May 2025 arXiv papers — page 55

Moallison F. Cavalcante, Bariş Çakmak, Marcus V. S. Bonança, Sebastian Deffner

Unitary quantum gates constitute the building blocks of Quantum Computing in the circuit paradigm. In this work, we engineer a locally driven two-qubit Hamiltonian whose instantaneous ground-state dynamics generates the controlled-NOT (CNOT) quantum gate. In practice, quantum gates have to be implemented in finite-time, hence non-adiabatic and external noise

Anton Ponomarev, Lutz Gröll, Veit Hagenmeyer

We estimate the lock-in domain of the origin of a current control system which is used in common DC/AC inverter designs. The system is a cascade connection of a 4-dimensional linear system (current controller, CC) followed by a two-dimensional nonlinear system (phase-locked loop, PLL). For the PLL, we construct a Lyapunov function via numerical approximation

Frederik Vonhoff, Maximilian J. Schilcher, David R. Reichman, David A. Egger

Predicting and explaining charge carrier transport in halide perovskites is a formidable challenge because of the unusual vibrational and electron-phonon coupling properties of these materials. This study explores charge carrier transport in two prototypical halide perovskite materials, MAPbBr$_3$ and MAPbI$_3$, using a dynamic disorder model. Focusing on th

Tao Wu, Jingyuan Chen, Wang Lin, Mengze Li

Large language models (LLMs) are revolutionizing education, with LLM-based agents playing a key role in simulating student behavior. A major challenge in student simulation is modeling the diverse learning patterns of students at various cognitive levels. However, current LLMs, typically trained as ``helpful assistants'', target at generating perfect respons

Qilong Wu, Yiyang Shao, Jun Wang, Xiaobo Sun

Leveraging high-quality joint representations from multimodal data can greatly enhance model performance in various machine-learning based applications. Recent multimodal learning methods, based on the multimodal information bottleneck (MIB) principle, aim to generate optimal MIB with maximal task-relevant information and minimal superfluous information via

M. Gorgone, G. Inferrera, F. Oliveri

An operatorial model of a system made by $N$ agents interacting each other with mechanisms that can be thought of as cooperative or competitive is presented. We associate to each agent an annihilation, creation and number fermionic operator, and interpret the mean values of the number operators over an initial condition as measures of the agents' wealth stat

A. Pointner, D. Thalheim, S. Belasi, L. Heinen

This study explores the correlation between iron mass on cell surfaces and the resultant magnetic field. Human colorectal cancer cells (HT29 line) were labeled with varying concentrations of SPIONs and imaged via a NV center widefield magnetic microscope. To assess the labeling efficacy, a convolutional neural network trained on simulated magnetic dipole dat

Marcel Aach, Cyril Blanc, Andreas Lintermann, Kurt De Grave

Artificial intelligence and machine learning models deployed on edge devices, e.g., for quality control in Additive Manufacturing (AM), are frequently small in size. Such models usually have to deliver highly accurate results within a short time frame. Methods that are commonly employed in literature start out with larger trained models and try to reduce the

Chenyang Ji, Sharon Xuesong Wang, Kai Zhang, Liang Wang

High-resolution spectrographs with precise radial velocity (PRV) capabilities require careful considerations in instrumental design and data processing in order to reach the 10 cm/s-level precision, which is needed for detecting Earth-like planets. In this work, we investigate the impact of fiber cross contamination on the RV precision via simulations, as mo

Anna Lueber, Adam J. Burgasser

The atmospheres of low-temperature stars, brown dwarfs, and exoplanets are challenging to model due to strong molecular features and complex gas and condensate chemistry. Self-consistent atmosphere models are commonly used for spectral fitting, but computational limits restrict the production of finely-sampled multi-dimensional parameter grids, necessitating

Giacomo Albi, Giacomo Dimarco, Federica Ferrarese, Lorenzo Pareschi

Magnetic fusion aims to confine high-temperature plasma within a device, enabling the fusion of deuterium and tritium nuclei to release energy. Due to the very large temperatures involved, it is essential to isolate the plasma from the device walls to prevent structural damage and the external magnetic fields play a fundamental role in achieving this confine

Tewodros Amdeberhan, Mircea Merca

In this paper, we investigate the arithmetic properties of the difference between the number of partitions of a positive integer $n$ with even crank and those with odd crank, denoted $C(n)=c_e(n)-c_o(n)$. Inspired by Ramanujan's classical congruences for the partition function $p(n)$, we establish a Ramanujan-type congruence for $C(n)$, proving that $C(5n+4)

Jack Hong, Shilin Yan, Zehao Xiao, Jiayin Cai

In this work, we propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input resolution on tracking performance. Our empirical study reveals that while scaling each factor leads to significant improvements in tracking accuracy, naive training suffers from su

Christopher Barrie, Petter Törnberg

Ashery et al. recently argue that large language models (LLMs), when paired to play a classic "naming game," spontaneously develop linguistic conventions reminiscent of human social norms. Here, we show that their results are better explained by data leakage: the models simply reproduce conventions they already encountered during pre-training. Despite the au

Vladislav Shkapenyuk, Divesh Srivastava, Theodore Johnson, Parisa Ghane

Large Language Models (LLMs) have recently become sophisticated enough to automate many tasks ranging from pattern finding to writing assistance to code generation. In this paper, we examine text-to-SQL generation. We have observed from decades of experience that the most difficult part of query development lies in understanding the database contents. These

Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen

Large language models (LLMs) have demonstrated strong performance in general-purpose machine translation, but their effectiveness in complex, domain-sensitive translation tasks remains underexplored. Recent advancements in Large Reasoning Models (LRMs), raise the question of whether structured reasoning can enhance translation quality across diverse domains.

Swetha Ganesh, Vaneet Aggarwal

Actor-Critic methods are widely used for their scalability, yet existing theoretical guarantees for infinite-horizon average-reward Markov Decision Processes (MDPs) often rely on restrictive ergodicity assumptions. We propose NAC-B, a Natural Actor-Critic with Batching, that achieves order-optimal regret of $\tilde{O}(\sqrt{T})$ in infinite-horizon average-r

Jianqiao Zheng, Xueqian Li, Hemanth Saratchandran, Simon Lucey

Convolutional Neural Networks (CNNs) inherently encode strong inductive biases, enabling effective generalization on small-scale datasets. In this paper, we propose integrating this inductive bias into ViTs, not through an architectural intervention but solely through initialization. The motivation here is to have a ViT that can enjoy strong CNN-like perform

Guilherme Grams, Nikolai N. Shchechilin, Théau Diverrès, Anthea F. Fantina

We investigate the effects of temperature on the properties of the inner crust of a non-accreting neutron star. To this aim, we employ two different treatments: the compressible liquid drop model (CLDM) and the temperature-dependent extended Thomas-Fermi (TETF) method. Our systematic comparison shows an agreement between the two methods on their predictions

Tong Wu, Zhiyong Chen, Dazhi He, Feng Yang

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semant

Anji Liu, Zilei Shao, Guy Van den Broeck

Probabilistic Circuits (PCs) offer a computationally scalable framework for generative modeling, supporting exact and efficient inference of a wide range of probabilistic queries. While recent advances have significantly improved the expressiveness and scalability of PCs, effectively training their parameters remains a challenge. In particular, a widely used

Emre Yilmaz, Philipp Bekemeyer

Determining onflow parameters is crucial from the perspectives of wind tunnel testing and regular flight and wind turbine operations. These parameters have traditionally been predicted via direct measurements which might lead to challenges in case of sensor faults. Alternatively, a data-driven prediction model based on surface pressure data can be used to de

Mircea Cimpoeas

We present a new combinatorial approach to the computation of the (real) Fourier expansions of $\cos^n(t)$ and $\sin^n(t)$, where $n\geq 1$ is an integer. As an application, we compute the Fourier expansions of $f(t)=\frac{1}{a-\cos t}$ and $g(t)=\frac{1}{a-\sin t}$, where $a\in\mathbb R$ with $|a|>1$.

Jan Lang, Zdeněk Mihula

We investigate the operator-theoretic property of strict singularity for optimal Sobolev embeddings within the general framework of rearrangement-invariant function spaces (r.i. spaces). More specifically, we focus on studying the ``quality'' of non-compactness for optimal Sobolev embeddings $V^m_0X(\Omega)\to Y_X(\Omega)$, where $X$ is a given r.i. space an

Wenjing Ren, Xin Dong, Yangjie Cui, Binqi Yang

In cluttered spaces, such as forests, drone picking up a payload via an abseil claw is an open challenge, as the cable is likely tangled and blocked by the branches and obstacles. To address such a challenge, in this work, a cooperative aerial system is proposed, which consists of a payload drone and a dexterous rappelling end droid. The two ends are linked

J. C. C. Capella, A. Fonseca, Pablo L. Saldanha, D. Felinto

In this work we review the Weisskopf-Wigner formalism for spontaneous emission considering the spatial modes of light as well as external atomic degrees of freedom which we introduce in the theory by modeling the atom as a wavepacket in momentum space with a given initial uncertainty. We perform a purity calculation in order to quantify the entanglement enco

Alkis Koudounas, Moreno La Quatra, Elena Baralis

Recent advances in conversational AI have demonstrated impressive capabilities in single-turn responses, yet multi-turn dialogues remain challenging for even the most sophisticated language models. Current dialogue datasets are limited in their emotional range, domain diversity, turn depth, and are predominantly text-only, hindering progress in developing mo

Marco Falconi, Benjamin Hinrichs

In this short communication we discuss the ultraviolet renormalization of the van Hove-Miyatake scalar field, generated by any distributional source. An abstract algebraic approach, based on the study of a special class of ground states of the van Hove-Miyatake dynamical map is compared with an Hamiltonian renormalization that makes use of a non-unitary dres

Naoki Agata, Takeo Igarashi

We introduce a novel method for controlling a motion sequence using an arbitrary temporal control sequence using temporal alignment. Temporal alignment of motion has gained significant attention owing to its applications in motion control and retargeting. Traditional methods rely on either learned or hand-craft cross-domain mappings between frames in the ori

Viviana del Barco, Gustavo Infanti, Exequiel Rivas, Paul Schwahn

We present a formalization, in the theorem prover Lean, of the classification of solvable Lie algebras of dimension at most three over arbitrary fields. Lie algebras are algebraic objects which encode infinitesimal symmetries, and as such ubiquitous in geometry and physics. Our work involves explicit calculations on the level of the underlying vector spaces

Shouxia Wang, Jiguo Cao, Hua Liu, Jinhong You

It is of great interest to test the equality of the means in two samples of functional data. Past research has predominantly concentrated on low-dimensional functional data, a focus that may not hold up in high-dimensional scenarios. In this article, we propose a novel two-sample test for the mean functions of high-dimensional functional data, employing a mu

Bilel Cherif, Tamas Bisztray, Richard A. Dubniczky, Aaesha Aldahmani

Digital Forensics and Incident Response (DFIR) involves analyzing digital evidence to support legal investigations. Large Language Models (LLMs) offer new opportunities in DFIR tasks such as log analysis and memory forensics, but their susceptibility to errors and hallucinations raises concerns in high-stakes contexts. Despite growing interest, there is no c

Kanglei Zhou, Hubert P. H. Shum, Frederick W. B. Li, Xingxing Zhang

Long-term Action Quality Assessment (AQA) aims to evaluate the quantitative performance of actions in long videos. However, existing methods face challenges due to domain shifts between the pre-trained large-scale action recognition backbones and the specific AQA task, thereby hindering their performance. This arises since fine-tuning resource-intensive back

Kilian Sennrich, Sina Ahmadi

Knowledge graphs offer an excellent solution for representing the lexical-semantic structures of lexicographic data. However, working with the SPARQL query language represents a considerable hurdle for many non-expert users who could benefit from the advantages of this technology. This paper addresses the challenge of creating natural language interfaces for

Bianca Kretz, Willi Freeden, Volker Michel

Poroelasticity can be classified with geophysics and describes the interaction between solids deformation and the pore pressure in a porous medium. The investigation of this effect is anywhere interesting where a porous medium and a fluid come together into play, for example this is the case in geothermics. More precisely, it is an important aspect in reserv

Jiayuan Su, Fulin Lin, Zhaopeng Feng, Han Zheng

Recent advances in Large Reasoning Models (LRMs) have significantly improved long-chain reasoning capabilities over Large Language Models (LLMs). However, LRMs often produce unnecessarily lengthy outputs even for simple queries, leading to inefficiencies or even accuracy degradation compared to LLMs. To overcome this, we propose CP-Router, a training-free an

Antti Koskela, Tejas Kulkarni

Achieving differential privacy (DP) guarantees in fully decentralized machine learning is challenging due to the absence of a central aggregator and varying trust assumptions among nodes. We present a framework for DP analysis of decentralized gossip-based averaging algorithms with additive node-level noise, from arbitrary views of nodes in a graph. We prese

Vojtěch Chlan, Martin Adamec, Oleg Heczko

The local environment of Mn atoms in stoichiometric Ni-Mn-Ga Heusler alloys was investigated using Nuclear Magnetic Resonance (NMR) and interpreted with the help of Density Functional Theory (DFT) methods. In cubic austenite, the significant amount of structural defects was observed in \mn NMR experiments and interpreted using DFT calculations as individual

Jie Yu, Charlotte Gehan, Saskia Hekker, Michaël Bazot

Stellar activity is fundamental to stellar evolution and the formation and habitability of exoplanets. The interaction between convective motions and rotation in cool stars results in a dynamo process that drives magnetic surface activity. In single stars, activity increases with rotation rate until it saturates for stars with rotation periods Prot < 3 - 10

Zheng Zhang, Shaocheng Lan, Lei Song, Jiang Bian

In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks during inference using only a few demonstrations. However, ICL performance is highly dependent on the selection of these demonstrations. Recent work explores retrieval-based methods for selecting query-specific demonstrations, but these approaches often rely on surrogate obj

Yu Wang, Junshu Dai, Yuchen Ying, Hanyang Yuan

Human mobility prediction is crucial for applications ranging from location-based recommendations to urban planning, which aims to forecast users' next location visits based on historical trajectories. While existing mobility prediction models excel at capturing sequential patterns through diverse architectures for different scenarios, they are hindered by t

Eric Zhao, Jessica Dai, Pranjal Awasthi

Recent progress in strengthening the capabilities of large language models has stemmed from applying reinforcement learning to domains with automatically verifiable outcomes. A key question is whether we can similarly use RL to optimize for outcomes in domains where evaluating outcomes inherently requires human feedback; for example, in tasks like deep resea

Sven Zschocke

In a recent investigation, the initial value problem of light propagation in the gravitational field of a body at rest with monopole and quadrupole structure has been determined in the second post-Newtonian (2PN) approximation. In reality, the light source as well as the observer are located at finite distances from the solar system bodies. This fact require

Neha Singh, Tomasz Bulik, Aleksandra Olejak

The Einstein Telescope (ET) is a proposed third-generation, wide-band gravitational wave (GW) detector which will have an improved detection sensitivity in low frequencies, leading to a longer observation time in the detection band and higher detection rate for binary neutron stars (BNSs). Despite the fact that ET will have a higher detection rate, a large f

Uriel Feige

We consider fair allocations of indivisible goods to agents with general monotone valuations. We observe that it is useful to introduce a new share-based fairness notion, the {\em residual maximin share} (RMMS). This share is {\em feasible} and {\em self maximizing}. Its value is at least as large as the MXS for monotone valuations, and at least as large as

Fernanda Oliveira, Bruno Ribeiro, Wiliam S. Hipólito-Ricaldi, Felipe Avila

Several models based on General Relativity and Modified Gravity aim to reproduce the observed universe with precision comparable to the flat-$\Lambda$CDM cosmological model. In this study, we investigate the consistency of some of these models with current high-redshift cosmic data, assessing their ability to simultaneously describe both the background expan

Zhongzhan Huang, Guoming Ling, Shanshan Zhong, Hefeng Wu

Long Context Understanding (LCU) is a critical area for exploration in current large language models (LLMs). However, due to the inherently lengthy nature of long-text data, existing LCU benchmarks for LLMs often result in prohibitively high evaluation costs, like testing time and inference expenses. Through extensive experimentation, we discover that existi

Anton Ponomarev, Lutz Gröll

We consider a two-dimensional SISO LTI system closed by uncertain linear feedback. The feedback gain is time-varying, bounded, and has a bounded derivative (both bounds are known). We investigate the asymptotic stability of this system under all admissible behaviors of the gain. Note that the situation is similar to the classical absolute stability problem o

Yong Liu, Jinshan Pan, Yinchuan Li, Qingji Dong

Diffusion models have shown great potential in generating realistic image detail. However, adapting these models to video super-resolution (VSR) remains challenging due to their inherent stochasticity and lack of temporal modeling. Previous methods have attempted to mitigate this issue by incorporating motion information and temporal layers. However, unrelia

I. F. Cunha, A. C. Lehum

We investigate tree-level scattering processes involving quarks ($q$) and gluons ($g$) mediated by graviton exchange in the framework of Agravity, a dimensionless and renormalizable theory of quadratic quantum gravity. Focusing on the ultra-Planckian regime, characterized by the Mandelstam variable $s = (p_1 + p_2)^2$, which corresponds to the total energy s

Jihyung Lee, Jin-Seop Lee, Jaehoon Lee, YunSeok Choi

Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smaller LLMs (e.g., Llama 3.1-8B) are used. This indicates that

Hui Chen, Miao Xiong, Yujie Lu, Wei Han

Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmark for evaluating AI agents on open-ended machine learning research. MLR-Bench includes three key components: (1) 201 research tasks sourced from NeurIPS, ICLR, and ICML workshops c

Andrew Zamai, Nathanael Fijalkow, Boris Mansencal, Laurent Simon

The differential diagnosis of neurodegenerative dementias is a challenging clinical task, mainly because of the overlap in symptom presentation and the similarity of patterns observed in structural neuroimaging. To improve diagnostic efficiency and accuracy, deep learning-based methods such as Convolutional Neural Networks and Vision Transformers have been p

Ondřej Straka, Jindřich Duník, Pau Closas, Tales Imbiriba

State-space estimation and tracking rely on accurate dynamical models to perform well. However, obtaining an vaccurate dynamical model for complex scenarios or adapting to changes in the system poses challenges to the estimation process. Recently, augmented physics-based models (APBMs) appear as an appealing strategy to cope with these challenges where the c

Rong-Cheng Tu, Wenhao Sun, Hanzhe You, Yingjie Wang

Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a compositional query, consisting of a reference image and a modifying text-without relying on annotated training data. Existing approaches often generate a synthetic target text using large language models (LLMs) to serve as an intermediate anchor between the compositional quer

Elvir Karimov, Alexander Varlamov, Danil Ivanov, Dmitrii Korzh

Deep learning voice models are commonly used nowadays, but the safety processing of personal data, such as human identity and speech content, remains suspicious. To prevent malicious user identification, speaker anonymization methods were proposed. Current methods, particularly based on universal adversarial patch (UAP) applications, have drawbacks such as s

Joanes Lizarraga, Carmelo López-Mediavilla, Ander Urio

Recent works have demonstrated the necessity of capturing the local inhomogeneous physics in axion inflation, and showed new genuine features, most notably the extension of the inflationary period dictated by an electromagnetic slow-roll phase. In this work, we further investigate the model by performing a systematic study of the effect of the inflationary p

Siqi Kou, Qingyuan Tian, Hanwen Xu, Zihao Zeng

Large language models (LLMs) have demonstrated remarkable reasoning capabilities in math and coding, often bolstered by post-training on the chain-of-thoughts (CoTs) generated by stronger models. However, existing strategies for curating such training data predominantly rely on heuristics, limiting generalizability and failing to capture subtleties underlyin

Zhipeng Tong, Xiaoqi Sun, Guiya Qin, Jinpu Bai

This study systematically investigates the regulation mechanisms of backbone topology (tri-/tetracyclic arenes), substitution positions, and functional groups on charge transport properties through molecular design of triphenylamine-ethynylene fused acene derivatives. By integrating Marcus charge transfer theory with kinetic Monte Carlo simulations, we demon

Gokul Adethya, Bhanu Pratyush Mantha, Tianyang Wang, Xingjian Li

Cryo-electron tomography (cryo-ET) has emerged as a powerful technique for imaging macromolecular complexes in their near-native states. However, the localization of 3D particles in cellular environments still presents a significant challenge due to low signal-to-noise ratios and missing wedge artifacts. Deep learning approaches have shown great potential, b

Herbert Woisetschläger, Ryan Zhang, Shiqiang Wang, Hans-Arno Jacobsen

Open-weight large language model (LLM) zoos provide access to numerous high-quality models, but selecting the appropriate model for specific tasks remains challenging and requires technical expertise. Most users simply want factually correct, safe, and satisfying responses without concerning themselves with model technicalities, while inference service provi

Antoine Moulin, Gergely Neu, Luca Viano

We study the problem of offline imitation learning in Markov decision processes (MDPs), where the goal is to learn a well-performing policy given a dataset of state-action pairs generated by an expert policy. Complementing a recent line of work on this topic that assumes the expert belongs to a tractable class of known policies, we approach this problem from

Jinpeng Huang, Gangshan Jing

Graph rigidity theory studies the capability of a graph embedded in the Euclidean space to constrain its global geometric shape via local constraints among nodes and edges, and has been widely exploited in network localization and formation control. In recent years, the traditional rigidity theory has been extended by considering new types of local constrain

Naoyuki Terashita, Yusuke Tozaki, Hideaki Omote, Congkha Nguyen

The diagram is a visual representation of a relationship illustrated with edges (lines or arrows), which is widely used in industrial and scientific communication. Although recognizing diagrams is essential for vision language models (VLMs) to comprehend domain-specific knowledge, recent studies reveal that many VLMs fail to identify edges in images. We hypo

Huan Zhang, Shenghua Fan, Shuyu Dong, Yujin Zheng

Continual Learning with Pre-trained Models holds great promise for efficient adaptation across sequential tasks. However, most existing approaches freeze PTMs and rely on auxiliary modules like prompts or adapters, limiting model plasticity and leading to suboptimal generalization when facing significant distribution shifts. While full fine-tuning can improv

Qiming Liu, Yilin Chen, Peng Xu, Huizhong Shen

Ammonia (NH3) emissions significantly contribute to atmospheric pollution, yet discrepancies exist between bottom-up inventories and satellite-constrained top-down estimates, with the latter typically one-third higher. This study quantifies how assumptions about NH3 vertical distribution in satellite retrievals contribute to this gap. By implementing spatial

Keita Minato, Atsushi Taruya, Teppei Okumura, Maresuke Shiraishi

We investigate the prospects for probing large-scale statistical anisotropy through galaxy clustering and intrinsic alignments (IA) in Stage IV galaxy surveys. Specifically, we consider a dipolar modulation in the primordial power spectrum and evaluate the Fisher information matrix using the two-point statistics of both the galaxy clustering and IA. Our anal

Run Gu, Wei Xu, Zhaohui Yang, Dusit Niyato

Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages. Deep learning (DL)-based semantic communication can effectively cultivate the essential semantic knowledge for semantic extraction, transmission, and interpretation by leveraging massive labeled samples for downstream task train

Ran Yu, Zhuoren Li, Lu Xiong, Wei Han

Reinforcement learning (RL) has demonstrated potential in autonomous driving (AD) decision tasks. However, applying RL to urban AD, particularly in intersection scenarios, still faces significant challenges. The lack of safety constraints makes RL vulnerable to risks. Additionally, cognitive limitations and environmental randomness can lead to unreliable dec

Wenrui Li, Penghong Wang, Xingtao Wang, Wangmeng Zuo

Audio-visual zero-shot learning (ZSL) has been extensively researched for its capability to classify video data from unseen classes during training. Nevertheless, current methodologies often struggle with background scene biases and inadequate motion detail. This paper proposes a novel dual-stream Multi-Timescale Motion-Decoupled Spiking Transformer (MDST++)

Barbara Palumbo, Paolo Massa, Federico Benvenuto

In this work, we consider ill-posed inverse problems in which the forward operator is continuous and weakly closed, and the sought solution belongs to a weakly closed constraint set. We propose a regularization method based on minimizing the Tikhonov functional on a sequence of compact sets which is dense in the intersection between the domain of the forward

Scott Copeland, Sungguen Ryu, Kazunari Imai, Nicholas Krasco

Carbon quantum dots (CQDs) are a promising material for electronic applications due to their easy fabrication and interesting semiconductor properties. Further, CQDs exhibit quantum confinement and charging effects, which may lead not only to improved performances but also to devices with novel functionalities. Here, we investigate the electronic transport o

Wensi Sun, Yanshuang Chen, Wencheng Ji, Yi Zhou

Hierarchical dynamics in glass-forming systems span multiple timescales, from fast vibrations to slow structural rearrangements, appearing in both supercooled fluids and glassy states. Understanding how these diverse processes interact across timescales remains a central challenge. Here, by combining direct particle-level observations with a dynamic eigenmod

Yejin Son, Minseo Kim, Sungwoong Kim, Seungju Han

Large Language Models (LLMs) are increasingly used for decision making in embodied agents, yet existing safety evaluations often rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. This obscures our understanding of embodied safety and limits the selective deployment of LLMs in high-risk p

Fabian Kresse, Emily Yu, Christoph H. Lampert, Thomas A. Henzinger

Learning-based systems are increasingly deployed across various domains, yet the complexity of traditional neural networks poses significant challenges for formal verification. Unlike conventional neural networks, learned Logic Gate Networks (LGNs) replace multiplications with Boolean logic gates, yielding a sparse, netlist-like architecture that is inherent

Qixi Zheng, Yushen Chen, Zhikang Niu, Ziyang Ma

Flow-matching-based text-to-speech (TTS) models, such as Voicebox, E2 TTS, and F5-TTS, have attracted significant attention in recent years. These models require multiple sampling steps to reconstruct speech from noise, making inference speed a key challenge. Reducing the number of sampling steps can greatly improve inference efficiency. To this end, we intr

S. Asselie, J. -M. Nazon, R. Caldani, C. Roux-Spitz

We study the temporal dynamics of light interacting with a one-dimensional lattice of cold atoms. In such a system, a photonic band gap opens up, yielding an efficient Bragg reflection for an incident field incoming with the right angle and detuning. Here, we report two new effects appearing in the Bragg reflection. First, for some detunings, there is a ``fl

Gianluca Ceruti, Nicolas Crouseilles, Lukas Einkemmer

The numerical approximation of high-dimensional evolution equations poses significant computational challenges, particularly in kinetic theory and radiative transfer. In this work, we introduce the Galerkin Alternating Projection (GAP) scheme, a novel integrator derived within the Dynamical Low-Rank Approximation (DLRA) framework. We perform a rigorous error

Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato

In this paper, we introduce a deep learning solution for video activity recognition that leverages an innovative combination of convolutional layers with a linear-complexity attention mechanism. Moreover, we introduce a novel quantization mechanism to further improve the efficiency of our model during both training and inference. Our model maintains a reduce

Songlin Han

We study the error bound for a smooth weighted prime number theorem, and its implication to the zero-free region for the Riemann zeta function using the method of Pintz. We also give an application to the average number of smooth weighted Goldbach representations and generalize the result to the case of smooth weighted average k-Goldbach representations.

Zifeng Ding, Sikuan Yan, Zhangdie Yuan, Xianglong Hu

Temporal reasoning and planning are essential capabilities for large language models (LLMs), yet most existing benchmarks evaluate them in isolation and under limited forms of complexity. To address this gap, we introduce the Temporal Constraint-based Planning (TCP) benchmark that jointly assesses both capabilities. Each instance in TCP features a naturalist

Konrad K. Dabrowski, Tala Eagling-Vose, Noleen Köhler, Sebastian Ordyniak

We determine if the width of a graph class ${\cal G}$ changes from unbounded to bounded if we consider only those graphs from ${\cal G}$ whose diameter is bounded. As parameters we consider treedepth, pathwidth, treewidth and clique-width, and as graph classes we consider classes defined by forbidding some specific graph $F$ as a minor, induced subgraph or s

Takaaki Nomura, Yusuke Shimizu, Towa Takahashi

We revisit a supersymmetric flavor model based on the symmetries $SU(2)_L \times A_4 \times Z_3 \times U(1)_R$, which extends the original Altarelli and Feruglio construction by introducing flavon and driving superfields responsible for the spontaneous breaking of the flavor symmetry in order to obtain non-zero reactor angle. The vacuum alignments of flavon

Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang, Sheng-Jun Huang

Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset. To alleviate extrapolation errors, existing studies often uniformly regularize the value function or policy updates across all states. However, due to substantial variations in data quality, the fixed regularization strength often leads to a dilemma: Weak regularizat

Roland Berger, Jun Maillard

A Poincar\'e Van den Bergh duality theorem for strong Kc-Calabi-Yau algebras was obtained by R. Taillefer and the first author under the assumption that the derived functors of functors involved in the statement exist. We prove the existence of these derived functors by showing that the dg category defining the derived Koszul calculus is isomorphic to a dg c

Sebastian Groß, Stefan Heindorf, Philipp Terhörst

Traditional face recognition systems rely on extracting fixed face representations, known as templates, to store and verify identities. These representations are typically generated by neural networks that often lack explainability and raise concerns regarding fairness and privacy. In this work, we propose a novel model-template (MOTE) approach that replaces

Chen Sang, Yeqiang Qian, Jiale Zhang, Chunxiang Wang

For tasks such as urban digital twins, VR/AR/game scene design, or creating synthetic films, the traditional industrial approach often involves manually modeling scenes and using various rendering engines to complete the rendering process. This approach typically requires high labor costs and hardware demands, and can result in poor quality when replicating

Amirali Kaboli, Alex Mascolo, Amir Shaikhha

Join processing is a fundamental operation in database management systems; however, traditional join algorithms often encounter efficiency challenges when dealing with complex queries that produce intermediate results much larger than the final query output. The emergence of worst-case optimal join (WCOJ) algorithms represents a significant advancement, offe

Feyi Adesanya, Kanan Castro Silva, Valdemar V. Graciano Neto, Istvan David

Modern systems exhibit unprecedented complexity due to their increased scale, interconnectedness, and the heterogeneity of their digital and physical components. In response to scaling challenges, the system of systems paradigm proposes flexible aggregations of subsystems into a larger whole, while maintaining the independence of subsystems to various degree

Artem Petrov, Dmitrii Volkov

As AI systems become increasingly capable, understanding their offensive cyber potential is critical for informed governance and responsible deployment. However, it's hard to accurately bound their capabilities, and some prior evaluations dramatically underestimated them. The art of extracting maximum task-specific performance from AIs is called "AI elicitat

Jiangjie Chen, Qianyu He, Siyu Yuan, Aili Chen

Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoni

Javier Marín

We present Adjacent Possible Exploration (APE), a selective fine-tuning method for adapting large language models that systematically explores parameter modifications while maintaining model stability. Inspired by evolutionary optimization principles, APE evaluates multiple candidate parameter updates through fine-tuning on small data subsets and accepts onl

Xiaosen Wang, Shaokang Wang, Zhijin Ge, Yuyang Luo

Large Vision-Language Models (VLMs) have achieved remarkable success in understanding complex real-world scenarios and supporting data-driven decision-making processes. However, VLMs exhibit significant vulnerability against adversarial examples, either text or image, which can lead to various adversarial outcomes, e.g., jailbreaking, hijacking, and hallucin

Tore Gude, Marta Anna Zagorowska, Lars Struen Imsland

This paper develops a persistently exciting input generating Online Feedback Optimization (OFO) controller that estimates the sensitivity of a process ensuring minimal deviations from the descent direction while converging. This eliminates the need for random perturbations in feedback loop. The proposed controller is formulated as a bilevel optimization prog

Marten T. Raaphorst, Joan Enrique-Romero, Thanja Lamberts

Cyanopolyynes, a family of nitrogen containing carbon chains, are common in the interstellar medium and possibly form the backbone of species relevant to prebiotic chemistry. Following their gas phase formation, they are expected to freeze out on ice grains in cold interstellar regions. In this work we present the hydrogenation reaction network of isolated H

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(1.0087\pm0.0044)\times10^{10}$ $J/\psi$ events collected with the BESIII detector at the BEPCII storage ring, the reactions $\Sigma^{+}n\rightarrow\Lambda p$ and $\Sigma^{+}n\rightarrow\Sigma^{0}p$ are studied, where the $\Sigma^{+}$ baryon is produced in the process $J/\psi\rightarrow\Sigma^{+}\bar{\Sigma}^-$ and the neutron is a component of the $^

Weijie Du, Yangguang Yang, Zixin Liu, Chao Yang

To overcome the limitations of existing algorithms for solving self-bound quantum many-body problems -- such as those encountered in nuclear and particle physics -- that access only a restricted subset of energy levels and provide limited structural information, we introduce and demonstrate a novel quantum-classical approach capable of resolving the complete

Shuang Ao, Flora D. Salim, Simon Khan

Although LLMs demonstrate proficiency in several text-based reasoning and planning tasks, their implementation in robotics control is constrained by significant deficiencies: (1) LLM agents are designed to work mainly with textual inputs rather than visual conditions; (2) Current multimodal agents treat LLMs as static planners, which separates their reasonin

Zsolt Szabó, Stefan Gehr, Paolo Facchi, Kazuya Yuasa

A quantum system subject to an external perturbation can experience leakage between uncoupled regions of its energy spectrum separated by a gap. To quantify this phenomenon, we present two complementary results. First, we establish time-independent bounds on the distances between the true dynamics and the dynamics generated by block-diagonal effective evolut

Alexander K. Hartmann, Satya N. Majumdar

We provide an exact formula for the mean first-passage time (MFPT) to a target at the origin for a single particle diffusing on a $d$-dimensional hypercubic {\em lattice} starting from a fixed initial position $\vec R_0$ and resetting to $\vec R_0$ with a rate $r$. Previously known results in the continuous space are recovered in the scaling limit $r\to 0$,