November 2025 arXiv papers — page 5

Yuxiang Chen, Zuohan Wu, Ziwei Wang, Xiangning Yu

Large reasoning models (LRMs) have garnered significant attention from researchers owing to their exceptional capability in addressing complex tasks. Motivated by the observed human-like behaviors in their reasoning processes, this paper introduces a comprehensive taxonomy to characterize atomic reasoning steps and probe the ``psyche'' of LRM intelligence. S

Zach Lawrence, Jessica Yao, Chris Qin

Wind farms with integrated energy storage, or hybrid wind farms, are able to store energy and dispatch it to the grid following an operational strategy. For individual wind farms with integrated energy storage capacity, data-driven dispatch strategies using localized grid demand and market conditions as input parameters stand to maximize wind energy value. S

Kuiqing Tang

Rare-earth tritellurides ($R$Te$_3$) exhibit complex charge-density-wave (CDW) phases intertwined with lattice symmetry, offering a platform to explore unconventional symmetry breaking in correlated materials. Elasto-optical probing, which detects strain-induced changes in birefringence, provides a non-invasive approach to visualize anisotropy and emergent o

Xinyue Wang, Yuheng Jia, Hui Liu, Junhui Hou

Typical deep clustering methods, while achieving notable progress, can only provide one clustering result per dataset. This limitation arises from their assumption of a fixed underlying data distribution, which may fail to meet user needs and provide unsatisfactory clustering outcomes. Our work investigates how multi-modal large language models (MLLMs) can b

Lingling Fu, Yongfu Xue

Reward models (RMs) are a critical component of reinforcement learning from human feedback (RLHF). However, conventional dense RMs are susceptible to exploitation by policy models through biases or spurious correlations, resulting in reward hacking: RM scores increase during training while alignment with human preferences deteriorates, a problem that is furt

Xingtai Gui, Jianbo Zhao, Wencheng Han, Jikai Wang

End-to-end autonomous driving systems directly generate driving policies from raw sensor inputs. While these systems can extract effective environmental features for planning, relying on auxiliary perception tasks, developing perception annotation-free planning paradigms has become increasingly critical due to the high cost of manual perception annotation. I

Jiaming Xu, Jiayi Pan, Hanzhen Wang, Yongkang Zhou

In this paper, we point out that the objective of the retrieval algorithms is to align with the LLM, which is similar to the objective of knowledge distillation in LLMs. We analyze the similarity in information focus between the distilled language model(DLM) and the original LLM from the perspective of information theory, and thus propose a novel paradigm th

Hiroko Okada, Wako Aoki, Nozomu Tominaga, Satoshi Honda

We present the measurement of 26 elemental abundances of SMSS J022423.27$-$573705.1 (SMSS 0224$-$5737), an extremely metal-poor (EMP) star with a weak $r$-process signature. We report the measurements of N, O, V, Zn, and Ba, and the upper limits for Mo, Ru, Pd, Ag, and Eu for the first time. SMSS 0224$-$5737 exhibits low C abundance and high N and O abundanc

Harley Kaufman, Josh Meisel

We study the asymptotic behavior of the critical density of the activated random walk model as the sleep rate $\lambda$ tends to $0$ and $\infty$. For large $\lambda$, we prove new lower bounds in dimensions 1 and 2, showing that in one dimension the critical density approaches $1$ superpolynomially fast. For small $\lambda$, we prove a new lower bound in tw

Bohan Zhao, Zane Cao, Yongchao He

As large language models (LLMs) scale out with tensor parallelism (TP) and pipeline parallelism (PP) and production stacks have aggressively optimized the data plane (attention/GEMM and KV cache), sampling, the decision plane that turns logits into tokens, becomes a new bottleneck. This creates a structural holdout: sampling neither expands with TP nor balan

Deliang Wang, Peng Liu, Yan Ma, Rongkai Zhuang

Interactive image segmentation(IIS) plays a critical role in generating precise annotations for remote sensing imagery, where objects often exhibit scale variations, irregular boundaries and complex backgrounds. However, existing IIS methods, primarily designed for natural images, struggle to generalize to remote sensing domains due to limited annotated data

Daniel Loebell, Mingmar Sherpa, Ram Datta Bhatta

The Millennium Challenge Corporation (MCC), started in 2004 by the United States Congress, focuses on development initiatives involving good governance, sustainable economic growth, and poverty reduction. Since its inception, it has invested over 13 billion US dollars in 30 countries. Nepal is a recent beneficiary, signing a compact valued at 500 million US

Fanlong Zeng, Wensheng Gan

Covariate distribution shift occurs when certain structural features present in the test set are absent from the training set. It is a common type of out-of-distribution (OOD) problem, frequently encountered in real-world graph data with complex structures. Existing research has revealed that most out-of-the-box graph neural networks (GNNs) fail to account f

M. Ghadimi, V. Salari, S. Bakrani, M. Zomorodi

We present a generalized Deutsch-Jozsa (DJ) quantum algorithm that not only determines both the global type of an unknown Boolean function (constant or balanced) but also determines explicit output values of the function in a single oracle query. Unlike the original DJ algorithm, which identifies only whether a function is constant or balanced, our generaliz

Osama A. Marzouk

Using observation records of wind speeds from weather stations in the Sultanate of Oman between 2000 and 2023, we compute estimators of the two Weibull distribution parameters (namely, the Weibull distribution's shape parameter and the Weibull distribution's scale parameter) in 10 weather station locations within eight Omani governorates. The 10 weather stat

Emmanuella Avwerosuoghene Oghenekaro

Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have enabled transformative progress in medical imaging analysis. Deep learning-based computer vision models, such as convolutiona

Haoyu Shen, Weimin Lyu, Haotian Xu, Tengfei Ma

Vision-Language Models (VLMs) have achieved impressive progress in multimodal text generation, yet their rapid adoption raises increasing concerns about security vulnerabilities. Existing backdoor attacks against VLMs primarily rely on explicit pixel-level triggers or imperceptible perturbations injected into images. While effective, these approaches reduce

Zhuohua Liu, Kaiqi Huang, Qinxin Mei, Yuanqi Hu

Analog circuit optimization is typically framed as black-box search over arbitrary smooth functions, yet device physics constrains performance mappings to structured families: exponential device laws, rational transfer functions, and regime-dependent dynamics. Off-the-shelf Gaussian-process surrogates impose globally smooth, stationary priors that are misali

Ryan J. French, Maria D. Kazachenko, David Berghmans, Elke D'Huys

We present fast cadence and high resolution observations of flare ribbons from the Solar Orbiter Extreme Ultraviolet Imager (EUI). Utilizing the short-exposure observations from the EUI High Resolution Imager in EUV (HRIEUV), we find small-scale blob/bead-like kernel structures propagating within a hook at the end of a flare ribbon, during the impulsive phas

Yifan Xu, Xichen Ye, Yifan Chen, Qiaosheng Zhang

Quality of datasets plays an important role in large language model (LLM) alignment. In collecting human feedback, however, preference flipping is ubiquitous and causes corruption in data annotation; the issue necessitates the alignment algorithms with improved robustness against potential flipped pairs. To this end, this paper introduces a Flipping-Aware Di

Ming-Hsiu Wu, Ziqian Xie, Shuiwang Ji, Degui Zhi

Advancements in AI for science unlocks capabilities for critical drug discovery tasks such as protein-ligand binding affinity prediction. However, current models overfit to existing oversimplified datasets that does not represent naturally occurring and biologically relevant proteins with modifications. In this work, we curate a complete and modification-awa

Li Qianyang, Zhang Xingjun, Wang Shaoxun, Wei Jia

Long-term time series forecasting (LTSF) is a critical task in computational intelligence. While Transformer-based models effectively capture long-range dependencies, they often suffer from quadratic complexity and overfitting due to data sparsity. Conversely, efficient linear models struggle to depict complex non-linear local dynamics. Furthermore, existing

Chengzhi Yu, Yifan Xu, Yifan Chen, Wenyi Zhang

Recently, large vision-language models (LVLMs) have risen to be a promising approach for multimodal tasks. However, principled hallucination mitigation remains a critical challenge.In this work, we first analyze the data generation process in LVLM hallucination mitigation and affirm that on-policy data significantly outperforms off-policy data, which thus ca

Seongyeon Park, Jaeyong Song, Changmin Shin, Sukjin Kim

Dynamic random walks are fundamental to various graph analysis applications, offering advantages by adapting to evolving graph properties. Their runtime-dependent transition probabilities break down the pre-computation strategy that underpins most existing CPU and GPU static random walk optimizations. This leaves practitioners suffering from suboptimal frame

Raphaël Dulac, Zixia Wei

Elliptic de Sitter (dS) spacetime dS$/\mathbb{Z}_2$ is a non-time-orientable spacetime obtained by imposing an antipodal identification to global dS. Unlike QFT on global dS, whose vacuum state can be prepared by a no-boundary Euclidean path integral, the Euclidean elliptic dS does not define a wavefunction in the usual sense. We propose instead that the pat

Xinyue Cheng, Yalu Feng

In this paper, we study systematically the concentration properties of Finsler metric measure manifolds. We establish the relationships between the concentration properties and the observable diameter, isoperimetric inequalities and the first eigenvalue. In particular, as an application, we derive a Cheng type upper bound estimate for the first closed eigenv

Bryan Alvarez, Micah Dorton, Thomas Michael Keller, Lawrence Liu

Minimal prime graphs are connected graphs on at least two vertices whose complements satisfy the following conditions: triangle-freeness, 3-colorability, and edge-maximality with respect to the latter two properties. These graphs are prime graphs (or Gruenberg-Kegel graphs) of finite solvable groups with the maximum number of Frobenius actions among their Sy

Juan Barranco, Argelia Bernal, Víctor Jaramillo, Darío Núñez

We introduce a minimal Dark Standard Model (DSM) consisting of a single spin-0 particle with dark $U(1)$ gauge symmetry, and completely decoupled from the visible sector. Characterized only by the scalar mass $\mu$ and the dark charge $q$, this framework naturally gives rise to a rich phenomenology, including stable solitonic configurations that behave as da

Ka Chung Lai, Ahmet Cetinkaya

We propose a new neural network architecture called CAR-net (CAscade Refinement Network) to deblur images that are subject to rotational motion blur. Our architecture is specifically designed for the semi-blind scenarios where only noisy information of the rotational motion blur angle is available. The core of our approach is progressive refinement process t

Chenyi Zhang, Tao Shang, Chao Guo, Ruohan He

Variational quantum circuits face a critical trade-off between privacy and trainability. High expressivity required for robust privacy induces exponentially large dynamical Lie algebras. This structure inevitably leads to barren plateaus. Conversely, trainable models restricted to polynomial-sized algebras remain transparent to algebraic attacks. To resolve

Bahrul Ilmi Nasution, Floor Eijkelboom, Mark Elliot, Richard Allmendinger

Synthetic data generation is an important tool for privacy-preserving data sharing. Although diffusion models have set recent benchmarks, flow matching (FM) offers a promising alternative. This paper presents different ways to implement FM for tabular data synthesis. We provide a comprehensive empirical study that compares flow matching (FM and variational F

Amichai Lampert, Andrew Snowden, Tamar Ziegler

Let $K$ be a number field and $f_1,\ldots,f_s\in K[x_1,\ldots,x_n]$ forms of odd degrees. In 1957, Birch proved that if $n$ is sufficiently large then the forms always have a nontrivial zero in $K^n$. Apart from some small degrees, the number of variables required was so large that it has been described as "not even astronomical". We prove that, for any fixe

Hasi Hays, Yue Yu, William J. Richardson

Artificial intelligence (AI) is reshaping computational and network biology by enabling new approaches to decode cellular communication networks. We introduce Hierarchical Molecular Language Models (HMLMs), a novel framework that models cellular signaling as a specialized molecular language, where signaling molecules function as tokens, protein interactions

Manoj Belavadi, Kathie Cameron

Given a $k$-colouring of a graph $G$ and two of the colours, a $Kempe$ $chain$ is a connected component of the subgraph of $G$ induced by the vertices coloured with one of these two colours. A $Kempe$ $swap$ changes one colouring into another by interchanging the colours of the vertices in a Kempe chain. Two colourings are $Kempe$ $equivalent$ if each can be

Mengzhu Xu, Hanzhi Liu, Ningkang Peng, Qianyu Chen

Continual learning for video--language understanding is increasingly important as models face non-stationary data, domains, and query styles, yet prevailing solutions blur what should stay stable versus what should adapt, rely on static routing/capacity, or require replaying past videos. We aim to explicitly specify where stability lives and where plasticity

Yuwen Chen, Paul Goulart, Colin Jones

We propose a novel warmstarting method for primal-dual interior point methods based on a smoothing operator that generates a starting point on the central path from the previous optimum. Compared to traditional approaches that prioritize minimizing infeasibility residuals, our method focuses on maintaining proximity to the central path. Computation of a smoo

Kerry Seekamp

In 2023, Defant introduced toric promotion as a cyclic analogue of Sch\"utzenberger's well known promotion operator. Toric promotion is defined by a choice of simple graph $G$ and acts on the labeling of $G$ by a series of involutions. Defant described the orbit length of toric promotion on trees and showed that it does not depend on the initial labeling; we

Dingqiang Ye, Chao Fan, Kartik Narayan, Bingzhe Wu

Gait patterns play a critical role in human identification and healthcare analytics, yet current progress remains constrained by small, narrowly designed models that fail to scale or generalize. Building a unified gait foundation model requires addressing two longstanding barriers: (a) Scalability. Why have gait models historically failed to follow scaling l

Takuya Miyamoto

We prove the finiteness of leaps of modules of $m$-integrable derivations for algebras essentially of finite type and, more generally, for schemes essentially of finite type over an algebraically closed field of positive characteristic. This provides an affirmative answer to a question posed by L. Narv\'aez Macarro. As an application, we establish the cohere

Anand Pal

X-ray diffraction (XRD) peak broadening analysis remains a cornerstone for quantifying crystallite size and lattice microstrain in materials. Among various approaches, the Size Strain Plot (SSP) method is widely employed for its conceptual simplicity and ease of use. However, this study reveals that the equation most commonly applied in SSP analysis is dimen

Weijie Peng, Nanbing Li, Jin Luo, Shuai Wang

Functional verification relies on large simulation-based regressions. Traditional test selection relies on static test features and overlooks actual coverage behavior, wasting substantial simulation time, while constrained random stimuli generation depends on manually crafted distributions that are difficult to design and often ineffective. We present NOVA,

Ruofan Jiang

We study ordinary abelian schemes in characteristic $p$ and their moduli spaces from the perspective of char $p$ Mumford--Tate, log Ax--Lindemann, and geometric Andr\'e--Oort conjectures (abbreviated as $\MTT_p$, $\mathrm{logAL}_p$ and geoAO$_p$). In this paper, we achieve multiple goals: (\textbf{A}) establish the implication $\mathrm{MT}_p\Leftrightarrow \

Anish Lakkapragada

Classical statistical inference and learning theory often fail to explain the success of modern neural networks. A key reason is that these models are non-identifiable (singular), violating core assumptions behind PAC bounds and asymptotic normality. Singular learning theory (SLT), a physics-inspired framework grounded in algebraic geometry, has gained popul

Yoichiro Mori, Chanoknun Sintavanuruk, Truong-Son P. Van

We consider the problem of approximating the Langevin dynamics of inertial particles being transported by a background flow. In particular, we study an acceleration corrected advection-diffusion approximation to the Langevin dynamics, a popular approximation in the study of turbulent transport. We prove error estimates in the averaging regime in which the di

Xu Duan, Dongmei Chen

Latent generative models are increasingly shifting from traditional VAEs toward representation autoencoders and semantically aligned latent spaces, which lift images into higher-dimensional feature domains where semantic factors become more separable. Yet these spaces also contain geometric regularities that existing methods do not fully exploit--particularl

Wu Yonggang

The development of large language models (LLMs) is limited by a lack of explainability, the absence of a unifying theory, and prohibitive operational costs. We propose a neuro-theoretical framework for the emergence of intelligence in systems that is both functionally robust and biologically plausible. The model provides theoretical insights into cognitive p

Gunhee Cho, Jason Cheng, Evelyn Li

We develop a unified geometric framework for quantum circuit compilation based on quantized orbifold phases and their diagrammatic semantics. Physical qubit platforms impose heterogeneous phase resolutions, anisotropic Bloch-ball contractions, and hardware-dependent $2\pi$ winding behavior. We show that these effects admit a natural description on the weight

Gunhee Cho, Jessie Wang, Angela Yue

We develop a differential-geometric framework for variational quantum circuits in which noisy single- and multi-qubit parameter spaces are modeled by weighted projective lines (WPLs). Starting from the pure-state Bloch sphere CP1, we show that realistic hardware noise induces anisotropic contractions of the Bloch ball that can be represented by a pair of phy

Qingying Deng, Xian'an Jin, Qi Yan, Yexiang Yan

Building on prior work that established Matrix Quasi-tree Theorems for special embedded graphs, in this paper, we develop a comprehensive theory applicable to all embedded graphs. We introduce symbolic skew-adjacency matrices and reduction maps as key innovations, and prove that a specific polynomial derived from these matrices encodes all spanning quasi-tre

Yi Zhang, Yiwen Zhang, Yu Wang, Tong Chen

The powerful text understanding and generation capabilities of large language models (LLMs) have brought new vitality to general recommendation with implicit feedback. One possible strategy involves generating a unique user (or item) profile from historical interaction data, which is then mapped to a semantic representation in the language space. However, a

Braden Scherting, Otso Ovaskainen, Tomas Roslin, David B. Dunson

Accurate biodiversity monitoring is essential for effective environmental policy, yet current practices often rely on arbitrarily defined ecosystems, communities, and ad-hoc indicator species, limiting cost-efficiency and reproducibility. We present a model-based framework that infers ecological sub-communities and corresponding indicators in terms of habita

Dong In Lee, Hyungjun Doh, Seunggeun Chi, Runlin Duan

Recent progress in 4D representations, such as Dynamic NeRF and 4D Gaussian Splatting (4DGS), has enabled dynamic 4D scene reconstruction. However, text-driven 4D scene editing remains under-explored due to the challenge of ensuring both multi-view and temporal consistency across space and time during editing. Existing studies rely on 2D diffusion models tha

Kiri L. Wagstaff

Isolated digit classification has served as a motivating problem for decades of machine learning research. In real settings, numbers often occur as multiple digits, all written by the same person. Examples include ZIP Codes, handwritten check amounts, and appointment times. In this work, we leverage knowledge about the writers of NIST digit images to create

Ajinkya Borle, Charles Nicholas, Uchenna Chukwu, Mohammad-Ali Miri

Non-negative matrix factorization (NMF) is a matrix decomposition problem with applications in unsupervised learning. The general form of this problem (along with many of its variants) is NP-hard in nature. In our work, we explore how this problem could be solved with an energy-based optimization method suitable for certain machines with non-von Neumann arch

Alexander Stangl, Aiswarya Kethamkuzhi, Hervé Roussel, Cornelia Pop

High temperature superconductors, especially YBa$_2$Cu$_3$O$_{7-δ}$ (YBCO), are considered a key enabling technology towards a clean energy future. Hole doping in YBCO is a prerequisite for the emergence of its unchallenged superconducting properties. Up to now, research was focused on the under- and optimally doped region, due to practical limitations in re

Aladin Djuhera, Fernando Koch, Alecio Binotto

Inference over large-scale foundation models within heterogeneous edge environments necessitates a fundamentally reconfigurable orchestration substrate. Static partitioning of model layers presumes temporal stability across compute and network resources, which is misaligned with the volatility of real-world deployments. We introduce a framework in which both

F Rousse, M Fasi, A Dmytryshyn, M Gulliksson

We use the Gaussian Phase-Space Representation to solve the real-time dynamic of interacting fermions in 1D, 2D, and 3D systems. The method is exact up to a spiking point, which represents a limit on the practical simulation time. The spiking can be delayed, and the practical simulation time extended, by adjusting the gauges of the representation, resulting

Saiful R Mondal, Ahmad K. Al Abdulaali

This paper investigates the lemniscate starlikeness of analytic functions by deriving specific conditions on their power series coefficients. The study utilizes the Cauchy product of power series along with key inequalities involving the Pochhammer symbol and the Gamma function. The derived results are further applied to a number of special functions, provid

Angela Fuquen-Tibatá, Yuriria Cortés-Poza, J. Rogelio Pérez-Buendía

Coral colonies exhibit complex, self-similar branching architectures shaped by biochemical interactions and environmental constraints. To model their growth and calcification dynamics, we propose a novel p-adic reaction-diffusion framework defined over p-adic ultrametric spaces. The model incorporates biologically grounded reactions involving calcium and bic

Nuno Soares, António Grilo

The resource-constrained shortest path problem (RCSPP) is a fundamental NP-hard optimization challenge with broad applications, from network routing to autonomous navigation. This problem involves finding a path that minimizes a primary cost subject to a budget on a secondary resource. While various RCSPP solvers exist, they often face critical scalability l

Rubén Oncala, Joan Soto

Hybrid quarkonia -exotic hadrons with explicit gluonic degrees of freedom- have gained increasing attention in hadron spectroscopy, particularly with the ongoing discovery of new XYZ mesons. In this work, we update the spectrum of heavy hybrid mesons in the charmonium and bottomonium sectors using the Born-Oppenheimer Effective Field Theory framework, by inc

Shizhe Diao, Yu Yang, Yonggan Fu, Xin Dong

Pre-training datasets are typically collected from web content and lack inherent domain divisions. For instance, widely used datasets like Common Crawl do not include explicit domain labels, while manually curating labeled datasets such as The Pile is labor-intensive. Consequently, identifying an optimal pre-training data mixture remains a challenging proble

Yuanyuan Fang, Zekai Yu

We employ a novel approach,based on homological mirror symmetry for Landau-Ginzburg models,to demonstrate the non-existence of crepant resolutions for certain weighted homogeneous Gorenstein compound Du Val singularities.Physically,this implies that such singularities cannot serve as holographic backgrounds for four dimensional N=1 superconformal quiver gaug

Michał Kowalewski, Piotr Oprocha

We compare quasi-graphs and generalized $\sin(1/x)$-type continua, which are two classes of continua that generalize topological graphs and contain the Warsaw circle as a nontrivial common element. We show that neither class is a subset of the other, provide some characterizations, and present illustrative examples. We unify both approaches by considering th

Breanna E. Green, Ashley L. Shea, Pengfei Zhao, Drew B. Margolin

Generative artificial intelligence tools, like ChatGPT, are an increasingly utilized resource among computational social scientists. Nevertheless, there remains space for improved understanding of the performance of ChatGPT in complex tasks such as classifying and annotating datasets containing nuanced language. Method. In this paper, we measure the performa

Yaswanth Chittepu, Raghavendra Addanki, Tung Mai, Anup Rao

The development of autonomous machine learning (ML) agents capable of end-to-end data science workflows represents a significant frontier in artificial intelligence. These agents must orchestrate complex sequences of data analysis, feature engineering, model selection, and hyperparameter optimization, tasks that require sophisticated planning and iteration.

Javira Altmann, Lorenzo Bernardinis, Peter Skands, Valentina Zaccolo

Data from the LHC show a rise in strange-hadron production with charged-particle multiplicity in pp collisions. The Monte-Carlo event generator PYTHIA, using its default Monash tune, instead predicts constant strangeness. We investigate a mechanism invoked during hadronization called string closepacking, where overlapping strings generate a background field,

He-Yen Hsieh, Hong Wang, H. T. Kung

Diffusion-based large language models (dLLMs) refine token generations through iterative denoising, but answers often stabilize before all steps complete. We propose EDIT (Early Diffusion Inference Termination), an inference-time criterion that adaptively stops denoising once sufficient reasoning stability relative to training-time reasoning is detected. EDI

Chelsea Drum, James. G. Nagy, Lucas Onisk

We investigate the regularizing behavior of an iterative Krylov subspace method for the solution of linear inverse problems in precisions lower than double. Recent works have considered the projection of iterated Tikhonov methods using Krylov subspaces for both computational efficiency and an additional regularizing effect. To investigate the regularizing be

Jungwoo Ho

We study a structured permutation scheme for two-sample testing that restricts permutations to single cross-swaps between block-selected representatives. Our analysis yields three main results. First, we provide an exact validity construction that applies to any fixed restricted permutation set. Second, for both the difference of sample means and the unbiase

Lisa M Voigt

Gravitational lensing of background galaxies by intervening matter is a powerful probe of the cosmological model. In the era of Stage IV surveys, contamination from galaxies below the detection threshold has emerged as a significant source of bias. Adopting a noise-bias-free machine-learning method to estimate shear, we quantify the impact of faint galaxies

Song Liu

We study the problem of learning disentangled signals from data using non-linear Independent Component Analysis (ICA). Motivated by advances in self-supervised learning, we propose to learn self-sufficient signals: A recovered signal should be able to reconstruct a missing value of its own from all remaining components without relying on any other signals. W

Jorge Cayao, Masatoshi Sato

We study the emergence of the nonlocal Josephson effect in a system composed of three laterally coupled minimal Kitaev chains and exploit it to realize the nonlocal Josephson diode effect. We find that an imbalance between crossed Andreev reflections and electron cotunneling in the middle Kitaev chain gives rise to an asymmetric $2\pi$-periodic phase-depende

Tanmay Agrawal

Large Language Models have rapidly advanced in their ability to interpret and generate natural language. In enterprise settings, they are frequently augmented with closed-source domain knowledge to deliver more contextually informed responses. However, operational constraints such as limited context windows and inconsistencies between pre-training data and s

Christian Mancas, Diana Christina Mancas

This paper presents a pseudocode algorithm for translating (Elementary) Mathematical Data Model ((E)MDM) schemes into Entity-Relationship data models. We prove that this algorithm is linear, sound, complete, and semi-optimal. As an example, we apply this algorithm to an (E)MDM scheme for a genealogical tree sub-universe. We also provide the main additional f

Alain Albouy, Jiexin Sun

The equations of the Newtonian $n$-body problem have a matrix form, where an $n\times n$ matrix depending on the masses and on the mutual distances appears as a factor. The $n$ eigenvalues of this matrix are real and nonnegative. In a motion of relative equilibrium, the configuration, called {\it central}, has constant mutual distances. The matrix is constan

Anik Sarker, Alan T. Asbeck

We address the correspondence-free alignment of two rotation sets on \(SO(3)\), a core task in calibration and registration that is often impeded by missing time alignment, outliers, and unknown axis conventions. Our key idea is to decompose each rotation into its \emph{Transformed Basis Vectors} (TBVs)-three unit vectors on \(S^2\)-and align the resulting s

Ahmed A. Al-habob, Octavia A. Dobre, Yindi Jing

This paper introduces an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery, sensing data gathering, and mobile edge computing (MEC) services. Three tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (user

Arthur F. Ramos, Tiago M. L. de Veras, Ruy J. G. B. de Queiroz, Anjolina G. de Oliveira

Lumsdaine (2010) and van den Berg-Garner (2011) proved that types in Martin-L\"of type theory carry the structure of weak {\omega}-groupoids. Their proofs, while foundational, rely on abstract properties of the identity type without providing explicit computational content for coherence witnesses. We establish an analogous result for computational paths -- a

Jan Batzner, Volker Stocker, Stefan Schmid, Gjergji Kasneci

Sycophantic response patterns in Large Language Models (LLMs) have been increasingly claimed in the literature. We review methodological challenges in measuring LLM sycophancy and identify five core operationalizations. Despite sycophancy being inherently human-centric, current research does not evaluate human perception. Our analysis highlights the difficul

Panagiotis Gavriilidis, George C. Alexandropoulos

The interplay between large antenna apertures and high carrier frequencies in future wireless systems gives rise to near-field communications, where the curvature of spherical wavefronts renders traditional far-field beamforming models inadequate. This chapter addresses the following fundamental questions on near-field operation: (i) What is the maximum dist

Sosuke Inui, Yinghe Qi, Yiming Xing, Charles Peretti

Electron-on-neon (eNe) qubits have recently emerged as a compelling platform for quantum computing, which combines the vacuum isolation advantages of trapped-ion qubits with the scalability of superconducting circuits. In this system, electrons are trapped in vacuum above a solid neon film deposited on superconducting microwave resonators, where they exhibit

Shengchun Yang, Amit Sravan Bora, Emil Matus, Gerhard Fettweis

Box Decoding is a sort-free tree-search MIMO detector whose complexity is independent of the QAM order, achieved by searching a fixed candidate box around a zero-forcing (ZF) estimate. However, without pruning, the number of visited nodes grows exponentially with the MIMO dimension, limiting scalability. This work proposes two deterministic, low-complexity,

Kate Finnerty, Tyler Genao, Jacob Mayle, Rakvi

In the 1970s, Serre proved that the adelic index of a non-CM elliptic curve over a number field is finite. More recently, Zywina conjectured the complete set of adelic indices for such curves over $\mathbb{Q}$. In this article, we prove that Zywina's conjecture is true for the family of non-CM elliptic curves over $\mathbb{Q}$ that admit a nontrivial rationa

Mohammed Latif Siddiq, Arvin Islam-Gomes, Natalie Sekerak, Joanna C. S. Santos

Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper presents the first large-scale, empirical study of reproducibility practices in LLM-for-SE research. We systematically mined and analyzed 640 papers published between 2017 and 2025

J. E. Drew, R. Greimel, J. Eislöffel, R. Raddi

The southern Galactic plane has been mapped at optical wavelengths and at under one-arcsecond angular resolution by the VST Photometric Ha Survey of the Galactic plane and bulge (VPHAS+). Anticipating the release of a uniform photometric calibration of the entire survey, we examine the properties of VPHAS+ ugriHa photometry of r < 19 mag. point sources in th

Beau Leighton-Trudel

We introduce a geometric scaling relation that characterizes the local scale behavior of correlations using the informational distance $d_E = K_0/\sqrt{I}$, where $I$ is the mutual information. We define a geometric conversion factor, $G \equiv \partial_r d_E$, which quantifies the local scale. We show that $G$ relates directly to $I$ via $G \propto I^{\kapp

Rankeya Datta, Noah Olander

A field extension $L/K$ of characteristic $p > 0$ is formally \'etale if and only if the relative Frobenius of $L/K$ is an isomorphism. Inspired by this classical result, we explore whether the formally \'etale property for a map $R \to S$ of $\mathbf{F}_p$-algebras is characterized by isomorphism of the relative Frobenius $F_{S/R}$. While $F_{S/R}$ being an

Shanhui Liu, Rui Xu, Yunke Wang

Vision Mamba has emerged as a promising and efficient alternative to Vision Transformers, yet its efficiency remains fundamentally constrained by the number of input tokens. Existing token reduction approaches typically adopt token pruning or merging to reduce computation. However, they inherently lead to information loss as they discard or compress token re

Lo Cheikh, Vila Sergio

The aim of this paper is to characterize in terms of coding a set of limit points considered in a paper of F. Riquelme and A. Velozo corresponding to geodesic rays which spend less time in any compact region of a pair of pants with one cusp. Moreover in this particular context we reprove that the Hausdorff dimension of this set is equal 1/2 by explicit calcu

Boyd Franken, Hong-Hanh Nguyen-Le, Nhien-An Le-Khac

Digital forensics faces unprecedented challenges with the emergence of digital twins and metaverse technologies. This paper presents the first comparative analysis between blockchain-based and traditional database systems for managing digital twin evidence in forensic investigations. We conducted controlled experiments comparing the Ethereum blockchain with

Dominic Groß

This work develops a constraint-aware grid-forming (GFM) control that explicitly accounts for current limits and modulation limits within the GFM oscillator dynamics generating the GFM voltage reference (i.e., phase angle and magnitude). Broadly speaking, the voltage reference generated by the constraint-aware GFM control minimizes the deviation from convent

Milan Kroemer, Stefan Müller

We consider a geodesic $\gamma$ of length $2L$ in an oriented Riemannian manifold $(\mathcal M, g)$ and a thin tube $\Omega^*_h$ around $\gamma$ of radius $h$. We study an 'elastic' energy per unit volume $E_h(u)$ of maps $u$ from $\Omega^*_h$ into another oriented Riemannian manifold $(\tilde {\mathcal M},\tilde g)$. The energy $E_h$ is based on the squared

Ananya Krishna, Valentina Simon, Arjan Kohli

Machine learning technologies for protein function prediction are black box models. Despite their potential to identify key drug targets with high accuracy and accelerate therapy development, the adoption of these methods depends on verifying their findings. This study evaluates DeepFRI, a leading Graph Convolutional Network (GCN) based tool, using advanced

Razieh Ghaedi, AmirReza BabaAhmadi, Reyer Zwiggelaar, Xinqi Fan

Cross-domain facial expression recognition (CD-FER) remains difficult due to severe domain shift between training and deployment data. We propose Graph-Attention Network with Adversarial Domain Alignment (GAT-ADA), a hybrid framework that couples a ResNet-50 as backbone with a batch-level Graph Attention Network (GAT) to model inter-sample relations under sh

Cole Cappello, Andrew Hoegh

Tire degradation plays a critical role in Formula 1 race strategy, influencing both lap times and optimal pit-stop decisions. This paper introduces a Bayesian state-space modeling framework for estimating the latent degradation dynamics of Formula 1 tires using publicly available timing data from the FastF1 Python API. Lap times are modeled as a function of

Mahmoud El Hussieni

The increasing prevalence of thyroid cancer globally has led to the development of various computer-aided detection methods. Accurate segmentation of thyroid nodules is a critical first step in the development of AI-assisted clinical decision support systems. This study focuses on instance segmentation of thyroid nodules using YOLOv5 algorithms on ultrasound

Timur Sattarov, Marco Schreyer, Damian Borth

We introduce DP-FinDiff, a differentially private diffusion framework for synthesizing mixed-type tabular data. DP-FinDiff employs embedding-based representations for categorical features, reducing encoding overhead and scaling to high-dimensional datasets. To adapt DP-training to the diffusion process, we propose two privacy-aware training strategies: an ad

Amr Ahmadain, Ming Yang

We study the nonlinear sigma model (NLSM) worldsheet action describing the motion of closed bosonic strings in the target space of a two-dimensional (2D) flat cone in polar coordinates. We calculate the cylinder partition function. We first place the cylindrical worldsheet on a rectangular lattice before taking the continuum limit. We find an integer number

Arup Maity

We construct a class of Fourier multipliers whose associated operators are weak (1,1) bounded but fail to be weak (p, p) bounded for any 1 < p \leq \infty. Moreover, we show that this result is sharp.