dataesr/scientific-paragraphs-categorization huggingface dataset:
5  -Supplement 3), we found a large degree of divergence after mapping them onto the Glasser's atlas  ( Glasser et al. , 2016 ) , probably due to the primary motor and somatosensory cortex being parcellated into single strips in the atlas and not differentiating the somatotopic areas in the feature space. For image recognition, the ventral visual stream was identified in all three maps, but with different specific spatial locations. Overall, although some overlap existed between saliency maps and meta-analysis maps, there was no systematic correspondence. This likely reflects the fact that features identified through traditional statistical tests and predictive models are to some extent divergent  ( Bzdok and Ioannidis, 2019 ). Similar observations were made with contrast maps from the HCP dataset.
6. Temporal sensitivity of GCN on the fMRI time window. The temporal sensitivity of GCN was investigated with variable lengths of time windows, ranging from a single fMRI volume (0. 7s) to 10s with a step of 2 TRs (i. e. 1. 4s). (A) The performance of GCN annotation gradually increased as prolonging the time window of fMRI time series. It started with 56% of test accuracy on a single fMRI volume (cyan), quickly increased to 82% with 6s of fMRI data (green), and reached a plateau of 90% at 10s (purple). (B) The cognitive tasks showed high diversity in the sensitivity to the duration of time windows. Among the cognitive domains, the relational processing and working memory tasks were most sensitive to the time window and achieved the lowest decoding accuracy at all temporal scales. tures, rather than relying on confounding effects, for example motion artifacts.
GCN annotation starts with 46% of test accuracy in decoding the 21 cognitive states by using only 14 subjects (cyan). Then, the performance quickly increased to 77% by using 71 subjects (yellow) and reached a plateau of 85% with around 280 subjects (green). Among the cognitive domains (B), the relational processing and working memory were the most demanding tasks on the sample size, while the language and motor tasks were more robust to the size of the dataset. sions of all cognitive tasks. These subjects were again split into training (70%), validation (10%) and test (20%) sets. Generally, with more subjects, GCN achieved higher accuracy in decoding the 21 cognitive states ( Fig. 8  ). GCN annotation already achieved decent performance with a handful of subjects (average f1-score = 46% using 14 subjects).
g. semantic context  ( Huth et al. , 2012 ). But these studies did not directly attempt to characterize what amount of temporal data is required to perform meaningful brain decoding. The temporal resolution of fMRI decoding is intrinsically constrained by two factors, including the acquisition time for a whole-brain fMRI scan (i. e. TR) and the delay effect of hemodynamic response. With a common setting as 2 second, the acquisition time was pushed down to a third (TR = 720ms) in HCP database by using simultaneous multislice acquisitions  ( U ğurbil et al. , 2013 ) , which brings opportunities to investigate fine-grained temporal dynamics of brain activity. Using this dataset, Li and Fan successfully predicted the entire experimental design of the working memory task by using a sliding window approach  ( Li and Fan, 2019 ).
But each time window still took around 30s of fMRI signals as input for task inference. To which extent of a shorter time window the decoding model can work with is still unexplored. In this study, we applied graph convolutions on a short series of fMRI signals and investigated the temporal resolution of brain decoding by using variable time windows of fMRI scans, ranging from a single fMRI volume to the entire event trial. Leveraging the fast fMRI acquisition of HCP database, our model can annotate 21 cognitive conditions with a sub-second temporal resolution. In the meantime, the decoding performance was still impacted by the task-evoked hemodynamic response, for instance, higher decoding accuracy by using fMRI signals after the peak of HRF than before the peak. This phenomenon was observed not only for low-level functions like body movements, but also high-level cognitions such as working memory tasks, or even missing all experimental conditions together ( Table  2  and Fig.
Limitations and future applications In the current project, we only explore the block design of task-fMRI dataset, i. e. consisting of long events with repeated trials that in total last for more than 10s. However, it is still unclear how to generalize the decoding pipeline to naturalistic stimuli, for instance visual scenes from movies, which consists of short and fast-switching events. The measured BOLD signals might be a mixture of hemodynamic responses evoked by different task events. Early attempts have been made by adding independent regressors with delayed onsets  ( Nishimoto et al. , 2011 ). But the simple linear model only generates a blurred image from the average prediction of each category. One possible solution to this problem is to use a multi-label decoding model based on GCN.
, 2016 ). Some researchers have also applied anatomical connectivity to separate specific components in brain dynamics that were associated with high-level cognitions  ( Huang et al. , 2018 ; Medaglia et al. , 2018 ). In our next project, we will systematically investigate the impact of using different brain graphs on GCN models, for instance using a spatial graph, built based on spatial adjacency on the cortical surface, a functional graph, calculated using resting-state functional connectivity, as well as an anatomical graph derived from diffusion tractography. An interesting potential application of our work would be transfer learning. In natural image processing, it is common practice to take a model already trained on a large dataset, such as AlexNet trained on ImageNet  ( Krizhevsky et al.
, 2012 ) , and fine-tune the parameters of the trained model to accomplish a new task  ( Tajbakhsh et al. , 2016 ). This allows training complex models even in the absence of extensive training data. This problem of lacking a sufficiently large dataset for specific experimental questions is pervasive in medical imaging. Our model was made publicly available ( https://github. com/zhangyu2ustc/GCN _ fmri _ decoding ) and can be used as a reference model for domain adaptation, possibly making contributions in a variety of domains, including neurological and psychiatric disorders. It could also be applied in samples where extensive data is acquired on a few subjects, such as the individual brain charting (IBC) project  ( Pinho et al. , 2018 )  or the Courtois project on neuronal modelling (neuromod, https://docs.
cneuromod. ca ). https://db. humanconnectome. org/data/projects/HCP_1200. In total, fMRI data from 1113 subjects under 7 different task domains and resting-state were used i n this study. The minimal preprocessed fMRI data of the CIFTI format were used, which maps i ndividual fMRI time-series onto the standard surface template with 32k vertices per hemisphere. Our decoding pipeline, as well as the optimized decoding models and the construction of brain graphs, were made publicly available i n the following repository: https://github. com/zhangyu2ustc/GCN _ fmri _ decoding. git.
We present a method to automatically learn to segment dynamic objects using SLAM outliers. It requires only one monocular sequence per dynamic object for training and consists in localizing dynamic objects using SLAM outliers, creating their masks, and using these masks to train a semantic segmentation network. We integrate the trained network in ORB-SLAM 2 and LDSO. At runtime we remove features on dynamic objects, making the SLAM unaffected by them. We also propose a new stereo dataset and new metrics to evaluate SLAM robustness. Our dataset includes consensus inversions, i. e. , situations where the SLAM uses more features on dynamic objects that on the static background. Consensus inversions are challenging for SLAM as they may cause major SLAM failures. Our approach performs better than the State-of-the-Art on the TUM RGB-D dataset in monocular mode and on our dataset in both monocular and stereo modes.
1. Steps of our method. We only need one monocular sequence per dynamic object to make the SLAM robust and prevent major failures due to dynamic objects. We train one network for our Consensus Inversion dataset and one for TUM RGB-D. but our experiments show that it does not prevent consensus inversions. The second type uses geometry during training and semantic segmentation at runtime. An example is  [11] : although their approach reduces the risk of consensus inversion, it needs training sequences recording the same location at different times, does not detect objects that do not move between training sequences and requires a full stereo camera + LIDAR setup. Similarly to  [11] , we use geometrical information (inliers and outliers) to localize dynamic objects and construct their masks, which we use to train a network.
We integrate the trained network in an existing SLAM to make it Dynamic. However, our approach only needs one monocular sequence per dynamic object, while  [11]  requires at least two and up to eight in practice. Moreover, contrary to  [11] , we do not need stereo nor depth information, making our approach easier to implement and put into practice. We use our method to make ORB-SLAM 2  [15]  (Monoc-ular, Stereo and RGB-D) and LDSO  [16]  robust to dynamic objects without any manual annotations. In addition to our method, we created the dataset Consensus Inversion and created two new metrics, Penalized ATE RMSE and Success Rate, to quantify the robustness of Dynamic SLAMs.
IV. CONSENSUS INVERSION DATASET AND NEW METRICS To the best of our knowledge, there is no dataset nor appropriate metrics to test the robustness of SLAMs with respect to motion consensus inversions. Hence, we created the Consensus Inversion dataset, illustrated in Fig. 3 , made of two subsets: Dynamic and Static. We used a MYNT EYE D1000-120 stereo camera at 1280x720 / 30Hz. IMU data is recorded but not used. All sequences are about 500 to 1000 images long.
2: Penalized ATE RMSE max(L). (1 + τ ), if ATE RMSE is invalid. ATE RMSE, otherwise. (2) Conversely, we consider that a SLAM successfully processes a sequence if all these conditions are respected: 1) The ATE RMSE is known 2) The ATE RMSE is below a fixed threshold l max 3) The tracking rate is at least r gt -∆ r,max. The Success Rate of a SLAM on a dataset is the ratio of sequences that the SLAM successfully processes.
V. EXPERIMENTS A. Experimental setup 1) Datasets and parameters: we evaluate our method on the TUM RGB-D dataset  [29]  (dynamic sequences) and on the Consensus Inversion dataset. The purpose of the TUM RGB-D dataset is the evaluation of RGB-D SLAM systems; it was recorded using a Microsoft Kinect and the ground truth camera poses was obtained from a motion capture system. The sequences contain color and depth images at 640x480/30Hz. The dynamic sequences are a set of eight sequences that record people moving. We use ORB-SLAM 2  [15]  as the core SLAM algorithm for the main experiments and LDSO  [16]  specifically to test the extension to a Direct SLAM. We set the feature number to 3000 and use default/author settings otherwise. We use our method to train one network on the TUM RGB-D dataset (we use the sequences fr3 sitting static and fr3 walking static) Fig.
1) Comparison with the State-of-the-Art: dynamic objects, especially when causing consensus inversions, may cause early SLAM failure and decrease in the tracking rate of SLAMs, making the comparison of ATE RMSEs biased. To take both the trajectory error and the tracking rate into account we use our new metrics: the Penalized ATE RMSE and the Success Rate. The Penalized ATE RMSE integrates failures, so it is directly comparable between SLAMs, and a higher Success Rate expresses that a SLAM is less affected by dynamic objects. We evaluate the methods on the TUM RGB-D and Consensus Inversion datasets. Table  I  (cols. 2-6) shows the Penalized ATE RMSE of the State-of-the-Art. Our method performed better than others on our dataset (all modes) and on TUM RGB-D in monocular mode. It is in third place on TUM RGB-D in RGB-D mode.
Test dataset No seg. Mask R-CNN  [30]  PWC-Net  [31]  RVOS  [32]  COSNet  [26]  Consensus Inversion -Mono 0,0547 0,0693 0,0692 N/A N/A 0,0860 0,0760 0,0237 0,0144 0,0297 0,0089 Consensus Inversion -Stereo N/A 0,0627 0,0699 N/A N/A 0,0756 0,0630 0,0803 0,0116 0,0148 0,0094 TUM RGB-D -Mono 0,0892 0,1108 0,1101 N/A N/A 0,0252 0,0235 0,0335 0,0331 0,0267 0,0222 TUM RGB-D -RGB-D N/A 0,0206 0,0173 0,0190 0,0802 0,1077 0,0172 0,0790 0,0218 0,0245 0,0185   D including our method. We reached the standard of other Dynamic SLAMs that rely on manually annotated networks. Fig. 4  illustrates how the SLAM can output nonsense in presence of consensus inversions (motions that do not exist) and that we prevent it. On our dataset other Dynamic SLAMs performed poorly: they failed in hard / very hard sequences when the object was of an unknown class, e.
g. , dragon or dromedary. Even SLAMANTIC, that tries not to segment mobile objects that are not moving (e. g. a parked car) also failed in the very hard sequences as the objects are static during most of the sequence. Table  II  (cols. [2] [3] [4] [5] [6]  shows the Success Rate of the State-ofthe-Art. Our approach has the best performance in all cases. The results are coherent with the Penalized ATE RMSE: a higher Success Rates correspond to a lower Penalized ATE RMSE. Except for TUM RGB-D in RGB-D mode (success rate ≥ 85%), all results are below 75%. The results in monocular mode show that it is more difficult to handle monocular dynamic sequences with geometrical approaches, possibly due to the arbitrary scale of the SLAM. The success rates on our dataset (about 60%) shows that both geometrical approaches and hybrid ones combining geometrical/semantic approaches at runtime fail if there are consensus inversions caused by unknown objects.
VI. CONCLUSIONS In this paper we proposed a novel method to learn to segment dynamic objects using only one monocular sequence per dynamic object, which is an advantage in comparison to previous methods. More importantly, we do not need any manual labelling which makes our method much easier to use. We also presented the Consensus Inversion dataset and new metrics to evaluate the robustness of Dynamic SLAMs. We showed that consensus inversions can cause major SLAM failures, even to State-of-the-Art Dynamic SLAMs. We improved ORB-SLAM 2 monocular/stereo/RGB-D as well as LDSO and achieved top results in very challenging scenarios. Another advantage, in addition to preventing SLAM failures and improving motion estimation, is the improvement in map reconstruction. Tasks like relocalization and loop closing need accurate maps and should benefit from our approach.
Star-planet interaction model 2. 1. ESPEM: an overview ESPEM (French acronym for Evolution of Planetary Systems and Magnetism ; see  Benbakoura et al. 2019 ) is a numerical code computing the secular evolution of a star-planet system by following the semi-major axis of the planetary orbit as well as the stellar rotation rate. Furthermore, we assume here a coplanar and circular orbit, and a synchronized planetary rotation, as the reservoir of angular momentum of the planet is less important than the one in its orbit  (Guillot et al. 1996). In this model, we consider a two-layer solar-type star composed of a radiative core and a convective envelope. Tidal dissipation is only considered in the stellar envelope in this work. The core is interacting with the envelope through internal coupling, and the latter exchanges angular momentum with the orbit through tidal and magnetic interactions (SPMI).
Observational data Knowing the possible evolutions of planetary populations as well as the influence of our model physical parameters on the distributions of orbital and stellar rotation periods, we now aim to confront our results with the statistics of observed star-planet systems. To do so, we focus on data from the Kepler mission, and more specifically on two studies performed by  McQuillan et al. (2013, hereafter MMA13)  and  McQuillan et al. (2014, hereafter MMA14). The MMA14 study provides the largest homogeneous rotation dataset in the Kepler field to date, involving F-type to M-type stars; it includes 34,030 Kepler MS stars whose rotation period has been measured through an autocorrelation-based method, which represents 25. 6% of the 133,030 MS Kepler targets detected at that time (excluding known eclipsing binaries and Kepler objects of interest).
This dataset is used in the remainder of this study to design a synthetic stellar population taking into account the observational biases of the Kepler field as well as the potential influences of the stellar distribution in the galaxy. MMA13 is one of very few studies to date that combine the orbital period of detected exoplanets and the stellar rotation of their host star, making the associated dataset a valuable tool in the study of star-planet interactions. The main highlight of the study was the observed lack of close-in planets around fast rotators (we also refer the reader to Pont 2009). The MMA13 dataset includes 737 KOI with detected orbital and stellar rotation periods. The targets are all in the MS, and were selected using the same processes as in the MMA14 sample. Thus, comparing these two studies allows us to keep identical target selection processes as well as detection methods between the populations of isolated and planet-hosting stars.
Moreover, for the sake of consistency, as our model does not deal with interactions between several planets, we do not take into account multiplanetary systems in the MMA13 dataset. Therefore, in the remainder of this work we aim to compare ESPEM results with the MMA13 sample which has been filtered to exclude detected multiplanetary systems. As seen in the top panel of Fig. 15 , the distribution in stellar effective temperatures is marginally affected by the choice of dataset (MMA13 vs. MMA14) as well as the removal of multiplanetary systems (black line). Therefore, we can rely on the T eff distribution from the MMA14 study to design a stellar population and compare the ESPEM results to the filtered MMA13 sample without adding significant biases. In addition, as shown in the bottom panel of Fig.
Synthetic populations and Kepler observations: comparison of global distributions In the remainder of this work, we aim to generate a synthetic population of star-planet systems out of our whole sample of ESPEM simulations, and then to compare the distributions we A&A proofs: manuscript no. aanda obtain with the filtered MMA13 dataset. To do so, we first design a realistic population of stars based on the MMA14 study. For the sake of consistency, as we assume a two-layer structure for the star, we consider stellar masses ranging between 0. 5 and 1. 1 M with a bin size of 0. 1 M. This corresponds to effective temperatures of between 3700 and 6000 K, which allows us to cover most of the MMA14 sample. The associated surface gravity log g ranges between 4. 25 and 5. 07, which is consistent with the selection processes used in MMA13 and MMA14 to exclude likely giants  (Ciardi et al.
Acknowledgements. We would like to thank the anonymous referee for helpful comments and suggestions regarding our work. The authors thank  Emeline Bolmont  for the help and information she has generously provided. We also thank  Cillia Damiani  and  Noë Brucy-Ciaramella  for fruitful discussions regarding our work. The authors acknowledge funding from the  European Union 's  Horizon-2020 research and innovation programme  (Grant Agreement no. 776403  ExoplANETS-A) as well as the PLATO CNES funding at  CEA/IRFU/DAp. A. S. and A. S. B. acknowledge funding by  ERC   WHOLESUN 810218  grant,  INSU/PNST  and  CNES Solar Orbiter. A. S. and J. A. acknowledges funding from the  Programme National de Planétologie (PNP). S. M. and J. A. acknowledge funding by the  European Research Council  through the  ERC  grant  SPIRE 647383.
We make use of a dataset from a network of 30 rain gauges covering an area of 10 000 km 2 in southeastern Niger and providing 5-min rainfall records over a 28-yr period    (AMMA-CATCH 1990). This dataset allows assessing Stochastorm's capabilities and robustness to transcribe the different aspects of the rainfall regime in the Sahel, whether prescribed or not in the model, from event to subevent scales up to 5 min. Section 2 presents the model, particularly with regard to previous versions which provide the basis of the current model. Section 3 presents the new developments for the current version. Section 4 details how the model was implemented in the African Monsoon Multidisciplinary Analysis-Coupling the Tropical Atmosphere and the Hydrological Cycle (AMMA-CATCH) Niger study site. Section 5 presents the results of model calibration and simulation.
As an exploratory initial step, the parameters that may vary seasonally are first calibrated in a moving window over the data and then plotted to visualize their nonstationarity. From these plots, the types of possible functions for seasonal covariates are estimated visually. Then, statistical distributions using the different possible seasonal covariates are calibrated over the entire dataset. The covariate models are compared using AIC and likelihood ratio tests. The choice of seasonal covariates may also be informed by knowledge about local climatology in the study region. Parameters are obtained by maximum likelihood estimation unless otherwise noted.
b. Data used: AMMA-CATCH network The calibration and validation of the simulator is performed with a subset of the AMMA-CATCH network of recording rain gauges. AMMA-CATCH consists of ecohydrological data collection at three mesoscale sites in West Africa. It is unique for the region in both its spatial density and the length of continuous data recording, since 1990 in Niger, 1999 in Benin, and 2002 in Mali. The data are freely available from the AMMA-CATCH database  (AMMA-CATCH 1990). Stochastorm was calibrated on the AMMA-CATCH study site located in the Sahel near Niamey, Niger. The site features a set of 30 rain gauges located within a 120 3 160 km 2 area (Fig. 3  providing 28 years of data (1990-2017) at the 5-min time step.
c. Model implementation The following sections describe the implementation of the Stochastorm model on the AMMA-CATCH dataset located within the West African Sahel. Previous studies have shown that the assumption of spatial stationarity is valid for our study site  [Ali et al. (2003)  for the intermittence of rainfall events;  Vischel et al. (2009)  for the marginal distribution (gamma);  Panthou et al. (2014)  for the GPD distribution of extreme values]. Table  2  provides a summary of parameter values.
6 ). For the a and b parameters, a log function and an exponential function were chosen respectively to link their values with the covariate of cumulative event rainfall. See Table  2  for their specific equations. Speed and direction are determined using an input database generated from AMMA-CATCH data. The recorded event with the value of cumulative event rainfall (P) closest to a given simulated cumulative rainfall amount is identified. Then, the speed and direction that was measured for the recorded event is used for the simulated event. This approach resembles the selection of an analog event within the recorded data used in, e. g. ,  Chardon et al. (2014).
Evaluation of the model using AMMA-CATCH data (Sahel) From Stochastorm calibrated on AMMA-CATCH data, we generated 30 realizations over a period of 28 years, equivalent to that of the dataset. The model permits the user to select the simulation sites; we chose to simulate at the locations of the 30 AMMA-CATCH measurement stations in order to compare simulation results with observed data. The following sections describe the ability of the model simulations to reproduce both model parameters (i. e. , whether simulated outputs reflect calibrated parameters) and observed characteristics of storms not directly prescribed in the model (whether simulated outputs are coherent with observations).