dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The present paper aimed to design a Gaussian process regression model in the service of GHI forecasting at various time horizons (intrahour, intraday and intraweek). In existing research, only the squared exponential kernel k SE was used as the default choice (see  [21, 23] ); no due attention was paid to the kernel selection and the ensuing impact of the model's performance, which was thoroughly investigated and proven to be pertinent in this paper. A comparative study was carried out on several GPR models, ranging from rather simple models to more complex versions, such as quasiperiodic kernels that are the sum or product of a simple kernel and a periodic kernel. Two separate 45-day datasets have been used. The conclusions which arise are that GPR models predictably outperform the persistence model, and that quasiperiodic-kernel-based GPR models are particularly apt to model and forecast GHI.
A simple sparse GPR approach, the subset of data technique, has also been assessed; this consists of using only a part of the available data for training. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent: this study indicates that comparable performance (in terms of nRMSE) is obtained when using only 30% of the available training data, which leads to a 90% decrease of computation time. Additionally, even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels. Future work will focus on some of the best-performing quasiperiodic kernels that have been put forward in this paper and assess their performance using larger GHI datasets.
Thirty minute, 4 h and 48 h forecasts of GHI given by OSGPR models based on k SE and k Per×RQ (nine-day dataset, with seven days used for training and two days used for testing). Training data are randomly selected to obtain a data sparsity of 20 %. Box plots depicting nRMSE vs. training data sparsity for 30 min, 4 h and 48 h forecasts of GHI given by the k SE -based OSGPR model (summer dataset). Number of Monte Carlo runs: 100. Box plots depicting nRMSE vs. training data sparsity for 30 min, 4 h and 48 h forecasts of GHI given by the k Per×RQ -based OSGPR model (summer dataset). Number of Monte Carlo runs: 100.
Thirty minute, 4 h and 48 h forecasts of GHI given by OSGPR models based on k SE and k Per×RQ (nine-day dataset, with seven days used for training and two days used for testing). Training data are randomly selected to obtain a data sparsity of 20 %. Box plots depicting nRMSE vs. training data sparsity for 30 min, 4 h and 48 h forecasts of GHI given by the k SE -based OSGPR model (summer dataset). Number of Monte Carlo runs: 100. Box plots depicting nRMSE vs. training data sparsity for 30 min, 4 h and 48 h forecasts of GHI given by the k Per×RQ -based OSGPR model (summer dataset). Number of Monte Carlo runs: 100. 0. 6 Training dataset Testing dataset 0. 5 (30 min forecasts) nRMSE 0. 3 0. 4 0. 2 GHI [W m -2 ] GHI [W m -2 ] GHI [W m -2 ] 01/07 01/07 01/07 Figure 5. 0. 1 300 600 900 1,200 300 600 900 1,200 300 600 900 1,200 0.
5 0. 6 0. 7 0. 8 0. 9 1 1. 1 1. 2 1. 4 1. 3 nRMSE Figure 6. 0 0. 1 0. 2 0. 3 nRMSE 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 nRMSE Figure 7. 90% 03/07 (a) k SE -based OSGPR model 05/07 07/07 30 min forecasts 09/07 GHI data Test data 01/07 Randomly selected data 95% confidence interval 03/07 (b) k Per×RQ -based OSGPR model 05/07 07/07 30 min forecasts 09/07 OSGPR Models 03/07 05/07 07/07 09/07 4 h forecasts (c) k SE -based OSGPR model 01/07 03/07 05/07 07/07 09/07 4 h forecasts (d) k Per×RQ -based OSGPR model 03/07 05/07 07/07 09/07 48 h forecasts (e) k SE -based OSGPR model 01/07 03/07 05/07 07/07 09/07 48 h forecasts (f) k Per×RQ -based OSGPR model 90% 80% 70% 60% 50% 40% 30% 20% 10% Testing dataset Testing dataset (4 h forecasts) Training data sparsity 90% 80% 70% 60% 50% 40% 30% 20% 10% (48 h forecasts) Training data sparsity Training dataset Testing dataset (30 min forecasts) 90% 80% 70% 60% 50% 40% 30% 20% 10% Testing dataset (4 h forecasts) Training data sparsity 90% 80% 70% 60% 50% 40% 30% 20% 10% Testing dataset (48 h forecasts) Training data sparsity 80% 70% 60% 50% 40% 30% 20% 10% 0% 0.
2. 5. 1. Virtual environment conditions based on Grand Theft Auto V The two virtual environment conditions are based on two neighboring districts of the fictitious virtual city of Los Santos from the video game Grand Theft Auto V  (Rockstar Games, 2013). Grand Theft Auto V is a gaming software that embodies an avatar within a visually realistic environment (e. g. , daily solar cycle, pedestrians, vehicles, weather conditions). The experiment was carried out using the "director mode", which allows the environmental and visual parameters of Grand Theft Auto V to be controlled. The software was installed on an Asus TUF Gaming FX504 Series PC and displayed on a separate 19-inch screen in front of the participant. For this study, two virtual neighborhoods that were as similar as possible to the characteristics of European cities were chosen: A high density of landmarks mixing both commercial and residential buildings, including apartments as well as small shops and restaurants, an irregular mapping (urban blocks of different sizes and shapes) and no high-rise buildings.
2. 5. 2. Navigational assistants The two groups of participants used one out of two voice navigational assistants. This protocol, based on the "Wizard of Oz" technique, gave the navigational assistants the appearance of a fully functional software for the participant, although the features were actually operated discreetly by the experimenter  (Salber & Coutaz, 1993). A loudspeaker was wired to the experimenter's laptop, in order to broadcast instructions given by the navigational assistant during the wayfinding task. Both assistants used the same synthetic female voice. For both assistants, the formulations and pronunciations (vocabulary, grammar mode, diction) were based on the instructions provided by common navigational aids (GPS, navigation apps). In particular, the instructions given by the survey assistant were all based on the first vocal instruction given for a route by Google Maps, which is the most widely used navigational mobile app according to the tech business magazine The Manifest  (Panko, 2018).
Collected data During the wayfinding task, the participant's screen and the 3D Rudder status window were simultaneously recorded on video files using Open Broadcaster Software (Figure  6 ). The purpose of recording the 3D Rudder status window was to allow accurate analysis of the videos by confirming the participant's movement. Two videos, one for each neighborhood, were recorded for each participant. Ninety-two videos (15 hours) were thus collected for the sample of 46 participants. The questionnaires were completed by all the 46 participants.
Analyses Wayfinding performance was measured by coding the video recordings, using the Behavioral Observation Research Interactive Software (BORIS)  (Friard & Gamba, 2016). Three concepts were defined for these measurements: "Mistakes", "visual explorations" and "stops". A "mistake" in the wayfinding task refers to an event during which the participant moves outside of the path indicated by the navigational assistant, thus making a detour. A "visual exploration" designates a moment during which the participant makes head movements from top to bottom or from right to left. A "stop" refers to a moment during which the participant remains motionless. A stop was recorded as soon as the participant did not push the platform in any direction. The coding was therefore carried out by counting the numbers of mistakes, visual explorations and stops.
Wayfinding performance A MANOVA on the data collected in both experimental conditions of the wayfinding task was carried out on the following dependent variables: Number of mistakes, number of visual explorations and inter-event time intervals (in seconds) between two visual explorations, number of complete stops and inter-event time intervals (in seconds) between two stops. The analysis indicates a main effect of the navigational assistant conditions (Pillai's trace =. 33, F(3. 95), p <. 01, ηp² =. 33). Further univariate analyses applying the Benjamini-Hochberg multiple comparison correction with a false discovery rate of. 05 indicate an effect across all variables  (Benjamini & Hochberg, 1995). Participants made fewer mistakes with the landmark assistant than with the survey assistant. Participants also made fewer visual explorations with the landmark assistant than with the survey assistant, and less frequently with the landmark assistant than with the survey assistant.
Discussion This study aimed primarily to test the induced performance and perceived usability of a landmarkbased wayfinding aid. By doing so, we also wanted to operate and subsequently assess a video game-based methodology that could represent a starting point for studies in spatial navigation. We used performance measurements on video data, as well as a usability questionnaire, to identify how navigational aids shaped spatial navigation in the virtual environment of Grand Theft Auto V and what type of navigational assistant would be judged the most usable. Our analyses on the data collected highlight the benefits of the landmarkbased aid in wayfinding. As these results provide converging evidence with the literature on wayfinding, they also emphasize the satisfactory methodological qualities of a video game-based methodology.
Wayfinding performance: Widespread positive effect of the landmark assistant on effectiveness and efficiency of the trip Two navigational assistants were compared in this study. One represented an aid whose principles were based on the literature on the use of landmarks in wayfinding. The second was a survey-based aid similar to the most commonly-used aids available to the general public. Based on video data of the trips completed by the participants, we conducted analyses on three observable behaviors: Making a mistake, visually exploring the environment and stopping completely. For these three observable behaviors, several differences were observed between participants using these two navigational assistants. Firstly, regarding the effectiveness of the trip, participants using the landmark assistant made fewer mistakes than participants using the survey assistant.
Acknowledgments The authors would like to thank  Take-Two Interactive, Inc. for allowing us to use Grand Theft Auto V for this study. The authors express their especial thanks to  Mrs. Gena Feist , from the  New York City office , for taking the time to draft, review and validate all the legal documents. The authors would also like to express their sincere thanks to the team at  3D  Rudder, and especially  Mr. Emmanuel Fromy , for their significant help regarding the 3D Rudder platform setup for this study.
Calibration of parameters β and γT on experimental results The ACMEG-T model was used to simulate experimental results on five different types of normally consolidated (NC) clays  [8 -12]. According to (  1 ) and (  7 ), the key model parameters to be determined to characterise the thermo-plastic response are γT, β and βs. All the considered tests are done under NC conditions, thus by definition at initial state p' = p'c. For each experimental dataset, the parameter γT in the ACMEG-T model was calibrated by fitting the volumetric strain accumulated during thermal loading at constant isotropic effective stress. The parameter β, corresponding to the plastic rigidity index at constant temperature, was calibrated on isothermal standard mechanical tests provided by the same authors, as proposed by  [23].
Acknowlegdement We warmly thank  Oleg Lepski  for fruitful discussions about minimax rates in change-point estimation, which helped to significantly improve a first version of the paper. We are also grateful to  Sylvain Delattre  for his interesting comments about the simulation study. Moreover, we would like to thank the anonymous referees for their valuable comments and their suggestions, which allowed to improve the quality and readability of the manuscript. This research has been supported by  French National Research Agency (ANR)  as part of the projects FOREWER  ANR-14-CE05-0028  and  BASICS ANR-17-CE40-0001 , as well as  ANR-18-IDEX-0001 ,  IdEx Université de Paris.
Acknowledgements The authors acknowledge Safran Aircraft Engines for providing the financial support of this project and for giving permission to publish this work. The authors also acknowledge the ANRT for providing the CIFRE grant allowing us to achieve this work. Appendix A. Stress in a half-space due to a concentrated unit normal force at the surface origin(F ij ) where with ν, the Poisson's ratio of the isotropic half-space. Appendix B. Stresses in a half-space subject to normal pressure (M ij ) An isotropic half-space is submitted a uniform normal pressure σ n in a discretized surface area of 2∆x × 2∆y at the center point P (x , y , 0). The stress at an observation point Q(x, y, z) is given in  [49]  and  [93] : The contact between a sphere and an elastic half-space having respectively elastic constants (E 1 , ν 1 ) and (E 2 , ν 2 ), where the surface z = 0 is discretized into rectangular surface area of 2∆ 1 × 2∆ 2 , is now considered.
a b s t r a c t Transcriptomic and proteomic analyses were performed on three replicates of tomato fruit pericarp samples collected at nine developmental stages, each replicate resulting from the pooling of at least 15 fruits. For transcriptome analysis, Illumina-sequenced libraries were mapped on the tomato genome with the aim to obtain absolute quantification of mRNA abundance. To achieve this, spikes were added at the beginning of the RNA extraction procedure. From 34,725 possible transcripts identified in the tomato, 22,877 were quantified in at least one of the nine developmental stages. For the proteome analysis, label-free liquid chromatography coupled to tandem mass spectrometry (LC-MS/ MS) was used. Peptide ions, and subsequently the proteins from which they were derived, were quantified by integrating the signal intensities obtained from extracted ion currents (XIC) with the MassChroQ software.
Absolute concentrations of individual proteins were estimated for 2375 proteins by using a mixed effects model from log 10 -transformed intensities and normalized to the total protein content. Transcriptomics data are available via GEO repository with accession number GSE128739. The raw MS output files and identification data were deposited on-line using the PROTICdb database (http://moulon. inra. fr/protic/tomato_fruit_ development) and MS proteomics data have also been deposited to the ProteomeXchange with the dataset identifier PXD012877. The main added value of these quantitative datasets is their use in a mathematical model to estimate protein turnover in developing tomato fruit.
Data description Tomato plants (Solanum lycopersicum cv. Moneymaker) were grown under conditions of commercial production in a greenhouse in the south-west of France. Samples were taken from pericarp of tomato fruits, at nine stages of tomato fruit development, on the 5th, 6th and 7th trusses [1] (Fig. 1 ). Transcriptomics and proteomics have been performed on these samples. Hierarchical clustering (Fig. 2 ) and principal component analyzes (Fig. 3 ) provide an overview of the transcriptome and proteome changes throughout the tomato fruit development. Transcriptomic data are available via GEO with accession number GSE128739. For proteomic, raw files and data are available on-line using the PRO-TICdb database (http://moulon. inra. fr/protic/tomato_fruit_development) and the ProteomeXchange with identifier PXD012877; quantitative data of proteins are provided in tables.
Transcriptome analysis and quantification Genes were mapped to the Solanum lycopersicum HEINZ assembly v2. 40, concatenated with the chloroplast (gij544163592jrefjNC_007898. 3j) and mitochondrial genomes (gij209887431jgbjFJ374974. 1j), and an "artificial chromosome" containing the 8 spike sequences (Supplemental Appendix S1). Genome data was downloaded from S. lycopersicum 2. 5 and the corresponding ITAG2. 4 gene models were downloaded from https://solgenomics. net/(34,725 entries). The quality of library sequencing was checked with FastQC  [4]. Quality and adapter trimming was performed with Trimmomatic [5] v0. 32. Trimmed reads were mapped to their respective genomes with Star  [6]  v2. 4. 2a and the unique counts per locus were quantified with HTSeq [7] v0. 6. 1. The number of transcripts per million (TPM) was calculated from the unique counts and gene length.
The normalized number of fragments per kilobase per million (FPKM) was calculated with cufflinks v2. 2. 1. Briefly, quantification based on FPKM corresponds to the normalization of data by depth sequencing (summed fragment per sample) divided by one million followed by a normalization by the gene length. Nondefault parameters that were used are presented in Supplemental Appendix S1. FPKM were then converted to TPM quantification to get relative transcript abundance among samples. Spikes were quantified as any other transcript. In order to preserve the native dynamic of RNA concentration through tomato fruit development (highest concentration before expansion phase), a standard curve was calculated for each sample. Each standard curve was determined from spiked-in concentrations and corresponding TPM values of the spikes.
Peptides were separated using a linear gradient from 5 to 35% buffer B for 110 min at 300 nl min À1. One run took 120 min, including the regeneration step at 95% buffer B and the equilibration step at 100% buffer A. Peptide ions were analyzed using Xcalibur 2. 1 (Thermo Electron) with the following datadependent acquisition steps: (1) MS scan (mass-to-charge ratio (m/z) 300 to 1,400, 70,000 resolution, profile mode), (2) MS/MS (17,500 resolution, normalized collision energy of 30, profile mode). Step 2 was repeated for the eight major ions detected in step (1). Dynamic exclusion was set to 30 seconds. Xcalibur raw datafiles were transformed to mzXML open source format using msconvert software in the ProteoWizard 3. 0. 3706 package  [9]. During conversion, MS and MS/MS data were centroided.
Protein identification Protein identification was performed using the protein sequence database of S. lycopersicum Heinz assembly v2. 40 (ITAG2. 4) downloaded from https://solgenomics. net/(34,725 entries). A contaminant database containing the sequences of standard contaminants was also interrogated (58 entries with e. g. , trypsin, keratin, and serum albumin). The decoy database comprised the reverse sequences of tomato proteins. Database search was performed with X!Tandem (version 2015. 04. 01. 1; http://www. thegpm. org/TANDEM/) with the following settings. Carboxyamidomethylation of cysteine residues was set to static modification. Oxidation of methionine residues, acetylation or deamination of glutamine and cystein residues were set to possible modifications. Precursor mass precision was set to 10 ppm.
Peptide and protein quantification Peptide ions were quantified using extracted ion chromatograms (XIC) and the MassChroQ software  [14]  version 2. 2 with the following parameters: "ms2_1" alignment method, tendency_halfwindow of 10, MS1 smoothing halfwindow of 0, MS2 smoothing halfwindow of 15, "quant1" quantification method, XIC extraction based on max, min and max ppm range of 10, anti-spike half of 5, mean filter half hedge, minmax_half_edge and maxmin_half_edge respectively set to 2, 4, and 3. Detection thresholds on min and max at 30,000 and 50,000, respectively, peak post-matching mode. Peptides intensities of each sample were normalized using peptides intensities of a reference sample. In the reference sample, peptide ions extract of the 26 samples were pooled and analyzed (identification, quantification) using the same pipeline used for each sample.
2. 2. Fig. 2. 22 Fig. 2. Hierarchical clustering analysis of (A) transcript and (B) protein concentrations from tomato at nine developmental stages. The hierarchical clustering analysis was performed using Pearson's correlation on mean centered and scaled data. Hierarchical clustering analysis was performed using plyr, gplots and reshape2 packages from R studio (R 3. 3. 2; http://www. rstudio. com/).
© 2019 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons. org/licenses/by-nc-nd/4. 0/). Specifications Table Subject Plant Science Specific subject area Plant physiology, transcriptomic and proteomic quantitative data, tomato fruit development Type of data Tables and Figures How data were acquired Illumina-sequenced libraries for transcriptomics. tomato_fruit_ development) The proteomics data have also been deposited to the ProteomeXchange with the dataset identifier PXD128739 (http://proteomecentral. proteomexchange. org) Related research article [2] Isma Belouah, Christine Nazaret, Pierre P etriacq, Sylvain Prigent, Camille B enard, Virginie Mengin, M elisande Blein-Nicolas, Alisandra K. Denton, Thierry Balliau, S egol ene Aug e, Olivier Bouchez, Jean-Pierre Mazat, Mark Stitt, Bj€ orn Usadel, Michel Zivy, Bertrand Beauvoit, Yves Gibon, Sophie Colombi e Modeling Protein Destiny in Developing Fruit Plant Physiology 2019.
Numerical simulations Despite the small size of the test specimens, measurements have shown that during phase change the distribution of temperature and strain becomes heterogeneous. Indeed, temperatures at the surface and inside the specimen become different. The same applies to the axial and circumferential strains. Consequently, a finite element analysis was performed to simulate the laboratory experiments, using the COMSOL software, where the theoretical formalism presented in Section 2 was implemented using the nonlinear coefficient form PDE formulation. A constraint is added to the system of equations with regard to the salt concentration evolution. Indeed, it is known that in saline porous media subjected to freezing, the formation of ice induces an increase of the salt concentration in the remaining nonfrozen interstitial liquid until it reaches a solubility limit c sat at which salt may precipitate.
Instrumental UV-visible absorption spectra were acquired using a Genesys 10S UV-vis spectrophotometer (Thermo Scientific, France). IR spectra were acquired with a Frontier spectrometer (Perkin Elmer, France) equipped with a Quest multiple reflection diamond attenuated total reflectance (ATR) accessory (Specac, UK) using dried 2 µL drops of NF suspension and citrate solution. TEM experiments were carried out on a transmission electron microscope JEOL JEM-1011 (JEOL Inc. , Peabody, MA, USA) operating at an acceleration voltage of 100 kV. AuNF in aqueous suspension were deposited on carbon-coated Cu grids. Excess water was removed by filter paper. The obtained samples were left to dry under ambient air. Image J software was used to measure the size distribution of AuNF, and for each particle, two perpendicular dimensions were measured.
Acknowledgements The authors acknowledge funding from the following grants in the framework of the  MINERVA  project ( ERA-NET RUS PLUS program ):  RFBR 16-54-76013  (Russia),  01DJ16019 (Germany)  and  MENESR N°321  (France). The authors are grateful to  Dr. Stéphanie David  for her help with experimental design, to  Julien Burlaud-Gaillard  (the  IBiSA Electron Microscopy Facility, University of Tours ) for TEM images and to  Hiba Chamekh  for technical assistance.