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Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators

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arXiv 2024年

作者： Rajput, Kishansingh Schram, Malachi Edelen, Auralee Colen, Jonathan Kasparian, Armen Roussel, Ryan Carpenter, Adam Zhang, He Benesch, Jay Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Department of Computer Science University of Houston HoustonTX77204 United States Department of Computer Science Old Dominion University NorfolkVA23529 United States SLAC National Laboratory Menlo ParkCA94025 United States Joint Institute on Advanced Computing for Environmental Studies Old Dominion University NorfolkVA23539 United States Hampton Roads Biomedical Research Consortium PortsmouthVA23703 United States

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithm (GA), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems using a Deep Differentiable Reinforcement Learning (DDRL) algorithm in particle accelerators. We compare DDRL algorithm with Model Free Reinforcement Learning (MFRL), GA and Bayesian Optimization (BO) for simultaneous optimization of heat load and trip rates in the Continuous Electron Beam Accelerator Facility (CEBAF). The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraint for energy requirements of the beam. A physics-based surrogate model based on real data is developed. This surrogate model is differentiable and allows back-propagation of gradients. The results are evaluated in the form of a Pareto-front for two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems. © 2024, CC BY.

关键词： Genetic algorithms

High-Order Randomized Compiler for Hamiltonian Simulation

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PRX Quantum 2024年第2期5卷 020330-020330页

作者： Kouhei Nakaji Mohsen Bagherimehrab Alán Aspuru-Guzik Chemical Physics Theory Group Department of Chemistry University of Toronto Toronto Ontario Canada M5S 3H6 Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Department of Computer Science University of Toronto Toronto Ontario Canada M5S 2E4 Vector Institute for Artificial Intelligence Toronto Ontario Canada M5G 1M1 Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Ontario Canada M5S 3E5 Department of Materials Science & Engineering University of Toronto Toronto Ontario Canada M5S 3E4 Canadian Institute for Advanced Research (CIFAR) Toronto Ontario Canada M5S 1M1

Hamiltonian simulation is known to be one of the fundamental building blocks of a variety of quantum algorithms such as its most immediate application, that of simulating many-body systems to extract their physical properties. In this work, we present qSWIFT, a high-order randomized algorithm for Hamiltonian simulation. In qSWIFT, the required number of gates for a given precision is independent of the number of terms in the Hamiltonian, while the systematic error is exponentially reduced with regard to the order parameter. In this respect, our qSWIFT is a higher-order counterpart of the previously proposed quantum stochastic drift protocol (qDRIFT), the number of gates in which scales linearly with the inverse of the precision required. We construct the qSWIFT channel and establish a rigorous bound for the systematic error quantified by the diamond norm. qSWIFT provides an algorithm to estimate given physical quantities by using a system with one ancilla qubit, which is as simple as other product-formula-based approaches such as regular Trotter-Suzuki decompositions and qDRIFT. Our numerical experiment reveals that the required number of gates in qSWIFT is significantly reduced compared to qDRIFT. In particular, the advantage is significant for problems where high precision is required; e.g., to achieve a systematic relative propagation error of 10−6, the required number of gates in third-order qSWIFT is 1000 times smaller than that of qDRIFT.

关键词： Quantum computation Quantum simulation

Deep Multi-task Cascaded Acoustic Echo Cancellation and Noise Suppression

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Deep Multi-task Cascaded Acoustic Echo Cancellation and Nois...

International Symposium on Chinese Spoken Language Processing

作者： Junjie Li Meng Ge Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Huiyan Technology (Tianjin) Co. Ltd Tianjin China Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (纸本)9798350397970

With the growing need for online communication, removing the acoustic echo and background noise during voice and video calls has become a major problem. Recent studies show that deep learning based algorithms can successfully be applied to acoustic echo cancellation. These algorithms usually use one mask to remove acoustic echo and noise at the same time. Considering the patterns of acoustic echo and noise are different, hence we propose a multi-task cascaded framework with multiple masks named DMC-AEC to ease the difficulty of removing different interference, i.e., acoustic echo and noise. The DMC-AEC consists of three cascaded blocks, each block containing one mask. The first block takes mic and far-end signals to learn the auxiliary task of estimating the echo. The second block utilizes the estimated echo, far-end and mic signals to cancel acoustic echo. And in the third block, it takes the output of the second block and further suppresses noise. The DMC-AEC is trained on a synthetic dataset of ICASSP AEC Challenge.

关键词： Training Deep learning Microwave integrated circuits Echo cancellers Noise reduction Interference Multitasking

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

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arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

Quantum Simulation of the First-Quantized Pauli-Fierz Hamiltonian

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PRX Quantum 2024年第1期5卷 010345-010345页

作者： Priyanka Mukhopadhyay Torin F. Stetina Nathan Wiebe Department of Physical & Environmental Sciences University of Toronto Ontario M1C 1A4 Canada Simons Institute for the Theory of Computing Berkeley California 94720 USA Berkeley Quantum Information and Computation Center University of California Berkeley California 94720-1460 USA Department of Computer Science University of Toronto Ontario M5S 2E4 Canada Pacific Northwest National Laboratory Richland Washington 99354 USA Canadian Institute for Advanced Research Toronto Ontario M5G 1M1 Canada

We provide an explicit recursive divide-and-conquer approach for simulating quantum dynamics and derive a discrete first-quantized nonrelativistic QED Hamiltonian based on the many-particle Pauli-Fierz Hamiltonian. We apply this recursive divide-and-conquer algorithm to this Hamiltonian and compare it to a concrete simulation algorithm that uses qubitization. Our divide-and-conquer algorithm, using lowest-order Trotterization, scales for fixed grid spacing as O~(ΛN2η2t2/ϵ) for grid size N, η particles, simulation time t, field cutoff Λ, and error ϵ. Our qubitization algorithm scales as O~(N(η+N)(η+Λ2)tlog (1/ϵ)). This shows that even a naive partitioning and low-order splitting formula can yield, through our divide-and-conquer formalism, superior scaling to qubitization for large Λ. We compare the relative costs of these two algorithms on systems that are relevant for applications such as the spontaneous emission of photons and the photoionization of electrons. We observe that for different parameter regimes, one method can be favored over the other. Finally, we give new algorithmic and circuit-level techniques for gate optimization, including a new way of implementing a group of multicontrolled-X gates that can be used for better analysis of circuit cost.

关键词： Quantum simulation

Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

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arXiv 2024年

作者： Huan, Ouwen Yang, Yang Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

Cloud-based Decentralized Smart Healthcare for Patient Monitoring on Deep Learning

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Cloud-based Decentralized Smart Healthcare for Patient Monit...

Applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： Sripriya Arunachalam Shanthi H J G Sivagurunathan Shyamali Das D. Anand Thanga Raj M Computer Applications- PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS AMET University ECR Kanathur Department of Electronics and Communication Engineering St. Joseph's College of Engineering OMR Chennai Department of Artificial intelligence and Machine learning CMR institute of technology Bengaluru Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP Persistent Systems Limited Pune

Over the past few years, there has been a meteoric surge in the quantity of digital information available online for instantaneous sharing, persistent archiving, and inquiring. It has expanded the possibilities for using digital data that is both decentralised and ad hoc in order to make decisions quickly. At present, e-Healthcare is among the most sought-after sectors for EHR and telemedicine communication. Securing electronic health records (EHR) has become a topic of intense interest in recent years, with previous works employing a wide range of methods to better ensure the confidentiality and security of EHR at a reasonable price. There are a number of serious problems with the current research, including computational complexity, increased process time, information leakage, vulnerability to various assaults, scalability difficulty, etc. Clinical data analysis presents several difficulties, but disease prediction is one of the most significant ones. The suggested study aims to apply deep learning (DL) classification algorithms for disease prediction. A technique that utilises cloud computing, fog computing, and IoMT more recently has been presented for diagnosing illness. Fast DL classification analysis is performed in the fog layer. When compared to the alternative proposed model Bi-CNN, the healthcare model's efficiency in the Bi-LSTM simulation yields significantly better results: 97.31% of accuracy, 97.58% of recall, 96.90% of precision, 94.90% of F1-measure, 97.25% of specificity, and 94.80% of G-mean.

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

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A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Frank Kulwa Oluwarotimi Williams Samuel Mojisola Grace Asogbon Tolulope Tofunmi Oyemakinde Olumide Olayinka Obe Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Computer Science Federal University of Technology Akure Nigeria

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

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