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Proceedings of the 41st International Conference on Machine Learning

作者： Weixin Liang Zachary Izzo Yaohui Zhang Haley Lepp Hancheng Cao Xuandong Zhao Lingjiao Chen Haotian Ye Sheng Liu Zhi Huang Daniel A. McFarland James Y. Zou Department of Computer Science Stanford University Machine Learning Department NEC Labs America Department of Electrical Engineering Stanford University Graduate School of Education Stanford University Department of Computer Science and Department of Management Science and Engineering Stanford University Department of Computer Science UC Santa Barbara Department of Biomedical Data Science Stanford University Graduate School of Education and Department of Sociology and Graduate School of Business Stanford University Department of Computer Science and Department of Electrical Engineering and Department of Biomedical Data Science Stanford University

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

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Integrating Physical Wheelchairs and Virtual Basketball for Enhanced Access and Enjoyment in Wheelchair Basketball 6

Integrating Physical Wheelchairs and Virtual Basketball for ...

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6th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2024

作者： Hsu, Yu-Hung Fang, Iuan-Kai Wang, Chun Chen, Jui-Chien Hu, Min-Chun National Tsing Hua University Department of Computer Science Hsinchu Taiwan National Tsing Hua University International Intercollegiate Ph.D. Program Hsinchu Taiwan National Taiwan University Department of Electrical Engineering Taipei Taiwan University of Texas at Austin School of Information United States

ISBN: (纸本)9798350372021

Wheelchair basketball for people with disabilities is a niche market with limited attention and resources due to its smaller scale. We utilize the characteristics of VR technology, combining wearable devices with physical wheelchair seating, to create a virtual and real interactive game of wheelchair basketball. This enables people with disabilities to exercise at home and allows the general public to experience the challenges of disability sports, fostering empathy and emphasizing the importance of social justice. © 2024 IEEE.

关键词： Virtual reality

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Transparent Barriers: Natural Language Access Control Policies for XR-Enhanced Everyday Objects 25

Transparent Barriers: Natural Language Access Control Polici...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Taninaka, Kentaro Jain, Rahul Shi, Jingyu Takashio, Kazunori Ramani, Karthik Graduate School of Media and Governance Keio University Fujisawa Japan Department of Electrical and Computer Engineering Purdue University West Lafayette IN United States Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN United States Faculty of Environment and Information Studies Keio University Fujisawa-shi Japan School of Mechanical Engineering Purdue University West Lafayette IN United States

ISBN: (纸本)9798400713941

Extended Reality (XR)-enabled headsets that overlay digital content onto the physical world, are gradually finding their way into our daily life. This integration raises significant concerns about privacy and access control, especially in shared spaces where XR applications interact with everyday objects. Such issues remain subtle in the absence of widespread applications of XR and studies in shared spaces are required for a smooth progress. This study evaluated a prototype system facilitating natural language policy creation for flexible, context-aware access control of personal objects. We assessed its usability, focusing on balancing precision and user effort in creating access control policies. Qualitative interviews and task-based interactions provided insights into users' preferences and behaviors, informing future design directions. Findings revealed diverse user needs for controlling access to personal items in various situations, emphasizing the need for flexible, user-friendly access control in XR-enhanced shared spaces that respects boundaries and considers social contexts. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Access control Everyday objects Extended reality Mixed reality Natural language policies Tangible user interface

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Machine Learning Methods for Predicting the Necessity of Caesareans Section of Childbirth 5

Machine Learning Methods for Predicting the Necessity of Cae...

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5th IEEE International Conference on Information Technology, Information Systems and electrical engineering, ICITISEE 2021

作者： Sunge, Aswan Supriyadi Heryadi, Yaya Abdurachman, Edi Kartowisatro, Iman H. Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty Of Engineering Computer Science Departement - BINUS Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

In the process of delivery usually the baby comes out of the vagina but under some circumstances a cesarean section is performed. Caesarean section, on the one hand can have short-term and long-term effects for the mother but on the other hand it is a life-saving procedure for both mother and child. The purpose of this study is to predict whether or not a caesarean section is necessary for a pregnant mother using a machine learning classifier model and some features from medical records as input data. In this study, some machine models were explored including Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, Gradient Boosting and Support Vector Machine. The experiment found that Logistic Regression model achieved the highest performance as measured by average accuracy 95%, average precision 86%, average recall 95%, and average F1 86%. © 2021 IEEE.

关键词： Forecasting

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Gallium Oxide Schottky Barrier Diodes for Alpha Spectroscopy

Gallium Oxide Schottky Barrier Diodes for Alpha Spectroscopy

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2022 Transactions of the American Nuclear Society Annual Meeting, ANS 2022

作者： Heckert, Christopher Taylor, Neil R. Dhara, Sushovan Rajan, Siddharth Cao, Lei R. Blue, Thomas E. Department of Mechanical and Aerospace Engineering Nuclear Engineering Program The Ohio State University ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States

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UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition 16

UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Tr...

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16th International Workshop on Semantic Evaluation, SemEval 2022

作者： Schneider, Elisa Terumi Rubel Rivera-Zavala, Renzo M. Martinez, Paloma Moro, Claudia Paraiso, Emerson Cabrera Graduate Program on Informatics Pontifícia Universidade Catolica do Paraná Brazil Computer Science and Engineering Department Universidad Carlos III de Madrid Spain

ISBN: (纸本)9781955917803

This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team. We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts. Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text. Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in. The ensemble model for the code-mixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points. Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data. © 2022 Association for Computational Linguistics.

关键词： Deep learning

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Robust Nonlinear $\mathcal{W}_{\infty}$ Optimal Control for Input Nonaffine Systems

Robust Nonlinear $\mathcal{W}_{\infty}$ Optimal Control for ...

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European Control Conference (ECC)

作者： Daniel N. Cardoso Marco H. Terra Guilherme V. Raffo Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte MG Brazil Department of Electrical and Computer Engineering University of São Paulo São Carlos SP Brazil Department of Electronics Engineering Federal University of Minas Gerais (UFMG) Belo Horizonte MG Brazil

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We propose a novel robust nonlinear $\mathcal{W}_{\infty}$ optimal control method for dynamical systems with nonaffine control inputs. The nonlinear $\mathcal{W}_{\infty}$ control formulation extends the classic nonlinear $\mathcal{H}_{\infty}$ one, considering a weighted Sobolev norm of the cost variable. This approach assumes that the cost variable belongs to the weighted Sobolev space $\mathcal{W}_{m,p,\mathbf{\Gamma}}$ , ensuring continuity and differentiability up to degree $m$ in a certain domain $\Omega$ . Consequently, in addition to the well-known features provided by the $\mathcal{H}_{\infty}$ approach in terms of disturbance attenuation, the closed-loop system benefits from the enhanced transient performance. Here, the robust nonlinear $\mathcal{W}_{\infty}$ optimal control problem is formulated via dynamic programming for increased-order systems, and a particular solution is proposed to the resulting Hamilton-Jacobi equation, along with the corresponding stability analysis. To validate the proposed method and its versatility, we provide numerical results for the control of a quadrotor. Additionally, leveraging the inherent $\mathcal{L}_{2}$ -gain properties of our approach, we demonstrate that the resulting controller can achieve trajectory tracking with guaranteed asymptotic stability for the whole closed-loop system.

关键词： Asymptotic stability Visualization Costs Trajectory tracking Optimal control Stability analysis Dynamic programming

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Modeling and Designing Non-Pharmaceutical Interventions in Epidemics: A Submodular Approach

arXiv

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arXiv 2024年

作者： Cheng, Shiyu Niu, Luyao Ramasubramanian, Bhaskar Clark, Andrew Poovendran, Radha Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States Department of Electrical and Computer Engineering University of Washington SeattleWA United States Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA United States Electrical and Computer Engineering Program Western Washington University BellinghamWA United States

This paper considers the problem of designing non-pharmaceutical intervention (NPI) strategies, such as masking and social distancing, to slow the spread of a viral epidemic. We formulate the problem of jointly minimizing the infection probabilities of a population and the cost of NPIs based on a Susceptible-Infected-Susceptible (SIS) propagation model. To mitigate the complexity of the problem, we consider a steady-state approximation based on the quasi-stationary (endemic) distribution of the epidemic, and prove that the problem of selecting a minimum-cost strategy to satisfy a given bound on the quasi-stationary infection probabilities can be cast as a submodular optimization problem, which can be solved in polynomial time using the greedy algorithm. We carry out experiments to examine effects of implementing our NPI strategy on propagation and control of epidemics on a Watts-Strogatz small-world graph network. We find the NPI strategy reduces the steady state of infection probabilities of members of the population below a desired threshold value. © 2024, CC BY.

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Multi-RIS Intelligent Collaboration Empowered Secure MmWave D2D Communication

Multi-RIS Intelligent Collaboration Empowered Secure MmWave ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Hu, Jiawei Ju, Ying Wang, Haoyu Liu, Lei Pei, Qingqi Guo, Yinbo Wu, Celimuge Xidian University School of Telecommunications Engineering Xi'an710071 China University of California Irvine Department of Electrical Engineering and Computer Science CA92697 United States Wuxi Jinyun Zhilian Technology Co. Ltd. Wuxi214122 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350351255

Millimeter wave (mmWave) Device-to-Device (D2D) communication networks suffer high path loss and dynamic physical obstructions. Meanwhile, eavesdroppers can intercept confidential information by residing in the main or side lobe of the transmission beam. Fortunately, multiple distributed Reconfigurable Intelligent Surfaces (RISs) offer a valuable approach to support mobile D2D devices, mitigating blocking effects and enhancing data security. In this paper, we propose a deep reinforcement learning (DRL) based communication scheme for the multi-RIS aided dynamic mmWave D2D networks, which aims to maximize the total secrecy data volume of D2D users over each service period by jointly optimizing the RIS resource allocation and multi-RIS phase shift design. To implement the intelligent collaboration of the RISs, we design the DRL approach with a nested structure. Specifically, we adopt a proximal policy optimization (PPO) network with discrete actions to realize the RIS-User association. Subsequently, we integrate the multi-agent PPO (MAPPO) framework to derive the phase shift design, containing intricate dynamic competition and cooperation among RIS agents. In addition, we divide the RIS into multiple subarrays, each sharing the same reflection coefficient. This approach controls more RIS phases while ensuring training stability. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the secrecy performance of dynamic mmWave D2D networks. © 2024 IEEE.

关键词： Resource allocation

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Dissipativity of nonlinear ODE model of distribution voltage profile

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IFAC-PapersOnLine 2023年第2期56卷 6453-6458页

作者： Chiaki Kojima Yuya Muto Yoshihiko Susuki Department of Electrical and Electronic Engineering Faculty of Engineering Toyama Prefectural University 5180 Kurokawa Imizu Toyama 939-0398 Japan Department of Electrical and Computer Engineering Graduate School of Engineering Toyama Prefectural University 5180 Kurokawa Imizu Toyama 939-0398 Japan Department of Electrical Engineering Graduate School of Engineering Kyoto University Katsura Nishikyo-ku Kyoto 615-8510 Japan

In this paper, we consider a power distribution system consisting of a straight feeder line. A nonlinear ordinary differential equation (ODE) model is used to describe the voltage distribution profile over the feeder line. At first, we show the dissipativity of the subsystems corresponding to active and reactive powers. We also show that the dissipation rates of these subsystems coincide with the distribution loss given by a square of current amplitudes. Moreover, the entire distribution system is decomposed into two subsystems corresponding to voltage amplitude and phase. As a main result, we prove the dissipativity of these subsystems based on the decomposition. As a physical interpretation of these results, we clarify that the phenomena related to the gradients of the voltage amplitude and phase are induced in a typical power distribution system from the dissipation equalities. Finally, we discuss a reduction of distribution losses based on the dissipation rate of the subsystem of voltage amplitude.

关键词： Power distribution system Nonlinear systems Distributed parameter systems Behavioral systems Dissipativity Modeling of power systems Voltage distribution profile Smart grids

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