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1. Enhancing reproducibility and decentralization in single cell research with biocytometry

   https://doi.org/10.1101/2024.07.01.601489  

   Fikar, Pavel; Alvarez, Laura; Berne, Laura; Cienciala, Martin; Kan, Christopher; Kasl, Hynek; Luo, Mona; Novackova, Zuzana; Ordonez, Sheyla; Sramkova, Zuzana; et al (July 2024, bioRxiv)

   Abstract Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In a decentralized framework, spanning 78 assays conducted by 30 users across 12 distinct laboratories, biocytometry consistently demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside of a basic instruction manual. The results affirm biocytometry to be a viable solution for immunophenotyping applications demanding sensitivity as well as scalability and reproducibility and paves the way for decentralized analysis of rare cells in heterogeneous samples. 

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2. Efficient and Adaptive Decentralized Sparse Gaussian Process Regression for Environmental Sampling Using Autonomous Vehicles

   Norton, Tanner A. (June 2022, BYU Scholars Archive)

   In this thesis, I present a decentralized sparse Gaussian process regression (DSGPR) model with event-triggered, adaptive inducing points. This DSGPR model brings the advantages of sparse Gaussian process regression to a decentralized implementation. Being decentralized and sparse provides advantages that are ideal for multi-agent systems (MASs) performing environmental modeling. In this case, MASs need to model large amounts of information while having potential intermittent communication connections. Additionally, the model needs to correctly perform uncertainty propagation between autonomous agents and ensure high accuracy on the prediction. For the model to meet these requirements, a bounded and efficient real-time sparse Gaussian process regression (SGPR) model is needed. I improve real-time SGPR models in these regards by introducing an adaptation of the mean shift and fixed-width clustering algorithms called radial clustering. Radial clustering enables real-time SGPR models to have an adaptive number of inducing points through an efficient inducing point selection process. I show how this clustering approach scales better than other seminal Gaussian process regression (GPR) and SGPR models for real-time purposes while attaining similar prediction accuracy and uncertainty reduction performance. Furthermore, this thesis addresses common issues inherent in decentralized frameworks such as high computation costs, inter-agent message bandwidth restrictions, and data fusion integrity. These challenges are addressed in part through performing maximum consensus between local agent models which enables the MAS to gain the advantages of decentralization while keeping data fusion integrity. The inter-agent communication restrictions are addressed through the contribution of two message passing heuristics called the covariance reduction heuristic and the Bhattacharyya distance heuristic. These heuristics enable user to reduce message passing frequency and message size through the Bhattacharyya distance and properties of spatial kernels. The entire DSGPR framework is evaluated on multiple simulated random vector fields. The results show that this framework effectively estimates vector fields using multiple autonomous agents. This vector field is assumed to be a wind field; however, this framework may be applied to the estimation of other scalar or vector fields (e.g., fluids, magnetic fields, electricity, etc.). Keywords: Sparse Gaussian process regression, clustering, event-triggered, decentralized, sensor fusion, uncertainty propagation, inducing points 

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3. Toward live inter-domain network services on the ExoGENI testbed

   https://doi.org/10.1109/INFCOMW.2018.8407026  

   Yao, Yuanjun; Cao, Qiang; Farias, Rubens; Chase, Jeff; Orlikowski, Victor; Ruth, Paul; Cevik, Mert; Wang, Cong; Buraglio, Nick (April 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

   A key dimension of reproducibility in testbeds is stable performance that scales in regular and predictable ways in accordance with declarative specifications for virtual resources. We contend that reproducibility is crucial for elastic performance control in live experiments, in which testbed tenants (slices) provide services for real user traffic that varies over time. This paper gives an overview of ExoPlex, a framework for deploying network service providers (NSPs) as a basis for live inter-domain networking experiments on the ExoGENI testbed. As a motivating example, we show how to use ExoPlex to implement a virtual software-defined exchange (vSDX) as a tenant NSP. The vSDX implements security-managed interconnection of customer IP networks that peer with it via direct L2 links stitched dynamically into its slice. An elastic controller outside of the vSDX slice provisions network links and computing capacity for a scalable monitoring fabric within the tenant vSDX slice. The vSDX checks compliance of traffic flows with customer-specified interconnection policies, and blocks traffic from senders that trigger configured rules for intrusion detection in Bro security monitors. We present initial results showing the effect of resource provisioning on Bro performance within the vSDX. 

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4. JARVIS-Leaderboard: a large scale benchmark of materials design methods

   https://doi.org/10.1038/s41524-024-01259-w  

   Choudhary, Kamal; Wines, Daniel; Li, Kangming; Garrity, Kevin F.; Gupta, Vishu; Romero, Aldo H.; Krogel, Jaron T.; Saritas, Kayahan; Fuhr, Addis; Ganesh, Panchapakesan; et al (May 2024, npj Computational Materials)

   Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website:https://pages.nist.gov/jarvis_leaderboard/ 

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5. Your notebook is not crumby enough, REPLace it

   Michael Brachmann, William Spoth (January 2020, Conference on Innovative Data Systems Research (CIDR))

   Notebook and spreadsheet systems are currently the de-facto standard for data collection, preparation, and analysis. However, these systems have been criticized for their lack of reproducibility, versioning, and support for sharing. These shortcomings are particularly detrimental for data curation where data scientists iteratively build workflows to clean up and integrate data as a prerequisite for analysis. We present Vizier, an open-source tool that helps analysts to build and refine data pipelines. Vizier combines the flexibility of notebooks with the easy-to-use data manipulation interface of spreadsheets. Combined with advanced provenance tracking for both data and computational steps this enables reproducibility, versioning, and streamlined data exploration. Unique to Vizier is that it exposes potential issues with data, no matter whether they already exist in the input or are introduced by the operations of a notebook. We refer to such potential errors as data caveats. Caveats are propagated alongside data using principled techniques from uncertain data management. Vizier provides extensive user interface support for caveats, e.g., exposing them as summaries in a dedicated error view and highlighting cells with caveats in spreadsheets. 

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