dataesr/scientific-paragraphs-categorization huggingface dataset:
If we consider a 100 MHz clock (100. 10 6 cycles), we observe that the positioning/attitude control part of the application requires 70% of available cycles with the SW solution. The margin is too small to be safe within a Linux system where additional user processes are necessary. Moreover, a FPU unit is of real value for matrix operations that have high precision requirements. The second solution 'SW+FPU' can also be considered for the object positioning (steps 1. 1, 1. 2, 1. 3) that requires around 2 M of cycles, but the quaternion-based rotation of objects will be too greedy (66 M cycles) and would lead to a total of 80% of CPU use. As expected, the graphic part of the application is definitely out of the scope of any processor-based implementation, except for the 2D projection steps (3.
Based on the previous assumptions, 40 M cycles are required for a complete execution of the graphics part. Tables  2  and 3  give the implementation results of the OP HW IP on different Xilinx device families: Virtex 5 and 6 and Spartan 6. The last one exhibits the lowest clock frequency (73 MHz), which means 73 M available cycles and consequently a use rate equal to 55%. In the two other cases, the clock frequency is 120 MHz and the use rate drops to 33%. As a conclusion, we observe that a viable solution is a MB with a FPU, running an embedded Linux OS and enhanced with a HW OP IP. This architecture offers the expected performances to run augmented reality applications. We have also tested the Zynq case, which is different since this is an hybrid device based on a ARM dual-core Cortex A9 combined with FPGA (Artix 7 or Kintex 7) on a single chip.
Implementation cost The OP processor is specified as a VHDL code at RTL level. This code has been synthesized, placed, and routed for four Xilinx devices. Synthesis results are given in Table  2 , and projections on different FPGA devices and families are given in Table  3. The MB configuration implements a 4 K I/D cache and usual peripheral controllers (UART, MPMC, Flash, Ethernet, VGA). Table  2  gives the number of slices and BRAM blocks required for MB+FPU and OP implementation and the maximum frequency clock after place and route. Figure  15  shows the required size of local memory that grows linearly with the number of objects and polygons. We observe that a OP is half the cost of a MB+FPU and that all implementations can reach 100 MHz except for OP on Spartan 6 (73 MHz). Note also that the results do not include the Y-buffer memory, which are not synthesized on the FPGA in these cases.
Power and technologies considerations Profiling-based power estimation shows that the power consumption of an OP is 100 mA on a Zynq Z020 with a maximum activity and a 88-MHz clock frequency. The power consumption of the ARM processor running Linux is 185 mA on the same device with the same voltage and a 866-MHz clock frequency. It means that a total power consumption remains lower than 300 mA with an OP that fully occupy the available 88 Mcycles. In our case study, only 40 M are necessary, so about 50% of power savings could be obtained if power gating was efficiently applied. If we compare with the Snowball implementation, we can consider that the BT and MEMS power consumption remain identical. So the power difference is mainly due the Zynq and GPP+GPU (A9+Mali) implementations and corresponds to 95 mA, and it leads to power gains larger than 200 mW.
Allocations will progressively increase each year, to accommodate the increasing number of SNe host galaxies that DES will have accumulated in subsequent years. In the 2012B (2012 August to 2013 January) semester, a year before the start of DES and OzDES, the DECam was used to execute the DES SN program as part of its science verification (SV) phase of commissioning. In parallel, AAT/AAOmega-2dF time was awarded for the observation of the DES SN fields in a precursor programme 1 Or: Optical redshifts for the Dark Energy Survey. Figure  1. The 2dF spectrograph has 392 optical fibres to be placed at positions across a field of view 2. • 1 in diameter (large orange circle). This is well matched to the DECam field of view (image mosaic in the background), making spectroscopic follow-up very efficient.
Target selection There are three stages to target selection: (i) creation of the input catalogues, (ii) target prioritization, and (iii) target allocation. For each field, the input catalogues contain a large number of potential targets (step i), from which we select a prioritized set of 800 potential targets (step ii), which the 2dF configuration software uses to optimally allocate its fibres for each observation (step iii). In the first stage, science targets are provided by the science working groups within DES and OzDES. The working groups are responsible for updating their input catalogues between observing runs, e. g. removing objects that have reliable redshifts from earlier runs. The number of targets greatly exceeds the number of fibres, so not all targets can be observed. Based on the scientific importance of the targets, we assign a priority (larger number for higher priority) and a quota to each type of object, as defined in Table  3.
In the third stage, targets are allocated to fibres using the custom 2dF fibre configuration software. This software avoids fibre collisions (see Section 3. 1), while optimizing the target distribution so the highest priority targets are preferentially observed. A maximum of 392 fibres are allocated in this step including 25 for sky positions. Another eight fibres are placed on bright sources for guiding. The input list size of 800 balances efficiency and performance of the fibre configuration software. A sufficient number of objects is Table  3. OzDES target types and priorities. Highest priorities are allocated first, up to their quota. Objects are removed from the target pool based on the deselection criteria. In most cases, this is when the target has a successful redshift measurement, but for transients (which include SNe), it is after they have faded, and some are never deselected as they require constant monitoring (AGN and F stars).
Redshifting All spectra are visually inspected by OzDES team members 4  using the interactive redshifting software RUNZ, originally developed by Will Sutherland for the 2dFGRS. RUNZ first attempts an automatic redshift determination by both cross-correlating to a range of galaxy and stellar templates and searching for emission line matches. It then displays the spectrum and marks the locations of the common emission and absorption features at the best redshift estimate. A redshifter visually inspects the fit and determines whether the redshift is correct. A number of interactive tools are provided, e. g. to switch to a different template, to mark a specific emission line, to add a comment, or to input the redshift directly. Uncertainties on the redshifts are calculated by RUNZ based on the width of the cross-correlation peak or the fit to emission lines.
SN hosts show lower completeness across a wide magnitude range than either ELGs or LRGs (see Fig. 4 ). This sample is selected based on criteria independent of whether the galaxy is of early or late type. The redshift range may be constrained by the selection criteria, but knowing the target type does not directly help in determining a redshift. At a given magnitude, the redshift completeness grows with integration time. Many of the faint targets are expected to be observed in multiple seasons and accumulate significantly more signal than obtained so far. The data from Y1 do not have sufficient range of exposure time to determine this trend with much certainty. In O  'Neill et al. (in preparation) , we use data gathered by the end of Y2 to model how the number of redshifts acquired increase as a function of number of visits to the field, for various types of targets with different magnitude and redshift distributions.
Otherwise, the median value is used if more than two redshifts are available or a random selection is made assuming at least one measurement is correct. The external redshifts are chosen from other surveys to have the highest quality flag defined. For quality 4 redshifts, we calculate error rates assuming all the external redshifts are correct. However, we have re-examined the spectra where we measure different redshifts from the external sources and found no evidence that the external values are favoured. We are quite confident that our measurements are correct, so the error rates are upper limits. For quality 3 redshifts, we notice several errors in our measurements for AGN, although in three cases the data quality is not good enough to confirm either redshift. As shown in Table  4 , overall close to 100 per cent of redshifts are correct with quality flag 4 but only about 90 per cent of redshifts are correct with quality flag 3.
2004 ). We expect to accurately recover the radius-luminosity relationship for all three lines  (King et al. 2015). Based on data obtained from 2013 September through the end of 2014, we presently have 5 (6) or more spectroscopic epochs for 693 (455) AGN (70 per cent and 46 per cent of the sample). These numbers are close to the expected 7 epochs (in the first two seasons) for 500 AGN in our survey simulation  (King et al. 2015). For more than 10 per cent of the sample, we have acquired spectra in 8 epochs.
Unusual objects The large number of spectra taken by OzDES almost guarantees discoveries of rare events or objects. Even in the photometrically selected samples, outliers are expected to exist. Although an exhaustive search for unusual objects is beyond the scope of this work, we highlight this potential by noting some objects that clearly stand out when the spectra were visually inspected in RUNZ. These objects fall in a few broad categories. Although some of these categories are common to large transient or spectroscopic surveys, we include all of them to showcase the diversity of the OzDES data set. (i) Unusual transients: time sensitive observations of live transients are of high priority. To maximize the discovery space, we target all transients that satisfy a straightforward magnitude cut.
As discussed in Section 3. 6, studies of transient spectra have been complicated by host galaxy light contamination and data-reduction issues. A more systematic investigation will be carried out when data reduction is refined. (ii) Rare stars: at least four WD and M-dwarf binaries are recognized as Galactic objects with both a hot and a cool spectral components. We have also found a WD candidate that is likely a rare DQ WD (with carbon bands). (iii) Broad absorption line AGN: a large number of AGN are observed as reverberation mapping targets, radio sources, galaxies, or transients. More than a dozen of these exhibit extraordinary absorption line systems. Selected objects will be monitored throughout our survey. (iv) Serendipitous spectroscopic SN: an SN may be observed unintentionally for two reasons.
For example, targeting the brightest objects first maximizes the number of measured redshifts for radio galaxies, but such tactics only work when selection bias is not a major consideration. It is also desirable to abandon an unsuccessful target after certain number of exposures, while the maximum integration time allowed depends on the total number of redshifts anticipated for this particular target type. Short integration times allow faster recycling of fibres and more redshifts to be measured. During poor observing conditions, a backup programme is in place to measure redshifts for bright galaxies, along with high-priority targets such as AGN and active transients. As for all long-term projects, we expect to continue to refine our data quality, actively analyse new data and adapt the observing strategy in the coming years.
AC K N OW L E D G E M E N T S Parts of this research were conducted by the  Australian Research Council Centre of Excellence for All-sky Astrophysics  ( CAAS-TRO ), through project number  CE110001020. ACR acknowledges financial support provided by the  PAPDRJ CAPES/FAPERJ  Fellowship. This work was supported in part by the  US Department of Energy  contract to SLAC No. DE-AC02-76SF00515. BPS acknowledges support from the  Australian Research Council   Laureate Fellowship Grant   LF0992131. NS is the recipient of an  Australian Research Council Future Fellowship. FS  acknowledges financial support provided by  CAPES  under contract The data in this paper were based on observations obtained at the  Australian Astronomical Observatory  ( AAO programs A/2012B/11  and  A/2013B/12 , and  NOAO program NOAO/0278 ).
The DES data management system is supported by the  National Science Foundation  under Grant Number  AST-1138766. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2012-39559 ,  ESP2013-48274 ,  FPA2013-47986 , and  Centro de Excelencia Severo Ochoa   SEV-2012-0234 , some of which include ERDF funds from the  European Union. The Collaborating Institutions are  Argonne National Laboratory , the  University of California at Santa Cruz , the  University of Cambridge, Centro de Investigaciones Energeticas ,  Medioambientales y Tecnologicas-Madrid , the  University of Chicago ,  University College London , the  DES-Brazil Consortium , the  Eidgenössische Technische Hochschule (ETH) Zürich ,  Fermi National Accelerator Laboratory , the  University of Edinburgh , the  University of Illinois at Urbana-Champaign , the  Institut de Ciencies de l'Espai (IEEC/CSIC) , the  Institut de Fisica d'Altes Energies ,  Lawrence Berkeley National Laboratory , the  Ludwig-Maximilians Universität  and the associated  Excellence Cluster Universe , the  University of Michigan , the  National Optical Astronomy Observatory , the  University of Nottingham , The  Ohio State University , the  University of Pennsylvania , the  University of Portsmouth ,  SLAC National Accelerator Laboratory ,  Stanford University , the  University of Sussex , and  Texas A&M University.
Experimental Setup We test the proposed model to verify the linguistic plausibility and computational feasibility of using MORPH links to represent syntactically idiosyncratic MWEs in a dependency parser enriched with subcat features. Therefore, we train a probabilistic dependency parsing model on modified treebank, representing ADV+que and de+DET constructions using this special syntactic relation in-stead of pretokenization. Furthermore, in addition to regular features learned from the treebank, we also introduce and evaluate subcat features based on a lexicon of verbal valency, which helps identifying subordinative clauses and de prepositional phrases (see Section 5). We evaluate parsing precision and MWE identification on a test treebank and, more importantly, on a dataset built specifically to study the representation of our target constructions.
Data Sets and Resources French Treebank (FTB) The parser was trained on the French Treebank, a syntactically annotated corpus of news articles from Le Monde  (Abeillé et al. , 2003). We used the version which was transformed into dependency trees by  Candito et al. (2009) , and which was also used by  Candito and Constant (2014)  for experiments on MWE parsing. We used a standard split of 9,881 sentences (278K words) for training and 1,235 sentences for test (36K words). We applied simple rules to transform the flat representation of ADV+que and de+DET constructions into MORPH-linked individual tokens. All other MWEs are kept unchanged in training and test data. They are represented as single tokens, not decomposed into individual words. MORPH Dataset The test portion of the FTB contains relatively few instances of our target constructions (see Tables  4  and 6 ).
Since some of the phenomena are quite rare in the FTB test portion, we focus on the MORPH dataset, which contains around 100 instances of each target construction. We compare our approach with two simple baselines. The first one consists in pretokenizing ADV+que systematically as a single token, while de+DET is systematically left as two separate tokens. This baseline emulates the behavior of most parsing pipelines, which deal with functional complex words during tokenization. This corresponds to choosing the majority classes in the last row of Tables  1  and 2. For ADV+que, the precision of the baseline is 56. 4%. If we assume recall is 100%, this yields an F1 score of 72. 2%. For de+DET, however, recall is 0% since no MORPH link is predicted at all. Therefore, we only look at the baseline's precision of 63.
5%. A second, slightly more sophisticated baseline, consists in choosing the majority class for each individual construction and average precisions over the constructions. In this case, the average precision is 75. 3% for ADV+que and 76. 6% for de+DET. We compare our model to the one proposed by  Green et al. (2013). We used the pretrained model available as part of the Stanford parser 9. Their model outputs constituent trees, which were automatically converted to unlabeled dependency structures. We ignore the nature of the dependency link, only checking whether the target construction elements are linked in the correct order. Our experiments use the MACAON tool suite. For the FTB, gold POS and gold lemmas are given as input to the parser. In the case of the MORPH dataset, for which we do not have gold POS and lemmas, they are predicted by MACAON.
ADV+que Constructions Table  5  displays the precision, recall and F1 of the prediction of the MORPH dependency on the 730 ADV+que sentences of the MORPH dataset, without and with the use of subcat features. The scores obtained are lower than the same experiments on the FTB. Precision is higher than recall, which indicates that the parser has a tendency to underpredict. We also present the precision of the two baselines described in Section 6. 2. Only in two cases the per-construction majority baseline (indiv. ) outperforms our parser without subcat features. These two constructions do not tend to form complex conjunctions, that is, the parser overgenerates MORPH dependencies. Here, subcat features help increasing precision, systematically outperforming the baselines. The introduction of subcat features has a beneficial but limited impact on the results, increasing precision and lowering a bit recall, augmenting the tendency of the parser to under predict MORPH dependencies.
65 A ˚(PDB entry 1waw) to 0. 95-1. 10 A ˚for the apo and pseudo-apo forms and the complex with chitobiose, allowing the determination of the protonation states within the active site. This information was extended by hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. The results suggest a new mechanism involving changes in the conformation and protonation state of the catalytic triad, as well as a new role for Tyr27, providing new insights into the hydrolysis and transglycosylation activities.
, 2003). In 2002, the first X-ray crystal structure of CHIT1 (PDB entry 1guv) was determined at 2. 35 A ˚resolution by Fusetti and coworkers  (Fusetti et al. , 2002) ; subsequently, Rao and coworkers obtained a structure of CHIT1 in complex with the inhibitor argifin at 1. 65 A resolution (PDB entry 1waw;  Rao et al. , 2005). These structures showed that this enzyme shares the TIM-barrel threedimensional folding of GH18 chitinases, with the conserved catalytic motif (DXDXE) located in the active site  (Fusetti et al. , 2002). However, more detailed information is still required for a full explanation of proton-translocation processes. Thus, in order to extend our knowledge regarding the catalytic properties, we obtained new X-ray data to resolutions of between 0. 95 and 1. 10 A ˚for CHIT1 in apo and pseudo-apo forms and in complex with chitobiose, an N-acetylglucosamine (NAG) dimer.
2 M PST. X-ray diffraction data were collected on the X06DA (PXIII) beamline of the Swiss Light Source (SLS), Villigen, Switzerland. All data sets were integrated, merged and scaled using HKL-2000  (Otwinowski & Minor, 1997). The structures were solved by molecular replacement with  Phaser (McCoy et al. , 2007)  using the coordinates of the native structure of the same protein as an initial search model (PDB entry 1guv;  Fusetti et al. , 2002). The model was improved by alternating cycles of manual model building using Coot  (Emsley et al. , 2010)  with refinement using REFMAC5  (Murshudov et al. , 2011)  and using PHENIX  (Adams et al. , 2010). The stereochemical quality of the final model was assessed with MolProbity  (Chen et al. , 2010). To determine the protonation states of some of the polar residues in the active site, an additional refinement with removed stereochemical restraints for the relevant C-O bond lengths was performed using SHELXL full-matrix leastsquares refinement  (Sheldrick, 2008).
Quantum mechanics/molecular mechanics (QM/MM) calculations For combined QM/X-ray refinement we used the program QMRx based on the general-purpose DYNGA driver  (Parker et al. , 2003). This program is similar to other available codes capable of hybrid QM/X-ray refinement, such as ChemShell  (Metz et al. , 2014) , ComQum  (Ryde et al. , 2002)  and DivCon in PHENIX  (Borbulevych et al. , 2014). By using QMRx, we performed the QM calculations via the PM7 Hamiltonian  (Borbulevych et al. , 2014; Stewart, 2009). The accuracy of PM7 has been discussed in the literature in great detail  (Stewart, 2013). We use the MOZYME  (Stewart, 2009)  implementation of PM7 as available in MOPAC2012. In our calculations, we address the charge assignments in two significant ways: by drastically increasing the QM integral cutoffs and by introducing a dielectric screening function as recommended in the literature  (Andersson et al.
, 2013). The effect of these additions is a more accurate description of the system but at the cost of a large increase in the total CPU time, limiting the total number of conformations that can be explored in a reasonable amount of time. To compensate for this high computational cost the QM integral cutoff was varied, following MOPAC software recommendations, from CUTOFF = 6. 0 for GNORM = 20 to CUTOFF = 12. 0 for GNORM = 1. 0 in four steps. For the X-ray section QMRx either uses XPLOR-NIH  (Wick et al. , 2014)  or SHELX as a slave program to obtain the firstorder derivatives of the X-ray restraint function. Mixing of the QM and X-ray restraints was performed using the standard protocols as described in the literature  (Metz et al. , 2014; Ryde et al. , 2002; Yu et al. , 2006; Falklo ¨f et al.
, 2003)  and using Gaussian03 as a slave program  (Foresman & Frisch, 1996). Residues Tyr27, Asp136, Asp138, Glu140 and Tyr141 and the chitobiose appear as observed in the structures of CHIT1 in complex with chitobiose. Note that the total number of atoms needs to be constant; therefore, this motif is present in all calculations. All of the water molecules in direct contact with these residues and pointing towards the reaction centre were included. Calculations were performed using the PBEPBE1 Hamiltonian as implemented in Gaus-sian03 and the 6-31G* atomic basis sets  (Frisch et al. , 1998)  for all calculations. The step of integration was 0. 2 fs with T = 298 K using a Nose thermostat as implemented in DYNGA.
Atomic resolution structures of the catalytic domain of CHIT1 reveal a double conformation of key catalytic residues The previously reported crystal structures of CHIT1 in the apo form (PDB entry 1guv) or in complex with chitobiose (PDB entry 1lg1) determined by  Fusetti et al. (2002)  showed the catalytic domain at 2. 35 and 2. 78 A ˚resolution, respectively. Even in the presence of an inhibitor, the highest resolution obtained was 1. 65 A ˚(in complex with the inhibitor argifin; PDB entry 1waw;  Rao et al. , 2005). Since our aim was to perform detailed structural mechanistic studies and protonation-state analysis, we needed to improve the resolution of these structures to subatomic resolution. We therefore set up new crystallization conditions combined with microseeding, which allowed us to obtain crystals of CHIT1 with higher diffraction quality extending to approximately 1.