dataesr/scientific-paragraphs-categorization huggingface dataset:
Active object recognition in video Starting by comparing with the state-of-the-art in egocentric video, in Table 2 we include a comparison between our approach (denoted as 'Ours'), the reference methods, and some techniques that reported State-of-the-art results in each dataset: a) the discriminatively-trained Deformable Part Model (DPM)  [5] , a sliding window technique that has reported the state-of-the-art results for the ADL dataset  [45] ; and b) the object recognition approach designed and reported by the authors of the GTEA dataset  [22]. In addition, for the ADL dataset, we have also evaluated two methods that combine well-known object recognition approaches and saliency, namely: c) DPM over bounding boxes proposed in  [9]  (DPM + obj. ), which randomly samples bounding boxes and selects the most appropriate based on a measure of their objectness (likelihood to contain an object); and d) BoW applied over candidate bounding boxes proposed by the method described in  [10]  (BoW + SS), which applies a Selective Search (SS) to generate potential candidate locations for objects.
In both cases, we have followed the same setup described in the original papers  [9, 10]  to develop the object detector. However, in order to establish a fair comparison with our method, in BoW + SS we have used the same features (dense SURF features) of our proposal. As we can see from the results, our method consistently outperforms any other automatic approach in every dataset, as well as achieves close performance to the hypothetic case in which ground truth masks/bounding boxes are available (GTEA lacks Ground Truth in all videos so we cannot provide Bow + GT results for this dataset). In particular, in the GTEA dataset we achieve absolute improvements of 10% compared to the best reported approach for this dataset  [22]. The performance of DPM is significantly worse than ours under the constrained scenario (Dem@care dataset), whereas it gets closer results under the unconstrained one (ADL dataset).
In Fig. 5  we also include per-category results in the ADL dataset. the results vary significantly from one class to another. The poor results in some categories can be explained as follows: although the number of image samples of a class may be high enough (hundreds of thousands), they correspond to a small set of different object instances (no more than 10-15 different instances per category). Hence, if a category shows high intra-class variation (e. g. bed, blanket, container or cloth), it is not possible to obtain good generalizations with such limited training sets. Although using external databases might seem appealing, the work in  [45]  showed how the application of detectors trained in ImageNet  [48]  yielded poor results for this particular dataset. For categories in which the BoW + GT achieves notably better results than the automatic approaches, we have observed that some of the errors are found in images in which the object is present but not considered as active.
Active object recognition and fine-grained activity recognition in images With PPMI we aim to address the problem of fine-grained activity recognition with the set of 24 categories (12 instruments, considered the active objects, and 2 kind of manipulations). As already mentioned, for this dataset we have followed a slightly different experimental setup in order to provide a fair comparison with the other approaches reported in the literature. Following the setup described in the original paper  [46] , we have used a multi-scale grid with SIFT features  [35]  and incorporated a Spatial Pyramid Matching (SPM) with 4 levels and a vocabulary size of 1024 visual words. Due to the multi-scale grid, the VSR has been removed from our solution (although we still kept the nonuniform sampling). Furthermore, since we are working with still images, our spatio-temporal-geometric saliency has been substituted by the spatial method in  [11].
As reported in Table  3 , our approach achieves state-of-the-art results in this dataset. Although not very significantly, our method even outperforms  [14] , that learns discriminative spatial saliency maps associated with each category in the dataset. From our point of view, our results demonstrate that the application of our approach is not restricted to egocentric content and can therefore be applied to the detection of active objects in any kind of scene. Finally, we would also like to show that the limitations of our method arise when all the objects in a scene have to be recognized (and not only the active ones). This is the case of the Pascal VOC 2010 dataset, for which our method gives an AP = 56. 9, approximately ranking in the average of the official submissions.
A study of the computation time In Table  4 , we show a comparison between the average execution times of our proposal and the DPM to run one category object-detector in a test frame. We include results using a single threading (S. T. ) and multi-threading (M. T. ) in a 2. 10GHz computer with 4 cores and hyper-threading. For our proposal, the execution time comprises the whole processing pipeline shown in Fig. 2(a). It is worth noting that some of the computations for the spatial saliency map are implemented in GPU so they cannot be translated to S. T. case (spatial saliency takes about 0. 05 sec per frame in the GPU). The rest of the calculations are made with the CPU under the aforementioned circumstances. For the DPM, we run the implementation in  [5] , made in Matlab with optimized c routines for all the steps in the process that require most of the execution time.
It not only achieves notable improvements with respect to the baseline BoW, but also provides state-of-the-art results in all the considered egocentric datasets at very competitive computational times. Furthermore, it avoids human efforts devoted to bounding-box level database annotation as in both training and test sets the saliency maps are automatically computed. In addition, experiments over both 1st-person and 3rd-person camera view have demonstrated that our method can be applied to various types of content, as long as they contain active objects. The limitation of our method is revealed in scenarios where all present objects (active and non-active) have to be identified. In this case our saliency maps remove important visual information and restrict the performance of our approach. In the future, we aim to continue exploring novel ways to introduce perceptual modeling into classical pattern recognition problems in computer vision.
13 n the last few years, different computational tools that enable modeling and simulation of molecular and cellular biology phenomena have been developed. 14 However, the majority of these tools, such as E-Cell,  15  BetaWB,  16, 17  and Cell Illustrator,  18  own abstractions to model only intracellular behavior. Thus, they are not suitable to model cells in their social context, along with all those biological mechanisms Paper subject to independent expert blind peer review. all editorial decisions made by independent academic editor. Upon submission manuscript was subject to antiplagiarism scanning. Prior to publication all authors have given signed confirmation of agreement to article publication and compliance with all applicable ethical and legal requirements, including the accuracy of author and contributor information, disclosure of competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements of third parties.
Preterm infants Premature infants do not have fully functioning cerebral vessels or cardiovascular systems and therefore vital organ perfusion is vulnerable. Using NIRS and umbilical artery ABP, continuous measures of cerebral autoregulation can be obtained. Animal studies indicate that cerebral autoregulation starts to develop from around halfway through the gestational period  [85]. Furthermore, even when static autoregulation is developed, the preterm newborn probably sits close to the lower limit of autoregulation  [86]. Early human investigations using Xe CT and NIRS indicated that CBF, CO 2 reactivity, and cerebral autoregulation may be impaired in preterm infants  [87] [88] [89] [90] [91]  (Table  3 ). Further, more recent human data using TCD indicated that cerebral autoregulation is more impaired if the baby is more premature  [92].
There have been changes in team membership, but use of standard development practices enabled new team members to support, enhance, and work with the system. Issues reported from the user testing were prioritized and resolved by late 2015. In early 2016, a functional registration system (http://registration. ipni. org) was made publicly available for prospective users and is about to be publicized through appropriate channels. Fossil organisms. -Two sites are being developed for fossil organisms governed by the Code, the Fossil Plant Index (http://www. fossilplantnames. com/accounts/login/) and the International Fossil Plant Names Index (IFPNI; http://fossilplants. info/about). Although the two sites are being developed independently based on different programming environments, the principal persons involved have agreed that the sites have similar aims and will exchange data and information.
The CRIS data acquisition systems The CRIS experiment has two data acquisition systems: one for the control and monitoring of the laser systems and counting of MCP events, and one for the DSS. The first system, called CRISTAL (CRIS laser Tuning, Acquisition and Logging) aggregates data from and sends instructions to several computers via TCP/IP. In this way, data from two wavemeters (a WSU2 and a WSU6) and two National Instruments USB-6211 cards (monitoring e. g. photodiodes and counting MCP events) can be combined into a final dataset, which can be visualised during the acquisition. CRISTAL simultaneously updates a LabVIEW Shared Variable which is used by the Matisse Commander software to scan the laser frequency. The CRISTAL software can also update LabVIEW Shared Variables to control and monitor the RILIS narrowband Ti:Sa pulsed laser system  [14].
In addition, the software keeps an automatic logbook, which is supplemented by the users to provide an accurate description of the progress of the experiment. All the source code for CRISTAL is open source and written in Python  [15]. The second system is an 8-channel digital CAEN V1724 acquisition module equipped with on-board digital pulse processing algorithms developed in partnership with the Daresbury Laboratory. The data acquisition software is based on MIDAS (Multi Instance Data Acquisition System)  [16]  and records the data event by event, from which correlations in time or between detectors can be reconstructed offline.
This work was funded by the Science and Technology Facilities Council Consolidated Grant ST/F012071/1, Continuation Grant ST/J000159/1 and Ernest Rutherford Grant ST/L002868/1, The Royal Society of London, the IUAP-Belgian State Belgian Science Policy (BRIX network P7/12), FWO-Vlaanderen (Belgium) and GOA 10/010 and 15/010 from KU Leuven, ERC Consolidator Grant 648381, and the EU's Seventh Framework through ENSAR (506065). T. E. C. was supported by the STFC Ernest Rutherford Fellowship ST/J004189/1. K. T. F. was supported by STFC Advanced Fellowship Scheme Grant ST/G006415/1. K. M. L. was supported by  the FWO Pegasus Marie Curie Fellowship under Grant 267216. We acknowledge the financial aid from the Ed Schneiderman Fund at New York University.
We carried out a chromatin immunoprecipitation (ChIP) assay with the anti-Hdac1 antibody and with an antibody specific for the acetylated form of histone 4 (H4K12ac), whose levels are proportional to the rate of transcriptional activity. Immunoprecipitated chromatin fractions from P1 cortices were analyzed by semi-quantitative PCR and confirmed by QPCR using primers amplifying part of the matrix attachment region (MAR) in the Ctip2 locus  (Britanova et al. , 2008). As a control, we used primers amplifying a sequence in the last intron of the Rnd2 gene containing a previously described Nr2f1-binding site  (Alfano et al. , 2011). Our data show that a lower amount of Hdac1 binds the Ctip2 locus, which accordingly appears more acetylated and thereby more active (Figure  7C ). No Hdac1 binding and acetylation differences were observed with control primers, confirming specificity of its effect on the Ctip2 locus (Figure  7C ).
Statistical analysis of the distinctive morphological features Ctip2+/Satb2+ and Ctip2+/Satb2-populations were compared using non-parametric Wilcoxon Mann-Whitney U-test. Comparison of populations, based on the data collected from 40x images, was performed on n = 145 neurons. Comparison based on Radial Position and Bifurcation was performed on n = 52 neurons (52 matched neurons on 10x and 40x images). Custom clustering analysis was performed using MATLAB and to diminish scale impact on clustering, z-scale standardization was applied across all feature variables. The unsupervised k-means++ clustering algorithm was applied using squared Euclidean as distance measure. The optimal number of clusters was defined by comparing the quality of cluster separation and neuron features similarity within each cluster.
3 Na-GTP, 10 Na-phosphocreatine and 0. 1% biocytin (Sigma Aldrich, Merck, Germany). Recordings were performed using an AxoPatch 200B amplifier (Axon Instruments, Foster City, CA), filtered at 10 kHz and were not corrected for liquid junction potentials. Data were collected using ClampEx software and analyses of recorded responses were performed using Clampfit. The cells usually had a holding potential between -65 and -70 mV or were hold at these potentials. To calculate cell properties (resistance, time to peak, sag), currents from -200 pA to 80 pA were injected for 500 ms. The voltages were measured at the peak amplitude and at steady-state and the I-V curves were plotted. The slope of these curves corresponded to the resistance at peak (Rpeak) and at steady-state (Rss). The sag was measured as the difference of voltage at peak to the voltage at steady state when -200 pA was injected.
Acknowledgements We thank  J Visvader  for providing us the Lmo4 antibody,  HH Chen  for the shRNA Lmo4 construct,  Q Lu  for the pCdk5r1-Ires-GFP,  P Arlotta  for the pCdk5r1-Fezf2 IRES-GFP and  V Tarabykin  for the pCAG-Satb2 plasmids. We are also grateful to  J Sanes  for giving us the permission to use the Thy1-eYFP-H transgenic line. We thank  N Elganfoud  for technical help during this project and the  PRISM Microscopy Facility  for technical support. J Hazan  and  M Nieto  for constructive suggestions on the manuscript, and the whole Studer lab for fruitful discussions. This work was supported by the  "Agence Nationale de la Recherche " under grant reference #  ANR-13-BSV4-0011 , by the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" LABEX SIG-NALIFE under program  reference #  ANR-11-LABX-0028-01 , by the "Fondation Recherche Medicale; Equipe FRM 2011" # DEQ20110421321  and by the " Fondation Jerome Lejeune"  under n  ˚R13098AA  to MS.
One common complication in all these approaches is the need for analytical models that describe the subvoxel architecture. Highly restrictive assumptions are made on vessel geometry and/or physiological processes in order to derive closed-form solutions. Thus, the validity of such measurements may be limited to a narrow range of the vascular characteristics. The mean vascular geometry used yields and analytical solution valid for an average voxel, often neglecting that pathologies impact considerably the geometrical characteristics of the vascular network. A new concept called MR fingerprinting has been recently proposed to estimate MR relaxation times  9  and could overcome the limitations of analytical methods to measure microvascular properties. In the first study proposed by Ma et al. , pseudorandom MR acquisitions that lead to complicated signal evolutions or 'fingerprints' in every voxel are compared to a database (or 'dictionary') obtained from numerical simulations of the same experiment.
g. , blood volume, vessel diameter). Nuisance parameters can either be measured and incorporated in the numerical model or directly estimated using the fingerprinting approach. Second, the numerical simulations provide a reasonable description of the MR signal modulations over a large range of physiologically relevant input values. In many analytical approaches, experimental conditions have to be subdivided into separate regimes (static dephasing, diffusion narrowing, etc. ) to ensure accurate estimates. These regimes are however not fulfilled under several experimental conditions (e. g. , during the passage of a bolus  11  ). Third, vascular fingerprinting provides a real advantage in tissues with irregular vascular networks and pathophysiological abnormalities. Numerical models can be adapted to these irregular vascular networks and accurate parameter estimates will be obtained as long as the corresponding virtual voxels are incorporated in the database.
Briefly, after fixation and saturation, brain sections were incubated overnight at 4 °C with primary antibodies to anti-type-IV collagen (Southern Biotech, Clinisciences, Montrouge, France, 1/1000) then with horseradish peroxidase labeled extravidin. Colour was developed with 0. 5% (w/v) 3,3-diaminobenzidine (DAB), 0. 03% (v/v) hydrogen peroxide followed by counterstaining with hematoxylin. The sections were scanned using a slice scanner at 10X using a Zeiss axio scan system. Quantitative analysis was performed using an automatic segmentation with 7 processing steps (subtract background, colour deconvolution, make binary, remove outliers, despeckle binary, close binary, and fill holes binary) using the freely available image-processing program FIJI (an ImageJ distribution; https://fiji. sc/)  18.
Vessel characterization within 2 regions of interests (ROI; tumor and healthy striatum) was performed using Matlab (extraction of minor and major vessels length and vessels area; The MathWorks Inc. , Natick, MA, USA). Then metrics from each of the 4 slides per animal were pooled. Finally we computed the BV histo and the VSI histo for both ROIs using formulae previously published  14. Data acquisition. MRI was conducted with a horizontal bore 4. 7 T Biospec animal imager (Bruker Biospin, Ettlingen, Germany) with an actively decoupled cross-coil setup (body coil for radiofrequency transmission and quadrature surface coil for signal reception) and Paravision 5. 0. 1. After second-order shimming, the following MRI protocol was performed: Data processing for steady-state microvascular maps. All processing was performed on a voxel-by-voxel basis using custom code developed in the Matlab environment (The MathWorks Inc.
StO 2 maps were estimated using the quantitative BOLD approach described in refs 4 and 21. Briefly, a T 2 map was derived from the MSME data using a non-linear fitting algorithm. To correct for the macroscopic B0 inhomogeneities, the MGE3D was spatially averaged as described in refs 4 and 21, leading to an MGE3D dataset with a spatial resolution matching that of the T 2 and of the BVf maps. The following equation was then fitted voxel-wise to the MR signal decay collected beyond 10 ms echo time of the MGE3D, S(t): γ π =.       -. -. ∆. -.       χ S t Cte exp T t BVf Hct StO B t ( ) 1 4 3 (1 ) 0 (3) 2 2 0 Where ∆ χ 0 , the difference between the magnetic susceptibilities of fully oxygenated and fully deoxygenated hemoglobin was set to 3. 32 ppm (SI unit) and using T 2 and BVf from the corresponding maps.
Data processing for MR vascular fingerprinting maps. Fingerprints were defined as the signal ratio of the two GESFIDE acquisitions obtained pre-and post-injection of USPIO. Such a fingerprint is sensitive to magnetic inhomogeneity-based dephasing but not to macroscopic B0, B1, and microscopic T 2 effects  10. Because of the low signal-to-noise ratio (SNR), the last 8 echoes of the GESFIDE sequences were removed before computing the fingerprints. Numerical simulations. The MR signal was simulated with an iterative numerical approach that models the effects of water protons diffusing in magnetic field gradients produced by the vasculature. We briefly outline here the principal components of the simulation tool, which is described in detail in ref. 22. The voxel contains blood vessels (straight cylinders) that occupy BVf.
The effects of water diffusion are modeled by the convolution of the magnetization lattice with a discrete Gaussian kernel with diffusivity equal to ADC. The MR signal is calculated at each δ t by summing the transverse component of the magnetization lattice. Convolutions are performed in the Fourier space to reduce the computational cost. Matlab codes used for the numerical simulations can be found at https://bitbucket. org/NPann/mrvox2d.
Acknowledgements The authors acknowledge the excellent technical support of the  MRI Facility of Grenoble (UMS IRMaGe ). This work was supported by the  NIH  [ 1R21-NS087491 ] and the program  ANR "Imoxy" [2011-BSV5-004 ]. The MRI facility IRMaGe is partly funded by the  French program  ' Investissement d' Avenir ' run by the  French National Research Agency , grant ' Infrastructure d'avenir en Biologie Sante´ ' [ ANR-11-INBS-0006 ]. BL and NC received a stipend from the  Association pour la Recherche Contre le Cancer (ARC).
To address those issues, Islam et al. (10)  and Jaitin et al. (13)  stochastically tagged cDNA molecules during reverse transcription with short random nucleotide sequences (unique molecular identifiers, UMIs). The use of UMIs largely improved data quality since it allows counting of the initial unamplified cDNA molecules what is much less biased than counting transcript read numbers in heavily amplified cDNA. UMIs are introduced during reverse transcription either at the 5 or 3 end of the cDNA. In consequence, only the UMI tagged extremity of the transcript is recovered and sequenced after cDNA fragmentation. The vast majority of currently used approaches introduce the UMI via the oligo-dT reverse transcription primer and sequence the 3 terminal, UMI tagged, fragment of the cDNA  (14) (15) (16) (17).
cDNA synthesis, PCR amplification -microfluidic device Lysis, reverse transcription and PCR mixes were the same as for the tube controls. Lysis (7 l) and reverse transcription mix (8 l) were added to the wells of the microfluidic device specified in the script for the C1. 6. 5 l of PCR mix with one of the 96 barcodes was added to each of the outlet wells. The PCR mixes were backloaded from the outlet wells into the reaction chambers resulting in cell specific barcoding on the microfluidic chip during the PCR amplification. The script for the Fluidigm C1 has been submitted to the Fluidigm C1 OpenApp script repository (https://www. fluidigm. com/ c1openapp).
Trimmed reads were mapped against the human genome (hg19) and ERCC sequences using STAR aligner (v2. 4. 0a), with default parameters. STAR indices were generated using Ensembl GTF file (release 75). For molecule counting based on UMI counts, we used the Dropseq Core Computational Protocol version 1. 0. 1 (dropseq. jar) (16). Unless indicated otherwise, we used the uniq option (identical UMIs at two different transcript positions are only counted once) and edit distance = 1 (UMIs for a transcript that are potentially a substitution mutant of another UMI with higher read coverage of the same transcript are discarded).