dataesr/scientific-paragraphs-categorization huggingface dataset:
Subjects were instructed to refrain from talking, frowning and moving their limbs during fNIRS data collection periods within the protocol. Additionally, we applied sliding-window motion artifact rejection (SMAR;  Ayaz et al. , 2010) ; each channel used for calculating the asymmetry metric was inspected post hoc to identify motion artifacts during NF. For post hoc analyses, raw data were low-pass filtered using a finite impulse response filter with order 20 and 0. 1 Hz cut-off frequency  (Ayaz et al. , 2010). Following instructions and a practice block, each participant completed eight identical blocks, consisting of three epochs: Rest, View, and NF. Block design and the timing of epochs are presented in Figure  4  (also see  Charles et al. , 2015). During Rest, subjects were instructed to look at a crosshair at the center of a gray screen, to try to clear their head of thoughts and relax.
RESULTS AND DISCUSSION After data screening and filtering, we identified 136 valid blocks for analysis (eight blocks were collected from each 17 subjects). We defined block success as a statistically significant increase in average asymmetry during NF compared to the View epoch within the same block. Block success was determined post hoc using independent t-test with bootstrapping resampling method (1000 samples, 95% CI) with epoch type (View/NF) as independent variable and asymmetry scores as dependent variable, applying Bonferroni correction on a subject basis to control false discovery rate (one-tailed p threshold of 0. 0125). (Note that the entire NF epoch was considered to determine block success, not just above-threshold asymmetry values. ) Based on this criterion, 70 out of the total 136 blocks (51%) were successful.
FUNDING This work was supported by the European project  H2020 ARIA-VALUSPA (No 645378 ) and the  French ANR  project  MOCA  ( ANR-12-CORD-019 ). It was partially performed within the  Labex SMART  ( ANR-11-LABX-65 ) supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02.
The three methods used the following inverse relationship matrices: PBLUP:  '$ ; SSGBLUP: 𝑯 '𝟏 = 𝑨 '$ + 0 0 0 𝑮 @ '$ -𝑨 && '$ where  @ is as in  [25]  ; SSGBLUP_M:  (5)'$ =  (5)'$ + 0 0 0  '$ - && (5)'$ where  =  -  - ′/ with  = /2 (see the Methods section) and  (5) is as in  [1]. More details are given in Supplementary material. For computation we used blupf90  [28]. In the case of SSGBLUP_M we constructed all relationship matrices with own software, and then used the option user_file in blupf90.
DISCUSSION In this work, we have addressed the complex issue of conciliation of marker and pedigree information. Powell et al. [34]  argued that both IBS (at the markers) and IBD are measures of identity at causal genes and they are compatible notions. However, the incompatibility issue appears when mixing both kind of relationships  [5, 25, 35, 36]. Legarra  [7]  established how to solve the issue of comparing genetic variance across IBD, IBS or other measures of relationships. In this work, we have used, similar (but not identical) to Powell et al. [34] , a fixed reference ( constructed as a crossproduct of -1,0,1 genotypic codes) and tailored  (IBD, pedigree) to fit  (IBS, markers). Using a fixed reference has the advantage, compared to previous approaches, that genomic relationships are immutable (adding more genotypes to the database does not change the existing relationships) and they are unconditional on pedigree depth, that by construction is always limited and, in animal breeding, often heterogeneous.
Funding: CAGB, SML and RJCC were partially funded by grants  FONCyT PICT 2013-1661 ,  UBACyT 861/2011  and  PIP CONICET 833/2013. This work was partially financed by the  AdMixSel  project of the  INRA SELGEN  metaprogram (CAGB, AL and ZGV) as well as  INP Toulouse  (CAGB, AL). The project was partly supported by the  Toulouse Midi-Pyrenees Bioinformatics platform.
98, p <. 0001), and 2015 (mean ¼ 3. 62, p <. 0001) were better. Interpretation: While DBS may be easier to collect and transport than serum samples, they may not be as good for young children, especially those under age 1 year. DBS collection requires training and practice to ensure that DBS are large enough for analyte testing. Despite challenges, DBS yielded good results and proved an acceptable alternative to serum samples in a resource limited environment. Funding: Funding was provided by NIH, NIAID (5U01-AI-088671). Purpose: ARSOBO's binational program opened in 2010 with a construction shop in Nogales, Sonora. Individuals with disabilities are trained and employed to construct medical devices they use. Two wheelchair users construct all-terrain wheelchairs. An amputee fabricates limbs for others.
The development phase lasted from July, 2014 to May, 2015. Structure/Method/Design: We conducted a physician needs assessment through surveys (n ¼ 50), in-depth interviews (n ¼ 6), and a focus group discussion (4 participants) in three community healthcare centers in Shanghai. We developed patient-directed textmessages based on literatures and invited physicians for evaluation. We synthesized iterative prototyping and stakeholder input to refine the design of the android-based App, and used server with spring Model View Controller, Hibernet framework and MySQL database. We relied on cloud communication technology to set up the voice call system. Encryption and role-based passwords were adopted to ensure data security. We also conducted extensive beta testing internally. IRB approval was obtained from Fudan University and Duke University.
In the remainder of this paper, we briefly introduce the AWA web application, describing its architecture and user interface. The evaluation of the tool is reported in terms of how we structured a legal writing academic's feedback to refine the rhetorical parser implemented in AWA, and the methodology for harvesting student feedback. We then analyse student feedback, before concluding with a discussion of how current limitations can be tackled, and the challenge of "algorithmic accountability", a broader concern in critical discourse about data in society.
EVALUATION METHODOLOGY WITH THE LAW ACADEMIC Confusion matrix annotation We have developed a practical methodology for refining the quality of the parser, using a form of semantic annotation by the domain expert (the civil law academic leading the course) of AWA's output. Designed to be practical for the development of analytics tools with staff with limited time and resource, this is a rapid version of the more systematic coding that a qualitative data analyst would normally perform on a large corpus, using signal detection theory codes for True/False Positives/Negatives, to populate a confusion matrix: Law Lecturer Important Unimportant AWA Selected TP FP Unselected FN TN Thus, the lecturer was asked to highlight True Positives and Negatives where she agreed with AWA's highlighting and classification of a sentence, or its absence; False Positives where AWA highlighted a sentence she did not consider to be significant, and False Negatives where AWA ignored a sentence she considered to be important.
Qualitative coding of student feedback An analysis was conducted of the survey data (including comments), the written student reflections, and the lecturer notes from the oral presentations on AWA. For those completing the questionnaire, response frequencies were tabulated. Further analysis of the written content was conducted in NVivo (QSR International Pty Ltd, 2012) to identify thematic patterns across the content; these are reported in the next section, with broad patterns noted and exemplifications of the feedback given in brackets.
Previous iterations of Galaxy Zoo  (Lintott et al. 2008)  showed that the effect of using colour images (rather than monochrome or single filter images) for classifications is small, but that their inclusion greatly increases classifier engagement, resulting in significantly faster collection of quantitative visual morphologies. The physical angular sizes of the Galaxy Zoo CAN-DELS images were matched in different filters, using the native point-spread functions (PSFs). The images were combined with an asinh stretch (described in detail in  Lupton et al. 2004 ) with a non-linearity value of 4. 0, chosen to show clear features across a wide dynamic range. Sources in the dataset vary greatly in size and surface brightness, and a single set of values for channel scalings is not adequate to capture the variety of features across the images.
Once a pre-set classification minimum count has been reached, the subject is retired from the active classification pool. While more sophisticated task assignment might have led to efficiency savings (e. g. Waterhouse 2013), it would have changed the experience of what was a popular project  (Bowyer et al. 2015)  in unpredictable ways, and the infrastructure supporting the project did not at the time make such complexities possible. The initial goal for Galaxy Zoo CANDELS was thus simply to obtain at least 40 independent classifications for each galaxy. This uniform retirement limit was modified twice during the project. In the first instance, a pre-analysis of the dataset performed when the average number of classifications per galaxy had reached approximately 20 revealed 11,837 subjects where further classification was unlikely to provide significant additional information.
1, 0. 6, 0. 3), then the classifier's consistency for Task T00 for this classification is κt = [(1 -0. 1) + (1 -0. 6) + 0. 3] /3 = 0. 53. In the above example, the classifier's answer to Task T00 leads to the end of the workflow (Table  1 ), so this κt is also equal to the classifier's consistency for the overall classification, κi. More generally, the classification consistency is the answer-weighted average of the task consistencies: κi = t κtat t at , (3) where each sum is over the number of tasks the classifier completed during the classification. Following this calculation for the entire classification database, each classifier's average consistency is calculated as κ = 1 Ni i κi. (4) Averaging over a classifier's individual consistency values for all classifications effectively downweights those contributions from classifiers whose classifications regularly diverge from the consensus whilst preserving the diversity of classifications from classifiers who are on average consistent with each other.
For instance, a combination of Galaxy Zoo and CANDELS team visual classifications would likely be helpful in selecting a sample of interacting galaxies with the optimum combination of completeness and purity, while a combination of clumpy selections from each would allow for a comparison of clumpiness versus patchiness in galaxies with a range of clump configurations. 4. 5 "Disk-like" and "Featured" are not equivalent Using a different combination of CANDELS team classifications and Galaxy Zoo classifications, we can directly test how well galaxy features correlate with visually identified disks by comparing the "diskiness" parameter Dv  (McIntosh et al, in preparation)  assessed by CANDELS team members to the Galaxy Zoo "Features or disk" vote fraction in Figure  10. The Dv parameter is a visual assessment of light concentration intended to distinguish disks from spheroids even in the absence of features traditionally associated with disks, given high enough data quality.
We identify a sample of smooth disks at 1 z 3 as part of this Galaxy Zoo CANDELS data release. This data release includes 3 of the 5 legacy fields imaged by the CANDELS project. Future work in the collaboration between Galaxy Zoo and CANDELS includes the classification of the other 2 fields (EGS and GOODS-North). Other future studies of this rich dataset may include a more detailed study of clumpy structures in z > 1 galaxies and examination of mergers at these epochs, for comparison to local samples with similar selection methods  (Darg et al. 2010a,b). The public catalog of Galaxy Zoo CANDELS morphologies may be obtained from data. galaxyzoo. org.
ACKNOWLEDGMENTS The authors wish to thank the anonymous referee for helpful comments which improved this manuscript. Astropy (Astropy Collaboration et al. 2013), TOPCAT (Taylor 2005) and an OS X widget form of the JavaScript Cosmology Calculator (Wright 2006;Simpson et al. 2013) were used while preparing this paper. Figure 5 was created using SankeyMATIC at sankeymatic. com/build. BDS gratefully acknowledges support from the  Oxford Martin School and Balliol College ,  Oxford. Support  for this work was provided by the  National Aeronautics and Space Administration   through Einstein Postdoctoral Fellowship Award Number   PF5-160143  issued by the Chandra X-ray Observatory Center, which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of the  National Aeronautics Space Administration  under contract  NAS8-03060.
Introduction Cameroon is exposed to geohazards (flood, landslide, volcanic eruption;  [1] ). The International Disaster Database (www. emdat. be/ see "Database / Country Profile") points out four catastrophic recent  (2007) (2008) (2009) (2010) (2011) (2012)  floods that affected between 10 000 and more than 30 000 inhabitants. The Ndop Plain  (35 × 20 km ) is situated at a mean altitude of 1150 m along the Cameroon Volcanic Line (CVL). It is surrounded by a series of escarpments which climb up to 2151 m, corresponding to the peripheries of the Mounts Oku and Bamenda, the Mbam and Nkogam Massifs (Fig. 1 ). The CVL is interpreted as a mega shear zone that seems to be structurally subdivided into an important network of faults associated with a system of alternating horsts and grabens  [2] [3] [4] [5] [6] [7].
Methods Our cartography work involved the use of appropriate software such as Surfer 9, MapInfo 8. 5, ArcGIS 10. 1, Global Mapper 13 and 3DEM, to realize the various maps required for this study. The base maps used during this phase were topographic maps. The procedure employed was as follows: Topographic maps (1:50,000) of Nkambe 1b and Foumban 3d from the National Institute of Cartography (NIC) Cameroon were georeferenced, and vectors such as contour lines, rivers, localities and roads were digitized. The GeoTiff DEM of the Ndop was downloaded from NASA's Shuttle Radar Topography Mission (SRTM V2), with a resolution of 90 m and introduced and imported into the ArcGIS software. Surface parameters such as contours, reliefs, slope dippings, and slope orientations were realised using the 3D spatial analyst tool in the ArcGIS 10.
1 software. With the Global Mapper software, the DEM of the Ndop Plain was first introduced. The x, y and z data were exported to the Surfer software for further modelling, using the elevation grid and surfer grid in the American Standard Code for Information Interchange (ASCII) format. The landslide and flood hazard maps for the Ndop Plain were realised with combining or weighting the var-ious parameters in the ArcGIS software. The model employed in mapping the hazards was a data-driven bivariate hazard mapping model (see  [16, 30] ). The various parameters (predisposition factors) involved in the realization of the hazard map are rock type, soil type, land cover, slope dipping, slope orientation, nature (size) of river and proximity to river (see  [31] [32] [33] [34] , and references therein).
Slope orientation and slope dipping Very steep slopes surround the flat Ndop Plain (Fig. 4 ). As pointed out in many regions worldwide, landslides are linked to slopes  [16, 36, [42] [43] [44] [45] [46] [47]. Two characteristics are classically considered: slope orientation and slope dipping. These slope parameters were realised with the ArcGIS 10. 1 software using the DEM of the area in the 3D spatial analyst tool. An aspect map (slope orientation) is essential in slope failure analysis because of the varying exposure to sunlight and rainfall. Here, the slope orientations range from 0 to 360 ∘ , and are grouped into 10 classes: flat (-1), N (0-22. 5), NE (22. 5-67. 5), E (67. 5-112. 5), SE (112. 5-157. 5), S (157. 5-202. 5), SW (202. 5-247. 5), W (247. 5-292. 5), NW (292. 5-337. 5) and N once more (337.
3-D representation Precise knowledge of the relief of an area is vital when carrying out landslide studies. The role of relief in slope instability has been disputed with some authors  [49] [50] [51] [52]  arguing that altitude is a good indicator conditioning slope movements, while others  [53]  do not see any changes in slope movements between low altitudes and the high altitudes. The three dimensional representation of the relief of the area was done using the DEM, from which vertices of points with spatial references were generated with the Global Mapper 13 software. These vertices were then exported to the Surfer 9 software to generate a 3D representation of the area (Fig. 5c ). It is confirmed that the northern part of the studied area is very uneven and hilly compared to the central and southern parts of the area, corresponding to the plain.
Nature and proximity to river The nature of the rivers was determined from the dimension of the river channels. This was obtained by digitizing the 1:50,000 topographic maps of Nkambe 1b and Foumban 3d using the ArcGIS 10. 1 software and also from field work. Rivers were grouped into five classes, from the largest to the smallest: major river, main river, river, stream and temporal stream. The dimensions of the rivers increase as the rivers flow into lowlands and coalesce together. Generally, the wider and shallower a river channel, the higher the degree of flood hazard is. Moreover, some streams may cause severe floods when they receive an abnormal influx of water. In case of a flood, the areas close to a river channel are highly affected compared to distant areas. A ring buffer was realised at a distance of 600 m from the river at intervals of 100 m, 200 m, 400 m and 600 m and grouped into four classes (Fig.
Combining hazard parameters Many different types of landslide hazard zonation techniques have been developed over the last decades, and the difficulty lies in the weighting the factors  [32, [56] [57] [58] [59] [60]. In this paper, the parameters were weighted as follows: the model builder was used in the ArcGIS 10. 1 software and all the environmental settings were checked such as processing extent, raster analysis and cell size. The parameters were then introduced into the model builder and reclassified to realize floating points, continuous datasets, categorize datasets into ranges, and assign each range of values a discrete integer value. Each parameter was classified based on its influence on landslides and floods. Dipping slopes, for example, were reclassified by assigning new input field values to them.
Consequently, steeper slopes were assigned higher values and less steep slopes lower values. This was done for the other parameters used in the model. Using the connection tools, the parameters were connected with their input data, their corresponding tools and their resultant outputs raster. The model was then run to ensure functionality. Using the weighted overlay tool, the values of each dataset were then weighted  [44, 60] , and all input parameters were assigned each a percentage of influence or hazard index. The higher the percentage of influence, the greater impact a particular input parameter will have on landslides or floods. The percentage of influence for both landslides and floods are presented in Tables  2  and 3. Accordingly, the weighted overlay operation was done as follows; 1, 10, and 1 were typed in the From, To, and By fields in the weighted overlay tool box to avoid having to update the scale values after adding the input datasets.
Kinematic recording. An electromagnetic tracking device, the Polhemus Fastrak (SPACE FASTRAK, Colchester, VT, USA) was used to record the left wrist position in three-dimensional coordinates. A stationary transmitter was fixed 30 cm in front of the participant and established the global coordinate system. A mobile sensor was fixed with an adhesive tape on the dorsal aspect of the left wrist at the intersection point between the styloid process of the ulna and the middle finger. Data acquisition was synchronized with the stimulus presentation. Real-time 3-D position of the left wrist was tracked at 100 Hz during the execution of the task, permitting off-line calculation of wrist displacement over time and the velocity profile. Data analysis. Data analysis was made offline using Matlab software (Mathworks, USA).
A 3-D reconstruction of the wrist trajectory was performed and its tangential velocity profile was calculated and filtered with a fifth-order low pass filter at 10Hz. The action was divided in two phases: (A) reach-to-grasp and (B) bring-to-the-body. A Matlab script was used to determine the onset of the reach-to-grasp phase, calculated as five percent of the first peak of velocity  14. The beginning of phase B was determined as the lowest velocity profile value between phases A and B. The end of the bring-to-the-body phase (phase B) was defined as the smallest value after its peak. The tangential velocity profile of each separated phase was time-normalized by a linear interpolation of 200 points. A second Matlab script was designed to detect discrepant shapes of the velocity profile. The two phases of all trials were plotted together so that discrepant shapes from the participant´s median value for each condition and each phase could be marked and then excluded through visual inspection blind to the valence condition.
If one outlier phase was beyond the average the whole trial was discarded from the analysis. Less than 10% of trials were excluded using these criteria. Four participants were excluded from the kinematic analysis due to technical problems in data collection. The following parameters were calculated for the remaining 21 participants: movement time (MT), peak velocity (PV), time to peak velocity (TPV) and movement trajectory length (MTL) for reach-to-grasp and bring-to-the-body phases. Movement time was defined as the time interval between the onset and offset of each phase. Movement trajectory length was determined as the distance traveled for each participant in each phase. Peak Velocity corresponds to the maximal velocity attained for each participant in each phase. Time to peak velocity represents the timing of motion, i.
e. : the proportion between the duration of the acceleration and the deceleration times  12  , that is, the ratio between acceleration time (AT) and movement time (MT). This index indicates how long the acceleration time of a movement lasted with respect to the total duration of the movement. A ratio greater than 0. 5 indicates that the deceleration time is shorter than acceleration time. This parameter indicates the motor system strategy to perform the action, since the deceleration time increases with task demand  13. Statistical Analyses. A Three-way repeated measures Anova was performed using Statistica 7 software, with phases (reach-to-grasp and bring-to-the-body), valence (unpleasant, neutral and pleasant) and blocks (1, 2 and 3) as independent variables for each kinematics parameter.
Acknowledgements This work was funded by  Conselho Nacional de Desenvolvimento Científico e tecnológico CNPq  (Grant numbers  306817/2014-4  and  480108/2012-9 ),  Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro FAPERJ  (Grants  E-26/110. 526/2012and E26/010. 002902/2014 ) and  Financiadora de Estudos e Projetos FINEP (PROINFRA HOSPITALAR  grant  18. 569-8 ). This work has been produced as part of the activities of  FAPESP Research, Dissemination and Innovation Center for Neuromathematics-NeuroMat  (Grant  2013/07699-0 ,  FAPESP ). This work was also supported by  CAPES-COFECUB  (Project no. 819-14 ).