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When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC–NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform.

Database URL: https://www.opensciencechain.org.

 

Large-scale network-cloud ecosystems are fundamental infrastructures to support future 5G/6G services, and their resilience is a primary societal concern for the years to come. Differently from a single-entity ecosystem (in which one entity owns the whole infrastructure), in multi-entity ecosystems (in which the networks and datacenters are owned by different entities) cooperation among such different entities is crucial to achieve resilience against large-scale failures. Such cooperation is challenging since diffident entities may not disclose confidential information, e.g., detailed resource availability. To enhance the resilience of multi-entity ecosystems, carriers are important as all the entities rely on carriers’ communication services. Thus, in this study we investigate how to perform carrier cooperative recovery in case of large-scale failures/disasters. We propose a two-stage cooperative recovery planning by incorporating a coordinated scheduling for swift recovery. Through preliminary numerical evaluation, we confirm the potential benefit of carrier cooperation in terms of both recovery time and recovery cost/burden reduction. 

We investigate the problem of future disaster-resilient optical network-cloud ecosystems. We introduce our solutions considering openness/disaggregation and cooperation for single- and multi-entity network-cloud ecosystems, respectively. 

In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time‐varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generatein situthat are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time‐varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem — learning a function‐space neural network to reproduce flow map samples under a fixed integration scheme — leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.

 

ABSTRACT We report on the detection of pulsations of three pulsating subdwarf B stars observed by the Transiting Exoplanet Survey Satellite (TESS) satellite and our results of mode identification in these stars based on an asymptotic period relation. SB 459 (TIC 067584818), SB 815 (TIC 169285097), and PG 0342 + 026 (TIC 457168745) have been monitored during single sectors resulting in 27 d coverage. These data sets allowed for detecting, in each star, a few tens of frequencies that we interpreted as stellar oscillations. We found no multiplets, though we partially constrained mode geometry by means of period spacing, which recently became a key tool in analyses of pulsating subdwarf B stars. Standard routine that we have used allowed us to select candidates for trapped modes that surely bear signatures of non-uniform chemical profile inside the stars. We have also done statistical analysis using collected spectroscopic and asteroseismic data of previously known subdwarf B stars along with our three stars. Making use of high precision trigonometric parallaxes from the Gaia mission and spectral energy distributions we converted atmospheric parameters to stellar ones. Radii, masses, and luminosities are close to their canonical values for extreme horizontal branch stars. In particular, the stellar masses are close to the canonical one of 0.47 M⊙ for all three stars but uncertainties on the mass are large. The results of the analyses presented here will provide important constrains for asteroseismic modelling.