November 2025 arXiv papers — page 115

Dominik Gdesz

We generalize the $\alpha$-invariant introduced by Atiyah and Rees to an invariant of real spin bundles and use it to classify real bundles over $\mathbb{C}\mathrm{P}^3$ admitting spin structure. We apply this result to show that $\mathbb{R}\mathrm{P}^7$ can be smoothly embedded in $\mathbb{R}\mathrm{P}^{11}$.

Chaitanya Kumar Konda, Piyush Agrawal, Shivansh Srivastava, Manish Agrawal

This manuscript addresses the challenge of designing functionally graded materials (FGMs) for arbitrary-shaped domains. Towards this goal, the present work proposes a generic volume fraction profile generation algorithm based on Gaussian Process Regression (GPR). The proposed algorithm can handle complex-shaped domains and generate smooth FGM profiles while

Wang Luo, Di Wu, Hengyuan Na, Yinlin Zhu

Point cloud completion aims to reconstruct complete 3D shapes from partial observations, which is a challenging problem due to severe occlusions and missing geometry. Despite recent advances in multimodal techniques that leverage complementary RGB images to compensate for missing geometry, most methods still follow a Completion-by-Inpainting paradigm, synthe

Kaiyue Zhao, Dingqi Chen, Shaoyu Wang, Pan Hu

DatalogMTL extends the classical Datalog language with metric temporal logic (MTL), enabling expressive reasoning over temporal data. While existing reasoning approaches, such as materialisation based and automata based methods, offer soundness and completeness, they lack support for handling efficient dynamic updates, a crucial requirement for real-world ap

Yongheng Wang, Xiemin Mo, Tao Liu

Renewable charging stations (RCSs) that co-locate electric-vehicle (EV) charging with distributed generation (DG) can raise renewable utilization and improve distribution-network (DN) efficiency, yet their variability and the siting-dependent charging demand can overload feeders if placed poorly. This paper proposes a tri-level, two-stage stochastic-robust o

Sayantan Pramanik, M Girish Chandra

In this paper, we focus on the task of optimizing the parameters in Parametrized Quantum Circuits (PQCs). While popular algorithms, such as Simultaneous Perturbation Stochastic Approximation (SPSA), limit the number of circuit-execution to two per iteration, irrespective of the number of parameters in the circuit, they have their own challenges. These method

Quach Thi Thai Binh, Thuan Phuoc, Xuan Hai, Thang Bach Phan

The extensive use of pesticides and synthetic dyes poses critical threats to food safety, human health, and environmental sustainability, necessitating rapid and reliable detection methods. Raman spectroscopy offers molecularly specific fingerprints but suffers from spectral noise, fluorescence background, and band overlap, limiting its real-world applicabil

Anders Björn, Jana Björn, David Manolis

We study boundary regularity at the infinity point $\boldsymbol{\infty}$ for nonlinear elliptic equations of $p$-Laplace type in unbounded open sets $\Omega \subset \mathbf{R}^n$. We consider the case $p \ge n \ge 2$ and characterize the regularity at $\boldsymbol{\infty}$ by means of Wiener-type integrals. Our approach uses circular inversion, which maps $\

Xi-guang Wang, Tian-xiang Lu, Guang-hua Guo, Jamal Berakdar

A mechanism for electrically tunable PT-symmetric magnonic lasing and anti-lasing is proposed along with a device consisting of a current-biased region in a magnetically ordered planar waveguide. Within the bias area, several heavy-metal wires carrying dc charge current are periodically attached to the waveguide and exert so spatially periodic spin-orbit tor

Jie Chen, Liangmin Wang

Fuzzing is a widely used technique for detecting vulnerabilities in smart contracts, which generates transaction sequences to explore the execution paths of smart contracts. However, existing fuzzers are falling short in detecting sophisticated vulnerabilities that require specific attack transaction sequences with proper inputs to trigger, as they (i) prior

Vlad-Matei Angheluţă, Bogdan Gheorghe, Daniel Ioan, Ionela Prodan

This paper investigates the synthesis of controllers for displacement-based formation control in the presence of bounded disturbances, specifically focusing on uncertainties originating from measurement noise. While the literature frequently addresses such problems using stochastic frameworks, this work proposes a deterministic methodology grounded in set-th

Shuo Yin, Zhiyuan Yin, Yuqing Hou, Rui Liu

Hash center-based deep hashing methods improve upon pairwise or triplet-based approaches by assigning fixed hash centers to each class as learning targets, thereby avoiding the inefficiency of local similarity optimization. However, random center initialization often disregards inter-class semantic relationships. While existing two-stage methods mitigate thi

Jonas Pleyer

Cellular Agent-Based Models are commonly employed to describe a variety biological systems. Over the course of the past years, many modeling tools have emerged which solve particular research questions. In this short opinion piece, we argue that existing frameworks lack flexibility compared to the inherent underlying complexity that they should be able to re

Wenbin Mai, Minghui Liwang, Xinlei Yi, Xiaoyu Xia

Ensuring safe, robust, and scalable motion planning for multi-agent systems in dynamic and uncertain environments is a persistent challenge, driven by complex inter-agent interactions, stochastic disturbances, and model uncertainties. To overcome these challenges, particularly the computational complexity of coupled decision-making and the need for proactive

Yaocheng Zhang, Haohuan Huang, Zijun Song, Yuanheng Zhu

Tool-Integrated Reasoning (TIR) with search engines enables large language models to iteratively retrieve up-to-date external knowledge, enhancing adaptability and generalization in complex question-answering tasks. However, existing search agent pipelines typically depend on reinforcement learning based optimization, which often suffers from sparse outcome

Jonathan Chirinos-Rodríguez, Cédric Févotte, Emmanuel Soubies

In this paper, we study (noisy) linear systems, and their $\ell_0$-regularized optimization problems, coupled with general data fidelity terms. Recent approaches for solving this class of problems have proposed to consider non-convex exact continuous relaxations that preserve global minimizers while reducing the number of local minimizers. Within this framew

M. Barbillon, A. Recio-Blanco, P. de Laverny, P. A. Palicio

3D maps of interstellar dust are crucial for understanding the structure of the Milky Way interstellar medium to apply correction to astrophysical observations affected by dust. We aim at providing new extinction estimates in the Gaia BP/RP bands to study the dust distribution in the disc, to provide new views of the spatial distribution of extinction and to

Thong Bach, Dung Nguyen, Thao Minh Le, Truyen Tran

Large language models exhibit systematic vulnerabilities to adversarial attacks despite extensive safety alignment. We provide a mechanistic analysis revealing that position-dependent gradient weakening during autoregressive training creates signal decay, leading to incomplete safety learning where safety training fails to transform model preferences in late

Gustavo Polleti, Marlesson Santana, Eduardo Fontes

We introduced a multimodal foundational model for financial transactions that integrates both structured attributes and unstructured textual descriptions into a unified representation. By adapting masked language modeling to transaction sequences, we demonstrated that our approach not only outperforms classical feature engineering and discrete event sequence

Ramin Mehrabifard, Adriana Misuthova, Zdenko Machala

Cold air plasma typically generates reactive oxygen and nitrogen species (RONS), which are transported into water to produce plasma-activated water (PAW). This study examines the effect of PAW produced by three different plasma systems on lettuce: transient spark, fountain dielectric barrier discharge, and microwave plasma. Physiochemical PAW properties and

Jianyi Yu, Tengxiao Wang, Yuxuan Wang, Xiang Fu

Compute-in-memory (CIM) techniques are widely employed in energy-efficient artificial intelligent (AI) processors. They alleviate power and latency bottlenecks caused by extensive data movements between compute and storage units. To extend these benefits to Transformer, this brief proposes a digital CIM macro to compute attention score. To eliminate dynamic

Kaixiang Yang, Boyang Shen, Xin Li, Yuchen Dai

Text-guided image editing has advanced rapidly with the rise of diffusion models. While flow-based inversion-free methods offer high efficiency by avoiding latent inversion, they often fail to effectively integrate source information, leading to poor background preservation, spatial inconsistencies, and over-editing due to the lack of effective integration o

Neelotpal Dutta, Tianyu Zhang, Tao Liu, Yongxue Chen

Existing curved-layer-based process planning methods for multi-axis manufacturing address collisions only indirectly and generate toolpaths in a post-processing step, leaving toolpath geometry uncontrolled during optimization. We present an implicit neural field-based framework for multi-axis process planning that overcomes these limitations by embedding bot

Yuqi Xie, Shuhan Ye, Yi Yu, Chong Wang

The integration of event cameras and spiking neural networks (SNNs) promises energy-efficient visual intelligence, yet scarce event data and the sparsity of DVS outputs hinder effective training. Prior knowledge transfers from RGB to DVS often underperform because the distribution gap between modalities is substantial. In this work, we present Time-step Mixu

Jiayu Li, Yunhan Zhao, Xiang Zheng, Zonghuan Xu

Vision-Language-Action (VLA) models enable robots to interpret natural-language instructions and perform diverse tasks, yet their integration of perception, language, and control introduces new safety vulnerabilities. Despite growing interest in attacking such models, the effectiveness of existing techniques remains unclear due to the absence of a unified ev

Advik Sinha, Akshay Arjun, Abhijit Das, Joyjit Mukherjee

This work aims to develop a resource-efficient solution for obstacle-avoiding tracking control of a planar snake robot in a densely cluttered environment with obstacles. Particularly, Neuro-Evolution of Augmenting Topologies (NEAT) has been employed to generate dynamic gait parameters for the serpenoid gait function, which is implemented on the joint angles

Lifeng Shen, Liang Peng, Ruiwen Liu, Shuyin Xia

Modeling normal behavior in dynamic, nonlinear time series data is challenging for effective anomaly detection. Traditional methods, such as nearest neighbor and clustering approaches, often depend on rigid assumptions, such as a predefined number of reliable neighbors or clusters, which frequently break down in complex temporal scenarios. To address these l

W. Bock, L. Cristofaro, J. L. da Silva

In this paper, we investigate the stochastic counterpart of the generalized Wright analysis introduced in Beghin et al.~ in Integral Equations and Operator Theory, {\bf 97}, 2025. We define a new class of non-Gaussian and non-Markovian processes, called the generalized Fox-$H$ process, which extends well-known processes such as fractional Brownian motion and

Elias Niepötter, Adrian Grimm, Torbjørn Cunis

As the relevance of control systems capable of dealing with multiple objectives rises (e.g. being economic while maintaining a certain performance), multi-objective Switched Model Predictive Control combines all the advantages of Model Predictive Control while dealing with multiple objectives. We propose two novel frameworks, a nominal and a robust framework

Atreyee Bhattacharya, Sayoojya Prakash

Quasi-Einstein manifolds are well-studied generalizations of Einstein manifolds. This includes gradient Ricci solitons and has a natural correspondence with the warped product Einstein manifolds. A quasi-Einstein metric is said to be rigid when it reduces to an Einstein metric. On a different note, Einstein metrics can be viewed as fixed points of the Ricci

Jialiang Wang, Xiong Zhou, Xianming Liu, Gangfeng Hu

Mitigating the negative impact of noisy labels has been aperennial issue in supervised learning. Robust loss functions have emerged as a prevalent solution to this problem. In this work, we introduce the Variation Ratio as a novel property related to the robustness of loss functions, and propose a new family of robust loss functions, termed Variation-Bounded

Seokwon Song, Minsu Park, Gunhee Kim

Source attribution aims to enhance the reliability of AI-generated answers by including references for each statement, helping users validate the provided answers. However, existing work has primarily focused on text-only scenario and largely overlooked the role of multimodality. We introduce MAVIS, the first benchmark designed to evaluate multimodal source

Caroline Guinet, Sepideh Mirrahimi, Jean-Michel Roquejoffre

In this paper, we study an integro-differential equation which describes the evolutionary dynamics of a population structured by a phenotypic trait. This population undergoes asexual reproduction, competition, selection, and mutation. We provide an asymptotic analysis of the model, assuming that the mutations have small effects. A standard approach for the a

Pinxue Guo, Chongruo Wu, Xinyu Zhou, Lingyi Hong

Multimodal Large Language Models (MLLMs) have unlocked powerful cross-modal capabilities, but still significantly suffer from hallucinations. As such, accurate detection of hallucinations in MLLMs is imperative for ensuring their reliability in practical applications. To this end, guided by the principle of "Seeing is Believing", we introduce VBackChecker, a

Sahar Moghimian Hoosh, Ilia Kamyshev, Henni Ouerdane

Non-intrusive load monitoring (NILM) is an advanced load monitoring technique that uses data-driven algorithms to disaggregate the total power consumption of a household into the consumption of individual appliances. However, real-world NILM deployment still faces major challenges, including overfitting, low model generalization, and disaggregating a large n

Zheng Wang, Yifu Li, Yuchao Mei, Xinyu Sui

This paper presents a transformer-based three- transmission-line (Tline) series Doherty power amplifier (PA) implemented in 65-nm CMOS, targeting broadband K/Ka-band applications. By integrating an impedance-scaling network into the output matching structure, the design enables effective load modulation and reduced impedance transformation ratio (ITR) at pow

Muntahi Safwan Mahfi, Md. Manzurul Hasan, Gahangir Hossain

In this paper, we introduce OBHS (Optimized Block Huffman Scheme), a novel lossless audio compression algorithm tailored for real-time streaming applications. OBHS leverages block-wise Huffman coding with canonical code representation and intelligent fallback mechanisms to achieve high compression ratios while maintaining low computational complexity. Our al

Karol C. Jurzec, Tomasz Szydlo, Maciej Wielgosz

Spiking neural networks (SNNs) communicate via discrete spikes in time rather than continuous activations. Their event-driven nature offers advantages for temporal processing and energy efficiency on resource-constrained hardware, but training and deployment remain challenging. We present a lightweight C-based runtime for SNN inference on edge devices and op

Dongdong Zhao, Ranxin Fang, Changtian Song, Zhihui Liu

Open Set Recognition (OSR) requires models not only to accurately classify known classes but also to effectively reject unknown samples. However, when unknown samples are semantically similar to known classes, inter-class overlap in the feature space often causes models to assign unjustifiably high confidence to them, leading to misclassification as known cl

Letian Chen, Runhan Shi, Gufeng Yu, Yang Yang

Aligning molecular sequence representations (e.g., SMILES notations) with textual descriptions is critical for applications spanning drug discovery, materials design, and automated chemical literature analysis. Existing methodologies typically treat molecular captioning (molecule-to-text) and text-based molecular design (text-to-molecule) as separate tasks,

A. Zahra, S. A. Mardan, Muhammad Bilal Riaz, Javlon Rayimbaev

This study examines the dynamical evolution of self-gravitating systems in the presence of exotic matter within the framework of $f(R)$ gravity. Specifically, we have adopted the Starobinsky model $f(R) = R + \alpha R^2$, which incorporates higher-order curvature corrections to describe nonlinear gravitational behavior. The analysis focuses on the nonlinear

Qingyu Zhang, Chunlei Xin, Xuanang Chen, Yaojie Lu

Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a significant challenge for even state-of-the-art Large Language Models (LLMs). A lack of task-specific data often limits previous works, and direct LLM application suffers from strategic b

Zhenqiang Ye, Jinjie Lu, Tianlong Gu, Fengrui Hao

Graph neural networks (GNNs) have emerged as the mainstream paradigm for graph representation learning due to their effective message aggregation. However, this advantage also amplifies biases inherent in graph topology, raising fairness concerns. Existing fairness-aware GNNs provide satisfactory performance on fairness metrics such as Statistical Parity and

Quanxing Xu, Ling Zhou, Feifei Zhang, Jinyu Tian

Large Language Models (LLMs) have become a crucial tool in Visual Question Answering (VQA) for handling knowledge-intensive questions in few-shot or zero-shot scenarios. However, their reliance on massive training datasets often causes them to inherit language biases during the acquisition of knowledge. This limitation imposes two key constraints on existing

Bingbing Wang, Zhixin Bai, Zhengda Jin, Zihan Wang

The rapid proliferation of multimodal social media content has driven research in Multimodal Conversational Stance Detection (MCSD), which aims to interpret users' attitudes toward specific targets within complex discussions. However, existing studies remain limited by: **1) pseudo-multimodality**, where visual cues appear only in source posts while comments

Hongyang Yang, Xiao-Yang Liu, Qingwei Wu

Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check all S&P 500 stocks (the Standard & Poor's 500). In this paper, we propose a practical scheme that recommends stocks from S&P 500 using machine learning. Our basic idea is to buy and

C. A. S. Vieira, B. J. Santos, J. G. Duque, E. M. Bittar

The Iridium-based oxides are the subject of great recent interest due to the non-conventional physics that may emerge from the strong spin-orbit coupling present in 5d ions. Here, we explore the coupling between Ir and Co in the La2-xBaxCoIrO6 perovskites (x = 0, 0.5, 0.75 and 1.0), where the structural, electronic, and magnetic properties of the series are

Md Rahat Hasan, Kazi Ahmed Akbar Munim, Md. Forkan Uddin

We formulate an optimization problem for joint RU allocation and C-SR to maximize the throughput of a multi-AP coordinated WiFi system. The optimization problem is found to be a non-linear integer programming problem. We solve the problem for several network scenarios using an optimization tool. The joint design significantly improves throughput compared to

Samer Houri, Rachid Haouari, Bart P. Weekers, Veronique Rochus

This work treats the dynamics of pairs of microelectromechanical ultrasound transducers (MUTs) that are immersed in water and acoustically coupled through the fluid medium. A series of these transducer pairs with varying diameters (and thus resonance frequency) and pitch separation (and thus coupling strength) are fabricated and measured. The work presented

Fengming Yu, Qingyu Meng, Haiwei Pan, Kejia Zhang

With the rapid development of deep learning, large language models have shown strong capabilities in complex reasoning tasks such as mathematical equation solving. However, their substantial computational and storage costs hinder practical deployment. This paper proposes a lightweight optimization method that integrates dynamic attention head pruning with kn

Zejiao Liu, Junqi Tu, Yitian Hong, Luolin Xiong

In cooperative Multi-Agent Reinforcement Learning (MARL), efficient exploration is crucial for optimizing the performance of joint policy. However, existing methods often update joint policies via independent agent exploration, without coordination among agents, which inherently constrains the expressive capacity and exploration of joint policies. To address

Yi Wang, Ruoyi Fang, Anzhuo Xie, Hanrui Feng

This study addresses the problem of dynamic anomaly detection in accounting transactions and proposes a real-time detection method based on a Transformer to tackle the challenges of hidden abnormal behaviors and high timeliness requirements in complex trading environments. The approach first models accounting transaction data by representing multi-dimensiona

Wanlong Fang, Tianle Zhang, Alvin Chan

Multimodal learning often relies on aligning representations across modalities to enable effective information integration, an approach traditionally assumed to be universally beneficial. However, prior research has primarily taken an observational approach, examining naturally occurring alignment in multimodal data and exploring its correlation with model p

Hongyang Yang, Xiao-Yang Liu, Shan Zhong, Anwar Walid

Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent an

Partha Kumar Das, Xuchen Liu, Sascha Hilgenfeldt

Systematic deflection of microparticles off of initial streamlines is a fundamental task in microfluidics, aiming at applications including sorting, accumulation, or capture of the transported particles. In a large class of setups, including Deterministic Lateral Displacement and porous media filtering, particles in non-inertial (Stokes) flows are deflected

Luxin Xu, Changliang Ren

Quantum batteries have attracted significant attention as efficient quantum energy storage devices.In this work, we propose a nonlinear two-photon driving quantum battery model featuring nonreciprocal dynamics that enables a highly efficient unidirectional charging mechanism through environmental engineering. Using a Markovian master-equation approach, we de

Ruiqi Cheng, Huijun Di, Jian Li, Feng Liu

Accurate 3D scene motion perception significantly enhances the safety and reliability of an autonomous driving system. Benefiting from its all-weather operational capability and unique perceptual properties, 4D mmWave radar has emerged as an essential component in advanced autonomous driving. However, sparse and noisy radar points often lead to imprecise mot

Piotr Pęzik, Konrad Kaczyński, Maria Szymańska, Filip Żarnecki

Large Language Models (LLMs) are pretrained on textual data up to a specific temporal cutoff. This creates a strict knowledge boundary beyond which models cannot provide accurate information without querying external sources. More subtly, when this limitation is unknown or ignored, LLMs may inadvertently blend outdated time-sensitive information with general

Nabarun Mandal, Sagnik Chakraborty, Ranjeet Singh, Jhionathan de Lima

Two-dimensional (2D) materials, due to their remarkable physical and chemical properties, hold significant potential for future optical and electrical applications. In this study, the synthesis of 2D phlogopite (magnesium-rich mica) via liquid-phase exfoliation (LPE) is reported using an efficient and scalable procedure. XRD structural analysis revealed a pr

Chengyi Liu, Xiao Chen, Shijie Wang, Wenqi Fan

In the era of information explosion, Recommender Systems (RS) are essential for alleviating information overload and providing personalized user experiences. Recent advances in diffusion-based generative recommenders have shown promise in capturing the dynamic nature of user preferences. These approaches explore a broader range of user interests by progressi

Lanxue Zhang, Yuqiang Xie, Fang Fang, Fanglong Dong

Large Language Models demonstrate strong reasoning capabilities, which can be effectively compressed into smaller models. However, existing datasets and fine-tuning approaches still face challenges that lead to catastrophic forgetting, particularly for models smaller than 8B. First, most datasets typically ignore the relationship between training data knowle

Neda Bagheri Renani, Maryam Jaefarzadeh, Daniel Sevcovic

We present a head-to-head evaluation of the Improved Inexact--Newton--Smart (INS) algorithm against a primal--dual interior-point framework for large-scale nonlinear optimization. On extensive synthetic benchmarks, the interior-point method converges with roughly one third fewer iterations and about one half the computation time relative to INS, while attain

Junyi Xie

In this note, we present recent progress on rigidity problems in one-dimensional complex dynamics, including the proof of Dynamical Andr\'e-Oort conjecture for curves and generic injectivity of multiplier spectrum. The proofs combine ideas from algebraic geometry, Arakelov geometry and complex dynamics.

Felipe Fujita, Hideyuki Takada

In this paper, we explore the effectiveness of combining fine-tuning and backtranslation on a small Japanese corpus for neural machine translation. Starting from a baseline English{\textrightarrow}Japanese model (COMET = 0.460), we first apply backtranslation (BT) using synthetic data generated from monolingual Japanese corpora, yielding a modest increase (C

Qianfan Wang, Jifan Liang, Peihong Yuan, Ken R. Duffy

Future beyond-5G and 6G systems demand ultra-reliable, low-latency communication with short blocklengths, motivating the development of universal decoding algorithms. Guessing decoding, which infers the noise or codeword candidate in order of decreasing (exact or approximate) likelihood, offers a universal framework applicable to short codes. In this paper,

Tianxiang Zhang, Peipeng Yu, Zhihua Xia, Longchen Dai

The proliferation of sophisticated deepfakes poses significant threats to information integrity. While DINOv2 shows promise for detection, existing fine-tuning approaches treat it as generic binary classification, overlooking distinct artifacts inherent to different deepfake methods. To address this, we propose a DeepFake Fine-Grained Adapter (DFF-Adapter) f

Hemant Kumar Gehlot, Mohammad Shirzadi, Junhao Gan, Ahad N. Zehmakan

Social media has transformed global communication, yet its network structure can systematically distort perceptions through effects like the majority illusion and echo chambers. We introduce the perception gap index, a graph-based measure that quantifies local-global opinion divergence, which can be viewed as a generalization of the majority illusion to cont

Oriane de Leuze, Maxime Berthe, Sophie Hermans, Benoît Hackens

Understanding charge transport in networks of two-dimensional crystals is essential for developing reliable applications such as chemiresistors or electromagnetic shields. For this purpose, intra- and inter-flake contributions to the network resistance must be disentangled. MXenes, such as Ti3C2Tx, are prime examples of 2D crystals often employed as thin net

Tammy Glazer, Gilles Q. Hacheme, Akram Zaytar, Luana Marotti

We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models. We pair building footprint and height data from existing datasets with quarterly PlanetScope basemap satellite images to train a multi-task deep learning model that predicts building density and bui

Sayad Ibna Azad, Md. Atiqur Rahman

We introduce BdSL-SPOTER, a pose-based transformer framework for accurate and efficient recognition of Bengali Sign Language (BdSL). BdSL-SPOTER extends the SPOTER paradigm with cultural specific preprocessing and a compact four-layer transformer encoder featuring optimized learnable positional encodings, while employing curriculum learning to enhance genera

Abhisha Garg, Akash Kumar, Suraj Srivastava, Nimish Yadav

This work conceives the Bayesian Group-Sparse Regression (BGSR) for the estimation of a spatial and frequency wideband, i.e., a dual wideband channel in Multi-User (MU) THz hybrid MIMO scenarios. We develop a practical dual wideband THz channel model that incorporates absorption losses, reflection losses, diffused ray modeling and angles of arrival/departure

Jian Zhou, Sihao Lin, Shuai Fu, Zerui Li

Many recent Vision-Language-Action models employ diffusion or flow-matching backbones with hundreds of millions of parameters for action generation. However, unlike image synthesis where the output spans millions of diverse pixels, a manipulation policy generates only short sequences of low-dimensional, physically correlated action values, a far simpler targ

Tianle Cheng, Zeyan Zhang, Kaifeng Gao, Jun Xiao

Recent advancements in diffusion-based video generation have produced impressive and high-fidelity short videos. To extend these successes to generate coherent long videos, most video diffusion models (VDMs) generate videos in an autoregressive manner, i.e., generating subsequent frames conditioned on previous ones. There are generally two primary paradigms:

Xianhao Zhou, Jianghao Wu, Ku Zhao, Jinlong He

Generating synthetic CT images from CBCT or MRI has a potential for efficient radiation dose planning and adaptive radiotherapy. However, existing CNN-based models lack global semantic understanding, while Transformers often overfit small medical datasets due to high model capacity and weak inductive bias. To address these limitations, we propose a DINOv3-Gu

Shuhan Ye, Yi Yu, Qixin Zhang, Chenqi Kong

Brain-inspired Spiking neural networks (SNNs) promise energy-efficient intelligence via event-driven, sparse computation, but deeper architectures inflate parameters and computational cost, hindering their edge deployment. Recent progress in SNN pruning helps alleviate this burden, yet existing efforts fall into only two families: \emph{unstructured} pruning

Xi Chen, Ziyun Jin

This paper investigates Calder\'on's problem on a conformally transversally anisotropic manifold $ (M,g) $ of dimension $n \geq 3$, where the conductivity $ a(s,x,p) $ might depend on both the electric potential and the electric field. We establish that for all $(t,x)\in \mathbb{R}\times M$ and $\beta \in \mathbb{N}^{1+n}$ the derivatives $ \partial_{(s,p)}^

Shuhan Ye, Yi Yu, Qixin Zhang, Chenqi Kong

Event cameras sense brightness changes and output binary asynchronous event streams, attracting increasing attention. Their bio-inspired dynamics align well with spiking neural networks (SNNs), offering a promising energy-efficient alternative to conventional vision systems. However, SNNs remain costly to train due to temporal coding, which limits their prac

Víctor A. Vicente-Benítez

We present the construction of an integral transmutation operator for the Schr\"odinger equation \[ -y'' + q(x)y = \lambda y, \quad x \in J, \ \lambda \in \mathbb{C}, \] in the case where $q$ is the distributional derivative of an $L^2$ function on a bounded interval $J \subset \mathbb{R}$. Such a transmutation operator transforms solutions of $ v'' + \lambd

Lóránt Nagy, Miklós Rásonyi

We consider a discrete-time model of a financial market where a risky asset is bought and sold with transactions having a transient price impact. It is shown that the corresponding utility maximization problem admits a solution. We manage to remove some unnatural restrictions on the market depth and resilience processes that were present in earlier work. A n

Emil Horobet

In this article, we study the generalized modern portfolio theory, with utility functions admitting higher-order cumulants. We establish that under certain genericity conditions, the utility function has a constant number of complex critical points. We study the discriminant locus of complex critical points with multiplicity. Finally, we switch our attention

Cynthia A. Huang, Tina Lam

The ability to programmatically retrieve vast quantities of data from online sources has given rise to increasing usage of web-scraped datasets for various purposes across government, industry and academia. Contemporaneously, there has also been growing discussion about the statistical qualities and limitations of collecting from online data sources and anal

Yu Zheng, Kezhi Wang, Wenji Xi, Gang Yu

Modeling indoor radio propagation is crucial for wireless network planning and optimization. However, existing approaches often rely on labor-intensive manual modeling of geometry and material properties, resulting in limited scalability and efficiency. To overcome these challenges, this paper presents SenseRay-3D, a generalizable and physics-informed end-to

Kiran Kumar Saha, Sweta Tiwari

In this paper, we study a boundary blow-up problem for real $(N-1)$-Monge-Amp\`{e}re equations of the form \begin{equation} \nonumber \left \{ \begin{aligned} & \operatorname{\det}^{\frac{1}{N-1}}\left(\Delta zI-D^{2}z\right)=K(|x|)f(z) && \text{ in } \Omega, & z(x) \to \infty \text{ as } \dist(x,\partial\Omega) \to 0, \end{aligned} \right. \end{equation} wh

Shengqin Jiang, Tianqi Kong, Yuankai Qi, Haokui Zhang

Prompt-based continual learning methods fine-tune only a small set of additional learnable parameters while keeping the pre-trained model's parameters frozen. It enables efficient adaptation to new tasks while mitigating the risk of catastrophic forgetting. These methods typically attach one independent task-specific prompt to each layer of pre-trained model

Yanchang Fu, Qiyue Yin, Shengda Liu, Pei Xu

Excessive abstraction is a critical challenge in hand abstraction-a task specific to games like Texas hold'em-when solving large-scale imperfect-information games, as it impairs AI performance. This issue arises from extreme implementations of imperfect-recall abstraction, which entirely discard historical information. This paper presents KrwEmd, the first p

Duaa Abdullah

In this study, we first reviewed the traditional astrolabe design methods and identified potential sources of manufacturing error. We then proposed an analytical approach using computer assistance to develop designs for the astrolabe components. This approach marks a pioneering step toward designing and producing a physical astrolabe model aided by computer

Jiayin Che, Sheng Ye, Shiqi Shen, Weiyan Li

We study tunneling ionization of HeH+ in strong elliptical laser fields numerically and analytically. The calculated photoelectron momentum distribution (PMD) show two different offset angles corresponding to ionization events occurring in the first and the second half cycles of one laser cycle. When the larger angle is greater than the angle of a model symm

Konstantinos Efstathiou, Tobias Våge Henriksen, Sonja Hohloch

In this paper we introduce a new bifurcation in Hamiltonian systems, which we call the double flip bifurcation. The Hamiltonian depends on two parameters, one of which controls the double flip bifurcation. The result of the bifurcation is the occurrence of two Hamiltonian flip bifurcations with respect to the other parameter. The two Hamiltonian flip bifurca

Ji-Ping Jin, Chen-Bin Feng, Rui Fan, Chi-Man Vong

Image stitching often faces challenges due to varying capture angles, positional differences, and object movements, leading to misalignments and visual discrepancies. Traditional seam carving methods neglect semantic information, causing disruptions in foreground continuity. We introduce SemanticStitch, a deep learning-based framework that incorporates seman

Yanchang Fu, Shengda Liu, Pei Xu, Kaiqi Huang

High-quality information set abstraction remains a core challenge in solving large-scale imperfect-information extensive-form games (IIEFGs)--such as no-limit Texas Hold'em--where the finite nature of spatial resources hinders solving strategies for the full game. State-of-the-art AI methods rely on pre-trained discrete clustering for abstraction, yet their

Abhishek Sebastian

Fiber Specklegram Sensors (FSS) are highly effective for environmental monitoring, particularly for detecting temperature variations. However, the nonlinear nature of specklegram data presents significant challenges for accurate temperature prediction. This study investigates the use of transformer-based architectures, including Vision Transformers (ViTs), S

Lokender Singh, Saksham Kumar, Chandan Kumar

Multilabel image categorization has drawn interest recently because of its numerous computer vision applications. The proposed work introduces a novel method for classifying multilabel images using the COCO-2014 dataset and a modified ResNet-101 architecture. By simulating label dependencies and uncertainties, the approach uses probabilistic reasoning to imp

Tushar Vatsa, Vibha Belavadi, Priya Shanmugasundaram, Suhas Suresha

Multimodal creative assistants decompose user goals and route tasks to subagents for layout, styling, retrieval, and generation. Retrieval quality is pivotal, yet failures can arise at several stages: understanding user intent, choosing content types, finding candidates (recall), or ranking results. Meanwhile, sending and processing images is costly, making

Marco S. Kirsch, Georgios G. Pyrialakos, Richard Altenkirch, Mahmoud A. Selim

In recent years, a self-consistent optical thermodynamic framework has emerged that offers a systematic methodology to understand, harness and exploit the complex collective dynamics of multimode nonlinear systems. These developments now allow consideration of a series of longstanding problems in optics, including the prospect of funnelling the entire power

Hongxuan Li, Wencheng Zhu, Huiying Xu, Xinzhong Zhu

Vector quantization has emerged as a powerful tool in large-scale multimodal models, unifying heterogeneous representations through discrete token encoding. However, its effectiveness hinges on robust codebook design. Current prototype-based approaches relying on trainable vectors or clustered centroids fall short in representativeness and interpretability,

H. Shinya, F. Ito, N. Kabeya, Y. Mizukami

CeRu$_2$Si$_2$ is a well-known heavy fermion paramagnet, and substituting Ge for Si induces antiferromagnetism. This antiferromagnetism is Ising-like and has a tricritical point in the magnetic field ($H$) -temperature ($T$) phase diagram. Since the temperature of the tricritical point is expected to decrease with increasing pressure ($P$), we investigated t

Md. Najmul Islam, Md. Miner Hossain Rimon, Shah Sadek-E-Akbor Shamim, Zarif Mohaimen Fahad

Diabetes is a serious worldwide health issue, and successful intervention depends on early detection. However, overlapping risk factors and data asymmetry make prediction difficult. To use extensive health survey data to create a machine learning framework for diabetes classification that is both accurate and comprehensible, to produce results that will aid

Dengming Zhang, Weitao You, Jingxiong Li, Weishen Lin

Emotion understanding is critical for making Large Language Models (LLMs) more general, reliable, and aligned with humans. Art conveys emotion through the joint design of visual and auditory elements, yet most prior work is human-centered or single-modality, overlooking the emotion intentionally expressed by the artwork. Meanwhile, current Audio-Visual Langu

Dong-Hee Shin, Deok-Joong Lee, Young-Han Son, Tae-Eui Kam

Adaptive treatment strategies (ATS) are sequential decision-making processes that enable personalized care by dynamically adjusting treatment decisions in response to evolving patient symptoms. While reinforcement learning (RL) offers a promising approach for optimizing ATS, its conventional online trial-and-error learning mechanism is not permissible in cli

Xinyue Yu, Youqing Fang, Pingyu Wu, Guoyang Ye

Generating expressive and controllable human speech is one of the core goals of generative artificial intelligence, but its progress has long been constrained by two fundamental challenges: the deep entanglement of speech factors and the coarse granularity of existing control mechanisms. To overcome these challenges, we have proposed a novel framework called