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Nonsaturated large magnetoresistance and transport dynamics in a n-doped cryogenic WSe2 field-effect device

Physical Review B 2025年第12期111卷 125307-125307页

作者： Wei-Chen Lin Ching-Chen Yeh Jia-Zhu Zou Nhat Anh Nguyen Phan Inayat Uddin Hai Yen Le Thi Kenji Watanabe Takashi Taniguchi Gil-Ho Kim Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Physics National Taiwan University Taipei 106 Taiwan Department of Electrical and Computer Engineering Sungkyunkwan University (SKKU) Suwon 164 Republic of Korea Sungkyunkwan Advanced Institute of Nano Technology (SAINT) Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan

Interest in the two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) continues to intensify, driven by their suitable band gaps to supplant silicon as next-generation semiconductor materials. Among various TMDs, tungsten diselenide (WSe2) is renowned for its superior electrical properties in carrier density and mobility under ambient conditions. Despite its notable attributes, the behavior of monolayer WSe2 in the electron-doped regime under cryogenic conditions remains largely uncharted, particularly concerning its magnetotransport properties. In this study, we reveal the transport mechanisms of monolayer WSe2 from high temperatures down to the cryogenic regime. As evident by Efros–Shklovskii variable-range hopping (E-S VRH) in the cryogenic regime, strong Coulomb interactions arise between electrons. Above 8 K, an uncommon nonsaturated quadratic large magnetoresistance (MR) can be explained by the wave-function shrinkage model, which is consistent with the E-S VRH transport mechanism. Notably, the nonsaturated quadratic large MR shows a magnitude up to 1740% at 13 T. These findings underscore the potential applications for monolayer WSe2 in cryogenic field-effect devices, magnetic sensors, and memory devices and mark a significant advance in magnetotransport research.

关键词： Monolayers

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: DATASETS, MODELS, TOOLS, AND METRICS FOR CROSS-DATASET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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The Wilhelm Tell Dataset of Affordance Demonstrations 25

The Wilhelm Tell Dataset of Affordance Demonstrations

引用

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Rachel Ringe Mihai Pomarlan Nikolaos Tsiogkas Stefano De Giorgis Maria Hedblom Rainer Malaka Digital Media Lab University of Bremen Bremen Germany Applied Linguistics University of Bremen Bremen Germany Department of Computer Science KU Leuven Leuven Belgium National Research Council Institute of Cognitive Sciences and Technologies Catania Italy Department of Computing JÖnkÖping School of Engineering JÖnkÖping Sweden

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Affordances - i.e. possibilities for action that an environment or objects in it provide - are important for robots operating in human environments to perceive. Existing approaches train such capabilities on annotated static images or shapes. This work presents a novel dataset for affordance learning of common household tasks. Unlike previous approaches, our dataset consists of video sequences demonstrating the tasks from first- and third-person perspectives, along with metadata about the affordances that are manifested in the task, and is aimed towards training perception systems to recognize affordance manifestations. The demonstrations were collected from several participants and in total record about seven hours of human activity. The variety of task performances also allows studying preparatory maneuvers that people may perform for a task, such as how they arrange their task space, which is also relevant for collaborative service robots.

关键词： Training Service robots Shape Affordances Video sequences Human-robot interaction Collaboration Metadata

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A review of the numerical methods for solving the binary Allen–Cahn equation

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Physica A: Statistical Mechanics and its Applications 2025年 670卷

作者： Lee, Hyun Geun Li, Yibao Yang, Junxiang Kwak, Soobin Hwang, Youngjin Ham, Seokjun Kim, Hyundong Jyoti Nam, Yunjae Kim, Junseok Department of Mathematics Dongguk University Seoul04620 Korea Republic of School of Mathematics and Statistics Xi'an Jiaotong University Xi'an710049 China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Department of Mathematics Korea University Seoul02841 Korea Republic of Department of Mathematics and Physics Gangneung-Wonju National University Gangneung25457 Korea Republic of Smart infrastructure Gangneung-Wonju National University Gangneung25457 Korea Republic of The Institute of Basic Science Korea University Seoul02841 Korea Republic of Program in Actuarial Science and Financial Engineering Korea University Seoul02841 Korea Republic of

In this review, we present an overview of numerical methods to solve the binary Allen–Cahn (AC) equation, which is extensively used to model phase separation processes in materials science. It describes the time-dependent evolution of interfaces between two phases and accounts for both local reaction kinetics and diffusion effects. This equation plays a critical role in understanding the behavior of interfaces. The AC equation has various applications across fields such as materials science, physics, and biology, where it helps to analyze and predict phenomena such as phase transitions, grain boundary motion, and pattern formation in complex systems. Its importance lies in its ability to model the dynamics of interfaces and help the study of pattern formation and phase transitions in diverse environments. We discuss various computational methodologies developed for this important mathematical model and describe their strengths, limitations, and applications in diverse scientific domains. © 2025 Elsevier B.V.

关键词： Rate constants

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Visual Control and Mapping for UAV-Based Platform Inspection

Visual Control and Mapping for UAV-Based Platform Inspection

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leonardo A. Fagundes Carlos M. Soria Raquel F. Vassallo Alexandre S. Brandão Department of Electrical Engineering Graduate Program on Computer Science Federal University of Viçosa Viçosa MG Brazil Facultad de Ingeniería Instituto de Automática Universidad Nacional de San Juan Argentine Department of Electric Engineering Federal University of Espirito Santo Vitória ES Brazil

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Autonomous take-off and landing capabilities are crucial in UAV vision-based missions, ensuring adaptive navigation, especially in challenging environments where realtime identification and interaction with a variety of landing platforms are required. In this context, this paper presents a servo-visual controller that uses pattern detection and color segmentation techniques to identify take-off/landing platforms and estimate their current orientation. The proposed system was subjected to experimental validation with four platforms positioned in different orientations, heights, and positions, demonstrating its versatility in various conditions. Our study addresses the Flying Robots Trial League challenge, which emulates mapping and inspection tasks in offshore platforms.

关键词： Visualization Offshore installations Navigation Color Inspection Autonomous aerial vehicles Aircraft Drones Aerospace control

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

引用

2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

arXiv

引用

arXiv 2025年

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing211111 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025, CC BY.

关键词： Unmanned surface vehicles

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Small Polaron-Induced Ultrafast Ferroelectric Restoration in BiFeO3

引用

Physical Review X 2025年第2期15卷 021046-021046页

作者： Wenfan Chen Tian Wang Chun-Chieh Yu Yuancheng Jing Xiaosong Li Wei Xiong Materials Science and Engineering Program University of California San Diego La Jolla California 92093 USA Department of Chemistry University of Washington Seattle Washington 98195 USA Department of Chemistry University of California San Diego La Jolla California 92093 USA Department of Electrical and Computer Science Engineering University of California San Diego La Jolla California 92093 USA

In this report, we apply a suite of ultrafast spectroscopic techniques and advanced calculations to reveal the interplay between electronic and lattice degrees of freedom in ferroelectric BiFeO3. Using transient sum frequency generation spectroscopy, which is sensitive to electronic polarizations, we observe a transient electronic dipole reduction upon optical excitation which recovers at 0.5 and 10 ps timescale. The time-dependent density functional theory calculation reveals that both ligand-metal charge transfer and local excitation transition occurred upon photo excitation. To reveal the nature of electronic dipole restoration, we employ transient extreme ultraviolet (EUV) spectroscopy—an element-specific ultrafast technique that follows charge dynamics of Bi, Fe, and O altogether. The transient EUV dynamics observed both ultrafast free charge carrier relaxation to excitons, as well as polaron formation. However, a timescale comparison suggests that only the polaron formation is responsible for the 0.5 ps electronic dipole restoration, whereas the faster electronic relaxation does not contribute to the ferroelectric property changes. Multireference configuration interaction calculation further corroborates this result by showing both Fe and Bi atoms shift from the ground state equilibrium—leading to the polaron formation. Our result disentangles the multidegrees of freedom in ultrafast ferroelectric modulation and identifies the pivotal motion—a local polaron formation—for the fast ferroelectric recovery. It provides crucial insights on the specific lattice distortion that could modulate properties or phase transitions of condensed matter materials.

关键词： Light polarization

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Effect of Music Training in Neural Responses to Emotional Speech Prosody: Insights from EEG and Brain Network Analysis 4th

Effect of Music Training in Neural Responses to Emotional...

引用

4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Gao, Ci-Jun Liu, Xucheng Jia, Gufeng Yang, Hongzhe Tao, Wei Wu, Haiyan Huang, Ruey-Song Wan, Feng Department of Psychology Faculty of Social Sciences University of Macau Taipa China Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Taipa China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9789819640003

Speech prosody conveys crucial information during social communication. However, the neural mechanisms underlying speech pro-sody in the human brain remain unclear. The primary aim of this study was to explore the impact of long-term musical training on the recognition of emotional speech prosody. Employing a task that isolated prosody recognition without semantic content interference, we recorded behavioral and electrophysiological data. Participants were categorized into two groups: individuals with systematic musical training and those without any formal musical training. Our findings indicate that the musician group exhibited enhanced neural activity in the frontal region, characterized by significant differences in the P50, P200, P3a, P3b, and P600 components. Additionally, a larger Power Spectral Density (PSD) was observed in the frontal, occipital, and parietal regions of musicians, particularly in the theta and alpha frequency bands. Graph theoretical analysis also indicated that musically trained individuals demonstrated an increased local coefficient and more concentrated degree. This study provides a comprehensive analysis of the neural alteration during speech prosody recognition attributable to long-term musical training, incorporating both static and dynamic analyses, with a focus on temporal and spatial dimensions. Our study provides new insights into the neural mechanisms induced by speech prosody. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Power spectral density

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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