dataesr/scientific-paragraphs-categorization huggingface dataset:
V USER INTERFACE: CORPUS NAVIGATION VIA CONCEPT NETWORKS The user interface 29 (UI) gives access to our sample of the Transcribe Bentham corpus via a full-text search index and thanks to a navigable rendering of the concept networks described above. The search index can be used to access contexts for the network nodes. Besides, a type of map called heatmap depicts the temporal evolution of the corpus content. The goal of the UI is to provide an overview of the corpus. As a first requirement, the networks should reflect a domain expert's knowledge of the corpus. Optimally, the networks and the connections between concepts in them might suggest new research ideas to a scholar (see the UI evaluation section for discussion). Our UI complements prior platforms to navigate the corpus: The Bentham Papers Database 11 and UCL Library's platform.
12 The Bentham Papers Database offers a detailed metadata-based search. Both UCL Libraries' tool and our UI search for query terms in the complete text of transcripts. Whereas their application returns the image and transcribed text for manuscripts matching a query, our UI returns the text of each matching manuscript, with the query terms highlighted, and with date facets. The other way in which our UI complements the prior ones is by allowing us to navigate the corpus using concept networks; this possibility was not available in the tools just cited.
https://dh. fbk. eu/technologies/kd. This opinion is based on an informal inspection of the tool's results on the Bentham corpus. The tool provides a weight for each term extracted, indicative of the term's importance in its document and in the corpus. Like in Yatea, the ratio of single-word to multitoken terms extracted is configurable. KD has been tested on the SemEval 2010 keyphrase extraction dataset [Kim et al. , 2010] , ranking 2 th to 4 th depending on the evaluation mode.
07. 00 software (Agilent, Santa Clara, USA). The data processing consisted of (1) application of a frequency filter of 80% on feature's presence in one condition, (2) normalization of signal intensities using cyclic loess algorithm, (3) imputation of missing values using KNN5 and (4) correction of batch effect using Combat according to animal's sacrifice day. Data were analyzed with Mass Profiler Professional (MPP; Agilent Technologies, Santa Clara, CA, USA) using unpaired student's t-test followed by Benjamini Hochberg correction. Mass spectrometry signals that discriminated the two conditions based on a corrected p-value of 0. 375 (FDR < 37. 5%) and a fold change > 1. 2, were subjected to tandem mass spectrometry for lipid identification. Cholesterol, hydroxycholesterol, desmosterol, and lathosterol, were identified separately and validated using their corresponding standards.
MRI Data Analysis Perfusion was calculated voxel-by-voxel using Formula 1 in  [82] , with some parameters assumed constant: brain/blood partition coefficient = 0. 9 mL/g, mouse arterial blood transit time = 0. 08 s, tagging efficacy = 0. 67, T 1b = 2. 3 s, T 1 = 1. 53 s, T 1sat = 0. 57 s, M a z (w) = 0. 48 s. The anatomical scans were first manually co-registered rigidly using ITK-SNAP  [83]  to a previously generated mouse template, then co-registered non-linearly with advanced normalization tools (ANTs)  [84]. This provided the ANTs transformations to co-register the perfusion images. Co-registration results were inspected in details, and registration parameters were stored as potential confounding variables. Two-sample t-test statistical parameter mapping (SPM) voxel-by-voxel analyses were run on the perfusion images.
Amyloid Deposits Staining Brain cryosections were fixed in 4% (w/v) paraformaldehyde (PFA) in phosphate-buffered saline (PBS) for 15 min at room temperature. After washing in PBS, the sections were incubated 15 min at room temperature with 0. 2% (w/v) thioflavin-S (Sigma-Aldrich, Saint-Louis, MO, USA) solution in 80% ethanol followed by washing in 80% ethanol (1 min), 70% ethanol (1 min). Slices were washed twice in distilled water, covered by mounting medium and coverslips for imaging. All quantifications were performed using Image J software, version 1. 8.
Cerebral Microvessel Density Staining After brain fixation in 4% PFA, brain sections were incubated for 1 h at room temperature in PBS containing 0. 5% (v/v) Tween-20 and 4% (w/v) bovine serum albumin (BSA). After washing with PBS, slides were incubated at 4 • C for 24h in PBS containing the polyclonal rabbit anti-collagen IV antibody, (1:100, Abcam ab19808, Cambridge, UK) with 1% BSA. After washing with PBS, brain slices were incubated at room temperature for 2 h in PBS containing Alexa Fluor 647 donkey anti-rabbit IgG (1:500, Abcam, Cambridge, UK) and DAPI solution (1:600, Thermo Fisher Scientific, Waltham, MA, USA) with 1% BSA. After washing, slides were covered by mounting medium and coverslips for imaging. Cerebral microvessel density was quantified using Image J software version 1. 8 as a percentage of collagen IV-positive area per ROI as previously  [36].
Statistical Analyses The minimal number of mice required per experiment was based on the variability of the data collected during our previous studies  [36]. Student's t-tests were performed with Prism 6 (La Jolla, CA, USA) to assess differences between groups. Data are expressed as means ± SD. Statistical significance was set at p < 0. 05. The interquartile range (IQR) was calculated as the third quartile (Q3) minus the first quartile (Q1). Values below Q1-1. 5 IQR or above Q3+1. 5 IQR were excluded from the analyses.
Presentation of the Data and Preliminary Analysis The Tohoku tsunami was the result of a magnitude 9. 1 undersea megathrust earthquake that occurred on Friday March 11th of 2011 at 2:46 p. m. local time (JST). It triggered powerful tsunami waves that may have reached heights of up to 40 m and laid waste to coastal towns of the Tohoku's Iwate Prefecture, traveling up to 5 km inland in the Sendai area  [1]. To analyze the aftermath of this disaster using the previously presented deep-learning algorithm, we use images from the ASTER program. We kept the Near-Infrared, Red and Green bands with a resolution of 15 m. The optical images we use are from 19 March 2011, 29 November 2010 and 7 July 2010 (Figure  6 ), see Table  1  for their detailed characteristics. We use two images before disaster because the closest image taken on November 2010 has high percent of cloud coverage above the potentially damaged area, though it has a lower variance of seasonal changes compared with the image taken on July 2010.
Hardware and Software All the experiments presented in this paper were run on a computer equipped with a NVDIA Xp Titan, an Intel Core i7 6850K, and 32 Go of DDR4 RAM. For the software part, all algorithms were implemented using Python for regular clustering methods and PyTorch for deep-learning algorithms. All visualizations were realized using QGIS. In Table  5 , we show the training and fine-tuning times for the different algorithms used in this paper.
Experiment Protocols A heating/cooling ramp of 1 °C/min is applied on the same principle as the DSC analysis performed for validation. The plate being highly conductive, the heating rate being low, and the droplets being small (~5 mm diameter), the temperature of the plate is assumed to be uniform and the temperature of the droplets is assumed to always be equal to the plate one. Therefore, the thermocouple placed on the plate is assumed to record the temperature of each droplet at any time and allows the association of a signal variation to a specific transition temperature. During the heating cycle, the IR camera simultaneously records the photonic flux emitted by each droplet placed on the aluminum plate. Its acquisition rate is set to 7 Hz to match the best compromise between the amount of gathered data and the processing time.
Experiment Protocols A heating/cooling ramp of 1 • C/min is applied on the same principle as the DSC analysis performed for validation. The plate being highly conductive, the heating rate being low, and the droplets being small (~5 mm diameter), the temperature of the plate is assumed to be uniform and the temperature of the droplets is assumed to always be equal to the plate one. Therefore, the thermocouple placed on the plate is assumed to record the temperature of each droplet at any time and allows the association of a signal variation to a specific transition temperature. During the heating cycle, the IR camera simultaneously records the photonic flux emitted by each droplet placed on the aluminum plate. Its acquisition rate is set to 7 Hz to match the best compromise between the amount of gathered data and the processing time.
Once elementary objects have been conveniently labelled (each droplet on the plate is identified by a different arbitrary color), the DL signals associated to each droplet can then be easily retrieved. In the second step, a droplet-by-droplet analysis is carried out to identify the temperature at which each droplet undergoes a transition. As a droplet can have hundreds of pixels (depending on the size of the droplet), the disparity in the signal of each plotted pixel makes the determination rather difficult, as can be seen in Figure  2. The first step of the data processing consists of identifying the pixels corresponding to each individual droplet in the recorded raw infrared images. The associated DL signals are then extracted, which allows to reduce the whole set of raw images to one single image by averaging the DL signals on time.
DSC Analysis DSC analysis was performed at ambient atmosphere using a DSC 131 supplied by Setaram (Caluire, France). Two series of experiments were performed, one with a 1 • C/min heating ramp for a preliminary estimation and the other one at a rate of 0. 3 • C/min for more accurate transition detections. As for the studied compositions, 8 molar fractions of PA-SA (0/100, 15/85, 30/70, 40/60, 55/45, 70/30, 86/14, and 100/0 in molar percentages of PA/SA) were chosen to assess the best phase diagram possible in the least amount of time (knowing that this procedure can be really time-consuming when accurate results are required). The molar fraction was chosen to depict a coherent phase diagram in a minimal amount of data while providing insightful information regarding detected transitions using the IRT method and providing additional/complementary data in comparison with literature  [15].
Thermodynamic Modeling of the Solid-Liquid Equilibrium Most models aiming at modeling the solid-liquid equilibrium of organic compounds' binary systems are either limited to the depiction of simple eutectic systems or rely on experimental data to fit unknown parameters. A model was implemented using Matlab Software (MathWorks, Natick, MA, USA) for this study. It is a combination of the models described in Reference  [18]  accounting for the polymorphic behavior of fatty acids and in Reference  [19]  introducing means of modeling the effect of the peritectic transition on the liquidus line. When a pure component is cooled/heated, different solid-solid transitions may occur. Most models tend to neglect those transitions because of their weak enthalpy value. However, fatty acids show a strongly polymorphic behavior.
Thus, asymmetries in the CCFs hint at the presence of a few granules (or alternatively, at the presence of nonspherically-symmetric shock waves) in the atmosphere of µ Cep which, in any case, behaves differently than the atmospheres of Mira stars. Figure  3  compares the visual light curve extracted from the American Association of Variable Star Observers (AAVSO) database  (Kafka 2018)  to the Gaussian-fit or M 1 velocities in the different masks as well as to the TiO-band temperatures. First, Fig. 3  reveals that the TiO-band temperature variation is mostly in phase with the light variation. This agreement, in turn, confirms the reliability of our temperature-derivation method (see Sect. 2. 2). Second, the maximum peak-to-peak amplitude of the M 1 velocity variations (middle panel of Fig.
3D radiative-hydrodynamics simulations and detailed radiative transfer Following  Kravchenko et al. (2018) , we used the same 3D RHD simulation of an RSG star computed with the CO5BOLD code  (Freytag et al. 2012) , where the combined compressible hydrodynamics and non-local radiative-transfer equations are solved on a Cartesian grid. The code takes into account molecular opacities; however, the radiation transport is treated in a "gray" approximation and ignores radiation pressure and dust opacities. The model geometry is of the kind referred to as "star-in-a-box". It is assumed that solar abundances are appropriate for RSG stars. The 3D simulations are time-dependent, characterized by realistic input physics, and reproduce the effects of convection and non-radial waves  (Chiavassa et al. 2011).
Acknowledgements. K. K. acknowledges the support of a  FRIA (FNRS)  fellowship. S. V. E. thanks to  Fondation ULB  for its support. This work is based on observations obtained with the HERMES spectrograph, which is supported by the  Fund for Scientific Research of Flanders (FWO) ,  Belgium , the  Research Council of K. U. Leuven, Belgium , the  Fonds de la Recherche Scientifique (F. R. S. -FNRS) ,  Belgium , the  Royal Observatory of Belgium , the  Observatoire de Genève, Switzerland  and the  Thüringer Landessternwarte Tautenburg, Germany. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research.
NMR 1 H- 15 N transverse relaxation optimized spectroscopy (TROSY) for PRE measurements was recorded at 20 • C using a Bruker HDIII wide-bore 800 MHz spectrometer. Spectra were recorded in the States-TPPI/PFG mode for quadrature detection with carrier frequencies for 1 H N and 15 N of 4. 73 and 120. 0 ppm, respectively. The samples contained 0. 4 mM MAX, 25 mM MES, and 25 mM NaCl (pH 5. 5) in a 90% H 2 O/10% D 2 O mixture. All NMR spectra were processed and analyzed using NMRPipe and SPARKY. (11, 12)  A squared and 60 • phaseshifted sine bell window function was applied in all dimensions for apodization. Time domain data were zero-filled to twice the data set size, prior to Fourier transformation. 1 H- 15 N cross peak assignments were obtained from the biological magnetic resonance data base (BMRB) entry 5956 and the work by Sauvé et al.
Molecular dynamics simulations To visualize the structural fluctuations of the NTD domain of the MAX 2 /DNA complex in solution, we performed molecular dynamics (MD) simulations using GROMACS 2018. 3  (22). The crystal structure of a human MAX 2 /DNA complex (PDB ID: 1HLO) was used in our simulations to build our initial atomistic model. The complex was confined with 21750 water molecules in a dodecahedral box so that the edges of the box were always at least 1 nm away from the complex. The structure was electro-neutralized with Na + ions, and Na + Cl -ions were added to achieve a salt concentration of 25 mM as used in the experiments. NVT equilibration was performed at P = 1 bar and T = 310, 320 and 330 K in a constrained box for 80 ps, with a step of 2 fs using the Verlet cutoff scheme set to 1.
The molecular structure of the MTSL label is indicated. Red bars indicate residues that were excluded from the analysis due to weak signals or overlap. (C) The intensity ratios V are mapped onto the crystal structure of MAX 2 /DNA (PDB entry 1HLO; for a DNA-free NMR-derived solution structure see PDB entry 1R05). The color code is indicated at the bottom. Residues 38-42, located in the HLH motif (indicated by the dashed box), are >2. 5 nm distant from the labeling site (green dot). Contrary to the experimental observation, no strong signal reductions would therefore be anticipated for these residues based on the crystal structure analysis. (D) Zoom on the dashed box in (C). The reduced intensity ratio V in the HLH region indicates conformational fluctuations that lead to reduced distances between the SL and the HLH motif.
Band density was quantified in each of three replicate blots using ImageJ software (version 1. 48v; National Institute of Health, Bethesda, MD, United States) and the abundance of each protein of interest determined relative to GAPDH labeling intensity. Full length immunoblots of the antipro-AKAP4, AKAP4, and GAPDH antibodies used throughout this study are presented in Supplementary Figure  S3.
The goal of this work is to build the basis for a smartphone application that provides functionalities for recording human motion data, train machine learning algorithms and recognize professional gestures. First, we take advantage of the new mobile phone cameras, either infrared or stereoscopic, to record RGB-D data. Then, a bottom-up pose estimation algorithm based on Deep Learning extracts the 2D human skeleton and exports the 3rd dimension using the depth. Finally, we use a gesture recognition engine, which is based on K-means and Hidden Markov Models (HMMs). The performance of the machine learning algorithm has been tested with professional gestures using a silk-weaving and a TV-assembly datasets.
Gesture recognition methods The implementation of deep learning for gesture recognition has become the common practice and can lead to very good results. The ChaLearn LAP Largescale Isolated Gesture Recognition Challenge from the ICCV 2017, crowned  [9]  [10]  [11]  as the best deep learning algorithms for gesture recognition. However, the need for large training databases is not compatible with the constraints professional gestures where datasets are quite small. Dynamic Time Warping (DTW) and Hidden Markov Models (HMMs) are machine learning methods that are widely used in pattern recognition. DTW is a template-based approach that is based on a temporal re-scaling of a reference motion signal and its comparison with the input motion signal, such as in  [12]. DTW can be good for doing one-shot learning, while HMMs is a robust durationindependent model-based approach.
Video recording using the smartphone A specific module for depth recording has been developed in order to use any iOS device equipped with an RGB and/or RGB-D sensor to record data. More precisely, the new iPhone XS has been used in this work. On the one hand, iPhone XS uses a dual rear camera, composed by a wideangle lens and a telephoto lens, to capture the disparity. The normalized disparity is defined as the inverse of the depth. In the Figure  2  on the left is shown how the rear camera captures the disparity. First, the observed object is reflected on the image plane of each camera. Then, the mathematical relation tying the depth (Z), baseline distance (B), disparity (D) and focal length (F), showed in equation 1, results in the normalized disparity. Finally, the iPhone needs to filter and post-process the disparity to smooth the edges and fill the holes, which requires a heavy computation.
Pose estimation Once the data is recorded, it is sent to the GPU server. For compression purposes, the RGB-D data is converted to jpeg format, then websockets (RFC 6455) are used to send it. We chose to use the client library Starscream. The goal of pose estimation is to obtain a series of keypoints that can be used by a gesture recognition engine as input, enabling to train a model that adapts to different situations or environments. OpenPose, in our case, estimates 25 body keypoints, 2x21 hand keypoints and 70 face keypoints. However, some of the estimated keypoints are useless for the recognition, either they are occluded or they do not carry any information about the gesture. Therefore, a joint selection module has been added to the application to give the possibility to the user to select the most appropriate keypoints depending on the use-case.
Gesture recognition The joints obtained with the pose estimation and the data obtained from the depth camera are the input to the gesture classification algorithm. To make the recognition invariant to the position of each person in the frame, the neck joint has been taken as a reference point, and any frame without neck has been discarded. The gesture recognition engine is based on supervised learning. Before making the gesture recognition, a labelled database can be manually created by the user, by manually selecting starting and ending time stamps of each gesture. Once the database has been labelled, the gesture recognition engine uses k-Means to obtain discrete-valued observations. Then, Hidden Markov Models is used to train the discrete data and to determine a gesture recognition accuracy.
Datasets The first dataset used, TV Assembly dataset (TVA), is made up of RGB-D sequences recorded from a top mounted view at a conveyor surface factory. Two different users have been recorded. Each sequence contains around 10. 000 RGB frames together with the depth. Table  2  shows the four gestures identified and labeled during the sequences along with the skeleton of each of them. The second dataset, Silk Weaving dataset (SW), contains sequences recorded from a lateral view and three different positions in a silk weaver museum. Three clear gestures have been identified and showed in 3 with their corresponding skeleton. In order to evaluate the performance of our pipeline and the algorithms we use the 80%-20% evaluation criteria. We randomly divide our dataset in 80% training set and 20% as testing set, repeating this procedure 10 times and computing the average values to generate the confusion matrix.
We also use the Recall (Rc), Precision (Pc) and f-score metrics. The TVA dataset contains 48 repetitions of each gesture and the SW dataset contains 88 repetitions. Moreover, for the gesture recognition engine, we selected 30 clusters for the K-Means algorithm and 4 states for the HMMs, which follow an ergodic topology. Finally, five different tests have been done in order to compare the gesture recognition accuracy using the following criteria: 2D against 3D, 2 joints against 7 joints, different camera positions and mixing gestures from the two datasets.
Pose estimation comparison using the TVA and SW datasets A comparison of the pose estimation using the two datasets has been made. We compared the number of frames per gestures with 1. the percentage of frames without any estimated skeleton, thus without estimation at all; 2. the percentage of frames without any reference point, thus without the neck and 3. the percentage of frames having the minimum useful estimation, thus at least the neck. The results are shown on Table  4  and we can affirm that the lateral views provided by the SW dataset have a better potential, than the TVA dataset, since a full skeleton has been estimated for all the frames. In the top mounted view of the TVA dataset, the algorithm struggled to estimate any information in 43% of frames for G2 and in 36% of frames for G4, because the user is not captured in many frames.
With regard to the TVA dataset, we would expect that these results might have an impact in the gesture recognition accuracy. We would also expect that the small duration of the G5 might also affect its recognition accuracy. 6. 2 Gesture recognition comparisons using the TVA dataset: 2D vs 3D and 2 vs 7 joints The joints selected for training the gesture recognition engine with the TVA dataset are the upper-body joints. Table  5  compares the recognition accuracy by using 2 (wrists) or 7 (wrists, elbow, shoulders and head) joints in the 2D or 3D space. The highest results are obtained by using 7 joints in 3D, while the worst with 2 joints in 2D. Moreover, as we expected, the low number of frames with the minimal useful pose estimation for G2 and G4 impacted the recognition accuracy for these gestures, while G1 and G3, which have data that give good pose estimation, achieved very high accuracy.