dataesr/scientific-paragraphs-categorization huggingface dataset:
The data were randomized and subaveraged across three trials within each trial type (as described above). Importantly, the z-score normalization was performed on the training and test dataset separately. For each time-point within the first 300 ms of each saccade trial, the classifier would then label the trial as face or house according to the learned patterns of the peripheral fixation trials (chance ϭ 50%). Classification accuracy for each participant was determined from 10 iterations of randomizing and subaveraging the data in each trial type. Group analysis was performed as described in the above paragraph. MVPA of the postsaccadic time-period was performed using the central fixation condition. The postsaccadic time-period was defined from saccade offset to 300 ms after saccade offset.
Acknowledgments Thanks to  Ying Chen  for posing for the photos in Figure 2 and Figure  10 ,  Carlijn van Alphen  for help with data collection, and  Sophie Wuerger ,  Anya Hurlbert ,  David Peterzell ,  Jules Davidoff ,  Karl Gegenfurtner , and  David Brainard  for comments. The work was supported by  ERC   Advanced Grant ''FEEL' ' No. 323674  to  J. Kevin O'Regan. Chris Racey  is funded by a  European Research Council  project '' CATEGORIES ,'' ref. 280635. Commercial relationships: none. Corresponding author:  Christoph Witzel. Email: cwitzel@daad-alumni. de. Address:  Justus-Liebig-Universität Gießen, Gießen, Germany.
Participants were asked to Think Aloud to illuminate difficulties encountered during task performance and were video-recorded with Morae software. Following two TUG trials, we provided participants paper-based prototypes of the feedback interface with their age-normed TUG time and estimated fall risk, conducted interviews focused on user experience, and administered the System Usability Scale (SUS). Nine of 10 participants performed the test correctly although six had questions about the instructions. Eight individuals expressed interest in monthly testing. Eight participants comprehended the TUG results graphics, but offered suggestions to enhance clarity. Satisfaction, rated with the SUS was high at 83. 4%. Because deviations from specified sitting and walking positions resulted in missing data, clear environmental landmarks will be required for older adults to perform the TUG independently.
Guinea pigs Pups were placed in a grey plastic circular arena (diameter 45 cm; height 30 cm). The arena was placed within a big box (80 × 80 × 100 cm). Inside, a white light bulb provided strong illumination. A microphone (IMG stage-line) was placed just above the arena and connected to a pre-amplifier (set at 70 dB) that was connected to an audio filter, which was in turn connected to a PC. Calls were automatically analyzed using the software program Ultravox 2. 0 (Noldus, Wageningen, The Netherlands), and the number of calls was quantified. Calls shorter than 15 ms were discarded and calls separated by b 15 ms were counted as one call.
Data collection The RRS-short form is a self-administered rumination questionnaire of ten items describing two dimensions: brooding and reflection. For each item, each subject indicates the frequency of each event on a 4-point scale ranging from 1 ("almost never") to 4 ("almost always"). Sociodemographic variables including age, sex, marital status (in couple versus alone), living arrangement (alone or living with a partner/family/collective), education level (primary/high school versus university level), and employment status (employed versus unemployed) were reported. Clinical characteristics were duration of illness, age at illness onset, number of previous hospitalizations due to depression, number of depressive episodes, depression severity using the 13-item self-rating Beck Depression Inventory (BDI; total score range 0-39, with higher scores indicating greater depression and scores $16 corresponding to a diagnosis of severe depression),  28, 29  and anxiety severity using the 20-item self-rating State-Trait Anxiety Inventorysituational/state anxiety (STAI-S; total score range 20-80, with higher scores indicating greater anxiety).
45, 46 Data analyses were performed using SPSS 20. 0 (IBM Corporation, Armonk, NY, USA), Winsteps ® Rasch measurement computer program (Winsteps 3. 91. 0; Linacre JM. OR, USA) for Rasch analysis, Stata 9. 0 (StataCorp LP, College Station, TX, USA) for DIF analyses, and AMOS 20. 0 (IBM Corporation) for confirmatory factor analysis.
Fourth, we used a self-rating scale (BDI) to measure the severity of depression. However, several studies support a satisfactory convergent validity between the BDI and more standardized objective tools, such as the Hamilton Psychiatric Rating Scale for Depression and the Montgomery-Åsberg Depression Rating Scale. 29, 63, 64 The Inventory of Depressive Symptomatology would have been of interest, as it contains one item (16 -"Outlook [self]") that allows the assessment of the intensity of ruminations considering major and minor defects in self. Finally, important data concerning external validity were not collected in our study. In particular, the cross-sectional nature of our study prevented us from making longitudinal predictions in terms of mid-and long-term change in clinical state (eg, response to treatment, remission, and relapse).
Acknowledgments This work was supported by operating grants to H. -Y. M. C. from the  Canadian Institutes of Health Research (CIHR)  (MOP# 126090 ) and the  Natural Sciences and Engineering Research Council (NSERC)  of  Canada  ( RGPIN-2016-05563 ), and to K. O. from the  National Institutes of Health  ( R01MH103361 ). H. -Y. M. C. is a  Tier II Canada Research Chair (CRC) in Molecular Genetics of Biological Clocks. L. M. -V. was supported by a graduate scholarship from  Consejo Nacional de Ciencia y Tecnologia (CONACyT) of Mexico.
Statistical Analyses Fear expectation mean percentages and SCR amplitudes were calculated by averaging four subsequent consecutive values resulting from the CS+ and CS-conditions. The averaged four responses, along the conditioning, extinction and recall phases provided nine mean values for each CS-type: one corresponding to habituation (H; the four last), four to conditioning (C1, C2, C3, and C4) and four to extinction (E1, E2, E3, and E4). Results obtained for CS-were subtracted to those for CS+. Because the data did not follow the normality law and/or did not respect the variance equality, non-parametric tests were performed using sigma plot software (v12). Five participants had no electrodermal conditioning, that is, they had no SCR larger than 0. 01 for any of the CS during acquisition.
Participants and experimental protocol The experiment was carried out on sixteen (six females) righthanded healthy undergraduate students, with ethical-committee approval and informed written consent. We determined the required size through the G* power software  (Faul et al. , 2009)  by setting the expected effect size at 0. 28 (estimated from previous studies on mirror and counter-mirror effects;  Ticini et al. , 2012, Barchiesi and Cattaneo, 2013; Cavallo et al. , 2013; Catmur et al. , 2007) , the significance level at 0. 05, and the desired power at 0. 80. Participants were naive to the purpose of the experiment. They seated comfortably in the experimental room with the forearms on a table. Their right hand was positioned between two buttons (starting position), one located on the right side of the little finger and the other on the left side of the index finger (Figure  1A ).
Data analysis Peak-to-peak MEP amplitudes were calculated as the absolute distance between the minimum and maximum values observed within a search window starting at 10 ms and ending at 80 ms after the TMS pulse. We discarded MEPs with detectable background activity preceding the TMS pulse in EB (0. 5%), TP (2. 5%), and in the two series of eight pulses at the beginning and at the end of the experiment (1. 4%). Statistical analysis was performed using the standard tests of null hypothesis on mean MEP (collapsed across muscles, see  Ticini et al. , 2012)  as dependent variable. We used the partial eta squared (gp2) to determine the effect size and set the alpha level at 0. 05 for all statistical tests. When needed, we also computed Bayes Factors (BF 10 ) in JASP  (Love et al. , 2015) , which allow to estimate the likelihood of the null hypothesis relative to the alternative hypothesis  (Rouder et al.
The first round was performed with primers polis4 and PolOR or NDR1 to PolOR followed by Polis2 to Uni2 or Polis4 to Uni2, respectively. PCR amplifications were performed using a Long Expand PCR kit (Roche Applied Science) according to the manufacturer's instructions. Amplified fragments were agarose gel purified and externally Sanger sequenced. Phylogenetic analyses were carried out on viral nucleotide sequence alignments by using MUSCLE in the MEGA v7 software package. Ignoring all gaps, an alignment of 319 bp was investigated, corresponding to base pairs 3727 to 4047 of SIVmusOI81 (accession number KF304707. 1), a highly conserved region. The best-fitting distance model of nucleotide substitution for each alignment was inferred using the maximum likelihood (ML) method, with goodness of fit measured by the Bayesian information criterion (BIC) in TOPALI v2.
The robot could thus retrieve a specific memory and the state of the world at a given time. Figure  2  provides details of the ABM architecture and its functioning. The ABM is implemented as an SQL database and a set of C++ functions that access and process the ABM contexts. In Figure  2A  the human and robot interact in a physical space, with actions involving objects, and they interact socially via spoken language. Figure  2B  illustrates the system architecture, where information about actions performed on objects is perceived by ReactVision TM and updates the world model in the Object Property Collector (OPC), which feeds into the ABM. Note that part of the contents of the world model is visible in the display in the upper left corner of Figure  2A. Figure  2C  illustrates the interaction between the components of the system.
The structure of the episodic memory storage is illustrated in Figure  2D. When the robot FIGURE 2 | (A) iCub robot interacting with the human. Mental model displayed on screen in upper left. (B) System architecture overview. Human and iCub interact face-to-face across the ReacTable, which communicates object locations via ReactVision to the object property collector (OPC). The Supervisor coordinates spoken language and physical interaction with the iCub via spoken language technology in the Audio interface. The autobiographical memory system encodes world states and their transitions due to human and robot action as encoded in the OPC. (C) Overview of the memory functioning including the ABM SQL Database, the Supervisor, the Reasoning module, and the OPC. This provides a partial zoom in on (B).
That is, the robot has a perceptual system to observe actions and a motor system to perform actions  (Lallée et al. , 2011) , a language system to understand and produce simple language related to action  (Dominey and Boucher, 2005b; Hinaut and Dominey, 2013; Hinaut et al. , 2014 Hinaut et al. , , 2015)) , and the ABM infrastructure described above. While these are minimal requirements, one can also consider that these could be embedded as part of a more complete system  (Lallee and Verschure, 2015). In the human, the neural infrastructure for autobiographical memory is significant  (Svoboda et al. , 2006) , indicating its importance in human cognition. We also made a significant investment in the ABM system that we developed for the iCub, as described above. We now review a series of studies that together demonstrate how aspects of each of Neisser's four levels can be achieved with the support of ABM.
SOMs were used to allow the iCub to learn its body schema  (Lallee and Dominey, 2013). Figure  7  illustrates how through experience, the iCub has learned the association between a proprioceptive posture, the camera image of the hand in that posture, and the spoken name of that posture (in this case the word "scissors" in the "rock, scissors, paper" game). In this body-schema context,  Hoffmann et al. (2010)  note that in the majority of studies on the development of robot body schema's typically only vision and proprioception are used in the multimodal context. The use of vision, proprioception and simple language demonstrates the extensibility of such multimodal approaches. These two systems: the forward model implemented in the ABM, and the MMCM body schema can provide to the robot a way to interpret the consequences of his own action upon the world and upon itself, thus constituting a first step toward development of the ecological self.
Via the use of an interface between the ABM and the language system, the robot can access its own history while interacting with a human. This provides a new dimension of shared experience over extended time, in the context of the development of the narrative self. We used the ABM system integrated into the Nao robot, in a series of experiments with naïve subjects teaching the robot how to repair a broken electronic card as a cooperative activity and shared plan with the subject. Again, this produced a significant data base in the ABM which could then be used as the contents in dialog between the human and the iCub, illustrating the narrative self. Here is a dialog excerpt from such an interaction:  An instance corresponds to an entry in the autobiographical memory and can be triggered by any software component of the iCub (sensor, effector, reasoning).
Creating the interface between the language system and the ABM allowed the first steps toward the narrative self. The robot could express its long term experience in language, extending the cooperative interaction capabilities. The past thus becomes available for interrogation by the human partner, in order to understand why a shared plan was learned in a certain way, who taught the shared plan, etc. (Sorce et al. , 2015). The crucial next step was to allow narrative to enrich the experience of the iCub beyond what the iCub can directly perceive  (Bruner, 1990). Narrative enrichment takes place when narrative creates links between events that were not visible. When the human says to the iCub "I gave you the toy because you wanted it, " the human is introducing a causal link between the mental state of wanting, and the physical action of giving.
, 2014). This constitutes one of the high points of primate social cognition  (Tomasello et al. , 2005) , in the context of the interpersonal self. The conceptual self-the acquired representation of one's characteristics-was also shown to be supported by the ABM. Indeed, the representation of self characteristics were used to allow the iCub to reason about what it was capable of doing, here in the context of a challenging cognitive task in the form of the Tower of Hanoi. This game can be characterized in terms of conceptual knowledge of rules about how the pieces can be moved. By observing legal moves being performed, the ABM encoded the corresponding preconditions and post conditions and generalized this to encode the rules of the game. We thus Simulating consequences  (Dearden and Demiris, 2005; Pointeau et al.
0. All variables described in the methods section were used in the model with 20 imputed datasets, and country-and neighbourhood identifiers were used as auxiliary variables. As a sensitivity analysis, complete case analysis was conducted on the original dataset; results are shown in supplementary tables S1a, S1b1, S1b2 and S1c. Additionally, we conducted a sensitivity analysis with sleep categorized into short, normal and long sleep duration  (Hirshkowitz et al. , 2015). Results of the a-and c-paths are shown in supplementary tables S2a1, S2a2 and S2b. All multilevel logistic regression analyses were conducted using STATA version 12. 1. We also performed linear regression analyses using a continuous BMI variable as dependent variable; this resulted in similar results as the main analyses (results not shown).
Drift-Diffusion Models (DDM) are widely used to model behaviour. Accounting for trial-to-trial variations of the parameters in DDM leads to better fits and allows researchers to investigate variations of parameters over time. However, parameter estimation with conventional integration techniques is slow and cannot be applied to large problems. Here we propose to rely on Variational Bayesian inference to estimate the posterior probability of the parameters at the local (trial) and global (subject and population) levels. We present a data-driven model that uses Inference Networks to estimate an approximate distribution for each of the local parameters given an arbitrary set of regressors. We apply the method to simulated and real datasets and show that it converges within reasonable time frame, achieves good fitting performance and highlights complex auto-correlation structures between trials.
While the Full DDM is more realistic, it is also much more complex and computationally expensive. So far, most methods used to fit the Full DDM to behavioural datasets have relied on Numerical Quadrature (NQ)  (10; 11; 12; 13; 14). However, NQ is computationally expensive, which precludes its use for large problems. Typically, only trial-to-trial variability in three of the four parameters of the DDM are assessed (drift, start-point and non-decision time). Also, large datasets (e. g. > 100 trials per subject) take an unreasonable amount of time to be optimized. Another issue with many implementations of the DDM lies in the core assumption of the method used to fit the parameters to the data. Usually, a Maximum Likelihood Estimate (MLE; i. e. finding the most likely value parameters would endorse in order to generate the data at hand) is used  (10; 15; 16; 11; 17; 18; 12; 13) , although a few Bayesian attempts have been made  (19; 20; 14).
Crucially, the validity of the MLE approach is based on some asymptotic properties of the summary statistics of a given dataset when the amount of data tends to infinity  (21). However, as we have seen, the computational cost of the numerical methods commonly used to optimize the Full DDM prevents us from fitting large datasets. Hence, we face a dilemma: a large data set will prevent us from computing the ML using classical NQ algorithms, and a small dataset set will violate the assumptions of the MLE. In this paper, we will show that the Full DDM finds a natural formulation in Bayesian Statistics (Section 2. 1 and Section 2. 2), and that we can use this formulation to solve efficiently this problem using recent advances in Variational Inference (Section 2. 3). This method will be first implemented in a simple case of (non-)linear Regression Model (Section 2.
(1) If the absorbing boundary is ¬b (i. e. the upper boundary), the above equation can be adjusted by setting w = 1 -w and v = -v. Together, the sum of the First Passage Time Densities at the two boundaries forms a valid probability distribution, i. e. : β∈{b,¬b} ∞ t=t0 W iener(t, β; z)dt = 1 and W iener(t, β; z) ≥ 0 for t ∈ [t 0 , ∞] and β ∈ b, ¬b. (2) The function in Equation (1) involves an infinite sum, and must therefore be implemented through approximations. Navarro et al. (23)  provided a method to truncate this infinite sum to the appropriate number of terms κ as a function of truncation error. Here, we used their approximation with a numerical precision of 10 -32 to estimate the parameters value. The simplest and most intuitive way to fit the DDM to behavioural data is to look for the parameter setting that maximizes the likelihood of observing this dataset given the parameters and the model.
If tempting, this approach overlooks a crucial detail of the DDM: if the true parameters vary from trial-to-trial, the fit of the average of these parameters will suffer from some specific, consistent biases that can have an important impact on the interpretation  (5). For instance, a high variability in the value of the starting point with mean. 5 will generally produce a dataset that will be better fitted by a point-estimate w * <. 5. Accounting for the trial-to-trial variability in w with distribution parameters θ w such that w ∼ p(w; θ w ) solves this problem.
The Full DDM as a Bayesian model The MLE of the Full DDM as formulated by  (10)  consists in finding the value θ * that maximizes the likelihood function p(y; θ) for y = {RT t,s , C t,s ∀s, t} (and therefore the posterior probability under an unnormalized, unbounded uniform prior), and to consider this value as a consistent estimator of the true value of θ. This choice can be justified from the asymptotic normality of the posterior distribution theorem  (21) : as the dataset grows, and considering that the data are generated according from the same process, the posterior distribution approaches a multivariate Gaussian distribution with a mean close to the true value θ 0 and covariance matrix equal to the inverse of the Observed Fisher Information matrix, i. e. θ * → θ 0 as N → ∞ and p(θ) ≈ N (θ * , I(θ * ) -1 ), where I(θ * ) = -2 θ * log p(D; θ).
Also, following the Bernstein-von Mises theorem  (24; 25; 26) , we can see that the choice of the prior becomes irrelevant to the posterior as the amount of data grows, which further justifies the MLE technique for large datasets. When maximizing a likelihood function, it is usually easier to work with its log transform, because it alleviates the need for computing the product of possibly small values, thereby avoiding possible computational underflow issues. Moreover, it is a valid approach since the log function is monotonically increasing, and therefore the log-likelihood function shares the same maxima as the likelihood function. If we are to use the MLE method for the Full DDM for a set of S subjects with T trials per subject, we therefore need to solve the following optimization problem: θ * = arg max θ p(y; θ) = arg max θ 1 S S s=1 1 T T t=1 (y t,s ; θ s ) where (y t,s ; θ s ) = log W iener(y t,s ; z)p(z t ; θ s )d z (3) For simplicity, we will consider that the DDM parameters all lie in an unbounded space, so that their prior probability can be formulated as a Gaussian with parameters θ = {µ, σ} (see Appendix A for justification and implementation).
Inference Network Flexibility and sparsity are two of the main desired properties of the approximate posteriors we want to fit to our dataset. A solution to reduce the number of variational parameters consists in using a direct, deterministic mapping from the datapoints to a given approximate posterior δ t = f (y t ; ξ) where ξ is the vector of parameters of the function f. This leads the approximate posterior to look like q(z t | y t ; ξ) (Figure  3 ). Our goal is now to optimize the mapping function parameters ξ instead of the local variational posteriors δ t. This optimization can be achieved using some Stochastic Gradient Descent algorithm, as we will see shortly. This use of an inference network (IN) is known as Neural Variational Inference and Learning (NVIL)  (33)  or amortized variational inference  (34).
It alleviates the need of optimizing each of the local approximate posteriors, which can be problematic for large datasets based on stochastic gradient descent optimization where each point has to be visited many times to obtain an accurate estimate of the true posterior, especially when the prior is optimized (or inferred) at the same time. This technique has led to many applications  (31; 35; 34; 36; 37; 38; 39) , because it allows the model to find a likely posterior for datapoints that might not have been seen yet, and, for large problems, it is cheaper than estimating each local variational parameters separately. Comparison of Algorithm 2 and Algorithm 3 highlights the difference between Amortized inference and classical VB method in the VEM context. Algorithm 2: Classical E step for the VEM algorithm (Algorithm 1) Data: Choices and reaction times y = {y 1 , y 2 ,.