dataesr/scientific-paragraphs-categorization huggingface dataset:
All 10 trips were considerably shorter than the mean trip duration (1. 5 hr vs. 10 hr), which supported that these movements were likely not foraging trips. In total, we recorded 111 foraging trips from 50 individuals during incubation, in 31 of which more than one trip was recorded for, and 212 foraging trips from 54 individuals during chick rearing, in 45 of which more than one trip was recorded for. All individuals with multiple trips recorded were personality tested (one individual with a single trip recorded during incubation was not personality tested). To standardize data resolution and to account for occasional missing GPS points, we used adehabitatLT  (Calenge, 2015)  to linearly interpolate tracks to intervals of 10 min during incubation and 2 min during chick rearing. To identify foraging sites from GPS tracks, we classified each GPS point as one of three behavioural states using hidden Markov models (HMMs).
Hidden Markov models are a type of state-space model, which decompose observed time-series data (here, movement) into an observed sequence of discrete behavioural states. Hidden Markov models were fitted using the moveHMM function from the movehmm package  (Michelot, Langrock, & Patterson, 2016) , which we provided with starting parameters informed by previous work using HMMs to describe kittiwake foraging behaviour  (Trevail et al. , 2019; Appendix S5). Based on the distributions of step lengths between GPS points (described by a gamma distribution) and turning angles (described by a von Mises distribution) between consecutive GPS points, HMMs classified each point as one of three behavioural states: foraging, resting or travelling. We used the Viterbi algorithm to estimate the most likely sequence of states to have generated the observed movement patterns  (Zucchini, MacDonald, & Langrock, 2016).
| Data analysis We carried out analyses in r v3. 5. 1 (R Core Team, 2018), using the package lme4  (Bates, Mächler, Bolker, & Walker, 2014)  for linear mixed-effects models (LMMs). To determine statistical significance of fixed effects, we used ANOVA comparisons of models with and without each variable in turn. We checked model assumptions of normality and homoscedasticity by visual inspection of residual plots. To estimate individual boldness, we used a principal component analysis (PCA) to collapse the five behaviour variables into a single test score (PC1). We estimated adjusted repeatability  (Nakagawa & Schielzeth, 2010)  of PC1 using the r package rptr  (Stoffel, Nakagawa, & Schielzeth, 2017) , including fixed effects to adjust for test date, breeding stage (incubation or chick rearing), observer and test number.
Rationale and Aims The FACETS program is not available in all areas of the United Kingdom. Work commitments, mobility or cognitive impairments, rurality and transport issues, or personal preferences might mean digital delivery would be more convenient or appealing for some pwMS. The MS Society was therefore keen to consider alternative delivery models. The project aims were to undertake a consultation to: 1. Gather views from HCPs and pwMS about a Web-based model of service delivery, considering aspects of delivery format and mode. 2. Obtain feedback about how best to adapt FACETS for Web-based delivery (known as cFACETS).
Participants, Recruitment, and Consent We aimed to conduct 8 telephone interviews with HCPs. HCPs were identified via the project lead's networks and via an MS Society database. They were sent an information sheet and a copy of the consent form via email. Before the interview, the consultation coordinator answered any questions they had, checked whether they were willing to be audio-recorded, and explained the consent process. If they wished to take part, their verbal consent was recorded at the start of the telephone consultation. If they preferred not to be audio-recorded, they were given the option of providing written consent, with notes taken instead. We planned to conduct consultation groups with pwMS in 3 UK locations (London, Bristol, and Liverpool). PwMS were identified via a gatekeeper (these were HCPs) at each location.
Overview In total, telephone interviews with 8 HCPs (6 occupational therapists and 2 physiotherapists) took place, 4 of whom were identified via the project lead's networks and 4 from an MS Society database. All HCPs were willing to be audio-recorded. All had been working as HCPs for 10 years or more and had delivered FACETS 72 times in total (mean 9 [SD 3. 6] times, range 4 to 15). Four were based in the community, 3 worked in hospitals, and 1 worked in an inpatient setting. All had attended the FACETS facilitator training provided by the MS Society with the exception of one HCP who had been trained by a FACETS-trained colleague. Interview durations ranged from 36 to 81 min (mean 61 [SD 16. 6] min).
Giunti et al have called for much greater involvement of pwMS and HCPs before digital solutions are implemented  [27, 28]. In a number of health care implementations offered to date, user requirements, existing patterns of use, and HCP reflections have not been considered before or during the development of a solution  [29, 30]. It is imperative that consideration is given to the requirements of pwMS throughout the development, prototyping, and implementation of any digital solutions  [16, 17, 31]. Feedback from this consultation has highlighted the opportunities and challenges of designing for an online audience of pwMS in terms of user requirements, design elements, and structural and pacing considerations. A paced delivery format would mean that group members could work through the cFACETS program as a closed cohort.
Other suggested options for improving engagement included using video and audio elements to either replace or complement different program sections and using appropriately pitched interactive characters-animated therapists have been successfully used in other interventions  [45] -and interactive quizzes and polls. Participants also suggested how a Web-based solution could potentially link with HCPs in terms of delivery of and complementing existing care, alongside the continuation of the successful F2F program. There are potential benefits from enabling clinical teams to interface with an online solution-such as possibilities for remote monitoring and support. HCP involvement in cFACETS would vary depending upon the types of solutions suggested and their resource and workload implications.
Digitizing FACETS also presents challenges in terms of successfully replicating group dynamics-a key program component-and mirroring the pacing of the 6-session format where concepts and materials are introduced in a structured and staged manner allowing time for familiarization, reflection, and practice. Findings from the consultation suggested that consideration be given to the inclusion of a human support aspect to maintain adherence and increase the likelihood of behavior change  [46] [47] [48]. Even acknowledging these challenges, the potential of a digital environment should not be overlooked. Relative to other long-term conditions, MS is currently under-served by health technology. New technology enhancements offer opportunities for personalized electronic health solutions relevant to pwMS and the management of fatigue  [16, 17]  such as voice-activated speakers  [49]  and the ability to collect live biometric data by using plug-in oximeters and wearable monitoring devices (although accuracy still needs improving)  [50].
Acknowledgments ST and PT conceived and designed the study. SC carried out the interviews with HCPs. SC and ST conducted 2 consultation groups and ST conducted 1 consultation group. SC charted the data from the interviews and consultation groups. ST and AP conducted the data analysis. AP led the preparation of the paper. All authors critically reviewed and revised the paper and participated in the final approval. The authors wish to thank all the pwMS and HCPs who took part in the consultation groups and interviews. Funding for the cFACETS consultation was provided by the  MS Society of the United Kingdom.
Determination of Library Concentration To determine the library concentration for each sample, the Kapa Biosystems qPCR Kit (KK4824, Cliniscience, Nanterre, France) was used. For each reaction, the mixture consisted of 12 µL Mastermix, 0. 4 µL ROX dye, 3. 6 µL H 2 0, and 4 µL template (standards or library diluted at 1/10000). The samples were run on an ABI PRISM 7900HT system (High ROX) at 95 • C for 5 min, 35 cycles of 95 • C for 30 s, and 65 • C for 45 s, with data collection followed by a dissociation curve. Standards corresponded to an amplicon of 452 bp, and the amplicon of NPP with a barcode corresponded to 115 bp. The ratio was applied to determine the concentration of each library.
Statistical Analysis The differentially expressed miRNA probe-sets were filtered with query parameters by using a signal threshold, the detection p-value, and the call rate to detect miRNAs (Perseus software)  [22]. The false discovery rate (FDR) was determined with threshold 0. 01 and log2 (fold change <-2 and > +2 or <-2 and >+4) to identify differences in miRNA expression. Heatmaps and hierarchical clustering involved using z-scores transformed from the original normalized values. GraphPad prism software was used for receiver operating characteristics (ROCs) and area under the ROC curve (AUC) determination. The generalized ROC criterion  [23]  with the best linear combination (virtual marker) of miRNA expression is the maximized AUC  [24].
Results To identify miRNAs differentially expressed in JIA versus SA, we analyzed the miRNome in SE and SF from the exploratory cohort (JIA: n = 5; SA: n = 3). Clinical and biological characteristics of participants are summarized in Table  1  and Supplementary Figure  S1. Of the 2083 miRNAs analyzed, >50% were detectable in SF and SE samples with CPM ≥20. To address differences in SE and SF miRNA levels between SA and JIA patients, data were analyzed by principal component analysis (PCA) and hierarchical clustering methods (Figure  1 ). PCA of all detectable miRNAs showed a separation between SA and JIA when quantified in SF samples (Figure  1A ), which suggests specific disease-related changes in miRNA expression within inflamed joints. However, PCA of SE samples less clearly separated overall SA and JIA patterns.
An SF Combination of Three miRNAs Differentiates JIA from SA at Initial Presentation To further validate our results with another detection technique that is easy to perform in the clinic, we measured the expression of the 19 miRNAs identified in SF by using RT-qPCR analysis of a validation cohort (n = 9/group). Four SF miRNAs (miR-146a-5p, miR-150-5p, miR-155, and miR-342-5p) were significantly upregulated at p < 0. 01 in JIA versus SA and 1 (miR-6764-5p) was significantly downregulated (Figure  3A ). Next, we used miRNA expression levels in SF as biological indicators for JIA and performed an ROC analysis to determine AUC values. AUC values were >0. 9 for miR-155, miR-150-5p, miR-146a-5p, miR-6764-5p, and miR-342-5p, which suggests high diagnostic performance. To optimize the classification performance of miRNA levels in SF, we determined a new threshold by using mROC software.
Funding: This research was funded by  INSERM (Institut National de la Santé et Recherche Médicale) , the  University of Montpellier , the  Arthritis Fondation Courtin , N. N. received a fellowship from  Arthritis R&D  (convention Cifre  N • 2015/1454 ), SPILF grant, the  Montpellier University Hospital Centre  ( AOI 466 ARTEMIS UF9361-2  to EJ) and the  Fondation de l'Avenir  (N •  AP-RMA-16-033 ).
Snow and sea ice data Every 2-3 days a sample site was chosen at an undisturbed location randomly selected within a 1. 2-km radius throughout the project. At the sample site, 5-8 snow depth measurements were made with a metal ruler. A minimum of four ice cores were collected using an ice corer (2015: Kovacs Mark V 14-cm diameter corer for nutrients and algal biomass, Kovacs Mark IV 9-cm diameter corer for temperature and salinity; 2016: Kovacs Mark IV 9-cm diameter corer). The sea ice thickness and freeboard, i. e. , the height of the sea ice surface from the sea surface, were measured through the ice core holes using a thickness gauge (Kovacs Enterprise). Two full ice cores were sampled as described in  Miller et al. (2015)  to measure vertical profiles of temperature and bulk salinity. Ice temperature was measured at 10-cm intervals using a high-precision thermometer (Testo 720; ±0.
The day of the snow melt onset was identified as the date after which the snow thickness stopped increasing and when the air temperature approached 0°C during the day. A positive air temperature was associated with a strong decrease in the near-infrared (1000 nm) albedo (measured every sampling day with a custom-built radiometer, Solalb, LGGE), making it a good indicator for snow melt onset in our case (see  Verin et al. , 2019 , for more details). Sea ice concentration was inferred from the satellite AMSR2 radiometer dataset on a 3. 125-km grid  (Spreen et al. , 2008). The closest pixel of sea ice concentration was less than 500 m from the study site. The sea ice breakup was determined as the date when the sea ice concentration was below 80% and when major cracks appeared around the study site, i.
Optical measurements In-water multispectral radiometric data were acquired every 2-3 days using an underwater spectroradiometer (IcePRO, Biospherical Instrument Inc. ). The IcePRO is a modified version of the C-OPS made by the same manufacturer  (Compact Optical Profiling System; Hooker et al. , 2013)  to measure downwelling irradiance (E d (z,λ)) and upwelling radiance (L u (z,λ)) under the ice to a depth z of up to 100 m, together with above surface downwelling irradiance E d (0 + ,λ). Photosynthetically available radiation (PAR) at each depth (PAR(z), mol photons m -2 s -1 ) was calculated as follows: ( ) ( ) λ λ = ∫ 700 400 , d PAR z E z d Daily photosynthetically available radiation (PAR 24h (z), mol photons m -2 d -1 ) in the water column was calculated by multiplying surface PAR 24h (0 + ) by the instantaneous transmittance, t(z), profile calculated from the C-OPS as follows: ( ) ( ) ( ) = + × 24 24 0 h h PAR z PAR t z where t(z) = PAR(z)/PAR(0 + ).
Hydrographical data and methods Conductivity, temperature and depth (CTD) profiles were collected using a Sea-Bird Electronics 19plus V2 CTD system (factory calibrated prior to the expedition) deployed from a 1-m 2 hole in the ice under a tent (Polarhaven, Weatherhaven) from the surface water down to 350 m. The data were post-processed according to the standard procedures recommended by the manufacturer and averaged into 1-m vertical bins. Ocean current profiles in the water column were measured using a downward looking 300 kHz Sentinel Workhorse Acoustic Doppler Current Profiler (ADCP) by RDI Teledyne mounted just beneath the sea ice bottom through a 25-cm auger hole. The first valid bin was located at a depth of about 4 m; subsequent bins were spaced with a 2-m vertical resolution and data were collected every 30 min.
Acknowledgements This project would not have been possible without the support of the  Hamlet of Qikiqtarjuaq  and the members of the community as well as the  Inuksuit School and its Principal Jacqueline Arsenault. The community contributed to the scientific campaign by facilitating logistical support and sharing local knowledge. The project is conducted under the scientific coordination of the  Canada Excellence Research Chair in Remote Sensing of Canada's New Art. 34, page 17 of 22 Arctic frontier and the  CNRS  and  Université Laval Takuvik Joint International laboratory  ( UMI3376 ). We thank  Françoise Pinczon du Sel  and  Eric Brossier  from  Vagabond  for assistance in the field and  Yves Christen  for fixing our valuable snowmobiles. We also thank  Guislain Bécu  (Takuvik),  Pascal Guillot  ( UQAR-ISMER ),  Shawn Mederic  ( Amundsen Science ) for the data processing and quality control of the C-OPS, CTD and ADCP respectively.
Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance.
Introduction In the last two decades, background subtraction for video taken by static cameras has been one of the most active research topics in computer vision due to a big amount of applications including intelligent surveillance as human activities in public spaces, traffic monitoring and industrial machine vision  [16, 17, 20, 21, 96]. A big variety of models was used for background/foreground separation such as statistical models  [49, 150, 180, 192] , fuzzy models  [6, 8, 15] , subspace learning models  [47, 53, 146] , robust PCA models  [86, 87, 94, 95, 97] , and neural networks models  [154, 156, 164]. Similarly as PCA models which renewed interest for this task due to the theoretical advances in robust PCA made in 2009 by Candès et al. [26]  after an empty period, neural networks progressively renews interest in this field since 2014  [213]  due to the practical advances in deep neural networks which are currently usable due to the availability of large-scale datasets  [62]   [203]  for the training, and the progress in computational hardware ability  1.
The background is automatically modeled through the neurons weights of the network. Each pixel is represented by a neural map with n × n weight vectors. The weights vectors of the neurons are initialized with the corresponding color pixel values using the HSV color space. Once the model is initialized, each new pixel information from a new video frame is compared to its current model to determine if the pixel corresponds to the background or to the foreground. In further works, SOBS was improved in several variants such as Multivalued SOBS  [124] , SC-SOBS  [126] , 3dSOBS+  [128] , Simplified SOM  [27] , Neural-Fuzzy SOM  [28]  and MILSOBS  [59] ) which allow this method to be in the leader methods on the CDnet 2012 dataset  [62]  during a long time. SOBS show only interesting performance for stopped object detection  [123, 125, 127].
But, one of the main disadvantages of SOBS based methods is the need to manual adjust at least four parameters. Recently, deep learning methods based on Deep Neural Networks (DNNs) with Convolutional Neural Networks (CNNs also called ConvNets) allow to alleviate the disadvantages of these previous approaches based on conventional neural networks  [163]   [118]   [66]. While CNNs existed for a long time, their success and then their use in computer vision was limited during a long period due to the size of the available training sets, the size of the considered networks, and the computational capacity. The breakthrough was made by Krizhevsky et al. [104]  who used a supervised training of a large network with 8 layers and millions of parameters on the ImageNet dataset  [45]  with 1 million training images.
in 2016  showing the big interest of deep neural networks in this field. Furthermore, each new method is in the top algorithms on the CDnet 2014 dataset by offering a big gap of performance compared to conventional approaches. In addition, deep neural networks was also employed in background initialization, foreground detection enhancement, ground-truth generation and deep learned features showing its potential in all the field of background subtraction. -In this context, we provide an exhaustive comparative survey regarding DNNs approaches used in the field of background background initialization, background subtraction, foreground detection and features. For this, we compare them in terms of architecture and performance. The rest of this paper is as follows. First, we provide in Section 2 a short reminder on the different key points in deep neural networks for novices.
In Section 3, we review the methods based on deep neural networks for background generation in video. In Section 4, we provide the methods based on deep neural networks for background subtraction with a full comparative overview in terms of architecture and challenges. In Section 5, deep learned features in this field are surveyed. In addition, we also provide a discussion about the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset in Section 7, and concluding remarks are given in Section 8. 2 Deep Neural Networks: A Short Overview.
Features Aspects As seen in the previous part, DNNs are determined by their architecture that becomes more and more sophisticated over time. Practically, an architecture consists of different layers classified as input layer, hidden layer and output layer. Each layers contains a number of neurons that are activated or not following an activation function. This activation function can be viewed as the mapping of the input to the output via a nonlinear transform function at each node. In literature, different activation functions can be found as the sigmoid function  [48] , Rectified Linear Unit (ReLU)  [148] , and Probabilistic ReLU (PReLU)  [74]. Once the architecture is determined and the activation function is chosen, the DNN need to be trained using a large-scale dataset such as ImageNet dataset  [104] , CIFAR-10 dataset and ILSVRC 2015 dataset for classification tasks.
For this, the architecture is exposed to the training dataset to learn the weights of each neurons in each layer. The parameters are learned via a cost function that are minimized on the desired output and the predicted one. The most common method for training is the back-propagation. Usually, the gradient of the error function computed on the correct output and the predicted one is propagated back to the beginning of the network in order to update its parameters. For this, it requires a gradient descent algorithm. Batch normalization which normalizes mini-batches can also be used to accelerate learning because it employs higher learning rates, and also regularizes the learning. For vocabulary, an epoch is a complete pass through a given dataset, and thus is the number of time where the neural network has been exposed to every record of the dataset once.
Theoretical Aspects Theoretical aspects concern mainly the understanding and the provability of DNNs  [145, 194, 195, 219] , but also their properties in presence of adversarial perturbations  [34, 137, 138, 139, 140, 186, 231] , and their robustness in presence of noisy labels  [189]. For this, the principle key features to design DNNs need to be mathematically investigated as follows  [194, 195] : -Architecture: The number, the size and the type of the layers are key characteristics of an architecture as well as the classes of functions that can be approximated by a feed-forward neural network. The key issue is how the chosen architecture impact expressiveness. -Optimization: It concerns the way to train the DNNs. This issue contains two aspects which are the datasets used for the training, and mostly the algorithm to optimize the network.