dataesr/scientific-paragraphs-categorization huggingface dataset:
2601, =5. 74e-18), on 0back tasks ( =-0. 3592, =1. 13e-33) and on 2back tasks ( = -0. 1173, = 0. 0001). This analysis was done by using all subjects from the HCP S1200 database ( =1074 of all subjects with available behavioral and imaging data for WM tasks). The significant correlations were sustained after controlling for the effect of confounds including age, gender, handedness and head motion ( =0. 4659, =5. 74e-59 for the average accuracy; =-0. 2552, =2. 0e-16 for the reaction time). Moreover, the segregation of ChebNet representations as well as inscanner behavioral performance during WM tasks were significantly heritable in HCP population (h 2 =0. 2882 for ChebNet representations, h 2 =0. 5624 and 0. 4118 for average accuracy and reaction time in WM tasks, see Table  S4  for all heritability estimates) and demonstrated significant shared genetic variance in ChebNet representations and behavioral scores (ρ =0.
This gain in brain decoding was mainly contributed by high-level integration of brain dynamics not only within limited subsets of brain regions but also between multiple brain networks. Specifically, we used high-order ChebNet graph convolution to encode the complex forms of functional integration during cognitive processes and captured hierarchical representations of brain activities at different levels. This hierarchical organizational pattern as well as the decoding accuracy was selectively impacted by the K-order in graph convolution, due to different organizational principles of the cognitive tasks. For segregated brain function like motor execution, the K=1 model achieved the best performance and revealed a stable 2-level hierarchy in neural representations. By contrast, for high-order cognition such as visual working-memory tasks, the model plateaued at K=5 and uncovered a tripartite organization in neural activity.
Compared to previous linear and nonlinear decoding models, our proposed decoding model provided a generalized solution over a large population and a variety of cognitive domains. Besides, our model outperformed other approaches on the same dataset (as shown in Table  S3 ), most of which followed the functional segregation assumption by predicting cognitive states from localized features of each brain parcel covering the entire cerebral cortex. After incorporating the network architecture of the human brain and integrating information flow within functional networks (e. g. first-order GCN), the classification accuracy was largely improved (90%, as stated in (  17 )). The decoding accuracy was further improved after taking into account the high-order interactions on the graph, not only within functional networks but also across multiple brain systems (93% using the 5-order ChebNet model).
e. much lower decoding accuracy in the cue phase compared to the movement blocks, as indicated in the Figure  6A  in  (17). Consequently, the visual cortex was not detected in the saliency maps of Motor tasks (Figure  2A ). On the other hand, some brain regions with absence of strong activations might show high saliency to the prediction (i. e. high saliency but with low activation scores). For instance, high saliency values were detected in the bilateral area OP4 for tongue movements and in the left area PHA3 for recognition of both place and face images. These patterns were not detected by random but instead highly consistent across a number of subjects (Figure  2C  and D ). By contrast, when using the GLM approach, these regions were not detected in either the group activation maps from HCP database (Figure  5 -S1 in (  17 )) or meta-analysis from a collection of previous studies (Figure  5 -S2 in (  17 )).
Materials and Methods fMRI Datasets and Preprocessing We used the block-design task-fMRI dataset from the Human Connectome Project S1200 release (https://db. humanconnectome. org/data/projects/HCP_1200). The minimal preprocessed fMRI data in CIFTI formats were selected. The preprocessing pipelines includes two steps (  40  "dtseries. nii" file), followed by surface-based smoothing using a geodesic Gaussian algorithm. Further details on fMRI data acquisition, task design and preprocessing can be found in  (38, 40). The task fMRI database includes six cognitive domains, which are emotion, language, motor, relational, social, and working memory. In total, there are 21 different experimental conditions. We excluded the two gambling conditions in our analysis due to the short event design of the gambling trials (1.
g. 10 second). Shorter time windows were discarded in the process. The remaining time-series were treated as independent data samples during model training. As a result, we generated a large number of fMRI time-series matrices from all cognitive domains, i. e. a short time-series with duration of for each of brain parcels,. The entire dataset consists of over 1000 subjects for each cognitive domain (see Table  S2  for detailed information), in total of 14,895 functional runs across the six cognitive domains, and 138,662 data samples of fMRI signals when using a 10s time window (i. e. 15 functional volumes at TR=0. 72s). Step 3: Spatiotemporal graph convolutions using ChebNet Graph convolution relies on the graph Laplacian, which is a smooth operator characterizing the magnitude of signal changes between adjacent nodes.
3) Consequently, the ChebNet graph convolution is defined as: * = =0 T (Eq. 4) where = 2 is a normalized version of graph Laplacian with being the largest eigenvalue, θ k is the model parameter to be learned at each order of the Chebychev polynomials. It has been proved that the ChebNet graph convolution was naturally K-localized in space by taking up to K th order Chebychev polynomials  (18) , which means that each ChebNet convolutional layer integrates the context of brain activity within a K-step neighborhood. Brain-decoding pipeline using ChebNet graph convolution We used a similar decoding pipeline as proposed in (  17  The entire dataset was split into training (60%), validation (20%), test (20%) sets using a subjectspecific split scheme, which ensures that all fMRI data from the same subject was assigned to only one of the three sets.
Heritability analysis of brain representations and behavioral performances For the heritability estimates of brain response and behavioral performance during WM tasks, we used the Sequential Oligogenic Linkage Analysis Routines (SOLAR) Eclipse software package ( http://www. nitrc. org/projects/se_linux ). SOLAR relies on the maximum variance decomposition of the covariance matrix Ω for a pedigree: Ω = 2Φσ 2 + 2 (Eq. 7) where σ 2 is the genetic variance due to the additive genetic factors, Φ is the kinship matrix representing the pairwise kinship coefficients among all individuals, 2 is the variance due to individual-specific environmental effects and measurement error, and is an identity matrix. Narrow sense heritability is defined as the fraction of phenotypic variance σ 2 attributable to additive genetic factors: ℎ 2 = σ 2 σ 2.
We used the Language tasks as an example to illustrate that ChebNet graph convolutions captured hemodynamic response in the temporal domain and brain activations in the spatial domain. First of all, various temporal convolutional kernels were learned at the first ChebNet layer (1 st column), which resembled the hemodynamic response function in BOLD signals. Second, using these temporal filters, the corresponding "activation maps" in the first ChebNet layer were extracted and grouped according to the task conditions, e. g. story (2 nd column) and math (3 rd column) for the Language tasks. These activation maps demonstrated a possible explanation of the biological basis behind spatiotemporal graph convolutions. For instance, when the temporal filter only focused on brain activity within 0-6s after task onset (3 rd row), the classical language network and frontoparietal network were detected for the story and math condition respectively, responsible for language comprehension and numerical processing during the cognitive task.
By contrast, when the temporal filter focused on brain activity 5-10s after task onset (4 th row), the motor and sensory cortex were detected for both conditions, responsible for button press during the task. When both time windows were taken into account (1 st row, 0-10s after task onset), the brain response in the language network and frontoparietal network were further enhanced while the activity in the auditory cortex was weakened. Similar analysis on the Motor tasks was shown in Figure  1 -Supplement 2 in  (17). We used the Language tasks as an example to illustrate the hierarchical organization of layer representations in ChebNet. At each ChebNet layer, the layer activations were extracted and saved as feature representations for the following analysis. First, the similarity of representations was calculated using CKA with a linear kernel (A), which illustrated a hierarchical organization of layer representations in ChebNet.
These representations were then projected onto a 2-dimensional space using t-SNE (1 st row in C) which indicated a nice disassociation between different task conditions (e. g. story vs math for the Language task). After that, the "activation map" associated with each task condition (2 nd row in C) were calculated by averaging the layer representations across all data samples of the same category and mapped back onto the cortical surface. These representations resembled the actual brain activation maps detected by the canonical GLM approach (B), provided by  (38) , downloaded from neurovault (https://neurovault. org/collections/457/ ). The confusion matrix was normalized by each task condition (row) such that each element in the matrix shows the recall score, i. e. among all predictions how many of them are positive predictions.
Acknowledgment This work was partially supported by the  Major Scientific Project of Zhejiang Lab  (No. 2020ND8AD01 ),  Courtois foundation  through the  Courtois NeuroMod Project  and the  IVADO Postdoctoral   Scholarships Program. PB is supported by a salary award of " Fonds de recherche du Québec -Santé ",  chercheur boursier junior 2.
Data and materials availability We used the block-design task-fMRI dataset from the Human Connectome Project S1200 release, downloaded from https://db. humanconnectome. org/data/projects/HCP_1200. In total, fMRI data from 1095 unique subjects under six different task domains and resting-state were used in this study. The minimal preprocessed fMRI data of the CIFTI format were used, which maps individual fMRI timeseries onto the standard surface template with 32k vertices per hemisphere. Our decoding pipeline, as well as the optimized decoding models and the construction of brain graphs, were made publicly available in the following repository: https://github. com/zhangyu2ustc/gcn_tutorial_test. git.
In this context, the main challenge of C-ITSs is to ensure appropriate allocation of communication network resources between the deployed services and to ensure a primacy of safety critical services. The wireless communication technologies defined for vehicle communications implement mechanisms for QoS management. ITS-G5 uses the enhanced distributed channel access (EDCA) mechanism to set up queues offering differentiated access parameters to network resources according to the priorities of the data flows. LTE-V2X offers different QoS mechanisms related to the operating modes (mode 3 and mode 4) for vehicular communications. These mechanisms are also based on differentiated access to network resources based on the prioritization of data flows. QoS mechanisms can allow C-ITSs to ensure the proper functioning of their services while taking into account the limited resources offered by communication technologies.
The DCC has been introduced to control and dynamically adjust data exchange to reduce traffic loads. It operates on the different layers of ITS station (ITS-S) architecture, as depicted in Fig. 3. DCC management contains all information provided by DCC access related to the channel occupancy and the controlled parameters to adjust traffic load. The DCC can adapt the traffic load through a set of algorithms such as transmit power control (TPC), transmit ratio control (TRC), and transmit data rate control (TDC). By using these algorithms, which adjust the transmit power, transfer rate, and other parameters related to control of the channel (e. g. , measured channel busy ratio (CBR), receiver signal strength indicator statistics (RSSI statistics), and frame transmission indication), the DCC can better support access management in high traffic load situations.
To ensure efficient traffic dissemination, the harmonized use of radio spectra for C-ITS applications in dedicated 5. 9 GHz band is divided in four different bands: • ITS G5A band: dedicated for safety applications. • ITS G5B band: dedicated for non-safety applications. • ITS G5C band: dedicated for Radio Local Area Network (RLAN), WLAN, etc. • ITS G5D band: dedicated for future ITS applications. As shown in Fig. 3 , we can identify channel spacing of 10 MHz for control channels (CCHs) and service channels (SCHs) to ensure data dissemination of safety and nonsafety messages. The available channels enable the use of multiple channels for different traffic communications. As an extension of the MAC layer, multi-channel operations (MCO) have been defined to allow a multi-channel access mode under the control of management layers and DCC management.
It was concluded that ITS-G5 outperforms the LTE-V2X mode when LTE radio resources are allocated for additional traffic. Reference  [20]  reported a performance comparison between ITS-G5 and cellular-based vehicular communication systems (referred to LTE-V2X). Realistic data traffic was simulated based on CAM and DENM messages in a real road scenario (CAM depending on the vehicle movement and DENM generated by Poisson distribution to model event-driven). End-to-End Delay and Packet Inter-Reception Time were measured as the performance metrics for both technologies. It was concluded that both technologies can support vehicular communications with realistic data traffic. Centralized scheduling in the base station allows an increase in the packet reception ratio for Cellular-VCS compared to ITS-G5.
In  [26] , the authors focus on the capabilities of edge computing servers in a 5G network to help in offloading services and bring them closer to the requester. They consider proactive migration based on intelligent resource management to determine the next edge server, which maintains low latency in high-mobility vehicles. In the simulation, the authors analysed the effects of prediction errors on the efficiency of service migration and showed that it induces additional latency. The evolution of communication technologies introduces more challenging issues to meet the continuing increase in application requirements. The significant performance provided by the next generation of ITS-G5 and C-V2X is expected to better support C-ITS services. The development of the next generation of mobile communication 5G  [27]  has accelerated in recent years, where its objective is to provide greater capability in terms of latency, data rate, mobility, massive connectivity, energy efficiency, spectrum efficiency, and so on.
To achieve these goals, 5G identifies three service categories, namely, enhanced mobile broadband (eMBB), massive machine type communication (mMTC) and ultra-reliable low latency communication (URLLC), to support a diverse VOLUME 9, 2021 set of use cases. These advances introduce additional capabilities to vehicular environments. In this direction, the 5G new radio (NR) introduced in the first phase of 5G development (3GPP Rel-15  [28] ) addresses V2X services. In this phase, 5G NR focuses on supporting eMBB use cases and some critical applications of URLLC. Additional capabilities were introduced in 3GPP Rel-16  [29] , expected to support eMBB, URLLC and mMTC use cases. These enhancements provide high levels of efficiency and capacity to advanced and automotive use cases (emergency electronic brake lights, software updates, platooning, high-definition maps, sensor data sharing, vulnerable road users and many more applications).
Bird distribution and abundance data We used data from eBird  (Sullivan et al. , 2014) , a unique online platform gathering hundreds of millions of bird observations across the globe. Observers report observations in checklists and provide information on sampling effort, allowing eBird data to be transformed into a dataset of relative abundance  (Sullivan et al. , 2009)  while controlling for the most common observation biases  (Johnston et al. , 2021) , as described below. We used the eBird dataset released in October 2019  (eBird, 2019). In addition, we used species distribution data (coarse polygons delimiting the species' ranges) as mapped by BirdLife International and HBW (2019), having aligned the taxonomies between these two datasets by following the latter (Supplementary Methods 1).
Building a standard abundance dataset We filtered the dataset following guidelines provided by the eBird team  (Sullivan et al. , 2009; Strimas-Mackey et al. , 2020; Johnston et al. , 2021) , similarly to the data filtering process described in  Cazalis et al. , (2020) ; see details in Supplementary Methods 2. To enable comparability of checklists, we restricted our dataset to recent observations  (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) , for which observers certified having reported all species identified, emerging from stationary counts or transects (with distance travelled <5km and duration between 0. 5-10 hours; duration is then controlled for in analyses), and only kept checklists made by experienced observers (Supplementary Methods 3). To avoid pseudo replication, we removed observation duplicates (i.
e. , checklists made on the same place and date). We removed "not approved" observations, as well as marine and coastal species and we restricted observations to the species breeding range and season (Supplementary Methods 2). The final dataset in our main analyses consisted of 59,583,879 observations and 404,086,397 inferred absences, structured into 3,449,486 checklists made by 44,013 observers and representing 4,362 species (all species whose breeding distribution intersects with the ecoregions considered in this study).
Bird species traits For each of the 4,362 species considered, we obtained data on eight species-level variables (that we call "traits" for simplicity; see Supplementary Methods 4). We extracted from BirdBase  (Şekercioğlu et al. , 2004, 2019)  species primary habitat, primary diet, migratory status, specialisation index, body mass and taxonomic Order. We extracted from BirdLife International (2019) Red List status that we used as a quantitative variable, and calculated species' breeding range size.
Protected areas We used spatial protected area data from the World Database on Protected Areas (UNEP-WCMC & IUCN, 2020), following the standard filtering procedure (UNEP-WCMC and IUCN, 2019) that excludes 'Man and Biosphere' reserves, protected areas with no associated polygons and those that are not yet implemented (i. e. , we kept only those 'designated', 'inscribed', or 'established').
Net Primary Productivity We calculated a Net Primary Productivity (NPP) value for each checklist, using NASA's Earth Observatory Team (2020) maps. We first created a raster layer by calculating for each cell the mean of NPP values from January 2014 to November 2016 (December 2016 data were not available). We then extracted for each checklist, the mean value of each pixel in this raster that intersected by at least 1% the 2. 5 km buffer around the checklist coordinates.
2) Building a standard abundance dataset We restricted our dataset to recent (2010-2019) observations, to increase synchronisation between bird records and landscape data. We kept only checklists for which observers certified having reported all species identified, thus obtaining a count dataset that included nondetections. Further, we filtered checklists based on sampling effort and protocol to create a set of checklists with relatively consistent effort. Specifically, we kept only checklists that reported a duration of sampling between 0. 5-10 hours and less than 5 km distance travelled arising from: the 'Stationary Points' protocol (i. e. , the observer did not move during sampling); the 'Travelling' protocol (i. e. , the observer moved during sampling); or 'Historical counts' (if they included information on duration and distance travelled).
This protocol selection excludes sampling events targeting particular taxonomic groups (e. g. wader surveys, nocturnal protocols) or using specific methods (e. g. banding). Each checklist is associated with a point, with coordinates provided by observers (often the middle of the route for 'Traveling' checklists). Pseudo-replication in the database may occur when several observers record birds at the same place at the same time. To eliminate these records, we first used the auk_unique function from the 'auk' package  (Strimas-Mackey et al. , 2017) , specifically designed to process eBird data. This function combines checklists from multiple observers reported to be birding together, in order to obtain a single checklist for each sampling event (the number of observers associated with each checklist alters the detection probability, and will be subsequently controlled for).
We excluded from the dataset "not approved" observations (corresponding to exotic, escaped or feral individuals), as well as domestic species, but kept established invasive species. Using the auk_rollup function from the 'auk' package, we removed subspecies, moving all observations to the species level. We also excluded marine species, defined as species with 'sea' or 'coastal' as primary habitat (cf. bird species traits section below). In many species, individuals are the most territorial and selective in terms of habitat requirements when breeding  (Zuckerberg et al. , 2016). We thus focused our analyses on potentially breeding individuals, by narrowing each species' observations records to the respective breeding season and breeding grounds. For each 10° latitudinal band, we derived the broad breeding season (all species considered together) based on the temporal distribution of records coded as 'breeding' in the eBird database (e.
g. , 6 April to 9 August for latitudes 50°N to 60°N; all year round for latitudes 10°S to 10°N; see Supplementary Methods 3). Within these dates, we then focused on the observations made in the breeding grounds of each species (as mapped by BirdLife International and HBW (  2019 )). We have restricted our dataset of bird records to observations made within each species' broad breeding season and breeding grounds. Bird breeding season is known to vary with latitude  (Baker, 1939). Although not all species within a given latitude necessarily breed at the same time  (Baker, 1939) , we did not have sufficiently detailed information to take these differences into account, and have instead assumed that within a given latitudinal band all species breed within the same broad season. To delimit the breeding season within each 10° latitudinal band, we studied the temporal distribution of the breeding codes that are sometimes associated with eBird observations (available for 1.
2% of the observations in our dataset). First, we restricted the breeding codes to those that either correspond to probable breeding or confirmed breeding (i. e. , by removing codes: "Flyover", which does not correspond to a breeding behaviour; and "In appropriate habitat" and "Singing male", which correspond to possible breeding only). We then plotted the temporal distribution of these breeding records (all species combined) per 10° latitudinal bands (Fig. S1 ). Based on these distributions, we considered that breeding occurs all year round between latitudes -10° and 10°. For other latitudinal bands, we defined the limits of the breeding season (Table  S1 ) as the period containing 95% of the observations with breeding codes, bounded by circular quantiles at 2. 5 and 97. 5% of observations (black lines on Fig.