Air pollution has become a serious public health problem for many cities around the world. To find the causes of air pollution, the propagation processes of air pollutants must be studied at a large spatial scale. However, the complex and dynamic wind fields lead to highly uncertain pollutant transportation. The state-of-the-art data mining approaches cannot fully support the extensive analysis of such uncertain spatiotemporal propagation processes across multiple districts without the integration of domain knowledge. The limitation of these automated approaches motivates us to design and develop AirVis, a novel visual analytics system that assists domain experts in efficiently capturing and interpreting the uncertain propagation patterns of air pollution based on graph visualizations. Designing such a system poses three challenges: a) the extraction of propagation patterns; b) the scalability of pattern presentations; and c) the analysis of propagation processes. To address these challenges, we develop a novel pattern mining framework to model pollutant transportation and extract frequent propagation patterns efficiently from large-scale atmospheric data. Furthermore, we organize the extracted patterns hierarchically based on the minimum description length (MDL) principle and empower expert users to explore and analyze these patterns effectively on the basis of pattern topologies. We demonstrated the effectiveness of our approach through two case studies conducted with a real-world dataset and positive feedback from domain experts.

AirVis: Visual Analytics of Air Pollution Propagation

Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions research directions regarding the type of explanations and the evaluation of explanations and interpretability. 

/pdf/explainable-ai-for-time-series-classification-a-review-1fx5libp.pdf

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing

Welcome to the 2011 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D)! These are the proceedings of the symposium being held February 18-20, 2011 in San Francisco, California. This is the fifteenth meeting of the symposium that started as a workshop in Chapel Hill, North Carolina in 1986. I3D has evolved to become a vibrant, stimulating, and collegial meeting place for researchers, practitioners, students, and enthusiasts in the area of interactive computer graphics techniques, with an emphasis on computer games.

This year the symposium received 64 paper submissions of which 24 were selected by a rigorous peer review process to appear in these proceedings. Over the years I3D has acquired an enviable reputation for high quality and timeliness of the content selected for presentation. We are pleased to continue to excel in that fine tradition this year as well. We would like to commend the authors of the submissions, and especially the ones that were selected, for contributing their efforts, insights, and ideas for presentation at I3D.

This year 67 members of the International Program Committee as well as 5 external reviewers participated in the careful and diligent review and papers selection process. Every paper received at least three reviews and there were extensive online discussions in which the program committee members and reviewers weighed in on the relative merits of each paper. We would like to thank the committee members and the reviewers for their extraordinary time and effort in the selection process.

Symposium on Interactive 3D Graphics and Games

The use and creation of machine‐learning‐based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks such as a lack of explainability and behaving as a black‐box model. During the last few years, Visual Analytics has provided several proposals to cope with these drawbacks, supporting the emerging eXplainable Deep Learning field. This survey aims to (i) systematically report the contributions of Visual Analytics for eXplainable Deep Learning; (ii) spot gaps and challenges; (iii) serve as an anthology of visual analytical solutions ready to be exploited and put into operation by the Deep Learning community (architects, trainers and end users) and (iv) prove the degree of maturity, ease of integration and results for specific domains. The survey concludes by identifying future research challenges and bridging activities that are helpful to strengthen the role of Visual Analytics as effective support for eXplainable Deep Learning and to foster the adoption of Visual Analytics solutions in the eXplainable Deep Learning community. An interactive explorable version of this survey is available online at https://aware‐diag‐sapienza.github.io/VA4XDL.

State of the Art of Visual Analytics for eXplainable Deep Learning

Bipartite graphs model the key relations in many large scale real-world data: customers purchasing items, legislators voting for bills, people's affiliation with different social groups, faults occurring in vehicles, etc. However, it is challenging to visualize large scale bipartite graphs with tens of thousands or even more nodes or edges. In this paper, we propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. It addresses the key trade-off that often occurs for visualizing large scale and noisy data: acquiring a clear and uncluttered overview while maximizing the information content in it. We formulate the visual summarization task as a co-clustering problem and propose an efficient algorithm based on locality sensitive hashing (LSH) that can easily scale to large graphs under reasonable interactive time constraints that previous related methods cannot satisfy. The method leads to the opportunity of introducing a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. In the framework, we also introduce a compact visual design inspired by adjacency list representation of graphs as the building block for a small multiples display to compare the bipartite relations for different subsets of data. We showcase the applicability and effectiveness of our approach by applying it on synthetic data with ground truth and performing case studies on real-world datasets from two application domains including roll-call vote record analysis and vehicle fault pattern analysis. Interviews with experts in the political science community and the automotive industry further highlight the benefits of our approach.

V i B r : Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle

Shadow aliasing due to limited storage precision has been plaguing discrete shadowing algorithms for decades We present a simple method to eliminate false self-shadowing through adaptive depth bias Unlike existing methods which simply set the weight of the bias based on surface slope or utilize the second nearest surface, we evaluate the bound of bias for each fragment and compute the optimal bias within the bound Our method introduces small overhead, preserves more shadow details than widely used constant bias and slope scale bias and works for common 2D shadow maps as well as 3D binary shadow volumes

/pdf/adaptive-depth-bias-for-shadow-maps-35q26lplcd.pdf

Adaptive depth bias for shadow maps

A system and method for providing a location based interactive informational display includes processing circuitry outputting on a display device a map of a region represented with a first level of detail and including a location focusing graphical indicia overlaid on a sub-region of the map, outputting on the display device a details frame that includes information of the sub-region with a second level of detail that is higher than the first level of detail, receiving user input for modifying the sub-region on which the location focusing graphical indicia is overlaid, and, responsive to the user input, modifying the display of the location focusing graphical indicia and modifying the details frame to include information at the second level of detail corresponding to the modified sub-region.

Interactive map informational lens

Voxelized shadow volumes [Wyman 2011] provide a discretized view-dependent representation of shadow volumes, but are limited to point or directional lights. We extend them to allow dynamic volumetric visibility from area light sources using imperfect shadow volumes. We show a coarser visibility sampling suffices for area lights. Combining this coarser resolution with a parallel shadow volume construction enables interactive rendering of dynamic volumetric shadows from area lights in homogeneous single-scattering media, at under 4x the cost of hard volumetric shadows.

Imperfect voxelized shadow volumes

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understand their structural properties. We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search in a database containing many individual graphs. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation, and perform subgraph matching in the latent space. Due to the complexity of the problem, it is still difficult to obtain accurate one-to-one node correspondences in the matching results that are crucial for visualization and interpretation. We, therefore, propose a novel GNN for node-alignment called NeuroAlign, to facilitate easy validation and interpretation of the query results. GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints. We demonstrate GraphQ through two example usage scenarios: analyzing reusable subroutines in program workflows and semantic scene graph search in images. Quantitative experiments show that NeuroAlign achieves 19%-29% improvement in node-alignment accuracy compared to baseline GNN and provides up to 100x speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.

Interactive Visual Pattern Search on Graph Data via Graph Representation Learning.

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