arXiv preprints from January 1, 2026 through September 23, 2026 — 00:36:51 EST
The origin of cosmic rays from outside the Solar system are unknown, as they are deflected by the interstellar magnetic field. Supernova remnants are the main candidate for cosmic rays up to PeV energies but due to lack of evidence, they cannot be concluded as the sources of the most energetic Galactic CRs. We investigate discrete...
This paper presents a meta-learning framework for credit risk assessment of Italian Small and Medium Enterprises (SMEs) that explicitly addresses the temporal misalignment of credit scoring models. The approach aligns financial statement reference dates with evaluation dates, mitigating bias arising from publication delays and...
This article describes the performance of the pair spectrometer installed in experimental Hall D at Jefferson Lab and its operation in multiple experiments with the GlueX detector. The primary purpose of the pair spectrometer is the precise determination of the flux of beam photons incident on the GlueX target, a critical input for...
In this work, we design and analyze a Discrete de Rham (DDR) scheme for a contact mechanics problem involving fractures along which a model of Tresca friction is considered. Our approach is based on a mixed formulation involving a displacement field and a Lagrange multiplier, enforcing the contact conditions, representing tractions at...
Colorectal liver metastases (CRLM) are a major cause of cancer-related mortality, and reliable detection on CT remains challenging in multi-centre settings. We developed a foundation model-based AI pipeline for patient-level classification and lesion-level detection of CRLM on contrast-enhanced CT, integrating uncertainty...
Efficient channel state information (CSI) feedback is critical for 6G extremely large-scale multiple-input multiple-output (XL-MIMO) systems to mitigate channel interference. However, the massive antenna scale imposes a severe burden on feedback overhead. Meanwhile, existing quantized feedback methods face dual challenges of limited...
Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machine learning framework for adiabatic lattice dynamics coupled to nonequilibrium electrons, and demonstrate it for a gating induced insulator to metal...
Memory is critical for dialogue agents to maintain coherence and enable continuous adaptation in long-term interactions. While existing memory mechanisms offer basic storage and retrieval capabilities, they are hindered by two primary limitations: (1) rigid memory granularity often disrupts semantic integrity, resulting in fragmented...
Food image segmentation is a critical task for dietary analysis, enabling accurate estimation of food volume and nutrients. However, current methods suffer from limited multi-view data and poor generalization to new viewpoints. We introduce BenchSeg, a novel multi-view food video segmentation dataset and benchmark. BenchSeg aggregates...
We study the use of large language models (LLMs) for physics instrument design and compare their performance to reinforcement learning (RL). Using only prompting, LLMs are given task constraints and summaries of prior high-scoring designs and propose complete detector configurations, which we evaluate with the same simulators and...
Reduced-order modeling lies at the interface of numerical analysis and data-driven scientific computing, providing principled ways to compress high-fidelity simulations in science and engineering. We propose a training framework that couples a continuous-time form of operator inference with the adjoint-state method to obtain robust...
Topological kagome magnets offer a rich landscape for exploring the intricate interplay of quantum interactions among geometry, topology, spin, and correlation. GdTi3Bi4 crystallizes in layered Ti based kagome nets intertwined with zigzag Gd chains along the a axis and orders antiferromagnetically below 15 K. Here, we present the...
Recent advances in large language models (LLMs) have enabled agents to autonomously execute complex, long-horizon tasks, yet planning remains a primary bottleneck for reliable task execution. Existing methods typically fall into two paradigms: step-wise planning, which is reactive but often short-sighted; and one-shot planning, which...
Oral presentation skills are a critical component of higher education, yet comprehensive datasets capturing real-world student performance across multiple modalities remain scarce. To address this gap, we present SOPHIAS (Student Oral Presentation monitoring for Holistic Insights & Analytics using Sensors), a 12-hour multimodal...
We consider the problem of Bose-Einstein condensed atoms, which are confined in a (quasi) one-dimensional toroidal potential. We focus on the case of an effective attractive interaction between the atoms. The formation of a localized blob (i.e., a ``bright" solitary wave) for sufficiently strong interactions provides an example of...
Optical computing offers ultrafast, energy-efficient alternatives to conventional digital processors, yet most implementations remain confined to single-channel processing, severely underutilizing light's information capacity. Here we demonstrate a self-optimizing multichannel optical computing architecture based on multi-plane light...
Generative AI systems often display highly uneven performance across tasks that appear ``nearby'': they can be excellent on one prompt and confidently wrong on another with only small changes in wording or context. We call this phenomenon Artificial Jagged Intelligence (AJI). This paper develops a tractable economic model of AJI that...
We prove that the reduced cross-sectional algebra of a Fell bundle with the approximation property over an inverse semigroup is exact if and only if the unit fiber of the Fell bundle is exact. This generalizes a recent result of the first-named author for actions of second countable locally compact Hausdorff groupoids on separable...
Extended reality is a fast-growing domain for which there is an increasing need to analyze and understand user behavior. In particular, understanding human visual attention during immersive experiences is crucial for many applications. The visualization and analysis of visual attention are commonly done by building fixation density...
The study of the Quark-Gluon Plasma (QGP), a deconfined state of nuclear matter, remains a central focus of high-energy heavy-ion collision experiments. The recent operation of the Large Hadron Collider (LHC) in Run 3 at the new center-of-mass energy of $\sqrt{s_{NN}}=5.36$ TeV necessitates theoretical predictions to characterize the...
We construct an $\ll^2$-solution (also known as a weakly low solution) to ${\mathrm{D}^2}$ within ${\mathrm{B}Σ^0_{3}}$ and prove the $\ll^2$-basis theorem for $\mathrm{RT}^2$ over ${\mathrm{B}Σ^0_{3}}$. The $\ll^2$-basis theorem is a variant of the low basis theorem, which has recently received focus in the context of the first-order...
Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-order generation. However, realizing these benefits in practice is non-trivial, as dLLMs inherently face an accuracy-parallelism trade-off. Despite increasing interest, existing methods typically...
We study information leakage in secure linear network coding schemes based on nested rank-metric codes. We show that the amount of information leaked to an adversary that observes a subset of network links is characterized by the conditional rank function of a representable $q$-polymatroid associated with the underlying rank-metric...
Several recent results from dynamic and sublinear graph coloring are surveyed. This problem is widely studied and has motivating applications like network topology control, constraint satisfaction, and real-time resource scheduling. Graph coloring algorithms are called colorers. In §1 are defined graph coloring, the dynamic model, and...
Multimodal emotion understanding requires effective integration of text, audio, and visual modalities for both discrete emotion recognition and continuous sentiment analysis. We present EGMF, a unified framework combining expert-guided multimodal fusion with large language models. Our approach features three specialized expert...