Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.

Automatic assistants could guide a person or a robot in performing new tasks, such as changing a car tire or repotting a plant. Creating such assistants, however, is non-trivial and requires understanding of visual and verbal content of a video. Towards this goal, we here address the problem of automatically learning the main steps of a task from a set of narrated instruction videos. We develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method sequentially clusters textual and visual representations of a task, where the two clustering problems are linked by joint constraints to obtain a single coherent sequence of steps in both modalities. To evaluate our method, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains videos for five different tasks with complex interactions between people and objects, captured in a variety of indoor and outdoor settings. We experimentally demonstrate that the proposed method can automatically discover, learn and localize the main steps of a task in input videos.

We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Second, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains about 800,000 frames for five different tasks1that include complex interactions between people and objects, and are captured in a variety of indoor and outdoor settings. Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner, the main steps to achieve the task and locate the steps in the input videos.

Vasu Sharma, Rajat Kulshreshtha, Puneet Singh, Nishant Agrawal, Akshay Kumar. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. 2015.

OpenFOAM is a software package for solving partial differential equations and is very popular for computational fluid dynamics in the automotive segment. In this work, we describe our evaluation of the performance of OpenFOAM with MPI-3 Remote Memory Access (RMA) one-sided communication on the Intel® Xeon Phi" coprocessor. Currently, OpenFOAM computes on a mesh that is decomposed among different MPI ranks, and it requires a high amount of communication between the neighboring ranks. MPI-3 offers RMA through a new API that decouples communication and synchronization. The aim is to achieve better performance with MPI-3 RMA routines as compared to the current two-sided asynchronous communication routines in OpenFOAM. We also showcase the challenges overcome in order to facilitate the different MPI-3 RMA routines in OpenFOAM. This discussion aims at analyzing the potential of MPI-3 RMA in OpenFOAM and benchmarking the performance on both the processor and the coprocessor. Our work also demonstrates that MPI-3 RMA in OpenFOAM can run in symmetric mode consisting of the Intel® Xeon® E5-2697v3 processor and the Intel® Xeon Phi™ 7120P coprocessor.

We introduce split-level I/O scheduling, a new framework that splits I/O scheduling logic across handlers at three layers of the storage stack: block, system call, and page cache. We demonstrate that traditional block-level I/O schedulers are unable to meet throughput, latency, and isolation goals. By utilizing the split-level framework, we build a variety of novel schedulers to readily achieve these goals: our Actually Fair Queuing scheduler reduces priority-misallocation by 28x; our Split-Deadline scheduler reduces tail latencies by 4x; our Split-Token scheduler reduces sensitivity to interference by 6x. We show that the framework is general and operates correctly with disparate file systems (ext4 and XFS). Finally, we demonstrate that split-level scheduling serves as a useful foundation for databases (SQLite and PostgreSQL), hypervisors (QEMU), and distributed file systems (HDFS), delivering improved isolation and performance in these important application scenarios.