dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets and Ground-Truth. The proposed method is tested on four different datasets (UTBM-1, UTBM-2, KITTI 05, and KITTI 06). The taken route for UTBM-1 dataset is shown in Figure  9   composed of 540 images while the testing database is composed of 520 images. The GPS information of each image is also collected. The popular KITTI benchmark dataset is also used to test our proposal. The KITTI Odometry dataset has 22 sequences containing a total of 44182 stereo images (39. 2 km). These sequences include environments with different characteristics and challenging situations such as perceptual aliasing and changes on scene. Among them, the datasets KITTI 05 and KITTI 06 that contain loop closures were selected to evaluate our method. There are 2761 and 1101 images in KITTI 05 and KITTI 06 datasets, respectively.
Image Preprocessing and Feature Extraction. In our work, for faster feature extraction, the original color images were downsampled into half scale size gray-scale image. That means images in datasets UTBM-1 and UTBM-2 were resized to 640 × 480 and the images in dataset KITTI 05 and KITTI 06 were resized to 613 × 235. In order to reduce the illumination influence on the outdoor image appearance, contrast-limited adaptive histogram equalization (CLAHE) method was used (see Section 3. 1). Moreover, as a pair of images is acquired at each instant, a disparity map can be computed easily using the SGBM (Semiglobal Block Matching) algorithm  [20]. After image preprocessing, binary descriptors (LBP, CLBP, CSLBP, CSLDP, and XCSLBP) are extracted with the following parameters: 8 sampling points and 3 pixels radius.
Performance of Different Binary Features. In this section, we compare binary features performance in two situations: with or without disparity map. Here the features are compared based on the Euclidean distance. Table  1  gives the  1 scores of the binary descriptors in two cases (without and with disparity information). It can be seen that LBP, CLBP, CSLBP, and CSLDP with disparity information improve the image retrieval ability as  1 scores are higher with disparity information than without disparity information. Among them, D-CSLBP is the best one; it achieves the highest  1 score. Figure  11  depicts the precision-recall curves obtained by the different binary features in two typical datasets UTBM-1 and KITTI 06. It can be seen that the performance of D-CSLBP is better than the performance with the features D-LBP, D-CLBP, D-CSLDP, and D-XCSLBP.
Performance of Multifeature Combination. In this section, we compare the performance of multifeature descriptor (D-CSLBP++HOG) with single independent feature descriptor. Figure  13  shows the precision-recall curves obtained with the different tested features: D-CSLBP, HOG, and D-CSLBP++HOG. It can be found that the binary feature D-CSLBP combined with HOG permits improving image retrieval performance. Combining D-CSLBP and HOG can achieve better result than each single feature, which means that the combination is useful for place recognition. Table  2  compares the  1 scores of different features with the state-of-the-art FAB-MAP method. It confirms that the multifeature D-CSLBP++HOG achieves better results than single feature. The  1 score of D-CSLBP++HOG provides the highest value for all the four datasets.
Furthermore, the proposed method outperforms the FAB-MAP method. For a better comprehension of the proposed multifeature, an example of distance matrices for UTBM-1 dataset is presented in Figure  14. Here, for clearly demonstrating the feature performance, the distance matrix  is normalized into 0-1 range. The distances of the same or similar images are close to 0 (red color), while, for the larger distances, the corresponding color is close to yellow. As plotted in Figure  14 (b), the ground-truth line is red. When perceptual aliasing occurs, some red points (noise) will appear which is outside the ground-truth line. In the distance matrix provided by our method using the D-CSLBP++HOG feature (see Figure  14(c )), it can be seen that the noise which appears around the diagonal (ground-truth line) due to perceptual aliasing is clearly reduced with respect to other feature approaches (CSLBP, D-CSLBP, and HOG).
Hash keys are constructed by applying  binary-valued hash functions to each image feature. The  binary-valued LSH functions consists of random projections and thresholds as 𝐻 test (𝐾) = sign (𝑤 ⊤ 𝑉 test + 𝑏) , 𝐻 train (𝐾) = sign (𝑤 ⊤ 𝑉 train + 𝑏) , (27) where  is a  dimensional data-independent random hyperplane, which is usually constructed from a standard Gaussian distribution  [24]. is a random intercept. For a normalized dataset with zero mean, the approximately balanced partition is obtained with  = 0. By applying  binary-valued hash functions to each image feature, high dimension multifeatures  test and  train are converted into a low  dimension bits  test and  train. Since  test and  train are binary bits, they can be more efficiently compared in low dimension space than original feature.
In our experiment, we compare the place recognition performance achieved with hashed multifeature of different binary lengths (2 8 ⋅ ⋅ ⋅ 2 12 bits) on four datasets in Figure  15. Since the image size is different, multifeature dimension in datasets UTBM-1 and UTBM-2 is 18696 while the multifeature dimension in KITTI 05 and KITTI 06 is 6432. It can be seen that, using 4096 and 2048 bits retains above 86% total place recognition performance. Table  3  shows the  1 score obtained from different hash bit lengths applied on the multifeature (D-CSLBP++HOG) of  Figure  15 : Visual localization results obtained by our system on four datasets. The trajectory of the vehicle is depicted with black lines; the loop closure zone is plotted by blue lines. Red points are correctly recognized locations at 100% precision by using our proposed approach.
There are no false positives in any case. It is noted that the loop closure zone of datasets UTBM-1 and UTBM-2 is the whole trajectory, while the loop closure zone of KITTI 05 and KITTI 06 is only parts of the trajectory in blue. our place recognition method. The average matching time is also presented. Here average matching time does not include the feature extraction time. The experiments were conducted on a laptop machine with intel i7-4700MQ CPU and 32 G RAM. As Table  3  shows, the average matching time using 4096 bits is almost half of the one using the Euclidean distance over the original full features. Compared with the full multifeature matching, hashing the original multifeature into 4096 bits makes the distance computation and comparison easier and faster. There is no doubt that, for large-scale datasets, the speed-up advantages can be more significant.
Visual Localization Results. In the previous section, 4096 bits obtained by hashing the original feature shows its good performance in place recognition. Therefore, in this section, we describe visual localization results achieved by 4096 hash bits. Figure  15  shows the final place recognition results for the different datasets at a precision level of 100%. For the datasets UTBM-1 and UTBM-2, we obtained 23. 81% and 11. 35% recall at the 100% precision, respectively, while in the KITTI 05 and KITTI 06 datasets, a recall rate of 17. 38% and 32. 39% is achieved, respectively, at the total correctly level. It should be noted that, at 100% precision level, the obtained place recognition result is totally correct. A correct place recognition means a successful visual localization; therefore, the higher th recognition rate (recall) at 100% precision is, the more robust the visual localization system is.
When adjusting the threshold value Th, the recognition precision is also changing. At 100% precision level, each recognized place is true positive and its localization error is small (depending on the ground-truth criteria, in our case it is 5 m). For achieving the 100% recognition precision level, threshold value is set to 0. 88 and 0. 58 for UTBM-1 and UTBM-2 datasets, respectively. When the threshold is set to 1, which means every image matching result is positive, in this case, the precision level is the lowest and there are many false matching for place recognition, which lead to huge localization error. In general, if small threshold is used, there are few false recognition cases. In addition, for visual recognition precision level below 100%, meaning that recognized places are not totally correct, some false recognition places appear.
For these false recognized places, the localization error can be very large, because the testing image can be wrongly matched to anyone in the training image database. That is also the reason why some locations have huge localization error. Table  4  gives the average localization error and recall ratio at different precision levels. For all these datasets, at 100% precision, the minimum localization error is 0 while the maximum error is not larger than 5 m. It should be noted that, at 100% precision level, some places can not be recognized and no localization results are obtained at these places. This problem can be easily solved by visual odometry technique or extra sensors (as LiDAR or Inertial Measurement Unit (IMU)).
Using this method, a multifeature set  train = {V train  }  train =1 of all training image pairs { train  }  train =1 is obtained. For a current testing image pair  test  , a multifeature V test  is also obtained. Then the image matching is conducted based on the Euclidean distance comparison between the multifeature V test  of the current testing image and all training image multifeatures V train  ( = [1, 2,. ,  train ]) from the training images dataset.
Feature Comparison and Image Matching. Feature comparison is performed based on the Euclidean distance between features. Each testing image multifeature V test  is compared with all the training images multifeatures V train  ( = [1, 2,. ,  train ]) of the training database. The distance  , between the multifeature V test  ( = [1, 2,. ,  test ]) of the testing image and multifeature vector V train  ( = [1, 2,. ,  train ]) of a training image is computed as follows:.
(a): the experimental vehicle traversed about 4 km in a typical outdoor environment. Three typical areas were traversed: urban city road (area a), lots of factories building (area b), and a nature scene surrounding a lake (area c). The training and testing data were collected at different times, respectively, in 2014/9/11 and 2014/9/5. The training database is composed of 849 images while the testing database is composed of 819 images. The average distance between two successive frames was around 3. 5 m. To tag the training images, GPS position of each image is obtained by a RTK-GPS receiver. The UTBM-2 dataset (Figure 9(b)) consists of a 2. 3 km route in Belfort city downtown acquired in 2014/9/5. The first traversal to acquire training dataset was performed in the morning and the second one was conducted in the afternoon to acquire testing dataset.
3. 4. 4 Feature Comparison and Image Matching. Feature comparison is performed based on the Euclidean distance between features. Each testing image multifeature V test  is compared with all the training images multifeatures V train  ( = [1, 2,. ,  train ]) of the training database. The distance  , between the multifeature V test  ( = [1, 2,. ,  test ]) of the testing image and multifeature vector V train  ( = [1, 2,. ,  train ]) of a training image is computed as follows:.
Thickness measurement setup and thickness profile reconstruction method To reduce the thickness uncertainty given by the manufacturer, a thickness measurement setup has been developed based on the α-transmission technique. The detection setup is made of an 241 Am source and a PIPS Silicon detector which has an energy resolution of 12 keV [9], leading to a measurement thickness precison of ±10 nm. The energy loss of alpha particles through a foil is measured and then converted in mylar thickness with the help of energy loss/thickness table, built with the SRIM software  [10]. A scan of the 14 × 22 cm 2 foil is performed and then the entire foil profile is reconstructed with a Delaunay triangulation interpolation  [11]. A typical thickness profile is presented in Fig. 3. The visible structures depend on the way the mylar foil is stretched and sticked to its frame.
It was also shown that using a foil of 0. 5 µm instead of the original 0. 9 µm one, has been successfully conducted leading to smaller energy loss corrections. Usage of foil thinner than 0. 9 µm was a technical challenge in itself. The next step for the development of the FALSTAFF spectrometer is to characterize the new axial ionization chamber, CALIBER. Its characterization will be made with a 252 Cf source and its energy calibration will be made with ion beams. A method for the fragment nuclear charge determination from the fragment energy loss profile in CALIBER will be investigated. The full characterization of the first arm of the spectrometer will be done in 2017 and the two-arm FALSTAFF spectrometer will be ready for data taking in 2018. This work was supported by EURATOM 7 Framework Programme CHANDA and the French programme NEEDS.
Therefore, future studies of both environmental or life style inducers of circadian disruption coupled with mechanistic or genetic marker studies in circadian rhythm pathways are needed. In this study, like other candidate pathway-based analyses  61  , we assigned SNPs to each of the circadian genes based on genomic location. Approaches that assign SNPs to a gene based on functionality such as a genetic influence on gene expression or expression quantitative risk loci (eQTL) might reveal more signals, but this type of approach relies heavily on the known eQTL function of the SNPs in the tissue of interest and, in fact, the eQTL effects on gene expression are typically tissue-specific  62. We attempted to evaluate the involvement of the top prostate cancer risk SNPs of AANAT and NPAS2 as functional eQTLs using RNA-seq and SNP data from ten normal brain tissues (GTEx).
We observed modest eQTL effects on AANAT and NPAS2 mRNA levels by the top risk SNPs, but no risk eQTL survived correction for multiple comparisons (data not shown). Importantly, published data suggest that the target tissue for melatonin synthesis is the pineal gland, while for circadian rhythm it is the superchiasmatic nucleus (SCN)  1. RNA-seq data for these normal brain tissues are not available in GTEx or to our knowledge from any other publically available database. Thus, whether the observed prostate cancer risk SNPs of AANAT and NPAS2 circadian genes are functional eQTLs, and whether the changes in mRNA levels in the pineal gland and SCN are associated with prostate cancer susceptibility remains to be determined. Our study has many strengths. Using genetic markers to examine circadian hypotheses minimizes the bias due to potential confounders, and therefore is a valuable complement to traditional epidemiologic studies (e.
generous support from the Ontario Ministry of Research and Innovation. PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Additionally, a subset of control samples were genotyped as part of the Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer GWAS (Yeager M, et al. Nat Genet. 2007;39(5):645-649), Colon CGEMS pancreatic cancer scan (PanScan) (Amundadottir L, et al. Nat Genet. 2009;41(9):986-990 and Petersen GM, et al. Nat Genet. 2010;42(3):224-228), and the Lung Cancer and Smoking study. The prostate and PanScan study datasets were accessed with appropriate approval through the dbGaP online resource (http://cgems. cancer. gov/data/) accession numbers phs000207.
v1. p1 and phs000206. v3. p2, respectively, and the lung datasets were accessed from the dbGaP website (http://www. ncbi. nlm. nih. gov/gap) through accession number phs000093. v2. p2. Funding for the Lung Cancer and Smoking study was provided by National Institutes of Health (NIH), Genes, Environment, and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping. PMH: National Institutes of Health (R01 CA076366 to PA Newcomb). VITAL: National Institutes of Health (K05 CA154337). WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.
Acknowledgement We thank  Dr. Andrew Bergen  and  Shailesh Kumar  ( NIH/NHLBI ) for the discussion on functional annotation and circadian rhythm. We recognize the following contributors from CORECT:  Stephanie L. Schmit ,  Fredrick R. Schumacher ,  Christopher K. Edlund ,  Gad Rennert ,  Eric Jacobs ,  Peter T. Campbell ,  John L. Hopper ,  Daniel D. Buchanan ,  Li Li ,  Michael Woods ,  Graham Giles. Other contributors from GECCO are listed in the supplementary materials.
A complete OsHAK1 clone has since become available in the Japanese cDNA database Knowledge-based Oryza Molecular biological Encyclopedia (KOME)  (Rice Full-Length Consortium et al. , 2003). We investigated the K + and Cs + transport activity of the encoded transporter by heterologous expression in yeast and analysis of the capacity of the transformed cells to deplete Cs + and K + from 30 µM solutions (same concentration as in the experiment with plants, Figure  1d ). The S. cerevisiae strain PLY246  (Bertl et al. , 2003) , impaired in K + uptake, was transformed with the pYPGE15 plasmid containing OsHAK1 coding sequence or no insert (empty vector control, EV). Yeast cells transformed with the plasmid allowing OsHAK1 expression efficiently took up Cs + and K + whereas cells transformed with the empty plasmid failed to do so (Figure  2 ).
Yeast growth conditions and Cs + and K + uptake in yeast The K + uptake deficient yeast (Saccharomyces cerevisiae) strain PLY246 (trk1∆ trk2∆ tok1∆)  (Bertl et al. , 2003)  was used for cation uptake experiments. OsHAK1 coding sequence was obtained from KOME database (https://dbarchive. biosciencedbc. jp/en/kome/desc. html ; clone AK119883)  (Rice Full-Length c et al. , 2003)  and cloned in the pYPGE15 vector  (Brunelli and Pall, 1993)  between BamHI and EcoRI sites. Yeast transformants containing either OsHAK1 in pYPGE15 or the empty vector (control transformants) were selected after plating on uracil-free SD medium supplemented with 100 mM K +  (Sherman, 1991). Cation uptake experiments were performed as described in  (Rubio et al. , 2000, Nieves-Cordones et.
Electrophysiological recordings in root cells with micro-electrodes Rice seedlings were grown in 0K solution for 5-7 days. Intact seedlings were fixed in a plexiglass chamber filled with 0K solution. Micro-electrode preparation, cell impalement and setup details were described in  (Mian et al. , 2011). After stabilization of the membrane potential in 0K solution was observed, membrane potential measurement solutions containing CsCl or KCl at different concentrations were percolated,in the plexiglass chamber, the chamber being washed with 0K solution between two successive measurements. A Michaelis-Menten equation was fitted to depolarization data obtained with the different CsCl and KCl concentrations.
Identification of an oshak1 homozygous T-DNA insertion mutant A T-DNA insertion mutant line of rice in the Dongjin genetic background was identified through Rice GE (the Rice Functional Genomic Express Database) (http://signal. salk. edu/cgi-bin/RiceGE) and ordered from POSTECH-RISD (Korea)  (Jeon et al. , 2000 , Jeong et al. , 2006). Identification of homozygous mutant plants was performed by PCR using primers described in Table  S5.
Acknowledgements We are grateful to  Jin Miao  for providing pOsgRNA and pHUbiCas9 vectors and to  Alonso Rodríguez-Navarro  for providing pYPGE15 vector, to  Murielle Portefaix  for transgenic plant production, to  Christian Chaine  and  Rémy Michel  for their help in greenhouse and to  Anne-Cécile Meunier  for assistance in sgRNA cloning. OsHAK1 cDNA was obtained from the  KOME library (Japan). The T-DNA insertion line in OsHAK1 was obtained from  Gynheung An. This work was supported by an  ANR   IA grant  ( ANR-11-RSNR-0005  Demeterres), to E. G. and H. S. , and by a  Precursory Research for Embryonic Science and Technology (PRESTO) program  from the  Japan Science and Technology Agency (JST)  ( 15665950 ) to K. T. The authors declare that there is no conflict of interest.
8 Scientifically, sharing makes it possible to compare or combine the data from different studies, and to more easily aggregate it for meta-analysis. It allows conclusions to be re-examined and verified or, occasionally, corrected, and it can allow new hypotheses to be tested. Sharing can therefore increase data validity, but it also squeezes more value from the original research investment, as well as helps to avoid unnecessary repetition of studies. The economic advantages of data reuse are one reason why governmental and intergovernmental agencies, as well as major research funders (eg, the Gates Foundation  9  and the Wellcome Trust 10 ), support data sharing. Ethically, data sharing provides a better way to honour the generosity of clinical trial participants, because it increases the utility of the data they provide and thus the value of their contribution.
Origin of this document The document has been prepared in the context of a specific working task of the European Union (EU) CORBEL (Coordinated Research Infrastructures Building Enduring Life-science Services) project (www. corbel-project. eu). CORBEL is designed to establish a collaborative and sustained framework of shared services across 11 participating European, European Strategy Forum on Research Infrastructures (ESFRI) biological and medical research infrastructures, to better support biomedical research in Europe and accelerate its translation into medical care. One of the objectives of this working task is to develop procedures to provide the scientific community with access, on request, to the individual participant data (IPD) from previous clinical trials for reanalyses, secondary analyses and meta-analyses.
This activity is led by the European Clinical Research Infrastructure Network (ECRIN-ERIC), an ESFRI research infrastructure that provides guidance, consulting and operations management for multinational clinical trials on a not-for-profit basis (www. ecrin. org). ECRIN already requests that the investigators it supports commit to make anonymised IPD data sets available to the scientific community on request. To be clear, throughout this document we use IPD to refer to all of the participant data available from a trial, and not just the data supporting the conclusions of a specific published paper. Such data will therefore normally be the data sets used for the various analyses, after appropriate de-identification and pseudonymisation or anonymisation measures have been applied. The goal is to develop a framework in which, ultimately, all of the participant-level data from any trial become available to those who can demonstrate they can make appropriate use of them.