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41st International Conference on Machine Learning, ICML 2024

作者： Greshler, Nir Eli, David Ben Rabinovitz, Carmel Guetta, Gabi Gispan, Liran Zohar, Guy Tamar, Aviv General Motors Advanced Technical Center Israel Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel

The combination of Monte Carlo tree search and neural networks has revolutionized online planning. As neural network approximations are often imperfect, we ask whether uncertainty estimates about the network outputs could be used to improve planning. We develop a Bayesian planning approach that facilitates such uncertainty quantification, inspired by classical ideas from the meta-reasoning literature. We propose a Thompson sampling based algorithm for searching the tree of possible actions, for which we prove the first (to our knowledge) finite time Bayesian regret bound, and propose an efficient implementation for a restricted family of posterior distributions. In addition we propose a variant of the Bayes-UCB method applied to trees. Empirically, we demonstrate that on the ProcGen Maze and Leaper environments, when the uncertainty estimates are accurate but the neural network output is inaccurate, our Bayesian approach searches the tree much more effectively. In addition, we investigate whether popular uncertainty estimation methods are accurate enough to yield significant gains in planning. Our code is available at: https://***/nirgreshler/bayesian-online-planning. Copyright 2024 by the author(s)

关键词： Intelligent systems

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Ultra-low-defect homoepitaxial micro-LEDs with enhanced efficiency and monochromaticity for high-PPI AR/MR displays

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PhotoniX 2024年第1期5卷 335-356页

作者： Yibo Liu Guobin Wang Feng Feng Mengyuan Zhanghu Zhengnan Yuan Zichun Li Ke Xu Hoi Sing Kwok Zhaojun Liu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong SARChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of SciencesJiangsu Institute of Advanced SemiconductorsSuzhouChina

The issue of brightness in strong ambient light conditions is one of the critical obstacles restricting the application of augmented reality(AR)and mixed reality(MR).Gallium nitride(GaN)-based micro-LEDs,renowned for their exceptional brightness and stability,are considered the foremost contenders for AR ***,conventional heteroepitaxial growth micro-LED devices confront formidable challenges,including substantial wavelength shifts and efficiency *** this paper,we firstly demonstrated the high-quality homoepitaxial GaN-on-GaN micro-LEDs microdisplay,and thoroughly analyzed the possible benefits for free-standing GaN substrate from the material-level characterization to device optoelectronic properties and microdisplay application compared with sapphire *** GaN-on-GaN structure exhibits a superior crystal quality with ultra-low threading dislocation densities(TDDs)of~105 cm^(-2),which is three orders of magnitude lower than that of *** an in-depth size-dependent optoelectronic analysis of blue/green emission GaN-on-GaN/Sapphire micro-LEDs from 100×100 shrink to 3×3μm^2),real that a lower forward voltage and series resistance,a consistent emission wavelength(1.21 nm for blue and 4.79 nm for green@500 A/cm2),coupled with a notable reduction in efficiency droop ratios(15.6%for blue and 28.5%for green@500 A/cm^(2))and expanded color gamut(103.57%over Rec.2020)within GaN-on-GaN 10μm *** but not least,the GaN-on-GaN micro-display with 3000 pixels per inch(PPI)showcased enhanced display uniformity and higher luminance in comparison to its GaN-on-Sapphire counterpart,demonstrating significant potentials for high-brightness AR/MR applications under strong ambient light.

关键词： optoelectronic ambient epitaxial

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Mobile Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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A multi-modal approach for identifying schizophrenia using cross-modal attention 46

A multi-modal approach for identifying schizophrenia using c...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Premananth, Gowtham Siriwarden, Yashish M. Resnik, Philip Espy-Wilson, Carol University of Maryland Department of Electrical and Computer Engineering College ParkMD United States University of Maryland Institute for Advanced Computer Studies College ParkMD United States

ISBN: (纸本)9798350371499

This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms. We developed a multi-modal schizophrenia classification system using audio, video, and text. Facial action units and vocal tract variables were extracted as low-level features from video and audio respectively, which were then used to compute high-level coordination features that served as the inputs from the audio and video modalities. Context-independent text embeddings extracted from transcriptions of speech were used as the input for the text modality. The multi-modal system is developed by fusing a segment-to-session-level classifier for video and audio modalities with a text model based on a Hierarchical Attention Network (HAN), with cross-modal attention. The proposed multi-modal system outperforms the previous state-of-the-art multi-modal system by 8.53% in the weighted average F1 score. © 2024 IEEE.

关键词： Image coding

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Enhancing WRSN Sustainability Through On-Demand Directional Wireless Charging With Multiple Green-Powered Mobile Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 18019-18030页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Ding, Jun Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China New Jersey Institute of Technology Advanced Networking Laboratory Helen and John C. Hartmann Department of Electrical and Computer Engineering NewarkNJ07102 United States

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime. © 2014 IEEE.

关键词： Wireless charging

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Conformity, Confabulation, and Impersonation: Persona Inconstancy in Multi-Agent LLM Collaboration 2

Conformity, Confabulation, and Impersonation: Persona Incons...

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2nd Workshop on Cross-Cultural Considerations in NLP, C3NLP 2024

作者： Baltaji, Razan Hemmatian, Babak Varshney, Lav R. Department of Electrical and Computer Engineering United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States

ISBN: (纸本)9798891761469

This study explores the sources of instability in maintaining cultural personas and opinions within multi-agent LLM systems. Drawing on simulations of inter-cultural collaboration and debate, we analyze agents’ pre- and post-discussion private responses alongside chat transcripts to assess the stability of cultural personas and the impact of opinion diversity on group outcomes. Our findings suggest that multi-agent discussions can encourage collective decisions that reflect diverse perspectives, yet this benefit is tempered by the agents’ susceptibility to conformity due to perceived peer pressure and challenges in maintaining consistent personas and opinions. Counterintuitively, instructions that encourage debate in support of one’s opinions increase the rate of inconstancy. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs will remain untapped. © 2024 Association for Computational Linguistics.

关键词： Chatbots

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A Hyperbolic Micromagnet for Multiple Spin Qubits with Fast Rabi Oscillations and High Addressability

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第4期45卷 526-529页

作者： Chang, Che-Hao Li, Yu-Cheng Wu, Yu-Jui Liao, Chen-Yao Lin, Min-Jui Tsao, Hung-Yu Li, Jiun-Yun Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Graduate School of Advanced Technology Taipei10617 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan

We demonstrate a hyperbolic micromagnet to increase the manipulation speed of electron spins in silicon quantum dots using electric dipole spin resonance (EDSR). For single qubits, the hyperbolic magnet provides a large gradient of transverse magnetic fields of 4.9 mT/nm with the corresponding Rabi frequency ( 69 MHz). We then demonstrate that this hyperbolic micromagnet can drive a linear array of qubits with Rabi frequencies of 19 69 MHz and a high addressability (minimum difference of Larmor frequencies) up to 590 MHz to suppress the crosstalk between different qubits. We show that 15 qubits can be effectively addressed for high fidelity by this hyperbolic micromagnet. © 1980-2012 IEEE.

关键词： Magnetic fields

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Quantum-Enhanced Zero Trust Security: Evolution, Implementation, and Application 1

Quantum-Enhanced Zero Trust Security: Evolution, Implementat...

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Lin, Jun Jiang, Qiu Zhang, Weiyong Lin, Zihao Du, Xiaojiang Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350366778

In today's fast-paced digital economy, the widespread adoption of new technologies brings about significant challenges in network security. This paper delves into the evolution of Zero Trust network architecture, marking a significant shift from traditional perimeter-centric security methods. It embraces the 'never trust, always verify' philosophy, moving away from conventional approaches. Subsequently, the paper explores the strategic implementation of Zero Trust, focusing on essential components such as Policy Enforcement Points (PEP) and Identity and Access Management (IAM). It also addresses the challenges in deploying Zero Trust architectures, including the high demands for real-time detection, and suggests viable solutions to these challenges. An important aspect of this study is the emerging role of quantum technology in enhancing network security. The integration of quantum technology with Zero Trust principles is poised to offer more robust network protection, particularly with the advancements in quantum key research. Current research in quantum keys opens new avenues for applying the Zero Trust philosophy in practical scenarios. This fusion is instrumental in laying the foundation for a more secure and innovative network security framework, aiming to effectively counter the evolving digital threats and strengthen the infrastructure of the digital economy against potential vulnerabilities. © 2024 IEEE.

关键词： Network Security Quantum Technology Zero Trust

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Benchmarking Large Language Models for Persian: A Preliminary study Focusing on ChatGPT 30

Benchmarking Large Language Models for Persian: A Preliminar...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Abaskohi, Amirhossein Baruni, Sara Masoudi, Mostafa Abbasi, Nesa Babalou, Mohammad Hadi Edalat, Ali Kamahi, Sepehr Sani, Samin Mahdizadeh Naghavian, Nikoo Namazifard, Danial Sadeghi, Pouya Yaghoobzadeh, Yadollah Computer Science Department University of British Columbia Vancouver Canada School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

ISBN: (纸本)9782493814104

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pretrained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles. We have made our codes, prompts, and data available here: https://***/Ipouyall/Benchmarking_ChatGPT_for_Persian. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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Extending LLMs to New Languages: A Case study of Llama and Persian Adaptation 31

Extending LLMs to New Languages: A Case Study of Llama and P...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761964

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model's performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model's initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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