March 2025 arXiv papers — page 168

Kha Vo, Chin-Teng Lin

Inspired by a graph-based technique for predicting molecular properties in quantum chemistry -- atoms' position within molecules in three-dimensional space -- we present Q-MARL, a completely decentralised learning architecture that supports very large-scale multi-agent reinforcement learning scenarios without the need for strong assumptions like common rewar

Kexin Di, Xiuxing Li, Yuyang Han, Ziyu Li

Few-shot image classification has become a popular research topic for its wide application in real-world scenarios, however the problem of supervision collapse induced by single image-level annotation remains a major challenge. Existing methods aim to tackle this problem by locating and aligning relevant local features. However, the high intra-class variabil

Nadav Borenstein

Computational Social Science (CSS) is an emerging field driven by the unprecedented availability of human-generated content for researchers. This field, however, presents a unique set of challenges due to the nature of the theories and datasets it explores, including highly subjective tasks and complex, unstructured textual corpora. Among these challenges, o

Simon Grall, Ignacio Madrid, Aramis Dufour, Helen Sands

Bioelectrochemistry is crucial for understanding biological functions and driving applications in synthetic biology, healthcare, and catalysis. However, current simulation methods fail to capture both the stochastic nature of molecular motion and electron transfer across the relevant picosecond-to-minute timescales. We present QBIOL, a web-accessible softwar

Sreelakshmi M, Akhilesh Ranjan

In this work, we estimate the mass spectra and decay properties of charmonium ($c \bar c$) using a non-relativistic potential model. We employ a potential model incorporating a Coulomb like term, representing one gluon exchange at short distances, and a screening term representing quark confinement at long distances. Spin-dependent corrections are also added

Ouxiang Li, Yuan Wang, Xinting Hu, Houcheng Jiang

Erasing concepts from large-scale text-to-image (T2I) diffusion models has become increasingly crucial due to the growing concerns over copyright infringement, offensive content, and privacy violations. In scalable applications, fine-tuning-based methods are time-consuming to precisely erase multiple target concepts, while real-time editing-based methods oft

J Dantas, P Silva, L Fiondella, C Melo

Blockchain technology has emerged, and many previous studies have assessed its performance issues. However, less attention has been paid to the dependability attributes, which have been a critical topic in service provisioning, considering public or private infrastructures. This paper introduces analytical models to assess the availability of private blockch

Boeun Kim, Hea In Jeong, JungHoon Sung, Yihua Cheng

This paper introduces Motion Personalization, a new task that generates personalized motions aligned with text descriptions using several basic motions containing Persona. To support this novel task, we introduce a new large-scale motion dataset called PerMo (PersonaMotion), which captures the unique personas of multiple actors. We also propose a multi-modal

Ruidong Chen, Honglin Guo, Lanjun Wang, Chenyu Zhang

Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images. To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts. However, current studies struggle to fully erase malicious concepts implicitly embedded in pr

Jingjing Li, Weitang Li, Xiaoxiao Xiao, Limin Liu

Accelerating quantum dynamical simulations with quantum computing has received considerable attention but remains a significant challenge. In variational quantum algorithms for quantum dynamics, designing an expressive and shallow-depth parameterized quantum circuit (PQC) is a key difficulty. Here, we proposed a multi-set variational quantum dynamics algorit

Lukas Graf, Tobias Harks, Julian Schwarz

A seminal result of [Fleischer et al. and Karakostas and Kolliopulos, both FOCS 2004] states that system optimal multi-commodity static network flows are always implementable as tolled Wardrop equilibrium flows even if users have heterogeneous value-of-time sensitivities. Their proof uses LP-duality to characterize the general implementability of network flo

Yongchun Lu, Liying Kang, Yisai Xue

Let \( \mathcal{F} \) be a family of graphs. The generalized Tur\'an number \( \operatorname{ex}(n, K_r, \mathcal{F}) \) is the maximum number of $K_r$ in an \( n \)-vertex graph that does not contain any member of \( \mathcal{F} \) as a subgraph. Recently, Alon and Frankl initiated the study of Tur\'an problems with bounded matching number. In this paper, w

Yuzhou Xiao, Senchun Chai, Li Dai, Yuanqing Xia

This paper addresses the challenge of amplitude-unbounded false data injection (FDI) attacks targeting the sensor-to-controller (S-C) channel in cyber-physical systems (CPSs). We introduce a resilient tube-based model predictive control (MPC) scheme. This scheme incorporates a threshold-based attack detector and a control sequence buffer to enhance system se

Gonzalo Mancera, Daniel DeAlcala, Julian Fierrez, Ruben Tolosana

This work adapts and studies the gradient-based Membership Inference Test (gMINT) to the classification of text based on LLMs. MINT is a general approach intended to determine if given data was used for training machine learning models, and this work focuses on its application to the domain of Natural Language Processing. Using gradient-based analysis, the M

Yunhong Che, Vivek N. Lam, Jinwook Rhyu, Joachim Schaeffer

Diverse usage patterns induce complex and variable aging behaviors in lithium-ion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery's current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery's degradation trajectory, are

Ho-Yeon Won, Cédric Lorcé

We study in detail the relativistic distributions of energy, longitudinal momentum, longitudinal energy flux, and longitudinal thrust inside nucleons based on the quantum phase-space formalism. Similar to recent studies on the electromagnetic current, we include the effects of the nucleon polarization and show that the latter are essential for understanding

Xin Liao, Rui-Jing Sun, Emi Minamitani, Lian-Zhi Yang

Electron charging play key roles in physiochemical processes, whose intrinsic stabilization in single molecules is desirable for tailoring molecular functionality and developing molecular devices, but remains elusive on surfaces. Here, we show molecular charge states can be self-stabilized via intramolecular distortion in single bis(phthalocyaninato)terbium(

Harsh Mathur, L. S. Anusha, Devang Agnihotri

Ca {\sc i} 4227 \AA\, line is a strong resonance line formed in the Solar chromosphere. At the limb, it produces the largest scattering polarization signal. So far, modeling the linear polarization in this line has been limited to the use of one-dimensional semi-empirical models of the solar atmosphere. In this paper, we use three-dimensional magnetohydrodyn

Kentaro Inoue

We study the log version of the prismatic Dieudonn\'{e} theory established by Ansch\"{u}tz-Le Bras. By applying this result to the integral toroidal compactification of a Shimura variety of Hodge type, we extend the prismatic realization, originally constructed by Imai-Kato-Youcis, to the compactification. This extension enables us to prove Lovering's conjec

Yusuke Hashimoto, Xue Jia, Hao Li, Takaaki Tomai

Materials informatics (MI), emerging from the integration of materials science and data science, is expected to significantly accelerate material development and discovery. The data used in MI are derived from both computational and experimental studies; however, their integration remains challenging. In our previous study, we reported the integration of the

Chongming Gao, Mengyao Gao, Chenxiao Fan, Shuai Yuan

While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likelihood maximization objective, compromising recommendation diversity and fairness. To address this, we present Flow-guided fine-tuning recommender (Flower), which replaces SFT with a G

Grik Tadevosyan, Valerii Serpiva, Aleksey Fedoseev, Roohan Ahmed Khan

We introduce AttentionSwarm, a novel benchmark designed to evaluate safe and efficient swarm control in a dynamic drone racing scenario. Central to our approach is the Attention Model-Based Control Barrier Function (CBF) framework, which integrates attention mechanisms with safety-critical control theory to enable real-time collision avoidance and trajectory

R. Spencer Hallyburton, David Hunt, Yiwei He, Judy He

Attacks on sensing and perception threaten the safe deployment of autonomous vehicles (AVs). Security-aware sensor fusion helps mitigate threats but requires accurate field of view (FOV) estimation which has not been evaluated autonomy. To address this gap, we adapt classical computer graphics algorithms to develop the first autonomy-relevant FOV estimators

Simon Hakvoort, Bastien Francois, Kirien Whan, Sjoerd Dirksen

Recent statistical postprocessing methods for wind speed forecasts have incorporated linear models and neural networks to produce more skillful probabilistic forecasts in the low-to-medium wind speed range. At the same time, these methods struggle in the high-to-extreme wind speed range. In this work, we aim to increase the performance in this range by train

Alberto S. Cattaneo, Filippo Fila-Robattino

This note examines the BV formulation of $N=1$, $D=4$ supergravity in the first-order Palatini--Cartan framework. Challenges in achieving an off-shell formulation are addressed by introducing corrections to the rank 2 BV action, offering in addition a solid foundation for the study of the theory on manifolds with boundary.

Feiyu An, Junyi Ye, Huanshu Tan

The evaporation dynamics of sessile drops are crucial for material deposition in applications like inkjet printing and pharmaceutical development. However, the evaporation behavior of high molecular weight polymer solutions and their impact on deposit morphology and flow fields are not well understood. This study investigates the evaporation dynamics and dep

Qizhi Zheng, Zhongze Luo, Meiyan Guo, Xinzhu Wang

Accurate, real-time object detection on resource-constrained hardware is critical for anomaly-behavior monitoring. We introduce HGO-YOLO, a lightweight detector that combines GhostHGNetv2 with an optimized parameter-sharing head (OptiConvDetect) to deliver an outstanding accuracy-efficiency trade-off. By embedding GhostConv into the HGNetv2 backbone with mul

Yuzhuo Wang, Tianxiong Gao, Yufan Niu, Ying Hu

Mean field theory is commonly employed to study nonequilibrium dynamics in hot Rydberg atomic ensembles, but the fundamental mechanism behind the generation of the mean-field interactions remains poorly understood. In this work, we experimentally observe a time-delay effect in the buildup of mean-field interaction, which reveals the key role of collision ion

Luis D. Reyes Vargas, Martin J. Menten, Johannes C. Paetzold, Nassir Navab

Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for preserving connectivity in curvilinear structures, aiding medical tasks like vessel segmentation. Existing compatible skeletonization algorithms face significant trade-offs: morpholo

Michael Kerber, Florian Russold

Graphcodes were recently introduced as a technique to employ two-parameter persistence modules in machine learning tasks (Kerber and Russold, NeurIPS 2024). We show in this work that a compressed version of graphcodes yields a description of a two-parameter module that is equivalent to a presentation of the module. This alternative representation as a graph

Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero-Durmus

Human physiological signals tend to exhibit both global and local structures: the former are shared across a population, while the latter reflect inter-individual variability. For instance, kinetic measurements of the gait cycle during locomotion present common characteristics, although idiosyncrasies may be observed due to biomechanical disposition or patho

Kangan Qian, Jinyu Miao, Ziang Luo, Zheng Fu

Accurate and reliable spatial and motion information plays a pivotal role in autonomous driving systems. However, object-level perception models struggle with handling open scenario categories and lack precise intrinsic geometry. On the other hand, occupancy-based class-agnostic methods excel in representing scenes but fail to ensure physics consistency and

Mohsen Asgharzadeh, Mohammad Golshani, Saharon Shelah

We present a class of abelian groups that exhibit a high degree of freeness while possessing no non-trivial homomorphisms to a canonical free object. Unlike prior investigations, which primarily focused on torsion-free groups, our work broadens the scope to include groups with torsion. Our main focus is on p-groups, for which we formulate and prove the Trivi

Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou

DeepSeek R1, and o1 have demonstrated powerful reasoning capabilities in the text domain through stable large-scale reinforcement learning. To enable broader applications, some works have attempted to transfer these capabilities to multimodal reasoning. However, these efforts have been limited by the limited difficulty of selected tasks and relatively small

Yannick Oswald

Prevailing top-down systems in politics and economics struggle to keep pace with the pressing challenges of the 21st century, such as climate change, social inequality and conflict. Bottom-up democratisation and participatory approaches in politics and economics are increasingly seen as promising alternatives to confront and overcome these issues, often with

Jiang Lin, Zili Yi

Video style transfer aims to alter the style of a video while preserving its content. Previous methods often struggle with content leakage and style misalignment, particularly when using image-driven approaches that aim to transfer precise styles. In this work, we introduce Trajectory Reset Attention Control (TRAC), a novel method that allows for high-qualit

Amitava Choudhuri, Madan Mohan Panja, Benoy Talukdar

We construct the equation of Duffing oscillator in a dissipative medium using certain concepts from elementary mechanics. The Duffing equation (DE) without damping can be solved analytically. This is not true for a DE that involves a damping term. We remove the damping term from a linearly damped DE and thus obtain a simple analytical solution x(t) of the da

Patrizio Angelini, Sabine Cornelsen, Carolina Haase, Michael Hoffmann

A dichotomous ordinal graph consists of an undirected graph with a partition of the edges into short and long edges. A geometric realization of a dichotomous ordinal graph $G$ in a metric space $X$ is a drawing of $G$ in $X$ in which every long edge is strictly longer than every short edge. We call a graph $G$ pandichotomous in $X$ if $G$ admits a geometric

Yi-Lin Wei, Mu Lin, Yuhao Lin, Jian-Jian Jiang

Language-guided robot dexterous generation enables robots to grasp and manipulate objects based on human commands. However, previous data-driven methods are hard to understand intention and execute grasping with unseen categories in the open set. In this work, we explore a new task, Open-set Language-guided Dexterous Grasp, and find that the main challenge i

Aaron Grapentin, Christian A. Hans, Jörg Raisch

In this paper, we present an advanced wind turbine control scheme for power maximization as well as for active power control, which is designed using $\mathcal{H}_\infty$ loop-shaping. Our approach involves the synthesis of two separate controllers for two different operating modes. To ensure smooth transitions between these modes, we implement a bumpless tr

Yiqing Xie, Alex Xie, Divyanshu Sheth, Pengfei Liu

We present RepoST, a scalable method to construct environments that provide execution feedback for repository-level code generation for both training and evaluation. Unlike existing works that aim to build entire repositories for execution, which is challenging for both human and LLMs, we provide execution feedback with sandbox testing, which isolates a give

Michael Neri, Tuomas Virtanen

In this work, we investigate the generalization of a multi-channel learning-based replay speech detector, which employs adaptive beamforming and detection, across different microphone arrays. In general, deep neural network-based microphone array processing techniques generalize poorly to unseen array types, i.e., showing a significant training-test mismatch

Zhengjie Kang, Hao Li, Shuo Wang, Jiaojiao Li

Learning quantum Hamiltonians with high precision is important for quantum physics and quantum information science. We propose a multi-stage neural network framework that significantly enhances Hamiltonian learning precision through successive network optimization of residual errors. Our approach utilizes time-series data from single-qubit Pauli measurements

Filippo Fila-Robattino

This paper contains a review of the theoretical foundations of Clifford algebras, spinors and spinor bundles in the so-called co-frame formalism. A compact index-free notation is introduced, along with a series of identities useful for computations in supergravity theories.

C. P. Larson, E. Yelton, K. Dodge, K. Okubo

When a high-energy particle, such as a $\gamma$-ray or muon, impacts the substrate of a superconducting qubit chip, large numbers of electron-hole pairs and phonons are created. The ensuing dynamics of the electrons and holes changes the local offset-charge environment for qubits near the impact site. The phonons that are produced have energy above the super

Carl Olsson, Yaroslava Lochman, Johan Malmport, Christopher Zach

Rotation averaging is a key subproblem in applications of computer vision and robotics. Many methods for solving this problem exist, and there are also several theoretical results analyzing difficulty and optimality. However, one aspect that most of these have in common is a focus on the isotropic setting, where the intrinsic uncertainties in the measurement

Eetu Tunturi, David Diaz-Guerra, Archontis Politis, Tuomas Virtanen

Music source separation is the task of separating a mixture of instruments into constituent tracks. Music source separation models are typically trained using only audio data, although additional information can be used to improve the model's separation capability. In this paper, we propose two ways of using musical scores to aid music source separation: a s

Shuai Tang, Jiachao Wu, Ning Zhou

This paper generalizes the encoding of argumentation frameworks beyond the classical 2-valued propositional logic system ($PL_2$) to 3-valued propositional logic systems ($PL_3$s) and fuzzy propositional logic systems ($PL_{[0,1]}s$), employing two key encodings: normal encoding ($ec_1$) and regular encoding ($ec_2$). Specifically, via $ec_1$ and $ec_2$, we

Qingqing Peng, Yikan Liu

This paper is concerned with the energy decay of a viscoelastic variable coefficient wave equation with nonlocality in time as well as nonlinear damping and polynomial nonlinear terms. Using the Lyapunov method, we establish a polynomial energy decay for the solution under relatively weak assumptions regarding the kernel of the nonlocal term. More specifical

Xiyu Gu, Matthias Pezzutto, Luca Schenato, Subhrakanti Dey

The emerging computing continuum paves the way for exploiting multiple computing devices, ranging from the edge to the cloud, to implement the control algorithm. Different computing units over the continuum are characterized by different computational capabilities and communication latencies, thus resulting in different control performances and advocating fo

Christoph Karg, Sebastian Stricker, Lisa Hutschenreiter, Bogdan Savchynskyy

In this work we present a novel approach for unsupervised multi-graph matching, which applies to problems for which a Gaussian distribution of keypoint features can be assumed. We leverage cycle consistency as loss for self-supervised learning, and determine Gaussian parameters through Bayesian Optimization, yielding a highly efficient approach that scales t

Johan Edstedt, Georg Bökman, Mårten Wadenbäck, Michael Felsberg

Keypoints are what enable Structure-from-Motion (SfM) systems to scale to thousands of images. However, designing a keypoint detection objective is a non-trivial task, as SfM is non-differentiable. Typically, an auxiliary objective involving a descriptor is optimized. This however induces a dependency on the descriptor, which is undesirable. In this paper we

Nils Philipp Walter, Jilles Vreeken, Jonas Fischer

Attribution methods reveal which input features a neural network uses for a prediction, adding transparency to their decisions. A common problem is that these attributions seem unspecific, highlighting both important and irrelevant features. We revisit the common attribution pipeline and observe that using logits as attribution target is a main cause of this

Jonas Luhrmann, Wilhelm Schlag, Sohrab Shahshahani

We study the evolution of the Gross-Pitaevskii equation linearized around the Ginzburg-Landau vortex of degree one under equivariant symmetry. Among the main results of this work, we determine the spectrum of the linearized operator, uncover a remarkable $L^2$-norm growth phenomenon related to a zero-energy resonance, and provide a complete construction of t

Aslak Fellman, Jesper Byggmästar, Fredric Granberg, Flyura Djurabekova

We develop a machine-learned interatomic potential for AlCrCuFeNi high-entropy alloys (HEA) using a diverse set of structures from density functional theory calculated including magnetic effects. The potential is based on the computationally efficient tabulated version of the Gaussian approximation potential method (tabGAP) and is a general-purpose model for

Antoine Van Biesbroeck, Clément Gauchy, Cyril Feau, Josselin Garnier

A seismic fragility curve expresses the probability of failure of a structure conditional to an intensity measure (IM) derived from seismic signals. When only limited data is available, the practitioner often refers to the probit-lognormal model coupled with maximum likelihood estimation (MLE) to obtain estimates of these curves. This means that only a binar

Antoine Joux, Rocco Mora

In this work, we introduce a novel variant of the multivariate quadratic problem, which is at the core of one of the most promising post-quantum alternatives: multivariate cryptography. In this variant, the solution of a given multivariate quadratic system must also be regular, i.e. if it is split into multiple blocks of consecutive entries with the same fix

Jakub Growiec, Klaus Prettner

Recent advances in artificial intelligence (AI) have led to a wide range of predictions about its long-term impact on humanity. A central focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from unprecedented economic g

Yiting Mao, Dajun Tao, Shengyuan Zhang, Tian Qi

In the field of robot target recognition research driven by artificial intelligence (AI), factors such as the disordered distribution of targets, the complexity of the environment, the massive scale of data, and noise interference have significantly restricted the improvement of target recognition accuracy. Against the backdrop of the continuous iteration an

V. Borka Jovanović, D. Borka, P. Jovanović

Here we want to investigate X-shaped radio galaxy 3C 315, which is a FRII source, but it lies very close to the FRI/FRII borderline. We used publicly available data from Leahy's atlas of double radio-sources and NASA/IPAC Extragalactic Database (NED) in order to investigate its flux density, as well as the spectral index distribution. We obtained spectral in

Haoran Xu, Peixi Peng, Guang Tan, Yiqian Chang

Occupancy World Models (OWMs) aim to predict future scenes via 3D voxelized representations of the environment to support intelligent motion planning. Existing approaches typically generate full future occupancy states from VAE-style latent encodings, which can be computationally expensive and redundant. We propose Delta-Triplane Transformers (DTT), a novel

Paolo Acampora, Jimmy Lamboley

In this paper, we focus on the famous Talenti's symmetrization inequality, more precisely its $L^p$ corollary asserting that the $L^p$-norm of the solution to $-\Delta v=f^\sharp$ is higher than the $L^p$-norm of the solution to $-\Delta u=f$ (we are considering Dirichlet boundary conditions, and $f^\sharp$ denotes the Schwarz symmetrization of $f:\Omega\to\

Muhammad Anisuzzaman Talukder

Two-photon terahertz (THz) quantum cascade lasers (QCLs) can immensely improve the conventional applications of THz frequency sources and open windows to new applications due to their ability to generate quantum-entangled twin photon beams. The rich intrinsic non-linearity in a two-photon THz QCL arises from cascaded photon transitions, leading to time-resol

Jack Dippel, Austin Eide, Pawel Pralat, Daniel Willhalm

The Sudoku number $s(G)$ of graph $G$ with chromatic number $\chi(G)$ is the smallest partial $\chi(G)$-colouring of $G$ that determines a unique $\chi(G)$-colouring of the entire graph. We show that the Sudoku number of the random $3$-regular graph $\mathcal{G}_{n,3}$ satisfies $s(\mathcal{G}_{n,3}) \leq (1+o(1))\frac{n}{3}$ asymptotically almost surely. We

Xing Xie, Jiawei Liu, Ziyue Lin, Huijie Fan

We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoregressive LLMs without architectural modifications. Different from prior works that require complex architectural redesigns, ARRA aligns LLM's hidden states with visual representations from external visual foundati

V. F. Maisi

Jaynes-Cummings Hamiltonian provides the elemental description of a two-level system interacting with a photonic mode. In this work, we review the Jaynes-Cummings formalism for an electronic two-level system. The purpose is to summarize and connect together the key theory concepts relevant for the transmission response probed typically in the experiments. Th

Xin Guan, Yiyuan Li, Xu Liu, Jinhong You

Change-plane analysis is a pivotal tool for identifying subgroups within a heterogeneous population, yet it presents challenges when applied to functional data. In this paper, we consider a change-plane model within the framework of functional response quantile regression, capable of identifying and testing subgroups in non-Gaussian functional responses with

Kang Xu, Yukun Wang, Dandan Li

Current quantum devices typically lack full qubit connectivity, making it difficult to directly execute logical circuits on quantum devices. This limitation necessitates quantum circuit mapping algorithms to insert SWAP gates, dynamically remapping logical qubits to physical qubits and transforming logical circuits into physical circuits that comply with dev

Changshun Wu, Weicheng He, Chih-Hong Cheng, Xiaowei Huang

Out-of-distribution (OoD) inputs pose a persistent challenge to deep learning models, often triggering overconfident predictions on non-target objects. While prior work has primarily focused on refining scoring functions and adjusting test-time thresholds, such algorithmic improvements offer only incremental gains. We argue that a rethinking of the entire de

Nghia Bui, Guergana Savova, Lijing Wang

The impact of random seeds in fine-tuning large language models (LLMs) has been largely overlooked despite its potential influence on model performance.In this study, we systematically evaluate the effects of random seeds on LLMs using the GLUE and SuperGLUE benchmarks. We analyze the macro-level impact through traditional metrics like accuracy and F1, calcu

Haotian Deng, Siyuan He, Songlin Jia, Yuyan Bao

Local reasoning about programs that combine aliasing and mutable state is a longstanding challenge. Existing approaches -- ownership systems, linear and affine types, uniqueness types, and lexical effect tracking -- impose global restrictions such as uniqueness or linearity, or rely on shallow syntactic analyses. These designs fall short with higher-order fu

Mehdi Hirari, Fabio Centofanti, Mia Hubert, Stefan Van Aelst

Multilinear Principal Component Analysis (MPCA) is an important tool for analyzing tensor data. It performs dimension reduction similar to PCA for multivariate data. However, standard MPCA is sensitive to outliers. It is highly influenced by observations deviating from the bulk of the data, called casewise outliers, as well as by individual outlying cells in

Gabriella Waters, Phillip Honenberger

The understanding of bias in AI is currently undergoing a revolution. Initially understood as errors or flaws, biases are increasingly recognized as integral to AI systems and sometimes preferable to less biased alternatives. In this paper, we review the reasons for this changed understanding and provide new guidance on two questions: First, how should we th

Kunal Chavan, Keertan Balaji, Spoorti Barigidad, Samba Raju Chiluveru

With an increasing demand for assistive technologies that promote the independence and mobility of visually impaired people, this study suggests an innovative real-time system that gives audio descriptions of a user's surroundings to improve situational awareness. The system acquires live video input and processes it with a quantized and fine-tuned Florence-

Zhiyu He, Saverio Bolognani, Florian Dörfler, Michael Muehlebach

Distribution shifts have long been regarded as troublesome external forces that a decision-maker should either counteract or conform to. An intriguing feedback phenomenon termed decision dependence arises when the deployed decision affects the environment and alters the data-generating distribution. In the realm of performative prediction, this is encoded by

Yubo Zhao, Qi Wu, Yifan Wang, Yu-Wing Tai

This paper advances motion agents empowered by large language models (LLMs) toward autonomous navigation in dynamic and cluttered environments, significantly surpassing first and recent seminal but limited studies on LLM's spatial reasoning, where movements are restricted in four directions in simple, static environments in the presence of only single agents

Nils Prigge

We study the Gelfand-Fuks cohomology of smooth vector fields on $S^d$ relative to $\mathrm{SO}(d+1)$ following a method by Haefliger that uses tools from rational homotopy theory. In particular, we show that $H^*(\mathrm{BSO}(4);\mathbb{R})$ injects into the relative Gelfand-Fuks cohomology which corrects a claim by Haefliger. Moreover, for $S^3$ the relativ

Yiming He, Yuchen Wang, Yunjia Zhang, Shuguang Li

Soft crawling robots exhibit efficient locomotion across various terrains and demonstrate robustness to diverse environmental conditions. Here, we propose a valveless soft-legged robot that integrates a pair of rotary bellows-enclosed soft transmission systems (R-BESTS). The proposed R-BESTS can directly transmit the servo rotation into leg swing motion. A t

Guanxuan Jiang, Shirao Yang, Yuyang Wang, Pan Hui

As large language models (LLMs) become increasingly capable of autonomous decision-making, they introduce new challenges and opportunities for human-AI cooperation in mixed-motive contexts. While prior research has primarily examined AI in assistive or cooperative roles, little is known about how humans interact with AI agents perceived as independent and st

Steven W. Su, Yaqi Li, Kairui Guo, Rob Duffield

The key to robot-assisted rehabilitation lies in the design of the human-machine interface, which must accommodate the needs of both patients and machines. Current interface designs primarily focus on machine control algorithms, often requiring patients to spend considerable time adapting. In this paper, we introduce a novel approach based on the Cooperative

Nicholas Braun Rodrigues, Gregorio Chinni

The global analytic hypoellipticity is proved for a class of second order partial differential equations with non-negative characteristic form globally defined on the torus. The class considered in this work generalizes at some degree the class of sum of squares considered by Bove-Chinni and also by Cordaro-Himonas.

Jiho Lee, Hayun Lee, Jonghyeon Kim, Kyungjae Lee

In robot task planning, large language models (LLMs) have shown significant promise in generating complex and long-horizon action sequences. However, it is observed that LLMs often produce responses that sound plausible but are not accurate. To address these problems, existing methods typically employ predefined error sets or external knowledge sources, requ

Zacharie Idriss, Raghu Raj

Quantitative inversion algorithms allow for the reconstruction of electrical properties (such as permittivity, and conductivity) for every point in a scene. However, they are challenging to use on measured datasets due to the need to know the incident wave field in the scene. In general, this is unknown due to factors such as antenna characteristics, path lo

Rui Qiao, Zhaoxuan Wu, Jingtan Wang, Pang Wei Koh

Machine learning models often have uneven performance among subpopulations (a.k.a., groups) in the data distributions. This poses a significant challenge for the models to generalize when the proportions of the groups shift during deployment. To improve robustness to such shifts, existing approaches have developed strategies that train models or perform hype

Weijia Wu, Zeyu Zhu, Mike Zheng Shou

Existing long-form video generation frameworks lack automated planning, requiring manual input for storylines, scenes, cinematography, and character interactions, resulting in high costs and inefficiencies. To address these challenges, we present MovieAgent, an automated movie generation via multi-agent Chain of Thought (CoT) planning. MovieAgent offers two

Aeysha Bhatti, Trudie Sandrock, Johane Nienkemper-Swanepoel

Machine learning algorithms permeate the day-to-day aspects of our lives and therefore studying the fairness of these algorithms before implementation is crucial. One way in which bias can manifest in a dataset is through missing values. Missing data are often assumed to be missing completely randomly; in reality the propensity of data being missing is often

Jun Wang, Tongsheng Shen, Dexin Zhao, Feitian Zhang

The artificial lateral line (ALL) is a bioinspired flow sensing system for underwater robots, comprising of distributed flow sensors. The ALL has been successfully applied to detect the undulatory flow fields generated by body undulation and tail-flapping of bioinspired robotic fish. However, its feasibility and performance in sensing the undulatory flow fie

Grace A. Batalla-Falcon, Lucas A. Cieza, Roberto Lavin, Millarca Valenzuela

We present mid-infrared transmission spectra from 2 to 23 microns of the 23 Atacama Desert chondrites of different types (carbonaceous Ornans and ordinary of H, L, and LL groups) as well as of some pure minerals (olivine and diopside). We focus on the characteristics of silicate at 10 and 20 microns, analyzing the influence of composition and grain size on p

George S. Theodoropoulos, Andreas Patakis, Andreas Tritsarolis, Yannis Theodoridis

Movements of maritime vessels are inherently complex and challenging to model due to the dynamic and often unpredictable nature of maritime operations. Even within structured maritime environments, such as shipping lanes and port approaches, where vessels adhere to navigational rules and predefined sea routes, uncovering underlying patterns is far from trivi

Asimina Marousi, Vassilis M. Charitopoulos

This paper presents a novel algorithm integrating global and robust optimization methods to solve continuous non-convex quadratic problems under convex uncertainty sets. The proposed Robust spatial branch-and-bound (RsBB) algorithm combines the principles of spatial branch-and-bound (sBB) with robust cutting planes. We apply the RsBB algorithm to quadratical

Fuming Li, Shilie Pan, Zhihua Yang

Material systems with lone-pair electrons have long been a treasure trove in the search for large second harmonic generation effects. Revealing the origin of second harmonic generation in nonlinear optical materials can provide theoretical guidance for the design of new materials. In this work, the origin of second harmonic generation in non-centrosymmetric

Simon B. Hollweger, Anna Werkovits, Oliver T. Hofmann

The intentional growth of metastable surface structures of organic molecules adsorbed on inorganic substrates is a challenging task. It is usually unclear which kinetic mechanism leads to the metastable surface polymorph after a deposition experiment. In this work we investigate a growth procedure that allows to intentionally grow a defined metastable surfac

Bo Huang, Wenlun Xu, Qizhuo Han, Haodong Jing

While diffusion models have achieved remarkable progress in style transfer tasks, existing methods typically rely on fine-tuning or optimizing pre-trained models during inference, leading to high computational costs and challenges in balancing content preservation with style integration. To address these limitations, we introduce AttenST, a training-free att

Luyi Jiang, Jiayuan Chen, Lu Lu, Xinwei Peng

The evaluation and improvement of medical large language models (LLMs) are critical for their real-world deployment, particularly in ensuring accuracy, safety, and ethical alignment. Existing frameworks inadequately dissect domain-specific error patterns or address cross-modal challenges. This study introduces a granular error taxonomy through systematic ana

Moritz Langer, Sai A. Dhurjati, Yared G. Zena, Ahmad Rahimi

We present the fabrication process of bright $GaAs$ quantum dot (QD) photon sources by non-deterministic embedding into broadband monolithic $Al_{0.15}Ga_{0.85}As$ microlens arrays on gold-coated substrates. Arrays of cylindrical photoresist templates, with diameters ranging from $2$ $\mu m$ to $5$ $\mu m$, are thermally reflowed and subsequently transferred

Alejandro David Cayuela Tudela, Javier Pastor-Galindo, Pantaleone Nespoli, José A. Ruipérez-Valiente

Ontologies provide a systematic framework for organizing and leveraging knowledge, enabling smarter and more effective decision-making. In order to advance in the capitalization and augmentation of intelligence related to nowadays cyberoperations, the proposed Influence Operation Ontology (IOO) establishes the main entities and relationships to model offensi

Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang

We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug trial are usually patients of the relevant disease; A/B tests on mobile applications target existing users only, and gene perturbation studies typically focus on specific cell type

Fabio Renda

In this article we prove that $E(n)$-coactions over a finite-dimensional algebra $A$ are classified by tuples $(\varphi, d_1, ... , d_n)$ consisting of an involution $\varphi$ and a family $(d_i)_{i=1,...,n}$ of $\varphi$-derivations satisfying appropriate conditions. Tuples of maps can be replaced by tuples of suitable elements $(c, u_1, . . . , u_n)$, when

Jiarui Wu, Yujin Wang, Lingen Li, Zhang Fan

Photo finishing tuning aims to automate the manual tuning process of the photo finishing pipeline, like Adobe Lightroom or Darktable. Previous works either use zeroth-order optimization, which is slow when the set of parameters increases, or rely on a differentiable proxy of the target finishing pipeline, which is hard to train. To overcome these challenges,

Markus Bläser, Yinan Li, Youming Qiao, Alexander Rogovskyy

Let $\varphi:V\times V\to W$ be a bilinear map of finite vector spaces $V$ and $W$ over a finite field $\mathbb{F}_q$. We present asymptotic bounds on the number of isomorphism classes of bilinear maps under the natural action of $\mathrm{GL}(V)$ and $\mathrm{GL}(W)$, when $\dim(V)$ and $\dim(W)$ are linearly related. As motivations and applications of the r