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ieee International symposium on Biomedical Imaging

作者： Arijit Patra Tapabrata Chakraborti Data & Translational Sciences UCB UK Dept of Med Phys & Biomed Eng UCL UK

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

There has been a recent strategic push globally to integrate digital pathology analysis into national health programs, which has been further encouraged by the emergence of large pathology foundation models which promise to make analysis of such data possible at scale in future. However, large scale clinical deployment of such systems remain a challenge in settings where datasets arrive over extended time, and there are requirements of adaptation to new classes of data over time. Also, in many clinical and pharmaceutical settings, retention of past data is often complicated by privacy issues, legal requirements, or storage constraints. Existing deep architectures are susceptible to decline in performance when trained on new tasks over time, in a phenomenon termed as catastrophic forgetting, addressing which is one of the key challenges towards enabling robust learning systems. This is particularly compounded in digital pathology as often new classes will have heterogeneous samples in limited numbers which need to be learned robustly in an explainable and trustworthy manner, as required by emerging legislation worldwide that call for AI safety and transparency in high risk applications like healthcare. We mitigate catastrophic forgetting by designing enhanced representation learning for histology image analysis, which not only improves continual learning performance under non-stationary data environments, but does so in an interpretable, and hence trustworthy manner.

关键词： Continuing education Learning systems Representation learning Histopathology Legislation Medical services Safety Reliability Biomedical imaging Pharmaceuticals

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Spatial Partitioning Strategies for Memory-Efficient Ray Tracing of Particles 10

Spatial Partitioning Strategies for Memory-Efficient Ray Tra...

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Gralka, Patrick Wald, Ingo Geringer, Sergej Reina, Guido Ertl, Thomas Univ Stuttgart Visualizat Res Ctr Stuttgart Germany NVIDIA Corp Santa Clara CA USA Univ Stuttgart Inst Visualizat & Interact Syst Stuttgart Germany

ISBN: (纸本)9781728184685

3D particle data is relevant for a wide range of scientific domains, from molecular dynamics to astrophysics. Simulations in these domains can produce datasets containing millions or billions of particles and rendering needs to be in high quality and interactive to support the scientists in exploring and understanding the structure of their data. One general baseline approach is to represent particles as spheres and employ ray tracing as a rendering technique. However, ray tracing requires the data to be organized in acceleration data structures like bounding volume hierarchies (BVH) to achieve interactive frame rates. Modern GPUs provide hardware acceleration for traversing such data structures but are more limited in memory than CPUs. In this paper, we evaluate different acceleration data structures for sphere-based datasets, including particle kD trees, with respect to their scalability regarding both memory size and speed, and we analyze how these data structures can benefit from hardware acceleration. We show that a bricking of data results in the most effective BVH, both fast to traverse utilizing hardware acceleration and with a reasonably small memory footprint. Additionally, we present a hybrid acceleration data structure that has negligible memory overhead and still ensures reasonable traversal speed. Based on our results, visualization tools and APIs for the ray tracing can provide overall better performance by adapting to the needs of particle-centric application scenarios.

关键词： Particle data acceleration data structure ray tracing

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RANDOM FOREST-BASED DIFFUSION INFORMATION GEOMETRY FOR SUPERVISED visualization AND data EXPLORATION 21

RANDOM FOREST-BASED DIFFUSION INFORMATION GEOMETRY FOR SUPER...

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ieee Statistical Signal Processing Workshop (SSP)

作者： Rhodes, Jake S. Cutler, Adele Wolf, Guy Moon, Kevin R. Utah State Univ Dept Math & Stat Logan UT 84322 USA Univ Montreal Dept Math & Stat Montreal PQ Canada Univ Montreal Mila Quebec AI Inst Montreal PQ Canada

ISBN: (纸本)9781728157672

Most dimensionality reduction techniques to date are unsupervised;they do not take class labels into account (e.g., PCA, MDS, t-SNE, Isomap). Such methods require large amounts of data and are often sensitive to noise that may obfuscate important patterns in the data. Various attempts at supervised dimensionality reduction methods that take into account auxiliary annotations (e.g., class labels) have been successfully implemented with goals of increased classification accuracy or improved data visualization. In this paper, we describe a novel supervised visualization technique based on random forest proximities and a diffusion-based information geometry. We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structure in data, while demonstrating the spatial relevance of features important for the supervised task. Importantly, our approach is robust to noise and parameter tuning, thus making it simple to use while producing reliable visualizations for data exploration.

关键词： Dimensionality reduction Information geometry data visualization Signal processing algorithms Signal processing Reliability Task analysis

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Distribution-based Particle data Reduction for In-situ analysis and visualization of large-scale N-body Cosmological Simulations 13

Distribution-based Particle Data Reduction for In-situ Analy...

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ieee Pacific visualization symposium (PacificVis)

作者： Li, Guan Xu, Jiayi Zhang, Tianchi Shan, Guihua Shen, Han-Wei Wang, Ko-Chih Liao, Shihong Lu, Zhonghua Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Ohio State Univ Columbus OH 43210 USA Chinese Acad Sci Natl Astron Observ Beijing Peoples R China Natl Taiwan Normal Univ Taipei Taiwan

ISBN: (数字)9781728156972

ISBN: (纸本)9781728156972

Cosmological N-body simulation is an important tool for scientists to study the evolution of the universe. With the increase of computing power, billions of particles of high space-time fidelity can be simulated by supercomputers. However, limited computer storage can only hold a small subset of the simulation output for analysis, which makes the understanding of the underlying cosmological phenomena difficult. To alleviate the problem, we design an in-situ data reduction method for large-scale unstructured particle data. During the data generation phase, we use a combined k-dimensional partitioning and Gaussian mixture model approach to reduce the data by utilizing probability distributions. We offer a model evaluation criterion to examine the quality of the probabilistic distribution models, which allows us to identify and improve low-quality models. After the in-situ processing, the particle data size is greatly reduced, which satisfies the requirements from the domain experts. By comparing the astronomical attributes and visualizations of the reconstructed data with the raw data, we demonstrate the effectiveness of our in-situ particle data reduction technique.

关键词： Human-centered computing visualization visualization application domains Scientific visualization

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Design of a Lightweight Privacy Communication Network System for Medium Enterprises

Design of a Lightweight Privacy Communication Network System...

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ieee Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Ruixiang Suo Wenxuan Qiao Xinyang Bai Yuyang Zhang Ping Dong School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Medium and large enterprises need to exchange confidential information frequently. Traditional encryption techniques can only protect the security of communication data, but the privacy of the communicating entities' identity information cannot be effectively protected. Currently, common solutions include virtual private networks (VPNs) and single/multiple agent-based forwarding mechanisms. However, the egress nodes of VPNs are susceptible to attacks and monitoring; agents in single-agent forwarding mechanisms are easy targets for single points of failure and attacks. Medium and large enterprises urgently need a solution that ensures communication privacy while being cost-effective and easy to manage. Therefore, this paper proposes a lightweight privacy communication network system based on a multi-agent mechanism to realize the non-association of communication parties and provide transparent transmission services. In order to schedule and manage the whole system flexibly, this paper implements a remote service and a visualization service in a Directory Server. In addition, an intelligent multi-objective optimization analysis of the route selection algorithm is introduced to ensure the anti-tracking and high efficiency of data transmission; meanwhile, traffic obfuscation technology is used to prevent attackers from recognizing the communication content and the identities of both parties through the traffic pattern, which improves the transmission and management security of the system. Test results show that the system effectively protects information communication relationships in medium and large enterprises.

关键词： Privacy data privacy Technological innovation data visualization Traffic control Virtual private networks Internet Communication networks Protection Optimization

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A Grammar-Based Approach for Applying visualization Taxonomies to Interaction Logs

A Grammar-Based Approach for Applying Visualization Taxonomi...

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24th Eurographics/ieee VGTC Conference on visualization (EuroVis)

作者： Gathani, Sneha Monadjemi, Shayan Ottley, Alvitta Battle, Leilani Univ Maryland College Pk MD 20742 USA Washington Univ St Louis MO 63110 USA Univ Washington Seattle WA 98195 USA

Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their high-level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to user interaction logs. This paper seeks to bridge the gap between visualization task taxonomies and interaction log data by making the taxonomies more actionable for interaction log analysis. To achieve this, we leverage structural parallels between how people express themselves through interactions and language by reformulating existing theories as regular grammars. We represent interactions as terminals within a regular grammar, similar to the role of individual words in a language, and patterns of interactions or non-terminals as regular expressions over these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven existing visualization taxonomies and develop code to apply them to three public interaction log datasets. In analyzing these regular grammars, we find that the taxonomies at the low-level (i.e., terminals) show mixed results in expressing multiple interaction log datasets, and taxonomies at the high-level (i.e., regular expressions) have limited expressiveness, due to primarily two challenges: inconsistencies in interaction log dataset granularity and structure, and under-expressiveness of certain terminals. Based on our findings, we suggest new research directions for the visualization community to augment existing taxonomies, develop new ones, and build better interaction log recording processes to facilitate the data-driven development of user behavior taxonomies.

关键词： visualization

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Nuwa: An Authoring Tool for Graph visualizations

Nuwa: An Authoring Tool for Graph Visualizations

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Dongming Han Haiyang Zhu Wei Chen Jiacheng Pan Ran Chen Xumeng Wang Zhen Wen Luoxuan Weng Minfeng Zhu Yingcai Wu Rüdiger Westermann State Key Lab of CAD&CG Zhejiang University China Hithink RoyalFlush Information Network Co. Ltd. China Wuchan Zhongda Digital Technology Co. Ltd. China Laboratory of Art and Archaeology Image (Zhejiang University) Ministry of Education China College of Computer Science Nankai University China Zhejiang University China Computer Graphics and Visualization Technical University of Munich Germany

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

关键词： Analytical models Authoring systems Costs Semantics data visualization Prototypes Programming

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Generative Memory-Guided Semantic Reasoning Model for Image Inpainting

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第11期32卷 7432-7447页

作者： Feng, Xin Pei, Wenjie Li, Fengjun Chen, Fanglin Zhang, David Lu, Guangming Harbin Inst Technol Shenzhen Dept Comp Sci Shenzhen 518057 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China

The critical challenge of single image inpainting stems from accurate semantic inference via limited information while maintaining image quality. Typical methods for semantic image inpainting train an encoder-decoder network by learning a one-to-one mapping from the corrupted image to the inpainted version. While such methods perform well on images with small corrupted regions, it is challenging for these methods to deal with images with large corrupted area due to two potential limitations. 1) Such one-to-one mapping paradigm tends to overfit each single training pair of images;2) The inter-image prior knowledge about the general distribution patterns of visual semantics, which can be transferred across images sharing similar semantics, is not explicitly exploited. In this paper, we propose the Generative Memory-guided Semantic Reasoning Model (GM-SRM), which infers the content of corrupted regions based on not only the known regions of the corrupted image, but also the learned inter-image reasoning priors characterizing the generalizable semantic distribution patterns between similar images. In particular, the proposed GM-SRM first pre-learns a generative memory from the whole training data to explicitly learn the distribution of different semantic patterns. Then the learned memory are leveraged to retrieve the matching semantics for the current corrupted image to perform semantic reasoning during image inpainting. While the encoder-decoder network is used for guaranteeing the pixel-level content consistency, our generative priors are favorable for performing high-level semantic reasoning, which is particularly effective for inferring semantic content for large corrupted area. Extensive experiments on Paris Street View, CelebA-HQ, and Places2 benchmarks demonstrate that our GM-SRM outperforms the state-of-the-art methods for image inpainting in terms of both visual quality and quantitative metrics.

关键词： Semantics Cognition Decoding Training Image restoration Image edge detection visualization Image inpainting generative memory image synthesis semantic reasoning

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Statistical Characterization of Polarimetric Time-Frequency Coherent Indicator

Statistical Characterization of Polarimetric Time-Frequency ...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Canbin Hu Hongyun Chen Xiaokun Sun Zekai Yun Laurent Ferro-Famil College of Information Science and Technology Beijing University of Chemical Technology China ISAE-SUPAERO University of Toulouse Toulouse France CESBIO University of Toulouse Toulouse France

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The polarimetric time-frequency coherent indicator has been proposed and effectively applied, especially for target detection. In this paper, based on the complex Wishart distribution, an associated asymptotic probability for the statistical test of the polarimetric time-frequency coherence is analytically derived. The goodness of fit for the probability density function is tested with Monte Carlo simulated data. It can be observed that the estimated probability density function curve fit well with the theoretical value.

关键词： Time-frequency analysis visualization Maximum likelihood estimation Maximum likelihood detection Monte Carlo methods Statistical distributions Object detection

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A Text Mining based Entity Extraction Model for Power Plant Fault

A Text Mining based Entity Extraction Model for Power Plant ...

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2023 CAA symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Meng, Yuwei Yu, Rongdong Yang, Xiaorong Li, Songyue Fu, Junwei Zhou, Zhiguang Zhejiang Energy Digital Technology Co. Ltd Zhejiang China Zhejiang Energy Technology Research Institute Co. Ltd Zhejiang China Zhejiang University of Finance & Economics Zhejiang China Hangzhou Dianzi University Zhejiang China Zhejiang University State Key Lab of CAD & CG Zhejiang China

ISBN: (纸本)9798350337754

Entity extraction is widely used in power fault diagnosis to extract valuable knowledge from a large amount of power fault data, supporting fault diagnosis and prevention. Traditional entity extraction methods mainly rely on fixed rules and procedures, which cannot be dynamically learned or changed with new data or knowledge. When dealing with complex and variable power plant fault data, traditional methods have limitations. This paper proposes a text mining-based entity extraction method that updates the model by learning fault data characteristics. This makes the model more adaptive and flexible to cope with complex and variable data. We compared and analyzed experimental results based on real fault data. The results show that our method effectively extracts fault entities from power plants and has a high accuracy and recall rate. This has a significant reference value for improving the efficiency and accuracy of power plant fault processing. © 2023 ieee.

关键词： Failure analysis

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