March 2025 arXiv papers — page 54

Xiangzhe Kong, Zishen Zhang, Ziting Zhang, Rui Jiao

The design of target-specific molecules such as small molecules, peptides, and antibodies is vital for biological research and drug discovery. Existing generative methods are restricted to single-domain molecules, failing to address versatile therapeutic needs or utilize cross-domain transferability to enhance model performance. In this paper, we introduce U

Lin Chen, Yuchen Mao, Guochuan Zhang

Existence of long arithmetic progression in sumsets and subset sums has been studied extensively in the field of additive combinatorics. These additive combinatorics results play a central role in the recent progress of fundamental problems in theoretical computer science including Knapsack and Subset Sum. The non-constructiveness of relevant additive combin

Baoyu Wang, Xin He, Jianjun Luo, Yitong Chen

Ferroelectric polarization switching, achieved by mechanical forces, enables the storage of stress information in ferroelectrics, and holds promise for human-interfacing applications. The prevailing mechanical approach is locally induced flexoelectricity with large strain gradients. However, this approach usually requires huge mechanical pressures, which gre

Daniel A. N. Vargas

Motivated by classical works of Gauss and Euler on the AGM, Ono and his collaborators investigated the union of AGM sequences over finite fields $\mathbb{F}_q$, where $q \equiv 3 \bmod 4$, which they refer to as swarms of jellyfish. A recent preprint extends some of their results to all finite fields with odd characteristic. For $q \equiv 5 \bmod 8$, we reve

Junqi Lai, Guoxin Wei

Brendle proved Lawson conjecture about minimal embedded torus in the round three-dimensional sphere. Carlotto and Schulz constructed a minimal embedded three-dimensional hypertorus in the round four-dimensional sphere and conjectured that their hypertorus is a unique minimal embedded three-dimensional hypertorus in the round four-dimensional sphere. In this

Haoqiang Lin, Haokun Wen, Xuemeng Song, Meng Liu

Composed Image Retrieval (CIR) allows users to search target images with a multimodal query, comprising a reference image and a modification text that describes the user's modification demand over the reference image. Nevertheless, due to the expensive labor cost of training data annotation, recent researchers have shifted to the challenging task of zero-sho

Guanglu Dong, Xiangyu Liao, Mingyang Li, Guihuan Guo

Generative Adversarial Networks (GANs) have been widely applied to image super-resolution (SR) to enhance the perceptual quality. However, most existing GAN-based SR methods typically perform coarse-grained discrimination directly on images and ignore the semantic information of images, making it challenging for the super resolution networks (SRN) to learn f

Jingtao Zhang, Xi Chen

This work introduces a novel multilevel Monte Carlo (MLMC) metamodeling approach for variance function estimation. Although devising an efficient experimental design for simulation metamodeling can be elusive, the MLMC-based approach addresses this challenge by dynamically adjusting the number of design points and budget allocation at each level, thereby aut

Naoki Yonezawa

This study evaluates the robustness of Proof of Team Sprint (PoTS) against adversarial attacks through simulations, focusing on the attacker win rate and computational efficiency under varying team sizes (\( N \)) and attacker ratios (\( \alpha \)). Our results demonstrate that PoTS effectively reduces an attacker's ability to dominate the consensus process.

Xiaoqing Zhang, Hanfeng Shi, Xiangyu Li, Haili Ye

Parkinson's disease (PD) is a prevalent neurodegenerative disorder globally. The eye's retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain le

Nguyen Huynh Thao Nhi, Huynh Viet Khanh

Let $K$ be a field and $E$ be a graph. Let $L_K(E)$ be the Leavitt path algebra of $E$ over $K$ with the standard involution $^\star$. We investigate the set of skew-symmetric elements, $\mathbf{K}_{L_K(E)}=\{x\in L_K(E) : x^{\star}=-x\}$, and show that for any simple $L_K(E)$ containing a cycle, $[\mathbf{K}_{L_K(E)}, \mathbf{K}_{L_K(E)}]\ne\mathbf{K}_{L_K(

Lakeisha M. Ramos Rosado, David K. Sing, Natalie H. Allen, Hannah R. Wakeford

We present a new and extended transmission spectrum of the warm Neptune HAT-P-26b spanning wavelengths between 0.29 - 5.0 microns. This spectrum is derived from new HST STIS G430L observations from the PanCET program, a reanalysis of the previously published HST STIS G750L data, along with the previously published HST WFC3 IR G102 and G141 data, and the two

Naoki Yonezawa

This paper presents an empirical evaluation of the Proof of Team Sprint (PoTS) consensus algorithm, focusing on reward fairness, energy efficiency, system stability, and scalability. We conducted large-scale simulations comparing PoTS with conventional Proof of Work (PoW) across various team sizes and computational conditions. In PoW, the highest-performance

Yifei Wang, Shihan Kong, Zhanhua Xin, Kaiwei Zhu

Autonomous underwater vehicles (AUVs) are essential for marine exploration and research. However, conventional designs often struggle with limited maneuverability in complex, dynamic underwater environments. This paper introduces an innovative orientation-adjustable thruster AUV (OATAUV), equipped with a redundant vector thruster configuration that enables f

Vasiliki Fragkou, Roberto Vázquez, Quentin A. Parker, Denise R. Gonçalves

Planetary nebulae (PNe) that are physical members of open star clusters (OCs) are valuable for stellar evolution studies. They are extremely rare, with only three such instances confirmed in our Galaxy. Here, we confirm the physical association of PN NGC 2818 with OC NGC 2818A, an association long debated in the literature, adding a fourth object to the samp

Dashen Yan

We discover an explicit construction of non-degenerate $\mathbb{Z}_{2}$-harmonic functions on $\mathbb{R}^{n},n\geq 3$, using a variant of ellipsoidal coordinates on $\mathbb{R}^{n}$. The branching set of these examples is a codimension-$2$ ellipsoid, providing the first known family of non-degenerate $\mathbb{Z}_{2}$-harmonic $1$-forms on $\mathbb{R}^{n}$ w

Yubo Li, Xinyu Yao, Rema Padman

Despite the outstanding performance of deep learning models in clinical prediction tasks, explainability remains a significant challenge. Inspired by transformer architectures, we introduce the Temporal-Feature Cross Attention Mechanism (TFCAM), a novel deep learning framework designed to capture dynamic interactions among clinical features across time, enha

Hengcheng Zhu, Valerio Terragni, Lili Wei, Shing-Chi Cheung

Mock assertions provide developers with a powerful means to validate program behaviors that are unobservable to test assertions. Despite their significance, they are rarely considered by automated test generation techniques. Effective generation of mock assertions requires understanding how they are used in practice. Although previous studies highlighted the

Yang Ren, Hai Jiang, Menglong Yang, Wei Li

RAW-to-sRGB mapping, or the simulation of the traditional camera image signal processor (ISP), aims to generate DSLR-quality sRGB images from raw data captured by smartphone sensors. Despite achieving comparable results to sophisticated handcrafted camera ISP solutions, existing learning-based methods still struggle with detail disparity and color distortion

Wu-Hsiung Huang

In this paper, we establish a "global" Morse index theorem. Given a hypersurface $M^{n}$ of constant mean curvature, immersed in $\mathbb{R}^{n+1}$. Consider a continuous deformation of "generalized" Lipschitz domain $D(t)$ enlarging in $M^{n}$. The topological type of $D(t)$ is permitted to change along $t$, so that $D(t)$ has an arbitrary shape which can "

Feiyang Wang, Xiaomin Yu, Wangyu Wu

Proving Rubik's Cube theorems at the high level represents a notable milestone in human-level spatial imagination and logic thinking and reasoning. Traditional Rubik's Cube robots, relying on complex vision systems and fixed algorithms, often struggle to adapt to complex and dynamic scenarios. To overcome this limitation, we introduce CubeRobot, a novel visi

Amogh Inamdar, Uzay Macar, Michel Vazirani, Michael Tarnow

The study of propositional logic -- fundamental to the theory of computing -- is a cornerstone of the undergraduate computer science curriculum. Learning to solve logical proofs requires repeated guided practice, but undergraduate students often lack access to on-demand tutoring in a judgment-free environment. In this work, we highlight the need for guided p

Wenjuan Qin, Weiran Wang, Yuming Yang, Tao Gui

The study investigates the efficacy of pre-trained language models (PLMs) in analyzing argumentative moves in a longitudinal learner corpus. Prior studies on argumentative moves often rely on qualitative analysis and manual coding, limiting their efficiency and generalizability. The study aims to: 1) to assess the reliability of PLMs in analyzing argumentati

Junle Liu, Yun Zhang, Zixi Guo

Feature Coding for Machines (FCM) aims to compress intermediate features effectively for remote intelligent analytics, which is crucial for future intelligent visual applications. In this paper, we propose a Multiscale Feature Importance-based Bit Allocation (MFIBA) for end-to-end FCM. First, we find that the importance of features for machine vision tasks v

Huynh Viet Khanh, Le Qui Danh

Let $L_K(E)$ be the Leavitt path algebra of a directed graph $E$ over a field $K$. In this paper, we determine $E$ and $K$ for the Lie algebra $\mathbf{K}_{L_K(E)}$ and the Jordan algebra $\mathbf{S}_{L_K(E)}$ arising from $L_K(E)$ with respect to the standard involution to be solvable.

Ben Rahman

Semantic segmentation has made significant strides in pixel-level image understanding, yet it remains limited in capturing contextual and semantic relationships between objects. Current models, such as CNN and Transformer-based architectures, excel at identifying pixel-level features but fail to distinguish semantically similar objects (e.g., "doctor" vs. "n

David Whiting

Boundary measurement matrices associated to networks on a plane correspond to certain totally nonnegative Grassmannians as shown previously by A. Postnikov. In this paper, we look to generalize this result by categorizing the boundary measurements associated to networks on a cylinder of maximal rank 2 and 3. In particular, we show that the maximal rank 3 mat

Junfeng Liu, Christopher T. Symons, Ranga Raju Vatsavai

Recent advancements in AI-driven conversational agents have exhibited immense potential of AI applications. Effective response generation is crucial to the success of these agents. While extensive research has focused on leveraging multiple auxiliary data sources (e.g., knowledge bases and personas) to enhance response generation, existing methods often stru

Max Ward, Mary Richardson, Mihir Metkar

mRNA technology has revolutionized vaccine development, protein replacement therapies, and cancer immunotherapies, offering rapid production and precise control over sequence and efficacy. However, the inherent instability of mRNA poses significant challenges for drug storage and distribution, particularly in resource-limited regions. Co-optimizing RNA struc

Valentin Anfray, Hong-Yan Shih

Epidemic spreading often occurs in spatially heterogeneous environments, yet how quenched heterogeneity reshapes its onset and critical dynamics remains poorly understood. The diffusive epidemic process, a minimal reaction-diffusion model whose absorbing-state transition is controlled by the relative diffusion of healthy and infected species, provides a natu

Zhiyao Ren, Yibing Zhan, Baosheng Yu, Dacheng Tao

Text-to-image generation has become increasingly popular, but achieving the desired images often requires extensive prompt engineering. In this paper, we explore how to decode textual prompts from reference images, a process we refer to as image reverse prompt engineering. This technique enables us to gain insights from reference images, understand the creat

Xuechen Liang, Meiling Tao, Yinghui Xia, Jianhui Wang

Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making, lack of long-term memory, and limited context windows in dynamic environments. To address these issues, this paper proposes an innovative framework Memory-Enhanced Agents with Reflective S

George Hodgkins, Mark Madler, Joseph Izraelevitz

In this work, we explore an object-based programming model for filling the space between shared memory and distributed systems programming. We argue that the natural representation for resources distributed across a memory network (e.g. RDMA or CXL) is the traditional shared memory object. This concurrent object (which we call a "channel" object) exports tra

Jeong Hyeok Cha, Hyun-Yong Lee, Heung-Sik Kim

Studying the real-time dynamics of strongly correlated systems poses significant challenges, which have recently become more manageable thanks to advances in density matrix renormalization group (DMRG) and tensor network methods. A notable development in this area is the introduction of a complex-time evolution scheme for tensor network states, originally su

Ephraim Linder, Sofya Raskhodnikova, Adam Smith, Thomas Steinke

We provide tools for sharing sensitive data when the data curator does not know in advance what questions an (untrusted) analyst might ask about the data. The analyst can specify a program that they want the curator to run on the dataset. We model the program as a black-box function $f$. We study differentially private algorithms, called privacy wrappers, th

Songyi Gao, Zuolin Tu, Rong-Jun Qin, Yi-Hao Sun

Offline reinforcement learning (RL) aims to learn from historical data without requiring (costly) access to the environment. To facilitate offline RL research, we previously introduced NeoRL, which highlighted that datasets from real-world tasks are often conservative and limited. With years of experience applying offline RL to various domains, we have ident

Elizabeth A. Silber, Daniel C. Bowman

The reentry of the OSIRIS-REx Sample Return Capsule (SRC) on September 24, 2023, presented a rare opportunity to study atmospheric entry dynamics through a dense network of ground-based infrasound sensors. As the first interplanetary capsule to reenter over the United States since Stardust in 2006, this event allowed for unprecedented observations of infraso

Sarah Pungitore, Shashank Yadav, Vignesh Subbian

Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional methods typically involve extensive manual data review. While machine learning and natural language processing advancements have helped, further improvements are needed. Few studies have explored using Large Language

Mohd Shoaib, Navonil Mustafee, Varun Ramamohan

Abstraction or substitution and aggregation are the most widely used simulation model simplification operations. Abstraction involves replacing subsystems within a discrete-event simulation (DES) with one or more quantities - typically random variables - representing the lengths of stay in the subsystems(s) in question to create a `simplified' system compris

Fucai Ke, Vijay Kumar B G, Xingjian Leng, Zhixi Cai

Visual reasoning (VR), which is crucial in many fields for enabling human-like visual understanding, remains highly challenging. Recently, compositional visual reasoning approaches, which leverage the reasoning abilities of large language models (LLMs) with integrated tools to solve problems, have shown promise as more effective strategies than end-to-end VR

Jingyao Sun, Qilu Zhang, Di Ma, Tianyu Jia

Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we propose a structured and sparse partial least sq

Ruiyi Wang, Yushuo Zheng, Zicheng Zhang, Chunyi Li

Existing real-world image dehazing methods primarily attempt to fine-tune pre-trained models or adapt their inference procedures, thus heavily relying on the pre-trained models and associated training data. Moreover, restoring heavily distorted information under dense haze requires generative diffusion models, whose potential in dehazing remains underutilize

Xiaozhe Hu, Miroslav Kuchta, Kent-Andre Mardal, Xue Wang

In this paper, we propose a parameter-robust preconditioner for the coupled Stokes-Darcy problem equipped with various boundary conditions, enforcing the mass conservation at the interface via a Lagrange multiplier. We rigorously establish that the coupled system is well-posed with respect to physical parameters and mesh size and provide a framework for cons

Jiali Cheng, Hadi Amiri

Large language models (LLMs) are the foundation of many AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding their ability to perform fine-grained linguistic annotation tasks, such as detecting nouns or verbs, or identifying more complex syntactic structures like clauses in input texts.

Xunchuan Liu, Pak-Shing Li

Inferring three-dimensional structures from two-dimensional maps remains a major challenge due to line of sight degeneracies. We present Cloud2to3, a flexible framework to reconstruct three-dimensional (3D) volumetric density fields from two-dimensional (2D) maps by generalizing the inverse Abel transform and the AVIATOR algorithm. The pipeline decomposes a

Hui Chen, Liangyu Liu, Xianchao Xiu, Wanquan Liu

Hyperspectral unmixing (HU) is a critical yet challenging task in remote sensing. However, existing nonnegative matrix factorization (NMF) methods with graph learning mostly focus on first-order or second-order nearest neighbor relationships and usually require manual parameter tuning, which fails to characterize intrinsic data structures. To address the abo

Farhana Keya, Gollam Rabby, Prasenjit Mitra, Sahar Vahdati

Every scientific discovery starts with an idea inspired by prior work, interdisciplinary concepts, and emerging challenges. Recent advancements in large language models (LLMs) trained on scientific corpora have driven interest in AI-supported idea generation. However, generating context-aware, high-quality, and innovative ideas remains challenging. We introd

Emily Dautenhahn, Laurent Saloff-Coste

Faber-Krahn functions provide lower bounds on the first Dirichlet eigenvalue of the Laplacian and are useful because they imply heat kernel upper bounds. In this paper, we are interested in Faber-Krahn functions and heat kernel estimates for a certain class of graphs consisting of "sufficiently nice pages" (satisfying a Harnack inequality) glued together via

Tom Bertalan, George A. Kevrekidis, Eleni D Koronaki, Siddhartha Mishra

Classically, to solve differential equation problems, it is necessary to specify sufficient initial and/or boundary conditions so as to allow the existence of a unique solution. Well-posedness of differential equation problems thus involves studying the existence and uniqueness of solutions, and their dependence to such pre-specified conditions. However, in

Tian Chong, Han Luo, Lingen Lu

By using the ABP method developed by Cabr\'e and Brendle, we establish some Sobolev inequalities for compact domains and submanifolds in a complete Riemannian manifold with lower quadratic curvature decay

Zeqiang Wei, Kai Jin, Zeyi Hou, Kuan Song

Transformers bring significantly improved performance to the light field image super-resolution task due to their long-range dependency modeling capability. However, the inherently high computational complexity of their core self-attention mechanism has increasingly hindered their advancement in this task. To address this issue, we first introduce the LF-VSS

Kelaiti Xiao, Liang Yang, Dongyu Zhang, Paerhati Tulajiang

We introduce VisualQuest, a novel dataset designed to rigorously evaluate multimodal large language models (MLLMs) on abstract visual reasoning tasks that require the integration of symbolic, cultural, and linguistic knowledge. Unlike existing benchmarks that focus on direct image captioning or classification of realistic images, VisualQuest comprises 3,551

Matheus Kunzler Maldaner, Wesley Hanwen Deng, Jason Hong, Ken Holstein

While generative AI systems have gained popularity in diverse applications, their potential to produce harmful outputs limits their trustworthiness and usability in different applications. Recent years have seen growing interest in engaging diverse AI users in auditing generative AI that might impact their lives. To this end, we propose MIRAGE as a web-based

Takaaki Nomura, Hiroshi Okada

We propose a new type of lepton seesaw model introducing a modular $A_4$ flavor symmetry in which isospin doublet vector fermions play an important role in constructing seesaw mechanisms for both the charged-lepton mass matrix and the neutrino one. The charged-lepton mass matrix is induced via the Dirac seesaw with a two-by-two block mass matrix. On the othe

Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu

A long-standing challenge in tomography is the 'missing wedge' problem, which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints. This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image. To tackle this challenge, we propose an approach du

Qingyan Ren, Liming Tang

In this paper, the generalized Loop Heisenberg-Virasoro algebra is introduced. Firstly, we determine the derivations on the generalized Loop Heisenberg-Virasoro algebra. Then we show that all 2-local derivations are derivations. Furthermore, we determine the biderivations on the generalized Loop Heisenberg-Virasoro algebra are inner biderivations and give th

Ruoyu Miao, Christiana Charalambous

Based on the proposed time-varying JLCM (Miao and Charalambous, 2022), the heterogeneous random covariance matrix can also be considered, and a regression submodel for the variance-covariance matrix of the multivariate latent random effects can be added to the joint latent class model. A general joint latent class model with heterogeneous random-effects mode

Tenzan Araki, Joseph F. Goodwin, Bálint Koczor

In contrast to monolithic devices, modular, networked quantum architectures are based on interconnecting smaller quantum hardware nodes using quantum communication links, and offer a promising approach to scalability. Virtual distillation (VD) is a technique that can, under ideal conditions, suppress errors exponentially as the number of quantum state copies

Hiêp Hàn, Carlos Hoppen, Nicolas Moro Müller, Dionatan Ricardo Schmidt

In this paper we show that for $r\geq 12$ and any sufficiently large $n$-vertex graph $G$ the number of $r$-edge-colorings of $G$ with no rainbow $K_4$ is at most $r^{ex(n,K_4)}$, where $ex(n,K_4)$ denotes the Tur\'{a}n number of $K_4$. Moreover, $G$ attains equality if and only if it is the Tur\'{a}n graph $T_3(n)$. The bound on the number of colors $r\geq

Mick Gardner, Audrey Billhymer, Rebecca Kamerer, Joanna Schmit

Quantitative ultrasound (QUS) characterizes the composition of cells to distinguish diseased from healthy tissue. QUS can reflect the complexity of the tumor and detect early lymph node (LN) metastasis ex vivo. The objective in this study was to gather preliminary QUS and cytokine data from dogs undergoing radiation therapy and correlate QUS data with both L

Félix Loubaton

We construct an adjunction between $m$-categories internal to $(\infty,n)$-categories, called $(n,m)$-double $\infty$-categories, and filtrations $A_0\to \dots\to A_m$ where for all $i<m$, $A_i$ is a $(n+i)$-category. We show that this adjunction induces an equivalence between $(n,m)$-double $\infty$-categories admitting enough companions and filtrations suc

Alexander P Browning, Michael J Chappell, Hamid Rahkooy, Torkel E Loman

Stochasticity plays a key role in many biological systems, necessitating the calibration of stochastic mathematical models to interpret associated data. For model parameters to be estimated reliably, it is typically the case that they must be structurally identifiable. Yet, while theory underlying structural identifiability analysis for deterministic differe

Hao Guo, Jianfei Zhu, Wei Fan, Chunzhi Yi

Referring expression comprehension (REC) aims at achieving object localization based on natural language descriptions. However, existing REC approaches are constrained by object category descriptions and single-attribute intention descriptions, hindering their application in real-world scenarios. In natural human-robot interactions, users often express their

Jiaqi Liao, Hong Liu, Guiying Yan

A theorem of Kleitman states that a collection of binary vectors with diameter d has cardinality at most that of a Hamming ball of radius d/2. In this paper, we give a q-analog of it.

Nikta Akbarpour, Ahmad Saleem Mirza, Erfan Raoofian, Fatemeh Fard

Ruby is a widely used open-source programming language, valued for its simplicity, especially in web development. Despite its popularity, with over one million users on GitHub, little is known about the issues faced by Ruby developers. This study aims to investigate the key topics, trends, and difficulties faced by Ruby developers by analyzing over 498,000 R

Huixin Gao, Tanya Evans, Anna Fergusson

This scoping review examines the use of student explanation strategies in postsecondary mathematics and statistics education. We analyzed 46 peer-reviewed articles published between 2014 and 2024, categorizing student explanations into three main types: self-explanation, peer explanation and explanation to fictitious others. The review synthesizes the theore

Mark P. Hertzberg, Rachel Nathan, Suzanna E. Semaan

While the two derivative action of gravitation is specified uniquely, higher derivative operators are also allowed with coefficients that are not specified uniquely by effective field theory. We focus on a four derivative operator in which the Riemann tensor couples directly to the electromagnetic field $a\,R_{\mu\nu\alpha\beta}F^{\mu\nu}F^{\alpha\beta}$. We

Nasir Alfaraj, Amr S. Helmy

Silicon-based integrated photonics has demonstrated significant advances in miniaturization and performance, yet critical challenges remain in achieving efficient on-chip communication at high bandwidths. Plasmonic devices on silicon and silicon-on-insulator substrates offer a promising solution, enabling subwavelength light confinement and enhanced light-ma

Zhengxuan Wang, Ruqian Wu, Chunlan Ma, Shijing Gong

Recently discovered, altermagnetism represents a third class of collinear magnets. These materials exhibit zero net magnetization, similar to antiferromagnets, but display anomalous transport properties resembling those of ferromagnets. Altermagnetic materials manifest various anomalous electronic transport phenomena, including the anomalous Hall effect, ano

Rui Loja Fernandes, Wilmer Smilde

We develop a new framework of relative algebroids to address existence and classification problems of geometric structures subject to partial differential equations.

Xinpeng Liu, Zeyi Huang, Fumio Okura, Yasuyuki Matsushita

Novel view synthesis has demonstrated impressive progress recently, with 3D Gaussian splatting (3DGS) offering efficient training time and photorealistic real-time rendering. However, reliance on Cartesian coordinates limits 3DGS's performance on distant objects, which is important for reconstructing unbounded outdoor environments. We found that, despite its

S. R. Mane

We present a general formula for the particular solution of an inhomogeneous linear difference equation with variable coefficients. The answer is expressed as a weighted sum of fundamental solutions of the associated linear difference equation. This corresponds to an initial value problem in the case of linear differential equations. We remark that Green's f

Manuel Cabezas, Alexander Fribergh, Mark Holmes, Edwin Perkins

We study the behaviour of the rescaled minimal subtree containing the origin and K random vertices selected from a random critical (sufficiently spread-out, and in dimensions d > 8) lattice tree conditioned to survive until time ns, in the limit as n goes to infinity. We prove joint weak convergence of various quantities associated with these subtrees under

Ludovic Jami, François-Xavier Gauci, Céline Cohen, Xavier Noblin

Vascular networks exhibit a remarkable diversity of architectures and transport mechanisms across biological systems. Inspired by embolism propagation in plant xylem, where air invades water-filled conduits under negative pressure, we study air penetration in compliant one-dimensional hydrodynamic networks experiencing mass loss by pervaporation. Using a the

Seungtaek Kim, Jonghyup Lee, Kyoungseok Han, Seibum B. Choi

To address the computational challenges of Model Predictive Control (MPC), recent research has studied using imitation learning to approximate MPC with a computationally efficient Deep Neural Network (DNN). However, this introduces a common issue in learning-based control, the simulation-to-reality (sim-to-real) gap. Inspired by Robust Tube MPC, this study p

Alexandra P. Klipfel, David I. Kaiser

The KM3NeT Collaboration recently announced the detection of a neutrino with energy 220 PeV. One possible source of such ultra-high-energy particles is the rapid emission of energetic Hawking radiation from a primordial black hole (PBH) near the end of its evaporation lifetime. The mass distribution for PBHs features a power-law tail for small masses; a smal

Naomi Sweeting

The Bloch-Kato conjecture predicts a far-reaching connection between orders of vanishing of $L$-functions and the ranks of Selmer groups of $p$-adic Galois representations. In this article, we consider the four-dimensional, symplectic Galois representations arising from automorphic representations $\pi$ of $\mathrm{GSp}_4(\mathbb A_{\mathbb Q})$ with trivial

Xiaoming Qi, Jingyang Zhang, Huazhu Fu, Guanyu Yang

Federated continual learning (FCL) offers an emerging pattern to facilitate the applicability of federated learning (FL) in real-world scenarios, where tasks evolve dynamically and asynchronously across clients, especially in medical scenario. Existing server-side FCL methods in nature domain construct a continually learnable server model by client aggregati

Hojung Choi, Jun En Low, Tae Myung Huh, Seongheon Hong

We introduce CoinFT, a capacitive 6-axis force/torque (F/T) sensor that is compact, light, low-cost, and robust with an average root-mean-squared error of 0.16N for force and 1.08mNm for moment when the input ranges from 0~14N and 0~5N in normal and shear directions, respectively. CoinFT is a stack of two rigid PCBs with comb-shaped electrodes connected by a

Joao Barata, Carlos A. Salgado, Joao M. Silva

The states of matter produced in the early stage of heavy ion collisions can be highly anisotropic. If such a feature is sufficiently pronounced, one should expect the final particle distribution inside jets to reflect it in the form of non-trivial angle correlations. In this talk, we discuss a first step in exploring such correlations by studying how a $q\b

Najeebullah, Maaz Salman, Zar Nawab Khan Swati

Digital image spoofing has emerged as a significant security threat in biometric authentication systems, particularly those relying on facial recognition. This study evaluates the performance of three vision based models, MobileNetV2, ResNET50, and Vision Transformer, ViT, for spoof detection in image classification, utilizing a dataset of 150,986 images div

Tomoki Ohsawa, Andrea Richaud, Roy Goodman

We perform an asymptotic analysis of massive point-vortex dynamics in Bose--Einstein condensates in the small-mass limit $\varepsilon \to 0$. We define two distinguished manifolds in the phase space of the dynamics. We call the first the kinematic subspace $\mathcal{K}$, whereas the second is an almost-invariant set $\mathcal{S}$ called a ``slow manifold.''

Rachael Boyd, Corey Bregman, Jan Steinebrunner

Let $H_g$ denote the 4-dimensional handlebody of genus $g$ and $U_g$ its boundary. We show that for all $g \ge 0$ the map from $B Homeo(H_g)$ to $B Homeo(U_g)$ induced by restriction to the boundary admits a section.

Han Chen, Zicong Jiang, Zining Zhang, Bingsheng He

We introduce LogQuant, a groundbreaking 2-bit quantization technique for KV Cache in large language model (LLM) inference, delivering substantial memory savings while preserving superior performance. Previous methods either assume that later tokens are more important or attempt to predict important tokens based on earlier attention patterns. Both approaches,

Carlos Contreras, José Garrido, Eugene Levin

In this paper we proposed the homotopy approach for solving the nonlinear Balitsky-Kovchegov (BK) evolution equation with running QCD coupling. The approach consists of two steps. First, is the analytic solution to the nonlinear evolution equation for the simplified, leading twist kernel. Second, is the iteration procedure that allow us to calculate correcti

Francisco Mena, Diego Arenas, Miro Miranda, Andreas Dengel

In recent years, the development of robust multi-source models has emerged in the Earth Observation (EO) field. These are models that leverage data from diverse sources to improve predictive accuracy when there is missing data. Despite these advancements, the factors influencing the varying effectiveness of such models remain poorly understood. In this study

M. T. Ziemba, J. Phrompao, F. Jung, I. M. Rabey

A plethora of studies ranging from precision physics to quantum information employ ions, highly excited neutral atoms or polar molecules as tools. Motivated by the need for miniaturization, scalability and controllability, the particles are often trapped close to electrodes imprinted on dielectric surfaces. However, such geometry makes the particles suscepti

M. D. Moldavskaya, L. E. Golub, V. V. Bel&#39;kov, N. N. Mikhailov

We report the observation and comprehensive study of the complex nonlinear intensity dependence of the magneto-photogalvanic effect (MPGE) current induced by terahertz (THz) radiation in Cd$_{x}$Hg$_{1-x}$Te films with inverted ($x = 0.15$) and noninverted ($x = 0.22$) band structures. The nonlinearities are studied for the resonant MPGE caused by cyclotron

Davis Ranney, Yufei Wang, A. Adam Ding, Yunsi Fei

The USB protocol has become a ubiquitous standard for connecting peripherals to computers, making its security a critical concern. A recent research study demonstrated the potential to exploit weaknesses in well-established protocols, such as PCIe, and created a side-channel for leaking sensitive information by leveraging congestion within shared interfaces.

Hansjörg Geiges, Jakob Hedicke, Murat Sağlam

We show that an overtwisted contact structure on a closed, oriented 3-manifold can be defined by a contact form having a Bott-integrable Reeb flow if and only if the Poincaré dual of its Euler class is represented by a graph link.

Achintya Sajeendran, Timothy C. Ralph

We present a modification to a &#39;rotating&#39; version of the dynamical Alcubierre spacetime, which was previously shown to permit closed timelike curves. We find that if the effective rotation rate is made dependent on the spacetime coordinates within the bubble, a class of closed timelike curves are promoted to spatially circular geodesics. These paths

Yiming Ma, Victor Sanchez, Tanaya Guha

We propose CLIP-EBC, the first fully CLIP-based model for accurate crowd density estimation. While the CLIP model has demonstrated remarkable success in addressing recognition tasks such as zero-shot image classification, its potential for counting has been largely unexplored due to the inherent challenges in transforming a regression problem, such as counti

Yong Yang, Geraint F. Lewis, Denis Erkal, Ting S. Li

The Ophiuchus stellar stream presents a puzzle due to its complicated morphology, with a substructure perpendicular to the main track (spur), a broadened tail (fanning), and a shorter than expected angular extent given its old stellar population and short orbital period. The location of the stream approaches the Galactic center, implying a possible connectio

Mark S. Bartlett, Elizabeth Cultra, Nathan Geldner, Amilcare Porporato

Quantifying watershed process variability consistently with climate change and ecohydrological dynamics remains a central challenge in hydrology. Stochastic ecohydrology characterizes hydrologic variability through probability distributions that link climate, hydrology, and ecology. However, these approaches are often limited to small spatial scales (e.g., p

Yoshihiro Kawasaki, Masahiro N. Machida

We perform one-dimensional protoplanetary disk evolution calculations to investigate the impact of the magnetohydrodynamic (MHD) disk wind on disk evolution and dust particle growth.To examine the effect of the MHD disk wind, we compare calculations with and without it. In disk evolution calculations, episodic accretion events (or outbursts) occur repeatedly

Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu

Recovering the underlying Directed Acyclic Graph (DAG) structures from observational data presents a formidable challenge, partly due to the combinatorial nature of the DAG-constrained optimization problem. Recently, researchers have identified gradient vanishing as one of the primary obstacles in differentiable DAG learning and have proposed several DAG con

Patrick Diehl, Nojoud Nader, Maxim Moraru, Steven R. Brandt

The rapid evolution of large language models (LLMs) has opened new possibilities for automating various tasks in software development. This paper evaluates the capabilities of the Llama 2-70B model in automating these tasks for scientific applications written in commonly used programming languages. Using representative test problems, we assess the model's ca

Alejandro Corichi, Angel Garcia Chung, Federico Zadra

The Entropic Uncertainty Relations (EUR) result from inequalities that are intrinsic to the Hilbert space and its dual with no direct connection to the Canonical Commutation Relations. Bialynicky-Mielcisnky obtained them in \cite{bialynicki1975uncertainty} attending Hilbert spaces with a Lebesgue measure. The analysis of these EUR in the context of singular

Bilal Alsallakh, Timothy Wroge, Vivek Miglani, Narine Kokhlikyan

We explore the symmetry of the mean k x k weight kernel in each layer of various convolutional neural networks. Unlike individual neurons, the mean kernels in internal layers tend to be symmetric about their centers instead of favoring specific directions. We investigate why this symmetry emerges in various datasets and models, and how it is impacted by cert

A. P. Milone, A. F. Marino, M. Bernizzoni, F. Muratore

Almost all globular clusters (GCs) contain multiple populations consisting of stars with varying helium and light-element abundances. These populations include first-population stars, which exhibit similar chemical compositions to halo-field stars with comparable [Fe/H], and second-population stars, characterized by enhanced He and N abundances along with re