dataesr/scientific-paragraphs-categorization huggingface dataset:
This database provides fMRI images of a big number of subjects from multiple international sites with no prior coordination and with different ranges of age, IQ and gender among other characteristics. It is to note that our approach permits to deal with the computational issues related to the large size of the ABIDE database, thanks to the elimination process which helps in alleviating the network. Hence, in the present work we propose the elimination of brain connectivities as an approach to address three major concerns: -Investigation of long-range underconnectivity theory related to ASD. -Enhancement of the classification of ASD subjects. -Generalization of the investigation using large datasets, and dealing with the computational demands of such datasets. The remainder of this paper is organized as follows.
Data ABIDE database is partitioned in two datasets, ABIDE I and ABIDE II. We decided to work with ABIDE I because a preprocessed version is proposed  [28]. This later offers a base for comparison that diminishes the impact of the preprocessing pipeline between research approaches. Since according to  [29]  and  [23]  the final results of any analysis is highly correlated with the choice of the preprocessing pipline. In concordance with this, the used preprocessing pipeline is the C-PAC  [30] , which was also used in  [21]. The ABIDE I dataset involved 17 international sites, sharing previously collected resting state functional magnetic resonance imaging (Rs-fMRI), anatomical and phenotypic datasets made available for data sharing with the broader scientific community. This dataset was the first available for sharing and yields 1112 subjects (539 individuals with ASD and 573 typical controls) with ages between 7 and 64 years.
Long-range and local connectivity matrices In this step, we firstly create clusters of the AAL atlas ROIs using Scipy (a Python-based ecosystem of open-source software for mathematics)  [46]. This first allocates each ROI to a separate cluster, then merges them using the equations described in 2. 4 till the creation of the final cluster that contains all the ROIs (Figure. similarity measures have been tested to bring out their impact on the results. Their values and results are reported below in the experimental results section. Fig. 4  Dendrogram of the AAL Atlas ROIs.
Experimental results In this work, the connectivity of autistic brains is studied in a highly heterogeneous setting and on large test datasets. Methods of our approach are first applied to the whole ABIDE I dataset. Then, to verify the impact of different criteria (age, gender, handedness) on the results, the same methods are also applied on different sub-datasets. Information on these laters are stated in Table. 1. As mentioned before, the proposed approach starts by computing the cor- The first method of the proposed approach (M-MST) eliminates the MST's extracted connections from the initial connectivity matrices. After this elimination, we remark a slight increase in the accuracy values of sub-datasets D1, D2, and D5 but in D3 and D4 the accuracy decreases. However, an important result shows up, which is the decrease of sensitivity in all datasets no matter the value of accuracy.
This same result was also found in  [27]  with different criterions sub-datasets. Which means that the eliminated connections contain information about the autistic brain connectivity deficit. These results are contained in Table. 2 that shows the results of the initial matrices classification compared to classification after the elimination of MST values. In the second method (HC-LRM) we divide the brain into several clusters. The hierarchical clustering permits to create different groups of clusters depending on the dissimilarity measure value. This latter is chosen empirically by testing different values to extract different clusters of ROIs. Then using these clusters, we construct the long-range and local connectivity matrices of the first dataset D1 and classify them separately. The metrics of this classification are stored in Table.
3, where we notice an increased accuracy of the inter-connectivity matrices classification using dissimilarity values of 80, 90 and 100 with a mean of 6-8 ROIs per cluster. However, after tests on the other  (HC-LRM) leads to better accuracy results than the intra-connectivity (HC-LM). The highest intra-connectivity classification result in the whole dataset (D1) is 65. 09%. It was achieved with a 300 dissimilarity distance value (3 clusters of ROIs). But this value is still lower than the accuracy achieved without clustering (68. 13%). That is why we focus on the inter-connectivity in the remainder of this work. The results of Table. 4 stems from classifying inter-connectivity matrices of clusters constructed using a dissimilarity value of 80. They show an improvement of the three classification metrics (accuracy, sensitivity and specificity) of the inter-connectivity matrices when compared to the initial matrices in all datasets.
In other words, the elimination of the clusters' local connections guarantees better results even when using different criterions datasets. This supports the brain's communication deficit in autism, and precisely the longrange deficit. From the results presented in Table. 2 and Table. 4, we can conclude that both    [20]  60. 0 62. 0 58. 0 in Table. 2 after removing MST from the initial matrices. In other words, the elimination of the minimum spanning tree has a direct impact on the sensitivity (the ability of ASD prediction), which means that the weakest connections extracted using the MST contain information about autism. Therefore, after their elimination the sensitivity always drops. Table. 5 summarizes those results. Comparing our approach's methods with the state-of-art approaches applied to ABIDE I, reported in Table.
Discussion In this work, we studied the autistic brain connectivity in a highly heterogeneous setting and on large test datasets from ABIDE I. The problem of using ABIDE is the uncontrolled heterogeneity due to the multi-site fMRI data acquisition that influences the results. The uncontrolled heterogeneity poses great challenges to develop brain-based classifiers for psychiatric illnesses in general. Without forgetting the sources of uncontrolled variation that can arise across sites as the scanner type, pulse sequence and sample composition. But since our objective is to simplify the process of autism detection and since the heterogeneity is part of the real setting, we used the datasets with little changes. Only the images with quality issues were eliminated. That is why using sub-datasets with certain criteria helps to meld those challenges and might even give more weight to findings from the whole dataset.
Then, we tested multiple ASD specific methods and combined them to improve the accuracy of ASD detection. The accuracy obtained is 69. 76% on the whole dataset D1, as can be seen in Table. 5, which exceeds previously published ABIDE findings  [21] ,  [20]. The elimination of the minimum spanning tree led to a diminution in the sensitivity in all datasets. Since the sensitivity mirrors the classification of the autistic subjects, its decrease implies that the eliminated weak connections contain biomarkers relative to autism, and supports the autism underconnectivity theories. The result of this elimination was reported in Table. 2. On the other hand, the elimination of intra-connectivity (the clusters local connectivity) using the clusters constructed with the hierarchical clustering improved accuracy and sensitivity.
This latter increased from 61. 9% to 63. 07% which indicates a long-range connectivity deficit in ASD. This can be seen in Table. 4 that reports the elimination of intra-connectivity in all datasets. The importance of the choice of the dissimilarity distance used to construct the clusters was also highlighted by testing multiple values. A good choice can strongly improve the prediction accuracy and sensitivity, as seen in Table. 3. Later, when the minimum spanning tree was again eliminated from the longrange connectivity matrices, the above findings of Table. 2 were confirmed in Table. 5. Where, again, the elimination of the weak connections extracted from the inter-connectivity matrices showed a decrease in the sensitivity. Which gives more weight to the under-connectivity deficit in Autism, especially between the long-range regions.
Hence, we conclude the existence of the longrange under-connectivity deficit in the autistic brain. In this study, the results showed evidence of a deficit in the long-range con- nectivity between the regions of the autistic brain that is independent of age, gender and handedness. This was confirmed by the classification results of the sub-datasets that showed the same pattern as the classification applied on the whole dataset. An accuracy of 70% proves that the proposed approach is very promising and leads to accurate prediction. As the elimination of inter-connectivity enhances the accuracy while the elimination of the MST impacts the sensitivity. Their combined use highlighted the long-range underconnectivity and permitted better results compared to the state-of-art, as can be seen in Table.
Conclusion In this paper, we suggested a novel approach based on ASD adaptive methods to verify autism theories about brain connectivity. Our aim was to improve the accuracy of the detection of this disorder while keeping up with the advance in the autism research. We used ABIDE I a large dataset with data from different world sites. We applied the minimum spanning tree and the hierarchical clustering to extract the weak connections, and the local/long-range connections, respectively. A combination of these two methods, was used to construct connectivity matrices to verify the long-range underconnectivity. From the results, we were able to detect a long-range connectivity deficit and proved that it is independent of age, gender and other criteria as the handedness. Furthermore, an under-connectivity problem was highlighted by the pattern found in the specificity and the sensitivity values.
remains the suitable tuned dissimilarity value. Another point that jumps to attention in Table. 3 is that the inter-connectivity 1 2 datasets, 80 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65.
VEFI includes a flux-gate magnetometer providing DC measurements at 1 vector/sec and AC-coupled measurements at 16 vector/sec, as well as a fast, fixed-bias Langmuir probe that serves as the input signal to trigger the VEFI burst memory collection of high time resolution wave data when plasma density depletions are encountered in the low latitude nighttime ionosphere. A bi-directional optical lightning detector designed by the University of Washington (UW) provides continuous average lightning counts at different irradiance levels as well as high time resolution optical lightning emissions captured in the burst memory. The VEFI central electronics box receives inputs from all of the sensors and includes a configurable burst memory with 1-8 channels at sample rates as high as 32 ks/s per channel.
Introduction The Communications/Navigation Outage Forecast System (C/NOFS) satellite is a US Air Force space weather mission designed to understand and forecast the ionospheric conditions that create the broad spectrum of irregularities that subsequently give rise to scintillations and outages of navigation and communication radiowave signals. With a long-term goal of forecasting such outages, the C/NOFS program included state-of-the-art instruments on a low inclination satellite, coordinated ground-based measurements, high level data processing and analysis, and theory and modeling that were combined to advance our understanding of the unstable low latitude ionosphere as well as to advance predictive capabilities. As outlined in de La  Beaujardière et al. (2004) , the C/NOFS program had three scientific goals: 1.
Accordingly, the C/NOFS mission included a requirement for vector DC electric field measurements sampled at 1 sample/second (s/sec) with 0. 5 mV/m accuracy with an 0. 1 mV/m accuracy "goal". This specific requirement was established by the C/NOFS modeling team  (Retterer et al. 2005). In addition to the DC electric field measurements, the same electric field instrument would gather vector measurements of the AC electric field component of the irregularities that cause scintillations. Thus, the C/NOFS satellite included a combined DC and wave, or AC, electric field detector which together were designed to address the following key science objectives: Fig. 1  Plasma density data gathered on NASA's Atmosphere Explorer-E satellite as it pierced a series of nighttime depletions (a "spread-F" event) shown in the top panel with associated strong, backscatter radar echoes observed simultaneously in Kwajalein Atoll, shown in the bottom panel  (Tsunoda et al.
C/NOFS Satellite Overview Before proceeding, we provide an overview of the C/NOFS satellite and mission which provides important context for the VEFI investigation. The C/NOFS satellite mission was a US Air Force Research Laboratory project that was part of the US Department of Defense Space Test Program. It was managed by the Air Force Research Laboratory, originally at Hanscom AFB, MA, and now at Kirtland AFB, NM. The C/NOFS Data Center and operations were carried out initially at Hanscom AFB and later at Kirtland AFB. The C/NOFS satellite is depicted in Fig. 2. A photograph of C/NOFS on the Pegasus vehicle is shown in Fig. 3. The satellite was designed to be ∼ 1 m in diameter to maximize The C/NOFS spacecraft was designed, built, and tested by Spectrum Astro, of Mesa, Arizona, which later became General Dynamics.
The VEFI A/D converter for high frequency waves is the 12-bit SEi 9240LPRP converter, which ran at 8 MHz sample rate. The digital electronics controlled numerous MUX circuits to optimize the performance of the instrument and maintain flexibility for different signal inputs. Analog multiplexers were implemented to provide simultaneous sampling of critical signal triplets (e. g. , Bx, By, and Bz; V12, V34, and V56). The MUX configuration provided redundancy for the core mission objectives in that critical signals could be routed to different A/D converters, on command, in the event of an anomaly. In a later section, the digital architecture is described from the standpoint of the IDPU and DSP software and processors. We now present the other sensors that were part of the VEFI, the flux-gate magnetometer, fixed-bias Langmuir probe, and optical lightning detectors.
The DSP enhanced the functionality of the VEFI by computing the Fast Fourier Transform (FFT) on the data as well as implementing digital filtering and the burst memory functionality. The IDPU flight software ran on an 80C86 microprocessor while the DSP flight software ran on a 320C25 microprocessor. A detailed block diagram of the VEFI digital architecture showing the different banks of ADCs, the MUXes, and the IDPU and DSP local buses is provided in Fig. 39. This diagram includes the memory and control logic as well as the 1553 interface to the spacecraft.
IDPU Software The IDPU software was broken into two parts: the bootstrap loader and the main program. When power was first applied to VEFI, the IDPU began executing the bootstrap loader software stored in PROM. This program fit within 1. 5 kB and initialized the 1553 interface, RAM, FPGAs and various internal variables. After initialization, the software checked if the current start-up was caused by the application of power (cold start) or by a ground command or a watchdog timer reset (warm start). The bootstrap loader would abort the automatic boot process and wait for operators to manually load the flight software after 16 consecutive warm starts. It is worth noting that during the entire C/NOFS mission, there was only one VEFI warm start caused by the watchdog timer. The IDPU main flight software managed all of the activities of the instrument.
DSP Software The DSP flight software executed on the 320C25 microprocessor running at 24 MHz with 8 MB RAM and 4 KB ROM. It managed all of the higher speed signal processing tasks that the IDPU could not handle and was in charge of the burst memory system. It was also written in assembly language for speed and also ran without the aid of an operating system. The DSP implemented a special 512-point Radix-4/Radix-2 real-FFT algorithm to transform 1024 data samples for the survey mode FFTs. This algorithm transformed the data using radix-4 butterflies in the first stages and a final radix-2 butterfly stage completed the transform. The reverse indexing required to order the data was complex as not only binary but also quaternary bit-reverse addressing was required. The benefit of using this algorithm was the speed of execution as it took less than half the time a 1024-point radix-2 FFT algorithm took.
Triggers include: Electric Field Wave Power (from the 12-channel filter bank) Plasma Density Depletions Lightning Flashes Pre-assigned Time Trigger criteria included plasma density depletions detected by an onboard algorithm provided as part of the VEFI software, electric field wave power amplitude from any of the 12 selectable filter bank channels, optical lightning detector irradiance, a combination of these, or a selected pre-determined time. Examples of pre-selected times correspond to when the C/NOFS satellite was in conjunction with a ground-based radar or in conjunction with a burn of the Space Shuttle. The trigger algorithm embedded in VEFI flight software continuously scores events that are encountered; the bursts with the highest scores are subsequently telemetered to the ground.
Acknowledgements We thank the reviewers for their useful comments. We acknowledge helpful comments from  Dr. John Bonnell  and  Dr. Harri Laakso. The C/NOFS mission, conceived and developed by the  Air Force Research Laboratory  (AFRL), was sponsored and executed by the  USAF Space Test Program. We acknowledge, with thanks, the project leadership at AFRL, in particular  Dr. Odile de la Beaujardière ,  Dr. Donald Hunton , and  Ms. Louise Gentile. The VEFI instrument at  Goddard  relied on a highly dedicated team and support staff which encompassed all aspects of the instrument design, fabrication, and test, the detailed processing and analysis of the data, and the management and stewardship of personnel and resources. We salute in particular the dedicated support and expert contributions of  Ms.
Shaded areas correspond to ± SEM around the group average (n = 20 participants). Source data for this figure are provided as a Source data file. reactivated around 1750-2000 ms, probably in anticipation of the response, since the response screen appeared between 2000 ms and 3000 ms. All this is consistent with results of previous studies using the same approach  [31] [32] [33] [34]. Also, as expected, classification performance declined with declining SNR, but it still maintained a qualitatively similar pattern of generalization over time (Fig. 2B ). Second, in order to further investigate the underlying brain dynamics, we analyzed the mean and variability of the trial-by-trial projected activity derived from MVPA. The mean projected activity displayed a non-linear increase with SNR for all time windows beyond 150 ms, compatible with either non-linear unimodal or bifurcation dynamics (Fig.
2C ; non-linearity was assessed by verifying that the derivative of the curve changed significantly as a function of SNR using one-way repeated measures ANOVAs at each time window: after 150 ms all F(4,76) >4. 9; all p-FDRcorrected <0. 005). The trial-to-trial variability, in contrast, displayed either a flat or slightly increasing profile for latencies up to 200 ms  Link between bifurcation dynamics and behavioral report during the active sessions. The data are so far consistent with qualitative predictions of the bifurcation model for latencies beyond 250-300 ms post-stimulus. Does the link hold for finer details of the observed neural activity? We assessed the distributions of trial-to-trial projected activity at various SNRs and compared them with the predictions from our models. The bifurcation model predicts markedly different distributions compared to the unimodal linear or non-linear models, since it is the only model that predicts bimodal distributions in response to stimulations around the perceptual threshold (Fig.
e. , the sensitivity of the classifier trained at a given time window (indicated by the shaded area and black arrow) and tested across the whole duration of a trial. The third column shows mean projected activity as a function of SNR for each time window of interest (arbitrary unit). The last column shows the variability of projected activity across trials as a function of SNR, i. e. , the standard deviation of the projected activity across trials (arbitrary unit). Error bars correspond to ± SEM around the group average (n = 20 participants). Source data for this figure are provided as a Source data file. shown in Fig. 3E. During baseline, the protected exceedance probability of the Null model was slightly above the other two as expected. Then, there was a first period between 0 and 250 ms after stimulus onset where the unimodal model showed the highest probability.
The shaded areas correspond to SEM (n = 20 participants). The periods of significant correlation are denoted as thick lines of the corresponding color (one-sided t-tests, p < 0. 05, FDR corrected). E Bayesian model comparison: protected exceedance probability at the group level for the unimodal non-linear, bifurcation, and null models. F Prediction of behavioral audibility from the neural projected activity under the assumption of bifurcation dynamics. Group average performance in predicting trial-by-trial audibility responses (heard versus not-heard) is shown for intermediate SNRs (-11, -9, and -7 dB). Thick dots denote periods were prediction performance was better than chance (two-sided t-tests, p < 0. 05, FDR corrected). The shaded areas correspond to SEM (n = 20 participants). Source data for this figure are provided as a Source data file.
All SNRs are included in this analysis. The shaded areas are SEMs (n = 20 participants). Thick dots denote periods were prediction performance was better than chance (two-sided t-tests, p < 0. 05, FDR corrected). Source data for this figure are provided as a Source data file. probes, participants reported either focusing on the visual and amodal tasks or on their own thoughts; they very rarely reported an absence of content or sleepiness (Supplementary Fig. 5 ). Overall, the sound constituted 19% of the reported mind-wandering contents, and this proportion increased with the intensity of the sound that preceded a mind-wandering probe (Supplementary Fig. 5 ). We thus assessed if we could predict whether or not a participant's mindwandering was directed toward the sounds or something else on a particular trial, based on the preceding neural activity.
The remaining 20 participants, all right-handed, included 10 women, and had a mean age of 23. 4 years (range 21-29). The study was validated by the ethics committee of Paris Descartes (CERES). Participants all gave informed consent and received a compensation of 20 € per hour of attendance. Stimuli. The stimuli were two vowels, French "a" and "e", of the same duration (200 ms). They were synthesized using the MBROLA software (v3. 02b)  66  with the following parameters: allophones 'a' and 'e' from the FR4 database (French female voice), 200 ms duration, 200 Hz pitch at 50% of the duration. These two vowels were chosen because of their close identification performance profiles, as revealed by behavioral pilots. Vowels were superimposed on a special background noise that equalizes the masking of all frequencies for human ears: Threshold Equalizing Noise or TEN  26.