dataesr/scientific-paragraphs-categorization huggingface dataset:
evaluation of methodological quality We performed a qualitative evaluation of the 52 studies that were included in the review. We used the Physiotherapy Evidence Database (PEDro) scale. 18 This scale contains 11 criteria that evaluate the methodological quality of studies. Each fulfilled criterion is worth 1 point, and nonfulfilled criterion is worth 0 point. Total score between 9 and 11 points means a good methodological quality, total score between 6 and 8 means medium methodological quality, and total score of less than 6 points means low methodological quality. Tables 1-3 report levels of methodological quality of the studies that used, respectively, cognitive training alone, physical training alone, and cognitive and physical training together. In the following sections, we will first present the impact of cognitive and physical training on cognition, and brain structure and function.
Acknowledgments We thank  Tim Pownall , native English speaker, for English editing. This work was supported by the  LabEx Cortex  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (Agence Nationale de la Recherche ).
Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based cameras. These properties make event-based cameras an ideal choice for autonomous vehicles, robot navigation or UAV vision, among others. However, the accuracy of event-based object classification algorithms, which is of crucial importance for any reliable system working in real-world conditions, is still far behind their framebased counterparts. Two main reasons for this performance gap are: 1. The lack of effective low-level representations and architectures for event-based object classification and 2. The absence of large real-world event-based datasets. In this paper we address both problems.
Finally, thanks to their logarithmic sensitivity to illumination changes, event-based cameras also have a much larger dynamic range, exceeding 120dB  [52]. These characteristics make event-based cameras particularly interesting for applications with strong constraints on latency (e. g. autonomous navigation), power consumption (e. g. UAV vision and IoT), or bandwidth (e. g. tracking and surveillance). However, due to the novelty of the field, the performance of event-based systems in real-world conditions is still inferior to their frame-based counterparts  [28, 66]. We argue that two main limiting factors of event-based algorithms are: 1. the limited amount of work on low-level feature representations and architectures for event-based object classification; 2. the lack of large event-based datasets acquired in real-world conditions.
This results in an event-based architecture which is significantly faster and more accurate than existing ones  [30, 33, 46]. Driven by brain-like asynchronous event based computations, this new architecture offers the perspective of a new class of machine learning algorithms that focus the computational effort only on active parts of the network. Finally, motivated by the importance of large-scale datasets for the recent progress of Computer Vision systems  [16, 28, 37] , we also present a new real-world event-based dataset dedicated to car classification. This dataset is composed of about 24k samples acquired from a car driving in urban and motorway environments. These samples were annotated using a semi-automatic protocol, which we describe below. To the best of our knowledge this is the largest labeled event-based dataset acquired in real-world conditions.
e. whether the illuminance intensity was increasing (ON events) or decreasing (OFF events). The conditional exposure measurement block measures the absolute luminous intensity observed by a pixel  [49]. In the ATIS, the measurement itself is not triggered at fixed frame-rate, but only when a change in the observed scene is detected by the relative change detector. In this work, the luminous intensity measures from the ATIS camera were used only to generate ground-truth annotations for the dataset presented in Sec. 5. By contrast, the object classification pipeline was designed to operate on change-events only, in order to support generic event-based cameras, whether or not they include the ATIS feature to generate grey levels. In this way, any event-based camera can be used to demonstrate the potential of our approach, while leaving the possibility for further improvement when gray level information is available  [39].
Frame-based Features and Object Classification There is a vast literature on spatial  [40, 13, 67, 57]  and spatiotemporal  [31, 73, 60]  feature descriptors for frame-based Computer Vision. Early approaches mainly focus on handcrafting feature representations for a given problem by using domain knowledge. Well-designed features combined with shallow classifiers have driven research in object recognition for many decades  [72, 13, 18]  and helped understanding and modeling important properties of the object classification problem, such as local geometric invari-ants, color and light properties, etc. [74, 1]. In the last few years, the availability of large datasets  [16, 37]  and effective learning algorithms  [32, 26, 68]  shifted the research direction towards data driven learning of feature representations  [2, 22].
Event-based Datasets An issue of previous work on event-based object classification is that the proposed solutions are tested either on very small datasets  [64, 30] , or on datasets generated by converting standard videos or images to an event-based representation  [48, 23, 35]. In the first case, the small size of the test set prevents an accurate evaluation of the methods. In the second case, the dataset size is large enough to create a valid tool for testing new algorithms. However, since the datasets are generated from static images, the real dynamics of a scene and the temporal resolution of event-based cameras can not be fully employed and there is no guarantee that a method tested on this kind of artificial data will behave similarly in real-world conditions. The authors of  [44]  released an event-based dataset adapted to test visual odometry algorithms.
Unfortunately, this dataset does not contain labeled information for an object recognition task. The need of large real-world datasets is a major slowing factor for event-based vision  [70]. By releasing a new labeled real-world event-based dataset, and defining an efficient semi-automated protocol based on a single eventbased camera, we intend to accelerate progress toward a robust and accurate event-based object classifier. 2 )) is computed by considering only the times t (x i + z, q) of the last events in a neighborhood of e i (orange dashed line). As a consequence, noisy events can have a large weight in Tei. This is visible from the spurious peaks in the surface Tei. (b) By contrast, the definition of Local Memory Time Surface T ei of Eq. (  3 ), considers the contribution of all past events in a spatio-temporal window N (z,q) (e i ).
Datasets We validated our approach on five different datasets: four datasets generated by converting standard framebased datasets to events (namely, the N-MNIST  [48] , N-Caltech101  [48] , MNIST-DVS  [63]  and CIFAR10-DVS  [35]  datasets) and a novel dataset, recorded from real-world scenes and introduced for the first time in this paper, which we call N-CARS. We made the N-CARS dataset publicly available for download at http://www. prophesee. ai/dataset-n-cars/.
Datasets Converted from Frames N-MNIST, N-Caltech101, MNIST-DVS and CIFAR10-DVS are four publicly available datasets created by converting the popular frame-based MNIST  [32] , Caltech101  [20]  and CIFAR10  [27]  to an event-based representation. N-MNIST and N-Caltech101 were obtained by displaying each sample image on an LCD monitor, while an ATIS sensor (Section 2) was moving in front of it  [48]. Similarly, the MNIST-DVS and CIFAR10-DVS datasets were created by displaying a moving image on a monitor and recorded with a fixed DVS sensor  [63]. In both cases, the result is a conversion of the images of the original datasets into a stream of events suited for evaluating event-based object classification. Fig. 4(a, b ) shows some representative examples of the datasets generated from frames, for the N-MNIST and N-Caltech101.
Dataset Acquired Directly as Events: N-CARS The datasets described in the previous section are good datasets for a first evaluation of event-based classifiers. However, since they were generated by displaying images on a monitor, they are not very representative of data from real-world situations. The main shortcoming results from the limited and predefined motion of the objects. To overcome these limitations, we created a new dataset by directly recording objects in urban environments with an event-based sensor. The dataset was obtained with the following semi-automatic protocol. First, we captured approximately 80 minutes of video using an ATIS camera (Section 2) mounted behind the windshield of a car. The driving was conducted in a natural way, without particular regards for video quality or content.
In a second stage, we converted gray-scale measurements from the ATIS sensor to conventional gray-scale images. We then processed them with a state-of-the-art object detector  [55, 56] , to automatically extract bounding boxes around cars and background samples. Finally, the data was manually cleaned to ensure that the samples were correctly labeled. Since the gray-scale measurements have the same time resolution of the change detection events, the gray-level images can be easily synchronized with the change detection events. Thus, the positions and timestamps of the bounding boxes can be directly used to extract the corresponding event-based samples from the full event stream. Thanks to our semi-automated protocol, we generated a two-class dataset composed of 12,336 car samples and 11,693 noncars samples (background).
The dataset was split in 7940 car and 7482 background training samples, and 4396 car and 4211 background testing samples. Each example lasts 100 milliseconds. More details on the dataset can be found in the supplementary material. We called this new dataset N-CARS. As shown in Fig. 4 (c) the N-CARS is a challenging dataset, containing cars at different poses, speeds and occlusions, as well as a large variety of background scenarios.
Event-based Object Classification Once the features have been extracted from the events sequences of the database, the problem reduces to a conventional classification problem. To highlight the contribution of our feature representation to classification accuracy, we used a simple linear SVM classifier in all our experiments. A more complex classifier, such as non-linear SVM or Convolutional Neural Networks, could be used to further improve the results. The parameters for all methods were optimized by splitting the training set and using 20% of the data for validation. Once the best settings were found, the classifier was We noticed little influence of the ρ and τ parameters to accuracy, while small K's improved performance for low resolution inputs. When the input duration is larger than the value of ∆t used to compute the time surfaces (Eq.
Results on the Datasets Converted from Frames The results for the N-MNIST, N-Caltech101, MNIST-DVS and CIFAR10-DVS datasets are given in Tab. 1. As it is usually done, we report the results in terms of classification accuracy. The complete set of parameters used for the methods are reported in the supplementary material. Our method has the highest classification rate ever reported for an event-based classification method. The performance improvement is higher for the more challenging N-Caltech101 and CIFAR10-DVS datasets. HOTS and a predefined Gabor-SNN have similar performance, while the H-First learning mechanism is too simple to reach good performance. Results on the N-CARS Datasets For the N-CARS dataset, the HATS parameters used are K = 10, ρ = 3 and τ = 10 9 µs. In this case, block normalization was not applied because it did not improve results.
Since the N-CARS dataset contains only two classes, cars and non-cars, we can consider it as a binary classification problem. Therefore, we also analyze the performance of the methods using ROC curves analysis  [19]. The Area Under the Curve (AUC) and the accuracy (Acc. ) for our method and the baselines are shown in Tab. 2, while the ROC curves are presented in the supplementary material. From the results, we see that our method outperforms the baselines by a large margin. The variability contained in a real-world dataset, such as the N-CARS one, is too large for both the H-First and HOTS learning algorithms to converge to a good feature representation. A predefined Gabor-SNN architecture has better accuracy than H-First and HOTS, but still 11% lower than our method. The spatio-temporal regularization implemented in our method is more robust to the noise and variability contained in the dataset.
Latency and Computational Time Latency is a crucial characteristic for many applications requiring fast reaction time. In this section, we compare HATS , HOTS and Gabor-SNN in terms of their computational time and latency on the N-CARS dataset. All methods are implemented in C++ and run on a laptop equipped with an Intel i7 CPU (64bits, 2. 7GHz) and 16GB of RAM. Tab. 3 compares the average computational times to process a sample. Average computational time per sample was computed by dividing the total time spent to compute the features on the full training set by the number of training samples. As we can see, our method is more than 20x faster than HOTS and almost 40x times faster than a 2-layer SNN. In particular our method is 13 times faster than real time. We also report the average number of events processed per second in Kilo-events per second (Kev/s).
5 ). The results were averaged over 5 repetitions. By using only 10ms of events, HATS has higher performance than the baselines applied to the full 100ms events stream. The performance of HATS does not completely saturate, probably due to the presence of cars with really small apparent motion in the dataset. We also notice that the performance of Gabor-SNN is unstable, especially for low latency. This is due to the spiking architecture of Gabor-SNN for which small variations in the input of a layer can cause large differences at its output.
Conclusion and Future Work In this work, we presented a new feature representation for event-based object recognition by introducing the notion of Histograms of Averaged Time Surfaces. It validates the idea that information is contained in the relative time between events, provided a regularization scheme is intro-Table  3 : Average computational times per sample (the lower the better) and average number of events processed per second, in Kilo-events per second Kev/s (the higher the better), on the N-CARS dataset. Since each sample is 100ms long, our method is more than 13 times faster than real time, while HOTS and Gabor-SNN are respectively 1,5 and 2,8 times slower than real time.
Baker dataset (Tarn Plateau and Park Butte). The depleted mantle composition is from  Salters and Stracke (2004) , with the exception of the Mg isotope composition (-0. 25‰), which is the canonical mantle value described in  Teng et al. (2010a)  and elsewhere  (Huang et al. , 2011; Hu et al. , 2016b; Wang et al. , 2016). The lower FIGURE 6 | AFC (assimilation-fractional crystallization) and bulk mixing models for δ 26 Mg vs. wt% MgO (A) and 87 Sr/ 86 Sr (B) for the North Cascades samples and available literature data. The colored circles represent those samples identified as most primitive from Mt. Baker and the Bridge River Cones, while the colored triangles represent samples that exhibit crustal contamination. The dashed and dotted line represents bulk mixing between depleted mantle and subducted sediment; the dashed line represents bulk mixing between primitive magma and subducted sediment; the solid line represents assimilation and fractional crystallization of lower continental crust by the primitive magma.
24 ± 0. 07 to -0. 10 ± 0. 07‰). The Cascade samples (-0. 33 ± 0. 07 to -0. 15 ± 0. 06‰) have similar Mg isotope compositions to the samples from all of those locations except the Philippines, which have unusually heavy compositions  (Li et al. , 2017). The present dataset fills in a gap in the Mg isotope data for arc volcanics from the circum-Pacific subduction zones, representing the margin along the northwestern coast of North America. The similarity to these other arc samples, including those from the Lesser Antilles in the Caribbean, demonstrates that the processes affecting Mg isotopes are likely consistent from margin to margin. Much of the combined data for the volcanic arcs worldwide can be approximated by the same assimilation and fractional crystallization model used with the North Cascades samples (Figure  6 ).
ACKNOWLEDGMENTS We would like to thank  Shui-Jiong Wang  for his extensive help throughout this study,  Florence Yuen ,  Khadijah Karrington Homolka , and  Jiarui Zhou  for their work in the clean lab, and  Kwan-Nang Pang  and  I. Stewart McCallum  for their insightful comments. P. Adam  and  J. Tepper  are acknowledged for providing samples from the  Bridge River Cones  and the Chilliwack batholith, respectively. We would also like to thank  Julia Ribeiro  for her careful editing and  Philip Pogge von Strandmann ,  Kristina J. Walowski , and  Thomas Zambardi  for their constructive comments. This work was supported by an  NSF  grant ( EAR-17407706 ) to F-ZT.
In particular, one important property of the Green function is that if the function u in Equation (  5 ) is constant and equal to 1, then we find that for any x ∈ Ω ∂Ω ∂G ∂n (x, y) dy = 1. ( 37 ) We build function g to estimate the Green's function as defined in Equation (  23 ) on a domain featuring two inner circle boundaries of centers 2i and -1 -2i and of radii 1. and 0. 5, respectively, and bounded by an outer circle of radius 5. centered at the origin. We then evaluate the quality of the estimation by comparing the integral of its normal derivative to 1: ε = 1 - ∂Ω ∂g ∂n (x, y) dy. ( 38 ) The integral is computed using the quad function from the scipy Python library  [31] , and based on a technique from the Fortran library QUADPACK. Figure  3  is a measure of the quality of the estimation from Equation (37), given by the logarithm of ε, relative to the number of collocation points at a given point x = -2 interior to the domain and for a given number of terms in the Laurent expansion N = 2, N = 5 or N = 10.
Two hundred and twenty-five patients were screened. Of the 58 participants eligible for the study, seven subjects dropped out (three participants without WMH and four with WMH), yielding 51 patients who completed the intervention program (39 women and 12 men; age=75. 3±6. 2 years) as well as preand postintervention assessments. Fourteen participants did not complete the follow-up assessment (seven MCI-WMH and seven MCI-non-WMH). Written informed consent was obtained from all participants. This study was approved by the ethical committee from Paris Descartes University's Institutional Review Board (N°IRB: 20161300001072) and was registered at ClinicalTrials. gov as NCT03195803.
Intervention All participants benefited from the same CCS program, developed using the concept of cognitive stimulation,  [35] [36] [37]  an ecologic approach based on engaging participants in a range of cognitive exercises in a group setting, thus aiming to stimulate cognitive functions and promote social interactions. The participants attended a 1 and a half hour session twice a week, during a period of 3 months (24 sessions in total) in Broca hospital. Three experienced neuropsychologists conducted group sessions with five to seven participants per group. The cognitive exercises were selected from the "KODRO" software (Altera-Group, Paris, France), a web-based platform, developed for older adults. We chose KODRO for its large content of playful cognitive exercises and for the ecologic nature of some of the exercises, which can be applied in everyday life.
Outcomes Participants were assessed at baseline (T0), immediately after the 3-month intervention (T3) and during a 3-month follow-up postintervention (T6). Neuropsychologists involved in data collection at any point of the assessment process were blinded to which group participants were part of. Outcomes were composed of neuropsychological and psychosocial measures. At baseline, we collected sociodemographic data such as age and sex as well as education level and premorbid intelligence, as assessed by the French adaptation of the National Adult Reading Test. 38 The presence or absence of hippocampal atrophy was also recorded.
Importantly, while the bulk of histones are replaced by protamine in sperm, 1% in mice and up to 10% of histones in humans are retained in genomic DNA  5. It is now widely accepted that remodelling of chromatin architecture plays an important role in transgenerational inheritance (reviewed in  6  ). Moreover, ectopic overexpression of the human histone lysine demethylase, KDM1a, during mouse spermatogenesis resulted in the transgenerational inheritance of abnormalities in the limbs, skeleton and skin development. This data supports the idea that histone methylation could be a mediator of transgenerational effects  7. The whole-genome datasets cataloguing human and bovine sperm nucleosome-retaining fractions provide evidence that genes encoding proteins with signal transduction and mitochondrial functions are enriched in the histone-containing parts of the genome  8.
1% in F1 (lower panel), nuclei are stained with DAPI and are shown in blue. Germ cells are negative for AMH, whereas Sertoli cells are positive. DAPI-stained cells represent all cells. In control, ESR1 staining appears as red nuclear and cytoplasmic; in CD-treated, as preferential nuclear staining. (E) Quantitative analysis of ESR1 signal in nuclei was performed using ImageJ software and presented as relative fluorescence +/-SEM, (p = 0. 001, n = 3 control, n = 3 CD samples, t-test). and 2. 5% in F3 CD), in addition to incomplete synapsing (16. 5% in F1 and 5. 8% in F3 compared to 1. 2% in both control F1 and F3, Table  1 ). To determine whether synapsing defects are due to inefficient DNA repair, we immunostained slides for the DMC1 protein, which specifically binds to single-strand DNA (ssDNA)  41.
We found that Pou5f1 has increased expression 1. 5 times in F1 males (p-value = 0. 048). The analysis of RNA-seq of F3 males showed that Pou5f1 is not differentially expressed. Based on our qPCR data, we found that master regulator of imprinting genes, Zfp57, has significantly reduced expression levels in F1 adult males (p-value = 0. 046) but not in F3 males. The transcriptomics analysis in the F3 generation whole testis using strand-specific paired-end high-throughput RNA sequencing revealed the changes of total 377 genes corresponding to 412 differentially expressed transcripts (Fold Change (FC) > 2 and FDR < 0. 1) (Supplementary Tables  1  and 2 , Supplementary Fig. 5 ). To determine the processes that could be perturbed in testis as a result of these changes, we performed a functional annotation of the differentially expressed genes (DEGs) using the Gene Ontology (GO) terms identified by DAVID.