November 2025 arXiv papers — page 153

Rui-Qing Sun, Ang Li, Zhijing Wu, Tian Lan

Talking Face Generation (TFG) methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have recently achieved impressive progress in personalized talking head synthesis. However, existing methods typically require several minutes of reference video for meticulous preprocessing and fitting, resulting in hours of preparation time and lim

Yash Raj Singh

Modern large language model-based reasoning systems frequently recompute similar reasoning steps across tasks, wasting computational resources, inflating inference latency, and limiting reproducibility. These inefficiencies underscore the need for persistent reasoning mechanisms that can recall and reuse prior computational traces. We introduce Graph-Memoize

Aurelien Perera

Understanding how molecular correlations give rise to mesoscale organization is central to the physics of complex fluids such as hydrogen-bonded mixtures. In this work, we develop a mesoscale bridge formalism that connects the site-site Ornstein-Zernike (SSOZ) framework to the field theoretical Teubner-Strey (TS) approach. This bridge highlights how local or

Akihiro Higashitani, Kenta Ueyama

We call a standard graded commutative $\Bbbk$-algebra cyclotomic if its $h$-polynomial has all its roots on the unit circle in the complex plane. Complete intersections provide typical examples of cyclotomic algebras, since the $h$-polynomial of any standard graded complete intersection is a product of polynomials of the form $1 + t + \cdots + t^{m-1}$. We r

Ji-Ha Park, Heon-Gyu Kwak, Gi-Hwan Shin, Yoo-In Jeon

Brain-computer interface (BCI) research, while promising, has largely been confined to static and fixed environments, limiting real-world applicability. To move towards practical BCI, we introduce a real-time wireless imagined speech electroencephalogram (EEG) decoding system designed for flexibility and everyday use. Our framework focuses on practicality, d

Seyedehanita Madani, Rama Chellappa, Vishal M. Patel

Change detection (CD) is fundamental to computer vision and remote sensing, supporting applications in environmental monitoring, disaster response, and urban development. Most CD models assume co-registered inputs, yet real-world imagery often exhibits parallax, viewpoint shifts, and long temporal gaps that cause severe misalignment. Traditional two stage me

Sida Huang, Siqi Huang, Ping Luo, Hongyuan Zhang

With the development of diffusion models, enhancing spatial controllability in text-to-image generation has become a vital challenge. As a representative task for addressing this challenge, layout-to-image generation aims to generate images that are spatially consistent with the given layout condition. Existing layout-to-image methods typically introduce the

Yazmín Ibáñez-García, Jean Christoph Jung, Vincent Michielini, Filip Murlak

We clarify the complexity of answering unions of conjunctive queries over knowledge bases formulated in the description logic $\mathcal S$, the extension of $\mathcal{ALC}$ with transitive roles. Contrary to what existing partial results suggested, we show that the problem is in fact 2ExpTime-complete; hardness already holds in the presence of two transitive

Jeong-Hoon Kim, Jinwoo Nam, Geunsik Jo

Personalized mathematics education is growing rapidly, creating a strong demand for large sets of similar practice problems. Yet existing studies on mathematics problem generation have focused on data augmentation for training neural language models rather than on direct educational deployment. To bridge this gap, we define a new task, Isomorphic Math Proble

Andrei Ol'khovatov

This paper is a continuation of a series of works, devoted to various aspects of the 1908 Tunguska event. This usually refers to an explosive phenomenon associated with the appearance of a forestfall, named nowadays as the Kulikovskii one. However, several other notable natural phenomena occurred in the Central Siberia on June 30, 1908. This paper considers

Xueyao Zhang, Chaoren Wang, Huan Liao, Ziniu Li

Aligning large generative models with human feedback is a critical challenge. In speech synthesis, this is particularly pronounced due to the lack of a large-scale human preference dataset, which hinders the development of models that truly align with human perception. To address this, we introduce SpeechJudge, a comprehensive suite comprising a dataset, a b

Dang Nha Nguyen, Hai Dang Nguyen, Khoa Tho Anh Nguyen

Data augmentation in time series forecasting plays a crucial role in enhancing model performance by introducing variability while maintaining the underlying temporal patterns. However, time series data offers fewer augmentation strategies compared to fields such as image or text, with advanced techniques like Mixup rarely being used. In this work, we propose

Yihang Wu, Ahmad Chaddad

Despite the remarkable performance of deep models in medical imaging, they still require source data for training, which limits their potential in light of privacy concerns. Federated learning (FL), as a decentralized learning framework that trains a shared model with multiple hospitals (a.k.a., FL clients), provides a feasible solution. However, data hetero

Selim Ahmet Iz, Mustafa Unel

This paper presents a novel image-based path planning algorithm that was developed using computer vision techniques, as well as its comparative analysis with well-known deterministic and probabilistic algorithms, namely A* and Probabilistic Road Map algorithm (PRM). The terrain depth has a significant impact on the calculated path safety. The craters and hil

Okan Arif Guvenkaya, Selim Ahmet Iz, Mustafa Unel

Obstacle avoidance and path planning are essential for guiding unmanned ground vehicles (UGVs) through environments that are densely populated with dynamic obstacles. This paper develops a novel approach that combines tangentbased path planning and extrapolation methods to create a new decision-making algorithm for local path planning. In the assumed scenari

Akif Hamid, Orchi Hassan

Resistive random access memory (RRAM) is a promising candidate for next-generation nonvolatile memory (NVM) and in-memory computing applications. Compact models are essential for analyzing the circuit and system-level performance of experimental RRAM devices. However, most existing RRAM compact models rely on multiple fitting parameters to reproduce the devi

Zhiwen Yang, Yuxin Peng

Camera-based 3D semantic scene completion (SSC) plays a crucial role in autonomous driving, enabling voxelized 3D scene understanding for effective scene perception and decision-making. Existing SSC methods have shown efficacy in improving 3D scene representations, but suffer from the inherent input-output dimension gap and annotation-reality density gap, wh

Shuang Liu, Zhirun Zhang, Jinhao Dong, Zan Wang

Question-answering software is becoming increasingly integrated into our daily lives, with prominent examples including Apple Siri and Amazon Alexa. Ensuring the quality of such systems is critical, as incorrect answers could lead to significant harm. Current state-of-the-art testing approaches apply metamorphic relations to existing test datasets, generatin

Bingyu Li, Tao Huo, Da Zhang, Zhiyuan Zhao

Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain largely limited to terrestrial scenes. To bridge this gap, we introduce \textbf{AquaOV255}, the first large-scale and fine-grained underwater segmentation dataset containing 255 categories and over 20K images, cove

Byung-Kwan Ko, Soowon Kim, Seo-Hyun Lee

Achieving robust generalization across individuals remains a major challenge in electroencephalogram based imagined speech decoding due to substantial variability in neural activity patterns. This study examined how training dynamics and lightweight subject specific adaptation influence cross subject performance in a neural decoding framework. A cyclic inter

Weixuan Ou, Yanzhao Zheng, Shuoshuo Sun, Wei Zhang

Reinforcement Learning (RL) has been shown to improve the capabilities of large language models (LLMs). However, applying RL to open-domain tasks faces two key challenges: (1) the inherent subjectivity of these tasks prevents the verifiable rewards as required by Reinforcement Learning with Verifiable Rewards (RLVR); (2) Reinforcement Learning from Human Fee

Byeong-Il Ham, Hyun-Bin Kim, Jeonguk Kang, Keun Ha Choi

In this paper, we propose a footstep planning strategy based on model predictive control (MPC) that enables robust regulation of body orientation against undesired body rotations by optimizing footstep placement. Model-based locomotion approaches typically adopt heuristic methods or planning based on the linear inverted pendulum model. These methods account

Eunyeong Ko, Soowon Kim, Ha-Na Jo

Individuals with aphasia experience severe difficulty in real-time verbal communication, while most imagined speech decoding approaches remain limited to offline analysis or computationally demanding models. To address this limitation, we propose a two-session experimental framework consisting of an offline data acquisition phase and a subsequent online feed

Yoonho Lee, Joseph Boen, Chelsea Finn

We introduce \textit{Feedback Descent}, a framework that optimizes text artifacts -- prompts, code, and molecules -- through structured textual feedback, rather than relying solely on scalar rewards. By preserving detailed critiques instead of compressing them to binary preferences, Feedback Descent widens the information bottleneck in preference learning, e

Jung-Sun Lee, Ha-Na Jo, Eunyeong Ko

Human speech production encompasses multiple modes such as perceived, overt, whispered, and imagined, each reflecting distinct neural mechanisms. Among these, theta-band synchrony has been closely associated with language processing, attentional control, and inner speech. However, previous studies have largely focused on a single mode, such as overt speech,

Søren Eilers, Efren Ruiz

We establish logical equivalence between statements involving * the Cuntz C*-algebra $\mathcal O_\infty$ with its canonical diagonal; * graph C*-algebras with their canonical diagonals; * Leavitt path algebras over general fields with their canonical diagonals; * Leavitt path algebras over $\mathbb Z$; * topological full groups; * groupoids; and * the automo

Narendra Singh Yadav, Pavan Kumar Perepu

Several computer vision applications like vehicle license plate recognition, captcha recognition, printed or handwriting character recognition from images etc., text polarity detection and binarization are the important preprocessing tasks. To analyze any image, it has to be converted to a simple binary image. This binarization process requires the knowledge

Kosei Fujiki, Michitaka Kohara, Kotaro Shinmyo, Yu-ki Suzuki

In this paper, we demonstrate that the first law of holographic pseudo-entropy, which is a non-Hermitian generalization of entanglement entropy in a two-dimensional conformal field theory (CFT), is equivalent to the perturbative Einstein equation in three-dimensional de Sitter (dS) space, assuming the dS/CFT correspondence. Our analysis reveals that the geod

Nur Shazwani Kamarudin, Ghazaleh Beigi, Lydia Manikonda, Huan Liu

There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies

Zhen He, Nika Salia, Xiutao Zhu

Caro, Patk\'os, and Tuza initiated a systematic study of the bipartite Tur\'an number for trees, and in particular asked for the extremal number of edges in connected bipartite graphs with prescribed color-class sizes that contain no paths of given lengths. In this paper, we determine these numbers exactly and describe all corresponding extremal configuratio

Jun-Young Kim, Young-Seok Kweon, Gi-Hwan Shin, Seong-Whan Lee

Adaptive reasoning enables humans to flexibly adjust inference strategies when environmental rules or contexts change, yet its underlying neural dynamics remain unclear. This study investigated the neurophysiological mechanisms of adaptive reasoning using a card-sorting paradigm combined with electroencephalography and compared human performance with that of

Zhenyu Gu, Yanchen Xu, Sida Huang, Yubin Guo

Rectified Flow (RF) has been widely used as an effective generative model. Although RF is primarily based on probability flow Ordinary Differential Equations (ODE), recent studies have shown that injecting noise through reverse-time Stochastic Differential Equations (SDE) for sampling can achieve superior generative performance. Inspired by Positive-incentiv

Hengjun Xu, Takashi Goda

In this paper, we develop constructive algorithms for generating quasi-uniform point sets and sequences over arbitrary two-dimensional triangular domains. Our proposed method, called the \emph{Voronoi-guided greedy packing} algorithm, iteratively selects the point farthest from the current set among a finite candidate set determined by the Voronoi diagram of

Miroslav Lžičař

We introduce CellARC, a synthetic benchmark for abstraction and reasoning built from multicolor 1D cellular automata (CA). Each episode has five support pairs and one query serialized in 256 tokens, enabling rapid iteration with small models while exposing a controllable task space with explicit knobs for alphabet size k, radius r, rule family, Langton's lam

Mohamed Abdalmoaty, Roy S. Smith

A recently developed data-driven Kalman filter requires offline measurement of the process disturbance; a requirement that is often unmet for many practical applications. We propose a solution that parametrizes the Kalman filter exclusively using measured input and output data. The key idea is to use the innovations form which naturally accounts for the proc

B. K. Kwaśniewski, A. Mundey

We study self-similar groupoid actions on arbitrary directed graphs together with $\mathbb{T}$-valued twists that exhaust the second cohomology group of the associated Zappa-Sz\'ep product category. We define and analyse the associated universal, reduced, and essential $C^*$-algebras, along with their Toeplitz versions and core subalgebras. In fact, we devel

Zhao Yu, Xiuping Wu, Liangjun Ke

Reinforcement learning (RL) has been recognized as a powerful tool for robot control tasks. RL typically employs reward functions to define task objectives and guide agent learning. However, since the reward function serves the dual purpose of defining the optimal goal and guiding learning, it is challenging to design the reward function manually, which ofte

Youneng Bao, Yulong Cheng, Yiping Liu, Yichen Yang

Prevailing quantization techniques in Learned Image Compression (LIC) typically employ a static, uniform bit-width across all layers, failing to adapt to the highly diverse data distributions and sensitivity characteristics inherent in LIC models. This leads to a suboptimal trade-off between performance and efficiency. In this paper, we introduce DynaQuant,

Haoning Li, Qinghua Huang

Negative sampling (NS) strategies play a crucial role in knowledge graph representation. In order to overcome the limitations of existing negative sampling strategies, such as vulnerability to false negatives, limited generalization, and lack of control over sample hardness, we propose DANS-KGC (Diffusion-based Adaptive Negative Sampling for Knowledge Graph

Arvid Siqveland

In arXiv:2511.04191 we constructed schemes of objects in small categories which contained a set of basepoints with local representing (localizing) objects. Here we prove that the category $\cat{Rings}$ of associative rings with unit has a certain set of basepoints for which localizing rings exist. We take the set of base points $B$ to be the set of rings on

Ihab Tabbara, Yuxuan Yang, Hussein Sibai

Safety assurance is a fundamental requirement for deploying learning-enabled autonomous systems. Hamilton-Jacobi (HJ) reachability analysis is a fundamental method for formally verifying safety and generating safe controllers. However, computing the HJ value function that characterizes the backward reachable set (BRS) of a set of user-defined failure states

Chuanfu Xiao, Jiaxin Zeng

Tensors, especially higher-order tensors, are typically represented in low-rank formats to preserve the main information of the high-dimensional data while saving memory space. In practice, only a small fraction elements in high-dimensional data are of interest, such as the $k$ largest or smallest elements. Thus, retrieving the $k$ largest/smallest elements

Chaeri Kim, Jaeyeon Bae, Taehwan Kim

Deep learning models have been successful in many areas but understanding their behaviors still remains a black-box. Most prior explainable AI (XAI) approaches have focused on interpreting and explaining how models make predictions. In contrast, we would like to understand how data can be explained with deep learning model training and propose a novel approa

Dengcan Liu, Jiahao Li, Zheren Fu, Yi Tu

Reward models (RMs) are a core component in the post-training of large language models (LLMs), serving as proxies for human preference evaluation and guiding model alignment. However, training reliable RMs under limited resources remains challenging due to the reliance on large-scale preference annotations and the high cost of fine-tuning LLMs. To address th

Ha-Na Jo, Jung-Sun Lee, Eunyeong Ko

Dysarthria impairs motor control of speech, often resulting in reduced intelligibility and frequent misarticulations. Although interest in brain-computer interface technologies is growing, electroencephalogram (EEG)-based communication support for individuals with dysarthria remains limited. To address this gap, we recorded EEG data from one participant with

Shihao Li, Jiachen Li, Jiamin Xu, Dongmei Chen

We present \textsc{S2C} (Specification-to-Certified-Controller), a multi-agent framework that maps natural-language requirements to certified $\mathcal{H}_\infty$ state-feedback controllers via LMI synthesis. \textsc{S2C} coordinates five roles -- \textit{SpecInt} (spec extraction), \textit{Solv} (bounded-real lemma (BRL) LMI), \textit{Tester} (Monte Carlo a

Choon-Jie Wong, Adam A. Larkin, Jie Bao, Maria Skyllas-Kazacos

Aluminium is manufactured through the Hall-H\'eroult process, which is very energy intensive. Power modulation, as an industrial-scale demand-side power management approach, allows aluminium smelters to operate with variable power consumption rates and as such be powered by renewable energy sources. In this way, aluminium smelting cells can be used as a larg

Yizhou Zhang

Empirical scaling laws describe how test loss and other performance metrics depend on model size, dataset size, and compute. While such laws are consistent within specific regimes, apparently distinct scaling behaviors have been reported for related settings such as model compression. Motivated by recent progress in spectral analyses of neural representation

Yeon-Woo Choi, Hye-Bin Shin, Dan Li

Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user attention fluctuates, brain states shift over time, or irregular artifacts appear during interaction. To mitigate these issues, we introduce a user state-aware electroencephalogram (EEG) filtering framework that

Soowon Kim, Byung-Kwan Ko, Seo-Hyun Lee

Non-invasive brain-computer interfaces that decode spoken commands from electroencephalogram must be both accurate and trustworthy. We present a confidence-aware decoding framework that couples deep ensembles of compact, speech-oriented convolutional networks with post-hoc calibration and selective classification. Uncertainty is quantified using ensemble-bas

Sicong Zang, Shuhui Gao, Zhijun Fang

Generating sketches with specific patterns as expected, i.e., manipulating sketches in a controllable way, is a popular task. Recent studies control sketch features at stroke-level by editing values of stroke embeddings as conditions. However, in order to provide generator a global view about what a sketch is going to be drawn, all these edited conditions sh

Chenhao Dang, Jing Ma

A persistent challenge in text classification (TC) is that enhancing model robustness against adversarial attacks typically degrades performance on clean data. We argue that this challenge can be resolved by modeling the distribution of clean samples in the encoder embedding manifold. To this end, we propose the Manifold-Correcting Causal Flow (MC^2F), a two

Yinglei Zhu, Xuguang Dong, Qiyao Wang, Qi Shao

Dynamic modeling and control are critical for unleashing soft robots' potential, yet remain challenging due to their complex constitutive behaviors and real-world operating conditions. Bio-inspired musculoskeletal robots, which integrate rigid skeletons with soft actuators, combine high load-bearing capacity with inherent flexibility. Although actuation dyna

Dechuang Chen, Sibo Wang, Qintian Guo

Graphs are a ubiquitous data structure in diverse domains such as machine learning, social networks, and data mining. As real-world graphs continue to grow beyond the memory capacity of single machines, out-of-core graph processing systems have emerged as a viable solution. Yet, existing systems that rely on strictly synchronous, iteration-by-iteration execu

Si-Hyun Kim, Heon-Gyu Kwak, Byoung-Hee Kwon, Seong-Whan Lee

Brain-computer interface (BCI) aims to decode motor intent from noninvasive neural signals to enable control of external devices, but practical deployment remains limited by noise and variability in motor imagery (MI)-based electroencephalogram (EEG) signals. This work investigates a hierarchical and meta-cognitive decoding framework for four-class MI classi

Jiaqi Wang, Liutao Yu, Xiongri Shen, Sihang Guo

Spiking neural networks (SNNs) offer a promising path toward energy-efficient speech command recognition (SCR) by leveraging their event-driven processing paradigm. However, existing SNN-based SCR methods often struggle to capture rich temporal dependencies and contextual information from speech due to limited temporal modeling and binary spike-based represe

Jessica Gumowski, Krishna Manaswi Digumarti, David Howard

Organisms in nature, such as Cephalopods and Pachyderms, exploit stiffness modulation to achieve amazing dexterity in the control of their appendages. In this paper, we explore the phenomenon of layer jamming, which is a popular stiffness modulation mechanism that provides an equivalent capability for soft robots. More specifically, we focus on mechanical la

Ehsan Alamdari, Rouhollah Amiri

This paper presents a unified framework for robust three-dimensional (3-D) source localization using a network of sensors equipped with one-dimensional (1-D) linear arrays. While such arrays offer practical advantages in terms of cost and size, existing localization methods suffer from a fundamental limitation: their performance degrades significantly as the

Suraj Kumar Pandit, Abhinay Pandey, Athreya Shankar, Krishna R. Nandipati

We study the polaritonic states and dynamics of multiple Jahn-Teller (JT) active molecules coupled to the modes of a Fabry-Perot cavity. We find that collective effects dramatically alter the interplay of electronic, vibrational and cavity angular momenta, giving rise to markedly different polaritonic spectra and dynamics even when going from one to two JT m

Neelesh Kumar Shukla, Pranay Sanghvi

In democracies like India, people are free to express their views and demands. Sometimes this causes situations of civil unrest such as protests, rallies, and marches. These events may be disruptive in nature and are often held without prior permission from the competent authority. Forecasting these events helps administrative officials take necessary action

Shihao Li, Jiachen Li, Jiamin Xu, Christopher Martin

We study how trajectory value depends on the learning algorithm in policy-gradient control. Using Trajectory Shapley in an uncertain LQR, we find a negative correlation between Persistence of Excitation (PE) and marginal value under vanilla REINFORCE ($r\approx-0.38$). We prove a variance-mediated mechanism: (i) for fixed energy, higher PE yields lower gradi

Yilin Gao, Shuguang Dou, Junzhou Li, Zhiheng Yu

Recent advances in diffusion models have achieved remarkable success in isolated computer vision tasks such as text-to-image generation, depth estimation, and optical flow. However, these models are often restricted by a ``single-task-single-model'' paradigm, severely limiting their generalizability and scalability in multi-task scenarios. Motivated by the c

Xingyu Li, Xiaolei Liu, Cheng Liu, Yixiao Xu

As large language models (LLMs) scale, their inference incurs substantial computational resources, exposing them to energy-latency attacks, where crafted prompts induce high energy and latency cost. Existing attack methods aim to prolong output by delaying the generation of termination symbols. However, as the output grows longer, controlling the termination

Huajie Song, Haitao Xu

The bulk boundary correspondence, one of the most significant features of topological matter, theoretically connects the existence of edge modes at the boundary with topological invariants of the bulk spectral bands. However, it remains unspecified in realistic examples how large the size of a lattice should be for the correspondence to take effect. In this

Cixiao Zhang, Yin Xu, Xinghao Guo, Xiaowu Ou

In wideband near-field arrays, frequency-dependent array responses cause wavefronts at different frequencies to deviate from that at the center frequency, producing beam squint and degrading multi-user performance. True-time-delay (TTD) circuits can realign the frequency dependence but require large delay ranges and intricate calibration, limiting scalabilit

Shan-Xia Bao, Ren Zhao, Yun-Zhi Du

Taking the mass, charge, hair parameter, and cosmological constant of the 5-dimensional de Sitter hairy spacetime as the state variables of a thermodynamic system, and based on the satisfaction of the universal first law of thermodynamics, we obtain the effective thermodynamic quantities of the spacetime. The thermodynamic properties of the system in the coe

Chenxi Lin, Weikang Yuan, Zhuoren Jiang, Biao Huang

Understanding human attitudes, preferences, and behaviors through social surveys is essential for academic research and policymaking. Yet traditional surveys face persistent challenges, including fixed-question formats, high costs, limited adaptability, and difficulties ensuring cross-cultural equivalence. While recent studies explore large language models (

Mingxiang Liu, Damián Marelli, Minyue Fu, Qianqian Cai

We study the problem of designing a state feedback linear quadratic Gaussian (LQG) controller for a system in which the system matrices as well as the process noise covariance are unknown. We do a rigorous comparison between two approaches. The first is the classic one in which a system identification stage is used to estimate the unknown parameters, which a

Taif Abdullah Enaoufal, Tarek Saanouni

This paper is devoted to the analysis of a focusing nonlinear biharmonic Schr\"odinger equation in the presence of an unbounded growing up inhomogeneous term. The first main contribution of this work is the derivation of an inhomogeneous Gagliardo-Nirenberg inequality adapted to the unbounded weight, which provides the necessary control over the nonlinear te

Nima Anari, Carlo Baronio, CJ Chen, Alireza Haqi

We present parallel algorithms to accelerate sampling via counting in two settings: any-order autoregressive models and denoising diffusion models. An any-order autoregressive model accesses a target distribution $\mu$ on $[q]^n$ through an oracle that provides conditional marginals, while a denoising diffusion model accesses a target distribution $\mu$ on $

Ramesh Pramanik, Ramu K Yadav, Sakuntala Chatterjee

When exposed to a time-periodic chemical signal, an \textit{E.~coli} cell responds by modulating its receptor activity in a similar time-periodic manner. However, there exists a phase lag between the applied signal and the activity response. We study the variation of the activity amplitude and phase lag as a function of the applied frequency~$\omega$, using

Keita Takahashi

We extend Beem's three completeness notions -- finite compactness, timelike Cauchy completeness, and Condition A -- originally defined for spacetimes, to Lorentzian length spaces and study their relationships. We prove that finite compactness implies timelike Cauchy completeness and that timelike Cauchy completeness implies Condition A for globally hyperboli

Frank Gilson

We develop a unified framework for iterated symmetric extensions with countable support and, more generally, with $<\kappa$-support. Set-length iterations are treated uniformly, and when the iteration template is first-order definable over a Godel-Bernays ground with Global Choice, the construction extends to class-length iterations. At limit stages with $\m

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri

Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such

Yuto Nishikawa, Tomoo Yokoyama

This paper shows that discrete Morse-Bott theory can be developed as a natural extension of R. Forman's discrete Morse theory by improving the definition of the discrete Morse-Bott function introduced by S. Yaptieu. To this end, we demonstrate that the combinatorial structure of critical cells can be extended to critical sets intuitively. Furthermore, we est

Shinwoo Park, Hyejin Park, Hyeseon Ahn, Yo-Sub Han

Large language models now draft news, legal analyses, and software code with human-level fluency. At the same time, regulations such as the EU AI Act mandate that each synthetic passage carry an imperceptible, machine-verifiable mark for provenance. Conventional logit-based watermarks satisfy this requirement by selecting a pseudorandom green vocabulary at e

Sunghun Yang, Minhyeok Lee, Jungho Lee, Sangyoun Lee

Monocular 3D object detection offers a cost-effective solution for autonomous driving but suffers from ill-posed depth and limited field of view. These constraints cause a lack of geometric cues and reduced accuracy in occluded or truncated scenes. While recent approaches incorporate additional depth information to address geometric ambiguity, they overlook

Changseok Kim, Jong-Hak Woo

We present a modified outflow model and its application to constrain ionized outflow properties of active galactic nuclei (AGNs). By adding a rotating disk component to the biconical outflow model of Bae & Woo, we find that models with a rotating disk require faster launching velocities ($\lesssim$ 1500 km s$^{-1}$) than outflow-only models to be consistent

Geuntae Park, Jiwon Yi, Taehyun Rhee, Kwanguk Kim

We present TouchWalker, a real-time system for controlling full-body avatar locomotion using finger-walking gestures on a touchscreen. The system comprises two main components: TouchWalker-MotionNet, a neural motion generator that synthesizes full-body avatar motion on a per-frame basis from temporally sparse two-finger input, and TouchWalker-UI, a compact t

Jason Li

We obtain the first near-linear time deterministic algorithm for negative-weight single-source shortest paths on integer-weighted graphs. Our main ingredient is a deterministic construction of a padded decomposition on directed graphs, which may be of independent interest.

Wei Wang

Deep learning architectures are highly diverse. To prove their universal approximation properties, existing works typically rely on model-specific proofs. Generally, they construct a dedicated mathematical formulation for each architecture (e.g., fully connected networks, CNNs, or Transformers) and then prove their universal approximability. However, this ap

Yu-Long Yan, Quan Cheng, Xiao-Ping Zheng, Xiao-Yue Yu

Newborn magnetars are important gravitational wave sources due to their ultra-strong magnetic fields and fast spins, and the entire population in the Universe may significantly contribute to the stochastic gravitational wave background (SGWB). In this work, we investigate the SGWB from newborn magnetars and assess its detectability by the advanced LIGO (aLIG

T. Fukui

We formulate gradient flow dynamics generated by two natural actions of the quantum metric for an isolated set of Bloch bands. Specializing to two spatial dimensions, we derive Bogomolny-type lower bounds that relate these actions to the Chern number and show that the bounds are saturated by (anti-)holomorphic projector configurations. Along the flows, the a

Shahram Mehry, Ali Eisapoor Khasadan

Let $R$ be a commutative ring with identity. We introduce a novel bipartite graph $\mathcal{B}(R)$, the \textit{bipartite zero-divisor--unit graph}, whose vertex set is the disjoint union of the nonzero zero-divisors $Z(R)^*$ and the unit group $U(R)$. A vertex $z \in Z(R)^*$ is adjacent to $u \in U(R)$ if and only if $z + u \in Z(R)$. This construction prov

Byeongseon Go, Changhun Oh, Hyunseok Jeong

Gaussian boson sampling (GBS) is a prominent candidate for the experimental demonstration of quantum advantage. However, while the current implementations of GBS are unavoidably subject to noise, the robustness of the classical intractability of GBS against noise remains largely unexplored. In this work, we establish the complexity-theoretic foundations for

Hae-Won Jo, Yeong-Jun Cho

Dynamic Scene Graph Generation (DSGG) models how object relations evolve over time in videos. However, existing methods are trained only on annotated object pairs and lack guidance for non-related pairs, making it difficult to identify meaningful relations during inference. In this paper, we propose Relation Scoring Network (RS-Net), a modular framework that

Fernando Gottardo, Benjamin J. Carey, Nathaniel Bawden, Glen I. Harris

Optomechanical sensors enable exquisitely sensitive force measurements, with emerging applications across quantum technologies, standards, fundamental science, and engineering. Magnetometry is among the most promising applications, where chip-scale optomechanical sensors offer high sensitivity without the cryogenics or magnetic shielding required by competin

Xiangling Chen, Yi Mei, Mengjie Zhang

Recent advances in neural neighborhood search methods have shown potential in tackling Vehicle Routing Problems (VRPs). However, most existing approaches rely on simplistic state representations and fuse heterogeneous information via naive concatenation, limiting their ability to capture rich structural and semantic context. To address these limitations, we

Chen-Bo Zhu

This set of lecture notes on local theta correspondence is the written version of a mini-course the author gave in March of 2025 for the program ``Representation Theory and Noncommutative Geometry" at the Institut Henri Poincar\'e, Paris. The emphasis is on the Archimedean theory, which concerns representations of classical Lie groups. Section 1 is about the

Animesh Banik, Md. Shihab Khan, Rafid Masrur Khan, Syed Emad Uddin Shubha

We introduce an efficient and versatile quantum teleportation protocol for specific types of n-qubit entangled states. By employing a partially entangled Greenberger-Horne-Zeilinger (GHZ) state as the quantum channel and an optimal Positive Operator-Valued Measure (POVM) based on an improved reciprocal state formulation, we achieve unambiguous state discrimi

Xiaoyue Liu, Yujia Xu, Benoit Montreuil

With the growth of data-driven services and expansion of mobile application usage, traditional methods of capacity and resource planning methods may not be efficient and often fall short in meeting rapid changes in the business landscape. Motivated by modularity, containerization, and open sharing concepts from Physical Internet (PI), this paper proposes an

S. J. Sebastian, S. S. Islam, R. Kolay, S. Mohanty

The three-dimensional (3D) magnet KBaFe$_2$(PO$_4$)$_3$ hosts a double-trillium lattice of Fe$^{3+}$ (spin, $S=5/2$) ions offering a prototypical platform to study the frustration induced effects in 3D. Through magnetization, specific heat, $^{31}$P nuclear magnetic resonance (NMR), and muon spin relaxation ($\mu$SR) experiments, supported by first principle

Vamsikrishna Thota, Hardik Prajapati, Yuvraj Joshi, Shubhangi Rathi

Early and accurate detection of cardiac arrhythmias is vital for timely diagnosis and intervention. We propose a lightweight deep learning model combining 1D Convolutional Neural Networks (CNN), attention mechanisms, and Bidirectional Long Short-Term Memory (BiLSTM) for classifying arrhythmias from both 12-lead and single-lead ECGs. Evaluated on the CPSC 201

Jay Chooi, Kevin Cong, Russell Li, Lillian Sun

As deep learning methods increasingly utilize sensitive data on a widespread scale, differential privacy (DP) offers formal guarantees to protect against information leakage during model training. A significant challenge remains in implementing DP optimizers that retain strong performance while preserving privacy. Recent advances introduced ever more efficie

Sunghyun Wee, Suyoung Kim, Hyeonjin Kim, Kyomin Hwang

Post-Training Quantization (PTQ) has become the de-facto standard for efficient LLM deployment, yet its optimization objective remains fundamentally incomplete. Standard PTQ methods minimize reconstruction error (e.g., MSE or KL divergence) without accounting for behavioral alignment--a critical property instilled through safety fine-tuning. We demonstrate t

Aditya Mitra, Sibi Chakkaravarthy Sethuraman, Devi Priya V S

As automation bot technology and Artificial Intelligence is evolving rapidly, conventional human verification techniques like voice CAPTCHAs and knowledge-based authentication are becoming less effective. Bots and scrapers with Artificial Intelligence (AI) capabilities can now detect and solve visual challenges, emulate human like typing patterns, and avoid

Guido Claro, Pamela Muller, Luis Nowak, Alejandra Perini

In this paper matrix quantitative weighted estimates on spaces of homogeneous type, such as endpoint estimates, strong type estimates are provided. To that end we extend some earlier results on convex body domination due to Nazarov, Petermichl, Treil and Volberg to this setting. We also provide a $T(1)$ alike convex body domination result analogous to the on

Zizheng Guo, Haichuan Liu, Xizhe Shi, Shenglu Hua

We introduce in this paper, HeteroSTA, the first CPU-GPU heterogeneous timing analysis engine that efficiently supports: (1) a set of delay calculation models providing versatile accuracy-speed choices without relying on an external golden tool, (2) robust support for industry formats, including especially the .sdc constraints containing all common timing ex

Shahram Mehry, Mansour Molaeinejad

Let $M$ be a left $R$-module. We define the \emph{homomorphism submodule graph} $\Gamma_{\mathrm{Hom}}(M)$ as the simple graph whose vertices are the proper submodules of $M$, with an edge between distinct vertices $N_1$ and $N_2$ if and only if $\mathrm{Hom}_R(N_1, M/N_2) \ne 0$ or $\mathrm{Hom}_R(N_2, M/N_1) \ne 0$. This graph encodes homological informati

Yiqiao Bao, Anindya De, Shivam Nadimpalli, Rocco A. Servedio

We consider the problem of testing whether an unknown low-degree polynomial $p$ over $\mathbb{R}^n$ is sparse versus far from sparse, given access to noisy evaluations of the polynomial $p$ at \emph{randomly chosen points}. This is a property-testing analogue of classical problems on learning sparse low-degree polynomials with noise, extending the work of Ch