arXiv preprints from January 1, 2026 through September 22, 2026 — 17:28:21 EST
We study federated online reinforcement learning with linear function approximation. While recent multi-agent reinforcement learning algorithms achieve strong regret guarantees, they typically require sharing raw trajectories. This reliance incurs a communication cost that scales linearly with the number of episodes and violates the...
Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy gamma rays by imaging the nanosecond Cherenkov flash of the air shower they initiate in the Earth's atmosphere. For four decades the first steps of IACT event reconstruction have been essentially unchanged, relying on a heavy parameterisation and dimensionality...
In this paper, recent advances in integrated microwave photonics (IMWP) are reviewed, including material platforms, integration technologies, and system functionalities. Emerging opportunities and future perspectives are discussed. Silicon (Si) and silicon nitride (SiN) provide dense routing, programmable filtering, and low-loss...
The hot circumgalactic medium (CGM) of a galaxy inevitably rotates more slowly than the cold gas in the disc it surrounds, due to a higher pressure support against gravity. If it accretes vertically onto the disc, angular momentum conservation leads to radial flows, advection of metals inwards and the steepening of a metallicity...
The formation of a superlattice pattern in two-frequency-driven Faraday waves discovered and named SSS-I by Arbell & Fineberg (1998, 2002) is investigated by means of Direct Numerical Simulations (DNS) of the full three-dimensional Navier--Stokes equations with a free surface. Two simulations with distinct quasi-hexagonal initial...
We present a map of the cosmic microwave background (CMB) lensing potential reconstructed from observations taken during the 2019 and 2020 seasons with the third-generation camera on the South Pole Telescope (SPT), covering the $1500\,{\rm deg}^{2}$ SPT-3G Main field, referred to as the SPT-3G D1 dataset. From the multi-frequency...
Silicon photomultipliers (SiPMs) are widely used in photon-counting applications, such as positron emission tomography or particle physics, where precise timestamps and photoelectron number information are both required. In this work, a Time-to-Digital Converter with amplitude measurement capabilities was designed using an Artix-7...
We report the first experimental observation of a steady-state, microwave-driven inverse Faraday effect in a high-temperature superconductor. Circularly polarized microwave radiation generates a helicity-dependent response in an epitaxial $\mathrm{YBa_2Cu_3O_{7-δ}}$ film, detected using homodyne Hall transport. The optomagnetic...
We investigate the dissociation of the neutral pion in hot magnetized quark matter within the two-flavor Nambu--Jona-Lasinio model. At zero magnetic field, the Mott temperature is conventionally determined by $m_{π^0}(T_{\rm Mott})=2M(T_{\rm Mott})$, above which a real pole ceases to exist. At finite magnetic field, Landau...
High-purity germanium (HPGe) detectors with thin amorphous-germanium (a-Ge) contacts can provide bipolar charge blocking and surface passivation while minimizing contact-related inactive thickness. We report the fabrication and characterization of a p-type inverted coaxial point-contact (ICPC) HPGe detector using thin a-Ge contacts in...
Cluster algorithms, such as the Swendsen--Wang and Wolff methods, are among the most successful MCMC methods for mitigating critical slowing down in statistical systems. These constructive cluster algorithms, however, fail in the presence of even extremely weak frustration. Here, we sidestep this fundamental limitation by learning...
Automatic vehicle location (AVL) data produced by transit vehicles is invaluable in performance studies, but turning raw AVL points into a detailed view of vehicle stop-and-gos is burdensome: the datasets are sparse, noisy, and prone to blunders. While recent research has explored methods of reconstructing trajectories describing the...
Calibration under model misspecification is inherently ill-posed because calibration parameters and structural discrepancy are statistically confounded without additional assumptions. Bayesian formulations address this ambiguity through prior and covariance modeling choices, including multi-response observations and cross-source...
Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and combinatorial hypothesis spaces, however, constructing probabilistic representations remains challenging because both the active model components and their associated parameters are...
Agent evaluations often use one benchmark to choose a workflow and then search for task types where its advantage weakens, so both conclusions are selected from the same data. We introduce Selection-Aware Semantic Stress Testing (\SASST{}), which learns a task reweighting from pre-execution features on discovery tasks and evaluates...
Extreme and rare changes in space mission solar irradiance records are scientifically relevant but difficult to quantify because these records are finite, instrument dependent, and affected by observational gaps and time varying measurement quality. We evaluated extreme daily logarithmic changes in the band integrated 0.1-7.0 nm...
Constructing confidence regions for stochastic gradient descent (SGD) ideally requires estimating the asymptotic covariance matrix, a severe computational bottleneck in high dimensions. Traditional cancellation-based batch means methods bypass this estimation but require inverting a sample batch covariance matrix. This introduces...
Natural-language tasks can elicit different verdicts from protocol-following evaluators that receive the same declared information. We study aggregate disambiguation systems (ADSs). Given a task and a candidate solution, each evaluator casts a binary vote on whether the solution should be accepted, and the system aggregates the votes...
Clinical validation studies of predictive tests are usually designed to focus on sensitivity ($Se$) and specificity ($Sp$), while statistical power is often calculated on regression-effect scales (e.g., risk ratio, hazard ratio). However, these quantities are statistically connected. Here, we provide closed-form links from...
We study identification and estimation of moments of random coefficients in short linear panels, allowing the number of heterogeneous coefficients to exceed the number of equations observed for each unit. Under moment homogeneity, different regressor histories impose restrictions on the same moment vector. We give necessary and...
When do text embeddings work as inputs to empirical analysis? Their use rests on an assumption: that we can trade text for its low-dimensional embedding, and lose little in doing so. I make that assumption precise under a generative model in which documents are mixtures of latent topics. I study two uses---clustering units in...
Tariff threats can move household beliefs before policy is enacted, yet their rapidly changing language is difficult to study with conventional surveys. We build a multi-agent system that turns 300 households from the Michigan Surveys of Consumers into persistent large-language-model agents exposed to social-media information over...
Stablecoins, typically pegged to fiat currencies, cannot achieve true stability because they inherit fluctuations in the underlying unit of account. To overcome this limitation, we introduce a stablecoin pegged to the Maximum Likelihood Value (MLV), a newly defined unit of account derived as the most probable configuration of latent...
Posted prices for AI inference have fallen steadily since 2024, yet the measured speed of that fall depends almost entirely on the method of measurement. This paper constructs quality-adjusted price indices for the AI inference market from public data. The panel assembles 21,024 posted-price observations across 3,208 models and 86...
We develop a computational framework for Bayesian decision-making. We show that as long as no action is optimal in every state, Bayes-optimal choice is intractable. This hardness need not arise from large action, state, or signal spaces, nor from a complicated represented utility function: extracting enough information from a...