dataesr/scientific-paragraphs-categorization huggingface dataset:
V. PERFORMANCE EVALUATION To assess the above use-case, a scenario with K CUs is considered, where due to the heterogeneous spacetime traffic patterns and radio conditions at various transmission/reception points (TRPs) over the RAN,  the corresponding MSs hold non-independent identically distributed (NIID) datasets of size D k,n = 1000 that have been collected in our evaluation from a live LTEadvanced network. The datasets include, as input features, the hourly traffics of the main over-the-top (OTT) applications, channel quality indicator (CQI) and MIMO full-rank usage, while the considered supervised output is the CPU load. The assumed slices are: • enhanced Mobile BroadBand (eMBB): NetFlix, Youtube and Facebook high and ultra-definition videos, • Social Media: Facebook, Facebook Messages, Whatsapp and Instagram, • Browsing: Apple, HTTP and QUIC, By coding the samples of all the datastes, the AEs' models weights as well as the SLA violation metrics in 32 bits format, we quantify the overhead induced by both the proposed stochastic policy-FL and a fully centralized SLA-constrained learning (CSCL) baseline  [14]  as depicted in Figure  5a  where m = 100.
Intuitively, since only the AE models' weights are exchanged in the FL setup instead of the raw dataset, as is the case in CSCL, the overhead is expected to be significantly reduced. Concretely, upon the convergence point of the stochastic policy-FL, i. e. , round 50 more than ×30 overhead reduction is obtained, with a minimum of ×22 reduction at round 80. While also dramatically reducing the CPU SLA violation rate compared to the FedAvg unconstrained algorithm  [15]  as showcased by Figure  5b.
Statement of need Due to the similarities in the methods used in the fields of textual reconstruction and evolutionary biology, textual scholars have begun to employ software created for biologists to analyze texts. Specifically, textual scholars are now using sequence alignment algorithms and phylogenetic tree-building packages to help reconstruct ancient texts  (Maas, 2013; Phillips-Rodriguez, 2007; Salemans, 2000). However, as bioinformatics becomes increasingly sophisticated, its models and algorithms have become more specific and less applicable to non-biological sequences. helayo has been designed from the ground up to perform multiple sequence alignment specifically for Sanskrit texts. Since Sanskrit has been written in over a dozen different scripts, each with their own orthographic peculiarities depending on their time and place, helayo performs a crucial pre-processing step in which the texts are normalized so that they can be compared meaningfully  (Li, 2017).
helayo can also tokenize texts either as individual characters or as akṣaras, since the Brahmic scripts used to write Sanskrit are abugidas, in which consonant and vowel pairs are written as a single unit. In addition, a web-based matrix editor can be used to edit an alignment. It can also automatically reconstruct a text based on an alignment and a phylogenetic tree using the Fitch algorithm  (Fitch, 1971). A full tutorial, with example files, is available at https://chchch. github. io/ sanskrit-alignment/docs.
Implementation helayo is written in Haskell and implements the Center Star multiple sequence alignment algorithm  (Gusfield, 1997, pp. 347-350)  with an affine gap penalty model  (Li, 2021). It can be run in three different tokenization modes (character, akṣara, or whitespace-delimited word) and outputs a TEI XML file which can then be edited using the matrix editor. The matrix editor is written in Javascript and can be used either online or offline. It loads both TEI XML alignments produced by helayo as well as phylogenetic trees in NeXML format, which can be used together to reconstruct a text.
Discussion Previous work on textual phylogeny has focused on texts in Western languages  (Barbrook et al. , 1998) , and, consequently, the tools developed from those projects embody assumptions about how Western languages are spoken, written, and transmitted (e. g. Dekker & Middell, 2019) , in the same way that tools produced for bioinformatics embody assumptions about biological sequences. While any set of sequences -composed of text in various languages, of DNA, of proteins -can be analyzed using the same fundamental algorithms, the results are meaningless unless we use domain-specific knowledge to refine those algorithms and to interpret those results. helayo, in turn, has been conceived specifically to align Sanskrit texts, and a great deal of accumulated expertise in Sanskrit philology informs its design.
, 2010) , a similar model of the human brain, a 'digital twin' of its cellular structure, would be in the range of several PBytes. The interactive exploration (as opposed to simple storage and visualization) of such a dataset is beyond the capacities of current computing, and creates significant challenges in this field  (Amunts and Lippert, 2021). Data coming from electron-microscopy, e. g. , multi-beam electron-microscopy, result, for small samples at nanometer resolution, in comparable data sizes  (Eberle and Zeidler, 2018). Big data problems also appear when moving from single brain data with high spatial or An anatomical 3D model @ 1 micron resolution isotropic needs 2-3 PByte storage per brain To optimize the computation of fiber tracts with a spatial resolution of 60 microns isotropic would require years for the whole Human brain with current technology Neuronal network training to extract structural features in images with a spatial resolution of 1x1x20 microns would require, for the whole brain, 100 days at whole brain level with current technology A 10 seconds point-neuron simulation including 4 million neurons requires 10 minutes of computation on EBRAINS' Fenix system (400 core hours) One second of simulation of a network of 450,000 cells with a high level of details of the hippocampus CA1 region requires at least 20,000 cores and needs 130,000 core hours on the Piz Daint supercomputer at CSCS in Lugano, Switzerland.
Simulation of the binding of a single substance at the molecular level with QM/MM (quantum mechanics/molecular mechanics): 20 million core hours on the JUWELS supercomputer at JSC, Germany. temporal resolution to large cohort studies with thousands of subjects, necessary to address intersubject variability. Large cohort studies are used to study the relationship of structural, functional, behavioral, life-style, health and genetic data in thousands of subjects, which are necessary to identify weak factors and their interactions in brain diseases. For example, the UK biobank provides a unique data set of about 500. 000 participants  (Bycroft et al. , 2018). Neuroimaging PheWAS was recently introduced as a webbased system to analyze gene-brain relationships, and could be used to study the influences of the apolipoprotein E (APOE) gene on various brain morphological properties in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort; benchmark tests on the UK biobank were performed as well  (Zhao et al.
EBRAINS offers different services (https://ebrains. eu/services/ ) for curating and sharing data and models, contributing to and accessing brain atlases, using modeling and simulation tools, running closed-loop AI and neurorobotics experiments, retrieving medical brain activity data, and computations based on high-performance computing. The idea behind this is to enable workflows that seamlessly connect elements of the different services. To prove this, so-called showcases have been developed by the HBP (Box 3). Integrating brain data and knowledge from different research approaches requires curation, proper annotation and provenance tracking. Through the EBRAINS Knowledge Graph, a flexible and scalable metadata management system accompanied by a search user interface, data are made findable, accessible, interoperable and reusable, i.
, 2021a). (C) Microscopical data are available through the BigBrain model  (Amunts et al. , 2013). The atlas provides different types of data in a common spatial framework and allows switching between template spaces. Brain atlases have a central role to visualize brain data in their spatial context, e. g. , to interpret neuroimaging data from living human subject and patients, but also to derive therefrom input for subsequent analysis and model building. Comparative approaches targeting cross-species differences and similarities represent an important field of brain research, but there is still a gap in linking the atlases of the different brains under a common technological umbrella, which creates difficulties, e. g. , in understanding homologies. The HBP human brain atlas aims to address this need, and to develop an atlas framework which allows reference to maps of human brain organization, those of rodents, and in the future also monkey brains.
The BigBrain is compatible to atlas data from neuroimaging, and serves as an input for simulation, e. g. , using The Virtual Brain (see Showcase 1, Box 3). Julich-Brain is a part of the Human Brain Atlas and serves as a cytoarchitectonic reference, while taking inter-subject variability into account  (Amunts et al. , 2020). It is linked to a comprehensive map of DTI-based fiber tracts  (Guevara et al. , 2017; Guevara et al. , 2012) , functional parcellation schemes based on multiple fMRI in a well-defined group of subjects  (Pinho et al. , 2021a) , which provide insights into the cognitive dimension of brain parcellation. MR-based approaches are central to open up applications into in vivo imaging, which is relevant for medical research. Being on EBRAINS allows, for example, directly linking information from the atlases with models and simulation.
On top of the Fenix infrastructure, any type of scientific digital service platform can be operated via RESTful APIs (https://fenix-ri. eu/ ). Fenix that emerged from computer science research in the HBP is an infrastructure-as-a-service (IaaS) for EBRAINS. It has been developed to master the big data challenge of modern brain research. Generic-purpose and domain-specific services provide access to scalable and interactive computing resources via simple-to-use interfaces.
As such, EBRAINS services provides an opportunity for researchers working on this convergence, in the form of a toolchain that connects conceptual exploration to application prototyping and finally implementation. Edge computing applications are poised to benefit most from the emergence of neuromorphic chips capable of both energy-efficient, low-latency processing of data streams and concurrent learning based thereon. Autonomous robotics will also greatly benefit from such chips, insofar as they are in all likelihood key enabling technologies towards the implementation of complex cognitive functions such as decision-making, situational awareness, contextual adaptability, etc. Understanding how those arise from the human brain, both at the computational and implementation level, is a challenge taken on by the HBP.
, 2019b). Conceptual clarification and analysis are the basis for addressing more practical issues raised by neuroscientific research from data protection autonomy and identity concerns  (Amadio et al. , 2018) ). EBRAINS is expected to adopt an inclusive and co-creative way of working, engaging with multiple audiences and communities to discuss ethical issues, developing novel insights into responsible innovations and their clinical and societal applications (https://ebrains. eu/discover/ ).
Conclusions To achieve a comprehensive understanding of the human brain, its connectome and parcellations means understanding the multi-level organization of the brain as an embodied network and complex system enabling perception, action, consciousness and cognition. Combining the perspectives of multi-level brain organization with embodiment is not only relevant to capture the full scope of brain diseases and to be able to develop new therapies, but also for the development of neuro-inspired technologies, and future neurorobotics. There is an urgent need to accelerate efforts for mental and brain health by making full use of insights from brain research and modern digital tools. Based on use cases from neurology already available in EBRAINS, including the Medical Informatics Platform and the Human Intracerebral EEG Data Platform, it is now being further developed to support research in mental health, psychiatric disorders, neurosurgery, and neuroradiology, but also more broadly in the medical field.
Insights into fundamental questions of brain organization will provide the key to new computing technologies, artificial neuronal networks, cognitive computing and neurorobotics as an integrative overarching technology both for experimentation and for substantially advancing real robotics. Making such technologies more "neuro-inspired" is expected to significantly speed up their development. Neurorobotics and neuromorphic computing will benefit from being increasingly neuro-inspired. The amount of brain data is increasing rapidly. The effort in terms of time, knowledge and methodology needed to make it findable, accessible, interoperable and reusable (FAIR) has long been underestimated and resources should be planned, from the very beginning of each research project, to address this. The Human Brain Atlas allows access to multiple brain data according to their spatial organization through viewers, but also fully programmed software coupling.
This might be a game changer for analyses of big and complex data on systems of the highest performance, but also for modeling and simulation, which become biologically more realistic. Modeling and simulation have started to develop from different angles, and they used different approaches. But now we are in a position where we can link them, which enables bridging the different scales, to better constrain and to verify results of simulation. Collaboration across boundaries of institutions, sectors, nations, research disciplines and cultures is indispensable for progress in neuroscience. Moreover, insights from brain research will increasingly influence learning and education and have an impact on our society. To stay ahead of emerging ethical, societal and legal issues, and to strengthen the societal benefit and acceptability of its findings, EBRAINS need structures and strategies for engaging in dialogue with communities on issues of immediate and long-term relevance, including data ethics, neuroethics, animal use and well-being, dual use, gender equality and diversity.
We therefore created an additional version of the Brian model, designed as a class-based template, similar to the Brian2 and NEURON implementations, and also incorporated the SciUnit interface. We shall refer to these two sets of models as Original and Brian1. Both these models employ the Brian simulator, and if the modified Brian1 implementation is a faithful reproduction of the Original implementation, they should produce identical responses. All the models are registered in the EBRAINS Model Catalog, thereby providing access to all associated metadata and validation results. The value of the initial resting potential for the models was not specified in the published article, and the published source code picks this randomly from a uniform distribution between -55. 0 and -75. 0 mV. For purposes of reproducibility and consistency, we deemed it essential to maintain a constant initial membrane potential across all simulation runs.
Discussion We have reproduced the simplified CA1 pyramidal cell model, developed by  Ferguson et al. (2014)  with the Brian2 and NEURON simulators. These implementations are able to reproduce the core features of the original model developed with Brian, but do not completely correspond to that reported in the published study. The original publication provides sufficient information for reproducing the model, but lacks some details with regards to the protocols followed for running the simulations. The availability of the original model, via ModelDB, is useful for perusing the source code, but does not alleviate the issues encountered in reproducing the simulation outcomes. Similar differences were found in the simulation outcomes when using the published model, from ModelDB, to reproduce the data in the published study.
, 2007; Ohyama et al. , 2013 ; Figure  1A ). In nature, rolling is only observed after exposure to a strong noxious stimulus, such as heat or a predator attack  (Ohyama et al. , 2015; Robertson et al. , 2013; Tracey et al. , 2003). Powerful genetic toolkits have advanced the observation and manipulation of larval behaviour at the cellular level, making Drosophila larvae particularly well-suited for studying the neural mechanisms underlying learning. In Drosophila, individual neurons are uniquely identifiable, with mor-Manuscript submitted to eLife phology and function preserved across animals  (Skeath and Thor, 2003; Wong et al. , 2002; Marin et al. , 2002; Jefferis et al. , 2007). Together with tissue-localised protein expression afforded by the GAL4-UAS binary expression system  (Fischer et al.
Couzin, 2020; Luxem et al. , 2020). While real-time behaviour detection of casts and runs has been developed for a single animal  (Schulze et al. , 2015) , our study of operant conditioning in freely behaving Drosophila larvae required efficient, real-time behaviour detection of multiple animals. We built a system to simultaneously track up to 16 larvae in real time, using LabVIEW for the user interface and algorithm implementation (Figure  1E ). Instrumental to this software architecture was the fast image processing speed afforded by FPGA-based parallelisation (Soares dos  Santos and Ferreira, 2014; Li et al. , 2011; Zhang et al. , 2017). Neuroscientists have adapted FPGA's real-time analysis capabilities  (Shirvaikar and Bushnaq, 2009; Uzun et al. , 2005; Chiuchisan, 2013; Yasukawa et al.
, 2016)  to track rats  (Chen et al. , 2005) , zebrafish larvae  (Cong et al. , 2017) , and fluorescently labelled neurons in freely behaving Drosophila larvae  (Karagyozov et al. , 2018 ). In our system, the FPGA and host computer worked together to read the raw camera images, detect eligible objects, and extract and process object features (i. e. contour, head and tail position, and body axis) (Figure  1E ). Larval body shape, velocity, and direction of motion facilitated robust behaviour detection which, in turn, drove closed-loop optogenetic and thermogenetic stimulation. All relevant experiment parameters and time-series data were output for offline analysis through a custom MATLAB framework 5 of 36 Manuscript submitted to eLife (see Materials and methods).
c,f. Probability that a given bend was directed towards the stimulated side or, in the case of the uncorrelated training group, towards the left. Grey line indicates equal probability of 0. 5 for bends to either side. Statistics calculated from a two-sided Wilcoxon signed-rank test. b-f. Gal4 expression depicted as color-coded CNS. All data is shown as (mean ± s. e. m. ). n. s. For each larva, two measures served as a read-out for bend direction preference: i) the bend rate, measured as the number of bends per minute performed towards a given side, and ii) the probability that a given bend was directed towards the stimulated side, obtained by normalising the bend rate with the total number of bends performed by the larva in that minute. Individual larva variation in bend rate yielded different results for these measures at the population level.
Behaviour classifiers Behaviour classifiers were developed using a user interface similar to JAABA  (Kabra et al. , 2013). The underlying algorithms combined trained neural networks and empirically determined linear thresholds. We developed a MATLAB (MathWorks, Natick, Massachusetts) user interface with functions for data visualisation, manual annotation, and machine learning using the Neural Network Toolbox, the Deep Learning Toolbox, and the Statistics and Machine Learning Toolbox. Here we briefly describe the behaviour classifiers and provide performance results based on manual validation (Table  1 ). The bend classifier was based on predefined thresholds for temporally smoothed body shape features and was itself exponentially smoothed over time. Independent left and right classifiers were used to initially detect bend direction.
To detect left and right bends, these classifiers were combined with the smoothed bend classifier using an AND conjunction. The raw time series of left and right bends was further smoothed post-acquisition using a custom MATLAB script: two bends to the same side separated by less than 200 ms were combined into a single long bend, and short bends of less than 200 ms were removed from analysis. To improve left and right detection performance, we developed a classifier for circular larval contours. This ball classifier used a feed-forward neural network with a single fully connected hidden layer whose inputs were normalised values of eig_reduced, larva_arc_ratio, and larva_-area_ratio. The hidden layer consisted of five neurons with a hyperbolic tangent activation function. The output layer contained a single neuron and used a sigmoid activation function.
The neural network was trained in MATLAB on a manually annotated data set for 500 epochs using a cross-entropy loss function and scaled conjugate gradient backpropagation. If a ball was detected within the previous 1. 5 s, left and right classifiers were overwritten to match the last detected bend direction prior to the beginning of the ball. The back classifier detected individual backward peristaltic waves based on thresholds for smoothed tail velocity features combined with no ball detection within the previous 1. 5 s. Two different classifiers were used to detect crawling. forward detected longer forward crawl periods based on thresholds for smoothed tail velocity features combined with no ball detection  Calibration was performed by projecting square spots at fixed DMD pixel locations and linearly fitting the corresponding camera coordinates.
Data analysis Data analysis was conducted using custom MATLAB software. To ensure high quality data, it was necessary to remove invalid objects from the data set prior to behavioural analysis. These included corrupted objects (e. g. scratches on the plate or residual food) that the software mistook for larvae. They also included larvae that lost their object identity and were consequently detected for only part of the experiment (e. g. after temporarily reaching the plate's edge or touching other larvae). After equally splitting each experiment into 60 s time bins, we retained objects for analysis that fulfilled strict criteria: i) the object must have been detected in every frame of the bin; ii) the object's initial detection must have occurred at least 20 s prior to the start of the bin; iii) at no point during the bin did the smoothed velocity of the larval centroid exceed 1.
5 mm/s; and iv) the mean of the smoothed centroid velocity across the object's detection period in the bin was at least 0. 5 mm/s. To quantify the accuracy of this method, we manually reviewed 350 videos of objects flagged as valid for a given 60 s bin. In this group, we observed no severely corrupted objects. In one case (0. 3%), a larva briefly touched another larva. In another case (0. 3%), head and tail of a larva were falsely detected the majority of the time, leading to flipped detection of left and right bends. When analysing valid bin data for operant conditioning of bend direction preference, we counted, for each larva, the numbers of left and right bends initiated within the bin. This was defined as the bend rate towards the respective direction. The probability of the larva bending towards the side paired with the optogenetic stimulus was defined as the ratio of the number of bends towards this side to the total number of bends initiated in the bin.