dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of Study Dataset The core dataset of (currently 39, planned 40) video-recordings of remote consultations incorporates video, audio, and screen capture at both "ends," clinician and patient, for each consultation along with detailed transcriptions. Both studies captured 2 video streams, what the clinician sees and does in the clinic and what the patient sees and does at the remote site (the place where the patient consults from, typically, from the living room at home). To date, in 27 of the 39 consultations, we have recorded the clinician's end of the consultation using a small digital camcorder. We used the same or equivalent technology for the patient end to capture video and good quality voice recordings. In each of the consultations, the camera's field of view captures as much as possible of the individual and their orientation toward the screen (eg, a computer or tablet) as well as contextual detail in the room.
RenderX We synchronized screen capture and video files into one using video editing software such that the video of the computer screen can be played exactly in parallel with a video of the patient looking at the screen. We then aligned the patient's and clinician's "ends" in a single editable file. These synchronized files allowed us to zoom in and slow down events to examine interactions, judgments, interpretations  [82] , the bodily conduct of (patient and clinician) participants, and the ways in which objects (eg, mobile devices and patient records) come to gain significance at particular moments  [83]. We have also transcribed consultations using ELAN, a specialized program for transcribing and analyzing video and audio resources that has allowed us to capture verbal and nonverbal details of interactions and view these interactions repeatedly (a requirement of linguistic analysis  [56, 82] ) and annotate audio and video streams at the level of a sentence, word, comment, or any other linguistic feature.
Project Management and Governance The QuARC study will be based at the University of Oxford and include NHS partners in participating sites in Oxford and East London. The study is largely desk-based, involving a researcher with specialist experience in linguistics (LMS) bringing together and analyzing existing datasets. Meetings between team members will occur at least monthly by teleconference and 3-monthly face-to-face to share emerging findings and develop the analysis. The program will be supported by an independently chaired, intersectoral steering group with representation from health services, policy makers, lay members, and external academics. We anticipate that this group will serve as an intersectoral discussion forum, a conduit to national policy, and a link with front-line clinical teams.
We will also compare interaction across remote and face-to-face consultations. The latter will necessarily be limited given that our face-to-face comparator data consists of audio-recorded (ie, verbal) and not video-recorded (ie, visual) data. The study deliberately focuses on a small number of consultations undertaken in 3 clinical services in the English NHS. We anticipate rich insights into the communicative utility of the remote genre; however, caution will be needed in considering relevance to other settings and conditions. One of the key findings of our work on remote consultations to date has been that although some clinicians are very keen to use this format, others are reluctant or opposed. One major benefit of having written, agreed guidance for both patients and clinicians is that the more reluctant clinicians will (we anticipate) be more confident to try this approach themselves.
(1998) ], Markovian models  [Pachet (2002) ] and other machine learning techniques  [Conklin 1 and Witten (1995) ;  Dubnov et al. (2003) ]. The use of a factor oracle from the field of string processing, paved the way to the popular OMax interactive improvisation software  [Assayag et al. (2006) ;  Surges and Dubnov (2013) ]. Several ideas have spawned around the OMax project to approach the concept of polyphonic information in automatic improvisation. ImproteK  [Nika et al. (2017) ] has been developed for music based on temporal scenarios (for instance a chord chart in jazz music). This system uses prior knowledge of a scenario that can represent another dimension than the one being generated, to guide the improvisation. Donze et al. (2013)  use an automaton to control a melodic improvisation through rule based grammars with information from other dimensions.
Cuoricino The first step towards CUORE was the Cuoricino experiment. The detector consisted of 44 large-size crystals (5 × 5 × 5 cm 3 ) and 18 smaller crystals (3 × 3 × 6 cm 3 ) coming from the MiDBD array. Crystals were arranged in 13 floors, with 11 modules housing 4 large-size crystals each and 2 modules housing 9 small-size ones. The total mass of TeO 2 was 40. 7 kg, corresponding to about 11 kg of 130 Te. 24  Cuoricino took data from April 2003 to June 2008 for a total exposure of 19. 75 kg( 130 Te)•yr. It achieved an effective mean energy resolution of (5. 8 ± 2. 1) keV (FWHM) at the 2615 keV 208 Tl line. The background rates in the Region Of Interest (ROI: 2474-2580 keV) and in the α-region (2700-3900 keV) were (0. 153 ± 0. 006) counts/(keV•kg•y) and (0. 110 ± 0. 001) counts/(keV•kg•y), respectively.
27 3. 2. CUORE-0 CUORE-0 5 was a single CUORE-like tower made of 52 natural TeO 2 5 × 5 × 5 cm 3 cubic crystals, arranged on 13 floors, each floor having 4 crystals. The detector had 39 kg of active mass, corresponding to about 10. 8 kg of 130 Te. The tower was constructed using the same procedures and materials selected for CUORE, it was installed in late 2012 in the former Cuoricino cryostat at LNGS and cooled to an operating temperature of T 0 ≈12 mK. The main goal of the experiment was to validate the cleaning and assembling procedures designed for CUORE and demonstrate the improvement in the background rate with respect to Cuoricino. T e detector collected data from March 2013 until March 2015. After an initial optimization phase,  28  the final exposure of 130 Te was 9. 8 kg•yr. 29  CUORE-0 trigger thresholds ranged from 30 keV to 120 keV.
Detector read-out and acquisition We present here a brief description of the read-out chain and the data acquisition systems adopted in both CUORE-0 and CUORE (the systems used in the previous experiments were quite similar as similar was data production, later described). To read out the signal, each thermistor is biased in differential configuration with a bipolar voltage generator connected to a pair of load resistors, finally connected to the thermistor terminals. The resistance of the thermistor varies in time with the temperature, R(t), and the voltage across it, V R (t), is the bolometer signal. The value of the load resistors, R L , is chosen to be much higher than R(t) so that V R (t) results only dependent to R(t), following the law introduced in Eq. 3. The signal V R (t) is amplified by a low-noise front-end electronics,  36  filtered by a 6-pole active Bessel-Thomson filter  37  and then digitized with an 18-bit analog-to-digital converter  38, 39  (ADC) with a sampling rate of 1 kHz (125 Hz in CUORE-0).
The pulse amplitude is determined from the maximum value of the filtered waveform. The data are grouped into datasets, which last approximately one month. At the beginning and at the end of each dataset, the detector is exposed to a radioactive source to calibrate the detector. The CUORE calibration system consists of 12 low-intensity 232 Th sources attached to Kevlar strings. 41 During the calibration, the strings are lowered from room temperature into the cryogenic volume amongst and around the towers. In CUORE-0 and its precursors, calibration procedure were similar but the source was external to the cryostat. The data production is a procedure that converts the data from a series of triggered waveforms into a calibrated energy spectrum. The procedure developed for CUORE is similar to the one developed for CUORE-0, 42 but scaled to 1000 channels.
Higher-order multiplets are used only to evaluate the contribution to the background from muons. The signal cut efficiency as a function of energy is defined as the fraction of true signal events that pass all the event cuts. More details on these selection techniques are presented in dedicated papers. 42, 43    optimize the search for low energy rare events,  44  such as solar axions or WIMP scattering, the standard derivative trigger is replaced by an optimum trigger (OT), that allows us to lower the threshold below 30 keV. The OT algorithm used in CUORE is an improved version to that presented in the past. 45 The data buffer is divided into slices that are continuously filtered in the frequency domain with the optimal filter mentioned above. The filtered waveforms have an improved signalto-noise ratio, and baseline fluctuations are reduced.
We modeled the response based on the measured response to the 2615 keV calibration peak. The full calibration response was modeled using a five component un-binned extended maximum likelihood (UEML) fit to 2615 keV line for each detector in a dataset (see Figure  4 ). The mono-energetic peak is well described by a double-Gaussian. The secondary Gaussian has a slightly lower mean energy and accounts for ∼5% of the events under the peak. The reason for the presence of a secondary peak is still under investigation. The substructure was independent of the data analysis steps and the double-Gaussian reproduced the data well across the energy range. Once the lineshape is known, we fit the ROI to determine the yield of 0νββ events. We performed a simultaneous UEML fit in the ROI (Figure  5 ). The fit components included a double-Gaussian at the Q-value, another double-Gaussian accounting for the  60   Compton events from 208 Tl and α decays at the surface.
However, we did take the lineshape uncertainty into account and treated it as systematic uncertainty in our final calculations. Figure  7  shows the combined fit for the 2615 keV photopeak of all the channel-dataset pairs in CUORE. Following the procedure outlined for CUORE-0, we performed a simultaneous UEML fit in the ROI (2465-2475 keV) to determine the yield of 0νββ events (Figure  8 ). The fit components included a triple-Gaussian at the Q-value, another triple-Gaussian accounting for the  60  Co sum peak, and a continuum background attributed to multi-Compton events from 208 Tl and α decays at the surface. Figure  8  shows the 155 events that passed all the selection criteria together with the UEML fit. We observed a large negative fluctuation for a signal at the Q-value, leading to a best-fit decay rate of Γ0ν = [-1.
ose a variable binning of the spectra to maximize the single bin. The minimum bin size in the continuum is 15 keV, and bins with less than 30 counts are merged with their immediate neighbor. The fit extends from 118 keV to 7 MeV. The threshold at 118 keV is set to exclude the low-energy noise events (contaminating few datasets) and the nuclear recoil peak (which is mis-calibrated). In building the 2 spectrum, we require that the energy of each event is above threshold. An exception is set for events with E > 2. 7 MeV in coincidence with events below the fit threshold, to correctly build-up the Q-value peaks in the α region of 2 spectrum. Figure  10  shows the reconstruction of the M 1 spectrum (M 2 and Σ 2 reconstructions are shown in  43  ). Figure  12  shows the 2νββ contribution predicted by the fit.
The strategy adopted for this analysis is to exploit multiple coincidence patterns that are combined together to extract the partial decay widths: Γ 0 + 1 0ν and Γ 0 + 1 2ν. The events considered are M 2 and M 3 , corresponding to the simultaneous energy deposition in two or three nearby bolometers. One of the bolometers is the source of the decay. The other one or two are the spectators: they just detect the photons escaping from the source. The spectators are required to record a monochromatic signal, corresponding to the full conversion of one or more γs (this implies a dramatic reduction of the background counting rate). The 90% C. L. limits obtained with Cuoricino 67 are: A similar analysis, using CUORE-0 data and exploiting the reduced background levels and the improved performances of the detectors, has recently allowed to improve these limits.
The absorption length of 511-keV γ in TeO 2 is less than 2 cm and, hence, the probability that it will escape a detector is small. It is, therefore, not surprising that the cleanest of the signature (M 3 ) has, unfortunately, the smallest probability of detection for our crystals. The highest probability of detection corresponds to events in which one or both the 511-keV γ get absorbed in the primary crystal where the decay takes place. However, in these cases it is difficult to discriminate the background based on the multiplicity of the events. We used the entire CUORE-0 data set, which corresponds to 35. 2 kg•yr of TeO 2 exposure, for the search of ECβ + decay of 120 Te. We implemented a simultaneous unbinned Bayesian fit of the energy spectra for all signatures using the Bayesian Analysis Toolkit (BAT) software package.
123 Te decay The electron capture decay of 123 Te (a naturally occurring isotope of Te) to the ground state of 123 Sb, although expected to occur, has never been experimentally observed. This isotope has an isotopic abundance of 0. 908±0. 002 and a Q-value of 51. 9 keV. Because of the large spin difference between the parent and daughter nuclei, the EC decay occurs primarily from the L and M shells  72, 73  while the capture from the K shell is suppressed by about 3 orders of magnitude. A sensitive search for the K-shell EC performed with TeO 2 thermal detectors have been able to put only a lower limit on the half-live of 9. 2×10 16 yr. 74 Given the enormously increased exposure (the exposure used in the analysis of the cited result corresponds to a single hour of CUORE data taking), CUORE can improve this result by orders of magnitude.
CUORE-0 data have been used to evaluate this quenching factor by looking for simultaneous energy depositions in facing crystals, where the sum energy equals the Q-value of an alpha decay. A fraction of these events are generated by alpha-decaying contaminations located on the very surface of a crystal, so that the recoiling nucleus is fully contained in one crystal, while the emitted alpha particle is absorbed in the facing one. This analysis resulted in a value of the quenching factor compatible with unity. Another aspect of the detector response that has been studied is the energy scale uncertainty at the lower end of the spectrum. Calibration curves are typically built by measuring the detector response to γ radiation at energies starting from above 200 keV. The extrapolation of these response functions at lower energy can be checked by measuring the position of peaks associated to the absorption of Te X-rays (around 27 and 30 keV).
Acknowledgments The CUORE Collaboration thanks the directors and staff of the  Laboratori Nazionali del Gran Sasso  and the technical staff of our laboratories. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) ; the  National Science Foundation  under Grant Nos. NSF-PHY-0605119 ,  NSF-PHY-0500337 ,  NSF-PHY-0855314 ,  NSF-PHY-0902171 ,  NSF-PHY-0969852 ,  NSF-PHY-1307204 ,  NSF-PHY-1314881 ,  NSF-PHY-1401832 , and  NSF-PHY-1404205 ; the  Alfred P. Sloan Foundation ; the  University of Wisconsin Foundation ; and  Yale University. This material is also based upon work supported by the  US Department of Energy (DOE) Office of Science  under Contract Nos. DE-AC02-05CH11231 ,  DE-AC52-07NA27344 , and de-sc0012654; and by the  DOE Office of Science, Office of Nuclear Physics  under Contract Nos.
15 EnergyFig. 4. 154 Fig. 4. A 5-component fit to the 2615 keV calibration peak for a single detector in a CUORE-0 dataset: (a) A primary Gaussian for the main peak; (b) a secondary Gaussian at a slightly lower energy; (c) the smeared step-function; (d) the x-ray escape peak; and (e) a flat background. The primary and secondary Gaussians are considered to be the detector's response to a mono-energetic peak and are defined as the lineshape of the detector.
) on CWI measurements. The modeling approach of  Azzola et al. (2018)  combined a spectral element approach (SPECFEM2D) to study the seismic wave propagation within a diffusive medium, while elastic deformation of the medium was modeled using a finite element code (Code_Aster)  (Azzola et al. 2018). We analyze the wave propagation properties of a medium that reproduce both acoustically and mechanically the measurements on the Westerly Granite rock sample during heating from room temperature to 450 °C, with a specific focus on its thermo-elastic deformation. CWI of synthetic waveforms generated while heating or cooling are compared to laboratory measurements  (Griffiths et al. 2018). The first section describes the principle of the methods developed in both studies. As a framework for the numerical modeling, the experimental method of  Griffiths et al.
Numerical scheme description The numerical simulation relies on combining two codes. The iterative thermo-mechanical deformation of the mesh grid is first obtained using the finite element software Code_Aster (EDF R&D, general code for the study of the mechanical behavior of the structures). Each step of the loading process consists of the resolution of a linear thermal problem, which is combined to a mechanical loading that takes into account the 0. 3 MPa axial stress applied to the cored sample  (Griffiths et al. 2018). We first solve the thermal problem according to the following equation: Temperature conduction within the sample is treated as an evolutionary regime. Figure  2  shows the evolution of temperature with simulation time computed by Code_Aster at the center of the sample. The insert on the right of Fig.
Funding This work has been published under the framework of the LABEX  ANR-11-LABX-0050-G-EAU-THERMIE-PROFONDE  and benefits from a funding from the state managed by the  French National Research Agency (ANR)  as part of the  'Investments for the Future' program. We acknowledge  ANR  grant  CANTARE  ( ANR-15-CE06-0014-01 ). It has also been funded by the  EU projects DESTRESS  ( EU H2020 research and innovation program , grant agreement No. 691728 ).
Similar capabilities are offered by the software described in  [24]. Another step forward is made in MRiLab  [25]. In contrast to previous solutions, where each tissue type is modeled as a separate uniform compartment, it proposes a multi-pool exchanging model of interacting spins. The effective usage of this software is, however, constrained by the availability of a CUDA-enabled GPU as an execution platform. Imaging of the blood flow constitutes a separate research path within the field of MR simulations. A number of papers have been published, where implementations of the Time-of-Flight, black-blood angiography, or PCA protocols are described. From the viewpoint of this study the most relevant works are reported in  [14, 15, 26, 27] , which are strictly focused on PCA. However, those studies refer to simplistic structures, such as isolated flow channels-straight  [14] , or with stenosis  [27] , relatively large vessels with one side branch (e.
Overview of the methodology workflow In the part referring to arterial models constructed from real angiography data, the proposed framework can be in general encapsulated into five main steps, as illustrated in Fig. 1. Initially, the images must be collected from a group of patients or healthy volunteers. The data acquisition and its subsequent analysis step embraces two parallel paths: (1) high spatial-resolution scanning in order to achieve detailed information about renal vasculature topology, and (2) functional examination in order to acquire blood flow velocity profiles in the arteries of interest. In our study the data were gathered using CT angiography of the abdomen and Doppler ultrasound. After completion of vessel segmentation and having estimated the blood velocity, the procedure moves on to reconstruction of the arterial tree geometrical model and simulation of blood flow through it.
We accomplish both tasks in Comsol Multiphysics  [29] -a computational fluid dynamics Fig. 1  The overview of the experimental workflow implemented in the study software. The combined geometrical and functional model is then fed into the magnetic resonance imaging simulator, which implements phase contrast angiography sequence. For that purpose we employ our own solution  [28] , however each MRI simulator capable of tracking positions of moving virtual spins can be applied. Eventually, the synthesized velocity-encoded phase images are compared with the ground-truth data, i. e. velocity field simulated in Comsol. By playing with the PCA configuration parameters, one gains the knowledge about feasible impact of various imaging factors on the velocity estimation errors. In the following, each of the above-summarized five steps are described in detail and in application to the kidney.
Real data set acquisition In our study, the contrast-enhanced angiograms of the abdomen were acquired for four patients. In case of two patients, no abnormal changes in the main renal arteries were observed. In one patient there was a severe renal artery stenosis (RAS) diagnosed in the left kidney (cf. Fig. 2 ) and one patient had bilateral RAS graded by a specialist as moderate. The data sets were anonymized and processed after collecting a written informed consent from the patients and approval from the local ethics committee (Approval No. RNN/132/17/KE). The in-plane resolution of the images was equal to 0. 703 × 0. 703 mm 2 with the matrix size-512 × 512 pixels. The number of axial slices was varied from 571 to 770 (depending on patient) and the spacing between slices, as well as slice thickness was Fig.
2  CT data set for a patient with severe stenosis in the left renal artery (blue arrow)-axial, sagittal and coronal slices (bottom row) and volume reconstruction (top panel). Image visualization performed using Slicer 3D software  [30]  set to 0. 625 mm for each individual. Hence, there were overall eight kidney vasculatures reconstructed using real data sets, five of which were normally appearing structures and three degenerated ones referred to as normal or RAS models respectively. In parallel to CT acquisition, the DUS imaging was performed. The collected US images contain information about temporal blood flow velocity in renal artery and distal vessels, i. e. smaller-size vessels located further from the main feeding renal arteries. In the case of one RAS model, the measurement in the left RA was missing.
Partly, the algorithms implemented therein are based on the ITK image processing library  [33]. Based on the geometrical description of the kidney vessel tree, its 3-dimensional surface model is constructed. Vessel surface could be determined using visualization libraries (e. g. VTK  [34] ), and then exported in a 3-D object file format, such as stereolithography (STL). Such an approach is postulated e. g. in  [35]  in application to cardiac and carotid arteries. Although this mechanism appears to be straightforward and relatively automated, the generated surfaces embrace numerous defects, of which non-manifold edges and self-intersecting faces constitute the most dominant faults that cannot be entirely repaired by surface smoothing during post-processing. In effect, the STL models imported into a computational fluid dynamics (CFD) software may not be tractable as the composition of the volumetric mesh for degenerated surfaces fails.