March 2025 arXiv papers — page 35

Changjiang Bu, Lixiang Chen, Yongtang Shi

It is well known that the algebraic multiplicity of an eigenvalue of a graph (or real symmetric matrix) is equal to the dimension of its corresponding linear eigen-subspace, also known as the geometric multiplicity. However, for hypergraphs, the relationship between these two multiplicities remains an open problem. For a graph $G=(V,E)$ and $k \geq 3$, the $

Meghana Bhat, Saipriya Dubey, Shreedevi K. Masuti

Let $(R, \mathcal{M})$ be a local ring over a field $k$ with $k = R/\mathcal M$ and $J$ an ideal in $R$ such that $A =R/J$ is an Artinian Gorenstein (AG) $k$-algebra. In 1989, A. Iarrobino introduced the symmetric decomposition of the Hilbert function of $A$. This became a very powerful tool for classifying the Hilbert functions of AG $k$-algebras. In this a

Jingye Chen, Yuzhong Zhao, Yupan Huang, Lei Cui

Recent advances in generative models have significantly impacted game generation. However, despite producing high-quality graphics and adequately receiving player input, existing models often fail to maintain fundamental game properties such as numerical and spatial consistency. Numerical consistency ensures gameplay mechanics correctly reflect score changes

Jesper Leong, Anthony W. Thomas, Pierre A. M. Guichon

The possible existence of an H-dibaryon near the $\Lambda-\Lambda$ threshold has still not been decided experimentally. This raises the question of the potential effects on neutron stars if it does exist. We explore the consequences within the quark-meson coupling model, using the excluded volume formalism. While the H is abundant in heavy stars the maximum

Sanu Bera, Snehashis Mukherjee

In this article we investigate the algebra $U_q^+(B_2)$. Assume that $q$ is a primitive $m$-th root of unity with $m \geq 5$. We prove that $U_q^+(B_2)$ becomes a Polynomial Identity (PI) algebra. It was previously known that for such algebras the simple modules are finite-dimensional with dimension at most the PI degree. We determine the PI degree of $U_q^+

Jiahao Lyu, Minghua Zhao, Jing Hu, Xuewen Huang

Video anomaly detection (VAD) methods are mostly CNN-based or Transformer-based, achieving impressive results, but the focus on detection accuracy often comes at the expense of inference speed. The emergence of state space models in computer vision, exemplified by the Mamba model, demonstrates improved computational efficiency through selective scans and sho

Jesper Leong, Anthony W. Thomas, Pierre A. M. Guichon

The equation of state for dense nuclear matter in $\beta$-equilibrium is explored including the possibility of a doubly-strange H-particle. Consistent with experimental constraints, the mass of the H in free space is taken to be near the $\Lambda \, \Lambda$ threshold. Within the quark-meson coupling model, which we use, no new parameters are required to des

Uvini Balasuriya Mudiyanselage, Woojin Cho, Minju Jo, Noseong Park

In this study, we examine the potential of one of the ``superexpressive'' networks in the context of learning neural functions for representing complex signals and performing machine learning downstream tasks. Our focus is on evaluating their performance on computer vision and scientific machine learning tasks including signal representation/inverse problems

Chee Kian Yap, Arun Kumar Singh

The study of magnetic phenomena in low-dimensional systems has largely explored after the discovery of two-dimensional (2D) magnetic materials, such as CrI3 and Cr2Ge2Te6 in 2017. These materials presents intrinsic magnetic order, overcoming the limitations predicted by the Mermin-Wagner theorem, due to magnetic crystalline anisotropy energy. Among these, Cr

Shaik Jani Babu, Fan Hu, Linyu Zhu, Sonal Singhal

Reliability has become an increasing concern in modern computing. Integrated circuits (ICs) are the backbone of modern computing devices across industries, including artificial intelligence (AI), consumer electronics, healthcare, automotive, industrial, and aerospace. Moore Law has driven the semiconductor IC industry toward smaller dimensions, improved perf

Samra Irshad, Seungkyu Lee, Nassir Navab, Hong Joo Lee

The presence of adversarial examples in the physical world poses significant challenges to the deployment of Deep Neural Networks in safety-critical applications such as autonomous driving. Most existing methods for crafting physical-world adversarial examples are ad-hoc, relying on temporary modifications like shadows, laser beams, or stickers that are tail

Ritu Dey, Joydeep Ghosh, Tanmay M. Macwan, Kaushlender Singh

The 2-D edge plasma fluid transport code, UEDGE has been used to simulate the edge region of circular limiter plasmas of ADITYA-U for modelling the measured electron density profile. The limiter geometry of ADITYA-U has been introduced in the UEDGE code, which is primarily developed and used for divertor configuration. The computational mesh defining the lim

Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana, David Smith

In the era of data-driven machine-learning applications, privacy concerns and the scarcity of labeled data have become paramount challenges. These challenges are particularly pronounced in the domain of few-shot learning, where the ability to learn from limited labeled data is crucial. Privacy-preserving few-shot learning algorithms have emerged as a promisi

Michael T. Lopez, Cheska Elise Hung, Maria Regina Justina E. Estuar

This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five categories (English symptoms, Filipino symptoms, face wearing, quarantine, and new normal) were examined using both 15-day and 30

Olexandr Polishchuk, Dmytro Polishchuk

On the basis of structural and flow models of multilayer network system (MLNS), the main structural and functional importance indicators of separate layers in the process of intersystem interactions are calculated. With the help of influence and betweenness parameters of separate layers, their role as generators, final receivers and transitors of intersystem

Yuhan Wang

This research introduces an innovative method for identifying credit card fraud by combining the SMOTE-KMEANS technique with an ensemble machine learning model. The proposed model was benchmarked against traditional models such as logistic regression, decision trees, random forests, and support vector machines. Performance was evaluated using metrics, includ

Kanishka Ranaweera, David Smith, Pubudu N. Pathirana, Ming Ding

Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine learning. However, ensuring differential privacy (DP) in FL presents challenges due to the trade-off between model utility and privacy protection. Clipping gradients before aggregation

Eugene Denteh, Andrews Danyo, Joshua Kofi Asamoah, Blessing Agyei Kyem

Transportation planning plays a critical role in shaping urban development, economic mobility, and infrastructure sustainability. However, traditional planning methods often struggle to accurately predict long-term urban growth and transportation demands. This may sometimes result in infrastructure demolition to make room for current transportation planning

Ashish Sardana

This article surveys Evaluation models to automatically detect hallucinations in Retrieval-Augmented Generation (RAG), and presents a comprehensive benchmark of their performance across six RAG applications. Methods included in our study include: LLM-as-a-Judge, Prometheus, Lynx, the Hughes Hallucination Evaluation Model (HHEM), and the Trustworthy Language

Xiaoming Xue, Liang Feng, Yinglan Feng, Rui Liu

Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization. However, a pressing issue in this field is that the invention of new ETO algorithms has far outpaced the development of fundamental theories needed to clearly understa

João Eduardo Batista

Feature engineering is mandatory in the machine learning pipeline to obtain robust models. While evolutionary computation is well-known for its great results both in feature selection and feature construction, its methods are computationally expensive due to the large number of evaluations required to induce the final model. Part of the reason why these algo

Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana, David Smith

Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data while reducing communication costs compared with centralized machine learning algorithms. However, recent studies have shown that federated learning alone does not guarantee privacy, as private data may still be inferred from the

Víctor Tamames-Rodero, Andrés Moya, Roberto Javier López, Luis Manuel Sarro

Context: Astronomy and astrophysics demand rigorous handling of uncertainties to ensure the credibility of outcomes. The growing integration of artificial intelligence offers a novel avenue to address this necessity. This convergence presents an opportunity to create advanced models capable of quantifying diverse sources of uncertainty and automating complex

Seunghyun Lee, Nabarun Deb, Sumit Mukherjee

This paper establishes a CLT for linear statistics of the form $\langle \mathbf{q},\boldsymbol{\sigma} \rangle$ with quantitative Berry-Esseen bounds, where $\boldsymbol{\sigma}$ is an observation from an exponential family with a quadratic form as its sufficient statistic, in the \enquote{high-temperature} regime. We apply our general result to random field

Teruyuki Mishima, Xiao-Nan Lu, Masanori Sawa, Yukihiro Uchida

In this paper we elucidate the advantage of examining the connections between Hilbert-Kamke equations and geometric designs, or Chebyshev-type quadrature, for classical orthogonal polynomials. We first establish that if a $5$-design with $6$ rational points for a symmetric classical measure is parametrized by rational functions, then the corresponding measur

Yuhan Liu, Yixiong Zou, Yuhua Li, Ruixuan Li

Cross-Domain Few-Shot Segmentation (CDFSS) is proposed to transfer the pixel-level segmentation capabilities learned from large-scale source-domain datasets to downstream target-domain datasets, with only a few annotated images per class. In this paper, we focus on a well-observed but unresolved phenomenon in CDFSS: for target domains, particularly those dis

Naoya Arakawa, Kenji Yonemitsu

A time-periodic driving field can be used to generate and control transport phenomena. Any transport coefficients in the linear-response regime are restricted by the Onsager reciprocal relations, but these relations in periodically driven systems have been poorly understood. In particular, the Onsager reciprocal relation in spin transport of these systems is

Chengzhe Li, Lee V. White, Reza Fazeli, Anna Skobeleva

Certification of low emissions products is crucial to ensure that they can attract a "green premium" in international markets but need to be designed so that they do not impose undue regulatory burden or drive-up production costs. In this work, we employ energy system modelling to evaluate how different policy choices affect the cost and certified emissions

Jianping Jiang, Sike Lang

Let $\mathbb{T}$ be the two-dimensional triangular lattice, and $\mathbb{Z}$ the one-dimensional integer lattice. Let $\mathbb{T}\times \mathbb{Z}$ denote the Cartesian product graph. Consider the Ising model defined on this graph with inverse temperature $\beta$ and external field $h$, and let $\beta_c$ be the critical inverse temperature when $h=0$. We pro

M. Sreenidhi Iyengar, M. K Anirudh, P. H. Anantha Desik, M. P. Phaniraj

Refractory high-entropy alloys can function at temperatures exceeding those of nickel-based superalloys. Aluminum, as an alloying element, contributes multiple advantageous characteristics to various high-temperature alloys. The Aluminum containing RHEAs have the potential of being the best high temperature materials. In the present study we use the machine

Úlfar Erlingsson

The most important security benefit of software memory safety is easy to state: for C and C++ software, attackers can exploit most bugs and vulnerabilities to gain full, unfettered control of software behavior, whereas this is not true for most bugs in memory-safe software. Fortunately, this security benefit -- most bugs don't give attackers full control --

Jinwei Qi, Chaonan Ji, Sheng Xu, Peng Zhang

Real-time interactive video-chat portraits have been increasingly recognized as the future trend, particularly due to the remarkable progress made in text and voice chat technologies. However, existing methods primarily focus on real-time generation of head movements, but struggle to produce synchronized body motions that match these head actions. Additional

Songbo Hou, Xinxin Tian

In this paper, we develop a dynamic model of HIV infection that incorporates latent hosts, cytotoxic T lymphocyte (CTL) immunity, saturated incidence rates, and two transmission mechanisms: virus-to-cell and cell-to-cell transmission. The model has three kinds of delays: intracellular delay, replication of viruses delay, immune response delay. Initially, the

Yash Bhake, Preeti Rao

Temporal dynamics are among the cues to expres siveness in music performance in different cultures. In the case of Hindustani music, it is well known that expert vocalists often take liberties with the beat, intentionally not aligning their singing precisely with the relatively steady beat provided by the accompanying tabla. This becomes evident when compari

Seth Farrell, Chenghao Li, Hongzhan Yu, Ryo Yoshimitsu

The integration of autonomous mobile robots (AMRs) in industrial environments, particularly warehouses, has revolutionized logistics and operational efficiency. However, ensuring the safety of human workers in dynamic, shared spaces remains a critical challenge. This work proposes a novel methodology that leverages control barrier functions (CBFs) to enhance

Junjie Chen, Weilong Chen, Yifan Zuo, Yuming Fang

Category-agnostic pose estimation aims to locate keypoints on query images according to a few annotated support images for arbitrary novel classes. Existing methods generally extract support features via heatmap pooling, and obtain interacted features from support and query via cross-attention. Hence, these works neglect to mine fine-grained and structure-aw

Shifra Mandel, Julian Gerber, Kaya Mori, Ceaser Stringfield

The central $2\times0.8$ deg$^2$ region of our Galaxy contains $\sim10,000$ X-ray point sources that were detected by a series of Chandra observations over the last two decades. However, the limited bandpass of Chandra below 8 keV hampered their spectroscopic classification. In 2016, the initial NuSTAR Galactic center (GC) survey detected 77 X-ray sources ab

Hedong Yan

In order to reduce the cost of experimental evaluation for agents, we introduce a computational theory of evaluation for mini agents: build evaluation model to accelerate the evaluation procedures. We prove upper bounds of generalized error and generalized causal effect error of given evaluation models for infinite agents. We also prove efficiency, and consi

Ka Long Keith Ho, Hien Duy Nguyen

Variable selection comprises an important step in many modern statistical inference procedures. In the regression setting, when estimators cannot shrink irrelevant signals to zero, covariates without relationships to the response often manifest small but non-zero regression coefficients. The ad hoc procedure of discarding variables whose coefficients are sma

Shashi Chourasiya, Daniel R. Johnston

We prove new results related to the digital reverse $\overleftarrow{n}$ of a positive integer $n$ in a fixed base $b$. First we show that for $b\geq 26000$, there exists infinitely many primes $p$ such that $\overleftarrow{p}$ is square-free. Further, we show that for $b\geq 2$ there are infinitely many palindromes (with $n=\overleftarrow{n}$) that are cube-

Zihao Zheng, Xiuping Cui, Size Zheng, Maoliang Li

As the Mix-of-Experts (MoE) architecture increases the number of parameters in large models, there is an even greater need for model quantization. However, existing quantization methods overlook the expert dynamics of MoE across multiple datasets. Moreover, the existing static quantization cannot adapt MoE to various data change scenarios. In this paper, we

Yuhang Yao, Syed A. Jafar

Entanglement is known to significantly improve the performance (separately) of communication and detection schemes that utilize quantum resources. This work explores the simultaneous utility of quantum entanglement for (joint) communication and detection schemes, over channels that are convex combinations of identity, depolarization and erasure operators, bo

Farzad Ghafari, Josephine Dias, L Krister Shalm, Varun B Verma

Entanglement distribution is crucial for quantum communication and cryptography but is hindered by channel loss and decoherence. Noiseless linear amplification (NLA) is a probabilistic protocol that supports noiseless amplification without violating the no-cloning theorem, aiding in tasks like entanglement distillation and enhanced metrology. The probabilist

Aleksis Koski, Jani Onninen, Haiqing Xu

We give a full characterization of embeddings of the unit circle that admit a Sobolev homeomorphic extension to the unit disk. As a direct corollary, we establish that for quasiconvex target domains $\mathbb Y$, any homeomorphism $\varphi \colon \partial \mathbb{D} \to \partial \mathbb Y$ that admits a continuous $W^{1,p}$-extension to the unit disk $\mathbb

Rui Peng, Rachidi Salako, Yixiang Wu

In this paper, we investigate a parabolic-ODE SIS epidemic model with no-flux boundary conditions in a heterogeneous environment. The model incorporates a saturated infection mechanism \({SI}/(m(x) + S + I)\) with \(m \geq,\,\not\equiv 0\). This study is motivated by disease control strategies, such as quarantine and lockdown, that limit population movement.

Saelyne Yang, Anh Truong, Juho Kim, Dingzeyu Li

Tutorial videos are a valuable resource for people looking to learn new tasks. People often learn these skills by viewing multiple tutorial videos to get an overall understanding of a task by looking at different approaches to achieve the task. However, navigating through multiple videos can be time-consuming and mentally demanding as these videos are scatte

Gen Li, Ying Zhang, Xiaoguang Xu, Lei Shen

High density data storage and spin-logic devices require highly efficient all-electric control of spin moments. So far, charge-to-spin conversion through the spin Hall effect (SHE) highly limits to d-orbital materials associated with strong spin-orbit coupling (SOC), especially heavy metals. However, d-orbital heavy metals with strong SOC results in a short

Russell Tsuchida, Jiawei Liu, Cheng Soon Ong, Dino Sejdinovic

We introduce squared families, which are families of probability densities obtained by squaring a linear transformation of a statistic. Squared families are singular, however their singularity can easily be handled so that they form regular models. After handling the singularity, squared families possess many convenient properties. Their Fisher information i

Ziyi Zhou, Xiaoming Zhang, Shenghan Tan, Litian Zhang

The proliferation of fake news on social media platforms has exerted a substantial influence on society, leading to discernible impacts and deleterious consequences. Conventional deep learning methodologies employing small language models (SLMs) suffer from the necessity for extensive supervised training and the challenge of adapting to rapidly evolving circ

Wei Wang, Xianglong Zhang, Peng Xu, Rongmao Chen

Oblivious RAM (ORAM) allows a client to securely retrieve elements from outsourced servers without leakage about the accessed elements or their virtual addresses. Two-server ORAM, designed for secure two-party RAM computation, stores data across two non-colluding servers. However, many two-server ORAM schemes suffer from excessive local storage or high bandw

Jiajie Quan, Ao Tong, Yuxuan Cai, Xinwei He

In multi-class unsupervised anomaly detection(MUAD), reconstruction-based methods learn to map input images to normal patterns to identify anomalous pixels. However, this strategy easily falls into the well-known "learning shortcut" issue when decoders fail to capture normal patterns and reconstruct both normal and abnormal samples naively. To address that,

Shuaiyu Zhang, Xun Lin, Rongxiang Zhang, Yu Bai

The integration of pathologic images and genomic data for survival analysis has gained increasing attention with advances in multimodal learning. However, current methods often ignore biological characteristics, such as heterogeneity and sparsity, both within and across modalities, ultimately limiting their adaptability to clinical practice. To address these

Ran Wei, ZhiXiong Lan, Qing Yan, Ning Song

Background: Chromosome karyotype analysis is crucial for diagnosing hereditary diseases, yet detecting structural abnormalities remains challenging. While AI has shown promise in medical imaging, its effectiveness varies across modalities. Leveraging advances in Foundation Models that integrate multimodal medical imaging for robust feature extraction and acc

Chenyu Ren, Daihai He, Jian Huang

Proteins are essential components of all living organisms and play a critical role in cellular survival. They have a broad range of applications, from clinical treatments to material engineering. This versatility has spurred the development of protein design, with amino acid sequence design being a crucial step in the process. Recent advancements in deep gen

Teng Huang, Han Ding, Wenxin Sun, Cui Zhao

Wireless sensing systems, particularly those using mmWave technology, offer distinct advantages over traditional vision-based approaches, such as enhanced privacy and effectiveness in poor lighting conditions. These systems, leveraging FMCW signals, have shown success in human-centric applications like localization, gesture recognition, and so on. However, c

Deepak A. Suresh, Xinchao Zhou, Chen-Lung Hung, F. Robicheaux

We theoretically investigate the collective dipole-dipole interactions in atoms coupled to a nanophotonic microring resonator. The atoms can interact with each other through light-induced dipole-dipole interactions mediated by free space and through the resonator whispering-gallery modes. The differing characteristics and mismatched wavenumbers of these mode

Juan José Sánchez Medina

This research paper aims to compare different methods for calculating the distance to the Large Magellanic Cloud (\textit{LMC}). The distance, $d_{LMC}$, is determined using stellar parallax, variable stars (RR Lyrae and Classical Cepheids), redshift, and celestial mechanics, from which the systematic and standard errors are calculated. After analyzing each

Lang Cui, Pengfei Jiang, Tao An, Hongmin Cao

Understanding the formation mechanisms of stellar-mass black holes in X-ray binaries (BHXBs) remains a fundamental challenge in astrophysics. The natal kick velocities imparted during black hole formation provide crucial constraints on these formation channels. In this work, we present a new-epoch very long baseline interferometry (VLBI) observation of the G

Megumi Shidatsu, Nobuyuki Kawai, Hiroyuki Maehara, Emi Goto

We report on the optical spectroscopic monitoring of the X-ray transient MAXI J0709$-$159 (identified as the Be star LY CMa) performed for about 1.5 months after the X-ray detection with MAXI. The observed spectrum showed a double-peaked H$\alpha$ line with a peak-to-peak separation of $\sim 230$ km s$^{-1}$, suggestive of the Be disk origin. We also detecte

MPD Collaboration

The Multi-Purpose Detector (MPD) is one of the three experiments of the Nuclotron Ion Collider-fAcility (NICA) complex, which is currently under construction at the Joint Institute for Nuclear Research in Dubna. With collisions of heavy ions in the collider mode, the MPD will cover the energy range 4-11 GeV to scan the high baryon-density region of the QCD p

Xuao Zhang

We study the most general $G_2$-invariant ${\rm AdS}_4$ vacua in 11 dimensional supergravity preserving 4 real supercharges, with the goal of understanding the IR fixed points of the RG flow induced by the cubic deformation of the ABJM theory. We identify a new $G_2$-invariant background, which completes the web of RG flows connecting its holographic dual wi

Yinzhu Quan, Yujia Xu, Guanlin Chen, Frederick Benaben

The growing emphasis on energy efficiency and environmental sustainability in global supply chains introduces new challenges in the deployment of hyperconnected logistic hub networks. In current volatile, uncertain, complex, and ambiguous (VUCA) environments, dynamic risk assessment becomes essential to ensure successful hub deployment. However, traditional

Jamshid Sourati, Grace Shao

Uncertainty of scientific findings are typically reported through statistical metrics such as $p$-values, confidence intervals, etc. The magnitude of this objective uncertainty is reflected in the language used by the authors to report their findings primarily through expressions carrying uncertainty-inducing terms or phrases. This language uncertainty is a

Yang Xu, Saumya Choudhary, Long D. Nguyen, Matthew Klein

Transparent conducting oxides (TCO) such as indium-tin-oxide (ITO) exhibit strong optical nonlinearity in the frequency range where their permittivities are near zero. We leverage this nonlinear optical response to realize a sub-picosecond time-gate based on upconversion (or sum-) four-wave mixing (FWM) between two ultrashort pulses centered at the epsilon-n

Bing-Bing Li, Jun Jiang, Lei Wu, Deng-Hong Zhang

The Land$\acute{e}$ $g$ factors of Ba$^+$ are very important in high-precision measurement physics. The wave functions, energy levels, and Land$\acute{e}$ $g$ factors for the $6s$ $^{2}S_{1/2}$ and $5d$ $^{2}D_{3/2,5/2}$ states of Ba$^{+}$ ions were calculated using the multi-configuration Dirac-Hartree-Fock (MCDHF) method and the Model-QED method. The contr

Tian Wang, Pengcheng Zhang

Let $A$ be a $g$-dimensional abelian variety defined over a number field $F$. It is conjectured that the set of ordinary primes of $A$ over $F$ has positive density, and this is known to be true when $g=1, 2$, or for certain abelian varieties with extra endomorphisms. In this paper, we extend the family of abelian varieties whose sets of ordinary primes have

Guangbin Zhang, Yan Wang, Tianyao Huang, Yonina C. Eldar

We consider high angular resolution detection using distributed mobile platforms implemented with so-called partly calibrated arrays, where position errors between subarrays exist and the counterparts within each subarray are ideally calibrated. Since position errors between antenna arrays affect the coherent processing of measurements from these arrays, it

Yunquan Gao, Zhiguo Zhang, Praveen Kumar Donta, Chinmaya Kumar Dehury

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving demand for mobile device support. However, existing mobile inference frameworks often rely on a single processor per model, limiting hardware utilization and causing suboptimal performance and energy efficiency. Expanding DNN accessibility on mobile platforms requires ad

Ming Yean Lim

We give a formula for the number of irreducibles (with multiplicity) in the decomposition of the plethysm $s_\lambda[s_m]$ of Schur functions in terms of the number of lattice points in certain rational polytopes. In the case where $\lambda = n$ consists of a single part, we will give a combinatorial interpretation of this number as the cardinality of a set

Gongzhu Yin, Hongli Zhang, Yi Luo, Yuchen Yang

Temporal Knowledge Graph (TKG) forecasting is crucial for predicting future events using historical data. With the surge of Large Language Models (LLMs), recent studies have begun exploring their integration into TKG forecasting and achieved some success. However, they still face limitations such as limited input length, inefficient output generation, and re

John Guillamon, Cheng-Zhen Wang, Zin Lin, Tsampikos Kottos

Controlling electromagnetic wave propagation in multiple scattering systems is a challenging endeavor due to the extraordinary sensitivity generated by strong multi-path contributions at any given location. Overcoming such complexity has emerged as a central research theme in recent years, motivated both by a wide range of applications -- from wireless commu

Seokcheon Lee

Cosmological perturbation theory provides a fundamental framework for analyzing the evolution of density fluctuations and gravitational potentials in the Universe. It plays a crucial role in understanding large-scale structure formation and cosmic microwave background (CMB) anisotropies. In this study, we apply perturbation theory to the minimally extended v

Gus G. Xia

This paper introduces function alignment, a novel theory of mind and intelligence that is both intuitively compelling and structurally grounded. It explicitly models how meaning, interpretation, and analogy emerge from interactions among layered representations, forming a coherent framework capable not only of modeling minds but also of serving as a blueprin

Minjun Kim, Jaehyeon Choi, SeungJoo Lee, Jinhong Jung

How can we accurately classify graphs? Graph classification is a pivotal task in data mining with applications in social network analysis, web analysis, drug discovery, molecular property prediction, etc. Graph neural networks have achieved the state-of-the-art performance in graph classification, but they consistently struggle with overfitting. To mitigate

Zheng Tan, Yiwen Nie, Wenfa Wu, Guanyu Zhang

Demand is spiking in industrial fields for multidisciplinary forecasting, where a broad spectrum of sectors needs planning and forecasts to streamline intelligent business management, such as demand forecasting, product planning, inventory optimization, etc. Specifically, these tasks expecting intelligent approaches to learn from sequentially collected histo

Yuyin Chen, Yida Wang, Xueyang Zhang, Kun Zhan

Urban scene reconstruction requires modeling both static infrastructure and dynamic elements while supporting diverse environmental conditions. We present \textbf{StyledStreets}, a multi-style street simulator that achieves instruction-driven scene editing with guaranteed spatial and temporal consistency. Building on a state-of-the-art Gaussian Splatting fra

Nathan Kirk, T. Konstantin Rusch, Jakob Zech, Daniela Rus

Message-Passing Monte Carlo (MPMC) was recently introduced as a novel low-discrepancy sampling approach leveraging tools from geometric deep learning. While originally designed for generating uniform point sets, we extend this framework to sample from general multivariate probability distributions with known probability density function. Our proposed method,

Jing Zhu, Qu, Luo, Zheng Chu

In this paper, we propose a novel active reconfigurable intelligent surface (RIS)-assisted amplitude-domain reflection modulation (ADRM) transmission scheme, termed as ARIS-ADRM. This innovative approach leverages the additional degree of freedom (DoF) provided by the amplitude domain of the active RIS to perform index modulation (IM), thereby enhancing spec

Simone Franchini

This paper extends the Blanket representation of [Universal scaling limits for spin networks via martingale methods, Franchini, S., Proc. R. Soc. A, 481 (2025)] from systems with two-body interactions to multi-spin (or n-body) interactions. This generalization allows for the exploration of broader physical phenomena where higher order interactions are signif

Wanki Park, Chanuk Yang, Young-Seok Ghee, Hyung Kook Choi

The ability to emit and control single electrons in a dynamical manner enables their use in electron quantum optics and sensing. To characterize the electron states emitted with energy far above the Fermi energy, a dynamic barrier has been used. In this work, we extract the energy-time covariance matrix of single electrons by analyzing the energy variance ob

Yun Zhu, Le Hui, Hang Yang, Jianjun Qian

Both indoor and outdoor scene perceptions are essential for embodied intelligence. However, current sparse supervised 3D object detection methods focus solely on outdoor scenes without considering indoor settings. To this end, we propose a unified sparse supervised 3D object detection method for both indoor and outdoor scenes through learning class prototype

Yedan Shen, Kaixin Wu, Yuechen Ding, Jingyuan Wen

Generative retrieval (GR) has revolutionized document retrieval with the advent of large language models (LLMs), and LLM-based GR is gradually being adopted by the industry. Despite its remarkable advantages and potential, LLM-based GR suffers from hallucination and generates documents that are irrelevant to the query in some instances, severely challenging

Sabin Cautis, Rachel Ollivier

We identify the center of the generic affine Hecke algebra $H_q$ corresponding to some root datum with the semigroup algebra $\mathbb C[q][\check X^+]$ of the dominant chamber of its coweight lattice. This is done by first identifying a maximal commutative subalgebra $A_q \subset H_q$ with the Rees algebra associated to the semigroup algebra of the coweight

Shayan Boghani, Emin Kirimlioglu, Amrita Moturi, Hao-Ting Tso

We present a convex optimization framework for overcoming the limitations of Kubernetes Cluster Autoscaler by intelligently allocating diverse cloud resources while minimizing costs and fragmentation. Current Kubernetes scaling mechanisms are restricted to homogeneous scaling of existing node types, limiting cost-performance optimization possibilities. Our m

Ahmed Shoyeb Raihan, Zhichao Liu, Tanveer Hossain Bhuiyan, Imtiaz Ahmed

Accelerating the discovery and manufacturing of advanced materials with specific properties is a critical yet formidable challenge due to vast search space, high costs of experiments, and time-intensive nature of material characterization. In recent years, active learning, where a surrogate machine learning (ML) model mimics the scientific discovery process

Zhuojiang Cai, Jingkai Hong, Zhimin Wang, Feng Lu

Smartphones with large screens provide users with increased display and interaction space but pose challenges in reaching certain areas with the thumb when using the device with one hand. To address this, we introduce GazeSwipe, a multimodal interaction technique that combines eye gaze with finger-swipe gestures, enabling intuitive and low-friction reach on

Fumian Chen, Hui Fang

Information retrieval systems such as open web search and recommendation systems are ubiquitous and significantly impact how people receive and consume online information. Previous research has shown the importance of fairness in information retrieval systems to combat the issue of echo chambers and mitigate the rich-get-richer effect. Therefore, various fai

Nikhil Bachhawat

We present a conservative approach to the black hole singularity problem that remains within the framework of classical General Relativity (GR) supplemented by semiclassical quantum field theory (QFT). Our construction replaces the singular interior of a black hole with a null characteristic hypersurface that carries the exterior ADM data. The excised singul

Keisuke Hanada, Masahiro Kojima

Randomized controlled trials are the standard method for estimating causal effects, ensuring sufficient statistical power and confidence through adequate sample sizes. However, achieving such sample sizes is often challenging. This study proposes a novel method for estimating the average treatment effect (ATE) in a target population by integrating and recons

Joseph Carolan, Andrew M. Childs, Matt Kovacs-Deak, Luke Schaeffer

Ordered search is the task of finding an item in an ordered list using comparison queries. The best exact classical algorithm for this fundamental problem uses $\lceil \log_{2}{n}\rceil$ queries for a list of length $n$. Quantum computers can achieve a constant-factor speedup, but the best possible coefficient of $\log_{2}{n}$ for exact quantum algorithms is

Mohammad Ayyash, Sahel Ashhab

The dispersive regime of $n$-photon qubit-oscillator interactions is analyzed using Schrieffer-Wolff perturbation theory. Effective Hamiltonians are derived up to the second order in the perturbation parameters. These effective descriptions reveal higher-order qubit-oscillator cross-Kerr and oscillator self-Kerr terms. The cross-Kerr term combines a qubit Pa

Haoming Xu, Shuxun Wang, Yanqiu Zhao, Yi Zhong

This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models. This task aims to selectively erase sensitive knowledge from large language models, avoiding both over-forgetting and under-forgetting issues. We propose an unlearning system that leverages Model Merging (specifically TIES-Mergi

Yuxuan Zhu, Tengjun Jin, Stefanos Baziotis, Chengsong Zhang

After decades of research in approximate query processing (AQP), its adoption in the industry remains limited. Existing methods struggle to simultaneously provide user-specified error guarantees, eliminate maintenance overheads, and avoid modifications to database management systems. To address these challenges, we introduce two novel techniques, TAQA and BS

Shengyong Li, Yidian Fan, Xiang Li, Xinhui Ruan

Quantum control requires high-precision and robust control pulses to ensure optimal system performance. However, control sequences generated with a system model may suffer from model bias, leading to low fidelity. While model-free reinforcement learning (RL) methods have been developed to avoid such biases, training an RL agent from scratch can be time-consu

Yan Tang

Existing solutions to the hotspot prediction problem in the field of geographic information remain at a relatively preliminary stage. This study presents a novel approach for detecting and predicting geographical hotspots, utilizing point cloud-voxel-community partition clustering. By analyzing high-dimensional data, we represent spatial information through

Wei Liu, Jin Sun

We explore the potential for detecting the inelastic dark model (DM) with an additional $U(1)_D$ gauge symmetry at various types of lepton colliders. The new gauge boson $Z'$ resulting from the spontaneous breaking of $U(1)_D$ can act as a portal connecting the Standard Model fermions and DM fermions $\chi$, thereby facilitating the production of $Z'$ and it

Jinjie Mai, Wenxuan Zhu, Haozhe Liu, Bing Li

Reconstructing dynamic 3D scenes (i.e., 4D geometry) from monocular video is an important yet challenging problem. Conventional multiview geometry-based approaches often struggle with dynamic motion, whereas recent learning-based methods either require specialized 4D representation or sophisticated optimization. In this paper, we present Sora3R, a novel fram

Bo Liu, Laine Thomas, Rury R. Holman, Fan Li

It is increasingly common to augment randomized controlled trial with external controls from observational data, to evaluate the treatment effect of an intervention. Traditional approaches to treatment effect estimation involve ambiguous estimands and unrealistic or strong assumptions, such as mean exchangeability. We introduce a double-indexed notation for

Yunbo Long, Yuhan Liu, Liming Xu, Alexandra Brintrup

The emergence of autonomous Large Language Model (LLM) agents has created a new ecosystem of strategic, agent-to-agent interactions. However, a critical challenge remains unaddressed: in high-stakes, emotion-sensitive domains like debt collection, LLM agents pre-trained on human dialogue are vulnerable to exploitation by adversarial counterparts who simulate

Pablo Shmerkin, Alexia Yavicoli

We investigate variants of the Erd\H{o}s similarity problem for Cantor sets. We prove that under a mild Hausdorff or packing logarithmic dimension assumption, Cantor sets are not full measure universal, significantly improving the known fact that sets of positive Hausdorff dimension are not measure universal. We prove a weaker result for all Cantor sets $A$: