Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling

Human behavior is the potential and expressive capacity (mental, physical, and social) of human individuals or groups to respond to internal and external stimuli. We explore assortative matching as a typical human behavior in virtual networked communities. We propose a modeling approach based on MAS(Multi-Agent System) and policy-based reinforcement learning to simulate human behavior through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning serves specific agents who learn from the environment status and competitor behaviors, then optimize strategy to achieve better results. This work simulates both the individual and group level, showing some possible paths for forming relative competitive advantages. This modeling approach can help further analyze the evolutionary dynamics of human behavior, communities, and organizations on various socioeconomic topics.Comment: 2 pages, 800 words, Extended abstract for DHM of HCI International 202

INTRA-INDUSTRY TRADE BETWEEN TAIWAN AND ASEAN-5 IN THE AGRO-FOOD SECTOR: PATTERNS AND DETERMINANTS

This paper attempts to identify the patterns and determinants of the levels of IIT between Taiwan and ASEAN-5 during the past three decades. Our empirical results confirm the belief that IIT between Taiwan and ASEAN-5 in the agro-food sector has been growing over time. Although industry-specific factors like market size and product differentiation have desirable impacts on IIT, taste overlaps do not. No deterministic conclusion can be drawn from the effect of trade liberalization as implied by removing trade barriers when shaping the future development of IIT. However, the indirect effect arising from income and consumer preferences' convergence may be the main determinant in promoting intra-industry agro-food trade among the Asian countries.International Relations/Trade,

Exploring the Learning Difficulty of Data Theory and Measure

As learning difficulty is crucial for machine learning (e.g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigorous theoretical foundation. In addition, there is no formal definition of easy and hard samples even though they are crucial in many studies. This study attempts to conduct a pilot theoretical study for learning difficulty of samples. First, a theoretical definition of learning difficulty is proposed on the basis of the bias-variance trade-off theory on generalization error. Theoretical definitions of easy and hard samples are established on the basis of the proposed definition. A practical measure of learning difficulty is given as well inspired by the formal definition. Second, the properties for learning difficulty-based weighting strategies are explored. Subsequently, several classical weighting methods in machine learning can be well explained on account of explored properties. Third, the proposed measure is evaluated to verify its reasonability and superiority in terms of several main difficulty factors. The comparison in these experiments indicates that the proposed measure significantly outperforms the other measures throughout the experiments.Comment: Ou Wu is the corresponding author of this wor

Flow Investigation on the Directional Instability of Aircraft with the Single Vertical Tail

AbstractAgeneric aircraft with the single vertical tail usually lose its directional stability at medium angle of attack (typically 20° to 30°). A model with moderate sweptwing of 47.5° and a conventional vertical tail is investigated in order to identify physical mechanisms responsible for directional stability. The results show that vertical tail and fuselage are the main components of the aircraft that generate yawing momentby the tests of model parts mounted and dismounted. The broken down vortex at windward side of vertical tail is the main reason for reducingstable yawing moment of vertical tail. Moreover, the middle part of the fuselage including air inlet and forepart of the wing is the main region of the fuselageenhancing unstable yawing moment

System Information Decomposition

In order to characterize complex higher-order interactions among variables in a system, we introduce a new framework for decomposing the information entropy of variables in a system, termed System Information Decomposition (SID). Diverging from Partial Information Decomposition (PID) correlation methods, which quantify the interaction between a single target variable and a collection of source variables, SID extends those approaches by equally examining the interactions among all system variables. Specifically, we establish the robustness of the SID framework by proving all the information atoms are symmetric, which detaches the unique, redundant, and synergistic information from the specific target variable, empowering them to describe the relationship among variables. Additionally, we analyze the relationship between SID and existing information measures and propose several properties that SID quantitative methods should follow. Furthermore, by employing an illustrative example, we demonstrate that SID uncovers a higher-order interaction relationships among variables that cannot be captured by current measures of probability and information and provide two approximate calculation methods verified by this case. This advance in higher-order measures enables SID to explain why Holism posits that some systems cannot be decomposed without loss of characteristics under existing measures, and offers a potential quantitative framework for higher-order relationships across a broad spectrum of disciplines

mGenomeSubtractor: a web-based tool for parallel in silico subtractive hybridization analysis of multiple bacterial genomes

mGenomeSubtractor performs an mpiBLAST-based comparison of reference bacterial genomes against multiple user-selected genomes for investigation of strain variable accessory regions. With parallel computing architecture, mGenomeSubtractor is able to run rapid BLAST searches of the segmented reference genome against multiple subject genomes at the DNA or amino acid level within a minute. In addition to comparison of protein coding sequences, the highly flexible sliding window-based genome fragmentation approach offered can be used to identify short unique sequences within or between genes. mGenomeSubtractor provides powerful schematic outputs for exploration of identified core and accessory regions, including searches against databases of mobile genetic elements, virulence factors or bacterial essential genes, examination of G+C content and binucleotide distribution bias, and integrated primer design tools. mGenomeSubtractor also allows for the ready definition of species-specific gene pools based on available genomes. Pan-genomic arrays can be easily developed using the efficient oligonucleotide design tool. This simple high-throughput in silico ‘subtractive hybridization’ analytical tool will support the rapidly escalating number of comparative bacterial genomics studies aimed at defining genomic biomarkers of evolutionary lineage, phenotype, pathotype, environmental adaptation and/or disease-association of diverse bacterial species. mGenomeSubtractor is freely available to all users without any login requirement at: http://bioinfo-mml.sjtu.edu.cn/mGS/

[1-(4-Chloro­phen­yl)-5-hy­droxy-3-phenyl-1H-pyrazol-4-yl](thio­phen-2-yl)methanone

In the title compound, C20H13ClN2O2S, the chloro­phenyl, phenyl and thienoyl rings are oriented at dihedral angles 17.84 (7), 53.13 (8) and 34.03 (8)°, respectively, to the central pyrazole ring. An intra­molecular O—H⋯O hydrogen bond occurs. In the crystal, pairs of bifurcated O—H⋯O hydrogen bonds link mol­ecules into inversion dimers with R 2 2(12) graph-set motifs