infrastructure security teams

Abstract: The Minimum Shared-Power Edge Cut (MSPEC) problem seeks to disconnect two designated terminals with minimum total power by assigning powers to graph vertices and deleting every edge for which the sum of the powers at its endpoints reaches its edge weight. Previous work gave a fully polynomial-time approximation scheme for this problem, while its exact computational complexity remained open. We prove that general MSPEC with nonnegative edge weights can be solved exactly in polynomial time. Specifically, we first use linear programming duality for each fixed cut to reformulate MSPEC as minimizing the maximum terminal-relaxed matching weight over all $s$-$t$ cuts. Then, we construct a terminal-copy extended graph that transforms the maximum-weight matching model with unconstrained terminal capacities for a fixed cut into an equivalent ordinary maximum-weight matching model. Next, we extend the submodularity of the unweighted maximum-matching cut function to nonnegative edge weights and prove that the restriction obtained by fixing the terminal copies on prescribed sides remains submodular. Consequently, MSPEC reduces to exact submodular function minimization. Once an optimal cut is obtained, a corresponding optimal power assignment can be recovered. This result resolves the open question concerning the exact computational complexity of general MSPEC.

Abstract: During epidemics, limited resources dictate critical decisions about whom to isolate or monitor. Conventional public health strategies intuitively prioritize known infected individuals and their immediate contacts. Here, we reveal that this contact-centric approach is fundamentally suboptimal under realistic resource constraints. By formulating this challenge as target-aware network dismantling, we uncover a counter-intuitive, budget-dependent intervention transition. Generating-function analysis demonstrates that when intervention budgets are scarce, isolating structurally central individuals, rather than the immediate neighbours of the infected, protects a vastly larger population. The conventional neighbour-targeting strategy only becomes optimal as available resources increase. Because this critical transition point shifts dynamically with the intervention budget, network topology, and spatial distribution of infections, static strategies inevitably fail. To overcome this, we develop Risk-Aware Isolation Learning (RAIL), a deep reinforcement learning framework that adaptively selects optimal interventions based on the evolving residual network. Across diverse synthetic and real-world networks, RAIL consistently maximizes population protection and suppresses outbreak spread. Ultimately, our findings challenge prevailing epidemiological intuitions, establishing a generalizable framework for adaptive interventions against localized risks in public health and other complex systems.

Abstract: Monitoring lower airspace over critical infrastructure using cellular signals of opportunity is highly practical because transmitters are pre-deployed, licensed, and continuously active. Digital twins can evaluate the feasibility of such integrated sensing and communication (ISAC) architectures, but their predictive accuracy must be validated against real-world data. This paper reports validation results for TiamiTwin, a digital twin developed for bistatic ISAC drone sensing, using empirical measurements from an operational 5G deployment featuring a commercial band n41 gNB and a receiver separated by 572.8 m over a non-line-of-sight (NLOS) channel. TiamiTwin incorporates three parallel channel representations evaluated on a 240-subcarrier grid: the 3GPP TR 38.901 (Release 19) bistatic ISAC model, a ray-traced site model, and the captured field measurements. Empirical results demonstrate that both statistical and ray-tracing models under-predict the measured root-mean-square (RMS) delay spread by approximately a factor of three. Furthermore, target reflections sit 68 dB below static clutter in power, making target detection entirely dependent on Doppler separation to isolate the drone from zero-Doppler background returns. Despite this severe clutter environment, the target remains separable along 88% of the flight path in the delay, Doppler, or joint delay-Doppler domains.