dataesr/scientific-paragraphs-categorization huggingface dataset:
This model has been comprehensively described in  Personne et al. (2009)  and more recently in  Hansen et al. (2017). The model is forced every 3 h by net radiation, deep soil temperature (30 cm), air temperature, relative humidity, wind speed, rainfall, and atmospheric NH 3 concentration with monthly values from passive sampler measurements repeated every 3 h. Forcing also includes values of leaf area index (LAI, measured), canopy height Z h (estimated), roughness length Z 0 (0. 13 Z h ), displacement height D (0. 7 Z h ), stomatal emission potential (constant), ground emission potential (derived from measurements during field campaigns, constant the rest of the time), and measurement height Z ref (2 m). LAI was measured according to the methodology developed in  Mougin et al. (2014). Data from Dahra were measured monthly during the wet season and were not published (Mougin, personal communication).
Acknowledgements. This study was financed by the  French CNRS-INSU  ( Centre National de la Recherche Scientifique -institute National des Sciences de l'Univers ), through the  LEFE -CHAT comity (Les Enveloppes Fluides et l'Environnement -Chimie Atmosphérique ). The authors thank the  IRD (Institut de Recherche et de développement)  local support for logistical help in Senegal, and the  Centre de Recherches Zootechniques (CRZ) de Dahra of the Institut Sénégalais de Recherches Agricoles (ISRA)  for their logistical help during the field campaigns. TT was funded by  SNSB  ( Dnr95/16 ). Review statement. This paper was edited by  Jens-Arne Subke  and reviewed by two anonymous referees.
S3. The data and the code used to analyze the data will be made available upon request. ECoG electrode positions on the brain (Fig. 1 ) were determined as described in  (Parvizi et al. , 2012). Imaging data were obtained using a GE 3-Tesla Signa scanner at Stanford University. A high-resolution anatomical volume of the whole brain was acquired with a head coil using a T1-weighted SPGR pulse sequence. Data were aligned to the AC-PC plane and resampled to 1 mm isotropic voxels. Both fMRI data and electrocorticography (ECoG) electrode locations were aligned to this brain volume. This volume was segmented to separate gray from white matter, which was used to reconstruct the subject's cortical surface.
General data analysis, data preprocessing and spectral analysis All analyses were performed in MATLAB (MathWorks) and used the FieldTrip toolbox (http://www. fieldtriptoolbox. org/) (Oostenveld et al. , 2011). Raw signals were low-pass filtered at 250 Hz and downsampled from %3. 05 kHz to 1 kHz. To remove the common recording reference, we subtracted signals from neighboring electrodes from each other, and refer to the resulting bipolar derivation as a (recording) site; This reduced the data from 6 electrodes to 5 sites. A 60 Hz notch filter was applied during recording (Fig. 2 ; gray shaded areas). No further data selection or pre-treatment, like artifact rejection, was performed. For each of the 432 trials, we extracted a baseline epoch from À0. 5 to 0 s, and a stimulation epoch from 0. 3 to 0.
Classification For the analysis shown in Fig. 7 , we used a linear Support Vector Machine (SVM). Specifically, we used the Matlab functions "svmtrain" and "svmclassify" for training and classification, respectively. The SVM was repeatedly trained on the spectral power recorded during the multiple presentations of two different images, and then used to classify the power recorded during a retained presentation of one of the two images. In detail, the following procedure was applied: 1) For any given pair of images, the power spectra for each presentation of either one of the images were obtained; 2) The power spectrum for one selected presentation of one of the images was retained and later used for classification; 3) For the remaining power spectra, the number of spectra was matched between the two images by random subselection, and those spectra were used to train the SVM; 4) The trained SVM was applied to classify the retained power spectrum; 5) The classification was identified as either correct or incorrect.
, 2004; Hoogenboom et al. , 2006; Self et al. , 2016). These studies in human subjects used controlled stimuli like gratings, rather than natural images, but the similarity of the observed visually-induced gamma-band activity across species strongly argues against a general species difference  (Fries et al. , 2008). Thus, neither differences in species nor fixation control can explain the discrepancy between  Brunet et al. and Hermes et al. Rather, this  discrepancy is likely due to the way in which Hermes et al. arrived at their conclusion, as discussed in the following. As mentioned in the introduction, Hermes et al. analyzed the same dataset, yet arrived at the conclusion that gamma oscillations are "not necessary for seeing"  (Hermes et al. , 2015). They base their conclusion mainly on the finding that some stimuli that can be perceived do not lead to gamma reaching significance in their test.
Mutually predictive image parts are perceptually bound, as they likely belong to one object. Both perceptual binding and predictability are reflected in neuronal gamma-band synchronization  (Gray et al. , 1989; Peter et al. , 2019; Singer and Gray, 1995; Vinck and Bosman, 2016). Note that for colored surfaces, gamma merely requires predictability of color across space, as e. g. in a uniformly colored surface  (Peter et al. , 2019). For grayscale stimuli like typical gratings or the images used here, gamma requires predictability of luminance contrasts, i. e. image structure  (Gieselmann and Thiele, 2008; Vinck and Bosman, 2016). In the dataset analyzed here, images with weak structure (or weak structure inside the respective receptive field) most likely induced gamma below the detection threshold of Hermes et al.
2016). Avisoft SASLab Pro 5. 2 software (Avisoft Bioacoustics, Glienicke, Germany) was used to construct the playback stimuli. Before the experiments started, we confirmed that focal parent birds were absent (no birds were visible around the nest and no birds were calling within 2 min). A speaker attached to a bamboo stick was placed at a distance of 1 m from the focal nest. The duration of each experiment was 7 min, including 2 min playback, and 5 min of subsequent observations. Behavioral responses during 7 min were recorded, included response latency (when responses occurred) and response duration (duration from the onset of behavioral response to termination)  (Yu et al. 2016 ). Video recorders were set up to record the experimental process. Alarm calls of ORW were played back to ORW (referred to as "O-O playback" hereafter), and alarm calls of BRW were played back to BRW (referred to as "B-B playback" hereafter).
Statistical methods All data in this study were analyzed using R 3. 4. 3 software (http://www. r-proje ct. org). For the response variables during dummy experiments, generalized linear mixed models (GLMMs, glmer in R package lme4) with a Poisson error structure and log-link function were used for the number of attacks of focal parent birds and the maximum number of warblers being attracted. For the response variables during three categories of playback experiments, we used GLMMs with a Poisson error structure and log-link function for analyses of response latency and duration. In the event of a significant effect of treatment, we further performed post hoc pairwise comparison between treatments. Because two-group comparison after multiple comparisons will increase the probability of type I errors, we used Bonferroni correction to adjust P values (P.
Acknowledgements We would like to thank  Prof. Shanthakumar Kulasekar ,  Dr. Kazuhiro Goto  and two anonymous reviewers for their constructive and helpful comments on our manuscript. We are grateful to  Longwu Wang ,  Yongbo Yu , and  Ye Gong  for assistance with fieldwork. We also thank  Zhalong National Nature Reserve  for their support and permit to carry out this study. This work is supported by the  National Natural Science Foundation of China  ( 31470458 ,  31770419  to HW,  31772453  to WL), the  Open  Project  Program of Jilin Provincial Key Laboratory of Animal Resource Conservation and Utilization  ( 130028823 ), and the  Fundamental Research Funds for the Central Universities  ( 2412018QD009 ).
Capacitance of the dielectric layers The capacitance of each dielectric layer was determined using the equation of parallel plate capacitor: C = A × /d, where A is the area of the capacitor (8 mm × 2 mm), d is the thickness of each layer determined by the scanning electron microscopy measurement and surface profiler measurement (DektakXT surface profiler), and  is the permittivity of each dielectric film. Here, low-frequency permittivity was approximated as that at optical frequency and measured using an ellipsometer (VASE, J. A. Woollam; spectral range from 193 to 3200 nm). The measurement data and fitting process of the permittivity can be found in section S1 (fig. S3 ). The voltage drop across the CQD layer was calibrated via the calculation of the charge stored in insulator layers (see details in fig.
Acknowledgments Funding: We would like to acknowledge the financial support from the  Singapore Ministry of Education   AcRF Tier-1 grant  ( MOE-2017-T1-002-142 ). We are also grateful to acknowledge the financial support from the  Singapore National Research Foundation  under the program  NRF-NRFI2016-08 ; the  Competitive Research Program   NRF-CRP14-2014-03 ; and the  Singapore Agency for Science, Technology and Research (A*STAR) SERC   Pharos Program  under grant no.
Motivation: Although missing data are prevalent in genetic and genomics data sets, existing implementations of Gaussian mixture models (GMMs) require complete data. Standard practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates. Results: Here we present MGMM, an R package for fitting GMMs in the presence of missing data. Using three case studies on real and simulated data sets, we demonstrate that MGMM is more effective at recovering true cluster assignments than standard GMM following state of the art imputation.
towards the complete data. This can result in inaccurate posterior membership probabilities that place excess weight on clusters with lower missingness. Although methods have been described for estimating GMMs from incomplete data  (Ghahramani and Jordan, 1994) , there are no existing implementations in R. To fill this gap, we present MGMM, an computationally efficient package for maximum likelihood (ML) estimation of GMMs in the presence of missing data. In contrast to complete case analysis, our approach makes full use of the available data, and in contrast to imputation, our approach is unbiased, and correctly propagates uncertainty. We present three case studies demonstrating that ML estimation via our expectation conditional maximization (ECM) algorithm  (Meng and Rubin, 1993)  outperforms imputation followed by standard GMM in recovering the true cluster assignments.
Cancer Patient RNA-seq Data Set To compare MGMM with imputation plus GMM in more realistic settings, we applied both to a Cancer RNA-Seq data set. Gene expression was measured in n = 801 patients having 1 of 5 tumor types. MGMM remained highly effective across missingness levels, especially when combined with entropy-based filtering. k-NN, MICE plus filtering, and missForest were competitive with base MGMM. MICE without filtering under-performed on these data, perhaps due to the presence of non-linear cluster boundaries. Mean and median imputation were not competitive at missingness exceeding 20%.
| Effects of resource density on food niches and networks We analyzed the effects of R. ferrugineum floral density, taxonomic insect families, and their interactions on network and on food niche indices using mixed linear models fitted with the HLfit function of the spaMM package  (Rousset & Ferdy, 2014 ) and the prior. weights option that accounts for the different sizes of samples. We examined individual indices at family (Apidae, Empididae, and Syrphidae) instead of at species level since some species had either too few individuals or were present in only one patch. To control for spatial autocorrelations, a random patch effect was added to the models. Residuals normality was always checked, and Box-Cox  (Box & Cox, 1964)  power transformation was applied if required. For the proportion of individuals carrying R.
This work was supported by the French Laboratory of Excellence project "TULIP" (ANR-10-LABX-41; ANR-11-IDEX-0002-02) and by the Centre National de la Recherche Scientifique-INEE (ERESA program: DJ/ST/IP/2013/D-112). We thank John S. Terblanche for his helpful comments. study. AP, NE, MB, and CA collected the data; NE and SB performed laboratory works. CA, SB, and AP analyzed the data.
5 ppm. The electrodes, with a mass loading of 1. 68 mg cm À2 , were assembled in Swagelok-type cells with metallic Na as reference and counter electrode. The electrolyte used in all experiments was 1 M NaFSI in EC : DEC (volume ratio 4 : 6). Electrochemical measurements were carried out on VMP3 or MPG2 potentiostat/galvanostat instruments from Bio-Logic controlled by the EC-Lab software. Cyclic voltammetry (CV) was performed at different increasing scan rates, i. e. 0. 05 mV s À1 , 0. 1 mV s À1 , 0. 2 mV s À1 and 0. 5 mV s À1. The samples were cycled between 2. 8 V to 4. 0 V vs. Na/Na +. Galvanostatic cycling with potential limitation was carried out between 2. 8 V to 4. 0 V vs. Na/Na +. The cycling rates were C/10, C/5, C/2, C, 2C, 5C (each 25 cycles) and again C/10 (15 cycles). The currents corresponding to the chosen C-rates were calculated considering the theoretical capacity of NVP, which is 118 mA h g À1.
For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.
Moreover, a large set of actions can be distinguished from these movements  [29]. 3D skeletal data is not only invariant to camera-viewpoint but also can be estimated in real-time. Moreover, it is available for most of depth based action datasets  [30]. Hence, exploiting this data source for 3D human action recognition opens up opportunities for addressing the limitations of RGB-depth modalities-based solutions and so many skeleton-based action recognition approaches have been proposed  [19, 23, [31] [32] [33]. Our goal is to exploit the potential of low-cost consumer depth cameras for identifying salient spatio-temporal patterns in skeleton sequences and then explore them for improving the recognition of human actions using deep learning models. In the literature of skeleton-based action recognition, there are two main issues that need to be solved.
In particular, D-CNNs with architectures such as DenseNet  [50]  can improve accuracy in the image recognition task since this kind of network is able to prevent overfitting and degradation phenomena  [58]  by maximizing information flow and facilitating features reuse as each layer in its architecture has direct access to the features from previous layers. Therefore, we explore the use of DenseNet in this work and optimize this architecture for learning and recognizing human actions on the proposed image-based representation. The effectiveness of the proposed method is evaluated on four public benchmark RGB-D datasets, including MSR Action3D  [59] , KARD  [60] , SBU Kinect Interaction  [61]  and NTU-RGB+D datasets  [39]. The hypotheses above were reinforced since the experimental results show that we achieve state-of-the-art performance on all the reported benchmarks.
• Secondly, we present a deep learning framework (The implementation and models will be made publicly available at https://github. com/cerema-lab/Sensors-2018-HAR-SPMF). based on the DenseNet architecture  [50]  for learning discriminative features from the proposed Enhanced-SPMF and performing action classification. The framework directly learns an end-to-end mapping between skeleton sequences and their action labels with little pre-processing. We evaluate the proposed method on four highly competitive benchmark datasets and demonstrate significantly improvement over existing state-of-the-art approaches. Our computational efficiency evaluations show that the proposed method is able to achieve high-level of performance whilst requiring low computational time for both the training and inference stages.
Pose Features (PFs) Computation Given a skeleton sequence S with N frames, denoted by S = {F t }, where t = 1, 2, 3,. , N. Let p t j and p t k be the 3D coordinates of the j-th and k-th joints in F t. The Joint-Joint Distance J JD t jk between p t j and p t k at timestamp t is computed as J JD t jk = ||p t j -p t k || 2 , (t = 1, 2, 3,. , N), (1) where || • || 2 denotes the Euclidean distance between two joints. The joint distances obtained by Equation (  1 ) for all types of actions of a specific dataset range from D min = 0 to D max = max{J JD t jk }. We note this distance space as D original. In fact, D original can be transformed into a tensor-structure and fed directly to D-CNNs for learning action features. However, since D original is a high-dimensional space, it could lead D-CNNs to overfit as well as being time-consuming.
Building Global Action Map from PFs and MFs Based on the obtained PFs and MFs, we propose a skeleton-based representation called SPMF for 3D human action recognition. To this end, all PFs and MFs computed from the skeleton sequence S are concatenated into a single feature vector in temporal order from the beginning to the end of the action. It is a global representation for the whole skeleton sequence S without dependence on the range of action and can be obtained by SPMF = [PF 1 + + MF 1→2 + + PF 2 + +. + + PF t + + MF t→t+1 + + PF t+1. + + PF N-1 + + MF N-1→N + + PF N ]. (8) Figure  4  (top row) shows some SPMFs obtained from the MSR Action3D dataset  [59]  in which all images are resized to 32 × 32 pixels. Before computing the SPMF, a Savitzky-Golay smoothing filter  [37, 76]  is adopted to reduce the effect of noise on skeletal data.
The histogram of image I will be defined by H k = n k , (10) where n k is the number of pixels in I with intensity k. The probability of occurrence of intensity level k in I can be estimated by p k = n k r × c , (k = 0, 1, 2,. , L -1). ( 11 ) The histogram equalized image is defined by transforming the pixel intensities, n, of I by the function T(n) = floor((L -1) n ∑ k=0 p k ), (n = 0, 1, 2,. , L -1), (12) The Histogram Equalization (HE) method is used for increasing the global contrast of the image. However, it cannot solve the problem of increasing local contrast. To overcome this limitation, the image needs to be divided into R regions and the HE is then applied in each and every one of these regions. This technique is called the Adaptive Histogram Equalization algorithm (AHE)  [49]. The bottom row of Figure  4  shows samples of the enhanced motion map with R = 8 on 32 × 32 images, which we refer to it as Enhanced-SPMF, for some actions from the MSR Action 3D dataset  [59].
Network Design We propose to design and optimize deep DenseNets  [50]  for learning and classifying human actions on the Enhanced-SPMFs. To study how recognition performance varies with architecture size, we explore different network configurations. The following configurations are used in our experiments: DenseNet (L = 100, k = 12); DenseNet (L = 250, k = 24); and DenseNet (L = 190, k = 40), where L is the depth of the network and k is the network growth rate. On all datasets, we use three dense blocks on 32 × 32 input images. In this design, H l (•) is defined as Batch Normalization (BN)  [79] , followed by an advanced activation layer called Exponential Linear Unit (ELU)  [80]  and 3 × 3 Convolution (Conv. ). A Dropout  [80]  with a rate of 0. 2 is used after each Convolution to prevent overfitting.
After the feature extraction stage, a Fully Connected (FC) layer is used for classification task in which the number of neurons for this FC layer is equal to the number of action classes in each dataset. The proposed networks can be trained in an end-to-end manner by gradient descent using Adam update rule  [81]. During the training stage, we minimize a cross-entropy loss function, which is measured by the difference between the true action label y and the predicted action ŷ by the networks over the training samples X. In other words, the network will be trained to solve the following optimization problem Arg min W (L X (y, ŷ)) = Arg min W - 1 M M ∑ i=1 C ∑ j=1 y ij log ŷij , ( 16 ) where W is the set of weights that will be optimized by the model, M denotes the number of samples in training set X and C is the number of action classes.
Experiments We investigate the effectiveness of the proposed approach using four public benchmark action recognition datasets, comparing our method with current state-of-the-art models for each benchmark. We refer the reader to a survey by Zhang et al. [30]  for a full description of current RGB-D based action recognition datasets: MSR Action3D  [59] , KARD  [60] , SBU Kinect Interaction  [61] , NTU-RGB+D  [39]. The detailed description of each dataset is provided in Section 4. 1. The implementation and training methodology are described in Section 4. 2. [59] : This Kinect 1 captured dataset contains 20 actions performed by 10 subjects. Each skeleton is composed of 20 joints. The MSR Action3D dataset  [59]  is challenging due to its high inter-action similarities. There are 567 action sequences in total, however, 10 sequences are not valid since the skeletons were missing.
Thus, our experiments were conducted on 557 valid sequences. We follow the standard protocol proposed by Li et al. [59]. Specifically, the whole dataset is divided into three subsets: AS1, AS2 and AS3. Table  2  provides a list of actions in each subset, in which all subjects with IDs 1, 3, 5, 7, 9 are selected for training and the remaining subjects with IDs 2, 4, 6, 8, 10 are used for test. Very deep neural networks such as the deep DenseNet architecture require a lot of data to train and optimize. Unfortunately, there are only 557 skeleton sequences on the MSR Action3D dataset  [59]. Therefore, some data augmentation techniques, i. e. , random cropping, vertical flipping, and rotation with α = 90 • have been applied on this dataset to minimize overfitting. Figure  5  illustrates three data augmentation techniques that were used in our experiments.
Kinect Activity Recognition Dataset (KARD)  [60] : The KARD  [60]  is a Kinect 1 dataset that contains 18 actions and 540 video sequences in total. Each action is performed three times by 10 subjects. It is composed of RGB, depth and skeleton frames in which each skeleton frame contains 15 key joints. The authors of the dataset  [60]  proposed to divide it into three subsets (i. e. , Action Set 1, Action Set 2, and Action Set 3), as listed in Table  3. For each subset, three experiments have been proposed. Specifically, the first experiment (Experiment A) uses one-third of the dataset for training and the rest for test. Meanwhile, the second experiment (Experiment B) uses two-thirds of the dataset for training and the rest for test. The last experiment (Experiment C) uses half of the dataset for training and the other half for testing.