dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The Feed-a- Gene  project has received funding from the  European Union 's  H2020 Programme  under grant agreement no  633531. Farouk MESSAD  was supported by a  Regional grant  ( SAD, Brittany region ) from  France , and  Basile Koffi  was funded by the  Feed-a-Gene EU  project. Funders approved the aim of the study but had no roles on its design, data analysis, data interpretation, nor in the writing of the manuscript.
In this work, we tackle the problem of multi-norm and multilingual license plate (LP) detection and recognition in natural scene images. The system architecture use a pipeline with two deep learning stages. The first network was trained to detect license plates on the full raw image by using the latest state-of-theart deep learning based detector namely YOLOv2. The second stage is then applied on the cropped image to recognize captured license plate photographs. Two recognition engines are compared in this work: a segmentation-free approach based on a convolutional recurrent neural network where the recognition is carried out over the entire LP image without any prior segmentation and a joint detection/recognition approach that performs the recognition on the plate component level. We also introduced a new large-scale dataset for automatic LP recognition that includes 9.
The prediction of the full license plate number is based on the detected LP components order. We evaluated the proposed system on two datasets collected from real road surveillance and parking access control environments. The main contributions of this paper can be summarized as follows: • A real-time end-to-end ALPR system is proposed using a two-stage approach for multi-norm LP detection and recognition. The detection system is based on the state-of-the-art YOLOv2 object detection CNN. For the recognition stage, two LP recognition engines are compared based on a segmentation-free recognition approach using CRNN and a segmentation based detection/recognition approach using YOLOv2 object detection CNNs. We demonstrate that the second recognition approach performs better in the case of multi-norm Tunisian LP 1.
• In order to reduce the time and cost of annotation processing, we propose a semi-automatic annotation process of LP images to complete partial annotations with location boxes. To the best of our knowledge, the proposed semi-automatic process is completely original. • A new public multi-norm LP images dataset named GAP-LP 2 with 9, 175 fully annotated images (over than 80, 000 LP components) is provided freely for research purpose. To the best of our knowledge, GAP-LP is the largest-ever LP dataset available to the academic research community, making it suitable for trying and evaluating deep learning techniques. • We experimentally evaluate the benefits of data augmentation by generating synthetic LP examples in order to augment the training dataset and The rest of the paper is organized as follows.
In our case, the confidence value does not considerably affect the detection performance since each image in our datasets contains only one vehicle, so only one LP. So that, we keep only the detection with the largest confidence in cases where more than one LP is detected. In this work, the value of 0. 25 is empirically chosen based on recall/precision on the validation set. It guarantees that YOLO does not predict a LP bounding box when the confidence is low. Since all parameters are fixed, we train YOLO network on the fully annotated LP regions. For the detection stage, we run the network over the full raw input images without any pre-processing. Since the LP is detected, it is cropped and sent to the second stage for recognition (see Figure  2 ).
License plate recognition h t-1 h t h t+1 h t-1 h t h t+1 h t-1 h t h t+1 h t-1 h t h t+1 h t-1 h t h t+1 h t-1 h t h t+1 We reiterated this process until all the images of SN L are labeled at components level or no image of SN L can be correctly recognized. At each iteration, S j is added to SL and each LP image that is correctly annotated is removed from SN L. The detailed algorithm steps are given in Algorithm 1. We present in tables 2 and 3 the evolution of the semi-automatic annotation on GAP-LP and Radar LP image datasets 3 of varying levels of difficulty. For each iteration, we present the number of LP images which are correctly labeled at the component level. We also present the percentage (the total of all itera- 3 The description of the datasets is given in section 5 Algorithm 1 Semi-automatic annotation of LP components Inputs : for each image I i in SN L do 7: D = {I 1 ,.
, I N } Test I i with YOLO using M j-1 to detect its components labels 8: Concat LP components labels to obtain the sequence label GT (l i ) 9: if (GT (l i ) == L i ) then 10: S j ← S j {l i } 11: SN L ← SN L \ {l i } 12: end if 13: end for 14: SL ← SL S j 15: Fine-tuning of Yolo on S j to obtain M j 16:   The annotation quality of the semi-automatic annotation process is evaluated using 250 LP images with hand annotated component locations. We obtained an average IoU of 88 for the GAP-LP dataset and of 85 for Radar dataset which confirms the robustness of the proposed algorithm.
GAP-LP dataset The GAP-LP dataset images were acquired with different quality cameras under different resolutions, view angles and daylight lighting conditions to enable robust LP detection/recognition algorithms testing. GAP-LP dataset 4  is freely available to the research community. It is composed of 9175 fully annotated images for both LP detection and recognition. The dataset is split as follows : 7117 images for training, 456 for validation and 1602 images for test. Figure  5  shows some image samples from the GAP-LP dataset.
Radar dataset The Radar dataset is composed of 6448 JPEG color images taken from radars on different Tunisian roads. The images were taken from real scenes with complex backgrounds and under various lighting conditions (day, night, sunshiny,  The experimental section is divided into three parts: the LP detection as false negatives, F N. The recall and precision are then computed by: recall = T P (T P + F N ) (2) precision = T P (T P + F P ) (3) It can be observed from table 5 that our method achieved impressive detection rates with a precision of 100% and a recall of 100% on the GAP-LP dataset and 99. 09 on the Radar dataset. Here a detection is considered to be correct if the overlap between the detection and ground-truth bounding box is greater than 0. 5 (IoU > 0. 5).
Recognition results The LP recognition rate (LP-RR) is defined as the number of correctly recognized license plates divides by the total number of ground-truth samples. A We conclude that the decrease in similarity between the training and test sets seemed to have negative impact on the system performance. Using a training set composed of a mix of GAP-LP and Radar images, a relative improvement of 2. 67% is obtained on GAP-LP test set. Contrarily, the performance on the Radar test set is largely decreased. We conclude that augmenting the training set by more complex images can help the network to recognize LP images with simple conditions, but the reverse is not true. We present in tables 9 and 10 the LP recognition accuracy of the segmen-          We present in table 13 the character and the plate recognition rates of the proposed CRNN and YOLO recognition approaches and we compare their performance against five state-of-art results published on the AOLP dataset.
Conclusion In this paper, we have presented a robust real-time ALPR system using a pipeline with two deep learning stages. The LP detection stage is based on the state-of-the-art YOLOv2 object detection CNNs. For the second stage, we compare two recognition engines: a sequence labeling method which recognize the whole license plate without character-level segmentation and a joint detection/recognition approach that performs the recognition on the plate component level. The proposed system is robust to illumination and weather conditions and is capable to achieve a full LP recognition rate of 97. 67% in the GAP-LP dataset, and of 91. 46% in Radar dataset with a reasonable computational time. We also.
Redmon & Farhadi, 2016). YOLOv2 reframes object detection as a single regression problem, straight from image pixels to bounding box coordinates and class probabilities. It uses a multi-scale training method and offers a trade-off between speed and accuracy. To detect license plates, YOLO uniformly divides the image into cells. During training, the deep neural network takes images from the training dataset as input, forms a loss function that incorporates the crossentropy loss, the L2 regression loss and the randomness and back-propagates.
introduced a new public dataset for multi-norm and multilingual ALPR that includes 9, 175 fully annotated images. Compared to the existing datasets for this task, GAP-LP is the largest ALPR dataset making it suitable for trying and evaluating deep learning techniques. In order to reduce the time and cost of annotation processing, we have proposed a new semi-automatic annotation procedure of LP images with labeled component bounding boxes. Future work consists of integrating the recognition of vehicle make and model to improve the vehicle identity recognition process and help check correlation with data stored on police and homeland security databases.
Increment j17: until SN L == φ or Sj == φ tions) of the correctly labeled LP images. It can be shown that the proposed algorithm annotates 95. 11% of the dataset in only 7 iterations when starting with 500 images with detailed annotation. We show that 250 detailed annotated images are enough to annotate 96. 10% of the dataset as shown in the second line of table 2. It is interesting to note that since the first two iterations more than 90% of the LP images are annotated. The proposed semi-automatic annotation algorithm is also evaluated on Radar dataset containing license plates images in various conditions such as low resolution, severe weather conditions and variable lighting conditions. As shown in table 3, more than 80% of the images are automatically annotated starting with only 250 images with detailed annotation.
( Redmon & Farhadi, 2016). YOLOv2 reframes object detection as a single regression problem, straight from image pixels to bounding box coordinates and class probabilities. It uses a multi-scale training method and offers a trade-off between speed and accuracy. To detect license plates, YOLO uniformly divides the image into cells. During training, the deep neural network takes images from the training dataset as input, forms a loss function that incorporates the crossentropy loss, the L2 regression loss and the randomness and back-propagates.
Discussion To summarize, we have addressed a fundamental problem in complex systems: detecting, from scalar observations, the time scales involved in spatial interactions of oscillators with time-varying spectral components. Classical surrogate data tests require time-series to be stationary. Nevertheless, data recorded from real-world systems are generally noisy and non-stationary. In order to study their interactions we propose a complementary approach based on wavelet analysis. Wavelet coherence is generalized as a method for detecting transient but significant coherence between multivariate nonlinear signals. The classic surrogate algorithm is also generalized to produce non-stationary surrogates. Results from several artificial and real non-stationary, linear and nonlinear time series, demonstrate the advantages of our approach.
Datasets. To illustrate the detection of dynamic spatial correlation patterns on real-world time series, we study two systems: i) the weekly measles case notifications in seven large English cities studied in previous works  30, 31  ; and ii) an electroencephalographic (EEG) recording from a pediatric subject with intractable epileptic seizures  28, 29. Although our approach is applicable to any neuroimaging functional method (e. g. EEG, fMRI, and MEG signals) here we use the EEG as this modality of acquisition has the major feature that collective neural behaviors, i. e. , synchronization of cortical assemblies are reflected as time-varying interactions between EEG signals. The file studied here contains 21 EEG signals sampled at 256 Hz according to the 10-20 bipolar montage  28. In this study, all time series are first centered and set to have zero mean and unit variance.
The measles dataset were obtained from the Prof. B. Bolker's website at the McMaster University, Canada. They are freely available at https://ms. mcmaster. ca/~bolker/measdata. html. The EEG data was obtained from the open repository CHB-MIT Scalp EEG Database at https://www. physionet. org/pn6/chbmit. In case of any difficulty in obtaining the datasets mentioned above, the corresponding author can provide the data used upon request.
Stained sections were mounted in Vectashield containing DAPI (Vector Labs, Peterborough, UK) visualized by epifluorescence microscopy and a Nikon DXM1200C digital camera. The NIS-Elements BR 2. 30 software was used to estimate the number of TUNEL-positive cells per 1000 DAPI-positive cells. Statistical comparisons for TUNEL staining were performed using the Fisher's exact test and chi-square test with Yate's correction.
Exome sequence analysis Sequencing libraries were constructed using the NimbleGen Kit (Roche, West Sussex, UK) and libraries combined in pools of six for targeted capture, using the SeqCap EZ Mouse Exome SR v2. 2 (target regions available from ftp://ftp. jax. org/ Genome_Biology_mouse_exomes). Libraries were assessed pre-and post-capture for quality and yield, using a High Sensitivity DNA assay (Agilent, Santa Clara, CA, USA) and Library Quantification Kit (Kapa Biosystems, Gillingham, UK). Massive parallel sequencing was performed with six samples per flow cell lane, using the HiSeq2000 platform and SBS reagents (Illumina, San Diego, CA, USA) to generate 100-bp paired-end reads. Illumina Data Analysis Pipeline software (CASAVA 1. 8. 1) was used for initial base calling and data multiplexing.
Illumina reads were mapped to the mouse genome (mm9) using the Burrows-Wheeler Aligner (BWA)_v2  (31)  with the default parameters. Single-nucleotide variant (SNV) cells were made using a customized version of The Genome Analysis Toolkit (GATK)  (32)  with default parameters. Several triaging steps were made to reduce false positives. (33) The 17 Mouse Genome data set  (34)  was used to filter inbred SNP sites from the RCALC1 SNV data set, and common sites were removed from further investigation. The remaining SNVs were further filtered by removing sites with an allele frequency <35% and >80%, a read depth <3, and a quality score <200. The final RCALC1 SNV data set was annotated with NGS-SNP. (35) A sequence analysis, protein sequence alignment, and protein prediction Variants were validated in G2 mice by Sanger DNA sequencing, using appropriate gene-specific primers (Sigma, Gillingham, UK), followed by dideoxynucleotide sequencing using the BigDye Terminator v3.
1 Cycle Sequencing Kit (Life Technologies, Carlsbad, CA, USA) and an automated detection system (ABI3730 capillary sequencer; Applied Biosystems, Carlsbad, CA, USA). (36)  Protein sequences were aligned using ClustalW. (20)  MutationTaster (http://www. mutationtaster. org/) was used to predict the effects of mutations. (37)  BRD4 modeling was undertaken using the BRD4 structure in complex with an extracellular signal-regulated kinase 5 inhibitor (PDB: 5LRQ), and figures prepared using the PyMOL Molecular Graphics System, Schrodinger (https://pymol. org/). RNA extraction, cDNA expression profiling, and quantitative RT-PCR (qRT-PCR) Total RNA was extracted from whole kidney samples taken from >16-week-old RCALC1, or parental wild-type (WT) BALB/c and C3H mice (n ¼ 4 mice per group) using Trizol reagent (Invitrogen, Carlsbad, CA, USA).
After DNaseI-treatment, RNA was purified using an RNeasy Mini Kit (Qiagen). Nine micrograms of total RNA was used for firstand second-strand cDNA synthesis using the One-Cycle cDNA Synthesis Kit (Affymetrix, High Wycombe, UK) according to the manufacturer's instructions. Biotinylated cRNA was synthesized using the Genechip IVT Labelling Kit (Affymetrix). Fifteen micrograms of fragmented cRNA was hybridized for 16 hours at 45°C to Mouse Genome 430 2. 0 arrays (Affymetrix). After hybridization, arrays were washed and stained with streptavidin-phycoerythrin in the Affymetrix Fluidics Station 450 and scanned using the GeneChip Scanner 3000 7G. The image data were analyzed with GCOS 1. 4 using Affymetrix default analysis settings and global scaling as a normalization method. The data set of the arrays were normalized using Robust Multi-chip Average algorithm in respect to the sequence-specific probe affinities.
(38) Probe sets with marginal expression (maximum native signal <50) were removed and the remaining 17,811 probe sets were analyzed using the F-test. After ANOVA false discovery rate (FDR) multiple testing corrections,  (39)  individual differences between the data sets were investigated using the least significant difference (LSD) post hoc test. Genes with >1. 5-fold difference in expression (in the same direction) versus both parental strains were selected for further investigation. qRT-PCR reactions were performed in kidney samples from 4 to 5 mice using the QuantiTect SYBR Green Kit (Qiagen) and a Rotorgene 5 (Qiagen), as previously described. (40) All qRT-PCR test samples were normalized to the geometric mean of three housekeeper genes (cyclin D1 [Ccnd1], cyclin D2 [Ccnd2], and glyceraldehyde 3phosphate dehydrogenase [Gapdh]), as described previously).
(40) All primers were obtained from Quantitect (Qiagen). Threshold cycle (C T ) values were obtained from the start of the log phase on Rotorgene Q Series Software and C T values analyzed in Microsoft Excel 97-2010 using the Pfaffl method. (40, 41) Data for RCALC1 mice were expressed relative to wild-type littermates, which were set at 1. Statistical analyses were performed using the Student's t test.
Characterization of the BRD4-Met149Thr variant on protein structure To assess the potential pathogenicity of the BRD4-Met149Thr variant, we first examined online databases containing sequencing data from exome analysis in mice and humans. The BRD4-Met149Thr variant was absent from mouse populations, assessed  The co-segregation of the Brd4 +/M149T variant with renal calcification was investigated in four generations (G2-G5) of the RCALC1 mice. The Brd4 mutation co-segregated with the renal papillary calcification phenotype observed in RCALC1 mice over four generations. Between 72% and 93% of Brd4 +/+ mice did not have renal papillary calcification. 6 GORVIN ET AL. using The Jackson Laboratory database of exome sequencing data from 175 strains of mice,  (49)  and human populations, assessed using the exome aggregation browser (ExAc), which contains genetic information on 60,706 individuals,  (50)  thereby indicating that the BRD4 Met149Thr variant is not a common polymorphism.
The BRD4 Met149 residue is also highly evolutionarily conserved (Fig. 2C ), and bioinformatic analysis using MutationTaster software  (37)  predicted the BRD4-Met149Thr variant to be damaging. We therefore next assessed the effect of the BRD4-Met149Thr variant on BRD4 protein structure. 53) (54)  The Met149 residue is located in the aC-helix of BRD4 BD1 (Fig. 2E ), and previous studies have shown that Met149 is critical for histone binding and that mutation to Ala149 impairs this binding. (55) Structural modeling showed that the Met149 residue forms critical backbone contacts with three residues within aC (Fig. 2F ) and likely plays a pivotal role in maintaining the structural integrity of BD1. Mutation to Thr149 is predicted to retain these contacts and form an additional polar contact with Asp145 (Fig.
Acknowledgments We thank  Kumudika Gibson ,  Michelle Stewart ,  Terry Hacker ,  Caroline Barker ,  Anita Muller ,  Gabi Born , and  Stefan Amisten  for their technical support. This research was funded by the  European Union ,  EuReGene FP6  ( NYL, SEP, LB, RDC, RVT ); the  Medical Research Council UK  ( CMG, AACR, JJ, LB, PTC, TAH, RDC, SDB, RVT ; grant numbers  G9825289  and  G1000467 );  Kidney Research UK  (MJS, RVT); the  Wellcome Trust  ( NYL, AACR, BNA, RVT ); the  University of Oxford  (NYL); and a  Wellcome Trust   Investigator Award  (RVT).
Aberrant chromatin features identified in CLL In order to characterize CLL chromatin modifications in correlation with transcriptional activity, we analyzed the chromatin landscape and the transcriptome of CD19 + B cells from peripheral blood from 23 CLL patients and from 17 pools of non-malignant B cells (NBCs) of healthy donors  (Figs 1 and EV1, Appendix Fig S1 and Table S1, Datasets EV1 and EV2). While a number of pathophysiological processes such as microenvironmental signaling occur in secondary lymphoid organs of CLL patients  (Burger & Gribben, 2014) , the comprehensive analysis of different epigenetic layers required the acquisition of sufficient numbers of CD19 + B cells and was therefore conducted from peripheral blood. CLL patients were selected to assess the fundamental changes in the original, untreated, and nonevolved disease including both disease subtypes of IGHV mutated and non-mutated samples.
For selected samples, also ChIP-seq of EBF1 and CTCF was performed. In addition, RNA transcription was analyzed by strand-specific RNA-seq of long and short RNAs  (Appendix Fig S1A). The added value of this comprehensive analysis is apparent from inspection of the TCF4 gene. The histone modifications predict downstream enhancers and intronic enhancers that become activated in CLL cells as judged from the enrichment of  H3K4me1 and H3K27ac (Fig 1A). The predicted enhancer loci in this region were particularly extended (> 10 kb) and are therefore an example for so-called "super-enhancers" (SEs, see below;  Whyte et al, 2013). In order to systematically evaluate histone modification changes, we annotated chromatin with a 12-state ChromHMM Hidden Markov model  (Fig 1B). Chromatin states differed substantially between CLL samples and NBCs and showed transitions for repressive chromatin states 4, 5, and 6 (H3K9me3, H3K27me3) and potential enhancer states 1, 8, 9, and 11 (carrying H3K27ac and/or H3K4me1 while lacking H3K4me3; Fig  1B  and C , Datasets EV4, EV6-EV7).
Global changes in the promoter-associated H3K4me3 histone mark Next, we investigated changes at promoters. When calculating correlation functions using raw mapped reads for H3K4me3, a small but significant extension of this signal by two additional modified nucleosomes was detected (~400 base pairs, P < 0. 002, Figs 3A and B, and EV3A). We then extracted all extended H3K4me3 regions from peak calls with a P-value < 0. 05 and a median change of 400 bp. This yielded 2,785 regions for CLL and only two regions for NBCs (Dataset EV5). The 2,785 CLL-specific extended regions spanned over annotated TSSs  (Fig EV3A). We evaluated nucleosome occupancy using histone H3 maps obtained by MNase-ChIP-seq (Fig EV3B ) and detected a significant gain of nucleosomes at 2,639 out of the 2,785 promoters  (Figs 3C and EV3C, Dataset EV5).