August 2025 arXiv papers

Alexis Horde-Vo, Matt Duckham, Estrid He, Rafe Benli

Who is the "Batman" behind "Batman Street" in Melbourne? Understanding the historical, cultural, and societal narratives behind place names can reveal the rich context that has shaped a community. Although place names serve as essential spatial references in gazetteers, they often lack information about place name origins. Enriching these pla

Zixin Zhu, Kevin Duarte, Mamshad Nayeem Rizve, Chengyuan Xu

In text-to-image (T2I) generation, achieving fine-grained control over attributes - such as age or smile - remains challenging, even with detailed text prompts. Slider-based methods offer a solution for precise control of image attributes. Existing approaches typically train individual adapter for each attribute separately, overlooking the entanglement among

Jaeyeon Kim, Lee Cheuk-Kit, Carles Domingo-Enrich, Yilun Du

Masked diffusion models (MDMs) have recently emerged as a promising alternative to autoregressive models over discrete domains. MDMs generate sequences in an any-order, parallel fashion, enabling fast inference and strong performance on non-causal tasks. However, a crucial limitation is that they do not support token insertions and are thus limited to fixed-

P. L. Krapivsky, A. Yu. Plakhov

A compromise process describes the evolution of opinions through binary interactions. Opinions are real numbers, and at each step, two randomly selected agents reach a compromise by averaging their pre-interaction opinions. We prove that if the number $N$ of agents is a power of two, then consensus emerges after a finite number of compromise events with prob

Bo Fu, Zhe Chen, Rahul Chandan, Alex Barbosa

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveragin

Elena Espinosa, Rubén Rodríguez Álvarez, José Miranda, Rafael Larrosa

The advent of next-generation sequencing (NGS) has revolutionized genomic research by enabling cost-effective, high-throughput sequencing of a diverse range of organisms. This breakthrough has unleashed a "Cambrian explosion" in genomic data volume and diversity. This volume of workloads places genomics among the top four big data challenges anticipa

Scarlett Raine, Emilio Olivastri, Benjamin Moshirian, Tobias Fischer

Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral species within the next decade. Reef restoration is crucial, but its success hinges on introducing automation to upscale efforts. In this work, we present a highly configurable AI pipeline for the real-time deployment

Hai-Jun Su

This paper presents analytical solvers for four common types of algebraic equations encountered in robot kinematics: single trigonometric equations, single-angle trigonometric systems, two-angle trigonometric systems, and bilinear two-angle systems. These equations arise frequently in the kinematics problems, particularly in robot kinematics. We provide deta

J. Oliva-Maza, Y. Tomilov

We reveal new aspects of the structure of Hilbert space $C_0$-semigroups $\mathcal T = (T(t))_{t\ge 0}$ similar to semigroups of contractions. In particular, we prove that $\mathcal T$ is similar to a semigroup of contractions if and only if $\mathcal T$ is similar to a quasi-contraction $C_0$-semigroup and $T(t)$ is similar to a contraction for a single $t>

J. Oliva-Maza, Y. Tomilov

In the context of finite tensor products of Hilbert spaces, we prove that similarity of a tensor product of operator semigroups to a contraction semigroup is equivalent to the corresponding similarity for each factor, after an appropriate rescaling. A similar result holds with contractivity replaced by quasi-contractivity. This splitting phenomenon allows us

Soham Ghosh, Holger Boche, Marc Geitz

Quantum Physical Unclonable Functions (QPUFs) are hardware-based cryptographic primitives with strong theoretical security. This security stems from their modeling as Haar-random unitaries. However, implementing such unitaries on Intermediate-Scale Quantum devices is challenging due to exponential simulation complexity. Previous work tackled this using pseud

Zhi Zhao, Fatih Kızılaslan, Shixiong Wang, Manuela Zucknick

Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high-dimensional omics data should also augment existing survival modeling approaches for identifying tumor cell type-specific genes predictive of cancer patient survival. However, the

Pavle V. M. Blagojevic

We develop a topological framework in an attempt to generalize the classical colourful Caratheodory theorem by imposing an additional constraint. For that we introduce the notion of zero-avoding complexes and covering criteria for the existence of colourful transversals. Using the developed method in combination with the homological Nerve theorem of Meshulam

Shu Liu, Soujanya Ponnapalli, Shreya Shankar, Sepanta Zeighami

Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When working with data, agents employ a high-throughput process of exploration and solution formulation for the given task, one we call agentic speculation. The sheer volume and ineffi

Runjia Zeng, Guangyan Sun, Qifan Wang, Tong Geng

Considering deep neural networks as manifold mappers, the pretrain-then-fine-tune paradigm can be interpreted as a two-stage process: pretrain establishes a broad knowledge base, and fine-tune adjusts the model parameters to activate specific neural pathways to align with the target manifold. Although prior fine-tuning approaches demonstrate success, their r

Jorge Almeida, Alfredo Costa, Herman Goulet-Ouellet

This paper is the first in a series of three, about (relatively)free profinite semigroups and S-adic representations of minimal shift spaces. We associate to each primitive S-adic directivesequence ${\boldsymbolσ}$ a $\textit{profinite image}$ in the free profinite semigroup over the alphabet of the induced minimal shift space. When this profinite image cont

Liancheng Zheng, Zhen Tian, Yangfan He, Shuo Liu

This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving style representation that maps abstract traits to parameters such as speed, safety factors, and reaction time. These parameters are embedded into the MFG through a spatial influence

S. A. Paston, A. J. Ziyatdinov

We investigate the possibility of explaining the observed effects usually attributed to the existence of dark matter through a transition from GR to a modified theory of gravity - embedding gravity. Since this theory can be reformulated as GR with additional fictitious matter of embedding gravity (FMEG), which moves independently of ordinary matter, we analy

Zifeng Ding, Shenyang Huang, Zeyu Cao, Emma Kondrup

Forecasting future links is a central task in temporal graph (TG) reasoning, requiring models to leverage historical interactions to predict upcoming ones. Traditional neural approaches, such as temporal graph neural networks, achieve strong performance but lack explainability and cannot be applied to unseen graphs without retraining. Recent studies have beg

Chia-Hsuan Hsu, Jun-En Ding, Hsin-Ling Hsu, Chih-Ho Hsu

Medical question answering requires advanced reasoning that integrates domain knowledge with logical inference. However, existing large language models (LLMs) often generate reasoning chains that lack factual accuracy and clinical reliability. We propose Ranked Preference Reinforcement Optimization (RPRO), a novel framework that combines reinforcement learni

Jay Vaghasiya, Omkar Ghugarkar, Vishvesh Bhat, Vipul Dholaria

We introduce CoreThink, a state-of-the-art Reasoning Layer built upon a novel reasoning method called General Symbolics. This approach diverges from reasoning paradigms such as test-time scaling, Supervised Fine-Tuning (SFT), and Reinforcement Learning with Verifiable Rewards (RLVR). CoreThink General Symbolic Reasoner (GSR) is specifically structured around

Xiangchen Wang, Jinrui Zhang, Teng Wang, Haigang Zhang

Recent advancements in large video-language models have revolutionized video understanding tasks. However, their efficiency is significantly constrained by processing high volumes of visual tokens. Existing token compression strategies apply a fixed compression ratio, ignoring the variability in semantic density among different video clips. Consequently, thi

Håkon Kolderup

We generalize the classical "1089-number trick", which states that a certain combination of addition, subtraction and swapping the digits of a three-digit number will always output 1089. More precisely, we show that any pair of zero divisors $fg=0$ in the group ring ${\mathbb Z}[Σ_n]$ on the n-th symmetric group gives rise to a partition of the set o

Amin Jafarimoghaddam, Manuel Soler

Condensation trails (contrails) are increasingly recognized as a major contributor to aviation-induced atmospheric warming, rivaling the impact of carbon dioxide. Mitigating their climate effects requires accurate and computationally efficient models to inform avoidance strategies. Contrails evolve through distinct stages, from formation and rapid growth to

Kuranage Roche Rayan Ranasinghe, Zhaolin Wang, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu

We address the modeling and optimal beamforming (BF) design for multiple-input multiple-output (MIMO) continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. First, a comprehensive DD continuous MIMO (DDC MIMO) channel model that incorporates CAPAs at both the transmitter (TX) and receiver (RX) is derived, which is used to ob

Lun Ai, Johannes Langer, Ute Schmid, Stephen Muggleton

Active learning is a general learning mechanism shared by artificial and human learners. Whether AI can teach humans such a strategy that transfers across domains is an open question. Ultra Strong Machine Learning (USML), a system whose explanations quantifiably improve human out-of-sample performance compared to self-learning, is uniquely positioned to answ

Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate

The Linear Quadratic Gaussian (LQG) regulator is a cornerstone of optimal control theory, yet its performance can degrade significantly when the noise distributions deviate from the assumed Gaussian model. To address this limitation, this work proposes a distributionally robust generalization of the finite-horizon LQG control problem. Specifically, we assume

Arjun Basandrai, Shourya Jain, K. Ilanthenral

Traditional resampling methods for handling class imbalance typically uses fixed distributions, undersampling the majority or oversampling the minority. These static strategies ignore changes in class-wise learning difficulty, which can limit the overall performance of the model. This paper proposes an Adaptive Resampling-based Training (ART) method that per

H. Francisco, B. Thapa, S. B. Trickey, A. C. Cancio

Deorbitalization of a conventional meta-generalized-gradient exchange-correlation approximation replaces its dependence upon the Kohn-Sham kinetic energy density with a dependence on the density gradient and Laplacian. In principle, that simplification should provide improved computational performance relative to the original meta-GGA form because of the shi

Ethan P. Honda

Results are presented from numerical simulations of the flat-space nonlinear Maxwell-Klein-Gordon-Dirac equations. The introduction of a boson-fermion interaction allows a scalar vortex to act as a harmonic trap that can confine massive Dirac bound states. A parametric analysis is performed to understand the range of boson-fermion coupling strengths, Ginzbur

Denghang Hu, Taolue Chen, Philipp Rümmer, Fu Song

The theory of sequences, supported by many SMT solvers, can model program data types including bounded arrays and lists. Sequences are parameterized by the element data type and provide operations such as accessing elements, concatenation, forming sub-sequences and updating elements. Strings and sequences are intimately related; many operations, e.g., matchi

Ali Abbasian Ardakani, Afshin Mohammadi, Taha Yusuf Kuzan, Beyza Nur Kuzan

To develop and externally validate integrated ultrasound nomograms combining BIRADS features and quantitative morphometric characteristics, and to compare their performance with expert radiologists and state of the art large language models in biopsy recommendation and malignancy prediction for breast lesions. In this retrospective multicenter, multinational

Christopher M. Drupieski, Jonathan R. Kujawa

We consider the finite Weyl groups of classical type -- $W(A_{r})$ for $r \geq 1$, $W(B_{r}) = W(C_{r})$ for $r \geq 2$, and $W(D_{r})$ for $r \geq 4$ -- as supergroups in which the reflections are of odd superdegree. Viewing the corresponding complex group algebras as Lie superalgebras via the graded commutator bracket, we determine the structure of the Lie

Md Shahidul Salim, Lian Fu, Arav Adikesh Ramakrishnan, Zonghai Yao

We present MedCOD (Medical Chain-of-Dictionary), a hybrid framework designed to improve English-to-Spanish medical translation by integrating domain-specific structured knowledge into large language models (LLMs). MedCOD integrates domain-specific knowledge from both the Unified Medical Language System (UMLS) and the LLM-as-Knowledge-Base (LLM-KB) paradigm t

David Hirnschall

We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as time series classification task. As financial transaction data streams grow in scale and complexity, traditional methods often require large labeled datasets, struggle with time series of irregular sampling frequencies and varying sequence lengths. To

Lukas Pertl, Han Xuanyuan, Pietro Liò

Interpreting graph neural networks (GNNs) is difficult because message passing mixes signals and internal channels rarely align with human concepts. We study superposition, the sharing of directions by multiple features, directly in the latent space of GNNs. Using controlled experiments with unambiguous graph concepts, we extract features as (i) class-condit

Wijdane El Hadri, Maryem Jemri

Inspired by string theory topics, we investigate the Reissner Nordstrom AdS black holes in noncommutative spacetime with Lorentzian smeared distributions. Concretely, we study certain thermodynamic properties including the criticality behaviors by computing the relevant quantities. For large radius approximations, we first derive the asymptotic expansions of

Shanglin Wu, Lihui Liu, Jinho D. Choi, Kai Shu

Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory. Retrieval-Augmented Generation (RAG) paradigms mitigate this issue by incorporating external knowledge at inference time. However, such methods typically handle knowledge as unstructured text, which reduces retrieval accuracy

Zakaria El Jaafari

Monte Carlo Counterfactual Regret Minimization (MCCFR) has emerged as a cornerstone algorithm for solving extensive-form games, but its integration with deep neural networks introduces scale-dependent challenges that manifest differently across game complexities. This paper presents a comprehensive analysis of how neural MCCFR component effectiveness varies

Gaik Ambartsoumian, Rohit Kumar Mishra, Indrani Zamindar

Weighted V-line transforms map a symmetric tensor field of order $m\ge0$ to a linear combination of certain integrals of those fields along two rays emanating from the same vertex. A significant focus of current research in integral geometry centers on the inversion of V-line transforms in formally determined setups. Of particular interest are the restrictio

David Dukić, Goran Glavaš, Jan Šnajder

Sequence labeling (SL) tasks, where labels are assigned to tokens, are abundant in NLP (e.g., named entity recognition and aspect-based sentiment analysis). Owing to the intuition that they require bidirectional context, SL tasks are commonly tackled with encoder-only models. Recent work also shows that removing the causal mask in fine-tuning enables decoder

Antoine Detaille

Given a compact manifold $ \mathcal{N} $ embedded into $ \mathbb{R}^ν $ and a projection $ P $ that retracts $ \mathbb{R}^ν $ except a singular set of codimension $ \ell $ onto $ \mathcal{N} $, we investigate the maximal range of parameters $ s $ and $ p $ such that the projection $ P $ can be used to turn an $ \mathbb{R}^ν $-valued $ W^{s,p} $ map into an $

Disha Shetty, Supriyo Dutta, Palak Chawla, Akshaya Jayashankar

In this exploratory numerical study, we assess the suitability of Quantum Linear Solvers(QLSs)toward providing a quantum advantage for Networks-based Linear System Problems (NLSPs). NLSPs naturally arise from graphs, and are of importance as they are connected to real-world applications. The achievable advantage with a QLS for an NLSP depends on the interpla

Joongho Jo, Jongsun Park

3D Gaussian Splatting (3D-GS) has emerged as a promising alternative to neural radiance fields (NeRF) as it offers high speed as well as high image quality in novel view synthesis. Despite these advancements, 3D-GS still struggles to meet the frames per second (FPS) demands of real-time applications. In this paper, we introduce GS-TG, a tile-grouping-based a

C. W. Shih, G. Nukazuka, Y. Sugiyama, Y. Akiba

The Intermediate Silicon Tracker (INTT), a two-layer barrel silicon strip tracker, is a key component of the tracking system for sPHENIX at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory. The INTT is designed to enable the association of reconstructed tracks with individual RHIC bunch crossings. To evaluate the performance of pr

Jediah Tau, John T. Whelan

During LIGO-Virgo-KAGRA's third observing run (O3), simulated continuous gravitational wave (CW) signals were added to the LIGO detectors as hardware (HW) injections, which are physical injections into the interferometer control loop via auxiliary laser actuation on test-mass mirrors. These included two periodic signals mimicking a spinning neutron star

Kapitonets Kirill

The Hardy function $Z(t)=ζ(1/2+it)e^{iθ(t)}$ takes real values for real $t$ and its real zeros are zeros $ζ(s)$ on the critical line $1/2+it$. After discovering the critical value of the local maximum in 1956, Lehmer formulated the assumption that the Hardy function could have a negative local maximum or a positive local minimum. In the paper the Generalized

Yutong Gao, Maoyuan Shao, Xinyang Huang, Chuang Zhu

CLIP's success has demonstrated that prompt tuning can achieve robust cross-modal semantic alignment for tasks ranging from open-domain recognition to fine-grained classification. However, redundant or weakly relevant feature components introduce noise and incur unnecessary computational costs. In this work, we propose Spotlighter, a lightweight token-se

Cesare Vianello, Sofia Salvatore, Luca Salasnich

We study the beyond-mean-field Josephson dynamics of the relative phase between two coupled macroscopic quantum systems. Using a covariant background field method, we derive the one-loop only-phase quantum effective action and the corresponding equation of motion for the quantum average of the phase. These analytical results are benchmarked against the exact

O. B. Zaslavskii

We consider spherically symmetric static black hole configurations that obey the vacuum equation of state: $p_{r}=-ρ$, where $p_{r}$ is the radial pressure, $ρ$ being energy density. We find in a closed form the metric for an arbitrary equation of state for tangential pressure $p_{θ}(ρ)$. The corresponding formulas enable us to embrace compact Schwarzschild-

Maryam Mahdi Alhusseini, Mohammad Reza Feizi Derakhshi

In todays rapidly evolving digital landscape, safeguarding network infrastructures against cyberattacks has become a critical priority. This research presents an innovative AI-driven real-time intrusion detection framework designed to enhance network security, particularly in Wireless Sensor Networks (WSNs), Cloud Computing (CC), and Internet of Things (IoT)

Răzvan-Alexandru Smădu, Andreea Iuga, Dumitru-Clementin Cercel, Florin Pop

Satire, irony, and sarcasm are techniques typically used to express humor and critique, rather than deceive; however, they can occasionally be mistaken for factual reporting, akin to fake news. These techniques can be applied at a more granular level, allowing satirical information to be incorporated into news articles. In this paper, we introduce the first

Huanbiao Zhu, Andrea Carlo Marini, Mikael Kuusela, Larry Wasserman

The unfolding problem in particle physics is to make inferences about the true particle spectrum based on smeared observations from a detector. This is an ill-posed inverse problem, where small changes in the smeared distribution can lead to large fluctuations in the unfolded distribution. The forward operator is the response matrix which models the detector

Zonghai Yao, Talha Chafekar, Junda Wang, Shuo Han

Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and social barriers. We introduce ChatCLIDS, the first benchmark to rigorously evaluate LLM-driven persuasive dialogue for health behavior change. Our framework features a library of exp

Biaoyan Hu

$f$-electron systems exhibit a subtle interplay between strong spin--orbit coupling and crystal-field effects, producing complex energy landscapes that are computationally demanding. We introduce auxiliary functions, constructed by extending hydrogen-like wave functions through a modification of the Legendre function. These functions often possess a Möbius-l

Takuya Miyamoto

In this paper, we first study the lifting problem of Hasse-Schmidt derivations and then apply the results to the theory of locally trivial deformations of algebraic schemes in positive characteristic. As an application, we construct an algebraic curve whose locally trivial deformation functor does not satisfy Schlessinger's condition (H1).

Duaa Abdullah, Jasem Hamoud

This paper investigates the natural density and structural relationships within Fibonacci words, the density of a Fibonacci word is $\operatorname{DF}(F_k)=n/(n+m),$ where $m$ denote the number of zeros in a Fibonacci word and $n$ denote the units digit. Through analysis of these ratios and their convergence to powers of $φ$, we illustrate the intrinsic expo

Xiang Li, Yueci Su, Jiahao Liu, Zhiwei Lin

Traditional vulnerability detection methods rely heavily on predefined rule matching, which often fails to capture vulnerabilities accurately. With the rise of large language models (LLMs), leveraging their ability to understand code semantics has emerged as a promising direction for achieving more accurate and efficient vulnerability detection. However, cur

Yuqin Dai, Guoqing Wang, Yuan Wang, Kairan Dou

Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging external information to enhance reasoning presents the following challenges: (1) low signal-to-noise ratio, where answer-supportive external information is diluted by irrelevant material,

Hans G. W. van Dam

Advances in large language models (LLMs) and real-time speech recognition now make it possible to issue any graphical user interface (GUI) action through natural language and receive the corresponding system response directly through the GUI. Most production applications were never designed with speech in mind. This article provides a concrete architecture t

Vimal Kumar, Susmita Das, Mala Das

The first run for the dark matter direct search experiment at Jaduguda Underground Science Laboratory is presented in this article. The experiment named InDEx; the Indian Dark matter search Experiment has been initiated with superheated emulsion detector consisting of the droplets of tetrafluoroethane ($C_2H_2F_4$). The detector ran for an effective period o

Mariia Drozdova, Erica Lastufka, Vitaliy Kinakh, Taras Holotyak

Vision-Language Models (VLMs), such as recent Qwen and Gemini models, are positioned as general-purpose AI systems capable of reasoning across domains. Yet their capabilities in scientific imaging, especially on unfamiliar and potentially previously unseen data distributions, remain poorly understood. In this work, we assess whether generic VLMs, presumed to

Yong Su, Yiyi Chen, Shenghong Yi, Hui Feng

Cellular-connected UAV systems have enabled a wide range of low-altitude aerial services. However, these systems still face many challenges, such as frequent handovers and the inefficiency of traditional transport protocols. To better study these issues, we develop a modular and scalable simulation platform specifically designed for UAVs communication levera

Yizhe Zhang, Qiang Chen, Tao Zhou

The emergence of powerful, general-purpose omnimodels capable of processing diverse data modalities has raised a critical question: can these ``jack-of-all-trades'' systems perform on par with highly specialized models in knowledge-intensive domains? This work investigates this question within the high-stakes field of medical image segmentation. We c

Abdelrahman Sadallah, Tim Baumgärtner, Iryna Gurevych, Ted Briscoe

Providing constructive feedback to paper authors is a core component of peer review. With reviewers increasingly having less time to perform reviews, automated support systems are required to ensure high reviewing quality, thus making the feedback in reviews useful for authors. To this end, we identify four key aspects of review comments (individual points i

Plinio Guillel Pino Murillo

The purpose of this expository article is to give a down-to-hearth introduction to the notion of an arithmetic group and arithmetic manifold. To achieve this we have decided to bring two geometrical questions relating the growth of systole and kissing number in hyperbolic manifolds, as a motivational guide, whose answers so far are given by the use of arithm

Hideya Watanabe

The Berele row-insertion is a symplectic analogue of the Schensted row-insertion. In the present paper, we provide it with a representation theoretical interpretation via the quantum symmetric pairs of type $A\mathrm{II}$. As applications, we lift Berele's Robinson--Schensted correspondence and Kobayashi--Matsumura's Robinson--Schensted--Knuth (RSK f

Nataliya Arefyeva, Evgeny Polyakov

We propose a new approach to coarse-grained description of quantum evolution that provides an explicit recipe to construct and evaluate multi-time decoherent histories in a controlled way, applicable to non-Markovian and integrable systems. Specifically, we study local interaction quench of a local degree of freedom (an open quantum system) within a noninter

Michelle Elizabeth, Alicja Kasicka, Natalia Krawczyk, Magalie Ochs

The growing number of generative AI-based dialogue systems has made their evaluation a crucial challenge. This paper presents our contribution to this important problem through the Dialogue System Technology Challenge (DSTC-12, Track 1), where we developed models to predict dialogue-level, dimension-specific scores. Given the constraint of using relatively s

Attila Egri-Nagy, Chrystopher L. Nehaniv

Recent algorithmic advances in algebraic automata theory drew attention to semigroupoids (semicategories). These are mathematical descriptions of typed computational processes, but they have not been studied systematically in the context of automata. Here, we use relational programming to explore finite semigroupoids to improve our mathematical intuition abo

Emmanuel Jehanno, Romain Menegaux, Julien Mairal, Sergei Grudinin

Crystalline structure prediction is an essential prerequisite for designing materials with targeted properties. Yet, it is still an open challenge in materials design and drug discovery. Despite recent advances in computational materials science, accurately predicting three-dimensional non-polymeric crystal structures remains elusive. In this work, we focus

Zhijing Wu, Longguang Wang

Reconstructing dynamic 3D scenes from monocular video has broad applications in AR/VR, robotics, and autonomous navigation, but often fails due to severe motion blur caused by camera and object motion. Existing methods commonly follow a two-step pipeline, where camera poses are first estimated and then 3D Gaussians are optimized. Since blurring artifacts usu

Takumu Okazaki, Akira Terui, Masahiko Mikawa

An effective method for optimizing path planning for a specific model of a 6-degree-of-freedom (6-DOF) robot manipulator is presented as part of the motion planning of the manipulator using computer algebra. We assume that we are given a path in the form of a set of line segments that the end-effector should follow. We also assume that we have a method to so

Takumu Okazaki, Akira Terui, Masahiko Mikawa

We propose an effective method for solving the inverse kinematic problem of a specific model of 6-degree-of-freedom (6-DOF) robot manipulator using computer algebra. It is known that when the rotation axes of three consecutive rotational joints of a manipulator intersect at a single point, the inverse kinematics problem can be divided into determining positi

Ruiqi Wang, Wenjun Li, Jing Ren, Tongyu Song

The deployment of large-scale LoRaWAN networks requires jointly optimizing conflicting metrics like Packet Delivery Ratio (PDR) and Energy Efficiency (EE) by dynamically allocating transmission parameters, including Carrier Frequency, Spreading Factor, and Transmission Power. Existing methods often oversimplify this challenge, focusing on a single metric or

J. Ovalle

We present an exact, purely geometric account of the interior dynamics of Schwarzschild black holes, formulated without invoking any specific gravitational theory and free of additional charges beyond the total mass ${\cal M}$. We show that dynamical evolution generically produces new singularities, absent in the static case, whose resolution imposes highly

Gyehun Go, Satbyul Han, Ahyeon Choi, Eunjin Choi

Recent advances in text-to-music (TTM) generation have enabled controllable and expressive music creation using natural language prompts. However, the emotional fidelity of TTM systems remains largely underexplored compared to human preference or text alignment. In this study, we introduce AImoclips, a benchmark for evaluating how well TTM systems convey int

Hyungbo Shim, Jin Gyu Lee, B. D. O. Anderson

This paper proposes an adaptation mechanism for heterogeneous multi-agent systems to align the agents' internal parameters, based on enforced consensus through strong couplings. Unlike homogeneous systems, where exact consensus is attainable, the heterogeneity in node dynamics precludes perfect synchronization. Nonetheless, previous work has demonstrated

Zhuodong Jiang, Haoran Wang, Guoxi Huang, Brett Seymour

Accurate 3D reconstruction in degraded imaging conditions remains a key challenge in photogrammetry and neural rendering. In underwater environments, spatially varying visibility caused by scattering, attenuation, and sparse observations leads to highly non-uniform information quality. Existing 3D Gaussian Splatting (3DGS) methods typically optimize primitiv

Changin Choi, Wonseok Lee, Jungmin Ko, Wonjong Rhee

Knowledge-intensive visual question answering (VQA) requires external knowledge beyond image content, demanding precise visual grounding and coherent integration of visual and textual information. Although multimodal retrieval-augmented generation has achieved notable advances by incorporating external knowledge bases, existing approaches largely adopt singl

Anum Arslan, Wen-Bo Dong, Charles Gale, Sangyong Jeon

We give an analytical expression for the in-plane polarization $P^{x}$, in heavy-ion collisions that has, to our knowledge, not been measured in heavy-ion collision experiments. We also carry out a numerical study of $P^{x}$ using a hydrodynamic model simulation as a cross-check for the analytical formula. It is found that if the temperature-gradient contrib

R. N. Köhle, K. T. W. Menting, K. Mitra, J. H. M. ten Thije Boonkkamp

This paper introduces a fast and robust iterative scheme for the elliptic Monge-Ampère equation with Dirichlet boundary conditions. The Monge-Ampère equation is a nonlinear and degenerate equation, with applications in optimal transport, geometric optics, and differential geometry. The proposed method linearises the equation and uses a fixed-point iteration

Pei Liu, Qingtian Ning, Xinyan Lu, Haipeng Liu

The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs): they lack cognitive inertia. Operating on isolated snapshots, these models cannot form a continuous understanding of the environment, leading to erratic decision jitter and a failure to execute complex, multi-st

Andreas Sauer

We improve well-known results concerning normal families and shared values of meromorphic functions in the plane. In particular, we obtain two corollaries concerning meromorphic functions $f \colon {\mathbb C} \to {\widehat{\mathbb C}}$: i) If $f$ shares a non-zero finite value with $f'$, and such that $f'$ is bounded on the preimages of $f$ for a se

Ganxi Xu, Zhao-Rong Lai, Yuting Tang, Yonghao Song

Visual prostheses hold great promise for restoring vision in blind individuals. While researchers have successfully utilized M/EEG signals to evoke visual perceptions during the brain decoding stage of visual prostheses, the complementary process of converting images into M/EEG signals in the brain encoding stage remains largely unexplored, hindering the for

Zeyu Li, Annan Shu

This paper introduces a novel line segment detector, the Aligned Anchor Groups guided Line Segment Detector (AAGLSD), designed to detect line segments from images with high precision and completeness. The algorithm employs a hierarchical approach to extract candidate pixels with different saliency levels, including regular anchors and aligned anchor groups.

Dvir Avrahami, Amit Milstein, Caroline Chaux, Tirza Routtenberg

Optimization-based solvers play a central role in a wide range of signal processing and communication tasks. However, their applicability in latency-sensitive systems is limited by the sequential nature of iterative methods and the high computational cost per iteration. While deep unfolding has emerged as a powerful paradigm for converting iterative algorith

Ravi Shankar, Sheng Wong, Lin Li, Magdalena Bachmann

Reliable abstention is critical for retrieval-augmented generation (RAG) systems, particularly in safety-critical domains such as women's health, where incorrect answers can lead to harm. We present an energy-based model (EBM) that learns a smooth energy landscape over a dense semantic corpus of 2.6M guideline-derived questions, enabling the system to de

Pragun Jaswal, L. Hemanth Krishna, B. Srinivasu

Deep Neural Networks (DNNs) require highly efficient matrix multiplication engines for complex computations. This paper presents a systolic array architecture incorporating novel exact and approximate processing elements (PEs), designed using energy-efficient positive partial product and negative partial product cells, termed as PPC and NPPC, respectively. T

Philip Laryea Doku

In this work, we investigate the security of Elliptic Curve Cryptosystem (ECC) implementations against Side-Channel Analysis (SCA). ECC is well known for its efficiency and strong security, yet vulnerable to SCA which exploits physical information leaked during scalar multiplication (kP). Countermeasures such as regularity and atomicity exist; this thesis fo

Alexandr B. Plachenov, Aleksei P. Kiselev

A detailed investigation is presented of a simple unidirectional finite-energy solution of the 3D wave equation. Its asymptotics as a spatial point runs to infinity with the wave propagations speed is a standard spherical wave as z < 0, where z is a Cartesian coordinate, and has an additional factor logarithmic with respect to the distance as z > 0. Asymptot

Xiangyu Wang, Chenrong Liu, Xue Lin, Yu Tian

Quantum computing has made remarkable strides in recent years, as demonstrated by quantum supremacy experiments and the realization of high-fidelity, fault-tolerant gates. However, a major obstacle persists: practical real-world applications remain scarce, largely due to the inefficiency of loading classical data into quantum processors. Here, we propose an

Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do

Dandan Fan, Huiqiu Lin, Yinfen Zhu

The investigation of eigenvalue conditions for the existence of an $[a,b]$-factor originates in the work of Brouwer and Haemers (2005) on perfect matchings. In the decades since, spectral extremal problems related to $[a,b]$-factors have attracted considerable attention. In this paper, we establish a spectral radius condition that ensures the existence of an

Lee Hyun, Sohee Yoon, Jinwoo Park, Sue In Chae

AI-driven materials discovery that couples automated experimentation with algorithmic decision-making requires process aware recipe to property predictors that are accurate, calibrated, and physically admissible. We approach this as a reasoning problem with large reasoning models (LRMs). To instill reasoning capability into language models, we curate reasoni

Yangsong Zhang, Abdul Ahad Butt, Gül Varol, Ivan Laptev

Human motion generation has shown great advances thanks to the recent diffusion models trained on large-scale motion capture data. Most of existing works, however, currently target animation of isolated people in empty scenes. Meanwhile, synthesizing realistic human-object interactions in complex 3D scenes remains a critical challenge in computer graphics an

Xinyue Liu, Ziqiang Li, Yanfeng Ge, Yong Liu

Two-dimensional (2D) Janus materials show great promise as piezoelectric materials and photocatalysts for water splitting. In this work, we systematically investigated the piezoelectric and photocatalytic properties of the hexagonal Janus In$XY$ ($X$ = S, Se, Te; $Y$ = Cl, Br, I) monolayers (MLs) using first-principles calculations. Except for InSeCl ML, the

Dongdong Wei, Zong-Kuan Guo, Qiqi Fan

We investigate the evolution and formation of double-layered vacuum bubbles during cosmological phase transitions with multiple vacua. We employ lattice simulations to show that flyover transitions can produce double-layered vacuum bubbles by overcoming successive potential barriers, thereby suggesting a novel bubble vacuum configuration in cosmological phas

Qiying Hu, and Rui Sun, Yingying Liang, Qianli Zhou

Dempster--Shafer theory (DST) provides an attractive framework for multi-source information fusion. However, its effectiveness critically depends on how a BPA is constructed for each source. Existing methods often rely on heuristic mappings or restrict belief assignment to singleton hypotheses and the full set, thereby underutilizing the expressive belief st

G. E. Volovik

The processes of splitting and merging of black holes obey the composition law generated by the Tsallis-Cirto $δ=2$ statistics. The same composition law expresses the full entropy of the Reissner-Nordström black hole via the entropies of its outer and inner horizons. Here we apply this composition law to the thermodynamics of the Kerr black hole. As distinct