Data-Driven Sparse Structure Selection for Deep Neural Networks

Deep convolutional neural networks have liberated its extraordinary power on various tasks. However, it is still very challenging to deploy state-of-the-art models into real-world applications due to their high computational complexity. How can we design a compact and effective network without massive experiments and expert knowledge? In this paper, we propose a simple and effective framework to learn and prune deep models in an end-to-end manner. In our framework, a new type of parameter -- scaling factor is first introduced to scale the outputs of specific structures, such as neurons, groups or residual blocks. Then we add sparsity regularizations on these factors, and solve this optimization problem by a modified stochastic Accelerated Proximal Gradient (APG) method. By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Comparing with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles. We evaluate our method, Sparse Structure Selection with several state-of-the-art CNNs, and demonstrate very promising results with adaptive depth and width selection.Comment: ECCV Camera ready versio

Mission F, LM descent/phasing summary document

Lunar module descent/phasing summary for mission

Effect of Perceived Stress on Cytokine Production in Healthy College Students

Chronic psychological stress impairs antibody synthesis following influenza vaccination. Chronic stress also increases circulating levels of proinflammatory cytokines and glucocorticoids in elders and caregivers, which can impair antibody synthesis. The purpose of this study was to determine whether psychological stress increases ex vivo cytokine production or decreases glucocorticoid sensitivity (GCS) of peripheral blood leukocytes from healthy college students. A convenience sample of Reserve Officer Training Corps (ROTC) students completed the Perceived Stress Scale (PSS). Whole blood was incubated in the presence of influenza vaccine and dexamethasone to evaluate production of interleukin-6 (IL-6), interleukin-1-beta (IL-1β), tumor necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ). Multiple regression models controlling for age, gender, and grade point average revealed a negative relationship between PSS and GCS for vaccine-stimulated production of IL-1β, IL-6, and TNF-α. These data increase our understanding of the complex relationship between chronic stress and immune function

Quantitative multielement analysis using high energy particle bombardment

Charged particles ranging in energy from 0.8 to 4.0 MeV are used to induce resonant nuclear reactions, Coulomb excitation (gamma X-rays), and X-ray emission in both thick and thin targets. Quantitative analysis is possible for elements from Li to Pb in complex environmental samples, although the matrix can severely reduce the sensitivity. It is necessary to use a comparator technique for the gamma-rays, while for X-rays an internal standard can be used. A USGS standard rock is analyzed for a total of 28 elements. Water samples can be analyzed either by nebulizing the sample doped with Cs or Y onto a thin formvar film or by extracting the sample (with or without an internal standard) onto ion exchange resin which is pressed into a pellet

Study of a heat rejection system using capillary pumping

Results of an analytical study investigating the application of capillary pumping to the heat rejection loop of an advanced Rankine cycle power conversion system are presented. The feasibility of the concept of capillary pumping as an alternate to electromagnetic pumping is analytically demonstrated. Capillary pumping is shown to provide a potential for weight and electrical power saving and reliability through the use of redundant systems. A screen wick pump design with arterial feed lines was analytically developed. Advantages of this design are high thermodynamic and hydrodynamic efficiency, which provide a lightweight easily packaged system. Operational problems were identified which must be solved for successful application of capillary pumping. The most important are the development of start up and shutdown procedures, and development of a means of keeping noncondensibles from the system and of earth-bound testing procedures

Epics Should be Sung

Chance O’Neal is a senior majoring in English, focusing on Creative Writing. He plans on pursuing an MFA after completing his undergraduate and striving to work at a publishing company while he continues to publish more literary works

Parallel processors and nonlinear structural dynamics algorithms and software

The adaptation of a finite element program with explicit time integration to a massively parallel SIMD (single instruction multiple data) computer, the CONNECTION Machine is described. The adaptation required the development of a new algorithm, called the exchange algorithm, in which all nodal variables are allocated to the element with an exchange of nodal forces at each time step. The architectural and C* programming language features of the CONNECTION Machine are also summarized. Various alternate data structures and associated algorithms for nonlinear finite element analysis are discussed and compared. Results are presented which demonstrate that the CONNECTION Machine is capable of outperforming the CRAY XMP/14

An A Priori Knowledge Based Wiener Filtering Approach to Ultrasonic Scattering Amplitude Estimation

The Wiener filter is currently used in the ultrasonic scattering amplitude estimation problem as a means of desensitization during deconvolution [1,2,3]. The work summarized here focuses on a Wiener filtering approach which incorporates a priori flaw and noise information. It will be shown that this approach leads to improved scattering amplitude estimates and improved radius estimates