November 2024 arXiv papers — page 32

Xuzhe Dang, Stefan Edelkamp

Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-based motion planning algorithm that extends the RRT* algorithm by using a deep neural network to predict a distribution for sampling at each iteration. We evaluate SIL-RRT* on variou

Ahmed Akl, Abdelwahed Khamis, Zhe Wang, Ali Cheraghian

Visual Question Answering (VQA) systems are notoriously brittle under distribution shifts and data scarcity. While previous solutions-such as ensemble methods and data augmentation-can improve performance in isolation, they fail to generalise well across in-distribution (IID), out-of-distribution (OOD), and low-data settings simultaneously. We argue that thi

Ivica Kopriva

Majority subspace clustering (SC) algorithms depend on one or more hyperparameters that need to be carefully tuned for the SC algorithms to achieve high clustering performance. Hyperparameter optimization (HPO) is often performed using grid-search, assuming that some labeled data is available. In some domains, such as medicine, this assumption does not hold

Dominik Semmler, Josef A. Nossek, Michael Joham, Benedikt Böck

We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particular, we show that when incorporating these networks, the system model reduces to the same structure as if no mutual coupling is present. Hence, all algorithms and theoretical discus

Chao Ge, Ge Chen, Zhipeng Jiang

The frequent migration of large-scale users leads to the load imbalance of mobile communication networks, which causes resource waste and decreases user experience. To address the load balancing problem, this paper proposes a dynamic optimization framework for mobile communication networks inspired by the average consensus in multi-agent systems. In this fra

Lucia Coto Elena, Fernando Caballero, Luis Merino

Accurate robot odometry is essential for autonomous navigation. While numerous techniques have been developed based on various sensor suites, odometry estimation using only radar and IMU remains an underexplored area. Radar proves particularly valuable in environments where traditional sensors, like cameras or LiDAR, may struggle, especially in low-light con

Claudio Novelli, Giulia Sandri

This chapter explores the influence of Artificial Intelligence (AI) on digital democracy, focusing on four main areas: citizenship, participation, representation, and the public sphere. It traces the evolution from electronic to virtual and network democracy, underscoring how each stage has broadened democratic engagement through technology. Focusing on digi

Alexander Kunkel, Hei Yin Jowett Chan, Hsi-Yu Schive, Hsinhao Huang

This paper introduces a hybrid numerical scheme for the fuzzy dark matter model: It combines a wave-based approach to solve the Schr\"odinger equation using Fourier continuations with Gram polynomials and a fluid-based approach to solve the Hamilton-Jacobi-Madelung equations. This hybrid scheme facilitates zoom-in simulations for cosmological volumes beyond

Cem Ata Baykara, Ali Burak Ünal, Nico Pfeifer, Mete Akgün

Machine learning models often struggle with generalization in small, heterogeneous datasets due to domain shifts caused by variations in data collection and population differences. This challenge is particularly pronounced in biological data, where data is high-dimensional, small-scale, and decentralized across institutions. While federated domain adaptation

Spiros Cotsakis

We analyze the dynamics of the Friedmann-Lema\^itre universes taking into account the different roles played by the fluid parameter and the cosmological constant, as well as the degenerate character of the equations. We find that the Friedmann-Lema\^itre system reduces to four qualitatively inequivalent normal forms and write down the sets of all stable pert

Anum Arslan, Wen-Bo Dong, Guo-Liang Ma, Shi Pu

We present an analytically solvable model based on the blast-wave picture of heavy-ion collisions with flow-momentum correspondence. It can describe the key features of spin polarizations in heavy-ion collisions. With the analytical solution, we can clearly show that the spin polarization with respect to the reaction plane is governed by the directed flow, w

Alexander Capstick, Rahul G. Krishnan, Payam Barnaghi

Large language models (LLMs) acquire a breadth of information across various domains. However, their computational complexity, cost, and lack of transparency often hinder their direct application for predictive tasks where privacy and interpretability are paramount. In fields such as healthcare, biology, and finance, specialised and interpretable linear mode

Om Suhas Deshmukh, Sankalp Nagaonkar, Achyut Mani Tripathi, Ashish Mishra

The newly introduced Visual State Space Model (VMamba), which employs \textit{State Space Mechanisms} (SSM) to interpret images as sequences of patches, has shown exceptional performance compared to Vision Transformers (ViT) across various computer vision tasks. However, recent studies have highlighted that deep models are susceptible to adversarial attacks.

Henning Ulfarsson

Theorems relating permutations with objects in other fields of mathematics are often stated in terms of avoided patterns. Examples include various classes of Schubert varieties from algebraic geometry (Billey and Abe 2013), commuting functions in analysis (Baxter 1964), beta-shifts in dynamical systems (Elizalde 2011) and homology of representations (Sundara

Om Ramakisan Varma, Mala Kalra

The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are inspired by natural phenomena. Recently, the propagation of the new COVID-19 implied a burden on the public health system to suffer several deaths. Vaccination, mas

Shay Ben-Moshe

We prove an ambidexterity result for $\infty$-categories of $\infty$-categories admitting a collection of colimits. This unifies and extends two known phenomena: the identification of limits and colimits of presentable $\infty$-categories indexed by a space, and the $\infty$-semiadditivity of the $\infty$-category of $\infty$-categories with $\pi$-finite col

Pirzada Suhail, Hao Tang, Amit Sethi

Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios. Network inversion techniques offer a solution by allowing us t

Nuno Brito, Orlando Oliveira, Paulo J. Silva

The lattice computation of the one-particle irreducible ghost-gluon Green function in the Landau gauge is revisited with a set of large gauge ensembles. The large statistical ensembles enable a precise determination of this Green function over a wide range of momenta, accessing its IR and UV properties with a control on the lattice effects.

Bhaskar Shukla, Jasper Nongmaithem, David Dudal, Subhash Mahapatra

We investigate the role of both magnetic field and chemical potential on the emergence of chaotic dynamics in the QCD confining string from the holographic principle. An earlier developed bottom-up model of Einstein-Maxwell-dilaton gravity, which mimics QCD features quite well, is used. The qualitative information about the chaos is obtained using the Poinca

Haixia Liu

Neural collapse, a newly identified characteristic, describes a property of solutions during model training. In this paper, we explore neural collapse in the context of imbalanced data. We consider the $L$-extended unconstrained feature model with a bias term and provide a theoretical analysis of global minimizer. Our findings include: (1) Features within th

Yitaek Kim, Ersin Das, Jeeseop Kim, Aaron D. Ames

Input delays affect systems such as teleoperation and wirelessly autonomous connected vehicles, and may lead to safety violations. One promising way to ensure safety in the presence of delay is to employ control barrier functions (CBFs), and extensions thereof that account for uncertainty: delay adaptive CBFs (DaCBFs). This paper proposes an online adaptive

Abdul Rehman, Robbert W. E. van de Kruijs, Wesley T. E. van den Beld, Jacobus M. Sturm

Hydrogen, crucial for the green energy transition, poses a challenge due to its tendency to degrade surrounding wall materials. To harness hydrogen's potential, it is essential to identify materials' parameter(s) that modulate hydrogen-material interaction. In a recent publication, we have shown that the reduction (de-nitridation) of transition metal (TM)-ni

Stefania Gravina, Naveed A. Chishti, Simona Di Bernardo, Eugenio Fasci

We report on precision spectroscopy of the 6s$^2$ $^1$S$_0\to$6s6p $^3$P$_1$ intercombination line of mercury in the deep ultraviolet, by means of a frequency-comb referenced, wavelength-modulated, saturated absorption technique. This method allowed us to perform sub-Doppler investigations with an absolute frequency axis at 254 nm, while ensuring a relativel

Yikun Li, Ting Zhang, Ratnadira Widyasari, Yan Naing Tun

Accurate identification of software vulnerabilities is crucial for system integrity. Vulnerability datasets, often derived from the National Vulnerability Database (NVD) or directly from GitHub, are essential for training machine learning models to detect these security flaws. However, these datasets frequently suffer from significant noise, typically 40% to

Shuyu Yang, Yaxiong Wang, Li Zhu, Zhedong Zheng

Text-based person search aims to retrieve specific individuals across camera networks using natural language descriptions. However, current benchmarks often exhibit biases towards common actions like walking or standing, neglecting the critical need for identifying abnormal behaviors in real-world scenarios. To meet such demands, we propose a new task, text-

Chris J Mitchell, Peter R Wild

Analogously to de Bruijn sequences, Orientable sequences have application in automatic position-location applications and, until recently, studies of these sequences focused on the binary case. In recent work by Alhakim et al., recursive methods of construction were described for orientable sequences over arbitrary finite alphabets, requiring 'starter sequen

Václav Vavryčuk

In this paper, we re-evaluate the estimates of dust mass in galaxies and demonstrate that current dust models are incomplete and based on a priori assumptions. These models suffer from a circularity problem and account for only a small portion of dust, specifically submicron-sized grains. They overlook larger dust particles and other macroscopic bodies, desp

Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu

Querying causal effects from time-series data is important across various fields, including healthcare, economics, climate science, and epidemiology. However, this task becomes complex in the existence of time-varying latent confounders, which affect both treatment and outcome variables over time and can introduce bias in causal effect estimation. Traditiona

Alessio Lugnan, Stefano Biasi, Alessandro Foradori, Peter Bienstman

Photonic neuromorphic computing may offer promising applications for a broad range of photonic sensors, including optical fiber sensors, to enhance their functionality while avoiding loss of information, energy consumption, and latency due to optical-electrical conversion. However, time-dependent sensor signals usually exhibit much slower timescales than pho

Cheng-Hsiao Tsou, Ching-Chi Lin, Gen-Huey Chen

A broadcasting problem in heterogeneous tree networks with edge weight uncertainty under the postal model is considered in this paper. The broadcasting problem asks for a minmax-regret broadcast center, which minimizes the worst-case loss in the objective function. Due to the presence of edge weight uncertainty, it is not easy to attack the broadcasting prob

Duc-Vu Nguyen, Thang Chau Phan, Quoc-Nam Nguyen, Kiet Van Nguyen

In this paper, we aimed to develop a neural parser for Vietnamese based on simplified Head-Driven Phrase Structure Grammar (HPSG). The existing corpora, VietTreebank and VnDT, had around 15% of constituency and dependency tree pairs that did not adhere to simplified HPSG rules. To attempt to address the issue of the corpora not adhering to simplified HPSG ru

Giuseppe Viterbo, Tobias Buck

Galaxies evolve hierarchically through merging with lower-mass systems and the remnants of destroyed galaxies are a key indicator of the past assembly history of our Galaxy. However, accurately measuring the properties of the accreted galaxies and hence unraveling the Milky Way's (MW) formation history is a challenging task. Here we introduce CASBI (Chemical

Pedro Núñez

Given a geometric orbifold $(X,\Delta)$ in the sense of Campana, adapted reflexive differentials with respect to this orbifold are defined on suitably ramified covers of $X$. We show that if the orbifold $(X,\Delta)$ is klt, then any such reflexive differential form can be extended to a regular differential form on a resolution of singularities of the cover.

Yoshiaki Tsujimoto, Kentaro Wakui, Tadashi Kishimoto, Shigehito Miki

In photonic quantum information processing, quantum operations using nonlinear photon-photon interactions are vital for implementing two-qubit gates and enabling faithful entanglement swapping. However, due to the weak interaction between single photons, the all-photonic realization of such quantum operations has remained out of reach so far. Herein, we demo

Qianke Wang, Dawei Lyu, Jun Liu, Jian Wang

Controlled quantum gates play a crucial role in enabling quantum universal operations by facilitating interactions between qubits. Direct implementation of three-qubit gates simplifies the design of quantum circuits, thereby being conducive to performing complex quantum algorithms. Here, we propose and present an experimental demonstration of a quantum Toffo

Lehan He, Zeren Chen, Zhelun Shi, Tianyu Yu

The success of Direct Preference Optimization (DPO) in mitigating hallucinations in Vision Language Models (VLMs) critically hinges on the true reward gaps within preference pairs. However, current methods, typically relying on ranking or rewriting strategies, often struggle to optimize these reward gaps in a systematic way during data curation. A core diffi

Mingjing Li, Huihui Zhou, Xiaofeng Xu, Zhiwei Zhong

There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks (SNNs) based on backpropagation. We

Haggai Bonneau, Vincent Démery, Elie Raphaël

Particle-particle correlation functions in ionic systems control many of their macroscopic properties. In this work, we use stochastic density functional theory to compute these correlations, and then we analyze their long-range behavior. In particular, we study the system's response to a rapid change (quench) in the external electric field. We show that the

Ryutaro Yoshimi, Ryosuke Kurihara, Yoshihiro Okamura, Hikaru Handa

Emergent phenomena arising from nontrivial band structures based on topology and symmetry have been attracting keen interest in contemporary condensed-matter physics. Materials such as SnTe and PbTe are one such example, which demonstrate a topological phase transition while showing ferroelectric instability derived from their rock-salt structure. The ferroe

Fan Yang, Ru Zhen, Jianing Wang, Yanhao Zhang

AIGC images are prevalent across various fields, yet they frequently suffer from quality issues like artifacts and unnatural textures. Specialized models aim to predict defect region heatmaps but face two primary challenges: (1) lack of explainability, failing to provide reasons and analyses for subtle defects, and (2) inability to leverage common sense and

Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre, Philipp Plewa

Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and intra-observer variability. In 2024, Anonymous Company organized an internal challenge to develop models for automatic bone quantification. We prepared and annotat

Francesco Carulli, Andrea Erroi, Francesco Bruni, Matteo L. Zaffalon

Lead halide perovskite nanocrystals (LHP-NCs) embedded in a plastic matrix are highly promising for a variety of photonic technologies and are quickly gaining attention as ultrafast, radiation-resistant nanoscintillators for radiation detection. However, advancements in LHP-NC-based photonics are hindered by their well-known thermal instability, which makes

Minbin Huang, Runhui Huang, Han Shi, Yimeng Chen

The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a range of downstream applications, such as visual question answering and image captioning. However, the substantial computational costs associated with processing high-resolution im

Aliezer Martinez-Mesa, Llinersy Uranga-Pinna, Nadine Halberstadt, Sergey N. Yurchenko

We investigate the adsorption of molecular hydrogen on pristine zinc oxide (ZnO) platelets. The volumetric and gravimetric hydrogen storage capacities of the ZnO monolayers are evaluated in a broad range of thermodynamic conditions (i.e., for temperatures in the range 77 K < T < 450 K, and for external gas pressures up to 200 bar). The thermodynamic properti

Yuang Zhao, Tianyu Li, Jiadong Chen, Shenrong Ye

Long-term Time Series Forecasting (LTSF) is crucial across various domains, but complex deep models like Transformers are often prone to overfitting on extended sequences. Linear Fully Connected models have emerged as a powerful alternative, achieving competitive results with fewer parameters. However, their reliance on a single, monolithic weight matrix lea

Muzamil Shah, Shahid Qamar, Muhammad Waseem

This paper theoretically investigates the manipulation of the Photonic Spin Hall Effect (photonic SHE) using a four-level closed coherent control coupling scheme in a cavity. The atomic system is configured to function as a combined Tripod and Lambda (CTL), Lambda $\Lambda$, and $N$ level model by properly adjusting the control field strengths and their rela

Jun Yu Chen, Tao Gao

We present APT, an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex and creative structures within the Minecraft environment. Unlike previous approaches that primarily concentrate on skill-based open-world tasks or rely on image-based diffusion models for generating voxel-based structures, our method le

Zijian Li, Yan Wang, Bowen Guan, JianKai Yin

Facial Expression Recognition has a wide application prospect in social robotics, health care, driver fatigue monitoring, and many other practical scenarios. Automatic recognition of facial expressions has been extensively studied by the Computer Vision research society. But Facial Expression Recognition in real-world is still a challenging task, partially d

Sheng Wang, Yao Tian, Xiaodong Mei, Ge Sun

Decision-making and planning in autonomous driving critically reflect the safety of the system, making effective planning imperative. Current imitation learning-based planning algorithms often merge historical trajectories with present observations to predict future candidate paths. However, these algorithms typically assess the current and historical plans

Hendrik Kleikamp, Mario Ohlberger

In this contribution we present an abstract framework for adaptive model hierarchies together with several instances of hierarchies for specific applications. The hierarchy is particularly useful when integrated within an outer loop, for instance an optimization iteration or a Monte Carlo estimation where for a large set of requests answers fulfilling certai

Shahriar Soudeep, Md Abrar Jahin, M. F. Mridha

The detection and tracking of small, occluded objects such as pedestrians, cyclists, and motorbikes pose significant challenges for traffic surveillance systems because of their erratic movement, frequent occlusion, and poor visibility in dynamic urban environments. Traditional methods like YOLO11, while proficient in spatial feature extraction for precise d

Michael Blondin, Michaël Cadilhac, Xin-Yi Cui, Philipp Czerner

Ordered binary decision diagrams (OBDDs) are a fundamental data structure for the manipulation of Boolean functions, with strong applications to finite-state symbolic model checking. OBDDs allow for efficient algorithms using top-down dynamic programming. From an automata-theoretic perspective, OBDDs essentially are minimal deterministic finite automata reco

Zhengfei Kuang, Tianyuan Zhang, Kai Zhang, Hao Tan

We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training data. Instead of relying on large-scale annotated video datasets, we demonstrate high-quality video buffer estimation by leveraging single-image priors with tem

JiHwan Moon, Jihoon Park, Jungeun Kim, Jongseong Bae

Sign language translation (SLT) is challenging, as it involves converting sign language videos into natural language. Previous studies have prioritized accuracy over diversity. However, diversity is crucial for handling lexical and syntactic ambiguities in machine translation, suggesting it could similarly benefit SLT. In this work, we propose DiffSLT, a nov

Yan Cao, Ya-Ze Cheng, Gen-Liang Li, Yong Tang

Ultralight bosons, proposed as candidates for dark matter (DM), are predicted by various new physics models. In the presence of bosons with suitable masses, superradiant (SR) instability can naturally transform a spinning black hole (BH) into a gravitational atom (GA). Here we study the dynamics of intermediate mass-ratio inspirals (IMRIs) around a GA formed

Zuxiang Kong, Fei Peng, Chieu-Minh Tran

We consider a (possibly discrete) unimodular locally compact group $G$ with Haar measure $\mu_G$, and a compact $A\subseteq G$ of positive measure with $\mu_G(A^2)\leq K\mu_G(A)$. Let $H$ be a closed normal subgroup of G and $\pi: G \rightarrow G/H$ be the quotient map. With the further assumption that $A= A^{-1}$, we show $$\mu_{G/H}(\pi A ^2) \leq K^2 \mu_

Lars Rohwedder, Ashkan Safari, Tjark Vredeveld

Local search is a widely used technique for tackling challenging optimization problems, offering simplicity and strong empirical performance across various problem domains. In this paper, we address the problem of scheduling a set of jobs on identical parallel machines with the objective of makespan minimization, by considering a local search neighborhood, c

E. J. Howell, E. Burns, A. Goldstein

The short gamma-ray burst (sGRB), GRB~170817A, is often considered a rare event. However, its inferred event rate, $\mathcal{O}(100s)\ \text{Gpc}^{-3}\ \text{yr}^{-1}$, exceeds cosmic sGRB rate estimates from high-redshift samples by an order of magnitude. This discrepancy can be explained by geometric effects related to the structure of the relativistic jet

G. X. Dong, X. B. Wang, N. Michel, M. Płoszajczak

The $^{19}$F$(p,\gamma)$$^{20}$Ne reaction is the only process to break out of the CNO cycle at temperature below 0.1 GK and may serve as the origin of calcium in first generation of stars after the Big Bang. In the recent measurement, the Jinping Underground Nuclear Experiment (JUNA) obtained the rate of $^{19}$F$(p,\gamma)$$^{20}$Ne reaction, significantly

Bertrand Daignan-Fornier, Thomas Pradeu

Cancer is most commonly viewed as resulting from somatic mutations enhancing proliferation and invasion. Some hypotheses further propose that these new capacities reveal a breakdown of multicellularity allowing cancer cells to escape proliferation and cooperation control mechanisms that were implemented during evolution of multicellularity. Here we criticall

Julie Momméja

In 1998, the Getty Center hosted the ''Time and Bits: Managing Digital Continuity'' conference, gathering the founders and thinkers of two San Francisco non-profit organizations interested in long-term thinking and archiving: the Internet Archive and the Long Now Foundation. This chapter proposes to discuss two different ways of archiving through time, in di

Ian George, Alice Zheng, Akshay Bansal

The data processing inequality is central to information theory and motivates the study of monotonic divergences. However, it is not clear operationally we need to consider all such divergences. We establish a simple method for Pinsker inequalities as well as general bounds in terms of $\chi^{2}$-divergences for twice-differentiable $f$-divergences. These to

Junyuan Deng, Wei Yin, Xiaoyang Guo, Qian Zhang

In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essential for many 3D vision tasks. However, most existing methods depend on handcrafted assumptions or are constrained by limited training data, resulting in poor generalization across d

Baptiste Nicolas Huguet

We prove a curvature-dimension criterion and obtain logarithmic Sobolev inequalities for generalised Cauchy measures with optimal weights and explicit constants. In the one-dimensional case, this constant is even optimal. From these inequalities, we derive concentration results, which allow concluding the case of the pathological dimension two.

Yadikaer Maitiniyazi, Shinya Matsuzaki, Kin-ya Oda, Masatoshi Yamada

We investigate a gravitational model based on local Lorentz invariance and general coordinate invariance. The model incorporates classical scale invariance, which forbids dimensionful parameters, and the irreversible vierbein postulate, which enables continuous degenerate limits of the vierbein, both at a specific scale. Through the dynamics of the system, w

Zheng Chen, Xun Zhang, Wenbo Li, Renjing Pei

The development of multimodal large language models (MLLMs) enables the evaluation of image quality through natural language descriptions. This advancement allows for more detailed assessments. However, these MLLM-based IQA methods primarily rely on general contextual descriptions, sometimes limiting fine-grained quality assessment. To address this limitatio

Jia Jun Cheng Xian, Sadegh Mahdavi, Renjie Liao, Oliver Schulte

Generative models such as diffusion models have achieved remarkable success in state-of-the-art image and text tasks. Recently, score-based diffusion models have extended their success beyond image generation, showing competitive performance with discriminative methods in image {\em classification} tasks~\cite{zimmermann2021score}. However, their application

Yixiong Yang, Shilin Hu, Haoyu Wu, Ramon Baldrich

Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic scenes and isolated objects and struggle to perform well on challenging real-world data. To address this issue, we propose MLI-NeRF, which integrates \textbf{M}ultiple \textbf{L}ight

K. Kourmpetis, P. Laskos-Patkos, Ch. C. Moustakidis

Astrophysical observations play a crucial role in understanding the processes within compact stars. A recent study measured the central object in the HESS J1731-347 supernova remnant (SNR), estimating its mass at $M=0.77^{+0.20}_ {-0.17} \ M_\odot$ and radius at $R = 10.40^{+0.86}_{-0.78} \ \mathrm{km}$, identifying it as the lightest neutron star ever obser

Woojoo Lee, Mikyoung Lim, Sangwoo Kang

We investigate the inverse scattering problem for tracking the location and orientation of a moving scatterer using a single incident field. We solve the problem by adopting the optimization approach with the objective function defined by the discrepancy in far-field data. We rigorously derive formulas for the far-field data under translation and rotation of

Xiangyu Liu, Xiaomei Zhang, Zhiyuan Ma, Xiangyu Zhu

Recent advancements in 3D object reconstruction have been remarkable, yet most current 3D models rely heavily on existing 3D datasets. The scarcity of diverse 3D datasets results in limited generalization capabilities of 3D reconstruction models. In this paper, we propose a novel framework for boosting 3D reconstruction with multi-view refinement (MVBoost) b

Andrea Bulgarelli, Marco Panero

The study of entanglement in quantum field theories provides insight into universal properties which are typically challenging to extract by means of local observables. However, calculations of quantities related to entanglement in gauge theories are limited by ambiguities that stem from the non-factorizability of the Hilbert space. On the other hand, (2+1)-

Yihao Qin, Shangwen Wang, Yan Lei, Zhuo Zhang

The software development process is characterized by an iterative cycle of continuous functionality implementation and debugging, essential for the enhancement of software quality and adaptability to changing requirements. This process incorporates two isolatedly studied tasks: Code Search (CS), which retrieves reference code from a code corpus to aid in cod

Liwei Deng, Penghao Chen, Ximu Zeng, Tianfu Wang

High-dimensional approximate $K$ nearest neighbor search (AKNN) is a fundamental task for various applications, including information retrieval. Most existing algorithms for AKNN can be decomposed into two main components, i.e., candidate generation and distance comparison operations (DCOs). While different methods have unique ways of generating candidates,

Yi Zhang, Wei Cheng, Jia-Wei Zhang, Tao Zhong

In this paper, we investigate the helicity form factors (HFFs) of the $B_{(s)}$-meson decay into a scalar meson with a mass larger than 1~GeV, {\it i.e.,} $B \to a_0(1450)$, $B_{(s)} \to K_0^*(1430)$ and $B_{s} \to f_0(1500)$ by using light-cone sum rules approach. We take the standard currents for correlation functions. To enhance the precision of our calcu

Yusheng Luo, Dimitrios Ntalampekos

The object of the paper is to characterize gasket Julia sets of rational maps that can be uniformized by round gaskets. We restrict to rational maps without critical points on the Julia set. Under these conditions, we prove that a Julia set can be quasiconformally uniformized by a round gasket if and only if it is a fat gasket, i.e., boundaries of Fatou comp

Ruoxi Zhu, Zhengzhong Tu, Jiaming Liu, Alan C. Bovik

Restoring images captured under adverse weather conditions is a fundamental task for many computer vision applications. However, most existing weather restoration approaches are only capable of handling a specific type of degradation, which is often insufficient in real-world scenarios, such as rainy-snowy or rainy-hazy weather. Towards being able to address

Mike Pols, Geert Brocks, Sofía Calero, Shuxia Tao

Phonons in chiral crystal structures can be circularly polarized, making them chiral. Chiral phonons carry angular momentum, which is observable in heat currents, and, via coupling to electron spin, in spin currents. Two-dimensional (2D) halide perovskites, versatile direct band gap semiconductors, can easily form chiral structures by incorporating chiral or

Xijia Liu, Kreske Felix Ecker, Lina Schelin, Xavier de Luna

We present and study semi-parametric estimators for the mean of functional outcomes in situations where some of these outcomes are missing and covariate information is available on all units. Assuming that the missingness mechanism depends only on the covariates (missing at random assumption), we present two estimators for the functional mean parameter, usin

Yicheng Yang, Pengxiang Li, Lu Zhang, Liqian Ma

Subject-driven image inpainting has recently gained prominence in image editing with the rapid advancement of diffusion models. Beyond image guidance, recent studies have explored incorporating text guidance to achieve identity-preserved yet locally editable object inpainting. However, these methods still suffer from identity overfitting, where original attr

Joshua P. Connor, Sean T. Griffin, Kavish A. Purohit

The $\Delta$-Springer fibers $Y_{n,\lambda,s}$, introduced by Levinson, Woo, and the second author, generalize Springer fibers for $\mathrm{GL}_n(\mathbb{C})$ and give a geometric interpretation of the of the Delta Conjecture from algebraic combinatorics (at $t=0$). We prove that all irreducible components of the $\Delta$-Springer fiber $Y_{n,n-1}=Y_{n,(1^{n

Jiarui Wang, Huiyu Duan, Guangtao Zhai, Juntong Wang

The rapid advancement of large multimodal models (LMMs) has led to the rapid expansion of artificial intelligence generated videos (AIGVs), which highlights the pressing need for effective video quality assessment (VQA) models designed specifically for AIGVs. Current VQA models generally fall short in accurately assessing the perceptual quality of AIGVs due

Wei Luo, Yating Zhang, Wei Chen

The edge states of a quantum spin Hall insulator exhibit helical properties, which has generated significant interest in the field of spintronics. Although it is predicted theoretically that Rashba spin-orbit coupling can effectively regulate edge state spin, experimental characterization of this spin control remains challenging. Here, we propose utilizing c

Hyegang Son, Yonglak Son, Changhoon Kim, Young Geun Kim

Transformer-based large-scale pre-trained models achieve great success. Fine-tuning is the standard practice for leveraging these models in downstream tasks. Among the fine-tuning methods, adapter-tuning provides a parameter-efficient fine-tuning by introducing lightweight trainable modules while keeping most pre-trained parameters frozen. However, existing

Yoshihito Kuno, Yasuhiro Hatsugai

A domain-wall pump by an extended cluster model of $S=1/2$ spins is proposed with local $U(1)$ gauge invariance. Its snapshot ground state is gapped and doubly degenerated due to $\mathbb{Z}_2$ invariance, which is broken by an infinitesimal boundary magnetic field. The ground state associated with the spontaneous symmetry breaking (SSB) is still symmetry-pr

Weiqi Chen, Zhiqiang Zhou, Qingsong Wen, Liang Sun

Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the following reasons: 1) how to effectively learn complex dynamics and dependencies in time series; 2) diverse and complicated anomalous subsequences as well as the inherent variance

Hui-Yue Yang, Hui Chen, Ao Wang, Kai Chen

Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM through prompting often overlook the domain shift issue, where SAM performs well on natural images but struggles in industrial scenarios. Parameter-Efficient Fine-Tuning (PEFT) offe

Arnaud Guillin, Boris Nectoux, Liming Wu

In this work, we establish, for a strong Feller process, the large deviation principle for the occupation measure conditioned not to exit a given subregion. The rate function vanishes only at a unique measure, which is the so-called quasi-ergodic distribution of the process in this subregion. In addition, we show that the rate function is the Dirichlet form

Maël Godard, Luc Jaulin, Damien Massé

In engineering, models are often used to represent the behavior of a system. Estimators are then needed to approximate the values of the model's parameters based on observations. This approximation implies a difference between the values predicted by the model and the observations that have been made. It creates an uncertainty that can lead to dangerous deci

Chengxing Xie, Xiaoming Zhang, Linze Li, Yuqian Fu

Image super-resolution (SR) has significantly advanced through the adoption of Transformer architectures. However, conventional techniques aimed at enlarging the self-attention window to capture broader contexts come with inherent drawbacks, especially the significantly increased computational demands. Moreover, the feature perception within a fixed-size win

Fabian Isensee, Yannick Kirchhoff, Lars Kraemer, Maximilian Rokuss

This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augmentation strategies to address the uniqu

S. Tchuiaga, A. Ndiaye, C. Khoule, R. A. M. Mohameden

This paper investigates the transfer of classical geometric structures from a smooth manifold $M$ to its Weil bundle $(M^\mathbf A, \tilde\pi_M, M)$ associated with a Weil algebra $\mathbf A$. We show that various structures including locally conformal symplectic (lcs), locally conformal cosymplectic (lcc), contact, Jacobi, Sasakian, Walker, sub Riemannian,

Xinyu Zhang, Lingling Zhang, Yanrui Wu, Muye Huang

Visual Question Generation (VQG) research focuses predominantly on natural images while neglecting the diagram, which is a critical component in educational materials. To meet the needs of pedagogical assessment, we propose the Diagram-Driven Course Questions Generation (DDCQG) task and construct DiagramQG, a comprehensive dataset with 15,720 diagrams and 25

Jiatao Tan, Boyang Liu

The atomic superfluid quantum interference device (ASQUID) with tunable Josephson junctions is theoretically investigated. ASQUID is a device that can be used for the detection of rotation. In this work we establish an analytical theory for the ASQUID using the tunneling Hamiltonian method and find two physical quantities that can be used for the rotation se

Yuk-Kam Lau, Wonwoong Lee

Let $d(n)$ be the number of divisors of $n$. We investigate the average value of $d(a_f(p))^r$ for $r$ a positive integer and $a_f(p)$ the $p$-th Fourier coefficient of a cuspidal eigenform $f$ having integral Fourier coefficients, where $p$ is a prime subject to a constraint on the angle associated with the normalized Fourier coefficient.

Tianyi Wang, Mengxiao Huang, Harry Cheng, Xiao Zhang

Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial i

Yu Xie, Ke Ji, Jun He, Xiaofan Shen

Magnetic kagome lattices have attracted much attention recently due to the interplay of band topology with magnetism and electronic correlations, which give rise to a variety of exotic quantum states. A common structural distortion of the kagome lattice is the breathing mode, which can significantly influence the magnetism and band characteristics. However,

Xuanbing Zhu, Dunbin Shen, Zhongwen Rao, Huiyi Ma

Multivariate time series data provide a robust framework for future predictions by leveraging information across multiple dimensions, ensuring broad applicability in practical scenarios. However, their high dimensionality and mixing patterns pose significant challenges in establishing an interpretable and explicit mapping between historical and future series

Anton Frederik Thielmann, Soheila Samiee

Recent advancements in tabular deep learning (DL) have led to substantial performance improvements, surpassing the capabilities of traditional models. With the adoption of techniques from natural language processing (NLP), such as language model-based approaches, DL models for tabular data have also grown in complexity and size. Although tabular datasets do

Xinyu Hou, Zongsheng Yue, Xiaoming Li, Chen Change Loy

In this work, we show that we only need a single parameter $\omega$ to effectively control granularity in diffusion-based synthesis. This parameter is incorporated during the denoising steps of the diffusion model's reverse process. This simple approach does not require model retraining or architectural modifications and incurs negligible computational overh