December 2024 arXiv papers — page 142

Yanzhe Lyu, Kai Cheng, Xin Kang, Xuejin Chen

Recently, 3D Gaussian Splatting (3D-GS) has prevailed in novel view synthesis, achieving high fidelity and efficiency. However, it often struggles to capture rich details and complete geometry. Our analysis reveals that the 3D-GS densification operation lacks adaptiveness and faces a dilemma between geometry coverage and detail recovery. To address this, we

Muhayy Ud Din, Jan Rosell, Waseem Akram, Isiah Zaplana

Performing complex manipulation tasks in dynamic environments requires efficient Task and Motion Planning (TAMP) approaches that combine high-level symbolic plans with low-level motion control. Advances in Large Language Models (LLMs), such as GPT-4, are transforming task planning by offering natural language as an intuitive and flexible way to describe task

Ajay Jayachandran, Stefan Mueller, Tobias Brixner

An integral procedure in every coherent multidimensional spectroscopy experiment is to suppress undesired background signals. For that purpose, one can employ a particular phase-matching geometry or phase cycling, a procedure that was adapted from nuclear magnetic resonance (NMR) spectroscopy. In optical multidimensional spectroscopy, phase cycling has been

Yotam Ashkenazy, Shmuel Balberg

The properties of the stellar cluster surrounding Sagittarius A* can be assessed indirectly through the motion of the S-stars. Specifically, the current accuracy to which the prograde precession of the S2 star is measured allows to place significant constraints on the extended mass enclosed by its orbit. We suggest that high velocity destructive collisions (

Julio Careaga, Vanja Nikolić, Belkacem Said-Houari

We investigate a nonlinear multiphysics model motivated by ultrasound-enhanced drug delivery. The acoustic pressure field is modeled by Westervelt's quasilinear wave equation to adequately capture the nonlinear effects in ultrasound propagation. The nonlocal attenuation characteristic for soft biological media is modeled by acoustic damping of the time-fract

Renaud-Alexandre Pitaval, Xiaolei Tie

5G-Advanced and likely 6G will support a new low-power wake-up signal (LP-WUS) enabling low-power devices, equipped with a complementary ultra low-power receiver to monitor wireless traffic, to completely switch off their main radio. This orthogonal frequency-division multiplexed (OFDM) signal will emulate an on-off keying (OOK) modulation to enable very low

Álvaro I. Riquelme

In various geosciences branches, including mineral exploration, geometallurgical characterization on established mining operations, and remote sensing, the regionalized input variables are spatially well-sampled across the domain of interest, limiting the scope of spatial uncertainty quantification procedures. In turn, response outcomes such as the mineral p

Yik Lung Pang, Alessio Xompero, Changjae Oh, Andrea Cavallaro

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. However, relying on hand-crafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to detect transparent objects such as drinking glasses. In this work, we propose a stereo-based metho

Nicklas Jävergård, Grigor Nika, Adrian Muntean

We offer an insight into our mathematical endeavors, which aim to advance the foundational understanding of energy systems in a broad context, encompassing facets such as charge transport, energy storage, markets, and collective behavior. Our working techniques include a combination of well-posed mathematical models (both deterministic and stochastic), mathe

Smruti Jagtap, Kanika Jadhav, Rushikesh Temkar, Minal Deshmukh

The hearing-impaired community in India deserves the access to tools that help them communicate, however, there is limited known technology solutions that make use of Indian Sign Language (ISL) at present. Even though there are many ISL users, ISL cannot access social and education arenas because there is not yet an efficient technology to convert the ISL si

Sumit Paul, Danh Lephuoc, Manfred Hauswirth

In the autonomous vehicle and self-driving paradigm, cooperative perception or exchanging sensor information among vehicles over wireless communication has added a new dimension. Generally, an autonomous vehicle is a special type of robot that requires real-time, highly reliable sensor inputs due to functional safety. Autonomous vehicles are equipped with a

Nikolaos Karaliolios

We construct cocycles in $\mathbb{T} \times SU(2)$ over Diophantine rotations that are minimal and not uniquely ergodic. Such cocycles are dense in an open subset of cocycles over the fixed Diophantine rotation. By a standard argument, they are dense in the whole set of such cocycles if the rotation satisfies a full-measure arithmetic condition.

Franco Giovenzana, Luca Giovenzana

For a general cubic fourfold $Y$ with associated Fano variety of lines $ F $, we show that the monodromy group of the finite degree 16 rational Voisin self-map $ψ\colon F \dashrightarrow F$ is maximal. To achieve this, we investigate the intriguing interplay between $ ψ$ and the fixed locus of the antisymplectic involution on the LLSvS variety $ Z $, examine

Yossi Ben-Zion, Roi Einhorn Zarzecki, Joshua Glazer, Noah D. Finkelstein

Seemingly we are not so far from Star Trek's food replicator. Generative artificial intelligence is rapidly becoming an integral part of both science and education, offering not only automation of processes but also the dynamic creation of complex, personalized content for educational purposes. With such advancement, educators are now crafting exams, buildin

Wenbo Huang, Jinghui Zhang, Guang Li, Lei Zhang

In few-shot action recognition (FSAR), long sub-sequences of video naturally express entire actions more effectively. However, the high computational complexity of mainstream Transformer-based methods limits their application. Recent Mamba demonstrates efficiency in modeling long sequences, but directly applying Mamba to FSAR overlooks the importance of loca

Alexandra Müller, Tobias Schesny, Ben Steinfurth, Julien Weiss

Reinforcement learning is employed to optimize the periodic forcing signal of a pulsed blowing system that controls flow separation in a fully-turbulent $Re_\theta = 1000$ diffuser flow. Based on the state of the wind tunnel experiment that is determined with wall shear-stress measurements, Proximal Policy Optimization is used to iteratively adjust the forci

Chris D. White, Martin J. White

In recent years, there has been increasing collaboration between the fields of quantum computing and high energy physics, including using LHC processes such as top (anti-)quark pair production to perform high energy tests of quantum entanglement. In this proceeding, I will review another interesting property from quantum computing ("magic"), that is needed t

Weiwei Xu, Weijie Shen, Zheng-Jian Bai

The generalized singular value decomposition (GSVD) is a powerful tool for solving discrete ill-posed problems. In this paper, we propose a two-sided uniformly randomized GSVD algorithm for solving the large-scale discrete ill-posed problem with the general Tikhonov regularization. Based on two-sided uniform random sampling, the proposed algorithm can improv

Yuki Kadokawa, Hirotaka Tahara, Takamitsu Matsubara

In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving autonomous excavation using reinforcement learning (RL) through a rock excavation simulator. In the simulation, resolution can be defined by the particle size/number in the whole

Simon Vialaret

In contact geometry, a systolic inequality is a uniform upper bound on the shortest period of a closed Reeb orbit, in terms of the contact volume. We prove a general systolic inequality valid on Seifert bundles with non-zero Euler number for all contact forms that are invariant under the underlying circle action.

Nathanael Arkor, John Bourke, Joanna Ko

Many structures of interest in two-dimensional category theory have aspects that are inherently strict. This strictness is not a limitation, but rather plays a fundamental role in the theory of such structures. For instance, a monoidal fibration is - crucially - a strict monoidal functor, rather than a pseudo or lax monoidal functor. Other examples include m

Romain A. Meyer, Bram Venemans, Marcel Neeleman, Roberto Decarli

We present $0."026$ $(140\ \rm{pc})$ resolution ALMA observations of [C II] $158\ \mu\rm{m}$ and dust continuum emission of the $z=6.6$ quasar J0305--3150, resolved over $\sim 300-400$ independent resolution elements. The dust continuum emission is compact with $\sim 80\%$ recovered within $r<0."3$ $(1.6\ \rm{kpc})$, whereas the [C II] emission profile is co

Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Rongxiang Weng

Despite being empowered with alignment mechanisms, large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks that can compromise their alignment mechanisms. This vulnerability poses significant risks to real-world applications. Existing work faces challenges in both training efficiency and generalization capabilities (i.e., Reinf

Matthias Goy, Jan Krause, Ömer Bayraktar, Philippe Ancsin

This paper presents the development and implementation of a versatile ad-hoc metropolitan-range Quantum Key Distribution (QKD) network. The approach presented integrates various types of physical channels and QKD protocols, and a mix of trusted and untrusted nodes. Unlike conventional QKD networks that predominantly depend on either fiber-based or free-space

Jiaqi Zhang, Chen Gao, Liyuan Zhang, Yong Li

Recent advances in embodied agents with multimodal perception and reasoning capabilities based on large vision-language models (LVLMs), excel in autonomously interacting either real or cyber worlds, helping people make intelligent decisions in complex environments. However, the current works are normally optimized by golden action trajectories or ideal task-

Linna Xu, Yongli Zhu

As a key component of power system production simulation, load forecasting is critical for the stable operation of power systems. Machine learning methods prevail in this field. However, the limited training data can be a challenge. This paper proposes a generative model-assisted approach for load forecasting under small sample scenarios, consisting of two s

Sen Kong

This study explores the design of a memory-based dynamic event-triggered mechanisms (DETM) scheme for heterogeneous multi-agent systems (MASs) characterized by interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy models. To address the complex nonlinear uncertainties inherent in such systems, discrete IT2 T-S fuzzy models are employed to accurately capture system d

Loriano Bonora, Stefano G. Giaccari

This ia a review/research paper on anomalies applied to a bottom-up approach to standard model and gravity. It is divided in two parts. The first consists in a review proper of anomalies in quantum field theories. Anomalies are analyzed according to three different methods: a perturbative one based on Feynman diagram, a non-perturbative one relying on the Sc

Hao Chen, Kai Yi

Causal discovery is a crucial initial step in establishing causality from empirical data and background knowledge. Numerous algorithms have been developed for this purpose. Among them, the score-matching method has demonstrated superior performance across various evaluation metrics, particularly for the commonly encountered Additive Nonlinear Causal Models.

Kai Yuan, Jiahao Zhang, Yidi Wang, Xiaobing Pei

Adversarial attacks on Graph Neural Networks aim to perturb the performance of the learner by carefully modifying the graph topology and node attributes. Existing methods achieve attack stealthiness by constraining the modification budget and differences in graph properties. However, these methods typically disrupt task-relevant primary semantics directly, w

Eduardo Fernández, David T. Gay, Daniel Hartman, Danica Kosanović

We show that the fundamental group of framed circles in $S^1 \times D^3$ injects into the fundamental group of framed spheres in $S^2\times D^2$, so that the cokernel is the fundamental group of framed neat disks in $D^4$. In particular, grasper families of circles give rise to countably many nontrivial families of spheres. Ambient extensions of either of th

Seisho Abe

Understanding nuclear effects is essential for improving the sensitivity of neutrino oscillation measurements. Validating nuclear models solely through neutrino scattering data is challenging due to limited statistics and the broad energy spectrum of neutrinos. In contrast, electron scattering experiments provide abundant high-precision data with various mon

Alexander Meaney, Mikael A. K. Brix, Miika T. Nieminen, Samuli Siltanen

Total variation (TV) regularization is a popular reconstruction method for ill-posed imaging problems, and particularly useful for applications with piecewise constant targets. However, using TV for medical cone-beam computed X-ray tomography (CBCT) has been limited so far, mainly due to heavy computational loads at clinically relevant 3D resolutions and the

Koen van Greevenbroek, Johannes Schmidt, Marianne Zeyringer, Alexander Horsch

The EU targets 10 Mt of green hydrogen production by 2030, but has not committed to targets for 2040. Green hydrogen competes with carbon capture and storage, biomass and imports in reaching emissions reductions; earlier studies have demonstrated the great uncertainty in future cost-optimal development of green hydrogen. In spite of this, we show that Europe

Fei Hou, Fei Tao, Huicheng Yin

In our previous paper [Fei Hou, Fei Tao, Huicheng Yin, Global existence and scattering of small data smooth solutions to a class of quasilinear wave systems on $\mathbb{R}^2\times\mathbb{T}$, Preprint (2024), arXiv:2405.03242], for the $Q_0$-type quadratic nonlinearities, we have shown the global well-posedness and scattering properties of small data smooth

Ehsan Lotfi, Nikolay Banar, Nerses Yuzbashyan, Walter Daelemans

Statutory article retrieval plays a crucial role in making legal information more accessible to both laypeople and legal professionals. Multilingual countries like Belgium present unique challenges for retrieval models due to the need for handling legal issues in multiple languages. Building on the Belgian Statutory Article Retrieval Dataset (BSARD) in Frenc

Sławomir Kuźmar, Beata Woźniak-Jęchorek, David Bole

This study assesses how the growing availability of working from home (WFH) shapes office employees' preferences to move to the suburbs and pinpoints the socio-economic factors that drive those intentions. We focus on Poland, where the housing market is shaped by exceptionally high home-ownership rates and specific suburbanisation patterns. We surveyed city-

Karol Palka, Tomasz Pełka

Let $(X,D)$ be an open log del Pezzo surface of rank one, that is, $X$ is a normal projective surface of Picard rank one, the boundary $D$ is a reduced nonzero divisor on $X$, and the anti-log canonical divisor $-(K_X+D)$ is ample. We show that, up to well described exceptions in characteristics 2, 3 and 5, the smooth part of $X\setminus D$ admits an $\mathb

Brian L Burrows

The time-dependent Schrodinger equation is solved for two model problems for a non-Hermitian quantum system.A simple matrix model system is used to examine two critical problems for these systems: complex and non-observable energies and situations where the matrix is defective. In addition the stationary states for infinite dimensional model system, which is

Jonas Schulte-Sasse, Ben Steinfurth, Julien Weiss

Oil-flow visualizations represent a simple means to reveal time-averaged wall streamline patterns. Yet, the evaluation of such images can be a time-consuming process and is subjective to human perception. In this study, we present a fast and robust method to obtain quantitative insight based on qualitative oil-flow visualizations. Using a convolutional neura

Janina C. Letz, Julia Sauter

Let $\mathcal{T}$ be an algebraic triangulated category and $\mathcal{C}$ an extension-closed subcategory with $\operatorname{Hom}(\mathcal{C}, \Sigma^{<0} \mathcal{C})=0$. Then $\mathcal{C}$ has an exact structure induced from exact triangles in $\mathcal{T}$. Keller and Vossieck say that there exists a triangle functor $\operatorname{D}^b(\mathcal{C}) \to

Kichang Lee, Jaeho Jin, JaeYeon Park, Songkuk Kim

Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing solutions often address these issues separately, sacrificing either system robustness or model

Giuseppe Nisticò

The present work proposes an alternative approach to the problem of the emergence of classicality. Typical approaches developed in the literature derive the classical behaviour of a quantum system from conditions that concern the value of the parameters deemed responsible of non-classicality, like Planck constant. Our first step in addressing the problem is

Yuxing Deng, Yuehan Hao

In this paper, we study the fundamental group of the complete steady gradient Ricci soliton with nonnegative sectional curvature. We prove that the fundamental group of such a Ricci soliton is either trivial or infinite. As a corollary, we show that an $n$-dimensional complete $\kappa$-noncollapsed steady gradient Ricci soliton with nonnegative sectional cur

Yaorui Shi, Sihang Li, Taiyan Zhang, Xi Fang

Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven processes. However, molecules generated by artificial intelligence may unintentionally infringe on existing patents, posing legal and financial risks that impede the full automation o

Sergey A. Koryagin, Mikhail E. Viktorov

The compact laboratory stand ``Solar Wind'' (Inst. Appl. Phys. of Russ. Acad. Sci.) forms an arch structure of the coronal loop type, in which the plasma pressure varies from zero to values of the order of and above the magnetic pressure. The arc discharge in each of the bases of the magnetic tube creates a plasma that is characterized by a significantly hig

Simon Garhofer, Oliver Bringmann

The Quantum Approximate Optimization Algorithm (QAOA) and its derived variants are widely in use for approximating combinatorial optimization problem instances on gate-based Noisy Intermediate Scale Quantum (NISQ) computers. Commonly, circuits required for QAOA are constructed by first reformulating a given problem as a Quadratic Unconstrained Binary Optimiz

Zhi-Xiang Jin, Yuan-Hong Tao, Bing Yu, Shao-Ming Fei

In the quantitative theory of quantum coherence, the amount of coherence for given states can be meaningfully discussed only when referring to a preferred basis. One of the objections to this quantification is that the amount of coherence is an intrinsically basis-dependent quantity. This limitation can, however, be lifted when considering a set of quantum s

Jinwu Hu, Yufeng Wang, Shuhai Zhang, Kai Zhou

LLMs have demonstrated impressive performance across various language tasks. However, the strengths of LLMs can vary due to different architectures, model sizes, areas of training data, etc. Therefore, ensemble reasoning for the strengths of different LLM experts is critical to achieving consistent and satisfactory performance on diverse inputs across a wide

Jayita Dutta, Rui Chen, Virat Tara, Arka MAjumdar

Programmable photonic integrated circuits are expected to play an increasingly important role to enable high-bandwidth optical interconnects, and large-scale in-memory computing as needed to support the rise of artificial intelligence and machine learning technology. To that end, chalcogenide-based non-volatile phase-change materials (PCMs) present a promisi

Raanan Y. Rohekar, Yaniv Gurwicz, Sungduk Yu, Estelle Aflalo

Are generative pre-trained transformer (GPT) models, trained only to predict the next token, implicitly learning a world model from which sequences are generated one token at a time? We address this question by deriving a causal interpretation of the attention mechanism in GPT and presenting a causal world model that arises from this interpretation. Furtherm

Marek Tiits, Tarmo Kalvet, David McBee

This paper addresses critical questions surrounding the security of government-issued identity documents and their potential misuse, with an emphasis on understanding the perspectives of ordinary citizens across Europe and the United States of America. Drawing upon research on technology acceptance and diffusion, the research focuses on understanding the fac

Omar H. Khater, Basem Almadani, Farouq Aliyu, Esam Al-Nahari

Internet of Things (IoT)-based healthcare systems offer significant potential for improving healthcare delivery in humanitarian and resource-constrained environments, providing essential services to underserved populations in remote areas. However, limited network infrastructure in such regions makes reliable communication challenging for traditional IoT sys

Diederick Vermetten, Jeroen Rook, Oliver L. Preuß, Jacob de Nobel

Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anytime behavior of an algorithm can provide valuable insights for further developments. In the context of multi-objective optimization, this anytime perspective is not as widely adopte

Malavika Kayyil Veedu, Gemma Lavilley, Mohamadou Sy, Joan Goetz

Lanthanide nanoparticles (LnNPs) feature sharp emission lines together with millisecond emission lifetimes which makes them promising luminescent probes for biosensing and bioimaging. Although LnNPs are gathering a large interest, their photoluminescence properties at the single nanoparticle level remain largely unexplored. Here, we employ fluorescence corre

Natalya Weber, Christian Guckelsberger, Tom Froese

The Self-Optimization (SO) model can be considered as the third operational mode of the classical Hopfield Network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of artificial life. In this article, we draw attent

S. Borodachov, P. Boyvalenkov, P. Dragnev, D. Hardin

We establish upper and lower universal bounds for potentials of weighted designs on the sphere $\mathbb{S}^{n-1}$ that depend only on quadrature nodes and weights derived from the design structure. Our bounds hold for a large class of potentials that includes absolutely monotone functions. The classes of spherical designs attaining these bounds are character

Weihua Liu, Said Boumaraf, Jianwu Li, Chaochao Lin

Natural gradient descent (NGD) is a powerful optimization technique for machine learning, but the computational complexity of the inverse Fisher information matrix limits its application in training deep neural networks. To overcome this challenge, we propose a novel optimization method for training deep neural networks called structured natural gradient des

Ritwik Dhara, Shyamal Guchhait, Meghna Sarkar, Swain Ashutosh

Quantum weak measurements became extremely popular in classical optics to amplify small optical signals for fundamental interests and potential applications. Later, a more general extension, joint weak measurement has been proposed to extract weak value from a joint quantum measurement. However, the detection of joint weak value in the realm of classical opt

Rama Rawat, Haripada Roy

We establish the following fractional Hardy's inequality $$\int_{\mathbb{H}^n_+}\frac{|f(\xi)|^p}{x_1^{sp}|z|^\alpha}d\xi\leq C\int_{\mathbb{H}^n_+}\int_{\mathbb{H}^n_+}\frac{|f(\xi)-f(\xi')|^p}{d({\xi}^{-1}\circ \xi')^{Q+sp}|z'-z|^\alpha}d\xi'd\xi,\ \ \forall\,f\in C_c(\mathbb{H}^n_+)$$ for the half space $\mathbb{H}^n_+:=\{\xi=(z,t)=(x_1,x_2,\ldots, x_n, y

Fabio Bagagiolo, Cristina Giannotti, Andrea Spiro, Marta Zoppello

We give two proofs of the Kalman Theorem, alternative to the most common ones, which infer such a classical result of Control Theory using just very basic facts on flows of vector fields. These proofs are apt to be generalised in diverse directions -- in fact one of them has been already generalised, yielding new criteria for local controllability of non-lin

Siyaxolisa Kabane

Credit card fraud detection remains a critical challenge in financial security, with machine learning models like XGBoost(eXtreme gradient boosting) emerging as powerful tools for identifying fraudulent transactions. However, the inherent class imbalance in credit card transaction datasets poses significant challenges for model performance. Although sampling

Juan Sebastian Herrera-Carmona, Cristian Ortiz, James Waldron

We introduce and study module structures on both the dgla of multiplicative vector fields and the graded algebra of functions on Lie groupoids. We show that there is an associated structure of a graded Lie-Rinehart algebra on the vector fields of a differentiable stack over its smooth functions that is Morita invariant in an appropriate sense. Furthermore, w

Huanjian Zhou, Masashi Sugiyama

Sampling from high-dimensional probability distributions is fundamental in machine learning and statistics. As datasets grow larger, computational efficiency becomes increasingly important, particularly in reducing adaptive complexity, namely the number of sequential rounds required for sampling algorithms. While recent works have introduced several parallel

Maien Binjonaid

The broken phase of the Next-to two-Higgs-doublet model (N2HDM) constitutes an archetype of extended Higgs sectors. In the presence of a softly-broken $\mathrm{Z}_2$ symmetry throughout the scalar and Yukawa sectors, as the additional gauge singlet field does not interact with fermions, the model admits four variants of Yukawa interactions between the double

A. A. A. Limburg, T. E. W. Keur, R. F. E. Pleijers, S. Nijdam

The electric field is the driving force behind every plasma. Electric field induced second harmonic generation (E-FISH) is a diagnostic able to obtain the electric field with high temporal and spatial resolution, is considered non-invasive and can be applied to almost any type of plasma with high sensitivity. However, the high power laser beam used as a prob

Daniil Fedotov, Sergei Nechaev

We consider the orientational diffusion controlled by the hyperspherical Laplacian, $\nabla^2_D$, on the surface of the $D$--dimensional hypersphere in the limit $D \to \infty$. We find that for stretched paths with lengths relatively short compared to the hypersphere's radius, the finite-size corrections in orientational correlations are controlled by the K

Weihua Liu, Jianhua Qiu, Said Boumaraf, Chaochao lin

In response to the growing threat of deepfake technology, we introduce BENet, a Cross-Domain Robust Bias Expansion Network. BENet enhances the detection of fake faces by addressing limitations in current detectors related to variations across different types of fake face generation techniques, where ``cross-domain" refers to the diverse range of these deepfa

Kinshuk Vasisht, Navreet Kaur, Danish Pruthi

To deploy language models safely, it is crucial that they abstain from responding to inappropriate requests. Several prior studies test the safety promises of models based on their effectiveness in blocking malicious requests. In this work, we focus on evaluating the underlying techniques that cause models to abstain. We create SELECT, a benchmark derived fr

Javad Seraj, Mohammad Mahdi Mohajeri, Mohammad Javad Dousti, Majid Nili Ahmadabadi

Automatic evaluation by large language models (LLMs) is a prominent topic today; however, judgment and evaluation tasks are often subjective and influenced by various factors, making adaptation challenging. While many studies demonstrate the capabilities of state-of-the-art proprietary LLMs in comparison to human evaluators, they often struggle to adapt to r

Xuhui Zhang, Wenchao Liu, Jinke Ren, Huijun Xing

Federated learning (FL) has become a transformative paradigm for distributed machine learning across wireless networks. However, the performance of FL is often hindered by the unreliable communication links between resource-constrained Internet of Things (IoT) devices and the central server. To overcome this challenge, we propose a novel framework that emplo

Jordi De Jonghe, Samrat Sen

In the solar corona, magnetically sheared structures are unstable to both tearing and thermal instabilities in a coupled fashion. However, how the choice of linear perturbation modes influences the time-scale to achieve the thermal runaway in a coupled tearing-thermal coronal current sheet is not well understood to date. Here, we model a force-free Harris cu

Markus Uhlmann, Jos Derksen, Anthony Wachs, Lian-Ping Wang

In the present chapter we focus on the fundamentals of non-grid-conforming numerical approaches to simulating particulate flows, implementation issues and grid convergence vs. available reference data. The main idea is to avoid adapting the mesh (and - as much as possible - the discrete operators) to the time-dependent fluid domain with the aim to maximize c

Samira Elghaayda, Asad Ali, M. Y. Abd-Rabbou, Mostafa Mansour

A long-standing debate on Gibbons-Hawking (GH) decoherence centers on its unclear thermal nature. In this work, we investigate the robustness of quantum Fisher information (QFI) and local quantum uncertainty (LQU) in the presence of GH decoherence, using free-falling Unruh-DeWitt (UDW) detectors in de Sitter spacetime (dS-ST). The UDW detectors interact with

Denis N. Bulanov, Efim A. Khazanov, Andrey A. Shaykin, Artem V. Korzhimanov

A programming library was developed, based on Stratton-Chu diffraction integrals for calculating reflected optical fields. Dipole-type focusing schemes with tunable number of beams and mirror placements were studied, considering the influence of phase distortion and aberrations. The intensity above $3\times 10^{26}$ W/cm$^2$ was found theoretically attainabl

Kush Kumar Dewangan, Srinivas Rao S, Durbar Roy, Atish Roy Chowdhury

We study evaporation and precipitate formation mechanics of bacteria-laden liquid bridge using experimental and theoretical analysis. Aqueous suspension of motile and non-motile Salmonella Typhimurium and Pseudomonas aeruginosa typically found in contaminated food and water were used in liquid bridge configuration between hydrophilic substrates. Using invers

Mohammad Moein, Mohammadreza Molavi Hajiagha, Abdolali Faraji, Mohammadreza Tavakoli

While Online Learning is growing and becoming widespread, the associated curricula often suffer from a lack of coverage and outdated content. In this regard, a key question is how to dynamically define the topics that must be covered to thoroughly learn a subject (e.g., a course). Large Language Models (LLMs) are considered candidates that can be used to add

Aikaterini A. Anagnostopoulou, Dimitris G. Sotiropoulos, Konstantinos I. Tserpes

The paper presents a cradle-to-gate sustainability assessment methodology specifically designed to evaluate aircraft components in a robust and systematic manner. This methodology integrates multi-criteria decision-making (MCDM) analysis across ten criteria, categorized under environmental impact, cost, and performance. Environmental impact is analyzed throu

Philipp Christmann, Gerhard Weikum

This article presents the QUASAR system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. The system adopts a RAG-based architecture, with a pipeline of evidence retrieval followed by answer generation, with the latter powered by a moderate-sized language model. Additionally and uni

Philippe Blache, Emmanuele Chersoni, Giulia Rambelli, Alessandro Lenci

The mechanisms of comprehension during language processing remains an open question. Classically, building the meaning of a linguistic utterance is said to be incremental, step-by-step, based on a compositional process. However, many different works have shown for a long time that non-compositional phenomena are also at work. It is therefore necessary to pro

Maximilian Berbig

Motivated by the hint for time-dependent dynamical dark energy from an analysis of the DESI Baryon Accoustic Oscillation (BAO) data together with information from the Cosmic Microwave Background (CMB) and Supernovae (SN), we relax the assumption of a vanishing initial velocity for a quintessence field. In particular we focus on pseudo-Nambu-Goldstone-Boson (

Matias Koivurova, Rajneesh Joshi

We study the connection between cross-spectral purity and spatiotemporal separability of nonstationary (pulsed) scalar fields. It is found that in the case of complete coherence, there is a two-way relation between global cross-spectral purity and spatiotemporal separability of the field. Moreover, we show that cross-spectral purity generalizes the notion of

Anuj Nandi, Swapnil Singh, Bhavesh Jaiswal, Anand Jain

SHAPE (Spectro-polarimetry of HAbitable Planet Earth) is an experiment onboard the Chandrayaan-3 Mission, designed to study the spectro-polarimetric signatures of the habitable planet Earth in the near-infrared (NIR) wavelength range (1.0 - 1.7 $\mu$m). The spectro-polarimeter is the only scientific payload (experimental in nature) on the Propulsion Module (

Ashutosh Singh, Khushdeep Singh, Amit Kumar, Abhishek Shrivastava

In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various mac

Ai-Wei Guan, Chuan-Fu Yang, Natalia P. Bondarenko

In this paper, we study an inverse spectral problem for the fourth-order differential equation $y^{(4)} - (p y')' + q y = \lambda y$ with real-valued coefficients $p$ and $q$ of $L^2(0,1)$. We prove that, for near-constant coefficients, the two spectra corresponding to the Dirichlet and the Dirichlet-Neumann boundary conditions uniquely determine either $p$

Ahan Bhatt, Nandan Vaghela, Kush Dudhia

Knowledge Graphs (KGs) are essential for the functionality of GraphRAGs, a form of Retrieval-Augmented Generative Systems (RAGs) that excel in tasks requiring structured reasoning and semantic understanding. However, creating KGs for GraphRAGs remains a significant challenge due to accuracy and scalability limitations of traditional methods. This paper intro

Gayathri Dandugula, Santhosh Boddana, Sudesh Mirashi

Deploying radar object detection models on resource-constrained edge devices like the Raspberry Pi poses significant challenges due to the large size of the model and the limited computational power and the memory of the Pi. In this work, we explore the efficiency of Depthwise Separable Convolutions in radar object detection networks and integrate them into

Meihao Fan, Ju Fan, Nan Tang, Lei Cao

Answering natural language (NL) questions about tables, known as Tabular Question Answering (TQA), is crucial because it allows users to quickly and efficiently extract meaningful insights from structured data, effectively bridging the gap between human language and machine-readable formats. Many of these tables are derived from web sources or real-world sce

Manish Kumar, Mateusz Wasilewski

We study the KMS states on local quantum Cuntz-Krieger algebras associated to quantum graphs. Using their isomorphism to the Cuntz-Pimsner algebra of the quantum edge correspondence, we show that the general criteria for KMS states can be translated into statements about the underlying quantum adjacency operator, somewhat analogously to the case of classical

Harsha Sreekumar, E. Harikumar

We construct and analyse wormhole solutions in quantised space-time. The field equations are constructed from the deformed wormhole metric in the proper reference frame using tetrads. The spatial geometry of the wormhole is analysed in the kappa space-time. Further, the modifications to the conditions that ensure traversibility of the wormhole are studied an

Jihen Amara, Birgitta König-Ries, Sheeba Samuel

While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable disease detection. In this study, we apply the Automated Concept-based Explanation (ACE) method to plant disease classification using the widely adopted InceptionV3 model and the Plan

Billy Joe Franks, Moshe Eliasof, Semih Cantürk, Guy Wolf

Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced their performance across various graph learning tasks. However, the general applicability of these encodings and their potential to serve as foundational representations for graphs remain uncertain. This paper investigates t

Sudha Krishnamurthy

We propose a novel self-supervised approach for learning audio and visual representations from unlabeled videos, based on their correspondence. The approach uses an attention mechanism to learn the relative importance of convolutional features extracted at different resolutions from the audio and visual streams and uses the attention features to encode the a

Yufei Ma, Zihan Liang, Huangyu Dai, Ben Chen

The growing demand for larger-scale models in the development of \textbf{L}arge \textbf{L}anguage \textbf{M}odels (LLMs) poses challenges for efficient training within limited computational resources. Traditional fine-tuning methods often exhibit instability in multi-task learning and rely heavily on extensive training resources. Here, we propose MoDULA (\te

Iman Mohammed Attia

In the present paper, Probability weighted moments (PWMs) method for parameter estimation of the median based unit weibull (MBUW) distribution is discussed. The most widely used first order PWMs is compared with the higher order PWMs for parameter estimation of (MBUW) distribution. Asymptotic distribution of this PWM estimator is derived. This comparison is

Dilina Chandika Rajapakse, Douglas Leith

We introduce a new sequential transformer reinforcement learning architecture RLT4Rec and demonstrate that it achieves excellent performance in a range of item recommendation tasks. RLT4Rec uses a relatively simple transformer architecture that takes as input the user's (item,rating) history and outputs the next item to present to the user. Unlike existing R

Yunming Hui, Shihan Wang, Melisachew Wudage Chekol, Stevan Rudinac

The influence maximization (IM) problem involves identifying a set of key individuals in a social network who can maximize the spread of influence through their network connections. With the advent of geometric deep learning on graphs, great progress has been made towards better solutions for the IM problem. In this paper, we focus on the dynamic non-progres

Xuanxuan Yang, Yangming Zhang, Haofeng Chen, Gang Ma

Electrical Impedance Tomography (EIT) is a promising noninvasive imaging technique that reconstructs the spatial conductivity distribution from boundary voltage measurements. However, it poses a highly nonlinear and ill-posed inverse problem. Traditional regularization-based methods are sensitive to noise and often produce significant artifacts. Physics-Embe

Haiyang Peng, Deren Han, Linbin Li, Meng Huang

This paper aims to address the phase retrieval problem from subgaussian measurements with arbitrary noise, with a focus on devising robust and efficient algorithms for solving non-convex problems. To ensure uniqueness of solutions in the subgaussian setting, we explore two commonly used assumptions: either the subgaussian measurements satisfy a fourth-moment

Naoya Egawa, Kaoru Mizuta, Joji Nasu

Discrete time crystals (DTCs) have emerged as novel nonequilibrium phases of matter that spontaneously break discrete time-translation symmetry in periodically driven systems. Rigorous experimental validation of DTCs, which requires highly controllable quantum simulators, has stimulated extensive research across diverse fields in condensed matter physics and