November 2025 arXiv papers — page 44

Justin Bennett

Murray-type flux-radius laws, Gilbert-type concave transport costs, and Young-Herring triple-junction angle balances are usually treated as separate theories. This work shows that, within a natural class of quadratic, scale-free ledgers for branched networks, all three are different faces of a single structure controlled by one dimensionless index chi. Each

Dapeng Zhang, Fei Shen, Rui Zhao, Yinda Chen

Autonomous driving represents a prominent application of artificial intelligence. Recent approaches have shifted from focusing solely on common scenarios to addressing complex, long-tail situations such as subtle human behaviors, traffic accidents, and non-compliant driving patterns. Given the demonstrated capabilities of large language models (LLMs) in unde

Maryam Eftekharifar, Churun Zhang, Jialiang Wei, Xudong Cao

We present a framework that pioneers the prediction of photochemical conversion in complex three-dimensionally printed objects, introducing a challenging new computer vision task: predicting dense, non-visual volumetric physical properties from 3D visual data. This approach leverages the largest-ever optically printed 3D specimen dataset, comprising a large

Dapeng Zhang, Zhenlong Yuan, Zhangquan Chen, Chih-Ting Liao

Vision-Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving. However, existing VLAs often struggle with achieving efficient inference and generalizing to novel autonomous vehicle configurations and driving scenarios. In this paper, we propose Reasoning-VLA, a general and fast action-generation VLA framewo

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

We present a search for gravitational waves from inspiraling, planetary-mass ultra-compact binaries using data from the first part of the fourth observing run of LIGO, Virgo and KAGRA. Finding no evidence of such systems, we determine the maximum distance reach for such objects and their merger rate densities, independently of how they could have formed. The

Jun Jia, Hongyi Miao, Yingjie Zhou, Linhan Cao

With the rapid advancement of diffusion models, a variety of fine-tuning methods have been developed, enabling high-fidelity image generation with high similarity to the target content using only 3 to 5 training images. More recently, zero-shot generation methods have emerged, capable of producing highly realistic outputs from a single reference image withou

Mazyar Taghavi, Javad Vahidi

This study introduces a quantum inspired framework for optimizing the exploration exploitation tradeoff in multiagent reinforcement learning, applied to UAVassisted 6G network deployment. We consider a cooperative scenario where ten intelligent UAVs autonomously coordinate to maximize signal coverage and support efficient network expansion under partial obse

Haoxuan Wang, Jiachen Tao, Junyi Wu, Gaowen Liu

We present Motion Marionette, a zero-shot framework for rigid motion transfer from monocular source videos to single-view target images. Previous works typically employ geometric, generative, or simulation priors to guide the transfer process, but these external priors introduce auxiliary constraints that lead to trade-offs between generalizability and tempo

Pawan Kumar, Marta Krasowska, Joseph D. Berry

Understanding contact line dynamics on superhydrophobic surfaces with microscopic structures is essential for designing materials with reduced drag, anti-icing, self-cleaning, and anti-fouling properties. Using numerical simulations, we demonstrate that forces on droplets receding over structured surfaces are governed by microscale deformations near the cont

Mingyu Zhao, Zhanfu Yang, Yang Zhou, Zhaoyang Xia

This paper employs a multimodal approach for continuous sign recognition by first using ML for detecting the start and end frames of signs in videos of American Sign Language (ASL) sentences, and then by recognizing the segmented signs. For improved robustness we use 3D skeletal features extracted from sign language videos to take into account the convergenc

Pawan Kumar, Joseph D. Berry

A numerical model is proposed to simulate the adhesion, compression, and subsequent detachment of a micro-liter droplet from a superhydrophobic surface composed of chemically homogeneous pillars arranged in a periodic fashion, replicating a typical force probe microscopy experiment. We observe that as the droplet is pulled away from the surface, the net vert

Fangyi Chen, Shu Ge, Jian Qian, Christopher Harshaw

We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators in a design-based setting, where potential outcomes and covariates are deterministic. As each subject arrives, an adaptive procedure must select both a treatment assignment probability and a pair of linear predictors to be used in the AIPW estimator. Our goal is to constru

Tan Su

Sullivan--Simons developed a Cheeger--Simons differential character analogue for degree (0 mod 2) differential K-theory, giving a complete set of numerical invariants that determine a complex vector bundle with unitary connection on a base manifold X, up to Chern--Simons equivalence of the connection. In this paper we develop a degree (0 mod 8) differential

A. V. Tsiganov

The Lagrange identity expresses the second derivative of the moment of inertia of a system of material points through kinetic energy and homogeneous potential energy, from which follows the Jacobi well-known result on the instability of a system of gravitating bodies. In this work, it is proven that if a Hamiltonian system satisfies the Lagrange identity, th

Yunxiao Wang

Recent advances in artificial intelligence (AI), particularly deep learning, have led to widespread adoption across various applications. Yet, a fundamental challenge persists: how can we verify the correctness of AI model inference when model owners cannot (or will not) reveal their parameters? These parameters represent enormous training costs and valuable

Subhasis Nalui, Subhra Bhattacharya

We consider the inhomogeneous Morris-Thorne wormhole metric with matter tensors characterised by a novel linear equation of state in $f(R)$ gravity. Using the Einstein's field equations in metric $f(R)$ gravity we model solutions for both wormhole as well as $f(R)$ gravity. We obtain four different wormhole models, two wormholes are characterised by solid an

Jiaqi Liu, Kaiwen Xiong, Peng Xia, Yiyang Zhou

Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotated supervision. Recent self-rewarding approaches attempt to overcome this constraint by allowing models to act as their own critics or reward providers. Yet, purely text-based self

Yuyi Li, Daoyuan Chen, Zhen Wang, Yutong Lu

Large Vision-Language Models (LVLMs) show promise for scientific applications, yet open-source models still struggle with Scientific Visual Question Answering (SVQA), namely answering questions about figures from scientific papers. A key bottleneck is the lack of public, large-scale, high-quality SVQA datasets. Although recent work uses LVLMs to synthesize d

Sayantan Choudhury, Swapnil Kumar Singh, Satish Kumar Sahoo

We perform a precision investigation of smooth quintessential inflation in which a single canonical scalar field unifies the two known phases of cosmic acceleration. Using a CMB-normalized runaway exponential potential, we obtain sharply predictive inflationary observables: a red-tilted spectrum with $n_s = 0.964241$ and an exceptionally suppressed tensor-to

Parosh Aziz Abdulla, Yu-Fang Chen, Michal Hečko, Lukáš Holík

We present the first fully automatic framework for verifying relational properties of parameterized quantum programs, i.e., a program that, given an input size, generates a corresponding quantum circuit. We focus on verifying input-output correctness as well as equivalence. At the core of our approach is a new automata model, synchronized weighted tree autom

Shunsuke Nishimura, Takeyuki Tsuji, Takayuki Iwasaki, Mutsuko Hatano

Type-II superconductors exhibit hysteretic behavior due to the presence of quantum vortices, and the order in which temperature and external field are varied plays a decisive role. Here we take current, rather than magnetic field, as the external drive. We image the magnetic field of a high-$T_c$ cuprate superconductor strip after cooling. We confirm that ev

Yuanyuan Lin, Xiangyu Ouyang, Teng Zhang, Kaixin Sui

Tree search-based methods have made significant progress in enhancing the code generation capabilities of large language models. However, due to the difficulty in effectively evaluating intermediate algorithmic steps and the inability to locate and timely correct erroneous steps, these methods often generate incorrect code and incur increased computational c

Sixtus Dakurah

In recent years there has been a paradigm shift from the study of local task-related activation to the organization and functioning of large-scale functional and structural brain networks. However, a long-standing challenge in this large-scale brain network analysis is how to compare network organizations irrespective of their complexity. The maximum spannin

Fengyi Xu, Jun Ma, Waishan Qiu, Cui Guo

Crowdsourced social media imagery provides real-time visual evidence of urban flooding but often lacks reliable geographic metadata for emergency response. Existing Visual Place Recognition (VPR) models struggle to geo-localize these images due to cross-source domain shifts and visual distortions. We present VPR-AttLLM, a model-agnostic framework integrating

Shuoyan Xu, Yu Zhang, Eric J. Miller

Ride-hailing platforms are characterized by high-frequency, behavior-driven environments. Although survival analysis has been applied to recurrent events in other domains, its use in modeling ride-hailing driver behavior remains largely unexplored. This study formulates idle behavior as a recurrent survival process using large-scale platform data and propose

Valeria Rios-Vargas, Ezekiel Oyeniyi, Xuecheng Shao, Wala Fathelrahman Ibrahim Elsayed

Developing reliable pseudopotentials for orbital-free density functional theory (OF-DFT), especially for transition metals, remains a significant challenge. In this study, we provide a theoretical framework for analyzing pseudization strategies for OF-DFT calculations. From the analysis arises a proposed pseudization method which involves constructing local

Pantelis Stefanakis, Ming Shen

Phase-Based Ranging (PBR) offers several advantages for estimating distances between wirelessly connected devices, including high accuracy over large distances and the removal of the need for antenna arrays at each transceiver. This study investigates the use of Neural Network (NN)-based models for accurate PBR in three distinct environments: Openfield, Offi

Zhe Liu, Kai Han, Siqi Ma, Yan Zhu

Computer-aided diagnosis (CAD) technology can assist clinicians in evaluating liver lesions and intervening with treatment in time. Although CAD technology has advanced in recent years, the application scope of existing datasets remains relatively limited, typically supporting only single tasks, which has somewhat constrained the development of CAD technolog

Chenna Reddy Kammadhanam

The meteor occurrence height and decay time height are strongly dependent on local atmospheric conditions in the mesosphere and lower thermosphere (MLT)-region. In this study, we comparatively examine the seasonal behaviour of vertical distribution of meteor occurrence height and decay time height at two identical radars of conjugate polar latitudes, Esrange

Junhong Liu, Yuan Zhang, Tao Huang, Wenchao Xu

Knowledge distillation (KD) has proven highly effective for compressing large models and enhancing the performance of smaller ones. However, its effectiveness diminishes in cross-modal scenarios, such as vision-to-language distillation, where inconsistencies in representation across modalities lead to difficult knowledge transfer. To address this challenge,

Chi Liu, Tianqing Zhu, Wanlei Zhou, Wei Zhao

As deep image forgery powered by AI generative models, such as GANs, continues to challenge today's digital world, detecting AI-generated forgeries has become a vital security topic. Generalizability and robustness are two critical concerns of a forgery detector, determining its reliability when facing unknown GANs and noisy samples in an open world. Althoug

Chenglu Sun, Shuo Shen, Haonan Hu, Wei Zhou

Despite advancements in language-controlled reinforcement learning (LC-RL) for basic domains and straightforward commands (e.g., object manipulation and navigation), effectively extending LC-RL to comprehend and execute high-level or abstract instructions in complex, multi-agent environments, such as football games, remains a significant challenge. To addres

Mobina Nankali, Michael W. Levin

This work addresses electric vehicle (EV) charging station placement through a bi-level optimization model, where the upper-level planner maximizes net revenue by selecting station locations under budget constraints, while EV users at the lower level choose routes and charging stations to minimize travel and charging costs. To account for range anxiety, we c

Fan Ye

We establish a dimension formula for the unreduced singular instanton homology of dual knots $\widetilde{K}_{p/q}\subset S^3_{p/q}(K)$ for a knot $K\subset S^3$: $$ \dim I^\sharp(S^3_{p/q}(K),\widetilde{K}_{p/q},\omega; \mathbb{K}) = 2q \cdot r_{\mathbb{K}}(K) + 2|p - q \cdot \nu^\sharp_{\mathbb{K}}(K)|~\mathrm{for}~p/q\neq \nu^\sharp_{\mathbb{K}}(K), $$wher

Lei Ding, Tong Liu, Xuanguang Liu, Xiangyun Liu

Change detection (CD) in multitemporal remote sensing imagery presents significant challenges for fine-grained recognition, owing to heterogeneity and spatiotemporal misalignment. However, existing methodologies based on vision transformers or state-space models typically disrupt local structural consistency during temporal serialization, obscuring discrimin

Bowen Du, Jingshan Zhu, Baoliang Li, Mingwei Ge

Accurate prediction of turbulence kinetic energy (TKE) added by wind-turbine wakes is of significant scientific value for understanding the wake recovery mechanisms. Furthermore, this physical quantity is a critical input for engineering applications. In this study, we propose a novel wake-added TKE prediction model capable of accurately predict the three-di

Prabhat Kumar Chand, Anisur Rahaman Molla

Mobile agents have emerged as a powerful framework for solving fundamental graph problems in distributed settings in recent times. These agents, modelled as autonomous physical or software entities, possess local computation power, finite memory and have the ability to traverse a graph, offering efficient solutions to a range of classical problems. In this w

Ho Jang, Jackson C. Glass, Gia-Wei Chern

We show that Restricted Boltzmann Machines (RBMs) provide a flexible generative framework for modeling spin configurations in disordered yet strongly correlated phases of frustrated magnets. As a benchmark, we first demonstrate that an RBM can learn the zero-temperature ground-state manifold of the one-dimensional ANNNI model at its multiphase point, accurat

Chengyue Huang, Mellon M. Zhang, Robert Azarcon, Glen Chou

Vision-Language-Action (VLA) models inherit strong priors from pretrained Vision-Language Models (VLMs), but naive fine-tuning often disrupts these representations and harms generalization. Existing fixes -- freezing modules or applying uniform regularization -- either overconstrain adaptation or ignore the differing roles of VLA components. We present MAPS

Xiangyu Zhao, Yaling Shen, Yiwen Jiang, Zimu Wang

Depression is one of the most prevalent mental health disorders globally. In recent years, multi-modal data, such as speech, video, and transcripts, has been increasingly used to develop AI-assisted depression assessment systems. Large language models have further advanced this field due to their strong language understanding and generalization capabilities.

Heng Quan, Yi Zhang, Guy Dagan, Stephan Fueglistaler

Tropical regions may experience periodic extreme precipitation and suffer from associated periodic deluges in a warmer climate. Recent studies conducted small-domain (around 100 km x 100 km) atmospheric model simulations and found that precipitation transitions from a steady state to a periodic oscillation state in a hothouse climate when the sea surface tem

Qingyu Zhang, Puzhuo Liu, Peng Di, Chenxiong Qian

Version control relies on commit messages to convey the rationale for code changes, but these messages are often low quality and, more critically, inconsistent with their diffs-known as message-code inconsistency (MCI). MCIs mislead reviewers, hinder maintenance, contaminate research datasets, and may obscure security patches. Yet, no dedicated benchmark exi

Arun Chowdary Sanna

As AI agents become integral to enterprise workflows, their reliance on shared tool libraries and pre-trained components creates significant supply chain vulnerabilities. While previous work has demonstrated behavioral backdoor detection within individual LLM architectures, the critical question of cross-LLM generalization remains unexplored, a gap with seri

Richard Chapling

We consider the Schr\"odinger--Poisson system on the complete, simply-connected Riemannian manifolds of constant sectional curvature. We obtain closed-form stationary spherically-symmetric solutions for the homogeneous equations for certain dimensions, and give some basic examples of solutions with a nonzero background.

Daniel I Jackson, Emma L Jensen, Syed-Amad Hussain, Emre Sezgin

Large language model (LLM) proficiency in evaluating and quantifying their capacities remains uncertain. We conducted a controlled psychometric measurement study adapting the 10-item General Self-Efficacy Scale (GSES) to evaluate simulated self-assessment across 10 contemporary LLMs. Models completed the GSES under a no-task control condition and after three

Kasidis Arunruangsirilert, Pasapong Wongprasert, Jiro Katto

While Time Division Duplexing (TDD) 5G New Radio (NR) networks offers higher downlink throughput due to the utilization of the middle frequency band, the uplink performance is negatively impacted due to higher path loss associated with higher frequencies, which degrade the users QoE in less optimal conditions. With the growing demand for high performance upl

Kasidis Arunruangsirilert, Pasapong Wongprasert, Jiro Katto

While Uplink 256QAM (UL-256QAM) has been introduced since 2016 as a part of 3GPP Release 14, the adoption was quite poor as many Radio Access Network (RAN) and User Equipment (UE) vendors didn't support this feature. With the introduction of 5G, the support of UL-256QAM has been greatly improved due to a big re-haul of RAN by Mobile Network Operators (MNOs).

Eito Sato, Takahiro Wada

Haptic shared control (HSC) is effective in teleoperation when full autonomy is limited by uncertainty or sensing constraints. However, autonomous control performance achieved by maximizing HSC strength is limited because the dynamics of the joystick and human arm affect the robot's behavior. We propose a cooperative framework coupling a joystick-independent

Kasidis Arunruangsirilert

The exponential growth of User-Generated Content (UGC), especially High-Definition (HD) live video streaming, places a significant demand on the uplink capabilities of mobile networks. To address this, the 5G New Radio (NR) standard introduced key uplink enhancements, including Uplink Multi-Input Multi-Output (UL-MIMO) and Uplink 256QAM, to improve throughpu

Yu Tang, Siyuan Wang, Shuang Ren, Chuang Yang

The four-level heterodyne Rydberg atom receiver has garnered significant attention in microwave detection and communication due to its high sensitivity and phase measurement capabilities. Existing theoretical studies, primarily based on static solutions, are limited in characterizing the system's frequency response. To address this, this paper comprehensivel

Yutaka Jitsumatsu, Liangchen Sun

This paper proposes a Prony-based parallel two-stage method for delay-Doppler estimation in OTFS systems. By performing delay-first and Doppler-first estimations in parallel and fusing the results, the method resolves ambiguities caused by similar path characteristics. The simulation results demonstrate the superior accuracy and robustness of the proposed me

Mingkai Chen, Zijie Feng, Lei Wang, Yaser Khamayseh

In the 6G era, semantic collaboration among multiple embodied intelligent devices (MEIDs) becomes crucial for complex task execution. However, existing systems face challenges in multimodal information fusion, adaptive communication, and decision interpretability. To address these limitations, we propose a collaborative Conversational Embodied Intelligence N

Valerie Lockhart, Dan McCreary, Troy A. Peterson

Educational simulations have long been recognized as powerful tools for enhancing learning outcomes, yet their creation has traditionally required substantial resources and technical expertise. This paper introduces MicroSims a novel framework for creating lightweight, interactive educational simulations that can be rapidly generated using artificial intelli

Yoshua Bengio, Stephen Clare, Carina Prunkl, Maksym Andriushchenko

This second update to the 2025 International AI Safety Report assesses new developments in general-purpose AI risk management over the past year. It examines how researchers, public institutions, and AI developers are approaching risk management for general-purpose AI. In recent months, for example, three leading AI developers applied enhanced safeguards to

He Wang, Yueheng Wang, Ziyu Zhou, Hanxiang Liu

As online reviews increasingly drive consumer decisions, the impact of review interface design on rating authenticity remains under-explored. This research investigates how evaluation sequence ("Rating-First" vs. "Review-First") influences consumer ratings through three experiments and a large-scale secondary data analysis. The results reveal a significant p

Chen Chen, Yang Hang, Hui Shan Wang, Yang Wang

Different from hexagonal boron nitride (hBN) sheets, the bandgap of hBN nanoribbons (BNNRs) can be changed by spatial/electrostatic confinement. It has been predicted that a transverse electric field can narrow the bandgap and even cause an insulator-metal transition in BNNRs. However, experimentally introducing an overhigh electric field across the BNNR rem

GigaWorld Team, Angen Ye, Boyuan Wang, Chaojun Ni

World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explicitly as a data engine for Vision-Language-Action (VLA) learning. GigaWorld-0 integrates two synergistic components: GigaWorld-0-Video, which leverages large-scale video generation

Ali Tozar

Standard scaling theory dictates that disorder leads to immediate localization in one-dimensional Hermitian systems. We demonstrate that non-Hermitian topology fundamentally alters this paradigm, protecting transport up to a substantial critical disorder strength. By employing a numerically stable log-space transfer matrix approach up to thermodynamic scales

Xiangkai Ma, Lekai Xing, Han Zhang, Wenzhong Li

Vision-Language-Action (VLA) models built upon Chain-of-Thought (CoT) have achieved remarkable success in advancing general-purpose robotic agents, owing to its significant perceptual comprehension. Recently, since text-only CoT struggles to adequately capture scene details in complex spatial environments, a highly promising strategy involves leveraging visu

Farzad Ahmed, Joniel Augustine Jerome, Meliha Yetisgen, Özlem Uzuner

Objective: Clinical documentation contains factual, diagnostic, and management errors that can compromise patient safety. Large language models (LLMs) may help detect and correct such errors, but their behavior under different prompting strategies remains unclear. We evaluate zero-shot prompting, static prompting with random exemplars (SPR), and retrieval-au

Claire Gilson, Shi-Hao Li, Guo-Fu Yu

This paper presents a non-commutative generalization of the Pfaffian which we call a quasi-Pfaffian. This novel concept arises from solving linear systems with non-commutative skew-symmetric coefficients. A new non-commutative integrable system whose solutions are expressed in terms of these quasi-Pfaffians is presented. Derivative formulae and identities sa

Xiangkai Ma, Han Zhang, Wenzhong Li, Sanglu Lu

Large Multimodal Models (LMMs) have achieved remarkable progress in aligning and generating content across text and image modalities. However, the potential of using non-visual, continuous sequential, as a conditioning signal for high-fidelity image generation remains largely unexplored. Furthermore, existing methods that convert series into "pseudo-images"

Brani Vidakovic

This paper develops a unified framework for quantum wavelet shrinkage, extending classical denoising ideas into the quantum domain. Shrinkage is interpreted as a completely positive trace-preserving process, so attenuation of coefficients is carried out through controlled decoherence rather than nonlinear thresholding. Phase damping and ancilla-driven constr

Jiankuo Zhao, Xiangyu Zhu, Zidu Wang, Zhen Lei

Reconstructing high-fidelity and animatable 3D head avatars from monocular videos remains a challenging yet essential task. Existing methods based on 3D Gaussian Splatting typically bind Gaussians to mesh triangles and model deformations solely via Linear Blend Skinning, which results in rigid motion and limited expressiveness. Moreover, they lack specialize

Seyeon Park, Yajing Zhang, Michele Reticcioli, Cesare Franchini

Polarons, quasiparticles formed through interactions between lattice and charge carriers (electrons and holes), strongly influence the electronic and optical properties of functional materials. In nanostructured BiVO$_{4}$, polaron formation and dynamics govern photocatalytic efficiency and charge transport, yet the microscopic nature remains not fully resol

Shi-Wei Dai, Yan-Wei Shie, Tsung-Huan Yang, Lun-Wei Ku

Personalized Large Language Models (LLMs) have been shown to be an effective way to create more engaging and enjoyable user-AI interactions. While previous studies have explored using prompts to elicit specific personality traits in LLMs, they have not optimized these prompts to maximize personality expression. To address this limitation, we propose PersonaP

Kun Guo, Xuefei Li, Xijun Wang, Howard H. Yang

Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices without sharing raw data. While FL supports low-latency parallel training, it may converge to less accurate model. In contrast, SL achieves higher accuracy through sequential traini

Jiahui Sun, Junran Lu, Jinhui Yin, Yishuo Xu

Automatic extraction of road networks from aerial imagery is a fundamental task, yet prevailing methods rely on polylines that struggle to model curvilinear geometry. We maintain that road geometry is inherently curve-based and introduce the B\'ezier Graph, a differentiable parametric curve-based representation. The primary obstacle to this representation is

Kazunori Kohri, Haruki Takahashi

In this paper, we derive the upper bounds on the coupling of axion-like particles (ALPs) with photon as a function of the mass by considering axion-photon conversion in the Crab Nebula. Previous studies have not considered the influence of the magnetic field within the Crab Nebula. The magnetic field plays a crucial role through the Synchrotron Self-Compton

Jinghang Xu, Kun Guo, Wei Teng, Chenxi Liu

Artificial intelligence-generated content (AIGC) service provisioning in wireless edge networks involves two phases: content generation on edge servers and content transmission to mobile devices. In this paper, we take image generation as a representative application and propose a batch denoising framework, followed by a joint optimization of content generat

Thomas M Metz, Matthew Q Hill, Alice J O'Toole

Vision foundation models can perform generalized object classification in zero-shot mode, and face/person recognition when they are fine-tuned. However, fine-tuned models suffer from catastrophic forgetting. We create models that perform four tasks (object recognition, face recognition from high- and low-quality images, and person recognition from whole-body

Chaogui Kang, Lijian Luo, Qingfeng Guan, Yu Liu

Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Mor

Ziqiang Kong, Yu Feng, Han Gao, Ru Sun

The miniaturization of quantum Hall resistance standards (QHRS) using epitaxial graphene on silicon carbide necessitates understanding how device dimensions impact performance. This study reveals a pronounced scale-dependent carrier density in graphene Hall devices: under electron doping, carrier density decreases with increasing channel width (Wd), while th

Andrew Cupo, Hai-Ping Cheng, Chandrasekhar Ramanathan, Lorenza Viola

Ultrafast quantum matter experiments have validated predictions from Floquet theory - notably, the dynamical modification of the electronic band structure and the light-induced anomalous Hall effect, via monotonic modulation of the driving amplitude. Here, we demonstrate how new physics is uncovered by leveraging quantum optimal control techniques to design

Joon Suk Huh, Kirthevasan Kandasamy

Learning effective pricing strategies is crucial in digital marketplaces, especially when buyers' valuations are unknown and must be inferred through interaction. We study the online contextual pricing problem, where a seller observes a stream of context-valuation pairs and dynamically sets prices. Moreover, departing from traditional online learning framewo

Qiwei Liang, Boyang Cai, Minghao Lai, Sitong Zhuang

Despite strong results on recognition and segmentation, current 3D visual pre-training methods often underperform on robotic manipulation. We attribute this gap to two factors: the lack of state-action-state dynamics modeling and the unnecessary redundancy of explicit geometric reconstruction. We introduce AFRO, a self-supervised framework that learns dynami

Liang Gou, Archit Khare, Praneet Pabolu, Prachi Patel

We introduce the Cisco Time Series Model, a univariate zero-shot forecaster. This time series foundation model is the result of a general architectural innovation to a time series model enabling it to accept multiresolution input, applied to a popular decoder-only time series model (TimesFM). The resulting multiresolution decoder-only model is trained on ove

Kunie Ishioka, Gerson Mette, Steven Youngkin, Andreas Beyer

Lattice-matched GaP layers without extended defects can be grown on Si(001) substrate via a two-step growth procedure, consisting of low-temperature nucleation followed by high-temperature overgrowth. A transient reflectivity experiment on a thin, low-temperature nucleation layer discovered a previously unknown phonon mode at 2 THz upon below-bandgap optical

Kaifei Feng, Weidong Wang, Yucheng Gao, Fengming Liu

Broadband longitudinal-to-transverse mode conversion under normal incidence remains difficult to achieve, especially with structurally simple designs. Numerical simulations show that a single periodic row of inclined long-slits near a free surface enables high-efficiency broadband conversion, where the conversion rate exceeds 0.8 across a normalized-frequenc

Yijun Liu

We study a dynamic mechanism design problem with limited liability. A principal hires an agent to work over a finite horizon. The agent's costs are i.i.d. across periods and privately known, while his working status is publicly observable. The nonnegative-payment constraint distinguishes our problem from the dynamic screening literature. We identify cond

Zhentao Zhan, Xiaoliang Xu, Jingjing Wang, Junmei Wang

Graph Similarity Computation (GSC) is a fundamental graph related task where Graph Edit Distance (GED) serves as a prevalent metric. GED is determined by an optimal alignment between a pair of graphs that partitions each into aligned (zero-cost) and unaligned (cost-incurring) substructures. Due to NP-hard nature of exact GED computation, GED approximations b

Yiting Lu, Wei Luo, Peiyan Tu, Haoran Li

World Generation Models are emerging as a cornerstone of next-generation multimodal intelligence systems. Unlike traditional 2D visual generation, World Models aim to construct realistic, dynamic, and physically consistent 3D/4D worlds from images, videos, or text. These models not only need to produce high-fidelity visual content but also maintain coherence

Xuewen Liu, Zhikai Li, Jing Zhang, Mengjuan Chen

Diffusion Transformers dominate video generation, but the quadratic complexity of attention computation introduces substantial latency. Attention sparsity reduces computational costs by focusing on critical tokens while ignoring non-critical tokens. However, existing methods suffer from severe performance degradation. In this paper, we revisit attention spar

Haoqing Li, Jun Shi, Xianmeng Chen, Qiwei Jia

Deep learning methods face dual challenges of limited clinical samples and low inter-class differentiation among Diffuse Cystic Lung Diseases (DCLDs) in advancing Birt-Hogg-Dube syndrome (BHD) diagnosis via Computed Tomography (CT) imaging. While Multimodal Large Language Models (MLLMs) demonstrate diagnostic potential fo such rare diseases, the absence of d

Masahiro Hachimori, Kenji Kashiwabara

We prove that for the preorder induced by a function f: V -> V, the family of all order ideals is average-rare, that is, its normalized degree sum (nds) is nonpositive. As a base case in our reduction, we establish the same result for functional partial orders (or rooted forests). We also propose a conjecture related to Frankl's Conjecture. All proofs have b

Aurelio Vivas, Harold Castro

Data-intensive scientific workflows increasingly rely on high-performance computing (HPC) systems, complementing traditional Grid and Cloud platforms. However, workflow scheduling on HPC infrastructures remains challenging due to the prevalence of non-uniform memory access (NUMA) architectures. These systems require schedulers to account for data locality no

Nicholas J. Sorensen, Elham Zohari, Joshua S. Wildeman, Sigurd Flågan

Quantum sensors based on the nitrogen-vacancy (NV) center in diamond are leading platforms for high-sensitivity magnetometry with nanometer-scale resolution. State-of-the-art implementations, however, typically rely on bulky free-space optics or sacrifice spatial resolution to achieve high sensitivities. Here, we realize an integrated platform that overcomes

Inferix Team, Tianyu Feng, Yizeng Han, Jiahao He

World models serve as core simulators for fields such as agentic AI, embodied AI, and gaming, capable of generating long, physically realistic, and interactive high-quality videos. Moreover, scaling these models could unlock emergent capabilities in visual perception, understanding, and reasoning, paving the way for a new paradigm that moves beyond current L

Junhao Zhu, Lu Chen, Xiangyu Ke, Ziquan Fang

Multi-modal analytical processing has the potential to transform applications in e-commerce, healthcare, entertainment, and beyond. However, real-world adoption remains elusive due to the limited ability of traditional relational query operators to capture query semantics. The emergence of foundation models, particularly the large language models (LLMs), ope

Nikos Dimou, Alex McAvoy

Originating in evolutionary game theory, the class of "zero-determinant" strategies enables a player to unilaterally enforce linear payoff relationships in simple repeated games. An upshot of this kind of payoff constraint is that it can shape the incentives for the opponent in a predetermined way. An example is when a player ensures that the agents get equa

Byeongjun Park, Byung-Hoon Kim, Hyungjin Chung, Jong Chul Ye

We present ReDirector, a novel camera-controlled video retake generation method for dynamically captured variable-length videos. In particular, we rectify a common misuse of RoPE in previous works by aligning the spatiotemporal positions of the input video and the target retake. Moreover, we introduce Rotary Camera Encoding (RoCE), a camera-conditioned RoPE

Yun-Feng Tu, Chuan-Hsiang Han

This paper investigates asymptotically optimal importance sampling (IS) schemes for pricing European call options under the Heston stochastic volatility model. We focus on two distinct rare-event regimes where standard Monte Carlo methods suffer from significant variance deterioration: the limit as maturity approaches zero and the limit as the strike price t

Junlin Yang

This paper investigates how institutional learning and regional spillovers shape volatility dynamics in ASEAN equity markets. Using daily data for Indonesia, Malaysia, the Philippines, and Thailand from 2010 to 2024, we construct a high-frequency institutional learning index via a MIDAS-EPU approach. Unlike existing studies that treat institutional quality a

Maria Pia Gualdani, Nataša Pavlović, Justin Toyota, Dominic Wynter

In this manuscript we derive the quantum Landau operator as the weak-coupling limit of the quantum Boltzmann operator (also known as the Uehling-Uhlenbeck operator). We consider both Fermi-Dirac and Bose-Einstein statistics. Our approach is inspired by the work by Benedetto and Pulvirenti, where the classical Landau operator was derived from the quantum Bolt

Armen Petrosyan

For a non-compact, locally compact, sigma-compact Hausdorff space with a continuous proper exhaustion function, and an admissible weight, we study the weighted supremum norms measuring how fast a function approaches its limit at infinity, and their quotient modulo constants. Our main result is a reduction theorem: when the weight is unbounded, the infimum ov

Wentao Hu, Mingkuan Zhao, Shuangyong Song, Xiaoyan Zhu

Sparse Mixture-of-Experts (SMoE) architectures have enabled a new frontier in scaling Large Language Models (LLMs), offering superior performance by activating only a fraction of their total parameters during inference. However, their practical deployment is severely hampered by substantial static memory overhead, as all experts must be loaded into memory. E

Aashish K Gupta, Christopher Ness, Sina Haeri

Evacuating the powder trapped inside the complex cavities of Triply Periodic Minimal Surface (TPMS) structures remains a major challenge in metal-powder-based additive manufacturing. The Discrete Element Method offers valuable insights into this evacuation process, enabling the design of effective de-powdering strategies. In this study, we simulate gravity-d

Miguel Carvalho, Helder Dias, Bruno Martins

Vision-Language Models (VLMs) often struggle with tasks that require fine-grained image understanding, such as scene-text recognition or document analysis, due to perception limitations and visual fragmentation. To address these challenges, we introduce CropVLM as an external low-cost method for boosting performance, enabling VLMs to dynamically ''zoom in''

Guowei Dai, Yingxin Sun, Juncheng Wei, Yong Zhang

In this paper, by introducing two-point stationary-phase amplitude defect, we provide a partial positive answer to the Schiffer and Berenstein conjectures in $\mathbb{R}^2$. More precisely, assuming that a bounded uniformly convex domain $Ω\subset \mathbb{R}^2$ has a connected boundary of class $C^{2,ε}$ with $ε\in (0,1)$, we show that if, for some nonzero c

Koena Ronny Mabokela, Tim Schlippe, Mpho Raborife, Turgay Celik

Sentiment analysis is a helpful task to automatically analyse opinions and emotions on various topics in areas such as AI for Social Good, AI in Education or marketing. While many of the sentiment analysis systems are developed for English, many African languages are classified as low-resource languages due to the lack of digital language resources like text