October 2025 arXiv papers — page 115

Yanjie Gou, Jiangming Liu, Kouying Xue, Yi Hu

The rapid expansion of video game production necessitates the development of effective advertising and recommendation systems for online game platforms. Recommending and advertising games to users hinges on capturing their interest in games. However, existing representation learning methods crafted for handling billions of items in recommendation systems are

Jun Nian, Leopoldo A. Pando Zayas, Cong-Yuan Yue

Attempts to construct a low-temperature version of the fluid/gravity correspondence have faced obstacles manifested in the form of logarithmic terms in the frequency, $\log(\omega)$, leading to non-local in time constitutive relations for the stress tensor and the charge current. These difficulties can be broadly presented as a breakdown of the hydrodynamic

Federico Corberi, Eugenio Lippiello, Paolo Politi, Luca Smaldone

In this paper, we start reviewing the main features of the one-dimensional Ising model with long-range interactions, where the spin-spin coupling decays as a power law, $J(r) \propto r^{-\alpha}$. We then discuss the key properties of the one-dimensional voter model, in which two agents (spins) at distance $r$ interact with a power-law probability with the s

Azalea Gui, Woosung Choi, Junghyun Koo, Kazuki Shimada

The performance of deep learning models for music source separation heavily depends on training data quality. However, datasets are often corrupted by difficult-to-detect artifacts such as audio bleeding and label noise. Since the type and extent of contamination are typically unknown, cleaning methods targeting specific corruptions are often impractical. Th

Mohit Kaushik, Kuljit Kaur Chahal

Open-source software (OSS) projects depend on community engagement (CE) for longevity. However, CE's quantifiable impact on project dynamics and lifespan is underexplored. Objectives: This study defines CE in OSS, identifies key metrics, and evaluates their influence on project dynamics (releases, commits, branches) and lifespan. Methods: We analyzed 33,946

Yuta Inoue, Ken-ichi Kawarabayashi, Atsuyuki Miyashita

We show that any planar graph $G=(V,E)$ has a 5-coloring such that one color class contains at most $|V|/6$ vertices. In other words, there exists a partition of $V$ into five independent sets $\{V_1, \cdots, V_5\}$ such that $|V_5| \leq |V| / 6$. Our proof yields an $O(|V|^2)$-time algorithm to find such a partition, and unlike the Four Color Theorem, our p

Hongcheng Liu, Yixuan Hou, Heyang Liu, Yuhao Wang

While Speech Large Language Models (Speech-LLMs) show strong performance in many applications, their robustness is critically under-tested, especially to speech disfluency. Existing evaluations often rely on idealized inputs, overlooking common disfluencies, particularly those associated with conditions like Parkinson's disease. This work investigates whethe

Ajay Sharma

Governing authorities in sports often make changes to rules and regulations to increase competitiveness. One such change was made by the English Football Association in 1981 when it changed the rule for awarding points in the domestic league from two points for a win to three points. This study aims to measure this rule change's impact on the domestic league

Christopher Salazar, Krithika Manohar, Ashis G. Banerjee

Real-time forecasting from streaming data poses critical challenges: handling non-stationary dynamics, operating under strict computational limits, and adapting rapidly without catastrophic forgetting. However, many existing approaches face trade-offs between accuracy, adaptability, and efficiency, particularly when deployed in constrained computing environm

Anyi Li, Jiacheng Cen, Songyou Li, Mingze Li

Accurate prediction of ionic conductivity in electrolyte systems is crucial for advancing numerous scientific and technological applications. While significant progress has been made, current research faces two fundamental challenges: (1) the lack of high-quality standardized benchmarks, and (2) inadequate modeling of geometric structure and intermolecular i

Ryoto Ichiya

We consider the semilinear heat equation $u_t - \Delta u = f(u)$ in $\Omega = B_R(0) \subset \mathbb{R}^n$ with super-exponential nonlinearities $f(u) = e^{u^p}u^q$ ($p>1$, $q \in \{0\}\cup [1,\infty)$), nonnegative bounded radially symmetric initial data and 0-Dirichlet boundary condition. In this paper, we show the asymptotic blow-up behavior for nonnegati

Michael Herty, Yizhou Zhou

This work is concerned with a hierarchical framework of optimal control problems connecting interacting particle systems, the mean field limit equations, and associated hydrodynamic models. By assuming the existence of solutions, we establish the exponential turnpike property for each level of the hierarchy, showing that optimal trajectories remain close to

Chen Qian, Haoyu Zhang, Junnan Ma, Liuhong Zhu

Clinical adoption of multi-shot diffusion-weighted magnetic resonance imaging (multi-shot DWI) for body-wide tumor diagnostics is limited by severe motion-induced phase artifacts from respiration, peristalsis, and so on, compounded by multi-organ, multi-slice, multi-direction and multi-b-value complexities. Here, we introduce a reconstruction framework, LoSP

Pooja Batra, Ajay Sharma

In this paper, we analyse the impact of international migration on the food consumption and dietary diversity of left-behind households. Using the Kerala migration survey 2011, we study whether households with emigrants (on account of international migration) have higher consumption expenditure and improved dietary diversity than their non-migrating counterp

Bingyu Li, Feiyu Wang, Da Zhang, Zhiyuan Zhao

Most existing underwater instance segmentation approaches are constrained by close-vocabulary prediction, limiting their ability to recognize novel marine categories. To support evaluation, we introduce \textbf{MARIS} (\underline{Mar}ine Open-Vocabulary \underline{I}nstance \underline{S}egmentation), the first large-scale fine-grained benchmark for underwate

Sahil Kumar, Adithya Maurya K R, Mudit Dixit

Understanding and optimizing polysulfide adsorption and conversion processes are critical to mitigating shuttle effects and sluggish redox kinetics in lithium-sulfur batteries (LSBs). Here, we introduce a machine-learning-accelerated framework, Precise and Accurate Configuration Evaluation (PACE), that integrates Machine Learning Interatomic Potentials (MLIP

Céline Bougel, Sébastien Déjean, Caroline Giulioli, Philippe Saint-Pierre

Background: Clinical trials are designed to prove the efficacy of an intervention by means of model-based approaches involving parametric hypothesis testing. Issues arise when no effect is observed in the study population. Indeed, an effect may be present in a subgroup and the statistical test cannot detect it. To investigate this possibility, we proposed to

Trishan Mondal

We construct an action of the braid group on the bounded derived category of coherent sheaves on hypertoric varieties arising from hyperplane arrangements. Using wall-crossing equivalences associated to paths in the complexified complement of the hyperplane arrangement, we show that these equivalences under certain conditions yield a functor from the Deligne

Shichuan Wang, Tie Hu, Zihan Mei, Xuancheng Peng

Compared with traditional infrared thermal imaging, polarimetric imaging provides additional polarization information, which effectively enhances object contours and image contrast, with broad application in both military and civilian domains. However, the traditional long-wave infrared polarimetric camera suffers from severe thermal noise, low sensitivity a

Yael Naze, Gregor Rauw, Piotr A. Kolaczek-Szymanski, Nikolay Britavskiy

Multiplicity is ubiquitous among massive stars. While the stellar components usually display similar masses, some binaries with extremely low mass ratios were also observed. Some of them are primordial, while others arise from binary interactions. The identification of systems with extreme mass ratios brings valuable information, notably on the origin of fas

Peng Ren, Hai Yang

Generating long and stylized human motions in real time is critical for applications that demand continuous and responsive character control. Despite its importance, existing streaming approaches often operate directly in the raw motion space, leading to substantial computational overhead and making it difficult to maintain temporal stability. In contrast, l

Eduard Mychelkin, Gulnara Suliyeva, Maxim Makukov

The static antiscalar solution of the Einstein-Klein-Gordon equations in the form of the Papapetrou exponential metric had been interpreted as a traversable wormhole with a throat at \textit{r=M}. We aim to search for the effects which could be associated with this scale and only find that the topological Gauss-Bonnet invariant swaps sign, and the value of t

Tengjie Zheng, Jilan Mei, Di Wu, Lin Cheng

Accurate learning of system dynamics is becoming increasingly crucial for advanced control and decision-making in engineering. However, real-world systems often exhibit multiple channels and highly nonlinear transition dynamics, challenging traditional modeling methods. To enable online learning for these systems, this paper formulates the system as Gaussian

Nidhi Mehandiratta, Georgia V. Panopoulou, Eirik Gjerløw, Vincent Pelgrims

The correlation between optical starlight polarization and polarized thermal dust emission can be used to infer intrinsic dust properties. This correlation is quantified by the ratio Rp/p, which has been measured to be 5.42 +/- 0.05 MJy sr^-1 at 353 GHz when averaged over large areas of the sky. We investigate this correlation using newly published stellar p

Mingyang Sun, Pengxiang Ding, Weinan Zhang, Donglin Wang

While behavior cloning with flow/diffusion policies excels at learning complex skills from demonstrations, it remains vulnerable to distributional shift, and standard RL methods struggle to fine-tune these models due to their iterative inference process and the limitations of existing workarounds. In this work, we introduce the Stepwise Flow Policy (SWFP) fr

Davide Basso, Luca Bortolussi, Mirjana Videnovic-Misic, Husni Habal

The adoption of machine learning-based techniques for analog integrated circuit layout, unlike its digital counterpart, has been limited by the stringent requirements imposed by electric and problem-specific constraints, along with the interdependence of floorplanning and routing steps. In this work, we address a prevalent concern among layout engineers rega

L. Yildiz, D. Kayki, E. Gudekli

We present a gauge-invariant Schr\"odinger-type evolution that combines (i) weighted local diffusion, (ii) symmetric nonlocal exchange through a kernel operator, and (iii) a mean-free phase-resonant drive. The resulting Resonant Weighted Nonlocal Schr\"odinger (RWNS) equation exactly conserves mass and, when the drive is absent, admits a Hamiltonian structur

Ting-Yu Yen, Yu-Sheng Chiu, Shih-Hsuan Hung, Peter Wonka

Recent advances in 3D Gaussian Splatting (3DGS) have enabled high-quality, real-time novel-view synthesis from multi-view images. However, most existing methods assume the object is captured in a single, static pose, resulting in incomplete reconstructions that miss occluded or self-occluded regions. We introduce PFGS, a pose-aware 3DGS framework that addres

Haisheng Su, Junjie Zhang, Feixiang Song, Sanping Zhou

Detecting 3D objects accurately from multi-view 2D images is a challenging yet essential task in the field of autonomous driving. Current methods resort to integrating depth prediction to recover the spatial information for object query decoding, which necessitates explicit supervision from LiDAR points during the training phase. However, the predicted depth

Chitralekha Gupta, Soundarya Ramesh, Praveen Sasikumar, Kian Peen Yeo

Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence.

Kexin Zheng, Lauriane Teyssier, Yinan Zheng, Yu Luo

The recent development of zero-shot reinforcement learning (RL) has opened a new avenue for learning pre-trained generalist policies that can adapt to arbitrary new tasks in a zero-shot manner. While the popular Forward-Backward representations (FB) and related methods have shown promise in zero-shot RL, we empirically found that their modeling lacks express

Christian H. Weiß, Philipp Adämmer

We propose a flexible and robust nonparametric framework for testing spatial dependence in two- and three-dimensional random fields. Our approach involves converting spatial data into one-dimensional time series using space-filling Hilbert curves. We then apply ordinal pattern-based tests for serial dependence to this series. Because Hilbert curves preserve

Axel Flinth, Hubert Orlicki, Semira Einsele, Gerhard Wunder

Beyond its widespread application in signal and image processing, \emph{compressed sensing} principles have been greatly applied to secure information transmission (often termed 'compressive security'). In this scenario, the measurement matrix $Q$ acts as a one time pad encryption key (in complex number domain) which can achieve perfect information-theoretic

Jan Haskovec, Peter Markowich, Stefano Zampini

We investigate a scalar partial differential equation model for the formation of biological transportation networks. Starting from a discrete graph-based formulation on equilateral triangulations, we rigorously derive the corresponding continuum energy functional as the $\Gamma$-limit under network refinement and establish the existence of global minimizers.

Zhentao He, Chao Ji

In this paper, we study the nonrelativistic limit of normalized solutions for the following nonlinear Dirac equation (NLDE) on noncompact metric graph $\G$ with finitely many edges and a non-empty compact core $\K$ \begin{equation*} \D u - \omega u= \chi_\K\abs{u}^{p-2}u, \end{equation*} under the constraint $\int_\G\abs{u}^2\,dx = 1$, where $\D$ is the Dira

Jihong Huang, Shun Zhou

In this talk, we present the investigation of the invisible decays of a heavy massive neutrino into a lighter neutrino and a massless Nambu-Goldstone boson, i.e., $\nu_i^{} \to \nu_j^{} + \phi$. The total decay rates are calculated in the most general case, where the individual helicities of both parent and daughter neutrinos are specified. We then examine t

Zhaodong Yang, Ai-Ping Hu, Harish Ravichandar

Automating chicken shoulder deboning requires precise 6-DoF cutting through a partially occluded, deformable, multi-material joint, since contact with the bones presents serious health and safety risks. Our work makes both systems-level and algorithmic contributions to train and deploy a reactive force-feedback cutting policy that dynamically adapts a nomina

Huihui Li, Shunlong Luo, Yue Zhang

Two classically equivalent expressions of mutual information of probability distributions (classical bipartite states) diverge when extended to quantum systems, and this difference has been employed to define quantum discord, a quantifier of quantum correlations beyond entanglement. Similarly, equivalent expressions of classical Fisher information of paramet

Zezhong Tan, Hang Gao, Xinhong Ma, Feng Zhang

Recent Large Reasoning Models (LRMs) have achieved remarkable performance in solving complex problems via supervised fine-tuning (SFT) and reinforcement learning (RL). Although existing RL algorithms significantly enhance model accuracy, they still suffer from excessively lengthy responses and overthinking issues, resulting in increased inference latency and

Pranay Agarwal, D. Manjunath

The ubiquity of smartphones has fueled content consumption worldwide, leading to an ever-increasing demand for a better Internet experience. This has necessitated an upgrade of the capacity of the access network. The Internet service providers (ISPs) have been demanding that the content providers (CPs) share the cost of upgrading access network infrastructur

Shun Huang, Deyun Zhang, Sumei Fan, Gongzheng Tang

Wolff-Parkinson-White (WPW) syndrome, a congenital cardiac conduction abnormality with low prevalence, carries a significant risk of sudden cardiac death. Early identification remains challenging due to screening costs and professional resource scarcity. This retrospective real-world study systematically evaluates an integrated Artificial Intelligence-enable

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Minimally invasive surgery can benefit significantly from automated surgical tool detection, enabling advanced analysis and assistance. However, the limited availability of annotated data in surgical settings poses a challenge for training robust deep learning models. This paper introduces a novel staged adaptive fine-tuning approach consisting of two steps:

Sumbul Khan, Wei Ting Liow, Lay Kee Ang

As design thinking education grows in secondary and tertiary contexts, educators face the challenge of evaluating creative artefacts that combine visual and textual elements. Traditional rubric-based assessment is laborious, time-consuming, and inconsistent due to reliance on Teaching Assistants (TA) in large, multi-section cohorts. This paper presents an ex

Yao Zhou, Peng Ye

While non-Hermitian bulk systems and their sensitivity to boundary conditions have been extensively studied, how a non-Hermitian boundary affects the entanglement structure of Hermitian critical systems remains largely unexplored. Here we present a fully analytical framework by exactly solving a Hermitian gapless chain with a single non-Hermitian impurity ac

Renrui Li, Qingzhi Ma, Jiajie Xu, Lei Zhao

Cardinality estimation has long been crucial for cost-based database optimizers in identifying optimal query execution plans, attracting significant attention over the past decades. While recent advancements have significantly improved the accuracy of multi-table join query estimations, these methods introduce challenges such as higher space overhead, increa

Ruyuan Zhang, Jinguang Han, Liqun Chen

Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without disclosing their local data. To address privacy issues of gradient, several privacy-preserving machine-learning schemes based on multi-client functional encryption (MCFE) have been proposed. However, existing MCFE-b

Jinwoo Kim, Max Beier, Petar Bevanda, Nayun Kim

A key question in sequence modeling with neural networks is how to represent and learn highly nonlinear and probabilistic state dynamics. Operator theory views such dynamics as linear maps on Hilbert spaces containing mean embedding vectors of distributions, offering an appealing but currently overlooked perspective. We propose a new approach to sequence mod

Maonan Wang, Yirong Chen, Yuxin Cai, Aoyu Pang

Air-ground collaborative intelligence is becoming a key approach for next-generation urban intelligent transportation management, where aerial and ground systems work together on perception, communication, and decision-making. However, the lack of a unified multi-modal simulation environment has limited progress in studying cross-domain perception, coordinat

Jinwoo Baek

We introduce \textbf{Chebyshev Moment Regularization (CMR)}, a simple, architecture-agnostic loss that directly optimizes layer spectra. CMR jointly controls spectral edges via a log-condition proxy and shapes the interior via Chebyshev moments, with a decoupled, capped mixing rule that preserves task gradients. We prove strictly monotone descent for the con

Jiawei Jiang, Linping Xu, Dejun Zhang, Qingbo Huang

Neural audio coding has been shown to outperform classical audio coding at extremely low bitrates. However, the practical application of neural audio codecs is still limited by their elevated complexity. To address this challenge, we have developed a high-quality neural audio codec with a low-complexity decoder, named LDCodec (Low-complexity Decoder Neural A

Dechen Zhang, Zhenmei Shi, Yi Zhang, Yingyu Liang

Kernel ridge regression (KRR) is a foundational tool in machine learning, with recent work emphasizing its connections to neural networks. However, existing theory primarily addresses the i.i.d. setting, while real-world data often exhibits structured dependencies - particularly in applications like denoising score learning where multiple noisy observations

Wei Ting Liow, Sumbul Khan, Lay Kee Ang

Creating interdisciplinary design projects is time-consuming and cognitively demanding for teachers, requiring curriculum alignment, cross-subject integration, and careful sequencing. International research reports increasing teacher use of AI alongside persistent workload pressures, underscoring the need for planning support. This paper presents the Interdi

Zixun Wang, Ben Dai

Semantic segmentation labels each pixel in an image with its corresponding class, and is typically evaluated using the Intersection over Union (IoU) and Dice metrics to quantify the overlap between predicted and ground-truth segmentation masks. In the literature, most existing methods estimate pixel-wise class probabilities, then apply argmax or thresholding

Aaron P. Quiskamp, Graeme R. Flower, Maxim Goryachev, Michael E. Tobar

A recent independent dark photon (DP) focused reanalysis of existing data from the TASEH axion haloscope experiment reported a tentative DP dark matter signal with local significance $\sim 4.7\sigma$ at $f_X \simeq$ 4.71 GHz, corresponding to $m_X \simeq 19.5~\mu$eV and kinetic mixing $\epsilon \sim 6.5\times 10^{-15}$. Motivated by this report, we performed

Ye Yuan, Walter Kob, Hajime Tanaka

Granular materials densify under repeated mechanical perturbations, a nonequilibrium dynamics that underlies many natural and industrial processes. Because granular relaxation is governed by frictional contacts and energy dissipation, this aging behavior fundamentally differs from that of thermal glasses despite their apparent similarities. Here, we uncover

Arka Mallick, Swarnendu Sil

We study regularity results for local minimizers of variable growth variational problem in Heisenberg groups under suitable integrability assumption on the horizontal gradient of the exponent function. More precisely, our main focus is on the continuity properties of the horizontal gradient $\mathfrak{X} u$, where $u \in HW_{\text{loc}}^{1,1}$ is a local min

Chen Wang, Kai Jiang, Jinxiu Zhao, Ziheng Wang

In this work, we report high-performance atomic-layer-deposited indium oxide (In2O3) transistors with high gated-Hall mobility ({\mu}H) exceeding 100 cm2/Vs at room temperature (RT). It is found that the deposition of top hafnium oxide (HfO2) above the In2O3 channel significantly enhances its crystallization, leading to an average grain size of 97.2 nm in a

Xuance Jiang, Sayed Ali Akbar Ghorashi, Deyu Lu, Jennifer Cano

We propose a new pathway to the quantized anomalous Hall effect (QAHE) by coupling an altermagnet to a topological crystalline insulator (TCI). The former gaps the topological surface states of the TCI, thereby realizing the QAHE in a robust and switchable platform with near- vanishing magnetization. We demonstrate the feasibility of this approach by studyin

Gihwan Nam, Yeunhwan Lim, Jeremy W. Holt

We obtain a universal relation for the neutron star maximum mass arising from a particular combination of the saturation density ($n_0$), the effective mass ($m^*$), and (when present) the vector meson self-coupling constant ($\zeta$) within the relativistic mean-field model framework. Observations of massive neutron stars heavier than $\sim 2M_{\odot}$ have

Tim Kraus, Axel Sauer, Ingo Feldner

The rapid evolution of embedded systems, along with the growing variety and complexity of AI algorithms, necessitates a powerful hardware/software co-design methodology based on virtual prototyping technologies. The market offers a diverse range of simulation solutions, each with its unique technological approach and therefore strengths and weaknesses. Addit

Saqib Qamar

Automated skin lesion segmentation through dermoscopic analysis is essential for early skin cancer detection, yet remains challenging due to limited annotated training data. We present MIRA-U, a semi-supervised framework that combines uncertainty-aware teacher-student pseudo-labeling with a hybrid CNN-Transformer architecture. Our approach employs a teacher

Marvin Nabroth

The HADES experiment investigates the reaction products of heavy-ion collisions at a few GeV, providing access to QCD matter at high net-baryon densities. A particular focus is the reconstruction of higher-order cumulant ratios of proton and light nuclei multiplicity distributions, which are considered sensitive probes of criticality through their connection

Alejandro Escontrela, Justin Kerr, Arthur Allshire, Jonas Frey

We present a novel approach for photorealistic robot simulation that integrates 3D Gaussian Splatting as a drop-in renderer within vectorized physics simulators such as IsaacGym. This enables unprecedented speed -- exceeding 100,000 steps per second on consumer GPUs -- while maintaining high visual fidelity, which we showcase across diverse tasks. We additio

Long Chen, Ruchi Guo, Jingrong Wei, Jun Zou

A DualTPD method is proposed for solving nonlinear partial differential equations. The method is characterized by three main features. First, decoupling via Fenchel--Rockafellar duality is achieved, so that nonlinear terms are discretized by discontinuous finite element spaces, yielding block-diagonal mass matrices and closed-form updates. Second, improved c

Shyalan Ramesh, Scott Mann, Alex Stumpf

Autonomous Underwater vehicles must operate in strong currents, limited acoustic bandwidth, and persistent sensing requirements where conventional swarm optimisation methods are unreliable. This paper formulates an irreversible hydrodynamic deployment problem for Autonomous Underwater Vehicle (AUV) swarms and presents Nauplius Optimisation for Autonomous Hyd

Baode Wang, Biao Wu, Weizhen Li, Meng Fang

Document parsing from scanned images into structured formats remains a significant challenge due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Existing supervised fine-tuning methods often struggle to generalize across diverse document types, leading to poor performance, particularly on out-of-distribution data

Liping Li

Given an infinite set $\Omega$ and a ring $R$ as well as a group $G$ acting on them, we show that $G$ and a subgroup $H$ share the same canonical relational structure on $\Omega$ if and only if the restriction functor gives an equivalence from the category of discrete representations of $G$ to that of $H$. Moreover, the age of this relational structure satis

Heecheol Yun, Kwangmin Ki, Junghyun Lee, Eunho Yang

Ensembling Large Language Models (LLMs) has gained attention as a promising approach to surpass the performance of individual models by leveraging their complementary strengths. In particular, aggregating models' next-token probability distributions to select the next token has been shown to be effective in various tasks. However, while successful for short-

Catarina G Belem, Parker Glenn, Alfy Samuel, Anoop Kumar

Automatic readability assessment plays a key role in ensuring effective and accessible written communication. Despite significant progress, the field is hindered by inconsistent definitions of readability and measurements that rely on surface-level text properties. In this work, we investigate the factors shaping human perceptions of readability through the

Xavier Tan, Xiaoli Tang, Han Yu

Federated learning (FL) has gained prominence due to heightened concerns over data privacy. Privacy restrictions limit the visibility for data consumers (DCs) to accurately assess the capabilities and efforts of data owners (DOs). Thus, for open collaborative FL markets to thrive, effective incentive mechanisms are key as they can motivate data owners (DOs)

Joshua Li, Brendan Chharawala, Chang Shu, Xue Bin Peng

Animating realistic character interactions with the surrounding environment is important for autonomous agents in gaming, AR/VR, and robotics. However, current methods for human motion reconstruction struggle with accurately placing humans in 3D space. We introduce Scene-Human Aligned REconstruction (SHARE), a technique that leverages the scene geometry's in

Eric J. Sung, Benjamin Koch, Tobias Jenke, Hartmut Abele

Discrepancies between theory and recent qBounce data have prompted renewed scrutiny of how boundary conditions are implemented for ultracold neutrons bouncing above a mirror in Earth's gravity. We apply the theory of self-adjoint extensions to the linear gravitational potential on the half-line and derive the most general boundary condition that renders the

Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao

Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal graph structures. To bridge this gap, w

Shengkai Hu, Haozhe Qi, Jun Wan, Jiaxing Huang

Recent advances in deep learning have significantly improved facial landmark detection. However, existing facial landmark detection datasets often define different numbers of landmarks, and most mainstream methods can only be trained on a single dataset. This limits the model generalization to different datasets and hinders the development of a unified model

Yeichan Kim, Ilmun Kim, Seyoung Park

Transfer learning is a key component of modern machine learning, enhancing the performance of target tasks by leveraging diverse data sources. Simultaneously, overparameterized models such as the minimum-$\ell_2$-norm interpolator (MNI) in high-dimensional linear regression have garnered significant attention for their remarkable generalization capabilities,

Liviu-Mihai Stan, Ranulfo Bezerra, Shotaro Kojima, Tsige Tadesse Alemayoh

Reliable navigation in disaster-response and other unstructured indoor settings requires robots not only to avoid obstacles but also to recognise when those obstacles can be pushed aside. We present an adaptive, LiDAR and odometry-based path-planning framework that embeds this capability into the ROS2 Nav2 stack. A new Movable Obstacles Layer labels all LiDA

Radek Erban, Jan Haskovec

The effect of short-term and long-term memory on spontaneous aggregation of organisms is investigated using a stochastic agent-based model. Each individual modulates the amplitude of its random motion according to the perceived local density of neighbors. Memory is introduced via a chain of $K$~internal variables that allow agents to retain information about

Tseng Ying-Wei, Kao Yu-Ting, Chang Yeong-Jar, Ou Chia-Ho

Recent quantum-inspired methods based on the Simulated Annealing (SA) algorithm have shown strong potential for solving combinatorial optimization problems. However, Grover's algorithm [1] in gate-based quantum computing offers only a quadratic speedup, which remains impractical for large problem sizes. This paper proposes a hybrid approach that integrates S

Yuyuan Feng, Bin Ma, Enyan Dai

Extensive research has highlighted the vulnerability of graph neural networks (GNNs) to adversarial attacks, including manipulation, node injection, and the recently emerging threat of backdoor attacks. However, existing defenses typically focus on a single type of attack, lacking a unified approach to simultaneously defend against multiple threats. In this

Shamil Asgarli, Dragos Ghioca, Chi Hoi Yip

Motivated by a question of Erd\H{o}s on blocking sets in a projective plane that intersect every line only a few times, several authors have used unions of algebraic curves to construct such sets in $\mathbb{P}^2(\mathbb{F}_q)$. In this paper, we provide new constructions of blocking sets in $\mathbb{P}^2(\mathbb{F}_q)$ from a union of geometrically irreduci

Gahee Kim, Takamitsu Matsubara

Black-box simulators are widely used in robotics, but optimizing their parameters remains challenging due to inaccessible likelihoods. Simulation-Based Inference (SBI) tackles this issue using simulation-driven approaches, estimating the posterior from offline real observations and forward simulations. However, in black-box scenarios, preparing observations

Tella Rajashekhar Reddy, Atharva Deshmukh, Karan Tandon, Rohan Gandhi

Large language model (LLM) applications are blindfolded to the infrastructure underneath and generate tokens autoregressively, indifferent to the system load, thus risking inferencing latency inflation and poor user experience. Our first-cut controller, named beLLMan, enables the LLM infrastructure to actively and progressively signal the first-party LLM app

Saïd Benayadi, Sofiane Bouarroudj, Hamza El Ouali

In this paper, we introduce pre-Lie and pre-Leibniz superalgebras, which generalize pre-Lie and pre-Leibniz algebras to the super setting. Additionally, we define a Levi-Civita product associated with a symmetric non-degenerate bilinear form on a non-associative superalgebra. This leads to the definition of flat pseudo-Euclidean left Leibniz superalgebras as

Zailin Ma, Jiansheng Yang, Yaodong Yang

This paper studies the generalization properties of a recently proposed kernel method, the Random Feature models with Learnable Activation Functions (RFLAF). By applying a data-dependent sampling scheme for generating features, we provide by far the sharpest bounds on the required number of features for learning RFLAF in both the regression and classificatio

Shimpei Kobayashi, Sihao Zeng

We develop a loop group (DPW-type) representation for minimal Lagrangian surfaces in the complex quadric $Q_{2}\cong \mathbb S^{2}\times \mathbb S^{2}$, formulated via a flat family of connections $\{\nabla^\lambda\}_{\lambda\in \mathbb S^{1}}$ on a trivial bundle. We prove that minimality is equivalent to the flatness of $\nabla^\lambda$ for all $\lambda$,

Tatsuru Kikuchi

I develop a continuous functional framework for spatial treatment effects grounded in Navier-Stokes partial differential equations. Rather than discrete treatment parameters, the framework characterizes treatment intensity as continuous functions $\tau(\mathbf{x}, t)$ over space-time, enabling rigorous analysis of boundary evolution, spatial gradients, and c

Saeed Salehi

In the first, pre-college level part, we present a proof by mathematical induction for Cantor's theorem on the uncountability of the infinite binary strings and the real numbers. In the second, undergraduate-level part, we prove Cantor's powerset theorem by using transfinite induction. There, we will need the axiom of choice and the concept of ordina

Yunlu Wang, Yunlong Xiao, Pengcheng Hong, Ronggang Ping

Symmetry studies represent one of the most promising frontiers in particle physics research. This investigation focuses on exploring $P$ and $CP$ symmetries in the charm system through the measurement of asymmetry decay parameters in the three-body decay of $\Xi_c^{+}$. Incorporating electron and positron beam polarization effects and utilizing the helicity

Marcoen JTF Cabbolet

Recently, in Found. Phys. 53: 64 (2023), it has been argued that there is no reality to the PBR theorem. In Found. Phys. 54: 36 (2024), Hofer-Szab\'o has commented that the argument is flawed and that PBR theorem remains in tact. Here we reply to Hofer-Szab\'o by showing that his counterargument does not hold up, concluding that the PBR theorem has been disp

Jeewon Kim, Minho Oh, Hyun Myung

Scene graphs enhance 3D mapping capabilities in robotics by understanding the relationships between different spatial elements, such as rooms and objects. Recent research extends scene graphs to hierarchical layers, adding and leveraging constraints across these levels. This approach is tightly integrated with pose-graph optimization, improving both localiza

Tingqiao Xu, Ziru Zeng, Jiayu Chen

The quality of supervised fine-tuning (SFT) data is crucial for the performance of large multimodal models (LMMs), yet current data enhancement methods often suffer from factual errors and hallucinations due to inadequate visual perception. To address this challenge, we propose VERITAS, a pipeline that systematically integrates vision priors and multiple sta

Ashwini Kannan, Jaya Vasavi Pamidimukkala, Avinash Dakshinamoorthy, Soham Bopardikar

Protein folding is one of the age-old biological problems that refers to the mechanism of understanding and predicting how a protein's linear sequence of amino acids folds into its specific three dimensional structure.This structure is critical, as a protein's functionality is inherently linked to its final folded form. Misfolding can lead to severe diseases

Yicun Duan, Xinyue Li, Camille Avestruz, Jeffrey Regier

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will commence full-scale operations in 2026, yielding an unprecedented volume of astronomical images. Constructing an astronomical catalog, a table of imaged stars, galaxies, and their properties, is a fundamental step in most scientific workflows based on astronomical image data. Tradition

Arthémise Altman, Roland Tóbiás, Alexandr S. Bogomolov, Meissa L. Diouf

Precise frequency values have been determined for H$_2^{~16}$O radio lines appearing in protected line lists of the International Astronomical Union and the Panel on Frequency Allocations of the US National Academy of Sciences. The improved precision is attributable to a spectroscopic network built from a large set of near-infrared Lamb-dip lines augmented w

Bolei Ma, Yina Yao, Anna-Carolina Haensch

Large Language Models (LLMs) are increasingly applied to creative domains, yet their performance in classical Chinese poetry generation and evaluation remains poorly understood. We propose a three-step evaluation framework that combines computational metrics, LLM-as-a-judge assessment, and human expert validation. Using this framework, we evaluate six state-

Zhiyang Chen, Daliang Xu, Haiyang Shen, Chiheng Lou

Performing Retrieval-Augmented Generation (RAG) directly on mobile devices is promising for data privacy and responsiveness but is hindered by the architectural constraints of mobile NPUs. Specifically, current hardware struggles with the variable workloads intrinsic to RAG: the transition between processing extensive contexts and generating tokens incurs si

Andharini Dwi Cahyani, Moh. Wildan Fathoni, Fika Hastarita Rachman, Ari Basuki

Automated essay scoring (AES) is a vital area of research aiming to provide efficient and accurate assessment tools for evaluating written content. This study investigates the effectiveness of two popular similarity metrics, Jaccard coefficient, and Cosine similarity, within the context of vector space models(VSM)employing unigram, bigram, and trigram repres

Zihao Huo, Wenxuan Wang, Jian Xie, Yves H. Kwan

Rhombohedral multilayer graphene has emerged as a powerful platform for investigating flat-band-driven correlated phenomena, yet most aspects remain not understood. In this work, we systematically study the moire-dependent band topology in rhombohedral hexalayer graphene. For the first time we demonstrate that the moire twist angle plays a crucial role in th

Srijan Saket, Ikuhiro Ihara, Vaibhav Sharma, Danish Kalim

In modern recommendation systems and social media platforms like Meta, TikTok, and Instagram, large-scale ID-based features often require embedding tables that consume significant memory. Managing these embedding sizes can be challenging, leading to bulky models that are harder to deploy and maintain. In this paper, we introduce a method to automatically det

Venkat Ram Reddy Ganuthula, Manish Kumar Singh

This paper presents a novel game theoretic framework for analyzing academic dishonesty through the lens of a unique deterrent mechanism: forced exam script swapping between students caught copying. We model the strategic interactions between students as a non cooperative game with asymmetric information and examine three base scenarios asymmetric preparation