dataesr/scientific-paragraphs-categorization huggingface dataset:
Obtaining significant activations of brain circuits with our method requires the functional involvement of the brain areas at both ends of a circuit. As first examples of such functional exploration of white matter, we investigated the brain circuits supporting prominent activation tasks including motor, memory and language functions. As our knowledge of the fine circuitry of the motor system is mostly derived from animal and lesion studies, the Functionnectome applied to motor tasks offers the unique opportunity to explore these circuits in the healthy human brain. In that regard, our findings match the pathways suggested by the literature and were replicated with both hands and feet twice. We confirmed the involvement of the internal capsule, which is a well-established  51  part of the ascending and descending motor pathways that include the cortico-subcortical motor loops  52, 53  and the cortico-cerebellar pathways.
Indeed, this pathway has not been identified in the monkey brain, and its role in working memory in humans has so far only been hypothesized based on its location  37. This new result highlights the exploratory potential of the Functionnectome toward understanding the white matter support of complex cognitive systems. The language circuitry, and more so its semantic system, offers an even greater challenge as it cannot be explored in animal studies  41. The Functionnectome confirmed, for the first time in the healthy human brain, the structural-functional circuitry supporting semantic processes originally suggested by lesion studies  40  or intraoperative stimulation  41  in humans (i. e. the uncinate fasciculus, the inferior fronto-occipital fasciculus, the middle longitudinal fasciculus, and the posterior segment of the arcuate fasciculus).
The Functionnectome also revealed the involvement of the anterior corpus callosum in story comprehension. Whilst some language processes require the integration of the left and right hemispheres via the posterior corpus callosum  62  , the anterior corpus callosum has been implicated in semantic disorders (e. g. semantic dementia  63  ). The Functionnectome result also suggests an involvement of the fornix. As a limbic pathway, the fornix may play an important role in the colouring of the story comprehension with emotions and memories  46. Our results thus prompt a closer inquiry into the role of the anterior corpus callosum and fornix in semantic processes and offer a non-invasive tool to study its involvement in healthy participants. In sum, our application of the Functionnectome to classical fMRI allows the confirmation and the exploration of the involvement of circuits for specific tasks for the first time in the healthy human brain.
To upscale the validation of this network view of brain functioning, crowdsource analysis of additional data would be possible. To facility this application, we provide an open-source software that will allow for an easy use of the Functionnectome method to revisit already acquired fMRI datasets, either privately or publicly available (e. g. HCP  65  , UK Biobank 66 , ABIDE  67  ). The use of the Functionnectome is also not bound to the activation paradigm and can be combined with advanced fMRI statistics  7  to reveal the dynamic causal interaction between brain circuits. Additionally, the Functionnectome can leverage the wealth of MRI modalities to explore the involvement of white matter circuits in different aspects of brain dynamics. For example, it could be applied to restingstate functional connectivity or cortical thickness to open up new perspectives onto the study of functional synchronisation, cortical changes during development, and brain pathologies.
Future developments of the priors might include a separation into interhemispheric (i. e. commissural circuits), cortico-subcortical (i. e. projection circuits), and cortico-cortical connectivity (i. e. association circuits) to better disentangle brain circuitries. Finally, the computation of the Functionnectome using every brain voxel can be very computationally expensive and thus time-consuming. While we recommend using this procedure, we acknowledge that not all research teams have access to the computing power required to compute the "voxel-wise" Functionnectome over several participants in a reasonable time. To circumvent this potential constraint, we provide an option within the software allowing for the use of an atlas and its parcels instead of all voxels (i. e. 'simplified' priors) for a less computationally intensive "region-wise" analysis (see supplementary materials).
Acquisition parameters and preprocessing EPI acquisitions: Full description of the acquisition parameters have been described elsewhere  68. In brief, the data were acquired on a 3 Tesla Siemens Skyra scanner using a whole-brain EPI acquisition with a 32-channel head coil. The parameters were as follows: TE = 33. 1 ms, TR = 720 ms, flip angle = 52°, BW = 2290 Hz/Px, in-plane FOV = 208 × 180 mm, 72 slices, 2. 0 mm isotropic voxels, and a multi-band acceleration factor of 8. 1200 frames per acquisition for the restingstate fMRI, and the number of frames was task-dependent for the task-based fMRI. Each type of acquisition was acquired twice using a right-to-left and a leftto-right phase encoding. The EPI 4D acquisitions were then preprocessed through the "Minimal preprocessing pipeline" fMRIVolume  69  , which applies movement and distortion corrections and performs a registration to the MNI152 space.
The default HCP preprocessing pipeline (v3. 19. 0)  69  was applied to the data  72. In short, the susceptibility-induced off-resonance field was estimated from pairs of images with diffusion gradient applied with distortions going in opposite directions  73  and corrected for the whole diffusion-weighted dataset using TOPUP  74. Subsequently, motion and geometrical distortion were corrected using the EDDY tool as implemented in FSL. Next, we discarded the volumes with a b-value of 1000 s/mm-2 and whole-brain deterministic tractography was subsequently performed in the native DWI space using StarTrack software (https://www. mr-startrack. com). A damped Richardson-Lucy algorithm was applied for spherical deconvolutions  74, 75. A fixed fibre response corresponding to a shape factor of α = 1.
5 × 10-3 mm2 s-1 was adopted, coupled with the geometric damping parameter of 8. Two hundred algorithm iterations were run. The absolute threshold was defined as three times the spherical fibre orientation distribution (FOD) of a grey matter isotropic voxel and the relative threshold as 8% of the maximum amplitude of the FOD  76. A modified Euler algorithm  77  was used to perform the whole-brain streamline tractography, with an angle threshold of 35°, a step size of 0. 5 mm and a minimum streamline length of 15 mm. We co-registered the structural connectome data to the standard MNI 2 mm space using the following steps: first, whole-brain streamline tractography was converted into streamline density volumes where the intensities corresponded to the number of streamlines crossing each voxel.
Second, a study-specific template of streamline density volumes was generated using the Greedy symmetric diffeomorphic normalisation (GreedySyN) pipeline distributed with ANTs  77, 78. This provided an average template of the streamline density volumes for all subjects. The template was then co-registered with a standard 2 mm MNI152 template using flirt as implemented in FSL. This step produced a streamline density template in the MNI152 space. Third, individual streamline density volumes were registered to the streamline density template in the MNI152 space template and the same transformation was applied to the individual whole-brain streamline tractography using the trackmath tool distributed with the software package Tract Querier 79 using ANTs GreedySyn. This step produced a whole-brain streamline tractography in the standard MNI152 space.
Comparing anatomical priors and resting-state networks We followed the method developed by Jonathan O'Muircheartaigh and Jbabdi  32  to compare cortical maps derived from functional and structural connectivity. We first ran a group independent component analysis (ICA) on the resting-state data (both LR and RL acquisition of all 45 subjects) to extract 20 independent components (IC), using MELODIC (multivariate exploratory linear optimised decomposition into independent components, version 3. 15), available in the FMRIB Software Library (FSL)  74. Among these 20 functional ICs, 3 were identified as noise components, leaving 17 ICs as resting-state networks (RSN). In parallel, we performed the same ICA on the anatomical priors of the Functionnectome using the region-wise probability maps concatenated over a fourth dimension.
Processing of neuroimaging data The activation analysis was applied to both the original functional dataset and the functionnectome 4D volumes. Processing was identical except for the application of a traditional spatial smoothing (FWHM = 4 mm) on the functional dataset (i. e. as a usual step to improve the signal/noise ratio and misalignment). Specifically, no spatial smoothing is required for the functionnectomes 4D volumes. Usual smoothing aims at improving the signal/noise ratio (SNR) using a weighted average of the local signal, assuming that neighbouring voxels share some signal of interest. The functionnectome method combines the signal from distant yet structurally linked voxels, which has an analogous effect of improving the SNR, but is guided by actual brain circuits. The processing was done using FEAT (FMRI Expert Analysis Tool, v6.
00) from FSL (FMRIB's Software Library). First-level statistical analysis was carried out on each participant using FILM (FMRIB's Improved Linear Model)  83  with prewhitening. For each task, we used the main contrasts provided with the HCP dataset. Then, a group-level analysis was performed on all participants using the first stage of FLAME (FMRIB's Local Analysis of Mixed Effects)  84  , resulting in the z-maps (ztransformed t-maps) presented in the results (fig. 1 2 3 4 ). Note that the assumption of identically independently distributed residuals in the linear modeling applied to the functionnectome volumes should hold true, as the signal of the voxels results from a simple linear combination of the signals (the classic BOLD timeseries) for which the assumption was already considered valid.
Visualisation Functional z maps and functionnectome maps were displayed on a standard template in MRIcron (https://www. nitrc. org/projects/mricron). Labelling for cortical regions and white matter pathways were added manually by expert anatomists (SJF and MTS). The visualisation of 3D structures in 2D is limited and may appear ambiguous at times but the full trajectories of pathways were considered before labelling, especially in regions of overlap between white matter structures. The 3D renderings were generated using the associated z-map. All functionnectome maps (slices and 3D renderings) were masked to remove the grey-matter parts of the volume in order to improve readability of the figures. The mask used here was generated using the segmentation provided in the HCP dataset and was composed of the voxels defined as white-matter or brain stem in at least 10% of the subjects of the test-retest dataset.
available at https://osf. io/5zqwg/ and http://www. bcblab. com/BCB/Opendata. html). This dataset was derived from the diffusion-weighted imaging dataset of 100 participants acquired at 7 http://www. humanconnectome. org/study/hcp-young-adult/; WU-Minn Consortium; Principal investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657).
29 ( 29 available at https://osf. io/5zqwg/ and http://www. bcblab. com/BCB/Opendata. html). This dataset was derived from the diffusion-weighted imaging dataset of 100 participants acquired at 7 http://www. humanconnectome. org/study/hcp-young-adult/; WU-Minn Consortium; Principal investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657).
The CO 2 sinks as defined here conceptually include the response of the land (including inland waters and estuaries) and ocean (including coasts and territorial seas) to elevated CO 2 and changes in climate and other environmental conditions, although in practice not all processes are fully accounted for (see Sect. 2. 7). Global emissions and their partitioning among the atmosphere, ocean, and land are in reality in balance. Due to the combination of imperfect spatial and/or temporal data coverage, errors in each estimate, and smaller terms not included in our budget estimate (discussed in Sect. 2. 7), the independent estimates (1) to (5) above do not necessarily add up to zero. We therefore (a) additionally assess a set of global atmospheric inverse model results that by design close the global carbon balance (see Sect.
Currently, the researchers direct their focus towards facilitating Blockchain integration with the industry through the fourth upcoming version. Besides, another DLT platform named IOTA is introduced to tackle the proliferation of IoT devices. IOTA  [8, 9]  is a DLT solution primarily designed to respond to the massive IoT requirements. It is based on directed acyclic graph (DAG) with a specific ledger structure named "Tangle". DAG is advisable for the IoT environment since it can simultaneously address the incoming transactions (Txs)  [10]. These two DLTs are distinguished by their different data structure. Blockchain is based on a series of blocks that includes transactions, while IOTA is based on a graph where transactions are validated instantly  [11]. IOTA categorizes its network participants into full and light nodes.
Experiments and Results The WLC implementation can be performed with different scenarios. It can be installed either on the light node, on the full node, or both. We start by testing the WLC algorithm on the light node against a few Tangle nodes. The WLC is implemented in a private Tangle network consisting of several virtual machines that act as full nodes with different specs, and one virtual machine works as a light node. All the nodes are connected to the same network. The light node is represented by IOTA wallet software that can create and sign Tx only. Therefore, it should be connected to a full node to perform PoW tasks and attach its Tx to the Tangle. In this scenario, the light node runs the WLC algorithm of  [25]  to connect to one of the nodes in the list. Thus, load balance is achieved based on the WLC decision.
Simulation: Decentralized WLC To build a network while increasing the Tangle nodes, we use a simulation environment based on the Java compiler to simulate the WLC behavior within a decentralized environment. We first build the datacenter class to store all the network components. The simulation code is available on GitHub repository  [28]. The class node generates nodes with random CPU values ranging from 1 KHZ to 6 GHZ and random RAM ranging from 500 MB to 16 GB. Furthermore, a JSON file is introduced to manually determine the values of the resources. This file permits us to create nodes either with similar or different resources to test different scenarios, as shown in the next section. Each connection consumes a fixed amount of resources and deducts the same value from any assigned node.
∆U GaM D-dw (U (q)) = ∆U dihedral protein (U (q)) + ∆U stretch water (U (q)) + ∆U bend water (U (q)) This mode is enabled by the flexibility of the AMOEBA 03 water model  35  but is not compatible with rigid water models such as TIP3P  48  commonly used with the CHARMM and AMBER force field. 49  This framework allows to further reduce the computational cost gap between PFFs and nPFFs. This new mode, in addition to the other GaMD-dih and GaMDdual modes, is now available within the Tinker-HP software. 12, 15  In the following, we first tested its GPU scalability and performance on the STMV system (≃1 066 624 atoms), and its sampling efficiency is demonstrated on simulations of the alanine dipeptide and the CD2-CD58 complex. A technical appendix is present at the end of the manuscript and provides the formalism of the method and the associated debiasing equations.
Results and discussion Level 0: Efficiency and GPU scalability The GaMD implementation is such that only a small computational and communication (in parallel) overhead is added compared to cMD. The GaMD-dih and GaMD-dualwat have been considered on the STMV system (1 066 624 atoms) with the AMOEBA PFF and the 10 fs outer time-step HMR BAOAB-RESPA1 multiple-time-step integrator. 17 V100 GPUs from the national Jean Zay supercalculator have been used for all the benchmark computations. Similar scalability studies have been performed on the Jean Zay multi-CPUs (Figure  S1 ). The AMOEBA GPU simulations were performed on a single node as the multi-node extension of the AMOEBA PFF within the Tinker-HP package is still under development. On 1 and 2 GPUs (Figure  1 ), the GaMD data communications are negligible, 1%.
Modified Rao-Blackwell estimator Recently, Ding et al. 63, 64  derived the MBAR equations using Rao-Blackwell (RB) estimator. The RB theorem characterizes the transformation of a crude estimator into a better estimator that has smaller mean-squared-error w. r. t to the dataset. We wish to calculate the i ∈ [[1,. , M ]] relative free energies F * i of M thermodynamic states sampled independently, with potential U i. To compute the relative free energies, the system should be sampled according to Boltzmann distribution. We note q i,n the n ∈ [[1,. , N i ]] configurations sampled from state i. To compute the relative free energies of the M thermodynamic states, the configurations q i,n are combined and considered as samples from the generalized ensemble p i (q) ∝ e -β(U i (q)+b i ) where b i is an unknown biased energy.
Acknowledgement This work has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No  810367 ), project  EMC2  (JPP). FC thanks funding from the  French  state funds managed by the  CalSimLab LABEX  and the  ANR  within the  Investissements d'Avenir program  (reference  ANR11-IDEX-0004-02 ) and support from the  Direction Génerale de l'Armement (DGA) Maîtrise NRBC of the French Ministry of Defense. Computations have been performed at  GENCI (IDRIS, Orsay, France  and  TGCC ,  Bruyères le Chatel ) on grant no  A0070707671.
In general, we used a VENC about 20% higher than the maximum velocity spotted during the experiments. Having finished the MRV experiments, we could process the data and obtain velocity field images. Nevertheless, using directly the raw data produced very noisy images, like the example shown in Fig. 9a. To overcome this issue, we used masking to remove data of voxels that were not in the water, which provided a cleaner image that was better to visualize (Fig. 9b ). Furthermore, masking also allows us to calculate the mean bulk velocity of the flow with the MRV measurements and compare it with the flowmeter acquisitions. Finally, we gathered the three velocity components in only one image by using the color bar to represent the axial velocity intensity (z-direction) and vectors for the transverse velocities (xy-plan), all of them being normalized by the bulk mean velocity (Vz,ref, obtained using the flowmeter data) to better compare their magnitudes.
Test procedure We adopted the following procedure during our experiments once a test section was introduced into the MRI scanner and hydraulic circuit was filled with water. First, we started the pump and adjusted its velocity to provide a 250 lpm flow rate for two minutes minimum, intending to force trapped bubbles to leave the bundle. Afterwards, we purged the air at all the highest points of the hydraulic circuit and stopped the pump so we could place the test section at the first measurement position. Then, we performed signal intensity measurements to ensure the appropriate positioning of the test section and find a capillary tube filled with water to serve as a reference for the slice selection position. Next, the pump rotation velocity was set to supply the highest tested flow rate (in this study, about 245 lpm) and we started acquiring the flowmeter data and performing the MRV for the three velocity components at all the selected slices.
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process. In this project, we present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining. In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels. We show that a combination of MoCo, CLD, and geometric augmentations outperforms conventional models pretrained on Im-ageNet by a large margin.
Introduction Wildlife censuses help determine the exact number and the spatial-temporal distribution of wild animals, which is vital to assess living conditions and potential survival risks of wildlife species  [19, 2, 29]. Since recently, these hitherto manual surveys of wildlife reserves are increasingly replaced with counts derived automatically from images acquired by Unmanned Aerial Vehicles (UAVs), paired with deep learning models to automate the wildlife recognition task  [19, 18, 30, 20, 10]. These models are typically supervised, pretrained on large-scale curated datasets such as ImageNet  [7] , MS-COCO  [22]  and then fine-tuned on the target imagery. Irrespective of the type of fine-tuning, this last step requires thousands of animals to be annotated, and hence expert knowledge.
Meanwhile, large UAV campaigns can generate images in high numbers  [19] , with many containing either large numbers of animals, or none  [20] , both of which cases are cumbersome for manual annotators. Furthermore, the vastness of wildlife reserves mean that wildlife is a rare sight and background dominates the majority of images. This causes two problems: on the one hand, the datasets are strongly imbalanced toward the absence class; on the other hand, the objects of interest are extremely small compared with the original aerial image size, as illustrated in Figure  1. These two issues significantly downgrade the capacity of a supervised model to solve the recognition task  [19]. Hence, methods to reduce the labelling requirement are urgently needed. A promising direction to this end is to use selfsupervised learning (SSL), where models are first trained in a pretext task on the target images without the need for manually provided labels, and then fine-tuned on the actual objective (downstream task) with manual labels  [23].
In UAV acquisitions, as illustrated in Figure  1 , high image sampling frequencies will generate strong autocorrelations between acquisitions in short time intervals (similar to adjacent frames in a video). Also, the vastness of many wildlife areas result in consistent characteristics, and hence in repeated or similar patches in the dataset. Many instance discrimination based methods, such as NPID  [38] , MoCo  [13, 5] , and SimCLR [4], are based on the assumption that each instance is significantly different from others and that each instance can be treated as a separate category. The large similarity between training images mean that the negative pairs used in the contrastive learning process is likely to be composed of highly similar instances, which in turn compromises feature representations due to incorrect repulsion between similar images.