Coherence Time Effects on J/psi Production and Suppression in Relativistic Heavy Ion Collisions

Using a coherence time extracted from high precision proton-nucleus Drell-Yan measurements and a nuclear absorption cross section extracted from pA charmonium production experiments, we study J/psi production and absorption in nucleus-nucleus collisions. We find that coherence time effects are large enough to affect the measured J/psi-to-Drell-Yan ratio. The S+U data at 200A GeV/c measured by NA38 are reproduced quantitatively without the introduction of any new parameters. However, when compared with recent NA50 measurements for Pb+Pb at 158A GeV/c, the data is not reproduced in trend or in magnitude.Comment: 8 pages, 2 figure

Mapping New England: heating fuel usage in southern New England

Two maps of southern New England tap 2006 American Community Survey data to show the heating fuels most often used in owner-occupied housing units versus the fuels favored in renter-occupied units.Energy consumption - New England

Mapping New England: real estate owned properties in New England

Lenders often end up owning places they foreclose on. Understanding where such real estate owned (REO) properties cluster may help communities to target stabilization funds better.Foreclosure ; Real property

The golden years dilemma

With 3.2 million baby boomers eligible to retire this year, how many will be able to meet daily financial needs and still preserve home equity? The author advises seniors to plan carefully and learn about the many forms of assistance available.Retirement income ; Home equity loans ; Baby boom generation ; Population aging - New England

Mapping New England: child poverty by county

Childhood poverty is a concern in itself and also because it often leads to adult poverty. Communities & Banking looks at the region’s changing child poverty rate between 1997 and 2007.Poverty - New England ; Poor children - New England

Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model

Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task. The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian. In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn better attribute predictions. Our proposed model jointly predicts the coarse pose (view) of the pedestrian and learns specialized view-specific multi-label attribute predictions. We show in an extensive evaluation on three challenging datasets (PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.Comment: accepted BMVC 201

A study on the feasibility of a precise measurement of the τ\tau-dependence of the cross sections for Drell-Yan experiments at moderate energies

Recently, a reconsideration of Drell-Yan cross sections at moderate energies and masses has suggested the possibility of relevant enhancements of the cross sections in some kinematical regions. If confirmed, these predictions could largely affect the planning of Drell-Yan experiments aimed at transverse spin measurements after 2010. More in general, the problem is present of a precision measurement of the $\tau$ dependence of Drell-Yan cross sections. Here we discuss the feasibility of such a measurement within short time at the COMPASS apparatus, and its relevance for the PANDA experiment.Comment: 4 pages, 4 figures, in print on Physical Review

Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora

Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available