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This study explores enhancements in analysis speed, WAN bandwidth efficiency, and data storage management through an innovative data access strategy. The proposed model introduces specialized ‘delivery’ services for data preprocessing, which include filtering and reformatting tasks executed on dedicated hardware located alongside the data repositories at CERN’s Tier-0, Tier-1, or Tier-2 facilities. Positioned near the source storage, these services are crucial for limiting redundant data transfers and focus on sending only vital data to distant analysis sites, aiming to optimize network and storage use at those sites. Within the scope of the NSF-funded FABRIC Across Borders (FAB) initiative, we assess this model using an “in-network, edge” computing cluster at CERN, outfitted with substantial processing capabilities (CPU, GPU, and advanced network interfaces). This edge computing cluster features dedicated network peering arrangements that link CERN Tier-0, the FABRIC experimental network, and an analysis center at the University of Chicago, creating a solid foundation for our research. Central to our infrastructure is ServiceX, an R&D software project under the Data Organization, Management, and Access (DOMA) group of the Institute for Research and Innovation in Software for High Energy Physics - IRIS-HEP. ServiceX is a scalable filtering and reformatting service, designed to operate within a Kubernetes environment and deliver output to an S3 object store at an analysis facility. Our study assesses the impact of server-side delivery services in augmenting the existing HEP computing model, particularly evaluating their possible integration within the broader WAN infrastructure. This model could empower Tier-1 and Tier-2 centers to become efficient data distribution nodes, enabling a more cost-effective way to disseminate data to analysis sites and object stores, thereby improving data access and efficiency. This research is experimental and serves as a demonstrator of the capabilities and improvements that such integrated computing models could offer in the HL-LHC era. 

We explore the adoption of cloud-native tools and principles to forge flexible and scalable infrastructures, aimed at supporting analysis frameworks being developed for the ATLAS experiment in the High Luminosity Large Hadron Collider (HL-LHC) era. The project culminated in the creation of a federated platform, integrating Kubernetes clusters from various providers such as Tier-2 centers, Tier-3 centers, and from the IRIS-HEP Scalable Systems Laboratory, a National Science Foundation project. A unified interface was provided to streamline the management and scaling of containerized applications. Enhanced system scalability was achieved through integration with analysis facilities, enabling spillover of Jupyter/Binder notebooks and Dask workers to Tier-2 resources. We investigated flexible deployment options for a “stretched” (over the wide area network) cluster pattern, including a centralized “lights out management” model, remote administration of Kubernetes services, and a fully autonomous site-managed cluster approach, to accommodate varied operational and security requirements. The platform demonstrated its efficacy in multi-cluster demonstrators for low-latency analyses and advanced workflows with tools such as Coffea, ServiceX, Uproot and Dask, and RDataFrame, illustrating its ability to support various processing frameworks. The project also resulted in a robust user training infrastructure for ATLAS software and computing on-boarding events. 

The ATLAS experiment is currently developing columnar analysis frameworks which leverage the Python data science ecosystem. We describe the construction and operation of the infrastructure necessary to support demonstrations of these frameworks, with a focus on those from IRIS-HEP. One such demonstrator aims to process the compact ATLAS data format PHYSLITE at rates exceeding 200 Gbps. Various access configurations and setups on different sites are explored, including direct access to a dCache storage system via Xrootd, the use of ServiceX, and the use of multiple XCache servers equipped with NVMe storage devices. Integral to this study was the analysis of network traffic and bottlenecks, worker node scheduling and disk configurations, and the performance of an S3 object store. The system’s overall performance was measured as the number of processing cores scaled to over 2,000 and the volume of data accessed in an interactive session approached 200 TB. The presentation will delve into the operational details and findings related to the physical infrastructure that underpins these demonstrators. 

Abstract We give a new, algebraically computable formula for skein modules of closed 3-manifolds via Heegaard splittings. As an application, we prove that skein modules of closed 3-manifolds are finite-dimensional, resolving in the affirmative a conjecture of Witten. 

Abstract Public concern regarding the use of herbicides in urban areas (e.g., golf courses, parks, lawns) is increasing. Thus, there is a need for alternative methods for weed control that are safe for the public, effective against weeds, and yet selective to turfgrass and other desirable species. New molecular tools such as ribonucleic acid interference (RNAi) have the potential to meet all those requirements, but before these technologies can be implemented, it is critical to understand the perceptions of key stakeholders to facilitate adoption as well as regulatory processes. With this in mind, turfgrass system managers, such as golf course superintendents and lawn care providers, were surveyed to gain insight into the perception and potential adoption of RNAi technology for weed management. Based on survey results, turfgrass managers believe that cost of weed management and time spent managing weeds are the main challenges faced in their fields. When considering new weed management tools, survey respondents were most concerned about cost, efficacy, and efficiency of a new product. Survey respondents were also optimistic toward RNAi for weed management and would either use this technology in their own fields or be willing to conduct research to develop RNAi herbicides. Although respondents believed that the general public would have some concerns about this technology, they did not believe this to be the most important factor for them when choosing new weed management tools. The need for new herbicides to balance weed control challenges and public demands is a central factor for turfgrass managers’ willingness to use RNAi-based weed control in turfgrass systems. They believe their clientele will be accepting of RNAi tools, although further research is needed to investigate how a wider range of stakeholders perceive RNAi tools for turfgrass management more broadly. 

We study the question of dualizability in higher Morita categories of locally presentable tensor categories and braided tensor categories. Our main results are that the 3-category of rigid tensor categories with enough compact projectives is 2-dualizable, that the 4-category of rigid braided tensor categories with enough compact projectives is 3-dualizable, and that (in characteristic zero) the 4-category of braided multi-fusion categories is 4-dualizable. Via the cobordism hypothesis, this produces respectively two-, three- and four-dimensional framed local topological field theories. In particular, we produce a framed three-dimensional local topological field theory attached to the category of representations of a quantum group at any value of $$q$$ .