arXiv preprints from January 1, 2026 through September 23, 2026 — 21:12:19 EST
The far-ultraviolet (FUV) field, consisting of photons with energies between 6 and 13.6 electron volts, plays an important role in the chemical and thermodynamical evolution of the interstellar medium. FUV continuum radiation is produced by bright stars in the galaxy and absorbed by interstellar dust. We used newly available maps of...
Krylov and Lanczos approximations are used in quantum dynamics, quantum subspace methods, and Hamiltonian learning. A practical question is how long an $m$-dimensional Krylov truncation can be trusted. We argue that this time is fixed by causal propagation on the associated Jacobi chain. The relevant distance is not the Krylov index...
We present a three-dimensional model of the local interstellar radiation field (ISRF) in the ultraviolet (UV). Using UV flux measurements from the TD1 catalog and stellar distances from Gaia and Hipparcos, we construct a catalog of stars that we expect to dominate the UV flux in the nearby Galaxy. We use the radiative transfer code...
Can a finite quantum episode populate a classical future that the same prepared state never reaches deterministically? We show that it can, using inflation as a cosmological laboratory. In our supergravity realization, two classical continuations already exist, but the prepared state reaches only one without noise. A stable entropy...
Spontaneous breaking of a continuous symmetry produces a massless Goldstone mode that can evolve across a degenerate manifold at zero energy cost. Goldstone modes have been identified primarily through excitation spectra, mode softening or collective oscillations. However, their time-domain transport under intrinsic fluctuations and...
Charge-transfer-to-solvent (CTTS) excitations provide a chemically central route to generating hydrated electrons and initiating redox chemistry in solution, yet the earliest stage of CTTS---the formation of the excited state itself---is usually treated as instantaneous. Here we present a time-domain perspective of how CTTS character...
Power grid state estimation relies on sensor measurements that are increasingly vulnerable to adversarial corruption in cyberphysical environments, potentially leading to significant deviations in system observations. This motivates the need to identify critical measurement nodes whose compromise results in the most severe...
Camera-derived remote photoplethysmography (rPPG) is commonly validated through endpoint accuracy, but endpoint performance does not establish whether other physiological properties of source contact photoplethysmography (PPG) remain preserved recording by recording. We evaluated property-specific PPG-to-rPPG recoverability on 655...
Range-Doppler (rD) maps produced by chirp- sequence (CS) radar systems are fundamentally limited in reso- lution by bandwidth, carrier frequency, and coherent processing interval constraints. Improving resolution through hardware is often impractical due to regulatory, cost, and real-time operation requirements. In this work, we...
Data-driven signal decomposition methods decompose a signal into its underlying components in a flexible and adaptive way, taking into account the signal characteristics. Here we focus on univariate signals, whose decomposition is ill-posed. Classical univariate approaches -- like variational mode decomposition -- constrain the...
Three-phase alternating current is so deeply embedded in modern electric-power infrastructure that its phase order is often treated as self-evident. Historically, however, 1-phase and true 2-phase systems were commercially important, while commercial 6-phase transmission was later demonstrated. This paper reassesses why 3 phases...
Most single-stage 3D object detectors complete different tasks with the same extracted features. Nevertheless, it is impossible to project features into a common space that is adaptive for all the tasks. We present a novel task-aware deformable prediction (TADP) method for single-stage 3D object detection to solve this problem....
Expansion planning models usually fix storage duration before optimization, setting how much storage power to build but not for how long it can discharge. We present a generation-transmission-storage expansion framework in which candidates of many durations compete on annualized cost, making the duration mix an optimization outcome....
Understanding spoken dialogue requires joint reasoning over lexical content and paralinguistic acoustic signals such as emotion and conversational intent. However, existing evaluations often allow shortcuts based on transcripts or single-modality solutions, obscuring whether models genuinely ground predictions in speech. We formalize...
This paper develops a price-based distributed scheduling in an energy community whose members own behind-the-meter renewable generation with deferrable EV charging and price-elastic thermostatic loads. A coordinator transacts with the distribution utility under a Net Energy Metering tariff and broadcasts a community price to which...
Industrial Internet of Things (IIoT) networks widely adopt Ethernet technologies, such as 100BASE-TX, for industrial communications. As industrial networks continue to scale, reliable device authentication becomes increasingly important for preventing device impersonation and unauthorized access. Physical-layer fingerprinting (PLF)...
The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred data. This paper presents an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) system that operates as an Extreme Learning Machine (ELM) to execute Over-The-Air (OTA) binary...
Detecting the fetal abdominal circumference standard plane in low-cost obstetric blind sweeps is a highly imbalanced frame-classification problem: positive frames account for under 3% of a sequence, form short contiguous segments, and are poorly handled by off-the-shelf ultrasound and vision foundation models. We propose AnatoProto, a...
Emerging integrated sensing and communication (ISAC) systems based on distributed MIMO (D-MIMO) enable radio imaging by exploiting spatial diversity across multiple access points (APs). However, joint sensing and communication introduce mutual interference between communication and sensing signals. In this paper, we propose a downlink...
Islamic prayer is a structured movement activity that offers a distinctive setting for behavioral biometrics: all participants execute the same action sequence, so identity must be inferred from differences in execution. We collected inertial data from 95 participants using their own smartphones during nightly congregational Islamic...
This paper investigates decentralized multitask learning over networks when the underlying task relationships are unknown. While existing graph-regularized multitask frameworks typically assume a known structure, practical settings often require learning inter-task dependencies directly from distributed data. We propose a...
We study long-horizon Wasserstein-penalized minimax control for discrete-time stochastic linear systems with empirical disturbance data, in which adversarial disturbance distributions induce time-varying mean and covariance dynamics, making standard turnpike arguments not directly applicable. For possibly uncentered data, we...
Sustained forced oscillations in power systems, driven by large cyclic loads such as data centers, pose a challenge to conventional power system stabilizers (PSSs), which rely on fixed tuned parameters and limited adaptability. This paper investigates the use of Data-Enabled Predictive Control (DeePC) as a data-driven alternative for...
Ambient IoT (A-IoT) devices rely on energy harvesting and duty cycling to sustain operation, thereby fundamentally changing collaborative sensing compared with traditional always-ON sensor networks. In this paper, we study the joint deployment and sensing scheduling of A-IoT devices equipped with directional sensing. We explore four...
This paper proposes a novel data-driven algorithm to approximate the dominant eigenfunctions (aka.~modes) of the Koopman operator of nonlinear dynamical systems using neural networks. The relevance of learning the dominant Koopman modes is to approximate nonlinear dynamics by linear ones in a lifted space, thereby enabling simplified...