arXiv preprints from January 1, 2026 through September 27, 2026 — 17:40:31 EST
Vacuum systems are an intrinsic part of any accelerator around the world: all particles circulate under vacuum. This lecture gives rudiments on the fundamentals of vacuum science such as units, ideal gas law, partial pressure, mean free path, flow of molecules, conductance, pumping speed, and outgassing. An overview of standard vacuum...
Under certain conditions, the oscillating inflaton condensate filling the Universe after inflation can fragment and form so-called oscillons. These long-lived soliton-like field configurations can dominate the Universe for several $e$-folds of expansion, leading to an early matter-dominated phase preceding the standard radiation era....
Recent progress in multi-modal large language models (MLLMs) has significantly advanced video understanding. However, their performance on long-form videos remains limited by computational constraints and suboptimal frame selection. We present Think-Clip-Sample (TCS), a training-free framework that enhances long video understanding...
Stream-based monitoring assesses the health of safety-critical systems by transforming input streams of sensor measurements into output streams that determine a verdict. These inputs are often treated as accurate representations of the physical state, although real sensors introduce calibration and measurement errors. Such errors...
Climate change is intensifying human heat exposure, particularly in densely built urban centers of the Global South. Low-cost construction materials and high thermal-mass surfaces further exacerbate this risk. Yet scalable methods for assessing such heat-relevant building attributes remain scarce. We propose a machine learning...
We study an elastic Calderon-type inverse problem: recover the mass density $ρ(x)$ in a bounded domain $Ω\subset\mathbb{R}^3$ from the Neumann-to-Dirichlet map associated with the isotropic Lamé system $\mathcal{L}_{λ,μ}u+ω^2ρ(x)u=0$. We introduce a constructive strategy that embeds a subwavelength periodic array of resonant...
We report on the observation on proximity-induced superconductivity in the topological insulator BiSbTeSe2 coupled to a disordered superconductor, amorphous indium oxide (a-InO). Resistance temperature measurements reveal superconducting signatures at low temperatures, even when InO is in an insulating state, indicating the...
Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely focus on symbolic or weakly grounded environments, leaving their performance in physics-constrained real-world domains underexplored. We...
Mn-Sb site mixing directly impacts both the magnetic and topological properties of MnSb2Te4. This study reveals, unlike previously believed, that these anti-sites can be unevenly distributed within the crystal. To that end, a polycrystalline sample was created with a two-step synthesis using MnTe and Sb2Te3 as precursors. DC-SQUID...
Energy efficiency has become an integral aspect of modern computing infrastructure design, impacting the performance, cost, scalability, and durability of production systems. The incorporation of power actuation and sensing capabilities in CPU designs is indicative of this, enabling the deployment of system software that can actively...
This paper studies microfluidic molecular communication receivers with finite-capacity Langmuir adsorption driven by an effective surface concentration. In the reaction-limited regime, we derive a closed-form single-pulse response kernel and a symbol-rate recursion for on-off keying that explicitly exposes channel memory and...
Time-series forecasting is fundamental in industrial domains like manufacturing and smart factories. As systems evolve toward automation, models must operate on edge devices (e.g., PLCs, microcontrollers) with strict constraints on latency and memory, limiting parameters to a few thousand. Conventional deep architectures are often...
Analytical solutions for the yaw response of tail fins for small wind turbines, and wind vanes for wind direction measurement, are derived for any planform and any release angle $γ_0$. This extends current linear models limited to small $|γ_0|$ and low aspect ratio planforms. The equation studied here is the minimal form of the...
Achieving net-zero carbon emissions requires a transformation of energy systems, industrial processes, and consumption patterns. In particular, a transition towards that goal involves a gradual reduction of excess carbon emissions that are not essential for the well-functioning of society. In this paper we study this problem from a...
We present a unified framework for gravitational baryogenesis combining two mechanisms: (i) an entropy-clock source that generates a sign-definite baryon chemical potential mu_B proportional to d ln S/dt during irreversible entropy production, and (ii) CP violation from a gravitational theta term theta R Rtilde analogous to the QCD...
We derive explicitly the e-values with optimal (relative) growth rate in the worst case for testing the mean of a bounded random variable, thereby providing the first application of the (RE)GROW quality criteria beyond the assumption of mutually absolutely continuous hypotheses for e-values originally proposed by Grünwald et al....
Optical frequency combs provide a phase-coherent interface between optical and microwave domains, underpinning advances in precision metrology, spectroscopy, and time-frequency transfer. Most conventional comb architectures are limited to sub-gigahertz repetition rates, constraining integration and scalability. Here, we demonstrate a...
In this paper, we compute the renormalization factors for the gluino and gluon fields, the gauge parameter, the coupling constant, as well as the scalar, pseudoscalar, and axial-vector gluino bilinear operators in N=1 supersymmetric Yang-Mills (SYM) theory, using improved lattice actions. Our lattice formulation employs clover...
Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether they would actually save clinicians time and effort in their portal workload. We investigate LLM alignment with individual clinicians through a...
Germanium-on-Silicon (Ge-on-Si) is a promising, CMOS-compatible platform for integrated terahertz (THz) photonics, offering a low-cost alternative to III-V semiconductors. A primary challenge for Ge-based photoconductive antennas (PCAs), however, has been the long carrier lifetime of bulk Ge, preventing its use as a detector. Here, we...
Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (RAG), which shows great successes for enhancing large language models (LLMs), has not been well explored, due to the fundamental difference between LLM and...
Magnetic skyrmions are topologically protected spin textures known for their robustness against perturbations. Their topological stability makes them robust information carriers, ideal for tackling a key challenge in quantum computing: creating reliable, one-way links between different types of qubits. In this proof-of-concept study,...
Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introduce Neural Chain-of-Thought Search...
The exploration of novel topological insulators (TIs) beyond binary chalcogenides has been accelerated in pursuit of exotic quantum states and device applications. Here, the layered ternary chalcogenide Ge2Bi2Te5 is identified as a three-dimensional TI. The bulk electronic structure of Ge2Bi2Te5 features a hole-type Fermi surface at...
We develop a novel framework for modeling diffusion on complex networks by constructing Laplacian-like operators based on walks around a graph. Our approach introduces a parametric family of walk-based Laplacians that naturally incorporate memory effects by excluding or downweighting backtracking trajectories, where walkers...