dataesr/scientific-paragraphs-categorization huggingface dataset:
Data reduction & spectral calibration strategy "DRP" (Data Reduction Pipeline) comprises the "2D" part (2D-DRP) and the "1D" part (1D-DRP). The 2D-DRP, which is under development by PU, receives two-dimensional raw spectral FITS images read out from the detectors and produces one-dimensional, sky-subtracted, flux-and wavelength-calibrated spectra ready for scientific analyses. The 1D-DRP being developed by LAM then receives these 1D spectra and measures various parameters of spectroscopic features such as redshifts and emission line fluxes. After each exposure of the spectrograph detectors, the data will be processed by on-site DRP with calibration data sets taken in advance. 1D-DRP is applied to the reduced and calibrated 1D spectra and measured parameters are added to the database. As successive exposures are taken for the same objects at different nights and observation runs, deeper and higher quality spectra will be produced from from full, batch processing of all available data.
The output from this simulator has been matched with the preliminary data model for PFS that has recently been defined based on that of SDSS, and it will be used to develop and validate the software components such as 1D-DRP and the database. Meanwhile, the Princeton team has been developing a simulator of raw images from the spectrograph detectors where the ray-trace calculations of the optical model and detector characteristics such as bias and dark are considered. These simulated 2D images (Fig. 10 ) and spectra have been used to develop and validate the 2D-DRP. They have coded a prototype pipeline, with which the data from the real spectrograph module will be reduced and analyzed during the integration and test at LAM. The data will be crucial not only to characterize the spectrograph module but also to develop the algorithms of 2D-DRP for data modeling such as PSF characterization.
Based on our preliminary plans, ∼1. 5 years will be necessary to complete engineering observations (including a certain period for optimization and stabilization of the performance and operation), so we expect to start science operation and a 5-year PFS SSP survey from mid-late 2019. The science team is developing a detailed survey strategy to be refined in the next two years, and the technical team is committing to this by brushing up the estimates of on-sky instrument sensitivity and carrying out survey and data simulations. Information on the instrument development and survey strategy will be posted and updated on the PFS official website http://pfs. ipmu. jp/. In addition, news, events and milestones are reported in the PFS official blog http://pfs. ipmu. jp/blog/. PFS and HSC, a unique set of powerful survey instruments, will be crucial strategic pieces for the Subaru Telescope through the 2020s into the 2030s, allowing unique science by effective synergies with new generation ground-based and space missions such as TMT, LSST, Euclid and WFIRST.
Figure1. A few representations of the PFS strengths. Top left: the product of the mean galaxy space density and the galaxy clustering amplitude is plotted as a function of redshift and the case for the PFS cosmology program is compared with WFIRST and DESI. 10, 11 Top right: the cosmic star formation history 12  is illustrated with a wide range of redshifts that can be accessed in the PFS galaxy & AGN evolution survey exploiting the wide wavelength coverage. Bottom: a PFS pointing plan around the Sculptor dwarf galaxy 13, 14  as a part of the Galactic archaeology program is summarized, showing high efficiency thanks to the wide field and high multiplicity.
ACKNOWLEDGMENTS We appreciate all the contributions from the  PFS science team  to the instrument requirements definitions and the survey planning. We also thank the people involved with this PFS project in the past in any formats. Without their efforts and contributions, the project would not even exist now. We are grateful to the staffs (in addition to those on the author list) at  National Astronomical Observatory of Japan  and the  Subaru Telescope observatory  for their contributions to the development of the PFS instrument, the modifications of the telescope system and other infrastructures to accept PFS, the preparations of PFS system integration and engineering observations, and various other aspects such as the administrative supports. Our thanks should also go to the staffs at  Durham University, UK , for their supports of the development of the PFS fiber system as the consultancy.
As the need for precise handling of nuclear data covariances grows ever stronger, no information about covariances of prompt fission neutron spectra (PFNS) are available in the evaluated library JEFF-3. 2, although present in ENDF/B-VII. 1 and JENDL-4. 0 libraries for the main fissile isotopes. The aim of this work is to provide an estimation of covariance matrices related to PFNS, in the frame of some commonly used models for the evaluated files, such as the Maxwellian spectrum, the Watt spectrum, or the Madland-Nix spectrum. The evaluation of PFNS through these models involves an adjustment of model parameters to available experimental data, and the calculation of the spectrum variance-covariance matrix arising from experimental uncertainties. We present the results for thermal neutron induced fission of 235 U.
The systematic experimental uncertainties are propagated via the marginalization technique available in the CONRAD code. They are of great influence on the final covariance matrix, and therefore, on the spectrum uncertainty band width. In addition to this covariance estimation work, we have also investigated the importance on a reactor calculation of the fission spectrum model choice. A study of the vessel fluence depending on the PFNS model is presented. This is done through the propagation of neutrons emitted from a fission source in a simplified PWR using the TRIPOLI-4 ® code. This last study includes thermal fission spectra from the FIFRELIN Monte-Carlo code dedicated to the simulation of prompt particles emission during fission.
The Madland-Nix model  [1]  is built from the Weisskopf theory of fission fragment evaporation. The spectrum of each fragment, light or heavy, is described by two parameters: the fragment maximal temperature T m , assumed to be the same for light and heavy fragments (i. e. T L m = T H m = T m ), and the fragment kinetic energy per nucleon in the lab frame, E L,H f. The contributions of both fragments must be summed, and weighted by the ratio of the average fragment neutron multiplicity νL,H over the total average prompt neutron multiplicity νL + νH. However, as the Madland-Nix spectrum is quite insensitive to this ratio, we set the contribution of the fragments to 50% each. We finally consider the PFNS computed by the FIFRELIN code  [4, 5] , which simulates the whole process of fission fragment de-excitation.
Generation of the PFNS Variance-covariance Matrix Using CONRAD CONRAD  [6]  is a code developed at CEA-Cadarache, devoted to nuclear data evaluation. In particular, it allows adjusting the free parameters of theoretical models to measured data through the 04012-p. 2 Bayesian technique. The three models (Maxwellian, Watt and Madland-Nix) mentioned above were implemented and the model parameters were adjusted to the available experimental data with their random counting uncertainty. The FIFRELIN model of PFNS has no analytical form since it comes from a Monte-Carlo simulation, therefore it was not part of the present study. For Watt and Madland-Nix models, the prior parameter values were computed as suggested by Madland and Nix in reference  [1]. The Maxwellian parameter T prior value was set as commonly suggested in the literaturenamely, 1.
Conclusion and Perspectives The CONRAD code allows in a single calculation the adjustment of nuclear data model parameters, and the generation of the covariance matrix in the frame of this model. We presented the PFNS covariance matrix obtained for thermal fission of 235 U. The systematic uncertainty is propagated via the marginalization technique. The uncertainty of the final spectrum is of the same order than the uncertainties on the PFNS measurements used for the study, although the shape of the uncertainty as a function of neutron energy strongly depends on the PFNS model under study. The systematic uncertainty included in our calculation comes from the experimental uncertainty on normalization; we also show the influence of the neutron detection efficiency uncertainty. It is intended in the future to generate the covariance matrix associated to the PFNS from the FIFRELIN probabilistic code.
Stimulus Full-face, frontal view, black and white photographs of human faces (gender-matched), extracted from the FACES Database  (Ebner et al. , 2010) , and of a humanoid robot (iCub;  Pais et al. , 2013)  expressing either a positive (happiness) or a negative emotion (anger) were employed in a reaction time task (see Fig. 1 ). These two emotions were chosen because they are easily recognized and distinguished by healthy participants  (Pais et al. , 2013). The two non-face stimuli consisted of a plus or a minus sign symbol. Thus, 6 different stimuli were presented to the participants. All stimuli had a gray background. An editing program (Photoshop) was used to match pictures for luminance. Stimulus size was 620 × 480 pixels. It is also important to note that this protocol is an experiment part of another protocol in which participants have to actually perform a motor task with the iCub robot.
Statistical analysis We compared groups' characteristics using the Student t or χ 2 tests. A 3 (type of stimulus: human face, robot face, non-face stimulus) × 2 (emotion: positive and negative) × 2 (group: schizophrenia and healthy participants) repeated measures ANOVA was performed on reaction time (RT) and accuracy. Post-hoc analyses were performed using Bonferroni or paired t-test. Correlations between RT, number of correct answers and clinical symptoms were analyzed. RT was calculated as the time interval between the onset of the RT signal and the time when the participant pressed the keyboard to answer it. RTs lower than 0. 1 s were also discarded from further analysis. No upper threshold was established since participants had only 1 s to respond. RT data was normally distributed. SPSS (The Statistical Package for the Social Sciences version) 17.
TLS is utilized in various ways on the Internet, to protect web traffic (HTTPS), to secure the e-mail transport protocol (SMTP/TLS) and so on. However, the TCP connection required by TLS is costly, specially in constrained environments such as the Internet of Things (IoT). Datagram Transport Layer Security (DTLS)  [RM12]  is a modified version of the TLS protocol to use UDP as the underlying layer. It has been designed for purposes such as real-time applications. The IETF promotes its use on constrained devices that are utilized in the emerging wireless technologies such as Internet of Things (IoT). Despite the use of UDP, DTLS is still costly for two main reasons. First, the number of messages transmitted during the handshake delays the first data packet (which is emphasized in duty cycled network [VTW + 15]) and energy consumption.
Second, the DTLS header limits the room available for application data in a packet. This article presents an improved version of DTLS for the sake of speed, allowing quicker connection establishments. We reduce the amount of signalling packets and the header overhead, using IETF recommendations as a basis. In order to concentrate on the DTLS layer only, we do not make any assumption on other layers. As a consequence, we reduce the whole protocol stack to the bare minimum as shown in Figure  1 : neither IP nor transport layer nor application layer, in order to measure only the impact of DTLS over a communication network based on IEEE 802. 15. 4. Fig. 1. Classic protocol stack as in deployed networks, and our protocol stack to only measure the impact of the DTLS protocol in communications. As encryption speed is an important parameter in security protocol efficiency, we quantify in this paper the gain of using hardware encryption rather than software encryption.
We compare our proposal to the original DTLS protocol, and we also compare the software and hardware encryptions in order to measure the improvement of communication speed with a specialized hardware. We focus on the connection speed (handshake and application data exchange) and the memory (RAM and Flash) consumption. The rest of this paper is structured as follows. In Section 2, we introduce DTLS principles. Section 3 describes the objective of this article, and the choice of algorithms. We then explain our improvements over the DTLS protocol in Sections 4 and 5. Experimental study of performances has been conducted. We describe our hardware platform in Section 6, and we summarize performance results in Section 8. We describe related works in Section 9, before concluding in Section 10. The goal of DTLS is to secure a connection between two applications.
It is composed of an algorithm and cryptographic material negotiation, then an application data exchange with encryption (confidentiality), integrity checking and signature (authenticity). DTLS is layered above UDP as the transport protocol, where TLS is layered above TCP. This difference implies that DTLS doesn't have a transport layer protocol that handles packet losses and retransmission, reordering and fragmentation itself. Figure  2a  shows the different control packets exchanged during connection establishment, which are explained later in this section.
Improvements: redundancy during the application data exchange Once the connection is established, DTLS transmits the application data using two layers (Record and Application layers) whose headers are shown in figure  3. The Record part contains two fields (Epoch and Sequence Number) used to detect packet losses, duplication and unordered packet arrival. When Authenticated Encryption with Associated Data (AEAD) algorithm is used to secure the communication, the Application Data part contains both the payload and an Initialization Vector (IV). The IV has to be unique on every message and is conventionally composed of the Epoch and the Sequence Number from the Record part. We can collapse this redundant information (8 bytes) in the two layers, reducing the DTLS communication overhead from 29 to 21 bytes.
Epoch Sequence Number Version MSize Fig. 3. Redundancy in the headers, due to a conventional usage of the initialization vector in AEAD encryption. The gain is especially the higher packet payload available (97 bytes instead of 89 when used on a IEEE 802. 15. 4 network, hence 8. 2 %). Applications have more room to carry data on a single fly. In addition, the removal of this redundancy is expected to lower the transmission duration. The counterpart of this optimization is that it removes compatibility with current DTLS implementations.
Handshake performances The handshake duration is measured on the client, from the first message of the connection to the handshake final (Finished) message. The median of observed handshake durations done over 100 connections is about 233 214 µs with DTLS, and 186 660 µs with our optimized version of DTLS. The gain is 46. 5 ms, 19. 9 % of the duration of the handshake: the message removal lowers the number of exchanges, thus lower the total duration of CSMA-CA waiting. The removal of the Initialization Vector (IV) explained in section 5 brings several improvements: transmission is quicker because the message is smaller, leaving more room for application data and decreasing the use of fragmentation.
Data exchange performances Figure  4  shows our results over 3000 measurements for each configuration. The difference between DTLS and our optimized version is pronounced with the single byte message because difference of the message size to transmit is proportionally more important, the gain is about 2. 6 %. The difference between DTLS (optimized or not) and a non protected exchange is measured with a non-secure exchange, to put the cost of security into perspective. The message protection implies a RTT 3. 9 times higher than without protection. Encryption, plus the overhead in the packet payload for DTLS, significantly slows down the connection. Secondly, the message payload can be 8 bytes higher, thus the number of messages required for sending a certain amount of data is lower. For instance to send 1700 bytes, which represents 18 messages with the optimized version of DTLS, it is required to send 20 messages of the original DTLS.
Related Work Lithe [RSH + 13] takes another approach to reduce DTLS overhead: it relies upon 6LoWPAN to compress DTLS informations in order to gain up to 8 bytes during data exchange in the optimal case, where Epoch and Sequence Number fields (in the Record header) are truncated respectively to 8 and 16 bits (instead of 16 and 48 bits). This reduced width limits the number of exchanged messages in a single connection. Lithe does not remove redundant informations between the Record and Application layers. Furthermore, Lithe is tied to 6LoWPAN and is not independant from the lower layers. Robust Header Compression (ROHC) is a method described in several RFCs to compress headers, such as IP and UDP ones (amongst others). The key principle is not to send an header if it is the same than the previous one, or just to send the difference.
Our work leads to an improvement of the time spent on the handshake, of the RAM and Flash memories used by removing several messages. The most significant improvement is the larger payload of application data, due to the removal of Initialization Vector for AEAD algorithms, such as suggested by the DICE IETF working group. This improvement is at the cost of an incompatibility with current DTLS implementations. As future work, we could experiment new standardized algorithms for encryption (ChaCha20) and for integrity checking (Poly1305) to see the differences in memory, code size and processing time. Furthermore, the IETF working group on TLS 1. 3 tends to change completely the protocol, leading to a simpler and faster handshake, new mandatory-to-implement algorithms and to even change the fields.
The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large-scale data. It can handle missing data and pooled sequencing data. By contrast to populationbased approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved both in terms of statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2. 0 and later versions of the package.
When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other software for genome scans (BayeScan, hapflk, OutFLANK, sNMF). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10% with the exception of BayeScan that generates 40% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful software in scenarios of population divergence and range expansion. Because pcadapt handles next-generation sequencing data, it is a valuable tool for data analysis in molecular ecology.
, 2014; Hao et al. , 2016; Galinsky et al. , 2016; Chen et al. , 2016; Duforet-Frebourg et al. , 2016; Martins et al. , 2016). The last challenge arises from the nature of multilocus datasets generated from next generation sequencing platforms. Because datasets are massive with a large number of molecular markers, Monte Carlo methods usually implemented in Bayesian statistics may be prohibitively slow  (Lange et al. , 2014). Additionally, next generation sequencing data may contain a substantial proportion of missing data that should be accounted for  (Arnold et al. , 2013; Gautier et al. , 2013). To address the aforementioned challenges, we have developed the software pcadapt and the R package pcadapt. The software pcadapt is now deprecated and the R package only is 2 maintained. pcadapt assumes that markers excessively related with population structure are candidates for local adaptation.
Since its first release, pcadapt has substantially evolved both in terms of statistical approach and software implementation (Table  1 ). The first release of pcadapt was a command line C software. It implemented a Monte Carlo approach based on a Bayesian factor model  (Duforet-Frebourg et al. , 2014). The test statistic for outlier detection was a Bayes factor. Because Monte Carlo methods can be computationally prohibitive with massive NGS data, we then developed an alternative approach based on PCA. The first statistic based on PCA was the communality statistic, which measures the percentage of variation of a SNP explained by the first K principal components  (Duforet-Frebourg et al. , 2016). It was initially implemented with a commandline C software (the pcadapt fast command) before being implemented in the pcadapt R package.
We do not maintain C versions of pcadapt anymore. The whole analysis that goes from reading genotype files to detecting outlier SNPs can now be performed in R (R Core  Team, 2015). The 2. 0 and following versions of the R package implement a more powerful statistic for genome scans. The test statistic is a robust Mahalanobis distance. A vector containing K z-scores measures to what extent a SNP is related to the first K principal components. The Mahalanobis distance is then computed for each SNP to detect outliers for which the vector of z-scores do not follow the distribution of the main bulk of points. The term robust refers to the fact that the estimators of the mean and of the covariance matrix of z, which are required to compute Mahalanobis distances, are not sensitive to the presence of outliers in the dataset  (Maronna and Zamar, 2012).
In the following, we provide a comparison of statistical power that shows that Mahalanobis distance provides more powerful genome scans compared to the communality statistic and to the Bayes factor that were implemented in previous versions of pcadapt. In addition to comparing the different test statistics that were implemented in pcadapt, we compare statistic performance obtained with the 3. 0 version of pcadapt and with other software of genome scans. We use simulated data to compare software in terms of false discovery rate (FDR) and statistical power. We consider data simulated under different demographic models including island model, divergence model and range expansion. To perform comparisons, we include software that require to group individuals into populations : BayeScan  (Foll and Gaggiotti, 2008) , the F LK statistic as implemented in the hapflk software  (Bonhomme et al.
Input data The R package can handle different data formats for the genotype data matrix. In the version 3. 0 that is currently available on CRAN, the package can handle genotype data files in the vcf, ped and lfmm formats. In addition, the package can also handle a pcadapt format, which is a text file where each line contains the allele counts of all individuals at a given locus. When reading a genotype data matrix with the read. pcadapt function, a. pcadapt file is generated, which contains the genotype data in the pcadapt format.
When K > 1, the covariance matrix ⌃ is estimated with the Orthogonalized Gnanadesikan-Kettenring method that is a robust estimate of the covariance able to handle large-scale data  (Maronna and Zamar, 2012)  (covRob function of the robust R package). When K = 1, the variance is estimated with another robust estimate (cov. rob function of the MASS R package).