dataesr/scientific-paragraphs-categorization huggingface dataset:
Together with a low-resolution wavemeter measurement, the control unit can determine the absolute frequency of the SHG pulse. The obtained frequency value was used for lidar data analysis and for providing an error signal for active stabilization of the lidar transmitter. The underlying functional operating principle of the FRUIT is depicted in more detail in Figure  17. The frequency of each mode of the frequency comb is determined by the two degrees of freedom of the comb: repetition rate and offset frequency. Knowledge of these two values allows calculating together with the integer mode index n the absolute optical frequency of each comb mode. With the transfer unit, a part of the comb spectrum was selected and heterodyned with the OPA pulse. A single frequency comb mode was isolated by suppressing neighboring modes with an optical filter.
This is repeated for multiple source positions (or, more generally, source distributions). The set of acoustic time series thus measured will incorporate both the unscattered (directly transmitted) and scattered parts of the acoustic field. To facilitate practical measurement times, large arrays are typically used, consisting of many hundreds of elements. In recent years, a number of such systems have been developed for UST breast imaging, including ones based on ring arrays  [24, 1, 70] , rotating planar or linear arrays  [62, 3, 48] , and bowl-shaped detector arrays  [36, 2]. Once the data has been measured, the challenge is to use it to form accurate quantitative images of the tissue's acoustic properties.
With these variables, the contribution to the integral  (10)  for time t = T can be computed. The price we pay for the heavily reduced memory footprint is that we have to run three instead of two wave simulations to compute  (10). However, the simulation domain of the TR wave simulation only needs to enclose Γ and can therefore often be chosen smaller. Appendix B describes the implementation of the TR-based gradient computation using the k -space pseudospectral method implemented in the k-Wave Matlab Toolbox  [80]  and Section 4. 3 validates it numerically. In the case of absorption, i. e. , L = 0,  [85]  describes how to modify TR to recover the pressure fields (essentially, the sign of the absorption needs to be switched). Note however, that compared to the application in PAT image reconstruction considered in  [85] , no additional regularization is needed in our case as the boundary time series are not contaminated with measurement noise.
3ns. We set T to twice the length of the computational domain divided by c min 0 , which leads to T /∆t = 3912 time steps. The anatomically realistic numerical breast phantom used is part of the OA-Breast Database 1  , which was designed for (photo-)acoustic simulations and is described in  [60]. It is based on the tissue segmentation of a clinical contrast enhanced MRI of a breast in prone, free-hanging position. The original segmentation is fitted and interpolated into our setup. The tissue-dependent speed of sound values we use are the same as in  [60]  and can be found in the caption of Figure  1. The mass density ρ 0 was assumed constant at 1000 kg/m 3 for simplicity here. We distribute 2048 transducer locations over the half-sphere by the golden section method. Half of them will be emitting (sources, n s = 1024) while the other half will be receiving (sensors).
Further Extensions • The methods presented here were exclusively implemented in Matlab, using the GPU support offered by the Parallel Computing Toolbox. Implementing it using optimized CUDA code will lead to further performance gains  [69, 83, 84]. • While we presented the material in terms of a general acoustic parameter u, we only showcased the methods for u = c 0. The extension to acoustic density ρ 0 and attenuation (α 0 , y) is described in Appendix A and will be examined numerically in future work. • In this numerical proof-of-concept study, we used idealized assumptions on the transducer locations and properties. In forthcoming work, we will address realistic transducer and scan modeling. • Finally, we will demonstrate the application of this work to real data from experimental phantoms, healthy volunteers and breast-cancer patients acquired by the PAMMOTH scanner in the future.
In our collocation scheme, we will simply extract the pressure in a layer of grid points with a defined thickness as examined in Section 4. 3, which we will denote as p(∂Γ) for simplicity. The scheme to compute the TR-based gradient consists of three wave simulations, forward, adjoint, and time-reversal of which the latter two are interleaved. For simplicity, we only consider u = c 0 here (as in the numerical studies) and any scaling factors like ∆t have been omitted if they cancel out in the end. Algorithm 1 describes the whole iteration. Step 13 corrects for the fact that the first order scheme effectively uses the time derivative of the mass source term provided, not the term itself, cf. (6). Code will be released as part of a more general Matlab toolbox for ultrasonic and photoacoustic image reconstruction.
Rather, they merely safeguard against running the numerical wave simulation scheme with values of the parameter fields u that would lead to instability. For this reason, we enforce them by a simple projection of the updated variable u k at the end of each iteration (step 23). We use progressive iterate averaging with w(i) = i 3 , but only start averaging after the estimated energy increases for the first time (step 31). We use history size m h = 64 throughout the paper. Code will be released as part of a more general Matlab toolbox for deterministic and stochastic reconstruction.
It has been demonstrated experimentally that maltose, fructose, and galactose produce only small changes in the dielectric properties, at least three times less than the effects of glucose  [29]. Lastly, based on a person's posture, body shape, and tissue distribution, it will be very complicated to associate the same sensor for every diabetic individual without identifying their tissue distribution. In addition, the volume of blood in the measured region affects the data of the sensor. The EM sensor radiates the GHz EM field onto the human-is their use safe? As in mobile phones  [30] [31] [32] [33] [34] , a dosimetry study must be considered. The amount of radiation energy to which the human body is exposed is estimated using the specific absorption rate (SAR). The SAR is determined at the highest certified power level in laboratory conditions.
The Dielectric Properties of a Simulated Tissue Model in the Forearm In several studies, it is important to determine the tissue's dielectric characteristic accurately for EM safety evaluations and to analyze, design, and develop a biosensor that interacts with tissue using an EM near field. Indeed, EM sensor technologies are constantly subject to new constraints and requirements, particularly with respect to their size, frequency, and sensitivity. The tissue's dielectric characteristic was previously studied by the IT'IS foundation  [26]. In this simulation study, we exploited their experimental dielectric results to simulate a model of tissue using COMSOL Multiphysics software. The sensor was placed on the forearm area, and it is composed of several modified split-ring resonators (SRR). A magnetic field was used to feed the resonators.
This behavior, which will be studied, has an impact on the sensor and its SAR. Despite the current development of detailed computational models of specific organs, which are a prerequisite for specific applications, it is considerably difficult to simulate the anatomy of a real forearm considering its accurate dielectric parameters' distribution because of its heterogeneous behavior. Furthermore, those dielectric parameters depend on the frequency for each tissue constituent (see Figure  1 ), which means to study any EM sensor based on the resonance frequency shift, it is very important to simulate the tissue model considering this dependency. All dielectric property data were imported from the IT'IS foundation database to our model tissue on COMSOL software. Blood has a very high value of relative permittivity compared with other media due to its high water content, which means it contains higher energy storage in polarization and magnetization, and its value decreases as the frequency increases.
Part of the Forearm Was Modeled Using COMSOL Multiphysics ® Software To numerically evaluate the effects of exposure to radiation on the forearm, a previously used model that corresponds to overweight people was used. A fat layer of 1 mm was added between the skin layer and muscles with the vein. The model assumes that the dielectric properties of each tissue are spatially uniform and depend on the frequency. The common method of calculating the estimated SAR value in COMSOL is by using the following formula: E SAR = σ |E| 2 ρ ( 7 ) where σ is the conductivity of human tissue and ρ is the density (E| is the norm of the electric field. The SAR unit is W/kg). The boundary condition at the interface is given by n × E = 0 (8).
Setup and Participants The setup for the experiment is shown in Fig. 1  where the two children in the team sit across each other separated by a barrier. Each of them has a touch screen in front, to interact with the application. The humanoid robot (QTrobot) is placed sideways with respect to the participants, to be visible to both. As depicted in Fig. 4 , there are two RGB-D cameras that record the facial streams and one environment camera that films the entire scene. Two lavalier microphones, clipped on the participants, are used to record audio. We use two computers, connected to the screens and the robot, to manage the activity and the synchronous recording of the sensors. On the software side, each participant interacts with an instance of the JUSThink application while a separate robot application is used to manage the robot.
All of the applications communicate via Robot Operating System (ROS). Rosbags are used to record all of the participants' actions (the logs) as well as the robot actions. For more details on the hardware and the software setup, see  [46]. The study was conducted in two international schools in Switzerland over two weeks  3. Although the experimenters were always present in the room, the activity was autonomous with little to no intervention required. A total of 96 students participated ranging from 9 to 12 years old; however, to ensure that data used for the study is complete and nonfaulty across all sensing modalities (i. e. , video, audio and actions logs) as well as homogeneous (e. g. , we excluded a team in which participants were speaking French instead of English to communicate with each other), we omitted 28 students, resulting in a corpus of 68 participants (i.
A common way of calculating affective states, such as valence and arousal, is via the facial action units generated by Open-Face. For positive and negative valence, we build on action units (AUs) that correspond to positive and negative emotions, respectively, based on the findings from IMotions 4  that uses Affectiva 5  for emotion recognition. These findings are also similar to the ones in EmotioNet (  [10] ). Exponential moving average is applied to smoothen the data for each AU followed by taking an average of the AUs belonging to positive and negative emotions for positive and negative valence, respectively. We calculate arousal by taking average of all the AUs above a certain intensity at a given point in time. Regardless of the valence, the absolute value of arousal is calculated to measure the expressivity of a user.
For the smile extraction based on AUs, we base it on the findings from a smile authenticity study conducted by  [42]. OpenFace also generates gaze angles that can be used to determine the eye gaze direction in radians in world coordinates. These angles are averaged for both eyes and are converted into more easy to use format than the gaze vectors. Using these gaze angles, it can be approximated if a person is looking straight ahead, left or right. Lastly, voice activity detection (VAD) through audio stream is done by using the python wrapper for the opensource Google WebRTC Voice Activity Detection. All the audio features listed in Table  1  are computed on the output given by the Google WebRTC VAD. Assessing Forward Clusters: To cluster teams based on their behavior pattern, as captured by the 28 features listed in Table  1 , we first apply Principal Component Analysis (PCA) on the normalized features (we use min-max scaler to transform features by scaling each feature between a range of 0 and 1) which return three principal components (PCs).
The three principal components identified by the PCA account for 50% of the variance within the features dataset, with the fourth component only contributing for 8%. Then, by applying K-means clustering on the three PCs (with K=4 chosen in accordance with the inertia score), we end up with four clusters as shown in Fig. 7  where each cluster represents a different behavioral pattern. As outlined in the opening of this section, to investigate RQ1, we compute the average performance metric and learning outcomes for the teams in the clusters obtained from the analysis of behavioral features as shown in Fig. 8. In the rest of the analysis, we disregard cluster F 2 all since it is composed of only 2 data points. As the figure shows, while the three clusters F 0 all , F 1 all and F 3 all have similar average performance, they significantly differ in terms of learning outcomes, with clusters F 0 all and F 3 all having higher averages than cluster F 1 all (i.
6. 2. 1. Performing PCA and K-means clustering on the log features (first section of Table  1 ), returns 3 clusters along 2 significant PCs (accounting for 55% of the variance within the features dataset, with the fourth component only contributing for 10%) as shown in Fig. 10. The similarity matrix given in Fig. 12  between the backward (on learning outcomes and performance metric) and forward (on ). However, a Kruskal-Wallis test run pairwise for the forward clusters over the learning outcomes shown in Fig. 11  reports no statistically significant difference, with only near-significant results we get are for the pair (F 0 logs , F 2 logs ) (mean_LG_abs: p = 0. 060, mean_LG_rel: p = 0. 065, mean_LG_joint: p = 0. 096). Similarly, following the backward and forward approach when using only the video and audio features (see Figs.
6, support both hypotheses. Briefly, this paper explores the link between engagement and learning and, thus, proposes the concept of Productive Engagement, its validation in an HRI data set, and considerations on its consequences. Firstly, we conclude that there are behavioral features, pertaining to task or/and social engagement, that predict learning outcomes and that these features are sometimes disconnected from performance in the task. To elaborate on the statement, in light of the results in Sect. 3, we observe that the teams that end up achieving a higher learning gain (i. e. , cluster PF -Productive Failure and cluster PS -Productive Success in Fig. 6 ) in the JUSThink activity may or may not apparently perform well in the task itself. However, irrespective of their performance, the way those teams interact with the task and express themselves through speech, facial expressions and gaze is distinct from the behavior of the teams who achieve lower learning gains (i.
The model will, thus, help the robot to answer the question of when to intervene effectively. However, to determine what behavior to induce in the user while designing for effective robot interventions, the next logical step we envision for this research is the characterization of the forward clusters obtained in Sect. 6. 2. 1 in terms of the contributions of the single features, and emerging differences between high-and low-learners. The aim is to acquire a deeper understanding of the link between engagement and learning, and therefore reach a refined and more solid definition for Productive Engagement. Funding Open Access funding provided by EPFL Lausanne. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 765955.
OPTIMIZATION OF THE SUPERCONDUCTING SYSTEM The mass optimization of the superconducting system (superconducting machine and its cooling system) is then achieved by combining electromagnetic and thermal models. The detail of the optimization phase is shown in Fig. 7. We will use a genetic algorithm (GA) in order to find a solution to this highly non-linear problem. The optimization algorithm is the GA implemented in MATLAB. Start with a given cooling temperature, the geometric variables, seen in Table  1 , are randomly generated by the GA. The dimensions of the coil and the cooling temperature will size the Jc and thus the torque, the joule and iron losses as well as the mechanical stresses on the coil are obtained by a semi-analytical model  [17]. The rest of the parameters are used for the thermal and mass calculation of the machine.
Context. As about half of main-sequence stars reside in multiple star systems, it is important to consider the effect of binarity on the evolution of planetesimal belts in these complex systems. Aims. We aim to see whether debris belts evolving in between two stars may be impacted by the presence of the companion and whether this leaves any detectable signature that could be observed with current or future instruments. Methods. We consider a circumprimary parent body (PB) planetesimal belt that is placed just inside the stability limit between the 2 stars and use the state-of-the-art DyCoSS code to follow the coupled dynamical and collisional evolution of the dust produced by this PB belt. We explore several free parameters such as the belt's mass or the binary's mass ratio and orbital eccentricity.
We use the GraTeR package to produce 2-D luminosity maps and system-integrated SEDs Results. We confirm a preliminary result obtained by earlier DyCoSS studies, which is that the coupled effect of collisional activity, binary perturbations and stellar radiation pressure is able to place and maintain a halo of small grains in the dynamically unstable region between the 2 stars. In addition, several prominent spatial structures are identified, notably a single spiral arm stretching all the way from the PB belt to the companion star. We also identify a fainter and more compact disc around the secondary star, which is non-native and feeds off small grains from the unstable halo. Both the halo, spiral arm and secondary disc should be detectable on resolved images by instruments with capacities similar to SPHERE.
Basic principle We use an updated version of the DyCoSS code, whose characteristics we briefly summarize here (for a more thorough description of DyCoSS, see TBO12). DyCoSS was, alongside the relatively similar CGA code  (Stark & Kuchner, 2009) , the first code able to handle, albeit in a simplified way, the coupled effect of violent, fragment-producing collisions and dynamics (gravity and SPR). Its principle is to first run a classical N-body run for large parent bodies not affected by radiation pressure until a dynamical steady-state is reached. Each parent body is then used as a seed from which we generate a cloud of small debris affected by radiation pressure, and we run 10 different "debris" simulations for 10 different starting positions of the companion star on its orbit. In these simulations the density map of the parent bodies is used to estimate a local collisional destruction probability for each daughter particle.
We then record, at regularly spaced time intervals, the position of each surviving particle, until all particles have been removed (either by dynamical perturbations or collisional destruction). Finally, we recombine together these 10 different simulations to produce collisional steady-state maps of the system for different positions of the companion star. The main simplification in the DyCoSS code is that collisions are considered to be perfectly destructive: debris do not produce new debris but are removed after exceeding their estimated collisional lifetime. This constraint has been relaxed in the more sophisticated LIDT-DD code  (Kral et al. , 2013) , based on collisionally interacting "super particles", which is the first (and so far only) fully self-consistent code coupling collisions and dynamics.
Another upgrade concerns the spatial scale of the "debris" runs, which is no longer limited to the circumstellar region but now extends beyond the location of the companion star. We now also take into account the radiation pressure from this companion star. Last but not least, we now couple our collisional code to the state-of-the-art GraTeR radiation transfer code  (Augereau et al. , 1999)  to derive both realistic synthetic images at all wavelengths and global Spectral Energy Distributions (SEDs) of the integrated system. The GraTer package is used for both the contribution of the central star and that of the stellar companion.
7  and 8 ). Interestingly, both the observed bow and our arc-like feature are followed by a sharp density drop in the direction of the companion. Another similarity is that the diffuse axisymmetric halo that starts at the main ring is, for both HR 4796 and our high-stellar-wind case, more extended in the anti-companion direction. There are, however, some important differences. Firstly, the bow structure is located much closer to the primary star in the HR 4796 system, but closer to the companion star in our simulations. Secondly, the curvature of the bow is different: it is concave (when considered from the primary star) on the HR 4796 images but convex in our scenario. Last but not least, it is not clear whether the HR 4796 A & B system is actually a physical binary. Olofsson et al. (2019)  indeed computed the escape velocity of B with respect to A, using the parallaxes and proper motions provided by the second Gaia data release  (Gaia et al.
Nonetheless, based on previous detection and the typical surface brightness of those disks, we can estimate whether the secondary disk could be detected or not if it were at a distance of 20 pc. For instance,  Choquet al. (2017)  detected the extended and faint disk around 49 Cet with SPHERE, and reported a surface brightness of ∼ 150 µJy/arcsec 2 (in total intensity). The face-on disk around TWA 7 was detected with SPHERE at the level of ∼ 100 µJy/arcsec 2 in DPI mode (revised reduction of the dataset presented in  Olofsson et al. , 2018, Ren et al. , in prep, private communication). As demonstrated in  Esposito et al. (2020) , debris disks with surface brightness of the order of hundreds of µJy/arcsec 2 can be detected with second generation instruments such as the Gemini Planet Imager (GPI) in DPI  (Perrin et al.
However, close genomes (typically genomes of strains of the same species) show a high sequence similarity. The mapping of sequencing reads results in infer non-identified haplotypes, defining them as de novo approaches. Additionally, DESMAN operates on a multiple set of sequencing reads. PanPhlan  (Scholz et al. , 2016)  which uses a set of reference genomes and StrainPhlan  (Truong et al. , 2017)  which uses markers from reference genomes are complementary tools providing a gene family presence/absence matrix and strain identification only for the dominant strain, respectively. StrainEst  (Albanese & Donati, 2017)  and DiTASiC  (Fischer, Strauch & Renard, 2017 ) use a set of reference genomes, providing abundance estimation of strains present in the sample. Finally, while designed for metagenomics classification, Kraken2  (Wood, Lu & Langmead, 2019)  and KrakenUniq  (Breitwieser, Baker & Salzberg, 2018) , which can use a custom database of reference genomes, offer meaningful outputs to characterize metagenomic samples.