dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Processing Breathing rate data was recorded at a sampling rate of 1 Hz. Data was filtered using a median filter (a 7th order low pass filter with a 20 sample point window). Next, the re-sampled data was z-normalized (per subject) using a pre-performance interval of 10 min as baseline. The data was synchronized across subjects using the markers (timestamp) provided by the Bioharness sensor. We then segmented the breathing rates time-courses into 4 time intervals corresponding to the 4 duets.
Analysis Individual level analysis: For each spectator, we fitted the data to four (one per duet) multi-linear regression models using MATLAB R2007b (The MathWorks, Inc. , Natick, Massachusetts, USA). The regression equation was YBR(t) = aX1BR(t) + bX2BR(t). YBR is the breathing rate of the spectator during the performance of the duet. X1BR and X2BR are the breathing rates of the two dancers from the duet. To study the combined effect of the two dancers' breathing rate we extracted the model's R2 (amount of variance explained by the model) rather than the coefficients of the individual dancers. In order to estimate the overall effect of the 4 duets we then averaged the four R2 values. We shall name this measure the R2 score. Group level analysis: Individual responses to the four target questions were modeled using linear mixed models (lm4 package for R;  Bates et al.
Procedure Before the start of the live dance performance, participants were received in a quiet section of the theater Lobby. The participants were seated in front a computer that controlled the experimental events and recorded the responses via the Open Sesame software (version 0. 26). In the task, the participants had to judge the stimulus duration in milliseconds and give their responses on a numeric keypad. They were instructed that the stimulus duration was between 200 and 1800 ms. When the participants were ready, they started a trial by pressing the spacebar. A blank screen was presented for 500 ms and then a rectangle appeared. There were 6 stimulus durations: 400, 600, 800, 1000, 1200, and 1400 ms. A 500 ms blank screen followed the presentation of the rectangle and then a screen displaying a request for time estimation.
Acknowledgments This research has received financial support from the  French ANR  through the  ''Investissements d'avenir'' program  ( ANR-10-LABEX-80-01 ) as well as extensive logistical support from the stuff of the Laboratoire d'excellence Arts-H2H. This work would have been impossible without the generous and continuous collaboration and support by  Myriam Gourfink  and her company as well as the hard work of all members of the labodanse project. We also thank the  Centre National de la Danse (Pantin, France)  and the  Forum de Blanc-Mesnil  for their support and cooperation.
To evaluate the effect of physical practice and MI practice on motor performance, participants performed before the training session (pre-test) and after it (post-test), one block of 5 sequences at their maximum speed. Data from glove were processed with a customized software and the finger movement performance rate was extracted and expressed in Hz. All subjects became faster during the training sessions (either physical practice and MI practice). We quantified the learning effect by measuring the increase in the value of finger movement performance rate in the post-test block of 5 finger sequences. To verify that MI training was really proficient to induce motor learning, 8 additional right-handed subjects (4 males and 4 females, mean age 22. 6 ± 4. 5 years) were involved in a control experiment.
To control for this we took a conservative approach: RM-ANOVAs that included the factor TIME were calculated in two versions. One used the MEPs recorded after practice (i. e. , immediately before PAS) as baseline and the second used MEPs before practice as baseline. Only those results are reported that turned out to be significant in both versions of the statistics  (Rosenkranz et al. , 2007). All the above statistical analyses were performed separately for the PAS25 and the PAS10 groups. Post hoc analysis of significant interactions was performed by means of t-tests applying the Bonferroni correction for multiple comparisons when necessary. p-values of 0. 05 were considered as threshold for statistical significance. Statistical analysis was performed with SPSS 13. 0.
Methods Participants Fifteen adults (age range 18-30) were recruited from the French RISC database (http://www. risc. cnrs. fr). They all reported normal or corrected vision, and were naïve with respect to the goal of the study. Three additional participants were tested but discarded from all the analyses reported here, because they showed extreme behavior (distance > 3SD from the mean), two in terms of poor perceptual performance, and one for an extreme bias in the confidence task. Participants received 20 euros after their participation in two sessions in the study.
These individual preferences were remarkably stable across time, and replicated in a second session one week after the first session. This result was obtained in two ways, firstly by analyzing a selection of trial pairs showing matched performance, and secondly by evaluating the bias across the whole dataset using a regression approach. This stability is important as it allows us to conclude that the individual differences we report reveal distinct individual profiles across the population, rather than random fluctuations due to sampling or estimation error. The inter-individual variability we found in our study contrasts with a recent study by Zylberberg and colleagues (  [13] ) in which higher stimulus variability was systematically accompanied by lower accuracy but higher confidence. As shown previously (  [9] ), this counterintuitive result can be explained by a model based on signal detection theory (  [17] ), with the additional and critical assumption of a "unique criterion constraint" (  [18] ), by which the observer uses the same set of decision and confidence criteria across the two experimental conditions.
Acknowledgments We thank the  RISC unit  ( CNRS UMS3332 ) for their participants' database. The authors gratefully acknowledge exciting discussions with  Joshua Solomon ,  Jérôme Sackur ,  Jean Daunizeau , with colleagues at the  Laboratoire de Psychologie de la Perception , the  Centre d'Economie de la Sorbonne , the  Motivation Brain Behavior laboratory , and with attendees of the Vision Science Society conferences 2012 and 2013.
, 1997). The subjects were selected from a sample of archived data including 42 patients participating in BP experiment and their selection was based on a presence of EBS in at least two EEG electrodes (per patient; neighboring contacts of one electrode were not included). The intracerebral locations of recording contacts from which the EBSs records were included are in Table  1. All patients had implanted chronic depth multilead electrodes for diagnostic reasons (Hôpital Sainte-Anne, Service de Neurochirurgie; INSERM, U 97) and were informed that the experiment had no relevance to their clinical examination. All of them agreed to participate in the study. With exception of epilepsy the selected patients had no other neurological or psychiatric disorder and all of them were treated by antiepileptic medication.
3. Experimental design During the experiment, a patient laid comfortably on bed and watched a point on the video screen positioned approximately 150 cm in front of their eyes. Experimental data were recorder during two periods. In the first control period, the patient watched the screen on which a letter lasting 200 ms irregularly replaced the central point. In the second experimental period, the patient was instructed spontaneously and irregularly to move a joystick and was asked to maintain intervals of at least 10 sec between the successive movements without counting or maintaining any other rhythmic activity. Due to anatomical cross of the hemisphere and hand, the opposite hand to the explored hemisphere was used to move the joystick with approximately 35 movements in the second experimental period.
4. Data acquisition and processing The intracerebral EEG activity was recorded and averaged using a 32-channel amplifier device (Nihon-Kohden, bandpass between 0. 016 and 100 Hz), and using Cambridge electronic device (CED) 1401 + interface with SIGNAL 2 software. Each data frame had duration of 5 sec (4 sec before the EMG onset in the experimental block and 4 sec before the visual signal in the control block) and was digitized at a frequency of 100 Hz. All the records were taken with a binaural reference. The data processing was performed offline using artifact-free iEEG records only (the selection was based on visual inspection of the records by two experienced persons). In experimental block, all available trials were used for averaging so that the number of trials averaged slightly varied across the patients (minimal number was 28).
5. Statistical evaluation Statistical evaluation of the results was performed by software package Statistica'99 (StatSoft Inc. , Tulsa, U. S. A. ). The binominal distribution was calculated on the whole data set using the chi-squared test, the correlation between the EBS amplitude and the distance was calculated also on the whole set using Spearman R-value. The correlation between levels of activity synchrony in paired loci was calculated in every pair using the same test, and the comparisons of levels of activity synchrony between subgroups were calculated in these subgroups using ANOVA test.
ACKNOWLEDGEMENTS The study was supported by the project  CEITEC CZ. 1. 05/1. 1. 00/02. 0068from   European Regional Development Fund  and the grant  MSM 002 1620849  research grant provided by  Charles University (PRVOUK). The data were collected in Paris ( Hôpital Ste Anne, Service de Neurochirurgie; INSERM, U 97 ) by a currently no more existent research group headed by  Michel Lamarche. The authors thank to  Pierre Buser  and  Ivan Rektor  for their help.
Deep brain stimulation (DBS), an effective surgical treatment for Essential Tremor (ET), requires test stimulations in the thalamus to find the optimum site for permanent electrode implantation. During these test stimulations, the changes in tremor are only visually evaluated. This, along with other parameters, increases the subjectivity when comparing the efficacy of different thalamic nuclei. We developed a method to quantitatively evaluate tremor during the test stimulations of DBS surgery and applied to 6 ET patients undergoing this treatment. From the quantitative data collected, we identified effective stimulation amplitudes for every test stimulation position and compared it with the ones identified visually during the surgery. We also classified the data based on the thalamic nuclei in which the center of the stimulating contact was present during test stimulations.
Electrical stimulation of certain brain structures is performed by implanting electrodes and connecting them to a subcutaneously implanted neurostimulator. Although the use of DBS is increasing, the optimum target structure is still debated. The limited knowledge of the mechanisms of action of DBS being one of the reasons, we believe the under-utilization of intraoperatively obtained data is another. A typical DBS surgery is preceded by a planning session during which a trajectory is planned from an entry point in the skull to the target structure on the patient's anatomical images using stereotactic planning software. With the aid of stereotactic equipment, this trajectory is then explored during the surgery. In most of the surgeries, neuronal activity is recorded and test stimulations are performed at pre-planned positions on this trajectory while the patient is awake.
Surgical protocol The primary goal of the DBS surgery remains the same for all surgical centres: to determine the optimal implantation position. However, the actual procedure may differ significantly. Lemaire et al have described, in detail, the routine surgical procedure at the University Hospital in Clermont-Ferrand  [4]. For treating ET patients using DBS, the primary target structure is the Ventrointermedius nucleus (VIM) of the thalamus. At the University Hospital in Clermont-Ferrand, during the planning session on patient's images using a commercial stereotactic surgery planning software (iPlan Stereotaxy, Brainlab, Germany), the surgical team identifies and outlines different nuclei in the thalamus using MRI images of the patient  [5]. After the definition of target and entry point, test stimulation positions are planned on the resulting trajectory.
After performing micro-electrode recording (MER) at these positions, test stimulations are performed. For each test stimulation, the stimulation current is varied from 0 to 3 mA in steps of 0. 2 mA and the changes in tremor are observed visually. The highest reduction in tremor (Visually identified Change, VC) and the corresponding stimulation amplitude (Visually identified Amplitude, VA) at which it was observed are noted for every test stimulation location. Along with these, the occurrence of side-effects, their type and their corresponding amplitudes are also noted. On completion of all test stimulations, these data are discussed upon by the surgical team and a position is determined as the "final implant location" for the chronic DBS electrode.
Acceleration data recording Tremor was quantified during the surgery using a commercial acceleration sensor evaluation board (STEVALMKI022VI, ST Micro, Switzerland). Time-stamped acceleration values from 3 axes were obtained at a frequency of 400 Hz and a range of 8 g. The evaluation board was fixed in an in-house developed non-conductive printed plastic case which was attached to the patient's wrist using a Velcro strap. The acceleration data were recorded by in-house developed software installed on a laptop that was interfaced with the sensor via an USB cable. Furthermore, the recording software was also interfaced with the electro-physiology system to synchronize the acceleration data recording with the test stimulation data. For the simplification of acceleration data recording and analysis, a data recording protocol was defined for all the surgeries.
Post-operative data analysis Acceleration data were analysed post-operatively using Matlab (Mathwork, USA). In brief, the resultant acceleration was calculated from the three axes and filtered to remove low frequency (2 Hz) movements and high frequency noise (10 Hz). Statistical features were extracted from these filtered acceleration data and the features from different stimulation amplitudes were normalized (%) using the features from the baseline data. These normalized features were used to identify a dataset of 2 values for each test stimulation: 1. ) The change in acceleration features (Quantitatively identified Change, QC) at the visually identified best stimulation amplitude VA and 2. ) The stimulation amplitude (Quantitatively equivalent Amplitude, QA) at which the quantitative change QC was similar to VC.
These data were then statistically compared to the corresponding values from the visual evaluation (VC vs QC and VA vs QA) using the Wilcoxon two-sided signed rank test. In order to analyse the correlation with the anatomical position of the electrode, the anatomical structure present at the centre of the stimulating electrode was determined. Data were then grouped based on the thalamic nucleus attributed to the test stimulation position. Furthermore, from the acceleration data of every test stimulation, we also calculated the minimum stimulation amplitude required to obtain a 75% reduction in the statistical features compared to baseline (Amp75). These data along with the occurrence of side-effects were used to compare the efficiency of different structures in reducing the patient's tremor by a fixed amount.
Acknowledgments The authors are grateful to  Drs. Odile Boesflug-Tanguy ,  Christel Depienne ,  Frédéric Darios ,  Cyril Goizet ,  Annick Toutain  and  Khalid H. El-Hachimi  for helpful discussions. The authors' work was partially funded by the  French National Agency for Research  (to GS), the  European Union  ( Omics call of the 7th framework program ,  NEUROMICS , to GS;  E-rare program ,  NEU-ROLIPID , to GS), the  Tom Wahlig Foundation , the  VERUM Foundation  (to GS), the  Vestre Viken Health trust  (to JK) and the program " Investissements d'Avenir "  ANR-10-IAIHU-06  (to GS). CT was the recipient of a PhD fellowship from the  French Ministry for Research from the Doctoral School of the Ecole Pratique des Hautes Etudes.
Spike-based neuromorphic sensors such as retinas and cochleas, change the way in which the world is sampled. Instead of producing data sampled at a constant rate, these sensors output spikes that are asynchronous and event driven. The event-based nature of neuromorphic sensors implies a complete paradigm shift in current perception algorithms toward those that emphasize the importance of precise timing. The spikes produced by these sensors usually have a time resolution in the order of microseconds. This high temporal resolution is a crucial factor in learning tasks. It is also widely used in the field of biological neural networks. Sound localization for instance relies on detecting time lags between the two ears which, in the barn owl, reaches a temporal resolution of 5 µs. Current available neuromorphic computation platforms such as SpiNNaker often limit their users to a time resolution in the order of milliseconds that is not compatible with the asynchronous outputs of neuromorphic sensors.
, 2014) , a digital parallel architecture oriented to the simulation of large scale models of neural tissue. The approach introduced in this work leverages the eventdriven nature of the platform to perform simulations with increased temporal resolution. We introduce a new collection of tools (spike sources and monitors, neural and plasticity models) oriented to sub-millisecond event-driven simulations and characterize the temporal behavior of the platform at different levels. The paper is organized as follows: Section 2 describes the hardware and software architecture of SpiNNaker and its current limitations. Section 3 introduces our novel programming framework and the components provided for sub-millisecond simulation. Section 4 reports the time characterization of the platform using a new method for measuring latencies.
Software The SpiNNaker software can be divided into two parts, the software running on the chips and on the host. Each SpiNNaker chip runs an event-based Application Run-time Kernel (SARK) that has two threads, the scheduler and the dispatcher. The scheduler is responsible for queuing tasks based on a userdefined priority, while the dispatcher de-queues and executes them starting with the highest-priority task. Tasks with priorities set to minus one are pre-emptive, zero task priorities are nonqueueable and are executed directly from the scheduler, while tasks with priorities set to one and above are queueable  (Sharp et al. , 2011). The SpiNNaker application programming interface (API) is built on top of SARK and allows users to write sequential C code to describe event-based neuron and synapse models by assigning callback functions that respond to particular system events.
, 2009) , a high-level simulator-independent neural specification language, is used that allows users to described neural topologies and parameters using abstractions such as populations and projections. A tool named partition and configuration management (PACMAN)  (Galluppi et al. , 2012b)  is responsible for mapping a PyNN description to a SpiNNaker machine based on the available resources, generating and uploading the relevant binary files, initiating a simulation and fetching the results to the host for further analysis.
Limitations of the Current Implementation In the SpiNNaker software available at the time of this work, neural models are implemented in a time-driven fashion. These models use the Timer event (see Section 2. 2) to periodically update the state of the simulated neurons with a given timestep. Parallel to that update process, incoming spikes to the implemented neural population are processed through the Packet Received Event (see Section 2. 2). This event looks up the different synaptic weights and delays relative to each connection. When the synaptic delay has been retrieved, the future contribution of the spike to the membrane potential of a given neuron is stored in its associated Post-Synaptic Potential buffer (PSP buffer). To implement the actual delay, the PSP buffers are ring buffers comprising one cell per simulation timestep.
Dendritic Delays As we can see from the standard implementation provided with SpiNNaker, managing synaptic delays when simulating neurons can lead to an important memory usage. To solve this problem, we chose to implement these delays independently from the rest of the neural simulation. One dendritic delay core implements one particular delay value. When a packet containing a spike is received, it is stored in a ring buffer in DTCM (local memory of the core). Then, a second process schedules events to dispatch these spikes after the given delay of the core has elapsed. This allows very compact and efficient code because events are output in their order of arrival. Because there are a large number of cores available in a typical SpiNNaker machine, using one core per delay value is not troublesome.
This section describes a series of experiments conducted in order to investigate the intra-and inter-chip MC packet latencies as a function of the router's waiting time and synthetic traffic going through a link. For these experiments, a parameterized software was developed using the SpiNNaker API. The packet received callback priorities were set to minus one (pre-emptive) in order to ensure that packets were cleared from the communications controller immediately upon receipt. The timer tick callback priority, which was used by the cores for terminating the simulation after 60 s, was set to zero (nonqueueable priority). Finally, the priority of the callback function developed to generate MC packets was set to two (lowest queueable priority). The processor clocks were set to 200 MHz, routers and system buses to 100 MHz, while the off-die memory clocks to 133 MHz.
825 µs, Figure  4A. Within this time is included the software overhead of the SpiNNaker API required to write the MC packet to the communication controller, the time needed for the packet to traverse through the internal link to the router, the time required for the packet to go through the router and again through the internal link to the communication controller of the target core and finally the software overhead of receiving the packet from the communication controller. The aforementioned times can be expressed as: t Intra RTD = t SW Send + 2 • t local Link + t R + t SW Receive , (7) where t SW Send is the software overhead of the API required to write the key of a MC packet to the communication controller, t SW Receive is the time passed from handling the interrupt raised by the communication controller to branching to the callback function assigned to handle the packet received events, t R is the time required by the router to process a single MC packet, and finally t local Link is the time a single MC packet needs to go through the local links.