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摘要： Capital punishment policy deserves a more prominent place in the analysis of the politics of Taiwan. While Taiwan's political culture is on a par with the mainstream of the world's democratic politics, its public opinion on capital crime policy is under-researched. This study begins with an historical review of capital punishment in Taiwan. After that, we turn to an in-depth analysis of data from a random survey in Taipei and New Taipei, focusing on the government's retentionist policy rather than an exploration of general attitudes toward the death penalty. Results indicate that support for death penalty policy and for the Ministry of Justice's execution policy stems mainly from instrumental sources: Belief in the deterrent effect of capital sentences and faith in a "tough on crime" approach as a generic cure for crime. In contrast, those who believe in the efficacy of rehabilitation tend to oppose capital punishment policy. Unexpectedly, experience with crime victimization and perception of high crime levels in one's residential neighborhood reduce support for capital punishment policy. We end our analysis with a call to political leaders to exercise enlightened leadership on the death sentence policy.

关键词： bodies   capital punishment   death penalty   execution   racial capitalism   sovereignty   violence  

摘要： A thorough analysis of capital punishment from a political-geographical perspective is lacking in the discipline of geography. This is despite the fact that capital punishment overlaps with numerous geographic approaches, concepts, and areas of study. This study serves as a call to geographers to begin considering capital punishment's wide interaction and interrelation with developed areas of knowledge in the discipline. Specifically, political geographers stand to contribute in novel ways to wider discussions about capital punishment by analyzing executions relative to contemporary work in geography on violent, legal, and carceral spaces. I first introduce the need for geography's engagement with this pressing issue. Then, I clarify several terms needed to understand this violent practice. A third section highlights several pathways of development in the critical geographies of capital punishment. It is organized around four broad themes-critically mapping capital punishment, bodies in motion, politics of access, and executing the "other"-connecting capital punishment to developing concepts, literatures, and subdisciplines in geography. The final two sections outline an epistemological approach to the geographies of capital punishment and how legal and carceral geographies might contribute.

关键词： natural language processing   eBooks   reading motivation   adjunct questions  

摘要： We investigated how Natural Language Processing (NLP) algorithms could automatically grade answers to open-ended inference questions in web-based eBooks. This is a component of research on making reading more motivating to children and to increasing their comprehension. We obtained and graded a set of answers to open-ended questions embedded in a fiction novel written in English. Computer science students used a subset of the graded answers to develop algorithms designed to grade new answers to the questions. The algorithms utilized the story text, existing graded answers for a given question and publicly accessible databases in grading new responses. A computer science professor used another subset of the graded answers to evaluate the students' NLP algorithms and to select the best algorithm. The results showed that the best algorithm correctly graded approximately 85% of the real-world answers as correct, partly correct, or wrong. The best NLP algorithm was trained with questions and graded answers from a series of new text narratives in another language, Slovenian. The resulting NLP algorithm model was successfully used in fourth-grade language arts classes for providing feedback to student answers on open-ended questions in eBooks.

关键词： Drug repurposing   ARBs   COVID-19   Docking   MD   MM-GBSA   In vitro studies   Computer science   Physics   Materials science   Chemistry   Biological sciences  

摘要： Drug repurposing is the most rapid and economic way nowadays to rapidly provide effective drugs for our pandemic coronavirus disease 2019 (COVID-19). It was a great debate about ARBs whether to be stopped or continued for patients using them especially at the beginning of the COVID-19 pandemic. In this study, we carried out a virtual screening for almost all members of ARBs (nine) against COVID-19 main protease. Molecular docking as one of the important computational techniques was performed in this work. Interestingly, the tested compounds showed variable binding affinities in the order of N3 inhibitor (10, docked) > Fimasartan (8) > Candesartan (2) > Olmesartan (7) > Azilsartan (9) > Eprosartan (5) > Valsartan (3) > Losartan (1) > Irbesartan (6) > Telmisartan (4). Moreover, Fimasartan (8), Candesartan (2), and Olmesartan (7) were additionally estimated through molecular dynamic simulations monitored via computing the binding free energy using MM-GBSA. The results are promising for rapidly repurposing such drugs (especially, Fimasartan (8) and Candesartan (2)) after further preclinical and clinical studies either alone or in combination with others for the treatment of COVID-19 virus especially known to cause vasodilatation (to prevent blood coagulation) and to reduce inflammation and fibrosis (to prevent pulmonary fibrosis), with well-known safety profiles. In vitro, the virtual findings were consistent with the experimental testing of four representative ARBs. Out of the tested compounds, Olmesartan (7) showed the most promising anti-SARS-CoV-2 activity (IC50 = 1.808 mu M, and CC50 = 557.6 mu M) with high selectivity index (308.4) against SARS-CoV-2 in Vero E6 cells. This work may clarify and approve not only the safety of ARBs used by a large group of patients worldwide but also their possible effectiveness against the COVID-19 virus either as a prophylactic or treatment option. It intended also to give a clear spot on the structure-activity relationship (SAR) r

关键词： Optimization   Correlation   Computer science   Fans   Benchmark testing   Search problems   Entropy   Decomposition (grouping) strategy   deep grouping   large scale optimization   problem decomposition  

摘要： Many real-world problems contain a large number of decision variables which can be modeled as large scale global optimization (LSGO) problems. One effective way to solve an LSGO problem is to decompose it into smaller subproblems to solve. The existing works mainly focused on designing methods to decompose separable problems, while seldom focused on the decomposition of nonseparable large scale problems. Also, the existing decomposition methods only learn the interaction (correlation or interdependence) among variables to make the decomposition. In this article, we make the decomposition deeper: we not only consider the variable interaction but also take the essentialness of the variable into account to form a deep grouping method. To do this, we first design an essential/trivial variable detection scheme to support the deep decomposition for both separable problems and nonseparable problems. Based on it, we propose a new decomposition method called deep grouping method. Then, we design a new differential evolution (DE) algorithm with a new mutation strategy. By integrating all these, we propose a hybrid deep grouping (HDG) algorithm. Finally, the experiments are conducted on the widely used and most challenging LSGO benchmark suites, and the comparison results of the proposed algorithm with the state-of-the-art algorithms indicate the proposed algorithm is more effective.

关键词： Computer science   Mathematics   Statistics   Bioinformatics   Microbial genomics   Genomics   Transcriptomics   Dimension reduction   SIR   ICA  

摘要： Dimension reduction is often a preliminary step in the analysis of data sets with a large number of variables. Most classical, both supervised and unsupervised, dimension reduction methods such as principal component analysis (PCA), independent component analysis (ICA) or sliced inverse regression (SIR) can be formulated using one, two or several different scatter matrix functionals. Scatter matrices can be seen as different measures of multivariate dispersion and might highlight different features of the data and when compared might reveal interesting structures. Such analysis then searches for a projection onto an interesting (signal) part of the data, and it is also important to know the correct dimension of the signal subspace. These approaches usually make either no model assumptions or work in wide classes of semiparametric models. Theoretical results in the literature are however limited to the case where the sample size exceeds the number of variables which is hardly ever true for data sets encountered in bioinformatics. In this paper, we briefly review the relevant literature and explore if the dimension reduction tools can be used to find relevant and interesting subspaces for small-..-large-.. data sets. We illustrate the methods with a microarray dataset of prostate cancer patients and healthy controls.

关键词： Semantometrics   Classification   Citation network   Natural language processing  

摘要： Identification of important works and assessment of importance of publications in vast scientific corpora are challenging yet common tasks subjected by many research projects. While the influence of citations in finding seminal papers has been analysed thoroughly, citation-based approaches come with several problems. Their impracticality when confronted with new publications which did not yet receive any citations, area-dependent citation practices and different reasons for citing are only a few drawbacks of them. Methods relying on more than citations, for example semantic features such as words or topics contained in publications of citation networks, are regarded with less vigour while providing promising preliminary results. In this work we tackle the issue of classifying publications with their respective referenced and citing papers as either seminal, survey or uninfluential by utilising semantometrics. We use distance measures over words, semantics, topics and publication years of papers in their citation network to engineer features on which we predict the class of a publication. We present the SUSdblp dataset consisting of 1980 labelled entries to provide a means of evaluating this approach. A classification accuracy of up to .9247 was achieved when combining multiple types of features using semantometrics. This is +.1232 compared to the current state of the art (SOTA) which uses binary classification to identify papers from classes seminal and survey. The utilisation of one-vector representations for the ternary classification task resulted in an accuracy of .949 which is +.1475 compared to the binary SOTA. Classification based on information available at publication time derived with semantometrics resulted in an accuracy of .8152 while an accuracy of .9323 could be achieved when using one-vector representations.

关键词： S. platensis   Chitosan nanoparticles   Growth performance   Chemical composition P. monodon  

摘要： Study conducted to evaluate the impact of dietary incorporation of chitosan nanoparticles (CsNPs) bio-synthesis and Spirulina platensis on the growth performance and body composition of intensive cultured fingerling of black tiger shrimp (Penaeus monodon) in addition to the effect of the diets on heavy metals concentrations in water of culture cages. Six diets were prepared to contain approximately 44% crude protein and 499 k cal./100 g diet and supplemented by S. platensis and chitosan as follow: T1: control, T2: 0.05% S. platensis, T3: 0.1% S. platensis, T4: 1 ml chitosan, T5: 1 ml chitosan and 0.05% S. platensis and T6: 1 ml chitosan and 0.1% S. platensis. Shrimp fingerlings (1.24 +/- 0.01 g) were randomly distributed in 12 hapas at stocking density of 20 fingerlings for each. The hapas were divided into 6 replicate groups and placed in cages. Each group received a kind of the experimental diet twice daily at feeding rate of 5% shrimp biomass for 12 weeks. The results indicated that, the growth parameters;final body weight (FBW g), weight gain (WG g), specific growth rate (SGR%) and survival rate (SR%) were improved significantly all trials compared to the control group. The best values for these parameters were recorded in the group of T5 and T6 without significant differences between them. Shrimp body composition showed the highest protein and fat contents for the group of T5 and T6 with significant differences (P < 0.05) among all treatments. No significant differences were noted for the heavy metals concentrations between all treatments. In conclusion, the incorporation of chitosan nanoparticles (CsNPs) and S. platensis attained considerable improvement in growth performance of black tiger shrimp and further investigations are recommended to optimize the level of incorporation in the diets for shrimp species. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： Brain  

摘要： The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration. © 2020, The Author(s).

关键词： quantum computing   quantum communication   quantum computations   quantum computing fundamentals   computer science   classical computers   quantum mechanics  

摘要： We all have been using classical computers for a long time. Quantum computing uses the phenomena of quantum mechanics like superposition and entanglement. Quantum computations can help achieve for the breakthroughs we have been looking for in science, machine learning, financial planning, medicine, etc., where classical computers' computing power is not enough. It was not long back when quantum computing's applications in our life were all just theoretical. However, to utilise the power of quantum computations for real-life applications, several recent developments have been made. Keeping that in mind, this study aims to explore the existing and upcoming applications of quantum computing. In this study, they start with an introduction of quantum computing fundamentals, following which, they give a brief overview of various applications of quantum computing in several significant areas of computer science, such as cryptography, machine learning, deep learning, and quantum simulations. They also cover various real-life scenarios such as risk analysis, logistics, and satellite communication.