dataesr/scientific-paragraphs-categorization huggingface dataset:
This design resulted in equally many Regular and Irregular spacing trials (120 of each). Before starting the experiment, the participants practiced on six blocks containing five or six repetitions sequences each at 2, 8 and finally 15 Hz. The experimenter was in the room during the practice and gave verbal feedback. The participants were informed that the image streams would last longer than during the practice. The whole session including practice lasted 1-1. 5 h. procedure and stimulus details. The images were a randomly chosen subset of the ImageNet database training set  33  (http://image-net. org/), cropped symmetrically from all sides to square shape and equalized in resolution to 150 * 150 pixels. Each participant viewed approximately 16. 000 different images. No images other than the repeated images and the images in the response frames were presented more than once to any participant.
The data were then split in half with respect to this delay and averaged within the two groups in order to contrast short and long delays. The very longest and shortest delays were not included since they were uncommon. Short delays were 3-20 and long were 21-37 trials. Note that the data is pooled over the conditions in a random, non-balanced, way in this analysis. For analysis and stimulus generation we used Matlab and Psychtoolbox-3  35. The 95% confidence intervals (CIs) of the participant means were computed using the Matlab bootci function with 2000 iterations and the default bias corrected and accelerated percentile method. For the Bayesian t-tests, we used the freely available software JASP  36.
Data Availability Neither of the experiments reported in this article was formally preregistered. The modified images from the ImageNet database training set are available at the open science framework https://osf. io/t7ej9/?view_on-ly=ead6d92f5c8b4e90891edfc0c136b21c. Raw data and the details of the Bayesian analysis are available at https:// osf. io/m4fy6/?view_only=730253996a6e489f82d393a3751ca1be.
root, modification of the Powell hybrid method  [52] ) and low-level virtual machine acceleration through the package Numba  [53]. Response to modulations. In order to characterize the spike rate dynamics we calculate the response to small-amplitude sinusoidal variations of the mean input around a baseline, � I s ðtÞ ¼ � I 0 s þ � I 1 s sin ðotÞ, � I d ðtÞ ¼ � I 0 d þ � I 1 d sin ðotÞ , or to a weak sinusoidal field, cf. Eq  (7). These modulations translate to sinusoidal modulations of μ s (t) and μ d (t) in the system (  25 )-  (36). The parameters σ ss , σ ds , σ sd and σ dd remain constant. For mathematical convenience we write the modulations in complex form m s ðtÞ ¼ m 0 s þ m 1 s e iot m d ðtÞ ¼ m 0 d þ m 1 d e iot ; ð44Þ with small m 1 s > 0 and m 1 d > 0 (thereby introducing a companion system) and approximate the solution to first order, rðtÞ ¼ r 0 þ r1 ðoÞe iot , where r1 is a complex variable from which the response amplitude r 1 and phase shift ψ of the oscillatory spike rate r 0 + r 1 (ω)sin(ωt+ ψ(ω)) can be extracted in a straightforward way: r 1 ¼ jr 1 j, c ¼ arg ðr 1 Þ (see, e.
For EMG recording, bipolar electrodes were sutured into the neck muscles using non-absorbable polyamide sutures. Animals were monitored for respiration rate and body temperature during surgery, observed until anesthesia recovery, and allowed to recover from surgery for 1 week before recording. EEG and EMG data were continuously collected using PC running Dataquest software (Data Science International, St Paul, MN, USA) linked to a receiver base (RPC-1, Data Science, St Paul, MN, USA), placed on the floor of the home cage inhabited by the implanted animals. Electrodes and the telemetry transmitter were removed after 1 week of recording under the same surgical conditions as for implantation. The EEG data were analyzed with Neuroscore v2. 1. 0 (Data Science International, St Paul, MN, USA). Analysis focused on the active state, determined by locomotor activity recording (included in the telemetry data of EMG recordings).
Images of the blots were digitized with a ChemiDoc Imaging System (Bio-Rad) and quantified with the Image Lab Software 5. 2. 1 (Bio-Rad). Serial sections of the entire brain of APP/PS1 ΔE9 mice were stained for the evaluation of Aβ pathology, as well as inflammation (Iba1 and GFAP). Brain sections were rinsed with PBS, and then incubated in 0. 3% hydrogen peroxide for 20 min. Sections were then blocked with PBS-0. 5% Triton (Triton X-100, Sigma, St Louis, MO, USA) and 4. 5% normal goat serum (NGS) for 30 min before overnight incubation with BAM10 (Sigma, A3981, 1/10. 000), Iba1 (Wako, VA, USA, /1000), or GFAP (Dako, Denmark, 1/10000) antibodies at 4 °C. Sections were rinsed with PBS and then successively incubated with 1/1000 anti-mouse IgG secondary antibody (BA-9200; Vector Labs) at room temperature for 1 h and ABC Vectastain (Vector Labs) before DAB revelation (DAB SK4100 kit, Vector Labs).
Images of stained sections were digitized with a Zeiss Axio Scan. Z1 (Zeiss, Jena, Germany) whole slide imaging microscope at X20 (0. 22 μm in plane resolution). The scanned files were exported as jpeg RGB images with a 30% compression (0. 73 μm in plane resolution) using Zen 2. 0 (Zeiss, Jena, Germany). Sections stained for Aβ were blindly analyzed and Aβ in the hippocampus quantified using ImageJ software  [36]. Segmentation of β-amyloid deposits was performed in two sections corresponding to AP -1. 70/-2. 30 mm  [27]. It was based on the determination of a threshold defined as T = M signal + 10xSD signal ,where M signal and SD signal represent the mean and standard deviation of the signal within a CA1 region of interest in which β-amyloid deposits were not visible. Neuroinflammation was blindly evaluated after manual adjustment of a threshold adjusted to select the stained microglial and astroglial cells.
Stereological counting of NeuN-positive neurons The optical fractionator method  [37, 38] was used to obtain an unbiased stereological estimate of the total number of NeuN-positive cells in the CA1/2 and CA3 layers of the hippocampus, layers I, II, and III-VI of the entorhinal cortex, and the cingulate/retrosplenial cortex. Measurements were performed on the left hemisphere of the brain. Cells were counted using a Leica DM6000 microscope equipped with a digital color camera (MicroFireTM, Optronics, Goleta, CA, USA), an x-y motorized stage controller, a motorized z-axis, and Mercator stereology software (Explora Nova, La Rochelle, France). The regions were delineated using a 4X objective in accordance with the mouse lemur brain atlas  [31]. Section thickness (from 11 to 13 μm) was measured at three locations for each section analyzed.
One out of ten sections was sampled, leaving 200 μm intervals between two sampled sections. ASF is the area of the sampling fraction and TSF the thickness of the sampling fraction. Values of SSF, ASF, and TSF for each region are given in Additional file 1: Table  S3. All histological data (surface, volume measurements, and cell counts) were performed by an investigator (SL) blind to the group assignment of the animals.
Statistical analysis Statistical analyses were performed using GraphPad Prism software (San Diego, CA, USA). In most graphics, data are shown as scatter plots with median and interquartile interval. Control-inoculated animals, as well as animals inoculated with each Alzheimer brain homogenates are displayed with different color codes. Behavioral studies are not displayed as scatter plots but as mean ± standard deviation (SD) to represent cognition evolution over time. Cognitive and motor experiments were evaluated by twoway repeated measures ANOVA (post-inoculation delay, group) followed by Bonferroni's multiple comparisons post-hoc tests. Data normality and variance homogeneity were evaluated using Shapiro-Wilk and Cochran C tests, respectively, and data from behavioral experiments were reciprocally transformed to obtain normality and variance homogeneity.
Estimations for quantitative values used a two-sample t-test on the basis of the mean and standard deviation obtained for the different measures in this study (BiostaTGV module, https://biostatgv. sentiweb. fr/?mod-ule=etudes/sujets#). The standard deviation used for this estimation was the square root of the pooled variance from each group. Estimations for proportional values (proportion of lemurs with Aβ, CAA or Tau in the brain) were based on a chi-squared test (BiostaTGV module based on epiR package 0. 9-9. 6, https://biostatgv. sentiweb. fr/?module=etudes/sujets#).
Acknowledgements We thank  V. Buee-Scherrer  for biochemical evaluation of tau. We also thank the donors and the  Brain Donation Program  of the " GIE NeuroCEB "  Brain Bank run by a consortium of Patient Associations: ARSEP  (association for research on multiple sclerosis),  CSC  (cerebellar ataxias),  France Alzheimer  and  France Parkinson , with the support of  Fondation Plan Alzheimer  and  IHU A-ICM  for providing the brain samples used in this study. GIE Neuro-CEB:  Charles Duyckaerts ,  Véronique Sazdovitch ,  Sabrina Leclère-Turbant , and  Marie-Claire Artaud-Botté ; The national network includes the following neuropathologists:  Anne Vital (Bordeaux), Françoise Chapon (Caen), Jean-Louis Kemeny (Clermont-Ferrand), Claude-Alain Maurage & Vincent Deramecourt (Lille) ,  David Meyronet & Nathalie Streichenberger  (Lyon),  André Maues de Paula  (Marseille),  Valérie Rigau  (Montpellier),  Fanny Vandenbos-Burel (Nice) ,  Charles Duyckaerts (Paris) ,  Danielle Seilhean  (Paris),  Véronique Sazdovitch  (Paris),  Serge Milin  (Poitiers),  Dan Christian Chiforeanu (Rennes) ,  Annie Laquerrière (Rouen) ,  Béatrice Lannes (Strasbourg), Marie-Bernadette Delisle & Emmanuelle Uro-Coste (Toulouse).
Images were analyzed using NIS Elements D 4. 60. 00 (Nikon Instruments, Japan) and ImageJ (https://imagej. nih. gov/). Flow cytometry data were analyzed with FlowJo V10. Statistical analyses were performed in R (https://www. r-project. org/). Time-kill curves. Biphasic killing parameters were determined by fitting a biexponential mixed model to the Log 10 -transformed, normally distributed number of surviving cells (CFU/ml) using the R package nlme (https://cran. r-project. org/ web/packages/nlme/index. html). The model was based on the equation Log 10 (CFU) = Log 10 (N 0 Á e -k n Áτ þ P 0 Á e -k p Áτ ), with τ the treatment time (in hours), N 0 and P 0 the number of normal and persister cells at τ = 0, and k n and k p the rate at which normal and persister cells are killed (per hour)  25.
For uniphasic killing, the R package lme4 (https://cran. r-project. org/web/packages/lme4/index. html) was used to fit the equation Log 10 (CFU) = Log 10 ((N 0 + P 0 ). e -k. τ ). Akaike Information Criterion was used to assess both models. Multidrug tolerance. Surviving fractions were compared between conditions using one-way analysis of variance and post-hoc comparisons with Sidak's correction for multiple testing. When no colonies were detected, the value for the surviving fraction was set at half the detection limit. Distribution of awakening times. The variable "persister awakening time" was split into bins of 60 min. The number of observations in each bin was normalized, to obtain relative frequencies (freq) of awakening events. The nls function in R was used to fit an exponential distribution with equation Log 10 (freq) = Log 10 (b.
Acknowledgements We thank  Abram Aertsen  ( KU Leuven ) for sharing the hipA7 strain. E. M. W. and B. V. D. B are Research Foundation Flanders (FWO)-fellows. This research was funded by the  KU Leuven Research Council  ( PF/10/010 ;  C16/17/006 ),  FWO  ( G047112N ;  G0B2515N ;  G055517N ), and the  Flemish Institute for Biotechnology (VIB).
In order to determine if, despite these potential issues, digit-tracking is a robust and reliable approach for studying visual exploration, a group of 22 subjects performed a simple picture exploration task under either digit-tracking or simple eye-tracking conditions. Each subject explored 122 pictures, divided into two sets (A and B) of 61 pictures. One subgroup of 11 subjects explored set A in the blurred-imaged, digittracking condition and set B in full-resolution condition with their eye movements being recorded using an infrared eyetracking device. The second subgroup of subjects explored the same two sets of images but the assignment of recording method was reversed. Image sets, order of presentation and recording method were fully counterbalanced. For each image/subject couple, the probability density of exploration was revealed using kernel density estimates using fixation data weighted by fixations duration for eye-tracking measurements, and the regularly sampled center of the simulated central vision for digit-tracking measurements.
We used a well-validated convolutional neural network architecture for feature extraction (AlexNet Network  23  -Fig. 3a ). The selected model was first trained to classify a set of more than one million images extracted from the ImageNet database (http:// www. image-net. org) for the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC  24  ). Then, a second training phase was conducted in order to find the optimal linear combination of high-level feature maps that produce the saliency maps closest to observed human behavior (Fig. S4 ). Empirically-derived attention maps used for this second learning phase were computed from eye-tracking data in a first experiment and from digit-tracking data in a second one. The CNN architecture predicted human visual exploration behavior measured with either method (Cross-Correlation Score (CC) CCeye = 0.
Impairments of visuospatial attention are a common consequence of vascular or traumatic brain injury. We tested patients with right parietal lobe damage and a suspicion of hemispatial neglect. Bedside examination with the digit-tracking system easily revealed the extent of the patients' left neglect (see the single case example in Fig. 4a  and data for 4 other patients in Fig. S5 ). Atypical visual behaviors are also observed in autistic spectrum disorders (ASD)  [25] [26] [27] [28]. This is illustrated in Fig. 4b  showing the image exploration performance of a 14-year-old patient presenting clinical signs of ASD, with social avoidance and highly stereotyped, restricted, and repetitive patterns of behavior. As compared to an age-matched neurotypical subject, exploration of socially-rich images using the digit-tracking interface revealed a systematic tendency to avoid human figures, and a focus on nonsocial features.
A strong positive correlation coefficient indicates a highly attractive feature while a strong negative correlation indicates a highly avoided feature. c Correlation between the weights learned using eye and digit-tracking (Set A r pearson = 0. 95, p < 1 × 10 -128 -Set B r pearson = 0. 96, p < 1 × 10 -147 ). d High-level features are visualized by identifying in the picture database the most responsive pixels for the considered CNN channel. Example of the most attractive and the most avoided features corresponding to, respectively, the 3 most positively correlated and the 3 most negatively correlated channels of the CNN. Human explorations are particularly sensitive to eyes or eye-like areas, faces and highly contrasted details, while uniform areas with natural colors, textures, repetitive symbols are generally avoided.
It is therefore of interest to establish the suitability of digit-tracking for populations other than adult human subjects. We have initiated a developmental study assessing visual performances in children from several age groups. Preliminary results indicate that children as young as 3 years-old can explore images using the digit-track interface and show well-defined attention maps (Fig. S9 ). We also conducted tests in a macaque monkey who had access within its home environment to a touch sensitive display running the digit-track software. In order to encourage spontaneous image exploration, the monkey was simply rewarded when a pre-determined track-path length had been reached, with no time constraint. The monkey learned to interact with the device and showed expected attraction to significant features in social scenes (Fig.
The recent innovations have pushed computing capabilities to a level at which the latest biomimetic algorithms need to optimize a large number of parameters and resort to huge learning databases. Consequently, acquiring human behaviors on a new scale becomes mandatory. Classification algorithms need the labeling of a great number of images and acquiring such data is easily feasible through a WEB interface. But when some regression problems need to be solved, such as saliency map prediction, more complex behaviors need to be recorded. Saliency models can be used to overcome the limited spatial resolution of convolutional neural networks, they can be trained to solve specific object detection tasks and help neuroscientists understand how humans can effortlessly detect targets in their visual field using highly degraded peripheral vision.
Modeling the obtained data with a convolutional neural network revealed how subjects allocate their attentional resources as they explore pictures with social content, what key high-level visual features drive the attention in the two populations. Notably, we identified a deficit in the exploration of regions containing eyes, which is partially counterbalanced by a preserved exploration of other face regions. This result replicates the atypical face exploration behavior often reported in the ASD population  [25] [26] [27] [28]. It should be emphasized that the patient population tested consisted of highly rehabilitated young adults whose visual exploration behavior might reflect successful learning of social adaptation strategies. As the development of innovative strategies to improve social interactions in patients with ASD focus on eye-contact or eye-contact-like behavior  40, 41  , digit-tracking could prove useful in a clinical setting to detect, quantify and follow the evolution over time of this behavior.
2, σ: 3. 4) at the time of the study. All participants had normal or corrected to normal vision. Ethics committees approval. Tests conducted in human participants were approved by French (Sud-Ouest, project N°2018-A02037-48) and Italian Ethical Committees (Azienda Ospedaliero-Universitaria of Cagliari, project N°AOB/2013/ 1, EudraCT code 2013-003067-59) and prior to the inclusion in the study, a written informed consent was obtained from all participants and/or their legal representative, as appropriate. Tests conducted on non-human primates were in conformity with current guidelines and regulations on the care and use of laboratory animals (European Community Council Directive No. 86-609) and were conducted under a research protocol authorized by the French Ministry of Research Ethics board (APAFIS n°2015061213048343).
Picture database. Two sets of 61 pictures each (Set A and Set B, N = 122) representing natural and social scenes, humans, animals, objects and/or examples of abstract art were selected. The image database was deliberately varied in order to not preclude any exploration behavior by the tested populations. Each picture contained one or several salient features and was optimized for a 1280 × 1024 pixels screen resolution. The type and global content of the scenes were matched between the two subsets. The picture data base contains images depicting human bodies and faces because these stimuli induces characteristic exploration patterns that provide the best basis for methods comparisons and because these exploration patterns are also strongly diagnostic of autistic subjects' gaze behavior. Experimental procedure.
The stability of the calibration and the behavior of the subject was constantly checked by the operator during the experiment by, in real time, observing if the fixations on the fixation-cross was correctly realized and observed before each picture presentation. If any problems were detected during the recording, the data were excluded from the analysis in order to not bias the quality and stability of measurements. Using these procedure and experimental setup, the usually observed mean deviation between real and measured focalization point is in the order of one degree of visual angle  46. The Clearview 2. 7. 0 software was used for picture presentation, eye-gaze data recording and pre-processing. The experiment took place in a quiet, dedicated room. Pictures were displayed on 33. 8 × 27.
4 cm video monitor (1280 × 1024pixels) placed at a distance of 50 cm from the subject's eyes (0. 87°/cm-39°× 31°). Each trial began with the presentation of a fixation cross, prompting the subject to press a key in order to display a picture. The fixation cross stayed on the screen for 1 s and was replaced by a 4 s picture presentation. Due to a technical problem, data from two ASD patients during exploration of the set B images were not included in the analysis of eyetracking data. Digit-tracking method. Digit-tracking was implemented on a Microsoft Surface Pro 4 computer, configured as a tablet with a 2736 × 1824 pixels touch interface (267 Pixels Per Inch) and a physical dimension of 26 cm × 17. 3 cm (46. 5°× 31°of visual angle-0. 56°/cm-considering a median eyes-screen distance of 32 cm, range 27 to 37 cm).
Comparisons between digit-tracking and eye-tracking. Several analyses were conducted to compare visual exploration measures obtained with digit-tracking and eye-tracking methods. First fixations were identified in the continuous eyegaze data using a fixation filter (Fixation radius = 50 pixels, minimum duration = 50 ms). Only fixations in the picture area were considered for analysis. Then, for digit-tracking, the regularly sampled location of the center of the simulated foveal windows was transposed in the original picture space to ensure that all analyses are done in the same space with the same spatial resolution for the two methods (picture maximized to fit a 1280 × 1024 pixels rectangle). For each subject and each picture, the probability density of exploration, or attention map, was calculated from the fixations data using kernel density estimation with a Gaussian kernel weighted by fixation duration and a bandwidth of 30 pixels (≈1°of visual angle) in order to compensate for the usual imprecisions encountered with these methods (e.
g. , ref. 46  ). Finally, each density was scaled between 0 and 1 by dividing the whole density by the maximum density value and group estimates were calculated by averaging scaled densities recorded at the subject level and scaled using the same unity-based normalization procedure. The comparison between attention maps measured with eye-tracking and with digit-tracking was performed using a correlation analysis. For each picture and each recording method, a group attention map was computed by averaging the attention map across subjects, yielding two group-attention maps by picture, derived from two independent populations with two different recording method. Then, for each picture, the similarity between eye-tracking and digit-tracking measurements was evaluated using Pearson correlation for a total of 61 correlation coefficients for each dataset, and tested using non-parametric statistical hypothesis tests.
Thus, for each dataset, the median correlation was tested using Wilcoxon rank-sum test (Bonferroni-corrected for the 2 comparisons) and the difference between the two median correlations was tested with uncorrected Wilcoxon ranksum test. The sensitivity to the noise of a recording method can be estimated using a counterbalanced inter-subject correlation analysis. Inter-subject correlations were calculated, for each image/technology couple, as the average Pearson correlation coefficient between the attention maps of one subject with the attention maps of another subject. The higher the inter-subject correlation (ISC) the more similar is the exploration between subjects. The linear relationship between ISC calculated with eye-tracking and with digit-tracking was evaluated with Pearson correlation, and the difference was tested using Wilcoxon rank-sum test.