Biocuration is the process of analyzing biological or biomedical articles to organize biological data into data repositories using taxonomies and ontologies. Due to the expanding number of articles and the relatively small number of biocurators, automation is desired to improve the workflow of assessing articles worth curating. As figures convey essential information, automatically integrating images may improve curation. In this work, we instantiate and evaluate a first-in-kind, hybrid image+text document search system for biocuration. The system, MouseScholar, leverages an image modality taxonomy derived in collaboration with biocurators, in addition to figure segmentation, and classifiers components as a back-end and a streamlined front-end interface to search and present document results. We formally evaluated the system with ten biocurators on a mouse genome informatics biocuration dataset and collected feedback. The results demonstrate the benefits of blending text and image information when presenting scientific articles for biocuration. 
Index Terms - document search, biocuration

MouseScholar interface.

MouseScholar: Evaluating an Image+Text Search System for Biocuration

Rayleigh-Benard (RB) convection occurs in a fluid layer when there is a temperature difference between the top and bottom boundaries, resulting in the spontaneous formation of convective motion.  In this study, a direct numerical simulation has been performed aimed at elucidating the impact of introducing dispersed droplets into a single-phase flow on the heat transfer dynamics within this flow.  The dimensionless parameters considered in this study encompass a Prandtl number of 4, a Weber number of 6000, and a Rayleigh number of 10^8.  Dispersed droplets were incorporated over a range spanning from 0 to 50%.

Effects of employing liquid-liquid emulsions on heat transfer within a turbulent Rayleigh-Benard convection

In the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional checkpointing methods ineffective due to the significant slowdown in simulations while saving state data to disk. As we progress towards exascale and GPU-driven High-Performance Computing (HPC) and confront larger problem sizes, the choice becomes increasingly stark: to compromise data fidelity or to reduce resolution. To navigate this challenge, this study advocates for the use of in situ analysis and visualization techniques. These allow more frequent data &ldquo;snapshots&rdquo; to be taken directly from memory, thus avoiding the need for disruptive checkpointing. We detail our approach of instrumenting NekRS, a GPU-focused thermal-fluid simulation code employing the spectral element method (SEM), and describe varied in situ and in transit strategies for data rendering. Additionally, we provide concrete scientific use cases and report on runs performed on Polaris, Argonne Leadership Computing Facility’s (ALCF) 44 Petaflop supercomputer and Jülich Wizard for European Leadership Science (JUWELS) Booster, Jülich Supercomputing Centre’s (JSC) 71 Petaflop High Performance Computing (HPC) system, offering practical insight into the implications of our methodology.

Side view visualization of a RBC case.

Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI

SAGE3, the newest and most advanced generation of the Smart Amplified Group Environment, is an open-source software designed to facilitate collaboration among scientists, researchers, students, and professionals across various fields. This tutorial aims to introduce attendees to the capabilities of SAGE3, demonstrating its ability to enhance collaboration and productivity in diverse settings, from co-located office collaboration to remote collaboration to both at once, with diverse displays, from personal laptops to large-scale display walls. Participants will learn how to effectively use SAGE3 for brainstorming, data analysis, and presentation purposes, as well as installation of private collaboration servers and development of custom applications. 
CCS CONCEPTS: Human-centered computing, Collaborative and social computing systems and tools; Open source software; Visualization systems and tools; Interactive systems and tools. 
KEYWORDS: Large Displays, Space to Think, Collaboration, Visualization, Data Science, Data Analysis, Computational Narratives

SAGE3 for Interactive Collaborative Visualization, Analysis, and Storytelling

Applications in immersive environments have gained popularity for training, learning, and recreational tasks. Due to the increasing availability of sensors and data-capturing devices, research is extending the use of immersive environments to support visual analytics processes, including sensemaking and strategic immersion for interaction and task completion. In this work, we propose Personal Situated Analytics (PSA) framework to embed users into recorded meetings with support for multiple degrees of immersion in the Reality-Virtuality spectrum. Our proposed framework encompasses various stages such as tracking, data capturing, data cleaning, data synchronization, prototype building, and deploying the final product to end-user hardware. We evaluate this framework on a data analysis scenario between human subjects and a conversational AI agent. Our pilot study (n=12) using this framework compares user experiences when using two different devices: Hololens2 and Quest2.

A user embedded in a conversation using Personal Situated Analytics in Virtual Reality as virtual avatars.

Using Personal Situated Analytics (PSA) to Interpret Recorded Meetings

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine
learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system
developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large-scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms
based on during-treatment symptoms. It supports this goal through an SRM, clustering, aggregation back end, and a custom front
end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic
decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research. 
Index Terms: Temporal Data; Life Sciences; Mixed Initiative Human-Machine Analysis; Data Clustering and Aggregation

Longitudinal symptom analysis and prediction for head and neck patients (ICC treatment).

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

While cities around the world are looking for smart ways to channel new advances in data collection, management, and analysis to address their day-to-day problems, the complex nature of urban issues and the overwhelming amount of available structured and unstructured data have posed significant challenges in translating these efforts into actionable insights. In the past few years, urban visual analytics tools have significantly helped tackle these challenges. When analyzing a feature of interest, an urban scientist must transform, integrate and visualize different thematic (e.g., sunlight, flows) and physical (e.g., buildings, sidewalks) layers, oftentimes across multiple spatial and temporal scales. However, integrating and analyzing these layers requires expertise in different fields, increasing development time and effort. This makes the entire visual data exploration and system implementation hard for experienced programmers and also sets a high entry barrier for urban experts outside of computer science. With this in mind, in this paper, we present the Urban Toolkit (UTK), a flexible and extensible visualization framework that enables the easy authoring of web-based visualizations through a new high-level grammar specifically built with common urban use cases in mind. In order to facilitate the integration and visualization of different urban data, we also propose the concept of knots to merge thematic and physical urban layers. We evaluate our approach through use cases and a series of interviews with experts and practitioners from different domains, including urban accessibility, urban planning, architecture, and climate science. UTK is available at urbantk.org. 
Index Terms: Urban visual analytics, Urban analytics, Urban data, Visualization toolkit.

UTK abstracts spatial joins through the use of knots.

The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics

Weather forecasting is essential for decision-making and is usually performed using numerical modeling. Numerical weather models, in turn, are complex tools that require specialized training and laborious setup and are challenging even for weather experts. Moreover, weather simulations are data-intensive computations and may take hours to days to complete. When the simulation is finished, the experts face challenges analyzing its outputs, a large mass of spatiotemporal and multivariate data. From the simulation setup to the analysis of results, working with weather simulations involves several manual and error-prone steps. The complexity of the problem increases exponentially when the experts must deal with ensembles of simulations, a frequent task in their daily duties. To tackle these challenges, we propose ProWis: an interactive and provenance-oriented system to help weather experts build, manage, and analyze simulation ensembles at runtime. Our system follows a human-in-the-loop approach to enable the exploration of multiple atmospheric variables and weather scenarios. ProWis was built in close collaboration with weather experts, and we demonstrate its effectiveness by presenting two case studies of rainfall events in Brazil. 
Index Terms - Weather visualization, Ensemble visualization, Provenance management, WRF visual setup

Building, managing, and analyzing a Weather Research and Forecasting (WRF) model ensemble using PROWIS to study an extreme rainfall event in Maricá (RJ, Brazil) in April 2022.

ProWis: A visual approach for building, managing, and analyzing weather simulation ensembles in runtime

New web technologies have enabled the deployment of powerful GPU-based computational pipelines that run entirely in the web browser, opening a new frontier for accessible scientific visualization applications. However, these new capabilities do not address the memory constraints of lightweight end-user devices encountered when attempting to visualize the massive data sets produced by today&rsquo;s simulations and data acquisition systems. In this paper, we propose a novel implicit isosurface rendering algorithm for interactive visualization of massive volumes within a small memory footprint. We achieve this by progressively traversing a wavefront of rays through the volume and decompressing blocks of the data on-demand to perform implicit ray-isosurface intersections. The progressively rendered surface is displayed after each pass to improve interactivity. Furthermore, to accelerate rendering and increase GPU utilization, we introduce speculative ray-block intersection into our algorithm, where additional blocks are traversed and intersected speculatively along rays as other rays terminate to exploit additional parallelism in the workload. Our entire pipeline is run in parallel on the GPU to leverage the parallel computing power that is available even on lightweight end-user devices. We compare our algorithm to the state of the art in low-overhead isosurface extraction and demonstrate that it achieves 1.7×–5.7× reductions in memory overhead and up to 8.4× reductions in data decompressed.

Interactive full-resolution isosurface visualization of the 2048×2048×1920 Richtmyer-Meshkov (R-M) data set in the browser.

Speculative Progressive Raycasting for Memory Constrained Isosurface Visualization of Massive Volumes

Using immersive environments for training, education, and recreation is becoming increasingly popular among individuals. In recent years these environments have also been used to facilitate data analytics processes involving interaction and task completion. In this work, we discuss Personal Situated Analytics (PSA) a cross-platform framework to embed users into recorded conversations with support for multiple degrees of immersion in the Reality-Virtuality spectrum. Our development pipeline consisted of several stages: tracking, data capturing, data cleaning, data synchronization, prototype building, and deploying the final product to end-user hardware. We evaluate PSA by analyzing data from a recorded meeting involving human participants and a visual conversational AI agent. Our pilot study (n = 12) using this framework compares user experiences for MR and VR environments. 
Index Terms: Immersive Analytics, Situated Analytics, Visualization techniques, Human-centered computing

PSA is an immersive MR/VR framework for experiencing and analyzing recorded conversations and events. We see two participants represented as virtual avatars engaged in a conversation.

PSA: A Cross-Platform Framework for Situated Analytics in MR and VR

Estimating the human posture from an image or a video is a fundamental task in computer vision. It not only enhances other vision tasks like action recognition, person re-identification, and virtual try-on but also facilitates many real-world applications including robotics, healthcare, sports, and retail. An effective and efficient human pose estimation system can enable robots to acquire skills from demonstrations, support physical therapists in diagnosing and rehabilitating patients, assist sports analysts and coaches in tracking and training athletes, and empower retailers to establish employee-free stores. Thanks to the advancement of deep learning and the availability of large-scale datasets, the performance of state-of-the-art human pose estimation methods has drastically improved in recent years. These approaches can accurately estimate postures in daily activities and sports. However, several challenges persist. For example, (1) it remains challenging to estimate postures that occur rarely or are entirely absent in the training data; (2) handling complex scenarios, such as crowded environments, motion blur, low-light conditions, and occlusions, is still a formidable task; (3) there is a growing need to develop efficient models that can estimate human poses in real-time or on low-power devices; (4) exploring novel applications for human pose estimation that can bring societal benefits or transform industries is an exciting avenue of research. This special issue presents a collection of high-quality papers on human pose estimation and its applications. They cover a wide range of topics, from fundamental human pose models with improved accuracy and efficiency to applications in human behavior understanding and assessment, sign language recognition, and multi-camera calibration. Through this special issue, we would like to open the path toward more discussion between practitioners and researchers in this very important yet challenging field of human pose estimation.

Guest editorial: special issue on human pose estimation and its applications

Semi-Supervised Temporal Action Localization (SS-TAL) aims to improve the generalization ability of action detectors with large-scale unlabeled videos. Albeit the recent advancement, one of the major challenges still remains: noisy pseudo labels hinder efficient learning on abundant unlabeled videos, embodied as location biases and category errors. In this paper, we dive deep into such an important but understudied dilemma. To this end, we propose a unified framework, termed Noisy Pseudo-Label Learning, to handle both location biases and category errors. Specifically, our method is featured with (1) Noisy Label Ranking to rank pseudo labels based on the semantic confidence and boundary reliability, (2) Noisy Label Filtering to address the class-imbalance problem of pseudo labels caused by category errors, (3) Noisy Label Learning to penalize inconsistent boundary predictions to achieve noise-tolerant learning for heavy location biases. As a result, our method could effectively handle the label noise problem and improve the utilization of a large amount of unlabeled videos. Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate the effectiveness of our method. The code is available at github.com/kunnxia/NPL.

Learning from noisy pseudo labels for semi-supervised temporal action localization

* Authors: 
Belcaid, M., Leigh, J., Theriot, R., Kirshenbaum, N., Tabalba, R., Rogers, M. University of Hawai’i at Manoa, Honolulu, HI 
Johnson, A., Brown, M., Renambot, L., Long, L., Nishimoto, A., University of Illinois Chicago, Chicago, IL 
North C., Harden, J., Virginia Tech, Blacksburg, VA 
Translational software research bridges the gap between scientific innovations and practical applications, driving impactful societal advancements. However, developing such software is challenging due to interdisciplinary collaboration, technology adoption, and post-funding sustainability. This article presents the experiences and insights of the Scalable Adaptive Graphics Environment (SAGE) team, which has spent two decades developing translational, cross-disciplinary, collaboration tools to benefit computational science research. With a focus on SAGE and its next-generation iterations, we explore the inherent challenges in translational research, such as fostering cross-disciplinary collaboration, motivating technology adoption, and ensuring post-funding product sustainability. We also discuss the roles of funding agencies, policymakers, and academic institutions in promoting translational research. Although the journey is fraught with challenges, the societal impact and satisfaction derived from translational research underscore its significance in the broader scientific landscape. This article aims to encourage further conversation and the development of effective models for
translational software projects.

The process that defines domain science, computer science (CS), and sustainable translational research.

Reflecting on the Scalable Adaptive Graphics Environment Team’s 20-Year Translational Research Endeavor in Digital Collaboration Tools

Recent advances in precision medicine for treating Oropharyngeal cancer (OPC) have led to significant improvements in survival for patients. However, OPC survivors often are left with severe side effects that result from damage to vital tissues in the head and neck caused by secondary radiation received during treatment. These side effects can persist for months after treatment cessation, and diminish the patients’ quality of life. Existing guidelines for radiation therapy plans identify dose limits to individual organs with the goal to avoid or minimize permanent tissue damage and their related toxicity. However, existing tools fail to account for either the three-dimensional nature of dose distributions or the fact that critical organs often work as a system that can have multiple failure points. 
We present an unsupervised approach for stratifying patients based on three-dimensional dose distributions to multiple organs and evaluate their association with long-term patient-reported symptoms. We apply this method to predicting severe patient-reported symptoms 6 months after ceasing radiation therapy for OPC patients. For actionable interpretability, we introduce a rule-mining method that simplifies our strata into a set of dose thresholds, and demonstrate that these thresholds outperform existing dose treatment guidelines. 
Keywords: Radiation therapy; Oropharynx cancer; Head and Neck cancer; Stratification; Symptom Burden; Quality of Life

Results of a chi-squared test between covariates and membership in each set of clusters for each outcome.

Multi-Organ Spatial Stratification of 3-D Dose Distributions Improves Risk Prediction of Long-Term Self-Reported Severe Symptoms in Oropharyngeal Cancer Patients Receiving Radiotherapy: Development of a Pre-Treatment Decision Support Tool.

Motivation: Figures in biomedical papers communicate essential information with the potential to identify relevant documents in biomedical and clinical settings. However, academic search interfaces mainly search over text fields. 
Results: We describe a search system for biomedical documents that leverages image modalities and an existing index server. We integrate a problem-specific taxonomy of image modalities and image-based data into a custom search system. Our solution features a front-end interface to enhance classical document search results with image-related data, including page thumbnails, figures, captions, and image-modality information. We demonstrate the system on a subset of the CORD-19 document collection. A quantitative evaluation demonstrates higher precision and recall for biomedical document retrieval. A qualitative evaluation with domain experts further highlights our solution’s benefits to biomedical search. 
Availability and implementation: A demonstration is available at <a href="https://runachay.evl.uic.edu/scholar">https://runachay.evl.uic.edu/scholar</a>. Our code and image models can be accessed via <a href="https://github.com/uic-evl/bio-search">https://github.com/uic-evl/bio-search</a>. The dataset is continuously expanded. 
Image caption: Search interface displaying the results of a boolean query. (A) The color-coded legend displays the image modalities in the first level of the taxonomy. (B) The search bar, with a keyword box showing a boolean query, a time filter, and an image modalities filter limiting results to documents containing \textit{radiology} or \textit{fluorescence microscopy} figures. (C) The metadata of a result includes the document title, year, venue, authors, and the hits on the abstract and full text. Below is the count of figures and modalities in the document. (D) The enhanced document surrogate shows the image content, including the page thumbnail, extracted figure, subfigures and modalities, and caption. 
<a href="https://doi.org/10.1093/bioadv/vbad095">https://doi.org/10.1093/bioadv/vbad095</a>

Search interface displaying the results of a boolean query.

Enhancing biomedical search interfaces with images

Objective: Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC). 
Materials & methods: 675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified. These clusterings were combined into a 3-level patient stratification and included along with other known clinical features in a Cox model for predicting survival outcomes, and logistic regression for toxicity, using independent subsets for training and validation. 
Results: Four groups were identified and combined into a 3-level stratification. The inclusion of patient stratification in predictive models for 5-yr Overall survival (OS), 5-year recurrence free survival, (RFS) and Radiation associated dysphagia (RAD) consistently improved model performance measured using the area under the curve (AUC). Test set AUC improvements over models with clinical covariates, was 9 % for predicting OS, and 18 % for predicting RFS, and 7 % for predicting RAD. For models with both clinical and AJCC covariates, AUC improvement was 7 %, 9 %, and 2 % for OS, RFS, and RAD, respectively. 
Conclusion: Including data-driven patient stratifications considerably improve prognosis for survival and toxicity outcomes over the performance achieved by clinical staging and clinical covariates alone. These stratification generalize well to across cohorts, and sufficient information for reproducing these clusters is included. 
Keywords: Head & Neck cancer, Statistical and research methods, Machine learning, Radiation oncology, Health Informatics, Imaging, Risk stratification, Oropharyngeal cancer, Clustering, Radiomics

Average lymph node involvement and clinical staging categories of the 3 derived lymph node + radiomics clusters across both the training and validation datasets.

Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas

Honorable Mention in the EuroVis Best Paper Category 
Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine-learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph. 
Keywords: chemical space exploration, cheminformatics, multipartite graphs, data visualization 
CCS Concepts: Applied computing → Chemistry 
<iframe width="560" height="315" src="https://www.youtube.com/embed/kVpz6TDjQMw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

An overview of ChemoGraph.

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes, and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research. 
CCS Concepts Human-centered computing → Graph drawings

Overview of the taxonomy of DMVN visualization techniques along with illustrations of the categories.

The State of the Art in Visualizing Dynamic Multivariate Networks

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS,to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience. 
CCS Concepts 
Human-centered computing→Scientific visualization; Computing methodologies→Machine learning; Applied computing→Life and medical sciences

DASS interactive model building for head/neck cancer.

DASS Good: Explainable Data Mining of Oncology Imaging and Toxicity Data

Methane the main component of natural gas is also a potent greenhouse gas that requires robust monitoring, so that scientists, educators, and the general public can get better informed of its effect(s) on local/regional air quality and current short-term mitigations for climate change. However, current data visualizations for methane, and other scientific data, are either static 2-D graphs/plots or animated through using videos/gifs. These data visualization approaches can be limiting in that they lack interactiveness for an end user to gain exploratory insights by comparing and contrasting different areas of a dataset. This report thereby showcases how to build a functional and interactive visualization as the solution to the previously mentioned limitation. 
Keywords: Interactive Scientific Data Visualization, Climate Data Visualization, Methane Visualization

This figure shows the respective volumetric/spheric plots for 07/02/2021 at 1000 hPa. Figure 2:

Visualizing the NOAA CarbonTracker-CH4 3-D Mole Fraction (Pressure Corrected) Dataset

Background: Existing studies have reported that forest therapy utilizing sensory stimuli increases the activity of the parasympathetic nervous system and decreases the activity of the sympathetic nervous system. 
Purpose: To observe the effects of guided scenic meditation in a virtual reality environment on physiological stress. 
To perform a platform comparison by deploying the same meditative experience and testing its effectiveness using a room-scale display and a desktop computer. 
Significance: Rather than using a headset, the study uses a room-scale display - the CAVE2 Hybrid-Reality Environment - to test the effects of virtual reality environments on stress reduction. 
The platform comparison intends to showcase that if proven effective, the product is accessible, as desktop computers are far more affordable ($400 - $1000) and widely available relative to room-scale displays ($1 million+).

Fig 1. On the left is the CAVE2 version of the application. Fig 2. On the right is the desktop version of the application.

The Effects of Virtual Reality Environments on Physiological Stress: A Platform Comparison Between Room-Scale Displays and Desktop Computers (poster)

In the past, existing studies have proven the effectiveness of forest therapy as an option for stress reduction, but what happens when an individual is seeking stress relief but the outdoors is unavailable due to unpredictable weather or even a lack of accessibility? The purpose of this research project is to observe the effects of guided scenic meditation in a virtual reality environment on physiological stress. Additionally, it intends to perform a platform comparison by deploying the same meditative experience and testing its effectiveness using a room-scale display and a desktop computer, as the latter ($400 - $1000) is far more affordable and widely available relative to the former ($1 million+). Using both platforms, ten participants were asked to engage with a Unity-built meditative application that simulates the sights, sounds, and smells of a forest environment. Based on the data collected, the participants experienced a decrease in both heart rate and blood pressure, demonstrating the effectiveness of the simulation in reducing physiological stress. Similarly, according to the participants, their subjectively perceived stress levels decreased, resulting in a far more relaxed state than before. The results showcase that a virtual simulation of an environment actively contributes to stress reduction and can be used in cases when the outdoors is unavailable. The platform comparison demonstrates that the product
of the research - a guided scenic meditation application that simulates a forest - is accessible and does not require a million-dollar room-scale display in order to be effective in reducing stress.

The forest model developed in Unity.

The Effects of Virtual Reality Environments on Physiological Stress: A Platform Comparison Between Room-Scale Displays and Desktop Computers

While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool - TILE2NET - for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. TILE2NET also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. 
Keywords: Pedestrian network extraction, Semantic segmentation, Automated network generation, Pedestrian infrastructure

Model results showing detected sidewalk, crosswalk and footpath centerlines in a) Boston and Cambridge, b) Manhattan and parts of Brooklyn, c) Wash- ington, DC. The maps are shown at the same scale for comparison.

Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery

Weakly-supervised Video Anomaly Detection (WVAD) aims to detect abnormal events in videos given only video-level labels for training. Recent methods relying on multiple instance learning (MIL) and self-training achieve good performance, but they tend to focus on learning easy abnormal patterns while ignoring hard ones, e.g., unusual driving trajectory or over-speeding driving. How to detect hard anomalies is a critical but largely ignored problem in W-VAD. To tackle this challenge, we propose a novel framework, termed Abnormal Ratios guided Multi-phase Self-training (ARMS), for W-VAD. It includes a new abnormal ratio-based MIL (AR-MIL) loss and a new multi-phase self-training paradigm. The AR-MIL loss guides the learning of hard anomalies by enforcing a minimum ratio of abnormal snippets in an abnormal video and no abnormal snippets in a normal video. Our multi-phase self-training paradigm sequentially performs bootstrapping, hard anomalies mining, and adaptive self-training so as to address pseudo labeling on easy anomalies, detect hard anomalies, and setting adaptive abnormal ratios for different videos in a unified framework. Experimental results on three benchmark datasets, i.e., ShanghaiTech, UCF-Crime, and XD-Violence, show that ARMS outperforms all previous state-of-the-art methods and has a great advantage in detecting hard anomalies. 
Index Terms: Anomaly detection, weakly-supervised video anomaly detection, multiple instance learning.

Abnormal Ratios Guided Multi-Phase Self-Training for Weakly-Supervised Video Anomaly Detection

Contrails are condensation trails generated from emitted particles by aircraft engines, which perturb Earth’s radiation budget. Simulation modeling is used to interpret the formation and development of contrails. These simulations are computationally intensive and rely on high-performance computing solutions, and the contrail structures are not well defined. We propose a visual computing system to assist in defining contrails and their characteristics, as well as in the analysis of parameters for computer-generated aircraft engine simulations. The back end of our system leverages a contrail-formation criterion and clustering methods to detect contrails&rsquo; shape and evolution and identify similar simulation runs. The front-end system helps analyze contrails and their parameters across multiple simulation runs. The evaluation with domain experts shows this approach successfully aids in contrail data investigation. 
Index Terms: Scalar Field Data, Physical & Environmental Sciences, Mathematics, Feature Detection, Tracking & Transformation 
<a href="http://dx.doi.org/10.1109/TVCG.2022.3209356">http://dx.doi.org/10.1109/TVCG.2022.3209356</a>