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关键词： Computational science   Computer science   Scientific data   Predictive markers   Biomarkers   Computational models   Data mining   Machine learning  

摘要： Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

关键词： Abacuses   Capital cities   Capital punishment   Buildings   Molding  

摘要： In this article, I bring the constitutional jurisprudence of major East Asian courts into reconstructive dialogue with that of the United States, South Africa, and several former Soviet-bloc countries, on per se review of capital punishment. This fills in a gap in the literature, which has failed to reflect new developments in Asia. Besides analysing various review approaches, I extrapolate recurrent analytical issues and reconstruct dialogues among these court decisions. Moreover, I place the analysis in historical perspective by periodising the jurisprudential trajectory of the right to life. The contextualised reconstructive dialogues offer multilayered understanding of my central analytical argument: for any court that may conduct per se review of capital punishment in the future, the highly influential South African Makwanyane case does not settle the lesson. The transnational debate has been kept open by the Korean Constitutional Court’s decisions, as well as retrospectively by the US cases of Furman and Gregg. This argument has two major points. First, the crucial part of the reasoning in Makwanyane, namely that capital punishment cannot be proven to pass the necessity test under the proportionality review, is analytically inconclusive. The Korean Constitutional Court’s decision offers a direct contrast to this point. Second, the exercise of proportionality review of the Makwanyane Court does not attest to the neutrality and objectivity of proportionality review. Rather, what is really dispositive of the outcome are certain value choices inhering in per se review of capital punishment. © The Author(s), 2021.

关键词： Computer science   Information technology   Software   Computational biology and bioinformatics  

摘要： Field-programmable gate arrays can speed up applications ranging from genomic alignment to deep learning. Field-programmable gate arrays can speed up applications ranging from genomic alignment to deep learning.

关键词： Mathematics and computing   Computer science   Machine learning  

摘要： The machine-learning techniques could benefit other areas of maths that involve large data sets. The machine-learning techniques could benefit other areas of maths that involve large data sets.

关键词： Computer science   Interdisciplinary studies   Sociology   Science   Humanities and Social Sciences   multidisciplinary  

摘要： Mass selection into groups of like-minded individuals may be fragmenting and polarizing online society, particularly with respect to partisan differences(1-4). However, our ability to measure the social makeup of online communities and in turn, to understand the social organization of online platforms, is limited by the pseudonymous, unstructured and large-scale nature of digital discussion. Here we develop a neural-embedding methodology to quantify the positioning of online communities along social dimensions by leveraging large-scale patterns of aggregate behaviour. Applying our methodology to 5.1 billion comments made in 10,000 communities over 14 years on Reddit, we measure how the macroscale community structure is organized with respect to age, gender and US political partisanship. Examining political content, we find that Reddit underwent a significant polarization event around the 2016 US presidential election. Contrary to conventional wisdom, however, individual-level polarization is rare;the system-level shift in 2016 was disproportionately driven by the arrival of new users. Political polarization on Reddit is unrelated to previous activity on the platform and is instead temporally aligned with external events. We also observe a stark ideological asymmetry, with the sharp increase in polarization in 2016 being entirely attributable to changes in right-wing activity. This methodology is broadly applicable to the study of online interaction, and our findings have implications for the design of online platforms, understanding the social contexts of online behaviour, and quantifying the dynamics and mechanisms of online polarization.

关键词： Prognostic markers   Alzheimer's disease   Magnetic resonance imaging   Computer science  

摘要： Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies;2,832 participants;8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neuro-degeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.

关键词： Machine learning   Computer science   Mathematics and computing  

摘要： The resulting wind information and its statistical uncertainty, together with a small number of balloon-relevant parameters, were used to train a machine-learning system known as an artificial neural network, and ultimately improved decision-making time during flights compared with previously used control systems, using similar battery power. [...]the ability to fix a balloon's geographical position is crucial if balloons are to be used to build an aerial wireless network for telecommunications - an early objective of Project Loon, the owners of the balloons used in Bellemare and colleagues' study. Station-keeping a balloon for months at a time would allow long-term environmental monitoring, for example, of air quality over cities, of carbon fluxes from heat-stressed forests and of regions of thawing permafrost.

关键词： Formal methods  

摘要： The hit of the COVID-19 pandemic has hugely affected higher education in the world, and as a result, most of the physical classes have been (partially) replaced by online teaching platforms. This transition is challenging even for experienced software engineering instructors, as they were pushed to break their habits and tricks developed over the years . This paper is an experience report in teaching an undergraduate course (revolving theoretical computer science topics) for the first time in an online format, and some observations and ideas of how to engage students during online lectures. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer science   Pure mathematics   Statistics   Science   Humanities and Social Sciences   multidisciplinary  

摘要： A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in different areas of pure mathematics. The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems. Since the 1960s, mathematicians have used computers to assist in the discovery of patterns and formulation of conjectures(1), most famously in the Birch and Swinnerton-Dyer conjecture(2), a Millennium Prize Problem(3). Here we provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning-demonstrating a method by which machine learning can aid mathematicians in discovering new conjectures and theorems. We propose a process of using machine learning to discover potential patterns and relations between mathematical objects, understanding them with attribution techniques and using these observations to guide intuition and propose conjectures. We outline this machine-learning-guided framework and demonstrate its successful application to current research questions in distinct areas of pure mathematics, in each case showing how it led to meaningful mathematical contributions on important open problems: a new connection between the algebraic and geometric structure of knots, and a candidate algorithm predicted by the combinatorial invariance conjecture for symmetric groups(4). Our work may serve as a model for collaboration between the fields of mathematics and artificial intelligence (AI) that can achieve surprising results by leveraging the respective strengths of mathematicians and machine learning.