April 2024 arXiv papers — page 184

Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep

Entity disambiguation (ED), which links the mentions of ambiguous entities to their referent entities in a knowledge base, serves as a core component in entity linking (EL). Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark. Nevertheless, generative approaches suffer from

Maxim Zaytsev, Vyacheslav Akkerman

Hydrodynamic discontinuities in an external potential and incompressible flow are investigated. Using the reaction front as an example in a 2D stream, an overdetermined system of equations is obtained that describes its motion in terms of the surface itself. Assuming that the harmonic flux approaching discontinuity is additional smooth, these equations can b

Fei Wei, Ergute Bao, Xiaokui Xiao, Yin Yang

Local differential privacy (LDP) is a strong privacy standard that has been adopted by popular software systems. The main idea is that each individual perturbs their own data locally, and only submits the resulting noisy version to a data aggregator. Although much effort has been devoted to computing various types of aggregates and building machine learning

Qishuo Cheng

In recent decades, financial quantification has emerged and matured rapidly. For financial institutions such as funds, investment institutions are increasingly dissatisfied with the situation of passively constructing investment portfolios with average market returns, and are paying more and more attention to active quantitative strategy investment portfolio

Yuichiro Yoshida, Nayuta Takemori, Wataru Mizukami

We propose introducing an extended Hubbard Hamiltonian derived via the ab initio downfolding method, which was originally formulated for periodic materials, towards efficient quantum computing of molecular electronic structure calculations. By utilizing this method, the first-principles Hamiltonian of chemical systems can be coarse-grained by eliminating the

Xingyu Lan, Leni Yang, Zezhong Wang, Yun Wang

Storytelling is an ancient and precious human ability that has been rejuvenated in the digital age. Over the last decade, there has been a notable surge in the recognition and application of data storytelling, both in academia and industry. Recently, the rapid development of generative AI has brought new opportunities and challenges to this field, sparking n

Tong Zhou

Brylinski and Malgrange proved in 1986 that, for a monodromic algebraic D-module on a finite dimensional vector space over the complex numbers, its characteristic cycle is canonically identified with the characteristic cycle of its Fourier transform. We prove the exact analogue of this in the $\ell$-adic context.

James Anibal, Hannah Huth, Ming Li, Lindsey Hazen

Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in

Kaiman Lin, Yi Li, Mahdi Ghorbani-Asl, Zdenek Sofer

This study investigates the electronic band structure of Chromium Sulfur Bromide (CrSBr) through comprehensive photoluminescence (PL) characterization. We clearly identify low-temperature optical transitions between two closely adjacent conduction-band states and two different valence-band states. The analysis of the PL data robustly unveils energy splitting

Kunlong Liu, Trinabh Gupta

Privacy-preserving federated graph analytics is an emerging area of research. The goal is to run graph analytics queries over a set of devices that are organized as a graph while keeping the raw data on the devices rather than centralizing it. Further, no entity may learn any new information except for the final query result. For instance, a device may not l

Zihao Deng, Peng Gao, Williard Joshua Jose, Christopher Reardon

Coordinated multi-robot navigation is an essential ability for a team of robots operating in diverse environments. Robot teams often need to maintain specific formations, such as wedge formations, to enhance visibility, positioning, and efficiency during fast movement. However, complex environments such as narrow corridors challenge rigid team formations, wh

Zhiyuan He, Aashish Gottipati, Lili Qiu, Xufang Luo

We introduce NADA, the first framework to autonomously design network algorithms by leveraging the generative capabilities of large language models (LLMs). Starting with an existing algorithm implementation, NADA enables LLMs to create a wide variety of alternative designs in the form of code blocks. It then efficiently identifies the top-performing designs

Frank Palma Gomez, Ramon Sanabria, Yun-hsuan Sung, Daniel Cer

Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, w

Lin Li, Jianping Gou, Baosheng Yu, Lan Du

Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients. Despite its promise, FL encounters challenges such as high communication costs for large-scale models and the necessity for uniform model architectures across all clients and the server. These challenges severely restrict the practica

Melanie J. McGrath, Andreas Duenser, Justine Lacey, Cecile Paris

Collaborative human-AI (HAI) teaming combines the unique skills and capabilities of humans and machines in sustained teaming interactions leveraging the strengths of each. In tasks involving regular exposure to novelty and uncertainty, collaboration between adaptive, creative humans and powerful, precise artificial intelligence (AI) promises new solutions an

Hanqian Li, Ruinan Zhang, Ye Pan, Junchi Ren

Remote sensing target detection aims to identify and locate critical targets within remote sensing images, finding extensive applications in agriculture and urban planning. Feature pyramid networks (FPNs) are commonly used to extract multi-scale features. However, existing FPNs often overlook extracting low-level positional information and fine-grained conte

Xin Li, Ting Wang, Jin Guo, Yanlong Zhao

This paper studies system identification of high-dimensional ARMA models with binary-valued observations. The existing paper can only deal with the case where the regression term is only one-dimensional. In this paper, the ARMA model with arbitrary dimensions is considered, which is more challenging. Different from the identification of FIR models with binar

Zongrui Li, Zhan Lu, Haojie Yan, Boxin Shi

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and

Aditya Deshmukh, Venugopal V. Veeravalli, Gunjan Verma

We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion center is assumed to contain a pretrained linear regression model, trained on a dataset of the entire uncompressed data. At inference time, the sensors compress their observations

Yi Di Yuan, Swee Liang Wong, Jonathan Pan

Non-line-of-sight localization in signal-deprived environments is a challenging yet pertinent problem. Acoustic methods in such predominantly indoor scenarios encounter difficulty due to the reverberant nature. In this study, we aim to locate sound sources to specific locations within a virtual environment by leveraging physically grounded sound propagation

Jie Wang, Youmin Zhang

This tutorial provides a systematic introduction to Gaussian process learning-based model predictive control (GP-MPC), an advanced approach integrating Gaussian process (GP) with model predictive control (MPC) for enhanced control in complex systems. It begins with GP regression fundamentals, illustrating how it enriches MPC with enhanced predictive accuracy

Yang Liu

In this paper, the author considers the fractional mean field equation on a finite graph $G=(V,E)$, say \begin{equation*} (-\Delta)^s u=\rho\left(\dfrac{he^u}{\int_V he^ud\mu}-\dfrac{1}{|V|}\right),\quad\forall\,x\in V, \end{equation*} where $s\in(0,\,1)$, $\rho\in(-\infty,\,0)\cup(0,\,+\infty)$ are some fixed parameters, $h$ denotes a given real value funct

Licheng Wang, Luochen Xie, Gang Huang, Changsen Feng

The rate of change of frequency (RoCoF) is a critical factor in ensuring frequency security, particularly in power systems with low inertia. Currently, most RoCoF security constrained optimal inertia dispatch methods and inertia market mechanisms predominantly rely on the center of inertia (COI) model. This model, however, does not account for the disparitie

Sai Li, Linjun Zhang

Machine learning methods often assume that the test data have the same distribution as the training data. However, this assumption may not hold due to multiple levels of heterogeneity in applications, raising issues in algorithmic fairness and domain generalization. In this work, we address the problem of fair and generalizable machine learning by invariant

Ivan A. Aleksandrov, Konstantin S. Zhuravlev

Processes of diffusion of As and P atoms in InP and InAs, and atomic and energy structure of group-V vacancies and interstitial P and As atoms in InP and InAs have been investigated using density functional theory. Formation energies of group-V vacancies in InP and InAs and P and As interstitial atoms in InP and InAs have been calculated with hybrid function

Zijian Zhou, Caimei Wang, Xiaoheng Deng, Jianhao Lu

Although the decentralized storage technology based on the blockchain can effectively realize secure data storage on cloud services. However, there are still some problems in the existing schemes, such as low storage capacity and low efficiency. To address related issues, we propose a novel decentralized storage framework, which mainly includes four aspects:

Kimiko Hasegawa, Rin Sugiyama

Polynomials commute under composition are referred to as commuting polynomials. In this paper, we study division properties for commuting polynomials with rational (and integer) coefficients. As a consequence, we show an algebraic particularity of the commuting polynomials coming from weighted sums for cycle graphs with pendant edges (arXiv:2402.07209v1.). W

Seongmin Hwang, Daeyoung Han, Cheolkon Jung, Moongu Jeon

The surge in interest regarding image dehazing has led to notable advancements in deep learning-based single image dehazing approaches, exhibiting impressive performance in recent studies. Despite these strides, many existing methods fall short in meeting the efficiency demands of practical applications. In this paper, we introduce WaveDH, a novel and compac

Y. Kawamoto, T. Kikkawa, M. Kawamata, Y. Umemoto

Magnon spin currents in the ferrimagnetic garnet Tb3Fe5O12 with 4f electrons were examined through the spin-Seebeck effect and neutron scattering measurements. The compound shows a magnetic compensation, where the spin-Seebeck signal reverses above and below Tcomp = 249.5(4) K. Unpolarized neutron scattering unveils two major magnon branches with finite ener

Silin Du, Xiaowei Zhang

Large language models (LLMs) have exhibited memorable strategic behaviors in social deductive games. However, the significance of opinion leadership exhibited by LLM-based agents has been largely overlooked, which is crucial for practical applications in multi-agent and human-AI interaction settings. Opinion leaders are individuals who have a noticeable impa

Xingwu Chen, Difan Zou

We study the capabilities of the transformer architecture with varying depth. Specifically, we designed a novel set of sequence learning tasks to systematically evaluate and comprehend how the depth of transformer affects its ability to perform memorization, reasoning, generalization, and contextual generalization. We show a transformer with only one attenti

Bo Li, Xu-Tao Zeng, Qianhui Xu, Fan Yang

Determination of the magnetic structure and confirmation of the presence or absence of inversion ($\mathcal{P}$) and time reversal ($\mathcal{T}$) symmetry is imperative for correctly understanding the topological magnetic materials. Here high-quality single crystals of the layered manganese pnictide CaMnSb$_2$ are synthesized using the self-flux method. De

Erik Burman, Rebecca Durst, Miguel A. Fernández, Johnny Guzmán

We consider a parabolic-parabolic interface problem and construct a loosely coupled prediction-correction scheme based on the Robin-Robin splitting method analyzed in [J. Numer. Math., 31(1):59--77, 2023]. We show that the errors of the correction step converge at $\mathcal O((\Delta t)^2)$, under suitable convergence rate assumptions on the discrete time de

Ya-Chien Chang, Sicun Gao

Reinforcement learning for control over continuous spaces typically uses high-entropy stochastic policies, such as Gaussian distributions, for local exploration and estimating policy gradient to optimize performance. Many robotic control problems deal with complex unstable dynamics, where applying actions that are off the feasible control manifolds can quick

Junyao Pan, Pengfei Guo

In 2019, B\'ona and Smith introduced the notion of \emph{strong pattern avoidance}, that is, a permutation and its square both avoid a given pattern. In this paper, we enumerate the set of permutations $\pi$ which not only strongly avoid the pattern $312$ or $231$ but also avoid the pattern $\tau$, for $\tau\in S_3$ and some $\tau\in S_4$. One of them is to

Johnny Xi, Jana Osea, Zuheng Xu, Jason Hartford

Multimodal representation learning techniques typically rely on paired samples to learn common representations, but paired samples are challenging to collect in fields such as biology where measurement devices often destroy the samples. This paper presents an approach to address the challenge of aligning unpaired samples across disparate modalities in multim

Erik Burman, Rebecca Durst, Miguel A. Fernández, Johnny Guzmán

We consider a loosely coupled, non-iterative Robin-Robin coupling method proposed and analyzed in [J. Numer. Math., 31(1):59--77, 2023] for a parabolic-parabolic interface problem and prove estimates for the discrete time derivatives of the scalar field in different norms. When the interface is flat and perpendicular to two of the edges of the domain we prov

Chuandi Zhang, Junsen Xiang, Quanliang Zhu, Longfei Wu

The recent discovery of the spin supersolid candidate Na$_2$BaCo(PO$_4$)$_2$ stimulates numerous research interest on the triangular-lattice transition-metal phosphates. Here we report a comprehensive study on the structural, magnetic and magnetocaloric properties of polycrystalline Na$_2$$A$$T$(PO$_4$)$_2$ ($A$ = Ba, Sr; $T$ = Co, Ni, Mn). X-ray and neutron

Ning Wang, Guangming Zhu, HS Li, Liang Zhang

While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. These models, however, usually fall short in performance compared to their black

Akihiro Higashitani, Koichiro Tani

The goal of this paper is to study the possible monoids appearing as the associated monoids of the initial algebra of a finitely generated homogeneous $\Bbbk$-subalgebra of a polynomial ring $\Bbbk[x_1,\ldots,x_n]$. Clearly, any affine monoid can be realized since the initial algebra of the affine monoid $\Bbbk$-algebra is itself. On the other hand, the init

Chia-Hsuan Chang, Mary M. Lucas, Grace Lu-Yao, Christopher C. Yang

Cancer stage classification is important for making treatment and care management plans for oncology patients. Information on staging is often included in unstructured form in clinical, pathology, radiology and other free-text reports in the electronic health record system, requiring extensive work to parse and obtain. To facilitate the extraction of this in

Yu Xia, Xu Liu, Tong Yu, Sungchul Kim

Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported. Existing methods for alleviating hallucinations typically require costly human annotations to identify and correct hallucinations in LLM outputs. Moreover, most of these methods focus on a specific type of hallucinatio

Yehui Shen, Mingmin Liu, Huimin Lu, Xieyuanli Chen

Visual place recognition (VPR) plays a pivotal role in autonomous exploration and navigation of mobile robots within complex outdoor environments. While cost-effective and easily deployed, camera sensors are sensitive to lighting and weather changes, and even slight image alterations can greatly affect VPR efficiency and precision. Existing methods overcome

Arjun Vijaywargiya, Guosheng Fu, Stanley Osher, Wuchen Li

We develop a class of barycenter problems based on mean field control problems in three dimensions with associated reactive-diffusion systems of unnormalized multi-species densities. This problem is the generalization of the Wasserstein barycenter problem for single probability density functions. The primary objective is to present a comprehensive framework

Akshit Sharma, Sam Reinher, Dinesh Mehta, Bo Wu

Frequent Subgraph Mining (FSM) is the process of identifying common subgraph patterns that surpass a predefined frequency threshold. While FSM is widely applicable in fields like bioinformatics, chemical analysis, and social network anomaly detection, its execution remains time-consuming and complex. This complexity stems from the need to recognize high-freq

Yinqiu Liu, Ruichen Zhang, Hongyang Du, Dusit Niyato

Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely defined reward functions, which formulate the optimization objective and indicate the positive/negative progress towards the optimal. With the ever-increasing environmental complexi

Hasan M. El-Hasan, Frederick Wilhelm

Murphy and the second author showed that a generic closed Riemannian manifold has no totally geodesic submanifolds, provided it is at least four dimensional. Lytchak and Petrunin established the same thing in dimension 3. For the higher dimensional result, the generic set is open and dense in the $C^{q}$--topology for any $% q\geq 2.$ In Lytchak and Petrunin

Seokha Moon, Hongbeen Park, Jungphil Kwon, Jaekoo Lee

In autonomous driving and robotics, there is a growing interest in utilizing short-term historical data to enhance multi-camera 3D object detection, leveraging the continuous and correlated nature of input video streams. Recent work has focused on spatially aligning BEV-based features over timesteps. However, this is often limited as its gain does not scale

Xingmin Wang, Zihao Wang, Zachary Jerome, Henry X. Liu

This paper studies the traffic state estimation problem at signalized intersections with low penetration rate vehicle trajectory data. While many existing studies have proposed different methods to estimate unknown traffic states and parameters (e.g., penetration rate, queue length) with this data, most of them only provide a point estimation without knowing

Chaitali Bhattacharyya, Hanxiao Wang, Feng Zhang, Sungho Kim

Recent progress in generative AI, primarily through diffusion models, presents significant challenges for real-world deepfake detection. The increased realism in image details, diverse content, and widespread accessibility to the general public complicates the identification of these sophisticated deepfakes. Acknowledging the urgency to address the vulnerabi

Namyong Park, Ryan Rossi, Xing Wang, Antoine Simoulin

The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting the right GL model becomes increasingly difficult and time consuming as more and more GL models are developed. Accordingly, it is of great significance and practical value to equi

Gregory Moille, Daron Westly, Rahul Shrestha, Khoi Tuan Hoang

The development of broadband microresonator frequency combs at visible wavelengths is pivotal for the advancement of compact and fieldable optical atomic clocks and spectroscopy systems. Yet, their realization necessitates resonators with anomalous dispersion, an arduous task due to the prevailing normal dispersion regime of materials within the visible spec

Jesudara Omidokun, Darlington Egeonu, Bochen Jia, Liang Yang

This study presents an innovative computer vision framework designed to analyze human movements in industrial settings, aiming to enhance biomechanical analysis by integrating seamlessly with existing software. Through a combination of advanced imaging and modeling techniques, the framework allows for comprehensive scrutiny of human motion, providing valuabl

Manuel Salmeron, Herta Montoya, Edwin Patino, Ingrid E. Madera Sierra

To date, the prospect of using real-time hybrid simulation (RTHS) to study the effects of long-term or 'wear-and-tear' loads, such as exposure to harmful environmental conditions or fatigue, has remained underexplored. This study presents a conceptual framework to assess the impact of long-term degradation on infrastructure systems. The framework integrates

Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke

Adversarial ranking attacks have gained increasing attention due to their success in probing vulnerabilities, and, hence, enhancing the robustness, of neural ranking models. Conventional attack methods employ perturbations at a single granularity, e.g., word or sentence level, to target documents. However, limiting perturbations to a single level of granular

Daniel R Chavas, Suzana J Camargo, Michael K Tippett

Genesis potential indices (GPIs) are widely used to understand the climatology of tropical cyclones (TCs). However, the sign of projected future changes depends on how they incorporate environmental moisture. Recent theory combines potential intensity and mid-tropospheric moisture into a single quantity called the ventilated potential intensity, which remove

Christian Limberg, Artur Gonçalves, Bastien Rigault, Helmut Prendinger

In this article, we explore the potential of zero-shot Large Multimodal Models (LMMs) in the domain of drone perception. We focus on person detection and action recognition tasks and evaluate two prominent LMMs, namely YOLO-World and GPT-4V(ision) using a publicly available dataset captured from aerial views. Traditional deep learning approaches rely heavily

Samuel Pell, Andreas Willig

Recently, swarms or formations of drones have received increased interest both in the literature and in applications. To dynamically adapt to their operating environment, swarm members need to communicate wirelessly for control and coordination tasks. One fundamental communication pattern required for basic safety purposes, such as collision avoidance, is be

Manish Sanwal

In the domain of Natural Language Inference (NLI), especially in tasks involving the classification of multiple input texts, the Cross-Entropy Loss metric is widely employed as a standard for error measurement. However, this metric falls short in effectively evaluating a model's capacity to understand language entailments. In this study, we introduce an inno

Dehao Yuan, Cornelia Fermüller, Tahseen Rabbani, Furong Huang

We propose VecKM, a local point cloud geometry encoder that is descriptive and efficient to compute. VecKM leverages a unique approach by vectorizing a kernel mixture to represent the local point cloud. Such representation's descriptiveness is supported by two theorems that validate its ability to reconstruct and preserve the similarity of the local shape. U

Mrunmayi S. Deshpande, Nuria P. F. Lorente, Anthony Horton, Brent Miszalski

PyCPL provides full access to ESO's Common Pipeline Library ( CPL) for astronomical data reduction within a Python environment. Not only does it offer a Python interface to the robust CPL library, but it also lets users and developers fully utilise the rest of the scientific Python ecosystem. We have written a C++ layer to CPL and with pybind11 (a third-part

Jiawei Fu, Tara Slough

The credibility revolution advances the use of research designs that permit identification and estimation of causal effects. However, understanding which mechanisms produce measured causal effects remains a challenge. The dominant current approach to the quantitative evaluation of mechanisms relies on the detection of heterogeneous treatment effects (HTEs) w

Kenjiro Okawa, Yasutaka. Amagai, Norihiko Sakamoto, Nobu-Hisa Kaneko

The ac Harman method has been used for the direct estimation of dimensionless thermoelectric figure of merit (zT) through ac/dc resistance measurements. However, accurate zT estimation with a four-probe configuration is difficult owing to the occurrence of a thermal phase-delay in the heat flow with a low frequency current. This study reports an exact soluti

Joel Sol, Amir M. Soufi Enayati, Homayoun Najjaran

This paper addresses the challenge of geometric quality assurance in manufacturing, particularly when human assessment is required. It proposes using Blender, an open-source simulation tool, to create synthetic datasets for machine learning (ML) models. The process involves translating expert information into shape key parameters to simulate deformations, ge

Jintao Zou, Long-Cheng Gui, Ying Chen, Jian Liang

We perform the first lattice QCD study on the radiative decay of the scalar glueball to the vector meson $\phi$ in the quenched approximation. The calculations are carried out on three gauge ensembles with different lattice spacings, which enable us to do the continuum extrapolation. We first revisit the radiative $J/\psi$ decay into the scalar glueball $G$

Yuchen Fei, Yanmei Luo, Yan Wang, Jiaqi Cui

To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from corresponding low-dose PET (LPET) images. However, most current methods merely learn the mapping between single-dose-level LPET and SPET images, but omit the dose disparity of LPET i

Mohammad Habibur Rahaman, Samuel Harper, Chang-Min Lee, Kyu-Young Kim

Telecom C-band single photons exhibit the lowest attenuation in optical fibers, enabling long-haul quantum-secured communication. However, efficient coupling with optical fibers is crucial for these single photons to be effective carriers in long-distance transmission. In this work, we demonstrate an efficient fiber-coupled single photon source at the teleco

Sajid Bin Mahamud, Steve Butler, Hannah Graff, Nick Layman

Coalescing involves gluing one or more rooted graphs onto another graph. Under specific conditions, it is possible to start with cospectral graphs that are coalesced in similar ways that will result in new cospectral graphs. We present a sufficient condition for this based on the block structure of similarity matrices, possibly with additional constraints de

Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon

In this chapter, we delve into the formation and primary characteristics of two significant components within galaxy clusters: the brightest cluster galaxies (BCGs) and the intracluster light (ICL). Drawing upon recent and pertinent studies in the field, we explore the mechanisms driving their growth from high redshift to the present day, i.e., mergers and s

Tajmilur Rahman, Yuecai Zhu

Modern Software Engineering era is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating many parts of the software development workflow. Requirements Engineering (RE) is a crucial phase that begins the software development cycle through multiple discussions on a p

Raffaele Galliera, Thies Möhlenhof, Alessandro Amato, Daniel Duran

Effective operation and seamless cooperation of robotic systems are a fundamental component of next-generation technologies and applications. In contexts such as disaster response, swarm operations require coordinated behavior and mobility control to be handled in a distributed manner, with the quality of the agents' actions heavily relying on the communicat

Bradley Greig, Sarah E. I. Bosman, Frederick B. Davies, Dominika Ďurovčíková

Reconstructing the intrinsic Ly$\alpha$ line flux from high-$z$ QSOs can place constraints on the neutral hydrogen content of the intergalactic medium during reionisation. There are now $\gtrsim10$ different Ly$\alpha$ reconstruction pipelines using different methodologies to predict the Ly$\alpha$ line flux from correlations with the spectral information re

Ibne Farabi Shihab, Sudesh Ramesh Bhagat, Anuj Sharma

This study aims to compare the effectiveness of a robust ensemble model with the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate detection of sidewalks. Accurate sidewalk detection is crucial in improving road safety and urban planning. The study evaluated the model's performance on Cityscapes, Ade20k, and the Boston Dataset. The results s

Sakumi Sugawara

We study the Milnor fiber boundary for hyperplane arrangements in $\mathbb{C}^3$. This is one of the examples of non-isolated surface singularities, which are studied by N\'emethi--Szil\'ard. In this paper, we compute the first homology group of the Milnor fiber boundary for a generic arrangement, which gives an affirmative answer to the conjecture of Suciu.

Qi Guo, Xiaohong Li, Xiaofei Xie, Shangqing Liu

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods

Helena Shawn, Thompson Chyrikov, Jacob Lanet, Lam-chi Chen

Utilizing a low-dose CT approach significantly reduces the radiation exposure for patients, yet it introduces challenges, such as increased noise and artifacts in the resultant images, which can hinder accurate medical diagnostics. Traditional methods for noise reduction struggle with preserving image textures due to the complexity of modeling statistical pr

Joel Alves de Lima Júnior, Kiev Gama, Jorge da Silva Correia Neto

The use of the open innovation paradigm has been, over the past years, getting special attention in the public sector. Motivated by an urban environment that is increasingly more complex and challenging, several government agencies have been allocating financial resources and efforts to promote open and participative government initiatives. As a way to try a

Raffaele Galliera, Konstantinos Mitsopoulos, Niranjan Suri, Raffaele Romagnoli

Addressing complex cooperative tasks in safety-critical environments poses significant challenges for multi-agent systems, especially under conditions of partial observability. We focus on a dynamic network bridging task, where agents must learn to maintain a communication path between two moving targets. To ensure safety during training and deployment, we i

Luis Pabon, Johannes Köhler, John Irvin Alora, Patrick Benito Eberhard

In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many tr

Xiang Chen, Jinshan Pan, Jiangxin Dong

How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on single-scale rain appearance, we develop an end-to-end multi-scale Transformer that leverages the potentially useful features in various scales to facilitate high-quality image recons

Shuqi Li, Tianya Wu, Xinhui Huang, Jia Zhou

The Circular Electron Positron Collider (CEPC) has been proposed to enable more thorough and precise measurements of the properties of Higgs, W, and Z bosons, as well as to search for new physics. In response to the stringent performance requirements of the vertex detector for the CEPC, a baseline vertex detector prototype was tested and characterized for th

Bin Chen, Elynn Y. Chen, Stevenson Bolivar, Rong Chen

Matrix-variate data of high dimensions are frequently observed in finance and economics, spanning extended time periods, such as the long-term data on international trade flows among numerous countries. To address potential structural shifts and explore the matrix structure's informational context, we propose a time-varying matrix factor model. This model ac

Luc Devroye, Austin Eide, Pawel Pralat

Let $\mathcal{T}$ be a Galton-Watson tree with a given offspring distribution $\xi$, where $\xi$ is a $Z_{\geq 0}$-valued random variable with $E[\xi] = 1$ and $0 < \sigma^{2}:=Var[\xi] < \infty$. For $n \geq 1$, let $T_{n}$ be the tree $\mathcal{T}$ conditioned to have $n$ vertices. In this paper we investigate $b(T_n)$, the burning number of $T_n$. Our mai

Khaldi Said, Arioui Fatima Zahra, Hakem Ali

Our aim in this paper is to discuss the critical exponent in semi-linear structurally damped wave and beam equations with additional dispersion term. The special model we have in mind is $$ u_{tt}(t,x)+(-\Delta)^{\sigma}u(t,x)+(-\Delta)^{2\delta}u(t,x)+2(-\Delta)^{\delta}u_{t}(t,x)=\left|u(t,x)\right| ^{p} $$ where the initial displacement $u(0,x)=u_{0}(x)$,

Ziqian Bai, Feitong Tan, Sean Fanello, Rohit Pandey

3D head avatars built with neural implicit volumetric representations have achieved unprecedented levels of photorealism. However, the computational cost of these methods remains a significant barrier to their widespread adoption, particularly in real-time applications such as virtual reality and teleconferencing. While attempts have been made to develop fas

Rahul Saxena, Taeyoun Kim, Aman Mehra, Christina Baek

Estimating the out-of-distribution performance in regimes where labels are scarce is critical to safely deploy foundation models. Recently, it was shown that ensembles of neural networks observe the phenomena "agreement-on-the-line", which can be leveraged to reliably predict OOD performance without labels. However, in contrast to classical neural networks t

Yuchen Ding, Ling Zhu, Annalisa Pillepich, Glenn van de Ven

We studied the effects of cluster environments on galactic structures by using the TNG50 cosmological simulation and observed galaxies in the Fornax cluster. We focused on galaxies with stellar masses of $10^{8-12}M_{\odot}$ at z=0 that reside in Fornax-like clusters with total masses of $M_{200c} = 10^{13.4-14.3}M_{\odot}$. We characterized the stellar stru

Alejandro Cabo-Bizet

The continuum of holographic dual gravitational charges is recovered out of the discrete spectrum of $U(N)$ $\mathcal{N}=4$ SYM on $\mathbb{R}\times S^3\,$. In such a limit, the free energy of the free gauge theory is computed up to logarithmic contributions and exponentially suppressed contributions. Assuming the supergravity dual prediction to correctly ca

El Mustapha Ait Ben Hassi, Mariem Jakhoukh, Lahcen Maniar, Walid Zouhair

The primary focus of this paper is to establish the internal null controllability for the one-dimensional heat equation featuring dynamic boundary conditions. This achievement is realized by introducing a new Carleman estimate and an observability inequality for the corresponding backward system. In conclusion, the paper includes a set of numerical experimen

Christian Porter

In this paper, we revisit the theory of perfect unary forms over real quadratic fields. Specifically, we deduce an infinite family of real quadratic fields $\mathbb{Q}(\sqrt{d})$ when $d=2$ or $3$ mod $4$, such that there are three classes of perfect unary forms up to homothety and equivalence. This work, along with the work in \cite{unitred}, seems to sugge

Daniil Lisus, Keenan Burnett, David J. Yoon, Richard Poulton

Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike `fixed' automotive radar, commercially available spinning radar systems typically do not produce radial velocities due to the lack of repeated measurements in the same direction and the fundament

Mandar Sharma, Rutuja Murlidhar Taware, Pravesh Koirala, Nikhil Muralidhar

Off-the-shelf pre-trained language models have become the de facto standard in NLP pipelines for a multitude of downstream tasks. However, the inability of these models to properly encode numerals limits their performance on tasks requiring numeric comprehension. We introduce strategies to semantically prime numerals in any corpus by generating anchors gover

Veronica Valeros, Anna Širokova, Carlos Catania, Sebastian Garcia

Understanding cybercrime communications is paramount for cybersecurity defence. This often involves translating communications into English for processing, interpreting, and generating timely intelligence. The problem is that translation is hard. Human translation is slow, expensive, and scarce. Machine translation is inaccurate and biased. We propose using

Antonio Savino, Gautam Gala, Marcello Cinque, Gerhard Fohler

With the increasing use of multicore platforms to realize mixed-criticality systems, understanding the underlying shared resources, such as the memory hierarchy shared among cores, and achieving isolation between co-executing tasks running on the same platform with different criticality levels becomes relevant. In addition to safety considerations, a malicio

Fynn Bachmann, Cristina Sarasua, Abraham Bernstein

The effectiveness of Voting Advice Applications (VAA) is often compromised by the length of their questionnaires. To address user fatigue and incomplete responses, some applications (such as the Swiss Smartvote) offer a condensed version of their questionnaire. However, these condensed versions can not ensure the accuracy of recommended parties or candidates

Kento Sugiura, Manabu Nishimura, Yoshiharu Ishikawa

In the last decade, academic and industrial researchers have focused on persistent memory because of the development of the first practical product, Intel Optane. One of the main challenges of persistent memory programming is to guarantee consistent durability over separate memory addresses, and Wang et al. proposed a persistent multi-word compare-and-swap (

Jingxuan Wei, Nan Xu, Guiyong Chang, Yin Luo

In the fields of computer vision and natural language processing, multimodal chart question-answering, especially involving color, structure, and textless charts, poses significant challenges. Traditional methods, which typically involve either direct multimodal processing or a table-to-text conversion followed by language model analysis, have limitations in

Maximilian Bailey, Dmitry Fedosov, Federico Paratore, Fabio Grillo

The generation of fluid flows by autophoretic microswimmers has been proposed as a mechanism to enhance mass transport and mixing at the micro- and nanoscale. Here, we experimentally investigate the ability of model 2-D &#34;active baths&#34; of photocatalytic silica-titania Janus microspheres to enhance the diffusivity of tracer particles at different micro

Jie Wang, Krispin Davies

Hybrid track/wheel-legged robots combine the advantages of wheel-based and leg-based locomotion, granting adaptability across varied terrains through efficient transitions between rolling and walking modes. However, automating these transitions remains a significant challenge. In this paper, we introduce a method designed for autonomous mode transition in a

Laboni Sarker, Mara Downing, Achintya Desai, Tevfik Bultan

Rapid advances in the field of Large Language Models (LLMs) have made LLM-based code generation an important area for investigation. An LLM-based code generator takes a prompt as input and produces code that implements the requirements specified in the prompt. Many software requirements include mathematical formulas that specify the expected behavior of the