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As the backbone of global Internet connectivity, the submarine cable network (SCN) faces growing threats with serious economic and security implications. Strengthening its resilience requires a clear understanding of which cables and landing points are most critical. This depends on accurately mapping traffic onto the underlying infrastructure to identify vulnerabilities and assess their regional and global impact. Yet existing methods often lack the resolution and fidelity needed for such analysis, leaving researchers and policymakers without the insights to safeguard this vital system. This paper introduces Calypso, a framework for mapping traceroute paths to the submarine cables they traverse. Calypso integrates ownership records, routing metadata, and geographic constraints to infer cable usage despite the opacity of the SCN and challenges such as route virtualization and inland infrastructure. It also defines Route Stress, a traceroute-derived metric for estimating the relative importance of submarine cables. Through expert validation, failure analysis, and regional case studies, we demonstrate Calypso’s utility in revealing SCN dependencies and informing resilience efforts. 

We develop KnowThyself, an agentic assistant that advances large language model (LLM) interpretability. Existing tools provide useful insights but remain fragmented and code-intensive. KnowThyself consolidates these capabilities into a chat-based interface, where users can upload models, pose natural language questions, and obtain interactive visualizations with guided explanations. At its core, an orchestrator LLM first reformulates user queries, an agent router further directs them to specialized modules, and the outputs are finally contextualized into coherent explanations. This design lowers technical barriers and provides an extensible platform for LLM inspection. By embedding the whole process into a conversational workflow, KnowThyself offers a robust foundation for accessible LLM interpretability. 

Low Earth Orbit (LEO) satellite networks are emerging as backups for national-scale outages. While they have demonstrated value in small-scale disasters such as supporting first responders during hurricanes, their effectiveness during large-scale infrastructure failures remains underexplored. This paper evaluates the capacity of LEO networks to act as national failover infrastructure using six real-world submarine cable failures. The failure capacity provided by a LEO network to a specific nation depends on a few key factors: the size of the country, the distribution of the user terminals, and the policies of the network operator for spectrum allocation and traffic engineering. We find that coordinated policies between governments and network operators, especially regarding terminal placement and spectrum use, can improve failover capacity by up to 1.8× without requiring additional infrastructure. However, even under optimistic conditions with 200,000 terminals and a dedicated failover network, LEO networks can only restore 0.9–14.7% of lost submarine cable capacity in most cases. 

The Internet's connectivity relies on a fragile submarine cable network (SCN), yet existing tools fall short in assessing its criticality. We introduce Calypso, a new framework that leverages traceroute data to map traffic to submarine cables. Validated through real-world case studies, Calypso reveals hidden risks and offers new insights to enhancing SCN resilience. 

We develop a convergent reaction-drift-diffusion master equation (CRDDME) to facilitate the study of reaction processes in which spatial transport is influenced by drift due to one-body potential fields within general domain geometries. The generalized CRDDME is obtained through two steps. We first derive an unstructured grid jump process approximation for reversible diffusions, enabling the simulation of drift-diffusion processes where the drift arises due to a conservative field that biases particle motion. Leveraging the Edge-Averaged Finite Element method, our approach preserves detailed balance of drift-diffusion fluxes at equilibrium, and preserves an equilibrium Gibbs-Boltzmann distribution for particles undergoing drift-diffusion on the unstructured mesh. We next formulate a spatially-continuous volume reactivity particle-based reaction-drift-diffusion model for reversible reactions of the form A + B <–> C. A finite volume discretization is used to generate jump process approximations to reaction terms in this model. The discretization is developed to ensure the combined reaction-drift-diffusion jump process approximation is consistent with detailed balance of reaction fluxes holding at equilibrium, along with supporting a discrete version of the continuous equilibrium state. The new CRDDME model represents a continuous-time discrete-space jump process approximation to the underlying volume reactivity model. We demonstrate the convergence and accuracy of the new CRDDME through a number of numerical examples, and illustrate its use on an idealized model for membrane protein receptor dynamics in T cell signaling.