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2022 Systems and Information Engineering Design symposium, SIEDS 2022

作者： Wolfe, Cecily Feng, Yayi Chen, David Purcell, Edwin Talkington, Anne Dolatshahi, Sepideh Shakeri, Heman University of Virginia School of Data Science CharlottesvilleVA United States Department of Biomedical Engineering CharlottesvilleVA United States

ISBN: (纸本)9781665451116

The cellular composition of the tumor microenvironment can directly impact cancer progression and the efficacy of therapeutics. Understanding immune cell activity, the body's natural defense mechanism, in the vicinity of cancerous cells is essential for developing beneficial treatments. Single cell RNA sequencing (scRNA-seq) enables the examination of gene expression on an individual cell basis, providing crucial information regarding both the disturbances in cell functioning caused by cancer and cell-cell communication in the tumor microenvironment. This novel technique generates large amounts of data, which require proper processing. Various tools exist to facilitate this processing but need to be organized to standardize the workflow from data wrangling to visualization, cell type identification, and analysis of changes in cellular activity, both from the standpoint of malignant cells and immune stromal cells that eliminate them. We aimed to develop a standardized pipeline (GeoTyper, https://***/celineyayifeng/GeoTyper) that integrates multiple scRNA-seq tools for processing raw sequence data extracted from NCBI GEO, visualization of results, statistical analysis, and cell type identification. This pipeline leverages existing tools, such as Cellranger from 10X Genomics, Alevin, and Seurat, to cluster cells and identify cell types based on gene expression profiles. We successfully tested and validated the pipeline on several publicly available scRNA-seq datasets, resulting in clusters corresponding to distinct cell types. By determining the cell types and their respective frequencies in the tumor microenvironment across multiple cancers, this workflow will help quantify changes in gene expression related to cell-cell communication and identify possible therapeutic targets. © 2022 ieee.

关键词： Pipelines

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Efficient Spatio-Temporal Contrastive Learning for Skeleton-Based 3-D Action Recognition

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 405-417页

作者： Gao, Xuehao Yang, Yang Zhang, Yimeng Li, Maosen Yu, Jin-Gang Du, Shaoyi Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Key Lab Multimedia Proc & Transmiss Shanghai 200240 Peoples R China South China Univ Technol Sch Automation Sci & Engn Guangzhou 510641 Peoples R China Pazhou Lab Ctr Brain Comp Interface Guangzhou 510335 Peoples R China

In this paper, we propose a simple yet effective self-supervised method called spatio-temporal contrastive learning (ST-CL) for 3D skeleton-based action recognition. ST-CL acquires action-specific features by regarding the spatio-temporal continuity of motion tendency as the supervisory signal. To yield effective representations, ST-CL first designs some novel contrastive proxy tasks by providing different spatio-temporal observation scenes for the same 3D action and pulling them together in the embedding space. Second, three key components are devised in the action encoding to efficiently extract representations in contrastive tasks: (1) Information Representation introduces the awareness of joint type when analyzing motion dynamics. (2) Non-local GCN learns a data-driven graph topology structure and promotes a spatial message passing among long-range joints in each frame. (3) Multi-Scale TCN makes larger receptive fields for capturing richer longe-range temporal dynamics amomg adjacent frames. In ST-CL, these effective proxy tasks yield useful representations and efficient action encoding further enhances the representation capacity. As validated on four large-scale datasets, ST-CL is a strong baseline with high performance and efficiency for the contrastive learning study of the skeleton data. Compared to previous self-supervised methods, the proposed ST-CL achieves significant improvement consistently with a smaller model size and better training efficiency.

关键词： Task analysis Skeleton Encoding Three-dimensional displays Training Feature extraction visualization self-supervised method observation scene contrastive learning 3D action recognition

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Pik-Fix: Restoring and Colorizing Old Photos 23

Pik-Fix: Restoring and Colorizing Old Photos

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23rd ieee/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Xu, Runsheng Tu, Zhengzhong Du, Yuanqi Dong, Xiaoyu Li, Jinlong Meng, Zibo Ma, Jiaqi Bovik, Alan Yu, Hongkai Univ Calif Los Angeles Los Angeles CA USA Univ Texas Austin Austin TX 78712 USA Cornell Univ Ithaca NY 14853 USA Northwestern Univ Evanston IL 60208 USA Cleveland State Univ Cleveland OH 44115 USA Innopeak Technol Inc Palo Alto CA USA

ISBN: (纸本)9781665493468

Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer from severe and commingled degradation such as cracks, defocus, and color-fading, which are difficult to treat individually and harder to repair when they interact. Deep learning presents a plausible avenue, but the lack of large-scale datasets of old photos makes addressing this restoration task very challenging. Here we present a novel reference-based end-to-end learning framework that is able to both repair and colorize old, degraded pictures. Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals. The overall system makes uses of color histogram priors from reference images, which greatly reduces the need for large-scale training data. We have also created a first-of-a-kind public dataset of real old photos that are paired with ground truth "pristine" photos that have been manually restored by PhotoShop experts. We conducted extensive experiments on this dataset and synthetic datasets, and found that our method significantly outperforms previous state-of-the-art models using both qualitative comparisons and quantitative measurements. The code is available at https://***/DerrickXuNu/Pik-Fix.

关键词： Degradation Deep learning Histograms visualization Image color analysis Training data Maintenance engineering

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Block Channel analysis and Adaption Approach for Distributed Blind Equalization over Sensor Networks

Block Channel Analysis and Adaption Approach for Distributed...

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2024 International symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Chi, Sulin Shimamura, Tetsuya Otemon Gakuin University 2-1-15 Nishiai Osaka Ibaraki567-8502 Japan Graduate School of Science and Engineering Saitama University 255 Shimo-Okubo Saitama Sakura-ku338-8570 Japan

ISBN: (纸本)9798350389210

In the study of signal estimation, the performance of distributed blind equalization over wireless sensor network (WSN) is significantly affected by channel conditions. However, there has been few studies investigating the effect of channel conditions on distributed blind equalization over WSN. To address this problem, this paper considers that different channel and noise variance conditions for distributed blind equalization. Initially, a smaller step-size is used to mitigate the noise enhancement effect that is caused by a large step-size under ill-channel conditions. Then, a block channel analysis and adaption approach is proposed to analyze the performance of distributed blind equalization under current condition of channel. This approach determines whether to adjust a smaller step-size to a large value and re-designs the weight for each sensor by analyzing the corresponding channel conditions repeated over Q input data samples of equalizer. In the computer simulations, the average mean square error (MSE) is used to assess the performance of distributed blind equalization. Compared to the conventional method, the proposed method demonstrates improved performance for the distributed blind equalization. © 2024 ieee.

关键词： Mean square error

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In-Situ Techniques on GPU-Accelerated data-Intensive Applications 19

In-Situ Techniques on GPU-Accelerated Data-Intensive Applica...

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19th ieee International Conference on e-Science, e-Science 2023

作者： Ju, Yi Li, Mingshuai Perez, Adalberto Bellentani, Laura Jansson, Niclas Markidis, Stefano Schlatter, Philipp Laure, Erwin Max Planck Computing and Data Facility Germany Kth Royal Institute of Technology Sweden Cineca Italy Technical University of Munich Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

ISBN: (纸本)9798350322231

The computational power of High-Performance Computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization. © 2023 ieee.

关键词： Graphics processing unit

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HisVA: A Visual Analytics System for Studying History

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第12期28卷 4344-4359页

作者： Han, Dongyun Parsad, Gorakh Kim, Hwiyeon Shim, Jaekyom Kwon, Oh-Sang Son, Kyung A. Lee, Jooyoung Cho, Isaac Ko, Sungahn Utah State Univ Logan UT 84322 USA UNIST Dept Comp Sci Ulsan 44919 South Korea UNIST Leadership Ctr Ulsan 44919 South Korea UNIST Dept Biomed Engn Ulsan 44919 South Korea UNIST U Innovat Educ Ctr Ulsan 44919 South Korea UNIST Sch Liberal Arts Ulsan 44919 South Korea UNIST Sch Comp Sci & Engn Ulsan 44919 South Korea

Studying history involves many difficult tasks. Examples include searching for proper data in a large event space, understanding stories of historical events by time and space, and finding relationships among events that may not be apparent. Instructors who extensively use well-organized and well-argued materials (e.g., textbooks and online resources) can lead students to a narrow perspective in understanding history and prevent spontaneous investigation of historical events, with the students asking their own questions. In this article, we proposed HisVA, a visual analytics system that allows the efficient exploration of historical events from Wikipedia using three views: event, map, and resource. HisVA provides an effective event exploration space, where users can investigate relationships among historical events by reviewing and linking them in terms of space and time. To evaluate our system, we present two usage scenarios, a user study with a qualitative analysis of user exploration strategies, and in-class deployment results.

关键词： History Encyclopedias Internet Information services Electronic publishing Education Tools visualization for education event visualization studying history Wikipedia

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DECENTRALIZED SPATIALLY CONSTRAINED SOURCE-BASED MORPHOMETRY 19

DECENTRALIZED SPATIALLY CONSTRAINED SOURCE-BASED MORPHOMETRY

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19th ieee International symposium on Biomedical Imaging (ieee ISBI)

作者： Saha, Debbrata K. Silva, Rogers F. Baker, Bradley T. Calhoun, Vince D. Triinst Ctr Translat Res Neuroimaging & Data Sci Atlanta GA 30303 USA

ISBN: (纸本)9781665429238

There is growing interest in extracting multivariate patterns (covarying networks) from structural magnetic resonance imaging (sMRI) data to analyze brain morphometry. Constrained source-based morphometry (constrained SBM) is a hybrid approach which provides a fully automated strategy for extracting subject-specific parameters characterizing gray matter networks. In constrained SBM, constrained independent component analysis (ICA) is used to compute maximally independent sources and statistical analysis is used to identify sources significantly associated with variables of interest. However, constrained SBM is built on the assumption that the data are locally accessible. As such, it cannot take advantage of decentralized (i.e., federated) data. While open data repositories have grown in recent years, there are various reasons (e.g., privacy concerns for rare disease data, institutional or IRB policies, etc.) that restrict a large amount of existing data to local access only. To overcome this limitation, we introduce a novel approach: decentralized constrained source-based morphometry (dcSBM). In our approach, data samples are located at different sites and each site operates the constrained ICA in a distributed manner. Finally, a master node simply aggregates result estimates from each local site and runs the statistical analysis centrally. We apply our method to UK Biobank sMRI data and validate our results by comparing to centralized constrained SBM results.

关键词： sMRI SBM Federated System UK Biobank

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The Effect of Sprint Duration to the Velocity in a large-Scale Embedded Software Project 17

The Effect of Sprint Duration to the Velocity in a Large-Sca...

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17th ACM/ieee International symposium on Empirical Software Engineering and Measurement, ESEM 2023

作者： Park, Dong Seon Noh, Ji Young Lg Electronics Seoul Korea Republic of

ISBN: (纸本)9781665452236

Background: Since there are not many empirical studies on the effect of sprint duration on efficiency and productivity in large-scale agile software developments, we collected and analyzed data from large-scale embedded software development. Aim: Our objective is to analyze the effect of sprint duration on the velocity in scrum-based software development projects. Method: We selected five scrums with the least changes from a large-scale embedded software development project of a big company and measured the change in velocity when operating with 3-week and 2-week sprint durations under the same condition. Results: Our analysis revealed that the 2-week sprint duration had about 30 percent higher development velocity. The average velocity was analyzed in four milestones over about two and half years of period, and the one-tailed T-test with an assumption of unequal variances proved that the development cycle of a 2-week sprint duration was higher than that of a 3-week sprint duration. Conclusions: In this respect, this paper contributes to improving the productivity in large-scale embedded software projects. © 2023 ieee.

关键词： Software design

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Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT’s Potential to Apply Graph Layout Algorithms

Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT...

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25th Eurographics Conference on visualization, EuroVis 2023

作者： Di Bartolomeo, Sara Severi, Giorgio Schetinger, Victor Dunne, Cody Northeastern University United States TU Wien Austria

ISBN: (纸本)9783038682196

large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions—such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT, one of the most recent cutting-edge LLMs made available to the public. These algorithms are used to create readable graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs’ ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs’ capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs;a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing—using the Sugiyama algorithm as a sample case—while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials to reproduce our results is available on *** . © 2023 The Authors.

关键词： data visualization

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GNN-Based Malicious Network Entities Identification In large-Scale Network data

GNN-Based Malicious Network Entities Identification In Large...

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ieee/IFIP Network Operations and Management symposium

作者： Dvorak, Stepan Prochazka, Pavel Bajer, Lukas Cisco Syst Prague Czech Republic

ISBN: (纸本)9781665406017

A reliable database of Indicators of Compromise (IoC's) is a cornerstone of almost every malware detection system. Building the database and keeping it up-to-date is a lengthy and often manual process where each IoC should be manually reviewed and labeled by an analyst. In this paper, we focus on an automatic way of identifying IoC's intended to save analysts' time and scale to the volume of network data. We leverage relations of each IoC to other entities on the internet to build a heterogeneous graph. We formulate a classification task on this graph and apply graph neural networks (GNNs) in order to identify malicious domains. Our experiments show that the presented approach provides promising results on the task of identifying high-risk malware as well as legitimate domains classification.

关键词： databases Buildings Manuals Malware Graph neural networks Reliability Task analysis

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