March 2026 arXiv papers — page 81

Pierre-Henri Chavanis

We discuss the connection between logotropes and polytropes in astrophysics and cosmology. The logotropic equation of state $P=A\ln(\rho/\rho_P)$ may be seen as a degenerate form of the polytropic equation of state $P=K\rho^{\gamma}$ in the limit $\gamma\rightarrow 0$, $K\rightarrow\infty$ with $A=K\gamma$ fixed. The logotropic distribution function correspo

Vinay Sharma, Manish Jain

Large Language Models (LLMs) often exhibit strong linguistic abilities while remaining unreliable on multi-step reasoning tasks, particularly when deployed without additional training or fine-tuning. In this work, we study inference-time techniques to improve the reasoning accuracy of LLMs. We systematically evaluate three classes of inference-time strategie

Aakash Ravindra Shinde, Arianne Meijer - van de Griend, Jukka K. Nurminen

Variational Quantum Algorithms (VQAs) have been extensively researched for applications in Quantum Machine Learning (QML), Optimization, and Molecular simulations. Although designed for Noisy Intermediate-Scale Quantum (NISQ) devices, VQAs are predominantly evaluated classically due to uncertain results on noisy devices and limited resource availability. Rai

Bin Hu, Zipeng Qi, Guoxi Huang, Zunnan Xu

Single-view reference-to-video methods often struggle to preserve identity consistency under large facial-angle variations. This limitation naturally motivates the incorporation of multi-view facial references. However, simply introducing additional reference images exacerbates the \textit{copy-paste} problem, particularly the \textbf{\textit{view-dependent

Runze Sun, Yu Zheng, Zexuan Xiong, Zhongjin Qu

Combating hate speech on social media is critical for securing cyberspace, yet relies heavily on the efficacy of automated detection systems. As content formats evolve, hate speech is transitioning from solely plain text to complex multimodal expressions, making implicit attacks harder to spot. Current systems, however, often falter on these subtle cases, as

Clement Boateng Ampadu

Olatinwo [3] introduced contractive definitions of the derivative type, and gave a new characterization of the Banach contraction principle, and fixed point theorems for contractions defined implicitly. On the other hand Ampadu et.al [4] introduced derivative type contractions in the setting of multiplicative metric spaces. In this paper, we have obtained a

Egor Kovlakov, Rene Gerritsma

We demonstrate precision laser spectroscopy of a trapped $^{173}$Yb$^+$ ion that is not directly laser cooled by coupling it to ultracold atoms. The atomic bath continuously cools the internal degrees of freedom of the ion to its hyperfine ground state via spin-exchange collisions. Successful laser excitation is detected via state-selective charge transfer a

Trung V. Phan, Thomas Bauschert

Advanced Persistent Threats (APTs) are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning (DRL) enables autonomous cyber defense, its decisions are often opaque and difficult to trust in operational environments. This paper presents DeepXplain, an explainable DRL framework for stage-aware APT defense. Bu

Jiazhong Cen, Jiemin Fang, Sikuang Li, Guanjun Wu

High-quality 3D assets are essential for VR/AR, industrial design, and entertainment, motivating growing interest in generative models that create 3D content from user prompts. Most existing 3D generators, however, rely on a single conditioning modality: image-conditioned models achieve high visual fidelity by exploiting pixel-aligned cues but suffer from vi

Guilherme D. da Fonseca, Fabien Feschet, Yan Gerard

We describe the methods used by Team Shadoks to win the CG:SHOP 2026 Challenge on parallel reconfiguration of planar triangulations. An instance is a collection of triangulations of a common point set. We must select a center triangulation and find short parallel-flip paths from each input triangulation to the center, minimizing the sum of path lengths. Our

Dao Nguyen Van Anh, Steven Senger, Dung The Tran, Le Anh Vinh

We study the parabolic variant of the Erd\H os--Falconer distance problem in finite fields. That is, if $q$ is odd, we seek size thresholds beyond which any subset $E\subset \mathbb F_q^2$ will determine many distinct parabolic distances. This problem has a rich history because the parabolic distance functional shares many properties with the standard distan

Brianna Binder, Agnimitra Dasgupta, Assad Oberai

We propose closed-form conditional diffusion models for data assimilation. Diffusion models use data to learn the score function (defined as the gradient of the log-probability density of a data distribution), allowing them to generate new samples from the data distribution by reversing a noise injection process. While it is common to train neural networks t

Huan-Qiang Zhou, Qian-Qian Shi

Non-invertible Kramers-Wannier (KW) duality symmetries are constructed for the transverse-field Ising model (TFIM) at the self-dual point under various boundary conditions (BCs), as long as the resultant Hamiltonian commutes with the ${\rm Z}_2$ symmetry operator. This is achieved by introducing extra degrees of freedom into the Hilbert space, in order to tu

Zhengxian Wu, Kai Shi, Chuanrui Zhang, Zirui Liao

Recent progress in multimodal large language models has led to strong performance on reasoning tasks, but these improvements largely rely on high-quality annotated data or teacher-model distillation, both of which are costly and difficult to scale. To address this, we propose an unsupervised self-evolution training framework for multimodal reasoning that ach

Yuntian Bo, Yazhou Zhu, Piotr Koniusz, Haofeng Zhang

Conventional few-shot medical image segmentation (FSMIS) approaches face performance bottlenecks that hinder broader clinical applicability. Although the Segment Anything Model (SAM) exhibits strong category-agnostic segmentation capabilities, its direct application to medical images often leads to over-segmentation due to ambiguous anatomical boundaries. In

Shiwei Chen, Niruthikka Sritharan, Xiaolin Wen, Chenxi Zhang

Current Large Language Models (LLMs), especially Large Reasoning Models, can generate Chain-of-Thought (CoT) reasoning traces to illustrate how they produce final outputs, thereby facilitating trust calibration for users. However, these CoT reasoning traces are usually lengthy and tedious, and can contain various issues, such as logical and factual errors, w

Minjong Cheon

Weather forecasting is a fundamental problem for protecting lives and infrastructure from high-impact atmospheric events. Recently, data-driven weather forecasting methods based on deep learning have demonstrated strong performance, often reaching accuracy levels competitive with operational numerical systems. However, many existing models rely on large-scal

Soudeep Ghoshal, Sandipan Chakraborty, Pradipto Chowdhury, Himanshu Buckchash

Machine learning techniques, such as Transformers and Long Short-Term Memory (LSTM) networks, play a crucial role in Symbolic Music Generation (SMG). Existing literature indicates a difference between LSTMs and Transformers regarding their ability to model local melodic continuity versus maintaining global structural coherence. However, their specific proper

Fei Wang, Guoying Liang, Zecheng Zhao, Bao-Ming Xu

The description of states and dynamics in non-Hermitian systems is fundamentally linked to the choice of an appropriate theoretical framework -- a point of ongoing debate in the field. This work addresses this issue by proposing a consistent formulation that reconciles existing controversies and establishes a unified theoretical understanding. Our approach r

Zongjie Li, Chaozheng Wang, Yuchong Xie, Pingchuan Ma

Large Language Models are increasingly being considered for deployment in safety-critical military applications. However, current benchmarks suffer from structural blindspots that systematically overestimate model capabilities in real-world tactical scenarios. Existing frameworks typically ignore strict legal constraints based on International Humanitarian L

Hangci Du, Yougang Wang, Junqiang Ge

The IllustrisTNG simulation suite, particularly TNG50, was reported to have generated a notable population of elongated, bar-like structures within galaxies classified as Early-Type Galaxies (ETGs). In this work, we revisit the nature of these structures at $z=0$ using a morphology-agnostic census. We find that these features are ubiquitous ($f_{\rm bar} \si

Hang-Zheng Shen, Xian-Hao Wei, Xi-Wang Luo, Zheng-Wei Zhou

Topological lasers and random lasers embody two contrasting strategies for disorder management in photonics: the former suppresses disorder via protected edge transport, while the latter exploits multiple scattering for feedback. Here, we theoretically demonstrate that these seemingly incompatible paradigms can be unified through a topological Anderson rando

Pranav Hemanth, Sampriti Saha

Large language models (LLMs) are increasingly deployed for extended, multi-topic conversations, yet the flat, append-only structure of current conversation interfaces introduces a fundamental limitation: all context accumulates in a single unbounded window, causing topically distinct threads to bleed into one another and progressively degrade response qualit

A. Carvalho, A. Krone-Martins, A. Da Silva, J. P. Mimoso

We introduce a novel set of analytic approximations for five fundamental functions in spherically symmetric, inhomogeneous Lemaitre-Tolman-Bondi (LTB) cosmologies, derived via Symbolic Regression (SR). Focusing on the constrained Garcia-Bellido-Haugboelle (GBH) parameterization, we sample the four-dimensional LTB parameter space using the bubble LTB numerica

Zihan Fang, Qianru Wang, Haonan An, Zheng Lin

Large language models (LLMs) increasingly adopt Mixture-of-Experts (MoE) architectures to scale model capacity while reducing computation. Fine-tuning these MoE-based LLMs often requires access to distributed and privacy-sensitive data, making centralized fine-tuning impractical. Federated learning (FL) therefore provides a paradigm to collaboratively fine-t

Pierre Fayet

We define the hyperbolic form factor of a density distribution as its bilateral Laplace transform, related by duality or analytic continuation to its form factor. For a sphere it is given by $\Phi(x = kR) =\langle \cosh \vec k.\vec r\rangle=\langle\sinh kr /kr\rangle $, expanded as $\sum \frac{x^{2n}}{(2n+1)!} \frac{\langle r^{2n}\rangle}{R^{2n}} $, and simi

Zewen Jiang, Wei Wang, Shumei Jia, Yaqing Shi

Hot Jupiters (HJs), especially the Ultra-Hot Jupiters (UHJs), are ideal targets for robust atmospheric characterization, thanks to their high equilibrium temperatures and large atmospheric scale heights, which result from their proximity to their host stars and intense stellar irradiation. Here, we present atmospheric studies of five planets, namely WASP-50b

Zachary F. Mainen

Externalized reasoning is already exploited by transformer-based agents through chain-of-thought, but structured retrieval -- indexing over one's own reasoning state -- remains underexplored. We formalize the transformer context window as an I/O page and prove that tool-augmented agents with indexed external memory achieve exponentially lower retrieval cost

Ze Tao, Hongfu Zhou, Hanbing Liang, Fujun Liu

Coupled partial differential equations underpin a wide range of multiphysics systems, yet existing neural PDE solvers still struggle to resolve localized high-risk regions and often fail to preserve structural admissibility across coupled fields. To address these limitations, we propose the Partition-of-unity Information-geometric Certified Solver (PICS), a

Lina Alkarmi, Armin Sarabi, Mingyan Liu

While the size of a data breach is typically measured by the number of (consumer, customer, or user) records exposed or compromised, its economic impact is generally measured from the point of view of the corporation suffering the data breach: cost in crisis management, legal fees, drop in stock price, and so on. This study examines whether it is possible to

Xiangchen Liu, Hanghan Zheng, Jeil Jeong, Minsung Yoon

Vision-language Navigation (VLN) requires an agent to understand visual observations and language instructions to navigate in unseen environments. Most existing approaches rely on static scene assumptions and struggle to generalize in dynamic, real-world scenarios. To address this challenge, we propose DyGeoVLN, a dynamic geometry-aware VLN framework. Our me

Chayanin Chamachot

We evaluate whether factor-wise auxiliary dynamics supervision produces useful latent structure or improved robustness in simulated humanoid locomotion. DynaMITE -- a transformer encoder with a factored 24-d latent trained by per-factor auxiliary losses during proximal policy optimization (PPO) -- is compared against Long Short-Term Memory (LSTM), plain Tran

Bader Ammari

We consider regularized Brascamp-Lieb inequalities using the theory of optimal transportation, more precisely an anisotropic version of Caffarelli's contraction theorem. Furthermore, we provide a full picture concerning the issues of finiteness of the Brascamp-Lieb constant and of the existence of Gaussian extremizers. We also find all optimizers for these r

Prasannaa Kumar D., Gulshan Kumar, Jay Dhariwal, Seshan Srirangarajan

The rising demand for low-cost air quality monitors stems from increased public awareness and interest within the research community. These monitors play a pivotal role in empowering citizens and scientists to comprehend spatiotemporal variations in air quality parameters, aiding in the formulation of effective mitigation policies. The primary challenge lies

Navid Ardakanian

We investigate whether the type-I seesaw mechanism can rescue the $Z_3$ Froggatt--Nielsen framework for neutrinos and find that it cannot. With right-handed Majorana masses carrying the $Z_3$ charge structure dictated by the Majorana bilinear -- where suppression powers follow $(q_i+q_j)\bmod 3$ -- the mass matrix contains an unsuppressed off-diagonal entry

Yiheng Xiong, Ting Su, Jingling Sun, Jue Wang

Property-based testing (PBT) is a popular software testing methodology and is effective in validating the functionality of mobile applications (apps for short). However, its adoption in practice remains limited, largely due to the manual effort and technical expertise required to specify executable properties. In this experience paper, we propose a novel str

Luca Maranzana, Koki Shinada, Ying-Ming Xie, Sergey Artyukhin

The geometry of electronic quantum states plays an important role in the equilibrium and transport properties of solids. While the Berry curvature is known to influence electron motion, recent work has shown that the quantum metric also affects the motion of electron wave packets beyond the adiabatic approximation. To connect this nonadiabatic dynamics to ma

Herbert Batte, Prosper Kaggwa

Let $(F_n)_{n\ge0}$ and $(L_n)_{n\ge0}$ denote the sequences of Fibonacci and Lucas numbers respectively. This paper determines all Lucas numbers that can be represented as base $b$ mixed concatenations of a Fibonacci number and a Lucas number. Mathematically, we study of two Diophantine equations $L_n=b^dL_m+F_k$ and $L_n=b^dF_m+L_k$, where $d$ is the numbe

Thiago Campos

While the quasi-local thermodynamics of spherically symmetric black holes is well described by pressure and volume, extending this framework to rotating spacetimes poses a significant challenge. Rotation induces an oblate deformation of the horizon, breaking the direct functional dependence between geometric volume and area. In this work, we resolve this dif

Wen Zhanjie, Guo Jingqiao

As artificial intelligence and generative large language models drive industrial upgrading, capital markets increasingly focus on AI-themed listed firms. Information asymmetry and technological opacity lower the cost of exaggerating AI capabilities relative to genuine R&D, spurring widespread AI Washing. Using China's A-share market from 2018Q1 to 2025Q2, we

Weiye Wang, Chen Chen, Junxue Zhang, Zhusheng Wang

Distributed prefix caching has become a core technique for efficient LLM serving. However, for long-context requests with high cache hit ratios, retrieving reusable KVCache blocks from remote servers has emerged as a new performance bottleneck. Such network-intensive LLM inference is expected to become increasingly common as agentic AI workloads continue to

Andrew Kiruluta

Large language models deliver strong generative performance but at the cost of massive parameter counts, memory use, and decoding latency. Prior work has shown that pruning and structured sparsity can preserve accuracy under substantial compression, while prompt-compression methods reduce latency by removing redundant input tokens. However, these two directi

Zikang Dong, Haidong Li

In this article, we apply the resonance method to derive conditional Omega results for logarithmic derivatives of quadratic Dirichlet $L$-functions. We improve a previous result of Mortada and Murty \cite{MM13}, as well as generalize some results of Yang \cite{yang2023omegatheoremslogarithmicderivatives}.

Cole J. Errico, Alberto Padovan, Daniel J. Bodony

Non-intrusive reduced-order modeling techniques are necessary for systems that are simulated using black-box solvers or known only from data. For systems exhibiting large transients and operating far away from equilibria, current non-intrusive models often exhibit poor forecasting accuracy and can even be unstable in infinite or finite time. Recent developme

Rajsekhar Bhattacharyya, Tony J. Puthenpurakal, Sudeshna Roy, Jyoti Singh

Let $C$ be a commutative Noetherian ring containing a field $K$ of characteristic zero. Let $R=C[X_1, \ldots, X_n, Y_1, \ldots, Y_m]$ be a polynomial ring over $C$ with $\mathrm{bideg}~ c=(0,0)$ for all $c \in C$, $\mathrm{bideg}~ X_i=(1,0)$ and $\mathrm{bideg}~ Y_j=(0,1)$ for $i=1, \ldots, n$ and $j=1, \ldots, m$. Let $I$ be a bihomogeneous ideal in $R$. In

Samson Owusu-Ensaw, Benoit F. Sehba, Ransford T. Tweneboanah

The Hardy operator is not bounded on the space of integrable functions on the positive half-line and its discrete counterpart on summable sequences. we introduce a modified Hardy operator obtained by subtracting a natural corrective term, and characterize the largest subspace of integrable functions on which this modified operator maps into integrable functi

Jingwen Tong, Fang Liu, Linkai Xv, Shiliang Lu

LLM agents are emerging as a key enabler for autonomous wireless network management. Reliably deploying them, however, demands benchmarks that reflect real engineering risk. Existing wireless benchmarks evaluate single isolated capabilities and treat all errors uniformly, missing both cascaded-chain failures and catastrophic unit confusions (\textit{e.g.}, d

Miaoxin Liu, Alvin J. K. Chua

Efficiently sampling from high-dimensional, multi-modal posteriors is a central challenge in Bayesian inference for astrophysics, especially gravitational-wave astronomy. Popular families of methods like Markov-chain Monte Carlo, nested sampling, and importance sampling all rely on proposal distributions to guide exploration. Because prior knowledge of the t

Alberlucia Rafael Soarez, Daniel Kim, Mariana Costa, Alejandro Torre

Knowledge distillation has emerged as a powerful technique for compressing large language models (LLMs) into efficient, deployable architectures while preserving their advanced capabilities. Recent advances in low-rank knowledge distillation, particularly methods like Low-Rank Clone (LRC), have demonstrated remarkable empirical success, achieving comparable

Kaung Myat Kyaw, Khush Agarwal, Jonathan Chan

Combining multiple knowledge graphs (KGs) across linguistic boundaries is a persistent challenge due to semantic heterogeneity and the complexity of graph environments. We propose a framework for cross-lingual graph fusion, leveraging the in-context reasoning and multilingual semantic priors of Large Language Models (LLMs). The framework implements structura

Sing-Yuan Yeh, Yi-An Wu, Hau-Tieng Wu, Mao-Pei Tsui

We propose a landmark-constrained algorithm, LA-VDM (Landmark Accelerated Vector Diffusion Maps), to accelerate the Vector Diffusion Maps (VDM) framework built upon the Graph Connection Laplacian (GCL), which captures pairwise connection relationships within complex datasets. LA-VDM introduces a novel two-stage normalization that effectively address nonunifo

Yuansheng Bu, Ziyin Song, Zhong Fang, Quansheng Wu

We investigate the evolution of Anomalous Hall Conductivity (AHC) in a coplanar and collinear antiferromagnetic system with varying spin canting angles. A tight-binding model based on three t2g-orbitals in a body-centered tetragonal lattice is constructed, where the inclusion of third-nearest neighbor hopping is demonstrated to be essential for capturing the

Mohammed El Amine Lazouni, Leila Ryma Lazouni, Zineb Aziza Elaouaber, Mohammed Ammar

In this paper, we present CornOrb, a publicly accessible multimodal dataset of Orbscan corneal topography images and clinical annotations collected from patients in Algeria. The dataset comprises 1,454 eyes from 744 patients, including 889 normal eyes and 565 keratoconus cases. For each eye, four corneal maps are provided (axial curvature, anterior elevation

M. Cherifi, Aude Sportisse, Xujia Zhu, Mohammed Nabil El Korso

Missing covariate data pose a significant challenge to statistical inference and machine learning, particularly for classification tasks like logistic regression. Classical iterative approaches (EM, multiple imputation) are often computationally intensive, sensitive to high missingness rates, and limited in uncertainty propagation. Recent deep generative mod

Le Liu, Junrui Liu, Yunhan Gao, Ziheng Wang

Aspect-based recommendation methods extract aspect terms from reviews, such as price, to model fine-grained user preferences on items, making them a critical approach in personalized recommender systems. Existing methods utilize graphs to represent the relationships among users, items, and aspect terms, modeling user preferences based on graph neural network

Huan-Qiang Zhou

It is shown that there is a hidden connection between the two well-studied sequences of the Temperley-Lieb (TL) integrable models -- the $q$-state quantum Potts (QP) models at the self-dual points and the staggered ${\rm SU}(n)$ spin-$s$ chains with $n=2s+1$ ($s \ge 1$), in addition to the uniform ${\rm SU}(2)$ spin-$1/2$ Heisenberg model. For each sequence,

Mayukh Mukherjee

A classical theorem of Colin de Verdi\`ere shows that on a closed manifold of fixed topology one can prescribe an arbitrary finite portion of the Laplace-Beltrami spectrum (including multiplicities, subject to the usual topological constraints) by choosing a sufficiently heterogeneous smooth metric. In this paper, we study the same inverse problem under the

Tung T. Nguyen, Nguyen Duy Tân

Graphs defined over a finite ring are well-studied in the literature. Due to their nature, these types of graphs connect several branches of mathematics, including algebra, number theory, matrix theory, and representation theory. In recent work, we studied $U$-unitary Cayley graphs over a finite commutative ring, which unifies several constructions of graphs

Huan-Qiang Zhou, Ian P. McCulloch, Murray T. Batchelor

Green parafermions, originally introduced by Green and extended by Greenberg and Messiah through trilinear and relative trilinear commutation relations beyond Bose-Fermi statistics, are generally regarded as mathematical curiosities without physical realization. We show that these paraparticles can in fact emerge as composite excitations in a broad class of

Haoyu Qiao, Hao Zhang, Shanwen Mao, Siyao Cheng

Large language models (LLMs) deliver impressive capabilities but incur substantial inference latency and cost, which hinders their deployment in latency-sensitive and resource-constrained scenarios. Cloud-edge-device collaborative inference has emerged as a promising paradigm by dynamically routing queries to models of different capacities across tiers. In t

Osamu Hirose, Emanuele Rodola

Nonrigid registration is conventionally divided into point set registration, which aligns sparse geometries, and image registration, which aligns continuous intensity fields on regular grids. However, this dichotomy creates a critical bottleneck for emerging scientific data, such as spatial transcriptomics, where high-dimensional vector-valued functions, e.g

Faisal Ahmed

Accurate classification of brain tumors from magnetic resonance imaging (MRI) plays a critical role in early diagnosis and effective treatment planning. In this study, we propose a deep learning framework based on Vision Transformers (ViT) enhanced with colormap-based feature representation to improve multi-class brain tumor classification performance. The p

Young-Seo Chang, Yatong An, Jae-Sang Hyun

We propose DepthTCM, a physics-aware end-to-end framework for depth map compression. In our framework of DepthTCM, the high-bit depth map is first converted to a conventional 3-channel image representation losslessly using a method inspired by a physical sinusoidal fringe pattern based profiliometry system, then the 3-channel color image is encoded and decod

Zhongyang Li, Yaqian Li, Faming Fang, Rinyoichi Takezoe

Multimodal large language models suffer from severe computational and memory bottlenecks, as the number of visual tokens far exceeds that of textual tokens. While recent methods employ projector modules to align and compress visual tokens into text-aligned features, they typically depend on fixed heuristics that limit adaptability across diverse scenarios. I

Di Lu, Yongzhi Liao, Xutong Mu, Lele Zheng

Host-acting agents promise a convenient interaction model in which users specify goals and the system determines how to realize them. We argue that this convenience introduces a distinct security problem: semantic under-specification in goal specification. User instructions are typically goal-oriented, yet they often leave process constraints, safety boundar

Evangelos Papoutsellis, Margaret A. G. Duff, Jakob S. Jørgensen, Sam Porter

The Core Imaging Library (CIL) is an open-source versatile Python framework for solving inverse problems with special emphasis on imaging applications such as computed tomography (CT), using a plug-in architecture for data and operators, interfacing to toolboxes such as ASTRA, TIGRE and SIRF. A key component of CIL is its optimisation module enabling users t

Jacopo Ballerini, Magali Pino, Michal Kuděj, Alberto Ferraris

This study investigates how entrepreneurial orientation (EO) affects governance of SaaS platforms in SMEs, including strategy alignment and long-term governance performance. This study uses SaaS as a hybrid governance model to examine how transaction cost variables affect strategic alignment and how EO moderates these associations. The research uses multi-st

Jinyu Xu, Tianqi Hu, Xiaonan Hu, Letian Zhou

Visually cataloging and quantifying the natural world requires pushing the boundaries of both detailed visual classification and counting at scale. Despite significant progress, particularly in crowd and traffic analysis, the fine-grained, taxonomy-aware plant counting remains underexplored in vision. In contrast to crowds, plants exhibit nonrigid morphologi

Keito Inoshita, Michiaki Omura, Tsukasa Yamanaka, Go Maeda

While AI-assisted writing has been widely reported to improve essay quality, its impact on the structural diversity of student thinking remains unexplored. Analyzing 6,875 essays across five conditions (Human-only, AI-only, and three Human+AI prompt strategies), we provide the first empirical evidence of a Quality-Homogenization Tradeoff, in which substantia

Chao Lin, Ze Tao, Fujun Liu

Accurately resolving steady electrohydrodynamic (EHD) flows presents a formidable computational challenge due to the strong nonlinear coupling between charged-particle density, velocity fields, and electric potential. These interactions frequently induce sharp transition layers, crossing fronts, and multiscale spatial structures, which notoriously degrade th

Haitao Hu, Jing Li, Jiafu Tang, Bo Zeng

The bilevel facility location problem (BO-FLP) is one of the core optimization problems behind the design of many decentralized industrial systems, e.g., supply chain systems where a supplier constructs some critical facilities and then uses them to serve retailers in a cost-effective fashion, while retailers directly handle customers aiming to minimize the

Haoguang Zhou, Siyi Wang, Jingyao Wu, James Bailey

Modern speech systems increasingly use discretized self-supervised speech representations for compression and integration with token-based models, yet their impact on emotional information remains unclear. We study how residual vector quantization (RVQ) reshapes emotional information in discrete speech representations from both representation- and task-level

Vaishnavi Gajendragad, Akanksha Gupta, Nadia Bihari Padhan, Rahul Pandit

Microswimmers in turbulent flows often navigate complex, heterogeneous, and obstacle-rich environments, where they exhibit intricate behaviors such as trapping at and escape from obstacles. We generalize recent $\mathcal{Q}-$learning methods of J.K. Alageshan \textit{et al.} [Phys.Rev.E \textbf{101}, 043110 (2020)] and A. Gupta \textit{et al.} [Physics of Fl

Ting Han, Xiangyi Xie, Yiping Chen, Yumeng Du

In this work, we present SYSU-HiRoads, a large-scale hierarchical road dataset, and RoadReasoner, a vision-language-geometry framework for automatic multi-grade road mapping from remote sensing imagery. SYSU-HiRoads is built from GF-2 imagery covering 3631 km2 in Henan Province, China, and contains 1079 image tiles at 0.8 m spatial resolution. Each tile is a

Fedor B. Lyudogovskiy

For each positive integer $n$, let $G_n$ be the graph of integer partitions of $n$, where two partitions are adjacent if one is obtained from the other by an elementary transfer of a cell in the Ferrers diagram, followed by reordering. Previous work has studied the global homotopy type of the clique complex $Cl(G_n)$ and the local combinatorics of $G_n$ at a

Joyce S. Y. Lau, Zihui Jing, Clement P. L. Chan, Louis C. F. Ng

SENSO is a motion-captured virtual reality serious game utilizing multisensory (visual, auditory, olfactory) stimuli to enhance cognitive and motor functions in older adults. This study evaluated its usability and performance among healthy seniors to establish normative baselines for predicting mild cognitive impairment (MCI) and dementia risk. Methods: Fort

Sotiris Skaperas, Arsenia Chorti

The robustness of angle of arrival (AoA) as a physical layer authentication (PLA) feature under spoofing attacks is studied, assuming a digital uniform linear array verifier. The verifier estimates the AoA assuming a legitimate user's single source model, whereas the received signal is generated by a multi antenna adversary at a different angle, leading to a

Manuel Gonzalez-Espinoza, Samuel Lepe, Joel F. Saavedra, Francisco Tello-Ortiz

We study the cosmological and thermodynamic implications of holographic dark energy derived from the Kaniadakis deformation of the Bekenstein-Hawking entropy. Within a spatially flat FLRW background, the generalized entropy leads to an effective dark energy density containing an infrared correction proportional to $H^{-2}$, modifying the dynamics of the appa

Shenghan Chen, Yiming Liu, Yanzhen Wang, Yujia Wang

Balancing performance trade-off on long-tail (LT) data distributions remains a long-standing challenge. In this paper, we posit that this dilemma stems from a phenomenon called "tail performance degradation" (the model tends to severely overfit on head classes while quickly forgetting tail classes) and pose a solution from a loss landscape perspective. We ob

Sandipan Pramanik, Emily B. Wilson, Henry D. Kalter, Agbessi Amouzou

Accurate estimation of cause-specific mortality fractions (CSMFs), the percentage of deaths attributable to each cause in a population, is essential for global health monitoring. Challenge arises because computer-coded verbal autopsy (CCVA) algorithms, commonly used to estimate CSMFs, frequently misclassify the cause of death (COD). This misclassification is

Dipendra Gupta, Edmund T. Liu, Gregory P. Bewley

The shape of the turbulent energy spectrum in the dissipation range, where viscous effects dominate, remains an open question despite decades of work. We report an experimental investigation of intermediate dissipation range energy spectra in turbulent shear layers at Taylor-scale Reynolds numbers, $Re_\lambda$, ranging from approximately 450 to 1500, which

Armand Coudray, Romain Gicquaud

This paper revisits the classical construction of initial data using the conformal method, as originally proposed by Holst, Nagy, and Tsogtgerel and later refined by Maxwell. We demonstrate that the existence of the solution can be proven using the Banach fixed point theorem, whereas the original proof relied on the Schauder fixed point theorem. This new app

Tian Xia, Matthew Sinclair, Andreas Schuh, Fabio De Sousa Ribeiro

Counterfactual image generation enables controlled data augmentation, bias mitigation, and disease modeling. However, existing methods guided by external classifiers or regressors are limited to subject-level factors (e.g., age) and fail to produce localized structural changes, often resulting in global artifacts. Pixel-level guidance using segmentation mask

Shih-Ying Hsu, Xunchuan Liu, Sheng-Yuan Liu, Tie Liu

Molecular inventories in starless cores are powerful tools for probing the physical and chemical structures at the earliest stages of star formation. Wide-band spectral scans are invaluable for obtaining a comprehensive view of the chemical composition. In this paper, we present the first results from the project Q/W-band Observations toward Starless Cores i

Lujia Liang, Lei Zhang

Open Radio Access Network (O-RAN) is a major advancement in the telecommunications field, providing standardized interfaces that promote interoperability between different vendors' technologies, thereby enhancing network flexibility and reducing operational expenses. By leveraging cutting-edge developments in network virtualization and artificial intelligenc

Wenzhuo Cheng, Ke Ding, Xin Dong, Yong He

Conversion rate (CVR) prediction models play a vital role in recommendation and advertising systems. Recent research on multi-scenario recommendation shows that learning a unified model to serve multiple scenarios is effective for improving overall performance. However, it remains challenging to improve model prediction performance across scenarios at low mo

Haolun Zheng, Yu He, Tailun Chen, Shuo Shao

Text-to-image (T2I) models such as Stable Diffusion and DALLE remain susceptible to generating harmful or Not-Safe-For-Work (NSFW) content under jailbreak attacks despite deployed safety filters. Existing jailbreak attacks either rely on proxy-loss optimization instead of the true end-to-end objective, or depend on large-scale and costly RL-trained generator

Fanli Zhou, Hao Chen, Pengxiang Xu, Kai Yang

Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder complexity demands atomistic simulations that achieve high accuracy, efficiency, and generalizability across vast spatial

Thomas Mendelson, Joshua Francois, Galit Lahav, Tammy Riklin-Raviv

Accurate delineation of individual cells in microscopy videos is essential for studying cellular dynamics, yet separating touching or overlapping instances remains a persistent challenge. Although foundation-model for segmentation such as SAM have broadened the accessibility of image segmentation, they still struggle to separate nearby cell instances in dens

Enrique Lopez-Rodriguez

Polycyclic aromatic hydrocarbons (PAHs) are commonly used as proxies for star formation, molecular gas content, and other interstellar medium (ISM) properties in our Galaxy and other galaxies. Given their abundance and brightness, polarization measurements of PAH features could, in principle, provide a probe of the ISM magnetic field and intrinsic PAH proper

Pierre Cardaliaguet, Joe Jackson, Panagiotis E. Souganidis

We study a high-dimensional stochastic optimization problem which features both control and stopping. In particular, a central planner steers a large population of particles, and can also remove particles at any time by paying a penalty. In the limit, we obtain a mean field control problem with discontinuous dynamics, in the sense that the controlled Fokker-

S. V. Streltsov, H. Y. Huang, A. Ushakov, C. I. Wu

The kagome lattice naturally hosts flat bands, Dirac fermions, and van Hove singularities, yet whether its geometry can stabilize orbital-selective phases - a hallmark of Hund's physics in multi-orbital correlated systems - has remained an open question. Here, we combine resonant inelastic X-ray scattering with density functional theory and dynamical mean-fi

Muhammad Muneeb, David B. Ascher

Polygenic risk score (PRS) tools differ substantially in statistical assumptions, input requirements, and implementation complexity, making direct comparison difficult. We developed a harmonized, implementation-aware benchmarking framework to evaluate 46 PRS tools across seven binary UK Biobank phenotypes and one continuous trait under three model configurat

Hoshang Heydari

We present a geometric formulation of quantum mechanics based on the symplectic structure of the projective Hilbert space. Building upon the standard K\"ahler framework, we introduce an extension in which the symplectic structure is allowed to couple to a metric-affine background geometry, leading to a deformation of the Hamiltonian flow on the state space.

Mihaly A. Csirik, Andre Laestadius

The non-uniform (or inhomogeneous) electron gas has received much attention in many-body quantum mechanics and quantum chemistry in the early days of density functional theory, mainly as a theoretical device to construct gradient approximations via linear response theory. In this article, motivated by the recent works of Lewin, Lieb and Seiringer, we propose

Zili Wang, Cong Wu

Let $\mathcal{M}_{2N}(\delta_1, \delta_2,\dots, \delta_N)$ be the moduli space of centrally symmetric convex polyhedral surfaces with $2N$ labeled vertices and prescribed cone-deficits $\delta_1$, $\delta_2$, $\dots$, $\delta_N$. We show that $\mathcal{M}_{2N}(\delta_1, \delta_2,\dots, \delta_N)$ has the structure of a real hyperbolic manifold of dimension $

Marco Ghirlanda

Based on the Reid-Shepherd-Barron-Tai criterion for canonical and terminal quotient singularities, we characterize canonicity and terminality of a toric variety in terms of its local class group actions. Specializing it to the Picard number one setting, we arrive at a classification algorithm for canonical and terminal fake weighted projective spaces in any

Alex Shvets

We develop a geometric framework for anonymous shuffle experiments based on an anchored affine likelihood-ratio law: a mean-zero measure on the regular simplex polytope. Every finite-output d-ary channel corresponds, up to refinements, to a unique anchored law, and conversely. On privacy: among all epsilon_0-LDP channels, binary randomized response universal

Shibing Chen, Alessio Figalli, Yi Ru-Ya Zhang

In this paper, we study minimizers of the Chon\'e--Rochet variational problem in dimension two. We first establish global $C^1$ regularity on arbitrary bounded convex domains, and then prove global $C^{1,1}$ regularity on bounded strictly convex domains or, more generally, whenever the zero set of $u$ has positive measure. Next, we construct smooth bounded c

Lijingze Xiao, Jinhong Du, Yang Cong, Supeng Diao

Grasping target objects is a fundamental skill for robotic manipulation, but in cluttered environments with stacked or occluded objects, a single-step grasp is often insufficient. To address this, previous work has introduced pushing as an auxiliary action to create graspable space. However, these methods often struggle with both stability and efficiency bec