143 research outputs found

    The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

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    International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

    Neural Transition-based Parsing of Library Deprecations

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    This paper tackles the challenging problem of automating code updates to fix deprecated API usages of open source libraries by analyzing their release notes. Our system employs a three-tier architecture: first, a web crawler service retrieves deprecation documentation from the web; then a specially built parser processes those text documents into tree-structured representations; finally, a client IDE plugin locates and fixes identified deprecated usages of libraries in a given codebase. The focus of this paper in particular is the parsing component. We introduce a novel transition-based parser in two variants: based on a classical feature engineered classifier and a neural tree encoder. To confirm the effectiveness of our method, we gathered and labeled a set of 426 API deprecations from 7 well-known Python data science libraries, and demonstrated our approach decisively outperforms a non-trivial neural machine translation baseline.Comment: 11 pages + references and appendix (14 total). This is an edited version of our rejected submission to ESEC/FSE 2022 to include a citation of our earlier short paper and remove all content pertaining to the demo paper submission currently under review for ICSE 202

    The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

    No full text
    International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

    Estudio y evaluaciĂłn del algoritmo PSO acoplado a un modelo de inteligencia artificial para la optimizaciĂłn de un motor de encendido por compresiĂłn

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    [ES] Los algoritmos de optimizaciĂłn han sido herramientas exitosas que se han utilizado para optimizar parĂĄmetros del motor tanto en experimentos como en simulaciones computacionales. En este trabajo se implementa un mĂ©todo hĂ­brido de optimizaciĂłn que acopla el Particle Swarm Optimization (PSO) con redes neuronales y tĂ©cnicas de cĂĄlculo CFD con la finalidad de reducir el consumo y las emisiones de NOx de un motor de encendido por compresiĂłn. Para evaluar el desempeño de la metodologĂ­a que se propone, los resultados se comparan con datos obtenidos previamente a partir del algoritmo PSO bĂĄsico. Los parĂĄmetros que definen el sistema de combustiĂłn y que se varĂ­an durante el estudio son: 5 variables relativas a la geometrĂ­a del pistĂłn, nĂșmero de orificios del inyector, ĂĄngulo del chorro, presiĂłn de inyecciĂłn de combustible, nĂșmero de swirl y tasa de EGR.[EN] Optimization algorithms have been useful tools that had allowed to optimize engine parameters in both computational simulations and experiments, in the last decades. In this work, a hybrid optimization method that couples Particle Swarm Optimization (PSO) and Novelty Search (NS) algorithms with neural networks and CFD computational techniques is implemented in order to find a combustion chamber design and an input parameter configuration that reduces fuel consumption and pollutant emissions in a compression ignition engine. To evaluate the performance of the proposed methodology, the results are compared with data previously obtained from the basic PSO algorithm. The parameters that define the combustion system and those that will be modified during the study are the following: 5 variables related to the piston geometry, the number of injector orifices, the spray angle, the injection pressure, the swirl number and the Exhaust Gas Recirculation (EGR).[CA] Els algoritmes d'optimitzaciĂł han sigut ferramentes que s'han utilitzat per a optimitzar parĂ metres del motor tant en experiments com en simulacions computacionals en les Ășltimes dĂšcades. En aquest treball s'implementa un mĂštode hĂ­brid d'optimitzaciĂł que acobla els algorismes Particle Swarm Optimization (PSO) i Novelty Search (NS) amb xarxes neuronals i tĂšcniques de cĂ lcul CFD amb la finalitat de trobar un disseny de cambra de combustiĂł i uns parĂ metres d'entrada que permeten reduir el consum i les emissions de NOx i partĂ­cules de sutge d'un motor d'encesa per compressiĂł. Per a avaluar l'acompliment de la metodologia que es proposa, els resultats es comparen amb dades obtingudes prĂšviament a partir de l'algoritme PSO bĂ sic. Els parĂ metres que defineixen el sistema de combustiĂł i que es pretenen variar durant l'estudi sĂłn: 5 variables relatives a la geometria del pistĂł, el nombre d'orificis de l'injector, l'angle del doll, la pressiĂł d'injecciĂł de combustible, el nĂșmero de swirl i la taxa de gasos recirculats d’escapament (EGR).Muñoz Navarro, N. (2021). Estudio y evaluaciĂłn del algoritmo PSO acoplado a un modelo de inteligencia artificial para la optimizaciĂłn de un motor de encendido por compresiĂłn. Universitat PolitĂšcnica de ValĂšncia. http://hdl.handle.net/10251/172252TFG

    The AXIOM project (Agile, eXtensible, fast I/O Module)

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    Abstract-The AXIOM project (Agile, eXtensible, fast I/O Module) aims at researching new software/hardware architectures for the future Cyber-Physical Systems (CPSs). These systems are expected to react in real-time, provide enough computational power for the assigned tasks, consume the least possible energy for such task (energy efficiency), scale up through modularity, allow for an easy programmability across performance scaling, and exploit at best existing standards at minimal costs. Current solutions for providing enough computational power are mainly based on multi-or many-core architectures. For example, some current research projects (such as ADEPT or P-SOCRATES) are already investigating how to join efforts from the High-Performance Computing (HPC) and the Embedded Computing domains, which are both focused on high power efficiency, while GPUs and new Dataflow platforms such as Maxeler, or in general FPGAs, are claimed as the most energy efficient. We present the project's initial approach, ideas and key concepts, and describe the AXIOM preliminary architecture. Our starting point uses power efficient multi-core nodes, such as ARM cores and FPGA accelerators on the same die, as in the Xilinx Zynq. We will work to provide an integrated environment that supports programmability of the parallel, interconnected nodes that form a CPS system, and evaluate our ideas using demanding test application scenarios

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    SĂ©ptimo desafĂ­o por la erradicaciĂłn de la violencia contra las mujeres del Institut Universitari d’Estudis Feministes i de GĂšnere "PurificaciĂłn Escribano" de la Universitat Jaume

    Data stream classification using random feature functions and novel method combinations

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    Big Data streams are being generated in a faster, bigger, and more commonplace. In this scenario, Hoeffding Trees are an established method for classification. Several extensions exist, including high performing ensemble setups such as online and leveraging bagging. Also, k-nearest neighbors is a popular choice, with most extensions dealing with the inherent performance limitations over a potentially-infinite stream. At the same time, gradient descent methods are becoming increasingly popular, owing in part to the successes of deep learning. Although deep neural networks can learn incrementally, they have so far proved too sensitive to hyper-parameter options and initial conditions to be considered an effective 'off -the-shelf' data-streams solution. In this work, we look at combinations of Hoeffding-trees, nearest neighbor, and gradient descent methods with a streaming preprocessing approach in the form of a random feature functions filter for additional predictive power. We further extend the investigation to implementing methods on GPUs, which we test on some large real-world datasets, and show the benefits of using GPUs for data-stream learning due to their high scalability. Our empirical evaluation yields positive results for the novel approaches that we experiment with, highlighting important issues, and shed light on promising future directions in approaches to data-stream classification. (C) 2016 Elsevier Inc. All rights reserved.Peer ReviewedPostprint (author's final draft

    Efficient exception handling support for GPUs

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    Operating systems have long relied on the exception handling mechanism to implement numerous virtual memory features and optimizations. However, today's GPUs have a limited support for exceptions, which prevents implementation of such techniques. The existing solution forwards GPU memory faults to the CPU while the faulting instruction is stalled in the GPU pipeline. This approach prevents preemption of the faulting threads, and results in underutilized hardware resources while the page fault is being resolved by the CPU. In this paper, we present three schemes for supporting GPU exceptions that allow the system software to preempt and restart the execution of the faulting code. There is a trade-off between the performance overhead introduced by adding exception support and the additional complexity. Our solutions range from 90% of the baseline performance with no area overheads, to 99.2% of the baseline performance with less than 1% area and 2% power overheads. Experimental results also show 10% performance improvement on some benchmarks when using this support to context switch the GPU during page migrations, to hide their latency. We further observe up to 1.75x average speedup when implementing lazy memory allocation on the GPU, also possible thanks to our exception handling support.We would like to thank anonymous reviewers, Lluis Vilanova and Javier Cabezas for their help in improving this paper. Early discussions with Steve Keckler, Arslan Zulfiqar, Jack Choquette and Olivier Giroux had a major influence on this work, for which we are very grateful. This work is supported by Nvidia through the GPU Center of Excellence program, the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), the Spanish Ministry of Science and Technology (TIN2015-65316-P) and by the Generalitat de Catalunya (grants 2014-SGR-1051 and 2014-SGR-1272). Nacho Navarro passed away before this paper was published. This work would have not been possible without his guidance, support, and dedication. A memory of him will always live in his students, colleagues and loved ones.Peer ReviewedPostprint (published version

    An open benchmark implementation for multi-CPU multi-GPU pedestrian detection in automotive systems

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    Modern and future automotive systems incorporate several Advanced Driving Assistance Systems (ADAS). Those systems require significant performance that cannot be provided with traditional automotive processors and programming models. Multicore CPUs and Nvidia GPUs using CUDA are currently considered by both automotive industry and research community to provide the necessary computational power. However, despite several recent published works in this domain, there is an absolute lack of open implementations of GPU-based ADAS software, that can be used for benchmarking candidate platforms. In this work, we present a multi-CPU and GPU implementation of an open implementation of a pedestrian detection benchmark based on the Viola-Jones image recognition algorithm. We present our optimization strategies and evaluate our implementation on a multiprocessor system featuring multiple GPUs, showing an overall 88.5× speedup over the sequential version.This work has been supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316P, the HiPEAC Network of Excellence and a Microsoft sponsored ACM SRC. The first two authors acknowledge Dr. Petrisor for her assistance in understanding and using the sequential version of the benchmark and dedicate this article to the memory of the late beloved advisor prof. Nacho Navarro, without whom this work would not have been possible.Peer Reviewe
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