dataesr/scientific-paragraphs-categorization huggingface dataset:
A connection can also be made between tangential diffusion and the double back propeller (Drucker and LeCun, 1992) and the opposing formation. (Szegedy et al. , 2014b; Goodfellow et al. , 2014b). Double backprop makes Jacobi more compact, while contentious training finds inputs that are geographically close to the original inputs and then trains the model to produce the same output for those inputs as the original inputs. To allow for tangential propagation and dataset expansion using manually specified transformations, the model must be invariant with respect to certain directions of change defined in the input. To ensure that the SSD performs as well as possible, a significant portion has been devoted to the process of developing location data for multiple objects from the ground up. A much better method would be to first train the final detection layers of the SSD network to distinguish between the many types of ground robots and their respective locations.
Many datasets have a natural order that places consecutive samples highly correlated with each other. For example, we may have a medical record consisting of a long list of test results from blood samples. This list can be structured so that the first five blood samples will be drawn at different times from sample only once or even perform an incomplete analysis of the training set. This is because the size of some datasets is increasing faster than the capacity of computers can keep up with the growth. Overfitting is not a problem when working with a very large set of drives; Therefore, poor fit and computational efficiency become major problems. For a discussion of the increasing number of training examples and the impact of computational bottlenecks on generalization errors, see also. Bottou and Bousquet (2008).
This initialization approach is also inspired by a deep mesh model that defines it as a series of non-linear matrix multiplications. This initialization strategy ensures that the total number of training iterations required to achieve convergence is not affected by the depth of the dataset under investigation. Increasing the scaling factor g brings the network closer to the region where the activation norm increases as it propagates forward along the network, but the gradient norm increases as it spreads backwards across the network. Sussillo (2014) has shown that a correct gain factor configuration is sufficient to train neural networks up to a depth of 1000 without using vertical initializations. One of the key lessons of this method is the realization that in feedforward networks, activations and gradients can expand or contract at each stage of forward or backward propagation, similar to the behavior of a random walk.
In fact, we often need to think of equilibrium as a hyperparameter whose ideal value is fairly close to the theoretical estimates, but not exactly equal. In fact, the optimal scale value of the weights lies somewhere between these two extremes. If available computational resources allow, it is generally a good idea to consider the initial scale of the weights at each level as hyperparameters and to randomly select these scales using a hyperparameter search technique mentioned in B. In other words, the initial weight scale for each layer should be considered as a hyperparameter. Another option for a hyperparameter is whether the initialization will be intensive or infrequent. It is also possible to manually search for scales that give the best initial results. When choosing initial scales, it makes sense to look at the firing range or the standard deviation of the gradients in a single mini dataset.
This can serve as a solid rule of thumb. If the weights are not large enough, the deeper the activations go in the network, the narrower the activation range that occurs during the mini-stack. Finally, it is possible to construct a network with acceptable initial activations by continuously identifying the first layer with unacceptably small activations and then increasing the weights of that layer. If learning is still too slow at this point, it might make sense to consider the range or standard deviation of the gradients in addition to the activations. This is possible if the slopes still change too much. Because it relies on feedback of the behavior of the first model to a single dataset, not feedback from a trained model to the validation set, this technique can in principle be automated and is generally less computationally intensive than a hyperparameter.
Double backprop training and competitor training require the model to be invariant in all aspects of input change, provided that the change in question is negligible. Just as enlarging the dataset is the non-infinite equivalent of tangential spread, enemy training can be thought of as the non-infinite equivalent of double rear propeller. The multi-tangent classifier developed by Rifai et al. , 2011c eliminates the need to know the tangent vectors beforehand. Autoencoders can do a variety of things, as we'll see below.
Confocal Microscopy and Image Analysis Single and/or double-immunolabeled images from each region of interest (dorsal striatum) were obtained using sequential laser scanning confocal microscopy (Zeiss LSM510 META, Oberkochen, Germany). Images were acquired at the level of the dorsal striatum (rostral level). Photomicrographs were obtained with the following band-pass and longpass filter setting: GFP (band pass filter: 505-530), Cy3 (band-pass filter: 560-615) and Cy5 (long-pass filter 650). Quantifications were performed in 325. 75 µm × 325. 75 µm confocal images. Immunofluorescent striatal cells (expressing IEGs and/or eGFP) were counted (absolute number) blindly of the treatment/group by using the cell counter plugin of the ImageJ software taking as standard reference a fixed threshold of fluorescence.
Statistical Analysis All statistical comparisons were performed with two-sided tests in Prism 6 (GraphPad Software, La Jolla, CA, USA). The distribution of data was determined with a Shapiro-Wilk normality test. No sample size calculations were performed. To determine outliers in every experimental group, we performed the Grubbs' test in Prism. No outliers were identified. All the data were analyzed using either Student's t-test with equal variances or one-way ANOVA. In all cases, the significance threshold was automatically set at p < 0. 05. The oneway ANOVA analysis, where treatment was the independent variable, was followed by a Bonferroni post hoc test for specific comparisons only when the overall ANOVA revealed a significant difference (at least p < 0. 05) among groups. All the values and statistical analyses are reported in the figure legends.
1A ). Finally, the simulated firing activity of each E pool is transformed into BOLD-like signals using the Balloon-Windkessel (BK) model  30  (Fig. 1C ), allowing a direct comparison with empirical fMRI data. The DMF has been used to simulate BOLD signals measured during an ample range of different conditions and global brain states  12, 15, 17, [31] [32] [33] [34]. Despite these achievements, a few limitations prevent the DMF model from being more widely exploited by the scientific community. The main limitation is that current publicly available implementations of the DMF model are computationally expensive, with demanding time and memory requirements. This problem is exacerbated by two further aspects of the DMF: 1) the need to calibrate the model's Feedback Inhibition Control (FIC) parameters, which stabilise the firing rates of E pools, and have been shown to provide more realistic activity 10 and richer dynamics  35  ; and 2) the need to optimise the model's free parameters to fit statistical features of a given empirical dataset.
Finally, using the Bayesian Optimization algorithm, we fit an fMRI dataset parcellated at two different spatial scales (N=90 and N=1000 regions). Using less than 350 optimization iterations, which can be performed in personal computers in matter of minutes, we achieve an excellent agreement between empirical and simulated data for both scales, showing the performance and broad applicability of the FastDMF to different scenarios. Furthermore, to facilitate the use of FastDMF by the community, we provide all codes that implement FastDMF. FastDMF is coded in C++ and is usable on both Matlab and Python and can be found on https://gitlab. com/concog/fastdmf.
Fast and efficient computational implementation The first advancement to make the FastDMF widely usable is to provide an open-source implementation with two main advantages: faster execution time and less memory usage. This is achieved through three main improvements: i) The core of FastDMF is written in C++ and takes advantage of the advanced linear algebra library Eigen,  40   given the large difference in timescale between the BOLD and the firing rate signals, allows FastDMF to radically reduce memory usage through shared finite-size buffer. 1  To benchmark our implementation, we ran simulations of varying lengths using the AAL90 structural connectome  36  and measured both memory usage and execution time. As expected, FastDMF was faster and more memory-efficient than the public Matlab implementation  12.
Thanks to the circular buffer, memory is effectively constant for all simulation times (Fig. 1D ) and runtime per second of simulated activity is less than half of the Matlab DMF (Fig. 1E ). Finally, in addition to the underlying C++ implementation, the FastDMF library incorporates interfaces for both Matlab and Python, using the Mex  41  and Boost Python 42 libraries respectively. These enable researchers to run and analyse simulations from a language of their choice, thus making the integration of FastDMF in their existing pipelines easier.
10  , and it is implemented in the scripts accompanying the publication of Ref. 12  and gives the optimal local inhibitory feedback N-dimensional vector (J opt ) for a given G, where N is the number of regions. However, for a standard setup these scripts took more than 2 months on a 32-core cluster to converge. Even worse, running the model using different connectivity matrices would require to separately optimize the FIC for each one of them, severely hindering the endeavour of investigating how structure shapes function through biophysical DMF modelling. Despite the lack of prior information regarding the expected J opt in the DMF models, results from other type of whole-brain models suggest that J opt is correlated with local connectivity measures of the structural connectivity  35. Accordingly, here we show that a first-order analytical solution for a stationary state where the expected values of firing rates are fixed to 3.
Schaefer 1000 As described in Ref. 1  , we used the Human Connectome Project (HCP) database, which contains diffusion spectrum and T2-weighted neuroimaging data. Specifically, we estimated the structural connectivity using HCP dMRI dataset provided by the Special HCP dMRI, which uses excellent protocols taking 89 minutes for each of 32 HCP participants at the MGH centre. A detailed description of the acquisition parameters can be found in the HCP website. 60. The precise preprocessing is described in details in Horn and colleagues  61  , In brief, the data were processed by using a q-sampling imaging algorithm implemented in DSI studio (http://dsi-studio. labsolver. org). A white-matter mask was computed by segmenting the T2-weighted images and co-registering the images to the b0 image of the diffusion data using SPM12.
Acknowledgements R. H. is funded by  CONICYT  scholarship  CONICYT-PFCHA/Doctorado Nacional/2018-21180428. P. M. is funded by the  Wellcome Trust  (grant no. 210920/Z/18/Z ). F. R. is supported by the  Ad Astra Chandaria Foundation. A. I. L. is funded by a  Gates Cambridge Scholarship  ( OPP 1144 ). E. T is funded by  Agencia Nacional De Promocion Cientifica Y Tecnologica (Argentina)  (Grant No. PICT-2018-03103 ). R. C. was supported by the  Human Brain Project ,  H2020-945539. G. D. is supported by the  Spanish Research Project  (ref. PID2019-105772GB-I00 AEI FEDER EU ), funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI)  and  European Regional Development Funds (FEDER) ;  HBP SGA3 Human Brain Project Specific Grant Agreement 3  (Grant Agreement No.
Simulation of Hearing-Aid Processing A 5-channel HA simulator implemented in MATLAB TM  (Moore et al. , 2010a)  was used to amplify the speech signals. The frequency ranges of the five HA channels were 0. 1-0. 7, 0. 7-1. 4, 1. 4-2. 8, 2. 8-5. 6, and 5. 6-8 kHz. In each channel, the simulator used two dynamic range compressors placed in series: the wide dynamic range compression function was applied in the first compressor, while the second compressor was used as a limiter. For further details about the implementation of the HA simulator, see  Fontan et al. (2020c).
Simulation of Hearing Loss The functioning of the HL simulator, also implemented in MATLAB TM , is detailed in  Nejime and Moore (1997). As done in Gonçalves Braz et al. (  2022 ), the simulator was used to mimic two of the perceptual consequences of age-related HL: Based on the input audiogram, a linear filter simulated the elevation of hearing thresholds, while loudness recruitment was simulated by raising the signal envelope  (Moore and Glasberg, 1993).
ACKNOWLEDGMENTS The experiments presented in this manuscript were carried out using the OSIRIM platform (http://osirim. irit. fr/)that is administered by the  Institut de Recherche en Informatique de Toulouse (IRIT)  and supported by the  French National Center for Scientific Research (CNRS) , the  Occitanie Region , the  French Government , and the  European Regional Development Fund (ERDF). MS was funded by the  NIHR Manchester Biomedical Research Centre (United Kingdom ).
GIF videos using StimulateOpenGL (StimGL) II v. 20160216 (https://github. com/cculianu/StimulateOpenGL_II). Each DLP was configured to parse the 24-bit RGB images in BRG order as 4-bit sequences of information per pixel to yield a moving pattern frame rate of 360Hz. Temporal synchrony of the two projectors was verified by highspeed videography (1000 fps; SA3 Fastcam, Photron, UK), filming two test targets, one from each projector, moving along synchronised circular trajectories with offset radii (Figure  1C ). Visual stimuli were synchronised with electrophysiology traces via a photodiode sensing a small 50x50 pixel box alternating black and white every frame.
𝑆 Butterfly photoreceptor electrophysiology and receptive field mapping Argynnis paphia butterflies were wild caught in Ljubljana, Slovenia in August 2021. Six photoreceptors from three Argynnis paphia adult males were recorded intracellularly using sharp borosilicate microelectrodes, filled with 3M KCl (R=80 MΩ). Recording electrodes were advanced through a small hole cut in the dorsal aspect of the eye; a 50 μm Ag/AgCl wire in the opposite eye served as a reference. Details of preparation and positioning are described elsewhere  (Belušič et al. , 2021). Signals were amplified in bridge mode through a SEC-10LX amplifier (NPI, Germany) and digitised at 30 kHz on a USB-6215 DAQ (National Instruments, USA). Data was collected and analysed in MATLAB. First, photoreceptor spectral sensitivity was determined using flash stimulation from a LED spectral array, combined with a diffraction grating  (Belušič et al.
, 2016 (Belušič et al. , , 2021)). Photoreceptor receptive fields were then mapped by measuring membrane depolarisations in response to a series of 400 small (0. (Huang and Krapp, 2013) , and digitised at 30kHz on a NI USB-6215 DAQ (National Instruments, USA). Data was collected in MATLAB (Mathworks, USA). Extracellular action potential (spike) times were detected offline in Spike2 software (CED, UK) using a manually adjusted voltage threshold. Clusters of spike units were isolated manually after projecting the spike waveforms onto their first three principal components. In all cases, a single unit was detectable in the raw trace. LPTC local motion sensitivities (LMS) and local preferred directions (LPD) were calculated using a fast stimulation protocol  (Krapp and Hengstenberg, 1997 ) (Figure  S3 ).
Acknowledgments We are grateful to the  Air Force Research Laboratory  for financially supporting this work. Thanks to all members of the  Department of Biology, University of Ljubljana  for their hospitality and offer of laboratory space over summer 2021. Thanks to  Jiaqi Huang ,  Doekele Stavenga ,  Mathias Wernet ,  Nick Roberts ,  Primož Pirih , and  Ric Wehling  for their insight and comments throughout the project.
Functional MRI The present study included a sample of 144 healthy righthanded volunteers (72 women, 27 ± 6 years, age range 19-53 years) from the BIL&GIN database (www. gin. cnrs. fr/ en/ curre nt-resea rch/ axis2/ bilgin-en/), which is a multimodal database harbouring imaging, psychometric, and genetic data to study the neural correlates of brain lateralisation  (Mazoyer et al. 2016). All participants presented without a history of developmental, neurological, or psychiatric diagnoses. They were not on medication and showed no abnormality on their structural brain MRI. All participants were French native speakers  (Mazoyer et al. 2016). On average, participants spent 4 years at a university. All participants (n = 144) were right-handed, and the group was balanced by sex (72 women). We used the functional MRI data (fMRI) previously published in  Hesling et al.
T-maps functional MRI activations The acquisition parameters and the pre-processing steps have been fully described by  Hesling et al. 2019. Briefly, the data were acquired on a 3 T Philips Intera Achieva scanner. After normalisation to the BIL&GIN template in the MNI space, the T2*-weighted echo-planar images (EPI) (TR = 2 s; TE = 35 ms; flip angle = 80°; 31 axial slices; field of view, 240 × 240 mm 2 ; isotropic voxel size, 3. 75 × 3. 75 × 3. 75 mm 3 ) were initially smoothed using a 6 mm full-width at halfmaximum Gaussian filter to each functional volume (194 T2*-weighted volumes). The data were processed using global linear modelling using SPM12 (https:// www. fil. ion. ucl. ac. uk/ spm/). For each participant, the BOLD variations of the Word-List reading task were contrasted against the cross-change detection task.
Decoding the functional activations with the disconnectome component maps Linear regression was applied to identify the statistical contribution of each component map (i. e. independent variables) to real group-level fMRI maps for reading (i. e. the dependent variable) (see Fig. 1 ) using SPSS 22 (https:// www. ibm. com/ produ cts/ spss-stati stics). The group-level fMRI maps (Fig. 3A ) were derived from a previously collected reading task in 144 healthy right-handed participants  (Hesling et al. 2019). The 46 disconnectome components (Thiebaut de Schotten et al. 2020) explained 50% of the variance of reading activation. The linear component provided a loading (Fig. 3B ) for each component that was subsequently used as an independent variable in a permuted linear regression (https:// fsl. fmrib.
Data availability Disconnectome maps and lesions were downloaded from the supplementary material of (Thiebaut de  Schotten et al. 2020)  The two sets of component maps and the atlas of white matter function (original and replication) are available at https:// ident ifiers. org/ neuro vault. colle ction: 7735. The scatterplot (fMRI and disconnectome) was done using the available python code http:// www. bcblab. com/ BCB/ Coding/ Entri es/ 2019/3/ 15_ Produ ce_ scatt erplo ts_ of_ corre lation_ betwe en_ 2_. nii_ files. html. Maps associated with this research are available on Neurovault. org (Collection: https:// ident ifiers. org/ neuro vault. colle ction: 12309. Individual files: fMRI: https:// ident ifiers. org/ neuro vault. image: 777434; Disco map: https:// ident ifiers. org/ neuro vault.
Figure1. Age-related variation for global and superficial amounts of cis-Vaccenyl Acetate in male flies. cis-Vaccenyl Acetate (cVA) was measured (in ng) in males of the following ages (indicated between the two series of graphs): less than 6 hours (<6h), 1 day (1d), 2 days (2d), 4 days (4d), 8 days (8d) and 12 days (12d). We measured both the global amount of cVA (A; obtained after a 24-hour extraction with dichloromethane) and the superficial amount of cVA (B; obtained after a 5-min hexane extraction). We compared these values in males of three genotypes: Canton-S (Cs), Dijon 2000 (Di2) and desat1 1573 homozygous mutant (desat1). Data are shown as box and whisker diagrams. The lower and upper box edges represent the first and third quartiles, while the median value is indicated by the inner small horizontal bar and the mean by the plain dot.
Data Analysis AIDEAL, a software developed in the IRIS laboratory, was used to analyze data recorded with the Pupil Labs eye tracker. To analyze the eye movements, AIDEAL treated the conjugate signal, i. e. , the L + R eye position/2. The saccade was defined as the time points where the peak velocity reached above or below 10% of the peak velocity; this corresponded to values above or below 40 • /s. AIDEAL defined the average velocity as the ratio of the total amplitude in degrees/time in seconds. The disconjugacy during saccades, or the evaluation of the binocular coordination, were measured as the difference in amplitude between the left and right eye signal. The difference in drift amplitude in the first 80 or 160 ms of fixation was calculated as the disconjugate drift. Eye movements with blinks or artifacts were automatically discarded by AIDEAL.
We also calculated the Spearman's Rho correlation between each of the postural and eye movement parameters in viewing each painting. For all analyses, the statistical significance was set at p ≤ 0. 05. We did not attempt to correct for multiple comparisons. All analyses were performed using SPSS version 25 (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25. 0. IBM Corp. : Armonk, NY, USA).
Postural Differences between Groups by Painting There were no significant differences in objective postural parameters measured with the accelerometer device between dyslexic and non-dyslexic children overall and for each individual painting. For all children, for all paintings, there was no difference in objective postural measurements between those who felt some movement and those who felt no movement at all. However, when examining the data for each of the three paintings, it seems that all subjects moved more in the anterior-posterior direction than in the mediolateral direction (see Figure  3a -c and Supplementary Table  S1 -S3).
Postural Differences between Groups by Painting There were no significant differences in objective postural parameters measured with the accelerometer device between dyslexic and non-dyslexic children overall and for each individual painting. For all children, for all paintings, there was no difference in objective postural measurements between those who felt some movement and those who felt no movement at all. However, when examining the data for each of the three paintings, it seems that all subjects moved more in the anterior-posterior direction than in the mediolateral direction (see Figure  3a-c  and Supplementary Tables  S1-S3 ).
Results Simulation of large-scale neural dynamics. To simulate neural activity at the individual whole-brain level we employed the homeostatic plasticity model recently developed in  21, 38. Figure  1  illustrates the main ingredients of our modeling strategy. Individual structural connectivity matrices are the key inputs of the stochastic model (Fig. 1a ). Imaging and behavioral data are taken from a large-scale stroke study described in previous publications  35, 51, 55. Structural connectivity data was available for 79 patients, acquired 3 months (t 1 ) and 1 year (t 2 ) after stroke onset. The same study includes data from 28 healthy controls, acquired twice 3 months apart. The difference in the time interval between stroke and controls is that diffusion imaging in patients was obtained only at three and 12 months since the diffusion signal is highly variable early post-injury.