arXiv preprints from January 1, 2026 through September 28, 2026 — 21:29:42 EST
In the past decades, static analysis has thrived in software, facilitating applications in bug detection, security, and program understanding. These advanced analyses are largely underpinned by general-purpose static analysis frameworks, which offer essential infrastructure to streamline their development. Conversely, hardware lacks...
This paper investigates how end-to-end (E2E) channel autoencoders (AEs) can achieve energy-efficient wideband communications by leveraging Walsh-Hadamard (WH) interleaved converters. WH interleaving enables high sampling rate analog-digital conversion with reduced power consumption using an analog WH transformation. We demonstrate...
We investigate the orbital dynamics of four-planet systems consisting of Earth-mass planets on initially-circular, coplanar orbits around a star of one solar mass. In our simulations, the innermost planet's semimajor axis is set at 1 AU, with subsequent semimajor axes spaced equally in terms of planets' mutual Hill radii. Several sets...
Physics-Informed Neural Networks (PINNs) represent a groundbreaking paradigm in scientific computing, seamlessly integrating the robust framework of deep learning with fundamental physical laws. This paper meticulously applies the standard PINN framework to solve the challenging one-dimensional nonlinear Fisher-KPP equation, a...
Cascaded liquid crystal spatial light modulators provide a versatile strategy for the generation of structured light and matter fields, with applications including optical communications, photonic computing, and topological field engineering. However, experimental imperfections, such as temperature-dependent liquid crystal response,...
We present the first empirical derivation of a continuous-time stochastic model for real-world opinion dynamics. Using longitudinal social-media data to infer users opinion on a binary climate-change topic, we reconstruct the underlying drift and diffusion functions governing individual opinion updates. We show that the observed...
Vision-Language-Action models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model embeddings. Recent advancements have introduced explicit intermediary reasoning-such as sub-task prediction (language)...
Energy levels of QED bound states, which depend on a number of independent mass parameters, can be calculated as matrix elements of the QED energy-momentum tensor trace. As an example of such system we consider muonic hydrogen. The leading one-loop corrections to its energy levels depend on the electron and muon masses. These...
Surface defects on Printed Circuit Boards (PCBs) directly compromise product reliability and safety. However, achieving high-precision detection is challenging because PCB defects are typically characterized by tiny sizes, high texture similarity, and uneven scale distributions. To address these challenges, this paper proposes a novel...
Credit assignment is a core challenge in multi-agent reinforcement learning (MARL), especially in large-scale systems with structured, local interactions. Graph-based Markov decision processes (GMDPs) capture such settings via an influence graph, but standard critics are poorly aligned with this structure: global value functions...
Accurate wetland mapping is essential for ecosystem monitoring, yet dense pixel-level annotation is prohibitively expensive and practical applications usually rely on sparse point labels, under which existing deep learning models perform poorly, while strong seasonal and inter-annual wetland dynamics further render single-date imagery...
In the context of pre-processing -- a scenario in which galaxies quench their star formation within substructures before falling into clusters -- we investigate the impact of environment on the physical and morphological properties of galaxies in Compact Groups (CGs), focusing specifically on a sample of Star-Forming Compact Groups...
The complexity of navigating digital privacy, safety, and security threats often falls directly on users. This leads to users seeking help from family and peers, platforms and advice guides, dedicated communities, and even large language models (LLMs). As a precursor to improving resources across this ecosystem, our community needs to...
This work describes a novel data-driven latent space inference framework built on paired autoencoders to handle observational inconsistencies when solving inverse problems. Our approach uses two autoencoders, one for the parameter space and one for the observation space, connected by learned mappings between the autoencoders' latent...
3D semantic occupancy prediction has emerged as a critical perception task for autonomous driving due to its ability to offer voxel-level semantic and geometric understanding of the environment. However, such a refined representation for large-scale scenes incurs prohibitive computation, posing a significant challenge to practical...
We study deterministic nonstationary discrete-time optimal control problems in both finite and infinite horizon. With the aid of Gateaux differentials, we prove a discrete-time maximum principle in analogy with the well-known continuous-time maximum principle. We show that this maximum principle, together with a transversality...
The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics dataset for the Mini Wheelbot, an open-source, quasi-symmetric balancing...
Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text. Intrinsic noise in CIR triplets incurs intrinsic uncertainty and threatens the model's robustness. Probabilistic learning approaches have shown promise in addressing such issues; however, they fall short for CIR due to their...
We establish power saving asymptotics for the sum of the divisor function along a binary quartic form, improving on work of Daniel. The proof involves an application of a recent two dimensional delta method due to Li, Rydin-Myerson, and Vishe and an exploitation of $\mathrm{GL}_2$ automorphic forms arising from the factorization of...
We present NAVIS (NAnochannel Velocity and thermal Interfacial Slip), a LAMMPS-Python scripted toolkit for computing the Navier (hydrodynamic) friction coefficient and Kapitza (thermal) resistance at arbitrary solid-fluid interfaces. NAVIS is based on equilibrium molecular dynamics (EMD) methods for calculating the linear response...
This report examines the open questions that remain unsolved following the measurements with antineutrons ($\bar n$) as probes conducted up to the 1990s at the LEAR facility at CERN. It also presents suggestions for possible new experiments at a future, upgraded AD complex, which can potentially provide access to new areas of physics.
The performance of constraint programming solvers is highly sensitive to the choice of their hyperparameters. Manually finding the best solver configuration is a difficult, time-consuming task that typically requires expert knowledge. In this paper, we introduce probe and solve algorithm, a novel two-phase framework for automated...
Theoretical spectroscopy, and more generally, electronic-structure theory, are powerful concepts for describing the complex many-body interactions in materials. They comprise a variety of methods that can capture all aspects, from ground-state properties to lattice excitations to different types of light-matter interaction, including...
Although much research has focused on AI explanations to support decisions in complex information-seeking tasks such as fact-checking, the role of evidence is surprisingly under-researched. In our study, we systematically varied explanation type, AI prediction certainty, and correctness of AI system advice for non-expert participants,...
Classifying the stratospheric polar vortex provides predictability for surface weather on extended-range timescales. However, providing a scientifically sound classification is challenging: all the definitions proposed in over 60 years of study depend on empirically chosen parameters and yield different results when one of them...