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arXiv 2021年

作者： Livneh, Yoav Yaacobi, Ami Orenstein, Meir Andrew & Erna Viterbi Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel Rafael Advanced Defense Systems Ltd. POB 2250 Haifa3102102 Israel

Chip-scale light-atom interactions are vital for the miniaturization of atomic sensing systems, including clocks, magnetometers, gyroscopes and more. Combining as many photonic elements as possible onto a photonic chip greatly reduces size and power consumption, where the critical elements are those interfacing between the 2D circuit and the 3D vapor cell. We introduce a new design method for large scale two-dimensional converter structures, enabling out-coupling of radiation from the photonic chip into the atomic medium. These structures allow light intensity and phase spatial distribution and polarization control, without external light-manipulating elements. Large, 100x100 μm2 structures were designed generating low divergence optical beams with high degree of circular polarization. Simulations obtain mean circular polarization contrast of better than 30 dB. © 2021, CC BY.

关键词： Circular polarization

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Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma

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Journal of Pathology Informatics 2024年 15卷 100359-100359页

作者： Hashimoto, Noriaki Hanada, Hiroyuki Miyoshi, Hiroaki Nagaishi, Miharu Sato, Kensaku Hontani, Hidekata Ohshima, Koichi Takeuchi, Ichiro RIKEN Center for Advanced Intelligence Project Furo-cho Chikusa-ku Nagoya 4648603 Japan Department of Pathology Kurume University School of Medicine 67 Asahi-machi Kurume 8300011 Japan Department of Computer Science Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya 4668555 Japan Department of Mechanical Systems Engineering Nagoya University Furo-cho Chikusa-ku Nagoya 4648603 Japan

In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis. © 2023 The Authors

关键词： Digital pathology Flow cytometry Mixture of experts Multiple instance learning Whole slide image

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling Data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

arXiv

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arXiv 2023年

作者： Cheng, Xiang Zhang, Haotian Zhang, Jianan Gao, Shijian Li, Sijiang Huang, Ziwei Bai, Lu Yang, Zonghui Zheng, Xinhu Yang, Liuqing The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China The Samsung Semiconductor Samsung SoC Research and Development Lab San DiegoCA92121 United States Shandong University Jinan250100 China Guangzhou510000 China Guangzhou510000 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. To fill the gap, we embark on an initial endeavor with the goal of establishing a unified framework of intelligent multi-modal sensing-communication integration by generalizing the concept of ISAC and providing a comprehensive review under this framework. Inspired by the human synesthesia, the so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such an intelligent integration and details its paradigm for the first time. We commence by justifying the necessity and potential of the new paradigm. Subsequently, we offer a rigorous definition of SoM and zoom into the detailed paradigm, which is summarized as three operational modes realizing the integration. To facilitate SoM research, we overview the prerequisite of SoM research, that is, mixed multi-modal (MMM) datasets, and introduce our work. Built upon the MMM datasets, we introduce the mapping relationships between multi-modal sensing and communications, and discuss how channel modeling can be customized to support the exploration of such relationships. Afterward, aiming at giving a comprehensive survey on the current research status of multi-modal sensing-communication integration, we cover the technological review on SoM-enhance-based and SoM-concert-based app

关键词： Beamforming

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DRAM-Less: Hardware Acceleration of Data Processing with New Memory

DRAM-Less: Hardware Acceleration of Data Processing with New...

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IEEE Symposium on High-Performance computer Architecture

作者： Jie Zhang Gyuyoung Park David Donofrio John Shalf Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology (KAIST) Computer Architecture and Memory Systems Laboratory Lawrence Berkeley National Laboratory

ISBN: (数字)9781728161495

ISBN: (纸本)9781728161501

General purpose hardware accelerators have become major data processing resources in many computing domains. However, the processing capability of hardware accelerations is often limited by costly software interventions and memory copies to support compulsory data movement between different processors and solid-state drives (SSDs). This in turn also wastes a significant amount of energy in modern accelerated systems. In this work, we propose, DRAM-less, a hardware automation approach that precisely integrates many state-of-the-art phase change memory (PRAM) modules into its data processing network to dramatically reduce unnecessary data copies with a minimum of software modifications. We implement a new memory controller that plugs a real 3x nm multi-partition PRAM to 28nm technology FPGA logic cells and interoperate its design into a real PCIe accelerator emulation platform. The evaluation results reveal that our DRAM-less achieves, on average, 47% better performance than advanced acceleration approaches that use a peer-to-peer DMA.

关键词： Phase change random access memory Data processing Hardware Acceleration Kernel Buffer storage

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Neural multi-objective combinatorial optimization with diversity enhancement 23

Neural multi-objective combinatorial optimization with diver...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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Additive Manufacturing of Ti6Al4V Alloy Through Laser Metal Deposition (LMD): An Elebotative Approach of Process Modelling Via Computational Fluid Dynamics

SSRN

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SSRN 2023年

作者： Rehman, Asif Ur Azher, Kashif Rehman, Aqsa Shah, Mussadiq Mahmood, Muhammed Arif Tüfekci, Celal Sami Salamci, Metin U. ERMAKSAN Bursa Turkey Department of Mechanical Engineering Faculty of Engineering Gazi University Ankara Turkey Additive Manufacturing Technologies Application and Research Center-EKTAM Ankara Turkey Department of Computer Science NFC-IET Punjab Multan60000 Pakistan Advanced Manufacturing Technologies Center of Excellence-URTEMM Ankara Turkey Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States

Additive manufacturing (AM) has revolutionized many industries. Laser Melting Deposition (LMD) offers remarkable utility in size and is an effective method in terms of energy efficiency. The interaction of powder with laser before coming in contact with the substrate helps to attain fair melting and deposition. However, there is a problem associated with non-uniformity and accumulation of metallic melt on the edges of the manufactured parts which needs to be understood. Herein, LMD process of Ti6Al4V alloy is presented. The forces defining the melt pool flow have been elaborated. The phenomenon of recoil pressure and Marangoni actions along the single tracks of the melt pool is defined via the computational fluid dynamics (CFD) analysis. The behaviour of the melt pool in conduction mode and to check whether it will form a keyhole region or not. The main factor which influences the melt pool dynamics are found to be surface tension and density differences. Two modes of heat transfer conduction and depression are discussed with the Marangoni actions. Conduction mode is mainly governed by Marangoni actions while the depression mode is by recoil pressure. The stream traces and velocity vectors are observed for single tracks LMD process. A similar Marangoni actions experimental investigation is compared with the phenomenon observed under CFD analysis. The Discrete Element Modelling (DEM) adds value to the study as variable particle diameters are considered which are near to the actual powder size distribution. Moreover, considering the conduction mode flow dynamics to avoid the depression mode and generation of keyhole region in the melt pool will lead to porosity. © 2023, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Neural Network Learning of Robot Dynamic Uncertainties and Observer-based External Disturbance Estimation for Impedance Control *

Neural Network Learning of Robot Dynamic Uncertainties and O...

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IEEE/ASME (AIM) International Conference on advanced Intelligent Mechatronics

作者： Teng Li Armin Badre Hamid D. Taghirad Mahdi Tavakoli Department of Electrical and Computer Engineering Faculty of Engineering University of Alberta Alberta Canada Western Upper Limb Facility Sturgeon Hospital St. Albert Alberta Canada Department of Surgery Faculty of Medicine & Dentistry Division of Orthopaedic Surgery University of Alberta Alberta Canada Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Estimation of dynamic uncertainties is a critical and fundamental problem when designing a control system for a robot. During robot-environment interaction, in addition to the internal dynamic model uncertainties, the external environment-exerted force will also enter the dynamics. For robot impedance control, an exact dynamic model of the robot is needed but usually not available. It has been shown that integrating an impedance controller with a disturbance observer can achieve accurate impedance control. However, it works only for robots in free motion but not robot-environment interaction. Although a disturbance observer is able to accurately estimate the dynamic uncertainties, the estimation is lumped uncertainties that contain all uncertainty sources including both the internal and the external disturbances. Without separating these two parts, the method of combining an impedance controller and an observer will result in the human-applied force being canceled instead of interacting with the robot. To solve this problem in this paper, we propose a framework for learning the internal disturbances and separating the external disturbances by integrating three entities: an impedance controller, a neural network (NN) model, and a disturbance observer. In the framework, the impedance controller provides compliant robot behavior, while the observer captures the lumped uncertainties, and the NN learns to separate the external disturbances. Simulation results of an application scenario with an obstructive virtual fixture demonstrate the effectiveness of the proposed framework.

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Learning contrastive embedding in low-dimensional space 22

Learning contrastive embedding in low-dimensional space

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Shuo Chen Chen Gong Jun Li Jian Yang Gang Niu Masashi Sugiyama RIKEN Center for Advanced Intelligence Project (AIP) Japan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China RIKEN Center for Advanced Intelligence Project (AIP) Japan and Graduate School of Frontier Sciences The University of Tokyo Japan

ISBN: (纸本)9781713871088

Contrastive learning (CL) pretrains feature embeddings to scatter instances in the feature space so that the training data can be well discriminated. Most existing CL techniques usually encourage learning such feature embeddings in the high-dimensional space to maximize the instance discrimination. However, this practice may lead to undesired results where the scattering instances are sparsely distributed in the high-dimensional feature space, making it difficult to capture the underlying similarity between pairwise instances. To this end, we propose a novel framework called contrastive learning with low-dimensional reconstruction (CLLR), which adopts a regularized projection layer to reduce the dimensionality of the feature embedding. In CLLR, we build the sparse/low-rank regularizer to adaptively reconstruct a low-dimensional projection space while preserving the basic objective for instance discrimination, and thus successfully learning contrastive embeddings that alleviate the above issue. Theoretically, we prove a tighter error bound for CLLR; empirically, the superiority of CLLR is demonstrated across multiple domains. Both theoretical and experimental results emphasize the significance of learning low-dimensional contrastive embeddings.

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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