dataesr/scientific-paragraphs-categorization huggingface dataset:
Temperature recovery In addition to the measurements list, a header is inserted in order to record information about the product. The header is illustrated with below example. The fictive product: • Name: "sorbet cassis 550 mL" -Size: 20 bytes equal to 20 characters. • Lot number: "3 574 100 075 093" -Size: 6 bytes. • Optimal temperature: 25°C -Size: 1 byte. • Maximal temperature: 30°C -Size: 1 byte. So as to easily read the information sent by the tag, a treatment is required on the phone. To do it, a Python's script is used because it is an ideal language to develop a prototype. In this first prototype, it is realized on a computer because Python is not implemented on our phone. Nevertheless, in future version, an Android application will be created to conjugate the reading and treatment.
The process of this treatment is based on two operations: the header reading and the shaping of the temperature derivations list around the optimal value. In the first step, the program read the header with the name product, its lot number, etc. and print them on the user's screen. For instead as previously, the identity of the product is rediscovered (Fig. 5 ). The following figure shows the result after processing the read values in the RFID sensor memory. Data have been retrieved from the cell phone and sent to a computer to then process them with a Python software. In the other step, the software extracts, thanks to the values number in the memory, the list of temperature derivations and then calculates the real temperature by reversing the conversion shown above (Fig. 6 ). Moreover, it formats in a graphic these values in order to easily represents a change in temperature.
The BAT uses picture and music stimuli to evoke situations and feelings related to adult attachment, such as loss, fear, parent-children relationships, or romantic relationships. It sits well within the psychometric tradition of projective tests, such as the Thematic Apperception Test  [12]. In 2019, we presented a machine learning methodology to automatically score the BAT using a small training data set, and we validated the use of a remote photoplethysmography (RPPG) algorithm to measure HRV in a contactless fashion as part of the BAT software  [13]. We have now renamed our test to the Multimodal Developmental Profile (MDP), because we hypothesize its stimuli and design can work for measuring not only adult attachment, but also several other dimensions of psychological functioning that are developmental in nature and crucial to the forming of psychopathology  [14].
Furthermore, the order of the stimuli themselves is such that stress and soothing themes are alternated, allowing us to get more contrast in our measurements of what each stimulus is doing to the person. A simple way of conceptualizing the MDP is as a series of dependent experiments. Each stimulus intends to evoke a certain range of reactions on its own but is also linked to the reactions that the next stimulus intends to evoke. For example, stimulus 11 will attempt to provoke fear, and stimulus 12 will attempt to evoke loss, whereas stimulus 13 will evoke a soothing comforting experience of human connection. We will be interested in the reactions to each of those stimuli separately, but we will, more importantly, be interested in the relationship between them, for example, "If the person was upset by the first 2 stimuli, were they able to calm down during the last one?" As the participant perceives the stimuli and responds aloud to them, the software automatically collects video and audio data and automatically extracts features from them.
Data Preparation All data preparation was performed in MATLAB 2021b (MathWorks). The MDP outputs a set of CSV files containing the structured data for each sense modality (facial expressions, linguistic analysis, etc). In most cases, this comes in the form of a table containing the timestamps as rows and the features as columns. We averaged each feature per stimulus (ie, an average of values for facial action unit 10 from the moment stimulus 3 was shown till the moment it disappeared). We discounted the first stimulus's results, the neutral one (see previous section), from all others so that we dealt solely with the variance produced by the test itself. Features were scaled to the -1 to 1 range, using either previous knowledge about the actual signal's minimum and maximum values, or the empirical minimum and maximum levels found within the signal in all our training samples for a given fold.
DERS-16 scores were also linearly scaled, to the 0-1 range, to allow for quicker training times and easier interpretation of results. An important step in our data preparation procedure was to uniformize our training sample with regards to the ground truth (ie, DERS-16 scores) so that all levels of the ground truth could be equally represented in terms of the number of samples being fed to our learning algorithm. Our code did this by binning the DERS-16 score, and up-sampling our data set until all bins (ie, all score levels) had the same number of cases representing them. This, of course, presented the problem of potentially overfitting these repeated cases. In the section about test time data augmentation, we present how we dealt with this problem.
RenderX The approach we used for creating the multimodal codexes is similar, yet it differs from DeepInsight's approach in that we implement a more modern and reliable dimensionality reduction method, the Uniform Manifold Approximation and Projection (UMAP)  [20]. Its strength is to better preserve the global structure of the data and thus the relationship between the features. In addition, we apply this procedure to a very specific kind of tabular data (multimodal sensing data). To the best of our knowledge, this has not been proposed before. Our proposed method to missing data imputation can be described by the following pseudocode: For each feature in the data set,  (1)  produce an image by disposing each feature vector in the dataset, EXCEPT the current one, as pixels in a grayscale image, with the intensity of the feature representing the pixel's intensity;  (2)  feed the created picture for each participant to a simple CNN consisting of 2 convolutional layers and a dense layer, the mission of which is to find visual patterns in the projected data that can predict the left-out feature; and (3) use the created model to predict the missing values corresponding to that feature.
In classical statistics, it is known that small samples should be fitted with models using relatively few degrees of freedom (ie, parameters) if one wants to prevent overfitting the training set. Typically, the best practice ratio is 10 to 1; ie, 10 times fewer degrees of freedom than data available. While that ideal might be too stringent when ported to modern machine learning, we still thought it was vital to keep it as a guiding principle. The fewer parameters we used, the least the network could overfit the data. Hence, our utilization of these layers. SGELU activation was recently introduced in a previous study by Yu et al  [22]. Yu et al took advantage of the already powerful GELU function, which represents nonlinearity by using the stochastic regularizer on an input (the cumulative distribution function derived from the Gaussian error function), which has shown several advantages over other activation functions and is currently implemented in modern natural language processing (NLP) transformer models.
Principal Findings Can computers detect emotion dysregulation in adults, by looking at their behavior and physiology during a set of controlled experiments? Can they generate "mental images" containing different sense modalities, like clinicians do? Can they do so in a sample that spans different cultures and languages? Can one train a deep multimodal fusion neural network using only a couple of thousand parameters? These are some of the questions we set out to answer in this work. This study evaluated the convergence validity of MDP's emotion XSL • FO RenderX dysregulation estimation with regard to DERS-16, a brief version of the "gold standard" measure for emotion dysregulation. We interpret our results as excellent evidence for convergence validity between MDP's emotion dysregulation estimation and the DERS-16 in our sample, suggesting that scores obtained using the MDP are valid measures of emotion dysregulation in adults.
It is important to reflect on the diversity of our sample. It spanned 3 continents and 2 languages, with a broad age range, and included individuals with psychopathology to represent the higher end of the emotion dysregulation spectrum. With that in mind, we believe it is impressive that emotion dysregulation estimations were so correlated with their DERS-16 counterparts for all folds, showing similar results. We think this shows a preliminary form of cross-cultural validity for the approach, adding to the evidence we found in our prior work  [13]. It also shows that the MDP is capable of assessing emotion dysregulation in adults with a psychopathology. We think the multimodal codex approach captures quite well the mental processes that occur in the mind of a clinician while conducting an assessment interview.
We attribute the success of our approach in large part to the good framing of the problem as spatiotemporal, and believe this representation of all sense modalities as a combined image is closer to the way we humans do multimodal fusion. To our knowledge, the MDP is the first test of its kind. It is a validated exposure-based psychometric test that implements deep multimodal fusion to analyze responses within a set of controlled experiments in order to measure psychological constructs. Its advantages over classical questionnaires and interview-based tests are manifold. They are as follows: the MDP takes less than 10 minutes to complete; it can be taken at home with a computer or tablet and is resilient to unpredictable variability in the test conditions; it is scored automatically in minutes; it is objective and replicable in its observations; it is holistic, taking into account language, voluntary and involuntary behavior, and physiology; it can be used in different cultures with only minimal translation efforts; and it can evolve over time, learning new scoring models based on different validated psychometric measures.
We think that at the heart of this concurrent view of machine learning, there is chaos in the form of randomness. Random noise has been added to our samples as data augmentation. There are random paths toward minima spearheaded by an increase in stochasticity due to small batches during training. There is randomness during prediction by implementing TTA. There is randomness in the random initialization of each kernel within each convolutional block, and the way the FMAP layers force them to ensemble. There is randomness in the automatic choice of the stimulus from the stimuli pool so that no single person experiences the exact same stimuli set. There is randomness in the random errors that occur in pretty much every one of the feature extraction processes implemented by the MDP software. Randomness might seem to be just noise, but what if, in reality, it is what allows us to separate signal from noise?.
Acknowledgments We want to thank  Gwenaëlle Persiaux  for her recruiting efforts in Lyon, France;  Nahed Boukadida  for her recruiting efforts in Tunisia;  Susana Tereno ,  Carole Marchand ,  Eva Hanras , and  Clara Falala-Séchet  for their recruiting efforts in Paris, France; and  Khalid Kalalou  and  Dominique Januel  for their recruiting efforts at  Etablissement Public De Santé Ville-Evrard in Saint-Denis, France. Funding for this publication (fees) was provided by  FP  and the  University of Bourgogne Franche-Compte.
The second term in Eq. (  52 ) is the high-energy contribution [∆α  (5)  had (-M 2 Z ) -∆α  (5)  had (-Q 2 0 )]. To estimate it, we follow Jegerlehner's idea  [ 17 ]  of utilizing the Adler function D(Q 2 ) defined in Eq. (  4 ). For sufficiently large Q 2 , the Adler function is calculable within perturbative QCD (pQCD) plus minor non-perturbative (NP) corrections  [ 16 , 135 ]. Our implementation of this approach, which we call pQCD  [Adler] , is based on the public code pQCDAdler by Jegerlehner  [ 136 ]. It takes into account full three-loop QCD with charm and bottom quark mass effects as well as massless four-and five-loop effects to improve high-energy tails. The code also accounts for the NP corrections via the operator product expansion and Padé approximants. We note that the pQCD  [Adler]  approach does not rely on the R-ratio integral, and does not suffer from the systematics of the cross-section data.
Left: The higher energy contribution [∆α (5) had (-M 2 Z ) -∆α (5) had (-Q 2 0 )] computed via the pQCD approach in Eq. (  53 ) using the pQCDAdler software package  [ 136 ]. Right: Results based on the KNT18[data] approach of Eq. (  54 ) using the R-ratio data with full covariance matrix  [ 31 , 133 ]. The red symbols in each panel are taken to produce the final estimates for each method, while the maxima and minima of the blue bands within the non-shaded region are used to estimate the uncertainty. panel the contribution from [∆α  (5)  had (-M 2 Z ) -∆α  (5)  had (-Q 2 0 )] has been determined in perturbative QCD via the Adler function (pQCD  [Adler] ), while in the right panel the same quantity has been evaluated using the R-ratio data and correlation matrix from KNT18 in Eq. ( 54 ). The blue bands represent the total error obtained by adding in quadrature all uncertainties that enter Eq.
Our result can thus replace estimates from the data-driven approach in studies that apply the running with energy to determine the electroweak mixing angle θ W in the Thomson limit, with the understanding that current lattice QCD results are limited to Q 2 ≈ 7 GeV 2. In the following, we compare our data with previous results in the literature. Phenomenological estimates of ∆ had sin 2 θ W (-Q 2 ) can be obtained by applying Jegerlehner's alphaQEDc19 software package  [ 129 ] , see Ref. [ 12 ]. Since alphaQEDc19 provides ∆α had and ∆α 2,had as primary quantities with their respective error estimates, we perform the comparison with our lattice determination for the running of the SU(2) gauge coupling α 2. As explained in Ref. [ 12 ] , the software package implements a modified flavor separation scheme assuming SU(3) flavor symmetry  [ 58 ] , which differs from the "perturbative" separation scheme advocated in Refs.
Acknowledgements We thank  Gilberto Colangelo ,  Jens Erler ,  Rodolfo Ferro-Hernández ,  Tim Harris ,  Martin Hoferichter ,  Fred Jegerlehner ,  Daniel Mohler ,  Massimo Passera ,  Thomas Teubner  and  Arianna Toniato  for valuable discussions. Further thanks go to  Simon Kuberski  for the determination of auxiliary data that were used in our analysis. We are grateful to  Bogdan Malaescu  and the authors of Refs. [ 11 , 132 ] for sharing their data for the running of α at Q 2 > 0. We thank  Alex Keshavarzi  and the authors of Refs. [ 31 , 133 ] for sharing the R-ratio data with covariance matrix, which were used to calculate tabulated data for the running of α at Q 2 > 0. Calculations for this project have been performed on the HPC clusters Clover and HIMster-II at  Helmholtz Institute Mainz and Mogon-II at Johannes Gutenberg-Universität (JGU) Mainz , on the HPC systems JUQUEEN and JUWELS at  Jülich Supercomputing Centre (JSC) , and on  Hazel Hen at Höchstleistungsrechenzentrum Stuttgart (HLRS)  [  141  ].
In this work, a methodology for the design and validation of a radiation monitoring (RadMON) system for electronic systems in particle accelerators is presented. The methodology expands the common radiation hardness assurance (RHA) procedure implemented at CERN, including new steps dedicated to both system-level testing, focused on a wireless device, and sensors characterization and readout validation. A case study demonstrating the validity of this methodology is proposed with the qualification of a novel battery-powered wireless RadMON system. This system not only represents the validation vehicle of the methodology but also an innovation in terms of monitoring platform due to its flexibility and improved capabilities. The application of this methodology allowed its full qualification, providing useful data in terms of resistance to radiation, lifetime, and failure rate in operation, demonstrating the validity of the testing strategy proposed in the article.
A. Component Qualification At CERN, as is the case for this monitoring system, systems can be highly distributed, and thus, the use of qualified COTS  [22]  components is the preferred solution. The CERN RHA standard already provides valuable information on component test methodologies, and the CERN database provides a wide selection of qualified components for a broad range of applications. However, very few low power and wireless applications were developed at CERN in the previous years, and thus, few suitable components were available in the database for such applications. In addition, test methodologies may not be adapted to them. Low power components can embed several internal features such as power saving, different operational or sleeps modes, and also work with much lower internal biasing.
From the data obtained from component level qualification, it was known that the two components sensitive to HeH were the Transmission and the Controller subsystems. Thus, one BatMon was irradiated by having in beam the Controller with the Transmission and the Power Management Subsystems (BatMon1) while the other having Controller with the Recovery and the Storage Subsystems (BatMon2). As anticipated in Section III-C, a hybrid solution was exploited ramping up the flux after a certain TID. During the test, the MCU was allowed to check all the subsystem functionalities helping to solve observability problems. In addition, the reset line and the power rails were checked using an oscilloscope. The different runs are reported in Table  III  with the corresponding cross section evaluated for each system.
The data presented in this article will be representative of the deployed parts in an initial deployment phase, where no more than 30 samples will be involved. Therefore, the number of samples tested is sufficient to ensure the reliability of the system. In the future, larger-scale production and deployment are expected, so testing will continue at CHARM and involve more samples. Nowadays, BatMons are fully qualified and are used for real measurements in super proton synchrotron (SPS). In the future, they will be installed in the LHC where the LoRa Network is available. Improved version of the system in terms of sensors and Main-Board, that will solve the weakness highlighted thanks to the test approach provided by the proposed methodology, are in development.
Multi-camera systems were recently introduced into laparoscopy to increase the narrow field of view of the surgeon. The video streams are stitched together to create a panorama that is easier for the surgeon to comprehend. Multi-camera prototypes for laparoscopy use quite basic algorithms and have only been evaluated on simple laparoscopic scenarios. The more recent stateof-the-art algorithms, mainly designed for the smartphone industry, have not yet been evaluated in laparoscopic conditions. We developed a simulated environment to generate a dataset of multi-view images displaying a wide range of laparoscopic situations, which is adaptable to any multi-camera system. We evaluated classical and state-of-the-art image stitching techniques used in non-medical applications on this dataset, including one unsupervised deep learning approach.
We show that classical techniques that use global homography fail to provide a clinically satisfactory rendering and that even the most recent techniques, despite providing high quality panorama images in non-medical situations, may suffer from poor alignment or severe distortions in simulated laparoscopic scenarios. We highlight the main advantages and flaws of each algorithm within a laparoscopic context, identify the main remaining challenges that are specific to laparoscopy, and propose methods to improve these approaches. We provide public access to the simulated environment and dataset.
Analysis In the literature, no image stitching algorithm has been tested in realistic conditions, e. g. , with organs and surgical instruments. The state-of-the-art stitching techniques for non-medical applications have also not yet been tested in laparoscopy. In parallel, publicly available datasets for the evaluation of these algorithms in laparoscopy are still missing. The closest publicly available dataset is the "Hamlyn Centre laparoscopic/endoscopic dataset", which gathers publicly available stereoscopic recordings of laparoscopy. However, in a stereoscopic endoscope, the cameras are very close to each other in order to perform 3D reconstruction. These images exhibit almost no parallax, making this dataset unsuitable for the evaluation of image stitching in multi-camera prototypes. It is also not possible to simulate a multi-camera system by combining two images from this dataset that were taken at different time frames from the same camera.
Goals and Contributions In light of this analysis, we defined two main goals highlighted in bold below. In Section 2, we review the main state-of-the-art techniques in the image stitching literature. In Section 3, we present a simulated environment that generates realistic images, viewed from a multi-camera prototype, in a wide range of typical laparoscopic scenarios. In Sections 4 and 5, we evaluate classical (i. e. , currently used in laparoscopic multi-camera prototypes) and state-of-the-art image stitching methods from the non-medical literature using our newly created dataset. Finally, we provide, in Section 6, some insights into the main limitations of current and state-of-the-art algorithms that are applied to laparoscopy. To our knowledge, this paper is the first to propose such an evaluation.
Apart from these mesh-based techniques, some non-mesh-based models directly compute 2D deformations on the whole overlap of the images. In  [27] , later referred to as the Perazzi method, optical flow was computed in the overlapping area to warp and align the images. This deformation was then smoothly extrapolated into non-overlap areas to avoid discontinuities. Finally, very recent methods use deep learning approaches to solve the image stitching problem. Some works  [28] [29] [30]  only focused on the homography estimation problem using supervised and unsupervised deep learning methods, while others  [31] [32] [33]  later developed supervised neural architectures for the whole image stitching pipeline. However, supervised approaches are not optimal since they are trained using synthetic ground truth datasets.
These datasets do not offer a complete equivalent for multi-camera scenarios as they do not reconstruct a reference panorama, but rather they are generated using an artificial camera point of view that encompasses the whole equivalent panorama FoV, which is not perfectly representative of the parallax error. This is later referred to as a "non-parallax" view. A very recent article proposed an unsupervised deep image stitching framework  [34] , later referred to as the UDIS method, for image stitching that does not require a supervised synthetic dataset. The UDIS framework is composed of: (a) an initial neural network for the global homography estimation and (b) a second architecture for alignment refinement with reconstruction networks. Out of all deep learning approaches, we focus on this unsupervised approach since it is the most recent and promising method.