dataesr/scientific-paragraphs-categorization huggingface dataset:
5 kHz using a combination of full and reduced event formats  [2]. The ambitious goal of the upgraded LHCb experiment in Run 3 (i. e. starting from 2022) is to remove the hardware trigger and directly process the full 30 MHz of data at an increased luminosity of 2 • 10 33 cm -2 s -1 in the HLT. At this luminosity, corresponding to a µ of around 6, it is no longer possible to efficiently identify bunch crossings of interest based purely on calorimeter and muon system information, as there is too much QCD background generated by the pileup pp collisions. [3]  It is necessary to instead fully read the detector out for every bunch crossing and fully perform the real-time processing in the HLT. This allows the much more sophisticated selections, in particular selections based on charged particle trajectories reconstructed in the whole of LHCb's tracker system, to be deployed already in HLT1.
Such a fully software trigger will not only allow LHCb to maintain its Run 1 and 2 efficiencies for muonic signatures, but will lead to a factor two improvement in efficiency for hadronic signatures compared to the calorimeter-based L0 trigger despite the harsher Run 3 environment. LHCb's Run 3 datataking conditions imply a data volume of around 32 Terabits (Tb) per second, comparable to what the ATLAS  [4]  and CMS  [5]  software triggers will be required to process during High-Luminosity LHC running from 2027 onwards. The design and delivery of LHCb's high-level trigger is therefore also one of the biggest computing challenges that the field of High Energy Physics is facing today. The closest current parallel to LHCb's system is that of the ALICE experiment  [6] , which will also operate a triggerless readout in Run 3 with an objective to reduce an input data rate of roughly 10 Tb/s to a manageable amount by performing a full detector reconstruction in real-time.
CPU-only architecture The CPU-only architecture is illustrated in Fig. 1. Briefly, it consists of • A set of custom FPGA cards, called TELL40, which together receive on average 32 Tb/s of data from LHCb's subdetectors; • A set of EB servers which host the TELL40 cards and implement a network protocol to bring the subdetector data fragments produced in a single LHC bunch crossing ("event" in LHCb nomenclature) together and then group O(1000) of these events into multi-event packets (MEPs) to minimize I/O overheads further down the line. The EB servers will be equipped with 32-core AMD EPYC (7502) CPUs; • An EFF illustrated on the bottom left of Fig. 1  which receives MEPs from the EB and executes HLT1. The memory available in the EB servers and HLT1 EFF nodes allows data to be buffered for O(20) seconds in case of temporary network or processing issues.
The HLT1 EFF servers are assumed to be equipped with the same 32-core AMD EPYC (7502) CPUs as the EB servers. When LHCb is not taking data this HLT1 EFF can also receive events from the disk servers (described below) and run the HLT2 process on them; • A high performance network, including high-speed network interface cards (NICs) located in the EB servers and EFF nodes as well as a large switch, connects the EB and HLT1 EFF and allows to transmit at a rate of 32 Tb/s. • The HLT1 process reduces the event rate by a factor of between 30-60, and an array of disk servers buffers this HLT1 output data while the detector alignment and calibration are performed in quasi-real-time. This disk buffer, whose size is a tunable parameter of the system as discussed later in Section 6, allows HLT1 output data to be buffered for O(100) hours in case of problems with the alignment and calibration which require specialist intervention; • A second EFF receives events from the disk servers once the alignment and calibration constants are available and runs the HLT2 process on them.
Hybrid architecture The hybrid architecture is illustrated in Fig. 2. It follows the same processing logic as the CPU-only solution: the full detector data is received by TELL40 boards and assembled into MEP packets by the EB servers, those MEP packets are then sent for processing by HLT1 and the events selected by HLT1 are recorded to the disk buffer for later processing by HLT2. Compared to the CPU-only solution it replaces the HLT1 EFF by GPU cards running HLT1 which are installed in the spare PCI express slots available in the EB servers. This allows HLT1 to reduce the data rate at the output of the EB by a factor of 30-60. This reduction in turn allows communication between the EB and EFF using a lower bandwidth (and consequently cheaper) network and removes the need to buy and install dedicated NICs in the EB and HLT1 EFF servers as they are already equipped with on-board 10Gb interfaces.
Acknowledgements This version of the article has been accepted for publication, after peer review, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi. org/10. 1007/s41781-021-00070-2. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MEiN  and  NCN UMO-2018/31/B/ST2/03998  (Poland);  MEN/IFA (Romania) ;  MSHE (Russia) ;  MICINN (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA).
The present study has the main purpose to experimentally investigate a turbulent momentum jet issued in a basin affected by rotation and in presence of porous obstructions. The experiments were carried out at the Coriolis Platform at LEGI Grenoble (FR). A large and unique set of velocity data was obtained by means of a Particle Image Velocimetry measurement technique while varying the rotation rate of the tank and the density of the canopy. The main differences in jet behavior in various flow configurations were assessed in terms of mean flow, turbulent kinetic energy and jet spreading. The jet trajectory was also detected. The results prove that obstructions with increasing density and increased rotation rates induce a more rapid abatement of both jet velocity and turbulent kinetic energy.
For the sake of brevity, experiments without rotation carried out as control tests, are not described in the present study, as they showed typical and expected trends (Ben  Meftah et al. 2015; Mossa and De Serio 2016; Mossa et al. 2017). Please refer to De  Serio et al. (2019)  for details. The adopted  modus operandi for processing the PIV data from all examined experiments was as follows. The PIV images acquired by each camera were successively coupled and correlated, to obtain the PIV measured velocity fields in the horizontal plane. As for the PIV images, velocities along the X direction are named u, while those in the Y direction are named v. The open-source ParaPIV software  (Wang 2018)  running in Matlab® was used to process the acquired images. Extensive parallelization (16 cores) was implemented to reduce computational times.
Conclusion The present study means to broaden a more thorough knowledge of momentum jets issued in obstructed environments, under the effect of Coriolis force. The experiments were carried out in the large-scale Coriolis platform at LEGI Grenoble (FR), where a large set of velocity data was obtained by means of a PIV system while varying the rotation rate of the tank and the density of the obstructions. The main results of this experimental analysis can be summed up as in the following. The rotation evidently affects the jet, inducing its propagation trajectory curving; at the same time, the jet velocity reduces with increasing distance from the outlet. Presence of obstructions array determines a more rapid decrease of the velocity magnitude along the jet trajectory. The rate of velocity decrease is higher when the array density is higher; while for equal array density, it is higher at greater tank rotation rates, thus manifesting the effect of Coriolis on the jet inertia.
Acknowledgments The experiments were carried out in the frame of the project  H+-CNRS-06 -JEVERB that received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 654110 , HYDRALAB+. All the authors are grateful to dr. Elvira Armenio  and dr. Annalisa Di Leonardo  for their cooperation during the execution of the experiments.
g. , mediating activation, suppression, modulation). While AC is typically measured in regard to monosynaptic connections, interareal neuronal interactions can extend to di-and polysynaptic interactions. While much of anatomical tract tracing, including the data used here, is focused on cortico-cortical connections, neuronal interactions may also use subcortical pathways  (Guillery and Sherman, 2002). Interareal neuronal interactions are subserved by neuronal entrainment and synchronization  (Bosman et al. , 2012; Brovelli et al. , 2004; Gregoriou et al. , 2009; Grothe et al. , 2012; Lobier et al. , 2018; Siegel et al. , 2008). Interareal neuronal synchronization can be assessed by local field potential (LFP) coherence, and entrainment can be assessed by LFP Granger causality (GC). Both coherence and GC can be determined per frequency, resulting in coherence or GC spectra.
, 2014a). Here, we establish the full variability in interareal power correlation, coherence, and GC across all pairs of simultaneously recorded sites and brain areas, which we directly relate to the full variability in interareal anatomical projection strength across those area pairs. We use a unique high-resolution micro-electrocorticography (mECoG) dataset providing simultaneous LFP signals from 218 recording sites distributed across 15 areas, in 2 awake macaques. The complete 218 3 218 matrices of power correlation, coherence, and GC revealed the respective interaction networks to consist of clearly defined modules, and that the coherence and GC networks are independent of the underlying power distributions. Intriguingly, those interaction networks agree partly for some pairs of frequency bands, while differing markedly between others, as observed in human subjects  (Williams et al.
In this way, FLN reflects the fraction of neurons projecting to A that originates in B. While the ma-jority of projections to a given cortical area arises from within the area itself ($80%), we are concerned here with projections arising from other areas and so estimate the extrinsic fraction of labeled neurons (FLNe)  (Markov et al. , 2011). We were interested in how interareal FC, assessed by coherence, power correlation, and GC, relates to interareal AC, assessed by FLNe. We used a dataset based on 28 retrograde tracer injections across 14 cortical areas (Figure  4A ). These 14 areas were identical to the 15 areas recorded electrophysiologically, except that they did not include the temporo-parietal area (TPt). The 14 areas resulted in a 14 3 14 matrix of 182 FLNe values (Figure  4C ). Note that this is a directed matrix of AC, in which FLNe from area A to area B is quantified independently of the FLNe in the reverse direction.
Data analysis All analyses were performed in MATLAB (MathWorks) using FieldTrip (https://www. fieldtriptoolbox. org) (Oostenveld et al. , 2011)  and custom scripts. Except otherwise noted, analyses used data recorded during the Post-cue period (as defined above, in the description of the visual attention task; see also Figure  1A ). The first 0. 3 s after cue onset were discarded to minimize cue-related transients. The remaining data until the first change of one of the stimuli (either target or distractor) were segmented into non-overlapping epochs of 1 s length. The exception to this are the analyses presented in Figures  S5A, S7A , and S7B. These analyses include data from the Baseline period, which was merely 0. 8 s long, and, after discarding 0. 3 s of post-fixation transients, left merely 0.
Data epochs of 1 s length were multitapered using three Slepian tapers  (Mitra and Pesaran, 1999)  and Fourier-transformed (using the FieldTrip function ''ft_freq_analysis'' with the configuration option ''mtmfft''), resulting in a spectral resolution of 1 Hz and a spectral smoothing of ± 1. 5 Hz. Data epochs of 0. 5 s length were multitapered using three Slepian tapers, zero-padded to 1 s, and Fourier-transformed (same FieldTrip approach), resulting in an interpolated spectral resolution of 1 Hz and a spectral smoothing of ± 3 Hz. The Fourier transforms were the basis for calculating the FC spectra, i. e. , coherence spectra  (Baker et al. , 1997) , spectra of power correlation across epochs  (Bruns et al. , 2000)  and the GC spectra. These FC spectra were first calculated per monkey, across all epochs of a given condition (Post-cue, Attended, Unattended, Baseline), and subsequently averaged over the two monkeys.
Retrograde tracing database Acquisition and analysis of the anatomical dataset has been reported in  (Chaudhuri et al. , 2015; Markov et al. , 2014a; Molnar et al. , 2020). Updates, atlases and additional information concerning the anatomical dataset that was used for this work is available at http://core-nets. org. We used the fraction of labeled neurons (FLNe, defined in the Results section) to quantify AC strength. We used the proportion of supragranular labeled neurons (SLN, also defined in the Results section) to quantify the feedforward or feedback nature of an anatomical projection. Furthermore, we used interareal white-matter distances. For comparison of FC with AC, we selected areas and the corresponding site pairs of the ECoG grids, if they were also injected with retrograde tracers.
We performed regression analyses for each of the 100 samples per predictor variable estimated from bootstrap over epochs, except for distance measures which do not change across trials. Modularity analysis was performed using the latest version of the Brain Connectivity toolbox and the modularity, agreement and consensus functions  (Rubinov and Sporns, 2010). For each of the three FC metrics separately, we computed a consensus community structure using the agreement matrix obtained from the concatenated degenerate partitions across the four frequency bands. This allowed to compare FC distributions between frequency bands, over the same set of modules (Figures  2  and S2 ). Degenerate partitions were obtained for each frequency band and FC metric by varying the resolution parameter between 0.
1-10 (the classical resolution parameter value being 1, smaller values detecting larger modules and higher values detecting smaller modules). The consensus partition was computed with a re-clustering resolution of 0. 25 (proportion of resolution parameters, for which any two vertices were assigned to the same class, across all four frequency bands), reapplied 100 times on the agreement matrix. The modularity values (q) reported on the margins of the matrices in Figures  2  and S2  were obtained with the classical resolution parameter of 1. All violin plots use bootstrap estimates over trials, their shape along the y axis uses a kernel density estimate with a self-optimized bandwidth of the density kernel (https://github. com/bastibe/Violinplot-Matlab/blob/master/violinplot. m). Statistical significance for average FC strength maps (Figure  3 ) was calculated by comparing experimentally obtained values for each site to values resulting from permutation of labels across sites i.
ACKNOWLEDGMENTS This work was supported by the  DFG  ( SPP 1665 FR2557/1-1 ,  FOR 1847 FR2557/2-1 ,  FR2557/5-1-CORNET ,  FR2557/6-1-NeuroTMR , and  FR2557/7-1-DualStreams , to P. F. ), the  EU  ( HEALTH-F2-2008-200728-BrainSynch ,  FP7-604102-HBP , and  FP7-600730-Magnetrodes , to P. F. ), a  European Young Investigator Award  (to P. F. ), the  National Institutes of Health  ( 1U54MH091657 -WU-Minn-Consortium-HCP , to P. F. ), the  LOEWE program  (NeFF, to P. F. ), the  ANR  ( LABEX  CORTEX  ANR-11-LABX-0042 ,  Universite de Lyon   ANR-11-IDEX-0007 ,  A2P2MC ANR-17-NENC-0004 ,  CORTICITY ANR-17-HBPR-0003 ,  RE-GION AUVERGNE-RHONE-ALPES SCUSI 1700933701 , to H. K. ), the  ERC  (Starting Grant SPATEMP, to M. V. ), and the  BMBF (BINDA ,  031L0167 , to M. V. ). The authors would like to thank  Olaf Sporns  for thoughtful discussions and advice on modularity analysis,  Jarrod Dowdall  for helpful support at some stages of the analysis, and  Georgios Spyropoulos  for insightful discussions on earlier versions of the manuscript.
Sown seeds were kept in the dark at 4°C for 72 h to break dormancy, and afterwards were grown at 22°C and Photosynthetically Active Radiation (PAR) of 100 lmol m À2 s À1 under short day (SD, 10 h : 14 h, light : dark), long day (LD, 16 h : 8 h, light : dark), equinoctial (12 h : 12 h, light : dark) or free-running (LL) photoperiods. For transcriptome analysis and validation of misregulated genes by quantitative reverse transcription PCR (qRT-PCR) analysis, total RNA was extracted from the four youngest leaves and the shoot apical meristems of plants grown under SD conditions for 25 d, and harvested at the end of the night (EN) immediately before the onset of the light, because bZIP63 is most expressed in meristem and young leaves  (Weltmeier et al. , 2009)  and at EN (DIURNAL Database: http://diurnal.
Microarray and data analysis Three biological replicates of each genotype (Wassilewskija (Ws) and bzip63-2) were used for transcriptome analysis. Each replicate was composed of a pool of the four youngest leaves and    (Contento et al. , 2004; Gibon et al. , 2004; Usadel et al. , 2008; Cookson et al. , 2016). (f) Clustering of energy stress associated genes misregulated in bzip63-2, induced by KIN10 ox and by various C starvation conditions and selected for validation in bzip63-3 through quantitative reverse transcription PCR. shoot apical meristem harvested from five plants. Hybridization on the GeneChip Arabidopsis ATH1 array was done in the LNBio (Brazilian Biosciences National Laboratory, Campinas, Brazil) Microarray Facility (LMA). Robust Multi-array Average (RMA) normalization was performed using Expression Console TM (Affymetrix) and statistical data analysis was performed using the AFFYLMGUI R package (R Core Team, 2018).
A Pvalue < 0. 05 was used as the cutoff for the selection of differentially expressed genes and expression level between genotypes. Overlaps with previously available transcriptome data were carried out using the webtool Venn Diagram (http://bioinformatics. psb. ugent. be/webtools/Venn/), and statistical significance of overlaps were estimated using a web-based software designed by Jim Lund (University of Kentucky), with statistical significance quantified using a hypergeometric test  (Kim et al. , 2001 ; http:// nemates. org/MA/progs/overlap_stats. cgi). The heatmap in Fig. 1 (f) was obtained using MULTIEXPERIMENT VIEWER  (Saeed et al. , 2003)  and over-representation of biological pathways were analyzed using MAPMAN  (Thimm et al. , 2004). Microarray data are available at GEO (accession number GSE119175).
The average amount of starch and maltose for each time point were measured from two independent experiments, with five biological replicates, and each replicate consisted of leaves collected from six 30-d-old plants (twotailed Student's t-test; n = 60 for starch and 30 for maltose; *, P < 0. 05; **, P < 0. 01; and Wilcoxon's test # , P < 0. 05). (e) Absolute transcript levels of starch degradation-related genes STARCH EXCESS1 (SEX1), PHOSPHOGLUCAN, WATER DIKINASE (PWD) and DISPROPORTIONATING ENZYME 2 (DPE2), are higher in bzip63-2 and bzip63-3 at the end of the night (EN). Data were obtained from three independent experiments, with three biological replicates, and each replicate consisted of four youngest leaves and the shoot apical meristems collected from five 25-d-old plants, differences were tested performing ANOVA and the different letters indicate groups that are significantly different (Tukey's honestly significant difference (HSD) test, P < 0.
Data were obtained from four independent experiments for HA-bZIP63-ox1 and seven for HA-bZIP63-ox2 (Student's t-test; *, P < 0. 05) for primer  pairs 1  and 2,  and one   area and the number of the rosette leaves were performed in an Eclipse 80i light microscope (Nikon, Tokyo, Japan). Samples were fixed using methanol, then cleared using lactic acid and mounted for analysis. The number of epidermal cells was obtained by counting the cells inside an area taken from 10 different positions across the leaf, corresponding to c. 10% of the total leaf area (leaves 1 and 3), and the total leaf area were measured, and from these values, the number of cells in the leaf epidermis was extrapolated. The Kolmogorov-Smirnov test was performed to ensure that the data were normally distributed and then Student's t-test was performed to identify significant differences between the bzip63-2 and Ws (P < 0.
Acknowledgements This work was supported by the  São Paulo Research Foundation (FAPESP)  (grant nos. 2008/52071-0, 2012/09351-8 ,  2014/ 04117-2  and  2015/06260-0 ;  BIOEN Program ),  2018/25710-4  ( FAPESP/UKRI-BBSRC  cooperation agreements),  2019/25993-9  ( FAPESP/Max Planck Institute cooperation agreements)  and  Conselho Nacional de Desenvolvimento Cient ıfico e Tecnol ogico (CNPq)  (grants  313104/2017-4  and  405520/2016-6 ).
A Reevaluation of TEC Precursor Claims Starting from the results of  He and Heki (2016) , we analyzed the same South American earthquakes to reproduce their results showing the pre-seismic enhancement. We demonstrate the subjective nature of their proposed polynomial fits. In this paper, we analyze TEC observations from the 2014 Iquique and 2010 Maule events. The processing of the TEC data was done using the SPECTRE code  (Lognonné et al. , 2006)  as described by  Eisenbeis et al. (2019). We show that the presence of a hole or an enhancement in the comparison between the TEC and the reference curve calculated using a polynomial fit strongly depends on the degree of the polynomial, as well as on the time window used to calculate it. Figure  2  shows the results of our attempt to reproduce the reference curve of  He and Heki (2016)  for the LYAR station used in their study.
We first used (upper left panel) the full time series (from 21 UTC to 5 UTC), excluding the time window from 40 min before until 20 min after the event (gray area in Figure  2 ), to compute the polynomial fit of third and fifth degree following the specifications of  He and Heki (2016). The results, for both third and fifth degree, do not fit the TEC data well and do not show any enhancement. The best fit to the TEC data appears to be that using the polynomial fit of ninth degree (upper right panel), which does not conform to the specifications set by  He and Heki (2016) , but was used in their later papers (e. g. ,  He & Heki, 2017). As shown in the upper right panel of Figure  2 , despite the polynomial fit of ninth degree reproducing the TEC data well, no enhancement is visible but rather a hole after the earthquake.
Data Availability Statement This work is entirely based on freely available GNSS data from the following web-resources: UNAVCO (www. unavco. org) using the Data Archiver Web Interface (https://www. unavco. org/data/gps-gnss/data-access-methods/dai2/app/dai2. html#), This research was supported by Program National de Télédétection Spatiale (PNTS, http://programmes. insu. cnrs. fr/pnts/) Grant No. PNTS-2020-16; by the CNES, granted project Global Ionospheric Seismology network & background; and by the Institut Universitaire de France (IUF). This is IPGP contribution 4191.