December 2024 arXiv papers — page 101

Amir Tosson, Mohammad Shokr, Mahmoud Al Humaidi, Eduard Mikayelyan

We address the identification of grain-corresponding Laue reflections in energy dispersive Laue diffraction (EDLD) experiments by formulating it as a clustering problem solvable through unsupervised machine learning (ML). To achieve reliable and efficient identification of grains in a Laue pattern, we employ a combination of clustering algorithms, namely hie

M. Andrés-Carcasona, M. Martínez, Ll. M. Mir, J. Mundet

In this article, we present results on the simulated performance of an instrumented baffle installed at the entrance of the vacuum towers hosting the end mirrors of Virgo's main Fabry-P\'erot cavities. The installation of instrumented baffles is part of the Advanced Virgo Plus upgrade in time for the O5 observing run. They were originally envisaged to be sus

Timur Biktagirov, Eugenio Otal, Leopold Trost, Patrick Dörflinger

Metal-organic frameworks (MOFs), with their high porosity and large internal surface area, provide versatile platforms for integrating spin centers with potential applications in catalysis and quantum sensing. Here, we identify a stable NH radical spin center in UiO-66-NH2, a zirconium-based MOF with aminoterephthalic acid (TPA-NH2) linkers. Using electron p

Han Liu, Tian Liu, Kai Huang

As urban logistics demand continues to grow, UAV delivery has become a key solution to improve delivery efficiency, reduce traffic congestion, and lower logistics costs. However, to fully leverage the potential of UAV delivery networks, efficient swarm scheduling and management are crucial. In this paper, we propose a real-time scheduling and management syst

Yu-Hsuan Huang, Ling Lo, Hongxia Xie, Hong-Han Shuai

Sequential recommendation (SR) systems predict user preferences by analyzing time-ordered interaction sequences. A common challenge for SR is data sparsity, as users typically interact with only a limited number of items. While contrastive learning has been employed in previous approaches to address the challenges, these methods often adopt binary labels, mi

Xavier Caruso, Quentin Gazda, Alexis Lucas

The aim of this paper is to discuss the notion of Wieferich primes in the context of Drinfeld modules. Our main result is a surprising connection between the proprety of a monic irreducible polynomial $\mathfrak p$ to be Wieferich and the $\mathfrak p$-adic valuation of special $L$-values of Drinfeld modules. This generalizes a theorem of Thakur for the Carl

Valentin Gillet

In this article, we study the similarity of the Polish operator topologies $\texttt{WOT}$, $\texttt{SOT}$, $\texttt{SOT}\mbox{$_{*}$}$ and $\texttt{SOT}\mbox{$^{*}$}$ on the set of the positive contractions on $\ell_p$ with $p > 1$. Using the notion of norming vector for a positive operator, we prove that these topologies are similar on $\mathcal{P}_1(\ell_2

Xiaokun Sun, Zeyu Cai, Ying Tai, Jian Yang

While haircut indicates distinct personality, existing avatar generation methods fail to model practical hair due to the data limitation or entangled representation. We propose StrandHead, a novel text-driven method capable of generating 3D hair strands and disentangled head avatars with strand-level attributes. Instead of using large-scale hair-text paired

Guanxiong Qu, Ji Zou, Daniel Loss, Tomoki Hirosawa

Nanoscale topological spin textures in magnetic systems are emerging as promising candidates for scalable quantum architectures. Despite their potential as qubits, previous studies have been limited to semiclassical approaches, leaving a critical gap: the lack of a fully quantum demonstration. Here, we address this challenge by employing the density-matrix r

Yan-Li Wang, Yu-Kuo Hsiao, Kai-Lei Wang, Chong-Chung Lih

We investigate the singly and doubly charmful two-body $\Omega_b^-$ decays using the light-front quark model. Our findings reveal that most branching fractions calculated in this study, such as ${\cal B}(\Omega_b^-\to\Xi^- D^0,\Xi^{-}D^{*0})$, can be ten to one hundred times larger than those reported in previous calculations. Additionally, we interpret the

Kémo Morvan

Given a complex analytic singularity $(X, 0)$, we show that if there exists an automorphism $F: (X, 0) \to (X, 0)$ that is contracting, then $(X, 0)$ is quasi-homogeneous.

Chang Xu, Ruixiang Zhang, Wen Yang, Haoran Zhu

Detecting oriented tiny objects, which are limited in appearance information yet prevalent in real-world applications, remains an intricate and under-explored problem. To address this, we systemically introduce a new dataset, benchmark, and a dynamic coarse-to-fine learning scheme in this study. Our proposed dataset, AI-TOD-R, features the smallest object si

Weifang Weng, Guoqiang Zhang, Zhenya Yan

In this paper, we investigate the large-order asymptotics of multi-rational solitons of the focusing complex modified Korteweg-de Vries (c-mKdV) equation with nonzero background via the Riemann-Hilbert problems. First, based on the Lax pair, inverse scattering transform, and a series of deformations, we construct a multi-rational soliton of the c-mKdV equati

Xianglong Zhang, Lihua You

Let $G$ be a connected graph of order $n$. A $\{P_{2},C_{3},P_{5},\mathcal{T}(3)\}$-factor of $G$ is a spanning subgraph of $G$ such that each component is isomorphic to a member in $\{P_{2},C_{3},P_{5},\mathcal{T}(3)\}$, where $\mathcal{T}(3)$ is a $\{1,2,3\}$-tree. The $A_{\alpha}$-spectral radius of $G$ is denoted by $\rho_{\alpha}(G)$. In this paper, we

Jingqian Wu, Shuo Zhu, Chutian Wang, Boxin Shi

Recent advancements in 3D Gaussian Splatting (3D-GS) have demonstrated the potential of using 3D Gaussian primitives for high-speed, high-fidelity, and cost-efficient novel view synthesis from continuously calibrated input views. However, conventional methods require high-frame-rate dense and high-quality sharp images, which are time-consuming and inefficien

Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li

Patch deformation-based methods have recently exhibited substantial effectiveness in multi-view stereo, due to the incorporation of deformable and expandable perception to reconstruct textureless areas. However, such approaches typically focus on exploring correlative reliable pixels to alleviate match ambiguity during patch deformation, but ignore the defor

Xinrui You, Liaoyuan Xiao, Yongguan Ke, Chaohong Lee

We study how nonlinear strength affects topological pumping of edge solitons by using nonlinear Gross-Pitaevskii equation. For weak nonlinear strength, the introduction of nonlinearity breaks the symmetry of the energy spectrum, which makes the topological pumping from the left edge to the right edges differ from the inverse process. For moderate nonlinear s

Ziye Zhu, Jiaming Hu, Yubo Yuan, Hua Wang

We discover that a large family of [Pb$_2$F$_2$]- and [Bi$_2$O$_2$]-based mixed-anion materials with a litharge-type structural unit are highly polarizable layered semiconductors on the edge of ferroelectricity. First-principles calculations demonstrate that in this family of materials, compounds as diverse as PbFBr, BiOCl, BiCuOSe, Bi$_2$OS$_2$, and Bi$_5$O

Katharina Prasse, Patrick Knab, Sascha Marton, Christian Bartelt

Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs (DCBMs), which redu

Qingliang Fan, Marcelo C. Medeiros, Hanming Yang, Songshan Yang

This paper considers the finite horizon portfolio rebalancing problem in terms of mean-variance optimization, where decisions are made based on current information on asset returns and transaction costs. The study's novelty is that the transaction costs are integrated within the optimization problem in a high-dimensional portfolio setting where the number of

Xiaozhe Li, Kai WU, Siyi Yang, YiZhan Qu

Recent advancements in text-to-video (T2V) generation have leveraged diffusion models to enhance visual coherence in videos synthesized from textual descriptions. However, existing research primarily focuses on object motion, often overlooking cinematic language, which is crucial for conveying emotion and narrative pacing in cinematography. To address this,

Lorenzo Cardarelli

Archaeological pottery documentation and study represents a crucial but time-consuming aspect of archaeology. While recent years have seen advances in digital documentation methods, vast amounts of legacy data remain locked in traditional publications. This paper introduces PyPotteryLens, an open-source framework that leverages deep learning to automate the

Zhen-Yang Peng, Ying-Dan Wang

We consider a quantum Otto-type heat engine constructed in an optomechanical system with which the cavity is chosen as the working substance. The cavity can effectively be coupled with hot thermal baths in nonequilibrium steady-states via optomechanical interaction. The mechanical mode with parametric drive fuels the cavity, and the utilization efficiency of

Isita Bagayatkar, Youngil Kim, Gene Tsudik

Internet of Things (IoT) devices are becoming increasingly commonplace in numerous public and semi-private settings. Currently, most such devices lack mechanisms to facilitate their discovery by casual (nearby) users who are not owners or operators. However, these users are potentially being sensed, and/or actuated upon, by these devices, without their knowl

Atsushi Murase, Hiro-aki Narita

We investigate the theta correspondence of cusp forms for the dual pair $(O^*(4),{\mathrm Sp}(1,1))$ originally introduced by Tsuneo Arakawa in the non-adelic setting. We call this Arakawa lifting. In this paper, reformulating the theta correspondence in the adelic setting, we provide commutation relations of Hecke operators satisfied by Arakawa lifting at a

Filippo Bigi, Marcel Langer, Michele Ceriotti

The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational chemistry and materials discovery. In this domain, rigorous enforcement of symmetry and conservation laws has traditionally been considered essential. For this reason, interatomic

Ioannis Chasandras, Odysseas S. Chlapanis, Ion Androutsopoulos

This paper presents the systems we developed for RIRAG-2025, a shared task that requires answering regulatory questions by retrieving relevant passages. The generated answers are evaluated using RePASs, a reference-free and model-based metric. Our systems use a combination of three retrieval models and a reranker. We show that by exploiting a neural componen

Sascha H. Hauck, Matthias Kabel, Mazen Ali, Nicolas R. Gauger

Homogenization is a fundamental technique for estimating the macroscopic properties of materials with microscale heterogeneity. Among Homogenization methods, the FFT-based Homogenization algorithm has become widely used due to its computational efficiency and ability to handle complex microstructures. Nevertheless, even with GPU acceleration, FFT-based Homog

Claudia M. Raiteri

We review the main topics in the field of blazar multiwavelength variability as a tool to understand the physics and structure of extragalactic jets and their central engine. We address issues such as the cross-correlation between flux variations at different frequencies, the mechanisms to explain the long-term and short-term variability, the size of the jet

Qian Zhang, Yi He, Yue Xiao, Xiaoli Zhang

As the Internet of Things (IoT) industry advances, the imperative to secure IoT devices has become increasingly critical. Current practices in both industry and academia advocate for the enhancement of device security through key installation. However, it has been observed that, in practice, IoT vendors frequently assign shared keys to batches of devices. Th

Antonio Alarcon, Francisco J. Lopez

Let $M$ be an open Riemann surface and $n\ge 3$ be an integer. In this paper we establish some generic properties (in Baire category sense) in the space of all conformal minimal immersions $M\to\mathbb{R}^n$ endowed with the compact-open topology, pointing out that a generic such immersion is chaotic in many ways. For instance, we show that a generic conform

Jian Wang, Xiao-Long Zhou, Ze-Min Shen, Dong-Yu Huang

Single atoms trapped in optical cavities exhibit immense potential as key nodes in future quantum information processing. They have already demonstrated significant advancement in various quantum technologies, particularly regarding the generation of nonclassical light. Here, we efficiently produce genuine photonic Bell states through the inelastic scatterin

Yu Zhao, Yifang Ban

Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilized for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detection of burned areas. Thus, satellites eq

Arthur Amalvy, Vincent Labatut, Richard Dufour

The automatic extraction of character networks from literary texts is generally carried out using natural language processing (NLP) cascading pipelines. While this approach is widespread, no study exists on the impact of low-level NLP tasks on their performance. In this article, we conduct such a study on a literary dataset, focusing on the role of named ent

Qing Wei, Wei Wang, Yifa Tang, Ralf Metzler

We consider the fractional Langevin equation far from equilibrium (FLEFE) to describe stochastic dynamics which do not obey the fluctuation-dissipation theorem, unlike the conventional fractional Langevin equation (FLE). The solution of this equation is Riemann-Liouville fractional Brownian motion (RL-FBM), also known in the literature as FBM II. Spurious no

Ali Hadj Djilani, Dominique Henry, Patrick Pons, Herve Aubert

In this paper a new method based on the fusion of optical and radar data is proposed to detect and remotely interrogate mobile and passive sensors. The sensors are detected in real time by using an optical camera, while their remote reading is carried out on the fly using a Frequency-Modulated Continuous-Wave radar. The proof-of-concept is established from t

Jingyu Xu, Yang Wang

With the increasing availability of multimodal data, many fields urgently require advanced architectures capable of effectively integrating these diverse data sources to address specific problems. This study proposes a hybrid recommendation model that combines the Mixture of Experts (MOE) framework with large language models to enhance the performance of rec

Yuchen Fu, Zifeng Cheng, Zhiwei Jiang, Zhonghui Wang

Extracting sentence embeddings from large language models (LLMs) is a promising direction, as LLMs have demonstrated stronger semantic understanding capabilities. Previous studies typically focus on prompt engineering to elicit sentence embeddings from LLMs by prompting the model to encode sentence information into the embedding of the last token. However, L

Yu Zhao, Sebastian Gerard, Yifang Ban

Wildfire monitoring and prediction are essential for understanding wildfire behaviour. With extensive Earth observation data, these tasks can be integrated and enhanced through multi-task deep learning models. We present a comprehensive multi-temporal remote sensing dataset for active fire detection, daily wildfire monitoring, and next-day wildfire predictio

Sungdong Lee, Joshua Bang, Youngrae Kim, Hyungwon Choi

Graphical model estimation from multi-omics data requires a balance between statistical estimation performance and computational scalability. We introduce a novel pseudolikelihood-based graphical model framework that reparameterizes the target precision matrix while preserving the sparsity pattern and estimates it by minimizing an $\ell_1$-penalized empirica

Karina Kvanchiani, Roman Kraynov, Elizaveta Petrova, Petr Surovcev

Accurate recognition and interpretation of sign language are crucial for enhancing communication accessibility for deaf and hard of hearing individuals. However, current approaches of Isolated Sign Language Recognition (ISLR) often face challenges such as low data quality and variability in gesturing speed. This paper introduces a comprehensive model trainin

Mario Rosenfelder, Hendrik Carius, Markus Herrmann-Wicklmayr, Peter Eberhard

In real-world applications of mobile robots, collision avoidance is of critical importance. Typically, global motion planning in constrained environments is addressed through high-level control schemes. However, additionally integrating local collision avoidance into robot motion control offers significant advantages. For instance, it reduces the reliance on

Yujie Chen, Jiangyan Yi, Cunhang Fan, Jianhua Tao

Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although current models perform well, their effectiveness diminishes when confronted with the diverse and evolving nature of real-world deepfakes. To address this issue, we propose a contin

Bowen Deng, Tong Wang, Lele Fu, Sheng Huang

Graph node clustering is a fundamental unsupervised task. Existing methods typically train an encoder through selfsupervised learning and then apply K-means to the encoder output. Some methods use this clustering result directly as the final assignment, while others initialize centroids based on this initial clustering and then finetune both the encoder and

Weilun Feng, Haotong Qin, Chuanguang Yang, Zhulin An

Diffusion models have received wide attention in generation tasks. However, the expensive computation cost prevents the application of diffusion models in resource-constrained scenarios. Quantization emerges as a practical solution that significantly saves storage and computation by reducing the bit-width of parameters. However, the existing quantization met

Bumhoo Lim, Masateru Ishiguro

Context. The Stardust flyby mission to Jupiter-family comet (JFC) 81P/Wild 2 (hereafter, 81P) captured its dense quasicircular depressions. Nevertheless, the formation mechanism remains a subject of ongoing debate. Aims. We aimed to study how cometary activity contributed to the formation and enlargement of these depressions by analyzing Stardust flyby image

Baptiste Chatelier, Vincent Corlay, Matthieu Crussière, Luc Le Magoarou

Reaping the benefits of multi-antenna communication systems in frequency division duplex (FDD) requires channel state information (CSI) reporting from mobile users to the base station (BS). Over the last decades, the amount of CSI to be collected has become very challenging owing to the dramatic increase of the number of antennas at BSs. To mitigate the over

Kai Pang, Haoyuan Sun, Zhiwei Wang

Let $(X,\omega)$ be a compact Hermitian manifold and let $\{\beta\}\in H^{1,1}(X,\mathbb R)$ be a real $(1,1)$-class with a smooth representative $\beta$, such that $\int_X\beta^n>0$. Assume that there is a bounded $\beta$-plurisubharmonic function $\rho$ on $X$. First, we provide a criterion for the weak convergence of non-pluripolar complex Monge-Amp\`ere

Audric Drogoul

This paper is devoted to an intrinsic geometrical classification of three-mirror telescopes. The problem is formulated as the study of the connected components of a semi-algebraic set. Under first order approximation, we give the general expression of the transfer matrix of a reflexive optical system. Thanks to this representation, we express the semi-algebr

Qiqi Xia, Xiaofeng Wang, Kai Li, Xiang Gao

With the development of wide-field surveys, a large amount of data on short-period W UMa contact binaries have been obtained. Continuous and uninterrupted light curves as well as high-resolution spectroscopic data are crucial in determining the absolute physical parameters. Targets with both TMTS light curves and LAMOST medium-resolution spectra were selecte

Ruitao Zhu, Yangsu Liu, Dagui Chen, Zhenjia Ma

Online advertising has become a core revenue driver for the internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Traditional auction mechanisms, like GSP, rely on the independent CTR assumption and fail to account for the influence of other displayed items, termed externalities. Recent advancement

Behzad Shayegh, Hobie H. -B. Lee, Xiaodan Zhu, Jackie Chi Kit Cheung

We address unsupervised dependency parsing by building an ensemble of diverse existing models through post hoc aggregation of their output dependency parse structures. We observe that these ensembles often suffer from low robustness against weak ensemble components due to error accumulation. To tackle this problem, we propose an efficient ensemble-selection

Ziyang Chen, Yiwen Ye, Feilong Tang, Yongsheng Pan

Domain generalization (DG) aims to enhance the ability of models trained on source domains to generalize effectively to unseen domains. Recently, Sharpness-Aware Minimization (SAM) has shown promise in this area by reducing the sharpness of the loss landscape to obtain more generalized models. However, SAM and its variants sometimes fail to guide the model t

Jisun Lee, Hyungki Im, Alper Atamtürk

We study the mixed-integer quadratic programming formulation of an $n$-period hybrid control problem with a convex quadratic cost function and linear dynamics. We first give the convex hull description of the single-period, two-mode problem in the original variable space through two new classes of valid cuts. These cuts are then generalized to the single-per

Jiaxu Wan, Hong Zhang, Ziqi He, Yangyan Deng

Point transformers have demonstrated remarkable progress in 3D understanding through expanded receptive fields (RF), but further expanding the RF leads to dilution in group attention and decreases detailed feature extraction capability. Proxy, which serves as abstract representations for simplifying feature maps, enables global RF. However, existing proxy-ba

Simon Torka, Sahin Albayrak

This paper reviews work published between 2002 and 2022 in the fields of Android malware, clone, and similarity detection. It examines the data sources, tools, and features used in existing research and identifies the need for a comprehensive, cross-domain dataset to facilitate interdisciplinary collaboration and the exploitation of synergies between differe

Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor

This technical report describes the MERaLiON-SpeechEncoder, a foundation model designed to support a wide range of downstream speech applications. Developed as part of Singapore's National Multimodal Large Language Model Programme, the MERaLiON-SpeechEncoder is tailored to address the speech processing needs in Singapore and the surrounding Southeast Asian r

Dennis Höning, Ludmila Carone, Philipp Baumeister, Kathy L. Chubb

Temperature-dependent biological productivity controls silicate weathering and thereby extends the potential habitable timespan of Earth. Models and theoretical considerations indicate that the runaway greenhouse on Earth-like exoplanets is generally accompanied by a dramatic increase in atmospheric H$_2$O and CO$_2$, which might be observed with the upcomin

Hervé Déjean

In this paper, we investigate how efficiently large language models (LLM) can be trained to check whether an answer is already stored in their parametric memory. We distill an LLM-as-a-judge to compute the IK (I Know) score. We found that this method is particularly beneficial in the context of retrieval-assisted augmented generation (RAG), with a respectabl

Quan Chen, Tingyu Wang, Rongfeng Lu, Yu Liu

UAV Geo-Localization faces significant challenges due to the drastic appearance discrepancy between dronecaptured images and satellite views. Existing methods typically assume a consistent scaling factor across views and rely on predefined partition alignment to extract viewpoint-invariant representations through part-level feature construction. However, thi

Chanuk Yang, Hwanchul Jung, Hyung Kook Choi, Yunchul Chung

We present systematic methods for compensating gate crosstalk effects in gate-defined quantum dots (QDs), to allow the observation of Coulomb blockade peaks from the few-electron regime (N = 1) to N \approx 20. Gate crosstalk, where adjustments to one gate voltage unintentionally affect other gate-controlled parameters, makes it difficult to control tunnelin

H. Dereli-Bégué, A. Pe'er, D. Bégué, F. Ryde

The X-ray light curves of gamma-ray bursts (GRBs) display complex features, including plateaus and flares, that challenge theoretical models. Here, we study the properties of flares that are observed in the early afterglow phase (up to a few thousand of seconds). We split the sample into two groups: bursts with and without X-ray plateau. We find that the dis

Wen-Shuai Zhang, Liang Tang

We apply the method of QCD sum rules to study the \(QQ\bar{Q}\bar{q}\) and \(QQ\bar{Q}\bar{s}\) tetraquark states, where $Q=c,b$ and $q=u,d$, with the quantum number \(J^P = 0^{+}\). We consider the contributions of vacuum condensates up to dimension-9 in the operator product expansion, and use the energy scale formula \(\mu = \sqrt{M_{X}^2 - (i\mathbb{M}_c

Junda Cheng, Zhipeng Cai, Zhaoxing Zhang, Wei Yin

Visual odometry (VO) aims to estimate camera poses from visual inputs -- a fundamental building block for many applications such as VR/AR and robotics. This work focuses on monocular RGB VO where the input is a monocular RGB video without IMU or 3D sensors. Existing approaches lack robustness under this challenging scenario and fail to generalize to unseen d

Panwang Xia, Lei Yu, Yi Wan, Qiong Wu

Cross-View Geo-Localization tackles the challenge of image geo-localization in GNSS-denied environments, including disaster response scenarios, urban canyons, and dense forests, by matching street-view query images with geo-tagged aerial-view reference images. However, current research often relies on benchmarks and methods that assume center-aligned setting

Brian Hopkins, Aram Tangboonduangjit

Mansour and Shattuck introduced the notion of water cells for integer compositions in 2018. We focus on compositions with parts restricted to 1 and 2 and consider the array of counts for such compositions of $n$ with $k$ water cells, establishing generating functions for the columns and diagonal sums, recurrences within the array in the spirit of Pascal's le

Olivier Ramaré

Let the $A$-cusps of a dense subset $\mathcal{P}^*\in[\sqrt{N},N]$ of primes be points $\alpha\in\mathbb{R}/\mathbb{Z}$ that are such that $|\sum_{\substack{p\in\mathcal{P}^*}} e(\alpha p)|\ge |\mathcal{P}^*|/A$. We establish that any $(1/N)$-well spaced subset of $A$-cusps contains at most $20A^2K\log(2A)$ points, where $K=N/(|\mathcal{P}^*|\log N)$. We fur

Mohsen Rashki

Machine learning (ML) has emerged as a powerful tool for tackling complex regression and classification tasks, yet its success often hinges on the quality of training data. This study introduces an ML paradigm inspired by domain knowledge of the structure of output function, akin to physics-informed ML, but rooted in probabilistic principles rather than phys

Hyun-kyu Ko, Dongheok Park, Youngin Park, Byeonghyeon Lee

3D super-resolution aims to reconstruct high-fidelity 3D models from low-resolution (LR) multi-view images. Early studies primarily focused on single-image super-resolution (SISR) models to upsample LR images into high-resolution images. However, these methods often lack view consistency because they operate independently on each image. Although various post

Runyu Lyu, Wenchi Cheng, Qinghe Du, Tony Q. S. Quek

Orbital angular momentum (OAM) technology enhances the spectrum and energy efficiency of wireless communications by enabling multiplexing over different OAM modes. However, classical information theory, which relies on scalar models and far-field approximations, cannot fully capture the unique characteristics of OAM-based systems, such as their complex elect

Daiki Iwata, Kanji Tanaka, Shoya Miyazaki, Kouki Terashima

In simultaneous localization and mapping, active loop closing (ALC) is an active vision problem that aims to visually guide a robot to maximize the chances of revisiting previously visited points, thereby resetting the drift errors accumulated in the incrementally built map during travel. However, current mainstream navigation strategies that leverage such i

Yujie Zhang, Shivam Aggarwal, Tulika Mitra

Mixture-of-Experts (MoE) models, though highly effective for various machine learning tasks, face significant deployment challenges on memory-constrained devices. While GPUs offer fast inference, their limited memory compared to CPUs means not all experts can be stored on the GPU simultaneously, necessitating frequent, costly data transfers from CPU memory,

Kousik Dhara, Harry Dym

This paper deals with (1) the truncated matrix Hamburger moment problem from the point of view of reproducing kernel Hilbert spaces of vector valued entire functions of the kind introduced and extensively studied by Louis de Branges and (2) the truncated matrix trigonometric moment problem viewed through an analogous class of spaces that are formulated with

Andrea Perin, Stephane Deny

Symmetries (transformations by group actions) are present in many datasets, and leveraging them holds considerable promise for improving predictions in machine learning. In this work, we aim to understand when and how deep networks -- with standard architectures trained in a standard, supervised way -- learn symmetries from data. Inspired by real-world scena

Dong In Lee, Hyeongcheol Park, Jiyoung Seo, Eunbyung Park

Recent advancements in 3D editing have highlighted the potential of text-driven methods in real-time, user-friendly AR/VR applications. However, current methods rely on 2D diffusion models without adequately considering multi-view information, resulting in multi-view inconsistency. While 3D Gaussian Splatting (3DGS) significantly improves rendering quality a

Xi Wang, Hongzhen Li, Heng Fang, Yichen Peng

Image rendering from line drawings is vital in design and image generation technologies reduce costs, yet professional line drawings demand preserving complex details. Text prompts struggle with accuracy, and image translation struggles with consistency and fine-grained control. We present LineArt, a framework that transfers complex appearance onto detailed

Keita Kobayashi, Hiroki Nakamura, Masahiko Okumura, Mitsuhiro Itakura

Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In this study, we employ machine learning molecular dynamics (MLMD) simulations to investigate the high-temperature thermal properties of thorium dioxide, which has a fluorite structure, and lithium oxide, which has

Hyeonchu Park, Byungjun Kim, Bugeun Kim

As large language models (LLMs) generate more human-like texts, concerns about the side effects of AI-generated texts (AIGT) have grown. So, researchers have developed methods for detecting AIGT. However, two challenges remain. First, the performance of detecting black-box LLMs is low because existing models focus on probabilistic features. Second, most AIGT

Shashikant Kumar, Gulshan Kumar, Ajay Kumar, Prakash Parida

Our study sets forth a carbon based two-dimensional (2D) kagome topological insulator without containing any metal atoms, that aligns the Fermi level with the Dirac point without the need for doping, overcoming a significant bottleneck issue observed in 2D metal-organic frameworks (MOFs)-based kagome structures. Our 2D kagome structure formed by creating pat

Yoshitaka Taguchi

Optical computing is emerging as a promising platform for energy-efficient, high-throughput hardware in deep learning. A key challenge lies in the realization of optical matrix-vector multiplication, which often requires $O(N^2)$ phase shifters for exact synthesis of $N \times N$ matrices, limiting scalability. In this study, we propose an approximate matrix

Jonathan Bennett, Michael G. Cowling

We establish a structure theorem for the Brascamp--Lieb constant formulated in the general setting of locally compact abelian groups. This extends and unifies the finiteness characterisations previously known for euclidean spaces and for finitely generated groups and their duals. We place particular emphasis on Fourier invariance throughout, reflecting the f

Le Shen, Rong Huang, Zhijie Wang

Garment restoration, the inverse of virtual try-on task, focuses on restoring standard garment from a person image, requiring accurate capture of garment details. However, existing methods often fail to preserve the identity of the garment or rely on complex processes. To address these limitations, we propose an improved diffusion model for restoring authent

Kunming Li, Mao Shan, Stephany Berrio Perez, Katie Luo

Traffic accidents are a global safety concern, resulting in numerous fatalities each year. A considerable number of these deaths are caused by animal-vehicle collisions (AVCs), which not only endanger human lives but also present serious risks to animal populations. This paper presents an innovative self-training methodology aimed at detecting rare animals,

Jiale Zhang, Qianxi Jia, Yang Liu, Wei Zhang

Stereo video conversion aims to transform monocular videos into immersive stereo format. Despite the advancements in novel view synthesis, it still remains two major challenges: i) difficulty of achieving high-fidelity and stable results, and ii) insufficiency of high-quality stereo video data. In this paper, we introduce SpatialMe, a novel stereo video conv

Yuxin Wang, Maresa Schröder, Dennis Frauen, Jonas Schweisthal

Constructing confidence intervals (CIs) for the average treatment effect (ATE) from patient records is crucial to assess the effectiveness and safety of drugs. However, patient records typically come from different hospitals, thus raising the question of how multiple observational datasets can be effectively combined for this purpose. In our paper, we propos

J. Kakol, A. Leiderman

In our previous paper we systematized several known equivalent definitions of Fr\'echet (G\^ ateaux) Differentiability Spaces and Asplund (weak Asplund) Spaces. As an application, we extended the classical Mazur's theorem, and also proved that the product of any family of Banach spaces $(E_{\alpha})$ is an Asplund lcs if and only if each $E_{\alpha}$ is Aspl

Vijay Prakash S

In an unbounded plane, straight lines are used extensively for mathematical analysis. They are tools of convenience. However, those with high slope values become unbounded at a faster rate than the independent variable. So, straight lines, in this work, are made to be bounded by introducing a parametric nonlinear term that is positive. The straight lines are

Shihan Wu, Ji Zhang, Pengpeng Zeng, Lianli Gao

Prompt tuning (PT) has long been recognized as an effective and efficient paradigm for transferring large pre-trained vision-language models (VLMs) to downstream tasks by learning a tiny set of context vectors. Nevertheless, in this work, we reveal that freezing the parameters of VLMs during learning the context vectors neither facilitates the transferabilit

George E. Andrews, Mohamed El Bachraoui

Motivated by two Legendre-type formulas for overpartitions, we derive a variety of their companions as Legendre theorems for overpartition pairs. This leads to equalities of subclasses of overpartitions and overpartition pairs.

Xiaoyi Ma, Pinghui Huang, Cong Yu, Ruobing Dong

Observations of protoplanetary disks have revealed the presence of both crescent-shaped and ring-like structures in dust continuum emission. These crescents are thought to arise from dust-trapping vortices generated by the Rossby Wave Instability (RWI), which induces density waves akin to those caused by planets. These vortices have the potential to create g

Guangsheng Bao, Yanbin Zhao, Juncai He, Yue Zhang

Advanced large language models (LLMs) can generate text almost indistinguishable from human-written text, highlighting the importance of LLM-generated text detection. However, current zero-shot techniques face challenges as white-box methods are restricted to use weaker open-source LLMs, and black-box methods are limited by partial observation from stronger

Radu Ioan Bot, Dang-Khoa Nguyen, Chunxiang Zong

In this paper, we derive a Fast Reflected Forward-Backward (Fast RFB) algorithm to solve the problem of finding a zero of the sum of a maximally monotone operator and a monotone and Lipschitz continuous operator in a real Hilbert space. Our approach extends the class of reflected forward-backward methods by introducing a Nesterov momentum term and a correcti

Catherine Leroux, Sophia F. Lin, Przemyslaw Bienias, Krishanu R. Sankar

One of the critical challenges solid-state quantum processors face is the presence of fabrication imperfections and two-level systems, which render certain qubits and gates either inoperable or much noisier than tolerable by quantum error correction protocols. To address this challenge, we develop a suite of novel and highly performant methods for adapting s

Koshi Oishi, Teruki Kato, Hiroya Makino, Seigo Ito

Forklifts are used extensively in various industrial settings and are in high demand for automation. In particular, counterbalance forklifts are highly versatile and employed in diverse scenarios. However, efforts to automate these processes are lacking, primarily owing to the absence of a safe and performance-verifiable development environment. This study p

Akifumi Takamizawa, Ryohei Hokari, Sota Kagami, Thu H. H. Le

A magneto-optical trap of cesium atoms was generated by applying a circularly polarized cooling laser beam onto a reflective two-dimensional diffraction grating with an aperture and by retroreflecting the incident beam passing through the aperture while reversing the circular polarization. The cooling laser beams comprised the incident, retroreflected, and f

Naoki Sato, Hideaki Iiduka

Graduated optimization is a global optimization technique that is used to minimize a multimodal nonconvex function by smoothing the objective function with noise and gradually refining the solution. This paper experimentally evaluates the performance of the explicit graduated optimization algorithm with an optimal noise scheduling derived from a previous stu

Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu

Understanding user intentions is challenging for online platforms. Recent work on intention knowledge graphs addresses this but often lacks focus on connecting intentions, which is crucial for modeling user behavior and predicting future actions. This paper introduces a framework to automatically generate an intention knowledge graph, capturing connections b

Wonje Choi, Woo Kyung Kim, Minjong Yoo, Honguk Woo

We address the challenge of utilizing large language models (LLMs) for complex embodied tasks, in the environment where decision-making systems operate timely on capacity-limited, off-the-shelf devices. We present DeDer, a framework for decomposing and distilling the embodied reasoning capabilities from LLMs to efficient, small language model (sLM)-based pol

Wei Hao, M. Atif Sultan, Li-Juan Liu, En Wang

Motivated by two news states $h_1(1911)$ and $X(2300)$ observed by BESIII, we have investigated the mass spectrum and the strong decay properties of the strangeonium mesons within the modified Godfrey-Isgur model by considering the screening effects. We have determined the free parameters using the masses and widths of the well established $s\bar{s}$ states

Alejandro Ortega, Luca Vilasi, Youjun Wang

We analyze the existence and multiplicity of positive solutions to a nonlocal elliptic problem involving the spectral fractional Laplace operator endowed with homogeneous mixed Dirichlet-Neumann boundary conditions and weighted critical nonlinearities. By means of variational methods and the Nehari manifold approach, we deduce the existence of multiple posit