March 2025 arXiv papers — page 45

L. A. Ureña-López, F. Lozano-Rodríguez, J. O. Román-Herrera, J. Aguilar

We present updated constraints on the parameters of an axion dark energy model, for which we took into account the properties of its characteristic potential and its full cosmological evolution. We show that the values of the axion parameters appear sufficiently constrained by the data, including the latest DESI DR1, and are consistent with the theoretical e

Ryotaro Shima, Alexander Davydov, Francesco Bullo

In this paper, we study the contractivity of Lur'e dynamical systems whose nonlinearity is either Lipschitz, incrementally sector bounded, or monotone. We consider both the discrete- and continuous-time settings. In each case, we provide state-independent linear matrix inequalities (LMIs) which are necessary and sufficient for contractivity. Additionally, we

RuiXi Qiao, Jie Cheng, Xingyuan Dai, Yonglin Tian

Skills have been introduced to offline reinforcement learning (RL) as temporal abstractions to tackle complex, long-horizon tasks, promoting consistent behavior and enabling meaningful exploration. While skills in offline RL are predominantly modeled within a continuous latent space, the potential of discrete skill spaces remains largely underexplored. In th

Rui-Si Zhou, Hui-Gen Liu, Li-Yong Zhou

The direct imaging of Earth-like planets in solar neighbors is challenging. Both transit and radial velocity (RV) methods suffer from noise due to stellar activity. By choosing a typical configuration of an X array interferometer, we used theoretical formulas to calculate the intrinsic Poisson noise and the noise of stellar activities. Assuming a fixed array

Shihao Zhou, Dayu Li, Jinshan Pan, Juncheng Zhou

Transformer-based approaches have gained significant attention in image restoration, where the core component, i.e, Multi-Head Attention (MHA), plays a crucial role in capturing diverse features and recovering high-quality results. In MHA, heads perform attention calculation independently from uniform split subspaces, and a redundancy issue is triggered to h

Keito Shimizu, Sotaro Sugishita

We investigate the asymptotic symmetries of quantum electrodynamics (QED) in three dimensions, demonstrating that their actions on asymptotic states are trivial under the assumption of confinement.

Mengqing Xue, Yifei Liu, Ling Guo, Shaoli Huang

Human-object interaction (HOI) synthesis is crucial for creating immersive and realistic experiences for applications such as virtual reality. Existing methods often rely on simplified object representations, such as the object's centroid or the nearest point to a human, to achieve physically plausible motions. However, these approaches may overlook geometri

Makoto Nakashima

In [CSZ23], the authors proved the convergence of the finite dimensional time distribution of the rescaled random fields derived from the discrete stochastic heat equation of $2d$-directed polymers in random environment in the critical window. The scaling limit is called critical $2d$ stochastic heat flow (SHF). In this paper, we will show that the critical

Boris Alexeev, Evan Conway, Matthieu Rosenfeld, Andrew V. Sutherland

Let $t(N)$ denote the largest number such that $N!$ can be expressed as the product of $N$ integers greater than or equal to $t(N)$. The bound $t(N)/N = 1/e-o(1)$ was apparently established in unpublished work of Erd\H{o}s, Selfridge, and Straus; but the proof is lost. Here we obtain the more precise asymptotic $$ \frac{t(N)}{N} = \frac{1}{e} - \frac{c_0}{\l

Zeyu Han, Zhi-Jian Song, Jia-Xin Zhang, Zheng-Yu Weng

The doping dependence of the superfluid density $\rho_{\text{s}}$ exhibits distinct behaviors in the underdoping and overdoping regimes of the cuprate, while the superconducting (SC) transition temperature $T_c$ generally scales with $\rho_{\text{s}}$. In this paper, we present a unified understanding of the superconducting transition temperature $T_c$ and $

Sheng Miao, Jiaxin Huang, Dongfeng Bai, Xu Yan

Novel view synthesis of urban scenes is essential for autonomous driving-related applications.Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization. We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner. Unlike

Fei Ma, Bing Yao

For real application and theoretical investigation of ordinary hypergraphs and non-ordinary hypergraphs, researchers need to establish standard rules and feasible operating methods. We propose a visualization tool for investigating hypergraphs by means of the natural topological structure of finite sets and their subsets, so we are able to construct various

Xianke Qiang, Zheng Chang, Ying-Chang Liang

Federated learning (FL) can fully leverage large-scale terminal data while ensuring privacy and security, and is considered as a distributed alternative for the centralized machine learning. However, the issue of data heterogeneity poses limitations on FL's performance. To address this challenge, artificial intelligence-generated content (AIGC) which is an i

Henggeng Han, Song Wang, Xue Li, Chuanjie Zheng

Utilizing the PHOENIX synthetic spectra, we investigated the impact of spectral resolution on the calculation of $S$-indices. We found that for spectra with a resolution lower than $\approx$30,000, it is crucial to calibrate $S$-indices for accurate estimations. This is especially essential for low-resolution spectral observations. We provided calibrations f

Jinyu Wang, Xianghui Fang, Nan Chen, Bo Qin

Despite advances in climate modeling, simulating the El Ni\~no-Southern Oscillation (ENSO) remains challenging due to its spatiotemporal diversity and complexity. To address this, we build upon existing model hierarchies to develop a new unified modeling platform, which provides practical, scalable, and accurate tools for advancing ENSO research. Within this

Lang Mei, Siyu Mo, Zhihan Yang, Chong Chen

Multimodal Retrieval-Augmented Generation (MRAG) enhances large language models (LLMs) by integrating multimodal data (text, images, videos) into retrieval and generation processes, overcoming the limitations of text-only Retrieval-Augmented Generation (RAG). While RAG improves response accuracy by incorporating external textual knowledge, MRAG extends this

Bahareh Golchin, Noushin Riahi

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topic

Jesus Salas

The Subset Sum problem, which asks whether a set of $n$ integers has a subset summing to a target $t$, is a fundamental NP-complete problem in cryptography and combinatorial optimization. The classical meet-in-the-middle (MIM) algorithm of Horowitz--Sahni runs in $\mathcal{O}^*(2^{n/2})$, which remains the best-known deterministic bound. Yet in practice, man

Yilin Tang, Hao Qin, Domenico de Ceglia, Wenkai Yang

Metasurfaces have long served as a cornerstone technique to enhance nonlinear processes, enabling frequency conversion, efficient light manipulation and integrated photonic devices. However, traditional bulk materials often suffer from high absorption losses, hindering the second harmonic generation (SHG) efficiency. Here, we develop a novel approach exploit

Yueye Wang, Wenyi Hu, Keyao Zhou, Chi Liu

As Artificial intelligence (AI) has been increasingly integrated into the medical field, the role of humans may become vague. While numerous studies highlight AI's potential, how humans and AI collaborate to maximize the combined clinical benefits remains unexplored. In this work, we analyze 270 screening scenarios from a health-economic perspective in a nat

Ryan C. Ng, Stéphane Larouche, Peter Y. Schneider, Aditi Munshi

Metaoptics are thin, planar surfaces consisting of many subwavelength optical resonators that can be designed to simultaneously control the amplitude, phase, and polarization to arbitrarily shape an optical wavefront much in the same manner as a traditional lens but with a much smaller form factor. The incorporation of metaoptics into a conventional optical

Oren Kraus, Federico Comitani, John Urbanik, Kian Kenyon-Dean

High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, enabling cell-based inference of drug-target interactions (DTI). However, the adoption of representation learning methods for HCS data has been hindered by the lack of accessible datasets and robust benchmarks. To

Heikki Mäntysaari, Yossathorn Tawabutr, Xuan-Bo Tong

We investigate the T-odd nucleon energy correlator (NEC) at small $x$ and establish its connection with the spin-dependent odderon. Probing the T-odd NEC involves measuring a single transverse spin asymmetry (SSA) for the energy pattern in the target fragmentation region in deep inelastic scattering. We find that while the inclusive energy pattern results in

Antoine Sédillot

In this article, we introduce topological adelic curves. Roughly speaking, a topological adelic curve is a topological space of (generalised) absolute values on a given field satisfying a product formula. Topological adelic curves are the topological counterpart to adelic curves introduced by Chen and Moriwaki. They aim at handling Arakelov geometry over pos

Lilan Dai, Shishuo Fu, Dun Qiu

Rational Dyck paths are the rational generalization of classical Dyck paths. They play an important role in Catalan combinatorics, and have multiple applications in algebra and geometry. Two statistics over rational Dyck paths called run and ratio-run are introduced. They both have symmetric joint distributions with the return statistic. We give combinatoria

Yu-Chen Huang, Zi-Gao Dai

Fast radio bursts (FRBs) are a type of highly-polarized, millisecond-duration electromagnetic pulses in the radio band, which are mostly produced at cosmological distances. These properties provide a natural laboratory for testing the extreme Faraday effect, a phenomenon in which two different propagation modes of a pulse separate after passing through a den

Meng Yang, Jun Chen, Wenping Gong, Longsheng Wei

Complicated nonlinear intensity differences, nonlinear local geometric distortions, noises and rotation transformation are main challenges in multimodal image matching. In order to solve these problems, we propose a method based on Frequency-domain Information of Local Energy Response called FILER. The core of FILER is the local energy response model based o

Shunsuke Kitou, Hajime Ishikawa, Yusuke Tokunaga, Masato Ueno

Among the thirty-two crystallographic point groups, 432 is the only one that lacks an inversion center but does not exhibit piezoelectricity. A gyroidal structure belongs to point group 432 and shows characteristic physical properties attributed to its distinctive strong isotropic network. Here, we investigate a gyroidal cobalt oxalate metal-organic framewor

Snigdh Sabharwal

We present a unified framework to systematically embed complex knotted and linked structures, beyond the torus family, into diverse topological phases, including Hopf insulators, classical spin liquids, topological semimetals, and non-Hermitian metals. Using rational maps and level sets of complex polynomials, we explicitly construct new topological models e

Xu Yang, Rui Wang, Kaiwen Li, Wenhua Li

The landscape of optimization problems has become increasingly complex, necessitating the development of advanced optimization techniques. Meta-Black-Box Optimization (MetaBBO), which involves refining the optimization algorithms themselves via meta-learning, has emerged as a promising approach. Recognizing the limitations in existing platforms, we presents

Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignm

Jai-chan Hwang, Hyerim Noh

A candidate for relativistic MOND with successful cosmology was proposed recently by using a Lorentz-violating vector field in Einstein's gravity. We show that the dynamic nature of the vector field makes it challenging to realize the MOND. Only in the stationary limit, thus excluding cosmological situations, one can achieve the MOND limit. We study the evol

Shanzhong Xie, Kan-Hao Xue, Zijian Zhou, Xiangshui Miao

Shell DFT-1/2 is a fast band gap rectification method that is versatile for semiconductor supercell and superlattice calculations, which involves two cutoff radii that have to be optimized. Although such optimization is trivial in terms of time cost for a primitive cell, in high-throughput calculations this can be a big concern because most materials are the

Xiduo Chen, Xingdong Feng, Antonio F. Galvao, Yeheng Ge

Obtaining valid treatment effect inference remains a challenging problem when dealing with numerous instruments and non-sparse control variables. In this paper, we propose a novel ridge regularization-based instrumental variables method for estimation and inference in the presence of both high-dimensional instrumental variables and high-dimensional control v

Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang

The explosion of Time Series (TS) data, driven by advancements in technology, necessitates sophisticated analytical methods. Modern management systems increasingly rely on analyzing this data, highlighting the importance of effcient processing techniques. State-of-the-art Machine Learning (ML) approaches for TS analysis and forecasting are becoming prevalent

Jiahe Li, Feiyu Wang, Xiaochao Qu, Chengjing Wu

Gaussian Splatting (GS)-based methods rely on sufficient training view coverage and perform synthesis on interpolated views. In this work, we tackle the more challenging and underexplored Extrapolated View Synthesis (EVS) task. Here we enable GS-based models trained with limited view coverage to generalize well to extrapolated views. To achieve our goal, we

Li Zhou, Kaijun Li, Aigen Li, Zheng Zhou

As the only moon in the solar system with a thick atmosphere, Titan is a compelling and enigmatic world containing a complex organic haze. Polycyclic aromatic hydrocarbon (PAH) molecules are believed to play an essential role in the formation of Titan's aerosols and haze layers. The existence of PAHs in Titan's upper atmosphere has been revealed by the detec

Tian Qin, Wei-Min Huang

We propose a novel two-stage framework of generative models named Debiasing Kernel-Based Generative Models (DKGM) with the insights from kernel density estimation (KDE) and stochastic approximation. In the first stage of DKGM, we employ KDE to bypass the obstacles in estimating the density of data without losing too much image quality. One characteristic of

KyeoReh Lee, Herve Hugonnet, Jae-Hong Lim, YongKeun Park

Quantitative phase imaging has been extensively studied in X-ray microtomography to improve the sensitivity and specificity of measurements, especially for low atomic number materials. However, obtaining quantitative phase images typically requires additional measurements or assumptions, which significantly limits the practical applicability. Here, we presen

Syed Ariff Syed Hesham, Yun Liu, Guolei Sun, Henghui Ding

Video semantic segmentation (VSS) plays a vital role in understanding the temporal evolution of scenes. Traditional methods often segment videos frame-by-frame or in a short temporal window, leading to limited temporal context, redundant computations, and heavy memory requirements. To this end, we introduce a Temporal Video State Space Sharing (TV3S) archite

Arnab Ganguly, Wasiur R. KhudaBukhsh

We consider a stochastic model of the Michaelis-Menten (MM) enzyme kinetic reactions in terms of Stochastic Differential Equations (SDEs) driven by Poisson Random Measures (PRMs). It has been argued that among various Quasi-Steady State Approximations (QSSAs) for the deterministic model of such chemical reactions, the total QSSA (tQSSA) is the most accurate

Joonhyun Jeong, Seyun Bae, Yeonsung Jung, Jaeryong Hwang

Despite the remarkable versatility of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) to generalize across both language and vision tasks, LLMs and MLLMs have shown vulnerability to jailbreaking, generating textual outputs that undermine safety, ethical, and bias standards when exposed to harmful or sensitive inputs. With the recent advancement of s

Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang, Ali Mohammad-Djafari

A significant advancement in Neural Network (NN) research is the integration of domain-specific knowledge through custom loss functions. This approach addresses a crucial challenge: how can models utilize physics or mathematical principles to enhance predictions when dealing with sparse, noisy, or incomplete data? Physics-Informed Neural Networks (PINNs) put

Dhruv Suri, Mohak Mangal

The increasing penetration of renewable energy sources introduces significant variability and uncertainty in modern power systems, making accurate state prediction critical for reliable grid operation. Conventional forecasting methods often neglect the power grid's inherent topology, limiting their ability to capture complex spatio temporal dependencies. Thi

Gil R. Cavalcanti

We extend the notion of topological T-duality from oriented sphere bundles to transgressive fibrations, a more general type fibration characterised by the abundance of transgressive elements. Examples of transgressive fibrations include principal $\mathrm{U}(n)$-bundles therefore our notion of T-duality belongs to the realm of non-Abelian T-duality. We prove

Mohamed Afane, Gabrielle Ebbrecht, Ying Wang, Juntao Chen

Quantum Neural Networks (QNNs) offer promising capabilities for complex data tasks, but are often constrained by limited qubit resources and high entanglement, which can hinder scalability and efficiency. In this paper, we introduce Adaptive Threshold Pruning (ATP), an encoding method that reduces entanglement and optimizes data complexity for efficient comp

Miriam Manoel, Leandro Nery

We explore a class of centrosymmetric matrices whose entries are polynomials in two variables, referred to as DNA matrices. Our motivation stems from an unexpected connection between these matrices and invariant polynomials under the action of a Lorentz rotation on the plane. Among several noteworthy properties, we establish that within a subclass of DNA mat

Nicolás Cuervo Ovalle, Isaac Goldbring, Netanel Levi

We establish that the complete theory of a Hilbert space equipped with a normal operator has the Schr\"oder-Bernstein property. This answers a question of Argoty, Berenstein, and the first-named author. We also prove an analogous statement for unbounded self-adjoint operators.

Yongshuai Liu, Xin Liu

Model-based reinforcement learning (MBRL) has demonstrated superior sample efficiency compared to model-free reinforcement learning (MFRL). However, the presence of inaccurate models can introduce biases during policy learning, resulting in misleading trajectories. The challenge lies in obtaining accurate models due to limited diverse training data, particul

Junyi Zhu, Ruicong Yao, Taha Ceritli, Savas Ozkan

Current network training paradigms primarily focus on either centralized or decentralized data regimes. However, in practice, data availability often exhibits a hybrid nature, where both regimes coexist. This hybrid setting presents new opportunities for model training, as the two regimes offer complementary trade-offs: decentralized data is abundant but sub

Rongxing Qiu, Weijun Fang

In modern storage technologies, symbol-pair codes have emerged as a crucial framework for addressing errors in channels where symbols are read in overlapping pairs to guard against pair errors. A symbol-pair code that meets the Singleton-type bound is called a maximum distance separable (MDS) symbol-pair code. MDS symbol-pair codes are optimal in the sense t

Zhenkai Qin, BaoZhong Wei, Caifeng Gao

With the acceleration of urbanization, the spatiotemporal characteristics of criminal activities have become increasingly complex. Accurate prediction of crime distribution is crucial for optimizing the allocation of police resources and preventing crime. This paper proposes LGSTime, a crime spatiotemporal prediction model that integrates Long Short-Term Mem

Dongliang Gong, Junyi Yang, Shu Zhang, Shashi Pandey

A large anomalous Nernst effect is essential for thermoelectric energy harvesting in the transverse geometry without external magnetic field. It is often connected with anomalous Hall effect, especially when electronic Berry curvature is believed to be the driving force. This approach implicitly assumes the same symmetry for the Nernst and Hall coefficients,

Takahiro Fuke, Masafumi Endo, Kohei Honda, Genya Ishigami

Reactive mobile robot navigation in unstructured environments is challenging when robots encounter unexpected obstacles that invalidate previously planned trajectories. Model predictive path integral control (MPPI) enables reactive planning, but still suffers from limited prediction horizons that lead to local minima traps near obstacles. Current solutions r

Jai-chan Hwang, Hyerim Noh

We study a massive-photon electrodynamics and magnetohydrodynamics (MHD) in the curved spacetime of Einstein's gravity. We consider a Proca-type photon mass and present equations in terms of electric and magnetic (EM) fields and the vector potential. We present the electrodynamics and MHD in the covariant and ADM formulations valid in general spacetime and i

Taichi Uyama, Luca Ricci, Marie Ygouf, Sean Andrews

HD~163296 is a Herbig Ae/Be star with multiple signposts of on-going planet formation on its disk, such as prominent rings and gaps, as well as kinematic features as identified by previous ALMA observations. We carried out JWST/NIRCam coronagraphic imaging using the F410M and F200W NIRCam filters, with the goal of detecting the emission from the putative you

Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang

Shewhart Control Charts (SCC)s are constructed under the assumption of normality and are widely recognized in statistical quality control by numerous researchers. Problems arise when the distribution of process data does not conform to a typical Normal Distribution (ND) or when there is insufficient evidence to confirm that the data has approximately ND. Add

C. L. Latune, M. B. Puthuveedu Shebeek, D. Sugny, S. Guérin

We introduce an energetically-optimal method inspired from Shortcut-To-Adiabaticity (STA) processes, named Quantum-Optimal-Shortcut-To-Energetics (QOSTE). QOSTE produces the same transformation as STA for a given protocol used in quantum technologies or thermodynamics, but at the lowest possible energy cost. In the general case of a N- level quantum system,

Cataldo Godano, Vincenzo Convertito, Anna Tramelli, Giuseppe Petrillo

We investigate the relationship between the cumulative number of earthquakes and ground uplift at the Campi Flegrei caldera (South Italy) during the ongoing unrest (2005-present). While previous studies have explored this correlation, we propose a nonlinear epidemic model that captures new features of the caldera system. Our model describes earthquakes' occu

Qi Zhao, Xingyu Ni, Ziyu Wang, Feng Cheng

We investigate how to enhance the physical fidelity of video generation models by leveraging synthetic videos derived from computer graphics pipelines. These rendered videos respect real-world physics, such as maintaining 3D consistency, and serve as a valuable resource that can potentially improve video generation models. To harness this potential, we propo

F. Adersh, M. Muhsin, M. Sahoo

We theoretically investigate the thermodynamic performance characteristics of an active magneto-gyrator taking into account the two-dimensional motion of an inertial charged active particle confined in an asymmetric parabolic potential and in contact with two heat baths kept at two different temperatures. A magnetic field of constant magnitude is applied in

Peter Schafhalter, Alexander Krentsel, Hongbo Wei, Joseph E. Gonzalez

Autonomous driving system progress has been driven by improvements in machine learning models, whose computational demands now exceed what edge devices alone can provide. The cloud offers abundant compute, but the network has long been treated as an unreliable bottleneck rather than a co-equal part of the autonomous vehicle control loop. We argue that this s

Pooja Rani, Jan-Andrea Bard, June Sallou, Alexander Boll

The rapid technological evolution has accelerated software development for various domains and use cases, contributing to a growing share of global carbon emissions. While recent large language models (LLMs) claim to assist developers in optimizing code for performance and energy efficiency, their efficacy in real-world scenarios remains under exploration. I

Muhammad Salar Khan, Hamza Umer

This study examines religious biases in AI-generated financial advice, focusing on ChatGPT's responses to financial queries. Using a prompt-based methodology and content analysis, we find that 50% of the financial emails generated by ChatGPT exhibit religious biases, with explicit biases present in both ingroup and outgroup interactions. While ingroup biases

Baris Donmez, Yanni Jiwan-Mercier, Sebastien Loranger, Gunes Karabulut Kurt

This study focuses on the feasibility analyses of the hybrid FSO and RF-based WPT system used in the realistic Cislunar environment, which is established by using STK HPOP software in which many external forces are incorporated. In our proposed multi-hop scheme, a solar-powered satellite (SPS) beams the laser power to the low lunar orbit (LLO) satellite in t

Zergham Ahmed, Joshua B. Tenenbaum, Christopher J. Bates, Samuel J. Gershman

Modern reinforcement learning (RL) systems have demonstrated remarkable capabilities in complex environments, such as video games. However, they still fall short of achieving human-like sample efficiency and adaptability when learning new domains. Theory-based reinforcement learning (TBRL) is an algorithmic framework specifically designed to address this gap

Yasuharu Okamoto

This paper introduces a technique to enhance the efficiency of quadratic machine learning models, particularly Field-Aware Factorization Machines (FFMs) handling binary data. Our approach strategically reduces model size through optimized feature selection based on the Ising model, maintaining comparable accuracy to the original model. By exploiting the adju

Chaofan Wu, Jiaheng Li, Jinghao Cao, Ming Li

Accurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sketch-Coloring framework OmniDepth-Occ. Additionally, our app

Lvcheng Chen, Zheng-Xin Liu

Antiferromagnets on the Shastry-Sutherland lattice have attracted lots of research interest due to the possible existence of deconfined criticality. In the present work, we study the $J_1$-$J_2$-$J_r$ model using Variational Monte Carlo (VMC) method, where $J_1$, $J_2$, $J_r$ stand for the nearest-neighbor, next nearest neighbor and ring exchange interaction

Robert A. Wittenmyer, Adriana Errico, Timothy R. Holt, Jonathan Horner

The planned NASA Habitable Worlds Observatory (HWO) flagship mission aims to image and spectroscopically characterise 25 Earth-size planets in the habitable zones of their stars. However, one giant planet in the habitable zone can ruin your whole day. Recent work has examined the current state of our knowledge on the presence or absence of such objects in sa

Hongwei Wen, Annika Betken, Wouter Koolen

Robust regression aims to develop methods for estimating an unknown regression function in the presence of outliers, heavy-tailed distributions, or contaminated data, which can severely impact performance. Most existing theoretical results in robust regression assume that the noise has a finite absolute mean, an assumption violated by certain distributions,

Kaiyue Feng, Yilun Zhao, Yixin Liu, Tianyu Yang

We introduce PHYSICS, a comprehensive benchmark for university-level physics problem solving. It contains 1297 expert-annotated problems covering six core areas: classical mechanics, quantum mechanics, thermodynamics and statistical mechanics, electromagnetism, atomic physics, and optics. Each problem requires advanced physics knowledge and mathematical reas

Hagit Attiya, Michael A. Bender, Martín Farach-Colton, Rotem Oshman

A history-independent data structure does not reveal the history of operations applied to it, only its current logical state, even if its internal state is examined. This paper studies history-independent concurrent dictionaries, in particular, hash tables, and establishes inherent bounds on their space requirements. This paper shows that there is a lock-fre

Maria Soledad Aronna, Gabriel de Lima Monteiro, Oscar Sierra

In this paper we address optimal control problems in which the system parameters follow a probability distribution, and the optimization is based on average performance. These problems, known as Riemann-Stieltjes optimal control or optimal ensemble control problems, involve uncertainties that influence system dynamics. Focusing on control-affine systems, we

Long Zhang, Zhongzhu Jiang, Yugang Zhang, Jing Zhang

Two-dimensional (2D) magnetic materials have attracted considerable interest owing to their potential applications in spintronics and fundamental investigations into low-dimensional magnetism. Cr2Te3, a quasi 2D non van der Waals magnet, exhibits a complex magnetic phase diagram due to competing magnetic interactions within and between layers. However, the p

Yihan He, Ming-Chun Hong, Qiming Ding, Chih-Sheng Lin

Molecular docking is a critical computational strategy in drug design and discovery, but the complex diversity of biomolecular structures and flexible binding conformations create an enormous search space that challenges conventional computing methods. Although quantum computing holds promise for these challenges, it remains constrained by scalability, hardw

Benjamin Tighe

We extend results of Looijenga--Lunts and Verbitsky and show that the total Lie algebra $\mathfrak g$ for the intersection cohomology of a primitive symplectic variety $X$ with isolated singularities is isomorphic to $$\mathfrak g \cong \mathfrak{so}\left(\left(IH^2(X, \mathbb Q), Q_X\right)\oplus \mathfrak h\right),$$ where $Q_X$ is the intersection Beauvil

Jiwon Chang, Fatemeh Nargesian

Combining query answering and data science workloads has become prevalent. An important class of such workloads is top-k queries with a scoring function implemented as an opaque UDF - a black box whose internal structure and scores on the search domain are unavailable. Some typical examples include costly calls to fuzzy classification and regression models.

Yuke Lou, Yiming Wang, Zhen Wu, Rui Zhao

Human-object interaction (HOI) synthesis is important for various applications, ranging from virtual reality to robotics. However, acquiring 3D HOI data is challenging due to its complexity and high cost, limiting existing methods to the narrow diversity of object types and interaction patterns in training datasets. This paper proposes a novel zero-shot HOI

Bicheng Ying, Zhe Li, Haibo Yang

This work tackles the fundamental challenges in Federated Learning (FL) posed by arbitrary client participation and data heterogeneity, prevalent characteristics in practical FL settings. It is well-established that popular FedAvg-style algorithms struggle with exact convergence and can suffer from slow convergence rates since a decaying learning rate is req

Rubén Muñoz--Bertrand

We give equivalences between given properties of a commutative ring, and other properties on its ring of Witt vectors. Amongst them, we characterise all commutative rings whose rings of Witt vectors are Noetherian. We define a new category of commutative rings called preduced rings, and explain how it is the category of rings whose ring of Witt vectors has n

Songyuan Liu, Shengbo Gong, Tianning Feng, Zewen Liu

The ongoing need for effective epidemic modeling has driven advancements in capturing the complex dynamics of infectious diseases. Traditional models, such as Susceptible-Infected-Recovered, and graph-based approaches often fail to account for higher-order interactions and the nuanced structure pattern inherent in human contact networks. This study introduce

Hend Abdel-Ghani, A. H. Abbas, Ivan S. Maksymov

The rising computational and energy demands of artificial intelligence systems urge the exploration of alternative software and hardware solutions that exploit physical effects for computation. According to machine learning theory, a neural network-based computational system must exhibit nonlinearity to effectively model complex patterns and relationships. T

Xiaobo Ma, Hyunsoo Noh, Ryan Hatch, James Tokishi

Urban transportation networks are vital for the efficient movement of people and goods, necessitating effective traffic management and planning. An integral part of traffic management is understanding the turning movement counts (TMCs) at intersections, Accurate TMCs at intersections are crucial for traffic signal control, congestion mitigation, and road saf

Jun Yuan, Kevin Miao, Heyin Oh, Isaac Walker

Effective error analysis is critical for the successful development and deployment of CVML models. One approach to understanding model errors is to summarize the common characteristics of error samples. This can be particularly challenging in tasks that utilize unstructured, complex data such as images, where patterns are not always obvious. Another method i

Siavash Mirzaei-Ghormish, Jeddy Bennett, Ryan M. Camacho

We present an efficient spin-photon interface for free-space vertical emission coupling. Using a \rev{dipole model}, we show that our design achieves a far-field collection efficiency of 96\% at the numerical aperture of 0.7 with a 95\% overlap to a Gaussian mode. Our approach is based on a dual perturbation layer design. The first perturbation layer extract

Pin-Jie Lin, Rishab Balasubramanian, Fengyuan Liu, Nikhil Kandpal

Modern LLMs struggle with efficient updates, as each new pretrained model version requires repeating expensive alignment processes. This challenge also applies to domain- or languagespecific models, where fine-tuning on specialized data must be redone for every new base model release. In this paper, we explore the transfer of fine-tuning updates between mode

Rajvardhan Oak, Zubair Shafiq

Pig-butchering scams have emerged as a complex form of fraud that combines elements of romance, investment fraud, and advanced social engineering tactics to systematically exploit victims. In this paper, we present the first qualitative analysis of pig-butchering scams, informed by in-depth semi-structured interviews with $N=26$ victims. We capture nuanced,

Semsi Coskun, Davood Damircheli, Robert Lipton

A field theory is presented for predicting damage and fracture in quasi-brittle materials. The approach taken here is new and blends a non-local constitutive law with a two-point phase field. In this formulation, the material displacement field is uniquely determined by the initial boundary value problem. The theory naturally satisfies energy balance, with p

Xavier Merino, Gabriella Pangelinan, Samuel Langborgh, Michael C. King

Automated one-to-many (1:N) face recognition is a powerful investigative tool commonly used by law enforcement agencies. In this context, potential matches resulting from automated 1:N recognition are reviewed by human examiners prior to possible use as investigative leads. While automated 1:N recognition can achieve near-perfect accuracy under ideal imaging

Dominika Ciupek, Maciej Malawski, Tomasz Pieciak

Artificial intelligence has transformed the perspective of medical imaging, leading to a genuine technological revolution in modern computer-assisted healthcare systems. However, ubiquitously featured deep learning (DL) systems require access to a considerable amount of data, facilitating proper knowledge extraction and generalization. Access to such extensi

Wayne Ge, James Oxley

It is well known that every sufficiently large connected graph has, as an induced subgraph, $K_n$, $K_{1,n}$, or an $n$-vertex path. A 2023 paper of Allred, Ding, and Oporowski identified a set of unavoidable induced subgraphs of sufficiently large $2$-connected graphs. In this paper, we establish a dual version of this theorem by focusing on the minors obta

Ran Tian, Kratarth Goel

Recent advancements in LLMs have revolutionized motion generation models in embodied applications. While LLM-type auto-regressive motion generation models benefit from training scalability, there remains a discrepancy between their token prediction objectives and human preferences. As a result, models pre-trained solely with token-prediction objectives often

Changye Li, Zhecheng Sheng, Trevor Cohen, Serguei Pakhomov

Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in naturalistic language samples can be useful for early screening of AD dementia. However, the nature of language deficits often requires test administrators to use various speech e

Changye Li, Weizhe Xu, Serguei Pakhomov, Ellen Bradley

Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently, clinical estimates of the severity of disorganized thinking have been shown to correlate with measures of how difficult speech transcripts would be for large language models (LLMs) to predict. However, LLMs' deployment challenges -- including privacy concerns,

Chang Chen, Hany Hamed, Doojin Baek, Taegu Kang

Long-horizon planning is crucial in complex environments, but diffusion-based planners like Diffuser are limited by the trajectory lengths observed during training. This creates a dilemma: long trajectories are needed for effective planning, yet they degrade model performance. In this paper, we introduce this extendable long-horizon planning challenge and pr

Albert W Reed, Connor Hashemi, Dennis Melamed, Nitesh Menon

Event-based sensors (EBS) are a promising new technology for star tracking due to their low latency and power efficiency, but prior work has thus far been evaluated exclusively in simulation with simplified signal models. We propose a novel algorithm for event-based star tracking, grounded in an analysis of the EBS circuit and an extended Kalman filter (EKF)

Santiago Montoya-Blandón, Andrés Ramírez-Hassan

The response of illicit drug consumers to policy changes like legalization is mediated by demand behavior. Since individual drug use is driven by many unobservable factors, accounting for unobserved heterogeneity becomes crucial for designing targeted policies. This paper introduces a finite Gaussian mixture of EASI demand systems to estimate joint demand fo

Yuyao Zhang, Jinghao Li, Yu-Wing Tai

Text-to-image (T2I) generation has made remarkable progress, yet existing systems still lack intuitive control over spatial composition, object consistency, and multi-step editing. We present $\textbf{LayerCraft}$, a modular framework that uses large language models (LLMs) as autonomous agents to orchestrate structured, layered image generation and editing.

Mayssam Tarighi Shaayesteh, Sara Memarian Esfahani, Hossein Mohit

This study examines how AI identity influences psychological empowerment and unethical AI behavior among college students, while also exploring the moderating role of IT mindfulness. Findings show that a strong AI identity enhances psychological empowerment and academic engagement but can also lead to increased unethical AI practices. Crucially, IT mindfulne