Antisymmetric magnetoresistance of the SrTiO3/LaAlO3 interface

The longitudinal resistance $R_{xx}$ of the SrTiO3/LaAlO3 interface with magnetic fields applied perpendicular to the interface has an antisymmetric term (namely, $R_{xx}(H)\neq R_{xx}(-H)$) which increases with decreasing temperature and increasing field. We argue that the origin of this phenomenon is a non-homogeneous Hall effect with clear contribution of an extraordinary Hall effect, suggesting the presence of non-uniform field-induced magnetization

Depth-Independent Lower bounds on the Communication Complexity of Read-Once Boolean Formulas

We show lower bounds of $\Omega(\sqrt{n})$ and $\Omega(n^{1/4})$ on the randomized and quantum communication complexity, respectively, of all $n$-variable read-once Boolean formulas. Our results complement the recent lower bound of $\Omega(n/8^d)$ by Leonardos and Saks and $\Omega(n/2^{\Omega(d\log d)})$ by Jayram, Kopparty and Raghavendra for randomized communication complexity of read-once Boolean formulas with depth $d$. We obtain our result by "embedding" either the Disjointness problem or its complement in any given read-once Boolean formula.Comment: 5 page

Online Fault Classification in HPC Systems through Machine Learning

As High-Performance Computing (HPC) systems strive towards the exascale goal, studies suggest that they will experience excessive failure rates. For this reason, detecting and classifying faults in HPC systems as they occur and initiating corrective actions before they can transform into failures will be essential for continued operation. In this paper, we propose a fault classification method for HPC systems based on machine learning that has been designed specifically to operate with live streamed data. We cast the problem and its solution within realistic operating constraints of online use. Our results show that almost perfect classification accuracy can be reached for different fault types with low computational overhead and minimal delay. We have based our study on a local dataset, which we make publicly available, that was acquired by injecting faults to an in-house experimental HPC system.Comment: Accepted for publication at the Euro-Par 2019 conferenc

RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

Transcriptomics:Quantifying Non-Uniform Read Distribution Using MapReduce

RNA-seq is a high-throughput Next-sequencing technique for estimating the concentration of all transcripts in a transcriptome. The method involves complex preparatory and post-processing steps which can introduce bias, and the technique produces a large amount of data [7, 19]. Two important challenges in processing RNA-seq data are therefore the ability to process a vast amount of data, and methods to quantify the bias in public RNA-seq datasets. We describe a novel analysis method, based on analysing sequence motif correlations, that employs MapReduce on Apache Spark to quantify bias in Next-generation sequencing (NGS) data at the deep exon level. Our implementation is designed specifically for processing large datasets and allows for scalability and deployment on cloud service providers offering MapReduce. In investigating the wild and mutant organism types in the species D. melanogaster we have found that motifs with runs of Gs (or their complement) exhibit low motif-pair correlations in comparison with other motif-pairs. This is independent of the mean exon GC content in the wild type data, but there is a mild dependence in the mutant data. Hence, whilst both datasets show the same trends, there is however significant variation between the two samples

Education and articulation: Laclau and Mouffe’s radical democracy in school

This paper outlines a theory of radical democratic education by addressing a key concept in Laclau and Mouffe’s Hegemony and Socialist Strategy: articulation. Through their concept of articulation, Laclau and Mouffe attempt to liberate Gramsci’s theory of hegemony from Marxist economism, and adapt it to a political sphere inhabited by a plurality of struggles and agents none of which is predominant. However, while for Gramsci the political process of hegemony formation has an explicit educational dimension, Laclau and Mouffe ignore this dimension altogether. My discussion starts with elaborating the concept of articulation and analysing it in terms of three dimensions: performance, connection and transformation. I then address the role of education in Gramsci’s politics, in which the figure of the intellectual is central, and argue that radical democratic education requires renouncing that figure. In the final section, I offer a theory of such education, in which both teacher and students articulate their political differences and identities

Improving Scalability and Maintenance of Software for High-Performance Scientific Computing by Combining MDE and Frameworks

International audienceIn recent years, numerical simulation has attracted increasing interest within industry and among academics. Paradoxically, the development and maintenance of high performance scientific computing software has become more complex due to the diversification of hardware architectures and their related programming languages and libraries. In this paper, we share our experience in using model-driven development for numerical simulation software. Our approach called MDE4HPC proposes to tackle development complexity by using a domain specific modeling language to describe abstract views of the software. We present and analyse the results obtained with its implementation when deriving this abstract model to target Arcane, a development framework for 2D and 3D numerical simulation software

Implementing Distributed Controllers for Systems with Priorities

Implementing a component-based system in a distributed way so that it ensures some global constraints is a challenging problem. We consider here abstract specifications consisting of a composition of components and a controller given in the form of a set of interactions and a priority order amongst them. In the context of distributed systems, such a controller must be executed in a distributed fashion while still respecting the global constraints imposed by interactions and priorities. We present in this paper an implementation of an algorithm that allows a distributed execution of systems with (binary) interactions and priorities. We also present a comprehensive simulation analysis that shows how sensitive to changes our algorithm is, in particular changes related to the degree of conflict in the system.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499

Strong Clonal Relatedness between Serum and Gut IgA despite Different Plasma Cell Origins

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