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arXiv 2025年

作者： Liu, Baopi Chen, Lu Xu, Wenjun Complex Systems Division Beijing Computational Science Research Center Beijing100193 China College of Physics Changchun Normal University Jilin Changchun130032 China School of Humanities and Basic Sciences Shenzhen Institute of Information Technology Shenzhen518116 China

In a fluid environment, flagellated microswimmers propel themselves by rotating their flagella. The morphology of these flagella significantly influences forward speed, swimming efficiency, and directional stability, which are critical for their survival. This study begins by simulating the three-dimensional motion trajectories of microswimmers to analyze their kinematic characteristics. The simulation results demonstrate that microswimmers can actively adjust their forward direction by modifying the orientation of their flagella. We subsequently perform numerical simulations to visualize the flow fields generated by a microswimmer and examine the hydrodynamic interactions between the cell body and the flagella, focusing on their impacts on forward speed and swimming efficiency. We conclude that forward speed and swimming efficiency are closely related to the filament radius, pitch angle, and contour length of the flagella, while the yaw angle of locomotion is determined by the helix radius and contour length of the flagella. We conclude that the pitch angle for maximum forward speed is slightly smaller than that for maximum swimming efficiency, which suggests that microswimmers can effectively alternate between states of maximum forward speed and maximum swimming efficiency by fine-tuning their pitch angle and adapting to varying ecological conditions. These morphological characteristics of microswimmers may result from species competition and natural selection. This research establishes an optimized model for microswimmers, providing valuable insights for the design of enhanced microrobots tailored to specific applications. © 2025, CC BY.

关键词： Kinematics

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Time-varying Normalizing Flow for Generative Modeling of Dynamical Signals

Time-varying Normalizing Flow for Generative Modeling of Dyn...

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European Signal Processing Conference (EUSIPCO)

作者： Anubhab Ghosh Aleix Espuña Fontcuberta Mohamed R.-H. Abdalmoaty Saikat Chatterjee Digital Futures School of Electrical Eng. and Comp. Science KTH Royal Institute of Technology Department of Information Technology Division of Systems and Control Uppsala University

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

We develop a time-varying normalizing flow (TVNF) for explicit generative modeling of dynamical signals. Being explicit, it can generate samples of dynamical signals, and compute the likelihood of a (given) dynamical signal sample. In the proposed model, signal flow in the layers of the normalizing flow is a function of time, which is realized using an encoded representation that is the output of a recurrent neural network (RNN). Given a set of dynamical signals, the parameters of TVNF are learned according to maximum-likelihood approach in conjunction with gradient descent (backpropagation). Use of the proposed model is illustrated for a toy application scenario - maximum-likelihood based speech-phone classification task.

关键词： Backpropagation Recurrent neural networks Computational modeling Toy manufacturing industry Europe Signal processing Task analysis

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Intelligent requirement-to-test-case traceability system via Natural Language Processing and Machine Learning

Intelligent requirement-to-test-case traceability system via...

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IEEE International Conference on Space Mission Challenges for information technology, SMC-IT

作者： Kae Sawada Marc Pomerantz Gus Razo Michael W. Clark Mission Control Systems Intg Test & Deployment Jet Propulsion Laboratory California Institute of Technology Pasadena CA Information And Data Management Jet Propulsion Laboratory California Institute of Technology Pasadena CA Natural Science Division Pasadena City College Pasadena CA

Accurate mapping of software requirements to tests is critical for ensuring high software reliability. However, the dynamic nature of software requirements throughout various mission phases necessitates the maintenance of traceable and measurable requirements throughout the entire mission life cycle. During the development phase, a predictable and controlled deployment, testing, and integration of software systems can strongly support a mission’s rapid innovation. Similarly, during the operation phase, timely application of patches and efficient evaluation and verification processes are vital. To address these challenges, we propose a novel method that combines Natural Language Processing (NLP) and Machine Learning (ML) to automate software requirement-to-test mapping. This method formalizes the process of reviewing the recommendations generated by the automated system, enabling engineers to improve software reliability, and reduce cost and development time.

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Representation of materials by kernel mean embedding

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Physical Review B 2023年第13期108卷 134107-134107页

作者： Minoru Kusaba Yoshihiro Hayashi Chang Liu Araki Wakiuchi Ryo Yoshida Research Organization of Information and Systems The Institute of Statistical Mathematics Tachikawa 190-8562 Japan SOKENDAI The Graduate Institute for Advanced Studies Tachikawa 190-8562 Japan JSR Corporation 3-103-9 Tonomachi Kawasaki-ku Kawasaki Kanagawa 210-0821 Japan Graduate School of Science and Technology Nara Institute of Science and Technology 8916-5 Takayama-cho Ikoma Nara 630-0192 Japan National Institute for Materials Science Research Network and Facility Services Division Tsukuba 305-0047 Japan

For using machine learning to predict material properties, the feature representation of the materials given to the model plays a fundamental role. A model describes material properties as a function of any given material system expressed as a fixed-length numeric vector, often called a descriptor. However, in most cases, the variables of interest are nontrivial for encoding their compositional or structural features, such as molecules, crystal systems, chemical compositions, and composite materials, into a fixed-length vector. Conventionally, to translate such a multicomponent system into a fixed-length vector, the distribution of predefined component features is summarized into a few summary statistics. The disadvantage of this reduction operation is that some distributional information, such as multimodality, is lost in the vectorization process. Here, we present a general class of material descriptors motivated by the machine-learning theory of kernel mean embedding. Unlike conventional descriptors, kernel mean embedding can retain all information regarding the distribution of component features in the vectorization process. Furthermore, the kernel mean descriptor uniquely determines the inverse map to the original material space. We demonstrate the expressive power and versatility of the kernel mean descriptor in various applications, including the prediction of the formation energy of inorganic compounds, prediction of the chemical composition to form quasicrystalline materials, and the use of force-field parameters to characterize polymeric materials.

关键词： Data mining Machine learning Statistical methods

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Single Image De-Raining by Multi Scale Fourier Transform Network

SSRN

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SSRN 2024年

作者： Zheng, Chaobing Yao, Yao Ying, Wenjian Wu, Shiqian Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan University of Science and Technology 947 Heping Avenue Wuhan430081 China Division of Mechanical Manufacturing and Intelligent Transportation Beijing Institute of Metrology Beijing100029 China College of Weapon Naval University of Engineering Wuhan430033 China

It is challenging to remove rain-steaks from a single image because the rain steaks are spatially varying in the rainy image. While data-driven rain removal algorithms have demonstrated promising results, they still face limitations, such as a heavy reliance on data and limited interpretability. This paper introduces a novel approach for single-image de-raining guided by Fourier Transform prior knowledge. Our method leverages inherent frequency domain information to effectively reduce rain streaks and restore image clarity. Initially, using Fourier transform to decompose the rainy image into amplitude and phase components, where raindrops are predominantly present in the amplitude image. Subsequently, data-driven algorithms are applied separately to process the amplitude and phase components. The Fourier inverse transform is then utilised to obtain enhanced features with improved clarity. In the final step, a multi-scale neural network incorporating a different scale attention mechanism is designed to enhance the processed features, thereby improving the algorithm's robustness. Experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods, both qualitatively and quantitatively. This innovative approach combines the power of the Fourier Transform with data-driven techniques, providing a more interpretable and effective solution for single-image de-raining. © 2024, The Authors. All rights reserved.

关键词： Rain

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Consumer technology in Task Offloading and Edge Resource Allocation: AIoT and Edge Computing for Next-Generation Communication

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IEEE Transactions on Consumer Electronics 2025年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Alghamdi, Azzah Ahanger, Tariq Ahamed Keshta, Ismail Xalikovich, Pardayev Abdunabi Soni, Mukesh Bhatt, Mohammed Wasim Convergence Department Korea University of Technology and Education Cheonan Korea Republic of Department of Computer Sciences College of Science University of Al Maarif Al Anbar31001 Iraq M.D. Research Intervention Treatment Institute HoustonTX United States Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Saudi Arabia Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia The Department of Accounting and Reporting Tashkent State University of Economics Tashkent Uzbekistan Division of Research and Development Lovely Professional University Phagwara India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

Artificial Intelligence of Things (AIoT) edge computing has emerged as a critical enabler for consumer technology, easing the load on distribution grid networks through enhanced data transmission and processing capabilities. The Internet of Things (IoT), a modern network paradigm, connects devices using sensors and communication mediums to enable seamless information exchange. However, the limited computational capacity of edge nodes poses challenges in optimizing AIoT edge resources compared to cloud computing. To address this, a cloud-edge three-layer framework for task offloading and edge resource allocation is proposed in the context of consumer technology and distribution grids. This model incorporates random tasks, limited resources, unequal processing power, and high latency requirements. It features two key phases: a resource auction using a multi-round iterative process and compute offloading managed by a Deep Reinforcement Learning (DRL) approach. Enhanced algorithms such as Double DQN and Dueling DQN are integrated into the job offloading process to improve performance. Simulations validate the convergence of the task offloading algorithm and demonstrate that the proposed methods significantly enhance computational efficiency and resource utilization for edge nodes. These advancements offer promising solutions for the dynamic needs of consumer technology and future communication networks. © 2025 IEEE.

关键词： Computation offloading

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Revisiting Modular Inversion Hidden Number Problem and Its Applications

Revisiting Modular Inversion Hidden Number Problem and Its A...

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作者： Xu, Jun Sarkar, Santanu Hu, Lei Wang, Huaxiong Pan, Yanbin Institute of Information Engineering Chinese Academy of Sciences State Key Laboratory of Information Security Beijing100085 China University of Chinese Academy of Sciences School of Cyber Security Beijing100049 China Indian Institute of Technology Madras Chennai600036 India Nanyang Technological University Division of Mathematical Sciences School of Physical and Mathematical Sciences Jurong West637371 Singapore Chinese Academy of Sciences Key Laboratory of Mathematics Mechanization NCMIS Academy of Mathematics and Systems Science Beijing100190 China

The Modular Inversion Hidden Number Problem (MIHNP), which was proposed at Asiacrypt 2001 by Boneh, Halevi, and Howgrave-Graham, is summarized as follows: Assume that the δ most significant bits of z are denoted by MSBδ(z). The goal is to retrieve the hidden number α ϵ p given many samples (ti,MSBδ((α + ti)-1 mod p)) for random ti ϵ p. MIHNP is a significant subset of Hidden Number Problems. Eichenauer and Lehn introduced the Inversive Congruential Generator (ICG) in 1986. It is basically characterized as follows: For iterated relations vi+1 = (avi-1 + b) mod p with a secret seed v0 ϵ p, each iteration produces MSBδ(vi+1) where i ≥ 0. The ICG family of pseudorandom number generators is a significant subclass of number-theoretic pseudorandom number generators. Sakai-Kasahara scheme is an identity-based encryption (IBE) system proposed by Sakai and Kasahara. It is one of the few commercially implemented identity-based encryption schemes. We explore the Coppersmith approach for solving a class of modular polynomial equations, which is derived from the recovery issue for the hidden number α in MIHNP and the secret seed v0 in ICG, respectively. Take a positive integer n = d3+o(1) for some positive integer constant d. We propose a heuristic technique for recovering the hidden number α or secret seed v0 with a probability close to 1 when δ/log2 p > 1/d+1 +o(1/d ). The attack's total time complexity is polynomial in the order of log2 p, with the complexity of the LLL algorithm increasing as dO(d) and the complexity of the Grobner basis computation increasing as dO(n). When d > 2, this asymptotic bound surpasses the asymptotic bound δ/log2 p > 1/3 established by Boneh, Halevi, and Howgrave-Graham at Asiacrypt 2001. This is the first time a more precise constraint for solving MIHNP is established, implying that the claim that MIHNP is difficult is violated whenever δ/log2 p © 1963-2012 IEEE.

关键词： Cryptography

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Understanding Older Adults' Perceptions and Challenges in Using AI-enabled Everyday Technologies

arXiv

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arXiv 2022年

作者： Shandilya, Esha Fan, Mingming School of Information Rochester Institute of Technology RochesterNY United States Guangzhou China Division of Integrative Systems and Design The Hong Kong University of Science and Technology Hong Kong

Artificial intelligence (AI)-enabled everyday technologies could help address age-related challenges like physical impairments and cognitive decline. While recent research studied older adults' experiences with specific AI-enabled products (e.g., conversational agents and assistive robots), it remains unknown how older adults perceive and experience current AI-enabled everyday technologies in general, which could impact their adoption of future AI-enabled products. We conducted a survey study (N=41) and semi-structured interviews (N=15) with older adults to understand their experiences and perceptions of AI. We found that older adults were enthusiastic about learning and using AI-enabled products, but they lacked learning avenues. Additionally, they worried when AI-enabled products outwitted their expectations, intruded on their privacy, or impacted their decision-making skills. Therefore, they held mixed views towards AI-enabled products such as AI, an aid, or an adversary. We conclude with design recommendations that make older adults feel inclusive, secure, and in control of their interactions with AI-enabled products. © 2022, CC BY-NC-ND.

关键词： Decision making

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Comparison of Temporal Decorrelation Decay Functions Over Land Cover Types for L- and C-vand SAR

Comparison of Temporal Decorrelation Decay Functions Over La...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yukio Endo Yu Morishita Ryu Sugimoto Ryo Natsuaki Masanobu Shimada Chiaki Tsutsumi Toru Kouyama Ryosuke Nakamura Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan Division of Architectural Civil and Environmental Engineering School of Science and Engineering Tokyo Denki University Saitama Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In repeat-pass interferometric synthetic aperture radar (In-SAR), the coherence loss derived from temporal decorrelation can be modeled using an exponential decay function. Many researches have investigated the relationships between decay parameters over various land-cover types for C-band SAR satellites. Although ALOS PALSAR provides many L-band SAR images, its revisit time was insufficient for understanding the rapid exponential drop in temporal decorrelation. Due to the lack of an appropriate archived dataset, analyzing the decay parameters for L-band SAR was a challenging task. In this study, we processed four-year interferometric pairs derived from ALOS-2 PALSAR-2 full-aperture ScanSAR images. This dataset has a 14 day minimum temporal interval. We selected Nairobi, Kenya, in the middle of the East African rift, as the study area and investigated how decay functions differ over multiple land-cover types. In addition, we compared decay models for Sentinel-1 interferograms. This study demonstrates that the decay parameters of L-band coherence completely differs across forest types, whereas C-band coherence is low coherence regardless of forest type.

关键词： Grasslands C-band Vegetation mapping Sentinel-1 Coherence Forestry Radar polarimetry

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A Study of Robustness of a Closed-Loop System for Blood Pressure Control

A Study of Robustness of a Closed-Loop System for Blood Pres...

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American Control Conference (ACC)

作者： Taylor E. Baum Bharadwaj Satchidanandan Munther A. Dahleh Emery N. Brown Electrical Engineering and Computer Science Department Massachusetts Institute of Technology MA USA Department of Electrical Engineering and Computer Science The Laboratory for Information & Decision Systems The Institute for Data Systems and Society at the Massachusetts Institute of Technology MA USA Department of Anesthesia Critical Care and Pain Medicine The Massachusetts General Hospital MA USA The Picower Institute for Learning and Memory The Massachusetts Institute of Technology MA USA The Division of Sleep Medicine Harvard Medical School MA USA

Wide variability of patient response to vasoactive drugs, and mechanical limits of infusion pumps lead to difficulties in achieving robust Closed-Loop Blood Pressure Control (CLBPC). Our goal is to refine a previously proposed CLBPC system which emphasizes the incorporation of mechanistic cardiovascular models, design simple Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) controllers, and assess their robustness to estimation errors in physiological parameters of patients that are expected to vary widely within a patient population. A pharmacokinetic-pharmacodynamic (PK-PD) model that quantifies the impact of drugs, phenylephrine and nicardipine, on a patient’s blood pressure is provided. A state space model is then formulated which incorporates the full dynamics of a patient’s blood pressure. We then implement MPC and PID control to simulate regulation of blood pressure and explore the robustness of such controllers to errors in estimates of patient physiological parameters.

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