dataesr/scientific-paragraphs-categorization huggingface dataset:
Algorithm 1 : 1 Learning procedure of HFedSNN: 𝑅 𝑔 is the total number of global rounds, 𝑟 is the fraction of selected participants in each round. Edge servers 𝐶 are indexed by 𝑐 and 𝐾 𝑐 is the client associated with the edge server 𝑐. On the client, 𝐷 𝑘 is the dataset available to client 𝑘, 𝐸 is the number of local epochs, and 𝜂 is the learning rate. 𝑇 is the total timestep, and 𝑉 𝑡ℎ represents the firing threshold. 𝐵 is the local batch size. 1: /* Edge and Cloud servers side *for each global round 𝑔 ∈ [1, 𝑅 𝑔 ] do 5:for each edge server 𝑐 ∈ 𝐶 do in parallel 6:Initialise 𝑤 𝑐,𝑔,0 ←-𝑤 𝑔 7:for each local round 𝑡 ∈ [1, 𝑅 𝑐 ] do 8:𝑤 𝑐,𝑔,𝑡 +1 ←-FedSNN(𝑤 𝑐,𝑔,𝑡 , 𝐾 𝑐 )𝑤 𝑔+1 ←-𝐹𝑒𝑑𝐴𝑣𝑔(𝑤 𝑐,𝑔,𝑅 𝑐 ) 12:end for 13: end procedure 14: procedure FedSNN(𝑤 𝑡 , 𝐾 𝑐 ) 15: 𝑃 𝑐 = 𝑟 ×|𝐾 𝑐 | 16: for each client 𝑘 ∈ 𝑃 𝑐 do in parallel 17: 𝑤 𝑘 𝑡 +1 ←-ClientUpdate(𝑘, 𝑤 𝑡 , 𝑇 , 𝑉 𝑡ℎ ) 𝑤 𝑡 +1 ←-𝐹𝑒𝑑𝐴𝑣𝑔(𝑤 𝑘 𝑡 +1 ) 20: end procedure 21: /* Client side */ 22: procedure ClientUpdate(𝑘, 𝑤, 𝑇 , 𝑉 𝑡ℎ ) 23: 𝛽 ←-(Split 𝐷 𝑘 into batch of size 𝐵) 24: for each batch 𝑏 ∈ 𝛽 do 25: for each epoch 𝑡 ∈ [1, 𝐸] do 26: Update 𝑤 using SNN(𝑏, 𝑤, 𝑉 𝑡ℎ , 𝜂) Δ𝑤 = 𝑤 -𝑤 0 30: end procedure 31: procedure SNN(𝑏, 𝑤, 𝑉 𝑡ℎ , 𝜂) 32: Initialise neuron potentials 𝑉 𝑖 = 0 for all neurons 𝑖 33: for each timestep 𝑡 ∈ [1,𝑇 ] do 34: Compute input current 𝐼 = 𝑤 𝑇 • 𝑏 35: Update neuron potentials 𝑉 𝑖 based on 𝐼 𝑖 36: If 𝑉 𝑖 > 𝑉 𝑡ℎ for any neuron 𝑖, emit spike and reset 𝑉 𝑖 37: Update weights 𝑤 based on emitted spikes and learning rule, with learning rate 𝜂 38: end for 39: end procedure user behavior and device characteristics.
The energy-efficient utilization of sensor nodes can be achieved using three control techniques, which are decentralized control, distributed control, and centralized control  [8]. In decentralized control, the nodes are divided into clusters. Each cluster has a central node that coordinates the activity of the nodes in each cluster. The activity of the nodes is therefore determined by the interaction of the central node of each cluster. For distributed control, each node makeslocal decisions with its partial knowledge of the entire network. This normally results in non-optimal routes in terms of energy consumption. By contrast, in centralized control, the network's global knowledge is known by the sink by means of its centralized database. The sink carries out the routing decisions. The centralized method can lead to optimal routes.
Simulation and Results Discussion The performance of the GA-based MSTs is first established for convergence using simulation. Secondly, the performance analysis of the proposed routing protocol is achieved with simulations using the performance metrics of network lifetime, number of alive sensor nodes (NAN), energy consumption, and computation time. These performance metrics of the CRPLEOGALSPI are compared with that of the recent CRPLOGARL  [13]  as a way of validation. The lifetime of the network is calculated as the time taken for the sink not to be reachable by the alive sensor node(s). The NAN is the number of alive sensor nodes at the lifetime of the network. The CRPLEOGALSPI and CRPLOGARL are coded with python 3. 8 under the "PyCharm" development environment. The graphical layout of the WSN is implemented with the python NetworkX module  [52].
The python codes are executed on the SLURM (Simple Linux Utility for Resource Management) cluster on the IRIT's OSIRIM platform. The Computer nodes of the OSIRIM platform adopted are the 4 AMD EPYC 7402 bi-processor computing nodes at 2. 8 GHz, with 48 processors and 512 GB of RAM each. These nodes enable more than 24 threads and/or 192 GB of RAM for the same process. The simulation parameters used to carry out the performance analysis and the network graph of the deployed WSN are shown in Table  2  and Figure  4 , respectively. The performance of the GA-based MSTs for the deployed network graph of the WSN given in Figure  4  is shown in Figure  5. As seen in Figure  5 , the GA-based MSTs is able to find 376 MSTs of the network graph for 1000 generations. This also implies the convergence of the solution of the GA-based MSTs in a reasonable number of generations.
Twelve ultrasonic sensors (SRF08) have been installed in four custom housings in the corners of the chassis of the wheelchair. Each housing contains three ultrasonic sensors covering a theoretical angle of 135º, where obstacles can be detected. Electric current sensors and voltage measurements are used to monitor the electric power flow through the two motors. Finally, a single-board computer (Raspberry Pi 3B+) acts as the ROS master, using a publisher-subscriber model. We refer the reader to  [16]  for more details on all these components, including the schematics of the hardware architecture. The wheeled platform developed at the Institut National des Sciences Appliquées (INSA) Rennes also builds on the Quickie Salsa M 2 wheelchair. It is equipped with 48 time-offlight (ToF) sensors organized in seven modules distributed along its perimeter: six modules of six sensors are located on each side and under the footplates, and one module of 12 sensors is installed behind the backrest (see Figure  3 ).
The classical design (known as a "symmetrical dual fisheye lens") includes two fisheye lenses pointing in opposite directions and two prisms that direct the light rays to two photosensitive elements [see Figure  5 (a)]. The dual-lens panorama design was introduced by Ricoh, in 2013 (Theta series), and it has been adopted by several other camera manufacturers over the past 10 years, e. g. , in the Insta360, Samsung Gear360, Madventure 360, Nikon KeyMission 360, and Garmin Virb 360. A twin-fisheye camera (Ricoh Theta S) installed on a mast overhead behind the user is the "Swiss Army knife" of sensors on INSA's smart wheelchair. In fact, it is used for driving assistance (together with an array of ToF sensors) and 3D scene reconstruction (for use in the wheelchair simulator or in an image-based localization module).
In this way, a digital "twin" of the real environment (with the same appearance and proportions) can be easily generated. For spherical photogrammetry, the dual-fisheye images from the Ricoh Theta are transformed into equirectangular images and fed into Agisoft Metashape, which yields dense, colored 3D point clouds with colors of photographic quality [see Figure  5(b) ]. If the camera pose relative to each image of the sequence is known, the resulting 3D reconstruction tends to be more accurate, and the computational cost is significantly reduced. In a classical data processing pipeline, Metashape relies on GNSS (Global Navigation Satellite System) measurements, which are typically available outdoors but not indoors. To address this issue in indoor environments, we first generated a sparse 3D model with the associated camera poses using OpenVSLAM  [20] , an off-the-shelf software package for visual simultaneous localization and mapping.
Driving Assistance The driving assistance solutions developed in the ADAPT project have been tested during two clinical trials: SWADAPT1 (NCT04072536) and SWADAPT2 (NCT04259151); see Figure  9 (a) and (c). Subjects with neurological disorders participated in these clinical studies. The main objective was to assess their driving performance with and without assistance. To this end, we measured the number of collisions and total time to completion in three standardized circuits of increasing difficulty. The SWADAPT1 clinical trial involved 25 users with expert wheelchair driving skills. The results of this trial indicate that the proposed assistance solutions are accurate, risk averse, and safe, with a high degree of acceptability. Moreover, even if the participants were already expert drivers, the study has shown that the use of the assistance module statistically significantly reduced the number of collisions during complex maneuvers  [6].
Training in VR The wheelchair simulator described in the "Wheelchair Simulator" section provides a high-fidelity, 3D immersive environment, and it offers the possibility to repeat the same circuit multiple times under identical experimental conditions. The user gets the impression of driving a real wheelchair, and safe navigation is guaranteed at all times. Shorter training sessions are thus necessary, and a wider array of (indoor/outdoor, obstacle-free/cluttered) environments and real-life conditions (variable light conditions, moving pedestrians) can be tested. Driving a real wheelchair could be dangerous for people with disabilities, requiring extensive training sessions to acquire the ability to move safely. The goal of the clinical trial SIMADAPT1 (NCT04171973) was to verify whether the performance observed on a real circuit was comparable to the one experienced on the wheelchair simulator.
In VR, the patients experienced a high sense of presence, and the level of cybersickness remained very low in the three circuits. In particular, the collected data indicate that using the simulator during a training phase could drastically reduce damage to the environment (walls, doors, and furniture) and driving accidents  [18]. The objective of the clinical trial SIMADAPT2 (NCT04894981) was to evaluate the impact of the immersive environment on VR driving performance. Three different conditions were compared in SIMADAPT2: with a Cave Automatic Virtual Environment (Immersia at IRISA/Inria Rennes), with a head-mounted display, and with a nonimmersive TV screen (see Figure  4 ). Overall, 18 wheelchair users with and without driving difficulties participated in this clinical study, organized in two sessions to comply with COVID-19 restrictions.
Acknowledgments This work was carried out as part of the  Interreg VA France (Channel) England  ( FCE) ADAPT (Assistive Devices for Empowering Disabled People Through Robotic Technologies ) project. The  Interreg   FCE program  is a European Territorial Cooperation program that aims to fund high-quality cooperation projects in the Channel border region between France and England. The program is funded by the  European Regional Development Fund (ERDF). The authors would like to thank  Éric Bazin  ( Institut d'Electronique et des Technologies du Numérique, INSA Rennes ) for his help during the development of the hardware platforms and the Inria associate team ISI4NAVE for supporting several exchanges among the French and English partners.
B. Specifications Of The Phantom DJI 3 Standard Battery As previously stated, we will evaluate the energy consumption of the UAV under various flying circumstances using power and thrust data acquired from computational fluid dynamics models. To accomplish this, we will need information regarding the Phantom DJI 3, which will be included in our research. The specifications of the phantom DJI 3 battery are presented in table 2  [37]. It is a 15. 2V 4480 mAh LiPo battery with a charging capability of 68W. Besides, it is developed exclusively for use with the Phantom 3 Standard drone and offers up to 25 minutes of flight time on a single charge  [37]. The battery image is presented in Fig. 10 , which is sourced from  [45]. These specifications combined with power consumption obtained from the CFD study will be used to calculate the battery energy consumption level of the drone when operating under different flight scenarios.
Fig. 27  presents the power consumption of the propellers under different hovering conditions. The bars represent the power required when correcting the thrust and the moving wall is traveling at speeds of 10 m/s and 15 m/s for 3000 rpm and 5000 rpm, respectively. As presented previously, when Propellers 1 and 2 are operating at 5000 rpm, Propeller 1 encounters a significant decrease in thrust while hovering near a moving wall at 15 m/s. To also ensure the stability of the UAV, the thrust control strategy was employed, leading to the appropriate increase in thrust. It is shown that the power consumption associated with this thrust control on propeller 1 is increased by approximately 8%. Finally, the power consumption and thrust force data obtained from the propellers' operation will be utilized to improve the energy estimations required for the quadcopter hovering and approach maneuvers near moving buses or trams.
N c A,b and a corresponding change to the TSD (Π) and thereby the attraction term F a N,Π. Hence, MORE and GoLF features are independent in formulation and can be combined to form MORE+GoLF-SPARKLING trajectories with temporally smooth k-space sampling pattern and gridded sampling in center of k-space. Both features are implemented in the SPARKLING package developed in Reference 12. In the ethos of open science, we will make the code available to the community on request for noncommercial research purposes only.
MRI acquisition parameters We carried out in silico and in vivo measurements to validate and benchmark the improvements of the new trajectories MORE and GoLF. In all the experiments, the target resolution is 0. 6mm 3 isotropic, with N x = N y = 384 (FOV x = FOV y = 23cm) and N z = 208 (FOV z = 12. 48cm). We use an AF of 20 (N c = 3969), except in the case of variable AF study. All the prospective scans were carried out on a clinical 3T MR system (Magnetom Prisma FIT , Siemens Healthcare) where the k-space data was acquired using a Siemens 64 channel Head/Neck coil. T * 2 -w images were obtained with flip angle of 15 • and slab selective excitation with TE of 20ms and Repetition Time (TR) of 37 ms. The k-space data was acquired with T Obs = 20. 48 ms (N s = 2048, hence Δt = 10 μs) and data were sampled by the analog-to-digital converter at t = 2 μs.
Varying acceleration factors Prospective k-space data was also acquired in vivo from the same healthy volunteer at varying AF values from 10 to 40 for both MORE-SPARKLING and GoLF+MORE-SPARKLING trajectories. The aim of this experiment was to assess how image quality evolves as a function of scan time. The results are presented in Figure  9. Overall, we observe that GoLF feature is crucial and provides less noisy and more detailed images as compared to the sole MORE-SPARKLING trajectories.
FUNDING INFORMATION This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 800945-NUMERICS-H2020-MSCA-COFUND-2017. This work was granted access to the CCRT High-Performance Computing (HPC) facility at CEA under the Grant CCRT2022-gilirach. This work was granted access to the HPC resources of IDRIS under the allocation 2021-AD011011153 made by GENCI.
These approaches do not normalise or quantitatively compare epigenomic levels of promoter and enhancer activity across species, and typically do not account for varying phylogenetic distances between species. To extend and improve these analyses, we applied and validated a phylogenetic modeling approach to promoter and enhancer epigenomic activities in two tissues from a four species phylogeny -in which we compare two mole-rat species with guinea pig and mouse as outgroup rodents. This strategy allowed us to quantitatively identify lineage-specific shifts in promoters and enhancers across mole-rat branches, investigate the associated biological processes, and assess the contribution of mole-rat specific changes in gene regulation to their characteristic evolutionary traits. The method we describe here improves on previously known issues with comparative analyses of functional genomics data, and could be extended for similar datasets across species, tissues and developmental stages.
At least two independent biological replicates from different animals were performed for each species and antibody. Wherever possible, tissues from young adult males were used. Tissues were prepared immediately post-mortem (typically within an hour) to maximize experimental quality. Post-mortem tissues were kept on ice until processed to minimize potential loss of protein-DNA interactions during post-mortem time. Sample allocations to experimental batches were randomised to ensure unbiased distributions of species, tissue, individual and sex, using the R/Bioconductor package OSAT  [64]. Tissues were prepared by direct perfusion of the liver with PBS, followed by dicing the whole organs (liver and heart) in small pieces around 1cm 3. Blood clots within the heart ventricles were removed. Cross-linking of the diced tissue was performed in 1% formaldehyde solution for 20 min, addition of 250 mM glycine and incubation for a further 10 min to neutralize the formaldehyde.
Computational analysis of ChIP-seq data and definition of regulatory regions Basic alignment and peak calling: Aligned bam files were obtained with bwa 0. 7. 17 and the Ensembl v99 assemblies HetGla_1. 0 (Naked mole-rat), DMR_v1. 0 (Damaraland mole-rat), Cavpor3. 0 (guinea pig) and GRCm38 (mouse). We used macs2  [66]  to call peaks for each ChiP-Seq replicate, using default parameters and "--keep-dup all" to retain duplicate reads. Before peak-calling, multi-mapping reads were removed and read-depth adjusted to 20 million uniquely mapped reads (or all available reads for low-depth libraries). Definition of regulatory regions from ChIP-seq peaks: We first constructed sets of reproducible peaks for each combination of histone mark, tissue and species by merging peaks identified across a minimum of two biological replicates (with minimum 50% length overlap).
Cross-mapping of regulatory regions across four rodent species Definition of orthologous regulatory regions: we used the LiftOver local software  [67]  to map genomic coordinates of the regulatory elements identified in naked mole-rat, Damaraland mole-rat and guinea pig to the mouse genome. LastZ pairwise whole-genome alignments with mouse were downloaded from Ensembl Compara v99 and converted from ensembl maf format to the UCSC chain format using UCSC tools (including mafToPsl, pslToChain and chainSwap, v357 for all software). We validated the correct implementation of the coordinates conversion step using LiftOver with the generated chain alignment files by comparing the resulting coordinates with those obtained from queries directly performed through the Ensembl API  [68]. We defined a set of "high-confidence orthologous regions" as 4-way orthologous regions for which we required robust LiftOver mapping of regulatory elements across the four genomes.
We took advantage of the approach implemented in the GreyListChIP R package, which flags regions with elevated signal in the input. In practice, for all pairs of input -H3K27ac sample ChIP experiments, we ran chipseq-greylist v1. 0. 2 with 100 bootstrap (--bootstraps 100), a simpler python implementation of GreyListChIP (available from https://github. com/roryk/chipseq-greylist). Two greylists were computed, one for each tissue, and including all regions flagged by chipseq-greylist in at least one input ChIP in any species. Finally, any region from the set of "high-confidence orthologous elements" overlapping with a greylisted region was removed before phylogenetic modeling. Across the different sets, the fraction of alignable elements that we exclude as greylisted ranged between 0. 5% and 2%.
We ran EVE's branch shift test  [38]  over normalized ChIP-seq reads using the evemodel R package  [71]  to detect regulatory activity shifts on the following branches of the four-species phylogeny: the ancestral mole-rat branch (Ancestral), the naked-mole rat branch (Hgla) and the Damaraland mole-rat branch (Fdam). Branch lengths in substitutions per site were extracted from Ensembl Compara v99  [68]  (specifically, from the species tree computed from pairwise whole-genome alignments, available at https://github. com/Ensembl/ensemblcompara/blob/release/99/conf/vertebrates/species_tree. branch_len. nw). For each of the four orthologous regulatory elements sets (Enhancers Heart, Enhancers Liver, Promoters Heart and Promoters Liver), we first estimated model parameters under the null model (selection without shift), in order to obtain realistic parameter values for simulations.
We again performed n=1,000 simulations for each of the four sets (Enhancers Heart, Enhancers Liver, Promoters Heart and Promoters Liver), and with varying proportions of simulations under the null and shift models (from 5 to 17. 5% of simulations with a shift). We performed simulations under the null and shift models using the average parameter values estimated from the data (parameters alpha, beta, theta0 and sigma), while the additional shift parameter value for the simulations with shift were drawn from a beta distribution Beta(alpha=8, beta=2)*3, selected to resemble empirically-estimated shift values. On average and across the four sets, testing for shift on the Ancestral branch at alpha = 0. 05 and without filtering on the value of the estimated shift yielded a true positive rate of 0.
Detection of regulatory activity shifts using differential binding analysis For comparison with phylogenetic modeling, we implemented the procedure described in the DiffBind R package  [39]  to identify regulatory regions differentially active in mole-rats compared to guinea pig and mouse. For each regulatory region, we measured regulatory activity as the raw number of H3K27ac ChIP-seq reads minus the raw number of input ChIPseq reads. We then normalized these counts across samples using the median of ratio method from the DESeq2 package  [73]. Finally, we ran the DESeq2 differential analysis procedure, contrasting a first group containing all samples from both mole-rats against a second group with samples from guinea pig and mouse. We filtered DESeq2 results to retain differentially active elements with FDR < 0.
Gene ontologies, pathways and genes enrichment tests We downloaded the mouse gene ontologies ("GO") data from the MGI database on the 20 th of April 2022, as well as C2 pathway annotations from MgDB on https://bioinf. wehi. edu. au/software/MSigDB/. We filtered GO data to retain only GO of the Biological Process ("BP") domain, and C2 pathways to retain only 1,397 pathways (mostly retaining REACTOME, KEGG and BIOCARTA pathways). We transferred mouse gene ontology and pathway annotations to the naked mole-rat and Damaraland mole-rat using gene orthologies from ensembl compara version 102, extracted with ensembl biomart  [68]. We reimplemented the region-based gene ontologies tests implemented in GREAT  [74] , allowing us to use custom genomes (the naked-mole rat and Damaraland mole-rat genomes) and the more recent gene ontology annotations downloaded from MGI.
For gene ontology and pathways enrichment tests, we used GREAT recommended tests (a) and (b) (i. e. tests against the whole genome as background). We retained only terms found enriched with both tests, ranked them according to BH adjusted p-value < 0. 05 of test (a), and selected the top 100 GO terms and top 20 C2 pathways. To identify single instances of significantly-enriched genes we used test (c), using the complete regulatory regions set of a given category as background (for instance all orthologous heart enhancers as background for nmrdmr up heart enhancers). Lastly, we validated our implementation by verifying that highly similar terms were found when using the GREAT web-server with the mouse genome and older gene ontology data from MGI. For regulatory elements with an activity shift in the ancestral mole-rat branch, we used the Damaraland mole-rat genome, which has a higher contiguity, to perform functional enrichment tests.