December 2025 arXiv papers — page 30

Jiahao Fan, Yuxin Qin, Wei Feng, Yanyin Chen

Product posters blend striking visuals with informative text to highlight the product and capture customer attention. However, crafting appealing posters and manually optimizing them based on online performance is laborious and resource-consuming. To address this, we introduce AutoPP, an automated pipeline for product poster generation and optimization that

Peter Koymans, Robert J. Lemke Oliver, Efthymios Sofos, Frank Thorne

We prove a composite case of the Cohen--Lenstra--Gerth heuristics. Specifically, we establish an asymptotic for the average $6$-torsion of the class group of quadratic number fields. We also prove Malle's conjecture for Galois $D_6$-extensions.

KaShun Shum, Binyuan Hui, Jiawei Chen, Lei Zhang

Execution-based feedback like unit testing is widely used in the development of coding agents through test-time scaling (TTS) and reinforcement learning (RL). This paradigm requires scalable and reliable collection of unit test cases to provide accurate feedback, and the resulting feedback is often sparse and cannot effectively distinguish between trajectori

Misir J. Mardanov, Telman K. Melikov, Samin T. Malik

This article is devoted to obtain new sufficient conditions for an extremum in problems of classical calculus of variations. The concept of a set of integrands is introduced. Using this concept, first and second order sufficient conditions for a weak and strong local minimum, as well as an absolute minimum were obtained. Also, this concept, in particular, al

Gurkirat Wadhwa, Priyank Sinha

Even large firms such as Walmart, Apple, and Coca-Cola face persistent fluctuations in costs, demand, and raw material availability. These are not \textit{rare events} and cannot be evaluated using traditional disruption models focused on infrequent events. Instead, sustained volatility induces opportunistic behavior, as firms repeatedly reconfigure partners

Zeyu Liang, Hailun Xia, Naichuan Zheng

While human action recognition has witnessed notable achievements, multimodal methods fusing RGB and skeleton modalities still suffer from their inherent heterogeneity and fail to fully exploit the complementary potential between them. In this paper, we propose PAN, the first human-centric graph representation learning framework for multimodal action recogni

Yafeng Tang, Xiaoou Ding, Jianzhuo Du, Zishuo Yan

Tabular data generation has become increasingly essential for enabling robust machine learning applications, which require large-scale, high-quality data. Existing solutions leverage generative models to learn original data distributions. However, real-world data are naturally heterogeneous with diverse distributions, making it challenging to obtain a univer

Nikolaos Cheimarios, Spyridoula Cheimariou

Logical paradoxes and inconsistent information pose deep challenges in epistemology and the philosophy of logic. Classical systems typically handle contradictions only through external checks or by altering the logical framework, as in Tarski's hierarchy or paraconsistent logics. We propose a novel approach: a quantum circuit architecture that intrinsically

Theo Datta, Kayla Huang, Sham Kakade, David Brandfonbrener

While most frontier models still use deterministic frequency-based tokenization algorithms such as byte-pair encoding (BPE), there has been significant recent work to design learned neural tokenizers. However, these schemes generally add to underlying language model complexity and force large changes to architecture, making them hard to implement at large sc

Zhongyin Peng, Chengrong Wang, Changjun Liu, Xiang Zhao

A novel method for enhancing microwave heating uniformity using a 2-bit coding metasurface is proposed. This metasurface is specially designed to scatter incident waves into multiple directions at 2.45 GHz rather than just one, significantly improving the electric field distribution uniformity within a cavity, and eliminating the need to redesign the cavity

Jikai Wang, Jianchao Tan, Yuxuan Hu, Jiayu Qin

Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel. However, the verification stage often becomes the dominant computational bottleneck, especially for long-context inputs and mixture-of-experts (MoE) models. Existing sparsification methods are designed primarily for standard token-by-token

Alexander Bednarek

This is the second of two papers studying both the geometric structure of Fano fibrations and the application to K\"ahler-Ricci flows developing a singularity in finite time. We assume that the K\"ahler-Ricci flow on a compact K\"ahler manifold has a rational initial metric and develops a singularity in finite time such that the manifold admits a Fano fibrat

Luca Melega, Tommaso Nottoli, Jürgen Gauss, Filippo Lipparini

We present a novel and efficient implementation of coupled-cluster with singles and doubles (CCSD) analytic gradients that combines the Cholesky decomposition (CD) of electron-repulsion integrals with the exploitation of Abelian point-group symmetry. This approach is particularly effective for medium-sized and large symmetric molecular systems. The CD of two

Xuan Pang, Danyao Wu, Pingzhi Yuan

Permutation polynomials over finite fields have extensive applications in various areas. Particularly, permutation polynomials with simple forms are of great interest. In recent papers, several classes of permutation polynomials of the form $L(X)+Tr_q^{q^3}(h(X))$ have been constructed. This paper further investigates permutation polynomials of such form ove

Kenny Workman, Zhen Yang, Harihara Muralidharan, Hannah Le

Spatial transcriptomics assays are rapidly increasing in scale and complexity, making computational analysis a major bottleneck in biological discovery. Although frontier AI agents have improved dramatically at software engineering and general data analysis, it remains unclear whether they can extract biological insight from messy, real-world spatial dataset

Dihang Guan, Hui He, Wenqing Hu, Jiaojiao Yang

We consider the wave propagation for a reaction-diffusion equation on the real line, with a random drift and Fisher-Kolmogorov-Petrovskii-Piscounov (FKPP) type nonlinear reaction. We show that when the average drift is positive, the asymptotic wave fronts propagating to the positive and negative directions are both pushed in the negative direction, leading t

Shen Zheng, Jiaran Cai, Yuansheng Guan, Shenneng Huang

Recent progress in diffusion models has significantly advanced the field of human image animation. While existing methods can generate temporally consistent results for short or regular motions, significant challenges remain, particularly in generating long-duration videos. Furthermore, the synthesis of fine-grained facial and hand details remains under-expl

Alexander Bednarek

This is the first of two papers studying both the geometric structure of Fano fibrations and the application to K\"ahler-Ricci flows developing a singularity in finite time. Given a Fano fibration which is generated by Kawamata's theorem from a compact K\"ahler manifold $X$ endowed with an ample, rational line bundle $L$ and non-nef canonical line bundle $K_

Elijah G. Marlin, Yukei S. Murakami, Dillon Brout, Jack W. Tweddle

ATLAS (Asteroid Terrestrial Last Alert System) is a time-domain survey using four telescopes, covering the entire sky. It has observed over 10,000 spectroscopically confirmed Type Ia supernovae (SNe~Ia), with thousands of cosmology-grade light curves (to be released as TITAN DR1). To prepare this massive, low-redshift dataset for cosmology, we evaluate and c

Sachin Pawar, Girish Keshav Palshikar, Anindita Sinha Banerjee, Nitin Ramrakhiyani

In this paper, we explore the problem of automatic statute prediction where for a given case description, a subset of relevant statutes are to be predicted. Here, the term "statute" refers to a section, a sub-section, or an article of any specific Act. Addressing this problem would be useful in several applications such as AI-assistant for lawyers and legal

Yosuke Onoue

This paper challenges the convention of using graph-theoretic shortest distance in stress-based graph drawing. We propose a new paradigm based on resistance distance, derived from the graph Laplacian's spectrum, which better captures global graph structure. This approach overcomes theoretical and computational limitations of traditional methods, as resistanc

Qing-Chang Zhao, Michal Dovciak, Han-Cheng Li, Lian Tao

We present a joint spectro-polarimetric analysis of the black hole X-ray binary GRS~1739--278 during its 2025 mini-outburst, using simultaneous observations from \ixpe\ and \nustar. The \ixpe\ data show a polarization degree of ${\rm PD} = (2.3 \pm 0.4)\%$ and a polarization angle of ${\rm PA} = 62^\circ \pm 5^\circ$ in the 2--8~keV range. The model-independ

Zehao Chen, Tianxiang Ai, Yifei Li, Gongxun Li

Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or final outputs while overlooking the rich internal representations and interactions across models.In this work, we introduce LLMBoost, a novel ensemble fine-tuning framework that breaks

J. P. S. Sandhu, M. Bhardwaj, N. Ananthkrishnan, A. Sharma

Traditional design principles for determining the optimal intake ramp or cone angles, for ensuring no flow spillage at the intake cowl under design conditions, and for the form of the terminal shock in the intake duct are revisited. We show that it is preferable to select the ramp or cone angles to be somewhat smaller than that suggested by the Oswatitsch cr

Chaoqi Liu, Haonan Chen, Sigmund H. Høeg, Shaoxiong Yao

Multitask learning poses significant challenges due to the highly multimodal and diverse nature of robot action distributions. However, effectively fitting policies to these complex task distributions is often difficult, and existing monolithic models often underfit the action distribution and lack the flexibility required for efficient adaptation. We introd

Carolina Aparício, Qi Shi, Bo Wen, Tesfaye Yadete

Addressing the challenge of multimodal data fusion in high-dimensional biomedical informatics, we propose MMCTOP, a MultiModal Clinical-Trial Outcome Prediction framework that integrates heterogeneous biomedical signals spanning (i) molecular structure representations, (ii) protocol metadata and long-form eligibility narratives, and (iii) disease ontologies.

Colin Geniet, Gunwoo Kim, Lucas Meijer

For some geometric graph classes, tractability of testing first-order formulas is precisely characterised by the graph parameter twin-width. This was first proved for interval graphs among others in [BCKKLT, IPEC '22], where the equivalence is called delineation, and more generally holds for circle graphs, rooted directed path graphs, and $H$-graphs when $H$

Hao-Yu Zhu, Shi-Jie Du, Lu Xu, Wei Shi

To overcome the high attrition rate and limited clinical translatability in drug discovery, we introduce the concept of Maximum Drug-Likeness (MDL) and develop an applicable Fivefold MDL strategy (5F-MDL) to reshape the screening paradigm. The 5F-MDL strategy integrates an ensemble of 33 deep learning sub-models to construct a 33-dimensional property spectru

Ruihao Jing, Cheng Gong, Yu Jiang, Boyu Zhu

Rare words remain a critical bottleneck for speech-to-text systems. While direct fine-tuning improves recognition of target words, it often incurs high cost, catastrophic forgetting, and limited scalability. To address these challenges, we propose a training-free paradigm based on task vectors for rare word recognition and translation. By defining task vecto

Shruti Aggarwal, Trasha Gupta, R. K. Agrawal, S. Indu

Quantum entanglement is a key resource in quantum computing and quantum information processing tasks. However, its quantification remains a major challenge since it cannot be directly extracted from physical observables. To address this issue, we study a few machine-learning based models to estimate the amount of entanglement in two-qubit as well as three-qu

Shi-jun Dang, Ji-guang Lu, Peng Jiang, Yu-lan Liu

Studies of subpulse variability in millisecond pulsars (MSPs) offer important constraints on their emission physics. Using the high sensitivity of FAST, we present the first identification of distinct single pulse fluctuation behaviour in PSR J1857+0943. We find that the third component(MP\_C3) of the main pulse may originate from a different region than the

Fatma Zürnacı-Yetiş

Two notable examples of dual functionals in approximation theory and computer-aided geometric design are the blossom and the divided difference operator. Both of these dual functionals satisfy a similar set of formulas and identities. Moreover, the divided differences of polynomials can be expressed in terms of the blossom. In this paper, an extended non-pol

Juyoung Bae, Moo Hyun Son, Jiale Peng, Wanting Qu

Digital crown design remains a labor-intensive bottleneck in restorative dentistry. We present CrownGen, a generative framework that automates patient-customized crown design using a denoising diffusion model on a novel tooth-level point cloud representation. The system employs two core components: a boundary prediction module to establish spatial priors and

Jialiang Shao, Ippei Obata, Dongdong Zhang

Cosmic birefringence, characterized by the observed rotation of the polarization plane of the cosmic microwave background (CMB) radiation, serves as a critical probe for testing theories beyond the standard cosmological scenario. As a major component of the universe, dark matter plays a pivotal role in cosmic evolution, particularly in the formation of large

Weichen Zhang, Peizhi Tang, Xin Zeng, Fanhang Man

Unmanned aerial vehicles (UAVs) have emerged as powerful embodied agents. One of the core abilities is autonomous navigation in large-scale three-dimensional environments. Existing navigation policies, however, are typically optimized for low-level objectives such as obstacle avoidance and trajectory smoothness, lacking the ability to incorporate high-level

Midhun T. Augustine

This paper presents a new approach to algorithmic composition, called predictive controlled music (PCM), which combines model predictive control (MPC) with music generation. PCM uses dynamic models to predict and optimize the music generation process, where musical notes are computed in a manner similar to an MPC problem by optimizing a performance measure.

Akiyoshi Uchida, Antonine Richard, Kentaro Uno, Miguel Olivares-Mendez

Knowing the inertia parameters of a grasped object is crucial for dynamics-aware manipulation, especially in space robotics with free-floating bases. This work addresses the problem of estimating the inertia parameters of an unknown target object during manipulation. We apply and extend an existing online identification method by incorporating momentum conse

Tingyang Sun, Ting He, Bo Ji, Parimal Parag

Large language models have demonstrated extraordinary performance in many AI tasks but are expensive to use, even after training, due to their requirement of high-end GPUs. Recently, a distributed system called PETALS was developed to lower the barrier for deploying LLMs by splitting the model blocks across multiple servers with low-end GPUs distributed over

Tianchen Deng, Wenhua Wu, Kunzhen Wu, Guangming Wang

Visual localization has traditionally been formulated as a pair-wise pose regression problem. Existing approaches mainly estimate relative poses between two images and employ a late-fusion strategy to obtain absolute pose estimates. However, the late motion average is often insufficient for effectively integrating spatial information, and its accuracy degrad

Kenta Iizuka, Akiyoshi Uchida, Kentaro Uno, Kazuya Yoshida

Approaching a tumbling target safely is a critical challenge in space debris removal missions utilizing robotic manipulators onboard servicing satellites. In this work, we propose a trajectory planning method based on nonlinear optimization for a close-range rendezvous to bring a free-floating, rotating debris object in a two-dimensional plane into the manip

Mo Wang, Junfeng Xia, Wenhao Ye, Enyu Liu

Foundation models are emerging as a powerful paradigm for fMRI analysis, but current approaches face a dual bottleneck of data- and training-efficiency. Atlas-based methods aggregate voxel signals into fixed regions of interest, reducing data dimensionality but discarding fine-grained spatial details, and requiring extremely large cohorts to train effectivel

Xiaokang Liu, Yuchen Yang, Yifei Sun, Jiang Bian

In multicenter biomedical research, integrating data from multiple decentralized sites provides more robust and generalizable findings due to its larger sample size and the ability to account for the between-site heterogeneity. However, sharing individual-level data across sites is often difficult due to patient privacy concerns and regulatory restrictions.

Marc S. Montalvo, Hamed Yaghoobian

Recent advances in large language models (LLMs) are transforming data-intensive domains, with finance representing a high-stakes environment where transparent and reproducible analysis of heterogeneous signals is essential. Traditional quantitative methods remain vulnerable to survivorship bias, while many AI-driven approaches struggle with signal integratio

Parth Agarwal, Navya Kommuri, Trizal Garg, Prisha Singhal

Cricket is the second most popular sport worldwide, with billions of fans seeking advanced statistical insights unavailable through standard web searches. Although LLMs have advanced significantly in Text-to-SQL tasks, their capability to handle domain-specific nuances and multilingual requirements in sports analytics remains under-explored. We present CricB

You Li, Guannan Zhao, Yuhao Ju, Yunqi He

We introduce LLA, an effective intellectual property (IP) protection scheme for generative AI models. LLA leverages the synergy between hardware and software to defend against various supply chain threats, including model theft, model corruption, and information leakage. On the software side, it embeds key bits into neurons that can trigger outliers to degra

Chinmay Pushkar, Sanchit Kabra, Dhruv Kumar, Jagat Sesh Challa

Large Language Models (LLMs) have demonstrated significant potential in automated software security, particularly in vulnerability detection. However, existing benchmarks primarily focus on isolated, single-vulnerability samples or function-level classification, failing to reflect the complexity of real-world software where multiple interacting vulnerabiliti

Ryunosuke Takahashi, Hayato Seno, Marin Takahashi, Shigetoshi Tomita

Ferroaxial order is characterized by the breaking of mirror symmetry parallel to the crystallographic principal axis, which often originates from spontaneous rotational distortions of the crystal lattice. Such rotational distortions are, by symmetry, allowed to couple to specific phonon modes. However, Raman-active phonons associated with these rotational di

Sultan Amed, Chan Yu Hang, Sayantan Banerjee

This paper presents PDx, an adaptive, machine learning operations (MLOps) driven decision system for forecasting credit risk using probability of default (PD) modeling in digital lending. While conventional PD models prioritize predictive accuracy during model development with complex machine learning algorithms, they often overlook continuous adaptation to

Nareg Ghazikhanian, Pavel Salev, Dayne Sasaki, Yayoi Takamura

In metal-insulator transition materials, a small perturbation can shift the delicate balance between competing or coexisting electronic phases, leading to dramatic changes of the material's properties. Using La0.7Sr0.3MnO3, a prototypical metal-insulator transition manganite, we show that local low-dose focused ion beam irradiation increases resistivity by s

Michael J. Cervia, Henry Lamm, Diyi Liu, Edison M. Murairi

Qudits offer the potential for low-overhead magic state distillation, although previous results for asymptotically good codes have required qudit dimension $q\gg 100$ or code length $\mathcal{N}\gg 100$. These parameters far exceed experimental demonstrations of qudit platforms, and thus motivate the search for better codes. Using a novel lifting procedure,

Xifeng Su, Shasha Xu

We consider the following mixed local and non-local critical elliptic equation: \begin{equation*}\label{0.1} \left\{ \begin{array}{lll} -\Delta u+(-\Delta)^su=\lambda h u^{p}+u^{2^*-1}, &\text{in}\,\, \mathbb{R}^n, u>0, &\text {in} \,\, \mathbb{R}^n, \lim\limits_{|x|\to\infty} u(x) = 0, \end{array} \right. \end{equation*} where $n\geqslant4, \,\, p\in (0,2^*

Aman Priyadarshi M. Kumar, Juie Shetye, Sean G. Sellers, Damian J. Christian

We present a uniform analysis of compact Ca II K (3934 \AA) brightenings that occur near flare kernels and assess their value as short-lead indicators of solar flare onset. Using high-cadence imaging from the Rapid Oscillations in the Solar Atmosphere (ROSA) instrument at the Dunn Solar Telescope (DST), we examine eight flare sequences (seven C-class and one

Jing-Yi Zeng, Guan-Hua Huang

This study investigates how to efficiently build a domain-specialized large language model (LLM) for statistics using the lightweight LLaMA-3.2-3B family as the foundation model (FM). We systematically compare three multi-stage training pipelines--starting from a base FM with no instruction-following capability, a base FM augmented with post-hoc instruction

Naen Xu, Jinghuai Zhang, Changjiang Li, Hengyu An

Large vision-language models (LVLMs) have achieved remarkable advancements in multimodal reasoning tasks. However, their widespread accessibility raises critical concerns about potential copyright infringement. Will LVLMs accurately recognize and comply with copyright regulations when encountering copyrighted content (i.e., user input, retrieved documents) i

Garima Rani, G. H. Philipp Nguyen, René Wittmann, Hartmut Löwen

Soft surfaces, spanning vastly different environmental and biomedical settings, are frequently colonised by surface-associated bacteria. Yet, how soft surfaces govern bacterial dynamics and their self-organisation into colonies remains poorly understood. Using experiments and agent-based modelling, we report the self-organisation of bacterial cells into nasc

Hiroyuki Yamase

We review recent theoretical progress on the charge dynamics of doped carriers in high-temperature cuprate superconductors. Advances in this field have clarified that doped charges in cuprates exhibit remarkably rich collective behavior, governed by the combined effects of strong electronic correlations, the intrinsic layered crystal structure, and long-rang

Hiroyuki Yamase

In this review, we first present compelling evidence from resonant inelastic x-ray scattering data that highlights the significance of the long-range Coulomb interaction in cuprate charge dynamics, particularly around the in-plane momentum q=(0,0). We show that these experimental observations are well-captured by the layered t-J-V model, which extends the st

Divyansh Srivastava, Akshay Mehra, Pranav Maneriker, Debopam Sanyal

Decoder-only autoregressive image generation typically relies on fixed-length tokenization schemes whose token counts grow quadratically with resolution, substantially increasing the computational and memory demands of attention. We present DPAR, a novel decoder-only autoregressive model that dynamically aggregates image tokens into a variable number of patc

Yiming Qian, Thorsten Neumann, Xueyining Huang, David Hardoon

We propose an explainable, privacy-preserving dataset distillation framework for collaborative financial fraud detection. A trained random forest is converted into transparent, axis-aligned rule regions (leaf hyperrectangles), and synthetic transactions are generated by uniformly sampling within each region. This produces a compact, auditable surrogate datas

Yihan Hu, Xuelin Chen, Xiaodong Cun

Existing video omnimatte methods typically rely on slow, multi-stage, or inference-time optimization pipelines that fail to fully exploit powerful generative priors, producing suboptimal decompositions. Our key insight is that, if a video inpainting model can be finetuned to remove the foreground-associated effects, then it must be inherently capable of perc

Simon Y. M. Gong, David G. L. Wang, K. Zhang

In this paper, we identify a new family of $e$-positive graphs, called the trinacria graphs $T_{(b+2)b2}$, thereby providing a partial answer to Stanley's question on which graphs are $e$-positive. The trinacria graph $T_{abc}$ is the graph on $a+b+c+3$ vertices obtained by attaching paths $P_a$, $P_b$ and~$P_c$ to the vertices of a triangle, respectively. O

Huatuan Sun, Yunshan Ma, Changguang Wu, Yanxin Zhang

Frozen Large Video Language Models (LVLMs) are increasingly employed in micro-video recommendation due to their strong multimodal understanding. However, their integration lacks systematic empirical evaluation: practitioners typically deploy LVLMs as fixed black-box feature extractors without systematically comparing alternative representation strategies. To

Jean-Marie Dufour, Tianyu He

This paper proposes asymptotically distribution-free inference methods for comparing a broad range of welfare indices across dependent samples, including those employed in inequality, poverty, and risk analysis. Two distinct situations are considered. \emph{First}, we propose asymptotic and bootstrap intersection methods which are completely robust to arbitr

Md Rafid Islam, Rafsan Jany, Akib Ahmed, Mohammad Ashrafuzzaman Khan

Diabetic retinopathy (DR) remains a leading cause of preventable blindness, yet large-scale screening is constrained by limited specialist availability and variable image quality across devices and populations. This work investigates whether feature-level fusion of complementary convolutional neural network (CNN) backbones can deliver accurate and efficient

Masayuki Kawarada, Kosuke Yamada, Antonio Tejero-de-Pablos, Naoto Inoue

Conditional image embeddings are feature representations that focus on specific aspects of an image indicated by a given textual condition (e.g., color, genre), which has been a challenging problem. Although recent vision foundation models, such as CLIP, offer rich representations of images, they are not designed to focus on a specified condition. In this pa

Darrin Bright, Rakshith Raj, Kanchan Keisham

Accurate food nutrition estimation from single images is challenging due to the loss of 3D information. While depth-based methods provide reliable geometry, they remain inaccessible on most smartphones because of depth-sensor requirements. To overcome this challenge, we propose PortionNet, a novel cross-modal knowledge distillation framework that learns geom

Qi Fan, An Zou, Yehan Ma

Large Language Models (LLMs) are increasingly deployed in time-critical systems, such as robotics, autonomous driving, embodied intelligence, and industrial automation, where generating accurate responses within a given time budget is crucial for decision-making, control, or safety-critical tasks. However, the auto-regressive generation process of LLMs makes

Shigeo Ohkubo

In 1954, following a five-year research period in the U.S., Professor Hideki Yukawa returned to Japan and visited Kochi on March 21 to attend the unveiling ceremony for the first statue of him ever built in Japan, a project initiated by the PTA of Yasu Elementary School in Yasu Town, Kochi Prefecture. By a coincidence of history, just three weeks prior on Ma

Haodong Lei, Hongsong Wang, Xin Geng, Liang Wang

Autoregressive (AR) image models achieve diffusion-level quality but suffer from sequential inference, requiring approximately 2,000 steps for a 576x576 image. Speculative decoding with draft trees accelerates LLMs yet underperforms on visual AR models due to spatially varying token prediction difficulty. We identify a key obstacle in applying speculative de

Lupiao Hu, Fasheng Wang, Fangmei Chen, Fuming Sun

Existing RGB-T salient object detection methods predominantly rely on manually aligned and annotated datasets, struggling to handle real-world scenarios with raw, unaligned RGB-T image pairs. In practical applications, due to significant cross-modal disparities such as spatial misalignment, scale variations, and viewpoint shifts, the performance of current m

Yuan He, Shi-Lei Xue

As the cover of embryos and adult organisms, epithelial tissues are subjected to substantial mechanical forces in tissue morphogenesis. However, the finite deformation behaviors of epithelial tissues remain largely unexplored. This study combines discrete vertex simulations with a multiscale constitutive model to investigate the necking behavior of epithelia

Kentaro Uno, Elian Neppel, Gustavo H. Diaz, Ashutosh Mishra

The allure of lunar surface exploration and development has recently captured widespread global attention. Robots have proved to be indispensable for exploring uncharted terrains, uncovering and leveraging local resources, and facilitating the construction of future human habitats. In this article, we introduce the modular and on-demand reconfigurable robot

Vedant Shah, Johan Obando-Ceron, Vineet Jain, Brian Bartoldson

The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL objective for LLM training involves a regularization term, which is the reverse Kullback-Leibler (KL) divergence between the trained policy and the reference policy. Since computing the KL divergence exactly is int

Kevin Zelaya, Jonathan Friedman, Mohammad-Ali Miri

Linear transformations are cornerstone operations utilized in modern computing, but are computationally expensive on current electronic platforms. Optical computing has been positioned as a new computing solution, promising high speed and energy efficiency by exploiting the available degrees of freedom of light. Although solutions exist in the optical domain

Noor Fatima, Hasan Faraz Khan, Muzammil Behzad

This work presents an attack-aware deepfake and image-forensics detector designed for robustness, well-calibrated probabilities, and transparent evidence under realistic deployment conditions. The method combines red-team training with randomized test-time defense in a two-stream architecture, where one stream encodes semantic content using a pretrained back

Jin-Yu Ni, Xia-Ming Zheng, Peng-Tao Wei, Da-Yong Liu

A variety of distinct anisotropic exchange interactions commonly exist in one magnetic material due to complex crystal, magnetic and orbital symmetries. Here we investigate the effects of multiple anisotropic exchange interactions on topological magnon in a honeycomb ferromagnet, and find a chirality-selective topological magnon phase transition induced by a

Jiaxin Liu, Peiyi Tu, Wenyu Chen, Yihong Zhuang

While Large Language Models (LLMs) have achieved remarkable success in cognitive and reasoning benchmarks, they exhibit a persistent deficit in anthropomorphic intelligence-the capacity to navigate complex social, emotional, and ethical nuances. This gap is particularly acute in the Chinese linguistic and cultural context, where a lack of specialized evaluat

Yuan-Jie Chen

To address the imperative for covert underwater swarm coordination, this paper introduces "Hydrodynamic Whispering," a near-field silent communication paradigm utilizing Artificial Lateral Line (ALL) arrays. Grounded in potential flow theory, we model the transmitter as an oscillating dipole source. The resulting pressure field exhibits steep nearfield atten

Yanning Jia, Fenzhuo Guo, Mengyan Li, Haifeng Dong

Not all entangled states can exhibit quantum steering, and determining whether a given entangled state is steerable is a crucial problem in quantum information theory. The main challenge lies in verifying the existence of a local hidden-state (LHS) model capable of reproducing all post-measurement assemblages generated by arbitrary measurements. To address t

Yu-Min Hu, Zhaoyu Han, Biao Lian

We study the phase diagram of a one-dimensional spin quantum breakdown model, which has an exponential $U(1)$ symmetry with charge unit decaying as $2^{-j}$ with site position $j$. By exact diagonalization and density matrix renormalization group, we show that the model with spin $S\ge2$ exhibits an exponential $U(1)$ spontaneous symmetry-breaking (SSB) phas

Jennifer Sawyer, Julian Allagan

This study extends previous hotspot and Chi-Square analysis by Sawyer \cite{sawyer2025hotspot} by integrating advanced statistical analysis, machine learning, and spatial modeling techniques to analyze five years (2019--2023) of traffic accident data from an 8.4-mile stretch of US 158 in Currituck County, NC. Building upon foundational statistical work, we a

Chuangxin Zhang, Guangfeng Lin, Enhui Zhao, Kaiyang Liao

Incremental learning often encounter challenges such as overfitting to new data and catastrophic forgetting of old data. Existing methods can effectively extend the model for new tasks while freezing the parameters of the old model, but ignore the necessity of structural efficiency to lead to the feature difference between modules and the class misalignment

T. R. Liu, Zheng Zhang, Y. X. Zhao

Crystallographic groups are conventionally studied in real space to characterize crystal symmetries. Recent work has recognized that when these symmetries are realized projectively, momentum space inherently accommodates nonsymmorphic symmetries, thereby evoking the concept of \textit{momentum-space crystallographic groups} (MCGs). Here, we reveal that the c

Gregory Berkolaiko, Jacob Shapiro, Beyer Chase White

The spectral localizer, introduced by Loring in 2015 and Loring and Schulz-Baldes in 2017, is a method to compute the (infinite volume) topological invariant of a quantum Hamiltonian on $\ZZ^d$, as the signature of the (finite) localizer matrix. We present a direct and elementary spectral-theoretic proof treating the $d=1$ and $d=2$ cases on an almost equal

Aayush Mainali, Sirjan Ghimire

Timing side-channels are an important threat to cryptography that still needs to be addressed in implementations, and the advent of post-quantum cryptography raises this issue because the lattice-based schemes may produce secret-dependent timing variability with the help of complex arithmetic and control flow. Since also real timing measurements are affected

Baorong Huang, Ali Asiri

High-quality parallel corpora are essential for Machine Translation (MT) research and translation teaching. However, Arabic-English resources remain scarce and existing datasets mainly consist of simple one-to-one mappings. In this paper, we present AlignAR, a generative sentence alignment method, and a new Arabic-English dataset comprising simple legal and

Deep Narayan Singh, Lagoon Biswal, Girish Naik, Manaswita Bose

In this paper, we study the ventilation airflow in a model classroom, where exhaust fans throw out the used air, to replace it with outdoor air through open door. Hybrid ventilation, or mechanically assisted natural ventilation, of this kind is used as a retrofit design to reduce infection risk from airborne transmission. The air stream entering the door for

Xiaokang Liu, Jie Hu, Naimin Jing, Yang Ning

In biomedical research, to obtain more accurate prediction results from a target study, leveraging information from multiple similar source studies is proved to be useful. However, in many biomedical applications based on real-world data, populations under consideration in different studies, e.g., clinical sites, can be heterogeneous, leading to challenges i

Joshua Enwright, Luca Francone, Joaquín Moraga, Hunter Spink

In this article, we introduce the notion of mutation semigroup algebras. This concept simultaneously generalizes cluster algebras and semigroup algebras. We show that, under some mild conditions on the singularities, the spectrum $U={\rm Spec}(R)$ of a mutation semigroup algebra $R$ admits a log Fano compactification $U\hookrightarrow X$. The compactificatio

Peng Jiang, Haoyu Zhang, Yi Dai, Tao Peng

Wall pressure fluctuations beneath the turbulent boundary layer of high-speed underwater vehicles are crucial for hydro-acoustics and acoustic stealth. However, a comprehensive understanding remains limited due to a lack of high-quality experimental data, particularly under realistic operational conditions. To address this gap, this study establishes the fir

Xiaoda Xu

This paper studies the expected $L_p$-discrepancy ($2 \leq p < \infty$) for stratified sampling schemes under importance sampling. We introduce a parametric family of equivolume partitions $\Omega_{\theta,\sim}$ and leverage recent exact formulas for the expected $L_2$-discrepancy \cite{xian2025improved}. Our main contribution is a weighted discrepancy reduc

Siyu Li, Chenwei Song, Wan Zhou, Xinyi Liu

This paper proposes a large language model (LLM) approach that integrates graph-structured information for knowledge reasoning in tobacco pest and disease control. Built upon the GraphRAG framework, the proposed method enhances knowledge retrieval and reasoning by explicitly incorporating structured information from a domain-specific knowledge graph. Specifi

Yanchen Chen, Daniel Andrés Díaz-Pachón

We introduce conserved active information $I^\oplus$, a symmetric extension of active information that quantifies net information gain/loss across the entire search space, respecting No-Free-Lunch conservation. Through Bernoulli and uniform-baseline examples, we show $I^\oplus$ reveals regimes hidden from KL divergence, such as when strong knowledge reduces

Renhong Liang, Mao Ye, Renkui Zheng, Jianhua Hao

van der Waals (vdW) epitaxy is conventionally regarded as a rotation-free and strain-free growth mode driven by weak, isotropic interactions, yet many interfaces paradoxically exhibit strictly locked orientations that defy standard surface-energy models. We resolve this inconsistency by establishing a unified quantitative framework for 2D-3D systems, in whic

Yunguo Yu

Automated clinical ICD-10 coding is a high-impact healthcare task requiring a balance between coverage, precision, and safety. While neural approaches achieve strong performance, they suffer from hallucination-generating invalid or unsupported codes-posing unacceptable risks in safety-critical clinical settings. Rule-based systems eliminate hallucination but

Renlong Jie, Longfeng Zhao, Chen Chu, Danyang Jia

While scholarly citations are pivotal for assessing academic impact, they often reflect systemic biases beyond research quality. This study examines a critical yet underexplored driver of citation disparities: authors' structural positions within scientific collaboration networks. Through a large-scale analysis of 17,942 papers from three top-tier machine le

M. H. Zeb, M. Z. Kabir

We tackle the challenge of predicting vibrational stability in inorganic semiconductors for high-throughput screening, an essential attribute for evaluating synthesizability alongside thermodynamic stability, frequently missing in prominent materials databases. We create a physics-informed neural network (PINN) that incorporates the Born stability requiremen

Peter Potaptchik, Cheuk-Kit Lee, Michael S. Albergo

We propose a simple, scalable algorithm for using stochastic interpolants to sample from unnormalized densities and for fine-tuning generative models. The approach, Tilt Matching, arises from a dynamical equation relating the flow matching velocity to one targeting the same distribution tilted by a reward, implicitly solving a stochastic optimal control prob

YuXiang Kong, JunFeng Hou, Jian Tang, Bingqing Zhu

Large language model (LLM)-based automatic speech recognition (ASR) has recently achieved strong performance across diverse tasks, yet contextual biasing for named entities and hotwords under large vocabularies remains challenging. In this work, we propose a scalable two-stage framework that integrates hotword retrieval with LLM-ASR adaptation. First, we ext

Xuanyu Chen, Yue Zheng, Junqing Zhang, Guanxiong Shen

The Internet of Drones (IoD) is an emerging and crucial paradigm enabling advanced applications that require seamless, secure communication across heterogeneous and untrusted domains. In such environments, access control and the transmission of sensitive data pose significant security challenges for IoD systems, necessitating the design of lightweight mutual