Generate To Adapt: Aligning Domains using Generative Adversarial Networks

Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.Comment: Accepted as spotlight talk at CVPR 2018. Code available here: https://github.com/yogeshbalaji/Generate_To_Adap

Single Field Baryogenesis

We propose a new variant of the Affleck-Dine baryogenesis mechanism in which a rolling scalar field couples directly to left- and right-handed neutrinos, generating a Dirac mass term through neutrino Yukawa interactions. In this setup, there are no explicitly CP violating couplings in the Lagrangian. The rolling scalar field is also taken to be uncharged under the $B - L$ quantum numbers. During the phase of rolling, scalar field decays generate a non-vanishing number density of left-handed neutrinos, which then induce a net baryon number density via electroweak sphaleron transitions.Comment: 4 pages, LaTe

Properties for Cellular Decks in Negative Bending

Cellular roof and floor decks may be formed by attaching essentially flat sheets to a hat-shapes or to fluted profiles by spot welding along the contact lines. This leads to a closed cellular deck unit suitable for such purposes as in-floor power distribution or communication systems. In positive bending, or when the flat sheet is in tension and stable, cellular deck flexural properties can be determined following the American Iron and Steel Institute Cold Formed Steel Design Manual. When the flat sheet is in compression, its contribution is not described in the AISI Manual since it is not continuously connected to the cell top. In earlier AISI Manual Commentaries, an approximate method was suggested for evaluating flat sheets in compression. Basically, the sheet was treated as a column between welds and, if this column did not buckle at limiting panel flexural stresses, the element edge could be treated as if it were continuously supported. Existing effective width formulas could then be used. For the vast majority of cellular deck applications, welds are not so closely spaced and column-like buckling can occur. The purpose of this study has been to address cases with larger weld spacings and to propose a general method for finding the effective width of sheets in compression when used in cellular decks

Enhancing Firefly Algorithm for better Network lifetime optimization in Healthcare Monitoring System - Cloud Computing Environment

The Internet of Things (IoT), a new phenomenon in the technology industry, is mostly responsible for updating healthcare systems by gathering and analyzing patient physiological data through wearable technology and sensor networks. It is difficult to process so much data from so many IoT devices in such a short amount of time. Maximizing the network lifetime is one of the most significant tasks faced by any wireless sensor network. The objective of the study described in this paper was to apply swarm intelligence metaheuristics to optimize the cluster head selection. In order to extend the lifetime of the WSN, we have implemented both the original firefly algorithm (FA) and the proposal for the revised FA. Additionally, According to the proposed study, sensitive data is created and stored by IoT devices, which are vulnerable to attack, and data processing is handled by the edge server. Standard security algorithms like AES, DES, and RSA make it difficult for the majority of IoT devices to function successfully because of their restricted resources. For real-time processing, visualization, and diagnosis, the real-time data is subsequently sent to a distant cloud server