Kinetic theory approach to the study of a curved shock-wave

Kinetic theory approach to curved shock wave with low Reynolds numbe

Design Methodology of Very Large Scale Integration

Very Large Scale Integration (VLSI) deals with systems complexity rather than transistor size or circuit performance. VLSI design methodology is supported by Computer Aided Design (CAD) and Design Automation (DA) tools, which help VLSI designers to implement more complex and guaranteed designs. The increasing growth in VLSI complexity dictates a hierarchical design approach and the need for hardware DA tools. This paper discusses the generalized Design Procedure for CAD circuit design; the commercial CADs offered by CALMA and the Caesar System, supported by the Berkeley design tools. A complete design of a Content Addressable Memory (CAM) cell, using the Caesar system, supported by Berkeley CAD tools, is illustrated

Recovery of cellular traction in three-dimensional nonlinear hyperelastic matrices

The traction exerted by a cell on the extra-cellular matrix (ECM) is critical to understanding and manipulating important biological processes such as stem cell differentiation, cancer cell metastasis, and embryonic morphogenesis. This traction is typically quantified through traction force microscopy (TFM). In TFM, the displacement of select markers inside the ECM is tracked, and is used in conjunction with an elasticity problem to reconstruct the traction field. Most applications of this technique thus far have assumed that the matrix behaves as a linear elastic solid that undergoes small deformation and infinitesimal strains. In this manuscript, we develop and implement a robust and efficient TFM methodology that overcomes these limitations by accounting for geometric and material nonlinearities in the ECM. We pose the TFM problem as an inverse problem and develop efficient adjoint-based minimization techniques to solve it. We test the effect of measurement noise on the proposed method, and examine the error incurred by not including nonlinear effects when solving the TFM problem. We present these results for in-silico traction fields that are applied to realistic geometric models of microglial and neuronal cells

A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data

Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of low-fidelity data. In the approach described in this paper, this is accomplished by constructing a graph Laplacian using the low-fidelity data and computing its low-lying spectrum. This spectrum is then used to cluster the data and identify points that are closest to the centroids of the clusters. High-fidelity data is then acquired for these key points. Thereafter, a transformation that maps every low-fidelity data point to its bi-fidelity counterpart is determined by minimizing the discrepancy between the bi- and high-fidelity data at the key points, and to preserve the underlying structure of the low-fidelity data distribution. The latter objective is achieved by relying, once again, on the spectral properties of the graph Laplacian. This method is applied to a problem in solid mechanics and another in aerodynamics. In both cases, this methods uses a small fraction of high-fidelity data to significantly improve the accuracy of a large set of low-fidelity data

A qualitative study on overcoming heterosexist harassment at work: indian cases

The Purpose of this paper is to understand the heterosexist harassment faced by Lesbian and Gay employees at the workplace in an Indian context where gender stereotypes are rooted throughout society. It also aims at exploring the coping mechanisms used by these gay/lesbian employees to deal with this harassment. In-depth interviews of six lesbian/gay employees from the NCR region of India were conducted for collecting data and information through open-ended questionnaire. The samples were selected through purposive non-probability sampling technique. Each interview has been explained through a case study by identifying themes and patterns based on cross-case synthesis, pattern matching and explanation building among them. The results revealed that the Lesbian/gay employees frequently experienced bullying, unwanted jokes, discrimination based on sexual orientation, sexual assault, dismissal from the job, social ostracism and isolation. Several coping strategies were identified which help the lesbian/gay employees to deal with these heterosexist harassments at workplace. Four broad categories of coping strategies were identified as support seeking, confrontation, inaction, and quitting. It was also revealed that participants resorted to secrecy and withdrawal as a way of managing labeling and stigma and to further avoid the subsequent heterosexual abuse. The findings of the study will advance the knowledge in the heterosexist harassments and coping mechanism used by lesbian/gay employees at workplace. The results contribute to meaningful social change to build safe work environments for Lesbian and gay employees

Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors

Inverse problems are notoriously difficult to solve because they can have no solutions, multiple solutions, or have solutions that vary significantly in response to small perturbations in measurements. Bayesian inference, which poses an inverse problem as a stochastic inference problem, addresses these difficulties and provides quantitative estimates of the inferred field and the associated uncertainty. However, it is difficult to employ when inferring vectors of large dimensions, and/or when prior information is available through previously acquired samples. In this paper, we describe how deep generative adversarial networks can be used to represent the prior distribution in Bayesian inference and overcome these challenges. We apply these ideas to inverse problems that are diverse in terms of the governing physical principles, sources of prior knowledge, type of measurement, and the extent of available information about measurement noise. In each case we apply the proposed approach to infer the most likely solution and quantitative estimates of uncertainty.Comment: Paper: 18 pages, 5 figures. Supplementary: 9 pages, 6 Figures, 2 Table