Cognizant

About: Cognizant is a company organization based out in Teaneck, New Jersey, United States. It is known for research contribution in the topics: Cloud computing & Test case. The organization has 485 authors who have published 412 publications receiving 3644 citations. The organization is also known as: Cognizant Technology Solutions Corporation & Cognizant Technology Solutions.

A review on small scale wind turbines

Abstract: Meeting future world energy needs while addressing climatic changes has led to greater strain on conventional power sources. One of the viable sustainable energy sources is wind. But the installation large scale wind farms has a potential impact on the climatic conditions, hence a decentralized small scale wind turbines is a sustainable option. This paper presents review of on different types of small scale wind turbines i.e., horizontal axis and vertical axis wind turbines. The performance, blade design, control and manufacturing of horizontal axis wind turbines were reviewed. Vertical axis wind turbines were categorized based on experimental and numerical studies. Also, the positioning of wind turbines and aero-acoustic aspects were presented. Additionally, lessons learnt from various studies/countries on actual installation of small wind turbines were presented.

A Multi-Organ Nucleus Segmentation Challenge

Abstract: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Everything as a Service (XaaS) on the Cloud: Origins, Current and Future Trends

Abstract: For several years now, scientists have been proposing numerous models for defining anything "as a service (aaS)", including discussions of products, processes, data a information management, and security as a service. In this paper, based on a thorough literature survey, we investigate the vast stream of the state of the art in Everything as a Service (XaaS). We then use this investigation to explore an integrated view of XaaS that will help propose approaches for migrating applications to the cloud and exposing them as services.

The Singular Values of Convolutional Layers

Abstract: We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective regularizer; for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2\% to 5.3\%.

The Singular Values of Convolutional Layers

Abstract: We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective regularizer; for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2\% to 5.3\%.