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Phase-bias-blind computational ghost imaging with optical phased array

Optics Express 2025年第10期33卷 21132-21141页

作者： Hirata, Sota Matsuda, Naoki Komatsu, Kento Tanemura, Takuo Horisaki, Ryoichi Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

We propose a method for computational ghost imaging (CGI) with an optical phased array (OPA), wherein phase biases on the phase shifters are intrinsically unknown. In our method, an object is illuminated with a number of light fields from the OPA using random phase shifts, and the light from the object is captured by a single-pixel detector. The object and the phase biases are simultaneously estimated from the measured intensity signals. We numerically demonstrate the proposed method. Furthermore, we present complex amplitude field acquisition in OPA-based CGI with intensity measurements performed without reference light by simply adding an iris to the detector. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computational imaging Imaging systems Phase shift Phased arrays Scattering media Spatial light modulators

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TeraPRI: Homomorphic Terahertz-Empowered Joint Wireless Power and Information Transfer with Privacy-Preserving for 6G-Autonomous Vehicles

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IEEE Transactions on Consumer Electronics 2025年

作者： Pauu, Kulaea Taueveeve Wu, Jun Bashir, Ali Kashif Waseda University Graduate School of Information Production and Systems Japan Manchester Metropolitan University Department of Computing and Mathematics United Kingdom

In sixth-generation networks, Terahertz technology enables high-speed, low-latency communication capabilities, while Mobile Edge computing (MEC) enhances remote computation, leveraging Autonomous Vehicles capable of providing computational resources and energy support, particularly in challenging environments where conventional terrestrial infrastructure is absent. However, challenges remain, particularly with the limited energy of end-user nodes and the constrained data capabilities of MEC nodes in THz networks, which require substantial energy to transmit large volumes of data. Efficient coordination of energy supply and information transfer is essential. Additionally, such networks can face signal interference and leakage of privacy-sensitive information, complicating data security and reliable communication. To address these challenges, we propose TeraPRI, a novel framework for homomorphic THz-empowered joint wireless power and information transfer with privacy preservation for 6G Autonomous Vehicles. First, we introduce a dual-mode adaptive resource allocation method that alternates between »harvest-and-then-transmit-mode» (HaT-mode) and »transmit-and-then-harvest-mode» (TaH-mode) based on end-user node demands. Second, we design a selective CKKS homomorphic encryption technique with lightweight thresholding for frequency allocation, enabling the MEC to securely assign unique THz sub-channels to end-user nodes in the low-THz band (0.1-1.0 THz) while enhancing communication security through randomized frequency hopping. Extensive simulations show that TeraPRI significantly improves power transfer, information transfer rates, and privacy over existing methods. © 1975-2011 IEEE.

关键词： Inductive power transmission

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Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer

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Computational and Structural Biotechnology Journal 2025年 27卷 1719-1731页

作者： Tseng, Tzu-Yang Hsieh, Chiao-Hui Liu, Jie-Yu Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies. © 2025 The Authors

关键词： Drug infusion

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VibWalk: Mapping Lower-limb Haptic Experiences of Everyday Walking 25

VibWalk: Mapping Lower-limb Haptic Experiences of Everyday W...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Shih Ying-Lei Dongxu Tang Weiming Hu Sang Ho Yoon Yitian Shao Faculty of Computing HITSZ Shenzhen Guangdong China Graduate School of Culture Technology KAIST Daejeon Republic of Korea Faculty of Computing HITSZ Shenzhen Guangdong China and National Key Laboratory of Smart Farm Technologies and Systems Harbin Heilongjiang China

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Security in Post-Quantum Era: A Comprehensive Survey on Lattice-Based Algorithms

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IEEE Access 2025年 13卷 89003-89024页

作者： Hien Nguyen Samsul Huda Yasuyuki Nogami Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Interdisciplinary Education and Research Field Okayama University Okayama Japan Graduate School of Environmental Life Natural Science and Technology Okayama University Okayama Japan

Lattice-based post-quantum cryptography (PQC) has attracted significant attention as a promising solution to the security challenges posed by quantum computing. Unlike traditional cryptographic algorithms, lattice-based schemes are expected to remain secure even in the presence of quantum attacks, making them essential for securing future data. Despite their strong theoretical foundations, lattice-based schemes face several practical challenges, particularly in optimizing performance and scalability for real-world applications. This survey provides a novel taxonomy that categorizes lattice-based PQC designs, with an emphasis on computational paradigms and security considerations. We systematically evaluate lattice-based PQC implementations across both software platforms, including central processing units and graphics processing units, as well as hardware platforms like field-programmable gate arrays and application-specific integrated circuits, highlighting their strengths and limitations. In addition, we explore the practical applications of lattice-based cryptography in fields such as secure communication, critical infrastructure, privacy-preserving data analytics, artificial intelligence, and trust and authentication systems. By offering a comprehensive overview of the current state of lattice-based PQC, this survey aims to provide valuable insights into the ongoing advancements and future research directions in the field as we transition to a post-quantum era.

关键词： Cryptography Surveys Security NIST Software Quantum computing Optimization Digital signatures Proposals Computers

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Surface EMG Sensing and Granular Gesture Recognition for Rehabilitative Pouring Tasks: A Case Study

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Biomimetics 2025年第4期10卷 229-229页

作者： Zhang, Congyi Zhou, Dalin Fang, Yinfeng Kubota, Naoyuki Ju, Zhaojie School of Computing University of Portsmouth PortsmouthPO1 2UP United Kingdom School of Communication Engineering Hangzhou Dianzi University Hangzhou310005 China Graduate School of Systems Design Tokyo Metropolitan University Tokyo192-0397 Japan

Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the pouring action. Pouring is a common yet complex movement requiring precise muscle coordination and control, making it an ideal focus for rehabilitation studies. This research proposes a granular computing-based deep learning approach utilizing ConvMixer architecture enhanced with feature fusion and granular computing to improve gesture recognition accuracy. Our findings indicate that the addition of hand-crafted features significantly improves model performance;specifically, the ConvMixer model’s accuracy improved from 0.9512 to 0.9929. These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities. © 2025 by the authors.

关键词： Functional electric stimulation

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Beyond the Dialogue: Multi-chatbot Group Motivational Interviewing for Premenstrual Syndrome (PMS) Management 25

Beyond the Dialogue: Multi-chatbot Group Motivational Interv...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Shixian Geng Remi Inayoshi Chi-Lan Yang Zefan Sramek Yuya Umeda Chiaki Kasahara Arissa J. Sato Simo Hosio Koji Yatani Interactive Intelligent Systems Laboratory The University of Tokyo Tokyo Japan Graduate School of Education (Division of Clinical Psychology) The University of Tokyo Tokyo Japan Graduate School of Interdisciplinary Information Studies The University of Tokyo Tokyo Japan Graduate School of Humanities and Sciences Ochanomizu University Tokyo Japan Department of Computer Sciences University of Wisconsin-Madison Madison Wisconsin USA Center for Ubiquitous Computing University of Oulu Oulu Finland

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Performance upper bound of a Grover-mixer quantum alternating operator ansatz

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Physical Review A 2025年第1期111卷 012401-012401页

作者： Ningyi Xie Jiahua Xu Tiejin Chen Xinwei Lee Yoshiyuki Saito Nobuyoshi Asai Dongsheng Cai Graduate School of Science and Technology University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan School of Computing and Augmented Intelligence Arizona State University Tempe Arizona 85281 USA Graduate School of Systems and Information Engineering University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan Present address: Singapore Management University Singapore. Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima 965-8580 Japan School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima 965-8580 Japan Faculty of Engineering Information and Systems University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan

The quantum alternating operator ansatz (QAOA) represents a branch of quantum algorithms for solving combinatorial optimization problems. A specific variant, the Grover-mixer (GM) QAOA, ensures uniform amplitude across states that share equivalent objective values. This property makes the algorithm independent of the problem structure, focusing instead on the distribution of objective values within the problem. In this work we provide an alternative proof for the upper bound on the probability of measuring a computational basis state from a GM QAOA circuit with a given depth, which is a critical factor in QAOA cost. Using this, we derive the upper bounds for the probability of sampling an optimal solution and for the approximation ratio of maximum optimization problems, both dependent on the objective value distribution. Through numerical analysis, we link the distribution to the problem size and build the regression models that relate the problem size, QAOA depth, and performance upper bound. Our results suggest that the GM QAOA provides a quadratic enhancement in sampling probability and requires circuit depth that scales exponentially with problem size to maintain consistent performance.

关键词： Combinatorial optimization

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-awareness In computing 25

Cyberoception: Finding A Painlessly-Measurable New Sense In ...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Tadashi Okoshi Zexiong Gao Yi Zhen Tan Takumi Karasawa Takeshi Miki Wataru Sasaki Rajesh Krishna Balan Faculty of Environment and Information Studies Keio University Fujisawa Kanagawa Japan Graduate School of Interdisciplinary Information Studies The University of Tokyo Tokyo Tokyo Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Singapore Graduate School of Media and Governance Keio University Fujisawa city Kanagawa Pref Japan Department of Engineering University of Cambridge Cambridge Cambridge United Kingdom Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan

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