The chameleon groups of Richard J. Thompson: automorphisms and dynamics

The automorphism groups of several of Thompson's countable groups of piecewise linear homeomorphisms of the line and circle are computed and it is shown that the outer automorphism groups of these groups are relatively small. These results can be interpreted as stability results for certain structures of PL functions on the circle. Machinery is developed to relate the structures on the circle to corresponding structures on the line

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

Modulation of IL-17 and Foxp3 Expression in the Prevention of Autoimmune Arthritis in Mice

©2010 Duarte et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Rheumatoid Arthritis (RA) is a chronic immune mediated disease associated with deregulation of many cell types. It has been reported that different T cell subsets have opposite effects in disease pathogenesis, in particular Th17 and Treg cells. Methodology and Findings: We investigated whether non-depleting anti-CD4 monoclonal antibodies, which have been reported as pro-tolerogenic, can lead to protection from chronic autoimmune arthritis in SKG mice – a recently described animal model of RA – by influencing the Th17/Treg balance. We found that non-depleting anti-CD4 prevented the onset of chronic autoimmune arthritis in SKG mice. Moreover, treated mice were protected from the induction of arthritis up to 60 days following anti-CD4 treatment, while remaining able to mount CD4-dependent immune responses to unrelated antigens. The antibody treatment also prevented disease progression in arthritic mice, although without leading to remission. Protection from arthritis was associated with an increased ratio of Foxp3, and decreased IL-17 producing T cells in the synovia. In vitro assays under Th17-polarizing conditions showed CD4-blockade prevents Th17 polarization, while favoring Foxp3 induction. Conclusions: Non-depleting anti-CD4 can therefore induce long-term protection from chronic autoimmune arthritis in SKG mice through reciprocal changes in the frequency of Treg and Th17 cells in peripheral tissues, thus shifting the balance towards immune tolerance.This work was funded by SUDOE, grant number IMMUNONET-SOE1/1P1/E014, and supported by a research grant from Fundação para a Ciência e Tecnologia (FCT), Portugal (FCT/POCI/SAU-MMO/55974/2004). JD, AA-D, and VGO are funded with scholarships from FCT (SFRH/BD/23631/2005, SFRH/BD/49093/2008, and SFRH/BPD/22575/2005)

Overview of research and development in subsurface fate and transport modeling

The US Department of Energy is responsible for the remediation of over 450 different subsurface-contaminated sites. Contaminant plumes at these sites range in volume from several to millions of cubic yards. The concentration of contaminants also ranges over several orders of magnitude. Contaminants include hazardous wastes such as heavy metals and organic chemicals, radioactive waste including tritium, uranium, and thorium, and mixed waste, which is a combination of hazardous and radioactive wastes. The physical form of the contaminants includes solutes, nonaqueous phase liquids (NAPLs), and vapor phase contaminants such as volatilized organic chemicals and radon. The subject of contaminant fate and transport modeling is multi-disciplinary, involving hydrology, geology, microbiology, chemistry, applied mathematics, computer science, and other areas of expertise. It is an issue of great significance in the United States and around the world. As such, many organizations have substantial programs in this area. In gathering data to prepare this report, a survey was performed of research and development work that is funded by US government agencies to improve the understanding and mechanistic modeling of processes that control contaminant movement through subsurface systems. Government agencies which fund programs that contain fate and transport modeling components include the Environmental Protection Agency, Nuclear Regulatory Commission, Department of Agriculture, Department of Energy, National Science Foundation, Department of Defense, United States Geological Survey, and National Institutes of Health

Transform Domain Two Dimensional And Diagonal Modular Principal Component Analysis For Facial Recognition Employing Different Windowing Techniques

Spatial domain facial recognition Modular IMage Principal Component Analysis (MIMPCA) has an improved recognition rate compared to the conventional PCA. In the MPCA, face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. In this work, the Transform Domain implementation of MPCA is presented. The facial image has two representations. The Two Dimensional MPCA (TD-2D-MPCA) and the Diagonal matrix MPCA (TD-Dia-MPCA). The sub-images are processed using both non-overlapping and overlapping windows. All the test results, for noise free and noisy images, using ORL, Yale and FERET databases achieved; 99.5%, 99.58% and 97.42% recognition accuracy respectively. Transform Domain implementations yield, computational and storage savings of at least 75% and 99.98%, respectively, compared to spatial domain. Sample results are given. © 2013 IEEE

Performance Evaluation Of Transform Domain Diagonal Principal Component Analysis For Facial Recognition Employing Different Pre-Processing Spatial Domain Approaches

Facial recognition using spatial domain Diagonal Principal Component Analysis (DiaPCA) algorithm produces better accuracy compared to the Two Dimensional PCA (2DPCA). Transform Domain - 2DPCA (TD2DPCA) retains the high recognition accuracy of the 2DPCA while considerably reducing storage requirements and computational complexity. In this work, the Transform Domain PCA implementation of the DiaPCA (TDDiaPCA) is presented. All the test results, for noise free and noisy images, consistently confirm the considerable storage and computational savings for different spatial domain pre-processing scenarios while retaining the high recognition rate. The performance is evaluated using ORL, Yale and FERET databases. Sample results are given. © 2012 IEEE