Predicting Future Sources of Mass Toxic Tort Litigation

The authors describe the efforts of an expert working group to identify potential sources, over the next five to ten years, of future mass litigation and report on the group\u27s consensus conclusions

Not yet the end of the world : political cultures of opposition and creation in the global youth climate justice movement

Based on participant observation and in-depth interviews with two dozen young climate justice activists at the U.N. climate summit COP19 in Warsaw, Poland, in November 2013, this research uses the concepts of “political cultures of opposition and of creation” to analyze the political orientations, discourse, and actions of global climate justice activists attempting to impact the negotiation of a universal climate treaty. Capturing relationships among experience, emotions, ideology, idioms, and organization, the concepts of political cultures of opposition and of creation shed light on the ability of these actors to fashion social movements of their own making. Through an analysis of actions in which youth delegates from divergent political cultures within the global climate justice movement worked collectively to realize a common vision, the formation and frictions of the larger global climate movement is made more legible to observers

This Will Change Everything: Teaching the Climate Crisis

We argue that U.S. sociologists have been woefully remiss in incorporating the climate crisis into our research agendas and even more, into our teaching. After laying out the gravity of the situation we issue a call for sociologists to consider whether they wish to continue this striking denial of responsibility to our students and to knowledge production. We then present four ways that we have infused our understanding of climate change, climate crisis, and climate justice into courses on global issues, social movements, inequality, and much more. We believe that “climate justice” – the key concept that drives our concern as scholar-activists working closely with undergraduate students – allows for a proper sociological emphasis on structured inequality and relational/intersectional thinking. The essay also points interested readers to resources that we have created, and invites them to contribute to a new project on writing case studies for teaching the climate crisis

Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis

The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support. However, supervised methods rely on a large number of labeled radiology images, which is a time-consuming and complex procedure requiring expert clinician input. Due to the relative scarcity of COVID-19 patient data and the costly labeling process, self-supervised learning methods have gained momentum and has been proposed achieving comparable results to fully supervised learning approaches. In this work, we study the effectiveness of self-supervised learning in the context of diagnosing COVID-19 disease from CXR images. We propose a multi-feature Vision Transformer (ViT) guided architecture where we deploy a cross-attention mechanism to learn information from both original CXR images and corresponding enhanced local phase CXR images. We demonstrate the performance of the baseline self-supervised learning models can be further improved by leveraging the local phase-based enhanced CXR images. By using 10\% labeled CXR scans, the proposed model achieves 91.10\% and 96.21\% overall accuracy tested on total 35,483 CXR images of healthy (8,851), regular pneumonia (6,045), and COVID-19 (18,159) scans and shows significant improvement over state-of-the-art techniques. Code is available https://github.com/endiqq/Multi-Feature-ViTComment: Accepted to the 2022 MICCAI Workshop on Medical Image Learning with Limited and Noisy Dat

Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis

Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to supplement medical decision-making and subsequent disease management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologists in the interpretation of the collected data. In this study, we propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales. We examine our model on various COVID-19 datasets acquired from different organizations to assess the generalization ability. Our experiments demonstrate that our method achieves state-of-art performance and has improved generalization capability, which is crucial for widespread deployment.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:00

A New Look at the Local White Dwarf Population

We have conducted a detailed new survey of the local population of white dwarfs lying within 20 pc of the Sun. A new revised catalog of local white dwarfs containing 122 entries (126 individual degenerate stars) is presented. This list contains 27 white dwarfs not included in a previous list from 2002, as well as new and recently published trigonometric parallaxes. In several cases new members of the local white dwarf population have come to light through accurate photometric distance estimates. In addition, a suspected new double degenerate system (WD 0423+120) has been identified. The 20 pc sample is currently estimated to be 80% complete. Using a variety of recent spectroscopic, photometric, and trigonometric distance determinations, we re-compute a space density of 4.8 ± 0.5 × 10−3 pc−3 corresponding to a mass density of 3.2 ± 0.3 × 10−3 M pc−3 from the complete portion of the sample within 13 pc. We find an overall mean mass for the local white dwarfs of 0.665 M, a value larger than most other non-volume-limited estimates. Although the sample is small, we find no evidence of a correlation between mass and temperature in which white dwarfs below 13,000 K are systematically more massive than those above this temperature. Within 20 pc 25% of the white dwarfs are in binary systems (including double degenerate systems). Approximately 6% are double degenerates and 6.5% are Sirius-like systems. The fraction of magnetic white dwarfs in the local population is found to be 13%