dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, a gain filter is used to correct for local variations of the gain, arising either from detector imperfections or the electronics themselves. A tail cancellation filter can be used to suppress the ion tails. The filtered data are fed into a pre-processor which contains hardware units for the cluster finding. The four CPUs (MIMD architecture) are used for the further processing. The local tracking procedure is discussed in detail in Section 12. 1. The MCMs are mounted on the ROB. On each board, 16 chips are used to sample and process the detector signals. A full detector chamber is covered by 8 ROBs (6 for chambers in stack 2). The read-out is organised in a multi-level tree. First, the data from four chips are collected by so-called column merger chips. The latter, in addition to processing the data from their own inputs, receive the data from three more MCMs.
Detector Control System The purpose of the DCS is to ensure safe detector conditions, to allow fail-safe, reliable and consistent monitoring and control of the detector, and to provide calibration data for offline reconstruction. In addition it provides detailed information on subsystem conditions and full functionality for expert monitoring and detector operation. Tools were implemented to reduce the operational complexity and the information on detector conditions to a level that allows operators to monitor and handle the detector in an intuitive and safe way. The TRD DCS is integrated with the rest of the ALICE detector control systems into one system which is operated by one operator. 18 : Overview of the DCS software architecture. The tree structure consists of device units (boxes) and logical control units (ellipses).
Architecture The hardware architecture of the DCS can be divided into three functional layers. The field layer contains the actual hardware to be controlled (power supplies, FEE, etc). The control layer consists of devices which collect and process information from the field layer and make it available to the supervisory layer. Finally, the devices of the control layer receive and process commands from the supervisory layer and distribute them to the field layer. The software on the supervisory layer is distributed over 11 server computers. It is based on the commercial Supervisory Control and Data Acquisition (SCADA) system PVSS II from the company ETM  [79] , now called Symatic WinCC  [80]. The implementation uses the CERN JCOP control framework  [81] , shared by all major LHC experiments.
This framework provides high flexibility and allows for easy integration of separately developed components in combination with dedicated software developed for the TRD, including Linux-based processes. The software architecture is a tree structure that represents (sub-)systems of the detector and its devices, as shown in Fig. 18. The entities at the bottom of the hierarchy represent the devices (device units), logical entities are represented by control units. The DCS system monitors and controls 89 low voltage (LV) power supplies with more than 200 channels, and 1044 high voltage channels. The system also monitors the electronics configuration of more than one million read-out channels, the GTU, and the cooling and gas systems.
These are a few examples: -In case of a failure of the cooling plant for the FEE, a PLC-based interlock disables the LV power supplies. -The temperature of the FEE is monitored at the control and supervisory level and interlocked with the PCU to switch off the devices in case of overheating or loss of communication to the SCADA system. -In case of a single LV channel trip, the corresponding FEE channels are consistently switched off. -Unstable LHC beam conditions, e. g. during injection or adjustment of the beam optics, pose a potential danger to gas-filled detectors. Therefore the HV settings are adapted to the LHC status (see Section 7. 2). At injection, the anode voltages are decreased automatically to an intermediate level to reduce the chamber gain. Restoring the nominal gain is inhibited until the LHC operators declare stable beams via a data interchange protocol.
High voltage The HV system comprises 36 HV modules in 5 crates. The 1044 HV channels, 1 of each polarity providing anode and drift voltage to each chamber, are controlled via a 250 kbit/s CAN bus through a dedicated Linux-based DIM server  [82]. The published DIM services, commands and remote procedure calls (RPC) resemble the logical structure of items used in commercial process control servers: the command to change a setting is confirmed by the server via a read back setting. In addition, the actual measured value from the device is published. Update rates for different services can be adjusted independently. The HV gain and drift velocity are equilibrated for each chamber individually to compensate for small differences in the chamber geometry. Changes of environmental conditions (atmospheric pressure and temperature) as well as small variations of the gas composition cause changes in gas gain and drift velocity.
e. the right to execute FSM commands and change the detector state, is only granted to a single operator at a time, and is represented by symbolic 'locks'. Operators can work on-site or access the DCS system remotely through appropriate gateways. The monitoring data acquired by the DCS system are stored in dedicated databases. Dedicated trending GUIs allow the experts to visualise the time dependence of the detector conditions. During data taking, the monitoring data needed for detector calibration is queried and made available for offline analysis (see Section 7. 3).
Errors observed during those tests, e. g. bitflips on individual connections on a read-out board, were cured by switching to spare lines or by masking channels from the read-out if a correction was not possible. After establishing the read-out, pedestal runs (without zero suppression) were recorded to determine the baseline and noise of each channel. If needed, further data were recorded to perform a Fourier analysis in order to identify and fix noise sources, e. g. caused by missing ground connections. In addition, these runs were used to identify inactive channels which cannot be read out. After each installation block of new supermodules, a dedicated calibration run was performed before the actual data taking. The detector is read out with radioactive 83m Kr distributed through the gas system (see Section 10.
2). Since this was usually the first high-rate data taking after the end-of-year shutdown (and installation), these runs and the preparations for them were an important step to get ready for the real data taking. Before each physics production run, periods of cosmic-ray data taking were scheduled to study the performance of the detector system, to align individual detector components (see Section 9. 1) and to provide reference spectra for particle identification (see  Section 11). Data were obtained with and without magnetic field. A two-level trigger condition was used to ensure sufficient statistics in the detector acceptance, even when only the first supermodules were installed in the horizontal plane (see Section 12. 3).
The detector contributed the same triggers as in the pp running scenario. -Pb-Pb collisions at √ s NN = 2. 76 TeV and 5. 02 TeV with maximum interaction rates of up to 8 kHz (minimum-bias and rare triggering). At the beginning of a fill, once all detectors within ALICE are ready for data taking, a global physics run is started. A run is defined in ALICE as an uninterrupted period of data taking, during which the conditions (trigger setup, participating detectors, etc. ) do not change. A run can last from a few minutes to several hours until either the experimental setup or conditions have to be changed or the beam is dumped. An additional end-of-run (EOR) reason is given by the occurrence of a problem related to a given detector or system. The detector parameters measured during a run, such as the voltages and currents of the anode and drift channels as well as temperatures of the FEE, are dumped at the EOR to the Offline Conditions Database (OCDB) via the Shuttle framework  [86, 87].
The relevant parameters can then be used in the offline reconstruction and analysis. In order to ensure sufficiently stable conditions during a run, any change, such as the failure of a part of the detector, e. g. due to a LV/HV trip, triggers the ending of the run. In order to avoid too frequent interruptions, the failure of a single chamber within a stack is ignored. Technically, this is realised using the so-called Majority Unit within DCS. All subcomponents of the TRD detector (infrastructure and gas system) are monitored via DCS (see Section 6). In case any entity deviates from nominal running conditions by pre-defined thresholds a warning is issued. The single entity is either recovered by the DCS operator in the ALICE Run Control Centre or by an expert intervention. During Run 1 data taking, most interventions were related to the recovery of single event upsets (SEU) and HV trips of problematic channels by re-configuration of the FEE or ramping up of the anode/drift channels.
Data quality assurance The Data Quality Monitoring framework (DQM) provides online feedback on the data and allows problems to be quickly spotted and identified during data taking. The Automatic MOnitoRing Environment (AMORE) was developed for ALICE  [95]  and allows run-based, detector-specific analyses on the raw data. The results are visualised in a dedicated user interface. The monitored observables, such as noise level, event size per supermodule, trigger timing, FEE not sending data, are compared with reference values or diagrams (depending on the data taking scenario). Deviations from the references indicate a problem to the operator. Based on the information obtained from the online DQM all runs are directly marked with a quality flag, both globally and for the individual ALICE subdetectors.
Alignment The physical alignment of the detectors during installation (see Section 2. 3) has a finite precision of the order of 1 mm for chambers within a supermodule and of 1 cm for supermodules in the spaceframe. The subsequent software alignment, i. e. accounting for the actual positions of supermodules and chambers in the reconstruction and simulation software, is the subject of this section. The alignment parameters (three shifts and three rotation parameters per alignable volume) are deduced from optical survey data and/or from reconstructed tracks. In the latter case, the obtained values have to be added to those already used during the reconstruction. The obtained alignment sets are stored in the OCDB and used in the subsequent reconstructions. The different alignment steps are described in the following subsections.
Pad noise and pad status calibration using pedestal runs Short pedestal runs are taken roughly once per month during data taking. In these runs, events are triggered at random instants and the data are recorded without zero suppression. At the end of the run, an automatic analysis of the pedestal data is performed on the computers of the DAQ system  [100]. Hundred events are sufficient to calculate the position of the baseline of the analogue pre-amplifier and shaper output (pedestal) and its fluctuation (noise) for all electronics channels. The results are subsequently collected by the Shuttle system  [87]  and transported to the OCDB. The mean noise is 1. 2 ADC counts, corresponding to an equivalent of 1200 electrons. The pad-by-pad r. m. s. value is 0. 17 counts. The precision of the measurement is 0.
015 counts (r. m. s. ). Pads that have a faulty connection to the FEE, are connected to a non working FEE channel, have excessive noise, or are bridged with a neighbour are marked in the OCDB and treated correspondingly during the data taking and reconstruction chain (pad status). 10. 2 Pad gain calibration using 83m Kr decays Pad-by-pad gain calibration of the TRD chambers is performed after every installation of new supermodules. It is done by injecting radioactive gas into the chambers and measuring the signals of the decay electrons. The method, developed by ALEPH  [101, 102]  and DELPHI  [103] , is also used to calibrate the ALICE TPC  [31]. Solid 83 Rb decays by electron capture into gaseous Kr and populates, among others, the isomeric state 83m Kr with an excitation energy of 41. 6 keV and a half-life of 1.
The correlation is fitted by a straight line. The effect of the pad tilt (dy/dz = tan(α), α = ± 2 • , see Section 2) is taken into account by adding the respective term to the global track inclination. The slope and the offset parameters give the drift velocity and the Lorentz angle, respectively. The gain calibration factor is determined by histogramming, for each chamber, the deposited charge divided by the path length and taking the mean of this distribution. The last stage of the chamber calibration is to identify chambers for which a satisfactory calibration cannot be obtained or whose parameter values are very different from the mean. These chambers are masked in the data analysis and in the respective simulation. The typical mean values, chamber-by-chamber variations, stability, and precision of the calibration parameters are shown in Table  8.
Since the reference creation requires a large data sample, the reference distributions are only produced after the full physics reconstruction pass. This means that the reference creation can only be done later, during data analysis rather than already during reconstruction. The references for the truncated mean and the likelihood methods are stored for this purpose in the Offline Analysis Database (OADB) and read from there in the initialisation phase of the analysis tasks  [115]. The pion efficiency for 1 GeV/c tracks is shown as a function of the electron efficiency and as a function of the number of detector layers providing signals for the various methods in Fig. 42. For all methods the pion rejection factor decreases as expected with decreasing number of contributing layers and a lower electron selection efficiency corresponds to a better pion rejection factor for all methods.
Acknowledgements The ALICE Collaboration would like to acknowledge the significant contributions to the  TRD  project given by:  A. Anjam ,  W. Amend ,  V. Aprodu ,  H. Baumeister ,  C. Dinca ,  N. Heine ,  T. Herold ,  D. Moisa ,  H. Hinke ,  H. Höbbel ,  V. Kiworra * , L. Prodan ,  A. Radu ,  W. Verhoeven ,  A. Wiesenäcker  and the  CERN  gas group of EP-DT. The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A.
5 mW/channel TRAP ADC depth 10 bit TRAP sampling frequency 10 MHz [78]differential PASA outputs are fed into the ADCs of the TRAP, the second ASIC on the same MCM. The PASA and TRAP parameters are listed in Table4. The TRAP is a custom-designed digital chip produced in the UMC 0. 18 µm process. The TRAP comprises cycling 10-bit ADCs for 21 channels, a digital filter chain, a hardware preprocessor, four two-stage pipelined CPUs with individual single-port, Hamming-protected instruction memories (IMEM, 4k x 24 bit), about 400 configuration registers usable by the hardware components, a quad-port Hamming-protected data memory (DMEM, 1k x 32 bit), and an arbitrated Hamming-protected data bank (DBANK, 256 x 32 bit)[78]. Three excess ADC channels are fed with the amplified analogue signal from the two adjacent MCMs to avoid tracking inefficiencies at the MCM boundaries.
We argue that in scene graph generation, it is unnecessary to require a parent word for every single word. We build our model on top of a neural depen-dency parser implementation  (Kiperwasser and Goldberg, 2016)  that is among the state-of-theart. We show that our carefully customized dependency parser is able to generate high quality scene graphs by learning from data. Specifically, we use the Visual Genome dataset  (Krishna et al. , 2017) , which provides rich amounts of region description -region graph pairs. We first align nodes in region graphs with words in the region descriptions using simple rules, and then use this alignment to train our customized dependency parser. We evaluate our parser by computing the F-score between the parsed scene graphs and ground truth scene graphs. We also apply our approach to image retrieval to show its effectiveness.
Scene Graphs The scene graph representation was proposed in  Johnson et al. (2015)  as a way to represent the rich, structured knowledge within an image. The nodes in a scene graph represent either an object, an attribute for an object, or a relationship between two objects. The edges depict the connection and association between two nodes. This representation is later adopted in the Visual Genome dataset  (Krishna et al. , 2017) , where a large number of scene graphs are annotated through crowd-sourcing. The scene graph representation has been proved useful in various problems including semantic image retrieval  (Johnson et al. , 2015) , visual question answering  (Teney et al. , 2016) , 3D scene synthesis  (Chang et al. , 2014) , and visual relationship detection  (Lu et al. , 2016). Excluding  Johnson et al.
Sentence-Graph Alignment We have shown the connection between nodes in scene graphs and words in dependency parsing. With alignment between nodes in scene graphs and words in the textual description, scene graph generation and dependency parsing becomes equivalent: we can construct the generated scene graph from the set of labeled edges returned by the dependency parser. Unfortunately, such alignment is not provided between the region graphs and region descriptions in the Visual Genome  (Krishna et al. , 2017)  dataset. Here we describe how we use simple yet reliable rules to do sentence-graph (word-node) alignment. There are two strategies that we could use in deciding whether to align a scene graph node d (whose label space is O ∪ A ∪ R) with a word/phrase w in the sentence: • Word-by-word match (WBW): d ↔ w only when d's label and w match word-for-word.
Implementation Details We train and evaluate our scene graph parsing model on (a subset of) the Visual Genome  (Krishna et al. , 2017)  dataset. Each image in Visual Genome contains a number of regions, and each region is annotated with both a region description and a region scene graph. Our training set is the intersection of Visual Genome and MS COCO  (Lin et al. , 2014)  train2014 set, which contains a total of 34027 images/ 1070145 regions. We evaluate on the intersection of Visual Genome and MS COCO val2014 set, which contains a total of 17471 images/ 547795 regions. In our experiments, the number of hidden units in BiLSTM is 256; the number of layers in BiL-STM is 2; the word embedding dimension is 200; the number of hidden units in MLP is 100. We use fixed learning rate 0. 001 and Adam optimizer (Kingma and Ba, 2014) with epsilon 0.
Application in Image Retrieval We test if the advantage of our parser can be propagated to computer vision tasks, such as image retrieval. We directly compare our parser with the Stanford Scene Graph Parser  (Schuster et al. , 2015)  on the development set and test set of the image retrieval dataset used in  Schuster et al. (2015)  (not Visual Genome). For every region in an image, there is a humanannotated region description and region scene graph. The queries are the region descriptions. If the region graph corresponding to the query is a subgraph of the complete graph of another image, then that image is added to the ground truth set for this query. All these are strictly following  Schuster et al. (2015). However, since we did not obtain nor reproduce the CRF model used in  Johnson et al.
Acknowledgments The majority of this work was done when YSW and XZ were visiting  Johns Hopkins University. We thank  Peter Anderson ,  Sebastian Schuster ,  Ranjay Krishna ,  Tsung-Yi Lin  for comments and help regarding the experiments. We also thank  Tianze Shi ,  Dingquan Wang ,  Chu-Cheng Lin  for discussion and feedback on the draft. This work was sponsored by the  National Science Foundation Center for Brains, Minds, and Machines NSF   CCF-1231216. CL also acknowledges an award from  Snap Inc.
Introduction Over the last decade, significant research efforts have been devoted to modeling sediment transport using two-phase flow models either with Eulerian-Lagrangian or Eulerian-Eulerian approaches. Contrary to classical sediment transport models relying on empirical formula for the bed-load and pickup flux for suspended-load  [1] , these new models are based on physical grounds allowing for in-depth investigation of sediment transport mechanisms at the intermediate scale, i. e. ranging between the particle scale and the mean flow length scales. Recently, we have developed a community multi-dimensional Eulerian two-phase flow sediment transport model, sedFOAM, under the platform OpenFOAM (https://github. com/sedfoam/sedfoam) [2, 3]. The aim of this contribution is to present some recent developments and the application of the model to scour around structures.
Acknowledgements MD thanks the  U. K's STFC  for financial support via grant  ST/P000274/1. MD also thanks the CERN theoretical physics department for a scientific associateship and for hospitality during the course of this work as well as the  School of Physics and Astronomy at the University of Manchester  for sabbatical leave which facilitated this work. MD thanks the  CEA Saclay and the French CNRS  for financial support and hospitality during the course of this work. JR thanks the  U. K's STFC  for financial support via grant  ST/N504178/1. GS is supported in part by the  French Agence Nationale de la Recherche , under grant  ANR-15-CE31-0016.
3D models of hNMDAR and their general behavior in MD simulations The available crystallographic data in the Protein Data Bank (PDB)  [20]  reporting the strongly modified (by deletion/insertion/replacement mutations and disulfide bridging) structure of NMDAR for Rattus norvegicus (pdb id: 4PE5) and for Xenopus laevis (pdb id: 4TLL, 4TLM) were used as templates for the construction of hNMDAR homology model. The template sequences are composed of GluN1/GluN2B chains. Considering the high similarity of GluN1/ GluN2B sequences from human with those from rats and frogs (99. 2/98. 6 and 91. 7/84. 7% respectively), the GluN1 and GluN2B chains were chosen as the optimal composition for deriving the appropriate homology model of hNMDAR (Fig 2A  and 2B ). Since the GluN2B is widely distributed in the adult brain and has been reported in a range of disorders  [21] , this model will potentially be a key target of positive allosteric modulators  [22].
The target and templates sequences The protein primary sequences of the human NMDAR (hNMDAR,) were retrieved from the UniprotDatabase (http://www. uniprot. org/uniprot). The GluN1-4a and GluN2B subunit variants were used as the target sequences. The crystallographic structures, encoded in Protein Data Base (PDB)  [20]  as 4PE5 (resolution of 3. 96 Å)  [14] , 4TLL and 4TLM (resolution of 3. 59 and 3. 77 Å respectively)  [15]  were chosen as the templates for homology modeling of the target. Alignment of sequences from hNMDAR (GluN1-4a: Q05586-2, GluN2B: Q13224-1), from Rattus norvegicus (rat) receptor (rNMDAR, template) (GluN1: P35439-5, GluN2B: Q00960) and from Xenopus laevis (frog) (fNMDAR, template) (GluN1: Q91977, GluN2B: A9QW73) was performed using Clustal 2. 1 (http://www. clustal. org/).