October 2023 arXiv papers — page 133

S. J. Nakoneczny, D. Alonso, M. Bilicki, D. J. Schwarz

We combine the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) catalogue with gravitational lensing maps from the Cosmic Microwave Background (CMB) to place constraints on the bias evolution of LoTSS radio galaxies, and on the amplitude of matter perturbations. We construct a flux-limited catalogue, and analyse its harmonic

Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng

As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models. This paper investigates the efficacy of these ``LLM evaluators'', particularly in using them to assess instruction following, a metric that ga

Yucheng Liu, Gordon Slade

We present a new proof of $|x|^{-(d-2)}$ decay of critical two-point functions for spread-out statistical mechanical models on $\mathbb{Z}^d$ above the upper critical dimension, based on the lace expansion and assuming appropriate diagrammatic estimates. Applications include spread-out models of the Ising model and self-avoiding walk in dimensions $d>4$, and

Robbie Mears, Kerrianne Harrington, William J. Wadsworth, Jonathan C. Knight

We report an anti-resonant hollow core fibre with ultraviolet transmission down to 190 nm, covering the entire UV-A, UV-B and much of the UV-C band. Guidance from 190 - 400 nm is achieved apart for a narrow high loss resonance band at 245 - 265 nm. The minimum attenuation is 0.13 dB/m at 235 nm and 0.16 dB/m at 325 nm. With an inscribed core diameter of ~ 12

Jonathan Bober, Andrew R. Booker, Min Lee, David Lowry-Duda

We prove the existence of "murmurations" in the family of holomorphic modular forms of level $1$ and weight $k\to\infty$, that is, correlations between their root numbers and Hecke eigenvalues at primes growing in proportion to the analytic conductor. This is the first demonstration of murmurations in an archimedean family.

Ziyue Huang, Mingming Zhang, Qingjie Liu, Wei Wang

The field of building detection from remote sensing images has made significant progress, but faces challenges in achieving high-accuracy detection due to the diversity in building appearances and the complexity of vast scenes. To address these challenges, we propose a novel approach called Context-Enhanced Detector (CEDet). Our approach utilizes a three-sta

Yuhe Liu, Changhua Pei, Longlong Xu, Bohan Chen

Information Technology (IT) Operations (Ops), particularly Artificial Intelligence for IT Operations (AIOps), is the guarantee for maintaining the orderly and stable operation of existing information systems. According to Gartner's prediction, the use of AI technology for automated IT operations has become a new trend. Large language models (LLMs) that have

Dan Cristofaro-Gardiner, Umberto Hryniewicz, Michael Hutchings, Hui Liu

We prove that every Reeb flow on a closed connected three-manifold has either two or infinitely many simple periodic orbits, assuming that the associated contact structure has torsion first Chern class. As a special case, we prove a conjecture of Hofer-Wysocki-Zehnder published in 2003 asserting that a smooth and autonomous Hamiltonian flow on $\mathbb{R}^4$

Yucheng Liu, Gordon Slade

We give conditions on a real-valued function $F$ on $\mathbb{Z}^d$, for $d>2$, which ensure that the solution $G$ to the convolution equation $(F*G)(x) = \delta_{0,x}$ has Gaussian decay $|x|^{-(d-2)}$ for large $|x|$. Precursors of our results were obtained in the 2000s, using intricate Fourier analysis. In 2022, a very simple deconvolution theorem was prov

Quan-feng Wu, Zhao Li

FeAmGen.jl is a Julia package designed to generate Feynman diagrams and their corresponding amplitudes for various processes in particle physics. Utilizing the models in the Universal Feynman Output (UFO) format and Qgraf for diagram generation, it also employs SymEngine.jl and Form for amplitude generation. Additionally, the package offers functions for con

Eleonora Lopez, Filippo Betello, Federico Carmignani, Eleonora Grassucci

Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnostic performance. However, distinguishing between malignant and benign masses in whole mammograms poses a challenge, as they appear nearly identical to an untrained eye, and the reg

Hai Huang, Zhengyu Zhao, Michael Backes, Yun Shen

Fine-tuning large pre-trained computer vision models is infeasible for resource-limited users. Visual prompt learning (VPL) has thus emerged to provide an efficient and flexible alternative to model fine-tuning through Visual Prompt as a Service (VPPTaaS). Specifically, the VPPTaaS provider optimizes a visual prompt given downstream data, and downstream user

Gregory Palmer, Chris Parry, Daniel J. B. Harrold, Chris Willis

The rapid increase in the number of cyber-attacks in recent years raises the need for principled methods for defending networks against malicious actors. Deep reinforcement learning (DRL) has emerged as a promising approach for mitigating these attacks. However, while DRL has shown much potential for cyber defence, numerous challenges must be overcome before

Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang

Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood m

Ernst Roell, Bastian Rieck

The Euler Characteristic Transform (ECT) has proven to be a powerful representation, combining geometrical and topological characteristics of shapes and graphs. However, the ECT was hitherto unable to learn task-specific representations. We overcome this issue and develop a novel computational layer that enables learning the ECT in an end-to-end fashion. Our

Hannah Rose Kirk, Andrew M. Bean, Bertie Vidgen, Paul Röttger

Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficient, effective and unbiased, especially for highly subjective human preferences and values. In this paper, we survey existing approaches for learning from human feedback, drawing on

Itamar Mor

Using profinite Galois descent, we compute the Brauer group of the $K(1)$-local category relative to Morava E-theory. At odd primes this group is generated by a cyclic algebra formed using any primitive $(p-1)$st root of unity, but at the prime two is a group of order $32$ with nontrivial extensions; we give explicit descriptions of the generators, and consi

C. L. Hale, D. J. Schwarz, P. N. Best, S. J. Nakoneczny

Covering $\sim$5600 deg$^2$ to rms sensitivities of $\sim$70$-$100 $\mu$Jy beam$^{-1}$, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency ($\sim$150 MHz) radio catalogue to date, making it an excellent tool for large-area radio cosmology studies. In this work, we use LoTSS-DR2 sources to investigate the angular two-

Theo Archambault, Arthur Filoche, Anastase Charantonis, Dominique Bereziat

Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space-borne altimetry provides valuable Sea Surface Height (SSH) measurements, used to estimate surface geostrophic currents. Due to the sensor technology employed, important gaps occur in SSH observations. Complete SSH maps are produced

Azqa Nadeem

Although many Computer Science (CS) programs offer cybersecurity courses, they are typically optional and placed at the periphery of the program. We advocate to integrate cybersecurity as a crosscutting concept in CS curricula, which is also consistent with latest cybersecurity curricular guidelines, e.g., CSEC2017. We describe our experience of implementing

Guilherme Vieira, Eleonora Grassucci, Marcos Eduardo Valle, Danilo Comminiello

Objects' rigid motions in 3D space are described by rotations and translations of a highly-correlated set of points, each with associated $x,y,z$ coordinates that real-valued networks consider as separate entities, losing information. Previous works exploit quaternion algebra and their ability to model rotations in 3D space. However, these algebras do not pr

Max H. C. van Riel, Tristan van Leeuwen, Cornelis A. T. van den Berg, Alessandro Sbrizzi

Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical systems at a high spatial and temporal resolution directly from k-

Nare Karapetyan, Ahmad Bilal Asghar, Amisha Bhaskar, Guangyao Shi

In this paper, we present an approach for coverage path planning for a team of an energy-constrained Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). Both the UAV and the UGV have predefined areas that they have to cover. The goal is to perform complete coverage by both robots while minimizing the coverage time. The UGV can also serve as a

Yi-Xiang Liu, Lingbang Zhu, Jeshurun Luke, J. J. Arfor Houwman

Coherent superpositions and entanglement are hallmarks of quantum mechanics, but they are fragile and can easily be perturbed by their environment. Selected isolated physical systems can maintain coherence and generate entanglement using well-controlled interactions. Chemical reactions, where bonds break and form, are highly dynamic quantum processes. A fund

Malte Röntgen, Xuelong Chen, Wenlong Gao, Maxim Pyzh

The Su-Schrieffer-Heeger (SSH) chain is the reference model of a one-dimensional topological insulator. Its topological nature can be explained by the quantization of the Zak phase, due to reflection symmetry of the unit cell, or of the winding number, due to chiral symmetry. Here, we harness recent graph-theoretical results to construct families of setups w

Claude Pruneau, Victor Gonzalez, Ana Marin, Sumit Basu

This paper formalizes the use of integral and differential cumulants for measurements of multi-particle event-by-event transverse momentum fluctuations, rapidity fluctuations, as well as net charge fluctuations. This enables the introduction of multi-particle balance functions, defined based on differential correlation functions (factorial cumulants), that s

Ivan Medina, Alexandre Drinko, Guilherme I. Correr, Pedro C. Azado

The energy extraction from quantum sources is a key task to develop new quantum devices such as quantum batteries (QB). In this context, one of the main figures of merit is the ergotropy, which measures the maximal amount of energy (as work) that can be extracted from the quantum source by means of unitary operations. One of the main issues to fully extract

Swati Patel, Patrick De Leenheer

In this work, we consider the periodic impulse control of a system modeled as a set of linear differential equations. We define a matrix that governs the qualitative behavior of the controlled system. This matrix depends on the period and effects of the control interventions. We investigate properties of the spectral radius of this matrix and in particular,

Hao Guo, Chen Shen, Rongcheng Zou, Pin Tao

Cooperation within asymmetric populations has garnered significant attention in evolutionary games. This paper explores cooperation evolution in populations with weak and strong players, using a game model where players choose between cooperation and defection. Asymmetry stems from different benefits for strong and weak cooperators, with their benefit ratio

Itay Kaplan, Binyamin Riahi, Arturo Rodriguez Fanlo

We prove that the group of automorphisms of the generic meet-tree expansion of an infinite non-unary free Fra\"{\i}ss\'{e} limit over a finite relational language is simple. As a prototypical case, the group of automorphism of the Rado meet-tree (i.e. the Fra\"{\i}ss\'{e} limit of finite graphs which are also meet-trees) is simple.

Gustav Nikopensius, Mohit Mayank, Orchid Chetia Phukan, Rajesh Sharma

Fact-checking is a crucial task as it ensures the prevention of misinformation. However, manual fact-checking cannot keep up with the rate at which false information is generated and disseminated online. Automated fact-checking by machines is significantly quicker than by humans. But for better trust and transparency of these automated systems, explainabilit

Matteo Mancanelli, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello

Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing. Hand in hand with their adoption, parameterized hypercomplex neural networks (PHNNs) are growing in size and no techniques have been adopted so far to control their convergence at a lar

Sumuk Shashidhar, Abhinav Chinta, Vaibhav Sahai, Zhenhailong Wang

The dominance of proprietary LLMs has led to restricted access and raised information privacy concerns. High-performing open-source alternatives are crucial for information-sensitive and high-volume applications but often lag behind in performance. To address this gap, we propose (1) A untargeted variant of iterative self-critique and self-refinement devoid

Anthony Conway

We describe a condition involving noncommutative Alexander modules which ensures that a knot with Alexander module $\mathbb{Z}[t^{\pm 1}]/(t-2) \oplus \mathbb{Z}[t^{\pm 1}]/(t^{-1}- 2)$ is topologically doubly slice. As an application, we show that a satellite knot $R_\eta(K)$ is doubly slice if the pattern $R$ has Alexander module $\mathbb{Z}[t^{\pm 1}]/(t-

Liangming Pan, Xinyuan Lu, Min-Yen Kan, Preslav Nakov

Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop

Bin Zhang, Hui Zhi, Jose Guadalupe Romero, David Navarro-Alarcon

In this paper, we propose a novel formation controller for nonholonomic agents to form general parametric curves. First, we derive a unified parametric representation for both open and closed curves. Then, a leader-follower formation controller is designed to form the parametric curves. We consider directed communications and constant input disturbances reje

Dennis Ogiermann, Daniel Balzani, Luigi E. Perotti

We present a new explicit local space-time adaptive framework to decrease the time required for monodomain simulations for cardiac electrophysiology. Based on the localized structure of the steep activation wavefront in solutions to monodomain problems, the proposed framework adopts small time steps and a tree-based adaptive mesh refinement scheme only in th

Siegfred Baluyot, Vorrapan Chandee, Xiannan Li

We study a new orthogonal family of $L$-functions associated with holomorphic Hecke newforms of level $q$, averaged over $q \asymp Q$. To illustrate our methods, we prove a one level density result for this family with the support of the Fourier transform of the test function being extended to be inside $(-4, 4)$. The main techniques developed in this paper

Yang Cao, Xinwei Sun, Yuan Yao

Multiple comparisons in hypothesis testing often encounter structural constraints in various applications. For instance, in structural Magnetic Resonance Imaging for Alzheimer's Disease, the focus extends beyond examining atrophic brain regions to include comparisons of anatomically adjacent regions. These constraints can be modeled as linear transformations

Arslan Mazitov, Maximilian A. Springer, Nataliya Lopanitsyna, Guillaume Fraux

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their component

G. Pirola, J. Juryšek, M. Balbo, D. Eckert

LHAASO J2108+5157 is a recently discovered source, detected in the Ultra-High-Energy band by the LHAASO collaboration. Two molecular clouds were identified in the direction coincident with LHAASO J2108+5157 and, from the spectra reported by LHAASO, there is no sign of an energy cutoff up to 200 TeV. This source makes a promising galactic PeVatron candidate.

Xinyu Zhang, Li Wang, Jian Chen, Cheng Fang

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental per

Max Downing

In [1] a conjecture for the modular transformation of the free fermion generalised Gibbs ensemble (GGE) was given where only the KdV charge associated to the weight four quasi primary field was inserted. In this paper we first generalise this conjecture to the case with an arbitrary, finite collection of KdV charges in the GGE. These GGEs are generalisations

Sumeet, M. Hörmann, K. P. Schmidt

We describe a hybrid quantum-classical approach to treat quantum many-body systems in the thermodynamic limit. This is done by combining numerical linked-cluster expansions (NLCE) with the variational quantum eigensolver (VQE). Here, the VQE algorithm is used as a cluster solver within the NLCE. We test our hybrid quantum-classical algorithm (NLCE$+$VQE) for

Chao-Ze Lu, Gaoyong Sun

We study many-body entanglements and spectra of the extended bosonic Hatano-Nelson model in the hard-core limit. We show that the system undergoes a phase transition from a gapless phase to a charge density wave phase accompanied by a $\mathcal{PT}$ transition in the first excited state. The phase transition is characterized by the crossing of the ground-sta

Josias Moukpe

Deep Learning methods have significantly advanced various data-driven tasks such as regression, classification, and forecasting. However, much of this progress has been predicated on the strong but often unrealistic assumption that training datasets are balanced with respect to the targets they contain. This misalignment with real-world conditions, where dat

Ali Maghami, Michele Ciavarella, Antonio Papangelo

The problem of the detachment of a sufficiently large flat indenter from a plane adhesive viscoelastic strip of thickness "b" is studied. For any given retraction speed, three different detachment regimes are found: (i) for very small "b" the detachment stress is constant and equal to the theoretical strength of the interface, (ii) for intermediate values of

Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis

In many interactive decision-making settings, there is latent and unobserved information that remains fixed. Consider, for example, a dialogue system, where complete information about a user, such as the user's preferences, is not given. In such an environment, the latent information remains fixed throughout each episode, since the identity of the user does

Karl Bringmann

Subset Sum Ratio is the following optimization problem: Given a set of $n$ positive numbers $I$, find disjoint subsets $X,Y \subseteq I$ minimizing the ratio $\max\{\Sigma(X)/\Sigma(Y),\Sigma(Y)/\Sigma(X)\}$, where $\Sigma(Z)$ denotes the sum of all elements of $Z$. Subset Sum Ratio is an optimization variant of the Equal Subset Sum problem. It was introduce

Alexey Garber, Žiga Virk, Nicolò Zava

Lattices and periodic point sets are well known objects from discrete geometry. They are also used in crystallography as one of the models of atomic structure of periodic crystals. In this paper we study the embedding properties of spaces of lattices and periodic point sets equipped with optimal bijection metrics (i.e., bottleneck and Euclidean bottleneck me

Alberto Cavallo, Irena Matkovič

We collect some observations about Legendrian links with non-vanishing contact invariants, mostly concerning the non-loose realizations of links and the addition of boundary-parallel half Giroux torsion. In particular, we show that every null-homologous link with irreducible complement admits a non-loose Legendrian realization with non-zero (at least) invari

Shubhabrata Mukherjee, Cory Beard, Sejun Song

Semantic communication aims to transmit meaningful and effective information, rather than focusing on individual symbols or bits. This results in benefits like reduced latency, bandwidth usage, and higher throughput compared with traditional communication. However, semantic communication poses significant challenges due to the need for universal metrics to b

Zichao Dong, Hang Ji, Xufeng Huang, Weikun Zhang

Point encoder is of vital importance for point cloud recognition. As the very beginning step of whole model pipeline, adding features from diverse sources and providing stronger feature encoding mechanism would provide better input for downstream modules. In our work, we proposed a novel PeP module to tackle above issue. PeP contains two main parts, a refine

J. M. Casas, L. Bonavera, J. González-Nuevo, G. Puglisi

Recovering the polarized cosmic microwave background (CMB) is essential for shedding light on the exponential expansion of the very early Universe, known as cosmic inflation. Achieving this goal requires not only improved instrumental sensitivity but also the development of robust and diverse data analysis techniques. In this work, we explore a novel compone

Luiza Pozzobon, Beyza Ermis, Patrick Lewis, Sara Hooker

Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of computationally intensive auxiliary models. Furthermore, previous approaches have often neglected the crucial factor of language's evolving nature over time. In this work, we present a comprehensive perspec

Caoyun Fan, Wenqing Chen, Jidong Tian, Yitian Li

Multi-Label Text Classification (MLTC) aims to assign the most relevant labels to each given text. Existing methods demonstrate that label dependency can help to improve the model's performance. However, the introduction of label dependency may cause the model to suffer from unwanted prediction bias. In this study, we attribute the bias to the model's misuse

Zikai Xiao, Zihan Chen, Songshang Liu, Hualiang Wang

Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in which each client holds a locally heterogeneous dataset; if the datasets can be globally aggregated, they jointly exhibit a long-tailed distribution. Under such a setting, existi

Souria Benalloua, Salima Saidi-Besbes, Abdelatif Bouyacoub, Eric Grelet

In this study, novel achiral mesogens containing 4,4'-biphenyl central core connected on both sides through an ester function to 1-(4-(alkyloxy)phenyl)-1H-[1,2,3]-triazolyl group have been synthesized and characterized. Different mesophases have been identified, with the appearance of both a periodic striped pattern and propeller-patterned droplets close to

Yuxuan Cai, Dingkang Liang, Dongliang Luo, Xinwei He

Defect detection is a critical research area in artificial intelligence. Recently, synthetic data-based self-supervised learning has shown great potential on this task. Although many sophisticated synthesizing strategies exist, little research has been done to investigate the robustness of models when faced with different strategies. In this paper, we focus

Laurenz Ruzicka, Bernhard Strobl, Bernhard Kohn, Clemens Heitzinger

Fingerprints have long been recognized as a unique and reliable means of personal identification. Central to the analysis and enhancement of fingerprints is the concept of the fingerprint core. Although the location of the core is used in many applications, to the best of our knowledge, this study is the first to investigate the empirical distribution of the

Luca Consolini, Marco Locatelli

We consider the speed planning problem for a vehicle moving along an assigned trajectory, under maximum speed, tangential and lateral acceleration, and jerk constraints. The problem is a nonconvex one, where nonconvexity is due to jerk constraints. We propose a convex relaxation, and we present various theoretical properties. In particular, we show that the

Dean S. Hazineh, Zechen Zhang, Jeffery Chiu

Foundation models exhibit significant capabilities in decision-making and logical deductions. Nonetheless, a continuing discourse persists regarding their genuine understanding of the world as opposed to mere stochastic mimicry. This paper meticulously examines a simple transformer trained for Othello, extending prior research to enhance comprehension of the

Raymond Fok, Joseph Chee Chang, Tal August, Amy X. Zhang

Navigating the vast scientific literature often starts with browsing a paper's abstract. However, when a reader seeks additional information, not present in the abstract, they face a costly cognitive chasm during their dive into the full text. To bridge this gap, we introduce recursively expandable abstracts, a novel interaction paradigm that dynamically exp

Davide Caprini, Francesco Battista, Pawel Zajdel, Giovanni Di Muccio

Although coveted in applications, few materials expand when subject to compression or contract under decompression, i.e., exhibit the negative compressibility phenomenon. A key step to achieve such counterintuitive behaviour is the destabilisations of (meta)stable equilibria of the constituents. Here, we propose a simple strategy to obtain negative compressi

Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju

Machine unlearning, the study of efficiently removing the impact of specific training instances on a model, has garnered increased attention in recent years due to regulatory guidelines such as the \emph{Right to be Forgotten}. Achieving precise unlearning typically involves fully retraining the model and is computationally infeasible in case of very large m

J. Thoppil S, Y. Waheed, S. Shit, I. D. Prasad

A van der Waals heterostructure containing an atomically thin monolayer transition-metal dichalcogenide as a single-photon emitting layer is emerging as an intriguing solid-state quantum-photonic platform. Here, we report the utilization of spin-coating of silica nanoparticles for deterministically creating the spectrally isolated, energetically stable, and

Chengyi Tu, Renfei Chen, Ying Fan, Xuwei Pan

Sustainability of common-pool resources hinges on the interplay between human and environmental systems. However, there is still a lack of a novel and comprehensive framework for modelling extraction of common-pool resources and cooperation of human agents that can account for different factors that shape the system behavior and outcomes. In particular, we s

Aamir Mandviwalla, Lake Yin, Boleslaw K. Szymanski

Regressions trained to predict the future activity of social media users need rich features for accurate predictions. Many advanced models exist to generate such features; however, the time complexities of their computations are often prohibitive when they run on enormous data-sets. Some studies have shown that simple semantic network features can be rich en

Stefan Adelbert, Anna B. T. Penzlin, Christoph M. Schäfer, Wilhelm Kley

In previous hydrodynamical simulations, we found a mechanism for nearly circular binary stars, like Kepler-413, to trap two planets in a stable 1:1 resonance. Therefore, the stability of coorbital configurations becomes a relevant question for planet formation around binary stars. Here, we investigate the coorbital planet stability using a Kepler-413 analogu

Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Brandon Augustino, Arriele Carr

Quantum linear system algorithms (QLSA) have the potential to speed up Interior Point Methods (IPM). However, a major challenge is that QLSAs are inexact and sensitive to the condition number of the coefficient matrices of linear systems. This sensitivity is exacerbated when the Newton systems arising in IPMs converge to a singular matrix. Recently, an Inexa

Osman Ülger, Yu Wang, Ysbrand Galama, Sezer Karaoglu

Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects. In this paper, we investigate the effectiveness of using such relationships for object detection and instance segmentation. To this end, we propose a Relational Prior-based Feature Enhancement Model (RP-FEM), a graph transfo

Ryan Ford, Kenneth Hutchison, Nicholas Felts, Benjamin Cheng

Understanding the impact of data set design on model training and performance can help alleviate the costs associated with generating remote sensing and overhead labeled data. This work examined the impact of training shifted window transformers using bounding boxes and segmentation labels, where the latter are more expensive to produce. We examined classifi

Rebecca Diesing, Stephan S. Meyer, Johannes Eser, Alexa Bukowski

The second generation of the Extreme Universe Space Observatory on a Super Pressure Balloon (EUSO-SPB2) is a balloon instrument that searched for ultra high energy cosmic rays (UHECRs) with energies above 1 EeV and very high energy neutrinos with energies above 1 PeV. EUSO-SPB2 consists of two telescopes: a fluorescence telescope pointed downward for the det

José M. Arrieta, Marcos Molina-Rodríguez, Lucas A. Santos

In this work we analyze the boundedness properties of the solutions of a nonautonomous parabolic degenerate logistic equation in a bounded domain. The equation is degenerate in the sense that the logistic nonlinearity vanishes in a moving region, $K(t)$, inside the domain. The boundedness character of the solutions depends not only on, roughly speaking, the

Jian Liu, Li Shen, Guo-Wei Wei

ChatGPT represents a significant milestone in the field of artificial intelligence (AI), finding widespread applications across diverse domains. However, its effectiveness in mathematical contexts has been somewhat constrained by its susceptibility to conceptual errors. Concurrently, topological data analysis (TDA), a relatively new discipline, has garnered

Weijia Yi, Nitin Jonathan Myers, Geethu Joseph

This paper develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems, the phase error

Yakir Aharonov, Daniel Collins, Sandu Popescu

Transfer of conserved quantities between two remote regions is generally assumed to be a rather trivial process: a flux of particles carrying the conserved quantities propagates from one region to another. We however demonstrate a flow of angular momentum from one region to another across a region of space in which there is a vanishingly small probability of

Pablo Martinez-Camblor, Sonia Perez-Fernandez, Lucas L. Dwiel, Wilder T. Doucette

The area under the receiver-operating characteristic curve (AUC) has become a popular index not only for measuring the overall prediction capacity of a marker but also the association strength between continuous and binary variables. In the current study, it has been used for comparing the association size of four different interventions involving impulsive

Jaffae Schroff, Xiangyang Ju

Event generators play an important role in all physics programs at the Large Hadron Collider and beyond. Dedicated efforts are required to tune the parameters of event generators to accurately describe data. There are many tuning methods ranging from expert-based manual tuning to surrogate function-based semi-automatic tuning, to machine learning-based re-we

Ion Grama, Hui Xiao

Let $(g_{n})_{n\geq 1}$ be a sequence of independent and identically distributed positive random $d\times d$ matrices, where $d\geq 2$ is an integer. For any starting point $x \in \mathbb{R}_+^d$ with $|x| = 1$ and $y \in \mathbb R$, we define the exit time $\tau_{x, y} = \inf \{ k \geq 1: y + \log |g_k \cdots g_1 x| < 0 \}$. In this paper, we investigate th

Udrea Păun

The pivot algorithm -- we also call it the pivot chain -- is an algorithm for approximately uniform sampling from $\Omega _{N},$ the set of $N$-step self-avoiding walks on $\mathbb{Z}^{d}$ ($N,$ $d\geq 1$). Based on this algorithm and the $G$ method, we construct another algorithm/chain, called the pivot$^{\text{+}}$ algorithm/chain, for approximately unifor

Minkun Kim

In this article, we explore two effective means to communicate the concept of walkability - 1) visualization, and 2) descriptive statistics. We introduce the concept of walkability as measuring the quality of an urban space based on the distance needed to walk from that space to a range of different social, environmental, and economic amenities. We use Dubli

Kapildeb Dolui, Lewis J. Conway, Christoph Heil, Timothy A. Strobel

A key challenge in materials discovery is to find high-temperature superconductors. Hydrogen and hydride materials have long been considered promising materials displaying conventional phonon-mediated superconductivity. However, the high pressures required to stabilize these materials have restricted their application. Here, we present results from high-thro

Oscar Fernando Romero Matamala

The detection of astrophysical neutrinos by IceCube in the TeV-PeV energy range motivates the development of instruments for observing these particles at higher energies. Moreover, the detection of very-high-energy (VHE) neutrinos could potentially bring constraints on ultra-high energy cosmic rays (UHECRs) source models. Tau neutrinos skimming the Earth und

Mingcheng Chen, Haoran Zhao, Yuxiang Zhao, Hulei Fan

Data-driven black-box model-based optimization (MBO) problems arise in a great number of practical application scenarios, where the goal is to find a design over the whole space maximizing a black-box target function based on a static offline dataset. In this work, we consider a more general but challenging MBO setting, named constrained MBO (CoMBO), where o

Gaston Giribet, Juan Laurnagaray, Bryan Malpartida, Julio Oliva

We study the scalar probe in the near-horizon region of near-extremal five-dimensional black holes and the problem of reattaching the asymptotic region. We consider the example of a Myers-Perry black hole with two independent angular momenta, for which the problem can be solved analytically in terms of the Riemann P-symbols and the confluent Heun special fun

Zeqi Ye, Hansheng Jiang

We study the dynamic pricing problem where the demand function is nonparametric and H\"older smooth, and we focus on adaptivity to the unknown H\"older smoothness parameter $\beta$ of the demand function. Traditionally the optimal dynamic pricing algorithm heavily relies on the knowledge of $\beta$ to achieve a minimax optimal regret of $\widetilde{O}(T^{\fr

Olivier Bélanger, Olfa Ben Yahia, Stéphane Martel, Antoine Lesage-Landry

High throughput satellites (HTSs) outpace traditional satellites due to their multi-beam transmission. The rise of low Earth orbit mega constellations amplifies HTS data rate demands to terabits/second with acceptable latency. This surge in data rate necessitates multiple modems, often exceeding single device capabilities. Consequently, satellites employ sev

M. A. Marques, R. Menezes

In this paper, we investigate multifield models in which the two-field BNRT model is coupled to a third field through mediator functions in the Lagrangian density. To conduct the investigation, we obtain the equations of motion and develop a first-order formalism based on energy minimization. Two possibilities are considered: i) the third field acting in the

Ziqi Wen, Tianqin Li, Zhi Jing, Tai Sing Lee

Deep learning models are known to exhibit a strong texture bias, while human tends to rely heavily on global shape structure for object recognition. The current benchmark for evaluating a model's global shape bias is a set of style-transferred images with the assumption that resistance to the attack of style transfer is related to the development of global s

Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou

LLMs confront inherent limitations in terms of its knowledge, memory, and action. The retrieval augmentation stands as a vital mechanism to address these limitations, which brings in useful information from external sources to augment the LLM. However, existing retrieval methods encounter two pressing issues. On one hand, the general retrievers are not prope

Tuan Minh Pham, Xiangyang Ju

Accurate simulation of detector responses to hadrons is paramount for all physics programs at the Large Hadron Collider (LHC). Central to this simulation is the modeling of hadronic interactions. Unfortunately, the absence of first-principle theoretical guidance has made this a formidable challenge. The state-of-the-art simulation tool, \textsc{Geant4}, curr

Guiwei Zhang, Yongfei Zhang, Zichang Tan

Visible-infrared person re-identification is challenging due to the large modality gap. To bridge the gap, most studies heavily rely on the correlation of visible-infrared holistic person images, which may perform poorly under severe distribution shifts. In contrast, we find that some cross-modal correlated high-frequency components contain discriminative vi

Fabio Cassini

Suitable discretizations through tensor product formulas of popular multidimensional operators (diffusion or diffusion--advection, for instance) lead to matrices with $d$-dimensional Kronecker sum structure. For evolutionary Partial Differential Equations containing such operators and integrated in time with exponential integrators, it is then of paramount i

Junteng Yao, Tuo Wu, Xiazhi Lai, Ming Jin

This paper investigates the efficacy of utilizing fluid antenna system (FAS) at a legitimate monitor to oversee suspicious communication. The monitor switches the antenna position to minimize its outage probability for enhancing the monitoring performance. Our objective is to maximize the average monitoring rate, whose expression involves the integral of the

K. Anesiadis, J. Hizanidis

We study the dynamics of identical Leaky Integrate-and-Fire (LIF) neurons on a multiplex composed of two ring networks with symmetric nonlocal coupling within each ring and one-to-one connections between rings. We investigate the impact of different intra-ring coupling strengths in the two rings for attractive and repulsive inter-ring coupling and show that

Junzhe Xu, Suling Duan, Chenwei Tang, Zhenan He

Zero-shot learning enables the model to recognize unseen categories with the aid of auxiliary semantic information such as attributes. Current works proposed to detect attributes from local image regions and align extracted features with class-level semantics. In this paper, we find that the choice between local and global features is not a zero-sum game, gl

Juan De Gregorio, David Sanchez, Raul Toral

Entropy estimation is a fundamental problem in information theory that has applications in various fields, including physics, biology, and computer science. Estimating the entropy of discrete sequences can be challenging due to limited data and the lack of unbiased estimators. Most existing entropy estimators are designed for sequences of independent events

Sosuke Inui, Mikai Hulse, Toshiaki Kanai, Wei Guo

Superfluid helium-4 (He II) is a widely adopted coolant in scientific and engineering applications owing to its exceptional heat transfer capabilities. However, boiling can spontaneously occur on a heating surface in He II when the heat flux exceeds a threshold value $q^*$, referred to as the peak heat flux. While the parameter $q^*$ holds paramount importan

Javier Pastor-Galindo, Pantaleone Nespoli, José A. Ruipérez-Valiente

This article presents the affordances that Generative Artificial Intelligence can have in misinformation and disinformation contexts, major threats to our digitalized society. We present a research framework to generate customized agent-based social networks for disinformation simulations that would enable understanding and evaluating the phenomena whilst di