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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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2024 IEEE high performance Extreme Computing conference, HPEC 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering MD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States

ISBN: (纸本)9798350387131

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. © 2024 IEEE.

关键词： Dimensionality reduction

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Sketching Very Large-scale Dynamic Attributed Networks More Practically 25

Sketching Very Large-scale Dynamic Attributed Networks More ...

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34th ACM Web conference, WWW 2025

作者： Wu, Wei Li, Shiqi Chen, Ling Li, Fangfang Luo, Chuan School of Computer Science and Engineering Central South University Changsha China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Beihang University Beijing China

ISBN: (纸本)9798400712746

Real-world networks, particularly those in web and social media, are dynamic with evolving node attributes and structures, often involving billions of nodes and edges. Dynamic attributed network embedding is a powerful tool for capturing these changes, enabling data owners and problem owners to better understand interactions and trends for more effective engagement and decision-making. While some existing algorithms are capable of handling very large-scale dynamic attributed networks with billions of nodes and edges, they often suffer from accuracy loss or high computational overhead. In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. By the sparse random projection technique in an incremental update manner, VLS2ketch significantly reduces the energy-intensive computational workload while maintaining accuracy. Also, we introduce an information decay mechanism, which adapts to temporally varying topologies and node attributes. This mechanism ensures that outdated information gradually diminishes over time. Extensive experiments on real-world very large-scale datasets demonstrate that our proposed VLS2ketch method delivers comparable embedding quality against the state-of-the-art learning-based competitors with dramatically reduced runtime. © 2025 Copyright held by the owner/author(s).

关键词： network embedding

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A Systematic Analysis of Deep Learning Algorithms in high-Dimensional Data Regimes of Limited Size 36

A Systematic Analysis of Deep Learning Algorithms in High-Di...

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36th IEEE International conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Jaxy, Simon Nowe, Ann Libin, Pieter Artificial Intelligence Lab Brussels Belgium Hasselt Belgium

ISBN: (纸本)9798331527235

There is a substantial demand for deep learning methods that can work with limited, high-dimensional, and noisy datasets. Nonetheless, current research mostly neglects this area, especially in the absence of prior expert knowledge or knowledge transfer. In this work, we bridge this gap by studying the performance of deep learning methods on the true data distribution in a limited, high-dimensional, and noisy data setting. To this end, we conduct a systematic evaluation that reduces the available training data while retaining the challenging properties mentioned above. Furthermore, we extensively search the space of hyperparameters and compare state-of-the-art architectures and models built and trained from scratch to advocate for the use of multi-objective tuning strategies. Our experiments highlight the lack of performative deep learning models in current literature and investigate the impact of training hyperparameters. We analyze the complexity of the models and demonstrate the advantage of choosing models tuned under multi-objective criteria in lower data regimes to reduce the likelihood to overfit. Lastly, we demonstrate the importance of selecting a proper inductive bias given a limited-sized dataset. Given our results, we conclude that tuning models using a multi-objective criterion results in simpler yet competitive models when reducing the number of data points. © 2024 IEEE.

关键词： Contrastive Learning

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The Inclusive conferences Survey: Analysis and validation of inclusivity at HPC conferences 23

The Inclusive Conferences Survey: Analysis and validation of...

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2023 Practice and Experience in Advanced Research Computing, PEARC 2023

作者： Lauer, A.J. Cordes Galbraith, Max Wolff, Milana Hart, David National Center for Atmospheric Research BoulderCO United States University of Minnesota MinneapolisMN United States University of Wyoming LaramieWY United States

ISBN: (纸本)9781450399852

As conferences and events take steps to welcome a broader range of people, it is important to accurately assess these efforts. We present and validate the Inclusive conferences Survey, a tool to measure perceptions of a welcoming atmosphere, conference inclusivity, and likelihood of recommending HPC conferences to new attendees. Our analysis showed that respondents who rated conferences as more inclusive were less likely to think of leaving the HPC field. We found significant differences in conference experiences based on gender identity, as well as differing perceptions of a welcoming environment between societally advantaged and disadvantaged groups. We recommend that HPC conferences and events continue inclusivity initiatives while using assessment tools such as the Inclusive conferences Survey to focus on areas for improvement. © 2023 Owner/Author.

关键词： conference Evaluation conferences D&I DEI Diversity and Inclusion HPC high performance Computing Sense of Belonging Under-representation

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Can Individuals be Repeatedly Selected for Mutation in Matrix-Based DE? A Comparative Study 11

Can Individuals be Repeatedly Selected for Mutation in Matri...

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11th International conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Wei, Ling-Ling Sun, Bing Li, Jian-Yu Yang, Qiang Xu, Jing Tang, Fei-Yi South China Normal University School of Computer Science Guangzhou510631 China Nankai University College of Artificial Intelligence Tianjin300350 China Nanjing University of Information Science and Technology School of Artificial Intelligence Jiangsu China Guangzhou Panyu Polytechnic Pazhou Lab School of Information Engineering Guangdong China

ISBN: (纸本)9798350385670

Evolutionary Computation (EC) is a class of algorithms derived from the simulation of natural phenomena, which faces the challenges of long running times and high computing loads resulting from large-scale problems. For this reason, the matrix-based EC (MEC) has been presented as an extension of classic EC algorithms to address large-scale optimization. The MEC can be implemented directly on high-performance computing resources, thus accelerating the computation of evolutionary operators. However, MEC is still in its infancy and lacks deeper analysis. Hence, this paper analyzes and investigates matrix-based differential evolution (MDE) and advances the development of related research through theoretical analysis and experimental validation, which has two contributions. First, through the comprehensive comparison of MDE and DE in different problems, we compare the solution quality and computational speed of MDE and DE, and show that the solution quality of MDE is not significantly different from that of DE, but the computational speed of MDE is much faster than that of DE. Second, we also further analyze the mutation mechanism of MDE, focusing on the effect of the repeatability of individuals in the mutation process on the performance of the algorithms, which provides a basis for future research on MDE of MEC. © 2024 IEEE.

关键词： differential evolution Evolutionary computation matrix-based evolutionary computation parallel computing

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Harmonizing Visual Text Comprehension and Generation 38

Harmonizing Visual Text Comprehension and Generation

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Zhen Tang, Jingqun Wu, Binghong Lin, Chunhui Wei, Shu Liu, Hao Tan, Xin Zhang, Zhizhong Huang, Can Xie, Yuan East China Normal University China ByteDance China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images and texts typically results in performance degradation due to the inherent inconsistency between vision and language modalities. To overcome this challenge, existing approaches resort to modality-specific data for supervised fine-tuning, necessitating distinct model instances. We propose Slide-LoRA, which dynamically aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Slide-LoRA harmonizes the generation of vision and language within a singular model instance, thereby facilitating a more unified generative process. Additionally, we develop a high-quality image caption dataset, DetailedTextCaps-100K, synthesized with a sophisticated closed-source MLLM to enhance visual text generation capabilities further. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach. Empowered by Slide-LoRA, TextHarmony achieves comparable performance to modality-specific fine-tuning results with only a 2% increase in parameters and shows an average improvement of 2.5% in visual text comprehension tasks and 4.0% in visual text generation tasks. Our work delineates the viability of an integrated approach to multimodal generation within the visual text domain, setting a foundation for subsequent inquiries. Code is available at https://***/bytedance/TextHarmony. © 2024 Neural information processing systems foundation. All rights reserved.

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Spreading Factor Allocation and Rate Adaption for Minimizing Age of Information in LoRaWAN 24

Spreading Factor Allocation and Rate Adaption for Minimizing...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Zhuoyao Xu, Xiaokang Zhao, Jin School of Computer Science Fudan University China

ISBN: (纸本)9798350319934

LoRaWAN has been widely deployed to support long-range connections for Internet of Things (IoT). However, due to frequent collisions, packet delivery performance in LoRaWAN has become a concern. Age of information (AoI) is used to measure information freshness, which refers to the elapsed time from when the information was first generated. For time-sensitive IoT applications, such as fire alarm, information freshness plays an important role. In this paper, we investigate the AoI model for packet sending and collision in LoRaWAN. To the best of our knowledge, this is the first attempt to improve LoRaWAN from the perspective of AoI. We then propose two efficient methods to minimize the AoI in LoRaWAN. Finally, our experiments show that the proposed methods can achieve the lowest AoI and the packet delivery ratio (PDR) is also improved by 52.14% on average compared with the default ADR scheme in LoRaWAN. © 2022 IEEE.

关键词： Internet of things

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Tuning of a Matrix-Matrix Multiplication Algorithm for Several GPUs Connected by Fast Communication Links 16th

Tuning of a Matrix-Matrix Multiplication Algorithm for Se...

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16th International conference on Parallel Computational Technologies, PCT 2022

作者： Choi, Yea Rem Nikolskiy, Vsevolod Stegailov, Vladimir HSE University Moscow Russia Joint Institute for High Temperatures of the RAS Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia

ISBN: (纸本)9783031116223

The usage of one of the latest high-performance hardware types (nodes with several GPUs connected by high bandwidth and low latency communication links), requires algorithms where the CPU is used only to manage the program execution, and GPUs are used for computations. In this work, we study an original GPU-only parallel matrix-matrix multiplication algorithm (C= αA∗ B+ βC ) for servers with multiple GPUs. The algorithm is implemented using CUDA. The performance of this multi-GPU GEMM algorithm and the method defining the optimal tile size using the hardware parameters and the matrix size are considered. The usability of the developed performance model by benchmarking two types of GPU servers is verified. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Medical Visual Prompting (MVP): A Unified Framework for Versatile and high-Quality Medical Image Segmentation

Medical Visual Prompting (MVP): A Unified Framework for Vers...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Yulin Huang, Guoheng Huang, Kai Lin, Zijin Zhong, Guo Luo, Shenghong Deng, Jie Zhou, Jian School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Sun Yat-Sen University Cancer Center Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China South China Hospital Shenzhen University Shenzhen China

ISBN: (纸本)9781665410205

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as the loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel Medical Visual Prompting (MVP) framework that leverages pre-training and prompting concepts from Natural Language Processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP framework enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate the superior performance of the proposed method in various challenging medical image tasks while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable. © 2024 IEEE.

关键词： Image segmentation

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Appearance- and Orientation-aware Fine-grained Rotated Ship Detection in high-Resolution Satellite Imagery

Appearance- and Orientation-aware Fine-grained Rotated Ship ...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yan Liu, Lingyi Bai, Yunpeng Li, Ying Shen, Qiang School of Software Northwestern Polytechnical University Xi'an China School of Computer Science Northwestern Polytechnical University Xi'an China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Ship detection using remote sensing imagery is a crucial research area with both military and civilian applications. However, it remains challenging due to limitations in current ship datasets, such as insufficient volume, incomplete annotations, and inaccuracies. Additionally, ships often exhibit arbitrary orientations, dense clustering, varying aspect ratios, and significant dimensional changes. To address these issues, this paper advances ship detection from both data and methodological perspectives. First, a new dataset, ORSISOD, is introduced. This dataset includes seven finely categorized ship types, annotated with rotated bounding boxes, which are more appropriate for ship detection than traditional horizontal boxes. Second, a novel rotated ship detection method is proposed, incorporating a Dynamic IOU Threshold Selection (DITS) module and a Positive Sample Quality Assessment (PSQA) module. DITS adjusts the IOU threshold based on ship size and shape, while PSQA assesses sample quality using ship aspect ratio and angle information. The ORSISOD dataset was tested on 12 object detection algorithms, providing benchmarks for ship detection. Furthermore, the proposed method was evaluated on both ORSISOD and DOTA datasets, demonstrating superior performance. © 2025 IEEE.

关键词： remote sensing images rotated object detection ship dataset ship detection

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