OverSketch: Approximate Matrix Multiplication for the Cloud

We propose OverSketch, an approximate algorithm for distributed matrix multiplication in serverless computing. OverSketch leverages ideas from matrix sketching and high-performance computing to enable cost-efficient multiplication that is resilient to faults and straggling nodes pervasive in low-cost serverless architectures. We establish statistical guarantees on the accuracy of OverSketch and empirically validate our results by solving a large-scale linear program using interior-point methods and demonstrate a 34% reduction in compute time on AWS Lambda.Comment: Published in Proc. IEEE Big Data 2018. Updated version provides details of distributed sketching and highlights other advantages of OverSketc

Mesenchymal chondrosarcoma of maxilla : a rare case report

Mesenchymal chondrosarcoma (MC) is a rare variant of chondrosarcoma (CS) that accounts for upto 3-9% of all CS and has high predilection for the head and neck region. It is usually seen in younger age group compared to conventional CS and maxillary anterior alveolus is the most common site. The tumor is most unusual as it has been described as a particularly aggressive neoplasm with a high tendency for late recurrence and delayed metastasis. It is a biphasic tumor with areas comprising of spindle cell mesenchyme interspread with areas of chondroid differentiation. A 75 year old male presented to us as a painless mass in maxilla. Contrast enhanced computed tomography (CECT) revealed a lytic expansile lesion in the left maxillary bone with foci of calcification within soft tissue lesion. Fine needle aspiration cytology (FNAC) and incisional biopsy was performed which confirmed the diagnosis of maxillary MC. The patient underwent right and left subtotal maxillectomy with 2 cm margins. The review of literature shows that very few cases of maxillary MC have been reported so far. Thus an attempt is made to add this rare case of MC of maxillary alveolus in the English literature. © Medicina Oral S. L

An Approach to Knowledge Management: The Contribution of Technical and Social Concepts

This paper attempts to identify models of knowledge acquisition and dissemination that are appropriate for the 21st century organizations facing complex and rapidly changing business environments. The specific aims of this paper are to (1) propose a new socio-technical model for Knowledge Management, (2) discuss some mature as well as emerging technologies that are now widely used for Knowledge Management, and (3) present few emerging learning systems and environments, and introduce how shadowy groups called “Communities of Practice” and “Share Groups” are transforming the acquisition, creation, packaging, and application of knowledge

Do we need entire training data for adversarial training?

Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suffer from vulnerability against adversarial attacks. In the past few years, numerous approaches have been proposed to tackle this problem by training networks using adversarial training. Almost all the approaches generate adversarial examples for the entire training dataset, thus increasing the training time drastically. We show that we can decrease the training time for any adversarial training algorithm by using only a subset of training data for adversarial training. To select the subset, we filter the adversarially-prone samples from the training data. We perform a simple adversarial attack on all training examples to filter this subset. In this attack, we add a small perturbation to each pixel and a few grid lines to the input image. We perform adversarial training on the adversarially-prone subset and mix it with vanilla training performed on the entire dataset. Our results show that when our method-agnostic approach is plugged into FGSM, we achieve a speedup of 3.52x on MNIST and 1.98x on the CIFAR-10 dataset with comparable robust accuracy. We also test our approach on state-of-the-art Free adversarial training and achieve a speedup of 1.2x in training time with a marginal drop in robust accuracy on the ImageNet dataset.Comment: 6 pages, 4 figure

Sodium-glucose cotransporter-2 inhibitors: updated evidence on their efficacy and safety in patients with type-2 diabetes

Management of type-2 diabetes mellitus (T2DM) is challenging. The scope of existing therapies toward T2DM has transformed remarkably. These large assortments of therapies have produced evidence-based data. Sodium-glucose cotransporter-2 inhibitor (SGLT-2i) is the most recent class of oral anti-hyperglycemic agents. They are approved by Food and Drug Administration for the treatment of diabetes mellitus. SGLT-2i has a unique mechanism of action and that lower glucose independent of insulin. They reduce renal tubular glucose reabsorption, thereby lowering blood glucose without stimulating the release of insulin. Additional advantages involve suitable effects on blood pressure and weight. According to guidelines of the American Association of Clinical Endocrinologists/ the American College of Endocrinology 2016, SGLT-2i (in the form of canagliflozin, dapagliflozin, and empagliflozin) is one of the acceptable alternatives to metformin as initial therapy towards T2DM. Canagliflozin, dapagliflozin, and empagliflozin reduce the cardiovascular risk in comparison to placebo as the part of standard care. This review article focuses on the clinical trials published over the past year and specifically the metabolic aspect of SGLT-2i and the adverse effects related to SGLT-2 inhibitors.

Knowledge-based Simulation System for Reliability and Performance Analysis of Computer Networks

Modeling and analysis of performance of computer networks is essential for ensuring smooth operation of an organization’s networks and preventing major failures. Mathematical analysis and simulation modeling are the common procedures for network system performance analysis. In this paper, a knowledge-based simulation system is developed that can be used for assessment and prediction of network performance and reliability

A fuzzy expert system for small business loan processing

In recent years, managerial applications of artificial intelligence, especially in the area of financial services, has received considerable attention. In this paper, a fiizzy logic expert system is developed for approvals of small business loans. Previous studies have used non-fuzzv expert systems. But the fuzzification of the variables used in a business loan approval decisionmaking promises more efficient results. Furthermore, another distinct feature of this paper is its focus on small business loans. CubiCalc fuzzy expert system shell is used to develop the expert system. Knowledge acquisition is made using the resources and expertise of a Small Business Development Center. Tests to establish generalizability and validity of the results are conducted