April 2024 arXiv papers — page 89

Giovanni Ziarelli, Nicola Parolini, Marco Verani

Since infectious pathogens start spreading into a susceptible population, mathematical models can provide policy makers with reliable forecasts and scenario analyses, which can be concretely implemented or solely consulted. In these complex epidemiological scenarios, machine learning architectures can play an important role, since they directly reconstruct d

Minghe Gao, Shuang Chen, Liang Pang, Yuan Yao

The remarkable performance of Multimodal Large Language Models (MLLMs) has unequivocally demonstrated their proficient understanding capabilities in handling a wide array of visual tasks. Nevertheless, the opaque nature of their black-box reasoning processes persists as an enigma, rendering them uninterpretable and struggling with hallucination. Their abilit

Jiri Rezek, Martin Kucera, Tomas Kozak, Radomir Cerstvy

In presented work, a reactive high-power impulse magnetron sputtering (r-HiPIMS) was used for high-rate deposition ( 170 nm/min) of Cu2O films. Films were deposited on a standard soda-lime glass (SLG) substrate at a temperature of 190C. As-deposited films exhibit poor hole mobility in the orders of 1 cm2/Vs. We have systematically studied the effect of laser

Jiaxing Zhao, Peng Zheng, Rui Ma

Creating large LiDAR datasets with pixel-level labeling poses significant challenges. While numerous data augmentation methods have been developed to reduce the reliance on manual labeling, these methods predominantly focus on static scenes and they overlook the importance of data augmentation for dynamic scenes, which is critical for autonomous driving. To

Ronny Ramlau, Bernadett Stadler

In this paper, we consider the atmospheric tomography operator, which describes the effect of turbulent atmospheric layers on incoming planar wavefronts. Given wavefronts from different guide stars, measured at a telescope, the inverse problem consists in the reconstruction of the turbulence above the telescope. We show that the available data is not suffici

Taehwa Choi, Seohyeon Park, Hunyong Cho, Sangbum Choi

Censored quantile regression has emerged as a prominent alternative to classical Cox's proportional hazards model or accelerated failure time model in both theoretical and applied statistics. While quantile regression has been extensively studied for right-censored survival data, methodologies for analyzing interval-censored data remain limited in the surviv

Frederic Kirstein, Jan Philip Wahle, Terry Ruas, Bela Gipp

Meeting summarization has become a critical task considering the increase in online interactions. While new techniques are introduced regularly, their evaluation uses metrics not designed to capture meeting-specific errors, undermining effective evaluation. This paper investigates what the frequently used automatic metrics capture and which errors they mask

Tim Browning, Pankaj Vishe, Shuntaro Yamagishi

We study the geometry of the space of rational curves on smooth complete intersections of low degree, which pass through a given set of points on the variety. The argument uses spreading out to a finite field, together with an adaptation to function fields of positive characteristic of work by Rydin Myerson on the circle method. Our work also allows us to ha

Pierre Lepagnol, Thomas Gerald, Sahar Ghannay, Christophe Servan

This study is part of the debate on the efficiency of large versus small language models for text classification by prompting.We assess the performance of small language models in zero-shot text classification, challenging the prevailing dominance of large models.Across 15 datasets, our investigation benchmarks language models from 77M to 40B parameters usin

Qinfeng Li, Zhiqiang Shen, Zhenghan Qin, Yangfan Xie

Proprietary large language models (LLMs) have been widely applied in various scenarios. Additionally, deploying LLMs on edge devices is trending for efficiency and privacy reasons. However, edge deployment of proprietary LLMs introduces new security challenges: edge-deployed models are exposed as white-box accessible to users, enabling adversaries to conduct

Sherry X. Chen, Yaron Vaxman, Elad Ben Baruch, David Asulin

Despite many attempts to leverage pre-trained text-to-image models (T2I) like Stable Diffusion (SD) for controllable image editing, producing good predictable results remains a challenge. Previous approaches have focused on either fine-tuning pre-trained T2I models on specific datasets to generate certain kinds of images (e.g., with a specific object or pers

Kangning Zhang, Yingjie Qin, Jiarui Jin, Yifan Liu

Multimodal recommendation focuses primarily on effectively exploiting both behavioral and multimodal information for the recommendation task. However, most existing models suffer from the following issues when fusing information from two different domains: (1) Previous works do not pay attention to the sufficient utilization of modal information by only usin

Xueyuan Gong, Zhiquan Liu, Yain-Whar Si, Xiaochen Yuan

Computing power has evolved into a foundational and indispensable resource in the area of deep learning, particularly in tasks such as Face Recognition (FR) model training on large-scale datasets, where multiple GPUs are often a necessity. Recognizing this challenge, some FR methods have started exploring ways to compress the fully-connected layer in FR mode

Etienne David, Jean Bellot, Sylvain Le Corff

Forecasting tasks using large datasets gathering thousands of heterogeneous time series is a crucial statistical problem in numerous sectors. The main challenge is to model a rich variety of time series, leverage any available external signals and provide sharp predictions with statistical guarantees. In this work, we propose a new forecasting model that com

Keren Shao, Ke Chen, Shlomo Dubnov

In this challenge, we disentangle the deep filters from the original DeepfilterNet and incorporate them into our Spec-UNet-based network to further improve a hybrid Demucs (hdemucs) based remixing pipeline. The motivation behind the use of the deep filter component lies at its potential in better handling temporal fine structures. We demonstrate an increment

Jinyulin Li, Wei Xie, Jinjin Liu, Qing Li

By measuring magnetizations of pristine and Ta-doped CsV$_{3}$Sb$_{5}$ single crystals, we have carried out systematic studies on the lower critical field, critical current density, and equilibrium magnetization of this kagome system. The lower critical field has been investigated in the two typical samples, and the temperature dependent lower critical field

Cassio F. Dantas, Raffaele Gaetano, Claudia Paris, Dino Ienco

Timely up-to-date land use/land cover (LULC) maps play a pivotal role in supporting agricultural territory management, environmental monitoring and facilitating well-informed and sustainable decision-making. Typically, when creating a land cover (LC) map, precise ground truth data is collected through time-consuming and expensive field campaigns. This data i

Kengo Tachihara, Naofumi Fukaya, Kazuki Tokuda, Yasumasa Yamasaki

We report on ALMA ACA observations of a high-density region of the Corona Australis cloud forming a young star cluster, and the results of resolving internal structures. In addition to embedded Class 0/I protostars in continuum, a number of complex dense filamentary structures are detected in the C18O and SO lines by the 7m array. These are sub-structures of

Qinsi Wang, Wei Hong Yang

Recently, the proximal Newton-type method and its variants have been generalized to solve composite optimization problems over the Stiefel manifold whose objective function is the summation of a smooth function and a nonsmooth function. In this paper, we propose an adaptive quadratically regularized proximal quasi-Newton method, named ARPQN, to solve this cl

Lianyu Hu, Wei Feng, Liqing Gao, Zekang Liu

In sign language, the conveyance of human body trajectories predominantly relies upon the coordinated movements of hands and facial expressions across successive frames. Despite the recent advancements of sign language understanding methods, they often solely focus on individual frames, inevitably overlooking the inter-frame correlations that are essential f

Hector O. Silva, Giovanni Tambalo, Kostas Glampedakis, Kent Yagi

The response of black holes to small perturbations is known to be partially described by a superposition of quasinormal modes. Despite their importance to enable strong-field tests of gravity, little to nothing is known about what overtones and quasinormal-mode amplitudes are like for black holes in extensions to general relativity. We take a first step in t

Hamed Hematian Hemati, Hamid Beigy

Efficiently modeling historical information is a critical component in addressing user queries within a conversational question-answering (QA) context, as historical context plays a vital role in clarifying the user's questions. However, irrelevant history induces noise in the reasoning process, especially for those questions with a considerable historical c

Tong Shen, Dong Li, Ziheng Gao, Lu Tian

Video Frame Interpolation (VFI) is a crucial technique in various applications such as slow-motion generation, frame rate conversion, video frame restoration etc. This paper introduces an efficient video frame interpolation framework that aims to strike a favorable balance between efficiency and quality. Our framework follows a general paradigm consisting of

Bonan Ruan, Jiahao Liu, Chuqi Zhang, Zhenkai Liang

Linux kernel vulnerability reproduction is a critical task in system security. To reproduce a kernel vulnerability, the vulnerable environment and the Proof of Concept (PoC) program are needed. Most existing research focuses on the generation of PoC, while the construction of environment is overlooked. However, establishing an effective vulnerable environmen

Emanuele Gentili, Davide Falessi

Context: Software specifications are usually written in natural language and may suffer from imprecision, ambiguity, and other quality issues, called thereafter, requirement smells. Requirement smells can hinder the development of a project in many aspects, such as delays, reworks, and low customer satisfaction. From an industrial perspective, we want to foc

Pingpeng Yuan, Yujiang Wang, Tianyu Ma, Siyuan He

Graph pattern matching, one of the fundamental graph mining problems, aims to extract structural patterns of interest from an input graph. The state-of-the-art graph matching algorithms and systems are mainly designed for undirected graphs. Directed graph matching is more complex than undirected graph matching because the edge direction must be taken into ac

Changsuk Oh, Dongseok Shim, Taekbeom Lee, H. Jin Kim

Object removal refers to the process of erasing designated objects from an image while preserving the overall appearance, and it is one area where image inpainting is widely used in real-world applications. The performance of an object remover is quantitatively evaluated by measuring the quality of object removal results, similar to how the performance of an

Xi Chen, Yumou Fei, Shyamal Patel

We give a distribution-free testing algorithm for decision lists with $\tilde{O}(n^{11/12}/\varepsilon^3)$ queries. This is the first sublinear algorithm for this problem, which shows that, unlike halfspaces, testing is strictly easier than learning for decision lists. Complementing the algorithm, we show that any distribution-free tester for decision lists

Molla Basir Ahamed, Sanju Mandal

The main objective of this study is to investigate the existence and forms of solutions of systems of general quadratic functional equations in $\mathbb{C}^n$. By utilizing Nevanlinna theory in $\mathbb{C}^n$, we explore the existence and form of solutions for the several systems of general quadratic difference and partial differential-difference equations o

Carlos Andrés Toro Cardona

We prove the impossibility of constructing free boundary minimal M\"obius bands in the Euclidean ball $\mathbb{B}^3$. This answers in the negative a question proposed by I. Fern\'andez, L. Hauswirth and P. Mira.

Qiyu Hou, Jun Wang, Meixuan Qiao, Lujun Tian

To overcome the limitations and challenges of current automatic table data annotation methods and random table data synthesis approaches, we propose a novel method for synthesizing annotation data specifically designed for table recognition. This method utilizes the structure and content of existing complex tables, facilitating the efficient creation of tabl

Deepu Kumar, Nguyen The Hoang, Yumin Sim, Youngsu Choi

The intricate interplay between spin and lattice degrees of freedom in two-dimensional magnetic materials plays a pivotal role in modifying their magnetic characteristics, engendering hybrid quasiparticles, and implementing functional devices. Herein, we present our comprehensive and in-depth investigations on magnetic and lattice excitations of MnPSe3-xSx (

Dingkun Zhang, Sijia Li, Chen Chen, Qingsong Xie

In the era of AIGC, the demand for low-budget or even on-device applications of diffusion models emerged. In terms of compressing the Stable Diffusion models (SDMs), several approaches have been proposed, and most of them leveraged the handcrafted layer removal methods to obtain smaller U-Nets, along with knowledge distillation to recover the network perform

Zhengyang Tang, Shuqiang Zhu

This paper examines the existence of centered co-circular central configurations in the general power-law potential n-body problem. We prove the nonexistence of such configurations when the system consists of n-3 equal masses and three arbitrary masses, under the condition that the three special masses are distinct or, if two of them are equal, not arranged

Ryo Nomura, Hideki Yagi

Two typical fixed-length random number generation problems in information theory are considered for general sources. One is the source resolvability problem and the other is the intrinsic randomness problem. In each of these problems, the optimum achievable rate with respect to the given approximation measure is one of our main concerns and has been characte

Farah Haneef

Automaton learning is a domain in which the target system is inferred by the automaton learning algorithm in the form of an automaton, by synthesizing a finite number of inputs and their corresponding outputs. Automaton learning makes use of a Minimally Adequate Teacher (MAT). The learner learns the target system by posing membership queries to the MAT. In t

Jiao Ou, Jiayu Wu, Che Liu, Fuzheng Zhang

Aligning large language models (LLMs) with human expectations requires high-quality instructional dialogues, which usually require instructions that are diverse and in-depth. Existing methods leverage two LLMs to interact for automatic collection: one simulating a user to pose instructions, and the other acting as a system agent to respond. However, these us

Anna Jové

Let f be a transcendental map, and let U be an attracting or parabolic basin, or a doubly parabolic Baker domain. Assume U is simply connected. Then, we prove that periodic points are dense in the boundary of U, under certain hypothesis on the postsingular set. This generalizes a result by F. Przytycki and A. Zdunik for rational maps. Our proof uses techniqu

Long Cao, Liwei Ge, Daochi Zhang, Xiang Li

Reducing computational scaling for simulating non-Markovian dissipative dynamics using artificial neural networks is both a major focus and formidable challenge in open quantum systems. To enable neural quantum states (NQSs), we encode environmental memory in dissipatons (quasiparticles with characteristic lifetimes), yielding the dissipaton-embedded quantum

Kunyang Song, Feiyu Jiang, Ke Zhu

We provide a new estimation method for conditional moment models via the martingale difference divergence (MDD).Our MDD-based estimation method is formed in the framework of a continuum of unconditional moment restrictions. Unlike the existing estimation methods in this framework, the MDD-based estimation method adopts a non-integrable weighting function, wh

Serena Dipierro, Edoardo Proietti Lippi, Caterina Sportelli, Enrico Valdinoci

We discuss the existence theory of a nonlinear problem of nonlocal type subject to Neumann boundary conditions. Differently from the existing literature, the elliptic operator under consideration is obtained as a superposition of operators of mixed order. The setting that we introduce is very general and comprises, for instance, the sum of two fractional Lap

M. Peterka, J. Seidl, T. Markovic, A. Loarte

This work presents the first analysis of the disruptive locked mode (LM) triggered by the dynamics of a confinement change. It shows that, under certain conditions, the LM threshold during the transient is significantly lower than expected from steady states. We investigate the sensitivity to a controlled $n = 1$ error field (EF) activated prior to the L-H t

Bogdan-Vasile Matioc, Christoph Walker

It is shown that semilinear parabolic evolution equations $u'=A+f(t,u)$ featuring H\"older continuous nonlinearities $ f=f(t,u)$ with at most linear growth possess global strong solutions for a general class of initial data. The abstract results are applied to a recent model describing front propagation in bushfires and in the context of a reaction-diffusion

Bernadett Stadler, Roberto Biasi, Mauro Manetti, Andreas Obereder

In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also

Amit Lavon, Smadar Shilo, Ayya Keshet, Eran Segal

UniFrac is a family of distance metrics over microbial abundances, that take taxonomic relatedness into account. Current tools and libraries for calculating UniFrac have specific requirements regarding the user's technical expertise, operating system, and pre-installed software, which might exclude potential users. FrackyFrac is a native command-line tool th

Trong-Hieu Nguyen, Anh-Cuong Le, Viet-Cuong Nguyen

The rapid advancement of large language models (LLMs) necessitates the development of new benchmarks to accurately assess their capabilities. To address this need for Vietnamese, this work aims to introduce ViLLM-Eval, the comprehensive evaluation suite designed to measure the advanced knowledge and reasoning abilities of foundation models within a Vietnames

Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-Wong

Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accuracy, monetary cost, and latency, and their accuracy further depends on the exact wording of the question (i.e., the specific prompt). At the same time, users often have a limit on

Mao Nagamine

In this paper, we discuss the computational approach to the results established by Okuyama and Saito. Although their results are often difficult to compute, we prove that, when the negative support of a fake exponent $v$ with respect to a generic weight $w$ is included in a certain set, solutions can be computed using only the reduced Gr\"{o}bner basis, and

Keyu Pan, Xiumei Wang, Xizhou Shen, Haoyi Zhou

In the last few decades, interference has been extensively studied in both the quantum and classical fields, which reveals light volatility and is widely used for high-precision measurements. We have put forward the phenomenon in which the discrete diffraction and interference phenomena, presented by the time-varying voltage of a Su-Schrieffer-Heeger (SSH) c

Anders Munch, Thomas A. Gerds, Mark J. van der Laan, Helene C. W. Rytgaard

We consider estimation of conditional hazard functions and densities over the class of multivariate c\`adl\`ag functions with uniformly bounded sectional variation norm when data are either fully observed or subject to right-censoring. We demonstrate that the empirical risk minimizer is either not well-defined or not consistent for estimation of conditional

Shengling Gao, Zhikun She, Quanyi Liang, Nan Zheng

Urban traffic resilience has gained increased attention, with most studies adopting an engineering perspective that assumes a single optimal equilibrium and prioritizes local recovery. On the other hand, systems may possess multiple metastable states, and ecological resilience is the ability to switch between these states according to perturbations. Control

Vikram Kashyap, Georgios Styliaris, Sara Mouradian, Juan Ignacio Cirac

Many-body open quantum systems, described by Lindbladian master equations, are a rich class of physical models that display complex equilibrium and out-of-equilibrium phenomena which remain to be understood. In this paper, we theoretically analyze noisy analogue quantum simulation of geometrically local open quantum systems and provide evidence that this pro

Alicia Vidler

Traditionally, assets are selected for inclusion in a portfolio (long or short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh and balance these securities using optimisation methods and other portfolio construction processes. Often, human PMs consider human analyst recommendations against the backdrop of the analyst's recommendatio

Shinichiro Ozaki, Yoshihiko Ide, Masaki Niimi, Masaru Yamashiro

Along the northern coast of Kyushu Island, significant storm surges were unlikely to occur because the strong wind does not blow directly to the coast when typhoons passes. However, during Typhoon Maysak, various areas along the coast experienced flooding due to the storm surges. Additionally, inundation occurred when the typhoon was more than 600 km away fr

Xiao-Yun Wang, Hui-Fang Zhou, Xiang Liu

In this work, the production of the hidden-strange pentaquarklike state $N^{*}(2080)$ via the $\pi^{-} p$ scattering process is studied by the effective Lagrangian approach. Concretely, we consider the $\rho$ meson exchange of $t$-channel and the nucleon exchange of $u$-channel, which are treated as the background terms, and take into account the contributio

Vera Serganova, Alexander Sherman, Dmitry Vaintrob

We introduce Sylow subgroups and $0$-groups to the theory of complex algebraic supergroups, which mimic Sylow subgroups and $p$-groups in the theory of finite groups. We prove that Sylow subgroups are always $0$-groups, and show that they are unique up to conjugacy. Further, we give an explicit classification of $0$-groups which will be very useful for futur

Henrik Axelsen, Johannes Rude Jensen, Omri Ross

Decentralized Autonomous Organizations (DAOs) have seen exponential growth and interest due to their potential to redefine organizational structure and governance. Despite this, there is a discrepancy between the ideals of autonomy and decentralization and the actual experiences of DAO stakeholders. The Information Systems (IS) literature has yet to fully ex

Htoo Wai Aung, Jiao Jiao Li, Yang An, Steven W. Su

Brain-Computer Interfaces connect the brain to external control devices, necessitating the accurate translation of brain signals such as from electroencephalography (EEG) into executable commands. Graph Neural Networks (GCN) have been increasingly applied for classifying EEG Motor Imagery signals, primarily because they incorporates the spatial relationships

A. F. Krenn, D. Kubyshkina, L. Fossati, J. A. Egger

The TOI-421 planetary system contains two sub-Neptune-type planets and is a prime target to study the formation and evolution of planets and their atmospheres. The inner planet is especially interesting as the existence of a hydrogen-dominated atmosphere at its orbital separation cannot be explained by current formation models without previous orbital migrat

Huimin Zhang, Chaoying Zhao

The orbital angular momentum (OAM) has attracted widespread attention due to its ability to carry information in multiple dimensions. However, a high-dimensional entanglement carrying OAM can be affected by environment and undergoes decoherence. Ensuring the stability and high fidelity of entangled states after transmission is a crucial part of quantum commu

Stanislav Pozdniakov, Jonathan Brazil, Solmaz Abdi, Aneesha Bakharia

Incorporating Generative AI (GenAI) and Large Language Models (LLMs) in education can enhance teaching efficiency and enrich student learning. Current LLM usage involves conversational user interfaces (CUIs) for tasks like generating materials or providing feedback. However, this presents challenges including the need for educator expertise in AI and CUIs, e

Harrison Winch, Keir K. Rogers, Renée Hložek, David J. E. Marsh

We calculate the abundance of UV-bright galaxies in the presence of ultralight axion (ULA) dark matter (DM), finding that axions suppress their formation with a non-trivial dependence on redshift and luminosity. We set limits on axion DM using both Planck cosmic microwave background (CMB) and UV luminosity function (UVLF) data. We exclude a single axion as a

Jingrong Wang, Bo Xu, Ronghe Jin, Shoujian Zhang

Accurate, continuous, and reliable positioning is a critical component of achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of a stand-alone sensor and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view ima

Xing Qianfan, Zhao Gang, Aoki Wako, Li Haining

The thorium and six second-peak r-process element (56<Z<72) abundances are determined for the alpha-poor star LAMOST J1124+4535 based on a high-resolution spectrum obtained with the High Dispersion Spectrograph (HDS) on the Subaru telescope. The age of J1124+4535 is 11.3$\pm$4.4 Gyr using thorium and other r-process element abundances. J1124+4535 is confirme

Feiwen Zhu, Arkadiusz Nowaczynski, Rundong Li, Jie Xin

AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict protein structures with lab-grade accuracy. However, its implementation does not include the necessary training code. OpenFold is the first trainable public reimplementation of AlphaFold. AlphaFold training procedure is prohibitively time-consuming, and gets diminishing b

Eve Cheng, Danny Cocks, Patrick Leslie

We propose a suit of methods to analyse the professional networks of MPs, showing how to analyse weak-tie connections between legislators and the connections between background charactersitic attributes. Applied to a novel dataset on the backgrounds of Australian MPs in the Australian Labor Party and the Liberal Party of Australia (1947-2019), we show that o

Endri Taka, Dimitrios Gourounas, Andreas Gerstlauer, Diana Marculescu

FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading FPGA vendors have enhanced their architectures to more efficiently support the computational demands of DL workloads. However, the two most prominent AI-optimized FPGAs, i.e., AMD/

Sandeep Sharma, A. Kani, M. Bhattacharya

We theoretically investigate PT symmetry, induced mechanical lasing and force sensing in an optically levitated nanoparticle with coupled oscillation modes. The coupling in the levitated system is created by the modulation of an asymmetric optical potential in the plane transverse to the beam trapping the nanoparticle. We show that such a coupling can lead t

Yongdong Luo, Haojia Lin, Xiawu Zheng, Yigeng Jiang

3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) are two crucial tasks in various 3D applications, which require both shared and complementary information in localization and visual-language relationships. Therefore, existing approaches adopt the two-stage "detect-then-describe/discriminate" pipeline, which relies heavily on the performance of the d

Muhamad Bhayuta Yudhi Putera, Melia Famiola

This study in the banking industry examines the influence of attitudinal loyalty on customer advocacy and cross buying behavior, alongside the moderating roles of Quality of Life and Corporate Social Responsibility support in the CSR fit and loyalty relationship. Employing Structural Equation Modeling, it reveals that higher attitudinal loyalty significantly

Takumi Kobayashi, Daisuke Akamatsu, Kazumoto Hosaka, Yusuke Hisai

We report on a reduced time variation of a time scale with respect to Coordinated Universal Time (UTC) by steering a hydrogen-maser-based time scale with a near-continuously operating optical lattice clock. The time scale is generated in a post-processing analysis for 230 days with a hydrogen maser with its fractional frequency stability limited by a flicker

Nicolas Ong, Hassan Shavarani, Anoop Sarkar

Despite remarkable strides made in the development of entity linking systems in recent years, a comprehensive comparative analysis of these systems using a unified framework is notably absent. This paper addresses this oversight by introducing a new black-box benchmark and conducting a comprehensive evaluation of all state-of-the-art entity linking methods.

Xin Xu, Yue Hu, Xu Zhang

The planar Tur\'{a}n number of a graph $H$, denoted by $ex_{\mathcal{P}}(n,H)$, is the maximum number of edges in an $n$-vertex $H$-free planar graph. Recently, D. Ghosh, et al. initiated the topic of double stars and prove that $ex_{\mathcal{P}}(n,S_{2,5})\leq \frac{20}{7}n$. In this paper, we continue to study this and give a sharp upper bound $ex_{\mathca

Mauro Costantini, Andrea Lucchini, Daniele Nemmi

Let $G$ be a finite almost simple group with socle $G_0$. In this paper we prove that whenever $G/G_0$ is abelian, then there exists an abelian subgroup $A$ of $G$ such that $G=AG_0$. We propose a few applications of this structural property of almost simple groups.

Zishun Feng, Joseph A. Sivak, Ashok K. Krishnamurthy

Cardiac amyloidosis, a rare and highly morbid condition, presents significant challenges for detection through echocardiography. Recently, there has been a surge in proposing machine-learning algorithms to identify cardiac amyloidosis, with the majority being imaging-based deep-learning approaches that require extensive data. In this study, we introduce a no

Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak

We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, wit

Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia

To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser. Existing unlearning research suffers from entangled training data and complex model architectures, incurring extremely high computational costs for large models. L

Zhiheng Lyu, Zhijing Jin, Fernando Gonzalez, Rada Mihalcea

Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review. Given a review and the sentiment associated with it, this work formulates SA as a combination of two tasks: (1) a causal discovery task that distinguishes whether a review "primes" the sentiment (Causal Hypothesis C1), or the sentiment "primes" the review (C

Ying Zhang, Yuezun Li, Bo Peng, Jiaran Zhou

The task of video inpainting detection is to expose the pixel-level inpainted regions within a video sequence. Existing methods usually focus on leveraging spatial and temporal inconsistencies. However, these methods typically employ fixed operations to combine spatial and temporal clues, limiting their applicability in different scenarios. In this paper, we

Huan Zhou, An Li, Shi-Jie Lin, Zhengxiang Li

Gravitational lensing effect is one of most significant observational probes to investigate compact dark matter/objects over a wide mass range. In this work, we first propose to derive the population information and the abundance of supermassive compact dark matter in the mass range $\sim10^5-10^7~M_{\odot}$ from 6 millilensed gamma-ray burst (GRB) candidate

Mohammad Shiri, Monalika Padma Reddy, Jiangwen Sun

Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer. Breast tissue histopathological examination is critical in diagnosing and classifying breast cancer. Although existing methods have shown promising results, there is still room for improvement in the classification accuracy and generalization of IDC using histopathology images. We p

Rijun Wang, Guanghao Zhang, Fulong Liang, Bo Wang

Using deep learning methods is a promising approach to improving bark removal efficiency and enhancing the quality of wood products. However, the lack of publicly available datasets for wood plate segmentation in bark removal processing poses challenges for researchers in this field. To address this issue, a benchmark for wood plate segmentation in bark remo

Mohannad Alhanahnah, Md Rashedul Hasan, Lisong Xu, Hamid Bagheri

Automatic Program Repair (APR) has garnered significant attention as a practical research domain focused on automatically fixing bugs in programs. While existing APR techniques primarily target imperative programming languages like C and Java, there is a growing need for effective solutions applicable to declarative software specification languages. This pap

Akifumi Wachi, Thien Q. Tran, Rei Sato, Takumi Tanabe

Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs). This paper formulates human value alignment as an optimization problem of the language model policy to maximize reward under a safety constraint, and then proposes an algorithm, Stepwise Alignment for Constrained Policy Opti

Mingchao Liu, Jinhua He, Zhen Guo, Jixing Ge

Infrared (IR) spectral energy distribution (SED) is the major tracer of protoplanetary disks. It was recently proposed to use the near-to-mid IR (or K-24) SED slope $\alpha$ defined between 2-24$\mu$m as a potential quantitative tracer of disk age. We critically examine the viability of this idea and confront it with additional statistics of IR luminosities

Ryan A. Revolinsky, Christopher J. Swenson, Nicholas M. Jordan, Y. Y. Lau

The Brillouin flow is a rectilinear, sheared electron fluid flow in a crossed electric field (E) and magnetic field (B), in the E x B direction with zero flow velocity and zero electric field at the surface with which the flow is in contact. It is broadly considered as the equilibrium electron flow in high power crossed-field devices including the magnetron

Hao Yan, Yuhong Guo

Federated learning aims to tackle the ``isolated data island" problem, where it trains a collective model from physically isolated clients while safeguarding the privacy of users' data. However, supervised federated learning necessitates that each client labels their data for training, which can be both time-consuming and resource-intensive, and may even be

James Y. Huang, Wenxuan Zhou, Fei Wang, Fred Morstatter

Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as copyrighted, biased, and private content has led to ethical and legal concerns. In response to these challenges, unlearning has emerged as a potential remedy for LLMs affected by pr

Luming Wang, Xu Zhang, Songyue Wang, Zhuolun Jiang

The growing memory demands of modern applications have driven the adoption of far memory technologies in data centers to provide cost-effective, high-capacity memory solutions. However, far memory presents new performance challenges because its access latencies are significantly longer and more variable than local DRAM. For applications to achieve acceptable

Xingchuan Zhu, Junsong Sun, Shou-Shu Gong, Wen Huang

Describing and achieving `unconventional' superconductivity remains a forefront challenge in quantum many-body physics. Here we use a unitary mapping, combined with the well-established properties of the attractive Hubbard model to demonstrate rigorously a Hamiltonian with a low temperature pair-density-wave (PDW) phase. We also show that the same mapping, w

Takafumi Niida, Sergei A. Voloshin

The strongly interacting system created in ultrarelativistic nuclear collisions behaves almost as an ideal fluid with rich patterns of the velocity field exhibiting strong vortical structure. Vorticity of the fluid, via spin-orbit coupling, leads to particle spin polarization. Due to the finite orbital momentum of the system, the polarization on average is n

Liwei Kang, Zirui Zhao, David Hsu, Wee Sun Lee

Chain-of-thought (CoT), tree-of-thought (ToT), and related techniques work surprisingly well in practice for some complex reasoning tasks with Large Language Models (LLMs), but why? This work seeks the underlying reasons by conducting experimental case studies and linking the performance benefits to well-established sample and computational complexity princi

Yukasa Murakami, Yuta Yamasaki, Masateru Tsunoda, Akito Monden

Cross-project defect prediction (CPDP) aims to use data from external projects as historical data may not be available from the same project. In CPDP, deciding on a particular historical project to build a training model can be difficult. To help with this decision, a Bandit Algorithm (BA) based approach has been proposed in prior research to select the most

Layla Sorkatti, Gunnar Traustason

We develop a structure theory for nilpotent symplectic alternating algebras.

Layla Sorkatti, Gunnar Traustason

In this paper and its sequel we continue our study of nilpotent symplectic alternating algebras. In particular we give a full classification of such algebras of dimension $10$ over any field. It is known that symplectic alternating algebras over $\mbox{GF}(3)$ correspond to a special rich class $\mathcal{C}$ of $2$-Engel $3$-groups of exponent $27$ and under

Renjie Lyu, Dingxin Zhang

We prove in most cases that a general smooth complete intersection in the projective space has no non-trivial automorphisms.

Paras Sheth, Tharindu Kumarage, Raha Moraffah, Aman Chadha

Content moderation faces a challenging task as social media's ability to spread hate speech contrasts with its role in promoting global connectivity. With rapidly evolving slang and hate speech, the adaptability of conventional deep learning to the fluid landscape of online dialogue remains limited. In response, causality inspired disentanglement has shown p

Xiang Ma, Haijian Sun, Rose Qingyang Hu, Yi Qian

Federated learning (FL) has emerged as a distributed machine learning (ML) technique that can protect local data privacy for participating clients and improve system efficiency. Instead of sharing raw data, FL exchanges intermediate learning parameters, such as gradients, among clients. This article presents an efficient wireless communication approach tailo

Ping Ren, Zhiqiang Zhao, Qian Yang

In recent years, with the changing landscape of international education and the growing demand from Chinese students, study abroad consultancy firms in China need to adopt transformational development strategies to address challenges and maintain competitiveness. This study investigated the relationships between key performance indicators and several factors

Nikolay Fedorov, Yuta Yamasaki, Masateru Tsunoda, Akito Monden

Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model, when a new data point is added. However, a module predicted as "non-defective" can result in fewer test cases for such modules. Thus, a defective module can be overlooked during testing. The erroneous test results are u