Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chromatic aberration, blur, exposure, noise, and color cast-- for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes

Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining

Numerous stream mining applications, such as visual detection, online patient monitoring, and video search and retrieval, are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness (i.e., delay) constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware presents new opportunities for energy saving as well as challenges. For example, employing low-performance processing nodes can save energy but may violate delay requirements, whereas employing high-performance processing nodes can deliver a fast response but may unnecessarily waste energy. Existing scheduling algorithms balance energy versus delay assuming constant processing and power requirements throughout the execution of a stream mining task and without exploiting hardware heterogeneity. In this paper, we propose a novel framework for dynamic scheduling for energy minimization (DSE) that leverages this emerging hardware heterogeneity. By optimally determining the processing speeds for hardware executing classifiers, DSE minimizes the average energy consumption while satisfying an average delay constraint. To assess the performance of DSE, we build a face detection application based on the Viola-Jones classifier chain and conduct experimental studies via heterogeneous processor system emulation. The results show that, under the same delay requirement, DSE reduces the average energy consumption by up to 50% in comparison to conventional scheduling that does not exploit hardware heterogeneity. We also demonstrate that DSE is robust against processing node switching overhead and model inaccuracy

Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that i) approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1200 and 2600 frames per second and using between 25 and 275 mW (effectively > 6000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.Comment: 7 pages, 6 figure

Large Scale Structures a Gradient Lines: the case of the Trkal Flow

A specific asymptotic expansion at large Reynolds numbers (R)for the long wavelength perturbation of a non stationary anisotropic helical solution of the force less Navier-Stokes equations (Trkal solutions) is effectively constructed of the Beltrami type terms through multi scaling analysis. The asymptotic procedure is proved to be valid for one specific value of the scaling parameter,namely for the square root of the Reynolds number (R).As a result large scale structures arise as gradient lines of the energy determined by the initial conditions for two anisotropic Beltrami flows of the same helicity.The same intitial conditions determine the boundaries of the vortex-velocity tubes, containing both streamlines and vortex linesComment: 27 pages, 2 figure

Electron-hadron shower discrimination in a liquid argon time projection chamber

By exploiting structural differences between electromagnetic and hadronic showers in a multivariate analysis we present an efficient Electron-Hadron discrimination algorithm for liquid argon time projection chambers, validated using Geant4 simulated data

View and Illumination Invariant Object Classification Based on 3D Color Histogram Using Convolutional Neural Networks

Object classification is an important step in visual recognition and semantic analysis of visual content. In this paper, we propose a method for classification of objects that is invariant to illumination color, illumination direction and viewpoint based on 3D color histogram. A 3D color histogram of an image is represented as a 2D image, to capture the color composition while preserving the neighborhood information of color bins, to realize the necessary visual cues for classification of objects. Also, the ability of convolutional neural network (CNN) to learn invariant visual patterns is exploited for object classification. The efficacy of the proposed method is demonstrated on Amsterdam Library of Object Images (ALOI) dataset captured under various illumination conditions and angles-of-view

Neutrino Quasielastic Scattering on Nuclear Targets: Parametrizing Transverse Enhancement (Meson Exchange Currents)

We present a parametrization of the observed enhancement in the transverse electron quasielastic (QE) response function for nucleons bound in carbon as a function of the square of the four momentum transfer ($Q^2$) in terms of a correction to the magnetic form factors of bound nucleons. The parametrization should also be applicable to the transverse cross section in neutrino scattering. If the transverse enhancement originates from meson exchange currents (MEC), then it is theoretically expected that any enhancement in the longitudinal or axial contributions is small. We present the predictions of the "Transverse Enhancement" model (which is based on electron scattering data only) for the $\nu_\mu, \bar{\nu}_\mu$ differential and total QE cross sections for nucleons bound in carbon. The $Q^2$ dependence of the transverse enhancement is observed to resolve much of the long standing discrepancy in the QE total cross sections and differential distributions between low energy and high energy neutrino experiments on nuclear targets.Comment: Revised Version- July 21, 2011: 17 pages, 20 Figures. To be published in Eur. Phys. J.

A first measurement of the interaction cross section of the tau neutrino

The DONuT experiment collected data in 1997 and published first results in 2000 based on four observed $\nu_\tau$ charged-current (CC) interactions. The final analysis of the data collected in the experiment is presented in this paper, based on $3.6 \times 10^{17}$ protons on target using the 800 GeV Tevatron beam at Fermilab. The number of observed $\nu_\tau$ CC interactions is 9, from a total of 578 observed neutrino interactions. We calculated the energy-independent part of the tau-neutrino CC cross section ($\nu + \bar \nu$), relative to the well-known $\nu_e$ and $\nu_\mu$ cross sections. The ratio $\sigma(\nu_\tau)$/$\sigma(\nu_{e,\mu})$ was found to be $1.37\pm0.35\pm0.77$. The $\nu_\tau$ CC cross section was found to be $0.72 \pm 0.24\pm0.36 \times 10^{-38}$ cm$^{2}\rm{GeV}^{-1}$. Both results are in agreement the Standard Model.Comment: 37 pages, 15 figure

A New Upper Limit for the Tau-Neutrino Magnetic Moment

Using a prompt neutrino beam in which a nu_tau component was identified for the first time, the nu_tau magnetic moment was measured based on a search for an anomalous increase in the number of neutrino-electron interactions. One such event was observed when 2.3 were expected from background processes, giving an upper 90% confidence limit of 3.9x10^-7 Bohr magnetons.Comment: 9 pages; 1 figur

New constraints on muon-neutrino to electron-neutrino transitions in MINOS

This paper reports results from a search for ν_μ → ν_e transitions by the MINOS experiment based on a 7×10^(20) protons-on-target exposure. Our observation of 54 candidate ν_e events in the far detector with a background of 49.1±7.0(stat)±2.7(syst) events predicted by the measurements in the near detector requires 2sin^2(2θ_(13))sin^2θ_(23)<0.12(0.20) at the 90% C.L. for the normal (inverted) mass hierarchy at δ_(CP)=0. The experiment sets the tightest limits to date on the value of θ_(13) for nearly all values of δ_(CP) for the normal neutrino mass hierarchy and maximal sin^2(2θ_(23))