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Adaptive strategy automation with large language models in paradoxical games

Physical Review Research 2025年第2期7卷 L022012-L022012页

作者： Kang Hao Cheong Jie Zhao Tao Wen Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore S637371 Singapore College of Computing and Data Science (CCDS) Nanyang Technological University Singapore S639798 Singapore

Finding an ideal sequence in games is crucial for maximizing gains in various scenarios. This process requires extensive investigation, which is both time consuming and demands significant domain expertise. The emergence of large language models (LLMs) represents a significant turning point in addressing this issue, attributable to their potent analytical capabilities. In this context, LLMs can serve to substantially alleviate the human labor needed to manage these complexities. Through comprehensive simulations of coin-tossing games, we have demonstrated that the adaptive switching strategies formulated by LLMs surpass predefined sequences in profitability when applied to certain paradoxical games. Furthermore, our experimental findings indicate that the proposed method not only automates the identification of effective strategies but also provides adaptability to various forms of these paradoxical games.

关键词： Computer simulation languages

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Reactivity,Regioselectivity,and Synthetic Application of 2-Pyrenyl Units in Scholl Reactions

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CCS Chemistry 2023年第9期5卷 2069-2077页

作者： Sai Ho Pun Ethan Chi Ho Wen Zeming Xia Han Chen Felix RFischer Qian Miao Department of Chemistry The Chinese University of Hong KongHong Kong Department of Chemistry University of CologneCologne Department of Chemistry University of CaliforniaBerkeleyCalifornia 94720 Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCalifornia 94720 Kavli Energy NanoScience Institute at the University of California Berkeley and the Lawrence Berkeley National Laboratory BerkeleyCalifornia 94720 Bakar Institute of Digital Materials for the Planet Division of ComputingData Scienceand SocietyUniversity of CaliforniaBerkeleyCalifornia 94720

We herein report the reactivity and regioselectivity of 2-pyrenyl as a coupling unit in Scholl *** the basis of the Scholl reactions of hexaarylbenzene substrates,we have found that pyrenyl units are preferably oxidized over naphthyl and phenyl units under appropriate Scholl reaction conditions,allowing divergent synthesis through a highly controllable intramolecular coupling *** find that the C1 and C3 positions of the 2-pyrenyl unit are the favorable sites for intramolecular coupling while C4 is not reactive to allow further *** reactivity and regioselectivity pattern can be explained by the spin density distribution,which shows that carbon-carbon bonds preferably form at sites with higher positive spin *** by these findings,we successfully synthesized a double helicene and a sextuple helicene through the controlled Scholl reactions of 2-pyrenyl units.

关键词： helicene polycyclic aromatics pyrene Scholl reactions spin density

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Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System

Advances in Privacy Preserving Federated Learning to Realize...

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Ravi Madduri Zilinghan Li Tarak Nandi Kibaek Kim Minseok Ryu Alex Rodriguez Data Science and Learning Division Argonne National Laboratory Lemont IL USA Mathematics and Computer Science Argonne National Laboratory Lemont IL USA School of Computing and Augmented Intelligence Arizona State University Tempe AZ USA Data Science and Learning Argonne National Laboratory Lemont IL USA

ISBN: (数字)9798350386745

ISBN: (纸本)9798350386752

The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable.

关键词： Training data privacy Technological innovation Federated learning Biological system modeling Medical services data models Real-time systems Security Protection

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Devil in the Noise: Detecting Advanced Persistent Threats with Backbone Extraction

Devil in the Noise: Detecting Advanced Persistent Threats wi...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Caio M. C. Viana Carlos H. G. Ferreira Fabricio Murai Aldri Luiz Dos Santos Lourenço Alves Pereira Júnior Computer Science Division Aeronautics Institute of Technology Brazil Department of Computing and Systems Federal University of Ouro Preto Brazil Data Science & Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Federal University of Minas Gerais Brazil

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

The use of host intrusion detection systems shows promising results in detecting APT campaigns due to the use of systems logs as source data to get more information about system environment. However, dealing with the increase of logs in time while tracking the execution context is a challenge for security analysts. Therefore, this work presents backbone extraction as a crucial preprocessing step, filtering out irrelevant logs. As the logs are modeled as provenance graphs, we discard spurious edges to detect residuals with distinctive node and edge distributions that indicate security threats. By applying our methodology to state-of-the-art benchmark datasets, we observed an increase in the performance of one-class classifiers by up to 62% on F1-score and 48% on recall in the Streamspot dataset and by up to 40% on F1-score and 33% on recall in the DARPA3 THEIA dataset. Moreover, our results indicate mitigation of the dependency explosion problem and underscore the ability of our methodology to improve the detection landscape by shrinking graph sizes without losing essential aspects to characterize attacks.

关键词： Computers Filtering Image edge detection Prevention and mitigation Noise Intrusion detection Benchmark testing Explosions Security

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XAI Based Cattle Identification with YOLO and SIFT Technique

XAI Based Cattle Identification with YOLO and SIFT Technique

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2024 IEEE International Conference on Future Machine Learning and data science, FMLDS 2024

作者： Katharria, Aashu Pant, Millie Deep, Kusum Devi, Indu Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand Roorkee India Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand Roorkee India Indian Institute of Technology Roorkee Department of Mathematics Uttarakhand Roorkee India ICAR-National Dairy Research Institute Livestock Production Management Division Haryana Karnal132001 India

ISBN: (纸本)9798350391213

In precision livestock farming, accurate cattle identification is essential for enhancing animal welfare, health monitoring, and productivity, while also supporting traceability and minimizing false insurance claims. This paper presents a novel approach for cattle identification using muzzle prints, with a focus on both efficiency and explainability. Taxicab metric, employed for efficient annotation of muzzle patterns significantly reduces the labeling time for training of YOLOv8 model. YOLOv8 is utilized for detecting muzzle prints in images, followed by SIFT (Scale-Invariant Feature Transform) for feature extraction and matching. The incorporation of Explainable AI (XAI) methods, particularly Grad-CAM, further enhances the transparency and interpretability of the SIFT model. © 2024 IEEE.

关键词： Precision agriculture

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Previs-Real:Interactive virtual previsualization system for news shooting rehearsal and evaluation

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 527-549页

作者： Che QU Shaocong WANG Chao ZHOU Tongchen ZHAO Rui GUO Cheng Wa WONG Chi DENG Bin JI Yuhui WEN Yuanchun SHI Yong-Jin LIU Department of Film and Television Technology Beijing Film AcademyBeijing 100088China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive ComputingTsinghua UniversityBeijing 100084China School of Social Sciences Tsinghua UniversityBeijing 100084China News Production and Broadcasting First Division China Media GroupBeijing 100038China School of Computer and Information Technology Bejing Jiaotong UniversityBejing 100044China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong UniversityBeijing 100044China

Background In the demanding field of live news broadcasting,the intricate studio production procedures and tight schedules pose significant challenges for physical rehearsals by *** paper explores the design and implementation of a lightweight virtual news previsualization system,leveraging virtual production technology and interaction design methods to address the lack of fidelity in presentations and manipulations,and the quantitative feedback of rehearsal effects in previous virtual *** Our system,Previs-Real,is informed by user investigation with professional cameramen and studio technicians,and adheres to principles of high fidelity,accurate replication of actual hardware operations,and real-time feedback on rehearsal *** system's software and hardware development are implemented based on Unreal Engine and accompanying toolsets,incorporating cutting-edge modeling and camera calibration *** We validated Previs-Real through a user study,demonstrating superior performance in previsualization shooting tasks using the virtual system compared to traditional camera *** findings,supported by both objective performance metrics and subjective responses,underline Previs-Real's effectiveness and potential in transforming news broadcasting *** Previs-Real eliminates the requirement for complex equipment interconnections and team coordination inherent in a physical studio by implementing methodologies complying the above principles,objectively resulting in a lightweight design of applicable version of virtual news previsualization *** offers a novel solution to the challenges in news studio previsualization by focusing on key operational features rather than full environment *** design approach is equally effective in the process of designing lightweight systems in other fields.

关键词： News broadcast Previsualization Human computer interaction design User-centered design Interface evaluation

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Method to simultaneously facilitate all jet physics tasks

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Physical Review D 2025年第5期111卷 054015-054015页

作者： Vinicius Mikuni Benjamin Nachman National Energy Research Scientific Computing Center Berkeley Lab Berkeley California 94720 USA Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Berkeley Institute for Data Science University of California Berkeley California 94720 USA

Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy, precision, or speed of all other jet physics tasks. This is demonstrated by training on a particular multiclass generation and classification task and then using the learned representation for different generation and classification tasks, for datasets with a different (full) detector simulation, for jets from a different collision system (pp versus ep), for generative models, for likelihood ratio estimation, and for anomaly detection. We consider our omnilearn approach thus as a jet-physics foundation model. It is made publicly available for use in any area where state-of-the-art precision is required for analyses involving jets and their substructure.

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Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Large Knowledge Graphs with Applications in Drug Repurposing

Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Lar...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Olasunkanmi, Olawumi Morris, Evan Kebede, Yaphet Lee, Harlin Ahalt, Stanley C. Tropsha, Alexander Bizon, Chris University of North Carolina at Chapel Hill Department of Computer Science Chapel HillNC United States University of North Carolina at Chapel Hill Renaissance Computing Institute Chapel HillNC United States University of North Carolina at Chapel Hill School of Data Science and Society Chapel HillNC United States University of North Carolina at Chapel Hill Division of Chemical Biology and Medicinal Chemistry Unc Eshelman School of Pharmacy Chapel HillNC United States

ISBN: (纸本)9798350362480

Knowledge graphs (KGs) represent the connections and relationships between real-world entities. We propose a link prediction framework on KGs named Enrichment-Driven GrAph Reasoner (EDGAR) that infers new edges by mining entity-local rules. This approach is based on enrichment analysis, a well-established statistical method used to calculate mechanisms common to a set of differentially expressed genes. EDGAR's inference results are inherently explainable and rankable, equipped with p-values for statistical significance of each enrichment-based rule. We demonstrate its effectiveness on a large-scale biomedical KG, ROBOKOP, focusing on drug repurposing for Alzheimer disease (AD) as a case study. Initially, we extracted 14 known drugs from the KG and identified 20 contextual biomarkers through enrichment analysis, shedding light on functional pathways relevant to the shared efficacy of drugs for AD. Subsequently, using the top 1,000 enrichment results, our enrichment-driven system identified 1,246 additional drug candidates for AD treatment. We validated the top 10 candidates using medical literature evidence. EDGAR is deployed within ROBOKOP, along with a web user interface. This is the first work to use enrichment analysis for either large graph completion or drug repurposing. © 2024 IEEE.

关键词： Knowledge graph

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ACE-RLHF: Automated Code Evaluation and Socratic Feedback Generation Tool using Large Language Models and Reinforcement Learning with Human Feedback

arXiv

引用

arXiv 2025年

作者： Rahman, Tasnia Kumar, Sathish A.P. Jha, Sumit Ramanathan, Arvind Department of Computer Science Cleveland State University ClevelandOH United States School of Computing and Information Sciences Florida International University MiamiFL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

Automated Program Repair tools are developed for generating feedback and suggesting a repair method for erroneous code. State of the art (SOTA) code repair methods rely on data-driven approaches and often fail to deliver solution for complicated programming questions. To interpret the natural language of unprecedented programming problems, using Large Language Models (LLMs) for code-feedback generation is crucial. LLMs generate more comprehensible feedback than compiler-generated error messages, and Reinforcement Learning with Human Feedback (RLHF) further enhances quality by integrating human-in-the-loop which helps novice students to lean programming from scratch interactively. We are applying RLHF fine-tuning technique for an expected Socratic response such as a question with hint to solve the programming issue. We are proposing code feedback generation tool by fine-tuning LLM with RLHF, Automated Code Evaluation with RLHF (ACE-RLHF), combining two open-source LLM models with two different SOTA optimization techniques. The quality of feedback is evaluated on two benchmark datasets containing basic and competition-level programming questions where the later is proposed by us. We achieved 2-5% higher accuracy than RL-free SOTA techniques using Llama-3-7B-Proximal-policy optimization in automated evaluation and similar or slightly higher accuracy compared to reward model-free RL with AI Feedback (RLAIF). We achieved almost 40% higher accuracy with GPT-3.5 Best-of-n optimization while performing manual evaluation. © 2025, CC BY.

关键词： Reinforcement learning

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Faithful Polarization Entanglement Distribution in the SeQUeNCe Quantum Network Simulator

Faithful Polarization Entanglement Distribution in the SeQUe...

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2024 Frontiers in Optics, FiO 2024

作者： Singal, Ansh Zang, Allen Ramesh, Anirudh Kolar, Alexander Chung, Joaquin Kettimuthu, Rajkumar Kumar, Prem Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Pritzker School of Molecular Engineering University of Chicago 5801 S Ellis Ave ChicagoIL60637 United States Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States

We simulate polarization entanglement distribution with the Fock state representation in the open-sourced quantum network simulator SeQUeNCe. Simulations are validated by comparing with experimental entanglement distribution results and unknown experimental parameters are estimated. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Polarization

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