ALBERT

Beschreibung: With the increasingly close relations between cities in China, it is of great significance to explore the regular characteristics of the intercity connection. Through Tencent’s population migration heat and Baidu map big data, this paper analyzes the regular characteristics of the relations between complex cities based on such index as the rich node propensity index, preference level index, and relative heat index and also investigates the influence of geographical proximity factors on the external relations of different cities. The research has the following results. Firstly, the relations between cities have obvious club characteristics. The rich nodes tend to connect with the rich nodes, while the nonrich nodes tend to connect with the nonrich nodes. Secondly, the connection between cities has the effect of hierarchical proximity, and cities mainly establish spatial connections with cities of the same level and adjacent level. Thirdly, the relations between cities also have the effect of geographical proximity, and the degree of influence of geographical proximity in low-level cities is greater than that in high-level cities. Fourthly, the external connection mode of high-level cities is to establish close contact with high-level cities adjacent to the level, with strong attraction to low-level cities adjacent to the location at the same time. The low-level cities are closely related to the high-level cities adjacent to the location and other cities of geographical proximity or adjacent level. This study helps to further understand the complex characteristics and laws of intercity connections and urban networks.

Beschreibung: In Nepal, forest management priority is shifting to scientific management from conventional management. Though, the forest officials claims that scientific management is beneficial to the forest user groups, comparative financial assessment with conventional management remains unexplored. Following a case study approach, this study compares financial efficiency of two forest management systems in the community forests, focusing on benefit-cost ratio. The study conducted documents review, focus group discussions, and rapid survey to quantify costs and benefits from each forest management system. Conventional management gave a higher benefit-cost ratio to the forest user groups, irrespective of whether forest products are sold at a subsidized price or par with the market price. However, scientific management required high forest management costs and thus had a lower benefit-cost ratio. Sensitivity analysis between two systems revealed that conventional management gave a higher benefit-cost ratio in all cases. The study concludes that forest user groups would bear financial loss if they do not fix the price of the timber at par with the market in scientific management, and in such a case, the tagged price will be beyond affordability of the forest users. Furthermore, scientific management has discouraged kind contribution of users in managing forest. Besides, social and environmental consequences of scientific management cannot be ignored. Hence, the study argues for reconsidering current scientific management considering likely economic and social consequences to the forest user groups.

Beschreibung: Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.

Beschreibung: As the externalized carrier of intrinsic value, value decision-making is an important factor affecting the social value system. As an old Chinese saying goes, “A friend in need is a friend indeed,” crisis environment provides the background for the conflicts of multiple values, while individual social value orientation (SVO) determines the ranking of the value states. This paper defined the SVO types by means of Slider Measure method on the basis of environment description, constructed a decision-making game model in accordance with SVO differences, and finally analysed the mechanism of people’s decision-making. Taking the epidemic situation as the background, this paper conducted an empirical analysis with the sample of college students. The results showed that the most SVO types of college students were prosocial orientation, followed by individualistic orientation, altruistic orientation, and competitive orientation. In the crisis environment, individual SVO type and decision-making constituted a mapping relationship. There was an equilibrium point in the decision of prosocial orientation, and the dominant decision of altruistic orientation or individualistic orientation is relatively stable.

Beschreibung: To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

Beschreibung: People are the most important factors of economy and the primary carriers of social culture. Cross-border migration brings economic and cultural impacts to the origin and destination and is also a key to reflect the international relations of related countries. In fact, the migration relationships of countries are complex and multilateral, but most traditional migration models are bilateral. Network theories could provide a better description of global migration to show the structure and statistical characteristics more clearly. Based on the estimated migration data and disparity filter algorithm, the networks describing the global multilateral migration relationships have been extracted among 200 countries over fifty years. The results show that the global migration networks during 1960–2015 exhibit a clustering and disassortative feature, implying globalized and multipolarized changes of migration during these years. The networks were embed into a Poincaré disk, yielding a typical and hierarchical “core-periphery” structure, which is associated with angular density distribution, and has been used to describe the “multicentering” trend since 1990s. Analysis on correlation and evolution of communities indicates the stability of most communities, yet some structural changes still exist since 1990s, which reflect that the important historical events are contributable to regional and even global migration patterns.

Beschreibung: Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.

Beschreibung: Portfolio investment is adopted by the venture capital to diversify those risks involved in project selection, investing or operating so that the venture capitalist can expect a relatively stable income and lower financing risks. Based on the design of portfolio investment contract with unlimited funds developed by Kanniainen and Keuschnigg, and Inderst et al., this article makes a modification and presents a model given the limitation of funds available for the venture capitalist. It is demonstrated that the marginal benefit of efforts paid by the entrepreneurs exceeds the marginal cost, given the limitation of funds available, which will conduce to a high-level engagement of the entrepreneurs. Thus, by adopting the design of renegotiation contract, the venture capitalist can manage to stimulate the entrepreneurs to make efforts, which is to result in moral hazard reduction.

Beschreibung: Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure.

Beschreibung: This paper considers the two-player location game in a closed-loop market with quantity competition. Based on the Cournot and Hotelling models, a circle model is established for a closed-loop market in which two players (firms) play a location game under quantity competition. Using a two-stage (location-then-quantity) pattern and backward induction method, the existence of subgame-perfect Nash equilibria is proved for the location game in the circle model with a minimum distance transportation cost function. In addition, sales strategies are proposed for the two players for every local market on the circle when the players are in the equilibrium positions. Finally, an algorithm for simulating the competitive dynamics of the closed-loop market is designed, and two numerical simulations are provided to substantiate the effectiveness of the obtained results.

Beschreibung: A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.

Beschreibung: In this paper, a delayed control strategy for a class of nonlinear underactuated fourth-order systems is developed. The proposal is based on the implementation of the tangent linearization technique, differential flatness, and a study of the σ-stabilization of the characteristic equation of the closed-loop system. The tangent linearization technique allows obtaining a local controllability property for the analyzed class of systems. Also, it can reduce the complexity of the global control design, through the use of a cascade connection of two second-order controllers instead of designing a global controller of the fourth-order system. The stabilizing behavior of the delayed controller design is supported by the σ-stability criterion, which provides the controller parameter selection to reach the maximum exponential decay rate on the system response. To illustrate the efficiency of the theoretical results, the proposal is experimentally assessed in two cases of study: a flexible joint system and a pendubot.

Beschreibung: Recently, knowledge graph embedding methods have attracted numerous researchers’ interest due to their outstanding effectiveness and robustness in knowledge representation. However, there are still some limitations in the existing methods. On the one hand, translation-based representation models focus on conceiving translation principles to represent knowledge from a global perspective, while they fail to learn various types of relational facts discriminatively. It is prone to make the entity congestion of complex relational facts in the embedding space reducing the precision of representation vectors associating with entities. On the other hand, parallel subgraphs extracted from the original graph are used to learn local relational facts discriminatively. However, it probably causes the relational fact damage of the original knowledge graph to some degree during the subgraph extraction. Thus, previous methods are unable to learn local and global knowledge representation uniformly. To that end, we propose a multiview translation learning model, named MvTransE, which learns relational facts from global-view and local-view perspectives, respectively. Specifically, we first construct multiple parallel subgraphs from an original knowledge graph by considering entity semantic and structural features simultaneously. Then, we embed the original graph and construct subgraphs into the corresponding global and local feature spaces. Finally, we propose a multiview fusion strategy to integrate multiview representations of relational facts. Extensive experiments on four public datasets demonstrate the superiority of our model in knowledge graph representation tasks compared to state-of-the-art methods.

Beschreibung: A multimode resource-constrained project scheduling problem (MRCPSP) may have multifeasible solutions, due to its nature of targeting multiobjectives. Given the NP-hard MRCPSP and intricate multiobjective algorithms, finding the optimized result among those solutions seems impossible. This paper adopts data envelopment analysis (DEA) to evaluate a series of solutions of an MRCPSP and to find an appropriate choice in an objective way. Our approach is applied to a typical MRCPSP in practice, and the results validate that DEA is an effective and objective method for MRCPSP solution selection.

Beschreibung: Based on the oligopoly game theory and the intellectual property rights protection policy, we investigate the complex dynamical behaviors of a mixed duopoly game with quadratic cost. In the new system, a few parameters are improved by considering intellectual property rights protection and the stability conditions of the Nash equilibrium point are discussed in detail. A set of the two-dimensional bifurcation diagrams is demonstrated by using numerical modeling, and these diagrams show abundant complex dynamical behaviors, such as coexistence of attractors, different bifurcation, and fractal structures. These dynamical properties can present the long-run effects of strengthening intellectual property protection.

Beschreibung: Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.

Beschreibung: Social networks are a complex system that members communicate, create new connections or destroy existing connections, and further deliver major impacts on each member’s life. Given the spread of the Internet and increased academic pressure, sedentary and prescreen behaviors are very common among adolescents; meanwhile, sports behaviors are gradually decreasing. This situation has had an adverse effect on health. This paper used a questionnaire survey to investigate the influence of social networks on adolescent sports behavior, including the intermediary role of social efficacy and moderating effect of self-presentation. The questionnaire survey was conducted on 568 students from 6 high schools in Shaanxi, Henan, and Shandong Provinces. After this, factor analysis and weighted least squares method were used for the empirical test. Based on theoretical and empirical analysis, this paper found the following: (1) Social networks of adolescents have obvious positive predictive effects on their sports behavior. A single online social network and an offline social network, instrumental network, emotional network, and mixed network have obvious positive predictive effects on adolescent sports behaviors. However, under the influence of multiple types of social networks, an offline social network has a negative predictive effect, while a mixed network has effects that are not as obvious. (2) Social efficacy plays an intermediary role in the relationship between social network and adolescent sports behavior. (3) The moderating effect of self-presentation is not significant.

Beschreibung: Forest biomass is an important ecological indicator for the sustainable management of forests. The aim of this study was to estimate forest aboveground biomass (AGB) by integrating SPOT-6 data with field-based measurements using the random forest (RF) algorithm. In total, 52 remote sensing variables, including spectral bands, vegetation indices, topography data, and textures, were extracted from SPOT-6 images to predict the forest AGB of Xuan Lien Nature Reserve, Vietnam. To determine the optimal predictor variables for AGB estimation, 10 different RF models were built. To evaluate these models, 10-fold cross-validation was applied. We found that a combination of spectral and vegetation indices and topography variables offer the highest prediction results (Radj2  = 0.74 and RMSE = 61.24 Mg ha−1). Adding texture features into the predictor variables did not improve the model performance. In addition, the SPOT-6 sensor has the potential to predict forest AGB using the RF algorithm.

Beschreibung: Agroecology was born as a competing theory to sciences derived from the Green Revolution like conventional agronomy or modernized animal husbandry. In recent years, several theoretical models or approaches have been developed in order to explain this science. However, any of them can explain its change or difference with its rival theories in a rational manner that allows assessment of its success. As a result, the aim of this study was to propose a rational model of scientific change based on main and auxiliary hypotheses. We found that seven basic principles have been formulated throughout theoretical books and papers as well as several auxiliary hypotheses that can be derived from them. These principles are as follows: (1) characteristic systemic principle of agroecology, (2) principle of biomimicry, (3) principle of biodiversity, (4) principle of specificity of agroecosystems, (5) principle of governance, (6) principle of socioecological resilience, and (7) principle of vulnerability. Also, three principles for food systems approach were retrieved. This model shows agroecology more like an organic theory that moves in different scales than a set of rival theories competing for success. However, a proper articulation and discussion of these basic principles is yet to be done.

Beschreibung: Background and Aims. An excessive and prolonged use of fertilizers undermines soils’ quality and, consequently, that of the crops they support, thus reducing the content of organic matter and generating environmental damages and problems to human health. Therefore, the use of biofertilizers such as cyanobacteria becomes a promising alternative. However, it is not always possible to generalize these fertilizers’ applicability, because microorganisms may be impacted by the physical and chemical variations of their environment. We will describe the action mechanisms or the characteristics of cyanobacteria involved in plant growth promotion for different crops in situ through a systematic review of scientific literature. Methods. A comprehensive search for original articles in two different databases, ScienceDirect and Scopus, was performed. We included in our search documents published from 2009 to 2018. After the screening process and the addition of gray literature publications, we obtained 23 articles for theoretical analysis. Results. The studies were distributed mainly in Asia and part of Africa, without any important temporal variation. They also showed a tendency to describe the use of cyanobacteria genera such as Anabaena sp., Nostoc sp., and Calothrix sp., besides mechanisms as N2 fixation, phosphate solubilization, phytohormone production, bioactive compounds excretion, and symbiotic associations, mainly on rice, wheat and corn crops. Conclusions. Cyanobacteria fertilizers used in situ are a widespread strategy, mainly in cereal crops. Their use is predominant in countries where cereal crops make an important contribution to their national economy. The great variety of mechanisms and characteristics of cyanobacteria used to promote plant growth in the field demonstrate the dramatic influence that physical, chemical, and biological variables have in their development.

Beschreibung: Height measurement for moving pedestrians is quite significant in many scenarios, such as pedestrian positioning, criminal suspect tracking, and virtual reality. Although some existing height measurement methods can detect the height of the static people, it is hard to measure height accurately for moving pedestrians. Considering the height fluctuations in dynamic situation, this paper proposes a real-time height measurement based on the Time-of-Flight (TOF) camera. Depth images in a continuous sequence are addressed to obtain the real-time height of the pedestrian with moving. Firstly, a normalization equation is presented to convert the depth image into the grey image for a lower time cost and better performance. Secondly, a difference-particle swarm optimization (D-PSO) algorithm is proposed to remove the complex background and reduce the noises. Thirdly, a segmentation algorithm based on the maximally stable extremal regions (MSERs) is introduced to extract the pedestrian head region. Then, a novel multilayer iterative average algorithm (MLIA) is developed for obtaining the height of dynamic pedestrians. Finally, Kalman filtering is used to improve the measurement accuracy by combining the current measurement and the height at the last moment. In addition, the VICON system is adopted as the ground truth to verify the proposed method, and the result shows that our method can accurately measure the real-time height of moving pedestrians.

Beschreibung: Empirical evidence shows complementarity between maize and soybean as a sustained agricultural system across North and South America as well as Eastern Europe. The potential application to sub-Saharan Africa motivates this literature review. Maize is one of the most important crops on the African subcontinent, accounting for over half of daily caloric intake in some regions. However, continuous cropping of maize has led to extensive degradation of soil and decrease in crop productivity and endangers household food and nutritional security. The cultivation of soybean holds great promise in improving agricultural systems in sub-Saharan Africa. Introducing soy into rotation with maize is a method to diversify diets, better nutritional status, reduce abiotic and biotic stresses, and improve soil fertility, while enhancing crop productivity and generating more income for farmers. However, limited access to extension services and other sources of technical support constrains adoption of the more complex rotation cropping system involving a new crop, soybean. Rotating soybean with maize too challenges farmers as there is not a specific prescription that can guide farmers operating across Africa’s diverse agroecological environments. Finally, soybean is an input-intensive crop requiring significant investment at planting, which may not allow small holders with limited resources and no access to credit.

Beschreibung: Since China’s first stock index futures, China Securities Index 300 (CSI300) stock index futures were published in 2010, and China’s stock index futures market is now in a period of rapid development and play a key role in price discovery. During 2014 to 2015, China’s stock index futures market fluctuated abnormally, and the overuse of high-frequency trading (HFT) strategies in the stock index futures market was blamed as the main reason of the abnormal volatility. To lower down market fluctuation, the regulatory institute then announced a series of trade restriction policy to prevent the overuse of HFT behaviour. However, until now, the impact of such trade restriction policy for HFT remains uncertain. To tackle this issue, based on minute-level HFT data from the CSI 300 index futures market, this paper aims to investigate the relationship between HFT and the exogenous liquidity risk and how HFT affects China’s stock index futures market on its liquidity using the liquidity-adjusted value at risk (LVaR) model. The findings indicate that HFT improves the return of the liquidity provider and reduces the exogenous liquidity risk significantly.