On the spectrum of operators concerned with the reduced singular Cauchy integral

We investigate spectrums of the reduced singular Cauchy operator and its real and imaginary components

HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity

Recent advances in deep learning for object detection

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning. Keywords: Object Detection, Deep Learning, Deep Convolutional Neural Network

Composite Fermions in Negative Effective Magnetic Field: A Monte-Carlo Study

The method of Jain and Kamilla [PRB {\bf 55}, R4895 (1997)] allows numerical generation of composite fermion trial wavefunctions for large numbers of electrons in high magnetic fields at filling fractions of the form nu=p/(2mp+1) with m and p positive integers. In the current paper we generalize this method to the case where the composite fermions are in an effective (mean) field with opposite sign from the actual physical field, i.e. when p is negative. We examine both the ground state energies and the low energy neutral excitation spectra of these states. Using particle-hole symmetry we can confirm the correctness of our method by comparing results for the series m=1 with p>0 (previously calculated by others) to our results for the conjugate series m=1 with p <0. Finally, we present similar results for ground state energies and low energy neutral excitations for the states with m=2 and p <0 which were not previously addressable, comparing our results to the m=1 case and the p > 0, m=2 cases.Comment: 11 page

Antinociception Following Implantation of AtT-20 and Genetically Modified AtT-20/hENK Cells in Rat Spinal Cord

AtT-20 cells, which produce β-endorphin, and AtT-20/hENK cells, which are AtT-20 cells transfected with a proenkephalin gene, were implanted in the rat spinal subarachnoid space in an effort to produce an antinociceptive effect. Host rats were tested for antinociceptive activity by standard nociceptive tests, tail flick and hot plate. Although cell implants had minimal effect on the basal response to thermal nociceptive stimuli, administration of the β2-adrenergic agonist isoproterenol produced antinociception in the cell-implanted group but not in the control group. The antinociceptive effect of isoproterenol was dose-related and could be blocked by the opioid antagonist naloxone. Immunohistochemical analysis of spinal cords revealed the presence of enkephalin-negative cells surrounding the spinal cord of rats receiving AtT-20 cell implants, and enkephalinpositive cells surrounding the spinal cord of rats. receiving AtT-20/hENK cell implants. These results suggest that opioid-releasing cells implanted around rat spinal cord can produce antinociception and may provide an alternative therapy for chronic pain