dataesr/scientific-paragraphs-categorization huggingface dataset:
TrackMate has a strong focus on interoperability and can be scripted (Jython in Fiji, MAT-LAB via MIJ  [13] ), exchange data with other tracking software (Icy  [14] , MTrackJ  [15] ) or analysis packages (MAT-LAB, R, Python). TrackMate is also a software platform that can easily be extended by adding new tracking algorithms, analysis, and visualization features. Thanks to these features, TrackMate has built a large user and developer base  [16] [17] [18]. Until now, TrackMate detectors were solely based on the Laplacian of Gaussian (LoG) filter. The LoG filter is efficient against sub-resolved particles  [19]  or other blob-like objects but performs poorly for textured objects, for objects with complex shapes, and other imaging modalities than fluorescence. These detectors are also limited to measuring the object's position and not their shape.
The new detectors work for 2D and 3D images when possible, but the display and analysis of object contours are currently limited to 2D images. However we can exploit Track-Mate to segment 3D objects, using a slice-by-slice approach. Instead of tracking objects over time, we tracked them across subsequent axial planes and linked them to recover 3D objects (Figure  2f , Supplementary Figure  7  and Movie 8-10). With this approach, TrackMate can accelerate the segmentation of challenging 3D datasets by using a custom DL model trained on 2D images, for instance, using interactive annotation tools such as Kaibu  [28]. We provide a detailed manual and step-by-step tutorials to facilitate the use of the new TrackMate detectors (Supplementary manual and Supplementary Figure  8 ). We also provide documentation aimed for developers to integrate their own DL and ML algorithms in TrackMate.
Altogether, TrackMate now enables powerful segmentation approaches for tracking purposes directly in Fiji within a user interface already familiar to many. We envision that by making complex tracking problems more easily solvable by scientists, this new version of TrackMate will accelerate discoveries made in Life-Sciences. Online methods and data availability statement. The version of TrackMate described here is available in the Fiji  [12]  software by simply updating it. TrackMate is documented on the ImageJ wiki: https://imagej. net/plugins/ trackmate/ and the documentation for the new features can be accessed from https://imagej. net/plugins/ trackmate/trackmate-v7-detectors. We also provide 11 test datasets that are made available via a dedicated Zenodo collection  [29].
thanks  Helen Blau  for giving us the permission to use the "Mouse hematopoietic stem cells in This study was supported by  France BioImaging  ( Investissement d'Avenir ;  ANR-10-INBS-04 , JYT), the  Academy of Finland (GJ) , the  Sigrid Juselius Foundation (GJ) , the  Cancer Society of Finland (GJ) , the  Åbo Akademi University Research Foundation  (GJ,  CoE CellMech ), the  Drug Discovery and Diagnostics strategic funding to Åbo Akademi University (GJ)  and the  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie grant agreement 841973  (JRWC). JWP was supported by  Health Campus Turku 2. 0 funded by the  Academy of Finland. RFL was supported by an  MRC   Skills development fellowship  ( MR/T027924/1 ). The Cell Imaging and  Cytometry Core facility  ( Turku Bioscience, University of Turku ,  Åbo Akademi University and Biocenter Finland)  is acknowledged for services, instrumentation, and expertise.
The archaeological map and geographic information systems The Ministry of Culture manages the national archaeological map, which is a mapped inventory of information relating to archaeology within the national territory, from its origins to the present day. The national archaeological map is updated at a regional level by each Regional Archaeology Service (SRA) of the Regional Direction for Cultural Affairs (DRAC) network -decentralised departments of the Ministry of Culture in each French region. It is now managed by the PATRIARCHE application. The SRAs are able to transfer part of the data from the archaeological map to the Atlas des Patrimoines, which is open access. In reality, few services have done so and the data uploaded has not been updated recently. To date, to consult recent data, it is still necessary to visit each SRA.
It is, therefore, not easy to have precise knowledge about the data and their quality. For the moment, no link has been established between CAVIAR, a national GIS managed by Inrap (see below), and the Atlas des Patrimoines. In parallel with the Atlas des Patrimoines, the SRA of Bretagne provides the GeoBretagne web application, which allows the general public to view and download part of the archaeological map data (operations, archaeological entities, regulatory zoning, etc. ) under an Etalab 2. 0 licence. The link between the data deposited in the Atlas des Patrimoines and those visible in GeoBretagne is not explicit. In the field of geographic information systems applied to archaeology, numerous regional or local, thematic or chronological initiatives have emerged over the last decade. We cannot mention them all, but we can emphasise the increasing use of geographic information systems, whether at the level of a research project, a town, a region or a national research institute.
In south-east France, a database for material and artefact management called Sisyphe has been developed, but it is little used and not finalised. Other regional archaeological services have chosen to develop databases with Filemaker Pro or simple Excel spreadsheets. Once again, there is great diversity in the IT tools used to manage archaeological material and associated documentation by the State's own departments. The establishment of shared preservation centres (museums, universities, associations, development-led archaeology operators, etc. ) for all documentation resulting from archaeological operations, both excavation archives and inventoried archaeological material, throughout France would certainly allow the adoption of common or at least interoperable tools.
PACTOLS is the main French thesaurus in archaeology, polyhierarchical, multilingual and scalable, in accordance with the FAIR principles, and is widely used by the French archaeological community. Financed by the Ministry of Higher Education, Research and Innovation, the general objective of the TGIR Huma-Num is to 'facilitate the digital turn in the humanities and social sciences research'. By encouraging the creation of field-specialised consortia, Huma-Num enhances scientific community building. Several consortia include archaeology in their approach (3D consortium, Medieval Sources Consortium -COSME, Paris Time Machine); the Consortium Mémoires des archéologues et des sites archéologiques (MASA), in which Inrap participates, is entirely dedicated to it. This consortium works on digital data stewardship in archaeology and preservation.
The challenge is threefold: to work on the data processing, to work on making it accessible and preserving it, and finally to encourage sharing and reuse. In addition to training and dissemination of good practices, a data management plan template for archaeological research projects has been developed. Within the framework of this consortium, the CITERES laboratory in Tours has also designed the OpenArchaeo platform, which aims to make archaeological datasets produced within the MASA consortium available on the semantic web and to offer an intuitive query interface for archaeological data, inspired by the ResearchSpace search engine set up by the British Museum. In addition to its support to consortia, the TGIR Huma-Num relies on a technical infrastructure that offers sustainable digital tools and services on a national and European scale by relying on a large network of partners and operators.
Huma-Num does not offer a search engine, nor a scientific editorial website, nor a data enrichment system. Its objectives are to propose solutions to host, to disseminate and to preserve the data produced by humanities and social sciences research projects. Several archaeological research projects use Huma-num services; it is difficult, however, to evaluate the proportion properly. In the field of development-led archaeology, because of the large amount of data produced, these services remain difficult to use on a large scale.
Sharing scientific publications In 2001, the CNRS developed an online platform HAL for referencing and disseminating published or unpublished articles, photographs and research work. It takes time for archaeological researchers to adopt this platform but for several years, it has been widely used in the community. Any document deposited in HAL has a unique and permanent URL and is archived in a sustainable way at the Centre Informatique National de l'Enseignement supérieur (CINES). Inrap has an institutional portal Hal-Inrap on this open archive platform. In 2020, the project 'Peer Community in Archaeology' or PCI Archaeology was created. This is a new peer review system for academic manuscripts in archaeology already deposited on preprint servers. The preprints recommended by PCI are full papers, they are scientifically validated and can be cited without the need to publish them in traditional scientific journals.
Some of them only offer a minimal cartographic online service that summarises their operations. The dynamics of opening and sharing data, supported by the European and national levels, in particular the Ministry of Research, naturally crosses the development-led archaeology sector. The stakes are all the more important, as it is in this disciplinary area that the vast majority of digital data produced in archaeology is concentrated. Inrap, as the central institute of the system, is in the process of developing a proactive policy for sharing the data resulting from its research. The objective is to move towards processes and datasets that comply with the FAIR principles. To follow this path, the entire process of creation, management and archiving is being transformed towards the formalisation of a natively digital and ultimately shareable data cycle.
Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyperparameters inspired by the readout layer concept in reservoir computing. Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition. The spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model. When trained with the unsupervised activity regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir yields better performance than state-of-the-art convolutional neural networks.
Another popular method of processing EMG signals is features extractions  [34]  and machine learning, but this requires tailoring the features  [29]  based on the problem at hand and the training is not end-to-end. Deep learning  [32]  methodologies, and non-spiking reservoir computing  [5]  have also been studied in the past for EMG signal processing, but such architectures are power-hungry and difficult to deploy on edge in real-time; moreover, the performance of deep neural networks is limited by the availability of large datasets. While there are studies done in the past to evaluate the performance of plastic spiking reservoirs for processing electroencephalogram (EEG) signals  [28] , such a study of EMG signal processing is not known to the authors. In this study, we wish to evaluate the impact of synaptic plasticity in recurrent connections on a reservoir performance.
In this work, we first discuss the sEMG dataset and spike encoding algorithm. To this end, we propose a novel yet straightforward algorithm to evaluate the encoding efficiency for the classification task. Subsequently, we simulate several different reservoir computers and ask whether the best performance is achieved on this edge of chaos. We use the CRITICAL  [6]  branching factor auto-regulation algorithm for driving the network to this state. We then evaluate multiple ingredients of a reservoir of spiking neurons (feeding input to the reservoir, properties of the neurons, connection density, and plasticity) and propose strategies for designing an efficient reservoir computing system. We report performance better than the present state of the art for sEMG-based gesture recognition for  [9, 14]  in the results section.
METHODS 2. 1 Dataset We use two publicly available hand gesture recognition datasets collected through the Myo armband. The armband has eight equally spaced sensors with a sampling rate of 200 Hz. The Roshambo dataset  [14]  consists of 3 gestures, collected from 10 participants, across 3 sessions. Each session comprises 5 trials for each gesture; hence there is 450 trials in the entire dataset. The initial and final 600 ms of each trial were trimmed off as done in  [25]  and  [24]  as to benchmark the performance equitably. The EMG subset of sensor fusion dataset, also referred to as the 5 class dataset  [9] , consists of 5 gestures. The dataset comprises recordings from 21 participants. Each participant took part in 3 sessions, and each session comprised five trials of each gesture. Each trial is for 2 seconds.
RESULTS AND DISCUSSIONS We propose two pipelines, the Spike Encoding & Evaluation Baseline as presented in Figure  1 , and an encoder followed by a CRITICALenabled reservoir as shown in Figure  2. Rather than minimizing the error in signal reconstruction, we optimize the encoding parameters by maximizing the accuracy of a supervised readout layer trained with the average rate vectors as shown in Figure  1. Data from 1 session was taken for testing from the three sessions, and the other two were used for training the machine learning classifier. The three-fold classification accuracy for two machine learning classifiers : LDA and SVM (RBF kernel) are reported in Table  3. We observe a higher accuracy for the SVM classifier than the LDA classifier and hence use that for comparing performance with literature (Table  4 ).
With the Spike Encoder & Evaluation Baseline we obtained 85. 44% accuracy using the Roshambo dataset and 65. 18% for the EMG subset of sensor fusion dataset. The classification performance for this method was found to be higher than the ones reported in the literature by using reservoirs with fixed weights in the case of the Roshambo dataset and by using spiking multilayer perceptron (MLP) and spiking convolutional neural network (CNN) for the sensor fusion dataset. 3 : Comparison of 3-fold cross-validation accuracies obtained with different readout methods (LDA and SVM with a radial basis function (RBF) kernel) for Roshambo dataset (3 class), and sensor fusion dataset (5 class) performed on encoded data, reservoir without plasticity and reservoir with CRITICAL plasticity. Note that we use reservoir with 320 neurons for 3 class problem, and 2048 neurons for 5 class problem, with a topology initialized from a small-world distribution with parameters coming from the optimization of a biorealistic eigenspectrum, as done in  [3].
budget and transmission bandwidth requirement. The Spike Encoding & Evaluation Baseline seems to be an excellent reference to which novel neuromorphic SNN classifiers can be compared. The architecture shown in Figure  2  uses a spiking reservoir with bio-inspired topology from  [3]  for features extraction. With plasticity in the reservoir performance improves further. We implemented the CRITICAL auto-regulation synaptic plasticity in synapses emerging from excitatory neurons. The classification performance for both datasets is reported in Table  3 , and compared in Table  4  with state of the art systems. The reservoir's performance are compared between fixed weights and with that of a reservoir with CRITICAL plasticity. Interestingly, the reservoir's performance with fixed weights was lower than that of the Spike Encoding & Evaluation Baseline with SVM readout for both datasets, indicating information loss in the reservoir's activity, possibly due to chaos.
This highlights the importance of using a reference system like our proposed Spike Encoding & Evaluation Baseline to challenge neural architectures. Moreover, the LDA classifier's performance was higher for the fixed weight reservoir than the Spike Encoding & Evaluation Baseline, which validates the proposition that a reservoir projects the signals to a linearly separable hyper-dimensional vector space. The reservoir performance with regulated activity was found to be more than 88% (320 reservoir neurons) for the Roshambo dataset and 70. 6% (2048 reservoir neurons) for the sensor fusion dataset. Importantly, the performance figure is higher than fixed weight reservoir  [24, 25]  for the Roshambo dataset, and the state-of-the-art CNN  [8]  for sensor fusion dataset as shown in Table  4. Hence, the initial hypothesis of the reservoir's increased performance at the edge of chaos is corroborated.
While "reservoir" is used as a common term, the methodology for the initalization and the plasticity rules within them widely differ between all the mentioned methods from the literature. The confusion matrix and receiver operating characteristic (ROC) curve for the Spike Encoding & Evaluation Baseline are shown in Figures  4(A     The accuracy for the correct class reaches up to 80%. Designing a system with commands associated with a similar muscle pathway, or low accuracy of differentiability should be avoided. The classification performance was plotted against the number of neurons for reservoir with fixed weights and regulated reservoir in Figures  6  and 8  and Figures  7  and 9  for Roshambo and sensor fusion dataset, respectively. The classification performance increases with the number of neurons consistently for the sensor fusion dataset with the SVM-based reservoir's readout.
The reservoir performance with regulated activity reached up to 89. 72% for the Roshambo EMG dataset  [14]  and 70. 6% for the EMG subset of sensor fusion dataset  [9]. In a real-world deployment, essentially, a null class should also be present to avoid the actuator command in the resting state. As the availability of open-source data is limited, such a system could not be trained and evaluated in this study. In the future, we plan to incorporate more gestures along with the resting-state class. Finally, the readout layer, although efficient, is not biologically plausible. In the future, we will explore more biologically plausible readout classifiers in the context of life-long learning.
) and 4(B) for the Roshambo dataset. The ROC curves are used to study the tradeoff between specificity (1 -false-positive rate) and sensitivity (true positive rate), with a classifier closer to the top left indicating better performance and area under curve (AOC) equal to one. AOC score is computed for each class and averaged irrespective of individual class size for computing the macro-average of the AOC. For computing the micro-average, the averaging is weighted by class size. The micro-average and macroave rage of the AOC are almost identical indicating class balance and uniform performance across dataset. The recognition for the correct class reaches up to 98% in the case of a regulated reservoir-based approach. Lower accuracy is seen for differentiating movement with similar muscle pathways, i.
This illustrates the high performance of event-based sensing of electrophysiological signals and low power Dataset Method Readout classifier Accuracy (𝜇 ± 𝜎) Spike Enc. & LDA 74. 61 ± 2. 44% Eval. Baseline SVM 85. 44 ± 0. 75% Roshambo Reservoir (No plasticity) LDA SVM 76. 44 ± 1. 80% 81. 83 ± 0. 93% Reservoir LDA 83. 06 ± 0. 92% (CRITICAL) SVM 88. 00 ± 0. 29% Spike Enc. & LDA 53. 03 ± 1. 38% Sensor fusion (EMG) Eval. Baseline Reservoir (No plasticity) Reservoir SVM LDA SVM LDA 65. 18 ± 3. 28% 55. 97 ± 2. 07% 62. 33 ± 2. 05% 61. 96 ± 2. 99% (CRITICAL) SVM 70. 60 ± 3. 65% Table.
Classification accuracy for the Roshambo dataset plotted against the number of reservoir neurons. The weights of reservoir are fixed (uniform distribution between 0 and 0. 25), and SVM or LDA is used in the readout layer. Classification accuracy for the Roshambo dataset plotted against the number of reservoir neurons. The CRITI-CAL learning rule is implemented in recurrent connections, and SVM or LDA is used in the readout layer. SVM LDA SVM LDA 100 100 75 75 Accuracy (%) 50 Accuracy (%) 50 25 25 0 32 64 128 256 320 512 768 1024 1536 2048 0 32 64 128 256 320 512 768 1024 1536 2048 Number of reservoir neurons (Fixed weights) Number of reservoir neurons (CRITICAL plasticity) Figure 6: Number of reservoir neurons (Fixed weights) Accuracy (%) 0 20 40 60 80 32 64 128 256 384 512 768 1024 SVM LDA 1536 2048 Accuracy (%) Figure 8: Number of reservoir neurons (CRITICAL plasticity) 0 20 40 60 80 32 64 128 256 384 512 768 1024 SVM LDA 1536 2048 Figure 7: Classification accuracy for the Sensor fusion Figure 9: Classification accuracy for sensor fusion dataset (5 dataset (5 class) plotted against the number of reservoir neu- class) plotted against the number of reservoir neurons.
Brain decoding aims to infer human cognition from recordings of neural activity using modern neuroimaging techniques. Studies so far often concentrated on a limited number of cognitive states and aimed to classifying patterns of brain activity within a local area. This procedure demonstrated a great success on classifying motor and sensory processes but showed limited power over higher cognitive functions. In this work, we investigate a high-order graph convolution model, named ChebNet, to model the segregation and integration organizational principles in neural dynamics, and to decode brain activity across a large number of cognitive domains. By leveraging our prior knowledge on brain organization using a graph-based model, ChebNet graph convolution learns a new representation from task-evoked neural activity, which demonstrates a highly predictive signature of cognitive states and task performance.
The ChebNet decoding model was evaluated using the cognitive battery of HCP task-fMRI dataset acquired from 1200 healthy subjects. Using the ChebNet-K5 model (i. e. ChebNet graph convolution with K=5), the six cognitive domains were nearly perfectly differentiated from each other by only using 10s of brain recordings (approximately the shortest duration of task conditions in HCP), with an average test accuracy of 96% (mean=95. 81%, STD =0. 15% by using 10 fold cross-validation with shuffle splits). Moreover, the pipeline was capable of distinguishing experimental conditions with fine cognitive granularity and fine temporal resolution, either across multiple domains (see Table  S3 ) or within each cognitive domain (see Table  S2 ), and achieved high decoding accuracy on both tasks. Among the six cognitive domains (as shown in Figure  1 -S2 and Table  S2 ), the language tasks (2 conditions, story vs math), and motor tasks (5 conditions, left/right hand, left/right foot and tongue) were the most easily recognizable conditions, and showed the highest precision and recall scores (F1score = 98.
Moreover, the different organizational patterns in ChebNet representations between cognitive tasks to some extent reflects different scales of information integration in cognitive processes, such that a deep ChebNet architecture was required to encode the complex forms of functional integration in WM, while a shallow ChebNet was sufficient to encode the segregation of localized brain activity during motor execution. The similarity of representations between ChebNet layers was first calculated using CKA with a linear kernel. A distance metric was then generated from the CKA matrix (dis = 1-cka). After that, the hierarchical clustering was applied to the distance matrix using Ward's linkage. The resulting dendrogram illustrated the hierarchical organization among ChebNet layers, for instance two-level organization in the Motor task and a tripartite organization in the Working-memory task.