October 2025 arXiv papers — page 220

Ziying Zhang, Yaqing Wang, Yuxuan Sun, Min Ye

Cold-start drug-target interaction (DTI) prediction focuses on interaction between novel drugs and proteins. Previous methods typically learn transferable interaction patterns between structures of drug and proteins to tackle it. However, insight from proteomics suggest that protein have multi-level structures and they all influence the DTI. Existing works u

Seunghyun Lee, Tae-Kyun Kim

Latest diffusion models have shown promising results in category-level 6D object pose estimation by modeling the conditional pose distribution with depth image input. The existing methods, however, suffer from slow convergence during training, learning its encoder with the diffusion denoising network in end-to-end fashion, and require an additional network t

Wei Chen, Shumo Cui, Kailiang Wu, Tao Xiong

This paper explores numerical schemes for Temple-class systems, which are integral to various applications including one-dimensional two-phase flow, elasticity, traffic flow, and sedimentation. Temple-class systems are characterized by conservative equations, with different pressure function expressions leading to specific models such as the Aw-Rascle-Zhang

Yingjing Xiao, Zhichao Huang, Junbin Ren, Haichuan Song

Hand pose tracking is essential for advancing applications in human-computer interaction. Current approaches, such as vision-based systems and wearable devices, face limitations in portability, usability, and practicality. We present a novel wearable system that reconstructs 3D hand pose and estimates per-finger forces using a minimal ring-watch sensor setup

Shuai Li, Huichao Wang

Magnetoresistance is a powerful probe for characterizing the intrinsic physics embedded in materials. Among its various manifestations, linear magnetoresistance has a long history and continues attracting research interest. In contemporary studies, a clear understanding of the magnetoresistance character of quantum origin is more crucial than ever for the st

Naihuan Jing, Yinlong Liu, Jian Zhang

Manin matrices are quantum linear transformations of general quantum spaces. In this paper, we study the $q$-analogue of super Manin matrices and obtain several quantum versions of classical identities, such as Jacobi's ratio theorem, Schur's complement theorem, Cayley's complementary theorem, Muir's law, Sylvester's theorem, MacMahon Master Theorem and Newt

Prakhar Paliwal, Atul Kabra, Manjesh Kumar Hanawal

Rapid digitization of critical infrastructure has made cyberwarfare one of the important dimensions of modern conflicts. Attacking the critical infrastructure is an attractive pre-emptive proposition for adversaries as it can be done remotely without crossing borders. Such attacks disturb the support systems of the opponents to launch any offensive activitie

Iasson Karafyllis, Miroslav Krstic

This short note shows that the Deadzone-Adapted Disturbance Suppression (DADS) adaptive control scheme is applicable to systems with unknown input coefficients. We study time-invariant, control-affine systems that satisfy the matching condition for which no bounds for the disturbance and the unknown parameters are known. The input coefficients can be time-va

Ziying Zhang, Yaqing Wang, Quanming Yao

Meta reasoning behaviors work as a skeleton to guide large language model (LLM) reasoning, thus help to improve reasoning performance. However, prior researches implement meta reasoning skeleton with manually designed structure, limiting ability to adapt to query-specific requirement and capture intricate logical dependency among reasoning steps. To deal wit

George Giapitzakis, Kimon Fountoulakis, Eshaan Nichani, Jason D. Lee

Semiautomata form a rich class of sequence-processing algorithms with applications in natural language processing, robotics, computational biology, and data mining. We establish the first Statistical Query hardness result for semiautomata under the uniform distribution over input words and initial states. We show that Statistical Query hardness can be establ

Wanxin Li, Yongjin P. Park, Khanh Dao Duc

Fairness testing evaluates whether a model satisfies a specified fairness criterion across different groups, yet most research has focused on classification models, leaving regression models underexplored. This paper introduces a framework for fairness testing in regression models, leveraging Wasserstein distance to project data distribution and focusing on

Masato Sakano, Shunsuke Akatsuka, Takato Yamamoto, Tianyishan Sun

Janus monolayer transition metal dichalcogenides (TMDs), created by post-growth substitution of the top chalcogen layer, represent a new direction for engineering 2D crystal properties. However, their rapid ambient degradation and the difficulty of obtaining large-area monolayer samples have limited the available experimental probes, leaving their detailed e

Liang Pan, Wei Chen, Jianxian Qiu, Tao Xiong

In this paper, we develop a high order structure-preserving local discontinuous Galerkin (DG) scheme for the compressible self-gravitating Euler equations, which pose great challenges due to the presence of time-dependent gravitational potential. The designed scheme is well-balanced for general polytropic equilibrium state and total energy conserving for mul

Yuxin Li, Eng Siong Chng, Cuntai Guan

Speech-based depression detection (SDD) has emerged as a non-invasive and scalable alternative to conventional clinical assessments. However, existing methods still struggle to capture robust depression-related speech characteristics, which are sparse and heterogeneous. Although pretrained self-supervised learning (SSL) models provide rich representations, m

Xinglong Luo, Ao Luo, Kunming Luo, Zhengning Wang

In this paper, we explore the problem of event-based meshflow estimation, a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start, we review the state-of-the-art in event-based flow estimation, highlighting two key areas for further research: i) the lack of meshflow-specific event datasets and methods, and ii

Jiawen Chang, Zhuda Yang, Changsong Zhou

The spatiotemporal patterns of neural dynamics are jointly shaped by directed structural interactions and heterogeneous intrinsic features of the neural components. Despite well-developed methods for estimating directionality in network connections from network of homogeneous nodes, how local heterogeneity impacts on directionality estimation remains poorly

Debayan Jana, Abhik Basu

We show that stochastically driven nonequilibrium conserved growth models admit generic strong coupling phases for sufficiently strong nonlocal chemical potentials underlying the dynamics. The models exhibit generic roughening transitions between perturbatively accessible weak coupling phases satisfying an exact relation between the scaling exponents in all

Ramzi Dakhmouche, Adrien Letellier, Hossein Gorji

Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. Yet, for LLM generation with multiple choice structure, the state-of-the-art in UQ is still dominated by the naive baseline given by the maximum softmax score. To address this shortcoming, we demon

Sergei V. Naydenov, Oleksii K. Kapustnyk, Igor M. Pritula, Dmitro S. Sofronov

In this study, binary $ZnSe:Fe^{2+}$ crystals and ternary $Zn_{1-x}Mg_{x}Se:Fe^{2+}$ crystals $(0 < x < 0.6)$ were grown by the vertical Bridgman method in graphite crucibles under high argon pressure. A comparative characterization of the structural, energetic, and optical parameters of the obtained crystals was performed. Theoretical explanations of the ob

John D. Ilee, Catherine Walsh, Jenny C. Calahan

Methanol (CH$_{3}$OH) ice is abundant in space and is a key feedstock for seeding chemical complexity in interstellar and circumstellar environments. Despite its ubiquity, gas-phase methanol has only been detected in one disk around a Solar-type star to date, TW Hya. Here we present new high sensitivity (~1 mJy/beam) observations of TW Hya with ALMA that det

Aniruddha Deshmukh

In this article, we prove an analogue of the Rubio de Francia's extrapolation theorem in the setting of Hausdorff capacities. We prove the result using techniques analogous to those in the classical setting and using the recently developed theory of capacitary Muckenhoupt weights.

Leonid A. Bunimovich, Emilio N. M. Cirillo, Matteo Colangeli, Lamberto Rondoni

We introduce a generalized version of the Kac ring model in which particles are of two types, black and white. Black particles modify the environment through which all particles move, thereby inducing indirect and potentially long-range interactions among them. Unlike the inert scatterers of Kac's original model, the scatterers in our setting possess interna

Wen-Xuan Zhang, Wen-Nian Liu, Duojie Jia

The limitation of flavor constituents for compact multiquarks is crucial for understanding the strong interaction at the low energy scale. Utilizing the MIT bag model that incorporates perturbative interactions and confinement energy $E_{\rm CON}$, we derive a critical bag radius $R_c=5.61\,$GeV$^{-1}$ from the condition $E_{\rm CON} < 0$ at zero temperature

Ramzi Dakhmouche, Hossein Gorji

Motivated by the remarkable success of Foundation Models (FMs) in language modeling, there has been growing interest in developing FMs for time series prediction, given the transformative power such models hold for science and engineering. This culminated in significant success of FMs in short-range forecasting settings. However, extrapolation or long-range

Ben Ansbacher, Malachy Guzman, Jordi Garcia-Ojalvo, Arjendu K Pattanayak

The development of multicellular organisms entails a deep connection between time-dependent biochemical processes taking place at the subcellular level, and the resulting macroscopic phenotypes that arise in populations of up to trillions of cells. A statistical mechanics of developmental processes would help to understand how microscopic genotypes map onto

Anushiya Arunan, Yan Qin, Xiaoli Li, U-Xuan Tan

Accurate battery capacity estimation is key to alleviating consumer concerns about battery performance and reliability of electric vehicles (EVs). However, practical data limitations imposed by stringent privacy regulations and labeled data shortages hamper the development of generalizable capacity estimation models that remain robust to real-world data dist

Zhiqiang Xu, Zili Xu, Xinyue Zhang

This paper aims to characterize the optimal frame for phase retrieval, defined as the frame whose condition number for phase retrieval attains its minimal value. In the context of the two-dimensional real case, we reveal the connection between optimal frames for phase retrieval and the perimeter-maximizing isodiametric problem, originally proposed by Reinhar

Chenxiang Ma, Xinyi Chen, Yujie Wu, Kay Chen Tan

Spiking neural networks (SNNs), recognized as an energy-efficient alternative to traditional artificial neural networks (ANNs), have advanced rapidly through the scaling of models and datasets. However, such scaling incurs considerable training overhead, posing challenges for researchers with limited computational resources and hindering the sustained develo

Peichao Lai, Jinhui Zhuang, Kexuan Zhang, Ningchang Xiong

Automating the conversion of UI images into web code is a critical task for front-end development and rapid prototyping. Advances in multimodal large language models (MLLMs) have made WebUI-to-Code increasingly feasible, yet existing benchmarks remain limited in data diversity and evaluation reliability. To address these issues, we present WebRenderBench, a

Tommy Mordo, Sagie Dekel, Omer Madmon, Moshe Tennenholtz

Competitive search is a setting where document publishers modify them to improve their ranking in response to a query. Recently, publishers have increasingly leveraged LLMs to generate and modify competitive content. We introduce Reinforcement Learning from Ranker Feedback (RLRF), a framework that trains LLMs using preference datasets derived from ranking co

Neri Merhav, Shlomo Shamai

We present a family of relatively simple and unified lower bounds on the capacity of the Gaussian channel under a set of pointwise additive input constraints. Specifically, the admissible channel input vectors $\bx = (x_1, \ldots, x_n)$ must satisfy $k$ additive cost constraints of the form $\sum_{i=1}^n \phi_j(x_i) \le n \Gamma_j$, $j = 1,2,\ldots,k$, which

Weikuo Wang, Yue Liao, Huan Luo

A major problem of kernel-based methods (e.g., least squares support vector machines, LS-SVMs) for solving linear/nonlinear ordinary differential equations (ODEs) is the prohibitive $O(an^3)$ ($a=1$ for linear ODEs and 27 for nonlinear ODEs) part of their computational complexity with increasing temporal discretization points $n$. We propose a novel Nystr\"o

Guixian Zhang, Guan Yuan, Ziqi Xu, Yanmei Zhang

Cognitive diagnostics in the Web-based Intelligent Education System (WIES) aims to assess students' mastery of knowledge concepts from heterogeneous, noisy interactions. Recent work has tried to utilize Large Language Models (LLMs) for cognitive diagnosis, yet LLMs struggle with structured data and are prone to noise-induced misjudgments. Specially, WIES's o

Emmanuel Coffie

We study a delayed stochastic interest rate model with superlinearly growing coefficients and develop novel analytical tools to investigate the properties of both the true solution and its truncated Euler-Maruyama (TEM) approximation. In particular, we prove that the true solution converges in probability to the truncated EM solution as the step size approac

Wei Wang, Tianhao Ma, Ming-Kun Xie, Gang Niu

Partial multi-label learning and complementary multi-label learning are two popular weakly supervised multi-label classification paradigms that aim to alleviate the high annotation costs of collecting precisely annotated multi-label data. In partial multi-label learning, each instance is annotated with a candidate label set, among which only some labels are

Nikita Gabdullin

Supervised learning (SL) methods are indispensable for neural network (NN) training used to perform classification tasks. While resulting in very high accuracy, SL training often requires making NN parameter number dependent on the number of classes, limiting their applicability when the number of classes is extremely large or unknown in advance. In this pap

Yitong Cui, Liu Liu, Baosheng Yu, Jiayan Qiu

Large language models (LLMs) have exhibited significant capabilities in addressing challenging problems throughout various fields, often through the use of agentic workflows that adhere to structured instructions and multi-step procedures. However, designing such workflows demands substantial manual effort, posing challenges to scalability and generalizabili

Nan Jiang, Tengyang Xie

This article introduces the theory of offline reinforcement learning in large state spaces, where good policies are learned from historical data without online interactions with the environment. Key concepts introduced include expressivity assumptions on function approximation (e.g., Bellman completeness vs. realizability) and data coverage (e.g., all-policy

Hyung Gyu Rho, Sian Lee

Modern preference alignment techniques, such as Best-of-N (BoN) sampling, rely on reward models trained with pairwise comparison data. While effective at learning relative preferences, this paradigm fails to capture a signal of response acceptability, leaving systems vulnerable to selecting the least bad of many unacceptable options. This is particularly pro

Fernando Ardana-Lamas, Seth L. Cousin, Juliette Lignieres, Jens Biegert

Electronic correlations occur on attosecond timescales, dictating how chemical bonds form, energy flows, and materials respond to light. Capturing such many-body processes requires light pulses of similar duration. The soft X-ray water window is vital because it encompasses the principle absorption edges of carbon, nitrogen, and oxygen that underpin chemistr

Prabhanjan Ananth, John Bostanci, Aditya Gulati, Yao-Ting Lin

Gluing theorem for random unitaries [Schuster, Haferkamp, Huang, QIP 2025] have found numerous applications, including designing low depth random unitaries [Schuster, Haferkamp, Huang, QIP 2025], random unitaries in ${\sf QAC0}$ [Foxman, Parham, Vasconcelos, Yuen'25] and generically shortening the key length of pseudorandom unitaries [Ananth, Bostanci, Gulat

Yuexin Cao, Yibei Li, Zhuo Zou, Xiaoming Hu

This paper studies the inverse optimal control problem for continuous-time linear quadratic regulators over finite-time horizon, aiming to reconstruct the control, state, and terminal cost matrices in the objective function from observed optimal inputs. Previous studies have mainly explored the recovery of state cost matrices under the assumptions that the s

Sher Khan, Raz Muhammad, Adil Hussain, Muhammad Sajjad

Cutaneous malignancies demand early detection for favorable outcomes, yet current diagnostics suffer from inter-observer variability and access disparities. While AI shows promise, existing dermatological systems are limited by homogeneous architectures, dataset biases across skin tones, and fragmented approaches that treat natural language processing as sep

Huanqing Guo, Junyong Zhang, Jiqiang Zheng

This paper studies the sharp $L^p$-$L^q$ boundedness of the Bochner-Riesz operator $S^{\delta}_{\lambda}(\mathcal{L}_{\mathbf{A}})$ associated with a scaling-critical magnetic Schr\"odinger operator $\mathcal{L}_{\mathbf{A}}$ on $\mathbb{R}^2$, where $\delta \in (-3/2, 0)$. We determine the conditions on the exponents $p$ and $q$ under which the operator is

Honglin Lin, Qizhi Pei, Xin Gao, Zhuoshi Pan

Reasoning capability is pivotal for Large Language Models (LLMs) to solve complex tasks, yet achieving reliable and scalable reasoning remains challenging. While Chain-of-Thought (CoT) prompting has become a mainstream approach, existing methods often suffer from uncontrolled generation, insufficient quality, and limited diversity in reasoning paths. Recent

Zixin Song, Bowen Zhang, Qian-Wen Zhang, Di Yin

Conditional Semantic Textual Similarity (C-STS) measures the semantic proximity between text segments under a specific condition, thereby overcoming the ambiguity inherent in traditional STS. However, existing methods are largely confined to discriminative models, failing to fully leverage recent breakthroughs in the NLP community involving Large Language Mo

Siddharth Bhandari, Abhishek Khetan

We investigate a geometric generalization of trifference, a concept introduced by Elias in 1988 in the study of zero-error channel capacity. In the discrete setting, a code C \subseteq {0,1,2}^n is trifferent if for any three distinct codewords x, y, z in C, there exists a coordinate i in [n] where x_i, y_i, z_i are all distinct. Determining the maximum size

Han Hu, Wei Minn, Yonghui Liu, Jiakun Liu

The permission mechanism in the Android Framework is integral to safeguarding the privacy of users by managing users' and processes' access to sensitive resources and operations. As such, developers need to be equipped with an in-depth understanding of API permissions to build robust Android apps. Unfortunately, the official API documentation by Android chro

S. V. Dzhenzher

We consider random linear continuous operators $\Omega \to \mathcal{L}(\mathcal{H}, \mathcal{H})$ on a Hilbert space $\mathcal{H}$. For example, such random operators may be random quantum channels. The Central Limit Theorem is known for the sums of i.i.d. random operators. Instead of the sum, there may be considered the composition of random exponents $e^{A

Amin Vahidi-Moghaddam, Sayed Pedram Haeri Boroujeni, Iman Jebellat, Ehsan Jebellat

One of the main challenges in modern control applications, particularly in robot and vehicle motion control, is achieving accurate, fast, and safe movement. To address this, optimal control policies have been developed to enforce safety while ensuring high performance. Since basic first-principles models of real systems are often available, model-based contr

Mohamad N. Nasser, Nafaa Chbili

In this article, we introduce the singular twin monoid and its corresponding group, constructed from both algebraic and topological perspectives. We then classify all complex homogeneous $2$-local representations of this constructed group. Moreover, we study the irreducibility of these representations and provide clear conditions under which irreducibility h

Santhosh Kumar Ravindran

The rise of artificial intelligence (AI) as super-capable assistants has transformed productivity and decision-making across domains. Yet, this integration raises critical concerns about value alignment - ensuring AI behaviors remain consistent with human ethics and intentions. A key risk is value drift, where AI systems deviate from aligned values due to ev

Ziyan Wang, Zheng Wang, Xingwei Qu, Qi Cheng

Reinforcement learning (RL) has become central to enhancing reasoning in large language models (LLMs). Yet on-policy algorithms such as Group Relative Policy Optimization (GRPO) often suffer in early training: noisy gradients from low-quality rollouts lead to unstable updates and inefficient exploration. We introduce Slow-Fast Policy Optimization (SFPO), a s

Zitian Gao, Haoming Luo, Lynx Chen, Jason Klein Liu

Recent studies have shown that diffusion language models achieve remarkable data efficiency under limited-data constraints, yet the underlying mechanisms remain unclear. In this work, we perform extensive ablation experiments to disentangle the sources of this efficiency. Our results show that random masking of input tokens plays the dominant role. We furthe

Rémy Degenne

The probability folder of Mathlib, Lean's mathematical library, makes a heavy use of Markov kernels. We present their definition and properties and describe the formalization of the disintegration theorem for Markov kernels. That theorem is used to define conditional probability distributions of random variables as well as posterior distributions. We then ex

Zongyin Deng, Qing Zhou, Yuhao Fang, Zijian Wang

This work presents TV-LoRA, a novel method for low-dose sparse-view CT reconstruction that combines a diffusion generative prior (NCSN++ with SDE modeling) and multi-regularization constraints, including anisotropic TV and nuclear norm (LoRA), within an ADMM framework. To address ill-posedness and texture loss under extremely sparse views, TV-LoRA integrates

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

We propose a new definition of characteristic polynomials of tensors based on a partition function of Grassmann variables. This new notion of characteristic polynomial addresses general tensors including totally antisymmetric ones, but not totally symmetric ones. Drawing an analogy with matrix eigenvalues obtained from the roots of their characteristic polyn

Junxi Yan, Zixi Wei, Qingyao Ai, Yiqun Liu

The cross-entropy scaling law has long served as a key tool for guiding the development of large language models. It shows that cross-entropy loss decreases in a predictable power-law rate as the model size increases. However, recent evidence indicates that this law breaks down at very large scales: the loss decreases more slowly than expected, which causes

Zheng Chen, Kewei Zhang, Xiaoyang Liu, Weihang Zhang

Demoir\'eing aims to remove moir\'e artifacts that often occur in images. While recent deep learning-based methods have achieved promising results, they typically require substantial computational resources, limiting their deployment on edge devices. Model quantization offers a compelling solution. However, directly applying existing quantization methods to

Jingxiang Zhang, Lujia Zhong

Large Language Models (LLMs) are increasingly expected to navigate the nuances of human emotion. While research confirms that LLMs can simulate emotional intelligence, their internal emotional mechanisms remain largely unexplored. This paper investigates the latent emotional representations within modern LLMs by asking: how, where, and for how long is emotio

Chetraj Pandey, Jinsu Hong, Anli Ji, Rafal A. Angryk

The prediction of solar flares is typically formulated as a binary classification task, distinguishing events as either Flare (FL) or No-Flare (NF) according to a specified threshold (for example, greater than or equal to C-class, M-class, or X-class). However, this binary framework neglects the inherent ordinal relationships among the sub-classes contained

Subhajit Sarkar, Gabriela Wójtowicz, Bartłomiej Gardas, Marek M. Rams

We examine the stationary--state equations for lattices with generalized Markovian dephasing and relaxation. When the Hamiltonian is quadratic, the single--particle correlation matrix has a closed system of equations even in the presence of these two processes. The resulting equations have a vectorized form related to, but distinct from, Lyapunov's equation.

T. T. Sergeev, E. S. Andrianov, A. A. Zyablovsky

We consider a quasi-PT-symmetric system of two resonators, one of which interacts with a finite-size environment. The interaction with the environment leads to energy losses in the resonators, and the finite size of the environment leads to a non-Markovian dynamics of the relaxation process. We demonstrate that non-Markovian processes in the quasi-PT-symmetr

Tong Mao, Jinchao Xu

We establish two related but logically distinct results for shallow ReLU$^k$ neural networks on the unit sphere $\SS^d$. First, for an arbitrary set of inner neural-network parameters, the best $\mathcal{L}^2(\SS^d)$ approximation of a fixed target function with smoothness $r>\tfrac{d+2k+1}{2}$ admits an asymptotic lower bound given by a constant multiple of

Youngjun Park, Minhyeok Kang, Chae-Yeun Park, Joonsuk Huh

Many quantum algorithms for ground-state preparation and energy estimation require the implementation of high-degree polynomials of a Hamiltonian to achieve better convergence rates. Their circuit implementation typically relies on quantum signal processing (QSP), whose circuit depth is proportional to the degree of the polynomial. Previous studies exploit t

Subhodip Panda, Varun M S, Shreyans Jain, Sarthak Kumar Maharana

For a responsible and safe deployment of diffusion models in various domains, regulating the generated outputs from these models is desirable because such models could generate undesired, violent, and obscene outputs. To tackle this problem, recent works use machine unlearning methodology to forget training data points containing these undesired features fro

Zhenyu Pan, Yucheng Lu, Han Liu

We present MetaFind, a scene-aware tri-modal compositional retrieval framework designed to enhance scene generation in the metaverse by retrieving 3D assets from large-scale repositories. MetaFind addresses two core challenges: (i) inconsistent asset retrieval that overlooks spatial, semantic, and stylistic constraints, and (ii) the absence of a standardized

Rijha Safdar, Danyail Mateen, Syed Taha Ali, Wajahat Hussain

Large Language Models (LLMs) have demonstrated exceptional progress in multiple domains of software engineering including software vulnerability detection. Using LLMs to automate vulnerability detection in the wild is an important and relatively under-explored problem. In this paper we propose QuiLL, the first comprehensive evaluation framework for real-worl

Shijie Xu, Zhizhong Zhang, Yan Huang, Tianyi Wang

Spintronics has emerged as a revolutionary frontier in the pursuit of faster, more energy-efficient, and technologically advanced electronics.

Shaohua Xue, Yuxuan Liu, Li-xin Li

We investigate the dilaton fluctuations near the string based on three classes of solutions of the 3D C-metric within the framework of the string-world holography. As a setup of holography, we focus on the asymptotic symmetry, recover the Virasoro algebra by central extension and get the central charge of the AdS3. Then we reduce the gravity on the brane as

Haijin Xie, Gongquan Zhang

New quality productive forces (NQPF), digital economy advancement, and artificial intelligence (AI) technologies are becoming crucial for promoting sustainable urban development. This study proposes a Multi-head Attention Deep & Cross Network (MADCN) framework, combining feature interaction modeling and attention mechanisms, to predict urban carbon emissions

Yijie Yang, Jian Shi, Dan Wang, Chenye Wu

The rapid growth of AI applications is dramatically increasing data center energy demand, exacerbating carbon emissions, and necessitating a shift towards 24/7 carbon-free energy (CFE). Unlike traditional annual energy matching, 24/7 CFE requires matching real-time electricity consumption with clean energy generation every hour, presenting significant challe

Behrooz Farkiani, Fan Liu, Patrick Crowley

Portable service mesh implementations enable Layer 4 to Layer 7 policy enforcement across heterogeneous infrastructures, yet they depend on the underlying network's connectivity and policies. Layer 3 network policies govern IP traffic regardless of whether upper layers authorize the flow. While these policies are integral to security, correct enforcement oft

Lele Liao, Qile Zhang, Ruofan Wu, Guanhua Fang

Evaluating large language models (LLMs) on comprehensive benchmarks is a cornerstone of their development, yet it's often computationally and financially prohibitive. While Item Response Theory (IRT) offers a promising path toward data-efficient evaluation by disentangling model capability from item difficulty, existing IRT-based methods are hampered by sign

Sukanya Samanta, Manohar Reddy

The interdiction of escaping adversaries in urban networks is a critical security challenge. State-of-the-art game-theoretic models, such as the Escape Interdiction Game (EIG), provide comprehensive frameworks but assume a highly dynamic interaction and entail significant computational complexity, which can be prohibitive for real-time applications. This pap

Xiangyu Guo, Ajay Bansal

This paper presents examples of using integrity constraints in stableKanren to encode numeric computations for problem solving. Then, we use one of the examples to introduce multiple ways to infuse heuristic knowledge and reduce solving time. stableKanren is an extension of miniKanren that supports normal logic programs under stable model semantics. stableKa

Aparna Nair-Kanneganti, Trevor J. Chan, Shir Goldfinger, Emily Mackay

Despite huge advances, LLMs still lack convenient and reliable methods to quantify the uncertainty in their responses, making them difficult to trust in high-stakes applications. One of the simplest approaches to eliciting more accurate answers is to select the mode of many responses, a technique known as ensembling. In this work, we expand on typical ensemb

Daichi Imagawa, Keiju Murata, Daisuke Yamamoto

We investigate the phenomenon of spacetime-localized response in a quantum critical spin system, with particular attention to how it depends on the spatial profile and operator content of the applied perturbation, as well as its robustness against increase of amplitude and temporal discretization. Motivated by recent theoretical proposals linking such respon

Kotaro J. Nishimura, Yuichi Sakumura, Kazushi Ikeda

Class imbalance is a common challenge in real-world binary classification tasks, often leading to predictions biased toward the majority class and reduced recognition of the minority class. This issue is particularly critical in domains such as medical diagnosis and anomaly detection, where correct classification of minority classes is essential. Conventiona

Yunfan Zhang, Kathleen McKeown, Smaranda Muresan

Large Language Models (LLMs) are typically trained to reflect a relatively uniform set of values, which limits their applicability to tasks that require understanding of nuanced human perspectives. Recent research has underscored the importance of enabling LLMs to support steerable pluralism -- the capacity to adopt a specific perspective and align generated

Bingtao Yang, Yujia Wang, Mengzhi Jiao, Hongwei Huo

Post-training quantization for reducing the storage of deep neural network models has been demonstrated to be an effective way in various tasks. However, low-bit quantization while maintaining model accuracy is a challenging problem. In this paper, we present a range estimation method to improve the quantization performance for post-training quantization. We

Matheus J. Ota, Ricardo Fukasawa

We study the vehicle routing problem with stochastic demands (VRPSD), an important variant of the classical capacitated vehicle routing problem in which customer demands are modeled as random variables. We develop the first algorithm for the VRPSD in the case where the demands are given by an empirical probability distribution of scenarios -- a data-driven v

Dan Leonte, Raphaël Huser, Almut E. D. Veraart

The growing availability of large and complex datasets has increased interest in temporal stochastic processes that can capture stylized facts such as marginal skewness, non-Gaussian tails, long memory, and even non-Markovian dynamics. While such models are often easy to simulate from, parameter estimation remains challenging. Simulation-based inference (SBI

Ayudh Saxena, Harsh Shah, Sandeep Routray, Rishi Rajesh Shah

Learning robust robotic control policies remains a major challenge due to the high cost of collecting labeled data, limited generalization to unseen environments, and difficulties in planning over long horizons. While Vision-Language-Action (VLA) models offer a promising solution by grounding natural language instructions into single-step control commands, t

Xu Shen, Song Wang, Zhen Tan, Laura Yao

Large language models (LLMs) increasingly rely on Chain-of-Thought (CoT) prompting to improve problem-solving and provide seemingly transparent explanations. However, growing evidence shows that CoT often fail to faithfully represent the underlying reasoning process, raising concerns about their reliability in high-risk applications. Although prior studies h

Bin Lei, Nuo Xu, Ali Payani, Mingyi Hong

Multimodal large language models (MLLMs) have markedly expanded the competence of graphical user-interface (GUI) systems, propelling them beyond controlled simulations into complex, real-world environments across diverse platforms. However, practical usefulness is still bounded by the reliability of visual grounding, i.e., mapping textual references to exact

Viet Hoang Pham, Hyo-Sung Ahn

This paper introduces a comprehensive strategy that integrates traffic perimeter control with traffic signal control to alleviate congestion in an urban traffic network (UTN). The strategy is formulated as a lexicographic multi-objective optimization problem, starting with the regulation of traffic inflows at boundary junctions to maximize the capacity while

Mohammad Salman, Hadi Zayyani, Hasan Abu Hilal, Mostafa Rashdan

This letter presents a novel method for estimating the position, velocity, and acceleration of a moving target using range-based measurements. Although most existing studies focus on position and velocity estimation, the framework of this letter is extended to include acceleration. To achieve this, we propose using the derivative of the range rate, in additi

Miguel Arratia, Jiajun Huang, Sean Preins, Sebastian Ritter

We developed a compact and rugged muon detector designed for deployment in boreholes. The detector uses a SiPM-on-tile approach in which silicon photomultipliers are directly coupled to scintillator tiles, thereby eliminating the need for wavelength-shifting fibers and long scintillator bars. The modular design is based on a 64-channel unit, 140~cm in length

Almamoon Alauthman, Abeer Al-Hyari

The paper addresses optimizing two of the most important performance parameters, packet loss, and delay, in the critical path optimization of LTE and 5G networks using metaheuristic algorithms to play a vital role in the smartphone user experience. In this context, nine metaheuristic algorithms, such as WOA, PSO, and ABC, have been studied for their effectiv

Linn Bieske, Carla Lorente

Recent advances in image editing have shifted from manual pixel manipulation to employing deep learning methods like stable diffusion models, which now leverage cross-attention mechanisms for text-driven control. This transition has simplified the editing process but also introduced variability in results, such as inconsistent hair color changes. Our researc

Zirui Wang, Jiajun Wu, Braden Teitge, Jessalyn Holodinsky

Large language models (LLMs) have become increasingly popular in medical domains to assist physicians with a variety of clinical and operational tasks. Given the fast-paced and high-stakes environment of emergency departments (EDs), small language models (SLMs), characterized by a reduction in parameter count compared to LLMs, offer significant potential due

Nelvin Tan, James Asikin Cheung, Yu-Ching Shih, Dong Yang

Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. More recently, they have been shown to be very effective in textual classification tasks, motivating the need to explain the LLMs' decisions. Motivated by practical constrains where LLMs are black-b

Lei Yu

The isoperimetric problem is a classic topic in geometric measure theory, yet critical questions regarding the characterization of optimal solutions -- even asymptotically optimal ones -- remain largely unresolved. In this paper, we investigate the large deviations asymptotics for the isoperimetric problem on the product Riemannian manifold $M^{n}$ endowed w

Meng Chen, Sicheng Ding

We prove that the $5$-canonical map of every minimal projective $3$-fold $X$ with $K_X^3\geq 86$ is stably birational onto its image, which loosens previous requirements $K_X^3>4355^3$ and $K_X^3>12^3$ respectively given by Todorov and Chen. The essential technical ingredient of this paper is an efficient utilization of a moving divisor which grows from glob

Xinhao Yao, Lu Yu, Xiaolin Hu, Fengwei Teng

The ongoing debate on whether reinforcement learning with verifiable rewards (RLVR) expands or shrinks the reasoning capabilities of large language models (LLMs) remains unresolved. Some studies contend that RLVR mainly improves sampling efficiency but at the expense of diversity and exploratory capacity, resulting in capability boundary shrinkage. In contra

Runze Wang

In a graph $G$, we define a set of vertices to be a \emph{strong hub set} if for any two vertices in $G$, we can find a path between them whose internal vertices are all in this set. We define the \emph{strong hub cover pebbling number} of $G$, denoted by $h_s^*(G)$, to be the smallest $t$ such that for any initial configuration with $t$ pebbles on $G$, we c

Jinseong Park, Yujin Choi, Jaewook Lee

With the increasing need to safeguard data privacy in machine learning models, differential privacy (DP) is one of the major frameworks to build privacy-preserving models. Support Vector Machines (SVMs) are widely used traditional machine learning models due to their robust margin guarantees and strong empirical performance in binary classification. However,

Yuchen Ding, Zhiwei Wang

We denote by $P^+(n)$ the largest prime factor of the integer $n$. In 1935, Erd\H os studied the quantity $T_c(x)$ defined by $$ T_c(x)=\big|\big\{p\le x: P^+(p-1)\ge p^c\big\}\big|, $$ and he proved $$ \limsup_{x\rightarrow \infty}\frac{T_c(x)}{\pi(x)}\rightarrow 0, \quad \text{as~}c\rightarrow 1. $$ Recently, Ding gave a quantitative form of Erd\H os' resu

Miguel Angel Guadarrama-García

In this note is given an algebraic solution to the problem 1997-6 proposed by D. A. Panov in the list of Arnold's problems \cite{Arnld2b}. In particular, it is shown that there does not exist a real polynomial function $f$ on the real euclidean plane, whose Hessian is positive in an open set bordered by smooth connected curve, and the parabolic curve of the