dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The distribution of the SBR values of visually normal examinations of patients with suspected Parkinsonism was not different from those of Hv or patients without dopaminergic degeneration. The large sample size of the databases analyzed confirmed the physiological dependencies published by large academic studies with Hv. The lower SBR threshold for age (-2SD), used to define an examination as degenerative, was not different from the reference databases. The ScanOnlyDB database, containing information on gender, can be proposed to users of CZT gamma cameras with the same protocol. The results presented herein enable the creation of a reference database from routine visually normal clinical scans by expert nuclear medicine physicians. The proposed post-reconstruction harmonization method can also facilitate the use of semi-quantitative analysis across centers or cameras.
, Right). HvDB: Healthy volunteer database; ScanOnlyDB: Visually normal scintigraphy database of patients with suspected Parkinsonism, blinded clinical information; NoDG5yearsDB: database of patients without dopaminergic degenerative pathology after a 5-year followup; ENC-DAT: European Normal Control Database of DaTSCAN ™ ; Siemens reference database (SiemensDB); CT: computerized tomography.
[16]  proposed a deep age-invariant model (AIM) for face recognition in the wild. Based on single end-to-end deep neural network with strong anti-occlusion ability, a novel face recognition method was proposed by Qiu et al. [17]  to discover the corrupted features and clean them. Aiming to further mitigate the resolution discrepancy due to the resolution limitations, Gao et al. [18]  proposed a hierarchical deep CNN feature set-based representation learning for face recognition. However, the current researches on occluded face recognition mainly focus on the occlusion of specific regions  [19] [20] [21] , including eyes and mouth. Moreover, many studies found that most face recognition models can not achieve best results on all face datasets. In recent years, the progress of deep learning has greatly promoted the improvement of the adaptability of face recognition model.
However, the current public face dataset lacks face samples from coal mine environment. Therefore, it is necessary to produce a miner face dataset to fine-tune the transfer model. In this paper, an improved face recognition method for underground coal dust occlusion based on transfer learning is proposed to solve the problem of random coal dust obscuration. Firstly, a novel DS-inception block is designed to reduce the amount of model parameters, and this work establishes a multiscale neural network named DSR-inception. Moreover, a joint supervised loss function based on center loss and softmax loss is proposed to adapt to face recognition classification task. In addition, experimental results show that the face recognition performance indexes of the proposed network in the homemade miner face dataset, such as accuracy, recall and F1-score, are superior to those of other classical face recognition models, and transfer model with joint supervised loss function can achieve the higher recognition accuracy.
Lastly, the validity of the proposed face recognition algorithm is verified by industrial test in the dugout tunnel of coal mine. The remainder of this paper is organized as follows. In section 2, the proposed improved algorithm and model architecture are introduced. In section 3, comparative experiments of model transfer strategy are carried out and the effectiveness of the improved algorithm is verified in the self-made miner face dataset. In section 4, a face recognition system is built and occluded face recognition experiment is carried out in underground coal mine. Conclusions and future works are summarized in section 5.
Dataset The pre-training dataset selected in this paper is the CelebA face dataset. The face data in CelebA is given in terms of face attributes for classification, and each face picture is given with face frame marker points. Appendix 4 shows a portion of the face images in the dataset. The homemade miner face dataset is named MF dataset. The MF dataset contains 40 people with 21 pictures each, including 7 pictures each of no coal ash obscuration, light coal ash obscuration and heavy coal ash obscuration, for a total of 840 pictures. Table  2  shows a group of face sample for the same person, and partial face samples of different  SNNBFR  [32]  93. 54 OPFaceNet  [33]  96. 83 IFDM  [34]  95. 64 LSCSR  [35]  96. 50 MFR  [36]  96. 78 MMPCANet  [37]  97. 18 Proposed 97. 26 X. Liu et al. miners in MF dataset are shown in Fig.
Contrast experiment In     of 20 groups of experiments, it can be seen that the F1-score of the model with joint supervised loss function is about 0. 5%~1. 5% higher than before, which verifies the effectiveness of joint supervised loss function. This paper also compares the proposed model with some new mainstream methods on CelebA face dataset, and the specific experimental results are shown in Table  5. In the real working environment of coal mine, the fine-tuned models on MF data set are used to detect and recognize faces with coal ash occlusion. The recognition results and prediction confidence of different algorithms are shown in Fig. 4 , and Fig. 5  shows facial recognition result of the proposed method at the coal face. As can be seen from the above figures and table, the performance of the proposed method on CelebA face dataset exceeds that of most existing methods.
Application of proposed approach in underground coal mine In order to further verify the feasibility of the proposed face recognition system, the face recognition system has been applied to the No. 23 coal mine tunnel of an underground coal mines company in Shandong Province. The mining monitoring camera KBA18W is selected to obtain images, and the signal is transmitted through the underground wireless router. The LED light source is used as auxiliary lighting equipment and the industrial test site equipment is set up as shown in Fig. 6(a). Before the industrial test, the face data of the staff members, a total of 6 people, is registered into the system. The system display interface is shown in Fig. 6.
Conclusions and future work In this paper, an improved face recognition method for underground coal mine based on transfer learning is proposed to solve the problem of random coal dust obscuration. A novel DS-inception block is designed to reduce the amount of model parameters, and a joint supervised loss function based on center loss and softmax loss is proposed to adapt to face recognition classification task. Compared with the other classical models such as Inceptionv1, VGG-16 and Resnet-18, the various evaluation indicators of the proposed multiscale neural network, including accuracy, recall and F1-score, achieve 97. 26%, 94. 17% and 95. 42%, respectively. In order to better adapt to the face recognition task in coal mine, a miner face dataset is made to fine-tune the transfer model, and the transfer model incorporating joint supervised loss can effectively improve the face recognition accuracy by about 0.
Acknowledgements This research is supported by the  National Natural Science Foundation of China  ( 51975568 ),  Natural Science Foundation of Jiangsu Province  ( BK20191341 ),  Jiangsu Funding Program   for Excellent Postdoctoral Talent  ( 2022ZB519 ),  China Postdoctoral Science Foundation  ( 2022M723387 ) the  Science Foundation of Donghai Laboratory  ( DH-2022KF0302 ), the  Priority Academic Program   Development of Jiangsu Higher Education Institution, China (PAPD). The research leading to these results has received funding from the  Norwegian Financial Mechanism   2014-2021  under Project Contract No  2020/37/K/ST8/ 02748.
this paper, stochastic gradient descent (SGD) is chosen as the optimizer of training, and the remaining hyperparameters, including batch size, initial learning rate a 0 , natural decay index β and epoch, are set to 64, 0. 01, 0. 05 and 150, respectively. Inceptionv1, VGG-16 and Resnet18 are selected as the comparison network models and trained on the CelebA face dataset. The accuracy and loss of models during training.
LAM-HTGTS. Genomic DNA (gDNA) was extracted from 10 million of 4 days stimulated B cells. LAM-HTGTS was performed as previously described (  22 ) with a Sµ bait. Data analysis of MiSeq sequencing reads was performed as previously described  (23). All sequence alignments were done with the mouse mm10 genome. Analysis of junction structure has been performed with CSReport tool  (24). Analysis of ATAC-seq. Nextera sequences were trimmed by TrimGalore  (25)  with only pairs of reads with their two reads of at least 55bp each were reported in the resulting fastqs and then processed by the nf-core/atacseq pipeline (26) using mm10 for reference genome. Differentially opened chromatin regions from comparisons of different conditions were extracted using DESeq2 (  27 ) from the DESeqDataSet previously produce by the pipeline  (26).
The normalized counts extracted from the DESeqDataSet were used to calculate the coefficient of variation (CV) of each consensus peaks. Peaks with coverage greater than or equal to the mean were ranked by increasing CV to find a suitable region for ATAC-qPCR normalization controls among the samples compared. The third least variable consensus peak hitting Lig1 gene from positions 13278266 to 13279324 of chromosome 7 on mm10 were selected to design primers for ATAC-qPCR normalization controls. Chromatin A/B compartments were inferred from the consensus peaks of nf-core/atacseq analysis (  26 ) at 3000kb resolution by an extended version (github trichelab/compartmap repository) of the original R package Compartmap  (28).
Acknowledgments We thank  BISCEm  and the animal core facility team for help with mouse work on both practical and regulatory aspects. We are grateful to  Emeline Lhuillier  and the  Genotoul Plateau GeT-Santé facility  () for technical assistance with ATAC-Seq sequencing. We thank  Vonick Sibut  for bio-informatic analysis advices on ATAC-Seq. We thank  Adam Yongxin Ye  and  Xuefei Zhang  for precious advices on 3C-HTGTS experiments (technical part and bio-informatic analysis),  Mehdi Alizadeh  and  Emilie Guérin  for advices on sequencing step. We are grateful to  Dr Brice Laffleur  for fruitfull scientific discussion and critical reading of the manuscript. We also thank  Pedro P Rocha  for scientific and technical discussions. We thank  Christelle Oblet  for her invaluable help in creating the graphical abstracts.
CB was supported by PhD fellowships from the  French Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation. MT was supported by PhD fellowships from the  French Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation  and the  Fonds Européen de Développement Régional (FEDER). JP was supported by  Institut CARNOT CALYM. This work was supported  Agence Nationale de la recherche  ( ANR-21-CE15-0001-0 ),  La Ligue Contre le Cancer  (comités  87 ,  23  to EP and SLN), the  Fondation ARC pour la recherche sur le cancer  (PhD continuation fellowship to CB and MT).
5°and  i = 2. 5°, allowing to cover a broad q z -range and thus to probe with finer detail the SLD profiles and thicknesses in the dry and fully swollen states. Here, q z denotes the momentum transfer vertical to the sample surface. Following a coherent summation method for data reduction,  [51]  reflectivity (R) curves for a q z -range between q z = 0 Å -1 and q z = 0. 23 Å -1 are reported in Figure  1a ,b, for C-PEOXA and L-PEOXA, respectively. Using refnx software for model-fitting,  [52]  the best fits to the ToF-NR curves for both brush types were obtained by applying a slab-multilayer model that sequentially included silicon (Si) (backing layer), a single  amorphous silicon oxide (SiO x ) layer, a single TiO 2 layer and different numbers of PLL-g-PEOXA layers. This description of polymer brushes swollen by water vapor agreed well with the modeling recently proposed by van Eck et al.
The apparently large values of SLD next to the TiO 2 layer (SLD dry, region 4 ≈ 2. 5 (× 10 -6 Å -2 )> SLD dry,PEOXA = 0. 985 (×10 -6 Å -2 )) in the dry state (RH = 3%) might imply the fast adsorption of D 2 O molecules probably from ambient atmosphere -even before exposure to D 2 O vapornear the grafting surface, which presumably took place at much shorter timescales compared to the time required for acquiring the individual datasets. Alternatively, this phenomenon might be due to the strong interaction between D 2 O and partially charged PLL backbones, which preferentially interact with (and lay down on) the TiO 2 surface and were more hygroscopic than the PEOXA grafts. [55] It is also known that the amide groups can be subject to H/D exchange, as reported for PNIPAM-based films and microgels.
Acknowledgements The authors acknowledge funding from the  German Ministry for Education and Research (BMBF)  withi'n the project " FlexiProb " (Grant No. 05K2016 ). The authors would like to thank  Stéphanie Jimenez  for her precious technical support during the experiments at the neutron reflectometer D17. The  Institut Laue-Langevin  and the neutron reflectometer D17 are acknowledged for beam time allocation and excellent equipment and support of measurements. P. W. acknowledges the  China Scholarship Council (CSC). A. V. would like to thank  Oleg Borisov  for helpful discussions. Open access funding enabled and organized by Projekt DEAL.
6, 10  In our study, the VIP regimen seemed to have a better response rate. An alternative in SR vaginal MGCT would be the use of four cycles of standard-dose cisplatin combined with etoposide and low-dose bleomycin regimen (PEB). 11, 12  Replacing cisplatin by carboplatin would be another alternative. 13, 14  Comparative analyses of these trials are in favor of similar efficacy and toxicity of PEB and JEB,  15, 16  but this is actually prospectively evaluated in the international AGCT1531 randomized clinical trial (NCT03067181). For patients with HiR disease, VIP regimen appears to be very efficient, as previously reported in the overall TGM-95 analysis. 6  A combined cisplatin and alkylating agents-based regimen was also tested by the Children's Oncology Group (COG) in HiR GCT, with cyclophosphamide added to PEB regimen with overall good tolerance and efficacy.
ACKNOWLEDGMENTS Thanks to the investigators of the following French centers, which contributed patients to this study:  Gustave Roussy  ( Villejuif) ,  Institut Curie (Paris) ,  Oscar   Lambret (Lille) ,  CHU Toulouse ,  Léon Bérard (Lyon) ,  CHU Bordeaux ,  CHU Montpellier , and  CHU Limoges. Thanks to  Linglart L. for the English revision.
Networks We evaluate the CNDP heuristics against both real-world and synthetic graphs. Real-world graphs are obtained from real-world networks of different sizes and types. Following the approach of  [15] , we selected graphs of five different types: Road Network (RN), Peer to Peer (P2P), Web Graph (WG), Social Network (SN), and Product-Co-Purchase (PCP). The networks have been collected from the following sources: the 9th DIMACS implementation challenge  [20] , the Stanford Large Network Dataset Collection  [21]  and the Ref. [22]  containing the graph academia-SN. Table  2  summarizes the characteristics of the graphs in our dataset, including a few topological properties. Synthetic graphs are generated using two well-known network models, Erdős-Rényi (ER) and Square Grid (SG) graphs, chosen in order to test the heuristics against specific network topologies.
More precisely, we use rectangular grids, not squared grids, in case of SG graphs. ER graphs are the most general null model to which all real graphs can be compared and are supposedly hard to disconnect. SG graphs, on the other hand, represent a stylized version of road networks, the only type of networks for which the CNH is sometimes outperformed by other heuristics (see Section 3. 1). We consider synthetic graphs of 5 K, 50 K and 100 K vertices, and tiny synthetic graphs of 30 nodes. Given their small size, tiny ER graphs are not guaranteed to present the topological properties that typically emerge in large ER graphs. However, these small ER and SG graphs enable us to qualitatively evaluate how close each heuristic can get to the optimal solution, at least at this tiny scale. To generate directed ER graphs, other than the number of vertices, we set the average out-degree to 7, in line with the average of all real-world networks in our dataset.
To generate directed SG graphs, we proceed as follows: (i) we generate the undirected version of the graph; (ii) we create the directed version of the graph by duplicating edges; (iii) we randomly remove a fraction (2%) of the edges. We decide to remove only 2% of the edges because the real-word RN graphs in our dataset have reciprocity 1, 1 and 0. 91, respectively (see Table  2 ). Note that it is not possible to increase the average degree of SG graphs without modifying the structure of the graph and thus the model. For each model and each graph size, we generated 10 independent graph instances -100 for tiny graphs -and we applied each heuristic to the giant SCC of all instances. Table  3  reports the main features of the giant SCC of our synthetic and tiny graphs, with properties averaged over the different instances.
Experimental setup We ran all the experiments on a 24-core AMD Ryzen Threadripper 3960X@3. 80 GHz system equipped with 256 GB of RAM. Few heuristics, due to their time complexity, require very long execution times. To deal with those cases we set an upper bound timeout of 172800 s (2 days) for each run. When the timeout expires, it executes a graceful shutdown procedure. Concerning the software, we developed a C implementation of CNH and bruteforce, whereas we leveraged existing solutions for the other centrality measures. We are aware that for some metrics there exist in the literature highly parallel and efficient implementations  [23, 24]. However, only few of them are freely available, and an efficient implementation is not available for all metrics. Thus, for seeking fairness in the comparison, we decided Table  2  Real-World Graphs -For each graph in the dataset we show: the name, the type, the number of nodes and edges, the density, the average total degree (avg.
Real-world graphs We start discussing the application of the standard strategy to realworld graphs. Figs. 2 3 4 5 6 show the results for each combination of 2 https://igraph. org 3 https://github. com/iac-cranic/seeking_critical_nodes_digraphs 4 https://networkx. org heuristic and graph. In each chart we show only the section that is more relevant for the discussion, by using a different limit for the 𝑥axis. The horizontal gray dotted lines in the charts denote the 50% of residual connectivity. Generally speaking, we observe that, by picking the right heuristic, we can reach the 50% of residual network connectivity by removing just a small subset of nodes -less than 3% of the initial number of nodes in almost all networks, about 5%-6% in amazon0601, p2p-Gnutella25 and rome99b. CNH is, overall, the best heuristic for identifying critical nodes, i.
We select a target 𝛼 ∈ [0, 1) and we aim at finding the fastest possible heuristic that degrades the connectivity of the network to some 𝛾 ≤ 𝛼𝛾 0 , where 𝛾 0 is the initial connectivity of the graph. In theory, this variant of the CNDP does not pose any constraint to the number 𝑘 of removed nodes. In practice, however, we do limit the number of nodes that each heuristic can remove to 𝑘 = 10% of the network, knowing from Section 3. 1 that 6% is enough to halve the connectivity of any graph in our dataset when the best heuristic for the graph is used. This is necessary for, at least, two reasons: (i) the maximum number of nodes that we can remove is often a constraint of the application, (ii) with no limits to 𝑘, removing all nodes of the network would always be the optimal solution to the problem.
Acknowledgment The authors would like to thank  Loukas Georgiadis  for useful discussions and comments. This work was partially supported by  TAILOR Project EU HORIZON 2020 Research and Innovation Programme  GA No  952215  https://tailor-network. eu; and  MIUR , the  Italian Ministry for Education, University and Research , under  PRIN Project AHeAD (Efficient Algorithms for HArnessing Networked Data ).
2C ). The tighter correlations may be due to the strong dependency of piRNA production mechanisms on chromatin marks FIG. 1. -Graphic summary of the study. Eight wild-type strains from D. melanogaster and D. simulans were included in the study. The present data sets are RNA-seq and ChIP-seq for H3K4me3 and H3K9me3 marks and were prepared from ovarian samples. They were analyzed in parallel with already published data produced from the same Drosophila strains: ovarian small RNA repertoires and genome assemblies based on Oxford Nanopore long-read sequencing  (Mohamed et al. 2020). For RNA-seq and ChIP-seq, TE-derived reads were analyzed at the TE family level, and gene-derived reads were analyzed in relation to TE insertions inside or near genes (therefore restricted to the TE insertions included within the bubbles).
Genome Annotation Genome assemblies were produced in  Mohamed et al. (2020)  and have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB50024 (https://www. ebi. ac. uk/ena/browser/ view/PRJEB50024). Throughout the present analysis, we kept scaffolds corresponding to complete chromosomes 2L, 2R, 3L, 3R, 4, and X.
TE Annotation We used RepeatMasker 4. 1. 0 (http://repeatmasker. org/) -species Drosophila in order to identify TE sequences in the assemblies, followed by OneCodeToFindThemAll (Bailly-Bechet et al. 2014) with default parameters, in order to parse RepeatMasker results. We include all TE sequences in the subsequent analyses, whether they are full length or truncated. TE genomic sequence abundance (bp) was computed using OneCodeToFindThemAll  (Bailly-Bechet et al. 2014). In order to determine which TE insertions were common (shared) to the four strains of a species or unique (private) to one strain, we used the following procedure. We used the GenomicRanges R package and the subsetByOverlaps function to build correspondence between gene and TE annotations of each genome assembly. For each gene and each functional region of each genome assembly for a given species, we extracted the family names of the TE insertions.
Acknowledgments We thank  Francois Sabot ,  Matthieu Boulesteix ,  Vincent Mérel , and  Daniel S Oliveira  for useful discussions and technical help. We thank the  GDR MobilET  for useful discussions. We thank  Gladys Mialdea ,  Justine Picarle ,  Sonia Janillon , and  Nelly Burlet  for technical help. This work was performed using the computing facilities of the CC LBBE/ PRABI. Sequencing was performed by the GenomEast platform, a member of the " France Génomique " consortium ( ANR-10-INBS-0009 ). This work was supported by  Fondation pour la Recherche Médicale  (grant  DEP20131128536 ) and  Agence Nationale de la Recherche  (grant  ExHyb ANR-14-CE19-0016-01 ).
Data Availability The RNA-seq data generated in this study have been submitted to the NCBI BioProject database (https://www. ncbi. nlm. nih. gov/bioproject/) under accession number PRJNA 795668. The ChIP-seq data generated in this study have been submitted to the NCBI BioProject database under accession number PRJNA796157. TE and gene annotations have been deposited to Zenodo doi: 10. 5281/zeno-.