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تسجيل مستخدم جديدEnd to end simulators: A flexible and scalable Cloud-Based architecture. Application to High Resolution Spectrographs ESPRESSO and ELT-HIRES
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Simulations of frames from existing and upcoming high-resolution spectrographs, targeted for high accuracy radial velocity measurements, are computationally demanding (both in time and space). We present in this paper an innovative approach based on both parallelization and distribution of the workload. By using NVIDIA CUDA custom-made kernels and state-of-the-art cloud-computing architectures in a Platform as a Service (PaaS) approach, we implemented a modular and scalable end-to-end simulator that is able to render synthetic frames with an accuracy of the order of few cm/sec, while keeping the computational time low. We applied our approach to two spectrographs. For VLT-ESPRESSO we give a sound comparison between the actual data and the simulations showing the obtained spectral formats and the recovered instrumental profile. We also simulate data for the upcoming HIRES at the ELT and investigate the overall performance in terms of computational time and scalability against the size of the problem. In addition we demonstrate the interface with data-reduction systems and we preliminary show that the data can be reduced successfully by existing methods.
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HIRES will be the high-resolution spectrograph of the European Extremely Large Telescope at optical and near-infrared wavelengths. It consists of three fibre-fed spectrographs providing a wavelength coverage of 0.4-1.8 mic (goal 0.35-1.8 mic) at a sp
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We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit fairseq. Espresso supports distributed
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The first generation of E-ELT instruments will include an optical-infrared High Resolution Spectrograph, conventionally indicated as EELT-HIRES, which will be capable of providing unique breakthroughs in the fields of exoplanets, star and planet form
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The Level 1 of the Planck LFI Data Processing Centre (DPC) is devoted to the handling of the scientific and housekeeping telemetry. It is a critical component of the Planck ground segment which has to strictly commit to the project schedule to be rea
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Recently, Transformer has gained success in automatic speech recognition (ASR) field. However, it is challenging to deploy a Transformer-based end-to-end (E2E) model for online speech recognition. In this paper, we propose the Transformer-based onlin
e CTC/attention E2E ASR architecture, which contains the chunk self-attention encoder (chunk-SAE) and the monotonic truncated attention (MTA) based self-attention decoder (SAD). Firstly, the chunk-SAE splits the speech into isolated chunks. To reduce the computational cost and improve the performance, we propose the state reuse chunk-SAE. Sencondly, the MTA based SAD truncates the speech features monotonically and performs attention on the truncated features. To support the online recognition, we integrate the state reuse chunk-SAE and the MTA based SAD into online CTC/attention architecture. We evaluate the proposed online models on the HKUST Mandarin ASR benchmark and achieve a 23.66% character error rate (CER) with a 320 ms latency. Our online model yields as little as 0.19% absolute CER degradation compared with the offline baseline, and achieves significant improvement over our prior work on Long Short-Term Memory (LSTM) based online E2E models.
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