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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第6期29卷 2875-2887页

作者： Wallinger, Markus Dobler, Alexander Noellenburg, Martin TU Wien Algorithms & Complex Grp A-1040 Vienna Austria

Linear diagrams are used to visualize set systems by depicting set memberships as horizontal line segments in a matrix, where each set is represented as a row and each element as a column. Each such line segment of a set is shown in a contiguous horizontal range of cells of the matrix indicating that the corresponding elements in the columns belong to the set. As each set occupies its own row in the matrix, the total height of the resulting visualization is as large as the number of sets in the instance. Such a linear diagram can be visually sparse and intersecting sets containing the same element might be represented by distant rows. To alleviate such undesirable effects, we present ***, a new approach that achieves a more space-efficient representation of linear diagrams. First, we minimize the total number of gaps in the horizontal segments by reordering columns, a criterion that has been shown to increase readability in linear diagrams. The main difference of *** to linear diagrams is that multiple non-intersecting sets can be positioned in the same row of the matrix. Furthermore, we present several different rendering variations for a matrix-based representation that utilize the proposed row compression. We implemented the different steps of our approach in a visualization pipeline using integer-linear programming, and suitable heuristics aiming at sufficiently fast computations in practice. We conducted both a quantitative evaluation and a small-scale user experiment to compare the effects of compressing linear diagrams.

关键词： Task analysis visualization data visualization Image color analysis Heuristic algorithms Taxonomy Sparse matrices Computational experiment linear diagrams set visualization user evaluation

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LLMER: Crafting Interactive Extended Reality Worlds with JSON data Generated by large Language Models

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第5期31卷 2715-2724页

作者： Chen, Jiangong Wu, Xiaoyi Lan, Tian Li, Bin Penn State Univ Dept Elect Engn University Pk PA 16802 USA George Washington Univ Dept Elect & Comp Engn Washington DC USA

The integration of large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization.

关键词： Three-dimensional displays Animation Codes Real-time systems Computer crashes Libraries Hands Extended reality data mining Training generative artificial intelligence large language model human-computer interaction

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visualization System for Analyzing the Characteristics of Areas with Frequent Water-related Accidents in River 28

Visualization System for Analyzing the Characteristics of Ar...

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28th International Conference on Information Visualisation (IV)

作者： Itoh, Masahiko Aizawa, Momoka Hokkaido Informat Univ Dept Informat Media Ebetsu Japan

ISBN: (纸本)9798350380170;9798350380163

Many water-related accidents occur not only at sea but also on rivers every year. However, water-related accidents are caused by multiple factors, making it difficult to understand the causes. Therefore, it is necessary to develop a system that automatically extracts areas of high occurrence of water-related accidents from a large amount of data and analyzes them in combination with other data to understand the cause of the accidents. In this paper, we first utilize DBSCAN to extract areas of high occurrence of water-related accidents in rivers. We then construct a feature analysis system to visualize and analyze these areas in combination with other data such as population and presence/absence of bridges. From the analysis results, we found that when bridges and campgrounds are located around high accident areas, people often dive from bridges and drink alcohol while camping.

关键词： Information visualization Visual Analytics System Water-related Accident

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Effect of Weight Quantization on Learning Models by Typical Case analysis

Effect of Weight Quantization on Learning Models by Typical ...

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ieee International symposium on Information Theory (ISIT)

作者： Kashiwamura, Shuhei Sakata, Ayaka Imaizumi, Masaaki Univ Tokyo Tokyo Japan Inst Stat Math Tokyo Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9798350382853;9798350382846

This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly increased computational resource requirements. To address this, quantizing model weights has become a prevalent practice in data analysis applications such as deep learning. Quantization is particularly vital for deploying large models on devices with limited computational resources. However, the selection of quantization hyperparameters, like the number of bits and value range for weight quantization, remains an underexplored area. In this study, we employ the typical case analysis from statistical physics, specifically the replica method, to explore the impact of hyperparameters on the quantization of simple learning models. Our analysis yields three key findings: (i) an unstable hyperparameter phase, known as replica symmetry breaking, occurs with a small number of bits and a large quantization width;(ii) there is an optimal quantization width that minimizes error;and (iii) quantization delays the onset of overparameterization, which mitigate overfitting as indicated by the double descent phenomenon. We also discover that non-uniform quantization can enhance stability. Additionally, we develop an approximate message-passing algorithm to validate our theoretical results.

关键词： Contrastive Learning

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Semi-Supervised Image Captioning by Adversarially Propagating Labeled data

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ieee ACCESS 2024年 12卷 93580-93592页

作者： Kim, Dong-Jin Oh, Tae-Hyun Choi, Jinsoo Kweon, In So Hanyang Univ Dept Data Sci Seoul 04763 South Korea POSTECH Dept Elect Engn Pohang 37673 South Korea POSTECH Grad Sch AI Pohang 37673 South Korea Yonsei Univ Inst Convergence Res & Educ Adv Technol Seoul 03722 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 34141 South Korea

We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Constructing a large-scale labeled image captioning dataset is expensive in terms of labor, time, and cost. In contrast to manually annotating all the training samples, separately collecting uni-modal datasets is immensely easier, e.g., a large-scale image dataset and a sentence dataset. We leverage such massive unpaired image and caption data upon standard paired data by learning to associate them. To this end, our novel semi-supervised learning method assigns pseudo-labels to unpaired images and captions in an adversarial learning fashion, where the joint distribution of image and caption is learned. This approach shows noticeable performance improvement even in challenging scenarios, including out-of-task data and web-crawled data. We also show that our proposed method is theoretically well-motivated and has a favorable global optimal property. Our extensive and comprehensive empirical results on captioning datasets, followed by a comprehensive analysis of the scarcely-paired COCO dataset, demonstrate the consistent effectiveness of our method compared to competing ones.

关键词： Task analysis data models Training Semisupervised learning visualization Natural languages Generative adversarial networks Closed captioning Image captioning unpaired captioning semi-supervised learning generative adversarial networks

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A Scanner Deeply: Predicting Gaze Heatmaps on visualizations Using Crowdsourced Eye Movement data

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 396-406页

作者： Shin, Sungbok Chung, Sunghyo Hong, Sanghyun Elmqvist, Niklas University of Maryland College Park United States Kakao Corp. Korea Republic of Oregon State University United States

Visual perception is a key component of data visualization. Much prior empirical work uses eye movement as a proxy to understand human visual perception. Diverse apparatus and techniques have been proposed to collect eye movements, but there is still no optimal approach. In this paper, we review 30 prior works for collecting eye movements based on three axes: (1) the tracker technology used to measure eye movements;(2) the image stimulus shown to participants;and (3) the collection methodology used to gather the data. Based on this taxonomy, we employ a webcam-based eyetracking approach using task-specific visualizations as the stimulus. The low technology requirement means that virtually anyone can participate, thus enabling us to collect data at large scale using crowdsourcing: approximately 12,000 samples in total. Choosing visualization images as stimulus means that the eye movements will be specific to perceptual tasks associated with visualization. We use these data to propose a Scanner Deeply, a virtual eyetracker model that, given an image of a visualization, generates a gaze heatmap for that image. We employ a computationally efficient, yet powerful convolutional neural network for our model. We compare the results of our work with results from the DVS model and a neural network trained on the Salicon dataset. The analysis of our gaze patterns enables us to understand how users grasp the structure of visualized data. We also make our stimulus dataset of visualization images available as part of this paper's contribution. © 2022 ieee.

关键词： Eye movements

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Exploring Effective Immersive Approaches to Visualizing WiFi 22

Exploring Effective Immersive Approaches to Visualizing WiFi

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22nd ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Rowden, Alexander Krokos, Eric Whitley, Kirsten Varshney, Amitabh

ISBN: (纸本)9798350328387

WiFi networks are essential to our daily lives, but their signals are not visible to us. Therefore, it is challenging to evaluate the health of a network or make changes to ensure an optimal configuration. Traditional visualization approaches, such as contour lines, are not intuitive and lead to challenges in the analysis and comprehension of networks. In this paper, we introduce two novel visualizations: Wavelines and Stacked Bars. We then compared these visualizations to the state-of-the-art visualization technique of contour lines. We carried out a user study with 32 participants to validate that our novel visualizations can improve user confidence, accuracy, and completion time for the tasks of router localization, ranking of signal strengths, channel interference, and router coverage. We selected these tasks after extensive discussions with domain experts. We believe that our findings will assist network analysts in visually understanding our increasingly rich signal environments.

关键词： Scalar Field data Application Motivated visualization Human-Subjects Qualitative Studies Human-Subjects Quantitative Studies data analysis Virtual Reality Immersive

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Nuwa: An Authoring Tool for Graph visualizations 17

Nuwa: An Authoring Tool for Graph Visualizations

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17th Pacific visualization Conference (PacificVis)

作者： Han, Dongming Zhu, Haiyang Chen, Wei Pan, Jiacheng Chen, Ran Wang, Xumeng Wen, Zhen Weng, Luoxuan Zhu, Minfeng Wu, Yingcai Westermann, Rudiger Zhejiang Univ State Key Lab CAD CG Hangzhou Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou Peoples R China Zhejiang Univ Lab Art & Archaeol Image Minist Educ Hangzhou Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Zhejiang Univ Hangzhou Peoples R China Tech Univ Munich Comp Graph & Visualizat Munich Germany Wuchan Zhongda Digital Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798350393811;9798350393804

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： Graph visualization Authoring tool

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A GENERATIVE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR EARTH OBSERVATION analysis

A GENERATIVE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR EARTH OBS...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Wagner, Otto Gordon, Jeffrey Mousa, Aya Terry, Brian Baptist, Joshua Yetkin, Oguz Borges, David Gowda, Sanjay NASA Analyt Mech Associates AMA Washington DC 20546 USA

ISBN: (纸本)9798350360332;9798350360325

Generative artificial intelligence (AI), specifically large Language Models (LLMS) such as generative pretrained transformers (GPTS) have emerged as a transformational technology in modern research and applications. This technology will revolutionize a wide variety of fields, including remote sensing and Earth observation. We explore the use of LLMS to improve and automate EO analysis, making these results more accessible to a broad range of decision makers. We develop a framework using this technology to support the Committee on Earth Observation Satellites (CEOS) and NASA's Open Science initiative. This framework leverages current work on Earth observation tools, including the Open data Cube (ODC) [1] and the OEA Algorithm Hub (AlgoHub). [2] In our work, we investigate the ability of large language models to orchestrate and reason with tools created to access geospatial data using the Open data Cube and the OEA Algorithm Hub [2]. In this paper we propose a natural language analytical framework for orchestrating Earth observation (EO) analysis. The framework uses large language models to interpret an analysis request, devise a solution based on ODC and AlgoHub tools, and execute the analysis. Our experimental investigations demonstrate that an LLM, with its embedded contextual knowledge, can effectively discern and sequence the necessary processing steps required to fulfill Earth observation analysis requests. This research demonstrates the potential of Generative AI (LLMs) in streamlining and enhancing EO analyses and empowering the observation community. In summary, Generative AI (LLMs) can enhance the capabilities of Earth observation analysis by automating tasks, providing natural language interfaces, and facilitating the extraction of valuable insights from the vast and complex datasets generated by remote sensing and EO technologies.

关键词： large Language Model Generative AI LangChain Earth Observation GPT Open data Cube Algorithm Hub

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ADAPTIVE SPATIAL AND MULTI-VARIABLE GENERALIZATION OF 4DVARNET IN OCEAN COLOUR REMOTE SENSING

ADAPTIVE SPATIAL AND MULTI-VARIABLE GENERALIZATION OF 4DVARN...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Dorffer, Clement Thi Thuy Nga Nguyen Fablet, Ronan Jourdin, Frederic IMT Atlantique UMR CNRS Lab STICC INRIA Team Odyssey Brest France Serv Hydrog & Oceanog Marine SHOM Brest France

ISBN: (纸本)9798350360332;9798350360325

This study presents an enhanced approach to ocean colour L4 product generation through the Adaptive Spatial and Multi-Variable Generalization of 4DVarNet - an innovative integration of deep neural networks with variational data assimilation proposed in [1]. We explore the model's capabilities in generalizing across various geographical regions and bio-optical variables using datasets of the North Sea and the Mediterranean Sea. Our analysis and visualization show that 4DVarNet demonstrates a notable ability to adapt and scale, reducing significantly training cost thanks to this generalization ability.

关键词： interpolation data-driven model variational data assimilation missing data suspended particulate matter observing system experiment ocean colour remote sensing Mediterranean North Sea

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