A High Power Hydrogen Target for Parity Violation Experiments

Parity-violating electron scattering measurements on hydrogen and deuterium, such as those underway at the Bates and CEBAF laboratories, require luminosities exceeding $10^{38}$cm$^{-2}$s$^{-1}$, resulting in large beam power deposition into cryogenic liquid. Such targets must be able to absorb 500 watts or more with minimal change in target density. A 40~cm long liquid hydrogen target, designed to absorb 500~watts of beam power without boiling, has been developed for the SAMPLE experiment at Bates. In recent tests with 40~$\mu$A of incident beam, no evidence was seen for density fluctuations in the target, at a sensitivity level of better than 1\%. A summary of the target design and operational experience will be presented.Comment: 13 pages, 9 postscript figure

Parity Violation with Electrons and Hadrons

A key question in understanding the structure of nucleons involves the role of sea quarks in their ground state electromagnetic properties such as charge and magnetism. Parity-violating electron scattering, when combined with determination of nucleon electromagnetic form factors from parity-conserving e-N scattering, provides another degree of freedom to separately determine the up, down and strange quark contributions to nucleon electromagnetic structure. Strange quarks are unique in that they are exclusively in the nucleon's sea. A program of experiments using parity violating electron scattering has been underway for approximately a decade, and results are beginning to emerge. This paper is a brief overview of the various experiments and their results to date along with a short-term outlook of what can be anticipated from experiments in the next few years.Comment: Invited talk at the 17th International IUPAP Conference on Few-Body Problems in Physic

Bayesian system identification of dynamical systems using highly informative training data

This paper is concerned with the Bayesian system identification of structural dynamical systems using experimentally obtained training data. It is motivated by situations where, from a large quantity of training data, one must select a subset to infer probabilistic models. To that end, using concepts from information theory, expressions are derived which allow one to approximate the effect that a set of training data will have on parameter uncertainty as well as the plausibility of candidate model structures. The usefulness of this concept is then demonstrated through the system identification of several dynamical systems using both physics-based and emulator models. The result is a rigorous scientific framework which can be used to select 'highly informative' subsets from large quantities of training data

An Energy Feedback System for the MIT/Bates Linear Accelerator

We report the development and implementation of an energy feedback system for the MIT/Bates Linear Accelerator Center. General requirements of the system are described, as are the specific requirements, features, and components of the system unique to its implementation at the Bates Laboratory. We demonstrate that with the system in operation, energy fluctuations correlated with the 60 Hz line voltage and with drifts of thermal origin are reduced by an order of magnitude

Templates for Convex Cone Problems with Applications to Sparse Signal Recovery

This paper develops a general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields. The approach works as follows: first, determine a conic formulation of the problem; second, determine its dual; third, apply smoothing; and fourth, solve using an optimal first-order method. A merit of this approach is its flexibility: for example, all compressed sensing problems can be solved via this approach. These include models with objective functionals such as the total-variation norm, ||Wx||_1 where W is arbitrary, or a combination thereof. In addition, the paper also introduces a number of technical contributions such as a novel continuation scheme, a novel approach for controlling the step size, and some new results showing that the smooth and unsmoothed problems are sometimes formally equivalent. Combined with our framework, these lead to novel, stable and computationally efficient algorithms. For instance, our general implementation is competitive with state-of-the-art methods for solving intensively studied problems such as the LASSO. Further, numerical experiments show that one can solve the Dantzig selector problem, for which no efficient large-scale solvers exist, in a few hundred iterations. Finally, the paper is accompanied with a software release. This software is not a single, monolithic solver; rather, it is a suite of programs and routines designed to serve as building blocks for constructing complete algorithms.Comment: The TFOCS software is available at http://tfocs.stanford.edu This version has updated reference

Repetitive Negative Thinking in Anticipation of a Stressor

Repetitive negative thinking (RNT) has been confirmed as a transdiagnostic phenomenon, but most measures of RNT are contaminated with diagnosis-specific content. The first aim of this study was to examine the structure of an anticipatory version of the Repetitive Thinking Questionnaire (RTQ-Ant) as a trans-emotional measure of anticipatory RNT. The original RTQ was completed with reference to a past stressor, whereas the RTQ-Ant instructs respondents to link their responses to a future stressor. The second aim was to test if the associations between a range of emotions (anxiety, depression, shame, anger, general distress) and the original post-stressor version of the RTQ would be replicated. Undergraduates (N = 175, 61% women) completed the RTQ-Ant, along with measures of various emotions, with reference to upcoming university exams. Principal axis factor analysis yielded many similarities between the original post-event RTQ and the RTQ-Ant, and some differences. The RTQ-Ant was comprised of two subscales: the RNT subscale measures engagement in repetitive thinking, negative thoughts about oneself, and ‘why’ questions; and the Isolated Contemplation (IC) subscale included items referring to isolating oneself and reflecting on negative thoughts, feelings, loneliness, and listening to sad music. RNT was more strongly related to negative emotions than IC. The RTQ-Ant appears to be a reliable measure of anticipatory RNT that is associated with a broad array of emotions

Search for a T_20 Analyzer for Deuterons

This work was supported by the National Science Foundation Grant NSF PHY 81-14339 and by Indiana Universit

Efficient MR Image Reconstruction for Compressed MR Imaging

Abstract. In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original prob-lem into L1 and TV norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Fi-nally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the recon-struction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for com-pressed MR image reconstruction.

Hypermagnetic Field Effects in the Thermal Bath of Chiral Fermions

The dispersion relations for leptons in the symmetric phase of the electroweak model in the presence of a constant hypermagnetic field are investigated. The one-loop fermion self-energies are calculated in the lowest Landau level approximation and used to show that the hypermagnetic field forbids the generation of the ''effective mass'' found as a pole of the fermions' propagators at high temperature and zero fields. In the considered approximation leptons behave as massless particles propagating only along the direction of the external field. The reported results can be of interest for the cosmological implications of primordial hypermagnetic fields.Comment: 5 page

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem