dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWF and FWF (Austria);.
During the 2011 heavy-ion datataking at the LHC, the Compact Muon Solenoid (CMS) experiment recorded PbPb collisions at a nucleon-nucleon centre-of-mass energy of √ s NN = 2. 76 TeV. The integrated luminosity of 150 µb -1 for the collected sample is sufficient to allow the first measurement of jet shapes in heavy-ion collisions. A reference measurement in pp collisions at the same centre-of-mass energy has been performed using the data sample from 2013. The pp integrated luminosity of 5. 3 pb -1 yields a kinematic reach for the jets similar to that in the PbPb data. The modification of the jet shapes in the PbPb collisions is studied in five classes of collision centrality: 0-10%, 10-30%, 30-50%, 50-70% and 70-100%.
Jet and track reconstruction Monte Carlo (MC) event generators have been used for evaluation of the jet and track reconstruction performance, particularly for determining the tracking efficiency, as well as the jet energy response and resolution. Jet events are generated by the PYTHIA MC generator  [39]  (version 6. 423, tune Z2  [40] ). These generated PYTHIA events are propagated through the CMS detector using the GEANT4 package  [41]  to simulate the detector response. In order to account for the influence of the underlying PbPb events, the PYTHIA events are embedded into fully simulated PbPb events, generated by HYDJET  [42]  (version 1. 8) that is tuned to reproduce the total particle multiplicities, charged-hadron spectra, and elliptic flow at all centralities. The embedding is done by mixing the simulated digital signal information from PYTHIA and HY-DJET, hereafter referred to as PYTHIA +HYDJET.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES (Croatia) ;  RPF (Cyprus) ;  MoER ,  SF0690030s09  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NKTH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  NSC (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Groups of BMT recipients were or were not given CD4 celldepleting mAb. In order to identify the best times to examine recipient hematopoiesis, we first evaluated the kinetics of rejection of MHC class I-deficient marrow by indirectly alloreactive CD8 T cells. We focused on the B cell lineage because it begins to show chimerism within a few days of conditioning and BMT  (20). On Days 4 and 7 post-BMT, no differences in B cell chimerism were observed between groups that did or did not receive depleting anti-CD4 mAb (Figure  3A ). By Day 11, no donor B cells were detected in the BM or in the blood of the CD4 T cell-depleted recipient mice (Figure  3A , B), while only a very low percentage of donor B cells remained in the spleen (data not shown). Next, we evaluated recipient hematopoiesis at the same time points for which donor chimerism was assessed above (Days 4, 7 and 11).
Cytotoxicity of recipient CD8 T cells against MHC class I-deficient BM To directly assess whether activated recipient CD8 T cells could be cytotoxic effector cells that rejected K b D b-/-allogeneic donor BMCs, cytotoxicity of CD8 T cells from rejecting animals was studied in a direct cytotoxicity assay. Enriched CD8 T cells from conditioned animals that had not received K b D b-/-donor BMCs did not kill either K b D b-/-or syngeneic B10. S blasts. In contrast, CD8 T cells from conditioned, CD4 cell-depleted animals that rejected the K b D b-/-donor BMCs showed cytotoxicity against K b D b-/-blasts but not against syngeneic B10. S blasts (Figure  7 ). These data demonstrate that recipient CD8 T cells from animals rejecting MHC class I-deficient BM developed specific cytotoxicity against the K b D b-/-donor, strongly suggesting that recipient CD8 T cells are the effector cells that kill transplanted K b D b-/-donor BMCs in vivo.
Acknowledgments We thank  Ms. Shavree Washington  for expert assistance with the manuscript and  Drs. Donna Farber ,  Gilles Benichou  and  Josef Kurtz  for helpful comments on the paper. This work was supported by  National Institutes of Health  Grants  R01 HL49915  and  P01 HL18646. F. H. was supported by the  Fondation pour la Recherche Médicale (France) , and the  American Society of Transplantation/American Society of Blood and Marrow Transplantation (USA). T. F. was supported by the  Swiss Foundation for Medical and Biological Grants/Novartis Switzerland  and the  Walter and Gertrud Siegenthaler Foundation (University of Zurich ). C. L. L. was supported by Government funds awarded by  DoD ,  Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG)  Fellowship,  32 CFR 168a.
These altitudes represent a vertical section of the planetary boundary layer defined by  Gregory (1961). Hourly climatic parameters were also obtained from the database including precipitation (mm), sun flux (W m À 2 ), relative humidity (%), pressure (hPa), altitude (m) and temperature (1C). The meteorological data GDAS (Global Data Assimilation System) used by this model were produced by the National Center for Environmental Prediction. The total distance traveled by air masses (km) was determined under POSTGRESQL/POSTGIS (http://www. postgresql. org) and the map projections were made with Quantum GIS version 1. 7. 0  (Development Team, 2011).
Inference of population structures by landscape clustering We determined the spatial population structure of P. syringae to test the hypothesis that genetic diversity of P. syringae populations in precipitation is different according to the sampling zone. We used a clustering model: GENELAND version 4. 0. 2 under R software  (Guillot et al. , 2005a (Guillot et al. , ,b, 2012) )  to (i) estimate the number of panmictic clusters (k), (ii) assign to each genetic cluster a membership to a sampling zone and (iii) assign individual strains to a cluster. The clustering approach can combine data on genotypic polymorphism with geographic coordinates projected on a rasterized map. The optimal number of clusters k and the posterior probabilities of cluster membership for any unit of the sampling map were identified by a Markov chain Monte Carlo procedure under the assumption that there is no admixture between populations and that populations are at Hardy-Weinberg equilibrium with linkage equilibrium between loci  (Guillot et al.
syringae. The cts sequences (427 base pairs, 80 SNPs) of the 209 strains of P. syringae selected randomly from snow and rain represented 54 unique haplotypes. Half of the haplotypes were represented by only one strain in the whole sample, and the three dominant haplotypes were represented by 28, 22 and 19 strains. GENELAND estimated that there were three clusters (k) whether spatial coordinates were included or not. These latter results were consistent with those obtained using the STRUCTURE 2. 3 software  (Pritchard et al. , 2000; Falush et al. , 2003)  (data not shown). Assuming admixture between populations, 89% of the haplotypes clearly belonged to only one cluster (q40. 95), indicating that the three clusters were highly divergent. As shown in Figures  4a-d , each cluster corresponded to a distinct spatial domain covering several sampling sites that are geographically close.
Acknowledgements We thank  Daniel Granier  and his team from the Super Sauze ski resort, for help with collection of samples. We are grateful to  Odile Berge  ( INRA, Avignon ) for her help in sampling and for enriching discussions, to  Ghislain Ge  ´niaux and  Michel Mourly  ( INRA, Avignon ) for help with spatial analysis of the trajectories, and to  Vaughan Phillips  for his insights in cloud physics. We gratefully acknowledge  Claire Troulet  for excellent technical assistance for the Botrytis cinerea experiments.
STT-MRAM Applied in Cache Hierarchy To evaluate our proposed methodology flow, we present an application case study based on the X. 264 video encoder. The processor used for this case study is a 32-bit Reduced Instruction Set Computing (RISC), dual-issue superscalar, out-of-order, speculating dynamic length pipeline (8-11 stages) processor, which is modeled after the ARMv7 architecture. The processor has a clock frequency of 1. 5 GHz, and we are running the Linux Operating System on the processor. The video that we wish to encode has a frame rate of 30 FPS and has a resolution of 1280 ˆ720. We also evaluated the impact of STT-MRAM for L2 cache, and we will compare its characteristics with a similar system employing SRAM. Table  8  summarizes the properties of 45 nm SRAM and STT-MRAM memory banks used as a 2-MB eight-way associative L2 cache.
Furthermore, the detailed parameters introduced here were extracted during the intrinsic analysis evaluation discussed in the previous section  [17]. The performance comparisons among memory cache banks, eight-way associative, 2-MB SRAM and STT-MRAM in the technology node of 45 nm on L2 cache are described in Table  9. Considering X. 264, the experimental results described in Table  9  are obtained based on the execution of the X. 264 benchmark using the Linux OS on top of the Gem5 calibrated with the memory bank latency for each technology. Based on Table  10 , we observe that the total CPU time increases from 16. 2 s to 17. 1 s, which is tolerable for the X. 264 application. Given the fact that the STT-MRAM hit latency is 2. 7ˆhigher than SRAM, yet we only see a 5% increase in execution time, we conclude that an increased latency of a hit in the L2 cache has a slight impact on the total CPU time.
In this particular case, the increase on the cache size was not enough to compensate for the penalty, due to the cache access delay. By employing write buffers and a novel cache access policy, the authors managed to achieve a similar performance while reducing the power consumption on the overall application (comprising all of the memory hierarchy) by almost 74%. They also presented a hybrid MRAM/SRAM cache organization, having 31 sets implemented in STT-MRAM and one set implemented in SRAM. The write-intensive data is kept in the SRAM part, in order to mitigate the higher write delay. A method for determining which datum is suitable for being placed in the SRAM set is also discussed. In this section, we have demonstrated the interest in using an L2 STT-MRAM cache for high performance applications.
Acknowledgments We would like to thank  Guillaume Prenat  and  Gregory Di Pendina , from Spintec at the  Commissariat à l'Énergie Atomiquee (CEA) in Grenoble , for their assistance on the physical details regarding the  STT-MTJ. ANR, due  to the grant of the SPIN and  MRAM based Architecture for Reliable and low power Systems (MARS)  projects, made this research and all research effort regarding STT-MRAM in France possible. Also, to  Adam Manzanares , my colleague for his time reviewing the text, fixing and improving it and suggestions made, that enriched the final text and presentation of this work, Thank you very much Adam.
Trigger and reconstruction The data used in the electron channel were recorded with a trigger requiring the presence of an energy cluster in the EM compartment of the calorimeter (EM cluster) with E T > 120 GeV. For the muon channel, matching tracks in the MS and ID with combined p T > 36 GeV are used to select events. In order to compensate for the small loss in the selection efficiency at high p T due to this matching, events are also recorded if a muon with p T > 40 GeV and |η| < 1. 05 is found in the MS. The average trigger efficiency (measured with respect to reconstructed objects) is above 99% in the electron channel and 80%-90% in the muon channel for the region of interest in this analysis. Each EM cluster with E T > 125 GeV and |η| < 1. 37 or 1. 52 < |η| < 2. 47 is considered as an electron candidate if it is matched to an ID track.
Statistical analysis and systematic uncertainties A Bayesian analysis is performed to set limits on the studied processes. For each candidate mass and decay channel, events are counted above an m T threshold. The optimisation of m Tmin is done separately for W ′ → ℓν and W * → ℓν. For each candidate mass, the m Tmin values that minimise the expected cross-section limits are obtained in the electron and muon channels separately, but for simplicity the lower value is used in both channels since this has a negligible impact on the final results. A similar optimisation is performed when setting the limits on DM production, and in this case a single m Tmin is chosen for each operator. The expected number of events in each channel is N exp = ε sig L int σB + N bkg , (6. 1) where L int is the integrated luminosity of the data sample, ε sig is the signal selection efficiency defined as the fraction of signal events that satisfy the event selection criteria as well as m T > m Tmin , N bkg is the expected number of background events, and σB is the cross-section times branching fraction.
It uses a macroblock classification based on rate-distortion (R-D) characteristics to generate three kinds of regions (called basic units). A weighted bit allocation per region is performed with predetermined factors in heuristic ways. Finally, a linear rate-quantization stepsize (R-QS) model and a distortion-quantization stepsize (D-QS) model compute a QP per basic unit. These techniques considered the R-Q linear model  [5, 6]  or the quadratic RC model  [3, 4]  and are useful for H. 264/AVC implementations. Meanwhile, the new HEVC standard has been recently finalized by ITU-T and ISO/IEC  [7]  and many works have focused on rate control and developed new R-Q schemes for it. In the reference software, two different algorithms have been proposed. The first one is based on a quadratic model and the mean absolute difference (MAD) between the original and the reconstructed signal  [8, 9].
In the second algorithm, an marwa meddeb et al. R-λ model that takes into account the hierarchical coding structure has been adopted  [10]. This model, initially introduced in HM. 10, has been improved in a recent version of the reference software (HM. 13). Adaptive bit allocation at the frame level has been introduced  [11]  by considering variable weights for each hierarchical level that depend on video content characteristics. Then, in  [12] , intra frame rate control has been modified by enabling basic unit level rate control. Recent works on high-efficiency video coding (HEVC) have proposed bit allocation approaches that take into account coding units (CUs) texture. In  [13] , CUs are classified referring to their depth in the quad tree and their coding type. Texture-based rate models for HEVC have been developed according to signal characteristics in different CU depths and coding types.
Rate models for three types of Cus of different texture levels have been constructed to deal with more complex content and to ensure more accurate rate control at the CU level. All the above-mentioned RC algorithms, which have been developed for HEVC, do not take into account the importance of particular regions of the frame. Therefore, we propose a new rate control scheme for video conferencing systems which processes the faces (ROIs) and the background separately. We propose two versions for this algorithm. The first one is based on the model implemented in the reference software HM. 10 [10]  performing the R-λ model on B-frames only. While, the second one considers the modified RC model introduced in HM. 13   [11]  that performs RC for both I and B frames. The reference controller is enhanced with three main features; first, using an object detection method, we detect our ROI and generate automatically a binary map (ROI map).
This model has been adopted in VM8 for MPEG4  [21] , H. 264/AVC  [22] , and also for HEVC  [23]. The accuracy of these models has been enhanced by introducing the so-called complexity of the source, using the per pixel gradient value in the R-Q model in  [24]. The sum of absolute transformed differences (SAD) has been adopted in  [25]. In a different way, the RC was improved by considering a representation in the ρ domain  [26]  as proposed in  [27]  and by taking into account additional parameters, like the frame rate  [28]. The most recent R-D model in the HEVC reference software is the R-λ model expressed as follows: λ = α R β , (5) where α and β are the model parameters  [10]. We note that this model defines a relationship between the rate in bits per pixel, R, and the Lagrange parameter λ which is used in RDO to decide the coding mode.
In this work, the model is used for each block. Thus, a QP is computed for each macroblock. In  [6] , a complete ROI-based controller is proposed. The scheme includes five steps, starting with region devising using the R-D characteristics of each macroblock. Macroblocks with similar characteristics are classified in the same basic unit and an overall bit allocation is performed using two linear models (R-QS and D-QS). A QP is computed for each basic unit. Finally, RDO is performed for each macroblock and models' parameters are updated as done in previous propositions. The above-mentioned algorithms provide a bit rate repartition that takes into account the high priority of the ROI. They have been developed considering linear and quadratic models and implemented in the H. 264/AVC JM software.
Complexity measure To perform bit allocation, a weight w B is computed for each CTU. In HM. 10, the weight is estimated by the prediction error (in form of MAD) between the current unit p and the coded unit p in the previous coded picture belonging to the same level  [10]. The weight of each CU of index i is defined as: w B (i ) = ⎛ ⎝ 1 N j ∈N | p j -p j | ⎞ ⎠ 2 , ( 10 ) where N is the number of pixels of the CTU. In the latest version of the reference software (HM. 13)  [31] , the CTUs weight for B-frames has been modified. It depends on the model parameters α i and β i at the CTU level, the λ of the picture and the number of pixels N. For a CTU of index i : w B (i ) = N λ α i 1 β i. (11) Finally, the complexity measure for I-frame (CTU) is calculated by deriving the sum of absolute Hadamard transformed difference (SATD) as described in  [12] : SATD = 7 k=0 7 =0 |h k | , (12) where h kl are the coefficients obtained after applying the Hadamard transform to the original 8 × 8 block.
R-λ model The R-λ implementation introduced in HEVC reference software uses different methods of QP computing and bit allocation for the I and the B frames. In HM. 10, bit allocation at the CTU level for intra frames was not considered. All the units have the same QP obtained at the frame level. For B-frames, the model introduced by (  5 ) in Section II is used at the CTU level and its parameters α and β are updated after encoding each unit. In HM. 13, to better control the rate allocation of intra coded frames, the complexity measure defined in (  12 ) and (  13 ) is additionally taken into consideration in the R-λ model as follows: λ i = α w I (i ) R i β. ( 14 ) For a CTU of index i , λ i depends on model parameters at the frame level. The parameters α and β remain constant for the entire frame; however, the number of allocated bits per pixel R i is computed per CTU.
In the first and the third algorithms (HM. 10 and HM. 9), I-frame bit allocation at the CTU level has not been yet introduced, so, all the frames were considered as B-frames except the first one (I-frame), while, for the extended version of our code in HM. 13 , we tested all the configurations. Three HD 720p sequences from class E have been tested: "Johnny", "KristenAndSara", "FourPeople"  [30]. As we can see in Fig. 5 , the selected test sequences have typical video conferencing content and different characteristics, like number of faces and ROI size. We used different bit rates, budget partitioning per-region and QP ranges to evaluate the performance of our approach.
B) Implementation and performed tests The introduced modifications have been done mainly in rate control class of the reference softwares HM. 10 [33], HM. 13  [31] , and HM. 9  [32]. A reference test "Ref " is performed using the rate control algorithm described in  [10]  and improved in  [11]. While evaluating the URQ model the reference used is described in  [9]. These first tests give us the reference performance: the ratio K between ROI bit rate and non-ROI bit rate, the bit budget used for encoding each region, the PSNR and the structural similarity (SSIM) index  [35]  of each region that goes from 0 to 100. Second, we activate all modified functions: we introduce a new bit repartitioning between regions by fixing a factor K and a large QP margin. Then we perform an evaluation test of our method that we note "New".
3 Data collected using the SLIPTA checklist reveal the current state of a laboratory's quality management systems and identify gaps in the 12 Quality System Essential areas defined by the Clinical and Laboratory Standards Institute. 4 Audit results are used to develop action plans and guide the selection of quality improvement activities for the SLMTA programme so as to help laboratories move toward national or international accreditation. The SLMTA programme in Vietnam and Cambodia began in 2010 when representatives from each country participated in a two-week Training-of-Trainers workshop. After stakeholder engagement, governmental approval and, in the case of Vietnam, planning with international partners, baseline audits were conducted in seven laboratories in Cambodia in June of 2011 using the standard paper-based SLIPTA checklist.
Results The baseline audit in Cambodia using the paper-based SLIPTA checklist had several limitations. Audits required at least one-and-a-half days, including two hours to manually fill out the 44-page SLIPTA checklist and calculate site scores in order to create the reports. Although audit teams gave verbal reports at the individual laboratory summation conferences immediately following the audits, final written reports to the laboratories were compiled remotely after the audit and delivered by mentors at their next visit, sometimes delayed as long as two weeks. Because the existing paperbased checklist is in a PDF or Microsoft ® Word format, multi-site data were not easily manipulated, reducing the analytical value. Additionally, the country team scanned and saved all final paper documents, requiring significant administrative time and costs for electronic data storage.