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QVis: A visual Analytics Tool for Exploring Noise and Errors in Quantum Computing Systems

QVis: A Visual Analytics Tool for Exploring Noise and Errors...

引用

Quantum Computing and Engineering (QCE), IEEE International conference on

作者： Chad A. Steed Junghoon Chae Samudra Dasgupta Travis S. Humble Oak Ridge National Laboratory Oak Ridge USA

We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum devices. We describe the implementations of these methods as well as applications to the analysis of a 127-qubit data set derived from the IBM washington processor over a 16-month period. Both human-interactive and semi-automated analytic methods are included to address requirements for visual exploration, thresholding, and clustering techniques. Our application of QVis to real-world scenarios demonstrates the ability to reveal noteworthy patterns in the behavior of the critical characterization metrics.

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Vision mamba mender 24

Vision mamba mender

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Proceedings of the 38th International conference on Neural Information Processing Systems

作者： Jiacong Hu Anda Cao Zunlei Feng Shengxuming Zhang Yi Wang Lingxiang Jia Mingli Song College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/.

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AirLens: Multi-Level visual exploration of Air Quality Evolution in Urban Agglomerations

AirLens: Multi-Level Visual Exploration of Air Quality Evolu...

引用

24th Eurographics/IEEE VGTC conference on visualization (EuroVis)

作者： Qu, Dezhan Lv, Cheng Lin, Yiming Zhang, Huijie Wang, Rong Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Intelligent Informat Proc Changchun Peoples R China

The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.

关键词： Feedback

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A Bayesian-Optimized Convolutional Neural Network to Decode Reach-to-Grasp from Macaque Dorsomedial visual Stream 8th

A Bayesian-Optimized Convolutional Neural Network to Decode ...

引用

8th Annual International conference on Machine Learning, Optimization and data science (LOD)

作者： Borra, Davide Filippini, Matteo Ursino, Mauro Fattori, Patrizia Magosso, Elisa Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon Cesena Campus Cesena Italy Univ Bologna Dept Biomed & Neuromotor Sci DIBINEM Bologna Italy Univ Bologna Alma Mater Res Inst Human Ctr Artificial Intellig Bologna Italy

ISBN: (纸本)9783031258909;9783031258916

Neural decoding is crucial to translate the neural activity for Brain-Computer Interfaces (BCIs) and provides information on how external variables (e.g., movement) are represented and encoded in the neural system. Convolutional neural networks (CNNs) are emerging as neural decoders for their high predictive power and are largely applied with electroencephalographic signals;these algorithms, by automatically learning the more relevant class-discriminative features, improve decoding performance over classic decoders based on handcrafted features. However, applications of CNNs for single-neuron decoding are still scarce and require further validation. In this study, a CNN architecture was designed via Bayesian optimization and was applied to decode different grip types from the activity of single neurons of the posterior parietal cortex of macaque (area V6A). The Bayesian-optimized CNN significantly outperformed a naive Bayes classifier, commonly used for neural decoding, and proved to be robust to a reduction of the number of cells and of training trials. Adopting a sliding window decoding approach with a high time resolution (5 ms), the CNN was able to capture grip-discriminant features early after cuing the animal, i.e., when the animal was only attending the object to grasp, further supporting that grip-related neural signatures are strongly encoded in V6A already duringmovement preparation. The proposed approach may have practical implications in invasive BCIs to realize accurate and robust decoders, and may be used together with explanation techniques to design a general tool for neural decoding and analysis, boosting our comprehension of neural encoding.

关键词： Neural decoding Convolutional neural networks Dorsomedial visual stream V6A Bayesian optimization

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visualization Tools for Machine Learning Pipelines: A Review

Visualization Tools for Machine Learning Pipelines: A Review

引用

Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Valentina Golendukhina Michael Felderer Lisa Sonnleithner University of Innsbruck Innsbruck Austria German Aerospace Center (DLR) Institute for Software Technology University of Cologne Cologne Germany CDL VaSiCS LIT CPS Lab Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

The increasing complexity and adoption of machine learning (ML) pipelines has led to a rising demand for effective visualization tools. This paper presents a comprehensive review of existing tools for visualizing data flow in machine learning (ML) pipelines. We highlight the tools’ purposes, integration methods, and visualization techniques. We collected and analyzed 22 open-source tools and concepts, analyzing their features and classifying them based on their primary purpose. Our analysis revealed five main purposes of visualization tools: exploration, explanation, visual development, comparison, monitoring. We provide an analysis of their integration methods, from standalone visual interfaces to code-level libraries, as well as a review of various visualization techniques, including Directed Acyclic Graphs (DAGs), pipeline matrices, and annotated visualizations. Our findings highlight the importance of visualization in enhancing the interpretability and efficiency of ML workflows. Moreover, the paper provides key limitations and challenges in current visualization methods to promote future research directions enhancing the usability and functionality of ML pipeline visualization tools.

关键词： visualization Directed acyclic graph Reviews Pipelines data visualization Machine learning visual systems Complexity theory Usability Monitoring

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A visual analytics approach for anomaly detection from a novel traffic light data

A visual analytics approach for anomaly detection from a nov...

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2021 conference on visualization and data analysis, VDA 2021

作者： Turner, Glenn Chen, Guoning Zhang, Yunpeng University of Houston Department of Computer Science United States Boise State University Dakota State University United States University of Melbourne Australia Imperial College London United Kingdom

Traffic signals are part of our critical infrastructure and protecting their integrity is a serious concern. Security flaws in traffic signal systems have been documented and effective detection of exploitation of these flaws remains a challenge. In this paper we present a visual analytics approach to look for anomalies in traffic signal data (i.e., abnormal traffic light patterns) that may indicate a compromise of the system. To our knowledge it is a first time a visual analytics approach is applied for the processing and exploration of traffic signal data. This system supports level-of-detail exploration with various visualization techniques. data cleaning and a number of preprocessing techniques for the extraction of summary information (e.g., traffic signal cycles) of the data are also performed before the visualization and data exploration. Our system successfully reveals the errors in the input data that would be difficult to capture with simple plots alone. In addition, our system captures some abnormal signal patterns that may indicate intrusions into the system. In summary, this work offers a new and effective way to study attacks or intrusions to traffic signal control systems via the visual analysis of traffic light signal patterns. © 2021, Society for Imaging Science and Technology.

关键词： Traffic signals

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exploration and analysis of Image-based Simulation Ensembles

Exploration and Analysis of Image-based Simulation Ensembles

引用

24th Eurographics conference on visualization, EuroVis 2022

作者： Dahshan, M. Turton, T.L. Polys, N. University of North Florida United States Los Alamos National Laboratory United States Virgina Tech United States

ISBN: (纸本)9783038681854

Scientists run simulation ensembles to study the behavior of a phenomenon using varying initial conditions or input parameters. However, the I/O bottlenecks hinder performing large-scale multidimensional simulations. In situ visualization approaches address the variability of I/O performance by processing output data during simulation time and saving predetermined visualizations in image databases. This poster proposes a visual analytics approach to exploring and analyzing image-based simulation ensembles, taking advantage of semantic interaction, feature extraction, and deep learning techniques. Our approach uses deep learning and local feature techniques to learn image features and pass them along with the input parameters to the visualization pipeline for in-depth exploration and analysis of parameter and ensemble spaces simultaneously. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： visualization

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Deployment of Reliable visual Inertial Odometry Approaches for Unmanned Aerial Vehicles in Real-world Environment

Deployment of Reliable Visual Inertial Odometry Approaches f...

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International conference on Unmanned Aircraft Systems (ICUAS)

作者： Bednar, Jan Petrlik, Matej Vivaldini, Kelen Cristiane Teixeira Saska, Martin Czech Tech Univ Fac Elect Engn Prague Czech Republic Univ Fed Sao Carlos Dept Comp Sao Carlos Brazil

ISBN: (纸本)9781665405935

Integration of visual Inertial Odometry (VIO) methods into a modular control system designed for deployment of Unmanned Aerial Vehicles (UAVs) and teams of cooperating UAVs in real-world conditions are presented in this paper. Reliability analysis and fair performance comparison of several methods integrated into a control pipeline for achieving full autonomy in real conditions is provided. Although most VIO algorithms achieve excellent localization precision and negligible drift on artificially created datasets, the aspects of reliability in non-ideal situations, robustness to degraded sensor data, and the effects of external disturbances and feedback control coupling are not well studied. These imperfections, which are inherently present in cases of real-world deployment of UAVs, negatively affect the ability of the most used VIO approaches to output a sensible pose estimation. We identify the conditions that are critical for a reliable flight under VIO localization and propose workarounds and compensations for situations in which such conditions cannot be achieved. The performance of the UAV system with integrated VIO methods is quantitatively analyzed w.r.t. RTK ground truth and the ability to provide reliable pose estimation for the feedback control is demonstrated onboard a UAV that is tracking dynamic trajectories under challenging illumination.

关键词： visual Inertial Odometry Unmanned Aerial Vehicle Trajectory Shaping Feedback Control Camera Orientation

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FamSearch: visual analysis of Genealogical data 16th

<i>FamSearch</i>: Visual Analysis of Genealogical Data

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16th International Symposium on visual Computing (ISVC)

作者： Burch, Michael Wallner, Guenter van de Wetering, Huub Tufail, Shahrukh Zandt-Sloot, Linda Gladkis, Stasius Hong, Minji Lepelaars, Carlo Univ Appl Sci Grisons Chur Switzerland Johannes Kepler Univ Linz Linz Austria Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783030904364;9783030904357

This paper describes an interactive visualization tool consisting of several views offering different perspectives on genealogical data that is stored and maintained by the Brabants Historical Information Center (BHIC). The dataset consists of several attributes, static as well as dynamic ones, containing information about several million people having lived in the Netherlands. With the tool, we can gain insights in family histories and build, confirm, and reject hypotheses, or search for one's own relatives having lived in that region. Genealogical, temporal, and geographical relations can be investigated and linked to each other to identify correlations. Moreover, family events such as marriages, divorces, births, deaths, and similar can be explored. To support these tasks the tool provides several algorithms and interactive visualizations that enable data exploration. visualizations include, amongst others, timeline diagrams, pedigree trees, population pyramids, sunbursts, and word clouds. We illustrate the usefulness of the tool by showcasing which and how patterns and anomalies can be found.

关键词： Genealogical data Information visualization Interactions Diagrammatic representations

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Connecting the Complementary-view Videos: Joint Camera Identification and Subject Association

Connecting the Complementary-view Videos: Joint Camera Ident...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Han, Ruize Gan, Yiyang Li, Jiacheng Wang, Feifan Feng, Wei Wang, Song Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We attempt to connect the data from complementary views, i.e., top view from drone-mounted cameras in the air, and side view from wearable cameras on the ground. Collaborative analysis of such complementary-view data can facilitate to build the air-ground cooperative visual system for various kinds of applications. This is a very challenging problem due to the large view difference between top and side views. In this paper, we develop a new approach that can simultaneously handle three tasks: i) localizing the side-view camera in the top view;ii) estimating the view direction of the side-view camera;iii) detecting and associating the same subjects on the ground across the complementary views. Our main idea is to explore the spatial position layout of the subjects in two views. In particular, we propose a spatial-aware position representation method to embed the spatial-position distribution of the subjects in different views. We further design a cross-view video collaboration framework composed of a camera identification module and a subject association module to simultaneously perform the above three tasks. We collect a new synthetic dataset consisting of top-view and side-view video sequence pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.

关键词： Performance evaluation Machine vision Video sequences Layout Collaboration visual systems Cameras

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