dataesr/scientific-paragraphs-categorization huggingface dataset:
due to the absence, for the moment, of clear mechanistic explanations for most of the systems. In MySinPat, mutations discovered in the past ten years are now enriched by weekly reports. MiSynPat, by allying sequence data, allelic composition in patients, sequence conservation, structural information, and bibliography, offers a homogeneous resource for in-depth analyses. The conservation/structural standpoint perfectly complements existing databases, whose main goal is to facilitate genomic investigation (e. g. , MSeqDR; Shen et al. , 2016). In addition, the embedded infrastructure and the automated mining and retrieval of literature data with minimal human intervention guarantee durability, regularity and up-to-date maintenance of the site. The unique configuration of MiSynPat explains why being restricted to a family of enzymes is a true added value and allows users a more comprehensive view of the field.
Contract grant sponsors:  Centre National de la Recherche Scientifique (CNRS) ;  Université de Strasbourg (Unistra) ;  Agence Nationale de la Recherche  (the  French National Programs "Investissement d'Avenir"  [ ANR-11-LABX-0057_MITOCROSS ] and [ ANR-10-BINF-03-02 ]; and  ReNaBi-IFB  [ ANR-11-INBS-0013 ]);  Fondation pour la Recherche Médicale  (projet DBI20131228569).
The 3D pieces were designed with Blender (Blender Foundation) and printed with a Makerbot printer (MakerBot R Industries). The river was represented by a wooden bulge pasted onto the box. Conductive tactile zones were created on landmarks, ramparts, and roads with small metallic pieces inserted into the wood or plastic pieces, which were connected to two Arduino-compatible Touch Boards R (Bare Conductive Ltd, London). One tactile knob (round head nail) for each rampart and two to five tactile elements (headless nails) for each road triggered audio descriptions when they were touched. Audio feedback included the names of the touched element, as well as the corresponding historical description. Audio files were created with the "Dspeech" (Dimitrios Coustoumbas) software in MP3 format, using the female voice "Virginie" (RealSpeak Solo).
ACKNOWLEDGMENTS We thank  INJA, Paris , and more specifically  Julien Berthier , special education teacher, as well as the 24 visually impaired students who volunteered to participate in the study. We thank  Andrea Boisadan  for assistance during the experimentation. Finally, we thank  CESDV-IJA  and " Cherchons Pour Voir ", and more specifically  Nathalie Bedouin  for precious assistance in producing tactile documents.
Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today's clusters already offer mechanisms to adjust the system to the resource requirements of an application, e. g. , by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energyefficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications.
We refer to such a behaviour as application dynamism. Examples of characteristics may involve compute intensity, workload granularity (load balancing), and parallel efficiency. Applications exhibiting such dynamic behaviour are the main target for our methodology. The READEX project aims at creating a tools-aided methodology for dynamic auto-tuning of high performance computing (HPC) applications for increased energy efficiency compared to the default system configuration. The methodology is divided into two parts: design time analysis (DTA) and runtime application tuning (RAT). The role of the DTA is to perform a detailed analysis of the application to unveil application dynamism. Once dynamism has been identified, care is taken to find the best tuning configuration for the individual code region of interest.
Typical examples of configurations may include the number of OpenMP threads and the CPU clock frequency. Every execution of a code region is called a runtime situation (rts). At the end of the DTA, a tuning model is created, which contains a description of the best found configurations. In order to reduce overhead, similar or identical rts's are merged into scenarios, a technique for dynamic tuning found in the embedded systems domain  [2, 3]. During production runs (RAT), the tuning model is used by a lightweight runtime library called the READEX Runtime Library (RRL). The role of the RRL is to perform adjustments according to the description found in the tuning model. The quality of the tuning model is evaluated at runtime by the RRL. A calibration mechanism can make adjustments to the tuning model to further refine configurations at runtime.
The work of Tiwari et al. [5]  targets static tuning. The authors have created a framework that statically evaluates different "computation kernels" and compiler optimisation flags to find an optimum in terms of execution time. Their work is based on Active Harmony  [6] , which is a well known framework for static and dynamic tuning. The application can define a search space and retrieve new configurations to test from a central tuning server. Contrary to the READEX approach, it is based on standard optimisation algorithms and not on expert knowledge about the tuning aspect. An approach taken by several projects is to overcome the problem of non-optimal programs by introducing new prototype languages or programming models, e. g. , the ENCORE  [7]  prototype language where the OmpSs programming model  [8]  is used to save energy by reducing the compute time.
The PEPPHER project  [9]  implements a framework that compiles multiple variants of the code for different types of architectures such as CPU's and GPU's. During execution, a runtime system decides which code path to execute. Although the authors seem to have energy efficiency as a possible objective in mind, they choose to focus on execution time as the main objective. The ANTAREX project  [10]  takes a similar approach. Using a Domain Specific Language (DSL) code can be distributed between multi-core CPUs and accelerators. An extra compilation step is introduced to translate the DSL into the intended programming language. While our work targets conventional HPC clusters, ANTAREX focuses on ARM-based systems. A manual approach for dynamic tuning has been described by Schöne and Molka  [11].
Application execution Our methodology builds on instrumentation of application and library functions as well as other regions of interest such as OpenMP parallel regions. We will use the term region to describe arbitrary code parts of the application. The set of all instrumented regions is called R instr , which denotes all regions that are instrumented and thus visible to the tuning system while the application is running. This set potentially contains a large number of regions that might run for only a short time, making a switch of parameters undesirable due to the cost associated with each switching operation. We thus need to reduce the set of regions to the set of significant regions R ⊆ R instr. A region r ∈ R is considered significant if it exhibits a certain execution time and thus is a suitable target for dynamic parameter switching.
Next, the instrumentation of the application is performed using automatic instrumentation techniques and potentially additional user instrumentation. In order to keep the runtime overhead low, the instrumentation has to be restricted to coarse granular program regions based on advanced automatic filtering techniques such as profile analysis for filter generation. The choice of a suitable instrumentation granularity is a common problem among performance analysis tools  [14]. With the aid of the instrumentation, significant regions can be detected in the application. All insignificant regions will be ignored and later added to the instrumentation filter. Based on the selection of significant regions, the tuning potential analysis will be performed on the basis of profiling data. This actual selection will be carried out either using existing profiling data or by performing an additional profiling run of the application.
Design-time analysis PTF will be extended to perform the DTA with the extensions depicted on the left side of Fig. 5. Most importantly, the DTA Management will control the overall execution of the DTA and includes two components: the DTA Process Management, which interacts with the PTF components to control the overall tuning workflow described in Sect. 5. 1. The RTS Management will handle the RTS Database, which will eventually contain all rts's and their selectors. Based on this database, the Scenario Identification component will construct the final application tuning model by aggregating the rts's into scenarios with corresponding classifiers according to their context and selector. The READEX tuning plugins are controlled by the plugin control and select suitable tuning parameter values based on expert knowledge applied to performance characteristics measurements.
The plugins make use of different search strategies and expert knowledge to shrink the search space. PTF provides multiple search strategies including exhaustive search, individual search, genetic search, and random search. The individual strategy optimizes tuning parameters assuming that they are independent. The genetic and random strategies are powerful search techniques for complex search spaces. Both can be enhanced in PTF with machine learning techniques to guide the selection of individuals based on previous tuning results. PTF's Analysis Component will be extended to provide hints to the design-time analysis, for example, the frequency of tuning cycles. The analysis component accesses performance data that is gathered in the Performance Database. The RRL (described below) is used to gather the performance data as well as to control the tuning parameters on the machines the parallel application is running on.
If a significant region has been detected, the RTS Detector collects all necessary information and passes it to the command and control (C&C) module, which passes it on to the TMM where the rts is mapped onto the upcoming scenario. The C&C receives the optimal configuration for the upcoming rts and applies it by sending the settings for the individual parameters to the parameter controller. The Parameter Controller controls the parameter settings through parameter control plugins, which provide a unified interface to the different parameters on the hardware and software layer. The input to the TMM can be twofold: 1. During DTA, the configuration of the TMM is provided by PTF through the Online Access Interface (OAI) by sending tuning requests to the RRL that contain information on rts's and the configurations to be tried in the current phase for each rts.
The PERMON  [18]  software package is an MPI-parallel implementation of the FETI method focusing on engineering applications and will be used as a target application throughout this section. The two main phases of the FETI method are pre-processing and solve. In the pre-processing stage, the stiffness matrix K is factorised and the natural coarse space matrix G and coarse problem matrix GG T are assembled. The latter matrix is also factorised. Both of these operations are among the most time consuming and thus the most energy consuming operations. The solver employs the CG algorithm, which consists of Sparse Matrix-Vector Multiplications (SpMV), vector dot products, or AXPY functions. For each iteration, it is necessary to apply the direct solver twice, i. e. , forward and backward solves for the so-called pseudoinverse action and the coarse problem solution.
At runtime, the lightweight READEX Runtime Library will be used to detect upcoming system scenarios and adapt the necessary system parameters dynamically based on the information provided by the tuning model. The runtime library will be based on Score-P, which has proven to be scalable on some of the largest HPC systems available today. By building on top of existing scalable software tools, we ensure both stability and usability of the READEX Tool Suite. On the one hand, these tools have been well tested by developers and users and thus provide a solid basis for the READEX software. On the other hand, they are already known to end users, which significantly reduces the entry barrier and allows for reuse of existing documentation. The READEX project employs a co-design approach in which selected applications are manually tuned for energy efficiency.
Acknowledgements The research leading to these results has received funding from the  European Union 's  Horizon 2020 Programme  under grant agreement number  671657. The authors would like to thank  Yury Oleynik  for his contributions. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
We used the straightforward objective criteria to preselect stimuli: three large picture databases conceived for the study of emotion were used: the Nencki Affective Picture System (NAPS  [57] ), the International Affective Picture System (IAPS  [58] ), and the Geneva Affective Picture Database (GAPED  [59] ). In some cases, none of these databases had enough pictures for some of the themes, so we turned to a stock picture service, iStockPhoto. We ended up with 126 preselected pictures.
Biometric Attachment Test (BAT) Administration Procedure The BAT automatic administration procedure was constructed using OpenSesame software, version 3. 1. 2 [62]. The full test duration is of 9 min. Before beginning, the participant is instructed to observe the visual or listen to the music stimuli as long as it is visible or audible, and then to describe aloud what they felt about it. During the test, each theme stimulus is automatically presented for 15 s, followed by a black screen displaying the phrase "What did you feel?" which shows for 25 s, whereas a countdown slider displays the available time to respond. It then shows the phrase "Thank you. Here is the following stimulus. ," for 5 s, followed by the following stimulus, and so forth. The test is administered with the person being alone with the computer in a room; aloneness can facilitate the activation of the attachment system  [1]  and renders the test situation closer to Ainsworth's SSP  [6].
Feature Extraction We conducted extensive feature extraction from each of the sense modalities captured during the BAT. All feature extraction procedures described below, including noise filtering processes, were performed programmatically without the need for human supervision. The interbeat interval (IBI) was automatically calculated from the HR data by proprietary algorithms of the Empatica E4 research wristband  [65]. The IBI files were cleaned of artifacts using Artiifact software, version 209  [66]. The same software was used for the extraction of heart rate variability (HRV) features (for a review of most standard HRV features, see  [67] ). We created a function in Microsoft Excel's Visual Basic for Applications version 7. 1 to automatically calculate Bayevsky stress index  [54]  from the IBI files.
From the EDA data, SCR, phasic maximal activity, and tonic skin conductance features were extracted using LedaLab software version 349  [68]. From the video data, facial expressions (such as anger and contempt, as well as the composites negative, neutral, and positive) were extracted using FACET's Emotient  [69]. A face size measure was extracted by the same software, which permits to establish the movement toward or away from the camera and thus the stimuli. Gaze direction was extracted using OpenFace  [70]. From the audio recordings of the participants' responses, paralinguistic acoustic features were extracted using the Cooperative Voice Analysis Repository for Speech Technologies (COVAREP) version 1. 2  [71]. They help identify a breathy, relaxed voice from a tense voice. We used Python and the French language model of Google's Cloud Speech API to generate automated transcripts of all responses.
Analyses A first exploratory analysis, conducted in MathWorks Matlab version R2016A, consisted of performing correlation matrices to uncover associations between attachment security as measured by the AAQ, the AAP, and the AMMI, and the features extracted from BAT responses. For the AAQ and the AAP, we used Pearson correlations, whereas Spearman rank was used for the AMMI due to the small number of assessed participants. Since AAQ measures attachment to romantic partners, it was evaluated vis-à-vis theme 7 ("attuned couple"). AAP attachment security is concerned with attachment figures in general, thus we evaluated it with regards to a composite formed by the mean of responses to theme 4 ("attuned father-child"), theme 7 ("attuned couple"), and theme 13 ("attuned mother-child"). As per the AMMI, since it yields security scores for mother, father, and partner separately, we explored each in relationship to the corresponding BAT theme (themes 13, 4, and 7, respectively).
A second, confirmatory analysis was conducted using IBM SPSS Statistics version 23 to verify if our exploratory findings were not a mere spurious byproduct of multiple hypotheses testing  [73]. We proceeded with a stringent approach consisting of producing a single "composite effects index" out of all available features (weighted in the same direction), then testing such index for a Pearson correlation vis-à-vis the variable of interest  [74, 75]. This approach circumvents the problem of type I errors often encountered in exploratory analyses. It also accounts for the problem of type II errors, which are likely when statistical correction procedures to control for family wise error rate (eg, Bonferroni correction) or false discovery rate (eg, Benjamini-Hochberg correction) are performed in studies with small samples, an elevated number of features, or small effect sizes such as ours  [76] [77] [78].
The Phasic maximal activity feature of EDA was not included for being redundant with respect to EDA's SCR. The tonic skin conductance feature was not included because it requires longer measuring durations to be meaningful (they were calculated for future analyses focusing on the totality of the BAT instead of just isolated themes). The total amount of features per theme that ended being added up in the composite effects index is of 61 (see Figure  2 ). A third analysis, conducted using IBM SPSS Statistics version 23, consisted in performing repeated measures analysis of variances (ANOVAs) on the BAT responses extracted features that were revealed as both statistically significant in their correlation to attachment security as well as theory consistent with attachment theory. The objective was to evaluate if those features yielded different results across different BAT stimuli sets or if they were consistently similar.
This analysis might also suggest that some of the features not showing a statistically significant association with attachment security might not achieve so because of a small sample size or small effect sizes. Composites help increase the effect size of features weighting in the same direction, statistically revealing their direction  [74]. We believe this sort of multimodal automatic appraisal of "the whole picture" that is an attachment-deactivating reaction, from a behavioral, psychophysiological, and linguistic standpoint, is a taste of what is becoming possible for psychometrics. Moreover, the fact that our data was obtained outside of a lab setting, using a consumer tablet and its webcam, a consumer-grade USB microphone, and a wireless wristband, attests to the pace at which sensing technology is advancing, offering a glimpse at how effortlessly these measures could be obtained in a close future.
Acknowledgments We wish to thank  Ville Evrard Clinical Research Unit 's Director,  Dr Dominique Januel , for her support in the form of financial help to purchase material for this study, as well as granting us access to patients in the  Ville Evrard Psychotherapy Center. The work depicted here is sponsored by the  US Army Research Laboratory (ARL)  under contract number  W911NF-14-D-0005. Statements and opinions expressed and content included do not necessarily reflect the position or the policy of the  US Government  and no official endorsement should be inferred.
The goal of the LUCID project is to identify impacts that are statistically robust, primarily in terms of being detectable, common among the different employed models, and focusing on signals that exceed the models' internal variability. LULCC is prescribed based on historical data starting from the beginning of the industrial revolution to present day conditions  [1, 3]. Preliminary results from the LUCID project were discussed by Pitman et al. [12] , who mainly focused on the Northern Hemisphere summer season. They investigated the impact of land cover changes on the atmosphere from pre-industrial period to present-day, using several Atmospheric General Circulation Models (AGCMs). They showed that model simulations in temperate regions indicate substantial changes in latent and sensible heat fluxes, albedo, and near-surface air temperature.
The model experiments from the LUCID project and the observation data sets are presented in Section 2, together with changes in land-cover since pre-industrial times. Section 3 presents a validation of rainfall and temperature as simulated by the climate models over two contrasted regions of West Africa: the Sahel and Guinea. Section 4 evaluates leaf area index as simulated by the LUCID models, and discusses how land-cover changes affect surface climate through changes in land surface characteristics and fluxes at the biosphere-atmosphere interface. Finally, Section 5 discusses the main findings, with an emphasis on changes caused by an increase in atmospheric greenhouse gases concentration since the pre-industrial time period.
LUCID Simulations In this study, we used the seven GCMs involved in the LUCID project  [12, 13, 21, 22]. The LSMs embedded in each GCM (hereafter GCM/LSMs), and their references are listed in Table  1. In the following, we will use the name of the GCM provided in Table  1  to ease reading of the text and figures' legends. Four types of simulations were conducted by GCMs groups to evaluate the impact of LULCC between the pre-industrial period and present day. Each experiment includes an ensemble (five members) of 30-years simulations, computed with prescribed sea surface temperatures SSTs and sea-ice extent (SIC), atmospheric CO 2 concentrations ([CO 2 ]) and land-cover maps Table  2. All models used the same SST/SIC dataset as boundary conditions (HadISST dataset of  [47]  and a combined crop/pasture data set derived from  [1, 3]  respectively.