March 2026 arXiv papers — page 26

Duanyi Yao, Changyue Li, Zhicong Huang, Cheng Hong

Vision-Language Models (VLMs) are increasingly deployed in consumer applications where users seek recommendations about products, dining, and services. We introduce Hidden Ads, a new class of backdoor attacks that exploit this recommendation-seeking behavior to inject unauthorized advertisements. Unlike traditional pattern-triggered backdoors that rely on ar

Cong Yi, Shi Pu, Long-Gang Pang, Guang-You Qin

Significant global polarization of $\Lambda$ hyperons along the direction of the orbital angular momentum has been measured in non-central heavy-ion collisions where the equation of state (EOS) of the produced dense matter is expected to change from intermediate to low colliding energies. We study the sensitivity of the global $\Lambda$ polarization to EOS i

Zhixuan Liu, Peter Schaldenbrand, Yijun Li, Long Mai

We present TokenDial, a framework for continuous, slider-style attribute control in pretrained text-to-video generation models. While modern generators produce strong holistic videos, they offer limited control over how much an attribute changes (e.g., effect intensity or motion magnitude) without drifting identity, background, or temporal coherence. TokenDi

Jiahao Niu, Rongjia Zheng, Wenju Xu, Wei-Shi Zheng

We present SGS-Intrinsic, an indoor inverse rendering framework that works well for sparse-view images. Unlike existing 3D Gaussian Splatting (3DGS) based methods that focus on object-centric reconstruction and fail to work under sparse view settings, our method allows to achieve high-quality geometry reconstruction and accurate disentanglement of material a

Gaurav Chaudhary, Laxmidhar Behera, Washim Uddin Mondal

Reinforcement Learning (RL) agents often struggle with inefficient exploration, particularly in environments with sparse rewards. Traditional exploration strategies can lead to slow learning and suboptimal performance because agents fail to systematically build on previously successful experiences, thereby reducing sample efficiency. To tackle this issue, we

Qingfeng Huang, Shengpu Huang, Tingyan Chen, Jing Fan

While Floquet engineering offers a powerful paradigm for manipulating topological phases, particularly Floquet Weyl semimetals, establishing an experimentally feasible strategy for tracking the dynamic evolution of such states remains a significant challenge. Here, we propose that the anomalous Hall effect (AHE), as a sensitive, all-electrical probe, can be

Anirudh Nakra, Min Wu

The widespread deployment of high-fidelity generative models has intensified the need for reliable mechanisms for provenance and content authentication. In-processing watermarking, embedding a signature into the generative model's synthesis procedure, has been advocated as a solution and is often reported to be robust to standard post-processing (such as geo

Tingyan Chen, Shengpu Huang, Jing Fan, Dong-Hui Xu

Achieving a giant anomalous Hall effect (AHE) and enabling its effective tuning are fundamental goals for topological spintronics. Magnetic Weyl semimetals hosting flat bands offer a promising route to maximize the AHE. However, while theoretical models are well-established, realistic material candidates remain scarce. Since the intrinsic anomalous Hall cond

Soghra Ghanavat, Abbas Sabour, Somayeh Mehrabankar

We investigate the dynamical transfer of bipartite entanglement through a two-leg spin-1/2 ladder governed by the anisotropic Heisenberg (XXZ-type) model with a selective magnetic field applied exclusively to the mediating rungs. Starting from a maximally entangled initial rung pair, we demonstrate high-fidelity entanglement transfer to the terminal pair (F_

F. -U. Caja-Lopez

We prove uniqueness of weak solutions to the fuzzy Landau equation and the multiespecies Landau system under suitable integrability assumptions. The results are based on explicit stability estimates in the 2-Wasserstein distance for a broader class of nonlinear equations with singular coefficients. Interestingly, this class includes the 2D incompressible Eul

Xiangyuan Xue, Yuyu Wang, Ruijie Yao, Xiaoyue Ni

Automatic speech recognition (ASR) has been extensively studied on neutral and stationary speech, yet its robustness under post-exercise physiological shift remains underexplored. Compared with resting speech, post-exercise speech often contains micro-breaths, non-semantic pauses, unstable phonation, and repetitions caused by reduced breath support, making t

Haifeng Huang, Yilun Chen, Zehan Wang, Jiangmiao Pang

Recent advancements in multi-modal large language models (MLLMs) have shown strong potential for 3D scene understanding. However, existing methods struggle with fine-grained object grounding and contextual reasoning, limiting their ability to interpret and interact with complex 3D environments. In this paper, we present Chat-Scene++, an MLLM framework that r

Yanjin Yue, Rui-Yang Gong, Shengyong Li, Ze-Liang Xiang

Waveguide quantum electrodynamics (WQED) provides a powerful platform for exploring quantum optical phenomena by enhancing atom-photon interactions through photon confinement in a waveguide. Here we investigate the photon-scattering dynamics of a weak coherent pulse incident from the left on a giant atom coupled to a bidirectional waveguide, focusing on effe

Xiaobing Chen, Weizhao Chen, Qihang Liu

Unconventional magnetism represents a paradigm shift in condensed matter physics, effectively bridging the fast, high-density advantages of antiferromagnets with the facile read-write capability of ferromagnets. Recent developments in spin space group theory have established a systematic methodology to decouple magnetic geometry from relativistic spin-orbit

Yuxi Lu, Kunqi Li, Zhidong Li, Xiaohan Su

Semantic segmentation of remote sensing imagery is fundamental to Earth observation. Achieving accurate results requires integrating not only optical images but also physical variables such as the Digital Elevation Model (DEM), Synthetic Aperture Radar (SAR) and Normalized Difference Vegetation Index (NDVI). Recent foundation models (FMs) leverage pre-traini

Xuenan Li, Michael I. Weinstein

Slowly varying nonuniform strains of non-magnetic wave propagating media with honeycomb symmetry induce an effective- or pseudo-magnetic field, a phenomenon observed first in graphene, and later in photonic crystals and other physical settings. Starting with a discrete nearest-neighbor tight-binding model of a non-uniformly strained honeycomb medium, we deri

Samsul Anwar, Siti Munawarah, Radhiah Radhiah

The rivalry between two football superstars Cristiano Ronaldo and Lionel Messi has always been a subject of extensive discussion. This study aimed to compare the level of consistency between the two players in scoring goals through 6 ways: right-footed kicks, left-footed kicks, penalty kicks, direct free kicks, long-range kicks, and headers. The data analyze

Wenxuan Zhang, Zhouchi Lin, Benzhuo Lu

Accurately characterizing the implied volatility curves is a central challenge in option pricing and risk management. The classical SABR model by Hagan et al. has been widely adopted in practice due to its well-defined stochastic volatility structure and its tractable closed-form approximation for Black implied volatility. However, under complex market condi

Chang Sun, Dongliang Liao, Changxing Ding

Open-vocabulary human-object interaction (HOI) detection aims to localize and recognize all human-object interactions in an image, including those unseen during training. Existing approaches usually rely on the collaboration between a conventional HOI detector and a Vision-Language Model (VLM) to recognize unseen HOI categories. However, feature fusion in th

Juan Xu, Huilong Lai, Yingying Cheng, Wenqiang Yang

In this paper, we report on the largest labelled dataset constructed so far for solving zero-dimensional square nonlinear systems with subdivision-based methods. A brief, non-exhaustive survey with emphasis on the literature from the past two decades is also provided to accompany with the dataset. The value of the dataset has been demonstrated through benchm

Jake Scally, Austin Myers, Ryan Carmichael, Phat Tran

Current quantum computers present significant noise, especially as circuit depth and qubit count increase. Prior work has demonstrated that erroneous outcomes exhibit some behavior in Hamming space, enabling improvements in the output distributions of NISQ-era computers. We present HAMMR-L: a principled post-processing technique for improving the fidelity of

Dongsoo Han

The emergence of artificial intelligence and robotics is catalyzing a profound transformation in the nature of human labor, fueling a contentious debate about the future of employment. While prominent studies predict substantial job displacement due to automation, historical precedents from past technological revolutions suggest that innovation tends to expa

Ke Xu, Ze Tao, Fujun Liu

Accurately and stably solving the incompressible Navier--Stokes equations with physics-informed neural networks (PINNs) remains challenging, particularly for sparse or noisy observations and for flow regimes in which the local balance among convection, diffusion, and pressure is difficult to capture. To address this issue, we propose a framework, denoted as

Xuanpu Zhao, Zhentao Tan, Dianmo Sheng, Tianxiang Chen

To enhance the perception and reasoning capabilities of multimodal large language models in complex visual scenes, recent research has introduced agent-based workflows. In these works, MLLMs autonomously utilize image cropping tool to analyze regions of interest for question answering. While existing training strategies, such as those employing supervised fi

Yizhe Li, Shixiao Wang, Jian K. Liu

Motor kinematics prediction (MKP) from electroencephalography (EEG) is an important research area for developing movement-related brain-computer interfaces (BCIs). While traditional methods often rely on convolutional neural networks (CNNs) or recurrent neural networks (RNNs), Transformer-based models have shown strong ability in modeling long sequential EEG

Zhaopeng Feng, Liangcai Su, Zhen Zhang, Xinyu Wang

As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing context management methods typically commit to a single fixed strategy throughout the entire trajectory. Such static designs may work well in some states, but they cannot adapt as the

Wankai He, Chengjie Yu

In this paper, motivated by our previous work \cite{HY}, we prove that the minimum of the first Dirichlet eigenvalues for the normalized combinatorial $p$-Laplacian on connected finite graphs with boundary consisting of $n$ edges is only achieved by the tadpole graph $T_{n,3}$. This result extends the Faber-Krahn inequality of Katsuda-Urakawa \cite{KU} to no

Kian Ming A. Chai, Edwin V. Bonilla

We introduce a novel one-parameter variational objective that lower bounds the data evidence and enables the estimation of approximate fractional posteriors. We extend this framework to hierarchical construction and Bayes posteriors, offering a versatile tool for probabilistic modelling. We demonstrate two cases where gradients can be obtained analytically a

Badhon Kumar, Rakibul Islam Kanak, Nishat Sultana, Jiachen Guo

Metal Laser Powder Bed Fusion (PBF-LB/M) is a leading additive manufacturing technique in which part quality and grain morphology are highly dependent on process parameters. Numerous studies of process variations, such as laser power, scan speed, and spot diameter, have demonstrated that they strongly influence melt pool dynamics; however, the effects of pow

Mengxi Yi, David Tyler

In this paper, we study properties of penalized and structured M-estimators of multivariate scatter, based on geodesically convex but not necessarily smooth penalty functions. Existence and uniqueness conditions for these penalized and structured estimators are given. However, we show that the standard fixed-point algorithm which is usually applied to an M-e

Alekhya Pachika, Lu Gao, Lingguang Song, Pan Lu

Considering recent advances in remote sensing satellite systems and computer vision algorithms, many satellite sensing platforms and sensors have been used to monitor the condition and usage of transportation infrastructure systems. The level of details that can be detected increases significantly with the increase of ground sample distance (GSD), which is a

Biman Bagchi

Helices are not generic outcomes of polymer collapse. Collapsed conformations of semiflexible polymers with isotropic attractions typically form globules, toroids, or rod-like structures, as seen in simulations and described by coarse-grained necklace and surface-tension models. Helical conformations, in contrast, are generally absent in minimal theories bas

Ahmad Mahmood, Paul W. Wiseman

Quantitatively mapping three-dimensional (3D) flow, diffusion, and particle density in crowded living cells remains challenging because most dynamic optical microscopy measurements are effectively planar and existing analysis methods struggle with dense, noisy volumetric data. We introduce volumetric spatio-temporal image correlation spectroscopy (vSTICS), a

Guan Ming Wong, Tianqi Hu, Samip Basnet, Chavdar Dutsov

We present the upgraded design, construction, and beam test results for the Muon Trigger Detector (MTD) developed for the muon Electric Dipole Moment (muEDM) experiment at the Paul Scherrer Institute (PSI) in Switzerland. This experiment aims to improve the sensitivity of the muon EDM measurement by more than three orders of magnitude beyond the current limi

Feiding, Yongkang Zhang, Yuhao Liao, Zijian Zeng

Vision--language models (VLMs) are increasingly aligned via Group Relative Policy Optimization (GRPO)-style training. However, relying solely on terminal outcome rewards yields sparse credit assignment in multi-step reasoning, weakening the linkage between visual evidence and intermediate steps and often causing unstable optimization and visual hallucination

Chongyang Zhao, Mingsong Li, Haodong Lu, Dong Gong

Multimodal Continual Instruction Tuning aims to continually enhance Large Vision Language Models (LVLMs) by learning from new data without forgetting previously acquired knowledge. Mixture of Experts (MoE) architectures naturally facilitate this by incrementally adding new experts and expanding routers while keeping the existing ones frozen. However, despite

Masaaki Tokieda

We present the full diagonalization of a non-quadratic bosonic Liouvillian with a two-body loss term. The Liouvillian is shown to be exactly diagonalizable in terms of left and right confluent hypergeometric functions, whose distinction arises from the noncommutative nature of superoperators. The resulting spectral decomposition yields the general solution o

Chuck Akemann

Let \(G\) be a non-discrete, locally compact group with Haar measure \(m\). We prove that there exists a compact set \(K \subset G\) with \(m(K)=0\) such that \(KK^{-1}\) contains a neighborhood of the identity. Moreover, such a set may be constructed inside any prescribed neighborhood of the identity.

Yuchang Liu, Wei Guo, Yan Jiang, Zheng Sun

This paper presents a class of novel high-order fully-discrete entropy stable (ES) discontinuous Galerkin (DG) schemes with explicit time discretization. The proposed methodology exploits a critical observation from [4] that the cell averages of classical DG solutions with forward Euler time stepping satisfy an ``entropy-stable-like'' property. Building on t

Muhammad Jahanzeb, Marvin Schmoll, Paul Weizel, Simon Majoni

We present the development and implementation of an experimental setup designed to investigate attosecond photoionization delays using a dual-sideband RABBITT (Reconstruction of Attosecond Beating By Interference of Two-Photon Transitions) technique. The setup utilizes an attosecond extreme ultraviolet source from high-harmonic generation driven by a carrier

Ishita Agarwal, Ankit Kundu, Christian M. Lange, Jonathan D. Hood

Standard macroscopic QED is built on the second-order Green's function for the electric field and discards open-system boundary terms. Here we develop a first-order electromagnetic operator approach that retains both $\mathbf{E}$ and $\mathbf{H}$ and keeps those boundary terms, naturally leading to a quantum input-output formalism. We recast Maxwell's equati

Aaron Winn, Justine Parmentier, Eleni Katifori, Martin Brandenbourger

Locally broken symmetries are used across fields to transport matter, particles and information in preferential directions. Beyond local mechanisms, spatially distributed nonlinearities in crystalline media have enabled non-reciprocal transport, a rectification mechanism that operates continuously across scales and frequencies. Here, we show that this concep

Jing Gu, Jinhui Chen, Jie Zhao

The chiral magnetic effect (CME) in relativistic heavy-ion collisions originates from a chirality imbalance among quarks within metastable QCD vacuum domains and may be linked to $CP$ violation, which is believed to play a crucial role in the matter-antimatter asymmetry of the universe. Over the past two decades, extensive experimental efforts at RHIC and th

Andrei S. Rapinchuk

Let $G$ be an absolutely almost simple simply connected algebraic group defined over a number field $K$, and let $M/K$ be the minimal Galois extension over which $G$ becomes an inner form of a split group. Assume that $G$ satisfies the Margulis-Platonov conjecture over $K$. We prove that if $S$ is a set of valuations of $K$ that contains all archimedean ones

Hanghang Cui, Arash Khalatbarisoltani, Jie Han, Wenxue Liu

Effective co-optimization of energy management strategy (EMS) and thermal management (TM) is crucial for optimizing fuel efficiency in hybrid electric vehicles (HEVs). Driving conditions significantly influence the performance of both EMS and TM in HEVs. This study presents a novel driving condition-aware integrated thermal and energy management (ITEM) frame

Robert Erdelyi, Adam Papp Levente Maucha, Philipp Pirro, Matthias Wagner

Magnonic devices exhibit strong amplitude-dependent nonlinearities, which are detrimental to signal integrity in radio-frequency (RF) signal processing applications. They also limit the power that such magnonic devices may process. In this paper we use micromagnetic simulations to characterize the nonlinearity of magnonic RF devices by investigating their in

Suraj Ranganath, Vaishak Menon, Anish Patnaik

Self-forcing video generation extends a short-horizon video model to longer rollouts by repeatedly feeding generated content back in as context. This scaling path immediately exposes a systems bottleneck: the key-value (KV) cache grows with rollout length, so longer videos require not only better generation quality but also substantially better memory behavi

Dipkumar Patel

We compress KV cache entries by quantizing angles in the Fast Walsh-Hadamard domain, where a random diagonal rotation makes consecutive element pairs approximately uniformly distributed on the unit circle. We extend this angular quantizer with per-layer early-boost, which independently configures K and V codebook sizes at each layer, allocating higher precis

Ferdinand Georg Frobenius, Ludwig Stickelberger

This is an English translation and digitisation of Frobenius' and Stickelberger's "On the theory of elliptic functions" first published in Journal fur die reine und angewandte Mathematik (Crelle's journal), 83, 175-179 (1877) with the title "Zur Theorie der elliptischen Functionen". The paper derives what is now known as the Frobenius and Stickelberger deter

Adrian Faigon

The particle in an expanding/contracting 1-dimension box is revisited in action-angle like variables with direct thermodynamic interpretation. An angle dependent potential is proposed accurately describing the mechanical behavior while also capturing thermodynamic evolution -- entropy production -- within a canonical Hamiltonian framework. Heat transfer at c

Mahdi Erfanian, Abolfazl Asudeh

We demonstrate NeedleDB, an open-source, deployment-ready database system for answering complex natural language queries over image data. Unlike existing approaches that rely on contrastive-learning embeddings (e.g., CLIP), which degrade on compositional or nuanced queries, NeedleDB leverages generative AI to synthesize guide images that represent the query

Cyrus S. McCrimmon, Pulong Ma

Risk assessment of hurricane-driven storm surge relies on deterministic computer models that produce outputs over a large spatial domain. The surge models can often be run at a range of fidelity levels, with greater precision yielding more accurate simulations. Improved accuracy comes with a significant increase in computational expense, necessitating the de

Mohsen Dehghankar, Abolfazl Asudeh

Matrix-vector multiplication is a fundamental building block in neural networks, vector databases, and large language models, particularly during inference. As a result, efficient matrix-vector multiplication engines directly translate into more efficient inference. Recent work has explored low-bit quantization of model weights, where matrices are represente

Rita Garcia, Ellie Lovellette, Xi Wu, Angela Zavaleta Bernuy

Replication studies play an important role in Computing Education Research (CER) by supporting the development of consistent and reliable scientific knowledge. However, prior research indicates that the CER community tends to prioritise novel contributions over replication. A 2019 SLR identified only 54 replication studies among 2,269 papers published betwee

Zhongying Deng, Cheng Tang, Ziyan Huang, Jiashi Lin

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical expertise and strict ethical and privacy

Tsung-Yu Yang, Meng-Chi Chen

Environmental, Social, and Governance (ESG) reports are central to investment decision-making, yet their length, heterogeneous content, and lack of standardized structure make manual analysis costly and inconsistent. We present ESGLens, a proof-of-concept framework combining retrieval-augmented generation (RAG) with prompt-engineered extraction to automate t

Xiaoting Li, Harry Joe

We develop an extreme value framework for CoVaR centered on $v(q \mid p ; C)$, the copula-adjusted probability level, or equivalently, the CoVaR on the uniform (0,1) scale. We characterize the possible tail regimes of $v(q \mid p ; C)$ through the limit behavior of the copula conditional distribution and show that these regimes are determined by the joint ta

Jianqing Fan, Zheng Tracy Ke, Zhaoyang Shi

Motivated by applications in statistics and machine learning, we consider a problem of unmixing convex combinations of nonparametric densities. Suppose we observe $n$ groups of samples, where the $i$th group consists of $N_i$ independent samples from a $d$-variate density $f_i(x)=\sum_{k=1}^K \pi_i(k)g_k(x)$. Here, each $g_k(x)$ is a nonparametric density, a

Yoshinori Namikawa

This is a continuation of arXiv: 2408.03012. We answer affirmatively Question 5.10 posed in the previous article. More precisely, let $(X, \omega)$ be a conical symplectic variety of dimension $2n$ with $wt(\omega) = 2$, which has a projective symplectic resolution. Assume that $X$ admits an effective Hamiltonian action of an $n$-dimensional algebraic torus

Gen Fukuzawa, Yebin Lee, Teruyasu Mizoguchi

Controlling ion transport is a fundamental challenge for advanced energy storage. Bilayer graphene offers a unique platform for modulating ion diffusion via twist-angle-dependent moire superlattices, yet conventional stacking configurations face an inherent trade-off: AA stacking provides stable Li intercalation but high diffusion barriers, while AB stacking

Ethan Fisk, Taeyoon Lee, Shenli Yuan

Controlling friction at the fingertip is fundamental to dexterous manipulation, yet remains difficult to realize in robotic hands. We present the design and analysis of a robotic fingertip equipped with passive rollers that can be selectively braked or pivoted to modulate contact friction and constraint directions. When unbraked, the rollers permit unconstra

Jakub Bąba, Jarosław A. Chudziak

Argument Mining (AM) is a foundational technology for automated writing evaluation, yet traditional supervised approaches rely heavily on expensive, domain-specific fine-tuning. While Large Language Models (LLMs) offer a training-free alternative, they often struggle with structural ambiguity, failing to distinguish between similar components like Claims and

Yen-Hsun Huang, Yu-Shiou Lin

Systematic reviews and meta-analyses (SR/MA) remain the gold standard for evidence synthesis, yet completing one typically requires 67 weeks and substantial expert effort. Recent large language model (LLM) systems have demonstrated strong performance on individual SR phases - screening (otto-SR: 96.7% sensitivity), extraction (Gartlehner et al.: 91.0% accura

Loïs Vanhée, Melania Borit

Realism is a central yet seemingly under-theorized concept in Agent-Based Modelling. This paper presents a Systematic Literature Review, aiming to identify how realism is currently operationalized and demonstrated. The results show that realism is often poorly defined and lacks a consistent conceptual framework. A wide variety of methods are used to achieve

Teddy X Cai, Nathan H Williamson, Peter J Basser

Diffusion exchange spectroscopy (DEXSY) is a method to probe exchange between domains of varying confinement. Analyses of DEXSY signals typically assume Gaussian diffusion within distinct compartments and first-order exchange kinetics between them. Other situations can yield DEXSY signal contrast with respect to mixing time, however, leading to potentially e

D. A. Saltykova, A. V. Yulin, I. A. Shelykh

We study nonequilibrium mode selection in dissipative exciton-polariton condensates incoherently pumped through an excitonic reservoir in the presence of pure energy relaxation. For a confined system in which a vortex mode is selected at threshold, we show that energy relaxation qualitatively changes the condensation scenario: as the pump increases, the asym

Giorgio Angelotti, Federica Nicolardi, Paul Henderson, W. Brent Seales

Reading the Herculaneum papyri is challenging because both the scrolls and the ink, which is carbon-based, are carbonized. In X-ray radiography and tomography, ink detection typically relies on density- or composition-driven contrast, but carbon ink on carbonized papyrus provides little attenuation contrast. Building on the morphological hypothesis, we show

Duraimurugan Rajamanickam

Statistical fairness metrics in AI-driven credit decisions conflate two causally distinct mechanisms: discrimination operating directly from a protected attribute to a credit outcome, and structural inequality propagating through legitimate financial features. We formalise this distinction using Pearl's framework of natural direct and indirect effects ap

Alexey Kabalnov

Additives of sparingly soluble components are known to slow down or completely inhibit Ostwald ripening in dispersed systems. In this paper series, our earlier model of the stabilization against Ostwald ripening is revisited and extended over the whole range of compositions, molar volumes of components, and their activity coefficients. In the first paper, a

Arun Govindankutty

As the number of qubits increases, quantum circuits become more complex and their state space grows rapidly. This makes functional verification challenging for conventional techniques. Ensuring correctness is especially critical for quantum error correction and entanglement generation. This paper presents a novel application of bit-vector based abstraction m

Eeshan Beohar, Abhirup Datta, Anshuman Tripathi, Samit Kumar Pal

The observations of the redshifted 21-cm signal from neutral hydrogen are a promising probe for understanding the Cosmic Dawn and the Epoch of Reionisation (EoR). One of the primary obstacles to the statistical detection of the Cosmological signal is the presence of residual foreground arising from gain calibration errors. Previous studies have shown that ga

Andriy Goychuk

Phase separation into compositionally and physically distinct domains is ubiquitous in (non)living matter ranging from alloys and emulsions to biomolecular condensates in cells. The organization of these domains can be controlled, for example, by nonequilibrium chemical reactions, external fields, or mechanical stresses. In this context, stationary states ca

Jakub Herko, Mark A. Caprio, Anna E. McCoy, Patrick J. Fasano

The group $\mathrm{SU}(3)$ has applications in several branches of physics. Many of these applications depend on availability of $\mathrm{SU}(3)$ coupling and recoupling coefficients. We have developed a modern Fortran library for calculation of the coupling coefficients, for both the $\mathrm{SU}(3)\supset\mathrm{U}(1)\times\mathrm{SU}(2)$ and $\mathrm{SU}(

Zhizhi Peng, Chonghe Zhao, Taotao Wang, Guofu Liao

Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data privacy, and model confidentiality. Zero-knowledge proofs (ZKPs) provide a compelling foundation for verifiable machine learning because they allow one party to certify that a training

Quankai Gao, Jiawei Yang, Qiangeng Xu, Le Chen

Learning human-object manipulation presents significant challenges due to its fine-grained and contact-rich nature of the motions involved. Traditional physics-based animation requires extensive modeling and manual setup, and more importantly, it neither generalizes well across diverse object morphologies nor scales effectively to real-world environment. To

Giorgio Giannone, Anna Clare Doris, Amin Heyrani Nobari, Kai Xu

Generating executable CAD programs from images requires alignment between visual geometry and symbolic program representations, a capability that current methods fail to learn reliably as design complexity increases. Existing fine-tuning approaches rely on either limited supervised datasets or expensive post-training pipelines, resulting in brittle systems t

Edgar A. Bering, Jakob Heikamp, Jack Kohav, Nir Lazarovich

We prove that any ascending chain of bounded rank subgroups in the fundamental group of a compact $3$-manifold stabilizes. We use geometrization to reduce the proof to fundamental groups of complete, finite-volume hyperbolic $3$-manifolds. To handle this case, we prove the following: In a toral relatively hyperbolic group, any ascending chain of bounded rank

Arjun Sharma, Sabarish V. Narayanan, Sarah Hormozi, Donald L. Koch

We develop a numerical framework to simulate the locomotion of a flagellated bacterium with a spheroidal head (such as Escherichia coli) in biological fluids like mucus, which are entangled polymer solutions exhibiting elasto-viscoplastic (EVP) rheology and porous microstructure. To account for the scale disparity between the large bacterial head and the sle

Yang Wang, Juan José García-Ripoll, Alan C. Santos

Combining decoherence protection with directional photon emission in a single waveguide quantum electrodynamics (QED) device remains an open challenge. Here we show that an artificial giant molecule -- strongly interacting artificial atoms coupled to a photonic waveguide at multiple spatially separated points -- achieves both: a fully operational decoherence

Shafayeth Jamil, Rehan Kapadia

When the system is linear, why should learning be nonlinear? Linear dynamical systems, the analytical backbone of control theory, signal processing and circuit analysis, have exact closed-form solutions via the state transition matrix. Yet when system parameters must be inferred from data, recent neural approaches offer flexibility at the cost of physical gu

Talha Ruzgar Akkus, Suayp Talha Kocabay, Kamer Ali Yuksel, Hassan Sawaf

The Forward-Forward (FF) algorithm trains networks layer-by-layer using a local "goodness function," yet sum-of-squares (SoS) has remained the only choice studied. We systematically explore the goodness-function design space and identify a unifying principle: the goodness function must be sensitive to the shape of neural activity, not its total energy. This

Xinyao Zhang, Chang Liu, Xiao Liang, Minghui Zheng

Precise segmentation of irregular and densely arranged components is essential for robotic disassembly and material recovery in electronic waste (e-waste) recycling. This study evaluates the impact of model architecture and scale on segmentation performance by comparing SAM2, a transformer-based vision model, with the lightweight YOLOv8 network. Both models

Eason Chen, Isabel Wang, Nina Yuan, Sophia Judicke

Behavioral analysis of tutoring dialogues is essential for understanding student learning, yet manual coding remains a bottleneck. We present a methodology where LLM coding agents autonomously improve the prompts used by LLM classifiers to label educational dialogues. In each iteration, a coding agent runs the classifier against human-labeled validation data

Masoud Heidary, Biresh Kumar Joardar

The dependability of AI models relies largely on the reliability of the underlying computation hardware. Hardware aging attacks can compromise the computing substrate and disrupt AI models over the long run. In this work, we present a new hardware aging attack that exploits commutative properties of addition to disrupt the multiply-and-add operation that for

Riccardo Terrenzi, Phongsakon Mark Konrad, Tim Lukas Adam, Serkan Ayvaz

Ad hoc dataset search requires matching underspecified natural-language queries against sparse, heterogeneous metadata records, a task where typical lexical or dense retrieval alone falls short. We reposition dataset search as a software-architecture problem and propose a bounded, auditable reference architecture for agentic hybrid retrieval that combines BM

Jacky Liang

We propose a stylized model of human-AI collaboration that isolates a mechanism we call the novelty bottleneck: the fraction of a task requiring human judgment creates an irreducible serial component analogous to Amdahl's Law in parallel computing. The model assumes that tasks decompose into atomic decisions, a fraction $\nu$ of which are "novel" (not covere

Jian Zhang, Shijie Zhou, Bangya Liu, Achuta Kadambi

Large vision-language models (VLMs) still struggle with reliable 3D spatial reasoning, a core capability for embodied and physical AI systems. This limitation arises from their inability to capture fine-grained 3D geometry and spatial relationships. While recent efforts have introduced multi-view geometry transformers into VLMs, they typically fuse only the

Danilo Polo Ojito, Emil Prodan

We first prove that the subalgebra $\mathcal{C}$ generated by the vertex and face operators of an abelian Kitaev model is a $C^\ast$-diagonal of the UHF algebra $\mathcal{A}$ of quasilocal observables. This gives us access to the Weyl groupoid $\mathcal{G}_\mathcal{C}$ associated with the $C^\ast$-inclusion $\mathcal{C} \hookrightarrow \mathcal{A}$, which su

Hricha Acharya, Zilin Jiang, Shengtong Zhang

We prove that for every integer $d \ge 3$, the median eigenvalues of any graph of maximum degree $d$ are bounded above by $\sqrt{d-1}$. We also prove that, in three separate cases, the median eigenvalues of a graph of maximum degree $d$ are bounded below by $-\sqrt{d-1}$: when the graph is triangle-free, when $d-1$ is a perfect square, or when $d \ge 75$. Th

Jiahe Shen

We investigate the $k$-cycle shuffle on repeated cards, namely on a deck consisting of $l$ identical copies of each of $m$ card types, with total size $n=ml$. We establish asymptotic results for the total variation mixing of this shuffle, including cutoff and explicit limiting profiles. For fixed $l$, we show that the walk exhibits cutoff at time $\frac{n}{k

Sungbae Chun

LayerNorm and RMSNorm impose fundamentally different geometric constraints on their outputs - and this difference has a precise, quantifiable consequence for model complexity. We prove that LayerNorm's mean-centering step, by confining data to a linear hyperplane (through the origin), reduces the Local Learning Coefficient (LLC) of the subsequent weight matr

Juan-Pablo Llerena-Córdova

Let $X$ be a compact Riemann surface of genus 2 and $D$ a very ample divisor with $\phi_D$ its associated embedding into $\mathbb{P}^{n}$. We consider the set $G_{X,D}$ of linear subspaces $W$ of $\mathbb{P}^n$ of codimension $2$ with projection $\pi_W$ such that $f_W = \pi_W \circ \phi_D$ is Galois, i.e. $f_W^*k(\mathbb{P}^1) \subseteq k(X)$ is a Galois ext

Tanvir Sohail, Burigede Liu, Swarnava Ghosh

We present a neural-operator-accelerated concurrent multiscale framework that couples atomistic simulations with continuum finite-element analysis for history-dependent materials, thereby making atomistic-continuum multiscale simulations of viscoelastic materials tractable. The approach replaces direct molecular dynamics (MD) evaluation of the constitutive r

Nikolas Chatzis, Angeliki Tsinouka, Katerina Papadimitriou, Niki Efthymiou

Accurate 6D pose estimation for robotic harvesting is fundamentally hindered by the biological deformability and high intra-class shape variability of agricultural produce. Instance-level methods fail in this setting, as obtaining exact 3D models for every unique piece of produce is practically infeasible, while category-level approaches that rely on a fixed

Luís Atayde, Simão Marques Nunes, Noemi Frusciante

We study a minimal modified gravity scenario in the symmetric teleparallel (non-metricity) formulation, focusing on an inverse non-metricity term with $f(Q)=Q+M^4 Q^{-1}$. The model does not introduce additional free parameters relative to $\Lambda$CDM, but modifies the late-time expansion and linear growth via an enhanced effective gravitational coupling. W

Mohammad Javad Najafirad, Shirantha Welikala

This paper presents a dissipativity-based distributed droop-free control and communication topology co-design framework for voltage regulation and current sharing in DC microgrids (MGs), where constant-power loads (CPLs) and voltage-source converter (VSC) input saturation introduce significant nonlinearities. In particular, CPLs introduce an inherently desta

Diana Savin

Let $\left(P_{n}\right)_{n\geq0}$ be the sequence of bi-periodic Padovan numbers and let $\left(M_{p_{n}}\right)_{n\geq0}$ be the sequence of bi-periodic Padovan matrices. In this article we study when these matrices are diagonalizable and we obtain a certain connection with the Lucas number sequence. We also obtain some connections of these matrices with th

Heli Elorreaga, Gonzalo Robledo, David Urrutia-Vergara

In this article, we complete the study of the equivalences between the properties of $h$--dichotomy, $h$--noncriticality and $h$--expansiveness of a linear nonautonomous ODE system which had been initiated in a previous work. Moreover, we extend a result of the generalized Floquet theory developed by T.A. Burton and J.S. Muldowney by providing a necessary an

Yiyang Jiang, Xudong Chen

In this paper, we focus on the class of complete $S$-partite graphs, for $S$ an undirected graph possibly with self-loops, and address the problem of finding largest $2$-regular subgraphs of these graphs, which can be formulated as an integer linear program. Roughly speaking, a complete $S$-partite graph is obtained by replacing every single node of $S$ with

Mahesh Natarajan, Xiaoye Li, Weiqun Zhang

We present AstraAI, a command-line interface (CLI) coding framework for high-performance computing (HPC) software development. AstraAI operates directly within a Linux terminal and integrates large language models (LLMs) with Retrieval-Augmented Generation (RAG) and Abstract Syntax Tree (AST)-based structural analysis to enable context-aware code generation