dataesr/scientific-paragraphs-categorization huggingface dataset:
This allows us to study how the Holocaust reconfigured the structure of the Jewish nuclear family-for example, multigenerational families are common, as are families in which one parent (in particular the father) is absent or the complete absence of family seen in those many individuals living alone-as well as to explore how family structures varied across the city-an important point as Jews coming to Budapest from other parts of Hungary tended to form communities based on where they came from. This last observation allows us to highlight one area of application in which the model we propose may be especially useful: migration studies. The model explicitly maps two elements of the place as we theorized it-location and locale-and does so using a dataset that is analogous to a modern census.
In these two tasks, deep learning using convolutional neural networks (CNNs) on perspective images is the state-of-the-art solution. This is mainly thanks to the emergence of large-scale datasets of perspective images with ground truth annotation, such as CamVid 1 and Cityscapes 2. This convenience is not available for omnidirectional images and motorized two-wheelers. Until now, there is no available dataset of omnidirectional real urban driving images with ground truth for this kind of vehicle. To compensate for this major issue, several contributions on semantic segmentation of fisheye images for the case of cars work on data augmentation by training the state-of-the-art CNNs on perspective images that were deformed with a distortion simulating a fisheye effect 3-5. On the other hand, some researchers proposed to encode directly the omnidirectional representation in the CNN 6.
• Is training networks developed for perspective representation on omnidirectional representations sufficient to have good results? Or, do we need to adapt CNNs for omnidirectional representations? • Do networks learn a universal representation when trained on omnidirectional images? And what are their performances on perspective images in this case? • Do networks with spherical convolutions give better results than the ones with planar convolutions? In order to answer these questions, we conduct several experiments using a set of OmniScape synthetic images with perspective, fisheye, and equirectangular projections of the same scene taken from both front sides of a motorcycle, images from CamVid and Cityscapes dataset, and fisheye images from or real annotated test set. First, we test several semantic segmentation networks on CamVid images and choose the four networks that give the best results.
Finally, the relevance of the two icosahedral-based models is compared to the best planar model for equirectangular images. Studies made on semantic segmentation of real fisheye images rarely present quantitative results, due to the scarcity of dataset that contains omnidirectional urban driving images ground truth. In this study, we present quantitative results in addition to qualitative ones. The remainder of this paper is organized as follows. "Related work" section presents different works on semantic segmentation of omnidirectional images. "The experimental approach" section introduces our experimental approach. "Results and discussions" section presents the results obtained and discusses them. Finally, "Conclusion and future work" section concludes the paper.
Related work Distinct studies were carried out on semantic segmentation of omnidirectional images to compensate for the lack of algorithms dedicated to this type of data, as succinctly presented in this section. Fisheye images. Fisheye cameras have a field of view that can reach 180°. Since CNNs for semantic segmentation are not designed for these images, and due to the scarcity of fisheye datasets with ground truth, researchers worked on the deformation of conventional images from Cityscapes or SYNTHIA  12  , by applying distortion to simulate the fisheye effect  [3] [4] [5] 13. The used distortion is described by r p = f tan(r f /f ) , which rep- resents the mapping from the fisheye image point P f = x f , y f to the perspective image point P p = x p , y p , where r 2 p = x p -u px 2 + y p -u py 2 is the square distance between the image point P p and the principal point U p = u px , u py in the perspective image, and r 2 f = x f -u fx 2 + y f -u fy 2 denotes the square distance between the image point P f and the principal point U f = u fx , u fy in the fisheye image.
This distortion only depends on the focal length f; thus, several focal lengths were set to simulate different fisheye images with their corresponding annotations. Using the images resulting from this transformation, Deng et al. 4  proposed OPP-net based on an Overlapping Pyramid Pooling module. Saez et al. 13  proposed an adaptation of Efficient Residual Factorized Network (ERFNet)  14  to fisheye road images in order to achieve real-time semantic segmentation and tested it on real fisheye images, but only qualitative results were exposed. Deng et al. 5  used the same method to achieve road scene semantic segmentation of fisheye surround-view cameras using restricted deformable convolution. The networks were trained on data from Cityscapes and SYNTHIA datasets and tested on real fisheye images.
Panoramic images. Xu et al. 15  used synthetic images captured from SYNTHIA to create a dataset of panoramic images by stitching images taken from different directions. Using these images, the authors show that panoramic images improve segmentation results. Yang et al. 16  proposed a panoramic annular semantic segmentation framework (PASS), such as the cited works for fisheye images, they made a data augmentation method by adding distortion to perspective images in the training set. They then used planar CNNs after unfolding and partitioning the panoramic images. Ma et al. 17  addressed the problem of semantic segmentation of panoramic images via an unsupervised domain adaptation method from perspective to panoramic images. Orhan et al. 18  achieved the same task as Ma et al. 17  by proposing a network that uses deformable convolution where the offsets Equirectangular images.
In all the experiments, we used RGB images with 15 semantic classes. It is worth noting that all the networks in this study are trained for 300 epochs and from scratch without data augmentation and domain adaptation modules. We directly take the hyper-parameters proposed in their respective publications. We do not fine-tune each network since this is not the purpose of the paper. In all the following experiments, we will use two metrics, the mean accuracy (mAcc) and the mean intersection over union (mIOU). The accuracy and the intersection over union are computed for each class separately, and then averaged over all classes to provide a global mean accuracy and mean intersection over union scores of the semantic segmentation predictions. A single GPU NVIDIA Tesla V100 SXM2 was used in all the experiments.
Networks selection. The goal of this experiment is to choose the four most relevant networks that we will use in the cross-modality. To choose these networks, we made a selection using real perspective images from CamVid Dataset among 11 networks representing different architectures proposed in the state-of-the-art on semantic segmentation of perspective images. The obtained results are listed in Table  1. We trained and tested all the networks on the same sets of 512 × 512 CamVid images. We used 700 images, 420 in the training set, 112 in the validation set, and the remaining 168 images in the test set. The images are segmented into 32 object classes. We mapped similar classes into 15 to have the same classes present in OmniScape. Figure  1  shows a CamVid image with ground truth. Some networks rely on a pre-trained ResNet for feature extraction.
Performance versus number of samples. We used the four selected networks and CamVid dataset with 100 to 700 images, using each time 60% in the training set, 24% in the validation set and 16% images in the test set. Figure  2  shows the evolution of the mAcc and mIoU when the size of the training set increases. We can see that the performance does not improve beyond 500 images. We have decided then to keep using 700 images in all the experiments. Cross-modality experiment. In this experiment, we used 700 captures from OmniScape, 700 images from CamVid, Cityscapes, and 15 images from our real fisheye images test set. The OmniScape Dataset provides synthetic omnidirectional images, namely 360 • equirectangular, fisheye, and catadioptric stereo RGB images from the two front sides of a motorcycle with semantic segmentation and depth map ground truth.
The images in OmniScape are annotated into 15 classes. For Equirectangular representation, we crop the images to keep just 180 • , which represents the front side, so all modalities can be fairly compared to each other. Figure  1  shows OmniScape different modalities used with semantic segmentation ground truth. Our test set contains real fisheye images, we also use these images under the perspective representation with a FOV 126 •. These real fisheye images are captured using the same disposition used in the OmniScape dataset; Stereo fisheye cameras placed in the two front sides of a motorcycle. We annotated 15 different images into 15 classes like the OmniScape dataset, using the open source tool for annotation PixelAnnotationTool  42. Figure  1  shows an example of images from this set with ground truth.
We split the 700 images of each modality like a standard cross validation problem into three sets: a training set of 420 images, a validation set of 112 images, and a test set of 168 images. We trained the four chosen networks on OmniScape images using fisheye, perspective, and 180 • equirectangular images and also CamVid and Cityscapes. Then, we tested all the trained networks on all these modalities, and on our test set of fisheye real images annotated manually. The class Void in CamVid represents far objects that are undefined, and in the OmniScape dataset, it represents the dark space surrounding the fisheye image. In this experiment, we dropped this class and we did not take it into account in the evaluation of the scores because it does not represent a piece of information. In Table  2  are listed the training and test sets along with the networks used in the cross-modality.
Conclusion and future work This paper takes stock of progress made on semantic segmentation of omnidirectional images. We presented a comparative study of semantic segmentation using equirectangular, fisheye, and perspective images, from real and synthetic datasets. By comparing different networks of semantic segmentation, we proved that networks developed for perspective images with planar convolutions when trained on omnidirectional images give good results and they are more robust against modality changes. We also made a comparison using equirectangular images with both planar convolution and different icosahedral-based solutions. The experiments show that planar convolution is better. As we noticed that networks used are sensitive to textures and environment changes, one solution can be to use networks performing image to image translation like pix2pix  45  to generate more realistic images using the OmniScape dataset since we lack datasets of real omnidirectional images with ground truth especially for the case of motorized two-wheelers.
Industry 4. 0 transforms classical industrial systems into more human-centric and digitized systems. Close human-robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, we propose to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and predict human trajectories and react to the motion of the human in order to reduce the occurrence of the collisions. The dataset for training is generated in a real environment in which a human and a robot are sharing their workspace. An Encoder-Decoder based network is proposed to predict the human hand trajectories. A Model Predictive Control (MPC) framework is also proposed, which is able to plan a collision-free trajectory in the shared workspace based on this human motion prediction.
In this paper, we extend our previous work by predicting trajectories of human hands with our online trajectory generation framework  [6]  to ensure a safe and efficient humanrobot collaboration in a shared workspace. The trajectories generated for cobots are fused with the predicted trajectories of a human hand to ensure a collision free environment. The perception module is composed of two parts: (1) detection and localization of the human hand in shared workspace, and (2) prediction of the human hand trajectory. Object detection from an RGB image is extensively studied since the development of deep learning libraries such as Openpose  [7]  and Mediapipe  [8] , which can achieve real-time skeleton detection of a human in a 2D images. However, the Cartesian 3D position of the human is necessary for the generation of the cobot trajectory.
Recently, deep learning based models are getting popular  [11, [21] [22] [23] [24]. Deep learning methods can take unprocessed data such as point clouds and produce human poses with high accuracy  [24]. However, deep learning models are data greedy models and require heavy data for training which are not easily available for human hand prediction, and datasets contain point clouds that are not suitable for real-time applications. Therefore, as a more accessible approach, estimating human poses from RGB images captured by regular cameras and then mapping the 2D information into 3D space is efficient and widely practiced in industry and academia  [6, 9]. The trivial way to estimate 3D human poses is to design an end-to-end network to predict the 3D key-point locations directly from 2D images  [25, 26].
Human Pose Detection The Algorithm 1 gives the set of instructions for collision free trajectory computation. In this section, we explain how to detect a human's position and train the model for prediction. Algorithm 1 Collision free trajectory computation. i ++; Input: U p k , S k , a p k , b p k Output: U k 1: i = 0; 2: while (||U k -U p k || 2 OR i ≤ k) do 3: U p k = U k ; The perception system used in this work is an ASUS Xtion Pro depth camera which provides cloud points, colour and depth images. We can then recover the (x,y,z) position in Cartesian space by combining color and depth image information. The advantage of working with 2D images is that we can directly use existing deep learning libraries such as OpenPose  [39]  or MediaPipe  [8] , which are fast and robust. Mediapipe is widely used for several real-time applications such as tracking  [40] , and sign language understanding  [41].
Experiments and Results As stated in the Introduction, the collision avoidance by predicting human motion prediction has potential applications in the industry. However, there is still no benchmark dataset for experimentation and learning. Therefore, very few works have been reported. In this paper, we generate a dataset with real cobot coordination. The Franka Emika Panda cobot is used for the experimentation. In our previous work, we used a similar cobot for collision detection  [6]. To generate the human hand trajectories, a human is asked to perform some tasks on a shared workspace with the cobot. The human hand moves to several different goals with some patterns with different speed and position; the patterns on which the hand should be moved is shown in Figure  5b , and the overall flow diagram for dataset generation is shown in Figure  5.
Acknowledgments Grant  PID2019-104009RB-I00  funded by  MCIN/AEI/10. 13039/501100011033  is gratefully acknowledged ( Agencia Estatal de Investigación (AEI)  and  Ministerio de Ciencia e Innovación (MCIN), Spain). Grant  T43-20R  financed by the  Aragón Government  is gratefully acknowledged. The authors would like to acknowledge the use of  Servicio General de Apoyo a la Investigación (SAI)  and the use of instrumentation as well as the technical advice provided by the  National Facility ELECMI ICTS, node "Laboratorio de Microscopias Avanzadas" at the University of Zaragoza.
Cerebellar-brain inhibition To probe neural changes within the cerebellum, we measured CBI between the right cerebellum and left M1 using dual-coil TMS. Note that before testing CBI, we first verified that the sole stimulation at the cervicomedullary junction did not elicit descending volleys into the spinal cord (see Data S1, section ''Cervicomedullar output'') and that the TMS parameters were similar between the two groups (see Data S1, section ''Rest motor threshold, MEPtarget, and CS intensity''). Thereafter, we found a modulation of CBI following mental practice, attested by the significant Group*Time interaction (F1,18 = 8. 42, p < 0. 01, hp2 = 0. 32, Figure  3 ). Interestingly, pairwise comparisons with Bonferroni corrections revealed that CBI was no longer present after practice for the MP group (Mean G SD: À9.
Accuracy was defined as the total number of correct sequences achieved per trial. Corticospinal excitability and cerebellar-brain inhibition (CBI) were also assessed in both groups before (Pre-Test) and after (Post-Test) actual trials. Neurophysiological measurements were made at rest, i. e. , the participants remained quiet without performing any task. In addition, CBI was probed during imagined movements for the mental practice group at Pre-Test. During the training session, the control group performed an attentional task, consisting of counting and memorizing a given number of red circles interspersed within white circles. The mental practice group performed 5 blocks of 10 imagined trials. The duration of both tasks was equivalent. Data S1, section ''Corticospinal excitability [Single-coil TMS]''), excluding neural adaptations between M1 and the target muscle after an acute session of mental practice.
Adaptive threshold-hunting technique This method has been recently developed to overcome the potential limitations of conventional pairedpulse TMS protocols, such as large variability in MEP amplitude and a ''floor/ceiling effect'' when the observed inhibition leads to complete MEP suppression  (Cirillo and Byblow, 2016; Van der Bos et al. , 2018). The hunting-threshold was defined as the TS intensity (expressed in percentage of the maximal stimulator output -%MSO) required to elicit the MEP target in the relaxed APB. To do so, we used an online freeware (TMS Motor Threshold Assessment Tool, MTAT 2. 0), based on a maximum-likelihood Parameter Estimation by Sequential Testing. Following previous studies, the software has been parameterized with assessment without a priori information  (Cirillo et al.
ACKNOWLEDGMENTS We thank  Anaı ¨s Gouteron  and  Davy Laroche  for the screening of TMS contraindications. We also thank  Cyril Sirandre  for developing the software that permitted us to perform dual-coil TMS measures. This work was supported by the  French-German ANR program in human and social sciences  (contract:  ANR-17-FRAL-0012-01 ).
Impulse excitation technique Damping ratios were measured based on the results of impulse excitations tests. Those tests were made following the methodology presented earlier  (Mazel and Tchoreloff, 2020; Meynard et al. , 2021). As peak shapes and damping ratios are impacted by the amount of energy given to the system to put it into motion, tablets were all dropped on a hard surface (here, stainless steel) at a fixed height (1 cm height) to minimize the variations of this energy during the experiments. In all the cases, to obtain a single rebound, tablets were dropped on their lateral band. The sample free vibrations induced by the impact were recorded using a microphone MM310 (Microtech Gefell GmbH, Gefell, Germany). The data acquisition system was a DEWE-43 coupled with the software Dewesoft X3 (Dewesoft, Trbovlje, Slovenia).
, 2021). In order to identify the mode shape related to mode A for all tablet dimensions studied, we followed the same methodology as in  (Meynard et al. , 2021)  using an undamped modal analysis. The study was performed using the finite element software Abaqus® 6. 13 (Dassault Systèmes, Vélizy-Villacoublay, France) with the Lanczos algorithm on 3-D free moderately thick cylindrical plates (representing flat-faced cylindrical tablets) on a mesh of about 40,000 nodes. More details on the finite element calculations used can be found in  (Meynard et al. , 2021). It was checked that the first mode observed always corresponded to the mode A for all the tablets with the dimensions studied in this article. The damping ratios ζ A were then characterized for all the tablets studied, using in each case, the first frequency peak of the spectrum.
Insufficient understanding of the mechanism that reversibly converts sulfur into lithium sulfide (Li2S) via soluble polysulfides (PS) hampers the realization of high performance lithium-sulfur (Li-S) cells. Typically Li2S formation is explained by direct electroreduction of a PS to Li2S; however, this is not consistent with the size and shape of the insulating Li2S deposits. Here, we use operando small and wide angle X-ray scattering (SAXS/WAXS) and operando small angle neutron scattering (SANS) to track the growth and dissolution of solid deposits from atomic to sub-micron scales during charge and discharge. Stochastic modelling based on the SANS data allows quantification of the chemical phase evolution during discharge and charge. We show that the deposit is comprised of nanocrystalline Li2S and smaller, solid short-chain PS particles, which we argue are likely Li2S2.
The data is the same as in Fig. 2d  and shown for direct comparison with SANS fits. g, Current signal and fit parameters as a function of time, obtained from PGRF model fits of the operando SANS data in b and c. The parameter V/Vmax corresponds to the relative amount of Li2S/Li2S2 deposit during the experiment. LY and LZ correspond to a correlation length of the Li2S and Li2S2 deposits, respectively. These length values are proportional to the actual particle sizes, which can be seen in d and e. Based on the q-position the particle/aggregate diameters were estimated to be around 26 nm and 2. 8 nm at the end of discharge (see Supplementary Figure  6 ). The Li2S aggregate size clearly increases during discharge, the Li2S2 particle size slightly decreases during discharge, before it increases at the beginning of charge.
On the other hand, the electrolyte determines redissolution and diffusion of the dissolved PSs, which is critical for approaching theoretical capacities. More broadly, the solid-state reduction mechanism from Li2S2 to Li2S indicates solidstate S conversion to be sufficiently facile, despite the poorly conducting nature of Li2S. This implies that solidstate S-to-Li2S conversion (SSC) is possible at practical rates if S/Li2S structures are properly engineered, which is a very important message for all Li-S design strategies seeking to avoid the polysulfide shuttling problem by utilizing SSC, but so far struggled to convert practical S amounts. Next to electron microscopy, electrochemical methods and Raman spectroscopy, the essential tools for these insights are operando SAXS/WAXS, operando SANS with contrast matching, and advanced data analysis using stochastic modeling.
A Biologic SP240potentiostat/galvanostat was used for electrochemical cycling. Operando SAXS/WAXS measurements were carried out on the Austrian SAXS beamline at the Synchrotron ELETTRA  44  (Trieste, Italy) using an X-ray wavelength of 0. 154 nm and a Pilatus 1M SAXS and Pilatus 100K WAXS detector (Dectris, Switzerland). During potentiostatic discharge / charge measurements, SAXS and WAXS patterns were collected with 1 s exposure time (to avoid radiation damage) and 60 s period (to avoid huge amounts of data). We discharged the cell at 2. 0 V vs. Li/Li + for 2. 5 hours (giving a capacity of 1520 mAh gC -1 ) and charged it at 2. 45 V vs. Li/Li + for maximum 2. 5 hours (to reverse the capacity of 1520 mAh gC -1 ). Operando SAXS data shown in Supplementary Figure  2  were recorded on a laboratory SAXS/WAXS instrument (SAXSpoint 2.