dataesr/scientific-paragraphs-categorization huggingface dataset:
The results were unchanged unless otherwise noted. To avoid arbitrary age-related grouping criteria, key analyses employed age as a continuous variable. However, for illustrative purposes only, we also present data dividing  Participants were told that some symbols might lead to winning more often than others. They were asked to try to discover which option was more advantageous by trial and error. Symbols were presented in fixed pairs, for 24 trials. There were four pairs of symbols in three conditions. In the asymmetric condition, one symbol led to winning more frequently than the other throughout the session. In the reversal condition, one symbol led to winning more frequently than the other for the first half of the trials, but these contingencies reversed in the second half. In the symmetric condition, both symbols were equally advantageous.
5. OOTs. In all trials, we recorded the time spent observing the outcomes of choices. This is the time between when the outcomes were presented and when participants pressed the same key they used to make their choice to pass to the next trial. Both time measures were log-transformed as this better approximated a normal distribution. These variables allow to assess the secondary hypothesis that age-related changes in RL are associated with changes in deliberation times or impulsivity. To retain all data points whilst accounting for non-independence of observations (e. g. , the fact that multiple responses came from the same participant), these five DVs were analysed using separate generalized mixed effects models (GLMMs), the details of which are described in the Supplementary Material (Supplementary Material 2).
We also assessed protected exceedance probabilities, which correct exceedance probabilities for the possibility that the observed differences in model evidence are due to chance. We used Aikaike weights for the calculation and forwarded them into the function VB_bms from the package bmsR (https://github. com/mattelisi/bmsR/). Finally, after model comparison, we also assessed parameter and model recoverability  (Wilson & Collins, 2019).
ACKNOWLEDGEMENTS S. -J. B. is funded by  Wellcome  (Grant Number  WT107496/Z/15/Z ), the  Jacobs Foundation , the  Wellspring Foundation  and the  University of Cambridge. S. P. is supported by the  Agence National de la Recherche  (CogFinAgent:  ANR-21-CE23-0002-02 ;  RELATIVE :  ANR-21-CE37-0008-01 ;  RANGE :  ANR-21-CE28-0024-01 ) and the  Departement d'Etudes Cognitives  ( FrontCog :  ANR-17-EURE-0017 ). E. J. K. was supported by an  MRC  studentship.
We proposed a solution based on I3D and optical flow to learn common characteristics between thermal and visible videos. For this purpose we proposed a new database to evaluate our solution. The new model comprises an optical flow extractor; a feature extractor based on I3D, a domain classifier, and an activity recognition classifier. We learn invariant characteristics computed from the optical flow. We have simulated several source domains, and we have shown that it is possible to obtain excellent results on a modality that was not used during the training. Such techniques can be used when there is only one source and one target domain.
Thus, many comprehensive databases are available for these modalities. On the other hand, only a few data exist for thermal images and even less for low-resolution ones. The main dataset on thermal images are  (Vadivelu et al. , 2016) ,  (Peñafort-Asturiano et al. , 2018)  and  (Martínez-Villaseñor et al. , 2019). In  (Vadivelu et al. , 2016) , the authors provide a dataset captured from FLIR ONE thermal cameras. The resolution of images was 640×480. In  (Peñafort-Asturiano et al. , 2018) , the authors acquired a dataset named FALL-UP by combining 6 infrared sensors, 2 normal cameras, 1 EEG, and 5 wearable sensors. In  (Martínez-Villaseñor et al. , 2019) , the authors provide a dataset named UP-Fall by combining 6 infrared sensors, 2 cameras, 5 IMUs, a gyroscope, ambient light, and 1 EEG. But even they are using thermal cameras, they only provided the thermal images features and no raw data.
In the literature, activity recognition domain generalization is most of the time focused on IXMAS  (Weinland et al. , 2006). This dataset has been widely used as a cross-view action recognition benchmark  (Li et al. , 2018; Li et al. , 2019). The videos have been collected from five different views. We think that generalization through different views does not demonstrate perfectly the performance of a domain generalization algorithm. In this dataset, different persons perform the same actions in different ways, so it is common that a model might not be able to recognize actions performed by new subjects not seen during training. Also, we cannot expect a model trained using indoor data to work well outdoors. This is why in this paper, we propose that the source and the target model do not share either the same persons, either the same views and even more neither the same modality (visible spectrum vs.
thermal). This paper will then try to answer the following question: is it possible to learn from classical modality datasets (e. g. , RGB) and infer thermal data. The main idea is then to train some networks on data collected and labeled for a fairly complete study of activity recognition from RGB images  (Tran et al. , 2018b)  and verify the accuracy of the inference on a much smaller database of low-resolution thermal images. To our best knowledge, it is the first time domain generalization is performed from visible to thermal. The source code is open 1. Our contributions are threefold: • This paper fills a gap in the literature on fall detection and activity recognition with thermal cameras. Indeed, most of the previous works learn and infer on the same database. In this paper, we propose to go further.
Visible domain and related One of the problems we faced for thermal-based activity recognition is the scarcity of datasets. Original domain generalization is used to adapt a model trained with a dataset into another dataset. Domain generalization is linked to domain adaptation and few-shot learning. But while in these techniques, the model sees the target data during training, in domain generalization, the model does not see the target dataset during training. The main reason why we want to apply domain generalization to our problem is that data acquisition takes time and annotation is time-consuming. Training a model using RGB video and inferring on Thermal videos will save us time. Indeed, there is a huge number of datasets for RGB images, but thermal datasets are sparser. A jth domain is defined as They are many ways to perform domain generalization: domain invariant features, hierarchical models, data augmentation and optimization algorithms.
Activity monitoring dataset Regarding data availability, there is a lot of data for human activity recognition  (Carreira et al. , 2018; Carreira et al. , 2019). Unfortunately, most of these datasets concern visible images. In the specific case of monitoring human activities as a goal of fall detection, the literature is also prolific regarding visible image  (Charfi et al. , 2012)  and recently about Kinect images  (Adhikari et al. , 2017). In  (Tran et al. , 2018b) , the authors propose a fall detection dataset with 8 falls types, 50 participants (20 women and 30 men). To collect the dataset, they use 7 overlapped Kinect and visible cameras and 2 WAX3 wireless accelerometers. We used this dataset as the source and collected a new dataset as the target. There are a few dataset using thermal images.
In  (Martínez-Villaseñor et al. , 2019) , the authors propose UP-Fall a fall detection dataset composed of 255 videos. They used 17 participants (8 men and 9 women). To acquire the dataset, they use 6 infrared sensors, 2 visible cameras, 5 IMUs with an accelerometer, a gyroscope, an ambient light, and 1 EEG. In  (Peñafort-Asturiano et al. , 2018) , the authors propose a new fall detection dataset composed of 255 videos. This dataset is acquired using 6 infrared sensors, 2 cameras, 1 EEG, 5 wearable inertial sensors.
Dataset Given that the dataset available in the state of the art was not satisfactory, we decided to collect a new dataset for thermal activity recognition. This is one of the core contributions of our paper. To acquire the images, we use Lepton 2. 5 FLIR cameras (Fig  1 ). In the Table  1 , there are the specifications of lepton 2. 5. These cameras are characterized by low resolution, noise, sudden brightness change (due to clutter refreshment), and halo effect. The main advantage is that these cameras are low-cost, thus can be used for industrial applications or for anyone wanting to install them in their homes. We used two cameras set in stereo to take advantage of the depth estimation. We set the stereo baseline to 16 cm for a tradeoff between congestion and the field of the view of the cameras.
We collected 713 videos following the same actions described in  (Tran et al. , 2018b) : walk, hand pick up, fall, crawl, sit then stand up, sit then fall, lie then sit up and lie then fall. Contrary to many other datasets in literature, our dataset has been acquired in real apartment situations. These rooms were furnished and therefore presented many occlusions to the cameras. Moreover, these data have been acquired for various room temperatures. Someone might suggest that our dataset is not big enough. Indeed, compared to other works in the literature we do not have enough data. However, we do not have the same objectives as these works. Indeed we place ourselves in the situation where a person could train a model a big dataset and deploy this model. So knowing that the performance of his model will not decrease even if the inference dataset is different from the training dataset.
Someone might suggest that our database is not large enough and does not contain enough views to do domain generalization. Compared for example to the IXMAX database (which is the standard database for domain generalization in activity detection), we have enough videos. Indeed, authors usually use only the first 5 actions (compared to our 8 actions). Moreover, they usually use 6 people (Alba, Andreas, Daniel, Hedlena, Julien and Nicolas) not taking into account irregular actions.
Source dataset For source dataset, we use the dataset CMDFALL provided by  (Tran et al. , 2018b). We choose this dataset because it involves 50 people and seven viewpoints with 20 activities. We modified the dataset for our purpose. First, they use 20 activities with some activities such as right fall and left fall. We have grouped with the activities that only differ in the sense that they have been performed left or right. Besides, we did not consider one of the cameras because it was placed on the ceiling. Moreover, given that their frame rate is 20 fps, we temporally down-sampled the video to 5 fps. As a result, we delete videos where the activities lasted less than 3 seconds. In the end, we got 6235 videos with some imbalance between classes (Table  3 ).
Data augmentation As shown in Table  3 , one of the problems we faced is that the cleaned CMDFALL dataset is imbalanced. There are many solutions to learn from imbalanced dataset  (Johnson and Khoshgoftaar, 2019) : data-level methods, algorithm-level methods, and hybrid methods. We chose to apply the data-level method through over-sampling with additive data augmentations in order to reach 1972 videos per class. The main problem of oversampling is over-fitting. Thus, we had a random affine transformation and ran- Random affine transformation Given a video v = {I 0 , • • • , I t-1 } with a frame size of m × n. For a given video a transformation matrix is randomly generated and applied to the frames.
The loss function of the model is defined as follows: min θ E ,θ C max θ 1 ,••• ,θ N S L C (C(E(x; θ E ); θ C ), y C ) - N S ∑ k=1 L k (D k (E(x; θ E ); θ k ), y k ) (4) where y C is the activity label ∈ {0, 1, 2, 3, 4, 5, 6, 7} of x, and y k is the domain label that is to say y k = 1 if x ∼ D k S , 0 otherwise. L C is the error between the predicted activity and the ground truth activity while L k is the error between the predicted domain and the ground truth domain. 4 Experiments Regarding the source dataset, videos have been originally recorded from 50 subjects. We use the first 40 persons for training and the remaining 10 subjects from testing. The source and the target datasets can be downloaded through this link tinyurl. com/eupz32ns.
Training settings We use a pretrained I3D backbone on the Kinetics dataset. The domain classifier is composed of two fully connected layers, followed by a ReLU and a dropout. There is also a last fully connected layer, followed by a softmax layer. The task classifier is composed of an average pooling followed by a dropout, a 3D convolutional layer, and a softmax layer. The model is trained using stochastic gradient descent for 20 epochs, and the initial learning rates are 0. 01 for both the task and the domain classifier. We evaluated three models: 1. Baseline: This model is composed of the traditional training. Here, we followed the procedure presented in  (Carreira and Zisserman, 2017). We first extracted the optical flow using TVL1 and then the model is trained normally without the domain generalization.
Baga dataset: frames during a fall from a bed dom occlusion to any video besides traditional dataaugmentation methods. The main idea is to be able to get an infinite number of videos from one video. The additive data augmentation techniques are described as follows:Random occlusion Randomly generate a rectangle with a random size r m × r n with r m ∈ [0, m] and r n ∈ [0, n]. The rectangle is randomly moved during the video.
Capture-Mark-Recapture (CMR) sampling strategy Birds were captured in their burrows at each colony, during incubation and/or chick-rearing period. Each individual was ringed with a single stainless-steel ring at its first capture, and every subsequent recapture was recorded. The dataset considered 2058 encounters from the three study colonies: Vila (n = 261 adults, data from 2007 to 2012 and 2016), M. Clara  (n = 415, 2010-2018)  and Cima (n = 418, 2010-2018). Forty-six males and 45 females on Vila (17. 6 and 17. 2 % of the total number of adults from Vila respectively), 46 males and 24 females on M. Clara (11. 1 and 5. 8 % respectively), and 14 males and 13 females on Cima (3. 3 and 3. 1 % respectively) were molecularly sexed following  Fridolfsson and Ellegren (1999). Sex was unknown for the remaining individuals.
Tracking data Since Bulwer's petrels face different environmental conditions in their breeding and non-breeding areas (a situation that may ultimately have different effects on their survival), we determined the areas used during the two seasons by fitting breeding adults with small Global Location Sensing loggers (GLS hereafter; n = 14 for Vila, 94 for M. Clara and 19 for Cima; more information in Table  C1  in Appendix C), leg-mounted with PVC rings over several years. The GLS models we used were Mk13, Mk14, and Mk18 from the British Antarctic Survey (BAS; Cambridge, UK), Mk4083 from BioTrack (Wareham, UK) and Intigeo-C65 from Migrate Technology (Cambridge, UK). GLS weighed from 1 to 2 g, which represents 0. 8 % to 2. 4 % of the body mass of a Bulwer's petrel (range: 83-132 g; authors' unpublished data).
Environmental data We evaluated the influence of environmental conditions on survival for each Bulwer's petrel population using four variables commonly related to seabirds' survival: (a) NAO (https://climatedataguide. ucar. edu/sites/ default/files/nao_pc_monthly. txt), (b) SOI (www. esrl. noaa. gov/psd/data/ correlation/soi. data), (c) SST (www. oceancolor. gsfc. nasa. gov/cgi/l3; monthly Aqua MODIS Sea Surface Temperature (11 μ daytime) product in °C, with a 9-km resolution), and (d) wind speed (marine. copernicus. eu/; Global Ocean Wind L4 Reprocessed Monthly Mean Observations product in m/s). Monthly estimates of NAO and SOI were averaged for the breeding and non-breeding seasons (months described above for each colony; Fig. D1  in Appendix D) and year. Mean SST and maximum wind speed estimates were calculated for each colony and season for the values that fell within the 50 % and 75 % KDE (Fig.
CMR models and statistical analyses We first evaluated whether the general model for single-state data with only time-dependent parameters (Cormack-Jolly-Seber [CJS] model) might be an acceptable starting point for our dataset. To do so, we carried out standard Goodness-Of-Fit (GOF) tests by group (males, females, unknown sex) using U-CARE 2. 3. 4 software  (Choquet et al. , 2009a). Second, we carried out multievent models with uncertainty in sex assignment to determine whether survival probabilities varied by colony, time (here, year) and sex, and if these probabilities were influenced by the environment. We assumed that individuals that had not been sexed comprised 50 % of males and 50 % of females (i. e. , we fixed the sex ratio to 0. 5 as explained in Appendix E). We further assumed that, at any time, an individual was in one of the following three states: alive male, alive female or dead.
Acknowledgements All the fieldwork was undertaken with the authorization of the  Regional Directorate of the Environment of the Azores, Dirección General de Protección de la Naturaleza del Gobierno de Canarias, and Direção Nacional do Ambiente of Cabo Verde. We thank  Andreia Leal ,  Anna Zango ,  Carlos Soria ,  Clara Rodríguez ,  Claudia Pich ,  Daniel Bragg ,  David Abizanda ,  Gabriel Tavares ,  Gilson J. Montrond ,  Helen Pargeter ,  Henri Ballot ,  Herculano Dinis ,  Iballa de Vicente ,  Jofre Sendrós ,  J. Evaldino Pereira ,  Laura Zango ,  Leia Navarro ,  Lucia López ,  Malvina Andris ,  Mariona Sardà ,  Nadito Barbosa ,  Nerea Sanchís ,  Nicole Azagury ,  Nuno Barros ,  Nuria Trilla ,  Rémi Fontaine ,  Ricard Marcos ,  Sarah Saldanha , and  Verónica Cortés  for helping with fieldwork;  Víctor Sanz  and  Sofía López  for some GLS analyses;  José Manuel  de los  Reyes-González  and  Zuzana Zajková  for developing the script to process and manage GLS data;  Virginia Morera-Pujol  for giving us a hand with R whenever needed;  Lucile Marescot ,  Frédéric Letourneaux ,  Ana Sanz , and  Giacomo Tavecchia  for their help with some demographic questions;  Sergi Torné  for letting us use his Bulwer's petrel drawing for this publication; and  Antón D.
Pérez-Rodríguez  and  Tim Janicke  for their recommendations. We finally thank two anonymous referees for constructive feedbacks. MCF was supported by a predoctoral grant from the  University of Barcelona  and the  H2020 Marie Skłodowska Curie Actions  ( H2020-MSCA- IF-2020 , ref. 101024166). JB was supported by the  Fundação para a Ciência e a Tecnologia  (project ref. PTDC/BIABDE/67286/2006 ), the  Fundo Europeu de Desenvolvimento Regional  (programme  COMPETE , ref. FCOMP-01-0124-FEDER-007061 ) and the  Programme MoniAves  (launched by the Regional Directorate of the Environment of the Azores and coordinated by  R. S. Santos ). VN was supported by a  Investigador Mar-AZ contract  ( 01-1045-FEDER-000140 ,  PO Açores 2020 ) and by a postdoctoral grant from the  Regional Fund for Science and Technology  ( FRCT/M3.
1. a/F/072/2016 ). RR was supported by a postdoctoral Ramón y Cajal contract of the  Spanish MINECO  ( RYC-2017-22055 ). This study was funded by  MINECO  ( CGL2006-01315/ BOS ,  CGL2009-11278/BOS ,  CGL2013-42585-P  and  CGL2016-78530-R ),  Fondos FEDER , the  Research Executive Agency (European Commission ,  FP7-PEOPLE-2013-CIG, 618841 ), the  MAVA Foundation pour la nature  ( MAVA17022 ) and  SEO/BirdLife  (project  LIFE+INDEMARES :  LIFE07NAT/ E/000732 ).
Rainfall are subject to local orography features and their intensities can be highly variable. In this context, identifying climatically coherent regions can greatly help interpreting rainfall patterns and improve the inference of return levels. In practice, partitioning a region of interest into homogeneous sub-regions is a delicate statistical task, especially in regards to modeling heavy rainfall features. In this work, our main goal is to propose and study a fast and efficient clustering algorithm. Compared to classical regional frequency analysis techniques, a key aspect is that our algorithm does not rely on the a priori choice of covariates. The proposed numerical scheme is only based on the precipitation dataset at hand, including low, moderate and heavy rainfall. In terms of inference, our approach builds on the easy-to-compute and reliable probability weighted moments commonly used in hydrology.
a. Number of clusters After computing the ratio ω using Eq. (  5 ) at each location, a number of clusters has to be chosen when applying PAM. To take into account rainfall variability, we randomly shuffle our whole dataset in space and time. This step should remove all spatial clustering and, hence under the null hypothesis of the absence of clusters, a base level for silhouette coefficients, see Eq. (  16 ), can be obtained and gives us a yardstick. In Figure  6 , solid black points indicate the difference between the base level and the level obtained without reshuffling for different cluster numbers, say k = 1,. , 20. We suggest to consider the number of clusters for which the discrepancy between partition of white noise and partition of real data is the most significant. A partition with two clusters clearly appears as the optimal choice.
To assess homogeneity within each of the two clusters, we compute the three RFA homogeneity tests (see  Hosking and Wallis 2005 , for the mathematical definition of these tests). We recall that, asymptotically, these three RFA tests reject homogeneity when they are far from a zero-mean Gaussian distribution (the variance depends on the sample size). They are also based on the assumption that rainfall follow a kappa distribution. However, there is no guarantee that a kappa distribution correctly fits the dataset at hand. This may explain why the three tests strongly reject the homogeneity hypothesis when applied to the two RFA clusters. Table  2  tells us that, even small sub-regions with 10% of locations have difficulty to be considered as homogeneous. For example, the second row indicates that the three RFA tests are far from zero, pointing out that the RFA northern cluster is strongly heterogeneous.
Acknowledgments Within the CDP-Trajectories framework, this work is supported by the  French National Research Agency  in the framework of the "  Investissements d'avenir" program  ( ANR-15-IDEX-02 ). We gratefully acknowledge financial support for this study provided by the  Swiss Federal Office for Environment (FOEN) , the  Swiss Federal Nuclear Safety Inspectorate (ENSI) , the  Federal Office for Civil Protection (FOCP) , and the  Federal Office of Meteorology and Climatology, MeteoSwiss , through the project  EXAR ("Evaluation of extreme Flooding Events within the Aare-Rhine hydrological system in Switzerland "). Part of this work was also supported by the  French national programs (FRAISE-LEFE/INSU  and  80 PRIME CNRS-INSU ), and the European  H2020   XAIDA  (Grant agreement ID: 101003469).
scRNA-seq of tumors reveals transcriptional trajectories governed by temporal patterning under all conditions To decipher the molecular mechanisms by which the inactivation of PRC2 and trx genes, alone or in combination, regulates the cellular composition of tumors and affects their growth, we performed scRNA-seq. We sequenced four conditions: the control tumors (pox n >pros RNAi ) (20) and the three perturbed tumors: E(z) RNAi [pox n >pros RNAi, E(z) RNAi ], trx RNAi (pox n >pros RNAi , trx RNAi ), and E(z) RNAi trx RNAi [pox n >pros RNAi , E(z) RNAi , trx RNAi ]. We dissected and dissociated tumors that persist in adults and proceeded to fluorescence-activated cell sorting (FACS) of GFP + tumor cells. We analyzed the transcriptomes of cells isolated from each type of tumors using the Seurat R package  (62).