dataesr/scientific-paragraphs-categorization huggingface dataset:
Digital contents The different 3D models (stomach and jaw) were made with the software Blender  [55] , these models must not have too many polygons to maintain the smooth running of the software  [58]. For the food selection stage, these are represented by 2D images from SU. VI. MAX study  [2, 3]. Once the food is selected, it appears as a cube, which will be easier for the patient to handle than a 2D image. The size of this cube is based on the quantity selected for the selected food.
Digital workshop presentation HOLO_NUTRI software has been used by patients during the DIGIT_WORK workshop which goal was to make patients aware of the post-surgery diet modification they will endure due to the bariatric surgery they will undergo. This workshop is composed of nine steps (also depicted in Table  2  for a more global understanding): session introduction (step 1), identic to step 1 of CONV_WORK workshop; debate around the central question "To feed, eat, when one has been operated on a bariatric surgery, what does it evoke you ?" (step 2), identic to step 2 of CONV_WORK workshop; computer scientists team presentation (step 3); hardware and its using presentation (step 4) by showing a video. A dedicated software named LEARN_EX has been developped by us to introduce basic gestures to control the HoloLens and essential to the good use of HOLO_NUTRI (Figure  13 ).
The computer scientist team gives information about this LEARN_EX learning exercise (step 5). This workshop starts with a discussion between all patients about the daily diet post-surgery (step 6), identic to step 5 of CONV_WORK workshop; patients are then invited to compose their own meal with HOLO_NUTRI software (step 7); the nutritionist delivers several advices depending on the food and quantities selected by the patient (step 8); feedback forms are then filled by patients in order to evaluate the session (step 9), identic to step 9 of CONV_WORK workshop. We have set up a specific communication to make patients able to use this technology and application as fast as possible: this communication is composed of laminated leaflets (surgery details, gestures to be used with the headset), several video sequences (basic gestures description to use both the headset and the application), and also a custom-made learning exercise LEARN_EX (to address specific gestures).
Experimental procedure The software presented in this article has been used in a hospital hosted workshop that aimed to help patient prepare for a bariatric surgery (Centre Hospi-  talier Émile Roux, Le Puy-en-Velay, France), several sessions of one hour and a half had been organized. For each one of them, a group of three patients were supposed to simultaneously handle first the headset HoloLens then to use the application HOLO_NUTRI. We, on purpose, limited the number of participants to three for the following reasons: size of the experimental room (Figure  14 ), only three HoloLens at our disposal, involvement of three researchers to help patients in the use of both the equipment and the software. To allow this simultaneous participation of three patients, we have used the HoloLens App application  [59]  from the Microsoft Store in order to connect (using WiFi) the three HoloLens to three different computers to get access to a video feedback of each patient to better help them in their use of both this hardware and the software (Figure  14 ).
• (Group 3, Table  5 ). 28 patients report not feeling the workload to be too much tiring (Q3). 24 patients have felt rather relaxed, satisfied and selfconfident while using the HOLO_NUTRI software (Q4). The duration of this experience has been perceived as rather satisfying by 28 patients (as a reminder, the session was planned to last one hour and a half with an effective use of HOLO_NUTRI of 18 minutes) (Q5), the two patients who have not    4 , Q2b). Three patients only have not perceived this experience as satisfying (Q7): two of them for the reason previously stated in the question about recommendation to another person (Q6), and the last patient has not given us any feedback. In a summary, this experience has been rather tough for three patients (difficulties in performing the required gestures willing to a certain annoyance and preventing them from fulfilling the workshop goals, i.
Acknowledgements Authors would like to thank  Arthur Jacquin , student in  DUT Informatique Graphique at IUT de Clermont-Ferrand, France  (site  Le Puy-en-Velay ) for the software design. This project is funded by a french national grant:  PEPS INSIS CNRS "Sciences de l'Ingénierie pour la Santé pour accompagner des projets translationnels ", 2017, project  AVACM (Assistance Visuelle Augmentée lors de Consultations Médicales ).
They occur with roughly the same energy throughout the history of the Universe and thus can be used as standard candles for measuring distances at cosmological scales. Since their application to cosmology became evident, SNe Ia have been among the main science drivers for many of 2 https://lsstdesc. org/ 3 https://cosmostatistics-initiative. org/ 4 https://github. com/COINtoolbox/RESSPECT/tree/master q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q i z g r the new generation of large scale astronomical surveysamong them the LSST. These surveys will gather photometric measurements of the brightness evolution (light curves) of thousands of supernovae candidates in a series of broad-band filters (see an example of simulated SN Ia light curve in Figure  1 ).
B. Data We used simulated data from the SuperNova Photometric Classification Challenge (SNPCC)  [11]. This data set was constructed to mimic observations taken by the Dark Energy Survey 6  (DES) during a period of 180 days. Distances were calculated assuming a standard cosmological model (Ω m = 0. 3, Omega Λ =0. 7, w = -1). Observation conditions at the telescope sight were derived from historical measurements of the ESSENCE project at the Cerro Tololo Inter-American Observatory  7  (CTIO) and incorporated to astrophysical templates. All these elements were incorporated using the SNANA 8  software  [12] , where all necessary configuration files for reproduction can be found. This data set contains three big classes of supernovae: types Ia, Ibc and II. The complete set contains 21,319 light curves in four broad-band DES filters, [g, r, i, z], of which 1,103 represent a population that was spectroscopicaly confirmed  9.
C. Experiment design We separated our data set into 3 groups: the full training sample, identified as spectroscopically confirmed by the SNPCC data set and formed by 1,103 objects (hereafter, original training); the validation and test samples formed by 1,000 objects each, taken from the 20,216 light curves tagged as purely photometric by the SNPCC data set and following its sub-population distribution; and the pool sample comprising the remaining 18,216 objects. Since our pre-processing step (Section II-D) requires a minimum of 5 observed points in each filter to deliver meaningful best-fit parameters, a complete input data matrix is only available starting from the 20 th day of the survey. This leaves only 160 active learning cycles (days) that we can use to build an optimal training sample.
In this work, we build upon the efforts reported in  [4]  and present for the first time a simulated data environment which simultaneously takes into account: 1) the necessity to estimate the current brightness of an object in order to make a decision about spectroscopic follow-up (only partial information is available at the time of labeling), 2) the evolution of training and pool samples with time, 3) the spectroscopic time required to observe each object in 2 different telescopes as a function of time (different labeling costs per day, object and budget source) and 4) the limited telescope time available per night (knapsack constraints). We tested the performance of random sampling (RS) as well as three batch active learning strategies based on uncertainty sampling. When using the original training sample provided within the SNPCC data set (1,103 objects) as a starting point, active learning strategies did not significantly improve upon RS.
ACKNOWLEDGMENT The authors would like to thank  David Kirkby  and  Connor Sheere  for insightful discussions. This work is part of the  Recommendation System for Spectroscopic Followup (RESSPECT)  project, governed by an inter-collaboration agreement signed between the  Cosmostatistics Initiative (COIN)  and the  LSST Dark Energy Science Collaboration (DESC). This research is supported in part by the  HPI Research Center in   Machine Learning  and  Data Science  at  UC Irvine. EEOI and SS acknowledge financial support from  CNRS   2017 MOMENTUM grant  under the project  Active Learning for Large Scale Sky Surveys. SGG and AKM acknowledge support by  FCT  under Project  CRISP PTDC/FIS-AST-31546/2017. This work was partly supported by the  Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of Houston.
Although the main mechanisms and evaporation of a blood pool seem to be similar to that of a gel, the characteristics of a pool have to be analysed as well since in practical cases the shape and size could vary significantly. Shape influence on the evaporation rate. In order to study the influence of the shape of a pool on its drying, Twenty pools of the same initial mass, 5. 24 ± 0. 47 g, but of different shapes were created. These were dried in the same humidity of 20%, at the same temperature of 23 ± 1 °C and on the same surface, a white tile. The initial mass, m i of a pool is known, thus the volume, V, can be calculated using the blood density: ρ = V m (2) i The area, A, and the perimeter, P, of each pool were measured using the imageJ software. From this, the height, h, of the pool can be estimated with the following approximation: = h V A (3) These dimensions characterise a blood pool.
We present a smooth surface interpolation method enabling to take discontinuities (e. g. faults) into account that can be applied to any dataset defined on a regular mesh. We use a second-derivative multi-scale minimization based on a conjugate gradient method. Our multi-scale approach allows the algorithm to process millions of points in a few seconds on a single-unit workstation. The interpolated surface is continuous, as well as its first derivative, except on some lines that have been specified as discontinuities. Application in geosciences are numerous, for instance when a structural model is to be built from points picked on seismic data. The resulting dip of interpolation extends the dip of the input data. The algorithm also works if faults are given by broken lines. We present results from a synthetic and real examples taking into account fault network.
Introduction The picking of surfaces, isochrones or faults, is difficult and uncertain because of low signal to noise ratio of surface seismic or other reasons, so that most often only a partial picking can be achieved. However, for some geoscience applications  (Schneider, 2002; Perrin and Rainaud, 2013) , a complete picking is required through these areas. An interpolator is thus needed with following characteristics: it has to be fast, able to process millions of data points, adaptable to all kinds of dataset (Fig. 1 ), and it has to preserve the dip of the data (if any). Moreover, the interpolator has also to take faults into account. Numerous interpolation techniques  (Awange et al. , 2018)  like polynomial interpolations  (Léger, 1999)  are not compatible with the above requirements, also spline interpolations are currently used in the scope of Computer Aided Design (CAD) to define object representation and to perform various manipulation of surfaces.
Method 2. 1 Principle We consider a rectangular regular mesh and the integer coordinates (i, j) discretize the coordinates X and Y. The step in i and j are unit, but the trace distance in meters is given to the program to manage possible anisotropies between X and Y. The data points lie on the mesh, and the unknown points also. To interpolate some given points, we have chosen to compute a surface that goes through all given points and to minimize its second derivative with the conjugated gradient method, which is simple to implement. However for some datasets, the number of iterations can be close to the number of unknown points, making the algorithm O(N 2 ), which this is too expensive for many applications. The aim of this paper is to remove this disadvantage. A solution to obtain a quick optimization is to initialize the model optimization by a model very close to the solution.
Circle This example has been designed to show the independence between the result and the initial model. The white circle, representing a closed discontinuity (Figs. 8a  and 8f ), is going to show how the interpolator manages all orientations of the fault. In this example we have only two given set of values (two little black-squares with brown and blue colors), one inside the white circle and the other outside. The dataset consists of two horizontal squares, one at 1800 m depth, the other at 2200 m depth. The size of these squares is 50 dots, and the size of the unknown domain is 700 by 500 dots. Figure  8a  shows the initial model which is the linear regression of the data. Because the same palette is chosen, the larger range saturates the dark brown and dark blue. In Figure  8b , the result of the first iteration has nothing to be compared with the initial model, and the two constant zones at 1800 and 2200 m are clearly found.
Real case study: Alwyn (north sea) We present the Alwyn dataset as a real field example. The size of this example is 13 km long and 5. 5 km wide, with steps of 12. 5 m in X and Y, which represents ~458 000 unknowns. Seismic sections have been picked and the result is a triangulation, the typical distance between points being about 100 m. These triangulation points are projected to the closest points of the regular mesh with the faults being edges. We have removed all the points closer than 350 m from a fault. This choice is the maximal value which preserve two points along the eastern edge, the two points allowing the dip computation. The reason to remove points close to the fault is also justified by a greater picking-uncertainty due to the signal-to-noise ratio. We made two interpolations in Figure  11 , without the faults in (a), and with the faults in (b).
Conclusion During several years, the presented multi-scale interpolation was used in various scope of work from Geosciences without faults to fill sparse information in 2D regular meshes. We bring into focus a multi-scale interpolator which has many advantages. The interpolation is fast because a million of unknowns requires only few seconds to compute. This fact is due to our multi-scale approach with several inversions instead of one, consequently the algorithm passes from N 2 to N. As well, the interpolation follows not only the value of the given data, but also the given dip (if any). From the various processed examples, one can also notice the arbitrary distribution of dataset, from 1 to N À 1 known points. Finally, from the last real case study from Alwyn, the interpolation is able to work with any network of fault lineaments.
We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the John Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.
1, 3. 4 for more details. In our approach, the number of daily reported cases is the value of a function that is a positive linear combination of N EF wavelets at the given day. We fix the number N of summands of EF wavelets entering in our modelling function (and in our applications N is usually taken to be 3 or 5). The wavelet series coefficients themselves are then obtained by machine learning based curve fitting methods with square loss function, see subsections 2. 2 and 4. 1. We then proceed with specific applications to Covid-19 scenarios. Here we present, now using in addition data-driven machine learning based curve fitting, some of our model's predictions to selected countries and US federal states, which are based on the currently existing respective data for these locations provided by the most recent numbers supplied by the Johns Hopkins University Covid-19 database.
" The recorded video of all these presentations was viewed more than 1,000 times. Two special days focused on "Embedded Artificial Intelligence" and "Silicon Photonics. " The sessions organized on these two hot topics covered new trends in cognitive algorithms, hardware architectures, software designs, emerging device technologies for AI, and on data communication via photonics for both data center/high-performance computing and optical networks on chip applications. Traditional DATE activities also include the PhD Forum and the University Booth, where university students and faculty can meet industrial representatives and showcase their research and outcomes. The PhD Forum, supported by EDAA, ACM SIGDA, and IEEE CEDA, gathered the work of 32 PhD students or recent graduates, who prepared a poster and/or a presentation introducing their research.
Acknowledgments The construction of the current virtual special issue required much effort from many people. The four guest editors are grateful to:  Alison Waldron ,  Amar Bhogals ,  Ashley Kieran Clift ,  David Spencer ,  Justyna Kasprzycka ,  Kavitha Narayanasamy , the publishers who launched in 2018 the idea of a joint special issue and constantly helped in the everyday care such an idea required;  Fatima Cardoso  and  Carlo Combi , the editors in chief of the two journals (The Breast and Artificial Intelligence in Medicine) who eagerly accepted the idea; the authors, who enthusiastically accepted the invitations, and carefully revised their manuscripts in guidance of the suggestions and recommendations from reviewers and guest editors; and the anonymous reviewers, whose valuable help set up the pillars on which this project was built.
In order to have a ground truth to validate the correct functionality of upVC, a simulated sequencing dataset has been generated using the HG38 Human reference genome from which 3,153,377 small variants have been inserted. Paternal and maternal chromosomes have been generated using the vcf2diploid tool  [22]. Short Illumina paired-end reads have been generated with the ART read simulator  [23]  with the following parameters: insert size = 400; standard deviation = 50; read length = 150bp; coverage = 30X. The resulting dataset contains 586 x 10 6 reads split into two fastq files. With reads of length 150bp, the index size for the human genome is equal to 120 GBytes. To GATK has been run with standard parameters. Compared to upVC, the quality is clearly better. However, upVC provides excellent results and legitimates our variant calling implementation on PIM architecture, knowing that the current code has plenty of room for improvement.
Brazilian Portuguese and Mexican Spanish corpora 2. 1. 1 Recording procedure and structure of the corpora Prior to the recording sessions at the Acoustic Phonetics Laboratory at UFRJ, a consent form regarding the scientific use of the recorded data was signed by the ten BP speakers. A SONY NEX-F3 video camera was used to record the upper body and face of the speakers who were seated against a dark background in a sound-attenuated room. Whereas the camera was positioned 90 cm from the speakers, a Zoom H4 recorder was positioned 20 cm from the speaker"s mouth, outside the view of the camera. The synchronization of the audio waves and the recorded video was made in the software Vegas Pro 14 (Magix, 2016). In the BP corpus, the sentence "Como você sabe" was produced as an assertion ("As you know.
Acoustic description and phonological notation In this study, forty BP and thirty MS sentences were analyzed. The phonological notation of the nuclear region of the assertive and interrogative intonational contours was made following the Autossegmental-Metric model  (Pierrehumbert, 1980). Sp_ToBI (Prieto and Roseano, 2018) was used for analyzing MS data and Portuguese_ToBI  (Frota et al. , 2015b)  for BP as well as descriptions provided by  Frota et al. (2015a)  and  Moraes (1998 Moraes ( , 2008)). Results of the acoustic description are presented in section 3. 1 of this article.
The two annotators watched each video clip, using the 22 AUs to describe the facial gestures. The presence of each feature was marked based on the subjective impression of the annotators. The number of observations of each AU for each of the 40 BP and 20 MS stimuli and for each annotator constitutes the dataset with 60 stimuli. However, in two stimuli (two assertions produced by male speakers, one from each language group), the speakers did not show any AUs. They were removed from the dataset; this limits the total number of stimuli to 58. In order to evaluate the interrater agreement between the two judges, Cohen"s Kappa was calculated  (Cohen, 1960). It measures the interrater accuracy, corrected for agreement by chance. The labels given by the two annotators were pooled in a contingency matrix having as lines each stimulus (58 lines) and as columns the AUs (22 columns).
Examples of the prototypical facial gestures for each speech act in both languages can be seen in Figures  4 to 7. In summary, the visual analysis of BP and MS data set showed that the stimuli can be sorted according to their intended speech acts on the basis of visual patterns produced by the speaker: thus, there is a coherence in the way speaker uses visual cues while performing assertions and questions. The analysis revealed that questions are produced in both languages with the same facial gestures, including eyebrow lowering, lid tightening and nose wrinkle. For assertions, there are different facial expressions: in BP, the head tilt movement plus blink are characteristic visual actions and, in MS, this speech act is linked to lip stretching as a visual cue. In this paper, the multimodal production of questions and assertions in Brazilian Portuguese and Mexican Spanish were described and compared.
Our hypotheses, stated in the introduction of this study, was that both languages would present specific auditory and visual cues for characterizing each speech act. The acoustic analysis showed that both languages encode the speech acts with specific prosodic cues. Whereas, in BP, a rising F0 in the nuclear stressed syllable is typical of echo questions, and a falling F0 in the nuclear stressed syllable is typical of assertions, in MS yes-no questions are characterized by a low stressed syllable followed by a rising boundary tone, and the assertions are characterized by either a falling-rising or a flat low F0 movement in the nucleus. The visual analysis in the Brazilian Portuguese and Mexican Spanish data disclosed different facial expressions produced by speakers when uttering assertions.