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arXiv 2025年

作者： Kebrich, Sebastian Engelhardt, Felix Franzmann, David Büsing, Christina Linßen, Jochen Heinrichs, Heidi Jülich52425 Germany RWTH Aachen University Teaching and Research Area Combinatorial Optimization Faculty of Mathematics Computer Science and Natural Sciences Aachen52074 Germany University of Siegen Chair for Energy Systems Analysis Department of Mechanical Engineering Siegen57076 Germany

Future greenhouse gas neutral energy systems will be dominated by variable renewable energy technologies. However, renewable electricity generation from wind and solar technologies, as well as electricity demand, varies with the weather. This work addresses the problem of determining optimal capacities for renewable technologies in energy systems that ensure sufficient electricity supply when dealing with multi–year time–series data. For this an iterative algorithm is proposed that starts by optimising an arbitrary starting time–series, followed by adding additional constraints and reoptimising the modified optimisation problem until sufficient energy supply is provided for all time–series, i.e. the solution is robust to weather and demand variations. This is evaluated in a computational study on a German energy system model. The results show that the iterative algorithm finds robust solutions for an increase of 2–2.5% in total annual cost for a simplified model in gurobipy and 2.9% for a model built in the open source model framework ***. Testing the feasibility for all non robust solutions showed that supply gaps occurred in at least some of the remaining years. Based on the results of this work, ensuring feasibility within an energy system model for multiple time–series boils down to two factors: 1.) ensuring sufficient back–up capacity to overcome periods of high demand combined with low electricity generation from wind and photovoltaic, and 2.) enforcing sufficient total annual electricity generation. Our proposed open source iterative algorithm is able to ensure this. For general modelling, it is recommended to (a) check for systematic effects of different years’ time–series on energy system models especially for wind, but also for photovoltaics, (b) include dark lull and cold period effects on generation and demand in time–series, and (c) assess the feasibility of energy system models using different time–*** Codes 90C10, 90C15, 90C90 Copyright

关键词： Critical path analysis

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A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education

arXiv

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arXiv 2024年

作者： Latif, Ehsan Zhou, Yifan Guo, Shuchen Gao, Yizhu Shi, Lehong Nyaaba, Matthew Lee, Gyeonggeon Zhang, Liang Bewersdorff, Arne Fang, Luyang Yang, Xiantong Zhao, Huaqin Jiang, Hanqi Lu, Haoran Li, Jiaxi Yu, Jichao Wu, Xuansheng You, Weihang Liu, Zhengliang Liu, Vincent Shung Wang, Hui Wu, Zihao Lu, Jin Dou, Fei Ma, Ping Liu, Ninghao Liu, Tianming Zhai, Xiaoming AI4STEM Education Center University of Georgia AthensGA United States National GENIUS Center AthensGA United States School of Computing University of Georgia AthensGA United States Department of Electrical Engineering and Computer Engineering University of Memphis MemphisTN United States Department of Natural Sciences and Science Education National Institute of Education Singapore Singapore School of Social Sciences and Technology Technical University of Munich Munich Germany Department of Statistics University of Georgia AthensGA United States Faculty of Psychology Beijing Normal University Beijing China Second Language Acquisition and Teaching University of Arizona TucsonAZ United States Department of Mathematics Science and Social Studies Education University of Georgia AthensGA United States Department of Workforce Education and Instructional Technology University of Georgia AthensGA United States School of Teacher Education Nanjing Normal University Jiangsu Nanjing China

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, having significant potential to revolutionize education and workforce development. To assess the state-of-art AI capacity, this study provides a comprehensive evaluation of OpenAI o1-preview’s ability to perform higher-order cognitive tasks across 14 distinct dimensions, including critical thinking, systems thinking, computational thinking, design thinking metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. In each dimension, we adopt existing instruments with high reliability and validity, such as the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to systematically assess o1-preview’s higher-order thinking in comparison to human performance. We found overall outperformance of o1-preview for most of the categories: • o1-preview achieved significantly outperform (150%) as compared to humans for systematic thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. • For logical reasoning, critical thinking, and quantitative reasoning, o1-preview slightly under-performed by 25% than human such as for logical reasoning. • For analogical reasoning, we found that both human and o1-preview achieve perfect scores for each given task. Nevertheless, we identify o1-preview’s limited capacity in thinking such as abstract reasoning, where humans (i.e., psychology students) perform better than o1-preview, indicating that human oversight remains crucial for tasks requiring high-level abstraction. The findings have broad implications for education, advocating a shift in educational paradigms to emphasize the development of complementary human skills, such as creativity, abstract reasoning, and critical thinking. This study highlights the transformative role of AI in educati

关键词： Systems thinking

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Predicting stroke with machine learning techniques in a sub-Saharan African population

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Neuroscience Informatics 2025年第3期5卷

作者： Benjamin Segun Aribisala Deirdre Edward Godwin Ogbole Onoja M. Akpa Segun Ayilara Fred Sarfo Olusola Olabanjo Adekunle Fakunle Babafemi Oluropo Macaulay Joseph Yaria Joshua Akinyemi Albert Akpalu Kolawole Wahab Reginald Obiako Morenikeji Komolafe Lukman Owolabi Godwin Osaigbovo Akinkunmi Paul Okekunle Arti Singh Philip Ibinaye Mayowa Owolabi Department of Radiology University of Chicago Chicago IL USA Department of Computer Science Lagos State University Lagos Nigeria Department of Computer Science Oduduwa University Nigeria Department of Radiology University of Ibadan Ibadan Nigeria Department of Epidemiology and Medical Statistics University of Ibadan Nigeria Department of Medicine Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Public Health College of Health Sciences Osun State University Osogbo Nigeria Department of Medicine College of Medicine University of Ibadan Ibadan Nigeria Department of Medicine University of Ghana Medical School Accra Ghana Department of Medicine University of Ilorin Teaching Hospital Ilorin Nigeria Department of Medicine Ahmadu Bello University Zaria Nigeria Department of Medicine Obafemi Awolowo University Teaching Hospital Ile-Ife Nigeria Department of Medicine Aminu Kano Teaching Hospital Kano Nigeria Department of Medicine University of Jos Jos Nigeria Department of Food and Nutrition Seoul National University Korea Department of Epidemiology and Biostatistics Kwame Nkrumah University of Science and Technology Ghana Centre for Genomic and Precision Medicine College of Medicine University of Ibadan Nigeria

Background Stroke is the second leading cause of death and the third leading cause of disability globally, including Africa, which bears its largest burden. Accurate models are needed in Africa to predict and prevent stroke occurrence. The aim of this study was to identify the best machine learning (ML) algorithm for stroke prediction. methods We assessed medical data of 4,236 subjects comprising 2,118 stroke patients and 2,118 controls from the SIREN database. Sixteen established vascular risk factors were evaluated in this study. These are addition of salt to food at table during eating, cardiac disease, diabetes mellitus, dyslipidemia, education, family history of cardiovascular disease, hypertension, income, low green leafy vegetable consumption, obesity, physical inactivity, regular meat consumption, regular sugar consumption, smoking, stress and use of tobacco. From these, we also selected the 11 topmost risk factors using Population-Attributable Risk ranking. Eleven ML models were built and empirically investigated using the 16 and the 11 risk factors. Results Our results showed that the 16 features-based classification (maximum AUC of 82.32%) had a slightly better performance than the 11 feature-based (maximum AUC 81.17%) algorithm. The result also showed that Artificial Neural Network (ANN) had the best performance amongst eleven algorithms investigated with AUC of 82.32%, sensitivity of 71.23%, specificity of 80.00%. Conclusion Machine Learning algorithms predicted stroke occurrence employing major risk factors in Sub-Saharan Africa better than regression models. Machine Learning, especially Artificial Neural Network, is recommended to enhance Afrocentric stroke prediction models for stroke risk factor quantification and control in Africa.

关键词： Stroke SIREN Sub-Sahara Africa Risk factors Machine learning Artificial neural network

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Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

arXiv

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arXiv 2021年

作者： Wang, Yikai Sun, Fuchun Huang, Wenbing He, Fengxiang Tao, Dacheng State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China JD Explore Academy *** Inc China

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge—it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction. At its core, CEN adaptively exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of CEN compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrate the advantage of each component we propose. Our code is available at https://***/yikaiw/CEN. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Smart system for detecting unauthorized entry into a smart home

Smart system for detecting unauthorized entry into a smart h...

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2020 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2020

作者： Sedova, Nelly A. Arkhipova, Zoya V. Lavrov, Evgeniy A. Ledovskikh, Irina A. Bazhenov, Ruslan I. Vladivostok State University of Economics and Service Department of Information Technology and Systems Vladivostok Russia Technologies Baikal State University Department of Mathematical Methods and Digital Irkutsk Russia Sumy State University Department of Computer Science Sumy Ukraine Pacific National University Department of Mathematics and Information Technology Khabarovsk Russia Sholom-Aleichem Priamursky State University Department of Information Systems and Mathematics Russia

ISBN: (纸本)9781728181790

The article covers the matter of the risk assessment of unauthorized entry into a smart home. Thereby the authors model and compare two approaches to the issue considered. The first technique actualizes a fuzzy system for the level detecting intrusion danger and warns about an unauthorized entry. But the second technique deals with a neuro-fuzzy system construction for assessing the level of unauthorized entry to the smart home. The authors present a structure and methods of training fuzzy and neuro-fuzzy systems to evaluate the level of unauthorized entry to the smart home. They carry out a series of experiments on the models of the designed systems. According to the results of comparative analysis, the authors recognize the application feasibility of a neuro-fuzzy system to assess the level of unauthorized entry to a smart home with a fuzzy logic inference algorithm based on Takagi Sugeno fuzzy model. Besides, the authors run a neuro-fuzzy system computer modelling/simulation to identify its best values. In particular, they declare a list of the best membership functions for generating input linguistic variables. They also propose a hybrid learning technique and the method of linear coefficients for developing terms of the output variable/signal. © 2020 IEEE.

关键词： Membership functions

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Rate Equations for Graphs 18th

Rate Equations for Graphs

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18th International Conference on Computational methods in Systems Biology, CMSB 2020

作者： Danos, Vincent Heindel, Tobias Honorato-Zimmer, Ricardo Stucki, Sandro CNRS ENS-PSL INRIA Paris France Institute of Commercial Information Technology and Quantitative Methods Technische Universität Berlin Berlin Germany Centro Interdisciplinario de Neurociencia de Valparaíso Universidad de Valparaíso Valparaiso Chile Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden

ISBN: (纸本)9783030603267

In this paper, we combine ideas from two different scientific traditions: 1) graph transformation systems (GTSs) stemming from the theory of formal languages and concurrency, and 2) mean field approximations (MFAs), a collection of approximation techniques ubiquitous in the study of complex dynamics. Using existing tools from algebraic graph rewriting, as well as new ones, we build a framework which generates rate equations for stochastic GTSs and from which one can derive MFAs of any order (no longer limited to the humanly computable). The procedure for deriving rate equations and their approximations can be automated. An implementation and example models are available online at https://***/fragger. We apply our techniques and tools to derive an expression for the mean velocity of a two-legged walker protein on DNA. © 2020, Springer Nature Switzerland AG.

关键词： Formal languages

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Learning discrete representations via constrained clustering for effective and efficient dense retrieval

arXiv

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arXiv 2021年

作者： Zhan, Jingtao Mao, Jiaxin Liu, Yiqun Guo, Jiafeng Zhang, Min Ma, Shaoping Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space. Therefore, we present RepCONC, a novel retrieval model that learns discrete Representations via CONstrained Clustering. RepCONC jointly trains dual-encoders and the Product Quantization (PQ) method to learn discrete document representations and enables fast approximate NNS with compact indexes. It models quantization as a constrained clustering process, which requires the document embeddings to be uniformly clustered around the quantization centroids and supports end-to-end optimization of the quantization method and dual-encoders. We theoretically demonstrate the importance of the uniform clustering constraint in RepCONC and derive an efficient approximate solution for constrained clustering by reducing it to an instance of the optimal transport problem. Besides constrained clustering, RepCONC further adopts a vector-based inverted file system (IVF) to support highly efficient vector search on CPUs. Extensive experiments on two popular ad-hoc retrieval benchmarks show that RepCONC achieves better ranking effectiveness than competitive vector quantization baselines under different compression ratio settings. It also substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Interpreting dense retrieval as mixture of topics

arXiv

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arXiv 2021年

Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we conduct an interpretation study of recently proposed DR models. Specifically, we first discretize the embeddings output by the document and query encoders. Based on the discrete representations, we analyze the attribution of input tokens. Both qualitative and quantitative experiments are carried out on public test collections. Results suggest that DR models pay attention to different aspects of input and extract various high-level topic representations. Therefore, we can regard the representations learned by DR models as a mixture of high-level topics. Copyright © 2021, The Authors. All rights reserved.

关键词： Mixtures

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Language and Gender Differences in Financial Literacy: The Role of Feminized, Masculinized, and Neutral Question Phrasing

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International Journal of Consumer Studies 2025年第4期49卷

作者： Andrzej Cwynar Piotr Białowolski Robert Porzak Dorota Weziak-Bialowolska Institute of Public Administration and Business WSEI University Lublin Poland Department of Economics Kozminski University Warsaw Poland Human Flourishing Program Harvard University Cambridge Massachusetts USA Institute of Psychology and Human Sciences WSEI University Lublin Poland Department of Quantitative Methods and Applications of Computer Science Kozminski University Warsaw Poland

We examine whether the phrasing of financial literacy test questions affects the test scores of women and men. To this end, we developed three language versions of a well-known set of financial literacy questions: feminized, masculinized, and neutral. We then surveyed a large, representative sample of adult Poles ( N = 4389), with respondents randomly assigned to one of these three language versions. Using item response theory (IRT), we examined how female and male respondents answered the questions and scored on the financial literacy test, depending on the language version. We found that both women and men performed differently contingent on the phrasing of questions. Through our analysis, we identified a subset of questions free from language bias. Utilizing this language bias-resistant set, we assessed financial literacy levels for both genders. Even in this language bias-resistant shortened test, a significant gender gap favoring men persisted, with a difference of 0.775 on the standardized scale. In a series of alternative models, we found that the gender gap in financial literacy was noticeably smaller when feminized language was used compared to both masculinized language and the language bias-resistant version of the test. This study confirms the existence of a gender gap in financial literacy but suggests that its magnitude may be overestimated when masculinized language is utilized in testing instruments.

关键词： financial literacy framing gender gender gap language wording

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An Algorithm for On-Line Image Segmentation of Multiple Paper Defects Based on Fast Two-Dimensional Threshold Method

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2020年第4期52卷 28-37页

作者： Qu, Yun-Hui Tang, Wei Feng, Bo Department of Electric and Control Engineering Shaanxi University of Science & Technology Shaanxi Xi’an710021 China Computer Teaching and Research Section Xi’an Medical University Shaanxi Xi’an710021 China

The two-dimensional threshold segmentation algorithm is time-consuming and cannot detect multiple paper defects. Considering these problems, a fast two-dimensional threshold method for on-line segmentation of multiple paper defects was proposed. The image subtraction operation was used first to segment the image with multiple paper defects. Then dividing two-dimensional threshold into two one-dimensional thresholds was used to accelerate the segmentation. At the same time, according to the gray distribution characteristics of the paper defect image after subtraction, the search range of the threshold was narrowed, and the selection of the optimal threshold was further accelerated. Experimental results showed that the algorithm proposed in this paper can accurately and quickly detect multiple paper defects, and could be effectively used for real-time detection of the paper production process. © 2020 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Defects

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