October 2025 arXiv papers — page 70

Xiaogang Jia, Qian Wang, Anrui Wang, Han A. Wang

Robotic manipulation systems benefit from complementary sensing modalities, where each provides unique environmental information. Point clouds capture detailed geometric structure, while RGB images provide rich semantic context. Current point cloud methods struggle to capture fine-grained detail, especially for complex tasks, which RGB methods lack geometric

Daniela Guericke, Rolf van der Hulst, Asal Karimpour, Ieke Schrader

In this work, we present the solution approach for the Integrated Healthcare Timetabling Competition 2024 submitted by Team Twente, which ultimately ranked third among the finalists. Our approach combines mixed-integer programming, constraint programming, and simulated annealing in a 3-phase solution approach based on decomposition into subproblems. In addit

Tiago V. Fernandes

Black holes exist all over our Universe, possessing a very wide range of masses. At the moment, they serve as a probe to test general relativity at astrophysical scales, but in the future they may also give us information about gravity at the microscale. Black holes seem to have thermodynamic properties, such as the Bekenstein-Hawking entropy, which are impo

Shuyuan Chen, Peng Zhang, Yifan Cui

Instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables. In this article, we develop a general nonparametric causal framework for identification and learning with multi-categorical or continuous instrumental variables. Specifically, the mean potential outcomes and the average treatment

Santiago Gil, Ecem E. Baş, Christian D. Jensen, Sebastian Engelsgaard

Distributed co-simulation plays a key role in enabling collaborative modeling and simulation by different stakeholders while protecting their Intellectual Property (IP). Although IP protection is provided implicitly by co-simulation, there is no consensus in the guidelines to conduct distributed co-simulation of continuous-time or hybrid systems with no expo

Neil Maiden, Konstantinos Zachos, James Lockerbie, Kostas Petrianakis

This paper presents a new computational model of creative outcomes, informed by creativity theories and techniques, which was implemented to generate more novel opportunities for innovation projects. The model implemented five functions that were developed to contribute to the generation of innovation opportunities with higher novelty without loss of usefuln

Xin Lu, Chuanqing Zhuang, Chenxi Jin, Zhengda Lu

Speech-driven 3D facial animation has attracted increasing interest since its potential to generate expressive and temporally synchronized digital humans. While recent works have begun to explore emotion-aware animation, they still depend on explicit one-hot encodings to represent identity and emotion with given emotion and identity labels, which limits thei

Takuya Kitamura, Genko Genov, Alon Salhov, Yutaka Kobayashi

Sub-picotesla level magnetometry has been demonstrated using negatively-charged nitrogen-vacancy (NV) centers in diamond by increasing the number of spins simultaneously used for sensing in an NV ensemble. However, such scale-up often introduces spatial inhomogeneities in detuning and control field amplitudes, which degrade sensitivity. Although several tech

Rubén Langarita, Jesús Alastruey-Benedé, Pablo Ibáñez-Marín, Santiago Marco-Sola

Multiple HPC applications are often bottlenecked by compute-intensive kernels implementing complex dependency patterns (data-dependency bound). Traditional general-purpose accelerators struggle to effectively exploit fine-grain parallelism due to limitations in implementing convoluted data-dependency patterns (like SIMD) and overheads due to synchronization

João Gonçalves da Silva, Giorgio Poggesi

The paper provides optimal quantitative stability estimates for the celebrated Alexandrov's Soap Bubble Theorem within the class of $C^{k,\alpha}$ domains, for any $k \ge 1$ and $0 < \alpha \leq 1$, by leveraging Gagliardo-Nirenberg-type interpolation inequalities. Optimal estimates of uniform closeness to a ball are established for $L^r$ deviations of the m

Rikpratik Sengupta, Chiranjeeb Singha

We investigate the gravitational collapse of a homogeneous dust cloud in the Shtanov Sahni braneworld model, which incorporates an extra timelike dimension. The interior of the collapsing configuration is modeled by a Friedmann Lemaitre spacetime, while the exterior is described by a Vaidya radiation envelope that eventually settles into a static Reissner No

Nicolas Delporte, Giacomo La Scala, Naoki Sasakura, Reiko Toriumi

Real eigenpairs of a real antisymmetric tensor of order $p$ and dimension $N$ can be defined as pairs of a real eigenvalue and $p$ orthonormal $N$-dimensional real eigenvectors. We compute the signed and the genuine distributions of such eigenvalues of Gaussian random real antisymmetric tensors by using a quantum field theoretical method. An analytic express

Nahual Sobrino

We investigate the thermoelectric transport properties of an interacting parallel double quantum dot in the Coulomb-blockade regime. Building on an analytical solution based on an equation-of-motion technique, we extend the formalism for the asymmetrically coupled situation and provide compact closed-form expressions for steady-state currents together with t

Fatma Almaz, Handan Oztekin

In this paper, Vn-slant helices and the harmonic curvature functions of Vn-slant helices are de ned in lightlike cone Qn+1, and the differential equations of the harmonic curvature functions Hi, 1<i<n-2 of Vn-slant helices are expressed by using constant vector field W that is the axis of Vn slant helices. Also, the necessary and sufficient conditions are gi

Yang Lv, Junwei Li, Jianfu Li, Yong Liu

An isolated calcium (Ca) atom has empty d-orbitals under ambient conditions. However, s-d band hybridization has been observed in both elemental Ca and compounds by manipulating thermodynamic conditions. Here, we reveal that the Ca 3d-band can even capture electrons from halogen atoms under pressure, exhibiting anionic behaviors in iodides. We predict a CsCl

Dang Vo Phuc

Stetkær&#39;s matrix (Levi--Civita) method is a powerful tool for functional equations on semigroups involving a homomorphism $σ$, as it yields a finite-dimensional invariant space under right translations and a corresponding matrix formalism. When $σ$ is an involutive anti-automorphism, however, the parametrized family of right translations reverses multipl

Julian Salt

The main goal of this contribution is to explain how to use interlacing techniques for LTI controllers implementation and analyze different struc- tures in this environment. These considerations lead to an important com- putation saving in constrained resource environments. It has been also intro- duced new procedures for obtaining the blocks related to diff

Qing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng Lim

Existing approaches for image-to-recipe retrieval have the implicit assumption that a food image can fully capture the details textually documented in its recipe. However, a food image only reflects the visual outcome of a cooked dish and not the underlying cooking process. Consequently, learning cross-modal representations to bridge the modality gap between

Lam Ngo, Huong Ha, Jeffrey Chan, Hongyu Zhang

Bayesian Optimization (BO) is a powerful tool for optimizing expensive black-box objective functions. While extensive research has been conducted on the single-objective optimization problem, the multi-objective optimization problem remains challenging. In this paper, we propose MOBO-OSD, a multi-objective Bayesian Optimization algorithm designed to generate

Z. B. Cui, Z. Q. Wang, P. Y. Liu, Y. Wang

Quantum networks and quantum repeaters represent the promising avenues for building large-scale quantum information systems, serving as foundational infrastructure for distributed quantum computing, long-distance quantum communication, and networked quantum sensing. A critical step in realizing a functional quantum network is the efficient and high-fidelity

S. Fantoni, M. Berni, R. Fognani, G. Fraterrigo

Articular cartilage showcases distinctive mechanical behaviour, attributable to its biphasic composition and hierarchical organization. Proteoglycans, essential constituents of the extracellular matrix, contribute to tissue swelling, stiffness, and viscoelasticity, thanks to the fixed charge density. Degenerative alterations in proteoglycan content and colla

Bjorn Remseth

Software platforms often act as structure preserving systems. They provide consistent interfaces and behaviors that remain stable under specific transformations that we denote as symmetries. This paper explores the idea that architectural robustness emerges from enforcing such structural regularities

Víctor Rampérez, Javier Soriano, David Lizcano, Juan A. Lara

Cloud computing has established itself as the support for the vast majority of emerging technologies, mainly due to the characteristic of elasticity it offers. Auto-scalers are the systems that enable this elasticity by acquiring and releasing resources on demand to ensure an agreed service level. In this article we present FLAS (Forecasted Load Auto-Scaling

Baoqing Yue, Jinyuan Zhou, Zixi Wei, Jingtao Zhan

Scaling laws aim to accurately predict model performance across different scales. Existing scaling-law studies almost exclusively rely on cross-entropy as the evaluation metric. However, cross-entropy provides only a partial view of performance: it measures the absolute probability assigned to the correct token, but ignores the relative ordering between corr

Shaltiel Shmidman, Avi Shmidman, Moshe Koppel

Since their initial release, BERT models have demonstrated exceptional performance on a variety of tasks, despite their relatively small size (BERT-base has ~100M parameters). Nevertheless, the architectural choices used in these models are outdated compared to newer transformer-based models such as Llama3 and Qwen3. In recent months, several architectures h

Yunpeng Bai, Haoxiang Li, Qixing Huang

Diffusion Transformers (DiTs) have emerged as the dominant architecture for visual generation, powering state-of-the-art image and video models. By representing images as patch tokens with positional encodings (PEs), DiTs combine Transformer scalability with spatial and temporal inductive biases. In this work, we revisit how DiTs organize visual content and

Anton A. Stoorvogel, Saeed Lotfifard, Ali Saberi

This paper presents a critical review of methods commonly employed in the literature for small signal stability analysis of inverter based resources (IBRs). It discusses the intended purposes of these methods and outlines both their proper and improper implementations. The paper provides insights into the applicability of these techniques, clarifies their in

Shuhei Aikawa, Aru Suzuki, Kei Yoshitake, Kanata Teshigawara

This paper focuses on forecasting hierarchical time-series data, where each higher-level observation equals the sum of its corresponding lower-level time series. In such contexts, the forecast values should be coherent, meaning that the forecast value of each parent series exactly matches the sum of the forecast values of its child series. Existing hierarchi

László Kozma, Michal Opler

Pattern-avoiding permutations are a central object of study in both combinatorics and theoretical computer science. In this paper we design a data structure that can store any size-$n$ permutation $\tau$ that avoids an arbitrary (and unknown) fixed pattern $\pi$ in the asymptotically optimal $O(n \lg{s_\pi})$ bits, where $s_\pi$ is the Stanley-Wilf limit of

Son T. Luu, Trung Vo, Hiep Nguyen, Khanh Quoc Tran

This paper presents the VLSP 2025 MLQA-TSR - the multimodal legal question answering on traffic sign regulation shared task at VLSP 2025. VLSP 2025 MLQA-TSR comprises two subtasks: multimodal legal retrieval and multimodal question answering. The goal is to advance research on Vietnamese multimodal legal text processing and to provide a benchmark dataset for

Anwar Ahmed Khan, Indrakshi Dey

Efficient medium access control (MAC) is critical for enabling low-latency and reliable communication in industrial Machine-to-Machine (M2M) net-works, where timely data delivery is essential for seamless operation. The presence of multi-priority data in high-risk industrial environments further adds to the challenges. The development of tens of MAC schemes

Rimpi Borah, J. Harshan

Analog Lagrange Coded Computing (ALCC) is a recently proposed computational paradigm wherein certain computations over analog datasets are efficiently performed using distributed worker nodes through floating point representation. While the vanilla version of ALCC is known to preserve the privacy of the datasets from the workers and also achieve resilience a

Wei Wu, Jun-Hong An

Quantum illumination uses quantum entanglement as a resource to enable higher-resolution detection of low-reflectivity targets than is possible with classical techniques. This revolutionary technology could transform modern radar. However, it is widely believed that the decoherence induced by the ubiquitous quantum noise destroys the superiority of quantum i

Tianyi Zhang, Florian Mai, Lucie Flek

Continual pretraining promises to adapt large language models (LLMs) to new domains using only unlabeled test-time data, but naively applying standard self-supervised objectives to instruction-tuned models is known to degrade their instruction-following capability and semantic representations. Existing fixes assume access to the original base model or rely o

Masoumeh Koohestani, Doost Ali Mojdeh, Mohsen Ghasemi

We consider Cayley sum graphs over the cyclic group $\mathbb{Z}_n$ and aim to explore several necessary and sufficient conditions for the existence of total perfect codes in these graphs. Specifically, we examine various cases for the connection set of the graph including when it is periodic, aperiodic, or square-free. To this end, we utilize a correspondenc

Jaehyung Seo, Hyeonseok Moon, Heuiseok Lim

Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. However, the impact of negated text on hallucination with LLMs remains largely unexplored. In this paper, we set three important yet unanswered research questions and aim to address them. To derive the answers, we investigate whether

Kota Asakura, Kazuki Yamamoto, Akihisa Koga

We study two-dimensional photonic crystals composed of elliptical dielectric rods arranged according to stealthy hyperuniform point patterns. These patterns are characterized by the structure factor, which vanishes for 0 < |k| <= K, where k is the wave number and K denotes the cutoff wave number specifying the stealthiness of the pattern. The optical propert

Gabriele Riccio, Michele Cantiello, Rebecca Habas, Nandini Hazra

The Surface Brightness Fluctuation method is one of the most reliable and efficient ways of measuring distances to galaxies within 100 Mpc. While recent implementations have increasingly relied on space-based observations, SBF remains effective when applied to ground-based data. In particular, deep, wide-field imaging surveys with sub-arcsecond seeing condit

Ben F. Tibola

We prove that for impulsive exposure patterns there is no uniform exponential energy law in wall-clock time t, which explains why past t-based unifications of continuous damping with impulses fail. We therefore replace t by a measure-valued clock, sigma, that aggregates absolutely continuous exposure and atomic doses within a single Lyapunov ledger. On this

Shu-Gui Yang, Xiang-bing Zeng, Feng Liu, Goran Ungar

Self-poisoning (SP) is ubiquitous in polymer crystallization, but has so far manifested itself visibly only as minima in growth rate vs. temperature in either monodisperse systems where e.g. unstable folded chains obstruct crystallization of stable extended chains, or in periodically segmented chains where unstable stems with n-1 segments disturb deposition

Zhenghao Xu, Qin Lu, Qingru Zhang, Liang Qiu

Reward model (RM) plays a pivotal role in reinforcement learning with human feedback (RLHF) for aligning large language models (LLMs). However, classical RMs trained on human preferences are vulnerable to reward hacking and generalize poorly to out-of-distribution (OOD) inputs. By contrast, strong LLM judges equipped with reasoning capabilities demonstrate s

Daniel Dadush, James B. Orlin, Aaron Sidford, László A. Végh

We provide faster strongly polynomial time algorithms solving maximum flow in structured $n$-node $m$-arc networks. Our results imply an $n^{\omega + o(1)}$-time strongly polynomial time algorithms for computing a maximum bipartite $b$-matching where $\omega$ is the matrix multiplication constant. Additionally, they imply an $m^{1 + o(1)} W$-time algorithm f

Daniel Gilkarov, Ran Dubin

Pretrained deep learning model sharing holds tremendous value for researchers and enterprises alike. It allows them to apply deep learning by fine-tuning models at a fraction of the cost of training a brand-new model. However, model sharing exposes end-users to cyber threats that leverage the models for malicious purposes. Attackers can use model sharing by

Max Nguyen, Vardan Adibekyan

The role of stellar metallicity in shaping planetary systems is central to our understanding of planet formation. While the core accretion paradigm is widely accepted as the dominant mechanism for forming low- and intermediate-mass planets, the origin of the most massive planets remains debated, with gravitational instability often invoked to explain their e

Muhammad Tayyab Khan, Zane Yong, Lequn Chen, Wenhe Feng

Engineering drawings are fundamental to manufacturing communication, serving as the primary medium for conveying design intent, tolerances, and production details. However, interpreting complex multi-view drawings with dense annotations remains challenging using manual methods, generic optical character recognition (OCR) systems, or traditional deep learning

H. Broadley, J. R. C. King, S. J. Lind

Meshless methods are commonly used to determine numerical solutions to partial differential equations (PDEs) for problems involving free surfaces and/or complex geometries, approximating spatial derivatives at collocation points via local kernels with a finite size. Despite their common use in turbulent flow simulations, the accuracy of meshless methods has

Yu-Tang Chang, Shih-Fang Chen

Analytical chemistry instruments provide physically meaningful signals for elucidating analyte composition and play important roles in material, biological, and food analysis. These instruments are valued for strong alignment with physical principles, enabling compound identification through pattern matching with chemical libraries. More reliable instruments

András Rácz, Tamás Borsos, András Veres, Benedek Csala

We present AttDet, a Transformer-inspired MIMO (Multiple Input Multiple Output) detection method that treats each transmit layer as a token and learns inter-stream interference via a lightweight self-attention mechanism. Queries and keys are derived directly from the estimated channel matrix, so attention scores quantify channel correlation. Values are initi

Aritra Roy, Enrico Grisan, John Buckeridge, Chiara Gattinoni

Since the advent of various pre-trained large language models, extracting structured knowledge from scientific text has experienced a revolutionary change compared with traditional machine learning or natural language processing techniques. Despite these advances, accessible automated tools that allow users to construct, validate, and visualise datasets from

Nick Fischer, Vasileios Nakos

We demonstrate that the best $k$-sparse approximation of a length-$n$ vector can be recovered within a $(1+\epsilon)$-factor approximation in $O((k/\epsilon) \log n)$ time using a non-adaptive linear sketch with $O((k/\epsilon) \log n)$ rows and $O(\log n)$ column sparsity. This improves the running time of the fastest-known sketch [Nakos, Song; STOC '19] by

Tianyi Zhang, Caihua Wan, Xiufeng Han

Determining critical points of phase transitions from partial data is essential to avoid abrupt system collapses and reducing experimental or computational costs. However, the complex physical systems and phase transition phenomena have long hindered the development of unified approaches applicable to both equilibrium and nonequilibrium phase transitions. In

Erik Burman, Lauri Oksanen, Janosch Preuss, Ziyao Zhao

We consider a unique continuation problem for the wave equation given data in a volumetric subset of the space time domain. In the absence of data on the lateral boundary of the space-time cylinder we prove that the solution can be continued with H\"older stability into a certain proper subset of the space-time domain. Additionally, we show that unique conti

Francesca Padovani, Bastian Bunzeck, Manar Ali, Omar Momen

We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we employ a variety of fine-tuning strategies to enforce "more communicative" text generations by our models. Although our models underperform on most standard BabyLM benchmarks, they exc

Manuel Mayo, María Isabel García de Soria, Pablo Maynar, José Javier Brey

The self-diffusion process of a hard sphere fluid confined by two parallel plates separated by a distance on the order of the particle diameter is studied. The starting point is a closed kinetic equation for the distribution function that takes into account the effects of the confinement and that is valid in the low-density limit. From it, the Boltzmann-Lore

Wenxuan Zhang, Yuan-Hao Jiang, Yang Cao, Yonghe Wu

Chunking strategies significantly impact the effectiveness of Retrieval-Augmented Generation (RAG) systems. Existing methods operate within fixed-granularity paradigms that rely on static boundary identification, limiting their adaptability to diverse query requirements. This paper presents FreeChunker, a Cross-Granularity Encoding Framework that fundamental

Daniel Grieser, Jørgen Olsen Lye

We study geodesics on a family $(M_\varepsilon)$ of manifolds that have a thin neck, which degenerate to a space with an incomplete cuspidal singularity as $\varepsilon\to0$. There are essentially two classes of geodesics passing the waist, i.e. the cross section where the neck is thinnest: 1. Those hitting the waist almost vertically. We find that these exh

Huayan Guo, Junhui Rao, Alex M. H. Wong, Ross Murch

Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffecti

Jin Zhan, Yongjun Zhang, Jiawen Zhang, Yu Liu

The origin of the strange metallic behavior observed in a wide range of quantum materials is an open challenge to condensed matter physics. Historically, strange metals were uniquely associated with antiferromagnetic quantum critical points (QCPs), but a new generation of materials reveals their association with uniform order parameters, such as ferromagneti

Amanda Wasielewski

W.J.T. Mitchell's influential essay 'What do pictures want?' shifts the theoretical focus away from the interpretative act of understanding pictures and from the motivations of the humans who create them to the possibility that the picture itself is an entity with agency and wants. In this article, I reframe Mitchell's question in light of contemporary AI im

Estelle Chigot, Dennis G. Wilson, Meriem Ghrib, Fabrice Jimenez

Deep vision models are now mature enough to be integrated in industrial and possibly critical applications such as autonomous navigation. Yet, data collection and labeling to train such models requires too much efforts and costs for a single company or product. This drawback is more significant in critical applications, where training data must include all p

Seunghoon Lee, Jeongwoo Choi, Byunggwan Son, Jaehyeon Moon

We present in this paper a novel post-training quantization (PTQ) method, dubbed AccuQuant, for diffusion models. We show analytically and empirically that quantization errors for diffusion models are accumulated over denoising steps in a sampling process. To alleviate the error accumulation problem, AccuQuant minimizes the discrepancies between outputs of a

Ashutosh Mishra, Shreya Santra, Elian Neppel, Edoardo M. Rossi Lombardi

Modular reconfigurable robots suit task-specific space operations, but the combinatorial growth of morphologies hinders unified control. We propose a decentralized reinforcement learning (Dec-RL) scheme where each module learns its own policy: wheel modules use Soft Actor-Critic (SAC) for locomotion and 7-DoF limbs use Proximal Policy Optimization (PPO) for

Marcus Prado, Romain Bachelard, Robin Kaiser, Felipe A. Pinheiro

Wave transport in complex media is determined by the nature of quasimodes at the microscopic level. In three dimensional disordered media, waves generally undergo a phase transition from diffusion to Anderson localization, characterized by exponentially localized modes. A remarkable exception are electromagnetic waves, whose vector-like nature prevents Ander

Haonan Bian

Knowledge Graphs (KGs) have long served as a fundamental infrastructure for structured knowledge representation and reasoning. With the advent of Large Language Models (LLMs), the construction of KGs has entered a new paradigm-shifting from rule-based and statistical pipelines to language-driven and generative frameworks. This survey provides a comprehensive

Wei Cao, Shanshan Wang

Expectile regression neural networks (ERNNs) are powerful tools for capturing heterogeneity and complex nonlinear structures in data. However, most existing research has primarily focused on fully observed data, with limited attention paid to scenarios involving censored observations. In this paper, we propose a data augmentation based ERNNs algorithm, terme

Ziyuan Gao

While frameworks such as the WHO Age-Friendly Cities have advanced urban aging policy, rural contexts demand fundamentally different analytical approaches. The spatial dispersion, terrain variability, and agricultural labor dependencies that characterize rural aging experiences require moving beyond service-domain frameworks toward spatial stress assessment

Chengpeng Li, Zhengyang Tang, Ziniu Li, Mingfeng Xue

Large reasoning models (LRMs) like OpenAI-o1 have shown impressive capabilities in natural language reasoning. However, these models frequently demonstrate inefficiencies or inaccuracies when tackling complex mathematical operations. While integrating computational tools such as Code Interpreters (CIs) offers a promising solution, it introduces a critical ch

Aaron Bernstein, Sayan Bhattacharya, Nick Fischer, Peter Kiss

We establish the first update-time separation between dynamic algorithms against oblivious adversaries and those against adaptive adversaries in natural dynamic graph problems, based on popular fine-grained complexity hypotheses. Specifically, under the combinatorial BMM hypothesis, we show that every combinatorial algorithm against an adaptive adversary for

D. Kucharski, A. Gaska, T. Kowaluk, K. Stepien

A reproducible deep learning framework is presented for surface metrology to predict surface texture parameters together with their reported standard uncertainties. Using a multi-instrument dataset spanning tactile and optical systems, measurement system type classification is addressed alongside coordinated regression of Ra, Rz, RONt and their uncertainty t

Vincenzo Pierro, Vincenzo Fiumara, Guerino Avallone, Giovanni Carapella

Accurate thermal modeling of Terminal Test Masses (TTMs) is crucial for optimizing the sensitivity of gravitational wave interferometers like Virgo. In fact, in such gravitational wave detectors even minimal laser power absorption can induce performance-limiting thermal effects. This paper presents a detailed investigation into the steady-state thermal behav

Clara Maathuis, Kasper Cools

In an era where AI (Artificial Intelligence) systems play an increasing role in the battlefield, ensuring responsible targeting demands rigorous assessment of potential collateral effects. In this context, a novel collateral damage assessment model for target engagement of AI systems in military operations is introduced. The model integrates temporal, spatia

Zixuan Wu, Hengyuan Zhang, Ting-Hsuan Chen, Yuliang Guo

Parking is a critical pillar of driving safety. While recent end-to-end (E2E) approaches have achieved promising in-domain results, robustness under domain shifts (e.g., weather and lighting changes) remains a key challenge. Rather than relying on additional data, in this paper, we propose Dino-Diffusion Parking (DDP), a domain-agnostic autonomous parking pi

Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus

The Schr\"odinger Bridge (SB) problem has become a fundamental tool in computational optimal transport and generative modeling. To address this problem, ideal methods such as Iterative Proportional Fitting and Iterative Markovian Fitting (IMF) have been proposed-alongside practical approximations like Diffusion Schr\"odinger Bridge and its Matching (DSBM) va

A. P. Kryukov, A. Yu. Razumov, A. P. Demichev, J. J. Dubenskaya

The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method

Chiyu Chen, Xinhao Song, Yunkai Chai, Yang Yao

Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile graphical user interfaces (GUIs). Operating in dynamic on-device ecosystems, which include notifications, pop-ups, and inter-app interactions, exposes them to a unique and underexplored threat vector: environmental injection. Unlike prompt-based attacks that manip

Anna Arias-Duart, Maria Eugenia Cardello, Atia Cortés

Artificial intelligence (AI) holds great promise for transforming healthcare. However, despite significant advances, the integration of AI solutions into real-world clinical practice remains limited. A major barrier is the quality and fairness of training data, which is often compromised by biased data collection practices. This paper draws on insights from

Kangli Wang, Qianxi Yi, Yuqi Ye, Shihao Li

Generalization remains a critical challenge in deep learning-based point cloud geometry compression. While existing methods perform well on standard benchmarks, their performance collapses in real-world scenarios due to two fundamental limitations: the lack of context models that are robust across diverse data densities, and the inability to efficiently adap

Yohai Reani, Omer Bobrowski

We introduce a novel approach for studying random k-coverage, using Morse theory for the k-nearest neighbor (k-NN) distance function. We prove a sharp phase transition for the number of critical points of the k-NN distance function, from which we conclude a phase transition for k-coverage. In addition, in the critical window our new framework enables us to p

Ajay Sridhar, Jennifer Pan, Satvik Sharma, Chelsea Finn

Humans routinely rely on memory to perform tasks, yet most robot policies lack this capability; our goal is to endow robot policies with the same ability. Naively conditioning on long observation histories is computationally expensive and brittle under covariate shift, while indiscriminate subsampling of history leads to irrelevant or redundant information.

Fengyuan Yu, Yuyuan Li, Xiaohua Feng, Junjie Fang

With the growing demand for safeguarding sensitive user information in recommender systems, recommendation attribute unlearning is receiving increasing attention. Existing studies predominantly focus on single-attribute unlearning. However, privacy protection requirements in the real world often involve multiple sensitive attributes and are dynamic. Existing

Yoshimine Kato, Tomoaki Nakamura, Katsuya Komorita, Kungen Teii

Rectification properties of semiconductor p-n junction diodes are the basic and important characteristics for electronic device evaluation, especially for novel semiconductor materials. Today's semiconductor devices' fabrication and integration processes require multibillion-dollar investments and are desired to be reduced or simplified. Therefore, low-cost

Jacob Kryczka, Artan Sheshmani

We consider the moduli space of rigidified perfect complexes with support on a general complete intersection Calabi-Yau threefold $X$ and its Tyurin degeneration $X\rightsquigarrow X_1\cup_SX_2$ to a complete intersection of Fano threefolds $X_1,X_2$ meeting along their anti-canonical divisor $S$. The corresponding derived dg moduli scheme over the generic f

D. M. Zhang, D. Y. Sun, X. G. Gong

Inspired by the Kadanoff transformation in the standard renormalization group theory, we propose a temporal renormalization scheme. A Boltzmann factor that explicitly depends on the renormalized timescale is constructed, permitting thermodynamic quantities to be evaluated self-consistently across different timescales. By applying the scheme to the long-time

Saber Ahmed, Natasha Crepeau, Gisel Flores, Osiano Isekenegbe

Place cells are neurons that act as biological position sensors, associated with and firing in response to regions of an environment to situate an organism in space. These associations are recorded in (combinatorial) neural codes, motivating the following mathematical question: Which neural codes are generated by a collection of convex open sets in Euclidean

Zelin Peng, Zhengqin Xu, Qingyang Liu, Xiaokang Yang

Multi-modal large language models (MLLMs) have emerged as a transformative approach for aligning visual and textual understanding. They typically require extremely high computational resources (e.g., thousands of GPUs) for training to achieve cross-modal alignment at multi-granularity levels. We argue that a key source of this inefficiency lies in the vision

Karol M. Dąbrowski, Mohammad Nooraiepour, Mohammad Masoudi

Underground storage of hydrogen and ammonia in geological formations is essential for renewable energy integration, but salt precipitation during gas injection may threaten storage performance. While extensively studied for CO2 systems, precipitation mechanisms in hydrogen-brine and ammonia-brine systems remain poorly understood. This study presents a compre

Hanbing Fang, Yu Li

In this paper, we establish a Lojasiewicz inequality for the pointed $\mathcal{W}$-entropy in the Ricci flow, under the assumption that the geometry near the base point is close to a standard cylinder $\mathbb{R}^k \times S^{n-k}$ or the quotient thereof. As an application, we prove the strong uniqueness of the cylindrical tangent flow at the first singular

Deepak Rajendraprasad, Durga R. Sankaranarayanan

For a finite simple undirected graph $G$, let $\gamma(G)$ denote the size of a smallest dominating set of $G$ and $\mu(G)$ denote the number of eigenvalues of the Laplacian matrix of $G$ in the interval $[0,1)$, counting multiplicities. Hedetniemi, Jacobs and Trevisan [Eur. J. Comb. 2016] showed that for any graph $G$, $\mu(G) \leqslant \gamma(G)$. Cardoso,

Michalis Gaitanas, Christos Efthymiopoulos, Ioannis Gkolias, George Voyatzis

We develop the 3D generalization of the planar analytical theory presented in Gaitanas et. al., 2024, which deals with states slightly perturbed from the exact `single-synchronous equilibrium state' (SSES) of the full two-body problem. The SSES corresponds to two non-spherical gravitationally interacting bodies, settled in nearly circular relative orbit, wit

Eduard Feireisl, Wladimir Neves

We apply a continuous data assimilation method to the Navier-Stokes-Fourier system governing the evolution of a compressible, rotating and thermally driven fluid. A rigorous proof of the tracking property is given in the asymptotic regime of low Mach and high Rossby and Froude numbers. Large data in the framework of weak solutions are considered.

Renaud A. L. Vallée, Rénal Backov

Light transport in strongly scattering porous photocatalytic materials governs the spatial distribution of absorbed photons and therefore the generation of charge carriers driving photocatalytic reactions. Yet translating measured optical properties of such media into intrinsic reaction rate constants remains challenging, as it requires simultaneously accoun

Wu Yichao, Wang Yirui, Ding Panpan, Wang Hailong

With the wide application of deep reinforcement learning (DRL) techniques in complex fields such as autonomous driving, intelligent manufacturing, and smart healthcare, how to improve its security and robustness in dynamic and changeable environments has become a core issue in current research. Especially in the face of adversarial attacks, DRL may suffer se

Mohammadali Rostami, Saeed Lotfifard, Mladen Kezunovic

This paper proposes a multi-actor coordination platform for the optimal utilization of smart buildings resources, including roof top PV generation and battery energy storage system (BESS), in active power distribution systems. The proposed multi-actor coordination includes the Smart Building Coordinator (SBC), Micro-Grid Coordinator (MGC) and Distribution Sy

Kazuhiro Kuboki

Kitaev chain is a one-dimensional spinless fermion model that has $p$-wave superconducting (SC) states and Majorana zero modes at the edge. Usually this model is analyzed by taking only SC order parameter (OP) into account, but the situation significantly changes when OPs other than the SCOP are included. It turns out that the SC state in the latter case is

Donghoon Ha, Jeong San Kim

We consider the discrimination of quantum sequences under maximum-confidence measurements and show that the optimal discrimination of a quantum sequence ensemble can always be factorized into that of each individual ensemble. In other words, the optimal quantum sequence discrimination under maximum-confidence measurements can be achieved just by performing a

Mingliang Zhai, Hansheng Liang, Xiaomeng Fan, Zhi Gao

Embodied Question Answering (EQA) requires agents to explore 3D environments to obtain observations and answer questions related to the scene. Existing methods leverage VLMs to directly explore the environment and answer questions without explicit thinking or planning, which limits their reasoning ability and results in excessive or inefficient exploration a

Mohit Kumar Jat, Kenji Watanabe, Takashi Taniguchi, Aveek Bid

We report the emergence of massless Dirac fermions in moir\'{e}-reconstructed bands of bilayer graphene (BLG) aligned with hexagonal boron nitride (hBN). Magnetotransport measurements reveal that while the primary BLG band retains a parabolic dispersion with a Berry phase of $2\pi$, the moir\'{e}-induced secondary bands at $n/n_0 = \pm 4$ host chiral massles

Sam de Regt, Siddharth Gandhi, Louis Siebenaler, Darío González Picos

In recent years, significant advances have been made in exoplanet and brown dwarf observations. By using state-of-the-art models, astronomers can determine properties of their atmospheres, such as temperatures, the presence of clouds, or the chemical abundances of molecules and atoms. Accurate and up-to-date opacities are crucial to avoid inconclusive or bia

Manuel Schönberger, Immanuel Trummer, Wolfgang Mauerer

Finding optimal join orders is among the most crucial steps to be performed by query optimisers. Though extensively studied in data management research, the problem remains far from solved: While query optimisers rely on exhaustive search methods to determine ideal solutions for small problems, such methods reach their limits once queries grow in size. Yet,

Anastasios Papazafeiropoulos, Pandelis Kourtessis, Dimitra I. Kaklamani, Iakovos S. Venieris

Stacked intelligent metasurface (SIM) is a promising enabler for next-generation high-capacity networks that exhibit better performance compared to its single-layer counterpart by means of just wave propagation. However, the study of ergodic mutual information (EMI) and outage probability for SIM-assisted multiple-input-multiple-output (MIMO) systems is not