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A Fluid Flow data Set for Machine Learning and its Application to Neural Flow Map Interpolation

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 1279-1289页

作者： Jakob, Jakob Gross, Markus Guenther, Tobias Swiss Fed Inst Technol Zurich Switzerland

In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visualization problems-requires us to share data more openly in order to enable more scientists to engage in data-driven research. In this paper, we construct a large fluid flow data set and apply it to a deep learning problem in scientific visualization. Parameterized by the Reynolds number, the data set contains a wide spectrum of laminar and turbulent fluid flow regimes. The full data set was simulated on a high-performance compute cluster and contains 8000 time-dependent 2D vector fields, accumulating to more than 16 TB in size. Using our public fluid data set, we trained deep convolutional neural networks in order to set a benchmark for an improved post-hoc Lagrangian fluid flow analysis. In in-situ settings, flow maps are exported and interpolated in order to assess the transport characteristics of time-dependent fluids. Using deep learning, we improve the accuracy of flow map interpolations, allowing a more precise flow analysis at a reduced memory IO footprint.

关键词： Machine learning Interpolation Image resolution data visualization Convolutional neural networks Feature extraction Scientific visualization deep learning flow maps

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A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第1期28卷 302-312页

作者： Qin, Yu Fasy, Brittany Terese Wenk, Carola Summa, Brian Tulane Univ New Orleans LA 70118 USA Montana State Univ Bozeman MT 59717 USA

Persistence diagrams have been widely used to quantify the underlying features of filtered topological spaces in data visualization. In many applications, computing distances between diagrams is essential;however, computing these distances has been challenging due to the computational cost. In this paper, we propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams, which allows for fast computation of distances. This framework is built upon a generative adversarial network (GAN) with a diagram distance loss function to steer the learning process. Instead of using standard representations, we hash diagrams into binary codes, which have natural advantages in large-scale tasks. The training of this model is domain-oblivious in that it can be computed purely from synthetic, randomly created diagrams. As a consequence, our proposed method is directly applicable to various datasets without the need for retraining the model. These binary codes, when compared using fast Hamming distance, better maintain topological similarity properties between datasets than other vectorized representations. To evaluate this method, we apply our framework to the problem of diagram clustering and we compare the quality and performance of our approach to the state-of-the-art. In addition, we show the scalability of our approach on a dataset with 10k persistence diagrams, which is not possible with current techniques. Moreover, our experimental results demonstrate that our method is significantly faster with the potential of less memory usage, while retaining comparable or better quality comparisons.

关键词： Binary codes Training Propagation losses Histograms Generative adversarial networks Hash functions Water resources Topological data analysis Persistence diagrams Persistence diagram distances Learned hashing Clustering

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An Empirical analysis on the Use and Reporting of National Security Letters 25

An Empirical Analysis on the Use and Reporting of National S...

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4th ACM symposium on Computer Science and Law, CS and LAW 2025

作者： Bellon, Alex Haller, Miro Labunets, Andrey Liu, Enze Savage, Stefan UC San Diego San DiegoCA United States

ISBN: (纸本)9798400714214

Government investigatory and surveillance powers are important tools for examining crime and protecting public safety. However, since these tools must be employed in secret, it can be challenging to identify abuses or changes in use that could be of significant public interest. In this paper, we evaluate this phenomenon in the context of National Security Letters (NSLs). NSLs are a form of legal process that empowers parts of the United States federal government to request certain pieces of information for national security purposes. After initial concerns about the lack of public oversight, Congress worked to increase transparency by mandating government agencies to publish aggregated statistics on the NSL usage and by allowing the private sector to report information on NSLs in transparency reports. The implicit goal is that these transparency mechanisms should deter large-scale abuse by making it visible. We evaluate how well these mechanisms work by carefully analyzing the full range of publicly available data related to NSL use. Our findings suggest that they may not lead to the desired public scrutiny as we find published information requires significant manual effort to collect and parse data due to the lack of structure and context. Moreover, we discovered mistakes (subsequently fixed after our reporting to the ODNI), which suggests a lack of active auditing. Taken together, our case study of NSLs provides insights and suggestions for the successful construction of transparency mechanisms that enable effective public auditing. © 2025 Copyright held by the owner/author(s).

关键词： NSL

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2023 ieee 29th International symposium for Design and Technology in Electronic Packaging, SIITME 2023 - Conference Proceedings

2023 IEEE 29th International Symposium for Design and Techno...

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29th ieee International symposium for Design and Technology in Electronic Packaging, SIITME 2023

ISBN: (纸本)9798350344110

The proceedings contain 75 papers. The topics discussed include: increasing data transmission reliability using pseudo-spread-spectrum short-range radio networks;data switch in long line 1-wire networks for monitoring system of large sensor arrays;real-time data analysis using industrial sensors;IoT system for vine disease monitoring;electrochemical corrosion assessment of low-Ag SAC lead-free solder alloys;design of a temperature controller for an oven;electronic hive management system for a beekeeping vehicle;design of a laboratory platform for buck-boost converter characterization;thermocouple fabrics for energy harvesting in smart textiles – applications and prototype;speed control model of DC motor based on a parameter investigation prototype;and improving the properties of conducting polymers in self-doping variant.

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Rigorous Floating-Point Round-Off Error analysis in PRECiSA 4.0 26th

Rigorous Floating-Point Round-Off Error Analysis in PRECiSA ...

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26th International symposium on Formal Methods (FM)

作者： Titolol, Laura Moscato, Mariano Feliu, Marco A. Masci, Paolo Munoz, Cesar A. Analyt Mech Associates Inc Hampton VA 23666 USA NASA Langley Res Ctr Hampton VA 23665 USA

ISBN: (纸本)9783031711763;9783031711770

Small round-off errors in safety-critical systems can lead to catastrophic consequences. In this context, determining if the result computed by a floating-point program is accurate enough with respect to its ideal real-number counterpart is essential. This paper presents PRECiSA 4.0, a tool that rigorously estimates the accumulated round-off error of a floating-point program. PRECiSA 4.0 combines static analysis, optimization techniques, and theorem proving to provide a modular approach for computing a provably correct round-off error estimation. PRECiSA 4.0 adds several features to previous versions of the tool that enhance its applicability and performance. These features include support for data collections such as lists, records, and tuples;support for recursion schemas;an updated floating-point formalization that closely characterizes the ieee-754 standard;an efficient and modular analysis of function calls that improves the performances for large programs;and a new user interface integrated into Visual Studio Code.

关键词： User interfaces

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Power Microservices Troubleshooting by Pretrained Language Model with Multi-source data 22

Power Microservices Troubleshooting by Pretrained Language M...

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22nd ieee International symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Ding, Shuai Xu, Yifei Lu, Zhuang Tang, Fan Li, Tong Ge, Jingguo Chinese Academy of Sciences Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Information Center Aier Eye Hospital Hunan China

ISBN: (纸本)9798331509712

Microservice has become the mainstream paradigm for developing cloud-native applications, but the intricate interdependencies between microservices and the vast amount of heterogeneous observable data (i.e. metrics, logs and traces) pose challenges for rapid troubleshooting. Several anomaly detection and root cause localization approaches that integrate multi-source data have been proposed. However, they are plagued with issues such as scarcity of high-quality data and insufficient model generalization. This is particularly evident when domain-specific models are trained from scratch for specific tasks. Recently, large Language Models (LLMs) have shown outstanding capabilities in time series analysis, due to multi-source data generated by distributed microservices exhibit intrinsic spatio-temporal characteristics. In view of this, we propose LLM4MST, an LLM-empowered microservice troubleshooting model. We first unify and represent multi-source data by extracting service invocation graphs, and model dependencies between microservices by using a message-passing based graph neural network to generate graph-level sequences. The graph-level representation is then aligned with the LLM, and the LLM is fine-tuned to capture complex spatio-temporal patterns, generating a global vector that represents the state of microservice system within a timeslot. LLM4MST achieves accurate anomaly detection and root cause localization by jointly training the end-to-end model. Experiments on real datasets show that LLM4MST exhibits excellent performance in both full-sample and few-shot scenarios, demonstrating the powerful ability of LLMs in cross-domain knowledge transfer and few-shot learning. © 2024 ieee.

关键词： Graph neural networks

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RDMP: A Reference Detection and Mask Propagation Pipeline for Numerous 3D Glomeruli Segmentation in large Volumes 22

RDMP: A Reference Detection and Mask Propagation Pipeline fo...

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22nd ieee International symposium on Biomedical Imaging, ISBI 2025

作者： Lin, Ziling Li, Zehua Chen, Zifan Lu, Yao Zhou, Fangxu Dong, Bin Zhang, Li Li, Haifeng Peking University Center for Data Science Beijing China Peking University First Hospital Beijing China College of Future Technology Peking University Beijing China Peking University Beijing International Center for Mathematical Research Beijing China Peking University Center for Machine Learning Research Beijing China Peking University National Biomedical Imaging Center Beijing China

ISBN: (纸本)9798331520526

Quantitative analysis of numerous repetitive structures in large volumes is one of the most challenging tasks in biomedical imaging, primarily due to its intensive manual requirements. Kidney glomeruli exemplify this challenge due to their dispersion across the cortical region, high density and microscopic size. To address this, we present a Reference Detection and Mask Propagation (RDMP) pipeline that combines object detection with 2D and 3D mask propagation, gradually achieving efficient segmentation of the entire volume by using a 2D reference example. Our method demonstrates robust performance, achieving a Dice Similarity Coefficient (DSC) of 0.9096 on microCT data, and when generalized to Synchrotron Radiation X-ray (SRX) data, reaching a DSC of 0.8431 with only minimal bounding box fine-tuning. Overall, this pipeline shows strong potential for efficient, large-scale kidney analysis, significantly reducing labor demands and accelerating kidney disease research. © 2025 ieee.

关键词： 3D Glomeruli Segmentation large-scale Volume Mask Propagation Reference Detection

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BUCEPHALUS: a BUsiness CEntric cybersecurity Platform for proActive analysis Using visual analyticS

BUCEPHALUS: a BUsiness CEntric cybersecurity Platform for pr...

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ieee symposium on visualization for Cyber Security (VizSec)

作者： Angelini, Marco Blasilli, Graziano Bonomi, Silvia Lenti, Simone Palleschi, Alessia Santucci, Giuseppe De Paoli, Emiliano Sapienza Univ Rome Rome Italy MBDA Italia Rome Italy

ISBN: (纸本)9781665420853

Analyzing and mitigating the threats that cyber-attacks pose on the services of a critical infrastructure is not a trivial activity. Research solutions have been developed using data about the devices used for implementing the services, services dependencies, network topology, and the vulnerabilities that can be exploited to attack the network. However, most of the proposed solutions fail to consider these aspects in an integrated fashion, allowing the user to understand global dependencies and weaknesses. This paper contributes this issue with BUCEPHALUS, a Visual Analytics solution providing

关键词： Cybersecurity Business Impact analysis Network Hardening Attack graphProactive analysis Visual Analytics What-if analysis

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Design and Implementation of a Logistic Map-Based Pseudo-Random Number Generator on FPGA

Design and Implementation of a Logistic Map-Based Pseudo-Ran...

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ieee International symposium on Embedded Multicore Socs (MCSoC)

作者： T. Hui Teo Maoyang Xiang Mostafa Elsharkawy Hen Rin Leao Mateo Jalen Andrew Calderon Jun Lei Lee Syarifuddin Azhar Bin Rosli Hui Ying See En Yu Lim Engineering Product Development Singapapore University of Technology and Design Singapore Nano-Electronic Engineering and Design Singapore University of Technology and Design Singapore Chang Gung University Taiwan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

Chaotic systems can be employed in hardware to generate pseudo-random numbers, which is essential for a wide range of applications, including cryptography, Very large Scale Integration (VLSI) testing, gaming, and simulations. Field-Programmable Gate Arrays (FPGAs) offer a flexible platform for implementing chaotic-based random number generators, integrating real-time data handling and visualization capabilities. This work presents the design and implementation of a pseudo-random number generator (PRNG) deployed on FPGA based on the logistic map for chaotic sequence generation. The generated pseudo-random numbers are further transformed via a Central Limit Theorem (CLT) function to produce a Gaussian distribution. The system architecture integrates several FPGA modules to support real-time interaction and data visualization: a clock generator, a Universal Asynchronous Receiver-Transmitter (UART) interface, a Xilinx Analog-to-Digital Converter (XADC), and a 7-segment display driver. These components enable the display of PRNG values directly on the FPGA and the transmission of data to a laptop for histogram analysis, confirming the Gaussian characteristics of the output.

关键词： Portable computers data handling data visualization Very large scale integration Generators Real-time systems Cryptography Field programmable gate arrays Logistics Testing

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Multimodal Identity Recognition of Spiking Neural Network Based on Brain-Inspired Reward Propagation 22

Multimodal Identity Recognition of Spiking Neural Network Ba...

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22nd ieee International symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Yang Zhang, Hongxia Li, Yahui Wang, Quanqiang Liu, Cheng Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng475004 China Henan University School of Computer and Information Engineering Henan Kaifeng475004 China Huaihe Hospital of Henan University Department of Clinical Laboratory Henan Kaifeng475004 China

ISBN: (纸本)9798331509712

Deep learning has strongly promoted the research progress of unimodal feature learning such as face recognition and speech recognition, but it still needs to be strengthened in multimodal data representation learning, especially the problem of high power consumption, which is not conducive to the real-time application of mobile devices under limited resources. In order to realize multimodal identity recognition for low-energy edge smart devices, this paper proposes a multimodal identity recognition method of spiking neural network based on brain-like mechanism (SNN-MIR-BiM). Simulating the attention mechanism of the cognitive system to specific information improves the response of multimodal features, mimics the regulation mechanism of the neurotransmitter dopamine in the nervous system, and releases reward signals from the recognition results, which affects the neuronal state and regulates the synaptic weights through global propagation, and then optimizes the network structure. Experimental results on large-scale audio and video datasets show that the proposed method can reach 98.94% recognition accuracy, and the energy consumption of the model is about 1/10 of that of CNN under the same architecture, which verifies the effectiveness of the attention mechanism and reward propagation in network design. This research is of great practical significance in the industry applications of unmanned autonomous systems such as human-computer interaction of intelligent machines and intelligent security. © 2024 ieee.

关键词： Face recognition

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