November 2024 arXiv papers — page 117

Wenkui Sun, Xiaoya Fan, Lijuan Jia, Tinyi Chu

Differential equations offer a foundational yet powerful framework for modeling interactions within complex dynamic systems and are widely applied across numerous scientific fields. One common challenge in this area is estimating the unknown parameters of these dynamic relationships. However, traditional numerical optimization methods rely on the selection o

Kaitao Tang, Thomas A. A. Adcock, Wouter Mostert

We present novel numerical simulations investigating the evolution of liquid droplets into bag-like structures in turbulent airflows. The droplet bag breakup problem is of significance for many multiphase processes in scientific and engineering applications. Turbulent fluctuations are introduced synthetically into a mean flow, and the droplet is inserted whe

F. Colauto, D. Carmo, A. M. H. de Andrade, A. A. M. Oliveira

A method for measuring the electrical connectivity between parts of a rectangular superconductor was developed for weak links making an arbitrary angle with the long side of the sample. The method is based on magneto-optical observation of characteristic lines where the critical current makes discontinuous deviations in the flow direction to adapt to the non

Wouter Brinksma, William Wernsen, Evert Verduin, Herman Hilberink

This report investigates the relationship between software refactoring and behavior preservation. Existing behavior preservation analyses often lack comprehensive insights into refactoring rejections and do not provide actionable solutions. To address these issues, we developed a conceptual model to detect refactoring dangers, and created an Eclipse plugin b

Damien Chapon, Patrick Hennebelle

The Galactica simulation database is a platform designed to assist computational astrophysicists with their open science approach based on FAIR (Findable, Accessible, Interoperable, Reusable) principles. It offers the means to publish their numerical simulation projects, whatever their field of application or research theme and provides access to reduced dat

Ivan Vujmilovic, Sara Collins, Luka Leskovec, Emmanuel Ortiz-Pacheco

The determination of the $DD^{*}$ scattering amplitude from lattice QCD is complicated by long-range interactions. In particular, the L\"uscher method is no longer applicable in the kinematical region close to the left-hand cut. We tackle this problem by adopting plane-wave and effective-field-theoretic methods, which also address partial wave mixing. In add

Joyjit Kundu, Debjyoti Bhattacharjee, Nathan Josephsen, Ankit Pokhrel

Superconducting Digital (SCD) technology offers significant potential for enhancing the performance of next generation large scale compute workloads. By leveraging advanced lithography and a 300 mm platform, SCD devices can reduce energy consumption and boost computational power. This paper presents a cross-layer modeling approach to evaluate the system-leve

Kseniia S. Chernova, Andrei A. Stepanenko, Maxim A. Gorlach

Quantum brachistochrone method has recently emerged as a technique allowing one to implement the desired unitary evolution operator in a physical system within the minimal time. Here, we apply this approach to the problem of time-optimal quantum state transfer in the array of three qubits with time-varying nearest-neighbor couplings and analytically derive t

Luna Lomonaco, Lucas Oliveira, Miguel Ratis Laude

A central problem in the study of critical circle dynamics is understanding the regularity of Yoccoz conjugators - circle homeomorphisms that conjugate critical circle maps with irrational rotation numbers to their corresponding rigid rotations. One can approach this problem from a different angle by studying the regularity of extensions of these maps to the

Chengdong Dong, Vijayakumar Bhagavatula, Zhenyu Zhou, Ajay Kumar

The remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3D Gaussian splatting, offers a powerful alternative to GANs and diffusion models. These methods can produce high-fidelity images and lifelike avatars, highlighting the need for robust detection methods. In resp

Xinkai Wang, Shuo Zhang, Ziyi Zhao, Lifeng Zhu

While tangible user interface has shown its power in naturally interacting with rigid or soft objects, users cannot conveniently use different types of granular materials as the interaction media. We introduce DipMe as a smart device to recognize the types of granular media in real time, which can be used to connect the granular materials in the physical wor

Mojdeh Karbalaee Motalleb, Chafika Benzaid, Tarik Taleb, Marcos Katz

The evolution of wireless communication systems will be fundamentally impacted by an open radio access network (O-RAN), a new concept defining an intelligent architecture with enhanced flexibility, openness, and the ability to slice services more efficiently. For all its promises, and like any technological advancement, O-RAN is not without risks that need t

Tian-Nuo Li, Yun-He Li, Guo-Hong Du, Peng-Ju Wu

New insights from the Dark Energy Spectroscopic Instrument (DESI) 2024 baryon acoustic oscillations (BAO) data, in conjunction with cosmic microwave background (CMB) and Type Ia supernova (SN) data, suggest that dark energy may not be a cosmological constant. In this work, we investigate the cosmological implications of holographic dark energy (HDE) and inte

Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, Amine Mohamed Aboussalah

Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribution (OOD) data. These challenges are exacerbated when training data is limited in size or diversity. To address these issues, we introduce a theoretical framework using Rademacher

Sven Goluža, Tomislav Kovačević, Stjepan Begušić, Zvonko Kostanjčar

The sequential nature of decision-making in financial asset trading aligns naturally with the reinforcement learning (RL) framework, making RL a common approach in this domain. However, the low signal-to-noise ratio in financial markets results in noisy estimates of environment components, including the reward function, which hinders effective policy learnin

Changyu Wang, Yuan Xu, Hong Xu, Juan Liu

Near-eye display plays an important role in emerging spatial computing systems, providing a distinctive visual effect of virtual-real fusion. However, its application for all-day wear is greatly limited by the bulky structure, energy expenditure, and continuous battery heating. Here, we propose a lightweight holographic near-eye display system that takes adv

Orna Kupferman, Ofer Leshkowitz, Namma Shamash Halevy

We study automatic synthesis of systems that interact with their environment and maintain privacy against an observer to the interaction. The system and the environment interact via sets $I$ and $O$ of input and output signals. The input to the synthesis problem contains, in addition to a specification, also a list of secrets, a function $cost: I\cup O\right

Kilian Schweppe, Ludmila Moshagen, Georg Schildbach

This paper considers the application of Model Predictive Control (MPC) to a weighted coverage path planning (WCPP) problem. The problem appears in a wide range of practical applications, including search and rescue (SAR) missions. The basic setup is that one (or multiple) agents can move around a given search space and collect rewards from a given spatial di

Alexandros Alexakis

Many fluid-dynamical systems met in nature are quasi-two-dimensional: they are constrained to evolve in approximately two dimensions with little or no variation along the third direction. This has a drastic effect in the flow evolution because the properties of three dimensional turbulence are fundamentally different from those of two dimensional turbulence.

Suddhasattwa Brahma, Jaime Calderón-Figueroa, Xiancong Luo, David Seery

We analyse the evolution of the reduced density matrix of inflationary perturbations, coupled to a heavy entropic field via the leading-order term within the Effective Field Theory of Inflation, for two nearly de Sitter backgrounds. We perform a full quantum treatment of the open system and derive a Fokker-Planck equation to describe decoherence and the enta

Shijin Gong, Huihang Liu, Xinyu Zhang

We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem, where demand is influenced by both price and contextual features and is modeled without any structural assumptions. The unknown demand distribution results in a challenging conditional stochastic optimization problem, further complicated by decision-dependent unc

Martijn S. S. L. Oei, Martin J. Hardcastle, Roland Timmerman, Aivin R. D. J. G. I. B. Gast

Jets launched by supermassive black holes transport relativistic leptons, magnetic fields, and atomic nuclei from the centres of galaxies to their outskirts and beyond. These outflows embody the most energetic pathway by which galaxies respond to their Cosmic Web environment. Studying black hole feedback is an astrophysical frontier, providing insights on st

Caroline Magg, Hoel Kervadec, Clara I. Sánchez

The Segment Anything Model (SAM) and similar models build a family of promptable foundation models (FMs) for image and video segmentation. The object of interest is identified using prompts, such as bounding boxes or points. With these FMs becoming part of medical image segmentation, extensive evaluation studies are required to assess their strengths and wea

Rui Meng, Hangyu Zhao, Liang Jin, Bingxuan Xu

Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT). However, state-of-the-art Channel State Information (CSI)-based PLA schemes face challenges in recognizing

C. Marconcini, F. D'Eugenio, R. Maiolino, S. Arribas

We present JWST/NIRSpec integral field spectroscopic (IFS) observations of the \Lyalpha emitter CR7 at z ~ 6.6, observed as part of the GA-NIFS program. Using low-resolution PRISM (R ~ 100) data, we confirm a bright \Lyalpha emitter, and a diffuse \Lyalpha halo extending up to 3 kpc from the peak of ionized emission, both of them associated to the most massi

Raphaël Ollando, Seung Yeob Shin, Lionel C. Briand

Controllers for software-defined networks (SDNs) are centralised software components that enable advanced network functionalities, such as dynamic traffic engineering and network virtualisation. However, these functionalities increase the complexity of SDN controllers, making thorough testing crucial. SDN controllers are stateful, interacting with multiple n

Dan Braha, Marcus A. M. de Aguiar

We analyze the accuracy of collective decision-making in socially connected populations, where agents update binary choices through local interactions on a network. Each agent receives a private signal that is biased -- even marginally -- toward the correct alternative, and social influence mediates the aggregation of these signals. We show analytically that

Patrick Dondl, Martin Heida, Simone Hermann

This work examines a discrete elastic energy system with local interactions described by a discrete second-order functional in the symmetric gradient and additional non-local random long-range interactions. We analyze the asymptotic behavior of this model as the grid size tends to zero. Assuming that the occurrence of long-range interactions is Bernoulli dis

Lara Bergmann, David Leins, Robert Haschke, Klaus Neumann

Non-prehensile manipulation, such as pushing objects to a desired target position, is an important skill for robots to assist humans in everyday situations. However, the task is challenging due to the large variety of objects with different and sometimes unknown physical properties, such as shape, size, mass, and friction. This can lead to the object oversho

Swaroop Panda

Thought experiments are considered valuable tools in science, enabling the exploration of hypotheses and the examination of complex ideas in a conceptual, non-empirical framework. These thought experiments can be useful in design fiction for speculating future possibilities, examining existing and alternate scenarios in new ways or challenging current paradi

Morenikeji Neri

We explore the computational content of Kronecker's lemma via the proof-theoretic perspective of proof mining and utilise the resulting finitary variant of this fundamental result to provide new rates for the Strong Law of Large Numbers for random variables taking values in type $p$ Banach spaces, which in particular are very uniform in the sense that they d

Ban Zhang, Zhao-Qing Feng

Within the framework of Lanzhou quantum molecular dynamics transport model, the neutron-skin thickness and its impact on the nuclear dynamics induced by low-energy antiprotons are investigated thoroughly. The correlation of the neutron-skin thickness and stiffness of symmetry energy is implemented into the transport model via the Fermi distributions of the p

Saman Mazaheri Khamaneh, Tong Wu

In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization approach for robust power system operations t

Hua Chen

$F(R)$ models for dark energy generally exhibit a weak curvature singularity, which can be cured by adding an $R^2$ term. This correction allows for a unified description of primordial and late-time accelerated expansions. However, most existing models struggle to achieve this, as they become unstable over certain negative ranges of the Ricci scalar, where e

Nakwoo Kim, Hoseob Shin

We consider the 2nd integrated correlators of ${\cal N}=4$, $D=4$ super Yang-Mills theory, especially those which can be associated with ${\cal N}=1^*$ mass-deformed theories. We provide an analytic derivation of the integrals at supergravity tree level, which has not been available so far. Our result agrees with the previous results from the study of BPS so

Alexis Béjar-López, Alain Blaustein, Pierre-Emmanuel Jabin, Juan Soler

This paper investigates the long time dynamics of interacting particle systems subject to singular interactions. We consider a microscopic system of $N$ interacting point particles, where the time evolution of the joint distribution $f_N(t)$ is governed by the Liouville equation. Our primary objective is to analyze the system's behavior over extended time in

Zhanwen Wang, Michael J. Miksis, Petia M. Vlahovska

Nonlinear electrokinetic phenomena, where electrically driven fluid flows depend nonlinearly on the applied voltage, are commonly encountered in aqueous suspensions of colloidal particles. A prime example is the induced-charge electro-osmosis, driven by an electric field acting on diffuse charge induced near a polarizable surface. Nonlinear electrohydrodynam

Smitha V. Thampi, Ankush Bhaskar, Prateek Mayank, Bhargav Vaidya

The May 10, 2024 space weather event stands out as the most powerful storm recorded during the current solar cycle. This study employs a numerical framework utilizing a semi-empirical coronal model, along with HUXt (Heliospheric Upwind eXtrapolation with time-dependence) and cone-CME models for the inner heliosphere, to forecast solar wind velocity and the a

Kengo Hashimoto

A combinatorial game is a two-player game without hidden information or chance elements. One of the major approaches to analyzing games in combinatorial game theory is to break down a given game position into a disjunctive sum of multiple sub-positions, then evaluate the game value of each component of the sum, and finally integrate these game values to find

Felix Stahlberg, Jared Lichtarge, Shankar Kumar

We propose a novel parameter-efficient training (PET) method for large language models that adapts models to downstream tasks by optimizing a small subset of the existing model parameters. Unlike prior methods, this subset is not fixed in location but rather which parameters are modified evolves over the course of training. This dynamic parameter selection c

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

Parameterized quantum circuits are the core of new technologies such as variational quantum algorithms and quantum machine learning, which makes studying its properties a valuable task. We implement parameterized circuits with definite and indefinite causal order and compare their performance under particular descriptors. One of these is the expressibility,

Miroslav Mirchev, Lasko Basnarkov, Igor Mishkovski

We investigate searching efficiency of different kinds of random walk on complex networks which rely on local information and one-step memory. For the studied navigation strategies we obtained theoretical and numerical values for the graph mean first passage times as an indicator for the searching efficiency. The experiments with generated and real networks

Van Phuc Bui, Junya Shiraishi, Petar Popovski, Shashi Raj Pandey

Training a high-quality Federated Learning (FL) model at the network edge is challenged by limited transmission resources. Although various device scheduling strategies have been proposed, it remains unclear how scheduling decisions affect the FL model performance under temporal constraints. This is pronounced when the wireless medium is shared to enable the

Pengwei Yin, Jingjing Wang, Guanzhong Zeng, Di Xie

The ability of gaze estimation models to generalize is often significantly hindered by various factors unrelated to gaze, especially when the training dataset is limited. Current strategies aim to address this challenge through different domain generalization techniques, yet they have had limited success due to the risk of overfitting when solely relying on

Karl Mason, Daniel Kelly

This paper presents Low-cost Marine Autonomous Robotic Vehicle Explorer (Lo-MARVE), a novel autonomous underwater vehicle (AUV) designed to provide a low cost solution for underwater exploration and environmental monitoring in shallow water environments. Lo-MARVE offers a cost-effective alternative to existing AUVs, featuring a modular design, low-cost senso

L. M. Ramos, A. F. V. Cortez, M. Kuźniak, A. Gnat

To enhance the ionization yield of liquid argon time projection chambers (LArTPC) used in dark matter and neutrino experiments it was proposed the use of dopants in LAr, with ionization energies below the scintillation threshold of Ar. While dual-phase LArTPCs have excellent sensitivity to single ionization electrons, their compatibility with photosensitive

Dominik Borer, Jakob Buhmann, Martin Guay

Modern pose estimation models are trained on large, manually-labelled datasets which are costly and may not cover the full extent of human poses and appearances in the real world. With advances in neural rendering, analysis-by-synthesis and the ability to not only predict, but also render the pose, is becoming an appealing framework, which could alleviate th

Zhonghua Li, Shukun Wang

In this paper, we first introduce the notion of projective Banach Lie bialgebras as the projective tensor product analogue of Banach Lie bialgebras. Then we consider the completion of the classical Yang-Baxter equation and classical r-matrices, and propose the notions of the projective Yang-Baxter equation and projective r-matrices. As in the finite-dimensio

Gaëlle Aymeric, Brice Magdalou

This paper presents findings from a web-experiment on a representative sample of the French population. It examines the acceptability of the Pigou-Dalton principle of transfers, which posits that transferring income from an individual to a relatively poorer one, reduces overall inequality. While up to 60% of respondents reject standard transfers, the three a

Karlo Lelas

Vibrating systems can respond to an infinite number of initial conditions and the overall dynamics of the system can be strongly affected by them. Therefore, it is of practical importance to have methods by which we can determine the damping that is in some sense optimal for all initial conditions, or for a given set of initial conditions. For a single and m

Yingqi Gao, Yifu Liu, Xiaoxia Li, Xiaorong Shi

To tackle the challenges of large language model performance in natural language to SQL tasks, we introduce XiYan-SQL, an innovative framework that employs a multi-generator ensemble strategy to improve candidate generation. We introduce M-Schema, a semi-structured schema representation method designed to enhance the understanding of database structures. To

Srijan Bhattacharyya, Thomas Sayer, Andrés Montoya-Castillo

The exact quantum dynamics of lattice models can be computationally intensive, especially when aiming for large system sizes and extended simulation times necessary to converge transport coefficients. By leveraging finite memory times to access long-time dynamics using only short-time data, generalized master equations (GMEs) can offer a route to simulating

Sujan Maity, Soumik Das, Mainak Palit, Koushik Dey

Electron-magnon coupling reveals key insights into the interfacial properties between non-magnetic metals and magnetic insulators, influencing charge transport and spin dynamics. Here, we present temperature-dependent Raman spectroscopy and magneto-transport measurements of few-layer graphene (FLG)/antiferromagnetic FePS\(_3\) heterostructures. The magnon mo

Chao Liu, Chao Ma, Tieyan Chang, Xiaoli Wang

Quasi-two-dimensional averievites with triangle-kagome-triangle trilayers are of interest due to their rich structural and magnetic transitions and strong spin frustration that are expected to host quantum spin liquid ground state with suitable substitution or doping. Herein, we report growth of bulk single crystals of averievite CsClCu5V2O10 with dimensions

Tatiana Tatarenko, Maryam Kamgarpour

We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate of a payoff-based approach intended to learn a variational GNE (v-GNE) in such games. While convergent algorithms have recently been proposed in this setting given full or partial

Franz G. Fuchs, Ruben P. Bassa, Frida Lien

The weighted MAX k-CUT problem involves partitioning a weighted undirected graph into k subsets, or colors, to maximize the sum of the weights of edges between vertices in different subsets. This problem has significant applications across multiple domains. This paper explores encoding methods for MAX k-CUT on qubit systems, utilizing quantum approximate opt

Indranil Biswas, Sorin Dumitrescu, Archana S. Morye

Let $M$ be a compact complex manifold, and $D\, \subset\, M$ a reduced normal crossing divisor on it, such that the logarithmic tangent bundle $TM(-\log D)$ is holomorphically trivial. Let ${\mathbb A}$ denote the maximal connected subgroup of the group of all holomorphic automorphisms of $M$ that preserve the divisor $D$. Take a holomorphic Cartan geometry

Jun Xie, Wenxiao Li, Faqiang Wang, Liqiang Zhang

Morphological methods play a crucial role in remote sensing image processing, due to their ability to capture and preserve small structural details. However, most of the existing deep learning models for semantic segmentation are based on the encoder-decoder architecture including U-net and Segment Anything Model (SAM), where the downsampling process tends t

A. Hossain, J. Buescu

This article presents the construction of a non-affine hypersurface on an $n$-simplex in $\mathbb{R}^n$. Additionally, fractal dimension of the graph of a non-affine multivariate real-valued fractal function is estimated under certain conditions. Furthermore, the upper bound of the Hausdorff dimension of the invariant probability measure supported on the gra

Youjun Deng, Lingzheng Kong, Hongjie Li, Hongyu Liu

Subwavelength resonance is a vital acoustic phenomenon in contrasting media. The narrow bandgap width of single-layer resonator has prompted the exploration of multi-layer metamaterials as an effective alternative, which consist of alternating nests of high-contrast materials, called ``resonators'', and a background media. In this paper, we develop a general

Saul Santos, Vlad Niculae, Daniel McNamee, André F. T. Martins

Associative memory models, such as Hopfield networks and their modern variants, have garnered renewed interest due to advancements in memory capacity and connections with self-attention in transformers. In this work, we introduce a unified framework-Hopfield-Fenchel-Young networks-which generalizes these models to a broader family of energy functions. Our en

Miguel Jorquera Riera, Leon Loveridge

We study Heisenberg's uncertainty relation relative to a quantum reference frame (QRF). We introduce the QRF as a covariant phase-space observable, show that when described relative to it, position and momentum appear compatible, and derive novel, frame-relative uncertainty relations. This is achieved by constructing a joint observable for position and momen

Myungjin Kim, Bogoan Kim, Kyungsik Han

This study examines the role of vagueness in the design process and its strategic management for the effective human-AI interaction. While vagueness in the generation of design ideas promotes diverse interpretations and prevents fixation, excessive vagueness can lead to scattered results. Designers attempt to use image search tools or generative AIs (e.g., D

Rebecca Nevin, Aleksandra Ćiprijanović, Brian D. Nord

Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evi

Guoqing Zhang, Keita Fukuyama, Kazumasa Kishimoto, Tomohiro Kuroda

Summarizing patient clinical notes is vital for reducing documentation burdens. Current manual summarization makes medical staff struggle. We propose an automatic method using LLMs, but long inputs cause LLMs to lose context, reducing output quality especially in small size model. We used a 7B model, open-calm-7b, enhanced with Native Bayes Context Extend an

Roberta Musina, Alexander I. Nazarov

We continue our investigation of Hardy-type inequalities involving combinations of cylindrical and spherical weights. Compared to [Cora-Musina-Nazarov, Ann. Sc. Norm. Sup., 2024], where the quasi-spherical case was considered, we handle the full range of allowed parameters. This has led to the observation of new phenomena related to lack of compactness.

Manfred Buchacher

We investigate the problem of deciding whether the restriction of a rational function $r\in\mathbb{K}(x,y)$ to the curve associated with an irreducible polynomial $p\in\mathbb{K}[x,y]$ is the restriction of an element of $\mathbb{K}(x)+\mathbb{K}(y)$. We present an algorithm and a conjectural semi-algorithm for finding such elements depending on whether $p$

Jaroslaw Kornowicz

This study empirically examines the "Evaluative AI" framework, which aims to enhance the decision-making process for AI users by transitioning from a recommendation-based approach to a hypothesis-driven one. Rather than offering direct recommendations, this framework presents users pro and con evidence for hypotheses to support more informed decisions. Howev

Stepan Svirin, Artem Ryzhikov, Saraa Ali, Denis Derkach

The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced ML techniques. In our study, we i

Mahendra Gupta, Maitreyee Dutta, Chandresh Kumar Maurya

Speech-to-Speech Translation (S2ST) models transform speech from one language to another target language with the same linguistic information. S2ST is important for bridging the communication gap among communities and has diverse applications. In recent years, researchers have introduced direct S2ST models, which have the potential to translate speech withou

Ryuta Hatakenaka, Yoshiyuki Tagawa

We investigated the impact of a droplet on a heated surface in a depressurized environment, with a particular focus on the unique outcome observed under these conditions: magic carpet breakup. This phenomenon, first reported by Hatakenaka et al. [Int. J. Heat Mass Transf., 145, 118729(2019)], describes an explosive, widespread rebound of the drop. A newly-de

Youzhi Liu, Fanglong Yao, Yuanchang Yue, Guangluan Xu

Vision-and-Language Navigation (VLN), as a widely discussed research direction in embodied intelligence, aims to enable embodied agents to navigate in complicated visual environments through natural language commands. Most existing VLN methods focus on indoor ground robot scenarios. However, when applied to UAV VLN in outdoor urban scenes, it faces two signi

Yaroslav V. Kartashov, Dmitry A. Zezyulin

We predict the existence of double-flattop quantum droplets in atomic Bose-Bose mixtures. Solutions of this type have two flattop regions of nearly uniform atomic density corresponding to a compressed central core surrounded by a rarefied layer. The birth of these double-flattop quantum droplets is analytically described using a perturbation theory, which in

Yu-Chong Chen, Siyu Chen, Wei-Cong Huang, Qing Yang

Axion-photon oscillation effect provides a possible explanation for the presence of very-high-energy (VHE) $\gamma$-ray signals from distant sources. In this work, we propose a model-dependent method to select possible sources that may give sufficient constraints on the axion parameters. We investigate such effect in the spectra of active galactic nuclei (AG

W. K. Smithson, Xinhua Wang

Guided missile accuracy and precision is negatively impacted by seeker delay, more specifically by the delay introduced by a mechanical seeker gimbal and the computational time taken to process the raw data. To meet the demands and expectations of modern missiles systems, the impact of this hardware limitation must be reduced. This paper presents a new obser

Yifan Wen, Yanbing Zhang, Lei Wu

The plasma edge flow, situated at the intricate boundary between plasma and neutral particles, plays a pivotal role in the design of nuclear fusion devices such as divertors and pumps. Traditional numerical simulation methods, such as the direct simulation Monte Carlo approach and the discrete velocity method, are hindered by extensive computation times when

Alina Mailach, Sebastian Simon, Johannes Dorn, Norbert Siegmund

Background: Large language models (LLMs) have become a paramount interest of researchers and practitioners alike, yet a comprehensive overview of key considerations for those developing LLM-based systems is lacking. This study addresses this gap by collecting and mapping the topics practitioners discuss online, offering practical insights into where prioriti

Md Insiat Islam Rabby, Mohammad Ali Rob Sharif, Mohammad Tarequl Islam, Md. Rakidul Islam

This article delves into a numerical exploration of two-dimensional, incompressible, laminar flow within a confined diverging jet. The study aims to understand how variations in the inlet opening fraction and Reynolds number affect the heat transfer and flow patterns. The research employs the finite volume method with a collocated mesh to solve the governing

Eran Igra

We prove that if a smooth vector field $F$ of $S^3$ generates a sufficiently complicated heteroclinic knot, the flow also generates infinitely many periodic orbits, which persist under smooth perturbations which preserve the heteroclinic knot. Consequentially, we then associate a Template with the flow dynamics - regardless of whether $F$ satisfies any hyper

Penghui Yang, Chen-Chen Zong, Sheng-Jun Huang, Lei Feng

Traditional knowledge distillation focuses on aligning the student's predicted probabilities with both ground-truth labels and the teacher's predicted probabilities. However, the transition to predicted probabilities from logits would obscure certain indispensable information. To address this issue, it is intuitive to additionally introduce a logit-level los

Shuai S. A. Yuan, Xinyi Y. I. Xu, Jinpeng Yuan, Guoda Xie

Rydberg atom-based antennas exploit the quantum properties of highly excited Rydberg atoms, providing unique advantages over classical antennas, such as high sensitivity, broad frequency range, and compact size. Despite the increasing interests in their applications in antenna and communication engineering, two key properties, involving the lack of polarizat

Chengyuan Zhang, Yilin Zhang, Lei Zhu, Deyin Liu

This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is to create an optimal solution for situations where only a few examples of novel object classes are available, with no access to training data for base or old classes, while maintai

Markus Kirschmer, Jürgen Klüners

We arrange the orders in an algebraic number field in a tree. This tree can be used to enumerate all orders of bounded index in the maximal order as well as the orders over some given order.

Ashkan Nejad, Mohammad Reza Faraji, Xiaojun Qi

With the widespread adoption of digital devices equipped with cameras and the rapid development of Internet technology, numerous content-based image retrieval systems and novel image feature extraction techniques have emerged in recent years. This paper introduces a saliency map-based image retrieval approach using invariant Krawtchouk moments (SM-IKM) to en

Leonidas Askianakis

Grasping by a robot in unstructured environments is deemed a critical challenge because of the requirement for effective adaptation to a wide variation in object geometries, material properties, and other environmental factors. In this paper, we propose a novel framework for robotic grasping based on the idea of compressing high-dimensional target and grippe

Wolfgang J. R. Enzi, Coleman M. Krawczyk, Daniel J. Ballard, Thomas E. Collett

The nature of dark matter is poorly constrained on subgalactic scales. Alternative models to cold dark matter, such as warm dark matter or self-interacting dark matter, could produce very different dark haloes on these scales. One of the few known dark haloes smaller than a galaxy was discovered in the triple source plane strong lens system J0946+1006. Previ

Mohsen Fathi

We investigate the shadow properties of a rotating black hole with a weakly coupled global monopole charge, using a modified Newman-Janis algorithm. This study explores how this charge and rotational effects shape the black hole's shadow, causal structure, and ergoregions, with implications for distinguishing it from Kerr-like solutions. Analysis of null geo

Micha Kaiser, Paul Lohmann, Peter Ochieng, Billy Shi

Food consumption and production contribute significantly to global greenhouse gas emissions, making them crucial entry points for mitigating climate change and maintaining a liveable planet. Over the past two decades, food policy initiatives have explored interventions to reshape production and consumption patterns, focusing on reducing food waste and curbin

Jingrui Hou, Axel Finke, Georgina Cosma

Machine unlearning in neural information retrieval (IR) systems requires removing specific data whilst maintaining model performance. Applying existing machine unlearning methods to IR may compromise retrieval effectiveness or inadvertently expose unlearning actions due to the removal of particular items from the retrieved results presented to users. We form

Wei Guan, Jian Cao, Shiyou Qian, Jianqi Gao

Software systems often record important runtime information in logs to help with troubleshooting. Log-based anomaly detection has become a key research area that aims to identify system issues through log data, ultimately enhancing the reliability of software systems. Traditional deep learning methods often struggle to capture the semantic information embedd

James Adam, Roy Maartens, Julien Larena, Chris Clarkson

The Cosmological Principle is a cornerstone of the standard model of cosmology and shapes how we view the Universe and our place within it. It is imperative, then, to devise multiple observational tests which can identify and quantify possible violations of this foundational principle. One possible method of probing large-scale anisotropies involves the use

Tao Hu, Xinpei Jia, Runhao Han, Jia Yang

We investigate the effect of top Al2O3 interlayer thickness on the memory window (MW) of Si channel ferroelectric field-effect transistors (Si-FeFETs) with TiN/Al$_2$O$_3$/Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_x$/Si (MIFIS) gate structure. We find that the MW first increases and then remains almost constant with the increasing thickness of the top Al2O3. The phenom

Christian Kleiber, William H. Oliver, Tobias Buck

We present $\texttt{LAMINAR}$, a novel unsupervised machine learning pipeline designed to enhance the representation of structure within data via producing a more-informative distance metric. Analysis methods in the physical sciences often rely on standard metrics to define geometric relationships in data, which may fail to capture the underlying structure o

J. C. Muñoz-Hervás, B. Semin, M. Lorite-Díez, G. J. Michon

In this study, we investigate the flow around a canonical blunt body, specifically a D-shaped body of width $D$, in a closed water channel. Our goal is to explore near-wake flow modifications when a series of rigid and flexible plates ($l=1.8D$) divided into filaments ($h=0.2D$) are added. We focus on assessing the interaction between the flexible filaments

Noam Soker

In comparing the two alternative explosion mechanisms of core-collapse supernovae (CCSNe), I examine recent three-dimensional (3D) hydrodynamical simulations of CCSNe in the frame of the delayed-neutrino explosion mechanism (neutrino mechanism) and argue that these valuable simulations show that neutrino heating can supply a non-negligible fraction of the ex

F. Camilloni

The Blandford-Znajek mechanism is an electromagnetic manifestation of the Penrose process that currently constitutes the best theoretical candidate to explain the launching of relativistic jets by black holes. In this talk we offer a modern review about the Blandford-Znajek mechanism and the analytic construction of black hole magnetospheres. Higher order pe

Suhas S Kowshik, Abhishek Divekar, Vijit Malik

Large language models (LLMs) have demonstrated remarkable performance in diverse tasks using zero-shot and few-shot prompting. Even though their capabilities of data synthesis have been studied well in recent years, the generated data suffers from a lack of diversity, less adherence to the prompt, and potential biases that creep into the data from the genera

Jun Qi, Chao-Han Yang, Samuel Yen-Chi Chen, Pin-Yu Chen

Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This technique effectively separates approximation error from qubit count and removes the need for restrictive condition

Jun-chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma

In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log g), metallicity ([Fe/H]) and the abundance of alpha elements with respect to iron ([alpha/Fe]) for O-M-type stars within

Azmine Toushik Wasi, MD Shafikul Islam, Adipto Raihan Akib, Mahathir Mohammad Bappy

Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack