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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

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Technical Report: Pose Graph Optimization over Planar Unit Dual Quaternions: Improved Accuracy with Provably Convergent Riemannian Optimization

arXiv

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arXiv 2024年

作者： Warke, William D. Ramos, J. Humberto Ganesh, Prashant Brink, Kevin M. Hale, Matthew T. Mechanical and Aerospace Engineering University of Florida GainesvilleFL United States Mechanical and Aerospace Engineering University of Florida ShalimarFL United States Principal Research Engineer EpiSci Science Incorporated San DiegoCA United States Air Force Research Laboratory Eglin Air Force Base FL United States Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measurements can be highly correlated, which leads to degradation in the accuracy of PGO solutions that rely on this assumption. Therefore, in this paper we develop a novel algorithm derived from a realistic, correlated model of relative pose uncertainty, and we quantify the resulting improvement in the accuracy of the solutions we obtain relative to state-of-the-art PGO algorithms. Our approach utilizes Riemannian optimization on the planar unit dual quaternion (PUDQ) manifold, and we prove that it converges to first-order stationary points of a Lie-theoretic maximum likelihood objective. Then we show experimentally that, compared to state-of-the-art PGO algorithms, this algorithm produces estimation errors that are lower by 10% to 25% across several orders of magnitude of correlated noise levels and graph sizes. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Cell trajectory modulation: rapid microfluidic biophysical profiling of CAR T cell functional phenotypes

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Nature Communications 2025年第1期16卷 1-15页

作者： Zeming, Kerwin Kwek Quek, Kai Yun Sin, Wei-Xiang Teo, Denise Bei Lin Cheung, Ka-Wai Goh, Chin Ren Kairi, Faris Lee, Elizabeth Lim, Francesca Lorraine Wei Inng Seng, Michaela Su-Fern Soh, Shui Yen Birnbaum, Michael E. Han, Jongyoon Critical Analytics for Manufacturing of Personalized Medicine Singapore-MIT Alliance for Research and Technology (SMART) Singapore Singapore Department of Haematology Singapore General Hospital Singapore Singapore SingHealth Duke-NUS Oncology Academic Clinical Programme Sing Health Duke-NUS Academic Medical Centre Singapore Singapore SingHealth Duke-NUS Cell Therapy Centre SingHealth Duke-NUS Academic Medical Centre Singapore Singapore Department of Paediatric Hematology and Oncology KK Women’s and Children’s Hospital Singapore Singapore Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States

Chimeric Antigen Receptor (CAR) T cell therapy is a pivotal treatment for hematological malignancies. However, CAR T cell products exhibit batch-to-batch variability in cell number, quality, and in vivo efficacy due to donor-to-donor heterogeneity, and pre/post-manufacturing processes, and the manufacturing of such products necessitates careful testing, both post-manufacturing and pre-infusion. Here, we introduce the Cell Trajectory Modulation (CTM) assay, a microfluidic, label-free approach for the rapid evaluation of the functional attributes of CAR T cells based on biophysical features (i.e., size, deformability). CTM assay correlates with phenotypic metrics, including CD4:CD8 ratio, memory subtypes, and cytotoxic activity. Validated across multiple donors and culture platforms, the CTM assay requires fewer than 10,000 cells and delivers results within 10 minutes. Compared to labeled flow cytometry processing, the CTM assay offers real-time data to guide adaptive manufacturing workflows. Thus, the CTM assay offers an improvement over existing phenotypic assessments, marking a step forward in advancing CAR T cell therapy manufacturing. © The Author(s) 2025.

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KNOWM Memristors in a Bridge Synapse delay-based Reservoir Computing system for detection of epileptic seizures

arXiv

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arXiv 2025年

作者： Przyczyna, Dawid Hess, Grzegorz Szacilowski, Konrad AGH University of Science and Technology Academic Centre for Materials and Nanotechnology al. Mickiewicza 30 Kraków30-059 Poland AGH University of Science and Technology Faculty of Physics and Applied Computer Science al. Mickiewicza 30 Kraków30-059 Poland Jagiellonian University Department of Neurophysiology and Chronobiology Institute of Zoology and Biomedical Research ul. Gronostajowa 9 Kraków30-387 Poland

Nanodevices that show the potential for non-linear transformation of electrical signals and various forms of memory can be successfully used in new computational paradigms, such as neuromorphic or reservoir computing (RC). Dedicated hardware implementations based on functional neuromorphic structures significantly reduce energy consumption and/or increase computational capabilities of a given artificial neural network system. Concepts of RC, which as a flexible computational paradigm can be highly inclusive, are often used as a model to describe computations performed in materia. With mostly fixed internal structure, solid-state devices, especially memristors, are studied as computational substrates in various RC systems. In this work, we present single-node Echo State Machine (SNESM) RC system based on bridge synapse as a computational substrate (consisting of 4 memristors and a differential amplifier) used for epileptic seizure detection. KNOWM memristors were posed as ideal candidates because of their easy prototyping and reliability of operation. In this account, we present an application of commercially available KNOWM memristors in various neuromorphic applications, from simple analysis of switching and internal dynamics (elucidated form noise spectroscopy and total harmonic distortion analysis) to the classification and recognition of complex time series: epilepsy seizure recognition using a wrist-worn triaxial accelerometer. The results show that the evolution of the signal in a feedback loop helps improve the classification accuracy of the system for that task. The transformation in SNESM changes the correlation and distribution of the complexity parameters of the input signal. In general, there are more differences in the correlation of complexity parameters between the transformed signal and the input signal, which may explain the improvement in the classification scores. SNESM could prove to be a useful time series signal processing system designed to im

关键词： Differential amplifiers

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A Blockchain-Based Distributed Authentication and Dynamic Group Key Agreement Protocol 2nd

A Blockchain-Based Distributed Authentication and Dynamic Gr...

引用

2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Xu, Zisang Li, Feng Tan, Minfu Zhang, Jixin Computer and Communication Engineer Institute Changsha University of Science and Technology ChangshaHunan410114 China Big data development and Research Center Guangzhou College of Technology and Business Guangzhou528138 China School of Computer Science Hubei University of Technology WuhanHubei430068 China

ISBN: (纸本)9789811592126

With the rapid development of mobile networks, there are more and more application scenarios that require group communication. Group communication can transmit messages to all group members with minimal resources. The security of group communication depends on the security of the group key. Most existing group key agreement protocols are often flawed in performance, scalability, or security. Therefore, this paper proposes a blockchain-based distributed authentication and dynamic group key agreement protocol. Based on blockchain technology, this protocol improves the scalability and makes it possible to track malicious members. In addition, comparison with related protocols shows that our protocol reduces computational and communication costs. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Implementing hardware primitives based on memristive spatiotemporal variability into cryptography applications

引用

Chip 2023年第1期2卷 33-44页

作者： Bo Liu Yudi Zhao YinFeng Chang Han Hsiang Tai Hanyuan Liang Tsung-Cheng Chen Shiwei Feng Tuo-Hung Hou Chao-Sung Lai Faculty of Information Technology College of MicroelectronicsBeijing Univer-sity of TechnologyBeijing 100124BeijingChina School of Information and Communication Engineering Beijing Information Science&Technology Univer-sityBeijing 100101BeijingChina Artificial Intelligence and Green Technol-ogy Research Center Chang Gung UniversityGuishan Dist.Taoyuan 33302TaiwanChina Department of Electronic Engineering Chang Gung UniversityGuishan Dist.Taoyuan 33302TaiwanChina School of Electrical Engineer-ing and Computer Science The Pennsylvania State UniversityUniversity ParkPA 16802USA Institute of Electronics National Yang Ming Chiao Tung UniversityHsinchu 300TaiwanChina Department of Nephrology Chang Gung Memorial HospitalGuishan Dist.Linkou 33305TaiwanChina Department of Materials Engineering Ming Chi University of TechnologyTaishan Dist.New Taipei City 24301TaiwanChina

Implementing hardware primitives into cryptosystem has become a new trend in electronic ***,with intrinsic stochastic characteristics including the switching voltages,times and energies,as well as the fluctuations of the resistance state over time,could be a naturally good entropy source for cryptographic key *** this study,based on kinetic Monte Carlo Simula-tion,multiple Artificial Intelligence techniques,as well as kernel density map and time constant analysis,memristive spatiotemporal variability within graphene based conductive bridging RAM(CBRAM)have been synergistically analyzed to verify the inher-ent randomness of the memristive ***,the ran-dom number based on hardware primitives passed the Hamming Distance calculation with high randomness and uniqueness,and has been integrated into a Rivest-Shamir-Adleman(RSA)*** security of the holistic cryptosystem relies both the modular arithmetic algorithm and the intrinsic randomness of the hardware primitive(to be more reliable,the random num-ber could be as large as possible,better larger than 2048 bits as NIST suggested).The spatiotemporal-variability-based random number is highly random,physically unpredictable and machinelearningattack resilient,improving the robustness of the entire cryptosystem.

关键词： Spatiotemporal variability Graphene based memristor RTN True random number generator Rivest-Shamir-Adleman cryptosystem

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Construction of Hierarchical Matrix on Distributed Memory Systems using a Task Parallel Language

Construction of Hierarchical Matrix on Distributed Memory Sy...

引用

International Symposium on Computing and Networking Workshops (CANDARW)

作者： Zhengyang Bai Tasuku Hiraishi Akihiro Ida Masahiro Yasugi Keiichiro Fukazawa Gradudate School of Informatics Kyoto University Kyoto Japan Department of Information and Computer Science Kyoto Tachibana University Kyoto Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Yokohama Japan Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

A hierarchical matrix is an approximated form of the dense matrix that represents the N-by-N correlations of N objects. The construction of the hierarchical matrix is achieved by dividing a matrix into submatrices (partitioning), followed by calculating these submatrices' entries (filling). The matrix partitioning is divided into two sub-steps: cluster tree (CT) construction by dividing objects into clusters, hierarchically, and block cluster tree (BCT) construction by observing all cluster pairs at the same level of the cluster tree that satisfy a given admissibility condition. We can apply task parallelism to filling by treating each submatrix as a parallelization unit. In existing implementations, the partitioning is redundantly executed on every computing node, and filling is executed in parallel by assigning a set of tasks to each worker statically. However, we cannot get good load balance using such implementations because it is difficult to predict the workload of each task precisely. In this paper, we propose a new parallel implementation of the hierarchical matrix construction, where a BCT construction is executed in parallel using all workers of all computing nodes. When a worker finds a BCT leaf during BCT construction, it executes filling for the submatrix corresponding to the leaf. To get good load balance, we parallelized the BCT construction using the task parallel language Tascell, which enables easy and efficient parallelization of tree recursive algorithms by employing a dynamic load balancing strategy. In numerical experiments with 3D electric field analyses using up to 16 computing nodes each of which has 36 cores, our implementation achieved up to 1.9-fold speedups compared to an implementation using the static task assignment strategy.

关键词： Parallel languages Three-dimensional displays Correlation Heuristic algorithms Conferences Parallel processing Load management

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Multi-chain Storage Scheme Based on Hash Calendar and Recursive Zero-knowledge Proof

Multi-chain Storage Scheme Based on Hash Calendar and Recurs...

引用

computer Science and Blockchain (CCSB), International Conference on

作者： Chao Wang Zhongyuan Yao Yong Zhang Shangkun Guo Lindong Zhao Xueming Si Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China The Frontier Information Technology Research Institute Zhongyuan University of Technology China Department of Computer Science and Engineer SHANGHAI Jiao Tong University Shanghai China

Blockchain technology is currently a research hotspot in the computer field. However, with the rapid development of blockchain technology, poor storage scalability limits its further application. In response to this problem, this paper proposes a multi-chain storage scheme based on hash calendar and recursive zero-knowledge proof, which consists of two parts: off-chain and on-chain. The off-chain part uses hash calendar technology to store account information, and through recursive zero-knowledge proof of account state transitions, ensures the verifiability of off-chain state transitions and improves the security of the solution; and sets up proof generators and transactions The two roles of the executor improve the efficiency of transaction execution. A dynamically addable main and sub-chain array is designed on the chain. The sub-chains store the brief information of transactions and the world state. The main chain is responsible for verifying the correctness of off-chain state transitions. On-chain and off-chain collaborative storage reduces the space occupied by data., to realize the storage expansion of the blockchain. Finally, simulation experiments show that this scheme can effectively realize the storage expansion of blockchain.

关键词： computer science Access control Scalability Collaboration Generators Blockchains Arrays

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