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关键词： Computer science   Ethics   Machine learning   Society  

摘要： Scientists have been promised a front-row seat for the formulation of the EU's proposed AI regulatory structures. They should seize this opportunity to bridge some big gaps.

关键词： Computer science   Ethics   Technology   Society  

摘要： In comments submitted to the United Nations, three leaders of the Association for Mathematical Consciousness Science (AMCS) called for more funding to support research on consciousness and AL They said that scientific studies of the boundaries between conscious and unconscious systems are urgently needed, and they cited ethical and other issues that make it crucial to understand AI consciousness. [...]US President Joe Biden's executive order seeking responsible development of AI technology did not address issues raised by conscious AI systems. Understanding what could make AI conscious, the AMCS researchers say, is necessary to evaluate the implications of conscious AI systems for society, including their possible dangers. To mitigate the risks, the AMCS leaders call on governments and the private sector to fund more research on AI consciousness.

关键词： Behavioral research  

摘要： Child Impact Statements (CIS) are instrumental in helping to foreground the concerns and needs of minor community members who are too young to vote and often unable to advocate for themselves politically. While many politicians and policymakers assert they make decisions in the best interests of children, they often lack the necessary information to meaningfully accomplish this. CISs are akin to Environmental Impact Statements in that both give voice to constituents who are often under-represented in policymaking. This paper highlights an interdisciplinary collaboration between Social Science and Computer Science to create a CIS tool for policymakers and community members in Shelby County, TN. Furthermore, this type of collaboration is fruitful beyond the scope of the CIS tool. Social scientists and computer scientists can leverage their complementary skill sets in data management and data interpretation for the benefit of their communities, advance scientific knowledge, and bridge disciplinary divides within the academy. © 2024, CC BY.

关键词： Computer science   Genetics research   Machine learning   Magnetic resonance imaging   Neurodegenerative diseases  

摘要： Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes. Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

关键词： Computer science   Electrical and electronic engineering   Technology  

摘要： While federated learning (FL) is promising for efficient collaborative learning without revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high communication overhead, and struggles to adapt to heterogeneous models. Federated distillation (FD) emerges as an alternative paradigm to tackle these challenges, which transfers knowledge among clients instead of model parameters. Nevertheless, challenges arise due to variations in local data distributions and the absence of a well-trained teacher model, which leads to misleading and ambiguous knowledge sharing that significantly degrades model performance. To address these issues, this paper proposes a selective knowledge sharing mechanism for FD, termed Selective-FD, to identify accurate and precise knowledge from local and ensemble predictions, respectively. Empirical studies, backed by theoretical insights, demonstrate that our approach enhances the generalization capabilities of the FD framework and consistently outperforms baseline methods. We anticipate our study to enable a privacy-preserving, communication-efficient, and heterogeneity-adaptive federated training framework. While federated learning is promising for efficient collaborative learning without revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high communication overhead, and struggles to adapt to heterogeneous models. Here, the authors show a federated distillation method to tackle these challenges, which leverages the strengths of knowledge distillation in a federated learning setting.

关键词： Computer science   Machine learning   Mathematics and computing  

摘要： [...]its inventors say that the method, described in Nature on 14 December, could be applied to a variety of questions in maths and computer science (B. Romera-Paredes et al. Mathematical chatbot FunSearch automatically creates requests for a specially trained LLM, asking it to write short computer programs that can generate solutions to a particular mathematical problem. FunSearch was able to improve on the lower bound for n = 8 by generating sets of cards that satisfy all the requirements of the game.

关键词： Computer science   Image processing   Machine learning   Viral infection  

摘要： Features in images' backgrounds can spuriously correlate with the images' classes, representing background bias. They can influence the classifier's decisions, causing shortcut learning (Clever Hans effect). The phenomenon generates deep neural networks (DNNs) that perform well on standard evaluation datasets but generalize poorly to real-world data. Layer-wise Relevance Propagation (LRP) explains DNNs' decisions. Here, we show that the optimization of LRP heatmaps can minimize the background bias influence on deep classifiers, hindering shortcut learning. By not increasing run-time computational cost, the approach is light and fast. Furthermore, it applies to virtually any classification architecture. After injecting synthetic bias in images' backgrounds, we compared our approach (dubbed ISNet) to eight state-of-the-art DNNs, quantitatively demonstrating its superior robustness to background bias. Mixed datasets are common for COVID-19 and tuberculosis classification with chest X-rays, fostering background bias. By focusing on the lungs, the ISNet reduced shortcut learning. Thus, its generalization performance on external (out-of-distribution) test databases significantly surpassed all implemented benchmark models. Image background features can undesirably affect deep networks' decisions. Here, the authors show that the optimization of Layer-wise Relevance Propagation explanation heatmaps can hinder such influence, improving out-of-distribution generalization.

关键词： Computer science   Machine learning   Quantum information   Quantum physics   Technology  

摘要： Scientists are exploring the potential of quantum machine learning. But whether there are useful applications for the fusion of artificial intelligence and quantum computing is unclear.

关键词： CS teaching challenges   CS teaching strategies   computational thinking   K-12 computer science education   professional development  

摘要： This study aims to identify effective teaching strategies and challenges in computer science (CS) teaching and K-12 CS teachers' needs for professional development (PD). The data were collected through semi-structured interviews with five K-12 CS teachers and eight CS instructors in higher education and analyzed using thematic analysis. The findings indicated that (1) connecting the classroom to the real world, project-based assessment, and teamwork are effective teaching strategies;(2) teaching challenges include CS teachers' knowledge gaps, students' technology access and use, and elective courses;and (3) teachers' PD opportunities and needs include content and pedagogical knowledge and an ongoing learning community. The findings offer implications for K-12 CS education and teachers' PD.

关键词： 2型糖尿病   生存质量   自我管理行为   老年   影响因素  

摘要： 目的分析基于互动模式的社区自我管理活动对老年糖尿病患者生存质量的干预效果及影响因素,为提高患者生存质量提供依据。方法2016—2020年杭州市依托医联体建设及家庭医生签约服务资源,组建干预团队,以患者小组为单位开展系列自我管理活动干预。通过问卷调查方式收集基本信息、疾病状况、生存质量及自我管理行为情况。采用SPSS 19.0软件进行Friedman检验、Spearman相关检验及多重线性回归模型分析。结果共707例老年2型糖尿病患者纳入研究。干预前、干预4个月及干预10个月健康调查简表(SF-36)及2型糖尿病自我管理行为量表(2-DSCS)各维度得分差异具有统计学意义(χ^(2)>21.737,P<0.001)。干预4、10个月较干预前2-DSCS总分变化值与SF-36的各维度变化值均显著相关(r>0.125,P<0.01)。多重线性回归分析结果显示,生理健康(PCS)的影响因素有基线PCS得分、2-DSCS总分差值、伴2种及以上并发症,心理健康(MCS)的影响因素有基线MCS得分、2-DSCS总分变化值、伴有并发症及支付医疗费用比较困难(∣t∣>2.019,P<0.05)。结论基于互动模式的社区自我管理活动可有效改善患者管理行为,提升生存质量,且合并并发症、医疗费用支付能力对生存质量改善存在一定影响。