dataesr/scientific-paragraphs-categorization huggingface dataset:
ImageCLEF 2013: the tasks, the data and participation The 2013 edition of ImageCLEF consisted of three main tasks, plus one task associated with the AMIA 2013 meeting: the photo annotation and retrieval task, the plant identification task, the robot vision task and, jointly with AMIA, the medical task. These tasks had the goal to benchmark the annotation and retrieval of diverse images such as general photographic, as well as domainspecific tasks such as plant identification and robot vision. The overall aim is to support and promote research that addresses key challenges in the field including: visual image annotation with concepts at various levels of abstraction that relies not only on manual, and thus reliable, training data, but also on automatically acquired, and thus noisy, labelled samples, -scientific multimedia data management through the particular case of botanical data identification, and the shift in the area of robot vision from visual place recognition to multimodal place recognition.
The underlying citizen science project aims at covering the entire French flora with a sufficiently rich and balanced collection of pictures. The dataset used for the 2013 campaign covered 250 species of herbs and trees living in France area (i. e. the most represented ones in the whole collected social data since 2011). Contrary to the two previous years that were exclusively focused on leaf images (of tree species only), the coverage of the 2013 task was extended to six different types of view of the plant: leaf scans (or scan-like), leaf photographs, flower photographs, fruit photographs, bark photographs, and the entire view of the plant. A separate evaluation score was computed for the two main categories of images, i. e. scans (or scan-like pictures) vs. photographs (with natural background).
Proportions were around 42% of scans and scan-like pictures of leaves vs. 58% of photographs with a natural background (more precisely 16% of leaves, 18% of flowers, 8% of fruits, 8% of stems and 8% of entire). The whole database contained around 26k images collected by 327 distinct contributors, living in different regions in France, equipped with various cameras and at different periods of the year. This makes the task much more realistic than any previous data built for the evaluation of content-based plant identification methods. Objective and Task for the 2013 Edition The precise goal of the 2013 task was to retrieve the correct species among the top k species of a ranked list of returned species, one list for each image of a test dataset. Participants received a first training set of annotated images in order to explore different techniques and train their system.
Six weeks later participants received the test set containing images without species labels. Then participants were allowed to submit until 4 run files, most of the time related to variations of one same method. A particular attention was paid when splitting the data into training and test subsets to avoid any bias. Several pictures in the dataset might actually depict the same individual plant (or neighboring plants) observed in the same conditions (same person, day, device, lightening conditions, etc. ). Randomly splitting images in a nave way would therefore favor having such near-duplicate images in both the training and the test subsets, making the recognition much more easy. To avoid this bias, we therefore performed our random split at the observation level rather than at the image level thanks to associated metadata (observation id when available, author, date, etc.
Overall, an important remark is that the ranking of the runs did not change much from an organ to another one, fostering the idea that generic methods might solve heterogeneous fine-grained classification problems. Regarding metadata, one run did show that using the observation date complementary to the visual content was a simple and efficient way to obtain a gain of up to 5 points on the flower category (thanks to the relatively short flourishing season of many species). On the other side, the GPS information was not successfully exploited probably because the database doesnt contain dense enough observations to build an accurate geographic repartition of the species. With the emergence of more and more plant identification apps  [9]  [10], [11], [12] and the ecological urgency to build realworld and effective identification tools, we believe that the detailed results and conclusions of the task will be of high interest for the community  [13].
The robot vision task The Robot Vision task addresses two main problems related to semantic robot localization: place classification and object recognition. Participants are asked to answers the questions "where are you?" and "which object can you recognize in the scene?" when presented with a test sequence. Such test sequence contains depth and visual images acquired by a mobile robot with a RGB-D camera in a previously seen indoor environment. Past Editions The Robot Vision task started in 2009  [14] , with the main objective to compare different approaches to robot localization in a common scenario. The localization problem has always been managed from a semantic point of view, where no topological information is provided or required. Since its origin, new challenges have been introduced each new edition, from detection of unknown rooms  [14] , to generalization across floors  [15, ?] , to categorization problems  [16]  to multimodal data analysis  [17].
5 points -The room category has not been classified: 0. 0 points Object (8 different binary problems) -For each correctly classified object within the frame: +0. 125 points -For each misclassified object within the frame: -0. 125 points -For each object that was not classified: 0. 0 points The Data The dataset provided for the task consists of different sequences of depth (in Point Cloud Data (PCD) format  [18] ) and visual images acquired within a department building at the University of Alicante, Spain. Concretely, there are two labelled sequences for training, another labelled sequence provided for validation, and one unlabelled sequence for testing. Every image has been manually labelled with its corresponding room category and with a list of eight different objects to appear or not within it.
Due to it was acquired only in the first floor of the building, it does not contains any frame for three rooms: Warehouse, VisioConference and Hall. Fig. 6  shows the same scene represented in three different sequences: training1 (top), validation (middle) and test (bottom). The scene was acquired using visual images (left) and point cloud data files (right). Training, validation and test sequences were acquired within the same building at two different floors but with some variations in the lighting conditions (as can be observed in Fig. 6 ) and in the acquisition procedure (clockwise and counter clockwise, ground floor first or ground floor last). Participants were provided with running code for computing several feature descriptors  [19] [20] [21]  as well as SVM-based online  [22, 23]  and cure integration classifiers  [24, 25].
AMIA: the medical task The main objective of the medical ImageCLEF task is to compare content-based image retrieval (CBIR) systems in medicine, and in particular to determine how associated cross-language text can be used in combination with CBIR to improve retrieval and ranking. ImageCLEFmed evaluates retrieval systems with visual, semantic and mixed topics in several languages using since 2008 a data collection from the biomedical literature. Past Editions ImageCLEFmed started in 2004 with only an image-based retrieval task  [26]. In 2005, an automatic annotation task was introduced  [27]. The goal of this task was to find out how well the techniques can identify body orientation, body region, and biological system examined based on the images. The database consisted of 10,000 radiographs fully annotated with IRMA code, taken randomly from medical routine.
Between 2006 and 2009, ImageCLEFmed kept these two tasks in similar formats format but using larger and more complex databases each year  [28] [29] [30] [31] [32]. From 2008 to 2010, the database contained images from articles published in Radiology and Radiographics including the text of the captions and a link to the html of the full text articles. In 2009, a lung nodule detection task was tested. The goal of this task was to compare the performance of lung nodule detection techniques with a gold standard of manually identified nodules. The data for this task was a subset of the LIDC (Lung Image Database Consortium) database. From 2010 to 2012, there were three types of task: the traditional image-based retrieval, modality classification and case-based retrieval  [33] [34] [35]. The modality classification task was introduced since previous studies have shown that imaging modality is an important aspect of the image for medical retrieval.
ImageCLEFmed is running in a similar format as in 2012 but with a new task, the compound figure separation that became important as a large fraction of around 40% of the database of PubMed Central used contain compound figures and the sub images are otherwise not accessible for research. Another novelty in 2013 is that the modality classification task includes a large amount of compound images to make the task more difficult and realistic. The following tasks were offered in 2013: -Modality Classification: In user-studies, clinicians have indicated that modality is one of the most important filters that they would like to be able to limit their search by. Many image retrieval websites (Goldminer, Yottalook) allow users to limit the search results to a particular modality. However, this modality is typically extracted from the caption and is often not correct or present.
Studies have shown that the modality can be extracted from the image itself using visual features. Additionally, using the modality classification, the search results can be improved significantly. In 2013, a larger number of compound figures will be present making the task significantly harder but corresponding much more to the reality of biomedical journals. -Compound figure separation: As up to 40% of the figures in PubMed Central are compound figures, a major step in making the content of the compound figures accessible is the detection of compound figures and then their separation into sub figures that can subsequently be classified into modalities and made available for research. The task makes available training data with separation labels of the figures, and then a test data set where the labels were made available after the submission of the results.
Conclusion This paper presented an overview of the activities in the 2013 edition of the ImageCLEF lab. The sustained interest in the lab, witnessed by the growing number of registration and the sustained number of groups actually participating to the lab, make ImageCLEF an important resource in the multi lingual image annotation and retrieval research landscape. The ever growing amount of data available through the internet, and the growing demand of tools for accessing and exploiting them, will become one of the key focus for the 2014 edition of ImageCLEF, where we look forward to welcome back the medical task under the ImageCLEF umbrella.
Acknowledgments This work has been partially supported by the  Halser Foundation  (B. C. ), by the  LiMoSINe FP7  project under grant #  288024  (B. T. ), by the  Khresmoi  (grant #  257528 ) and  PROMISE  ( grant #  258191 ) FP 7 projects (H. M. ) and by the tranScriptorium  FP7  project under grant #  600707  (M. V. , R. P. ).
However, the drawbacks of the VibScoreEditor raised by the non-experts participants include the issue of users unaccustomed to musical notation as well as the difficulty to read strength and duration simultaneously. We believe these issues would be exacerbated with multiple actuators, not currently supported by the editor. Jonas  [17]  used another graphical representation of music by drawing inspiration from music and video editing software with their timeline interface. In this regard, he developed a tactile editor with a track-based graphical user interface for non-experts, who can simply create and edit patterns by switching motors on and off for certain durations and at certain intensities (see right of Fig. 2 ). It also supports multiple actuators through Arduino and MakeController platforms.
Participants positively evaluated the tool underlining that the timeline metaphor helped them design and test the vibrotactile patterns. Fig. 2. Left: the VibScoreEditor  [16]  uses a musical metaphor for editing (main window) that is an abstraction of the physical parameters associated with a vibrotactile signal (right windows). Right: the Tactile Editor  [17]. Another radically different and original approach to authoring haptic content is the TECHTILE toolkit  [18] , which aims to be a prototyping tool for easy and fun design and education of haptic media. It is based on a direct mapping from the audio signal to the vibration. Through a microphone, voice-coil actuators and a signal amplifier, users can easily record the sensation via audio and transfer it to a haptic sensation. First experience with haptics is surely improved, but fine-tuning still requires using audio editing software, which is not necessarily intuitive to all.
Moreover, the audio channel is richer than haptics and mapping sounds to haptics is not straightforward, in particular for systems including multiple actuators. However, the transfer between audio and haptics is an obvious source of inspiration for designing haptics patterns; this is why in future work the possibility to transpose audio content to haptic patterns using TactiPEd will be investigated. Touch Interaction. Given the advent of touchscreen interfaces, the possibility to program patterns directly by mapping touch properties to the vibration parameters has been explored. On the commercial side, the iPhone provides users with the possibility to record a custom-made vibration for contacts by simply tapping on the screen and recording the rhythm. Hong  [19]  investigated the mapping of user touch input to other vibration parameters to facilitate vibrotactile design.
Overview TactiPEd aims to provide a high-level interface accessible to non-technical experts (e. g. ergonomists and users) that facilitates the rapid prototyping and testing of tactile patterns. For that purpose, the editor is based on a graphical metaphor using the spatial layout of the device to tune its parameters. It also supports different types of actuators as well as multi-actuator devices so that pattern metaphors can be tested onto several devices. It has been implemented in Python and has been used on the Windows platform. The editor consists of four modules:  Graphical user interface: for creating, editing, opening device templates; creating and editing patterns; and providing access to the other modules.  File handler: for opening and saving device templates and pattern files via XML.
Communication Module and Internal Player/Recorder The communication module establishes the connection to the device via a defined communication protocol and sends the data appropriately to the hardware. In the GUI (see Fig. 5 ), in order to test a pattern with the internal player, the user first needs to select a protocol and the port where the device is connected before attempting a connection. Each protocol in the list corresponds to a protocol file. Each protocol file follows a protocol template that implements a defined set of functions used by the GUI, namely init (for initialization and opening the connection), closeSocket (for closing the connection), play (for testing the displayed pattern), stop, playRecorded-Pattern (for playing a pattern recorded on the device) and recordPattern (to record a pattern at a certain memory position on the device).
Results Effectiveness. Participants completed all the tasks without difficulty, leading to patterns they felt satisfied with, no matter the device used and the arrangement of actuators. Participants took longer on average to program on the gamepad but a t-test on the log-transformed data (for the assumption of normal distribution) revealed that the difference was not significant between the bracelet (M=245s, SE=45. 2) and the gamepad (M=285. 4, SE=54. 24) with t(8)=-1. 73, p >. 05 (see Fig. 9 ). There are also no significant differences between the user groups (see Fig. 9 ). Interestingly, some participants commented that it was more difficult to program patterns on the gamepad as the arrangement of actuators was less intuitive than a bracelet. patterns were prototyped in less than 5 minutes with the editor.
We refer to  [2, 6, 9, 12, 14, 15, 19, 24]  for previous works on the subject. The main goal of this work is to use recent GPU devices for semi-Lagrangian simulations of the Vlasov-Poisson system (1)-  (2). Indeed, looking for new algorithms that are highly scalable in the field of plasmas simulations (like tokamak plasmas or particle beams), it is important to mimic plasma devices more reliably. Particle methods have already been tested on such architectures, and good scalability has been obtained as in  [5, 30]. We mention a recent precursor work on the parallelization in GPU in the context of a gyrokinetic eulerian code GENE  [13]. Semi-Lagrangian algorithms dedicated to the simplified setting of the one-dimensionnal Vlasov-Poisson system have also recently been implemented in the CUDA framework (see  [22, 25] ).
Some details on the implementation We list here the CUDA kernel calls for information and give some descriptions of the variables. //for transposition transposeNoBankConflicts<<<grid, threads>>>(d_odata, f_d, N, N, 1); copy<<<grid, threads>>>(f_d, d_odata, N, N, 1); //for advection //for going to complex data real_to_complex<<<grid, threads>>> (f_d, f_complex_d, N); //for matrix computation: from alpha_d and i0_d returns w_d compute_coefficients<<<grid, threads>>> (w_d, alpha_d, i0_d, N, degree); cufftExecZ2Z (plan1d, w_d, w_d, CUFFT_FORWARD); //forward fft cufftExecZ2Z (plan1d, f_complex_d, f_complex_d, CUFFT_FORWARD); //for multiplication in Fourier space mult_complex_array<<<grid, threads>>> (f_complex_d, w_d, N); //backward fft cufftExecZ2Z (plan1d, f_complex_d, f_complex_d, CUFFT_INVERSE); //for going back to real data and multiply by scale factor complex _to_real_scaled<<<grid, threads>>> (f_complex_d, f_d, N, scale); //for computation of charge density compute_sum_v<<<grid_rho, threads_rho>>> (rho_d, f_d, N); The variables f d, d odata are real arrays of size N 2 ; f d represents the distribution function f.
Performance results Characteristics. We have tested the code on different computers with the following characteristics: We measure in the GPU codes the proportion of FFT which consists in: transform 1D real data to complex, computing the FFT, making the complex multiplication, computing the inverse FFT, transforming to real data (together with addition of δf modification, if we use the δf method). We add a diagnostic for having the proportion of time in the cufftExec routine; we note that this extra diagnostic can modify a little the time measures (when this is the case; new measures are given in brackets, see on Table  1 ). When the number of cells grows, the proportion of FFT time in total time grows, as shown on The results with a CPU code (vlaso) without OpenMP are given on Table  3 , top; in that code, the Landau test case is run with ∆t/2 advection in x, followed by ∆t advection in v and ∆t/2 advection in x and the last advection in x of iteration n is merged with the first advection in x of iteration n + 1.
RepeatsDB (http://repeatsdb. bio. unipd. it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10 745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins.
After all, the most common way to detect repeat families so far was to manually annotate the sequence family first and only afterwards visually recognize their structural repetitiveness. Such an approach is obviously difficult when dealing with the entire Protein Data Bank (PDB)  (26) , especially considering the many uncharacterized protein structures deposited by the main structural genomics consortia  (27). The systematic description of repeat structures becomes a question of using automated methods to detect them in protein structures. This field is relatively new, with only few available methods. One of the first attempts was made by the Thornton group  (28) , but is unfortunately no longer available. Some methods  (4, (29) (30) (31) (32) (33)  were developed to detect internal symmetries in proteins, but these may be difficult to adapt to the systematic classification of repeats.
Recently, our group has developed RAPHAEL  (34)  in an attempt to fill the gap for repeat detection from structure. Widely used structural classifications such as CATH  (35)  and SCOP (36) also do not explicitly annotate repeats in protein structures, although it may be possible to leverage individual annotations to find similar repeats. Some databases exist for the detection of repeats from sequence  (37) (38) (39) , but usually these are limited to short tandem repeats and do not take into account divergent repeats, such as solenoids or toroids. The main domain sequence databases such as Pfam  (40)  and SMART  (41)  do not excel at the annotation of these repeat types either, as coverage is rather low and many repeat units go undetected. For Pfam most of the largest clusters of human sequence regions not covered were recently found to be repeats  (42).
To the best of our knowledge, no database or classification is currently available for repeat structures. This is the motivation for our present work, and we introduce RepeatsDB as a way to fill this gap. The database was developed to provide a central resource for the systematic annotation and classification of repeats. Given the fact that the structure-based search and classification of repeat proteins is more complete than on the basis of sequences or key words, our database will allow more accurate assignment of proteins with repeats to the corresponding families. For example, it will be used to suggest a better subdivision of alpha-solenoid proteins where at present the boundaries between the structures with Armadillo, HEAT, TPR and other repeat types are frequently blurred.
Data curation The initial dataset for RepeatsDB was extracted from the PDB  (43). Repeat candidates were identified from the reduced PDB dataset with RAPHAEL  (34) , which uses a geometric approach imitating the work of a human curator (score cutoff 1). The resulting dataset consisted of >10 000 repeat candidates, stored in the database as 'predicted' entries, which underwent a classification and curation process. The dataset of predicted repeats was manually curated using a two-level annotation system. The first manual annotation level ('manually classified') classifies an entry into structural repeat class and subclass. This classification is based on previous work  (14) , where five classes of repeat structures are proposed, which are then further divided into subclasses. Class assignment is based mainly on repeat unit length and subclass assignment on secondary and tertiary structure features.