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  • Articles  (50,124)
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  • 1
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    Cross-Domain Matching with Squared-Loss Mutual Information (2015)
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    Publication Date: 2015-08-04
    Description: The goal of cross-domain matching (CDM) is to find correspondences between two sets of objects in different domains in an unsupervised way. CDM has various interesting applications, including photo album summarization where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system, and temporal alignment which aligns sequences such as videos that are potentially expressed using different features. In this paper, we propose an information-theoretic CDM framework based on squared-loss mutual information (SMI). The proposed approach can directly handle non-linearly related objects/sequences with different dimensions, with the ability that hyper-parameters can be objectively optimized by cross-validation. We apply the proposed method to several real-world problems including image matching, unpaired voice conversion, photo album summarization, cross-feature video and cross-domain video-to-mocap alignment, and Kinect -based action recognition, and experimentally demonstrate that the proposed method is a promising alternative to state-of-the-art CDM methods.
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  • 2
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    Semi-Continuity of Skeletons in Two-Manifold and Discrete Voronoi Approximation (2015)
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    Publication Date: 2015-08-04
    Description: The skeleton of a 2D shape is an important geometric structure in pattern analysis and computer vision. In this paper we study the skeleton of a 2D shape in a two-manifold $mathcal {M}$ , based on a geodesic metric. We present a formal definition of the skeleton $S(Omega )$ for a shape $Omega$ in $mathcal {M}$ and show several properties that make $S(Omega )$ distinct from its Euclidean counterpart in $mathbb {R}^2$ . We further prove that for a shape sequence $lbrace Omega _irbrace$ that converge to a shape $Omega$ in $mathcal {M}$ , the mapping $Omega righta- row overline{S}(Omega )$ is lower semi-continuous. A direct application of this result is that we can use a set $P$ of sample points to approximate the boundary of a 2D shape $Omega$ in $mathcal {M}$ , and the Voronoi diagram of $P$ inside $Omega subset mathcal {M}$ gives a good approximation to the skeleton $S(Omega )$ . Examples of skeleton computation in topography and brain morphometry are illustrated.
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  • 3
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    Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting (2015)
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    Publication Date: 2015-08-04
    Description: A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.
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  • 4
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    Recognising Planes in a Single Image (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: We present a novel method to recognise planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations based on spatiograms of gradients and colour, coupled with relevance vector machines for classification and regression. We first show that using hand-labelled training data, we are able to classify pre-segmented regions as being planar or not, and estimate their 3D orientation. We then incorporate the method into a segmentation algorithm to detect multiple planar structures from a previously unseen image.
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  • 5
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    IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
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  • 6
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    Sparse Multi-View Consistency for Object Segmentation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: Multiple view segmentation consists in segmenting objects simultaneously in several views. A key issue in that respect and compared to monocular settings is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. Second, we show how other modalities such as depth may be seamlessly integrated in the model and benefit the segmentation. The paper exposes a detailed set of experiments used to validate the algorithm, showing results comparable with the state of art, with reduced computational complexity. We also discuss the use of different modalities for specific situations, such as dealing with a low number of viewpoints or a scene with color ambiguities between foreground and background.
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  • 7
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    Multi-Orientation Scene Text Detection with Adaptive Clustering (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks, while most current research efforts only focus on horizontal or near horizontal scene text. In this paper, first we present a unified distance metric learning framework for adaptive hierarchical clustering, which can simultaneously learn similarity weights (to adaptively combine different feature similarities) and the clustering threshold (to automatically determine the number of clusters). Then, we propose an effective multi-orientation scene text detection system, which constructs text candidates by grouping characters based on this adaptive clustering. Our text candidates construction method consists of several sequential coarse-to-fine grouping steps: morphology-based grouping via single-link clustering, orientation-based grouping via divisive hierarchical clustering, and projection-based grouping also via divisive clustering. The effectiveness of our proposed system is evaluated on several public scene text databases, e.g., ICDAR Robust Reading Competition data sets (2011 and 2013), MSRA-TD500 and NEOCR. Specifically, on the multi-orientation text data set MSRA-TD500, the $f$ measure of our system is $71$ percent, much better than the state-of-the-art performance. We also construct and release a practical challenging multi-orientation scene text data set (USTB-SV1K), which is available at http://prir.ustb.edu.cn/TexStar/MOMV-text-detection/.
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  • 8
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    A Low-Cost Mechanism Exploiting Narrow-Width Values for Tolerating Hard Faults in ALU (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Digital circuits are expected to increasingly suffer from more hard faults due to technology scaling. Especially, a single hard fault in ALU (Arithmetic Logic Unit) might lead to a total failure in processors or significantly reduce their performance. To address these increasingly important problems, we propose a novel cost-efficient fault-tolerant mechanism for the ALU, called LIZARD. LIZARD employs two half-word ALUs, instead of a single full-word ALU, to perform computations with concurrent fault detection. When a fault is detected, the two ALUs are partitioned into four quarter-word ALUs. After diagnosing and isolating a faulty quarter-word ALU, LIZARD continues its operation using the remaining ones, which can detect and isolate another fault. Even though LIZARD uses narrow ALUs for computations, it adds negligible performance overhead through exploiting predictability of the results in the arithmetic computations. We also present the architectural modifications when employing LIZARD for scalar as well as superscalar processors. Through comparative evaluation, we demonstrate that LIZARD outperforms other competitive fault-tolerant mechanisms in terms of area, energy consumption, performance and reliability.
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  • 9
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    Hybrid Search Scheme for Social Networks Supported by Dynamic Weighted Distributed Label Clustering (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Information searches are the most common application within social networks. Normally, the social network is modeled as a network graph, consisting of nodes (In the rest of the paper, unless otherwise specified, we will use the terms “user” and “node” interchangeably.) representing users within the network and edges representing relationships between users. Choosing the appropriate nodes to form an auxiliary structure for supporting the effective query message spreading can reduce the troublesome repeated queries. To accomplish this, a hybrid search (HS) scheme is proposed. If the query message is received by a node belonging the auxiliary structure constructed by dynamic weighted distributed label clustering (DW-DLC), it would be flooded to all neighbors of the visited node; otherwise, it would be forwarded to one neighbor of the visited node. The DW-DLC based auxiliary structure can accelerate the process of obtaining required information within the network. The simulation results show that the HS+DW-DLC scheme can reduce the average searching delay time, even in a required-information-scarce social network. In addition, the proposed scheme can generate a relatively low amount of repeated messages to lower repeatedly asking social network users.
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  • 10
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    Radix-2 Division Algorithms with an Over-Redundant Digit Set (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: This paper presents a derivation of four radix-2 division algorithms by digit recurrence. Each division algorithm selects a quotient digit from the over-redundant digit set {−2, −1, 0, 1, 2}, and the selection of each quotient digit depends only on the two most-significant digits of the partial remainder in a redundant representation. Two algorithms use a two’s complement representation for the partial remainder and carry-save additions, and the other two algorithms use a binary signed-digit representation for the partial remainder and carry-free additions. Three algorithms are novel. The fourth algorithm has been presented before. Results from the synthesized netlists show that two of our fastest algorithms achieve an improvement of 10 percent in latency per iteration over a standard radix-2 SRT algorithm at the cost of 36 percent more power and 50 percent more area.
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  • 11
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    WaFS: A Workflow-Aware File System for Effective Storage Utilization in the Cloud (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: We present WaFS, a user-level file system, and a related scheduling algorithm for scientific workflow computation in the cloud. WaFS’s primary design goal is to automatically detect and gather the explicit and implicit data dependencies between workflow jobs, rather than high-performance file access. Using WaFS’s data, a workflow scheduler can either make effective cost-performance tradeoffs or improve storage utilization. Proper resource provisioning and storage utilization on pay-as-you-go clouds can be more cost effective than the uses of resources in traditional HPC systems. WaFS and the scheduler controls the number of concurrent workflow instances at runtime so that the storage is well used, while the total makespan (i.e., turnaround time for a workload) is not severely compromised. We describe the design and implementation of WaFS and the new workflow scheduling algorithm based on our previous work. We present empirical evidence of the acceptable overheads of our prototype WaFS and describe a simulation-based study, using representative workflows, to show the makespan benefits of our WaFS-enabled scheduling algorithm.
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  • 12
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    Granular Evaluation of Anomalies in Wireless Sensor Networks Using Dynamic Data Partitioning with an Entropy Criteria (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: This paper presents an anomaly detection model that is granular and distributed to accurately and efficiently identify sensed data anomalies within wireless sensor networks. A more decentralised mechanism is introduced with wider use of in-network processing on a hierarchical sensor node topology resulting in a robust framework for dynamic data domains. This efficiently addresses the big data issue that is encountered in large scale industrial sensor network applications. Data vectors on each node’s observation domain is first partitioned using an unsupervised approach that is adaptive regarding dynamic data streams using cumulative point-wise entropy and average relative density . Second order statistical analysis applied on average relative densities and mean entropy values is then used to differentiate anomalies through robust and adaptive thresholds that are responsive to a dynamic environment. Anomaly detection is then performed in a non-parametric and non-probabilistic manner over the different network tiers in the hierarchical topology in offering increased granularity for evaluation. Experiments were performed extensively using both real and artificial data distributions representative of different dynamic and multi-density observation domains. Results demonstrate higher accuracies in detection as more than 94 percent accompanied by a desirable reduction of more than 85 percent in communication costs when compared to existing centralized methods.
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  • 13
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    A Unified Framework for Event Summarization and Rare Event Detection from Multiple Views (2015)
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    Publication Date: 2015-08-04
    Description: A novel approach for event summarization and rare event detection is proposed. Unlike conventional methods that deal with event summarization and rare event detection independently, our method solves them in a single framework by transforming them into a graph editing problem. In our approach, a video is represented by a graph, each node of which indicates an event obtained by segmenting the video spatially and temporally. The edges between nodes describe the relationship between events. Based on the degree of relations, edges have different weights. After learning the graph structure, our method finds subgraphs that represent event summarization and rare events in the video by editing the graph, that is, merging its subgraphs or pruning its edges. The graph is edited to minimize a predefined energy model with the Markov Chain Monte Carlo (MCMC) method. The energy model consists of several parameters that represent the causality, frequency, and significance of events. We design a specific energy model that uses these parameters to satisfy each objective of event summarization and rare event detection. The proposed method is extended to obtain event summarization and rare event detection results across multiple videos captured from multiple views. For this purpose, the proposed method independently learns and edits each graph of individual videos for event summarization or rare event detection. Then, the method matches the extracted multiple graphs to each other, and constructs a single composite graph that represents event summarization or rare events from multiple views. Experimental results show that the proposed approach accurately summarizes multiple videos in a fully unsupervised manner . Moreover, the experiments demonstrate that the approach is advantageous in detecting rare transition of events .
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  • 14
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    Object Tracking Benchmark (2015)
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    Publication Date: 2015-08-04
    Description: Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
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  • 15
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    Fused Lasso Screening Rules via the Monotonicity of Subdifferentials (2015)
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    Publication Date: 2015-08-04
    Description: Fused Lasso is a popular regression technique that encodes the smoothness of the data. It has been applied successfully to many applications with a smooth feature structure. However, the computational cost of the existing solvers for fused Lasso is prohibitive when the feature dimension is extremely large. In this paper, we propose novel screening rules that are able to quickly identity the adjacent features with the same coefficients. As a result, the number of variables to be estimated can be significantly reduced, leading to substantial savings in computational cost and memory usage. To the best of our knowledge, the proposed approach is the first attempt to develop screening methods for the fused Lasso problem with general data matrix. Our major contributions are: 1) we derive a new dual formulation of fused Lasso that comes with several desirable properties; 2) we show that the new dual formulation of fused Lasso is equivalent to that of the standard Lasso by two affine transformations; 3) we propose a novel framework for developing effective and efficient screening rules for f used La sso via the m onotonicity of the s ubdifferentials (FLAMS). Some appealing features of FLAMS are: 1) our methods are safe in the sense that the detected adjacent features are guaranteed to have the same coefficients; 2) the dataset needs to be scanned only once to run the screening, whose computational cost is negligible compared to that of solving the fused Lasso; (3) FLAMS is independent of the solvers and can be integrated with any existing solvers. We have evaluated the proposed FLAMS rules on both synthetic and real datasets. The experiments indicate that FLAMS is very effective in identifying the adjacent features with the same coefficients. The speedup gained by FLAMS can be orders of magnitude.
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  • 16
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    Variational Infinite Hidden Conditional Random Fields (2015)
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    Publication Date: 2015-08-04
    Description: Hidden conditional random fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite hidden conditional random field is a hidden conditional random field with a countably infinite number of hidden states, which rids us not only of the necessity to specify a priori a fixed number of hidden states available but also of the problem of overfitting. Markov chain Monte Carlo (MCMC) sampling algorithms are often employed for inference in such models. However, convergence of such algorithms is rather difficult to verify, and as the complexity of the task at hand increases the computational cost of such algorithms often becomes prohibitive. These limitations can be overcome by variational techniques. In this paper, we present a generalized framework for infinite HCRF models, and a novel variational inference approach on a model based on coupled Dirichlet Process Mixtures, the HCRF-DPM. We show that the variational HCRF-DPM is able to converge to a correct number of represented hidden states, and performs as well as the best parametric HCRFs—chosen via cross-validation—for the difficult tasks of recognizing instances of agreement, disagreement, and pain in audiovisual sequences.
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  • 17
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    Single-Pedestrian Detection Aided by Two-Pedestrian Detection (2015)
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    Publication Date: 2015-08-04
    Description: In this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single- and two-pedestrian detectors, and to refine the single-pedestrian detection result using two-pedestrian detection. The two-pedestrian detector can integrate with any single-pedestrian detector. Twenty-five state-of-the-art single-pedestrian detection approaches are combined with the two-pedestrian detector on three widely used public datasets: Caltech, TUD-Brussels, and ETH. Experimental results show that our framework improves all these approaches. The average improvement is $9$ percent on the Caltech-Test dataset, $11$ percent on the TUD-Brussels dataset and $17$ percent on the ETH dataset in terms of average miss rate. The lowest average miss rate is reduced from $37$ to percent on the Caltech-Test dataset, from $55$ to $50$ percent on the TUD-Brussels dataset and from $43$ to $38$ percent on the ETH dataset.
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  • 18
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    STES : A Stream Cipher Based Low Cost Scheme for Securing Stored Data (2015)
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    Publication Date: 2015-08-07
    Description: The problem of securing data present on USB memories and SD cards has not been adequately addressed in the cryptography literature. While the formal notion of a tweakable enciphering scheme (TES) is well accepted as the proper primitive for secure data storage, the real challenge is to design a low cost TES which can perform at the data rates of the targeted memory devices. In this work, we provide the first answer to this problem. Our solution, called STES, combines a stream cipher with a XOR universal hash function. The security of STES is rigorously analyzed in the usual manner of provable security approach. By carefully defining appropriate variants of the multi-linear hash function and the pseudo-dot product based hash function we obtain controllable trade-offs between area and throughput. We combine the hash function with the recent hardware oriented stream ciphers, namely Mickey, Grain and Trivium. Our implementations are targeted towards two low cost FPGAs—Xilinx Spartan 3 and Lattice ICE40. Simulation results demonstrate that the speeds of encryption/decryption match the data rates of different USB and SD memories. We believe that our work opens up the possibility of actually putting FPGAs within controllers of such memories to perform low-level in-place encryption.
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  • 19
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    Automated Design Architecture for 1-D Cellular Automata Using Quantum Cellular Automata (2015)
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    Publication Date: 2015-08-07
    Description: Cellular automata (CAs) have been widely used to model and simulate physical systems and processes. CAs have also been successfully used as a VLSI architecture that proved to be very efficient at least in terms of silicon-area utilization and clock-speed maximization. Quantum cellular automata (QCAs) as one of the promising emerging technologies for nanoscale and quantum computing circuit implementation, provides very high scale integration, very high switching frequency and extremely low power characteristics. In this paper we present a new automated design architecture and a tool, namely DATICAQ (Design Automation Tool of 1-D CAs using QCAs), that builds a bridge between 1-D CAs as models of physical systems and processes and 1-D QCAs as nanoelectronic architecture. The QCA implementation of CAs not only drives the already developed CAs circuits to the nanoelectronics era but improves their performance significantly. The inputs of the proposed architecture are CA dimensionality, size, local rule, and initial and boundary conditions imposed by the particular problem. DATICAQ produces as output the layout of the QCA implementation of the particular 1-D CA model. Simulations of CA models for zero and periodic boundary conditions and the corresponding QCA circuits showed that the CA models have been successfully implemented.
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  • 20
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    Automated Model-Based Testing of Role-Based Access Control Using Predicate/Transition Nets (2015)
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    Publication Date: 2015-08-07
    Description: Role-based access control is an important access control method for securing computer systems. A role-based access control policy can be implemented incorrectly due to various reasons, such as programming errors. Defects in the implementation may lead to unauthorized access and security breaches. To reveal access control defects, this paper presents a model-based approach to automated generation of executable access control tests using predicate/transition nets. Role-permission test models are built by integrating declarative access control rules with functional test models or contracts (preconditions and postconditions) of the associated activities (the system functions). The access control tests are generated automatically from the test models to exercise the interactions of access control activities. They are transformed into executable code through a model-implementation mapping that maps the modeling elements to implementation constructs. The approach has been implemented in an industry-adopted test automation framework that supports the generation of test code in a variety of languages. The full model-based testing process has been applied to three systems implemented in Java. The effectiveness is evaluated through mutation analysis of role-based access control rules. The experiments show that the model-based approach is highly effective in detecting the seeded access control defects.
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  • 21
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    Data Partitioning on Multicore and Multi-GPU Platforms Using Functional Performance Models (2015)
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    Publication Date: 2015-08-07
    Description: Heterogeneous multiprocessor systems, which are composed of a mix of processing elements, such as commodity multicore processors, graphics processing units (GPUs), and others, have been widely used in scientific computing community. Software applications incorporate the code designed and optimized for different types of processing elements in order to exploit the computing power of such heterogeneous computing systems. In this paper, we consider the problem of optimal distribution of the workload of data-parallel scientific applications between processing elements of such heterogeneous computing systems. We present a solution that uses functional performance models (FPMs) of processing elements and FPM-based data partitioning algorithms. Efficiency of this approach is demonstrated by experiments with parallel matrix multiplication and numerical simulation of lid-driven cavity flow on hybrid servers and clusters.
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    $k$ -Times Attribute-Based Anonymous Access Control for Cloud Computing (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: In this paper, we propose a new notion called $k$ -times attribute-based anonymous access control , which is particularly designed for supporting cloud computing environment. In this new notion, a user can authenticate himself/herself to the cloud computing server anonymously. The server only knows the user acquires some required attributes, yet it does not know the identity of this user. In addition, we provide a $k$ -times limit for anonymous access control. That is, the server may limit a particular set of users (i.e., those users with the same set of attribute) to access the system for a maximum $k$ -times within a period or an event. Further additional access will be denied. We also prove the security of our instantiation. Our implementation result shows that our scheme is practical.
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    Proactive Data Migration for Improved Storage Availability in Large-Scale Data Centers (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: In face of high partial and complete disk failure rates and untimely system crashes, the executions of low-priority background tasks become increasingly frequent in large-scale data centers. However, the existing algorithms are all reactive optimizations and only exploit the temporal locality of workloads to reduce the user I/O requests during the low-priority background tasks. To address the problem, this paper proposes Intelligent Data Outsourcing (IDO), a zone-based and proactive data migration optimization, to significantly improve the efficiency of the low-priority background tasks. The main idea of IDO is to proactively identify the hot data zones of RAID-structured storage systems in the normal operational state. By leveraging the prediction tools to identify the upcoming events, IDO proactively migrates the data blocks belonging to the hot data zones on the degraded device to a surrogate RAID set in the large-scale data centers. Upon a disk failure or crash reboot, most user I/O requests addressed to the degraded RAID set can be serviced directly by the surrogate RAID set rather than the much slower degraded RAID set. Consequently, the performance of the background tasks and user I/O performance during the background tasks are improved simultaneously. Our lightweight prototype implementation of IDO and extensive trace-driven experiments on two case studies demonstrate that, compared with the existing state-of-the-art approaches, IDO effectively improves the performance of the low-priority background tasks. Moreover, IDO is portable and can be easily incorporated into any existing algorithms for RAID-structured storage systems.
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    MuR-DPA: Top-Down Levelled Multi-Replica Merkle Hash Tree Based Secure Public Auditing for Dynamic Big Data Storage on Cloud (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users’ direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a - ame replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.
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    Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-06
    Description: Systems based on bag-of-words models from image features collected at maxima of sparse interest point operators have been used successfully for both computer visual object and action recognition tasks. While the sparse, interest-point based approach to recognition is not inconsistent with visual processing in biological systems that operate in ‘saccade and fixate’ regimes, the methodology and emphasis in the human and the computer vision communities remains sharply distinct. Here, we make three contributions aiming to bridge this gap. First, we complement existing state-of-the art large scale dynamic computer vision annotated datasets like Hollywood-2  [1] and UCF Sports  [2] with human eye movements collected under the ecological constraints of visual action and scene context recognition tasks. To our knowledge these are the first large human eye tracking datasets to be collected and made publicly available for video, vision.imar.ro/eyetracking (497,107 frames, each viewed by 19 subjects), unique in terms of their (a) large scale and computer vision relevance, (b) dynamic, video stimuli, (c) task control, as well as free-viewing . Second, we introduce novel dynamic consistency and alignment measures , which underline the remarkable stability of patterns of visual search among subjects. Third, we leverage the significant amount of collected data in order to pursue studies and build automatic, end-to-end trainable computer vision systems based on human eye movements. Our studies not only shed light on the differences between computer vision spatio-temporal interest point image sampling strategies and the human fixations, as well as their impact for visual recognition performance, but also demonstrate that human fixations can be accurately predicted, and when used in an end-to-end automatic system, leveraging some of the advanced computer vision practice, can lead to state of the art results.
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    Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-06
    Description: We consider the problem of parameter estimation and energy minimization for a region-based semantic segmentation model. The model divides the pixels of an image into non-overlapping connected regions, each of which is to a semantic class. In the context of energy minimization, the main problem we face is the large number of putative pixel-to-region assignments. We address this problem by designing an accurate linear programming based approach for selecting the best set of regions from a large dictionary. The dictionary is constructed by merging and intersecting segments obtained from multiple bottom-up over-segmentations. The linear program is solved efficiently using dual decomposition. In the context of parameter estimation, the main problem we face is the lack of fully supervised data. We address this issue by developing a principled framework for parameter estimation using diverse data. More precisely, we propose a latent structural support vector machine formulation, where the latent variables model any missing information in the human annotation. Of particular interest to us are three types of annotations: (i) images segmented using generic foreground or background classes; (ii) images with bounding boxes specified for objects; and (iii) images labeled to indicate the presence of a class. Using large, publicly available datasets we show that our methods are able to significantly improve the accuracy of the region-based model.
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    Submodular Relaxation for Inference in Markov Random Fields (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-06
    Description: In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem. The task is known to be NP-hard in general and its practical importance motivates numerous approximate algorithms. We propose a submodular relaxation approach (SMR) based on a Lagrangian relaxation of the initial problem. Unlike the dual decomposition approach of Komodakis et al. [29] SMR does not decompose the graph structure of the initial problem but constructs a submodular energy that is minimized within the Lagrangian relaxation. Our approach is applicable to both pairwise and high-order MRFs and allows to take into account global potentials of certain types. We study theoretical properties of the proposed approach and evaluate it experimentally.
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    A Hypergraph-Based Reduction for Higher-Order Binary Markov Random Fields (2015)
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    Publication Date: 2015-06-06
    Description: Higher-order Markov Random Fields, which can capture important properties of natural images, have become increasingly important in computer vision. While graph cuts work well for first-order MRF’s, until recently they have rarely been effective for higher-order MRF’s. Ishikawa’s graph cut technique [1] , [2] shows great promise for many higher-order MRF’s. His method transforms an arbitrary higher-order MRF with binary labels into a first-order one with the same minima. If all the terms are submodular the exact solution can be easily found; otherwise, pseudoboolean optimization techniques can produce an optimal labeling for a subset of the variables. We present a new transformation with better performance than [1] , [2] , both theoretically and experimentally. While [1] , [2] transforms each higher-order term independently, we use the underlying hypergraph structure of the MRF to transform a group of terms at once. For $n$ binary variables, each of which appears in terms with $k$ other variables, at worst we produce $n$ non-submodular terms, while [1] , [2] produces $O(- k)$ . We identify a local completeness property under which our method perform even better, and show that under certain assumptions several important vision problems (including common variants of fusion moves) have this property. We show experimentally that our method produces smaller weight of non-submodular edges, and that this metric is directly related to the effectiveness of QPBO [3] . Running on the same field of experts dataset used in [1] , [2] we optimally label significantly more variables (96 versus 80 percent) and converge more rapidly to a lower energy. Preliminary experiments suggest that some other higher-order MRF’s used in stereo [4] and segmentation [5] are also locally complete and would thus benefit from our work.
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    GPstruct: Bayesian Structured Prediction Using Gaussian Processes (2015)
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    Publication Date: 2015-06-06
    Description: We introduce a conceptually novel structured prediction model, GPstruct , which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M $^3$ N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.
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    STAR: Strategy-Proof Double Auctions for Multi-Cloud, Multi-Tenant Bandwidth Reservation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-09
    Description: Bandwidth reservation has been recognized as a value-added service to the cloud provider in recent years. We consider an open market of cloud bandwidth reservation, in which cloud providers offer bandwidth reservation services to cloud tenants, especially online streaming service providers, who have strict requirements on the amount of bandwidth to guarantee their quality of services. In this paper, we model the open market as a double-sided auction, and propose the first family of ST rategy-proof double A uctions for multi-cloud, multi-tenant bandwidth R eservation (STAR). STAR contains two auction mechanisms. The first one, STAR-Grouping, divides the tenants into groups by a bid-independent way, and carefully matches the cloud providers with the tenant groups to form good trades. The second one, STAR-Padding, greedily matches the cloud providers with the tenants, and fills the partially reserved cloud provider(s) with a novel virtual padding tenant who can be a component of the auctioneer. Our analysis shows that both of the two auction mechanisms achieve strategy-proofness and ex-post budget balance. Our evaluation results show that they achieve good performance in terms of social welfare, cloud bandwidth utilization, and tenant satisfaction ratio.
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    Local-Deadline Assignment for Distributed Real-Time Systems (2015)
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    Publication Date: 2015-06-09
    Description: In a distributed real-time system (DRTS), jobs are often executed on a number of processors and must complete by their end-to-end deadlines. Job deadline requirements may be violated if resource competition among different jobs on a given processor is not considered. This paper introduces a distributed, locally optimal algorithm to assign local deadlines to the jobs on each processor without any restrictions on the mappings of the applications to the processors in the distributed soft real-time system. Improvedschedulability results are achieved by the algorithm since disparate workloads among the processors due to competing jobs havingdifferent paths are considered. Given its distributed nature, the proposed algorithm is adaptive to dynamic changes of the applications and avoids the overhead of global clock synchronization. In order to make the proposed algorithm more practical, two derivatives of the algorithm are proposed and compared. Simulation results based on randomly generated workloads indicate that the proposed approach outperforms existing work both in terms of the number of feasible jobs (between 51% and 313% on average) and the number of feasible task sets (between 12% and 71% on average).
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    Parallel Reproducible Summation (2015)
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    Publication Date: 2015-06-09
    Description: Reproducibility, i.e. getting bitwise identical floating point results from multiple runs of the same program, is a property that many users depend on either for debugging or correctness checking in many codes [10] . However, the combination of dynamic scheduling of parallel computing resources, and floating point nonassociativity, makes attaining reproducibility a challenge even for simple reduction operations like computing the sum of a vector of numbers in parallel. We propose a technique for floating point summation that is reproducible independent of the order of summation. Our technique uses Rump’s algorithm for error-free vector transformation [7] , and is much more efficient than using (possibly very) high precision arithmetic. Our algorithm reproducibly computes highly accurate results with an absolute error bound of $n cdot 2^{-28} cdot macheps cdot max _i |v_i|$ at a cost of $7n$ FLOPs and a small constant amount of extra memory usage. Higher accuracies are also possible by increasing the number of error-free transformations. As long as all operations are performed in to-nearest rounding mode, results computed by the proposed algorithms are reproducible for any run on any platform. In particular, our algorithm requires the minimum number of reductions, i.e. one reduction of an array of six double precision floating point numbers per sum, and hence is well suited for massively parallel environments.
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    Design of Hybrid Second-Level Caches (2015)
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    Publication Date: 2015-06-09
    Description: In recent years, embedded dynamic random-access memory (eDRAM) technology has been implemented in last-level caches due to its low leakage energy consumption and high density. However, the fact that eDRAM presents slower access time than static RAM (SRAM) technology has prevented its inclusion in higher levels of the cache hierarchy. This paper proposes to mingle SRAM and eDRAM banks within the data array of second-level (L2) caches. The main goal is to achieve the best trade-off among performance, energy, and area. To this end, two main directions have been followed. First, this paper explores the optimal percentage of banks for each technology. Second, the cache controller is redesigned to deal with performance and energy. Performance is addressed by keeping the most likely accessed blocks in fast SRAM banks. In addition, energy savings are further enhanced by avoiding unnecessary destructive reads of eDRAM blocks. Experimental results show that, compared to a conventional SRAM L2 cache, a hybrid approach requiring similar or even lower area speedups the performance on average by 5.9 percent, while the total energy savings are by 32 percent. For a 45 nm technology node, the energy-delay-area product confirms that a hybrid cache is a better design than the conventional SRAM cache regardless of the number of eDRAM banks, and also better than a conventional eDRAM cache when the number of SRAM banks is an eighth of the total number of cache banks.
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    Lattice-Based Signatures: Optimization and Implementation on Reconfigurable Hardware (2015)
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    Publication Date: 2015-06-09
    Description: Nearly all of the currently used signature schemes, such as RSA or DSA, are based either on the factoring assumption or the presumed intractability of the discrete logarithm problem. As a consequence, the appearance of quantum computers or algorithmic advances on these problems may lead to the unpleasant situation that a large number of today’s schemes will most likely need to be replaced with more secure alternatives. In this work we present such an alternative—an efficient signature scheme whose security is derived from the hardness of lattice problems. It is based on recent theoretical advances in lattice-based cryptography and is highly optimized for practicability and use in embedded systems. The public and secret keys are roughly $1.5$  kB and $0.3$  kB long, while the signature size is approximately $1.1$  kB for a security level of around $80$ bits. We provide implementation results on reconfigurable hardware (Spartan/Virtex-6) and demonstrate that the scheme is scalable, has low area consumption, and even outperforms classical schemes.
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    Optimal Task Placement with QoS Constraints in Geo-Distributed Data Centers Using DVFS (2015)
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    Publication Date: 2015-06-09
    Description: With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the task placement problem over geo-distributed data centers. We exploit the dynamic frequency scaling technique and formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an optimal solution is discovered for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the traditional resizing scheme, i.e., by activating/deactivating certain servers in data centers.
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    Methodology for Cycle-Accurate DRAM Performance Analysis (2015)
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    Publication Date: 2015-06-09
    Description: A new methodology for DRAM performance analysis has been proposed based on accurate characterization of DRAM bus cycles. The proposed methodology allows cycle-accurate performance analysis of arbitrary DRAM traces, obviates the need for functional simulations, allows accurate estimation of DRAM performance maximum, and enables root causing of suboptimal DRAM operation.
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    Efficient Learning of Image Super-Resolution and Compression Artifact Removal with Semi-Local Gaussian Processes (2015)
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    Publication Date: 2015-08-04
    Description: Improving the quality of degraded images is a key problem in image processing, but the breadth of the problem leads to domain-specific approaches for tasks such as super-resolution and compression artifact removal. Recent approaches have shown that a general approach is possible by learning application-specific models from examples; however, learning models sophisticated enough to generate high-quality images is computationally expensive, and so specific per-application or per-dataset models are impractical. To solve this problem, we present an efficient semi-local approximation scheme to large-scale Gaussian processes. This allows efficient learning of task-specific image enhancements from example images without reducing quality. As such, our algorithm can be easily customized to specific applications and datasets, and we show the efficiency and effectiveness of our approach across five domains: single-image super-resolution for scene, human face, and text images, and artifact removal in JPEG- and JPEG 2000-encoded images.
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    Learning Efficient Sparse and Low Rank Models (2015)
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    Publication Date: 2015-08-04
    Description: Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speed-up compared to the exact optimization algorithms.
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    Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination (2015)
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    Publication Date: 2015-08-04
    Description: CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.
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    Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (2015)
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    Publication Date: 2015-08-04
    Description: Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 $times$ 224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 $times$ faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this - ompetition.
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    Distribution Matching with the Bhattacharyya Similarity: A Bound Optimization Framework (2015)
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    Publication Date: 2015-08-04
    Description: We present efficient graph cut algorithms for three problems: (1) finding a region in an image, so that the histogram (or distribution) of an image feature within the region most closely matches a given model; (2) co-segmentation of image pairs and (3) interactive image segmentation with a user-provided bounding box. Each algorithm seeks the optimum of a global cost function based on the Bhattacharyya measure, a convenient alternative to other matching measures such as the Kullback–Leibler divergence. Our functionals are not directly amenable to graph cut optimization as they contain non-linear functions of fractional terms, which make the ensuing optimization problems challenging. We first derive a family of parametric bounds of the Bhattacharyya measure by introducing an auxiliary labeling. Then, we show that these bounds are auxiliary functions of the Bhattacharyya measure, a result which allows us to solve each problem efficiently via graph cuts. We show that the proposed optimization procedures converge within very few graph cut iterations. Comprehensive and various experiments, including quantitative and comparative evaluations over two databases, demonstrate the advantages of the proposed algorithms over related works in regard to optimality, computational load, accuracy and flexibility.
    Print ISSN: 0162-8828
    Electronic ISSN: 1939-3539
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    [Perspective] Thinking abstractly like a duck(ling) (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: In the film O Brother, Where Art Thou?, when one of a trio of bungling prison escapees angrily asks another, “Who elected you leader of this outfit?” his buddy smugly quips, “I figured it should be the one with the capacity for abstract thought.” Indeed, abstract conceptual thought is held to be so central to being human that the idea of someone being incapable of this kind of thinking is a subject for (sometimes rather cruel) humor. Interest in understanding the capacity for abstract thought has been a matter of serious consideration that dates back at least three centuries to the famous English philosopher John Locke. Locke confidently contended that “brutes abstract not” (1) and insisted that exhibiting abstract thought definitively divided humans from all other animals. However, no science then existed to confirm or refute Locke's contention. On page 286 of this issue, Martinho and Kacelnik (2) put the claim that animals are incapable of abstract thought to a strong behavioral test. Author: Edward A. Wasserman
    Keywords: Cognition
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [In Depth] First farmers' motley roots (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Farming was such a good idea when it was invented 10,000 to 12,000 years ago in the Fertile Crescent that it was quickly adopted by several different groups of people. According to three teams who used new techniques to gain glimpses of the nuclear DNA of the world's very first farmers, farming was adopted by at least three genetically distinct groups scattered across the Middle East and Anatolia. The research found that early farmers of Israel and Jordan were genetically distinct from those in the Zagros Mountains, and that both populations were distinct from the western Anatolians. This shows that farming wasn't spread initially by just one group of people, but that it was invented more than once—or was an idea that spread rapidly between groups. Author: Ann Gibbons
    Keywords: Ancient DNA
    Print ISSN: 0036-8075
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Policy Forum] Crisis informatics—New data for extraordinary times (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Crisis informatics is a multidisciplinary field combining computing and social science knowledge of disasters; its central tenet is that people use personal information and communication technology to respond to disaster in creative ways to cope with uncertainty. We study and develop computational support for collection and sociobehavioral analysis of online participation (i.e., tweets and Facebook posts) to address challenges in disaster warning, response, and recovery. Because such data are rarely tidy, we offer lessons—learned the hard way, as we have made every mistake described below—with respect to the opportunities and limitations of social media research on crisis events. Authors: Leysia Palen, Kenneth M. Anderson
    Keywords: Social Media Research
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Perspective] How much biodiversity loss is too much? (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: How much of something do we need to keep people safe and well? This question is frequently asked by those working in risk management. Across diverse sectors from flood protection to health care, practitioners assess risk as the product of the impact of a given event and the probability of its occurrence. Although these estimates are often uncertain, policy-makers must ultimately make spending decisions aimed at averting these risks, because the costs of inaction to society can be substantial. Biodiversity loss is a similarly critical, yet uncertain, issue. On page 288 of this issue, Newbold et al. (1) quantify global biodiversity losses, providing much-needed information on the encroachment of proposed “safe limits.” Author: Tom H. Oliver
    Keywords: Ecology
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Book Review] A toxic timeline (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: In the 1970s, residents of a Niagara Falls neighborhood realized that chemicals from a toxic waste dump had leached into their homes, parks, and neighborhood school. Their cancers, miscarriages, and myriad chronic ailments told the tale, and in 1978 they organized, filed lawsuits, and demanded intervention. The federal government eventually complied, evacuating the residents and creating the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA), also known as the Superfund Act, which provides a framework for cleaning up such sites. In his new book, Love Canal: A Toxic History from Colonial Times to the Present, Richard S. Newman urges us to see the Love Canal disaster stretched out in time, rooted in the long history of the Niagara Falls area. The crisis itself, he says, was an outcome of patterns established generations earlier that pitted developmental pressures against environmental and human health and created a "cycle of disposable land use that had long dominated area politics and economics." Author: Jacob Darwin Hamblin
    Keywords: Environmental Policy
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Introduction to Special Issue] Nature's Fury (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Authors: Brent Grocholski, Robert Coontz
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Letter] Mining undermining Brazil's environment (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Authors: Hani Rocha El Bizri, Jonathan Christopher Bausch Macedo, Adriano Pereira Paglia, Thaís Queiroz Morcatty
    Print ISSN: 0036-8075
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Special Issue News] Doomsday Machines (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: In San Diego, California, a six-story tower riddled with strain gauges and accelerometers rises from the platform of one of the world's biggest earthquake machines. This device—a sort of bull ride for buildings—is one in a network built around the United States to advance natural disaster science with more realistic and sophisticated tests. The National Science Foundation initiative has helped scientists simulate some of the most powerful and destructive forces on Earth, including earthquakes, tsunamis, and landslides. The work has led to new building standards and better ways to build or retrofit everything from wharves to older concrete buildings. Now, in a new $62 million, 5-year program, the network of doomsday machines is expanding to simulate hurricanes and tornadoes and is joining forces with computer modeling to study how things too big for a physical test—such as nuclear reactors or even an entire city—will weather what Mother Nature throws at them. Author: Warren Cornwall
    Keywords: Natural Hazards
    Print ISSN: 0036-8075
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Special Issue News] Thinking the Unthinkable (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: What are the greatest threats to humanity and human civilization? Scholars think a self-induced catastrophe such as nuclear war or a bioengineered pandemic is most likely to do us in. But extreme natural hazards—including threats from space and geologic upheavals here on Earth—could also do the job. Although common, moderately severe disasters such as earthquakes attract far more funding and attention than low-probability apocalyptic ones, a handful of researchers persists in thinking the unthinkable. With knowledge and planning, they say, it's possible to prepare for—or in some cases prevent—rare but devastating natural disasters such as blasts of particles from the sun, collisions with near-Earth asteroids like the one that wiped out the dinosaurs, and supervolcanoes that dwarf any eruptions in recorded history. Author: Julia Rosen
    Keywords: Natural Hazards
    Print ISSN: 0036-8075
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [This Week in Science] The innate wisdom of ducklings (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Author: Sacha Vignieri
    Keywords: Evolutionary Cognition
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Special Issue Review] Global trends in satellite-based emergency mapping (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Over the past 15 years, scientists and disaster responders have increasingly used satellite-based Earth observations for global rapid assessment of disaster situations. We review global trends in satellite rapid response and emergency mapping from 2000 to 2014, analyzing more than 1000 incidents in which satellite monitoring was used for assessing major disaster situations. We provide a synthesis of spatial patterns and temporal trends in global satellite emergency mapping efforts and show that satellite-based emergency mapping is most intensively deployed in Asia and Europe and follows well the geographic, physical, and temporal distributions of global natural disasters. We present an outlook on the future use of Earth observation technology for disaster response and mitigation by putting past and current developments into context and perspective. Authors: Stefan Voigt, Fabio Giulio-Tonolo, Josh Lyons, Jan Kučera, Brenda Jones, Tobias Schneiderhan, Gabriel Platzeck, Kazuya Kaku, Manzul Kumar Hazarika, Lorant Czaran, Suju Li, Wendi Pedersen, Godstime Kadiri James, Catherine Proy, Denis Macharia Muthike, Jerome Bequignon, Debarati Guha-Sapir
    Print ISSN: 0036-8075
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Special Issue Review] Human influence on tropical cyclone intensity (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Recent assessments agree that tropical cyclone intensity should increase as the climate warms. Less agreement exists on the detection of recent historical trends in tropical cyclone intensity. We interpret future and recent historical trends by using the theory of potential intensity, which predicts the maximum intensity achievable by a tropical cyclone in a given local environment. Although greenhouse gas–driven warming increases potential intensity, climate model simulations suggest that aerosol cooling has largely canceled that effect over the historical record. Large natural variability complicates analysis of trends, as do poleward shifts in the latitude of maximum intensity. In the absence of strong reductions in greenhouse gas emissions, future greenhouse gas forcing of potential intensity will increasingly dominate over aerosol forcing, leading to substantially larger increases in tropical cyclone intensities. Authors: Adam H. Sobel, Suzana J. Camargo, Timothy M. Hall, Chia-Ying Lee, Michael K. Tippett, Allison A. Wing
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [This Week in Science] The heat is on (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Author: H. Jesse Smith
    Keywords: Glaciers
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [This Week in Science] Adaptors conduct the EGFR symphony (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Author: Leslie K. Ferrarelli
    Keywords: Biochemistry
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Editors' Choice] Why pay more for medicine in some places? (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Author: Brad Wible
    Keywords: Health Economics
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Editors' Choice] A less personal cancer therapy? (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Author: Paula A. Kiberstis
    Keywords: Tumor Immunology
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Editors' Choice] Immigration and admixture in Europe (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Author: Andrew M. Sugden
    Keywords: Paleogenomics
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [New Products] New Products (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: A weekly roundup of information on newly offered instrumentation, apparatus, and laboratory materials of potential interest to researchers.
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Report] Discovery of robust in-plane ferroelectricity in atomic-thick SnTe (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: Stable ferroelectricity with high transition temperature in nanostructures is needed for miniaturizing ferroelectric devices. Here, we report the discovery of the stable in-plane spontaneous polarization in atomic-thick tin telluride (SnTe), down to a 1–unit cell (UC) limit. The ferroelectric transition temperature Tc of 1-UC SnTe film is greatly enhanced from the bulk value of 98 kelvin and reaches as high as 270 kelvin. Moreover, 2- to 4-UC SnTe films show robust ferroelectricity at room temperature. The interplay between semiconducting properties and ferroelectricity in this two-dimensional material may enable a wide range of applications in nonvolatile high-density memories, nanosensors, and electronics. Authors: Kai Chang, Junwei Liu, Haicheng Lin, Na Wang, Kun Zhao, Anmin Zhang, Feng Jin, Yong Zhong, Xiaopeng Hu, Wenhui Duan, Qingming Zhang, Liang Fu, Qi-Kun Xue, Xi Chen, Shuai-Hua Ji
    Print ISSN: 0036-8075
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Working Life] Wearing my disability with pride (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Author: Zachary S. Wiersma
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [Report] Ocean forcing of glacier retreat in the western Antarctic Peninsula (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-15
    Description: In recent decades, hundreds of glaciers draining the Antarctic Peninsula (63° to 70°S) have undergone systematic and progressive change. These changes are widely attributed to rapid increases in regional surface air temperature, but it is now clear that this cannot be the sole driver. Here, we identify a strong correspondence between mid-depth ocean temperatures and glacier-front changes along the ~1000-kilometer western coastline. In the south, glaciers that terminate in warm Circumpolar Deep Water have undergone considerable retreat, whereas those in the far northwest, which terminate in cooler waters, have not. Furthermore, a mid-ocean warming since the 1990s in the south is coincident with widespread acceleration of glacier retreat. We conclude that changes in ocean-induced melting are the primary cause of retreat for glaciers in this region. Authors: A. J. Cook, P. R. Holland, M. P. Meredith, T. Murray, A. Luckman, D. G. Vaughan
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    [Report] Return to quiescence of mouse neural stem cells by degradation of a proactivation protein (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Quiescence is essential for long-term maintenance of adult stem cells. Niche signals regulate the transit of stem cells from dormant to activated states. Here, we show that the E3-ubiquitin ligase Huwe1 (HECT, UBA, and WWE domain–containing 1) is required for proliferating stem cells of the adult mouse hippocampus to return to quiescence. Huwe1 destabilizes proactivation protein Ascl1 (achaete-scute family bHLH transcription factor 1) in proliferating hippocampal stem cells, which prevents accumulation of cyclin Ds and promotes the return to a resting state. When stem cells fail to return to quiescence, the proliferative stem cell pool becomes depleted. Thus, long-term maintenance of hippocampal neurogenesis depends on the return of stem cells to a transient quiescent state through the rapid degradation of a key proactivation factor. Authors: Noelia Urbán, Debbie L. C. van den Berg, Antoine Forget, Jimena Andersen, Jeroen A. A. Demmers, Charles Hunt, Olivier Ayrault, François Guillemot
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [This Week in Science] Show me a sign of sweetness to come (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2016-07-22
    Description: Author: Sacha Vignieri
    Keywords: Behavioral Ecology
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [This Week in Science] Cyclin suppresses antitumor immunity (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: Author: Kristen L. Mueller
    Keywords: Cancer Immunotherapy
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [This Week in Science] Eliminating paternal mitochondria (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: Author: Beverly A. Purnell
    Keywords: Mitochondria
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    [This Week in Science] How to link and separate memories (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: Author: Peter Stern
    Keywords: Memory Research
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    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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    Cover (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-08-05
    Description: Provides a listing of board members, committee members, editors, and society officers.
    Print ISSN: 0162-8828
    Electronic ISSN: 1939-3539
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Certain inner feelings and physiological states like pain are subjective states that cannot be directly measured, but can be estimated from spontaneous facial expressions. Since they are typically characterized by subtle movements of facial parts, analysis of the facial details is required. To this end, we formulate a new regression method for continuous estimation of the intensity of facial behavior interpretation, called Doubly Sparse Relevance Vector Machine (DSRVM). DSRVM enforces double sparsity by jointly selecting the most relevant training examples (a.k.a. relevance vectors) and the most important kernels associated with facial parts relevant for interpretation of observed facial expressions. This advances prior work on multi-kernel learning, where sparsity of relevant kernels is typically ignored. Empirical evaluation on challenging Shoulder Pain videos, and the benchmark DISFA and SEMAINE datasets demonstrate that DSRVM outperforms competing approaches with a multi-fold reduction of running times in training and testing.
    Print ISSN: 0162-8828
    Electronic ISSN: 1939-3539
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Low-Cost and High-Reduction Approaches for Power Droop during Launch-On-Shift Scan-Based Logic BIST (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-07-12
    Description: During at-speed test of high performance sequential ICs using scan-based Logic BIST, the IC activity factor (AF) induced by the applied test vectors is significantly higher than that experienced during its in field operation. Consequently, power droop (PD) may take place during both shift and capture phases, which will slow down the circuit under test (CUT) signal transitions. At capture, this phenomenon is likely to be erroneously recognized as due to delay faults. As a result, a false test fail may be generated, with consequent increase in yield loss. In this paper, we propose two approaches to reduce the PD generated at capture during at-speed test of sequential circuits with scan-based Logic BIST using the Launch-On-Shift scheme. Both approaches increase the correlation between adjacent bits of the scan chains with respect to conventional scan-based LBIST. This way, the AF of the scan chains at capture is reduced. Consequently, the AF of the CUT at capture, thus the PD at capture, is also reduced compared to conventional scan-based LBIST. The former approach, hereinafter referred to as Low-Cost Approach (LCA), enables a 50 percent reduction in the worst case magnitude of PD during conventional logic BIST. It requires a small cost in terms of area overhead (of approximately 1.5 percent on average), and it does not increase the number of test vectors over the conventional scan-based LBIST to achieve the same Fault Coverage (FC). Moreover, compared to three recent alternative solutions, LCA features a comparable AF in the scan chains at capture, while requiring lower test time and area overhead. The second approach, hereinafter referred to as High-Reduction Approach (HRA), enables scalable PD reductions at capture of up to 87 percent, with limited additional costs in terms of area overhead and number of required test vectors for a given target FC, over our LCA approach. Particularly, compared to two of the three recent alternative solutions mentioned above, HRA en- bles a significantly lower AF in the scan chains during the application of test vectors, while requiring either a comparable area overhead or a significantly lower test time. Compared to the remaining alternative solutions mentioned above, HRA enables a similar AF in the scan chains at capture (approximately 90 percent lower than conventional scan-based LBIST), while requiring a significantly lower test time (approximately 4.87 times on average lower number of test vectors) and comparable area overhead (of approximately 1.9 percent on average).
    Print ISSN: 0018-9340
    Electronic ISSN: 1557-9956
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    [In Depth] A time capsule from Bronze Age Britain (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Reconstructing daily life in the Bronze Age has been difficult in northern Europe. Most houses were poorly preserved, traced out by postholes or barren remains of hearths, and offer up only meager fragments of pottery. A major excavation near Peterborough, U.K., promises to fill in the picture. Archaeologists have dug up 3000-year-old roundhouses that were perched on stilts above a river, perhaps for defense or facilitating trade. The building materials and much of the contents are well-preserved because the five houses were quickly abandoned during a fire and then collapsed into a river. The rich array of artifacts includes textiles, wooden objects, metal tools, and complete sets of pottery. The arrangement of artifacts could indicate how various sections of the houses were used and perhaps new details about diet. The fact that all the buildings burned down, apparently at the same time, and the belongings were left behind, suggests the fires may have been part of an attack. Author: Erik Stokstad
    Keywords: Archaeology
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    [Feature] The Avenger (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: David Fajgenbaum was in his third year of medical school at the University of Pennsylvania (UPenn) 6 years ago, on an obstetrics-gynecology rotation, when he was first hit by night sweats, fatigue, and weight loss. His eventual diagnosis: a deadly form of Castleman disease, a rare immune disorder for which knowledge was in depressingly short supply. So Fajgenbaum decided to dedicate himself to taking down this disease. He abandoned plans to become an oncologist, skipped medical residency, and enrolled in business school instead—building a powerhouse network of hundreds of physicians, researchers, and drug company employees around the world to help him decipher Castleman. He co-authored papers with his doctor, wrote a case study about himself, proposed a new model of the disease, and currently coordinates a dozen Castleman studies from his small office at UPenn, where he is an assistant professor. Author: Jennifer Couzin-Frankel
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    [Perspective] Butterfly communities under threat (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Butterflies are better documented and monitored worldwide than any other nonpest taxon of insects (1). In the United Kingdom alone, volunteer recorders have sampled more than 750,000 km of repeat transects since 1976, equivalent to walking to the Moon and back counting butterflies (2). Such programs are revealing regional extinctions and population declines that began before 1900 (3, 4). In a recent study, Habel et al. report a similar story based on inventories of butterflies and burnet moths since 1840 in a protected area in Bavaria, Germany (5). The results reveal severe species losses: Scarce, specialized butterflies have largely disappeared, leaving ecosystems dominated by common generalist ones. Similar trends are seen across Europe (6) and beyond, with protected areas failing to conserve many species for which they were once famed. Author: Jeremy A. Thomas
    Keywords: Ecology
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    [Perspective] Chromatin controls behavior (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Chromatin structure stabilizes and compacts the genome to package it within the nucleus. This structure also serves as a dynamic regulator of gene expression, silencing or activating transcription depending on molecular signals impinging upon it. It has been understood for the past two decades that chromatin stabilizes gene readout after cell-fate determination, establishing and perpetuating the precise pattern of genes transcribed in a given cell to maintain its phenotype (1, 2). But what about dynamic regulation of chromatin structure and its biological role? On page 300 of this issue, Yang et al. (3) describe how dynamic regulation of chromatin remodeling controls cerebellar circuit development, function, and cerebellum-dependent learning and memory, and challenge prevailing epigenetics dogma in the central nervous system. Author: J. David Sweatt
    Keywords: Gene Expression
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    [In Depth] Brain scans are prone to false positives, study says (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: A new study suggests that common settings used in software for analyzing brain scans may lead to false positive results. Researchers led by Anders Eklund, an electrical engineer at Linköping University in Sweden, analyzed functional magnetic resonance imaging (fMRI) data from several public databases. Certain software settings, the team found, could give rise to a false positive result up to 70% of the time. In the context of a typical fMRI experiment, that could lead researchers to wrongly conclude that activity in a certain area of the brain plays a role in a cognitive function such as perception or memory. Author: Greg Miller
    Keywords: Neuroscience
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    [In Brief] News at a glance (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: In science news around the world, the United States sets final safety regulations for oil and gas drilling in its Arctic waters, Australian researchers announce that the AIDS epidemic in the country is over—but caution that too many people are still being infected with HIV, the U.S. Food and Drug Administration halts a trial of a cancer drug following the deaths of three young adults with leukemia, French researchers sharply criticize the nomination of a policy expert rather than a scientist as the next head of the country's agricultural research institute, and more. Also, an Italian judge clears bird flu expert Ilaria Capua of a series of criminal charges brought against her 2 years ago. And the world's largest population of chinstrap penguins may be in peril because of an erupting volcano on their remote south Atlantic island.
    Keywords: SCI COMMUN
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    [Editorial] Hazards without disasters (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: A natural hazard need not become a human disaster if society learns and applies lessons in preparation and resilience. Earthquake history speaks well to this—engineered structures need to stand up to strong shaking. Chile learned this lesson before its 2010 earthquake of magnitude 8.8. Because it had already enforced seismic provisions of building codes, there was little loss of life due to damage to buildings. Engineered structures also performed very well during the giant 2011 Tohoku earthquake in northeast Japan; however, approximately 20,000 lives were lost to the ensuing tsunami. What survival strategies are available for communities at risk for tsunamis? Author: Marcia McNutt
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    [Perspective] Ferroelectric chalcogenides—materials at the edge (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: A ferroelectric material possesses an intrinsic electric dipole (polarization) whose direction can be reversed with an applied field. Applications of ferroelectrics include nonvolatile memories and sensors, but for high-density electronic devices or nanoscale devices, a limitation has been that as a ferroelectric film gets thinner, the maximum temperature for retaining the dipole—the Curie temperature Tc—decreases (often well below room temperature). On page 274 of this issue, Chang et al. (1) show that ultrathin layers of tin telluride (SnTe) can display robust, room-temperature, ferroelectric properties with higher Tc than that of the bulk material. Authors: Bart J. Kooi, Beatriz Noheda
    Keywords: Ferroelectrics
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    [Book Review] Big data meets human health (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: There is a "veritable explosion" in the number of people using digital and wearable devices to record, analyze, and reflect upon data created by their own bodies and behaviors. In their new book, Self-Tracking, Gina Neff and Dawn Nafus tread carefully between the twin pitfalls of techno-utopianism and techno-dystopianism to develop a nuanced position that acknowledges both the opportunities and the challenges raised by this trend. Elad Yom-Tov's Crowdsourced Health discusses a different field of digital health, in which data are generated not through the use of wearable devices but from queries entered in search engines. Based on the premise that these searches mirror our offline behavior and that the Internet offers greater privacy and accessibility than many other possible sources of information, he shows how these data could reveal information about health that would be difficult or impossible to gather in other ways. The question that has yet to be answered is what should ultimately be done with all of these data—and by whom. Author: Conor Farrington
    Keywords: Health Analytics
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    [Letter] NextGen Voices: Submit Now (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
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    [Special Issue Review] Connecting slow earthquakes to huge earthquakes (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Slow earthquakes are characterized by a wide spectrum of fault slip behaviors and seismic radiation patterns that differ from those of traditional earthquakes. However, slow earthquakes and huge megathrust earthquakes can have common slip mechanisms and are located in neighboring regions of the seismogenic zone. The frequent occurrence of slow earthquakes may help to reveal the physics underlying megathrust events as useful analogs. Slow earthquakes may function as stress meters because of their high sensitivity to stress changes in the seismogenic zone. Episodic stress transfer to megathrust source faults leads to an increased probability of triggering huge earthquakes if the adjacent locked region is critically loaded. Careful and precise monitoring of slow earthquakes may provide new information on the likelihood of impending huge earthquakes. Authors: Kazushige Obara, Aitaro Kato
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    [Editors' Choice] Smashing bits to show asteroid strength (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Author: Keith T. Smith
    Keywords: Planetary Science
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    [Business Office Feature] Structural biology shapes up (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-15
    Description: Despite advances in the field of proteomics, protein folding still remains a mystery. Yet innovations in X-ray crystallography, electron microscopy, and data analysis (think robots and Google) are yielding answers about protein structures faster than ever before.Read the Feature (Full-Text HTML)Read the Feature (PDF)Read New Products (PDF) Author: Alan Dove
    Keywords: Business Office Feature
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    [In Depth] African HIV/tuberculosis institutes merge (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: Two prominent biomedical research institutes in South Africa's KwaZulu-Natal province announced that they plan to merge and form the Africa Health Research Institute. One of the partners, the Africa Centre for Population Health, has long focused on epidemiological and demographic studies. Its funder is the Wellcome Trust. The Howard Hughes Medical Institute founded the second partner, the KwaZulu-Natal Research Institute for Tuberculosis and HIV. The Africa Health Research Institute is a new lease on life for both struggling institutes, which plan to combine their clinical and basic research skills to address major research questions in both HIV and tuberculosis. The Wellcome Trust is eventually expected to take over, but, for now, both philanthropies are backing the endeavor. Author: Jon Cohen
    Keywords: Public Health
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    [In Depth] To save caribou, Alberta wants to fence them in (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: An imperiled caribou herd in western Alberta province in Canada could become a high-profile test case for a controversial plan to save some of Canada's woodland caribou from extinction: herding them into pens enclosing 100 square kilometers or more and ringed with electric fences, and killing or removing every predator inside. The approach, proposed last month by Alberta's government, is an attempt to arrest the decline of the animals, threatened by development and preyed on by wolves. But some caribou advocates are skeptical that the expensive pens will work. They also fear that the strategy, which the energy industry has helped fund, will undermine efforts to curb habitat destruction. Author: Warren Cornwall
    Keywords: Conservation
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    [In Depth] Do genomic conflicts drive evolution? (2016)
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    In: Science
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    Publication Date: 2016-07-22
    Description: Two billion years ago, an early cell swallowed an energy-producing microbe, giving birth to the mitochondria that are the hallmarks of all eukaryotes. Evolutionary biologists now think that was just the start of the influence that the cell's "powerhouses" have on the tree of life. Mitochondria, which can exist by the scores in a eukaryotic cell, have their own set of genes, which can replicate and mutate faster than the cell's better-known complement in the nucleus. Yet both genomes code for products that have to work together in the mitochondria. Researchers are now finding hints that cells' efforts to keep nuclear and mitochondrial genes in sync could play a major role in evolution. At a recent meeting, biologists suggested out-of-sync nuclear and mitochondrial genomes may explain many biological puzzles—from why some female birds prefer the reddest mates to the evolution of new species in both plants and animals. Author: Elizabeth Pennisi
    Keywords: Evolutionary Biology
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    [In Depth] Vaginal microbiome affects HIV risk (2016)
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    In: Science
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    Publication Date: 2016-07-22
    Description: South African teen girls and young women have astonishingly high rates of HIV infection, and researchers for years have suspected that there might be biological factors making them unusually susceptible to infection. New studies presented at the International AIDS Conference being held in Durban, South Africa—located in KwaZulu-Natal province, the hardest hit region in the country—suggest a possible culprit, Prevotella bivia, a bacterium found in the vagina that causes inflammation. The close examination of the vaginal microbiome found a second bacterium, Gardnerella, may help explain why a microbicide gel that contained the anti-HIV drug tenofovir failed to protect many uninfected women who used it in a clinical trial. In test tube experiments, Garnderella "gobbled up" tenofovir, rapidly reducing levels of the drug. Author: Jon Cohen
    Keywords: Infectious Disease
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    [In Depth] Wild bird comes when honey hunters call for help (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: Of all relationships between wild animals and people, few are more heartwarming than that of African honey hunters and a robin-sized bird called the greater honeyguide. Flitting and calling, the bird leads the way to a bee's nest and feasts on the wax after the honey hunters have raided it. Researchers have discovered that this mutualistic relationship is even tighter than it seemed, with the bird recognizing and responding to specific calls from its human partners. In the new work, the researchers quantify the benefit to people helped by the birds and test how much more guiding occurs when the honey hunters attract the birds first with a "trill-grunt" call. This work was done in Mozambique, but it seems honey hunters elsewhere also have special calls, albeit different ones. Author: Elizabeth Pennisi
    Keywords: Animal Behavior
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    [In Depth] A lichen ménage à trois (2016)
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    Publication Date: 2016-07-22
    Description: Lichen isn't much to look at—often just a gray, yellow-green, or garish orange crust on rock or bark. Yet lichens cover up to 6% of Earth's surface, by one estimate. Now, modern genomics is revealing that lichens are startlingly complex. For some 140 years, scientists have understood lichens to be a symbiosis between a fungus, which provides a physical structure and supplies moisture, and a photosynthesizing alga or cyanobacterium, which produces nutrients. Studies of gene activity have now revealed that many lichens are instead a threesome, with two fungi in the mix. The role of the second fungus, a yeast, is uncertain, and some lichen aficionados aren't convinced it is a true symbiotic partner. But others say it's time to throw the textbook understanding of lichens out the window. Author: Elizabeth Pennisi
    Keywords: Symbiosis
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    [Feature] Rules of the game (2016)
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    Publication Date: 2016-07-22
    Description: Proteins consist of long chains of building blocks known as amino acids that fold up into precise 3D shapes that govern their function. David Baker, a computational biochemist at the University of Washington, Seattle, has spent years deciphering the rules that govern how these amino acid chains fold, and develop software to predict the 3D shape unknown amino acid chains are likely to take. Recent improvements to this software from Baker and others now make it possible to extend such prediction to the majority of proteins in nature. That's likely to lead to novel insights for biochemists working to understand what all these proteins do. It is also allowing Baker and his colleagues to design novel proteins to work as everything from medicines to materials, and catalysts to biochemical sensors. Author: Robert F. Service
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    [Feature] Red star rising (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: China is in the midst of launching a clutch of space science missions, with four put into space within a 13-month span. Its lunar exploration program is also increasingly science-driven, with a sample return mission scheduled for next year and the first ever landing on the far side of the moon planned for 2018. Beginning in 2020, China will launch another round of four science missions and the nation's first Mars probe. Chinese space administrators say that to build on these advances, the space science program needs reliable annual funding, instead of the 5-year lump sums now provided. They also think merging the country's different space agencies could maximize the scientific impact and lead to greater efficiencies. Author: Dennis Normile
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    [In Brief] News at a glance (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: In science news around the world, a United Nations panel rules against China's claim to a vast swath of the South China Sea, the Canadian Institutes of Health Research reverses course on its controversial online peer-review system, a U.S. federal appeals court finds that the Navy failed to protect whales from its low-frequency sonar, and researchers at the International Space Station prepare to test a DNA sequencer in orbit. Also, U.K. science and universities minister Jo Johnson keeps his job amid a shakeup in the cabinet, researchers demonstrate single-atom memory storage using chlorine and copper, and scientists from the Sloan Digital Sky Survey III unveil the largest 3D map of the universe to date.
    Keywords: SCI COMMUN
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    [In Depth] Huge statue suggests early rise for Buddhism (2016)
    American Association for the Advancement of Science (AAAS)
    In: Science
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    Publication Date: 2016-07-22
    Description: Archaeologists excavating at the sprawling Buddhist complex of Bhamala Stupa, north of Islamabad, at first thought they were digging up yet another stone wall. But they soon realized they had discovered the shattered remains of a massive statue—a monumental reclining Buddha that stretched more than 15 meters, the length of a shipping container. Radiocarbon dates on wood recovered from the site came back at 240 C.E. to 390 C.E.—several centuries before Buddhists were thought to have created the massive sculptures common in temples across Asia. If confirmed, the early date would make this the oldest evidence of monumental Buddhist sculpture. And big statues have big implications, because they require wealthy patrons and rulers to fund their creation. Author: Andrew Lawler
    Keywords: Archaeology
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    Adherent Raindrop Modeling, Detectionand Removal in Video (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-08-05
    Description: Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Modeling, detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatio-temporal derivatives of raindrops. To accomplish the idea, we first model adherent raindrops using law of physics, and detect raindrops based on these models in combination with motion and intensity temporal derivatives of the input video. Having detected the raindrops, we remove them and restore the images based on an analysis that some areas of raindrops completely occludes the scene, and some other areas occlude only partially. For partially occluding areas, we restore them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity derivative. For completely occluding areas, we recover them by using a video completion technique. Experimental results using various real videos show the effectiveness of our method.
    Print ISSN: 0162-8828
    Electronic ISSN: 1939-3539
    Topics: Computer Science
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    Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled ‘seed’ image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.
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    Face Association for Videos Using Conditional Random Fields and Max-Margin Markov Networks (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-08-05
    Description: We address the video-based face association problem, in which one attempts to extract the face tracks of multiple subjects while maintaining label consistency. Traditional tracking algorithms have difficulty in handling this task, especially when challenging nuisance factors like motion blur, low resolution or significant camera motions are present. We demonstrate that contextual features, in addition to face appearance itself, play an important role in this case. We propose principled methods to combine multiple features using Conditional Random Fields and Max-Margin Markov networks to infer labels for the detected faces. Different from many existing approaches, our algorithms work in online mode and hence have a wider range of applications. We address issues such as parameter learning, inference and handling false positves/negatives that arise in the proposed approach. Finally, we evaluate our approach on several public databases.
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    Topics: Computer Science
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  • 97
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    Infinite Factorial Unbounded-State Hidden Markov Model (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markovmodels (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.
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    Topics: Computer Science
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  • 98
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    Max-Margin Action Prediction Machine (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-08-05
    Description: The speed with which intelligent systems can react to an action depends on how soon it can be recognized. The ability to recognize ongoing actions is critical in many applications, for example, spotting criminal activity. It is challenging, since decisions have to be made based on partial videos of temporally incomplete action executions. In this paper, we propose a novel discriminative multi-scale kernelized model for predicting the action class from a partially observed video. The proposed model captures temporal dynamics of human actions by explicitly considering all the history of observed features as well as features in smaller temporal segments. A compositional kernel is proposed to hierarchically capture the relationships between partial observations as well as the temporal segments, respectively. We develop a new learning formulation, which elegantly captures the temporal evolution over time, and enforces the label consistency between segments and corresponding partial videos. We prove that the proposed learning formulation minimizes the upper bound of the empirical risk. Experimental results on four public datasets show that the proposed approach outperforms state-of-the-art action prediction methods.
    Print ISSN: 0162-8828
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    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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  • 99
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    Factors of Transferability for a Generic ConvNet Representation (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-08-05
    Description: Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then, by optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We further show that these visual recognition tasks can be categorically ordered based on their similarity to the source task such that a correlation between the performance of tasks and their similarity to the source task w.r.t. the proposed factors is observed.
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    Factorized Graph Matching (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-08-05
    Description: Graph matching (GM) is a fundamental problem in computer science, and it plays a central role to solve correspondence problems in computer vision. GM problems that incorporate pairwise constraints can be formulated as a quadratic assignment problem (QAP). Although widely used, solving the correspondence problem through GM has two main limitations: (1) the QAP is NP-hard and difficult to approximate; (2) GM algorithms do not incorporate geometric constraints between nodes that are natural in computer vision problems. To address aforementioned problems, this paper proposes factorized graph matching (FGM). FGM factorizes the large pairwise affinity matrix into smaller matrices that encode the local structure of each graph and the pairwise affinity between edges. Four are the benefits that follow from this factorization: (1) There is no need to compute the costly (in space and time) pairwise affinity matrix; (2) The factorization allows the use of a path-following optimization algorithm, that leads to improved optimization strategies and matching performance; (3) Given the factorization, it becomes straight-forward to incorporate geometric transformations (rigid and non-rigid) to the GM problem. (4) Using a matrix formulation for the GM problem and the factorization, it is easy to reveal commonalities and differences between different GM methods. The factorization also provides a clean connection with other matching algorithms such as iterative closest point; Experimental results on synthetic and real databases illustrate how FGM outperforms state-of-the-art algorithms for GM. The code is available at http://humansensing.cs.cmu.edu/fgm .
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    Topics: Computer Science
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