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arXiv 2024年

作者： Li, Heng Li, Haojin Chen, Jianyu Fu, Huazhu Liu, Jiang The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Institute of High Performance Computing Agency for Science Technology and Research Singapore

Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced in clinical settings due to the scarcity of annotated data resulting from the professional and private nature of medical data. Despite the existence of decent solutions, many of them are hindered in clinical settings due to limitations in data collection and computational complexity. To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain. A frequency filter-based data augmentation strategy is first proposed to promote domain variability within a single-source domain by introducing variations in frequency space and blending homologous samples. Then Gaussian filter-based structural saliency is also leveraged to learn robust representations across augmented samples, further facilitating the training of generalizable segmentation models. To validate the effectiveness of RaffeSDG, we conducted extensive experiments involving out-of-domain inference on segmentation tasks for three human tissues imaged by four diverse modalities. Through thorough investigations and comparisons, compelling evidence was observed in these experiments, demonstrating the potential and generalizability of RaffeSDG. The code is available at https://***/liamheng/NonIID_Medical_Image_Segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

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Comparison of Multi-Phase Power Converters and Power Delivery Networks for Next-Generation Space Architectures

Comparison of Multi-Phase Power Converters and Power Deliver...

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IEEE Conference on Aerospace

作者： Nicholas Franconi Thomas Cook Christopher Wilson Alan D. George NASA Goddard Space Flight Center Science Data Processing Branch Code 587 Software Engineering Division Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh

Next-generation spacecraft developers are earnestly investigating the application of artificial intelligence (AI) algorithms onboard to enable new mission concepts for space exploration and science. However, the current generation of radiation-hardened processors are inferior compared to commercial-off-the-shelf alternatives in terms of the computational performance required by modern AI applications. To address this disparity, space-system designers have started employing novel radiation-tolerant architectures combining both commercial and radiation-hardened components to mitigate radiation effects at a system level. Unfortunately, developing single-board computers with radiation-tolerant, high-performance processors is challenging because designers must balance the sparce selection of radiation-hardened power converters and high-reliability decoupling capacitors with SmallSat/CubeSat area constraints and limited thermal conduction. Consequently, the next-generation of space processors, including the AMD-Xilinx Versal Adaptive Compute Acceleration Platform require demanding power solutions capable of supplying core rails with 0.8V ± 18mV and currents up to 150 A. In this paper, we present a multifaceted analysis of the power system and decoupling network for a future Versal-based design. We developed preliminary power estimates based on expected processor and FPGA resource utilization for common AI processing applications. These estimates drive the power system requirements and a comparative analysis of single-phase integrated converters and multi-phase discrete converters for high-current FPGA supplies. We develop a tradespace between the number of phases, input voltage, load current, switching frequency, power efficiency, and printed circuit board area. Finally, we design and simulate four power delivery networks based on commercial, high-reliability, and flight-qualified capacitors to compare the efficacy of 0201 decoupling capacitors in flight missions.

关键词： Rails Program processors Capacitors Voltage Power systems Resource management Artificial intelligence

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Solving 2 by 2 Grid Sudoku Problem using Grover’s Algorithm with Intel Quantum SDK

Solving 2 by 2 Grid Sudoku Problem using Grover’s Algorithm...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Tejas Shinde Yaknan John Gambo Kevin Rasch Helena Liebelt Rui Li High Performance Computing and Quantum Computing Deggendorf Institute of Technology Deggendorf Germany Quantum Computing Research Intel Inc Hillsboro Oregon US Department of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

Quantum technologies are moving towards enabling real world uses and as these technologies develop. The most important thing that differs Quantum from classical computers is that Quantum Computers can do “multiple” tasks at the same time as opposed to the classical computers doing a single task at a time. This phenomenon has led to the discovery of a few algorithms that are faster than regular computers. One such algorithm is the Grover’s Algorithm[l]. This algorithm has shown a few real-world applications such as Quantum Search Algorithm, Travelling salesman, etc[2]. In this paper, Grover’s algorithm is introduced, and a 2 x 2 Sudoku example is solved using Intel Quantum Software Development Kit[3]. The Intel Quantum SDK is quite new and in this paper we show how a simple Grover’s Problem can be implemented in the SDK. Sudoku is a popular number-based puzzle game played on a grid consisting of nine squares subdivided into smaller 3x3 grids. The objective is to fill the grid with numbers from 1 to 9, ensuring that each row, column, and 3x3 grid contains all numbers exactly once.

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Deep Perspective Transformation Based Vehicle Localization on Bird’s Eye View

arXiv

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arXiv 2023年

作者： Mahyar, Abtin Motamednia, Hossein Rahmati, Dara Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and engineering Shahid Beheshti University Tehran Iran

An accurate understanding of a self-driving vehicle’s surrounding environment is crucial for its navigation system. To enhance the effectiveness of existing algorithms and facilitate further research, it is essential to provide comprehensive data to the routing system. Traditional approaches rely on installing multiple sensors to simulate the environment, leading to high costs and complexity. In this paper, we propose an alternative solution by generating a top-down representation of the scene, enabling the extraction of distances and directions of other cars relative to the ego vehicle. We introduce a new synthesized dataset that offers extensive information about the ego vehicle and its environment in each frame, providing valuable resources for similar downstream tasks. Additionally, we present an architecture that transforms perspective view RGB images into bird’s-eye-view maps with segmented surrounding vehicles. This approach offers an efficient and cost-effective method for capturing crucial environmental information for self-driving cars. 1 © 2023, CC BY.

关键词： Birds

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Learning discretized neural networks under Ricci flow

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 18901-18944页

作者： Jun Chen Hanwen Chen Mengmeng Wang Guang Dai Ivor W. Tsang Yong Liu Institute of Cyber-Systems and Control Zhejiang University China and School of Computer Science and Technology Zhejiang Normal University China Institute of Cyber-Systems and Control Zhejiang University China SGIT AI Lab State Grid Corporation of China China Centre for Frontier Artificial Intelligence Research Agency for Science Technology and Research (A*STAR) Singapore and Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore and College of Computing and Data Science Nanyang Technological University Singapore

In this paper, we study Discretized Neural Networks (DNNs) composed of low-precision weights and activations, which suffer from either infinite or zero gradients due to the nondifferentiable discrete function during training. Most training-based DNNs in such scenarios employ the standard Straight-Through Estimator (STE) to approximate the gradient w.r.t. discrete values. However, the use of STE introduces the problem of gradient mismatch, arising from perturbations in the approximated gradient. To address this problem, this paper reveals that this mismatch can be interpreted as a metric perturbation in a Riemannian manifold, viewed through the lens of duality theory. Building on information geometry, we construct the Linearly Nearly Euclidean (LNE) manifold for DNNs, providing a background for addressing perturbations. By introducing a partial differential equation on metrics, i.e., the Ricci flow, we establish the dynamical stability and convergence of the LNE metric with the L2-norm perturbation. In contrast to previous perturbation theories with convergence rates in fractional powers, the metric perturbation under the Ricci ow exhibits exponential decay in the LNE manifold. Experimental results across various datasets demonstrate that our method achieves superior and more stable performance for DNNs compared to other representative training-based methods.

关键词： discretized neural networks gradient perturbation information geometry ricci flow riemannian manifold

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AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Li, Haojin Li, Heng Chen, Jianyu Zhong, Rihan Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Adaptive Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China Beijing Information Science & Technology University China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both training and test data, hindering the empirical decoupling of information by existing methods. To tackle this issue, we propose an Autonomous Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm, which leverages a frequency-based learnable information filter to autonomously decouple DVI and DII. Information Bottleneck (IB) and Self-supervision (SS) are incorporated to optimize the learnable frequency filter. The IB governs the information flow within the filter to diminish redundant DVI, while SS preserves DII in alignment with the specific task and image modality. Thus, the autonomous information filter can overcome domain shifts relying solely on target data. A series of experiments covering various medical image modalities and segmentation tasks were conducted to demonstrate the benefits of AIF-SFDA through comparisons with leading algorithms and ablation studies. The code is available at https://***/JingHuaMan/AIF-SFDA. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Workload Failure Prediction for data Centers

arXiv

引用

arXiv 2023年

作者： Li, Jie Wang, Rui Ali, Ghazanfar Dang, Tommy Sill, Alan Chen, Yong Department of Computer Science Texas Tech University Lubbock United States High-Performance Computing Center Texas Tech University Lubbock United States

Failed workloads that consumed significant computational resources in time and space affect the efficiency of data centers significantly and thus limit the amount of scientific work that can be achieved. While the computational power has increased significantly over the years, detection and prediction of workload failures have lagged far behind and will become increasingly critical as the system scale and complexity further increase. In this study, we analyze workload traces collected from a production cluster and train machine learning models on a large amount of data sets to predict workload failures. Our prediction models consist of a queue-time model that estimates the probability of workload failures before execution and a runtime model that predicts failures at runtime. Evaluation results show that the queue-time model and runtime model can predict workload failures with a maximum precision score of 90.61% and 97.75%, respectively. By integrating the runtime model with the job scheduler, it helps reduce CPU time, memory usage by up to 16.7% and 14.53%, respectively. © 2023, CC BY-NC-SA.

关键词： Big data

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AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation 39

AIF-SFDA: Autonomous Information Filter-driven Source-Free D...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

ISBN: (纸本)157735897X

Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both training and test data, hindering the empirical decoupling of information by existing methods. To tackle this issue, we propose an Autonomous Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm, which leverages a frequency-based learnable information filter to autonomously decouple DVI and DII. Information Bottleneck (IB) and Self-supervision (SS) are incorporated to optimize the learnable frequency filter. The IB governs the information flow within the filter to diminish redundant DVI, while SS preserves DII in alignment with the specific task and image modality. Thus, the autonomous information filter can overcome domain shifts relying solely on target data. A series of experiments covering various medical image modalities and segmentation tasks were conducted to demonstrate the benefits of AIF-SFDA through comparisons with leading algorithms and ablation studies. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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Inverse design of high entropy alloys using a deep interpretable scheme for materials attribution analysis

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Journal of Alloys and Compounds 2024年 976卷

作者： Lee, Chih-Yu Jui, Chia-Yung Yeh, An-Chou Chang, Yao-Jen Lee, Wen-Jay National Center for High-Performance Computing Taiwan Department of Material Science and Engineering National Tsing Hua University Taiwan High Entropy Materials Center National Tsing Hua University Taiwan

high entropy alloys (HEAs) have gained significant attention due to their unique combinations of properties and extensive compositional space. However, designing HEAs with specific properties has become increasingly complex and challenging. To address this issue, a Deep Interpretable Scheme for Material Attribution Analysis (DISMAA) was developed to systematically uncover the composition-structure-property (CSP) relationships of HEAs and generate novel compositions. The approach combines a deep generative model, trained with limited data, to create a continuous composition space of AlCoCrFeMnNi high entropy alloys, and additional deep learning models, trained with more data, to predict the corresponding phase and properties. Attribution and sensitivity analysis techniques are used to analyze the contribution of each element to different materials properties and phases. The distributions of composition, phase, mechanical property, and contribution of elements are visualized in the latent space, providing a novel and intuitive approach to analyze CSP relationships using deep learning. The efficacy of the method was validated, demonstrating the interpretability and rationality of the deep neural network model for material inverse design applications. This approach can be applied to tailor multiple properties and manufacturing parameters in various materials systems, providing scientists with a comprehensive context for designing HEAs. © 2023

关键词： Attribution analysis Generative model high entropy alloy Latent space Material design

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Impedance responses and size-dependent resonances in topolectrical circuits via the method of images

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Physical Review B 2023年第24期107卷 245114-245114页

作者： Haydar Sahin Zhuo Bin Siu S. M. Rafi-Ul-Islam Jian Feng Kong Mansoor B. A. Jalil Ching Hua Lee Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore Department of Physics National University of Singapore Singapore 117542 Singapore

Resonances in an electric circuit occur when capacitive and inductive components are present together. Such resonances appear in admittance measurements depending on the circuit's parameters and the driving AC frequency. In this study, we analyze the impedance characteristics of nontrivial topolectrical circuits such as one- and two-dimensional Su-Schrieffer-Heeger circuits and reveal that size-dependent anomalous impedance resonances inevitably arise in finite LC circuits. Through the method of images, we study how resonance modes in a multidimensional circuit array can be nontrivially modified by the reflection and interference of current from the structure and boundaries of the lattice. We derive analytic expressions for the impedance across two corner nodes of various lattice networks with homogeneous and heterogeneous circuit elements. We also derive the irregular dependency of the impedance resonance on the lattice size, and provide integral and dimensionally reduced expressions for the impedance in three dimensions and above.

关键词： Edge states Electrical conductivity Impedance Resistive switching Topological insulators Electronic structure Band structure methods Bloch wave theory Classical electromagnetism Tight-binding model

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