dataesr/scientific-paragraphs-categorization huggingface dataset:
05 and approximately 90% efficient for muons with |η| > 1. 05. The differences in the efficiency are mostly due to the different geometrical acceptance of the muon trigger system in these regions. For 8 TeV data, events were also recorded with a dimuon trigger requiring at least two muon candidates with transverse momenta of p T > 18 GeV and p T > 8 GeV. After applying data quality requirements, the total integrated luminosity of the selected data sample is 4. 5 ± 0. 1 fb -1 for 7 TeV data and 20. 3 ± 0. 6 fb -1 for 8 TeV data. The associated systematic uncertainties are summarised in Section 6. At the LHC, SM Higgs boson production is dominated by the gluon fusion (ggF) process with the next two most important contributions arising from the vector boson fusion (VBF) process and production in association with vector bosons (VH).
The observed data is consistent with the expected backgrounds. No evidence for a signal is observed and upper limits are set on the signal strength as a function of the Higgs boson mass. For a SM Higgs boson with a mass of 125. 5 GeV, the observed (expected) limit on the signal strength µ S at the 95% CL is 7. 0 (7. 2) times the SM prediction. Assuming a Higgs boson mass of 125. 5 GeV and the SM production cross section, which is allowed to vary within its uncertainty, the 95% CL upper limit on the H → µ + µ -branching ratio is 1. 5 × 10 -3. The ATLAS Collaboration.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federa-tion; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
X-ray single crystal structural analysis The crystallographic data for the title compound were collected on a Nonius Kappa CCD diffractometer at room temperature using graphite-monochromated MoKα radiation (λ= 0. 71073 Å). Data sets were integrated with the Denzo-SMN package  [14]  and corrected for Lorentz-polarization and absorption  [15]  effects. The crystal parameters and other experimental details of the data collection are summarized in Table  1. The structure was solved by direct methods with the SIR97 suite of programs  [16]  and refinement was performed on F 2 by full-matrix least-squares methods with all nonhydrogen atoms anisotropic. Hydrogen atoms were included on calculated positions, riding on their carrier atoms. All calculations were performed using SHELXL-97  [17]  implemented in the WINGX system of programs  [18].
DFT calculations The calculations were made with the Gaussian A. 09 software by use of the B3LYP method. The basis set was 6-31+G* for all atoms except for Cd and I for which the LanL2DZ pseudopotential was used. Three types of calculations were made (see below) and in each case both the NMR chemical shifts and the IR spectrum were calculated. S e p t e m b e r 1 0 , 2 0 1 4.
INTRODUCTION A moral dilemma emerges when different motivations of human behavior dictate opposite actions in a given decision-making context. In economic situations, the most appealing type of dilemma concerns the conflict between selfish monetary reward maximization and adherence to some ethical pro-social norm, especially when the latter implies an economic loss. The emotional implications of such conflicts seem to originate, from the interplay between a basic impulse for greedy money-seeking motivations and alternative, more sophisticated social and personal ethical norms 1. In this paper, we obtain behavioral data and emotional responses by the participants in a laboratory experiment based on a moral dilemma designed as a public procurement auction with the option of an anti-social bribe by the winner of the auction to the auctioneer.
Behavioral and physiological data collection procedures The experiment was computerized using the z-Tree toolbox  (Fischbacher, 2007). Continuous electrodermal activity was recorded during the entire experimental session using a BIOPAC MP150 system and four TEL100C telemetry modules (BIOPAC systems, Inc). Two Ag/AgCl electrodes filled with isotonic gel were placed on each subject's distal phalanges of the middle and the index fingers of the non-dominant hand. The BIOPAC amplifier applies a constant voltage of 0. 5 V to provide a continuous measure of the skin conductance level between the two electrodes, as this varies with sweat gland activity. Specifically, activation of the sympathetic nervous system due to emotional arousal results in marked increases of the skin conductance level. When evoked by particular experimental events, these increases in conductance are called skin conductance responses (SCRs)  (Dawson et al.
, 2007). The skin conductance signal was sampled at 125 Hz and low-pass filtered offline at 0. 5 Hz using a Butterworth digital filter. SCRs were automatically detected and their amplitudes were quantified using a custom version of the Matlab EDA toolbox freely available at: https://github. com/mateusjoffily/EDA. False SCRs were removed after visual inspection of the entire signal. SCRs were associated to a specific decision if their onset appeared at least 1. 0 s after subjects were informed about their choices and before the moment of the decision. SCRs were associated to a feedback event when their onset appeared between 1. 0 and 3. 0 s after the display of the feedback screen. Only responses above 0. 02 microSiemens (μS) were considered as valid. Group average SCRs were obtained by averaging across events of the same condition (e.
ACKNOWLEDGMENTS The authors gratefully acknowledge the three reviewers of this journal. Financial support by the  Spanish Ministry of Economía y Competitividad  (project  ECO2011-23634  and grant  JCI-2010-06790 ) is gratefully acknowledged. Also,  Tarek Jaber-López  acknowledges financial support by the  Universitat Jaume I  (doctoral grant  PREDOC/2010/25 ).
Patients and methods Data for this analysis were submitted to the Center for International Blood and Transplant Research (CIBMTR), a voluntary working group of more than 450 transplant centers worldwide who contribute data on consecutive allogeneic HCT to a statistical center housed at the Medical College of Wisconsin in Milwaukee and the National Marrow Donor Program (NMDP) in Minneapolis. Patients are followed longitudinally with yearly followup. Computerized checks for errors and on-site audits of participating centers ensure data quality. Physician review of data and additional requested information from reporting centers are included. Observational studies conducted by the CIBMTR are performed with a waiver of informed consent and in compliance with Health Insurance Portability and Accountability Act (HIPAA) regulations as determined by the Institutional Review Board and the Privacy Officer of the Medical College of Wisconsin.
Acknowledgments Funding Support: The CIBMTR is supported by  Public Health Service Grant/Cooperative Agreement   U24-CA76518  from the  National Cancer Institute (NCI) , the  National Heart, Lung and Blood Institute (NHLBI)  and the  National Institute of Allergy and Infectious Diseases (NIAID) ; a Grant/Cooperative Agreement  5U01HL069294  from  NHLBI  and  NCI ; a contract  HHSH234200637015C  with  Health Resources and Services Administration (HRSA/DHHS ); two Grants  N00014-06-1-0704  and  N00014-08-1-0058  from the  Office of Naval Research ; and grants from  AABB ;  Aetna ;  American Society for Blood and Marrow Transplantation; Amgen, Inc. ; Anonymous donation to the  Medical College of Wisconsin ;  Association of Medical Microbiology and Infectious Disease Canada ;  Astellas Pharma US, Inc.
HyMap Dataset The HyMap hyperspectral scanner (Manufactured by Integrated Spectronics Pty Ltd, Sydney, Australia) is operated by HyVista Corporation and weighs 200 kg. The spectral configuration of the HyMap sensor includes four spectral modules acquiring 32 bands each, totaling 128 spectral bands. The sensor operates in a tri-axis gyro stabilized platform to minimize image distortion due to aircraft motion. The system, integrated with the Global Position System (GPS) and an Inertia Monitoring Unit (IMU), can be rapidly adapted into any aircraft with a standard aerial camera port and is transported between international survey sites by air freight. Geolocation and image geocoding are achieved with DGPS and the integrated IMU. Laboratory calibration and operational system monitoring ensures that the imagery is calibrated as required for demanding spectral mapping tasks.
The HyMap datasets used in the study are Level 2 reflectance hyperspectral datasets that have been processed through radiometric calibration, geometric correction and atmospheric correction with the 6S model, totaling 122 bands without the six bands influenced by water vapor; the detailed technical parameters are listed in Table 1  [48]. A flowchart of the study is presented in Figure  2. The HyMap airborne data used in this study was captured on 24 October 2002 with a cloud-free day, in East Tien Shan Mountain, and there's almost no vegetation in the study area. The preprocessing of HyMap data occurred in several steps. The first was to convert the original Digital Number (DN) to radiance using the DN-to-radiance conversion factors that accompanied HyMap data. The next task was atmospheric correction.
The atmospheric effects were removed by the HyCorr for HyMap Atmospheric Correction (HHAC) module. The HHAC module was developed by the HyVista Co. Ltd. (Sydney, Australia) on the basis of the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) module. Then, the spectral reflectance of the captured HyMap data was rebuilt for the further analysis and the mineral mapping with the SAI.
Results With the SAI model and on the basis of knowledge regarding spectral absorption features, using the interactive data language (IDL software, produced by ITT Co. Ltd. , Boulder, CO, USA) to do coding of these absorption features, spectral absorption features relating to the main molecular bonds of Al-OH, Mg-OH and CO3 were extracted from HyMap hyperspectral data of the study area (See Figures  5  and 6 ). With these results from the HyMap hyperspectral data, mapping of the corresponding alteration minerals was carried out (See Figure  7 ).
The main component of the secondary mineral was kaolinite. Other mineral components are at very low percentages (no more than 3%), which can be neglected. Considering the low amount of the secondary minerals and other mineral components, a comparative analysis between the testing spectra and the standard spectra of the USGS mineral library was not carried out in this study. The illite identified using PIMA is a common clay mineral, a hydrated mica that shares with other Al-clays (i. e. , smectite and kaolin group) the AlOH feature located at approximately 2200 nm. The mineral illite has a property of being rich in potassium and aluminum but poor in iron. In this study, the illite identified using PIMA was regarded as Al-rich muscovite extracted from HyMap data on the basis of SAI modeling.
Quantitative analyses of the rock samples' spectra and the relative abundance of alteration minerals were determined using the IMSMAS. Based on the SAI and other spectral absorption features, the relationship between these spectral absorption features and the relative abundance of minerals was analyzed. The results showed that the spectral absorption band-depth and the SAI had a higher degree of consistency with the relative abundance of minerals than the other spectral absorption features, in particular the spectral absorption band-depth provides a better representation. Then, the ore prospecting potential of the study area was predicted in the paper. The distribution and close spatial relationship among the minerals extracted from the HyMap data showed good ore prospecting potential for the Goldmine area and the Copper-mine area.
Data collected by the network of Village Malaria Workers (the Malaria Information System, also known as the Village Malaria Worker Database or D0 system) are unlikely to reflect the transmission at community level and properly assess the actual parasite reservoir. Home-based treatment or treatment from the private health sector remain the first choice of many patients with fever  [9]. Furthermore, considering that many infections are asymptomatic, figures reported through surveillance systems underestimate the true burden of malaria infections. Symptomatic infections may contribute considerably to transmission, including in areas of low transmission intensity  [10] , and thus monitoring of symptomatic cases only (passive case detection) represents a poor indicator for progress towards elimination.
Study area The study was conducted from September to October 2013, at the end of the rainy season, in two neighbouring districts, Chhaeb and Chey Saen, located in the Northern Province of Preah Vihear, Cambodia (Figure  1 ). These rural districts are located at an altitude of 100 metres above sea level and their landscape alternates between forestry and cultivated areas. Chhaeb district covers an area of 2,500 km 2 and has a population of approximately 18,455 inhabitants residing in 26 villages (according to the National Cambodian Commune Database, based on the 2008 General Population Census  [14]. Chey Saen district covers 1,100 km 2 with a population of 21,407 inhabitants living in 21 villages. The climate is tropical with a wet monsoon season extending from mid-May to early October. Malaria transmission is seasonal, with a peak occurring usually in August and September.
Data collection For each participant, the age, sex, status (permanent resident, visitor or seasonal worker), use of mosquito net the night before the interview were recorded. History of fever in the last 24 hours previous to the interview and body temperature of individuals complaining of fever at the time of the survey or of history of fever within the previous 24 hours were collected as well. The bed nets owned by the household (found in the household at the time of the study) were observed and counted.
RT-PCR detection of Plasmodium Plasmodium DNA was detected using a qualitative Real-Time PCR (RT-PCR) assay targeting the Plasmodium cytochrome b gene  [15]. Raw data were emailed for validation to IPC in Phnom Penh. Results were available within 30 hours of blood collection, allowing to contact the participants with positive results and to provide free treatment within two days after the initial blood sampling.
Mutations in the K13-propeller domain gene (PF3D7_1343700) P. falciparum positive samples were screened for mutations in the K13-propeller domain gene  [13] , a molecular marker associated with artemisinin resistance, according to the procedure described on the Nature protocols website  [17]. The K13-propeller domain gene was amplified using nested PCR. PCR products were detected using 2% agarose gel electrophoresis and ethidium bromide staining. Double-strand sequencing of PCR products was performed by Macrogen (Korea). Electrophoregrams were visualized and analysed with CEQ2000 genetic analysis system software (Beckman Coulter, Villepinte, France). The amino acid sequences were compared with the wild-type amino acid sequences (GenBank accession number, XM_001350122). The presence of single nucleotide polymorphisms (SNPs) was confirmed by reading both the forward and the reverse strands.
Acknowledgements This study was sponsored by  Médecins Sans Frontières ,  Belgium. Dr. Khoy Bun Thanny , Director of  Preah Vihear Provincial Health Department, Cambodia , is acknowledged for authorizing this study. We are thankful to  Pascale Chaillet ,  Amine Dahmane ,  Mark Debackere , and  Pieter Pannus  for their support and relevant advice. We would also like to thank the study teams,  MSF  field team and the villagers who took part in this study. DM was supported by the  French Ministry of Foreign Affairs.
In addition, the protocol only works well when the fixed node is in direct range with the base station, and the initial authentication phase suffers from internal attacks. Sinks in the network can easily take the place of each other when they are not in communication range with the base station. The authors only give a quick informal security analysis of their solutions and they have not deployed their solutions on real motes. In  [16] , Manjusha et al. propose an authentication technique using ECC and ElGamal digital signature scheme for message authentication. The authors do not perform any security analysis of their solution. Moreover, the proposition is evaluated on a computer using MATLAB simulator, which makes it difficult to estimate compared with a real testbed implementation, since digital signatures and public key cryptographic primitives are very resource consuming operations.
They do not evaluate on real motes their solutions and the security analysis is only informal. One of the most known symmetric key systems that were proposed for wireless sensor networks is SPINS (Security Protocols for Sensor Networks)  [19] , which uses a simplified version of TESLA (Timed, Efficient, Streaming, Loss-tolerant Authentication) protocol  [20]. Perrig et al. test several symmetric encryption functions and MAC (Message Authentication Code) for their protocols using an extremely limited sensor network platform. They show that data transmission takes 71% of the time. In SPINS, the base station plays an essential role in the key establishment process. It is a lightweight cryptography protocol but has limited scalability and depends heavily on the base station. Munivel et al. in  [21]  propose a multi-hop key establishment between nodes called Micro-PKI, using ECC and MAC.
Formal Security Evaluation Evaluating the security of cryptographic protocols is not an easy task and flaws can readily occur in protocols. During the past decades, several tools  [3, 5, 28]  have been developed to automatically verify cryptographic protocols. We use Scyther  [5]  because it is one of the fastest as has been shown in  [29]  and also one of the most user friendly. Cas Cremers has developed an automatic tool called Scyther  [5]. It is a free tool available on all operating systems (Linux, Mac and Windows). This tool can automatically find attacks on cryptographic protocols and prove their security for bounded and unbounded numbers of sessions. One main advantage of Scyther is that it provides an easy way to model security properties like secrecy and authentication. Scyther uses the Dolev-Yao intruder model  [6].
PROTEOME NETWORKS The program EGN  (Halary et al. , 2013)  was used to calculate network parameters for all 84 selected genome-derived proteomes. The network edges in our analyses were established using the following criteria: protein sequence identity ≥40%, hit length must correspond to ≥20% of the smallest homolog, minimal hit length in amino acids is 25, and a BLASTP score <1e-5. Edges in homology-like networks were generated when the mutual match coverage (MMC) for a pair of sequences was ≥90, or 80, or 70% of both sequence lengths. Proteins were annotated using RPS-BLAST  (Marchler-Bauer et al. , 2011)  against the TIGRFAMs database  (Haft et al. , 2003)  with an e-value threshold of 1e-10. We also studied the distribution of proteins in the six EC classes (i. e. , EC1: oxidoreductases, EC2: transferases, EC3: hydrolases, EC4: lyases, EC5: isomerases, and EC6: ligases) in the partial similarity and homology-like networks (see below).
PROTEIN SIMILARITY NETWORKS AS A COMPLEMENT TO PHYLOGENETIC APPROACHES Sequence similarity networks tools are of growing importance because they allow comparative analysis of massive datasets without the need for multiple sequence alignments  (Yona et al. , 2000; Frickey and Lupas, 2004; Atkinson et al. , 2009; Bapteste et al. , 2012 Bapteste et al. , , 2013)). This is a major advantage because alignment and phylogenetic analysis of as few as 10,000 sequences becomes computationally demanding, leading to errors and poor resolution of nodes. This is caused by the decreasing number of phylogenetically informative sites in an alignment as sequence numbers rise  (Frickey and Lupas, 2004). Sequence similarity networks allow the analysis of very large datasets and they become more reliable as datasets increases in size because the larger number of pairwise relationships resolved within networks leads to a more accurate placement of sequences among putative homologs  (Frickey and Lupas, 2004).
INTERPRETING SEQUENCE SIMILARITY NETWORKS In sequence similarity networks, each node (circle) represents a protein sequence from the dataset. Two nodes are connected by an edge (a line linking two nodes) if the pair of sequences fulfills chosen similarity criteria such as percentage identity and coverage (i. e. , the ratio between the length of the matching parts and the total length of any two sequences). These criteria are applied in a BLAST all-versus-all analysis to produce a table of pairwise hits. Sequence similarity networks are displayed and analyzed as a set of connected components, that is, a partition of the network into sets of nodes for which there is an interconnecting path. This partition defines groups of putative homologous sequences or gene families, when the coverage between sequences is high.
Selecting different criteria (i. e. , BLAST cut-offs) will produce different networks and components from the same dataset. A stringent criterion (high percent protein identity, high MMC) will result in numerous, small, connected components of very similar sequences. A lower threshold will result in larger components (presumably representing more ancient relationships) being identified among the sequences. Partial similarity edges (introduced while lowering the minimal coverage and similarity thresholds) also usually results in a significant fraction of the sequences forming a single principal connected component (Figure  1A ), because partial similarity between sequences created for example by domain sharing introduces additional connections between the nodes of the network  (Haggerty et al.
Under that hypothesis, the ≥40% identity homology-like networks used here include proteins that share more ancient relationships than the 100% identity networks. The detection of putative homologs or "cluster of homologous genes"  (Halary et al. , 2010)  in the 84 prokaryotes resulted in the two principal components being composed of 1759 and 540 proteins. Given the complexity of this dataset, we focused on these principal components and analyzed them primarily through the lens of EC1 enzyme distribution because these are most relevant to our goal of understanding redox transitions over evolutionary time. For reference however, the distribution of the different EC enzyme classes in the homology-like networks i (Supplementary Figure  2 ) shows that ca. 30% of annotated EC enzymes fall in the EC1 class.