March 2024 arXiv papers — page 6

Guan-Cheng Zhou, Chen Chengb, Yan-zhou Chena

Real-time and high-precision situational awareness technology is critical for autonomous navigation of unmanned surface vehicles (USVs). In particular, robust and fast obstacle semantic segmentation methods are essential. However, distinguishing between the sea and the sky is challenging due to the differences between port and maritime environments. In this

Samiran Ghosh, Malay Banerjee, Vitaly Volpert

A new age-distributed immuno-epidemiological model with information-based vaccine uptake suggested in this work represents a system of integro-differential equations for the numbers of susceptible individuals, infected individuals, vaccinated individuals and recovered individuals. This model describes the influence of vaccination decision on epidemic progres

Gengming Zhang, Hao Cao, Kewei Hu, Yaoqiang Pan

Accurately identifying lychee-picking points in unstructured orchard environments and obtaining their coordinate locations is critical to the success of lychee-picking robots. However, traditional two-dimensional (2D) image-based object detection methods often struggle due to the complex geometric structures of branches, leaves and fruits, leading to incorre

Xiao-Li Zhang, Yong-Feng Huang, Ze-Cheng Zou

According to the hypothesis that strange quark matter may be the true ground state of matter at extremely high densities, strange quark stars should be stable and could exist in the Universe. It is possible that pulsars may actually be strange stars, but not neutron stars. Here we present a short review on recent progresses in the field of strange quark star

Renyang Liu, Kwok-Yan Lam, Wei Zhou, Sixing Wu

Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existing black-box attack methods still suffer from unsatisfactory performance due to the vast number of queries needed to optimize desired perturbations. Besides, the other critical chal

Zhenhua Liu, Tong Zhu, Jianxiang Xiang, Wenliang Chen

Data augmentation (DA) is crucial to mitigate model training instability and over-fitting problems in low-resource open-domain dialogue generation. However, traditional DA methods often neglect semantic data diversity, restricting the overall quality. Recently, large language models (LLM) have been used for DA to generate diversified dialogues. However, they

Chengyuan Li, Tianyu Zhang, Xusheng Du, Ye Zhang

Recent advances in generative artificial intelligence (AI) technologies have been significantly driven by models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and denoising diffusion probabilistic models (DDPMs). Although architects recognize the potential of generative AI in design, personal barriers often restrict their a

Chenghao Zhang, Gaofeng Meng, Bin Fan, Kun Tian

The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks, this needs gathering training data that covers a number of heterogeneous scenes at deployment time. However, training samples are typically acquired continuously in practical app

Duosheng Chen, Shihao Zhou, Jinshan Pan, Jinglei Shi

Exploring motion information is important for the motion deblurring task. Recent the window-based transformer approaches have achieved decent performance in image deblurring. Note that the motion causing blurry results is usually composed of translation and rotation movements and the window-shift operation in the Cartesian coordinate system by the window-bas

Tao Li, Qinghua Tao, Weihao Yan, Zehao Lei

Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning. Two branches of methods have been proposed to seek flat minima and improve generalization: one led by sharpness-aware minimization (SAM) minimizes the worst-case neighborhood loss through adversarial weight perturbation (AWP), and the ot

Andrea Ruo, Lorenzo Sabattini, Valeria Villani

In the field of control engineering, the connection between Signal Temporal Logic (STL) and time-varying Control Barrier Functions (CBF) has attracted considerable attention. CBFs have demonstrated notable success in ensuring the safety of critical applications by imposing constraints on system states, while STL allows for precisely specifying spatio-tempora

Ahmet Bingül, Mehmet Adıyaman

In this study, a procedure for designing a free-form lens for long-range LED illumination is presented. The geometrical form of the proposed lens is obtained by minimizing optical path lengths of the rays emitted from a point-like light source. Optical ray tracing simulations of two different LEDs and the free-form lens are performed by using Zemax OpticStud

Andrea Ruo, Lorenzo Sabattini, Valeria Villani

Over the past decade, a multitude of service robots have been developed to fulfill a wide range of practical purposes. Notably, roles such as reception and robotic guidance have garnered extensive popularity. In these positions, robots are progressively assuming the responsibilities traditionally held by human staff in assisting customers. Ensuring the safe

Andrea Ruo, Lorenzo Sabattini, Valeria Villani

A motion planning methodology based on the combination of Control Barrier Functions (CBF) and Signal Temporal Logic (STL) is employed in this paper. This methodology allows task completion at any point within a specified time interval, considering a dynamic system subject to velocity constraints. In this work, we apply this approach into the context of Socia

Zhen Gao, Lini Yuan, Pedro Reviriego, Shanshan Liu

Stable Diffusion is a popular Transformer-based model for image generation from text; it applies an image information creator to the input text and the visual knowledge is added in a step-by-step fashion to create an image that corresponds to the input text. However, this diffusion process can be corrupted by errors from the underlying hardware, which are es

Linghan Cai, Shenjin Huang, Ye Zhang, Jinpeng Lu

Multiple instance learning (MIL) is a robust paradigm for whole-slide pathological image (WSI) analysis, processing gigapixel-resolution images with slide-level labels. As pioneering efforts, attention-based MIL (ABMIL) and its variants are increasingly becoming popular due to the characteristics of simultaneously handling clinical diagnosis and tumor locali

Niels Dossche, Bert Abrath, Bart Coppens

Race conditions are a class of bugs in software where concurrent accesses to shared resources are not protected from each other. Consequences of race conditions include privilege escalation, denial of service, and memory corruption which can potentially lead to arbitrary code execution. However, in large code bases the exact rules as to which fields should b

Runmin Zhang, Zhu Yu, Zehua Sheng, Jiacheng Ying

Cross-spectral image guided denoising has shown its great potential in recovering clean images with rich details, such as using the near-infrared image to guide the denoising process of the visible one. To obtain such image pairs, a feasible and economical way is to employ a stereo system, which is widely used on mobile devices. Current works attempt to gene

Aayan Masood Pathan, Michele Pavon

We study in this paper optimal mass transport over a strongly connected, directed graph on a given discrete time interval. Differently from previous literature, we do not assume full knowledge of the initial and final goods distribution over the network nodes. In spite of the meager information, we show that it is possible to characterize the most likely flo

Sara Merino-Aceituno, Christian Schmeiser, Raphael Winter

The Vicsek-BGK equation is a kinetic model for alignment of particles moving with constant speed between stochastic reorientation events with sampling from a von Mises distribution. The spatially homogeneous model shows a steady state bifurcation with exchange of stability. The main result of this work is an extension of the bifurcation result to the spatial

Benjamin Berg, Benjamin Moseley, Weina Wang, Mor Harchol-Balter

Modern computing workloads are often composed of parallelizable jobs. A parallelizable job can be completed more quickly when run on additional servers. However, each job can only use a limited number of servers, known as its parallelizability level, which is determined by the type of computation the job performs and how it is implemented. Workloads generall

Takayuki Hara, Tatsuya Harada

The generation of 3D scenes from user-specified conditions offers a promising avenue for alleviating the production burden in 3D applications. Previous studies required significant effort to realize the desired scene, owing to limited control conditions. We propose a method for controlling and generating 3D scenes under multimodal conditions using partial im

Ankit Satpute, Noah Giessing, Andre Greiner-Petter, Moritz Schubotz

Large Language Models (LLMs) have demonstrated exceptional capabilities in various natural language tasks, often achieving performances that surpass those of humans. Despite these advancements, the domain of mathematics presents a distinctive challenge, primarily due to its specialized structure and the precision it demands. In this study, we adopted a two-s

Wenqi Ge, Chao Tang, Hong Zhang

Object search is a fundamental skill for household robots, yet the core problem lies in the robot's ability to locate the target object accurately. The dynamic nature of household environments, characterized by the arbitrary placement of daily objects by users, makes it challenging to perform target localization. To efficiently locate the target object, the

Sajal Hasan, Syed M. Arslan, Muhammad Imran, Rameez-ul Islam

Hyperentangled swapping is a quantum communication technique that involves the exchange of hyperentangled states, which are quantum states entangled in multiple degrees of freedom, to enable secure and efficient quantum information transfer. In this paper, we demonstrate schematics for the hyperentanglement swapping between separate pairs of neutral atoms th

Pedram Rabiee, Jesse B. Hoagg

We present a closed-form optimal control that satisfies both safety constraints (i.e., state constraints) and input constraints (e.g., actuator limits) using a composition of multiple control barrier functions (CBFs). This main contribution is obtained through the combination of several ideas. First, we present a method for constructing a single relaxed cont

Ahmed R. Sadik, Bodo Urban

Cooperative manufacturing is a new trend in industry, which depends on the existence of a collaborative robot. A collaborative robot is usually a light-weight robot which is capable of operating safely with a human co-worker in a shared work environment. During this cooperation, a vast amount of information is exchanged between the collaborative robot and th

Yiyang Chen, Chao Ji, Yunrui Cai, Tong Yan

Combining data-driven applications with control systems plays a key role in recent Autonomous Car research. This thesis offers a structured review of the latest literature on Deep Reinforcement Learning (DRL) within the realm of autonomous vehicle Path Planning and Control. It collects a series of DRL methodologies and algorithms and their applications in th

Pedro Henrique Barboza Rossetto, Jörg Frauendiener, Andrew Melatos

General relativistic corrections are calculated for the quadrupole moment of a magnetically confined mountain on an accreting neutron star. The hydromagnetic structure of the mountain satisfies the general relativistic Grad-Shafranov equation supplemented by the flux-freezing condition of ideal magnetohydrodynamics, as in previous calculations of the magneti

Giuseppe Castagna, Loïc Peyrot

We define and study "row polymorphism" for a type system with set-theoretic types, specifically union, intersection, and negation types. We consider record types that embed row variables and define a subtyping relation by interpreting types into sets of record values and by defining subtyping as the containment of interpretations. We define a functional calc

Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda, Takafumi Horie

In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications. Traditional methodologies for annotating datasets for NER models are challenged by high costs and variations in dataset quality. This research introduces a novel hybrid annotation approach tha

Chenyi Zhang, Yihan Hu, Henghui Ding, Humphrey Shi

Despite significant advancements in image matting, existing models heavily depend on manually-drawn trimaps for accurate results in natural image scenarios. However, the process of obtaining trimaps is time-consuming, lacking user-friendliness and device compatibility. This reliance greatly limits the practical application of all trimap-based matting methods

Guido Martinelli, Silvano Simula, Ludovico Vittorio

We present updated estimates of $\vert V_{cb} \vert$ and $R(D^{(*)})$ based on all the available theoretical and experimental data on semileptonic $B \to D^{(*)} \ell \nu_\ell$ decays. These values have been obtained by using the Dispersive Matrix method to describe the hadronic form factors. By analysing all the lattice data we get the theoretical values $R

Babette Bühler, Efe Bozkir, Hannah Deininger, Peter Gerjets

Successful learning depends on learners' ability to sustain attention, which is particularly challenging in online education due to limited teacher interaction. A potential indicator for attention is gaze synchrony, demonstrating predictive power for learning achievements in video-based learning in controlled experiments focusing on manipulating attention. T

Joseph M. Shunia

We introduce a new approach for generating combinatorial identities and formulas by the application of Kronecker substitution to polynomial expansions within quotient rings. Our main result enables the derivation of elementary arithmetic formulas for many C-recursive integer sequences directly from their characteristic polynomials. As sample applications, we

A. A. Molavi Choobini, S. S. Ghaffari-Oskooei

Self-focusing emerges as a nonlinear optical phenomenon resulting from an intense laser field and plasma interaction. This study investigates the self-focusing behavior of Gaussian laser beams within magnetized plasma environments utilizing a novel approach, source-dependent expansion. By employing source-dependent expansion, we explore the intricate dynamic

Robin Magnet, Maks Ovsjanikov

Deep functional maps have emerged in recent years as a prominent learning-based framework for non-rigid shape matching problems. While early methods in this domain only focused on learning in the functional domain, the latest techniques have demonstrated that by promoting consistency between functional and pointwise maps leads to significant improvements in

Michael Lacey, Ji Li, Brett D. Wick, Liangchuan Wu

We characterize the Schatten class $S^p$ of the commutator of Riesz transforms $[b,R_j]$ in $\mathbb R^n$ ($j=1,\ldots, n$) in the two weight setting for $n< p<\infty$, by introducing the condition that the symbol $b$ being in Besov spaces associated with the given two weights. At the critical index $p=n$, the commutator $[b,R_j]$ belongs to Schatten class $

Gérard Clément, Khireddine Nouicer

The most general set of static and spherically symmetric solutions for conformal Killing gravity coupled to Maxwell fields is presented in closed form. These solutions, depending on six parameters, include non-asymptotically flat black holes or naked singularities, non-asymptotically flat traversable wormholes, and (possibly singularity-free) closed universe

Wen Sheng, Zhong Zheng, Jiajun Liu, Han Lu

Background: Liver tumors are abnormal growths in the liver that can be either benign or malignant, with liver cancer being a significant health concern worldwide. However, there is no dataset for plain scan segmentation of liver tumors, nor any related algorithms. To fill this gap, we propose Plain Scan Liver Tumors(PSLT) and YNetr. Methods: A collection of

Pep Mulet

The isentropic compressible Cahn-Hilliard-Navier-Stokes equations is a system of fourth-order partial differential equations that model the evolution of some binary fluids under convection. The purpose of this paper is the design of efficient numerical schemes to approximate the solution of initial-boundary value problems with these equations. The efficiency

M. N. Ellingham, Joanna A. Ellis-Monaghan

In 1965 Edmonds showed that every eulerian graph has a bi-eulerian embedding, i.e., an embedding with exactly two faces, each bounded by an euler circuit. We refine this result by giving conditions for a graph to have a bi-eulerian embedding that is specifically orientable or nonorientable. We give connections to the maximum genus problem for directed embedd

Zdeněk Dvořák, Sergey Norin

A connected graph G is 3-flow-critical if G does not have a nowhere-zero 3-flow, but every proper contraction of G does. We prove that every n-vertex 3-flow-critical graph other than K_2 and K_4 has at least 5n/3 edges. This bound is tight up to lower-order terms, answering a question of Li et al. (2022). It also generalizes the result of Koester (1991) on t

Hao Sun, Rundong He, Zhongyi Han, Zhicong Lin

Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training, with the use of only a small number of labeled in-distribution (ID) images. Up to now, a mainstream strategy is based on large-scale vision-language models, such as CLIP. However, these methods overlook a crucial issue: the lack of re

Wenjun Lin, Yan Hu, Huazhu Fu, Mingming Yang

Instrument-tissue interaction detection task, which helps understand surgical activities, is vital for constructing computer-assisted surgery systems but with many challenges. Firstly, most models represent instrument-tissue interaction in a coarse-grained way which only focuses on classification and lacks the ability to automatically detect instruments and

Mingjing Chen, Weiguang Cui, Wenjuan Fang, Zhonglue Wen

The main systematics in cluster cosmology is the uncertainty in the mass-observable relation. In this paper, we focus on the most direct cluster observable in optical surveys, i.e. richness, and constrain the intrinsic mass-richness (MR) relation of clusters in THE THREE HUNDRED hydrodynamic simulations with two runs: GIZMO-SIMBA and GADGET-X. We find that m

Xingrui Gu, Zhixuan Wang, Irisa Jin, Zekun Wu

This research presents a novel multimodal data fusion methodology for pain behavior recognition, integrating statistical correlation analysis with human-centered insights. Our approach introduces two key innovations: 1) integrating data-driven statistical relevance weights into the fusion strategy to effectively utilize complementary information from heterog

Tzviel Frostig, Yoav Benjamini, Ruth Heller

Confidence intervals (CIs) are instrumental in statistical analysis, providing a range estimate of the parameters. In modern statistics, selective inference is common, where only certain parameters are highlighted. However, this selective approach can bias the inference, leading some to advocate for the use of CIs over p-values. To increase the flexibility o

Arjun P S, Andrew Melnik, Gora Chand Nandi

Recent advancements in Generative AI, particularly in Large Language Models (LLMs) and Large Vision-Language Models (LVLMs), offer new possibilities for integrating cognitive planning into robotic systems. In this work, we present a novel framework for solving the object goal navigation problem that generates efficient exploration strategies. Our approach en

Jose M. Montilla, Jan A. Siminski, Rafael Vazquez

In the domain of Space Situational Awareness (SSA), the challenges pertaining to orbit determination and catalog correlation are notably pronounced, partly attributable to the escalating presence of non-cooperative satellites engaging in unspecified maneuvers at irregular intervals. This study introduces an initial orbit determination methodology reliant upo

Felipe Cano, Nuria Corral, David Senovilla-Sanz

In this paper we describe how to compute a Saito basis of a cusp, a plane curve with only one Puiseux pair. Moreover, the 1-forms of the Saito basis that we compute are characterized in terms of their divisorial orders associated to the "cuspidal" divisor of the minimal reduction of singularities of the cusp. We also introduce a new family of analytic invari

Vince Maes, Ignace Bossuyt, Hannes Vandecasteele, Wouter Dekeyser

Large particle systems are often described by high-dimensional (linear) kinetic equations that are simulated using Monte Carlo methods for which the asymptotic convergence rate is independent of the dimensionality. Even though the asymptotic convergence rate is known, predicting the actual value of the statistical error remains a challenging problem. In this

Toon Ingelaere, Vince Maes, Giovanni Samaey

Kinetic equations describe physical processes in a high-dimensional phase space and are often simulated using Markov process-based Monte Carlo routines. The quantities of interest are typically defined on the lower-dimensional position space and estimated on a grid (histogram). In several applications, such as the construction of diffusion Monte Carlo-like t

Lishen Qu, Shihao Zhou, Jinshan Pan, Jinglei Shi

Intense light sources often produce flares in captured images at night, which deteriorates the visual quality and negatively affects downstream applications. In order to train an effective flare removal network, a reliable dataset is essential. The mainstream flare removal datasets are semi-synthetic to reduce human labour, but these datasets do not cover ty

Tatsuru Kikuchi

Recently, a couple of investigations related to symmetry breaking phenomena, 'spontaneous stochasticity' and 'ergodicity breaking' have led to significant impacts in a variety of fields related to the stochastic processes such as economics and finance. We investigate on the origins and effects of those original symmetries in the action from the mathematical

Yibo Miao, Yu Lei, Feng Zhou, Zhijie Deng

Low-shot image classification is a fundamental task in computer vision, and the emergence of large-scale vision-language models such as CLIP has greatly advanced the forefront of research in this field. However, most existing CLIP-based methods lack the flexibility to effectively incorporate other pre-trained models that encompass knowledge distinct from CLI

Alejandro García-Fernández, José Antonio Parejo, Antonio Ruiz-Cortés

The Software as a Service (SaaS) model is a distribution and licensing model that leverages pricing structures and subscriptions to profit. The utilization of such structures allows Information Systems (IS) to meet a diverse range of client needs, while offering improved flexibility and scalability. However, they increase the complexity of variability manage

F. Ayatollah Zadeh Shirazi, E. Hakimi, A. Hosseini, R. Rezavand

In the following text we compute the adjoint of weighted generalized shift operators over Hilbert spaces. We show for a conjugate invariant subset $A$ of $\mathbb C$, the additive semigroup generated by $A\cup\{0\}-$weighted generalized shifts over Hilbert space $\mathcal H$ is adjoint invariant if and only if $\mathcal H$ is a finite dimensional Hilbert spa

Saddam Hussain Khan

Alzheimer diseases (ADs) involves cognitive decline and abnormal brain protein accumulation, necessitating timely diagnosis for effective treatment. Therefore, CAD systems leveraging deep learning advancements have demonstrated success in AD detection but pose computational intricacies and the dataset minor contrast, structural, and texture variations. In th

Wei Guo, Meng He, Chuan Huang, Hengtao He

Within the realm of rapidly advancing wireless sensor networks (WSNs), distributed detection assumes a significant role in various practical applications. However, critical challenge lies in maintaining robust detection performance while operating within the constraints of limited bandwidth and energy resources. This paper introduces a novel approach that co

Ruyang Liu, Chen Li, Haoran Tang, Yixiao Ge

Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI interaction at the video level. However, how to effectively encode and understand videos in video-based dialogue systems remains to be solved. In this paper, we investigate a straightfo

Stefano Baranzini, Gian Marco Canneori, Susanna Terracini

We seek frozen planet orbits for the helium atom through an application of the Mountain Pass Lemma to the Lagrangian action functional. Our method applies to a wide class of gravitational-like interaction potentials thus generalising the results in [7] (Cieliebak, Frauenfelder and Volkov - 2023). We also let the charge of the two electrons tend to zero and p

Yang Hu

Applying advanced digital technologies such as artificial intelligence (AI), blockchain (BLC), bigdata analytics (BDA) and digital twin (DT)/simulations to enhance supply chain resilience (SCRes) has been widely discussed in light of the global pandemic, regional conflicts, and the technology revolution such as Industry 4.0 and 5.0. Previous studies are limi

G. Saxena, Dashty T. Akrawy, Ali H. Ahmed, Mamta Aggarwal

Various decay modes in superheavy nuclei have been of significant interest among which cluster radioactivity has recently gained sizable attention. The {\alpha}-decay being a predominant decay mode in the superheavy region, the accurate determination of cluster decay half-lives is also crucial in this region as it has tremendous potential to be explored as o

Kang Lin, Sebastian Eckart, Hao Liang, Alexander Hartung

Similar to the optical diffraction of light passing through a material grating, the Kapitza-Dirac effect occurs when an electron is diffracted by a standing light wave. In its original description the effect is time-independent. In the present work, we extend the Kapitza-Dirac concept to the time domain. By tracking the spatiotemporal evolution of a pulsed e

Md Saroar Jahan, Mourad Oussalah, Djamila Romaissa Beddia, Jhuma kabir Mim

The surge of interest in data augmentation within the realm of NLP has been driven by the need to address challenges posed by hate speech domains, the dynamic nature of social media vocabulary, and the demands for large-scale neural networks requiring extensive training data. However, the prevalent use of lexical substitution in data augmentation has raised

Ohood Ali AL-Sbaheen, Ahmed Al-Jamel, Mohamed Ghaleb Al-Masaeed

In this paper, we study the saturation effect in the energy or mass spectra of three quantum models with energy-dependent potentials: the harmonic oscillator, the hydrogen atom, and the heavy quarkonia. We used the method proposed in \cite{garcia2009exactly}, which is based on studying various canonical point and gauge transformations applied to a function,

Xingyu Ren, Jiankang Deng, Yuhao Cheng, Jia Guo

Recent 3D face reconstruction methods have made remarkable advancements, yet there remain huge challenges in monocular high-quality facial reflectance reconstruction. Existing methods rely on a large amount of light-stage captured data to learn facial reflectance models. However, the lack of subject diversity poses challenges in achieving good generalization

Seoyeon Bae, Yoon Kyung Lee, Jungcheol Lee, Jaeheon Kim

A growth mindset has shown promising outcomes for increasing empathy ability. However, stimulating a growth mindset in VR-based empathy interventions is under-explored. In the present study, we implemented prosocial VR content, Our Neighbor Hero, focusing on embodying a virtual character to modulate players' mindsets. The virtual body served as a stepping st

Juze Zhang, Jingyan Zhang, Zining Song, Zhanhe Shi

Humans naturally interact with both others and the surrounding multiple objects, engaging in various social activities. However, recent advances in modeling human-object interactions mostly focus on perceiving isolated individuals and objects, due to fundamental data scarcity. In this paper, we introduce HOI-M3, a novel large-scale dataset for modeling the i

Xu Cao, Yu-Tie Liang, Rong-Gang Ping

The self-polarization of relativistic electrons or positrons moving in a magnetic field at a storage ring occurs through the emission of spin-flip synchrotron radiation, known as the Sokolov-Ternov effect. The resulting transverse polarizations of the colliding electrons and positrons, away from the depolarization resonances, allow for precise investigation

Md Abrar Jahin, Md Sakib Hossain Shovon, M. F. Mridha, Md Rashedul Islam

Sentiment analysis is crucial for understanding public opinion and consumer behavior. Existing models face challenges with linguistic diversity, generalizability, and explainability. We propose TRABSA, a hybrid framework integrating transformer-based architectures, attention mechanisms, and BiLSTM networks to address this. Leveraging RoBERTa-trained on 124M

Reza Esmailvandi, Mahmoud Filali, Jorge Galindo

Let $\mathcal{A}$ be a weakly sequentially complete Banach algebra containing a bounded approximate identity that is an ideal in its second dual $\mathcal{A}^{\ast\ast}$, we call such an algebra a Wesebai algebra. In the present paper we examine the Arens regularity properties of closed ideals of algebras in the Wesebai class. We observe that, although Weseb

Jyoichi Kaneko, Keiji Matsumoto, Katsuyoshi Ohara, Tomohide Terasoma

We define a hypergeometric series in $m$ variables with $p+(p-1)m$ parameters, which reduces to the generalized hypergeometric series $_pF_{p-1}$ when $m=1$, and to Lauricella's hypergeometric series $F_C$ in $m$ variables when $p=2$. We give a system of hypergeometric differential equations annihilating the series. Under some non-integral conditions on para

Lasse Leskelä

This article develops an analytical framework for studying information divergences and likelihood ratios associated with Poisson processes and point patterns on general measurable spaces. The main results include explicit analytical formulas for Kullback-Leibler divergences, R\'enyi divergences, Hellinger distances, and likelihood ratios of the laws of Poiss

Yaozhong W. Qiu

We continue the $U$-bound program initiated in [J. Funct. Anal. 258, 814-851 (2010)] and prove super-Poincar\'e inequalities for a class of subelliptic probability measures defined on M\'etivier groups, the main ingredient in the proof being a Hardy-type inequality. In doing so, we recover and extend some previous results from the probabilistic viewpoint.

Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan

Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs, this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However, the existing camouflaged generation methods require specifying t

Kang Yin, Jinlin Wang, Licheng Lou, Xiao Xu

Sulfide Kesterite Cu2ZnSnS4 (CZTS), a nontoxic and low-cost photovoltaic material, has always being facing severe charge recombination and poor carrier transport, resulting in the cell efficiency record stagnating around 11% for years. Gradient bandgap is a promising approach to relieve these issues, however, has not been effectively realized in Kesterite so

Nikolay A. Vanyushkin, I. M. Efimov, Ashot H. Gevorgyan

In this paper, an analytical formula for the sensitivity of optical sensors based on one-dimensional photonic crystals (PCs) with a defect was derived for the first time. Based on this formula, a comparative analysis of the sensitivity of defected PCs with and without mirror symmetry was carried out. In addition, the exact values of sensitivity in the limit

Vsevolod Gubarev

We describe all Rota-Baxter operators $R$ of weight zero on the algebra $U_3(F)$ of upper-triangular matrices of order three over a field of characteristic 0. For this, we apply the following three ingredients: properties of $R(1)$, conjugation with suitable (anti)automorphisms of $U_3(F)$, and computation with the help of \texttt{Singular} of the Gr\"{o}bne

Shihao Zhou, Jinshan Pan, Jinglei Shi, Duosheng Chen

How to explore useful features from images as prompts to guide the deep image restoration models is an effective way to solve image restoration. In contrast to mining spatial relations within images as prompt, which leads to characteristics of different frequencies being neglected and further remaining subtle or undetectable artifacts in the restored image,

Jian Zhang, Chong Wang, Anran Li, Weisong Sun

Recently, Automated Vulnerability Localization (AVL) has attracted growing attention, aiming to facilitate diagnosis by pinpointing the specific lines of code responsible for vulnerabilities. Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in line-level vulnerability localization remains underexplored. In this wo

Kaveh Eftekharinasab

We prove two versions of a global implicit function theorem, which involve no loss of derivative, for Keller's $ C_c^1 $-mappings between arbitrary Fr\'{e}chet spaces. Subsequently, within this framework, we apply these theorems to establish the global existence and uniqueness of solutions to initial value problems that involve the loss of one derivative. Mo

Jihun Kim, Dahyun Kim, Hyungrok Jung, Taeil Oh

Deploying deep models in real-world scenarios entails a number of challenges, including computational efficiency and real-world (e.g., long-tailed) data distributions. We address the combined challenge of learning long-tailed distributions using highly resource-efficient binary neural networks as backbones. Specifically, we propose a calibrate-and-distill fr

V. S. D. S. Mahesh Akavarapu, Arnab Bhattacharya

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word s

N. V. Filina, S. S. Baturin

We present a theoretical description of charged particles with nonzero projection of the orbital angular momentum (OAM) in a uniform magnetic field with broken axial symmetry. The wave functions we find naturally account for the asymmetry of the magnetic field at the entrance of the solenoid through the continuous parameter and are a generalization of the La

Yuji Cao, Huan Zhao, Yuheng Cheng, Ting Shu

With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and high-level task planning. In this survey, we provide a comprehensive review of the existing literature in LLM-enhanced RL and

Zong-Jun Yao, Shi-Hao Li

In this paper, we mainly consider a combinatoric explanation for block Pfaffians in terms of non-intersecting paths, as a generalization of results obtained by Stembridge. As applications, we demonstrate how are generating functions of non-intersecting paths related to skew orthogonal polynomials and their deformations, including a new concept called multipl

Michel Bertemes

We present recent measurements from the Belle and Belle II experiment related to $CP$ violation in decays of charmed mesons via two complementary approaches. We also propose a new algorithm to determine the flavor of neutral charmed mesons.

Shihao Zhou, Duosheng Chen, Jinshan Pan, Jufeng Yang

Transformer-based approaches have achieved superior performance in image restoration, since they can model long-term dependencies well. However, the limitation in capturing local information restricts their capacity to remove degradations. While existing approaches attempt to mitigate this issue by incorporating convolutional operations, the core component i

Michael Cuntz, Bernhard Mühlherr

Dimitrov and Fioresi introduced an object that they call a generalized root system. This is a finite set of vectors in a euclidean space satisfying certain compatibilities between angles and sums and differences of elements. They conjecture that every generalized root system is equivalent to one associated to a restriction of a Weyl arrangement. In this note

Fei Gu, Longfei Hao, Bo Liang, Song Feng

Studying the universe through radio telescope observation is crucial. However, radio telescopes capture not only signals from the universe but also various interfering signals, known as Radio Frequency Interference (RFI). The presence of RFI can significantly impact data analysis. Ensuring the accuracy, reliability, and scientific integrity of research findi

Hongqiu Wu, Yan Wang, Xingyuan Liu, Hai Zhao

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game rules and autonomously generate game-play processes. The IDGE allows users to create games by issuing simple natural language instructions, which significantly lowers the barrier for game development. We app

Satoshi Ohya

Because of the absence of indistinguishability constraint, interparticle interactions between nonidentical particles have in general much more variety than those between identical particles. In particular, it is known that there exists a $U(2)$ family of two-body contact interactions between nonidentical particles in one spatial dimension. This paper studies

Jinjin Du, Thibault Vogt, Ningxuan Zheng, Wenhui Li

Rydberg atoms are currently a very fast advancing quantum platform. For many interesting and demanding applications, including quantum computation, fast detection of a Rydberg excitation or a Rydberg qubit for information readout would be one of the most desirable developments. We demonstrate single-shot and \textit{in situ} absorption imaging of individual

Yishay Manassen, Michael Averbukh, Zion Hazan, Yahel Tzuriel

We demonstrate ionization of a molecule with the bias voltage of a Scanning Tunnelling Microscope (STM) resulting in a coexistence of a neutral and ionic molecules, i.e. radical (paramagnetic) and non-radical (diamagnetic) states. This coexistence may be facilitated by a periodic switching between two bias voltages. The precession of the nucleus in the diama

Ruqian Zhang, Yijiao Zhang, Annie Qu, Zhongyi Zhu

The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this paper, we propose a novel B

Judy X Yang, Jun Zhou, Jing Wang, Hui Tian

Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently. Additionally, it incorporates a specialized block for spatia

Ye Qiao, Jingcheng Li, Haocheng Xu, Sitao Huang

Neural Architecture Search (NAS) is a powerful technique for discovering high-performing CNN architectures, but most existing methods rely on costly training or extensive sampling. Zero-shot NAS offers a training-free alternative by using proxies to predict architecture performance. However, existing proxies are often suboptimal -- frequently outperformed by

Yushuang Wu, Luyue Shi, Junhao Cai, Weihao Yuan

Generalizable 3D object reconstruction from single-view RGB-D images remains a challenging task, particularly with real-world data. Current state-of-the-art methods develop Transformer-based implicit field learning, necessitating an intensive learning paradigm that requires dense query-supervision uniformly sampled throughout the entire space. We propose a n