October 2025 arXiv papers — page 108

Utsav Dhanuka, Soham Poddar, Saptarshi Ghosh

In an era where public health is increasingly influenced by information shared on social media, combatting vaccine skepticism and misinformation has become a critical societal goal. Misleading narratives around vaccination have spread widely, creating barriers to achieving high immunisation rates and undermining trust in health recommendations. While efforts

Andrei Piryatinski, Nishaant Jacobus, Sameer Dambal, Eric R. Bittner

The use of quantum light to probe exciton properties in semiconductor and molecular nanostructures typically occurs in the low-intensity regime. A substantial enhancement of exciton-photon coupling can be achieved with photonic cavities, where excitons hybridize with cavity modes to form polariton states. To provide a theoretical framework for interpreting e

Arunava Patra, C. F. Sagar Zephania, Sagar Chakraborty

In a mix of prejudiced and unprejudiced individuals engaged in strategic interactions, the individual intensity of prejudice is expected to have effect on overall level of societal prejudice. High level of prejudice should lead to discrimination that may manifest as unfairness and, perhaps, even spite. In this paper, we investigate this idea in the classical

Jugal Gajjar, Kamalasankari Subramaniakuppusamy

We introduce the MultiLang Code Parser Dataset (MLCPD), a large-scale, language-agnostic dataset unifying syntactic and structural representations of code across ten major programming languages. MLCPD contains over seven million parsed source files normalized under our proposed universal Abstract Syntax Tree (AST) schema, enabling consistent cross-language r

Fuqun Han, Stanley Osher, Wuchen Li

In this work, we propose a sparse transformer architecture that incorporates prior information about the underlying data distribution directly into the transformer structure of the neural network. The design of the model is motivated by a special optimal transport problem, namely the regularized Wasserstein proximal operator, which admits a closed-form solut

Haocheng Yu, Krishan K. Ahuja, Lakshmi N. Sankar, Spencer H. Bryngelson

High sound pressure levels (SPL) pose notable risks in loud environments, particularly due to noise-induced hearing loss. Ill-fitting earplugs often lead to sound leakage, a phenomenon this study seeks to investigate. To validate our methodology, we first obtained computational and experimental acoustic transmission data for stand-alone slit resonators and o

Bibhuti Bhusan Dutta, Liza Devi, Biplob Sarkar, Asish Jyoti Boruah

The Rossby Wave Instability (RWI) has become an important concept in understanding the hydrodynamics (HDs) of accretion discs (ADs), especially in systems around black holes (BHs) where magnetic effects are either weak or absent. This instability is triggered by extrema (or sharp gradients) in the vortensity profile of the disc. Once activated, it leads to n

Supriya Nagesh, Karina Covarrubias, Robert El-Kareh, Shiva Prasad Kasiviswanathan

Background: Aspiration, the inhalation of foreign material into the lungs, significantly impacts surgical patient morbidity and mortality. This study develops a machine learning (ML) model to predict postoperative aspiration, enabling timely preventative interventions. Methods: From the MIMIC-IV database of over 400,000 hospital admissions, we identified 826

Sayak Mukherjee, Himanshu Sharma, Wenceslao Shaw Cortez, Genevieve Starke

This research investigates designing a supervisory feedback controller for a hybrid power plant that coordinates the wind, solar, and battery energy storage plants to meet the desired power demands. We have explored an online feedback control design that does not require detailed knowledge about the models, known as feedback optimization. The control inputs

Amir Azarmehr, Soheil Behnezhad, Mohammad Roghani, Aviad Rubinstein

How many adjacency matrix queries (also known as pair queries) are required to estimate the size of a maximum matching in an $n$-vertex graph $G$? We study this fundamental question in this paper. On the upper bound side, an algorithm of Bhattacharya, Kiss, and Saranurak [FOCS'23] gives an estimate that is within $\epsilon n$ of the right bound with $n^{2-\O

Shule Hao, Junpeng Bao, Wenli Li

Recent research in time series forecasting has explored integrating multimodal features into models to improve accuracy. However, the accuracy of such methods is constrained by three key challenges: inadequate extraction of fine-grained temporal patterns, suboptimal integration of multimodal information, and limited adaptability to dynamic multi-scale featur

Zekeriya Ender Eğer, Waris Khan, Priyabrata Maharana, Kandula Eswara Sai Kumar

This article investigates the identification of magnetic spin distributions in ferromagnetic materials by minimizing the system's free energy. Magnetic lattices of varying sizes are constructed, and the free energy is computed using an Ising model that accounts for spin-to-spin neighbor interactions and the influence of an external magnetic field. The proble

Songyuan Lu, Jingwen Hui, Jake Weeks, David B. Berry

Current spinal pain management procedures, such as radiofrequency ablation (RFA) and epidural steroid injection (ESI), rely on fluoroscopy for needle placement which exposes patients and physicians to ionizing radiation. In this paper, we investigate a radiation-free surgical navigation system for spinal pain management procedures that combines magnetic reso

Timothy M. Chan, Hsien-Chih Chang, Jie Gao, Sándor Kisfaludi-Bak

We give the first truly subquadratic time algorithm, with $O^*(n^{2-1/18})$ running time, for computing the diameter of an $n$-vertex unit-disk graph, resolving a central open problem in the literature. Our result is obtained as an instance of a general framework, applicable to different graph families and distance problems. Surprisingly, our framework compl

Pakwan Chanprakhon, Michael F. Reid, Jon-Paul R. Wells

High-resolution absorption and laser spectroscopy are used to determine electronic energy levels for Ho$^{3+}$ ions in K$_2$YF$_5$ microparticles. A total of 72 crystal-field energy levels, distributed among 8 multiplets, are assigned. This optical data is used for crystal-field modelling of the electronic structure of Ho$^{3+}$ in K$_2$YF$_5$. Partially-res

Chenrui Tie, Shengxiang Sun, Yudi Lin, Yanbo Wang

Assembly hinges on reliably forming connections between parts; yet most robotic approaches plan assembly sequences and part poses while treating connectors as an afterthought. Connections represent the foundational physical constraints of assembly execution; while task planning sequences operations, the precise establishment of these constraints ultimately d

Yan Gong, Zhi-yu Zhang, Christian Henkel, C. -H. Rosie Chen

Oxygen isotope abundances and their ratios are fingerprints of stellar evolution and therefore provide a powerful tool in tracing the enrichment history of galaxies. However, their behavior in low-metallicity dwarf galaxies remains largely unexplored. The Small Magellanic Cloud (SMC), a nearby analog of young high-redshift galaxies, offers an ideal laborator

Tong Zhang, Ru Zhang, Jianyi Liu, Zhen Yang

Existing concept erasure methods for text-to-image diffusion models commonly rely on fixed anchor strategies, which often lead to critical issues such as concept re-emergence and erosion. To address this, we conduct causal tracing to reveal the inherent sensitivity of erasure to anchor selection and define Sibling Exclusive Concepts as a superior class of an

Xinshun Zhang, Shaomin Chen, Wei Dou, Haoyang Fu

Deep underground experiments present a new avenue to probe the first interactions in extensive air showers or hadronic interactions in the extreme forward phase space. The China Jinping Underground Laboratory, characterized by a vertical rock overburden of 2,400~m, provides an exceptionally effective shield against cosmic muons with energies below 3~TeV. The

Pratham Singla, Shivank Garg, Ayush Singh, Ishan Garg

Recent advances in post-training techniques have endowed Large Language Models (LLMs) with enhanced capabilities for tackling complex, logic-intensive tasks through the generation of supplementary planning tokens. This development raises a fundamental question: Are these models aware of what they "learn" and "think"? To address this, we define three core com

Michael Rozowski

A large population limit of the parabolic-parabolic Patlak-Keller-Segel (PKS) system with degenerate, nonlinear diffusion, e.g., of porous medium-type $-\frac{m}{m-1}\mathrm{div}(\rho \nabla \rho^{m-1})$, is studied. We show, asymptotically, a sharp interface develops separating a region containing organisms arranged in a constant-in-time, uniform density fr

Wan-Peng Sun, Yong-Kun Zhang, Ji-Guo Zhang, Xiaohui Liu

The repeating behavior of fast radio bursts (FRBs) is regarded as a key clue to understanding their physical origin, yet reliably distinguishing repeaters from apparent non-repeaters with current observations remains challenging. Here we propose a physically interpretable and practically quantifiable classification framework based on spectral morphology. Usi

Hairi Bai, Xinyan Fan, Kuangnan Fang, Yan Zhang

We propose STANE (Shared and Time-specific Adaptive Network Embedding), a new joint embedding framework for dynamic networks that captures both stable global structures and localized temporal variations. To further improve the model's adaptability to transient changes, we introduce Sparse STANE, which models time-specific changes as sparse perturbations, the

Pachara Sawettamalya, Huacheng Yu

In this note, we present a simple algorithm for computing a \emph{$k$-connectivity certificate} in dynamic graph streams. Our algorithm uses $O(n \log^2 n \cdot \max\{k, \log n \log k\})$ bits of space which improves upon the $O(kn \log^3 n)$-space algorithm of Ahn, Guha, and McGregor (SODA'12). For the values of $k$ that are truly sublinear, our space usage

Eden Shaveet, Crystal Su, Daniel Hsu, Luis Gravano

Foodborne illnesses are gastrointestinal conditions caused by consuming contaminated food. Restaurants are critical venues to investigate outbreaks because they share sourcing, preparation, and distribution of foods. Public reporting of illness via formal channels is limited, whereas social media platforms host abundant user-generated content that can provid

Junha Song, Sangdoo Yun, Dongyoon Han, Jaegul Choo

A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarkable capabilities. This has created a void in the understanding of the vision encoder, which determines how MLLMs perceive images. The recent shift in MLLM training paradigms, from

Haiyue Sun, Qingdong He, Jinlong Peng, Peng Tang

Autoregressive Model (AR) has shown remarkable success in conditional image generation. However, these approaches for multiple reference generation struggle with decoupling different reference identities. In this work, we propose the TokenAR framework, specifically focused on a simple but effective token-level enhancement mechanism to address reference ident

Fatemeh Jafarian Dehkordi, Elahe Vedadi, Alireza Feizbakhsh, Yasaman Keshtkarjahromi

Striking a balance between protecting data privacy and enabling collaborative computation is a critical challenge for distributed machine learning. While privacy-preserving techniques for federated learning have been extensively developed, methods for scenarios involving bitwise operations, such as tree-based vertical federated learning (VFL), are still unde

Daniel Paul-Pena, C. Seshadhri

Counting small patterns in a large dataset is a fundamental algorithmic task. The most common version of this task is subgraph/homomorphism counting, wherein we count the number of occurrences of a small pattern graph $H$ in an input graph $G$. The study of this problem is a field in and of itself. Recently, both in theory and practice, there has been an int

Hongyuan Liu, Xinyang Liu, Guosheng Hu

The rapid advancement of Artificial Intelligence (AI) has created unprecedented demands for computational power, yet methods for evaluating the performance, efficiency, and environmental impact of deployed models remain fragmented. Current approaches often fail to provide a holistic view, making it difficult to compare and optimise systems across heterogeneo

Eric Zhu

Given an abelian variety $A$ over a number field, we consider the generalized Kummer varieties of $A$ coming from quotients of $A$ by an automorphism of prime order $p > 2$. We prove that the Brauer-Manin obstruction on these generalized Kummer varieties only can come from the $p$-primary part of the Brauer group. This is applied to show that certain familie

Nicholas J. Weaver, Joshua I. Faskowitz, Richard F. Betzel, Christopher W. Lynn

In the human brain, the allowed patterns of activity are constrained by the correlations between brain regions. Yet it remains unclear which correlations -- and how many -- are needed to predict large-scale neural activity. Here, we present an information-theoretic framework to identify the most important correlations, which provide the most accurate predict

Yi Wei, Shunpu Tang, Liang Zhao, Qiangian Yang

Recent advances in diffusion models have driven remarkable progress in image generation. However, the generation process remains computationally intensive, and users often need to iteratively refine prompts to achieve the desired results, further increasing latency and placing a heavy burden on cloud resources. To address this challenge, we propose Diffusion

Arpan Das

Let $p$ be a prime, and $F$ a non-archimedean local field with residue characteristic $p$ and ring of integers $\mathcal{O}_{F}$. Set $G_{S}:={\rm SL}_{2}(F)$and $K_{0}:={\rm SL}_{2}(\mathcal{O}_{F})$ . For a smooth irreducible $\bar{\mathbb{F}}_{p}$-representation $\sigma$ of $K_{0}$, we study the structure of the compact induction ${\rm ind}_{K_{0}}^{G_{S}

Neelarnab Raha

Brill-Noether theory of curves has played a crucial role in the study of curves and their moduli since the 19th century, and has been extensively studied by several authors. Clifford's theorem provides a starting point in determining the emptiness of Brill-Noether loci by providing an upper bound on $h^0(L)$ for a line bundle $L$ on a smooth curve $C$ in ter

Mo Zhou, Haoyang Ma, Rong Ge

Deep learning has led researchers to rethink the relationship between memorization and generalization. In many settings, memorization does not hurt generalization due to implicit regularization and may help by memorizing long-tailed examples. In this paper, we consider the synergy between memorization and simple composition -- the ability to make correct pre

Junno Yun, Yaşar Utku Alçalar, Mehmet Akçakaya

Algorithm unrolling methods have proven powerful for solving the regularized least squares problem in computational magnetic resonance imaging (MRI). These approaches unfold an iterative algorithm with a fixed number of iterations, typically alternating between a neural network-based proximal operator for regularization, a data fidelity operation and auxilia

Wenhao Wang, Longqi Cai, Taihong Xiao, Yuxiao Wang

This paper presents a systematic study of scaling laws for the deepfake detection task. Specifically, we analyze the model performance against the number of real image domains, deepfake generation methods, and training images. Since no existing dataset meets the scale requirements for this research, we construct ScaleDF, the largest dataset to date in this f

Rui Yang, Huining Li, Yiyi Long, Xiaojun Wu

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within sel

Shaojiang Zhu, Xinyuan You, Alexander Romanenko, Anna Grassellino

Quantum thermometry plays a critical role in the development of low-temperature sensors and quantum information platforms. In this work, we propose and theoretically analyze a hybrid circuit quantum electrodynamics architecture in which a superconducting qubit is dispersively coupled to two distinct bosonic modes: one initialized in a weak coherent state and

Siqi Cao, Shu Yang

Federated learning of causal estimands offers a powerful strategy to improve estimation efficiency by leveraging data from multiple study sites while preserving privacy. Existing literature has primarily focused on the average treatment effect using single data source, whereas our work addresses a broader class of causal measures across multiple sources. We

Georgios Aravanis, Nicholas Silionis, Jacopo Bardiani, Marco Giglio

Structural health monitoring (SHM) strategies involve the processing of structural response data to indirectly assess an asset's condition. These strategies can be enhanced for a group of structures, especially when they are similar, since mutual underlying physics are expected to exist. The concept behind population-based SHM exploits the sharing of data am

Moshe Kamensky, Rahim Moosa

Given an algebraic difference equation of the form \[\sigma^n(y)=f\big(y, \sigma(y),\dots,\sigma^{n-1}(y)\big)\] where $f$ is a rational function over a field $k$ of characteristic zero on which $\sigma$ acts trivially, it is shown that if there is a nontrivial algebraic relation amongst any number of $\sigma$-disjoint solutions, along with their $\sigma$-tr

R. N. Chen, Y. N. Zhang, J. M. Yao, J. Engel

We examine the effects of symmetry restoration on nuclear beta decay within the axially deformed proton-neutron quasiparticle random phase approximation (QRPA). We employ the proton-neutron finite-amplitude method (pnFAM) to compute transition amplitudes, and perform angular-momentum projection both after variation and after the QRPA to restore rotational sy

En Xu, Yilin Bi, Hongwei Hu, Xin Chen

The study of complex systems has attracted widespread attention from researchers in the fields of natural sciences, social sciences, and engineering. Prediction is one of the central issues in this field. Although most related studies have focused on prediction methods, research on the predictability of complex systems has received increasing attention acros

Olajumoke O. Adekunle, Joseph D. Akinyemi, Khadijat T. Ladoja, Olufade F. W. Onifade

Lung cancer, a malignancy originating in lung tissues, is commonly diagnosed and classified using medical imaging techniques, particularly computed tomography (CT). Despite the integration of machine learning and deep learning methods, the predictive efficacy of automated systems for lung cancer classification from CT images remains below the desired thresho

Crystal Su

Large language models (LLMs) often produce fluent reasoning steps while violating simple mathematical or logical constraints. We introduce MedRule-KG, a compact typed knowledge graph coupled with a symbolic verifier, designed to enforce mathematically interpretable rules in reasoning tasks. MedRule-KG encodes entities, relations, and three domain-inspired ru

Chi Zhang, Xian Huang, Wei Dong

UAVs equipped with a single depth camera encounter significant challenges in dynamic obstacle avoidance due to limited field of view and inevitable blind spots. While active vision strategies that steer onboard cameras have been proposed to expand sensing coverage, most existing methods separate motion planning from sensing considerations, resulting in less

Paul Disberg, Anne Lankreijer, Martyna Chruślińska, Andrew J. Levan

Both theoretical models and observations of collapsar created gamma-ray bursts -- typically long-duration gamma-ray bursts (LGRBs) -- suggest that these transients cannot occur at high metallicity, likely due to angular momentum losses via stellar winds for potential progenitor stars. However, the precise metallicity threshold (if it is a hard threshold) abo

Xin Wang, Yu Wang, Yunchao Liu, Jens Meiler

Ligand-based virtual screening (VS) is an essential step in drug discovery that evaluates large chemical libraries to identify compounds that potentially bind to a therapeutic target. However, VS faces three major challenges: class imbalance due to the low active rate, structural imbalance among active molecules where certain scaffolds dominate, and the need

Chaojie Wang, Xutong Li, Xiuyi Ma, Yuning Zhang

Quantum interference can produce a pivotal effective photon-photon interaction, enabling the exploration of various quantum information technologies that beyond the possibilities of classical physics. While such an effective interaction is fundamentally limited to the bosonic nature of photons and the restricted phase responses from commonly used unitary opt

Qinyu Xu

The transport and localization of RNA molecules, crucial for cellular function and development, involve a combination of diffusion and active transport mechanisms. Here, we are motivated by understanding the dynamics of RNA in Xenopus laevis oocytes. Fluorescence Recovery After Photobleaching (FRAP) is an experimental technique that is widely used to investi

Katsunori Iwasaki

We construct a lot of K3 surface automorphisms of positive entropy having rotation domains of ranks 1 and 2. To carry out this construction, we first lay theoretical foundations concerning equivariant linearization of nonlinear maps under resolutions of quotient singularities, linear models near exceptional components, Salem numbers and multipliers at period

Amena Khatun, Muhammad Usman

Quantum machine learning (QML) has emerged as a promising area of research for enhancing the performance of classical machine learning systems by leveraging quantum computational principles. However, practical deployment of QML remains limited due to current hardware constraints such as limited number of qubits and quantum noise. This chapter introduces a hy

Mohammad Abdul Rehman, Syed Imad Ali Shah, Abbas Anwar, Noor Islam

The remarkable capabilities of Large Language Models (LLMs) in natural language understanding and generation have sparked interest in their potential for cybersecurity applications, including password guessing. In this study, we conduct an empirical investigation into the efficacy of pre-trained LLMs for password cracking using synthetic user profiles. Speci

Paul R. Anderson, Amanda Peake, Shohreh Gholizadeh Siahmazgi

Quantum effects are studied in both Schwarzschild spacetime and a spacetime in which a null shell collapses to form a black hole via the vacuum polarization $\langle \phi^2 \rangle$ and stress-energy tensor $\langle T_{ab} \rangle$ for a massless minimally-coupled scalar field in two dimensions. For Schwarzschild spacetime, the Boulware, Unruh, and Hartle-Ha

Mohammad Abdul Rehman, Syed Imad Ali Shah, Abbas Anwar, Noor Islam

Large Language Models (LLMs) can reason over natural-language inputs, but their role in intrusion detection without fine-tuning remains uncertain. This study evaluates a prompt-only approach on UNSW-NB15 by converting each network flow to a compact textual record and augmenting it with lightweight, domain-inspired boolean flags (asymmetry, burst rate, TTL ir

Zhiwei Zhao, Chixiang Chen, Shuo Chen

Evaluating the effects of time-varying exposures is essential for longitudinal studies. The effect estimation becomes increasingly challenging when dealing with hundreds of time-dependent confounders. We propose a Marginal Structure Ensemble Learning Model (MASE) to provide a marginal structure model (MSM)-based robust estimator under the longitudinal settin

Muhammad Hamza Ali, Amritanshu Pandey

The high penetration of distributed energy resources, resulting in backfeed of power at the transmission and distribution interface, is causing conventional underfrequency load shedding (UFLS) schemes to become nonconforming. Adaptive schemes that update UFLS relay settings recursively in time offer a solution, but existing adaptive techniques that obtain UF

Yuan Ai, Xidong Mu, Pengbo Si, Yuanwei Liu

This letter proposes a novel pinching antenna systems (PASS) enabled non-orthogonal multiple access (NOMA) multi-access edge computing (MEC) framework. An optimization problem is formulated to minimize the maximum task delay by optimizing offloading ratios, transmit powers, and pinching antenna (PA) positions, subject to constraints on maximum transmit power

Ryoto Miyamoto, Xin Fan, Fuyuko Kido, Tsuneo Matsumoto

OpenLVLM-MIA is a new benchmark that highlights fundamental challenges in evaluating membership inference attacks (MIA) against large vision-language models (LVLMs). While prior work has reported high attack success rates, our analysis suggests that these results often arise from detecting distributional bias introduced during dataset construction rather tha

Jiangsheng Hu, Wei Ren, Xiaoyan Yang, Hanyang You

Let $(\mathcal{X}, \mathcal{Y})$ be a balanced pair in an abelian category $\mathcal{A}$. Denote by ${\bf K}_{\mathcal{E}\text{-}{\rm ac}}(\mathcal{X})$ the chain homotopy category of right $\mathcal{X}$-acyclic complexes with all items in $\mathcal{X}$, and dually by ${\bf K}_{\mathcal{E}\text{-}{\rm ac}}(\mathcal{Y})$ the chain homotopy category of left $\

Minfeng Qi, Zhongmin Cao, Qin Wang, Ningran Li

Preprint repositories become central infrastructures for scholarly communication. Their expansion transforms how research is circulated and evaluated before journal publication. Generative large language models (LLMs) introduce a further potential disruption by altering how manuscripts are written. While speculation abounds, systematic evidence of whether an

Saejin Oh, Xinyi Fang, I-Hsin Lin, Paris Dee

The development of automated experimental facilities and the digitization of experimental data have introduced numerous opportunities to radically advance chemical laboratories. As many laboratory tasks involve predicting and understanding previously unknown chemical relationships, machine learning (ML) approaches trained on experimental data can substantial

Yutong Wang, Haiyu Wang, Sai Qian Zhang

Vision-Language Models (VLMs) are integral to tasks such as image captioning and visual question answering, but their high computational cost, driven by large memory footprints and processing time, limits their scalability and real-time applicability. In this work, we propose leveraging Singular-Value Decomposition (SVD) over the joint query (Q), key (K), an

Nobuhiro Okabe, Yuki Omiya, Kazuhiro Nakazawa, Naomi Ota

We report a weak-lensing (WL) mass measurement for the merging cluster Abell 754 and impose constraints on the merger trajectory. The trajectory analysis adopts a two-body model with a point-mass approximation and dynamical friction, refined using numerical simulations of major mergers and characterized by Euler angles. We first conduct WL analysis using the

Yue Zheng, Xiufang Shi, Jiming Chen, Yuanchao Shu

Video anomaly detection (VAD) has rapidly advanced by recent development of Vision-Language Models (VLMs). While these models offer superior zero-shot detection capabilities, their immense computational cost and unstable visual grounding performance hinder real-time deployment. To overcome these challenges, we introduce Cerberus, a two-stage cascaded system

Yoonho Lee, Junseok Lee, Sangwoo Seo, Sungwon Kim

Despite the promising results of disentangled representation learning in discovering latent patterns in graph-structured data, few studies have explored disentanglement for hypergraph-structured data. Integrating hyperedge disentanglement into hypergraph neural networks enables models to leverage hidden hyperedge semantics, such as unannotated relations betw

Jianchao Xue, Ping Zhang, Jean-Claude Vial, Li Feng

In this paper we focus on the analysis of the multiwavelength spectroscopic observations of a quiescent prominence. The spectral and geometrical parameters in the prominence were derived and used to constrain the NLTE radiative transfer models. Applying this method with multiwavelength observations provides a good opportunity to reduce the large range of the

Han-Jie Xu, Shi-Wei Qu

In this article, a compact wideband circularly polarized antenna based on a miniaturized phase shifter with ultra-wideband operation is proposed. The proposed antenna is comprised of a pair of compact orthogonal ultra-wideband Vivaldi antennas and a miniaturized phase shifter. To achieve wideband impedance matching and miniaturization, parasitic radiation st

Madi Yerlanov, Nancy Rodriguez

This paper introduces a reaction-advection-diffusion system that models interactions among three actors: a target, a partaker, and a guardian. The framework is versatile, capturing phenomena ranging from the emergence and movement of crime hotspots in urban areas to shifts in public attitudes during critical events as individuals and control units move throu

Kay T. Xia, Thayer L. Anderson, Phelan Yu

The American scientific community is reeling from funding cuts and policy directives that will debilitate scientific research and education. The underlying hostilities fueling these attacks have intensified in recent years as the COVID-19 pandemic increased suspicion of scientific experts and the institutional embrace of diversity, equity, and inclusion (DEI

Ansh Aggarwal

We analyze algorithms for computing the $n$th prime $p_n$ and establish asymptotic bounds for several approaches. Using existing results on the complexity of evaluating the prime-counting function $\pi(x)$, we show that the binary search approach computes $p_n$ in $O(\sqrt{n} \, (\log n)^4)$ time. Assuming the Riemann Hypothesis and Cram\'er's conjecture, we

Di Zhang

Bootstrapping is a powerful statistical resampling technique for estimating the sampling distribution of an estimator. However, its computational cost becomes prohibitive for large datasets or a high number of resamples. This paper presents a theoretical analysis and design of parallel bootstrapping algorithms using the Message Passing Interface (MPI). We ad

Gavin Stewart, Avy Soffer

We study the asymptotics of the Schr\"odinger equation with time-dependent potential in dimension one. Assuming that the potential decays sufficiently rapidly as $|x| \to \infty$, we prove that the solution can be written as the sum of a free wave $e^{-it\Delta} u_+$ and a weakly bound component $u_{\text{wb}}(t)$. Moreover, we show that the weakly bound par

Yilin Wu, Anqi Li, Tucker Hermans, Fabio Ramos

Reasoning Vision Language Action (VLA) models improve robotic instruction-following by generating step-by-step textual plans before low-level actions, an approach inspired by Chain-of-Thought (CoT) reasoning in language models. Yet even with a correct textual plan, the generated actions can still miss the intended outcomes in the plan, especially in out-of-d

Yuxuan Yang

We use the extended Mukai vectors for hyper-K\"ahler manifolds to investigate the derived equivalences of the hyper-K\"ahler manifolds which are deformation equivalent to generalized Kummer varieties. Inspired by the idea for hyper-K\"ahler manifolds of $\mathrm{K3}^{[n]}$-type, we obtain an integral lattice which is proved to be invariant under the derived

Ernesto Vallejo, Pedro David Sánchez Salazar

We present a way of computing Kronecker coefficients that uses a new family of rational convex polytopes, called column-row polytopes. We give several different formulas for the computation. They are alternating sums of numbers of integer points of either column-row polytopes or faces of column-row polytopes. We also compute the maximal dimension of these po

Jason Fulman, Dennis Stanton

We observe that Anzanello's work on the proportion of derangements in affine classical groups over finite fields is related to symplectic and orthogonal Cohen-Lenstra type distributions on integer partitions. This leads to a proof of three q-polynomial identities conjectured by Anzanello, which were crucial for her work.

Song Bian, Minghao Yan, Anand Jayarajan, Gennady Pekhimenko

Large Language Models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated strong reasoning capabilities. To further enhance LLM capabilities, recent agentic systems, such as Deep Research, incorporate web interactions into LLM reasoning to mitigate uncertainties and reduce potential errors. However, existing research predominantly focuses on reasoni

T. M. Sitnova, L. I. Mashonkina, A. M. Romanovskaya, R. E. Giribaldi

We present a spectroscopic analysis of ten carbon enhanced metal-poor (CEMP) stars of type CEMP-s and CEMP-rs and determine their NLTE abundances of Ba and Eu, as well as the fractions of the odd Ba isotopes (F_odd). The Ba abundances inferred from the resonance Ba II 4554 and 4934 A lines depend on the adopted Ba isotope mixture. We perform calculations for

Harold Jones, Vineeth Krishna, Finn Larsen

We construct the phase diagram of supersymmetric ground states in AdS$_4\times S^7$ supergravity. BPS black holes exist only when the conserved charges satisfy a certain non-linear constraint. For other charge sectors, we propose two component configurations comprised of a core black hole that carries macroscopic entropy, and a gas that carries a macroscopic

Jingyue Huang, Zachary Novack, Phillip Long, Yupeng Hou

Discrete representation learning has shown promising results across various domains, including generation and understanding in image, speech and language. Inspired by these advances, we propose MuseTok, a tokenization method for symbolic music, and investigate its effectiveness in both music generation and understanding tasks. MuseTok employs the residual ve

Yuntian Wang, Xilin Yang, Che-Yung Shen, Nir Pillar

We introduce Universal and Transferable Adversarial Perturbations (UTAP) for pathology foundation models that reveal critical vulnerabilities in their capabilities. Optimized using deep learning, UTAP comprises a fixed and weak noise pattern that, when added to a pathology image, systematically disrupts the feature representation capabilities of multiple pat

Yubo Zhang, Da Ke, Rohan Maniar, Timo Lebeda

In density functional theory, the SCAN (Strongly Constrained and Appropriately Normed) and r2SCAN functionals significantly improve over generalized gradient approximation functionals such as PBE (Perdew-Burke-Ernzerhof) in predicting electronic, magnetic, and structural properties across various materials, including transition-metal compounds. However, ther

Hui Yang, Faisal Aqlan, Richard Zhao

The rapid evolution of modern manufacturing systems is driven by the integration of emerging metaverse technologies such as artificial intelligence (AI), digital twin (DT) with different forms of extended reality (XR) like virtual reality (VR), augmented reality (AR), and mixed reality (MR). These advances confront manufacturing workers with complex and evol

Vincenzo Fiorentini, Paola Alippi, Gianaurelio Cuniberti

Ab initio density-functional calculations show that orthorhombic Pca21 hafnia HfO2 mixed with vanadium at low concentration is a ferroelectric and ferromagnetic insulator. The multiorbital degeneracy of singly-occupied V states in the nominally 4+ ionic state is broken by magnetism, reduced symmetry, and local distortion, causing a single one-electron majori

Baicheng Li, Zike Yan, Dong Wu, Hongbin Zha

Human behaviors are the major causes of scene dynamics and inherently contain rich cues regarding the dynamics. This paper formalizes a new task of proactive scene decomposition and reconstruction, an online approach that leverages human-object interactions to iteratively disassemble and reconstruct the environment. By observing these intentional interaction

Tingting Zhu, Xiongtao Zhang

We study the emergent behavior of a second-order Kuramoto-type model with frustration effect on a strongly connected digraph. The main challenge arises from the lack of symmetry in this system, which renders standard approaches for symmetric models, such as the gradient-flow method and classical $\ell^p$ or $\ell^\infty$-type energy estimates, ineffective. T

Valentin Ovsienko

We describe the relationships between the notion of $q$-deformed rational numbers, introduced in our previous work with Sophie Morier-Genoud, and the theory of dimer models. We show that $q$-deformed rationals can be calculated in terms of perfect matchings of certain bipartite graphs, known as snake graphs, or ribbon tiles, etc. equipped with a certain weig

Murat Yessenov, Luca Sacchi, Alfonso Palmieri, Layton A. Hall

We report the first direct observation of the spatially structured Montgomery effect, a lensless self-imaging phenomenon that generalizes the Talbot effect to aperiodic structures, unfolding repeated tightly focused spots (~10 $\mu$m) in free space. Using a dynamic optical hologram to discretize radial spatial frequencies, we demonstrate self-imaging at dist

Babu G. V. R., Ratna Babu D, Alemayehu Negash

We prove the existence of common fixed points for three selfmaps $T,f$ and $g$ defined on a metric space $(X,d)$ satisfying, $T$ is $(f,g)$-weakly contractive; and the pairs $(T,f)$ and $(T,g)$ are weakly compatible. Also, for such $T,f$ and $g$, we prove the convergence of modified Mann iteration and modified Ishikawa iteration with respect to $T,f$ and $g$

Paulo R. A. Campos, Sandro M. Reia, José F. Fontanari

The standard Axelrod model of cultural dissemination, based on discrete cultural traits, exhibits a non-equilibrium phase transition but is inherently limited by its inability to continuously probe the critical behavior. We address this limitation by introducing a generalized Axelrod model utilizing continuous cultural traits confined to the interval $[0,1]$

Karim Barigou, Melanie Patten, Kenneth Q. Zhou

Climate change poses increasing challenges for mortality modeling and underscores the need to integrate climate-related variables into mortality forecasting. This study introduces a two-step approach that incorporates climate information from the Actuaries Climate Index (ACI) into mortality models. In the first step, we model region-specific seasonal mortali

T. Rashidi, V. Anari, O. Powles, G. C. MacLeod

We analyze a decade of 6.7 GHz methanol monitoring data in G9.62+0.20E, confirming the known periodicities of p1 = 241.3 +/- 2.3 d and p2 = 52.5 +/- 0.3 d, and identifying three new cycles at p3 = 127.0 +/- 1.6 d, p4 = 163.9 +/- 2.9 d, and p5 = 204.1 +/- 1.5 d. The 241.3-d and 204.1-d periods occur in multiple velocity channels, while the others are confined

Adel Ali, Alexey Belyanin

We study fermionic and bosonic systems coupled to a real or synthetic static gauge field that is quantized, so the field itself is a quantum degree of freedom and can exist in coherent superposition. A natural example is electrons on a quantum ring encircling a quantized magnetic flux (QMF) generated by a superconducting current. We show that coupling to a c

Jierui Peng, Yanyan Zhang, Yicheng Duan, Tuo Liang

The evaluation of Vision-Language-Action (VLA) agents is hindered by the coarse, end-task success metric that fails to provide precise skill diagnosis or measure robustness to real-world perturbations. This challenge is exacerbated by a fragmented data landscape that impedes reproducible research and the development of generalist models. To address these lim

Jose Guajardo, Ali Niknejad

This article presents an overview and analysis of spatial-to-spectral harmonic-modulated arrays (SHAs). Compared to traditional analog or digital beamforming arrays, SHAs enable concurrent multi-beamforming without requiring substantial hardware replication. SHAs replace the need for hardware replication with frequency-domain multiplexing. Furthermore, SHAs

Olivier Vincent, Patrick Dufour, Pierre Bergeron

White dwarf spectroscopic characterization is entering a big data era, with the number of spectroscopically characterized white dwarfs expected to grow from $\sim$100,000 to over 300,000 in upcoming years. Traditional methods like least-squares fitting and Markov Chain Monte Carlo have become computationally prohibitive for large-scale analysis, requiring mi

Faizuddin Ahmed, Edilberto O. Silva

In this paper, we examine the geodesic and thermodynamic properties of a Schwarzschild black hole with a cloud of strings (known as the Letelier black hole) immersed in a King dark matter (KDM) halo under an isotropic configuration. The dynamics of both photons and massive particles are analyzed in detail using the effective potential formalism, including pa