April 2024 arXiv papers — page 96

Nilson C. Bernardes

Our main goal is to prove that every invertible generalized hyperbolic operator on a Banach space has a stability property, known as time-dependent stability, which was introduced by J. M. Franks (Invent. Math. 24 (1974), 163--172) and is stronger than structural stability.

Radha N Somaiya, Muhammad Sajjad, Nirpendra Singh, Aftab Alam

Developing efficient electrocatalysts for CO$_2$ reduction into value-added products is crucial for the green economy. Inspired by the recent synthesis of Biphenylene (BPH), we have systematically investigated pristine, defective, and Cu-decorated BPH as an electrocatalyst for the CO$_2$ reduction reactions (CRR). Our first-principles calculations show the C

Giuseppe Tarollo, Tomaso Fontanini, Claudio Ferrari, Guido Borghi

Nowadays, deep learning models have reached incredible performance in the task of image generation. Plenty of literature works address the task of face generation and editing, with human and automatic systems that struggle to distinguish what's real from generated. Whereas most systems reached excellent visual generation quality, they still face difficulties

Feiyang Chen, Ziqian Luo, Lisang Zhou, Xueting Pan

Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks. However, their practical deployment is hampered by high computational and memory demands. This study addresses the challenge by evaluating four primary model compression techniques: quantization, low-rank approximation, knowl

A. Zabrodin

We revisit dispersionless version of the multicomponent KP hierarchy considered previously by Takasaki and Takebe. In contrast to their study, we do not fix any distinguished component treating all of them on equal footing. We obtain nonlinear equations for dispersionless tau-function (the F-function) and represent them using the trigonometric parametrizatio

Hao Feng, Yuanzhe Jia, Ruijia Xu, Mukesh Prasad

Image recognition techniques heavily rely on abundant labeled data, particularly in medical contexts. Addressing the challenges associated with obtaining labeled data has led to the prominence of self-supervised learning and semi-supervised learning, especially in scenarios with limited annotated data. In this paper, we proposed an innovative approach by int

Billy Pham, Huy Le

In this paper, we present a detailed approach and implementation to prove Ethereum full node using recursive SNARK, distributed general GKR and Groth16. Our protocol's name is Sisu whose architecture is based on distributed Virgo in zkBridge with some major improvements. Besides proving signature aggregation, we provide solutions to 2 hard problems in provin

Ravshan Ashurov, Ilyoskhuja Sulaymonov

The paper considers the initial-boundary value problem for equation $D^\rho_t u(x,t)+ (-\Delta)^\sigma u(x,t)=0$, $\rho\in (0,1)$, $\sigma>0$, in an N-dimensional domain $\Omega$ with a homogeneous Dirichlet condition. The fractional derivative is taken in the sense of Caputo. The main goal of the work is to solve the inverse problem of simultaneously determ

Kevin Marc Seja, Niclas Wall-Wennerdal, Tomas Löfwander, Mikael Fogelström

Pair-breaking edges of $d$-wave superconductors feature Andreev bound states at the Fermi energy. Since these states are energetically highly unfavorable they are susceptible to effects that shift them to finite energy. We investigate the free energy of two different mechanisms: spontaneous phase gradients in the superconducting order parameter and surface f

Dayin Chen, Xiaodan Shi, Haoran Zhang, Xuan Song

Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techniques, together with the omnipresence of smartphones, have redirected researchers'attention towards the exploration of phone-based ambient temperature estimation methods. However, e

Qihong Huang, He Huang, Bing Xu, Kaituo Zhang

Einstein--Cartan theory is a generalization of general relativity that introduces spacetime torsion. In this paper, we perform phase space analysis to investigate the evolution of the early universe in Einstein--Cartan theory. By studying the stability of critical points in the dynamical system, we find that there exist two stable critical points which repre

Chao Tang, Dehao Huang, Wenlong Dong, Ruinian Xu

Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain regions responsible for semantic and geometric reasoning during cognitive processes, modeling the intricate relat

Tian Chen, Xijun Hu, Zhen Wu

In this paper, we study the eigenvalue problem of stochastic Hamiltonian system driven by Brownian motion and Markov chain with boundary conditions and time-dependent coefficients. For any dimensional case, the existence of the first eigenvalue is proven and the corresponding eigenfunctions are constructed by virtue of dual transformation and generalized Ric

Martina Baiardi, Samuele Burattini, Giovanni Ciatto, Danilo Pianini

The execution of Belief-Desire-Intention (BDI) agents in a Multi-Agent System (MAS) can be practically implemented on top of low-level concurrency mechanisms that impact on efficiency, determinism, and reproducibility. We argue that developers should specify the MAS behaviour independently of the execution model, and choose or configure the concurrency model

Filip Chudy, Paweł Woźny

New differential-recurrence relations for B-spline basis functions are given. Using these relations, a recursive method for finding the Bernstein-B\'{e}zier coefficients of B-spline basis functions over a single knot span is proposed. The algorithm works for any knot sequence and has an asymptotically optimal computational complexity. Numerical experiments s

Takato Miura, Naoki Akai, Kohei Honda, Susumu Hara

This paper presents a reactive navigation method that leverages a Model Predictive Path Integral (MPPI) control enhanced with spline interpolation for the control input sequence and Stein Variational Gradient Descent (SVGD). The MPPI framework addresses a nonlinear optimization problem by determining an optimal sequence of control inputs through a sampling-b

Simone Farinelli

For the quantized Yang-Mills $3+1$ dimensional problem we introduce the Wilson loop, prove an extension of Elitzur's theorem and shown quark confinement for sufficiently small values of the bare coupling constant, provided the existence of the mass gap.

Yiqian Wu, Hao Xu, Xiangjun Tang, Xien Chen

Existing neural rendering-based text-to-3D-portrait generation methods typically make use of human geometry prior and diffusion models to obtain guidance. However, relying solely on geometry information introduces issues such as the Janus problem, over-saturation, and over-smoothing. We present Portrait3D, a novel neural rendering-based framework with a nove

Ziqi Zhao, Zhaochun Ren, Liu Yang, Yunsen Liang

Offline reinforcement learning (RL) aims to learn policies without online explorations. To enlarge the training data, model-based offline RL learns a dynamics model which is utilized as a virtual environment to generate simulation data and enhance policy learning. However, existing data augmentation methods for offline RL suffer from (i) trivial improvement

Peiyan Liu, Yuanzhe Shen, Yueqian Liu, Fengyu Quan

As fully-actuated systems, omnidirectional multirotor aerial vehicles (OMAVs) have more flexible maneuverability and advantages in aggressive flight in cluttered environments than traditional underactuated MAVs. %Due to the high dimensionality of configuration space, making the designed trajectory generation algorithm efficient is challenging. This paper aim

Pavel Bakhvalov

We consider the spectral difference method based on the p-th order Raviart~-- Thomas space (p=1,2,3) on regular triangular meshes for the scalar transport equation. The solution converges with the order p if the transport velocity is parallel to a family of mesh edges and with the order p+1 otherwise. We prove this fact for p=1 and show it for p=1,2,3 in num

Kyle Burke, Antoine Dailly, Nacim Oijid

Arc-Kayles is a game where two players alternate removing two adjacent vertices until no move is left, the winner being the player who played the last move. Introduced in 1978, its computational complexity is still open. More recently, subtraction games, where the players cannot disconnect the graph while removing vertices, were introduced. In particular, Ar

Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic, Ewa Deelman

The increasing growth of data volume, and the consequent explosion in demand for computational power, are affecting scientific computing, as shown by the rise of extreme data scientific workflows. As the need for computing power increases, quantum computing has been proposed as a way to deliver it. It may provide significant theoretical speedups for many sci

Xinyu Zhang, Weidong Jiang, Xiangfeng Qiu, Yongxiang Liu

Optimization of slow-time transmit sequence endows cognitive radar with the ability to suppress strong clutter in the range-Doppler domain. However, in practice, inaccurate target velocity information or random phase error would induce uncertainty about the actual target steering vector, which would in turn severely deteriorate the the performance of the slo

Weronika Hryniewska-Guzik, Luca Longo, Przemysław Biecek

Explainable Artificial Intelligence has gained significant attention due to the widespread use of complex deep learning models in high-stake domains such as medicine, finance, and autonomous cars. However, different explanations often present different aspects of the model's behavior. In this research manuscript, we explore the potential of ensembling explan

Noah Lewis, Jean Luca Bez, Surendra Byna

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of

John T. M. Campbell, Nicola Marchetti, John Dooley, Indrakshi Dey

Quantum state comparison, utilizing metrics like fidelity and trace distance, underpins the assessment of quantum networks within quantum information theory. While recent research has expanded theoretical understanding, incorporating error analysis and scalability considerations remains crucial for practical applications. The primary contribution of this let

F. Abadizaman, M. Kiaba, A. Dubroka

Using spectroscopic ellipsometry, we studied the optical conductivity of LaCoO$_3$ with various degrees of strain. The optical response of the compressively strained \lco\ film is qualitatively similar to the one of the unstrained LaCoO$_3$ polycrystalline sample and exhibits redistribution of the spectral weight between about 0.2 and 6 eV, which is most lik

Yuqi Wang, Boran Jiang, Yi Luo, Dawei He

Large language models (LLMs), such as GPT3.5, GPT4 and LLAMA2 perform surprisingly well and outperform human experts on many tasks. However, in many domain-specific evaluations, these LLMs often suffer from hallucination problems due to insufficient training of relevant corpus. Furthermore, fine-tuning large models may face problems such as the LLMs are not

Hongli Wen, Yang Xu

Human action recognition and performance assessment have been hot research topics in recent years. Recognition problems have mature solutions in the field of sign language, but past research in performance analysis has focused on competitive sports and medical training, overlooking the scoring assessment ,which is an important part of sign language teaching

Rozhin Yousefjani, Xingjian He, Angelo Carollo, Abolfazl Bayat

Stark systems in which a linear gradient field is applied across a many-body system have recently been proposed for quantum sensing. Here, we explore sensing capacity of Stark probes, in both single-particle and many-body interacting systems, for estimating nonlinear forms of the gradient fields. Our analysis reveals that, this estimation can achieve super-H

Deddy Jobson, Li Yilin, Naoki Nishimura, Yang Jie

The Randomized Controlled Trial (RCT) or A/B testing is considered the gold standard method for estimating causal effects. Fisher famously advocated randomly allocating experiment units into treatment and control groups to preclude systematic biases. We propose a variant of systematic sampling called Covariate Ordered Systematic Sampling (COSS). In COSS, we

MIT Hardness Group, Erik D. Demaine, Lilly Hall, Hayashi Layers

We prove PSPACE-hardness for fifteen games in the Super Mario Bros. 2D platforming video game series. Previously, only the original Super Mario Bros. was known to be PSPACE-hard (FUN 2016), though several of the games we study were known to be NP-hard (FUN 2014). Our reductions build door gadgets with open, close, and traverse traversals, in each case using

Lei Zhou

This paper aims to present objective methods for constructing new fuzzy sets from known fuzzy or classical sets, defined over the elements of a finite universe's superstructure. The paper proposes rules for assigning membership functions to these new fuzzy sets, leading to two important findings. Firstly, the property concerning the cardinality of a power se

Xinbu Cheng, Xinqi Huang, Mingyuan Rong, Zixiang Xu

For an $n$-vertex graph $G$, let $h(G)$ denote the smallest size of a subset of $V(G)$ such that it intersects every maximum independent set of $G$. A conjecture posed by Bollob\'{a}s, Erd\H{o}s and Tuza in early 90s remains widely open, asserting that for any $n$-vertex graph $G$, if the independence number $\alpha(G) =\Omega(n) $, then $h(G) = o(n)$. In th

Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez

Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2nd edition of the Face Recognitio

Manfred Buchacher

We present a semi-algorithm which for any irreducible $p\in\mathbb{K}[x,y]$ finds all elements of $\mathbb{K}(x) + \mathbb{K}(y)$ that are of the form $qp$ for some $q\in\mathbb{K}(x,y)$ whose denominator is not divisible by $p$.

Yepeng Ding, Arthur Gervais, Roger Wattenhofer, Hiroyuki Sato

Decentralized finance (DeFi) is revolutionizing the traditional centralized finance paradigm with its attractive features such as high availability, transparency, and tamper-proofing. However, attacks targeting DeFi services have severely damaged the DeFi market, as evidenced by our investigation of 80 real-world DeFi incidents from 2017 to 2022. Existing me

Boris S. Kerner, Sergey L. Klenov, Vincent Wiering, Michael Schreckenberg

We present a methodology of cooperative driving in vehicular traffic, in which for short-time traffic prediction rather than one of the statistical approaches of artificial intelligence (AI), we follow a qualitative different microscopic traffic prediction approach developed recently [Phys. Rev. E 106 (2022) 044307]. In the microscopic traffic prediction app

Henri Hudrisier, Mokhtar Ben Henda

E-learning is becoming a global phenomenon. Learning Arabic (or Arabic dialects), or learning one or several variants of Berber can be understood from a very local perspective (in the Maghreb for instance) or in the wider framework of the diaspora or even more broadly in a global world context (in case a Japanese or a Russian learns Arabic and Berber). Resou

H. Yang, T. Xue, L. Jiang, C. Xu

The Jinping Neutrino Experiment(JNE), conducted within the China Jinping Underground Laboratory, aims to detect and analyze of solar neutrinos, geo-neutrinos, and supernova neutrinos. A one-ton prototype will soon be in commision with an upgrade from 30 channels to 60 channels, which will increase the data bandwidth by one to two orders of magnitude and exce

Sabrina Bonandin, Michael Herty

We address an optimization problem where the cost function is the expectation of a random mapping. To tackle the problem two approaches based on the approximation of the objective function by consensus-based particle optimization methods on the search space are developed. The resulting methods are mathematically analyzed using a mean-field approximation and

Henri Hudrisier, Rachid Zghibi, Sihem Zghidi, Mokhtar Ben Henda

The project targets both oral corpus and the rich text resources written in the Maghreb region. It focuses particularly on the continuity, for more than 12 centuries, of a classical still alive Arabic language and on the extreme hybridization of vernacular languages sustained by the rich Libyan, Roman, Hebrew and Ottoman influences and by the more recent Fre

Daniel Koch, Akhilesh Nandan, Gayathri Ramesan, Aneta Koseska

Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, e.g., via cell surface receptors or sensory organs. Integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simp

Ilaria Ciaramaglia, Paola Goatin, Gabriella Puppo

We prove the well-posedness of weak entropy solutions of a scalar non-local traffic flow model with time delay. Existence is obtained by convergence of finite volume approximate solutions constructed by Lax-Friedrich and Hilliges-Weidlich schemes, while the L1 stability with respect to the initial data and the delay parameter relies on a Kruzkov-type doublin

Mgeni Makambi Mashauri, Alexandre Graell i Amat, Michael Lentmaier

We show that spatially coupled low-density parity-check (LDPC) codes yield robust performance over changing intersymbol interfere (ISI) channels with optimal and suboptimal detectors. We compare the performance with classical LDPC code design which involves optimizing the degree distribution for a given (known) channel. We demonstrate that these classical sc

Zhongkai Mi

In a previous joint paper with Wu and Yakimov, we gave an explicit description of the lowest discriminant ideal in Cayley-Hamilton Hopf algebras $(H,C,\mathrm{tr})$ with basic identity fiber, i.e. all irreducible representations over the kernel of the counit of the central Hopf subalgebra $C$ are one-dimensional. We first show in this work that the zero set

Yongming Huang, Xiaohu You, Hang Zhan, Shiwen He

Intelligent communications have played a pivotal role in shaping the evolution of 6G networks. Native artificial intelligence (AI) within green communication systems must meet stringent real-time requirements. To achieve this, deploying lightweight and resource-efficient AI models is necessary. However, as wireless networks generate a multitude of data field

Jarmo Mäkelä

Inspired by Einstein's Strong Principle of Equivalence we consider the effects of quantum mechanics to the gravity-like phenomena experienced by an observer in a uniformly accelerating motion in flat spacetime. Among other things, our model of quantum gravity, derived from the first principles, predicts the Unruh effect, and a discrete area spectrum for spac

Ayah Youssef, Hassan Noura, Abderrahim El Amrani, El Mostafa El Adel

Fault diagnosis in marine diesel engines is vital for maritime safety and operational efficiency.These engines are integral to marine vessels, and their reliable performance is crucial for safenavigation. Swift identification and resolution of faults are essential to prevent breakdowns,enhance safety, and reduce the risk of catastrophic failures at sea. Proa

Sarah Fakhoury, Markus Kuppe, Shuvendu K. Lahiri, Tahina Ramananandro

Improper parsing of attacker-controlled input is a leading source of software security vulnerabilities, especially when programmers transcribe informal format descriptions in RFCs into efficient parsing logic in low-level, memory unsafe languages. Several researchers have proposed formal specification languages for data formats from which efficient code can

Ioannis Dimitriou

In this paper, we study Markov-dependent reflected autoregressive processes, and other related models the analysis of which results in a vector-valued fixed-point functional equation of a certain type. In queueing terms, such processes describe the workload just before a customer arrival, which makes obsolete a fraction of the work already present, and where

Quentin Chauleur, Radu Chicireanu, Guillaume Dujardin, Jean-Claude Garreau

We consider the Gross-Pitaevskii equation with a confining ring potential with a Gaussian profile. By introducing a rotating sinusoidal perturbation, we numerically highlight the nucleation of quantum vortices in a particular regime throughout the dynamics. Numerical computations are made via a Strang splitting time integration and a two-point flux approxima

Mingze Sun, Yiqing Wang, Zhenyi Zhao

A novel crowd stampede detection and prediction algorithm based on Deformable DETR is proposed to address the challenges of detecting a large number of small targets and target occlusion in crowded airport and train station environments. In terms of model design, the algorithm incorporates a multi-scale feature fusion module to enlarge the receptive field an

Wenjie Lin, Zhen Liu, Chengzhi Jiang, Mingyan Han

In this paper, we address the Bracket Image Restoration and Enhancement (BracketIRE) task using a novel framework, which requires restoring a high-quality high dynamic range (HDR) image from a sequence of noisy, blurred, and low dynamic range (LDR) multi-exposure RAW inputs. To overcome this challenge, we present the IREANet, which improves the multiple expo

Enming Zhang, Bingke Zhu, Yingying Chen, Qinghai Miao

Vision-Language Models (VLMs), such as CLIP, play a foundational role in various cross-modal applications. To fully leverage VLMs' potential in adapting to downstream tasks, context optimization methods like Prompt Tuning are essential. However, one key limitation is the lack of diversity in prompt templates, whether they are hand-crafted or learned through

Payal Varshney, Adriano Lucieri, Christoph Balada, Andreas Dengel

Trustworthiness is a major prerequisite for the safe application of opaque deep learning models in high-stakes domains like medicine. Understanding the decision-making process not only contributes to fostering trust but might also reveal previously unknown decision criteria of complex models that could advance the state of medical research. The discovery of

Sungjun Cho, Beomjun Kim, Hyunuk Cho, Gyeongseob Seo

This work investigates a new erase scheme in NAND flash memory to improve the lifetime and performance of modern solid-state drives (SSDs). In NAND flash memory, an erase operation applies a high voltage (e.g., > 20 V) to flash cells for a long time (e.g., > 3.5 ms), which degrades cell endurance and potentially delays user I/O requests. While a large body o

Ruifeng Li, Dongzhan Zhou, Ancheng Shen, Ao Zhang

Artificial intelligence (AI) technology has demonstrated remarkable potential in drug dis-covery, where pharmacokinetics plays a crucial role in determining the dosage, safety, and efficacy of new drugs. A major challenge for AI-driven drug discovery (AIDD) is the scarcity of high-quality data, which often requires extensive wet-lab work. A typical example o

Haodong Wen, Bodong Du, Ruixun Liu, Deyu Meng

Recently, the optimization of polynomial filters within Spectral Graph Neural Networks (GNNs) has emerged as a prominent research focus. Existing spectral GNNs mainly emphasize polynomial properties in filter design, introducing computational overhead and neglecting the integration of crucial graph structure information. We argue that incorporating graph inf

Jiafu Wei, Chia-Ming Chang, Xi Yang, Takeo Igarashi

In real-world usage, existing GAN image generation tools come up short due to their lack of intuitive interfaces and limited flexibility. To overcome these limitations, we developed CanvasPic, an innovative tool for flexible GAN image generation. Our tool introduces a novel 2D layout design that allows users to intuitively control image attributes based on r

Luke W. Yerbury, Ricardo J. G. B. Campello, G. C. Livingston, Mark Goldsworthy

Relative Validity Indices (RVIs) such as the Silhouette Width Criterion and Davies Bouldin indices are the most widely used tools for evaluating and optimising clustering outcomes. Traditionally, their ability to rank collections of candidate dataset partitions has been used to guide the selection of the number of clusters, and to compare partitions from dif

Richard Löscher, Michael Reichelt, Olaf Steinbach

We consider a distributed optimal control problem subject to a parabolic evolution equation as constraint. The control will be considered in the energy norm of the anisotropic Sobolev space $[H_{0;,0}^{1,1/2}(Q)]^\ast$, such that the state equation of the partial differential equation defines an isomorphism onto $H^{1,1/2}_{0;0,}(Q)$. Thus, we can eliminate

Marc Peruzzetto, Bastien Colas, Clara Lévy, Jérémy Rohmer

For rockfall hazard assessment on areas more than several km2 in size, the quantification of runout probability is usually done empirically. Classical methods use statistical distributions of reach or energy angles derived from rockfall databases. However, other topographic descriptors can be derived from the topographic profiles along the rockfall path. Usi

Jiazhen Shao, Igor P. Ivanov, Mikko Korhonen

We continue classification of finite groups which can be used as symmetry group of the scalar sector of the four-Higgs-doublet model (4HDM). Our objective is to systematically construct non-abelian groups via the group extension procedure, starting from the abelian groups $A$ and their automorphism groups $\mathrm{Aut}(A)$. Previously, we considered all cycl

Mgeni Makambi Mashauri, Alexandre Graell i Amat, Michael Lentmaier

In this paper, we derive the exact input/output transfer functions of the optimal a-posteriori probability channel detector for a general ISI channel with erasures. Considering three channel impulse responses of different memory as an example, we compute the BP and MAP thresholds for regular spatially coupled LDPC codes with joint iterative detection and dec

M. T. Beltrán, M. Padovani, D. Galli, N. Áñez-López

Context. Dust polarization observations of the massive protocluster G31.41+0.31 carried out at ~1'' (~3750 au) resolution with the SMA at 870 microm have revealed one of the clearest examples to date of an hourglass-shaped magnetic field morphology in the high-massregime. Additionally, ~0.24'' (~900 au) resolution observations with ALMA at 1.3 mm have confir

Hyeonbin Hwang, Doyoung Kim, Seungone Kim, Seonghyeon Ye

Training on large amounts of rationales (i.e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs). However, acquiring human-authored rationales or augmenting rationales from proprietary models is costly and not scalable. In this paper, we study the problem of whether LLMs could self-improve their reasoning c

Solène Schropff, Fabien Petitpas, Eric Daniel

Analytical/quasi-analytical solutions are proposed for a steady, compressible, two-phase flow in mechanical equilibrium in a rectilinear duct subjected to heating followed by cooling. The flow is driven by the pressure ratio between a variable outlet pressure and an upstream tank. A critical pressure ratio distinguishes subsonic and supersonic outlet regimes

Nicoletta D'Angelo, Giada Adelfio, Jorge Mateu, Ottmar Cronie

Second-order statistics play a crucial role in analysing point processes. Previous research has specifically explored locally weighted second-order statistics for point processes, offering diagnostic tests in various spatial domains. However, there remains a need to improve inference for complex intensity functions, especially when the point process likeliho

Asal Khosravi, Zahed Rahmati, Ali Vefghi

With the growth of textual data across online platforms, sentiment analysis has become crucial for extracting insights from user-generated content. While traditional approaches and deep learning models have shown promise, they cannot often capture complex relationships between entities. In this paper, we propose leveraging Relational Graph Convolutional Netw

Bin Ren, Yawei Li, Nancy Mehta, Radu Timofte

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop netw

Runwei Guan, Rongsheng Hu, Zhuhao Zhou, Tianlang Xue

In reality, images often exhibit multiple degradations, such as rain and fog at night (triple degradations). However, in many cases, individuals may not want to remove all degradations, for instance, a blurry lens revealing a beautiful snowy landscape (double degradations). In such scenarios, people may only desire to deblur. These situations and requirement

Arnulf Hagen, Trond Michael Andersen

In April 2021 Stava bridge, a main bridge on E6 in Norway, was abruptly closed for traffic. A structural defect had seriously compromised the bridge structural integrity. The Norwegian Public Roads Administration (NPRA) closed it, made a temporary solution and reopened with severe traffic restrictions. The incident was alerted through what constitutes the br

Ziwen Yin, Luca Visinelli

We present novel findings concerning the parameter space of axion stars, extended object forming in dense dark matter environments through gravitational condensation. We emphasize their formation within the dense minihalos that potentially surround primordial black holes and in axion miniclusters. Our study investigates the relation between the radius and ma

Gourav Banerjee, Blesson Mathew, Suman Bhattacharyya, Ashish Devaraj

In a recent study, Banerjee et al. (2021) produced an atlas of all major emission lines found in a large sample of 115 Galactic field Be stars using the 2-m Himalayan Chandra Telescope (HCT) facility located at Ladakh, India. This paper presents our further exploration of these stars to estimate the electron density in their discs. Our study using Balmer dec

Chengran Yang, Marta Florido-Llin`as, Mile Gu, Thomas J. Elliott

Quantum technologies offer a promising route to the efficient sampling and analysis of stochastic processes, with potential applications across the sciences. Such quantum advantages rely on the preparation of a quantum sample state of the stochastic process, which requires a memory system to propagate correlations between the past and future of the process.

Jianqi Zhang, Wenwen Qiang, Jingyao Wang, Jiahuan Zhou

Transformer-based methods have achieved state-of-the-art performance in time series forecasting (TSF) by capturing positional and semantic topological relationships among input tokens. However, it remains unclear whether existing Transformers fully leverage the intrinsic topological structure among tokens throughout intermediate layers. Through empirical and

A. D. Drozdov, R. Hoj Jermiin, J. deClaville Christiansen

Recycling has become a predominant subject in industry and science due to a rising concern for the environment driven by high production volume of plastics. Replacement of virgin polymers with their recycled analogs is not always possible because recycled polymers cannot met the same property profiles as their virgin counterparts. To avoid deterioration of t

Qi Guo, Shanmin Pang, Xiaojun Jia, Yang Liu

Adversarial attacks, particularly \textbf{targeted} transfer-based attacks, can be used to assess the adversarial robustness of large visual-language models (VLMs), allowing for a more thorough examination of potential security flaws before deployment. However, previous transfer-based adversarial attacks incur high costs due to high iteration counts and comp

Rafaella Fiorelli, Mona Rajabali, Roberto Méndez-Romero, Akash Kumar

As nascent nonlinear oscillators, nano-constriction spin Hall nano-oscillators (SHNOs) represent a promising potential for integration into more complicated systems such as neural networks, magnetic field sensors, and radio frequency (RF) signal classification, their tunable high-frequency operating regime, easy synchronization, and CMOS compatibility can st

Xiaotian Yang, Weiqing Zhou, Qi Yao, Yunhai Li

We construct Sierpinski-carpet (SC) based on AA or AB bilayer graphene by atom vacancies, namely, SC-AA and SC-AB, to investigate the effects of interlayer coupling on the electronic properties of fractals. Compared with monolayer graphene SC, their density of states have similar features, such as Van-Hove singularities and edge states corresponding to the c

Yu Miao, Qing Yin

Complex networks play a crucial role in understanding physical, biological, social and technological systems. One of the most relevant features of graphs representing real systems is community structure. In this paper, for a specific partition of a given network, we prove the Cramer's moderate deviations of modularity for the partition when the size of the n

Rui Hu, Yahan Tu, Shuyu Wei, Dongyuan Lu

Despite achieving outstanding performance on various cross-modal tasks, current large vision-language models (LVLMs) still suffer from hallucination issues, manifesting as inconsistencies between their generated responses and the corresponding images. Prior research has implicated that the low quality of instruction data, particularly the skewed balance betw

William Y. C. Chen, Amy M. Fu

In light of the grammar given by Ji for the $(\alpha,\beta)$-Eulerian polynomials introduced by Carlitz and Scoville, we provide a labeling scheme for increasing binary trees. In this setting, we obtain a combinatorial interpretation of the $\gamma$-coefficients of the $\alpha$-Eulerian polynomials in terms of forests of planted 0-1-2-plane trees, which spec

Xufeng Yuan, Chaoying Zhao

Recently, meta-surface(MS) has emerged as a promising alternative method for generating vortex waves. At the same time, MS also face the problem of narrow bandwidth, in order to obtain a board bandwidth, the MS unit cells structure become more and more complex, which will deduce many inconveniences to the preparation process of MS device. Therefore, we want

Reihaneh Amini, Sanaz Saki Norouzi, Pascal Hitzler, Reza Amini

Ontology alignment, a critical process in the Semantic Web for detecting relationships between different ontologies, has traditionally focused on identifying so-called "simple" 1-to-1 relationships through class labels and properties comparison. The more practically useful exploration of more complex alignments remains a hard problem to automate, and as such

Juha Reinikainen, Vlad Stirbu, Teiko Heinosaari, Vesa Lappalainen

Quantum computing is a highly abstract scientific discipline, which, however, is expected to have great practical relevance in future information technology. This forces educators to seek new methods to teach quantum computing for students with diverse backgrounds and with no prior knowledge of quantum physics. We have developed an online course built around

Zhiyu Hu, Yang Zhang, Minghao Xiao, Wenjie Wang

The evolving paradigm of Large Language Model-based Recommendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommendation data. The inclusion of user data in LLMs raises privacy concerns. To protect users, the unlearning process in LLMRec, specifically removing unusable data (e.g., historical beha

Charl Ras, Matthew Tam, Daniel Uteda

In this work, we consider a nonsmooth minimisation problem in which the objective function can be represented as the maximum of finitely many smooth ``subfunctions''. First, we study a smooth min-max reformulation of the problem. Due to this smoothness, the model provides enhanced capability of exploiting the structure of the problem, when compared to method

Yu Miao, Qing Yin

In this paper, the model is a specific partition of a given network. Berry-Esseen bound and strong law of large numbers of modularity for the partition are proved when the size of the network gets large.

Zhiyu Zhang, Chenkaixiang Lu, Wenchong Tian, Zhenliang Liao

Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. Owin

Ryan Camacho, Cheng Huang

The current study aims to evaluate and investigate the development of projection-based reduced-order models (ROMs) for efficient and accurate RDE simulations. Specifically, we focus on assessing the projection-based ROM construction utilizing three different approaches: the linear static basis, nonlinear quadratic basis, and an adaptive model order reduction

Jiapeng Su, Qi Fan, Guangming Lu, Fanglin Chen

Few-shot semantic segmentation (FSS) has achieved great success on segmenting objects of novel classes, supported by only a few annotated samples. However, existing FSS methods often underperform in the presence of domain shifts, especially when encountering new domain styles that are unseen during training. It is suboptimal to directly adapt or generalize t

Fan Liu, Shuai Zhao, Zhiyong Cheng, Liqiang Nie

Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-order neighboring nodes. However, most existing GCN-based methods overlook the multiple interests of users

Christian Gück, Cyriana M. A. Roelofs, Stefan Faulstich

Anomaly detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain specific public datasets are scarce. Many comparisons of different approaches either use benchmarks composed of data from many different domains, inaccessible data or one of the few

Danil Afonchikov, Elena Kornaeva, Irina Makovik, Alexey Kornaev

Cells count become a challenging problem when the cells move in a continuous stream, and their boundaries are difficult for visual detection. To resolve this problem we modified the training and decision making processes using curriculum learning and multi-view predictions techniques, respectively.

Xiang Feng, Yongbo He, Linxi Chen, Yan Yang

Low-resolution (LR) multi-view capture limits the fidelity of 3D Gaussian Splatting (3DGS). 3DGS super-resolution (SR) is therefore important, yet challenging because it must recover missing high-frequency details while enforcing cross-view geometric consistency. We revisit SRGS, a simple baseline that couples plug-in 2D SR priors with geometry-aware cross-v

Hamed Babaei Giglou, Jennifer D'Souza, Felix Engel, Sören Auer

Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge or predictive models, with limited exploration of the potential of Large Language Models (LLMs). We present the LLMs4OM framework, a novel a

Avi Abu, Nikola Miskovic, Oleg Chebotar, Neven Cukrov

We present an algorithm for detecting and tracking underwater mobile objects using active acoustic transmission of broadband chirp signals whose reflections are received by a hydrophone array. The method overcomes the problem of high false alarm rate by applying a track-before-detect approach to the sequence of received reflections. A 2D time-space matrix is