Distributed Bayesian Probabilistic Matrix Factorization

Matrix factorization is a common machine learning technique for recommender systems. Despite its high prediction accuracy, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used on large scale data because of its high computational cost. In this paper we propose a distributed high-performance parallel implementation of BPMF on shared memory and distributed architectures. We show by using efficient load balancing using work stealing on a single node, and by using asynchronous communication in the distributed version we beat state of the art implementations

Hands-on tutorial: coarse-grained reconfigurable architectures-compilation and exploration

CGRAs consist of an array of a large number of functional units (FUs) interconnected by a mesh style network. Register files are distributed throughout the CGRAs to hold temporary values and are accessible only by a subset of FUs. The FUs can execute common word-level operations, including addition, subtraction, and multiplication. CGRA processors accelerate inner loops of applications by exploiting instruction level parallelism (ILP) and in some cases also data-level and task-level parallelism (DLP & TLP). The aim of this tutorial is to give insight in CGRA architectures, their compilation techniques, and to experience first hand how to do source code mapping on a CGRA. Therefore the tutorial consists of presentations as well as a hands-on session

HyperLoom: A platform for defining and executing scientific pipelines in distributed environments

Real-world scientific applications often encompass end-to-end data processing pipelines composed of a large number of interconnected computational tasks of various granularity. We introduce HyperLoom, an open source platform for defining and executing such pipelines in distributed environments and providing a Python interface for defining tasks. HyperLoom is a self-contained system that does not use an external scheduler for the actual execution of the task. We have successfully employed HyperLoom for executing chemogenomics pipelines used in pharmaceutic industry for novel drug discovery.6

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Architectures for Cognitive Radio Testbeds and Demonstrators – An Overview

Wireless communication standards are developed at an ever-increasing rate of pace, and significant amounts of effort is put into research for new communication methods and concepts. On the physical layer, such topics include MIMO, cooperative communication, and error control coding, whereas research on the medium access layer includes link control, network topology, and cognitive radio. At the same time, implementations are moving from traditional fixed hardware architectures towards software, allowing more efficient development. Today, field-programmable gate arrays (FPGAs) and regular desktop computers are fast enough to handle complete baseband processing chains, and there are several platforms, both open-source and commercial, providing such solutions. The aims of this paper is to give an overview of five of the available platforms and their characteristics, and compare the features and performance measures of the different systems

EuroEXA - D2.6: Final ported application software

This document describes the ported software of the EuroEXA applications to the single CRDB testbed and it discusses the experiences extracted from porting and optimization activities that should be actively taken into account in future redesign and optimization. This document accompanies the ported application software, found in the EuroEXA private repository (https://github.com/euroexa). In particular, this document describes the status of the software for each of the EuroEXA applications, sketches the redesign and optimization strategy for each application, discusses issues and difficulties faced during the porting activities and the relative lesson learned. A few preliminary evaluation results have been presented, however the full evaluation will be discussed in deliverable 2.8

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field