dataesr/scientific-paragraphs-categorization huggingface dataset:
2014) , and Centaur objects like Chariklo (Braga-  Ribas et al. 2014)  and Chiron  (Ortiz et al. 2015; Ruprecht et al. 2015). The observation of a multi-chord stellar occultation on 2014 November 15 increases that list to include the TNO (229762) 2007 UK 126 , the main topic of this paper. This TNO was discovered by  Schwamb et al. (2008)  in October 2007 with an estimated radius and albedo of 299. 5 ± 38. 9 km and 0. 167 +0. 058  -0. 038  (Santos-Sanz et al. 2012) , respectively. With a semi-major axis of 73. 81 AU, aphelion distance of 109. 7 AU, orbital period of 634. 13 yr, orbital eccentricity of 0. 492 and an inclination of 23. 34 degrees (JPL Small-Body Database Browser 2016), it is usually classified as a Scattered Disk Object -SDO -(according to  Gladman et al. (2008) ) or a member of the class of "Detached Objects" (see eg.
1 ). The times of the star disappearances (ingress) and re-appearances (egress) for the seven detections are listed on Table  3. All acquired data, with exception of the data from the two telescopes in San Pedro Martir, were in video format. A video time inserter (VTI) was used to place a time-stamp on each individual video frame. All RECON sites use IOTA-VTI that The VTI interacts with a GPS receiver to obtain the time, ideally with an absolute accuracy of a few milliseconds, and then superimpose the current time on each video field as it passes from the camera to the computer (see  Buie & Keller (2016) ). Unfortunately, no information is saved on any image header because of the video format.
DATA ANALYSIS Considerable detail is provided on video data that is applicable to RECON data in  Buie & Keller (2016). In particular, the discussion on frame and field interleaving of the data is especially relevant for these data. All of the RECON data were collected with a SENSEUP value of 128x, meaning that the integration time is equal to 128 times the field rate of the NTSC video signal, which resultted in integrations of approximately 2 seconds. The Urbana data were collected with a Watec camera integrating 128 video frames, or approximately 4 seconds. If there are no dropped frames or other problems, the RECON video will have 64 copies of each integration in the video data stream. The Urbana video will have 128 copies. Two independent analyses were performed on the video data to extract light curves and timing information.
GBR extraction Using AUDELA (a free and open source astronomy software for digital observations: CCD cameras, Webcams, etc. ) we extracted individual frames to FITS format from the video at a rate of 29. 97 frames per second. A careful check was done in all sets of images to verify whether the extracted time corresponds to the time printed at each frame. The frames were then grouped to match the corresponding SENSEUP value. The first and last frames of each 64-frame (or 128) sequence were identified by counting frames from a calibrated starting point and checked with a change in field brightness. They were then excluded and the other 62 (or 126) frames were averaged to obtain each image that corresponds to an individual exposure time. By not considering the first and last frame from a sequence, we avoid the need to de-interlace and re-interlace the frames, and we do not take into account any field from the previous or next sequence.
MWB extraction Accurate determination of the time of each integration is discussed in detail in  Buie & Keller (2016)  and requires locating the exact place in the video stream where a new integration is first seen. In this analysis, custom software was created in IDL for each site's data. The first step requires converting the AVI-format video data to individual image files. The freely available tool, ffmpeg 1  , was used to extract a sequence of images to individual PNG format files. In all cases only 90-120 seconds of data were extracted, centered if possible on the occultation chord. The basic flow of data processing contains some or all of the following steps, customized for each dataset. 1) Create a mean sky image. If possible to build, this image contains the general background gradient, typically from amplifier glow in one corner as well as hot pixels.
If there is a single frame point between the two levels, re-interlacing is required. 4) Extract source and comparison star fluxes from each frame. 5) Down-sample by averaging to a single measurement for each integration. 6) Apply timing formula from  Buie & Keller (2016)  to get absolute timing of each data point. The deviations from this set of steps is now described for each site in turn. The photometric extraction required some special handling in all cases. Four nearby field stars were measured to obtain both position and flux. When the occultation star was visible, its position was also measured. The offset for the occultation star relative to the brightest field star was determined for each dataset. This offset was used as the exact position for extracting the occultation star flux on each image.
In no case was there a dropped frame on an integration boundary. The photometric used was a 5-pixel object aperture and a sky annulus of 8-25 pixels. The final result, though laborious, was not affected by the dropped frames. Yerington -A mean sky frame was generated and subtracted. The data were re-interlaced and had no dropped frames. The photometry was generated from a 5-pixel object aperture and sky was determined from the mean of each frame. Bardecker/Gardnerville -The mean sky image could not be generated due to the excellent tracking at this site and the lack of separate calibration image sequences. The video data required re-interlacing but did not have any instances of dropped frames in the video data stream. A 5-pixel photometric aperture was used for the sources and a local sky value was determined for each with a sky annulus of 8-40 pixels.
The frame integration boundary was visually determined by seeing the change in the background noise. A single transition was sufficient to establish the timing for the entire sequence. Note that this site used a telescope with an equatorial mount. Tonopah -A mean sky frame was generated and subtracted. The data were re-interlaced and had no dropped frames. The photometry was generated from a 5-pixel object aperture and sky was determined from the mean of each frame. Olsen/Urbana -This dataset uses a similar but not identical setup to the standard RECON system. The camera was a Watec-120N+ and has a similar sensitivity and operation to the RECON MallinCAM cameras but can integrate twice as long. The timing was provided by a Kiwi OSD video time inserter. A sky frame was generated and subtracted.
Within one hour of their usual bedtime, participants were allowed to sleep undisturbed in the laboratory, after completing a Stanford Sleepiness Scale  [17]. Additional measures were collected the next day. The two days of practice closely reproduced the structure of a meditation retreat. Six 45-min sessions of sitting meditation, three in the morning, three in the afternoon, were separated by four 30-min sessions of walking meditation. The first 45-min sitting meditation session began at 8:15 am with a short guided meditation. The morning and afternoon sessions were separated by a 1-h lunch break, followed by a 30-min dharma talk. The order of Vipassana and Metta meditation sessions was randomized across participants. Two subjects didn't complete the Vipassana meditation session, and their data were excluded from the analyses.
Sleep PSG assessment and hdEEG data analysis Six mastoid-referenced channels, which included F3, F4, C3, C4, O1, and O2, together with a sub-mental electromyogram and an electrooculogram were utilized to perform sleep staging, in 30-second epochs according to standard criteria  [24]  using Alice 1 Sleepware (Philips Respironics, Murrysville, PA). Sleep EEG recordings were scored by a registered polysomnographic technologist who was blind to the experimental conditions. PSG recordings during the baseline session were reviewed by a board certified sleep medicine physician. Individuals with sleep disorders, which included sleep-related movement disorders (periodic limb movement arousal index >10 / h, 2 participants), and sleep-disordered breathing (apnea-hypopnea index >10 ⁄ h, 1 participant), were excluded from the analyses.
All-night sleep hdEEG recordings were collected at a sampling frequency of 500 Hz with vertex-referencing, using a NetAmps 300 amplifier and NetStation software (Electrical Geodesics Inc. , Eugene, OR). After applying a firstorder high-pass filter (Kaiser type, 0. 1 Hz) to eliminate the DC shift, data were band-pass filtered (1-50 Hz), and down-sampled to 128 Hz. Average-reference was performed after removing EEG channels in which artifacts affected most of the recording. Computation of the power spectral density was conducted on six-second epochs by means of a Welch's averaged modified periodogram with a Hamming window. The epochs were divided in 8 segments with 50% overlapping using the built-in MATLAB function pwelch. A threshold based on the mean power at either a low (1-4 Hz) or high (20-30 Hz) frequency band was used to detect and remove artefactual six-second epochs from NREM sleep data  [4, 25, 26].
Artifacts (i. e. , muscle twitches, eye movements, heartbeats) were detected and removed from REM data by using independent component analysis (ICA)  [27]. Power spectral density was computed on REM sleep data after removal of tonic and phasic REM epochs contaminated with artifacts. After excluding the electrodes located on the neck/face region, we adopted an unbiased approach to the analysis of the frequency power spectrum on the remaining 185 channels, integrating across 1-Hz non-overlapping frequency bins ranging from 1 to 40 Hz. Sleep cycles were defined according to the modified criteria  [28]  of Feinberg and Floyd  [29].
The condition factor had 3 levels (baseline, Vipassana, and Metta sessions). The sleep cycle factor had 3 levels (first, second, and third cycle). The sleep stage factor had 4 levels (N1, N2, N3, and REM). Total sleep time (TST) and wake after sleep onset (WASO) were entered in a separate ANOVA that included the condition factor and the sleep parameter factor (TST and WASO). For both ANOVA designs, the between group factor had two levels, long-term meditators and meditation-naïve controls. Greenhouse-Geisser correction was applied to p-value and degrees of freedom in case of violation of the sphericity assumption. Differences in topographical NREM and REM sleep hdEEG power between conditions were assessed with statistical non-parametric mapping (SnPM), a permutation approach that accounts for the multiple comparison problem inherent in the analysis of functional neuroimaging datasets  [30].
Specifically we adopted a suprathreshold cluster method that quantifies the size of connected regions (clusters) on the thresholded statistical images derived from the observed data and from randomly shuffling the observations between conditions. In this method, the size of each suprathreshold cluster in the observed data is compared against the permutation distribution of the maximal suprathreshold cluster size to obtain the corrected p-value  [30]. Specifically, 50000 unique combinations were run for each comparison in order to approximate the actual cluster distribution. The primary threshold tvalue was set to 2. 069, corresponding to α = 0. 05 for the given degrees of freedom. We implemented our in-house MATLAB-based SnPM software in order to control at once for the multiple comparisons deriving from testing numerous scalp channels (185 electrodes), frequency bins  (39) , and time points (the first 3 sleep cycles).
We used SnPM to investigate the effect of lifetime meditation experience on the changes in spontaneous brain activity during sleep after an intense day of meditation practice over scalp channels, frequency bins, and sleep cycles. To increase sensitivity, we reduced the frequency space by integrating the frequencies above 15 Hz across 5-Hz bins while keeping a 1-Hz resolution for traditional sleep rhythms  [28]. We also separately assessed the contribution of the time spent in the specific meditation style in which the participants were engaged during the day of practice (OM, FA, and LK). In order to control for the influence of outliers on the correlation analyses, we ran the permutation statistics without including data points above or below 1. 5 interquartile ranges from the median of practice hours across participants.
Acknowledgments We would like to thank  Donal G MacCoon  and  Brianna S Schuyler  for helping analyzing the self-report questionnaires in this study,  Robin I Goldman  for helpful feedback, and  Jeanette A Mumford  and  Armand Mensen  for statistical advice. We are grateful to the board certified sleep physician  Ruth M Benca  for reading the sleep recordings. We would like to acknowledge  Mary Lindstrom 's invaluable contribution to the statistical analyses of an earlier version of this work.
These progresses will lead to more efficient and reproducible science and help equip brain scientists with the arsenal of high-quality tools they need for big data analytics. The major challenge faced by researchers when validating brain imaging methods is the lack of a ground truth measure against which the outcome of their methods are compared. Unlike fields such as machine learning in natural images, there are only few public benchmark brain imaging datasets that get widely included in validation studies within a specific subfield. This lack of benchmarks makes it very difficult to compare validation results published on different image analysis methods. Developing reference benchmarks is very challenging because the physiological (e. g. , neuronal and metabolic) and physical (e. g. , magnetic and electrical) processes underlying brain imaging are extremely complex.
In "Evaluation of Second-Level Inference in fMRI Analysis," S. P. Roels et al. compare different modeling and inference techniques for general linear model (GLM) analysis of group functional magnetic resonance imaging. Although the GLM has been ubiquitous in fMRI research for decades, distinct variants remain popular to this date, and the validity of their underlying assumptions is not yet well established. How these variants behave in terms of reproducibility of results, instead of sensitivity and specificity, is also incompletely understood. S. P. Roels et al. implemented mixed effect simulations as well as a bootstrap analysis on real data to shed light on these questions. In "How Many Is Enough? Effect of Sample Size in Inter-Subject Correlation Analysis of fMRI" J. Pajula and J. Tohka evaluate intersubject correlation (ISC) based analysis of fMRI data with a large dataset consisting of 130 subjects.
They utilize split-half resampling to disclose the reproducibility of the analysis results with different sample sizes and additionally compare the analysis results using a large 130-subject dataset to the analysis results with smaller sample sizes. In "MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters" A. -S. Hincapie et al. evaluate the tuning of regularization hyperparameter within the minimum norm source reconstruction context when applied to magnetoencephalography (MEG) data. They addressed the question using Monte Carlo simulations of MEG data, where they generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths, thus providing a detailed evaluation framework within a new application context, the analysis of resting state functional connectivity using MEG data.
Introduction 'Omics' approaches such as genomics, metabolomics and metabonomics are used to improve our understanding of integrated functioning of living organisms, with each 'omics' generating high-density datasets  (Dumas, 2012; Nicholson et al. , 2002). Genes and metabolites provide complementary information about biological processes. Metabolic reactions are catalyzed by enzymes encoded by genes. The activity of these enzymes is regulated by transcriptional, translational and post-translational mechanisms impacting metabolism. Metabolic processes are also regulated by signaling transduction pathways, allowing the organism to adapt to environmental changes and maintain homeostasis. The Kyoto Encyclopaedia of Genes and Genomes (KEGG)  (Kanehisa and Goto, 2000)  is a popular reference database storing biological pathways and cellular processes from a wide range of reference organisms as graphical diagrams, where the nodes are biological entities (e.
g. genes, metabolites) and the edges represent the relationship between them. In the recent years, several tools have been developed for analyzing, editing and customizing KEGG metabolic  (Cottret et al. , 2010; Posma et al. , 2014)  or signaling  (Zhang and Wiemann, 2009)  pathways independently. However, these tools ignore the interconnection between the genome and metabolome, and therefore they do not provide an integrated overview of regulatory events leading to the observed metabolic patterns. We introduced an integrated metabolome and interactome mapping (iMIM) strategy to analyze the topology of protein interaction, signaling and metabolic networks, and implemented it as a standalone database and workflow  (Davidovic et al. , 2011). To make this network-modeling strategy accessible to a wider community, we have now developed an R-based approach, called MetaboSignal, which allows building organism-specific KEGG networks that account for the interaction between metabolites and genes (both metabolic and signaling).
Tissue-specific pruning is achieved using information from the Human Protein Atlas database  (Gatto, 2016; Uhlen et al. , 2010). The metabolic network is then merged with the signaling network to create the 'MetaboSignal' network that can be customized according to different criteria. For instance, undesired nodes can be removed or nodes representing isomers of the same molecule (e. g. a and b D-glucose) can be collapsed into a single node. MetaboSignal analyzes network topology using the igraph R-package  (Csardi, 2015)  to: (i) derive a gene-metabolite distance matrix based on shortest path lengths, (ii) compute centrality statistics and (iii) generate concise sub-networks based on betweenness-ranked shortest paths from a list of input genes to a list of metabolites. Finally, the original network or the sub-networks can be visualized in R.
Example To illustrate the functionality of our approach, we used transcriptomic and metabonomic datasets from the adipose tissue of rat congenic strains derived from the diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats (data and accession numbers available from  Dumas et al. , 2016). Quantitative-trait locus (QTL) analysis of these datasets identified eQTL-and mQTLresponsive genes and metabolites. A MetaboSignal network was built using all metabolic and signaling KEGG pathways from the rat adipose-tissue. 19 metabolites and 58 genes (38 metabolic-and 20 signaling-genes) significantly associated with one locus were mapped onto the MetaboSignal network. We computed shortest path lengths from the 38 metabolic-genes to the 19 metabolites in both the metabolic network (without signaling-genes, Fig.
Discussion 'MetaboSignal' is a versatile package integrating metabolic and signaling transduction pathways to build parsimonious visualizations of gene-metabolite associations based on the analysis of network topology. MetaboSignal is a biomolecular navigation system allowing the exploration of organism-specific, and even tissue-specific, relationships between any given gene and any given metabolite retrieved from KEGG. This approach is ideally suited to identify candidate genes in metabotype-QTL studies (e. g. trans-acting associations), or to identify biological pathways affected in transgenic models (e. g. knock-out, CRISPR-Cas9)  (Dumas, 2012). Finally, MetaboSignal is easily amenable to incorporate other pathway databases and types of interactions, such as protein interactions and transcription factor networks.
A. APoD image collection In this experiment, the NASA Astronomy Picture of the Day data set has been used. It is a nice example of growing image collection. Every day, a new astronomical picturesometimes a video -is put online, along with a brief description written by a professional astronomer. The project started in 1995 -almost 7600 entries up-to-date -and is still active -more than 14,000 likes and followers on Facebook and Google+ fan pages, respectively. Note that this use case deals with copyrighted images: "All the images on the APOD page are credited to the owner or institution where they originated. Neither NASA nor APOD can grant permission to use copyrighted images. " In the main page of the project website  [5] , one can see the new image and its information. A link to the archive and a textbased search engine are also available.
C. Toward an online scenario Thanks to the incremental property of our algorithm, our system can be deployed on a server, and daily take into account the new image posted on the APOD website. Figure  2  illustrates a feasible workflow. A deamon could run on the APoD eXplorer server. It will get the day information using the NASA API. If it is a video, we could directly update the main page of our system, and propose recent videos as recommendations. If it is an image, the deamon will download it and eventually convert it to be handle by our descriptor extraction application. As soon as, the descriptors is computed, we can discard the image from the server to avoid any copyright related issues. The new image is now described by the CLD and some meta data given by the API (e. g. title, url, etc.
Timeline JS  [16]  -an open source JavaScript library -has been used to implement the timelines. In Figure  5 , the interactive explore visualisation described in Section III is available. It consists of the graph visualation canvas (left) and controls/information panel (right). Linkurious JS  [17]  -another open source JavaScript library -has been used to manage the graph visualisation. The first level of the proposed hybrid structure is displayed in the main frame. Some of the available interactions are given below. a) Graph manipulation: Common interactions such as zoom, pan and graph dragging are available to allow the user to manipulate the graph and select a region of interest in the graph. Furthermore, if the user do not want to have a visual notification of the adjacencies and their information, he can hide the edges, that will not be rendered.
Introduction During the fission fragment de-excitation, nuclei emit neutrons and gammas to loose their excitation energy and angular momentum in order to reach their ground state. The properties of those prompt emitted particles is of particular interest for nuclear reactors, for example to quantify the damage they can cause on the nuclear reactor vessel which has a direct impact on its lifetime. A Monte-Carlo computer code named FIFRELIN  [1]   [2]  developed at CEA-Cadarache allows studying nuclear fission through the nuclear fission fragment de-excitation process. This is performed through a Monte-Carlo implementation of the so-called Hauser-Feshbach algorithm which takes into account the n/γ competition  [2]. Practically the notion of nuclear realization is used as proposed for capture reactions in  [3].