December 2024 arXiv papers — page 27

Aryan Agarwala, Guy Even

Designs of data structures for approximate membership queries with false-positive errors that support both insertions and deletions stipulate the following two conditions: (1) Duplicate insertions are prohibited, i.e., it is prohibited to insert an element $x$ if $x$ is currently a member of the dataset. (2) Deletions of nonelements are prohibited, i.e., it

Emiru Tsunoo, Yuki Saito, Wataru Nakata, Hiroshi Saruwatari

Real-time speech enhancement (SE) is essential to online speech communication. Causal SE models use only the previous context while predicting future information, such as phoneme continuation, may help performing causal SE. The phonetic information is often represented by quantizing latent features of self-supervised learning (SSL) models. This work is the f

Haripriya Madathil, Pranav Pradeep, Paul Nöel, Saül Vélez

Magnons, the quanta of spin angular momentum, can be excited in magnetic insulators by spin-flip scattering processes originated from currents applied to a heavy metal overlayer. The efficiency to generate non-equilibrium magnons across interfaces is parametrized by the spin conductance gs, a phenomenological constant that is considered to be dependent on th

P. Céspedes, F. H. Holik, A. P. Majtey

While the concept of entanglement for distinguishable particles is well established, defining entanglement and non-locality in systems of indistinguishable particles, which require the use of the (anti)symmetrization postulate, remains challenging, and multiple approaches have been proposed to address this issue. In this work we study the problem of detectin

Kemal Kirtac, Guido Germano

We investigate the efficacy of large language models (LLMs) in sentiment analysis of U.S. financial news and their potential in predicting stock market returns. We analyze a dataset comprising 965,375 news articles that span from January 1, 2010, to June 30, 2023; we focus on the performance of various LLMs, including BERT, OPT, FINBERT, and the traditional

Yukimi Goto, Tohru Koma

We study a lattice Nambu-Jona-Lasinio model with certain continuous chiral and two-flavor symmetries. For the Hamiltonian of the model, we construct a ground state which breaks the parity and flavor symmetries. In our argument, the chiral symmetry plays a crucial role for proving the violation of the parity and flavor symmetries, although the model does not

Nabo He, DanDan Li, Xiaohong Huang

Due to the vast address space of IPv6, the brute-force scanning methods originally applicable to IPv4 are no longer suitable for proactive scanning of IPv6. The recently proposed target generation algorithms have a low hit rate for existing IPv6 target generation algorithms, primarily because they do not accurately fit the distribution patterns of active IPv

Fatemeh Asgari, Valeria Vitelli, Uta Sailer

Handling latent variables in Structural Equation Models (SEMs) in a case where both the latent variables and their corresponding indicators in the measurement error part of the model are random curves presents significant challenges, especially with sparse data. In this paper, we develop a novel family of Functional Structural Equation Models (FSEMs) that in

Jason M. Pittman

Machine learning systems increasingly drive innovation across scientific fields and industry, yet challenges in compute overhead, specifically during inference, limit their scalability and sustainability. Responsible AI guardrails, essential for ensuring fairness, transparency, and privacy, further exacerbate these computational demands. This study addresses

Shubham Barua, Shantanu Desai

We apply data-motivated priors on the peak absolute magnitude of Type Ia supernovae ($M$) and the sound horizon at the drag epoch ($r_d$), to study how the $M-r_d$ degeneracy affects low redshift measurements of the Hubble constant, and then compare these estimates to the Planck estimated value of the Hubble constant. We use the data from Pantheon$+$, Cosmic

A. S. Serdyuk, V. A. Sorych, N. M. Sorych

For arbitrary nontrivial linear combinations of a finite number of Poisson kernels, the fulfillment of the Nagy condition is established for all numbers n, starting from some number. It is also proved for any n the existence of linear combinations of m Bernoulli kernels and linear combinations of m conjugate Poisson kernels that satisfy the Nikolsky conditio

Huiyu Duan, Qiang Hu, Jiarui Wang, Liu Yang

The rapid growth of user-generated content (UGC) videos has produced an urgent need for effective video quality assessment (VQA) algorithms to monitor video quality and guide optimization and recommendation procedures. However, current VQA models generally only give an overall rating for a UGC video, which lacks fine-grained labels for serving video processi

Xuyang Li, Chenyu Li, Gemine Vivone, Danfeng Hong

Remote Sensing (RS) data encapsulates rich multi-dimensional information essential for Earth observation. Its vast volume, diverse sources, and temporal continuity make it particularly well-suited for developing large Visual Foundation Models (VFMs). These models serve as powerful feature extractors, leveraging extensive RS data for pretraining and subsequen

Qian Lei, Chi Seng Pun

In this paper, we establish existence, uniqueness, and regularity properties of the solutions to multi-dimensional backward stochastic Volterra integral equations (BSVIEs), whose (possibly random) generator reflects nonlinear dependence on both the solution process and the martingale integrand component of the adapted solutions, as well as their diagonal pro

Vasiliy A. Es'kin, Alexey O. Malkhanov, Mikhail E. Smorkalov

The article discusses the development of various methods and techniques for initializing and training neural networks with a single hidden layer, as well as training a separable physics-informed neural network consisting of neural networks with a single hidden layer to solve physical problems described by ordinary differential equations (ODEs) and partial di

Jefferson Ederhion, Festus Zindozin, Hillary Owusu, Chukwurimazu Ozoemezim

The evolution of computer architecture has led to a paradigm shift from traditional single-core processors to multi-core and domain-specific architectures that address the increasing demands of modern computational workloads. This paper provides a comprehensive study of this evolution, highlighting the challenges and key advancements in the transition from s

Boris Arseniev, Dmitry Guskov, Richik Sengupta, Igor Zacharov

We explore the utilization of higher-order discretization techniques in optimizing the gate count needed for quantum computer based solutions of partial differential equations. To accomplish this, we present an efficient approach for decomposing $d$-band diagonal matrices into Pauli strings that are grouped into mutually commuting sets. Using numerical simul

Bellave S. Shivaram, Usha Sinha

This is a short historical note about the visit of Prof. G. Klipping of Fritz Haber Institute, Germany, to the low temperature physics laboratory of Indian Institute of Technology, Madras. During his visit in 1979, Klipping delivered a series of lectures on cryogenic practices and low temperature physics in the physics department. The authors, both Masters d

Yuquan Lin, Wensong Lin

Strengthened notions of a matching $M$ of a graph $G$ have been considered, requiring that the matching $M$ has some properties with respect to the subgraph $G_M$ of $G$ induced by the vertices covered by $M$: If $M$ is the unique perfect matching of $G_M$, then $M$ is a \emph{uniquely restricted matching} of $G$; if all the edges of $M$ are pendant edges of

Xingbo Fu, Zihan Chen, Yinhan He, Song Wang

Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data for graph-related downstream tasks, such as graph property prediction. In the real world, however, the graph data can suffer from significant distribution shifts across clients as

Aditya Bhattacharya, Simone Stumpf, Robin De Croon, Katrien Verbert

Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Although AI practitioners use various methods to reduce representation bias, their effectiveness is often constrained by insufficient domain knowledge in the debiasing process. To addres

Hui Liu, Shikai Jin

Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. Transcriptomic profiling provides a comprehensive reflection of phenotypic changes in cellular responses to external perturbations. In this paper, we propose XTransferCDR, a novel generative framework designed for feature decoupling and tran

Rongping Ye, Xiaobing Pei

With the rapid development of online social networks and the inadequacies in content moderation mechanisms, the detection of fake news has emerged as a pressing concern for the public. Various methods have been proposed for fake news detection, including text-based approaches as well as a series of graph-based approaches. However, the deceptive nature of fak

Rodrigo Moreira, Hugo G. V. O. da Cunha, Larissa F. Rodrigues Moreira, Flávio de Oliveira Silva

Monitoring heterogeneous infrastructures and applications is essential to cope with user requirements properly, but it still lacks enhancements. The well-known state-of-the-art methods and tools do not support seamless monitoring of bare-metal, low-cost infrastructures, neither hosted nor virtualized services with fine-grained details. This work proposes VIr

Zhiqiang Yan, Zhengxue Wang, Kun Wang, Jun Li

In this paper, we introduce the Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first time. Moving beyond direct completion using convolutional neural networks (CNNs), SigNet initially densifies sparse depth data through non-CNN densification tools to obtain coarse y

Norio Adachi

Let $v$ be a discrete valuation of a field $K$, which indicates that the valuation group of $v$ is isomorphic to the integers $\mathbb{Z}$ with the natural order, and let $L$ be a finite separable extension of $K$ with a complete set $\{V_1,V_2,...,V_g\}$ of extended valuations of $v$. Then it is well-known that the following basic equation holds: \[\sum_{j=

Lovepreet Singh, S. K. Tiwari

Let $g$ be an additive map on a division ring $D$. In this paper, we study the functional identity $G_{1}(y)g(y)G_{2}(y) = H(y)$, where $G_{1}(Y), G_{2}(Y)$, $H(Y)$ are generalized polynomials in $D_{G}[Y]$ such that both $G_{1}(Y)$ and $G_{2}(Y)$ are non-zero. By application of this result and its implications, we prove that if $D$ is a non-commutative divi

Lukas Weber, Miguel A. Morales, Johannes Flick, Shiwei Zhang

We perform extensive simulations of the two-dimensional cavity-coupled electron gas in a modulating potential as a minimal model for cavity quantum materials. These simulations are enabled by a newly developed quantum-electrodynamical (QED) auxiliary-field quantum Monte Carlo method. We present a procedure to greatly reduce finite-size effects in such calcul

Jingjing Zhao, Jing Su, Kaiquan Cai, Yanbo Zhu

A novel time-efficient framework is proposed for improving the robustness of a broadband multiple-input multiple-output (MIMO) system against unknown interference under rapidly-varying channels. A mean-squared error (MSE) minimization problem is formulated by optimizing the beamformers employed. Since the unknown interference statistics are the premise for s

Qiankang Wang, DaYun Qiu, Yi-Lei Tang

In this paper, we present an algorithm to generate the collider events of the GeV-scale oscillating sterile neutrinos with the ready-made event generation tools in the case that the crossing-widths among the nearly-degenerate fermionic fields arise. We prove the validity of our algorithm, and adopt some tricks for practical calculations. The formulations of

Gorapada Bera, Saman Habibi Esfahani, Yang Li

The local Donaldson-Scaduto conjecture predicts the existence and uniqueness of a special Lagrangian pair of pants with three asymptotically cylindrical ends in the Calabi-Yau 3-fold $X \times \mathbb{R}^2$, where $X$ is an ALE hyperk\"ahler 4-manifold of $A_2$-type. The existence of this special Lagrangian has previously been proved. In this paper, we prove

Basit Alawode, Shibani Hamza, Adarsh Ghimire, Divya Velayudhan

Informed by the success of the transformer model in various computer vision tasks, we design an end-to-end trainable model for the automatic detection and classification of bleeding and non-bleeding frames extracted from Wireless Capsule Endoscopy (WCE) videos. Based on the DETR model, our model uses the Resnet50 for feature extraction, the transformer encod

Maxime Ryckewaert, Diego Marcos, Christophe Botella, Maximilien Servajean

The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluable for understanding species distributions and their dynamics, but their use in a Species Distribution Model (SDM) is curtailed by sampling biases and the lack of information on ab

Zhouming Wu, Yifen Mu, Xiaoguang Yang

As the earliest and one of the most fundamental learning dynamics for computing NE, fictitious play (FP) has being receiving incessant research attention and finding games where FP would converge (games with FPP) is one central question in related fields. In this paper, we identify a new class of games with FPP, i.e., $3\times3$ games without IIP, based on t

Shamik Bhattacharjee, Kamlesh Marathe, Hitesh Kapoor, Nilesh Patil

Fantasy sports, particularly fantasy cricket, have garnered immense popularity in India in recent years, offering enthusiasts the opportunity to engage in strategic team-building and compete based on the real-world performance of professional athletes. In this paper, we address the challenge of optimizing fantasy cricket team selection using reinforcement le

Maria Matushko

We propose a trigonometric solution of the associative Yang-Baxter equation related to the queer Lie superalgebra which in its turn satisfies the quantum Yang-Baxter equation.

Luis A. Anchordoqui, Ignatios Antoniadis

Five dimensional (5D) uniform inflation describes a de Sitter (or approximate) solution of 5D Einstein equations, with cosmological constant and a 5D Planck scale $M_* \sim 10^9$ GeV. During the inflationary period all dimensions (compact and non-compact) expand exponentially in terms of the 5D proper time. This set-up requires about 40 $e$-folds to expand t

Hongliang Zhang, Shuo Chen, Lei Luo, Jian Yang

Spherical Sliced-Wasserstein (SSW) has recently been proposed to measure the discrepancy between spherical data distributions in various fields, such as geology, medical domains, computer vision, and deep representation learning. However, in the original SSW, all projection directions are treated equally, which is too idealistic and cannot accurately reflect

Yuxin You, Zhen Liu, Xiangchao Wen, Yongtao Zhang

Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through the development of graph neural networks (GNNs). However, GNNs are still deficient in generalizing to diverse graph data. Aiming to this issue

Matías Godoy, Manuel Torres-Valdebenito, Emilio Vilches

In this paper, we study the well-posedness of state-dependent and state-independent sweeping processes driven by prox-regular sets and perturbed by a history-dependent operator. Our approach, based on an enhanced version of Gronwall's lemma and fixed-point arguments, provides an efficient framework for analyzing sweeping processes. In particular, our finding

Genevieve Lam, Huang Dongyan, Weisi Lin

In this study, we focus on automated approaches to detect depression from clinical interviews using multi-modal machine learning (ML). Our approach differentiates from other successful ML methods such as context-aware analysis through feature engineering and end-to-end deep neural networks for depression detection utilizing the Distress Analysis Interview Co

Reza Hassanpour, Kasim Oztoprak, Niels Netten, Tony Busker

Machine learning models use high dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for

Yong Shang, Alexander Heinlein, Siddhartha Mishra, Fei Wang

Randomized neural networks (RaNNs), in which hidden layers remain fixed after random initialization, provide an efficient alternative for parameter optimization compared to fully parameterized networks. In this paper, RaNNs are integrated with overlapping Schwarz domain decomposition in two (main) ways: first, to formulate the least-squares problem with loca

Zekang Yang, Wang Zeng, Sheng Jin, Chen Qian

Designing effective neural architectures poses a significant challenge in deep learning. While Neural Architecture Search (NAS) automates the search for optimal architectures, existing methods are often constrained by predetermined search spaces and may miss critical neural architectures. In this paper, we introduce NADER (Neural Architecture Design via mult

Sujay Ray, Martin Eckstein, Philipp Werner

Understanding light-induced hidden orders is relevant for nonequilibrium materials control and future ultrafast technologies. Hidden superconducting order, in particular, has been a focus of recent experimental and theoretical efforts. In this study, we investigate the stability of light-induced $\eta$ pairing. Using a memory truncated implementation of none

Duarte Almeida, André Moitinho, Sandro Moreira

Context. The dissolution rate of open clusters (OCs) and integration of their stars into the Milky Way's field population has been previously explored using their age distribution. With the advent of the Gaia mission, we have an exceptional opportunity to revisit and enhance these studies with ages and masses from high quality data. Aims. To build a comprehe

Kun Cheng, Shuchao Li

Let $\lambda_2$ be the second largest eigenvalue of the adjacency matrix of a connected graph. In 2023, Li and Sun \cite{LiSun1} determined all the connected $\{K_{2,3}, K_4\}$-minor free graphs whose second largest eigenvalue $\lambda_2\le 1$. As a continuance of it, in this paper we completely identify all the connected $\{K_5,K_{3,3}\}$-minor free graphs

A. O. Ivanov, A. A. Tuzhilin

We show that the problem whether a given finite metric space can be embedded into $m$-dimensional rectilinear space can be reformulated in terms of the Gromov--Hausdorff distance between some special finite metric spaces.

Zahiriddin Rustamov, Ayham Zaitouny, Rafat Damseh, Nazar Zaki

Instance selection (IS) is a crucial technique in machine learning that aims to reduce dataset size while maintaining model performance. This paper introduces a novel method called Graph Attention-based Instance Selection (GAIS), which leverages Graph Attention Networks (GATs) to identify the most informative instances in a dataset. GAIS represents the data

Dengming Zhang, Weitao You, Ziheng Liu, Lingyun Sun

Dynamic Music Emotion Recognition (DMER) aims to predict the emotion of different moments in music, playing a crucial role in music information retrieval. The existing DMER methods struggle to capture long-term dependencies when dealing with sequence data, which limits their performance. Furthermore, these methods often overlook the influence of individual d

J. Castel, S. Cebrián, T. Dafni, D. Díez-Ibáñez

Background: we develop the concept of a Micromegas (MICRO-MEsh GAseous Structure) readout plane with an additional GEM (Gas Electron Multiplier) preamplification stage placed a few mm above it, to increase the maximum effective gain of the combined readout. We implement it and test it in realistic conditions for its application to low-background dark matter

Sandi Berk, Bojan Stopar

This paper examines mathematical models for processing classical horizontal geodetic (triangulation and trilateration) networks. Two rigorous parametric adjustment models are discussed. The first one is a well-known model of adjustment in the geodetic coordinate system. This model is completely rigorous (functional and stochastic parts) and uses unreduced di

Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman, Ronan Le Bras

Obeying precise constraints on top of multiple external attributes is a common computational problem underlying seemingly different domains, from controlled text generation to protein engineering. Existing language model (LM) controllability methods for multi-attribute constraint satisfaction often rely on specialized architectures or gradient-based classifi

Shikun Cui, Lili Wang, Wendong Wang

As is well known, for the 3D Patlak-Keller-Segel system, regardless of whether they are parabolic-elliptic or parabolic-parabolic forms, finite-time blow-up may occur for arbitrarily small values of the initial mass. In this paper, it is proved for the first time that one can prevent the finite-time blow-up when the initial mass is less than a certain critic

Zhi-Zhang Peng, You Wu, Lang Liu

The detection of a stochastic signal by recent pulsar timing array (PTA) collaborations, including NANOGrav, PPTA, EPTA+InPTA, CPTA and MPTA, has opened a new window to explore gravitational waves (GWs) at nanohertz frequencies. Motivated by the possibility that such a signal could arise from primordial gravitational waves (PGWs), we investigate the implicat

Xu-Hui Jiang, Chih-Ting Lu

Leptophilic axion-like particles (ALPs) exhibit rich phenomenology, focusing exclusively on interactions between an ALP and Standard Model (SM) leptons. Through integration by parts, it is shown that both the three-point interaction, $a\bar\ell\ell$ and the four-point interaction, $a\ell^-\nu W^+$, play significant roles, making the flavor portal particularl

Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Chenxiao Ma

We study reinforcement learning (RL) for decision processes with non-Markovian reward, in which high-level knowledge of the task in the form of reward machines is available to the learner. We consider probabilistic reward machines with initially unknown dynamics, and investigate RL under the average-reward criterion, where the learning performance is assesse

Yue Ming, Zhao-Xin Fu, Yan-Xiong Du

The Rydberg blockade effect plays an important role in realizing two-qubit gates in atomic arrays. Meanwhile, such mechanics will increase the crosstalk between atoms and enhance the decoherence. In this paper, we propose a new scheme to realize the controlled-phase gate without Rydberg blockade. The scheme works effectively with large atomic spacings and is

T-H. Hubert Chan, Qipeng Kuang, Quan Xue

We consider game-theoretically secure distributed protocols for coalition games that approximate the Shapley value with small multiplicative error. Since all known existing approximation algorithms for the Shapley value are randomized, it is a challenge to design efficient distributed protocols among mutually distrusted players when there is no central autho

Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu

Large language models (LLMs) have shown remarkable capabilities in general domains, but their application to multi-omics biology remains underexplored. To address this gap, we introduce Biology-Instructions, the first large-scale instruction-tuning dataset for multi-omics biological sequences, including DNA, RNA, proteins, and multi-molecules. This dataset b

Ru Ling, Meng Li, Bowen Liu, Zhibin Li

Traditional driving risk potential field model generally assumes uniform vehicle sizes, which fails to reflect the heterogeneity in real-world traffic environment. This study aims to develop an improved model by introducing the vehicle dimensions into the driving risk potential field framework. Vehicles are categorized based on size into standard-sized vehic

Ahmed Alhawwary, Janne Mustaniemi, Phong Nguyen-Ha, Janne Heikkilä

Portraits or selfie images taken from a close distance typically suffer from perspective distortion. In this paper, we propose an end-to-end deep learning-based rectification pipeline to mitigate the effects of perspective distortion. We learn to predict the facial depth by training a deep CNN. The estimated depth is utilized to adjust the camera-to-subject

H. N. Long

Two main ingredients of current particle physics such as local gauge symmetry and mass generation via the Higgs mechanism being basic ground of the Standard Model are widely confirmed by experimental data. However, some problems such as neutrino masses, dark matter, baryon asymmetry of Universe have clearly indicated that the Standard Model cannot be the ult

Mana Sakai, Takeru Matsuda, Tatsuya Kubokawa

Asymptotically unbiased priors, introduced by Hartigan (1965), are designed to achieve second-order unbiasedness of Bayes estimators. This paper extends Hartigan's framework to non-i.i.d. models by deriving a system of partial differential equations that characterizes asymptotically unbiased priors. Furthermore, we establish a necessary and sufficient condit

Inge S. Helland

The main purpose of this article is to prove that, under certain assumptions in a linear prediction setting, optimal methods based upon model reduction and even an optimal predictor can be provided. The optimality is formulated in terms of the expected mean square prediction error. The optimal model reduction turns out, under a certain assumption, to corresp

Giorgio Leone

We revisit and extend the construction of six-dimensional orientifolds built upon the $T^4/\mathbb{Z}_N$ orbifolds with a non-vanishing Kalb-Ramond background, both in the presence of $\mathcal{N}=(1,0)$ supersymmetry and Brane Supersymmetry Breaking, thus amending some statements present in the literature. In the $N=2$ case, we show how the gauge groups on

Wenjing Chen

With the rapid development of multimodal learning, the image-text matching task, as a bridge connecting vision and language, has become increasingly important. Based on existing research, this study proposes an innovative visual semantic embedding model, Multi-Headed Consensus-Aware Visual-Semantic Embedding (MH-CVSE). This model introduces a multi-head self

Ilyes Hammouda, Mohamed Ndaoud, Abd-Krim Seghouane

Convex and penalized robust regression methods often suffer from a persistent bias induced by large outliers, limiting their effectiveness in adversarial or heavy-tailed settings. In this work, we study a smooth redescending non-convex M-estimator, specifically the Welsch estimator, and show that it can eliminate this bias whenever it is statistically identi

Gabriele Carcassi, Andrea Oldofredi, Christine A. Aidala

This short note addresses the criticisms recently proposed by Shan Gao against our article "On the Reality of the Quantum State Once Again: A No-Go Theorem for {\psi}-Ontic Models" (Found. Phys. 54:14). The essay aims to respond to such objections and to show once again that the theorem proved in our paper is correct, and therefore true - contrary to Gao's c

Kaitong Sun, Si Wu, Guanping Xu, Lingwei Li

We present a comprehensive investigation into the physical properties of intermetallic ErPd$_2$Si$_2$, a compound renowned for its intriguing magnetic and electronic characteristics. We confirm the tetragonal crystal structure of ErPd$_2$Si$_2$ within the $I4/mmm$ space group. Notably, we observed anisotropic thermal expansion, with the lattice constant $a$

Soon-Yi Kang, Toshiki Matsusaka, Gyucheol Shin

Building on the results of Craig, van Ittersum, and Ono, we provide a refined understanding of MacMahon's partition functions and their variants, including their quasi-modular properties and new prime-detecting expressions.

Dongwei Sun, Jing Yao, Wu Xue, Changsheng Zhou

Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing.This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity.Current deep learning method

Yang Du, Yuqi Liu, Qin Jin

Cross-modal (e.g. image-text, video-text) retrieval is an important task in information retrieval and multimodal vision-language understanding field. Temporal understanding makes video-text retrieval more challenging than image-text retrieval. However, we find that the widely used video-text benchmarks have shortcomings in comprehensively assessing abilities

Artem A. Tsygankov, Bulat N. Galimzyanov, Anatolii V. Mokshin

Equilibrium antimony melt near the melting temperature is characterised by structural features that are not present in simple single-component liquids. The cause of these features may be long-lived structural formations that are not yet fully understood. The present work provides the detailed characterization of the structures formed in liquid antimony near

Kai Jiang, Meng Li, Juan Zhang, Lei Zhang

Numerically solving parabolic equations with quasiperiodic coefficients is a significant challenge due to the potential formation of space-filling quasiperiodic structures that lack translational symmetry or decay. In this paper, we introduce a highly accurate numerical method for solving time-dependent quasiperiodic parabolic equations. We discretize the sp

Gergő Nemes

In this paper, we investigate the asymptotic properties of the generalised trigonometric integral $\operatorname{ti}(a, z, \alpha)$ and its associated modulus and phase functions for large complex values of $z$. We derive asymptotic expansions for these functions, accompanied by explicit and computable error bounds. For real values of $a$, the function $\ope

Mahmoud Nabil, Lidia Contreras-Bernal, Gloria P. Moreno-Martinez, Jose Obrero-Perez

Metal halide perovskite solar cells (MHPSCs) hold great promise related to their high efficiency and low fabrication costs, but their long-term stability under environmental conditions remains a major challenge. In this study, we demonstrate an effective protection strategy to enhance the stability of MHPSCs through the incorporation of a double passivation

Long-Shun Lu, Lei-Yi Li, Cai-Dian Lü

The semi-inclusive decay processes of a top quark into a charged pseudo-scalar meson and a jet are studied within the framework of QCD factorization. The leading power of the decay matrix elements can be factorized into heavy-to-light quark transition current and a hadron matrix element up to next-to-leading order QCD corrections. We calculate one-loop virtu

Yishan Han, Biao Xu, Yao Wang, Shanxing Gao

Top-$K$ recommendation involves inferring latent user preferences and generating personalized recommendations accordingly, which is now ubiquitous in various decision systems. Nonetheless, recommender systems usually suffer from severe \textit{popularity bias}, leading to the over-recommendation of popular items. Such a bias deviates from the central aim of

Houtianfu Wang, Ozgur B. Akan

This paper provides a comprehensive overview of fundamentals and the latest research progress in gravitational communication, with a detailed historical review of gravitational wave generation and detection. Key aspects covered include the evolution of detection sensitivity and generation methods, modulation techniques, and gravitational communication channe

Peihan Sun, Manzhou Zhang, Renxian Yuan, Deming Li

This study introduces a novel real-time betatron tune measurement algorithm, utilizing Schottky signals and an FPGA-based backend architecture, specifically designed for rapidly ramping synchrotrons, with particular application to the Shanghai Advanced Proton Therapy (SAPT) facility. The developed algorithm demonstrates improved measurement accuracy under ch

Massimiliano Giona, Giuseppe Procopio, Chiara Pezzotti

The fluctuation-dissipation theory is grounded on the Langevin condition expressing the local independence between the thermal force and the particle velocity history. Upon hydrodynamic grounds, it is reasonable to relax this condition in order to account for the correlated fluid fluctuations, especially in the case of liquids, consistently with the inclusio

Bo Liang, Chang Liu, Tianyu Zhao, Minghui Du

\Acp{PTA} are essential tools for detecting the \ac{SGWB}, but their analysis faces significant computational challenges. Traditional methods like \ac{MCMC} struggle with high-dimensional parameter spaces where noise parameters often dominate, % while existing deep learning approaches fail to model the \ac{HD} correlation or are validated only on synthetic d

Simona Frenda, Andrea Piergentili, Beatrice Savoldi, Marco Madeddu

Gender-fair language aims at promoting gender equality by using terms and expressions that include all identities and avoid reinforcing gender stereotypes. Implementing gender-fair strategies is particularly challenging in heavily gender-marked languages, such as Italian. To address this, the Gender-Fair Generation challenge intends to help shift toward gend

Chiara Pezzotti, Massimiliano Giona, Giuseppe Procopio

The analysis of fluctuation-dissipation relations developed in Giona et al. (2024) for particle hydromechanics is extended to stochastic forcings alternative to Wiener processes, with the aim of addressing the occurrence of Gaussian equilibrium densities or alternatively the breaking of the Gaussian paradigm at equilibrium. Preliminarly, it is discussed how

Massimiliano Giona, Giuseppe Procopio, Chiara Pezzotti

The article provides a unitary and complete solution to the fluctuation-dissipation relations for particle hydromechanics in a generic fluid, accounting for the hydrodynamic fluid-particle interactions (including arbitrary memory kernels in the description of dissipative and fluid inertial effects) in linear hydrodynamic regimes, via the concepts of fluctuat

Qiude Zhang, Chunyu Lin, Zhijie Shen, Nie Lang

Monocular 3D object detection is challenging due to the lack of accurate depth. However, existing depth-assisted solutions still exhibit inferior performance, whose reason is universally acknowledged as the unsatisfactory accuracy of monocular depth estimation models. In this paper, we revisit monocular 3D object detection from the depth perspective and form

Devibala Esakkimuthu, Basherrudin Mahmud Ahmed A

Conditional Measurement scheme which employs linear optical elements and photon detection is the fertile ground for nonclassical state generation. We consider a simple setup that requires a coherent state and a number state as inputs of the beam splitter, and a photon detector. We show that by tuning the parameters involved in the setup, we can achieve optim

Suman Acharyya, Priodyuti Pradhan, Chandrakala Meena

Synchronization is an emergent and fundamental phenomenon in nature and engineered systems. Understanding the stability of a synchronized phenomenon is crucial for ensuring functionality in various complex systems. The stability of the synchronization phenomenon is extensively studied using the Master Stability Function (MSF). This powerful and elegant tool

Olga Klimanova, Nikita Rybin, Alexander Shapeev

We present an algorithm for accelerating the search of molecule's adsorption site based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning interatomic potential (moment tensor potential) to approximate the potential energy surface and an active learning algorithm for the automatic construction of an optimal training

Tushar Chugh, Fredrik Bruzelius, Balázs Kulcsár

This paper presents a robust position controller for electric power assisted steering and steer-by-wire force-feedback systems. A position controller is required in steering systems for haptic feedback control, advanced driver assistance systems and automated driving. However, the driver's \textit{physical} arm impedance causes an inertial uncertainty during

Arun K. Sharma, Shubhobrata Bhattacharya, Motahar Reza, Bishakh Bhattacharya

Traditional biometric systems have encountered significant setbacks due to various unavoidable factors, for example, wearing of face masks in face recognition-based biometrics and hygiene concerns in fingerprint-based biometrics. This paper proposes a novel lightweight vision transformer with phase-only cross-attention (POC-ViT) using dual biometric traits o

Muhammad A. Muttaqien, Ayanori Yorozu, Akihisa Ohya

This paper explores the integration of incremental curriculum learning (ICL) with deep reinforcement learning (DRL) techniques to facilitate mobile robot navigation through task-based human instruction. By adopting a curriculum that mirrors the progressive complexity encountered in human learning, our approach systematically enhances robots' ability to inter

Yuuki Ogawa, Satoru Hayami

We theoretically study the generation of net magnetization induced by static strain in $g$-wave altermagnets, which exhibit the symmetric spin-split band structure under collinear spin textures free from the relativistic spin-orbit coupling. By analyzing a tight-binding model in a two-dimensional tetragonal system, we show that the $g$-wave altermagnets give

Sasmita Dash, Amalendu Patnaik

The promising way to provide sufficient transmission capacity is by accessing transmission bands at higher carrier frequencies. This desire for higher carrier frequency or more bandwidth led the researchers to take advantage of the terahertz (THz) spectrum. The opportunity for large bandwidth in the THz band leads to the possibility of easy, high data rate t

Yabing Wang, Zhuotao Tian, Qingpei Guo, Zheng Qin

Visual Grounding aims to localize the referring object in an image given a natural language expression. Recent advancements in DETR-based visual grounding methods have attracted considerable attention, as they directly predict the coordinates of the target object without relying on additional efforts, such as pre-generated proposal candidates or pre-defined

Shuzhi Xu, Hiroki Kawabe, Kentaro Yaji

The design of porous infill structures presents significant challenges due to their complex geometric configurations, such as the accurate representation of geometric boundaries and the control of localized maximum stress. In current mainstream design methods, such as topology optimization, the analysis is often performed using pixel or voxel-based element a

Kosei Tanada, Yuka Iwanaga, Masayoshi Tsuchinaga, Yuji Nakamura

To use assistive robots in everyday life, a remote control system with common devices, such as 2D devices, is helpful to control the robots anytime and anywhere as intended. Hand-drawn sketches are one of the intuitive ways to control robots with 2D devices. However, since similar sketches have different intentions from scene to scene, existing work needs ad

Jungkyu Kim, Kibok Lee, Taeyoung Park

Masked autoencoders (MAEs) have recently demonstrated effectiveness in tabular data imputation. However, due to the inherent heterogeneity of tabular data, the uniform random masking strategy commonly used in MAEs can disrupt the distribution of missingness, leading to suboptimal performance. To address this, we propose a proportional masking strategy for MA

Yuetong Zhao

Assuming the generalized Riemann hypothesis, we evaluate sharp upper bounds for the shifted moments of quadratic Dirichlet L-functions with moduli 8p, where p ranges over odd primes. We then apply this result to prove bounds for the moments of quadratic Dirichlet character sums with prime moduli.