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IEEE International Conference on high performance computing and Communications (HPCC)

作者： Jinglun Ma Yanlong Qiu Canqun Yang Chengkun Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer Science and Techology National University of Defense Technology Changsha China

The prediction of interactions between biomedical entities is crucial for the discovery of therapeutics for diseases. The fundamental step is to build an interaction model with a comprehensive coverage on literature and curated databases. The massive amount of heterogeneous data in interaction networks of biomedical entities poses significant challenges to relevant prediction tasks. Previous research has mainly focused on constructing networks with limited types of entity relations. In this paper, we construct a large-scale heterogeneous graph model, BioNet+, using a data-driven approach to extract latent information and predict various relations among biomedical entities. BioNet+ provides a crucial update to our previous BioNet model, with the addition of disease entities. The model employs a graph convolution encoder to learn embedding information from the intricate interactions among chemicals (C), genes (G), diseases (D), and biological pathways (P). Furthermore, a decoder that applies two tensor decomposition methods is deployed to predict multiple types of relations, including CG, CD, and GD interactions. The model constructs a graph network consisting of 81,987 entity nodes and 206,195,703 interaction edges. To handle this massive model, BioNet+ reduces the computational complexity via data categorization and proposes a parallel model training algorithm for cross-node GPUs. Evaluation experiments demonstrate that BioNet+ achieves superior predictive performance, as demonstrated by its highest area under the receiver operating characteristic curve (AUROC) of 0.964.

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ExplSched: Maximizing Deep Learning Cluster Efficiency for Exploratory Jobs

ExplSched: Maximizing Deep Learning Cluster Efficiency for E...

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IEEE International Conference on Cluster computing

作者： Hongliang Li Hairui Zhao Zhewen Xu Xiang Li Haixiao Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China High Performance Computing Center Jilin University China

Resource management for Deep Learning (DL) clusters is essential for system efficiency and model training quality. Existing schedulers provided by DL frameworks are mostly adaptations from traditional HPC clusters and usually work on jobs’ makespan, assuming that DL training jobs finish completely. Unfortunately, it is reported that a fair amount of training jobs are exploratory jobs and often finish unsuccessfully (over 30%) in production clusters. This is due to the distinct characteristic of Deep Neural Network (DNN) training that it is an exploratory process of frequent user interventions, such as adjusting model structures, tuning hyperparameters, and exploring feature validity. Existing DL cluster schedulers using offline algorithms are not suitable for exploratory jobs when unexpected early terminations can cause noticeable resource waste. Moreover, DL training jobs are iterative and usually yield diminishing returns as they progress. Equally allocating resource among training iterations is not efficient, especially when dealing with exploratory jobs where it can worsen the degradation of system efficiency. The fundamental goal of a DL training job is to gain model quality improvement, usually indicated by the loss reduction (job profit) of a DNN model. This paper introduces a novel scheduling problem for exploratory jobs that seeks to maximize the overall training profit of a DL cluster. We propose ExplSched, an online scheduling solution based on the primal-dual framework, resulting in a competitive ratio of 2α that belongs to O(ln n). It uses a resource price function that emphasizes the importance of job profit to resource consumption ratio to make quick resource allocation decisions. Experimental results show that ExplSched achieved an average system utility improvement of 87.28% compared with other related work.

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Practical security of twin-field quantum key distribution with optical phase-locked loop under wavelength-switching attack

arXiv

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arXiv 2024年

作者： Peng, Qingquan Chen, Jiu-Peng Xing, Tianyi Wang, Dongyang Wang, Yizhi Liu, Yang Huang, Anqi Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Hunan Changsha410073 China Jinan Institute of Quantum Technology Hefei National Laboratory Jinan Branch Shandong Jinan250101 China

The twin-field class quantum key distribution (TF-class QKD) has experimentally demonstrated the ability to surpass the fundamental rate-distance limit without requiring a quantum repeater, as a revolutional milestone. In TF-class QKD implementation, an optical phase-locked loop (OPLL) structure is commonly employed to generate a reference light with correlated phase, ensuring coherence of optical fields between Alice and Bob. In this configuration, the reference light, typically located in the untrusted station Charlie, solely provides wavelength reference for OPLL and does not participate in quantum-state encoding. However, the reference light may open a door for Eve to enter the source stations that are supposed to be well protected. Here, by identifying vulnerabilities of an acousto-optic modulator (AOM) in the OPLL scheme, we propose and demonstrate a wavelength-switching attack on a TF-class QKD system. This attack involves Eve deliberately manipulating the wavelength of the reference light to increase mean photon number of prepared quantum states, while maintaining stable interference between Alice and Bob as required by TF-class QKD protocols. The maximum observed increase in mean photon number is 8.7%, which has been theoretically proven to compromise the security of a TF-class QKD system. Moreover, we have shown that with well calibration of the modulators, the attack can be eliminated. Through this study, we highlight the importance of system calibration in the practical security in TF-class QKD *** Codes 81P94 Copyright © 2024, The Authors. All rights reserved.

关键词： Modulators

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Bridging Clinical Gaps: Multi-Dataset Integration for Reliable Multi-Class Lung Disease Classification with DeepCRINet and Occlusion Sensitivity

Bridging Clinical Gaps: Multi-Dataset Integration for Reliab...

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IEEE Symposium on computers and Communications (ISCC)

作者： Naeem Ullah Javed Ali Khan Ivanoe De Falco Giovanna Sannino Department of Electrical Engineering and Information Technology University of Naples "Federico II" Naples Italy Department of Computer Science University of Hertfordshire Hatfield UK Institute for High-Performance Computing and Networking (ICAR) National Research Council (CNR) Naples Italy

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

This research presents DeepCRINet, a deep learning (DL) model designed for reliable performance across various Chest Radiography Images (CRIs) datasets, in response to the urgent need for quick and accurate lung disease identification utilizing CRIs. Our method builds on earlier research, which frequently used single-source datasets that might not adequately represent the heterogeneity present in clinical situations. Our model’s diagnostic adaptability and real-world dependability are improved by utilizing images from different datasets, which helps us overcome limitations such as dataset bias, robustness, generalizability, and underrepresentation of conditions. With validation on a broad dataset consisting of 14,096 images (from three different datasets), DeepCRINet provides a solution that demonstrates excellent flexibility in recognizing illnesses including TuBerculosis, Pneumonia, COVID-19, and Lung Opacity. Through data augmentation, we improve the dataset, supporting training and testing procedures and confirming the model’s ability to generalize. We used occlusion sensitivity as a kind of explainable AI to openly identify and visually emphasize regions important to proper classification. This ability not only shows that DeepCRINet is analytically better than other DL models and hybrid techniques, but it also improves patient outcomes and diagnosis, which makes it a vital tool for medical professionals like radiologists.

关键词： COVID-19 Training Analytical models Pneumonia Accuracy Tuberculosis Sensitivity analysis Robustness Diagnostic radiography Testing

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Coherent optical link phase noise control based on SPGD algorithm with optimal estimation 6

Coherent optical link phase noise control based on SPGD algo...

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6th Optics Young Scientist Summit, OYSS 2023

作者： Luo, Yihao Zhang, Jun Du, Shiyin Yan, Qiuquan Zhou, Tong Jiang, Tian College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences Pla China Beijing100071 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Institute of Quantum Information Science and Technology College of Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510672642

The Stochastic Parallel Gradient Descent (SPGD) algorithm is a widely used model-free optimization algorithm in adaptive optical systems. However, when affected by noise, the SPGD algorithm may incorrectly estimate the gradient, resulting in a significant decrease in its performance and limitations in its application in complex environments. To address this issue, this paper introduces the optimal estimation algorithm and adds a self-checking process to the SPGD algorithm, proposing a Kal-SPGD algorithm. The feasibility of the Kal-SPGD algorithm is verified through simulation and experimentation. The experimental results show that compared with the standard SPGD, the use of the Kal-SPGD algorithm can reduce the signal amplitude variance by up to 92.15%. The Kal-SPGD algorithm provides a more favorable control technique for adaptive optical systems. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Stochastic systems

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Spatial Reasoning Development in the K12 Environment 53

Spatial Reasoning Development in the K12 Environment

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Nite, Sandra B. Erdogan, Niyazi Gray, Trenton Lee, Seonhu Texas A&m University High Performance Research Computing College StationTX United States Educational Achievement College of Engineering Texas A&m University College StationTX United States Texas A&m University Department of Computer Science and Engineering College StationTX United States Allen Academy BryanTX United States

ISBN: (纸本)9798350336429

Spatial awareness, spatial, reasoning, spatial visualization, and spatial abilities are various terms all related to various ideas or aspects of spatial ability. From infancy throughout our life spans spatial awareness is embedded in our lives in ways we do not realize. Spatial reasoning is one of the foundational key concepts in mathematics and has been shown to be highly correlated with success in mathematics overall. higher levels of spatial reasoning are critical in many STEM fields. However, it is an area in which students struggle for competency. Studies have shown that boys generally are better than girls at spatial relationships, beginning at an early age. It is not clear how much is part of the difference in the ways the brain works and how much is part of the environment and experiences of children at play. However, research shows that spatial abilities can definitely be intentionally developed and improved. Thus, children of all ages could benefit from more intentional experiential learning in areas of spatial ability. Assessing various aspects of spatial reasoning is complex, but there are research-based types of activities that support the development of spatial abilities. Our theoretical framework is based on David Kolb's experiential learning theory. In this framework, a holistic approach is envisioned, recognizing the combination of cognition, environmental factors, and emotions in learning experiences. In a week-long summer camp, activities were focused on various aspects of spatial ability. These included flying drones, paper engineering through popups, and a spatial drawing activity. Each of the activities will be discussed in the context of the various aspect of spatial ability development and the importance of those aspects in STEM fields. Students completed a survey about how important they believed spatial skills were in science, mathematics, technology, and engineering. They reflected on the camp activities and their perception of the use of

关键词： spatial reasoning STEM camp

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Eosinophil Detection with Modified YOLOv3 Model in Large Pathology Image 4

Eosinophil Detection with Modified YOLOv3 Model in Large Pat...

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4th International Conference on computer Information science and Artificial Intelligence, CISAI 2021

作者： Wang, Xinyue Che, Yonggang Jiang, Nan Chen, Weijian Lan, Long National University of Defense Technology State Key Laboratory of High Performance Computing College of Computer Changsha China Hunan Children's Hospital Department of Pathology Changsha China

ISBN: (纸本)9781665406925

Eosinophil detection and counting have an important role in the treatment of eosinophilic gastroenteritis. Generally, the physicians diagnose the eosinophilic gastroenteritis depending on the number of eosinophil cells in the corresponding pathology images. However, manually identifying eosinophils in the large pathology image is time-consuming and boring. The accuracy of Eosinophil counting significantly relies on the experiences of professional pathologists, the manual counting also prone to missing and misclassification due to fatigue. In this paper, we introduce a modified YOLOv3 object detection method to automatically count the eosinophil. Specifically, to detect the small and crowded eosinophils in the large pathology image, we carefully redesign the scale and size of candidate boxes and make our kernels more sensitive to tiny eosinophils in the feature maps. To verify our method, we collect and label a real eosinophil dataset with totally 8469 eosinophils being annotated and distributing over 255 pathology images from 11 different classes. We implemented our proposed method on our dataset and demonstrated the feasibility and bright prospect for applying object detection technique into the treatments of eosinophilic gastroenteritis. © 2021 IEEE.

关键词： Object detection

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Numerical simulations of attachment-line boundary layer in hypersonic flow, Part I: roughness-induced subcritical transitions

arXiv

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arXiv 2024年

作者： Xi, Youcheng Yan, Bowen Yang, Guangwen Sha, Xinguo Zhu, Dehua Fu, Song School of Aerospace Engineering Tsinghua University Beijing100084 China Institute of High Performance Computing Department of Computer Science and Technology Tsinghua University Beijing100084 China China Academy of Aerospace Aerodynamics Beijing100074 China

The attachment-line boundary layer is critical in hypersonic flows because of its significant impact on heat transfer and aerodynamic performance. In this study, high-fidelity numerical simulations are conducted to analyze the subcritical roughness-induced laminar-turbulent transition at the leading-edge attachment-line boundary layer of a blunt swept body under hypersonic conditions. This simulation represents a significant advancement by successfully reproducing the complete leading-edge contamination process induced by surface roughness elements in a realistic configuration, thereby providing previously unattainable insights. Two roughness elements of different heights are examined. For the lower-height roughness element, additional unsteady perturbations are required to trigger a transition in the wake, suggesting that the flow field around the roughness element acts as a disturbance amplifier for upstream perturbations. Conversely, a higher roughness element can independently induce the transition. A low-frequency absolute instability is detected behind the roughness, leading to the formation of streaks. The secondary instabilities of these streaks are identified as the direct cause of the final transition. © 2024, CC BY.

关键词： Surface roughness

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

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Pronounced advance on typhoon track forecast with global convectionpermitting model

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science Bulletin 2025年第9期70卷 1424-1428页

作者： Jun Gu Chun Zhao Gudongze Li Jiawang Feng Mingyue Xu Qiuyan Du Zihan Xia Yubin Li Guanghua Chen Xiaoyu Hao Junshi Chen Hong An Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of ChinaHefei 230026China Laoshan Laboratory Qingdao 266237China CAS Center for Excellence in Comparative Planetology University of Science and Technology of ChinaHefei 230026China School of Atmospheric Physics Nanjing University of Information Science and TechnologyNanjing 210044China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

Typhoons pose significant threats to coastal and inland regions,with their impacts exacerbated by climate change and population growth[1].Recent studies have shown increased frequency of powerful storms,slower translation speeds,northward shifts in the Western North Pacific(WNP)basin,and a near tripling of global exposure to typhoons since 1970[1-4].These changes present new challenges for forecasters and researchers,particularly in predicting impacts on inland and high-latitude populations lacking prior exposure or resilience to typhoon effects.

关键词： translation speed global convection permitting model population growth recent track forecast typhoon population growth storm frequency climate change

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