November 2024 arXiv papers — page 173

Yuankai Fan, Tonghui Ren, Can Huang, Zhenying He

Natural Language Interfaces for Databases empower non-technical users to interact with data using natural language (NL). Advanced approaches, utilizing either neural sequence-to-sequence or more recent sophisticated large-scale language models, typically implement NL to SQL (NL2SQL) translation in an end-to-end fashion. However, like humans, these end-to-end

Rawan S. Abdulsadig, Bryan M. Williams, Nikolay Burlutskiy

Expression of human epidermal growth factor receptor 2 (HER2) is an important biomarker in breast cancer patients who can benefit from cost-effective automatic Hematoxylin and Eosin (H\&E) HER2 scoring. However, developing such scoring models requires large pixel-level annotated datasets. Transfer learning allows prior knowledge from different datasets to be

Beatrice Acciaio, Stephan Eckstein, Songyan Hou

We build a time-causal variational autoencoder (TC-VAE) for robust generation of financial time series data. Our approach imposes a causality constraint on the encoder and decoder networks, ensuring a causal transport from the real market time series to the fake generated time series. Specifically, we prove that the TC-VAE loss provides an upper bound on the

Dan Yang, Yu-Ting Rong

This study investigates the tetrahedral structure in $^{80}$Zr and Lambda ($\Lambda$) impurity effect in $^{81}_{~\Lambda}$Zr using the multidimensionally constrained relativistic Hartree-Bogoliubov model. The ground states of both $^{80}$Zr and $^{81}_{~\Lambda}$Zr exhibit a tetrahedral configuration, accompanied by prolate and axial-octupole shape isomers.

Youquan Xian, Xueying Zeng, Chunpei Li, Dongcheng Li

With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility a

I. Bandac, L. Berge, J. M. Calvo-Mozota, P. Carniti

Highly forbidden $\beta$ decays provide a sensitive test to nuclear models in a regime in which the decay goes through high spin-multipole states, similar to the neutrinoless double-$\beta$ decay process. There are only 3 nuclei ($^{50}$V, $^{113}$Cd, $^{115}$In) which undergo a $4^{\rm th}$ forbidden non-unique $\beta$ decay. In this work, we compare the ex

Francesco Invernici, Francesca Curati, Jelena Jakimov, Amirhossein Samavi

The world is facing a multitude of challenges that hinder the development of human civilization and the well-being of humanity on the planet. The Sustainable Development Goals (SDGs) were formulated by the United Nations in 2015 to address these global challenges by 2030. Natural language processing techniques can help uncover discussions on SDGs within rese

Yuda Feng, Yang Hu, Shi Li, Ruilong Zhang

We study the problem of assigning items to agents so as to maximize the \emph{weighted} Nash Social Welfare (NSW) under submodular valuations. The best-known result for the problem is an $O(nw_{\max})$-approximation due to Garg, Husic, Li, V\'egh, and Vondr\'ak~[STOC 2023], where $w_{\max}$ is the maximum weight over all agents. Obtaining a constant approxim

Haoyu Ma, Yushu Chen, Wenlai Zhao, Jinzhe Yang

Time series foundation models have demonstrated strong performance in zero-shot learning, making them well-suited for predicting rapidly evolving patterns in real-world applications where relevant training data are scarce. However, most of these models rely on the Transformer architecture, which incurs quadratic complexity as input length increases. To addre

Effie Papageorgiou

The main goal of this work is to study the $L^p$-asymptotic behavior of solutions to the heat equation on arbitrary rank Riemannian symmetric spaces of non-compact type $G/K$ for non-bi-$K$ invariant initial data. For initial data $u_0$ compactly supported or in a weighted $L^1(G/K)$ space with a weight depending on $p\in [1, \infty]$, we introduce a mass fu

Keke Zhai

This paper delves into the continuous post-training optimization methods for small language models, and proposes a continuous post-training alignment data construction method for small language models. The core of this method is based on the data guidance of large models, optimizing the diversity and accuracy of alignment data. In addition, to verify the eff

Emilio Olivastri, Jonathan Francis, Alberto Pretto, Niko Sünderhauf

The advent of generalist Large Language Models (LLMs) and Large Vision Models (VLMs) have streamlined the construction of semantically enriched maps that can enable robots to ground high-level reasoning and planning into their representations. One of the most widely used semantic map formats is the 3D Scene Graph, which captures both metric (low-level) and s

Yangning Li, Yinghui Li, Xinyu Wang, Yong Jiang

Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs). Although promising, existing heuristic mRAGs typically predefined fixed retrieval processes, which causes two issues: (1) Non-adaptive Retrieval Queries. (2) Overloaded Retrieval Queries. Howev

Nikolai Chukhin, Alexander S. Kulikov, Ivan Mihajlin, Arina Smirnova

Proving complexity lower bounds remains a challenging task: we only know how to prove conditional uniform lower bounds and nonuniform lower bounds in restricted circuit models. Williams (STOC 2010) showed how to derive nonuniform lower bounds from uniform upper bounds: by designing a fast algorithm for checking satisfiability of circuits, one gets a lower bo

Mohammad Kakooei, James Bailie, Markus B. Pettersson, Albin Söderberg

Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the complex interactions between human activities and natural resources. Existing global urban-rural datasets such as such as GHSL-SMOD, GHS Degree of Urbanisation, and GRUMP are ofte

Sraboni Dey, Kirandas P S, Deepshikha Jaiswal Nagar, Joy Mitra

Band-selective absorption and emission of thermal radiation in the infrared are of interest due to applications in emissivity coatings, infrared sensing, thermo-photovoltaics and solar energy harvesting. The broadband nature of thermal radiation presents distinct challenges in achieving spectral and angular selectivity, which are difficult to address by prev

Luca Seemungal, Ben Sharp

We prove a linear upper bound on the Morse index of closed constant mean curvature (CMC) surfaces in orientable three-manifolds in terms of genus, number of branch points and a Willmore-type energy.

Nikki Zabel, Alessandro Loni, Marc Sarzi, Paolo Serra

We combine new and archival MUSE observations with data from the MeerKAT Fornax Survey and the ALMA Fornax Cluster Survey to study the ionised, atomic, and molecular gas in six gas-rich dwarf galaxies in the Fornax cluster in detail. We compare the distributions and velocity fields of the three gas phases with each other, with MUSE white-light images, and wi

Lingjie Jiang, Shaohan Huang, Xun Wu, Furu Wei

Image aesthetics is a crucial metric in the field of image generation. However, textual aesthetics has not been sufficiently explored. With the widespread application of large language models (LLMs), previous work has primarily focused on the correctness of content and the helpfulness of responses. Nonetheless, providing responses with textual aesthetics is

Yulin Gong

We study the spectral distribution of damped waves on compact Anosov manifolds. Sj\"ostrand \cite{SJ1} proved that the imaginary parts of the majority of the eigenvalues concentrate near the average of the damping function, see also Anantharaman \cite{AN2}. In this paper, we prove that the most of eigenvalues actually lie in certain regions with imaginary pa

Quinten Eggerickx, Adam Wills, Ting-Chun Lin, Kristiaan De Greve

Geometrically local quantum codes, which are error correction codes embedded in $\mathbb{R}^D$ with checks acting only on qubits within a fixed spatial distance, have garnered significant interest. Recently, it has been demonstrated how to achieve geometrically local codes that maximize both the dimension and the distance, as well as the energy barrier of th

Mariem Ben Youssef, Peeranuch Poungsripong, Hugo Bidotti, Halidou Abdoul-Yasset

We report the redox switching of ferrocene moieties embedded in a double tunnel barrier plasmonic cavity fabricated from a click-chemistry self-assembled monolayers of ferrocenyl-alkylthiol on ultra flat gold surfaces, connected to a single poly(vinylpyrrolidone) capped silver nanocube, AgNC, which is contacted by the tip of a conductive-AFM to study the ele

Fabrianne Effendi, Anupam Chattopadhyay

Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capturing intricate relationships within money laundering networks. However, the effectiveness of AML solutions

E. E. Nokhrina

Measurement of a jet geometry transition region is an important instrument of assessing the jet ambient medium properties, plasma bulk motion acceleration, parameters of a black hole and location of a jet launching radius. In this work we explore the possibility of a presence of a core shift break, associated with the geometry and jet physical properties tra

Laura Vana-Gür, Rainer Hirk

In this paper we build a joint model which can accommodate for binary, ordinal and continuous responses, by assuming that the errors of the continuous variables and the errors underlying the ordinal and binary outcomes follow a multivariate normal distribution. We employ composite likelihood methods to estimate the model parameters and use composite likeliho

Taras Mel'nyk, Christian Rohde

A reduced-dimensional asymptotic modelling approach is presented for the analysis of two-phase flow in a thin cylinder with aperture of order $\mathcal{O}(\varepsilon),$ where $\varepsilon$ is a small positive parameter. We consider a nonlinear Muskat-Leverett two-phase flow model expressed in terms of a fractional flow formulation and Darcy's law with a sat

Liang Gao, Hai-Bin Yu, Thomas B. Schrøder, Jeppe C. Dyre

Given the vast differences in interaction details, describing the dynamics of structurally disordered materials in a unified theoretical framework presents a fundamental challenge to condensed-matter physics and materials science. Here, we numerically investigate a double-percolation scenario for the two most important relaxation processes of supercooled liq

Chao Xu, Xijia Tang, Guoqing Liu, Yuhua Qian

In many open environment applications, data are collected in the form of a stream, which exhibits an evolving distribution over time. How to design algorithms to track these evolving data distributions with provable guarantees, particularly in terms of the generalization ability, remains a formidable challenge. To handle this crucial but rarely studied probl

Pengkun Jiao, Na Zhao, Jingjing Chen, Yu-Gang Jiang

Open-set single-source domain generalization aims to use a single-source domain to learn a robust model that can be generalized to unknown target domains with both domain shifts and label shifts. The scarcity of the source domain and the unknown data distribution of the target domain pose a great challenge for domain-invariant feature learning and unknown cl

Hajime Koike, Hideki Takayasu, Misako Takayasu

The gravity model is a mathematical model that applies Newton's universal law of gravitation to socio-economic transport phenomena and has been widely used to describe world trade, intercity traffic flows, and business transactions for more than several decades. However, its strong nonlinearity and diverse network topology make a theoretical analysis difficu

Junxia Zhang, Xiangyu Ren, Maoqun Wang

A subset of vertices in a graph $G$ is considered a maximal dissociation set if it induces a subgraph with vertex degree at most 1 and it is not contained within any other dissociation sets. In this paper, it is shown that for $n\geq 3$, every unicyclic graph contains a minimum of $\lfloor n/2\rfloor+2$ maximal dissociation sets. We also show the graphs that

Dominic Schuhmacher, Leoni Carla Wirth

In this article, we derive Stein's method for approximating a spatial random graph by a generalised random geometric graph, which has vertices given by a finite Gibbs point process and edges based on a general connection function. Our main theorems provide explicit upper bounds for integral probability metrics and, at improved rates, a recently introduced Wa

Fangyang Zhan, Xin Jin, Da-Shuai Ma, Jing Fan

The gyrotropic magnetic effect (GME), which emerges as the low-frequency limit of natural gyrotopy, is a fundamental property of Bloch electrons on the Fermi surface in materials lacking inversion symmetry. While Weyl semimetals were among the first systems predicted to host the GME, this effect has not yet been experimentally observed in these materials. He

Yu-Mei Wu, Qing-Guo Huang

Pulsar Timing Arrays (PTAs) offer an independent method for searching for ultralight dark matter (ULDM), whose wavelike nature induces periodic oscillations in the arrival times of radio pulses. In addition to this gravitational effect, the direct coupling between ULDM and ordinary matter results in pulsar spin fluctuations and reference clock shifts, leadin

Ao Fu, Yi Zhou, Tao Zhou, Yi Yang

World models and video generation are pivotal technologies in the domain of autonomous driving, each playing a critical role in enhancing the robustness and reliability of autonomous systems. World models, which simulate the dynamics of real-world environments, and video generation models, which produce realistic video sequences, are increasingly being integ

Chao Ding, Shi Wang, Yaonan Wang, Weibo Gao

Quantum machine learning is considered one of the current research fields with immense potential. In recent years, Havl\'i\v{c}ek et al. [Nature 567, 209-212 (2019)] have proposed a quantum machine learning algorithm with quantum-enhanced feature spaces, which effectively addressed a binary classification problem on a superconducting processor and offered a

L. Cacciapuoti, A. Busso, R. Jansen, S. Pataraia

The Atomic Clock Ensemble in Space (ACES) mission is developing high performance clocks and links for space to test Einstein's theory of general relativity. From the International Space Station, the ACES payload will distribute a clock signal with fractional frequency stability and accuracy of 1E-16 establishing a worldwide network to compare clocks in space

Qinxuan Peng, Bolong Jiao, Hang Yu, Liao Sun

The Leggett-Garg inequality (LGI) is a powerful tool for distinguishing between quantum and classical properties in studies of macroscopic systems. Applying the LGI to non-Hermitian systems with dissipation presents a fascinating opportunity, as competing mechanisms can either strengthen or weaken LGI violations. On one hand, dissipation-induced nonlinear in

Licong Lin, Fangzhou Su, Wenlong Mou, Peng Ding

Resampling methods are especially well-suited to inference with estimators that provide only "black-box'' access. Jackknife is a form of resampling, widely used for bias correction and variance estimation, that is well-understood under classical scaling where the sample size $n$ grows for a fixed problem. We study its behavior in application to estimating fu

Behzad Eslam Panah, Angel Rincon, Narges Heidari

By considering a new model of nonlinear electrodynamics, known as the modified Maxwell (ModMax), and taking into account the topological and the cosmological constants in Einstein's gravity, we extract black hole solutions called Topological ModMax (A)dS black holes. The next step is to study the thermodynamic properties, quasinormal modes, and emission rate

Inigo Martin, Iker Heras, Ibai Coria, Mikel Abasolo

Since wire bearings were patented in 1936, they have been used in applications where weight saving is a key aspect. Nevertheless, little work can be found in literature regarding their structural behaviour. In order to predict how a wire bearing reacts under load, the most feasible way is to model and analyse it via Finite Elements (FE). However, the great a

Iker Heras, Josu Aguirrebeitia, Mikel Abasolo, Ibai Coria

This work introduces a methodology for the calculation of the load distribution in four-point contact slewing bearings considering ball preload, manufacturing errors and ring flexibility. The model is built by the formulation and minimization of the potential energy of the bearing. Comparing with the rigid rings assumption, the results show that ring deforma

Yingzhen Yang, Ping Li

We study nonparametric regression by an over-parameterized two-layer neural network trained by gradient descent (GD) in this paper. We show that, if the neural network is trained by GD with early stopping, then the trained network renders a sharp rate of the nonparametric regression risk of $\mathcal{O}(\epsilon_n^2)$, which is the same rate as that for the

Ibai Coria, Inigo Martin, Abdel-Hakim Bouzid, Iker Heras

Several factors can cause leakages in a bolted flange connection, external loads being the most important one. External loads such as those produced by misalignment introduce an external axial load combined with a bending moment which could lead to either an excessive compression of the gasket and its subsequent crushing, and/or a separation of the flanges c

Zhuoshuo Li, Jiong Zhang, Youbing Zeng, Jiaying Lin

Current brain surface-based prediction models often overlook the variability of regional attributes at the cortical feature level. While graph neural networks (GNNs) excel at capturing regional differences, they encounter challenges when dealing with complex, high-density graph structures. In this work, we consider the cortical surface mesh as a sparse graph

Zhan Li, Yongtao Wu, Yihang Chen, Francesco Tonin

Large vision-language models (VLLMs) exhibit promising capabilities for processing multi-modal tasks across various application scenarios. However, their emergence also raises significant data security concerns, given the potential inclusion of sensitive information, such as private photos and medical records, in their training datasets. Detecting inappropri

Mourad Choulli

This work is composed of two parts. We prove in the first part the uniqueness of the determination of the unbounded zero-order coefficient in a parabolic equation from boundary measurements. The novelty of our result is that it covers the largest class of unbounded zero-order coefficients. We establish in the second part a logarithmic stability inequality fo

Nguyen Xuan Tung, Trinh Van Chien, Hien Quoc Ngo, Won Joo Hwang

This paper proposes a distributed learning-based framework to tackle the sum ergodic rate maximization problem in cell-free massive multiple-input multiple-output (MIMO) systems by utilizing the graph neural network (GNN). Different from centralized schemes, which gather all the channel state information (CSI) at the central processing unit (CPU) for calcula

Lidija Stanovnik

Consider a $q$-ary block code satisfying the property that no $l$-letters long codeword's prefix occurs as a suffix of any codeword for $l$ inside some interval. We determine a general upper bound on the maximum size of these codes and a tighter bound for codes where overlaps with lengths not exceeding $k$ are prohibited. We then provide constructions for co

Hamin Jung

A significant generalization of the Erd\"os-R\'enyi random graph model is an `inhomogeneous' random graph where the edge probabilities vary according to vertex types. We identify the threshold value for this random graph with a finite number of vertex types to be connected and examine the model's behavior near this threshold value. In particular, we show tha

Shaoshi Chen, Hanqian Fang, Sergey Kitaev, Candice X. T. Zhang

In this paper, we propose a general framework that extends the theory of permutation patterns to higher dimensions and unifies several combinatorial objects studied in the literature. Our approach involves introducing the concept of a "level" for an element in a multi-dimensional permutation, which can be defined in multiple ways. We consider two natural def

M. M. Suleimanov, M. U. Nosirov, H. T. Yusupov, A. Chaves

We use the Dirac continuum model to study the propagation of electronic wave packets in graphene with periodically arranged circular potential steps. The time propagation of the wave packets are calculated using the split-operator method for different size, height and separation of the barriers. The time propagation of the wave packets is calculated using th

Hamid Mosadeq, Mohammad-Hossein Zare

Honeycomb lattice Kitaev magnets exhibit exotic magnetic properties governed by the Kitaev interaction. This study delves into $\alpha$-RuCl$_3$, a prototypical example described by effective Hamiltonians encompassing bond-dependent Kitaev interactions alongside additional terms such as the Heisenberg interaction and symmetric off-diagonal exchange interacti

Xuyu Zhang, Haofan Huang, Dawei Zhang, Songlin Zhuang

With fast developments in computational power and algorithms, deep learning has made breakthroughs and been applied in many fields. However, generalization remains to be a critical challenge, and the limited generalization capability severely constrains its practical applications. Hallucination issue is another unresolved conundrum haunting deep learning and

Maria Paszkiewicz-Idzik, Lukas Rebholz, Carsten Rockstuhl, Ivan Fernandez-Corbaton

We introduce the conformally-invariant scalar product, originally devised for radiation fields, to the study of the modes of optical resonators. This scalar product allows one to normalize and compare resonant modes using their corresponding radiation fields. Such fields are polychromatic fields free of divergences, which are determined from the complex freq

Binggwong Leung, Stanislav Gorb, Poramate Manoonpong

Dung beetles impressively coordinate their six legs simultaneously to effectively roll large dung balls. They are also capable of rolling dung balls varying in the weight on different terrains. The mechanisms underlying how their motor commands are adapted to walk and simultaneously roll balls (multitasking behavior) under different conditions remain unknown

Jerome Gilles, Stanley Osher

In this paper we present a new approach to deblur the effect of atmospheric turbulence in the case of long range imaging. Our method is based on an analytical formulation, the Fried kernel, of the atmosphere modulation transfer function (MTF) and a framelet based deconvolution algorithm. An important parameter is the refractive index structure which requires

Yu Mao, Jerome Gilles

We recently developed a new approach to get a stabilized image from a sequence of frames acquired through atmospheric turbulence. The goal of this algorihtm is to remove the geometric distortions due by the atmosphere movements. This method is based on a variational formulation and is efficiently solved by the use of Bregman iterations and the operator split

Jinqiu Deng, Ke Chen, Mingke Li, Daoping Zhang

Diffeomorphic image registration is crucial for various medical imaging applications because it can preserve the topology of the transformation. This study introduces DCCNN-LSTM-Reg, a learning framework that evolves dynamically and learns a symmetrical registration path by satisfying a specified control increment system. This framework aims to obtain symmet

Dalal Waadallah Shehab

This study investigates the translation of circumlocution from Arabic to English in a corpus of short stories by renowned Arabic authors. By analyzing the source and target texts, the study aims to identify and categorize circumlocution instances in Arabic and their corresponding renditions in English. The study employs Nida's (1964) translation theory as a

Wei Wu, Zhuoshi Pan, Chao Wang, Liyi Chen

Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challenges: performance degradation due to sequence lengths out-of-distribution, and excessively long inference times caused by the quadratic computational complexity of attention. These i

Per Östborn

The treatment of time in relativity does not conform to that in quantum theory. To resolve the discrepancy, a formalization of time is introduced in an accompanying paper, starting from the assumption that the treatment of time in physics must agree with our cognition. The formalization has two components: sequential time $n$ and relational time $t$. The evo

Joseph Chuang, Andrey Lazarev, Yunhe Sheng, Rong Tang

A Koszul duality-type correspondence between coderived categories of conilpotent differential graded Lie coalgebras and their Chevalley-Eilenberg differential graded algebras is established. This gives an interpretation of Lie coalgebra cohomology as a certain kind of derived functor. A similar correspondence is proved for coderived categories of commutative

Takeshi Kimura, Kohtaro Kato

The modern Hopfield network, proposed by Krotov and Hopfield, is a mathematical generalization of the Hopfield network, which is a basic model of associative memory that employs higher-order interactions. This study introduces an open quantum model for discrete modern Hopfield networks that generalizes the open quantum Hopfield network. Our model integrates

Fatemeh Rajabi-Alni, Alireza Bagheri, Behrouz Minaei-Bidgoli

The freeze tag problem (FTP) aims to awaken a swarm of robots with one or more initial awake robots as soon as possible. Each awake robot must touch a sleeping robot to wake it up. Once a robot is awakened, it can assist in awakening other sleeping robots. We study this problem inside a polygonal domain and present approximation algorithms for it.

Tianfeng Feng, Jue Xu, Wenjun Yu, Zekun Ye

Quantum simulation is a promising pathway toward practical quantum advantage by simulating large-scale quantum systems. In this work, we propose communication-efficient distributed quantum simulation protocols by exploring three quantum simulation algorithms, including the product formula, the truncated Taylor series, and the processing of quantum signals ov

Zhi Tan, Xiang Lv, Jie Xing, Shaoxiong Xie

The origin of frequently observed ultrahigh electric-induced longitudinal strain, ranging from 1% to 26%, remains an open question. Recent evidence suggests that this phenomenon is linked to the bending deformation of samples, but the mechanisms driving this bending and the strong dependence of nominal strain on sample thickness have yet to be fully understo

Fabio Bagarello

In a series of recent scientific contributions the role of bosonic and fermionic ladder operators in a macroscopic realm has been investigated. Creation, annihilation and number operators have been used in very different contexts, all sharing the same common main feature, i.e. the relevance of {\em discrete changes} in the description of the system. The main

Xiaoying Qu, Sibo Wang, Hui Tong

The equation of state (EOS) of extremely dense matter is crucial for understanding the properties of rotating neutron stars. Starting from the widely used realistic Bonn potentials rooted in a relativistic framework, we derive EOSs by performing the state-of-the-art relativistic Brueckner-Hartree-Fock (RBHF) calculations in the full Dirac space. The self-con

Brindhu Malani S, Eugen Klein, Rostyslav Lesyuk, Christian Klinke

The excellent optoelectronic properties, straightforward synthesis, and robust material stability of quasi-2D metal halide perovskites have made them a hot research topic for optoelectronic devices. In layered organic-inorganic perovskites, the optoelectronic properties are greatly influenced by morphology and quantum confinement, which are crucial for their

Futa Watabe, Koji Okuda

We study the lower bound of the entropy production in a one-dimensional underdamped Langevin system constrained by a time-dependent parabolic potential. We focus on minimizing the entropy production during transitions from a given initial distribution to a given final distribution taking a given finite time. We derive the conditions for achieving the minimum

Ankit Anand, Saeed Noori Gashti

Recent advancements in black hole thermodynamics have introduced corrections to elucidate the relationship between entropy and extremality bound of black holes. Traditionally, this relationship has been studied in the context of black holes characterized by Bekenstein-Hawking entropy. However, this study extends the investigation to encompass non-extensive g

Zubeyir Cinkir

We compute the total number of spanning trees for the generalized cone of the complete graph $K_n$ and a number of families of some modified bipartite graphs $K_{m,n}$. In particular, we obtain a new method of finding the number of spanning trees of $K_n$ and $K_{m,n}$. Our method relies on the vertex deletion formula for the number of spanning trees.

Percy Fernández Sánchez, Jorge Mozo Fernández

In this paper, we study the analytic classification of a class of nilpotent singularities of holomorphic foliations in $(\mathbb{C}^2,0)$, those exhibiting a Poincar\'e-Dulac type singularity in their reduction process. This analytic classification is based in the holonomy of a certain component of the exceptional divisor. Finally, as a consequence, we show

Makoto Nakamura, Takuma Yoshizumi

The Cauchy problem for semi-linear Klein-Gordon equations is considered in Friedmann-Lema\^itre-Robertson-Walker spacetimes. The local and global well-posedness of the Cauchy problem is considered in Sobolev spaces. The non-existence of global solutions is also considered.

Jun Cui

This study investigates the impact of software design model capabilities and data structure algorithm abilities on microservices architecture design within enterprises. Utilizing a qualitative methodology, the research involved in-depth interviews with software architects and developers who possess extensive experience in microservices implementation. The fi

Junhao Dong, Xinghua Qu, Z. Jane Wang, Yew-Soon Ong

Despite remarkable achievements in deep learning across various domains, its inherent vulnerability to adversarial examples still remains a critical concern for practical deployment. Adversarial training has emerged as one of the most effective defensive techniques for improving model robustness against such malicious inputs. However, existing adversarial tr

Alice P. Curtin, Ketan R. Sand, Ziggy Pleunis, Naman Jain

The Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) project has discovered the most repeating fast radio burst (FRB) sources of any telescope. However, most of the physical conclusions derived from this sample are based on data with a time resolution of $\sim$1 ms. In this work, we present for the first time a morphological analys

Duneesha Fernando, Maria A. Rodriguez, Rajkumar Buyya

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact the

Haoan Xu, Tianshu Zheng, Xinyi Xu, Yao Shen

Accurate automatic tissue segmentation in fetal brain MRI is a crucial step in clinical diagnosis but remains challenging, particularly due to the dynamically changing anatomy and tissue contrast during fetal development. Existing segmentation networks can only implicitly learn age-related features, leading to a decline in accuracy at extreme early or late g

Jinyin Chen, Minying Ma, Haibin Zheng, Qi Xuan

Recent research has constructed successful graph reconstruction attack (GRA) on GFL. But these attacks are still challenged in aspects of effectiveness and stealth. To address the issues, we propose the first Data Manipulation aided Reconstruction attack on GFL, dubbed as DMan4Rec. The malicious client is born to manipulate its locally collected data to enha

Deepa Gupta, Sachin Bhalekar

Mackey-Glass equation arises in the leukemia model. We generalize this equation to include fractional-order derivatives in two directions. The first generalization contains one whereas the second contains two fractional derivatives. Such generalizations improve the model because the nonlocal operators viz. fractional derivatives are more suitable for the nat

Tao Zhang, Ning Yan, Masood Mortazavi, Hoang H. Nguyen

Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning capability on many NLP tasks. Recasting an NLP task into a text-to-text generation task is a common practice so that generative LLMs can be prompted to resolve it. However, performing document-level relation extraction (DocRE) tasks with generative LLM

Kai Zhu, Chenkai Guo, Kuihao Yan, Xiaoqi Jia

Analyzing programs with loops is a challenging task, suffering from potential issues such as indeterminate number of iterations and exponential growth of control flow complexity. Loop summarization, as a static analysis method for concrete semantic interpretation, receives increasing focuses. It produces symbolic expressions semantically equivalent to the lo

Peter Akioyamen, Zixuan Yi, Ryan Marcus

Recent work in database query optimization has used complex machine learning strategies, such as customized reinforcement learning schemes. Surprisingly, we show that LLM embeddings of query text contain useful semantic information for query optimization. Specifically, we show that a simple binary classifier deciding between alternative query plans, trained

Bowei Du, Zhixuan Liao, Yanan Zhang, Zhi Cai

Developing accurate and efficient detectors for drone imagery is challenging due to the inherent complexity of aerial scenes. While some existing methods aim to achieve high accuracy by utilizing larger models, their computational cost is prohibitive for drones. Recently, Knowledge Distillation (KD) has shown promising potential for maintaining satisfactory

Weiguo Pian, Yiyang Nan, Shijian Deng, Shentong Mo

In this paper, we introduce a novel continual audio-visual sound separation task, aiming to continuously separate sound sources for new classes while preserving performance on previously learned classes, with the aid of visual guidance. This problem is crucial for practical visually guided auditory perception as it can significantly enhance the adaptability

Xuerong Guo, Han Wang, Yifan Xiao, Huaxi Chen

Fast Radio Burst (FRB) is an extremely energetic cosmic phenomenon of short duration. Discovered only recently and with its origin still unknown, FRBs have already started to play a significant role in studying the distribution and evolution of matter in the universe. FRBs can only be observed through radio telescopes, which produce petabytes of data, render

Pranav Gupta, Rishubh Singh, Pradeep Shenoy, Ravikiran Sarvadevabhatla

Multi-object multi-part scene segmentation is a challenging task whose complexity scales exponentially with part granularity and number of scene objects. To address the task, we propose a plug-and-play approach termed OLAF. First, we augment the input (RGB) with channels containing object-based structural cues (fg/bg mask, boundary edge mask). We propose a w

Mohammadreza Kouchaki, Minglong Zhang, Aly S. Abdalla, Guangchen Lan

In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum access and management. This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder for the de

Anh Le, Phat K. Huynh, Om P. Yadav, Chau Le

Many existing models struggle to predict nonlinear behavior during extreme weather conditions. This study proposes a multi-scale temporal analysis for failure prediction in energy systems using PMU data. The model integrates multi-scale analysis with machine learning to capture both short-term and long-term behavior. PMU data lacks labeled states despite log

Rasmus E. Benestad

A comprehensive geoscientific downscaling model strategy is presented outlining an approach that has evolved over the last 20 years, together with an explanation for its development, its technical aspects, and evaluation scheme. This effort has resulted in an open-source and free R-based tool, 'esd', for the benefit of sharing and improving the reproducibili

Mohammad Kakooei, Klaudia Solska, Adel Daoud

Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs). Achieving this requires high-frequency, granular data to capture neighborhood-level changes, particularly in data scarce regions such as low- and middle-income countries. To fill in the data gaps, recent computer vision methods combining machine learning (ML) with earth o

Deepika Sharma, Shubham Negi, Trishit Dutta, Amogh Agrawal

Spiking Neural Networks (SNNs), with their inherent recurrence, offer an efficient method for processing the asynchronous temporal data generated by Dynamic Vision Sensors (DVS), making them well-suited for event-based vision applications. However, existing SNN accelerators suffer from limitations in adaptability to diverse neuron models, bit precisions and

Shohei Taniguchi, Keno Harada, Gouki Minegishi, Yuta Oshima

Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., $\beta_2$, in a problem-dependent manner. There have been many attempts to fix the non-convergence (e.g., AMSGrad), but they require an impractical assumption that the gradient noise is u

Jinyin Chen, Haonan Ma, Haibin Zheng

Graph has become increasingly integral to the advancement of recommendation systems, particularly with the fast development of graph neural network(GNN). By exploring the virtue of rich node features and link information, GNN is designed to provide personalized and accurate suggestions. Meanwhile, the privacy leakage of GNN in such contexts has also captured

T. Chang, I. Holzman, S. Q. Lim, D. Holmes

The realization of a quantum computer represents a tremendous scientific and technological challenge due to the extreme fragility of quantum information. The physical support of information, namely the quantum bit or qubit, must at the same time be strongly coupled to other qubits by gates to compute information, and well decoupled from its environment to ke

Zhibin Wen, Bin Li

The goal of Multilingual Visual Answer Localization (MVAL) is to locate a video segment that answers a given multilingual question. Existing methods either focus solely on visual modality or integrate visual and subtitle modalities. However, these methods neglect the audio modality in videos, consequently leading to incomplete input information and poor perf

Fei Yang, Xuenan Xu, Mengyue Wu, Kai Yu

Pathological speech analysis has been of interest in the detection of certain diseases like depression and Alzheimer's disease and attracts much interest from researchers. However, previous pathological speech analysis models are commonly designed for a specific disease while overlooking the connection between diseases, which may constrain performance and lo

Jun Cui

This paper compares the impact of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) on software delivery effectiveness within enterprise environments. Using a qualitative research design, data were collected through in-depth interviews with developers and project managers from enterprises adopting TDD or BDD. Moreover, the findings reveal d