dataesr/scientific-paragraphs-categorization huggingface dataset:
An alternative method of multimodal aggregation is the convergence-divergence zone (CDZ)-based SOM combining all modalities in a single CDZ map. This method is not discussed in detail in this paper, though we mentioned it in the literature review. To our knowledge, the ReSOM model currently offers higher accuracy than the CDZ. But, with a linear growth rate in the number of lateral connections, the CDZ-based SOM is a worthy candidate for developing a more scalable system, achieving a more impressive speed gain for a greater number of modalities. 2) Python overhead: To speed up the prototype production and to simplify further integration with ready-made AI libraries, a significant part of the framework's code is written in Python. Unfortunately, non-optimized Python code entails a significant computation inefficiency and increase in execution time.
This is largely due to the need to serialize, split into packets and then assemble the counted activations. Such a decrease in the speed is not critical when the activation computation time significantly exceeds the transmission time. But in future implementations, with an increase in the calculation speed, this process will need to be optimized. This can be achieved either by rewriting the serialization-deserialization Python code in a low-level language as C/C++, or by reducing the limit on the size of forwarded packets. To solve this problem, we plan to implement a DMA interface between directly connected boards in a future SCALP system update.
The concept of SSN can be also combined with the SOM, as shown by other researchers  [27]. Thus we see great potential to develop our architecture by integrating it with SNNs for signal-type data processing. The ReSOM multimodal association learning methods explored in this work were performed sequentially in two phases: first, we trained the SOMs for unimodal classifications, and second we created and reinforced bidirectional connections between pairs of maps based on their activities on the same training dataset. We refer to this learning approach as asymmetric. This is particularly interesting in the context of offline learning when working on pre-established datasets. First, in a purely practical way, it gave a lot of flexibility since we could train the unimodal SOMs on their respective available data separately, then train the multimodal association based on a smaller synchronized multimodal dataset.
FUNDING This work has been supported by the  French government , through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002. It has been also supported by the  Swiss National Science Fund (SNSF)  through the international  SOMA  project with the reference  ANR-17-CE24-0036.
Using standard tools such as the rapid population synthesis codes StarTrack and COMPAS and the detailed stellar evolution code MESA, we examine as a case study the possible future evolution of Melnick 34, the most massive known binary star system (with initial component masses of 144 M and 131 M ). We show that, despite its fairly well-known orbital architecture, various assumptions regarding stellar and binary physics predict a wide variety of outcomes: from a close BH-BH binary (which would lead to a potentially detectable coalescence), through a wide BH-BH binary (which might be seen in microlensing observations), or a Thorne-Żytkow object, to a complete disruption of both objects by pair-instability supernovae. Thus since the future of massive binaries is inherently uncertain, sound predictions about the properties of BH-BH systems are highly challenging at this time.
From comparison with Bonn stellar evolutionary models at LMC metallicity  (Brott et al. 2011 ), R139 (P orb = 154 d, e = 0. 38) has a primary O supergiant mass of ∼ 80 M  (Mahy et al. 2020 ), R144 (P orb = 74 d, e = 0. 51) has a primary WNh mass of ∼ 110 M  (Shenar et al. 2021 ) and Melnick 34 (Mk 34, P orb = 155 d, e = 0. 68) -the current record-holder -has component WNh+WNh masses of 139 +21  -18 M and 127 ± 17 M  (Tehrani et al. 2019 ) with initial masses of 144 M and 131 M. Pollock et al. (2018)  first established that Mk 34 is a colliding wind binary from analysis of X-ray time series observations, while  Tehrani et al. (2019)  noted that the potential fate of Mk 34 involves a double stellar mass black hole binary merger. We predict the fate of Mk 34, using different physics assumptions, applying two rapid population synthesis codes (StarTrack and COMPAS) and the detailed stellar evolutionary code (MESA).
StarTrack calculations We use the population synthesis code StarTrack  (Belczynski et al. 2020a) , which employs analytic fits to evolutionary tracks of non-rotating stellar models  (Hurley et al. 2000). We adopt standard wind losses for massive stars from  Vink et al. (2001)  and LBV winds as (dM/dt) lbv = f lbv 10 -4 M yr -1 with f lbv = 1. 5 from  Belczynski et al. (2010a). For stars that overfill their Roche lobes, we initiate mass transfer between binary components and associated (if any) mass loss from binary systems. If the binary is not circularized by tidal interactions we circularize it (e new = 0) to periastron distance (a new = a(1 -e)) in one timestep and only then start Roche lobe overflow (RLOF). For nuclear-timescale mass transfer (NTMT) and thermal-timescale mass transfer (TTMT) we use the standard formalism, while we use a diagnostic mass ratio diagram as a criterion for common envelope (CE) development (see Sec.
COMPAS calculations We use the population synthesis code COMPAS  (Stevenson et al. 2017; Vigna-Gómez et al. 2018; Chattopadhyay et al. 2021) , which incorporates stellar  (Hurley et al. 2000)  and binary evolution  (Hurley et al. 2002)  with updated wind prescriptions for massive stars  (Vink et al. 2001; Belczynski et al. 2010a ). The LBV wind losses are as in StarTrack (see Sec. 2. 1). In its default set-up, the mass accretion during RLOF onto degenerate stars (i. e. black holes, neutron stars and white dwarfs) is Eddington limited in COMPAS. For nondegenerate stars, this accretion limit is set by the ratio of the rate at which mass is being donated to the rate of which mass can be incorporated into the companion star. These two rates, in turn, are determined by the thermal (Kelvin-Helmholtz) timescales of the donor and the companion respectively  (Kalogera & Webbink 1996).
For very massive stars this ratio is close to unity  (Yusof et al. 2013; Köhler et al. 2015). In contrast, a 144 M star at Z = 0. 006 metallicity evolved with StarTrack or COMPAS, even though significantly stripped through MS winds (M TAMS = 77. 4 M ) is far away from being a helium star with the helium core mass of only M core,TAMS = 34. 1 M. To correct for this and calibrate the properties of the StarTrack and COMPAS models at TAMS, we use the MESA 1D stellar-evolution code  (Paxton et al. 2011 (Paxton et al. , 2013 (Paxton et al. , 2015 (Paxton et al. , 2018 (Paxton et al. , 2019) )   2. We compute single models of 131 M and 144 M stars at Z = 0. 006 metallicity. The relative initial abundances of metals follow  Grevesse et al. (1996). We model convection by using mixing-length theory (Böhm-Vitense 1958) with a mixing-length parameter α = 2.
MESA models In this section we use the MESA code to check the outcome of the CE phase encountered in the three StarTrack models from Sec. 3. 1 (see  Sec. 2. 3. 3 and Klencki et al. 2021 , for the method). In the model MESA2 we evolve a star with M zams = 133 M , overshooting σ ov = 0. 2 and Dutch winds with f wind = 1. 5. This model produces at some point of its post-MS evolution a star with a mass of M = 80. 9 M and a He-core mass of M core = 62. 2 M , radius of R = 700 R T eff ≈ 9000K (an outer radiative envelope; the envelope would not become convective until least T eff  4500K Klencki et al. (2020)). At this point we calculate the envelope binding energy (obtained from integration of the mass distribution over the entire envelope) corrected for the internal energy of the envelope, then we subtract the BH accretion luminosity that effectively lowers binding energy, to obtain E bind = 6.
We thank  Lukasz Wyrzykowski ,  Tomasz Kamiński ,  Chris Fryer  and  Daniel Holz  for useful comments on the manuscript. KB, AR, AO acknowledge support from the  Polish National Science Center grant Maestro  ( 2018/30/A/ST9/00050 ). DC and SS acknowledge the support of the  Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav) , through project number  CE170100004. This work made use of the OzSTAR high performance computer which is funded by  Swinburne University of Technology  and the  National Collaborative Research Infrastructure Strategy (NCRIS). MCM acknowledges support from  NASA ADAP  grant  80NSSC21K0649. He performed part of his work on this paper at the  Aspen Center for Physics , which is supported by  National Science Foundation  grant  PHY-1607611.
Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools.
INTRODUCTION Rapid growth in FAIR (1) data, and an expanding range of open source analysis software packages, offer rich research opportunities in data intensive fields such as genome science. Galaxy offers powerful and practical solutions for analyzing this data by providing access to extensive hardware, tools, and data that can be adopted with relatively minimal training. The open source software allows scientists to efficiently manage their own data, and to share transparent, reproducible analyses. More than 8000 popular analysis software packages have been integrated with Galaxy and their use is supported via numerous topicbased training resources. The growing breadth of tools available in Galaxy enables diverse types of analysis and because all data manipulations are performed via tools (as opposed to ad-hoc scripts or manual editing), reproducibility is ensured.
Galaxy also offers an interactive workflow manager (2) that makes efficient use of compute infrastructure, and comes preloaded with access to terabytes of reference data. There is also an extensible visualization framework (https://usegalaxy. org/visualizations) with built-in track-based genome visualizations (https: //galaxyproject. org/learn/visualization/), multiple types of charts (barcharts, scatterplots, line charts, etc; https: //galaxyproject. org/learn/visualization/charts/), as well as more specialized visualization tools such as Cytoscape (3), NGL molecular visualizations  (4) , and geographic maps from OpenLayers. In addition to software, the project community offers access to free computing services on large research infrastructure around the world with most prominent installations residing in Australia, Europe, and the United States.
Self-hosted cloud deployments are also supported via the NHGRI AnVIL infrastructure (https://anvilproject. org/)  (5). All the software and services are accompanied with a growing library of training materials and events  (6). Started in 2005  (7) , the Galaxy project is now sustained by a strong, global community of users, educators, developers and administrators. The size of this community is continuing to grow across all areas of the Galaxy ecosystem. Highlighted in Figure  1  is the usage of the free services by the researchers while Figure  2    most frequently. We believe the size and diversity of this community is what sets Galaxy apart from alternative data analysis platforms. For a detailed perspective on the alternative workflow management systems and a comparison of technical features, please see Wratten et al.
Ability to browse external data repositories Public and private data repositories are a growing trend for hosting, aggregating, and sharing data. Galaxy can now represent these remote data resources as a UI-browsable filesystem, which users can upload to and download from. Remote file source plugins bundled with Galaxy include AWS S3 storage service, Google Cloud Storage, Google Drive, AnVIL, and Dropbox. To add a new data source module to Galaxy, one need only find or write a client library for the desired source that implements the PyFilesys-tem2 interface, and configure Galaxy to provision it with appropriate user credentials.
Initial support for batch operations Dataset collections allow the same operations, such as executing a tool or workflow, to be performed on a set of datasets. Collections have been enhanced to allow converting the datatype for all items in a collection as a batch operation. Another major development is in the rule builder, which allows data being imported into a collection to be structured based on defined rules (e. g. create a collection of dataset pairs from a list of accessions). The rule builder now also retains memory of the most recent rules that it ran, allowing users to re-run this saved rules list on a new collection of datasets.
Modernizing the framework Since its inception, Galaxy used the Web Server Gateway Interface (WSGI) convention for its web server functionality through various low-level Python libraries. Modern best practices, however, recommend the use of features such as standard asynchronous computing framework (asyncio), type annotations (mypy), data validation (pydantic) and live documentation (OpenAPI) for increased efficiency and maintainability. To utilize these advances, Galaxy has adopted the FastAPI web framework and can now be served as an Asynchronous Server Gateway Interface (ASGI) application. This transition has resulted in a simplified development experience with auto-generated Nucleic Acids Research, 2022 3 documentation, more robust parsing of parameters, better error handling and the ability to handle more user requests with fewer server resources.
Importantly, the API schema is automatically up-to-date and correct, and can be used to generate client libraries in many different programming languages. Finally, these updates provide the groundwork for interactive applications and notifications using the websocket protocol. An additional framework upgrade is the adoption of a message queue for long-running tasks. As the number of datasets that Galaxy must support continues to grow, it has become untenable to perform even trivial processing of such datasets in the web request-response cycle itself. We have extended Galaxy with support for Celery tasks, which allows such long-running operations to be offloaded from the server request. Slow operations, such as deleting datasets and exporting histories, have been transitioned to this background processing model with more planned.
Workflow best practices, invocations, and reports Galaxy's workflow editor offers powerful capabilities to formulate multi-step pipelines for analyzing thousands of samples, all from a graphical web interface. The editor has been upgraded to offer researchers automated advice for adhering to workflow development best practices. The advice includes support for automatically upgrading legacy workflow inputs to the current format, warnings about disconnected inputs, missing metadata for inputs, missing outputs, and missing license and creator metadata. Additionally, a new workflow invocation component was added that displays historic and currently executing workflow runs, while displaying input parameters, input datasets, input dataset collections, outputs, all workflow steps, jobs and subworkflows.
A complete administrator's toolset The growth of the Galaxy user community has also driven a professionalization of the Galaxy Administrator, and we teach ever larger courses to Galaxy Admins in response. This has likewise resulted in the development of numerous utilities and projects to make their lives easier. For example, gxadmin (https://galaxyproject. github. io/gxadmin) is a collection of commonly used SQL queries that provide infrastructure statistics for reporting and debugging. Nebulizer (https://github. com/pjbriggs/nebulizer) and Ephemeris (https://github. com/galaxyproject/ephemeris) help administrators automate tasks like importing data, installing tools, and managing users. parsec was cre- ated to expose all of the Galaxy API as a set of command line tools that can be composed in a UNIX-like manner.
Galaxy service for protected and private data One of the biggest changes in the Galaxy ecosystem is general availability of a Galaxy service for use with protected and private datasets. In the context of the NHGRI AnVIL project (5), we have implemented new capabilities that enable anyone to securely access Galaxy alongside patient records and >300 000 genomes, eliminating the need to download, store, and protect that data locally. AnVIL operates with US FedRAMP certification  (10). These strengthened privacy guarantees also open a door for researchers to upload their private data to this resource. This is particularly appealing for smaller institutions that do not have the resources to build their own secure data center, and can instead launch their own instances of Galaxy within a highly scalable cloud-computing environment, broadening and democratizing accessibility of data analysis options.
A community-curated repository of high-quality workflows The Intergalactic Workflow Commision (IWC; https:// github. com/galaxyproject/iwc) is a new community group that develops, collects, and improves Galaxy workflows. The workflows are maintained in an open repository as reusable components, and include such diverse topics as variant analysis of SARS-CoV-2 data and free energy calculations for molecular dynamics simulations. Anyone can contribute a workflow, which is then peer reviewed by the IWC according to pre-set guidelines. Once accepted, each workflow is continuously checked for best-practice conformance and tested during each new Galaxy release. The workflows are also automatically published to Dockstore  (12)  and WorkflowHub  (13)  and optionally synchronized to a list of Galaxy servers.
Analysis of public SARS-CoV-2 data After nearly two years of the global COVID-19 pandemic and numerous virus strains, pathogen genomic surveillance has become an essential public service. The current knowledge about the evolutionary dynamics of SARS-CoV-2 comes primarily from genome assemblies  (14). However, the availability of the read-level datasets used to build these assemblies lags behind the number of complete genome assemblies, making it impossible to confirm or investigate these genomes further, such as to further refine sequencing errors or detect potential co-infections. In addition, only a fraction of available read-level datasets are useful for transmission analysis because they lack necessary metadata (https://galaxyproject. org/projects/covid19/samples/). The Galaxy project now continuously ingests and analyzes high quality read-level datasets on public infrastructure, providing a platform for global pathogen monitoring  (15).
A machine learning toolkit Galaxy-ML (16) (Figure  4 ) is a new toolkit for Galaxy that features a large and general-purpose suite of supervised machine learning tools. With Galaxy's web-based user interface, an entire machine learning pipeline from normalization, feature selection, model definition, hyperparameter optimization and cross-fold evaluation can be created Nucleic Acids Research, 2022 5 and applied to large datasets using only a web browser. By leveraging analysis tools already available in Galaxy, comprehensive end-to-end analyses can be performed, beginning with primary analysis of -omics, imaging, or other large biomedical datasets and continuing to downstream machine learning tools that build and evaluate predictive machine learning models from features extracted from the primary data.
Since then, the community has organized additional, similar training events, including COVID-19 data analysis workshops, plant transcriptomics, machine learning, and single-cell analysis. To support this modality of training, we have also recently created the GTN video library (https: //gallantries. github. io/video-library/). (18) , single-cell RNA-Seq  (19)  and public health  (20)  as well as climate science as an all-new domain (https://climate. usegalaxy. eu/).
Data-local computing Galaxy manages the datasets that users analyze by storing a local copy of each dataset. Storing copies is becoming untenable as biomedical datasets grow and are distributed across local and cloud repositories. We are now working on support for data-local computing where Galaxy will only store a universally unique identifier (UUID) for a dataset and the data for a given analysis step will be fetched as required. This will reduce the resources needed for analyses and enable operating on public and private repositories.
ACKNOWLEDGEMENTS The growth of the  Galaxy  project is made possible by a growing community of world-wide users, developers, system administrators, and educators. We are extremely grateful to the  Texas Advanced Computing Center (TACC)  for hosting https://usegalaxy. org,ELIXIR (the research infrastructure for life-science data) for hosting https://usegalaxy. org. eu, and  Bioplatforms Australia  and the  Australian Research Data Commons  for hosting https://usegalaxy. org. au/.
The possibilities offered by Blockchain technology have been extended to a wider set of applications with the smart contract concept. These are programs that automatically run on a Blockchain network, and whose results are immutable  [5]. The purpose of Blockchain in SoC reconfiguration applications is to avoid third parties and to decentralize trust. Setting up the trust link between a chip and Blockchain data is not a trivial task. Moreover, the published protocol descriptions lead us to believe that trust flaws can appear when a component interacts with the Blockchain network. It is because of this potential design flaw that we propose to model and verify an approach from the literature, along with potential fixes. To this end, we use Verifpal, which is a tool designed to be relatively easy to use, and which can be used to model and verify cryptographic protocols  [6].
Solutions have been proposed to address interactions between a Blockchain and the external environment. Caldarelli  [10]  provides an overview of the research on oracles, which allow the use of off-chain data in smart contracts. To the best of our knowledge, oracles are not capable of reliably pushing data out of the Blockchain, as required in our case. Other studies have focused on modeling blockchain-based systems. Sukhwani et al. [11]  proposed a performance model of Hyperledger fabric, a permissioned Blockchain network, to evaluate its performance. Rocha and Ducassse  [12]  have worked on how to model software based on smart contracts using UML. Like Verifpal, there are several tools that can model protocols in order to verify them. Among the most popular are ProVerif and Tamarin. ProVerif is a tool by Bruno Blanchet  [13] , which is able to automatically translate a protocol description into Horn clauses, and to determine whether the desired security properties hold by resolution on these clauses.