De Guibert Pierre · Harvard Dataverse · 2022
The Level 1 (L1) SAETTA data consist in time and space locations of VHF sources that are radiated by lightning flashes in the 60-66 MHz VHF band. The L1 SAETTA data are processed from the L0 SAETTA raw data collected by the 12 sensors deployed over Corsica. Each VHF source is described by its time occurrence, its geo-location, its VHF amplitude, and two quality control parameters. The L1 SAETTA data are stored per 10-min period in ASCII format, and are archived per year, then per month, and finally per day.
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Emily L. Coderre (605359), Walter J. B. van Heuven (400667) · Figshare · 2015
<p>Bilinguals L1 Chinese waveforms at Cz and Pz for the a) 0 ms SOA; and b) −400 ms SOA. Significant effects from the running <i>t</i>-tests, within the N<sub>inc</sub> windows (shaded area), are indicated in bars underneath. Topographic maps show the N<sub>inc</sub> components, plus electrodes showing significant differences (<i>p</i><0.05) between incongruent and congruent trials across the specified window.</p>
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Pierre, de Guibert, Sylvain, Coquillat · AERIS - Pôle français de données et services pour l'atmosphère · 2021
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Xu, Huilin · Mendeley Data · 2026
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Boyer, Alexandre · Borealis · 2020
Supplemental data L1,L2,Nr5a1
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Shiwei YU · Figshare · 2020
Time: 04:00 UT on 22 August 2017
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Uschi Cop, Denis Drieghe · Figshare · 2015
Sentence level eye movement data for late Dutch-English bilinguals readign a complete novel in Dutch. Legend in second sheet.
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Uschi Cop, Denis Drieghe · Figshare · 2015
Sentence level eye movement data for late Dutch-English bilinguals readign a complete novel in Dutch. Legend in second sheet.
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Shiwei Yu · Figshare · 2020
Time: 04:00 UT on 22 August 2017
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Emma Ellen Kathrina BUCHTEL · OSF Preprints (OSF Preprints) · 2020
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Bessho, K. · TIB Data Manager · 2024
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Ankur Kath, Neha Paranjape, Sachin Kulkarni et al. · 2014
Cache performance can be improved by predicting what data will need to be present in the L1 cache. The prediction system will predict the target address of a future load or store instruction and will fetch that data into a small buffer cache a few cycles before the actually instruction is committed. There can be a window between two memory instructions wherein no memory operation takes place. This time can be used to pre-load the predicted address. There isn&apos;t a data correctness issue here because, if the prediction turns out to be incorrect where there will be a cache miss on the L1 Cache and it should take the same execution time that it does with the current architecture. 1.
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Daniel B. Seaton, Team Swap · 2012
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Jaakko Nevalainen, Denis Larocque · 2015
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Ιωάννης Δημητρίου · International Transactions in Operational Research · 1994
Abstract Let us consider n data measurements of a univariate process that have been altered by random errors. We assume that an underlying model function has a substantially smaller number of turning points than the observed ones. We propose algorithms that make least the sum of the moduli of the errors by requiring k monotonic sections, altered increasing and decreasing, in the sequence of the smoothed values. The main difficulty in this calculation is that the optimal positions of the joins of the monotonic sections have to be found automatically among so many combinations that it is impossible to test each one separately. Moreover, the calculation seems to be very intractable to general optimization techniques because O ( n k ) local minima can occur. It is shown that dynamic programming can be used for separating the data into optimal disjoint sections of adjacent data, where each section requires a single L 1 monotonic calculation. This procedure is highly efficient, requiring at most O ( k n ) computer operations and O ( n ) bes L 1 monotonic calculations to subranges of data for a global minimum.
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Sieder, Leah-Louisa · 2024
The CMS Experiment at the CERN Large Hadron Collider (LHC) relies on a Level-1 Trigger system (L1T) to process in real time all potential collisions, happeing at a rate of 40 MHz, and select the most promising ones for data acquisition and further processing. The CMS upgrades for the upcoming high-luminosity LHC run will vastly improve the quality of the L1T event reconstruction, providing opportunities for a complementary Data Scouting approach where physics analysis is performed on a data stream containing all collisions but limited to L1T reconstruction. This poster describes the future Data Scouting system, some first estimates of its physics capabilities, and the demonstration setups used to assess its technical feasibility. This work has been funded by the Eric & Wendy Schmidt Fund for Strategic Innovation through the CERN Next Generation Triggers project under grant agreement number SIF-2023-004.
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