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检索条件"任意字段=2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023"

共 417 条记录，以下是221-230 订阅

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Ray-space constrained multichannel Nonnegative Matrix Factorization for Audio Source Separation 32

Ray-Space constrained multichannel Nonnegative Matrix Factor...

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32nd european Signal processing conference (EUSIPCO)

作者： Munoz-Montoro, Antonio J. Olivieri, Marco Pezzoli, Mirco Carabias-Orti, Julio Antonacci, Fabio Sarti, Augusto Univ Jaen Dept Ingn Telecomunicac Linares Spain Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9789464593617;9798331519773

Non-negative matrix factorization (NMF) has been widely adopted for the blind separation of acoustic sources. In the context of VR and AR applications, when several microphones are available the adoption of sound field representations such as spherical harmonics or ray space is shown to be effective in the NMF context. In this work, we propose a dictionary-based NMF-based model considering ray-space-transformed signals. The novelty of this approach is to account for the explicit modelling of the frequency dependency of the sound propagation from the source positions to the sensors. Spatially-constrained approaches aim at exploiting spatial information to improve the separation performance;however, they may not take advantage of possible priors given by representations of the data. The proposed approach allows us to exploit the source location model of the Ray space through a predefined frequency-dependent dictionary of Ray space patterns. Results demonstrate the competitive performance of the proposed method with respect to state-of-the-art NMF-based algorithms using real recordings.

关键词： Ray space Non-negative matrix factorization (NMF) blind source separation (BSS) microphone array processing

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Tradeoff Between Performance and Reliability in FPGA Accelerated DNNs for space Applications

Tradeoff Between Performance and Reliability in FPGA Acceler...

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data handling & data processing conference (edhpc), european

作者： Ioanna Souvatzoglou Dimitris Agiakatsikas Aitzan Sari Vasileios Vlagkoulis Antonios Tavoularis Mihalis Psarakis Department of Informatics University of Piraeus Greece European Space Agency ESTEC Noordwijk The Netherlands

The space industry is interested in using Commercial-off-the-self (COTS) SRAM Field Programmable Gate Arrays (FPGAs) for Deep Neural Networks (DNN) inference acceleration due to their low non-recurring engineering costs, flexibility and performance per watt ratio. Specifically, FPGA DNN accelerators can be adopted in several AI-empowered onboard applications, such as autonomous failure detection, cloud detection, and other real-time data processing. However, SRAM FPGAs are sensitive to radiation effects, e.g., to Single Event Upsets (SEUs), causing significant reliability challenges. In this work, we examine the tradeoff between the performance and reliability of an FPGA-based dataflow Quantised DNN (QNN) accelerator by leveraging its hardware folding parameter. Hardware folding determines the level of computing resource sharing (or the level of parallelisation) between the neurons of each network layer. A low folding factor results in a highly parallelised (i.e., low-latency) QNN, while a high folding factor leads to a less parallelised QNN. Based on the AMD-Xilinx FINN, an open-source dataflow compiler for developing QNNs on FPGAs, we generated three versions of a QNN accelerator that performs image classification. The three QNN design versions (Max, Med, Min) were produced with different folding factors, i.e., maximum (Max), medium (Med) and minimum (Min) folding, respectively, and were implemented on a Zynq-7020 system-on-chip (SoC) FPGA. We performed statistical fault injection experiments in the FPGA’s configuration memory to estimate the SEU vulnerability of the three QNNs. In turn, we calculated the Mean Time Between Failure (MTBF) and the Mean Executions Between Failure (MEBF) metrics for a Low Earth Orbit (LEO) mission to examine how hardware folding affects the balance between performance and reliability. Our results showed that the Med QNN is the most reliable design (i.e., has the highest MTBF) and the Min is the least reliable one (i.e., has the lowest

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Clinical Natural Language processing and Health Interoperability to Support Knowledge Management and Governance in Rare Cancers 16th

Clinical Natural Language Processing and Health Interoperabi...

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Mediterranean conference on Medical and Biological Engineering and Computing (MEDICON) and International conference on Medical and Biological Engineering (CMBEBIH)

作者： Lopez-Perez, Laura Gaeta, Eugenio Alonso, Itziar Esteban, Alejo Gerardo Dominguez, Victor Mercalli, Franco Martinelli, Elena Trama, Annalisa Fernanda Cabrera, Maria Teresa Arredondo, Maria Fico, Giuseppe Univ Politecn Madrid Sch Telecommun Engn Photon Technol & Bioengn Dept Life Supporting Technol Madrid Spain MultiMed Engn Srl Parma Italy

ISBN: (纸本)9783031490613;9783031490620

Rare cancers are a significant public health challenge due to their low incidence and complex nature, resulting in limited knowledge and resources for patient care and research. To address this issue, we present a Rare Cancer data Ecosystem that enables the re-use of existing health data sources for rare cancers across european healthcare systems. Its main goal is to leverage emerging interoperability technologies and artificial intelligence approaches to improve the quality and organization of rare cancer patient care and advance health research. The ecosystem will be developed and tested through an european multidisciplinary project that will cover a regulatory-compliant data governance approach, a standard-based reference architecture, and a set of technical tools to share data, while ensuring data sovereignty policies. We aim to take part to the european Healthcare data space by contributing to an open, transparent digital ecosystem. Natural language processing and understanding tools will allow the extraction of health information stored in text form and support data findability and reusability. It will also include a secure pipeline for machine learning models development and deployment in a secure and reliable manner. The ecosystem will also include an augmented analytics and multimodal data navigator, providing a more natural and intuitive experience for users to explore and analyze rare cancer data. Overall, these features, after being piloted with several rare cancer data sources across different countries, will enable the efficient and secure use of existing health data sources for rare cancers, which can lead to improved patient care and health research.

关键词： Rare cancers data governance Federated learning

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Development Demands for Remote Sensing Information processing Technology: Inspired by the US PWSA

Development Demands for Remote Sensing Information Processin...

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IET International Radar conference 2023, IRC 2023

作者： Zheng, Lu Xiaotian, Lu Zitao, Cai Huijie, Zhao Guoqing, Liu Kan, Cheng Xiaoyang, Liang China Academy of Space Technology 104 Youyi Road Beijing China

ISBN: (纸本)9781839539954

This article analyzes the overall characteristics, hierarchical structure, and main mission objectives of the proliferated warfighter space architecture proposed by the United States space Development Agency. It primarily focuses on two layers of equipment: the tracking layer and the guardian layer, and analyzes the key technology requirements in the field of space information processing, exerting a certain influence on the development of related technologies. © The Institution of Engineering & Technology 2023.

关键词： data handling

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Learning space-Time Continuous Neural PDEs from Partially Observed States 37

Learning Space-Time Continuous Neural PDEs from Partially Ob...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Iakovlev, Valerii Heinonen, Markus Lahdesmaki, Harri Aalto Univ Dept Comp Sci Espoo Finland

ISBN: (纸本)9781713899921

We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatio-temporal grids. We propose a space-time continuous latent neural PDE model with an efficient probabilistic framework and a novel encoder design for improved data efficiency and grid independence. The latent state dynamics are governed by a PDE model that combines the collocation method and the method of lines. We employ amortized variational inference for approximate posterior estimation and utilize a multiple shooting technique for enhanced training speed and stability. Our model demonstrates state-of-the-art performance on complex synthetic and real-world datasets, overcoming limitations of previous approaches and effectively handling partially-observed data. The proposed model outperforms recent methods, showing its potential to advance data-driven PDE modeling and enabling robust, grid-independent modeling of complex partially-observed dynamic processes.

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A data LAKE PROTOTYPE SYSTEM FOR MULTIWAVELENGTH (MWA) AND MULTIMESSENGER ASTRONOMY (MMA) 16

A DATA LAKE PROTOTYPE SYSTEM FOR MULTIWAVELENGTH (MWA) AND M...

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16th International conference on ICT, Society and Human Beings, ICT 2023, the 15th International conference on e-Health, EH 2023, the 9th International conference on Connected Smart Cities, CSC 2023 and 8th International conference on Big data Analytics, data Mining and Computational Intelligence, BigDaCI 2023

作者： Bento, Matheus Rosa, Reinaldo Roberto de Almeida, Ulisses Barres INPE-MCTI Av. dos Astronautas 1758 SP São José dos CamposCEP 12227-010 Brazil Lab for Computing and Applied Mathematics COPDT-INPE-MCTI Av. dos Astronautas 1758 SP São José dos CamposCEP 12227-010 Brazil R. Dr. Xavier Sigaud 150 - Urca RJ Rio de JaneiroCEP 22290-180 Brazil

ISBN: (纸本)9789898704504

At the beginning of the 21st century, large telescopes are coming into operation, making astronomy a Big data science like few others. Recently, given this scenario, applied computing to astronomy and space sciences has pointed out major challenges involving data science and data engineering. Faced with such a challenge, this article presents a first prototype of a data lake as a strategic solution for heterogeneous data astronomy such as multi-wavelength astronomy (MWA), also including multi-messenger astronomy (MMA). The proposed prototype presents a three-layer architecture, composed of storage, processing and application layers. The storage layer incorporates S3 for raw data storage, MariaDB for simple data storage (crud) and TimescaleDB for complex data storage. The processing layer includes Laravel for CRUD operations and Python for handling large volumes of data. The application layer uses ReactJS with the *** library for analytical data visualization. The solution proposes to deliver, when necessary, agility and concatenation between heterogeneous data (light curve, spectra and images) coming from different silos (instruments and telescopes). Thus, the solution, within the MWA and MMA paradigms, incorporates data cube technology and can be customized for applications other than astronomy, such as space weather monitoring and debriefing in the context of space medicine. © ICT *** rights reserved.

关键词： Big data

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ROOM ADAPTATION OF TRAINING data FOR DISTANT SPEECH RECOGNITION 31

ROOM ADAPTATION OF TRAINING DATA FOR DISTANT SPEECH RECOGNIT...

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31st european Signal processing conference, EUSIPCO 2023

作者： Fosburgh, James Sharma, Dushyant Naylor, Patrick A. Research and Development Nuance Communications Inc United States Electrical and Electronic Engineering Imperial College London United Kingdom

ISBN: (纸本)9789464593600

We present a novel signal processing-based approach for estimating room impulse responses for augmentation of ASR training data that is best suited to the reverberation characteristics in a particular acoustic space. Our approach estimates an impulse response of a room by using a supervised adaptive system identification algorithm to extract the relative transfer function between a speech source played through a loudspeaker and recorded by a microphone. These impulse responses can then be applied to clean speech files to create an augmented training set for an ASR system. Given the availability of a small amount of this type of playback audio for a room, we show that an ASR model trained with our data augmentation approach can provide a 19% relative reduction in word error rate compared to a system using random augmentation. © 2023 european Signal processing conference, EUSIPCO. All rights reserved.

关键词： Impulse response

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data Mesh Adoption: A Multi-case and Multi-method Readiness Approach 20th

Data Mesh Adoption: A Multi-case and Multi-method Readiness ...

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20th european, Mediterranean, and Middle Eastern conference (EMCIS)

作者： Ramos, Isabel Santos, Maribel Yasmina Joshi, Divya Pratik, Sheetal Univ Minho ALGORITMI Res Ctr Guimaraes Portugal Thoughtworks Chicago IL USA Adidas Gurugram Haryana India

ISBN: (纸本)9783031564802;9783031564819

data Warehousing systems have been used to support Business Intelligence applications by ingesting operational data and providing analytical data. As data volume, variety, and velocity increased in Big data contexts, this data architecture needed to be modernised, and Big data Warehouses emerged as scalable, high-performance, and highly flexible processing systems capable of handling ever-increasing volumes of data. These monolithic techniques, however, create major challenges to data engineering teams in terms of design, development, management, and evolution. data Mesh emerged as a novel and disruptive concept aimed at data-driven businesses. The research detailed in this paper seeks to characterise data Mesh readiness by examining the elements that influence the adoption choice using the technology-organization-environment (TOE) paradigm. A survey and a set of interviews were used in a multi-case and multi-method approach. Researchers and data triangulation were implemented to ensure rigour and arrive at a comprehensive understanding of data Mesh adoption. The obtained results demonstrate the successful adoption of data Mesh once its benefits are well understood, with increased teams' creativity, data accuracy, data security, data governance and interoperability.

关键词： data Architectures data Mesh Technology Adoption

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NearPM: A Near-data processing System for Storage-Class Applications 23

NearPM: A Near-Data Processing System for Storage-Class Appl...

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18th european conference on Computer Systems (EuroSys)

作者： Seneviratne, Yasas Seemakhupt, Korakit Liu, Sihang Khan, Samira Univ Virginia Charlottesville VA 22903 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781450394871

Persistent Memory (PM) technologies enable both fast memory access and recovery in case of a failure. To ensure crash-consistent behavior, programs need to enforce persist ordering and employ mechanisms that introduce additional data movements such as logging, checkpointing, and shadow-paging. The emerging near-data processing (NDP) architectures can effectively reduce this overhead. In this work, we propose NearPM, a near-data processor that accelerates common, primitive operations that are crucial to crash consistency. Using these primitives, NearPM accelerates commonly-used crash-consistency mechanisms. NearPM further reduces the synchronization overheads between the NDP and the CPU by handling ordering near memory. We propose Partitioned Persist Ordering (PPO) that ensures a correct persist ordering between CPU and NDP devices, as well as among multiple NDP devices. We prototype NearPM on an FPGA platform. NearPM executes the data-intensive operations of crash-consistency mechanisms with correct ordering guarantees, while the rest of the program runs on the CPU. We evaluate nine PM workloads, each implemented in three crash consistency mechanisms: logging, checkpointing, and shadow paging. Overall, NearPM achieves 4.3-9.8x speedup in the NDP-offloaded operations and 1.22 - 1.35x speedup in the whole applications.

关键词： Persistent Memory Persistency Near-data processing Accelerator

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Autoencoder-Based Attribute Noise handling Method for Medical data 1

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29th International conference on Neural Information processing

作者： Ranvier, Thomas Elgazel, Haytham Coquery, Emmanuel Benabdeslem, Khalid Univ Lyon UCBL CNRS INSA LyonLIRISUMR 5205 43 Bd 11 Novembre 1918 F-69622 Villeurbanne France

ISBN: (数字)9789819916450

ISBN: (纸本)9789819916443;9789819916450

Medical datasets are particularly subject to attribute noise, that is, missing and erroneous values. Attribute noise is known to be largely detrimental to learning performances. To maximize future learning performances, it is primordial to deal with attribute noise before performing any inference. We propose a simple autoencoder-based pre-processing method that can correct mixed-type tabular data corrupted by attribute noise. No other method currently exists to entirely handle attribute noise in tabular data. We experimentally demonstrate that our method outperforms both state-of-the-art imputation methods and noise correction methods on several real-world medical datasets.

关键词： data Denoising data Imputation Attribute Noise Machine Learning Deep Learning

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