November 2024 arXiv papers — page 79

Miloslav Znojil

In spite of its unbroken ${\cal PT}-$symmetry, the popular imaginary cubic oscillator Hamiltonian $H^{(IC)}=p^2+{\rm i}x^3$ does not satisfy all of the necessary postulates of quantum mechanics. The failure is due to the ``intrinsic exceptional point'' (IEP) features of $H^{(IC)}$ and, in particular, to the phenomenon of a high-energy asymptotic parallelizat

Cédric Bonnafé

Adapting methods of previous papers by A. Sarti and the author, we construct K3 surfaces from invariants of the Weyl group of type $\Erm_6$. We study in details one of these surfaces, which turns out to have Picard number $20$: for this example, we describe an elliptic fibration (and its singular fibers), the Picard lattice and the transcendental lattice.

Natalie Matchett, Brian van Soelen

The gamma-ray binary HESS J0632+057 consists of a Be star and an undetected compact object in a $\sim$317 day orbit. The interpretation of the emission from this system is complicated by the lack of a clear orbital solution, as two different and incompatible orbital solutions were obtained by previous radial velocity studies of this source. In order to addre

Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa

Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning. To address this, we propose $\textbf{Additional Logic Training (ALT)}$, which aims to enhance LLMs' reasoning capabilities by program-generated logical reasoning samples. We first establish principles for designing high-quality samples by integr

Sanskriti Agrawal, Raktim Abir

We have shown that the small-$x$ evolution of the off-forward leading-log dipole scattering amplitudes, both pomeron and odderon, in the momentum space can be completely determined by the evolution of the respective forward amplitudes, with rescaled momenta. In position space, if there is translation symmetry (assumption of a large nucleus), the dipole cross

Debashree Chowdhury

The Fermi surface topology of a triple non-hermitian (NH) Weyl semimetal (WSM) driven by bi-circularly polarized light is presented in this study. A NH WSM in particular has remarkable outlines. Bi-circular light, however, modifies the symmetry features of non-hermitian triple Weyl and causes an unusual new kind of band swapping. We observe swapping between

Holger Gies, Felix Karbstein, Lars Maiwald

Laser pulse collisions are a promising tool for the investigation of light-by-light scattering phenomena induced by quantum vacuum fluctuations. Using the numerical code based on the vacuum emission picture and put forward in Blinne et al. (2019), we observe a strong dependence of the signal features on the transverse profiles of the colliding laser pulses i

Igor Podlubny

This discussion paper presents some parts of the work in progress. It is shown that G.W. Leibniz was the first who raised the question about geometric interpretation of fractional-order operators. Geometric interpretations of the Riemann--Liouville fractional integral and the Stieltjes integral are explained. Then, for the first time, a geometric interpretat

Hubert Plisiecki

This paper introduces the Semantic Propagation Graph Neural Network (SProp GNN), a machine learning sentiment analysis (SA) architecture that relies exclusively on syntactic structures and word-level emotional cues to predict emotions in text. By semantically blinding the model to information about specific words, it is robust to social biases such as politi

Elena Ancona, Roman Ya. Kezerashvili

The 6th International Symposium on Space Sailing (ISSS 2023) took place on June 5-9, 2023 at the New York City College of Technology, the City University of New York. Since its inauguration in Herrsching (Germany, 2007), the ISSS has been held in New York (USA, 2010), Glasgow (UK, 2013), Kyoto (Japan, 2017) and Aachen (Germany, 2019). During the five-day sym

Michael Zingale, Khanak Bhargava, Ryan Brady, Zhi Chen

Stellar evolution is driven by the changing composition of a star from nuclear reactions. At the late stages of evolution and during explosive events, the timescale can be short and drive strong hydrodynamic flows, making simulations of astrophysical reacting flows challenging. Over the past decades, the standard approach to modeling reactions in simulation

Eva Weiler, Christian Möstl, Emma E. Davies, Astrid Veronig

Forecasting the geomagnetic effects of solar coronal mass ejections (CMEs) is currently an unsolved problem. CMEs, responsible for the largest values of the north-south component of the interplanetary magnetic field, are the key driver of intense and extreme geomagnetic activity. Observations of southward interplanetary magnetic fields are currently only acc

Tommaso Nottoli, Mattia Bondanza, Filippo Lipparini, Benedetta Mennucci

We present a polarizable embedding quantum mechanics/molecular mechanics (QM/MM) framework for ground- and excited-state Complete Active Space Self-Consistent Field (CASSCF) calculations on molecules within complex environments, such as biological systems. These environments are modeled using the AMOEBA polarizable force field. This approach is implemented b

Rahul Chand, Ashutosh Shukla, Sneha Boby, G V Pavan Kumar

Artificial soft matter systems have appeared as important tools to harness mechanical motion for microscale manipulation. Typically, this motion is driven either by the external fields or by mutual interaction between the colloids. In the latter scenario, dynamics arise from non-reciprocal interaction among colloids within a chemical environment. In contrast

Marta A. Chabowska, Hristina Popova, Magdalena A. Załuska-Kotur

This study presents a comprehensive and innovative exploration of how the surface potential energy landscape influences meander formation. Using the Vicinal Cellular Automaton model, which distinguishes surface diffusion from adatom incorporation into the crystal, the research delves into various factors affecting surface pattern dynamics. By isolating the d

Sara Memarzadeh, Mateusz Gołębiewski, Maciej Krawczyk, Jarosław W. Kłos

This study investigates the nucleation, dynamics, and stationary configurations of Abrikosov vortices in hybrid superconductor-ferromagnetic nanostructures exposed to inhomogeneous magnetic fields generated by a ferromagnetic nanodot. Using time-dependent Ginzburg-Landau simulations and Maxwell's equations, we observe and provide an explanation for the evolu

Sebastiano Corli, Enrico Prati

The measurement-based architecture is a paradigm of quantum computing, relying on the entanglement of a cluster of qubits and the measurements of a subset of it, conditioning the state of the unmeasured output qubits. While methods to map the gate model circuits into the measurement-based are already available via intermediate steps, we introduce a new parad

Sean Papay, Roman Klinger, Sebastian Pado

While LLMs have grown popular in sequence labeling, linear-chain conditional random fields (CRFs) remain a popular alternative with the ability to directly model interactions between labels. However, the Markov assumption limits them to % only directly modeling interactions between adjacent labels. Weighted finite-state transducers (FSTs), in contrast, can m

Marc Roig Vilamala, Jack Furby, Julian de Gortari Briseno, Mani Srivastava

Effective communication is essential in collaborative tasks, so AI-equipped robots working alongside humans need to be able to explain their behaviour in order to cooperate effectively and earn trust. We analyse and classify communications among human participants collaborating to complete a simulated emergency response task. The analysis identifies messages

Azar C. Nakhl, Ben Harper, Maxwell West, Neil Dowling

This work augments the recently introduced Stabilizer Tensor Network (STN) protocol with magic state injection, reporting a new framework with significantly enhanced ability to simulate circuits with an extensive number of non-Clifford operations. Specifically, for random $T$-doped $N$-qubit Clifford circuits the computational cost of circuits prepared with

Xuan Wang, Weikai Zong, Xiao-Yu Ma, Stéphane Charpinet

As mixed with real pulsations, the reflection of super-Nyquist frequencies (SNFs) pose a threat to asteroseismic properties. Although SNFs have been studied in several pulsating stars, a systematic survey remains scarcely explored. Here we propose a method to identify SNFs from Kepler and TESS photometry by characterizing their periodic frequency modulations

Janik Pinter, Frederik Zahn, Maximilian Beichter, Ralf Mikut

The increasing penetration of renewable energy sources introduces significant challenges to power grid stability, primarily due to their inherent variability. A new opportunity for grid operation is the smart integration of electricity production combined with battery storages in residential buildings. This study explores how residential battery systems can

Peili Li, Zhuomei Li, Yunhai Xiao, Chao Ying

Sparse linear regression is one of the classic problems in the field of statistics, which has deep connections and high intersections with optimization, computation, and machine learning. To address the effective handling of high-dimensional data, the diversity of real noise, and the challenges in estimating standard deviation of the noise, we propose a nove

Shuangyi Wang, Haichuan Lin, Yiping Xie, Ziqi Wang

Transcatheter tricuspid valve replacement (TTVR) is the latest treatment for tricuspid regurgitation and is in the early stages of clinical adoption. Intelligent robotic approaches are expected to overcome the challenges of surgical manipulation and widespread dissemination, but systems and protocols with high clinical utility have not yet been reported. In

Tathagata Basu, Matthias C. M. Troffaes

Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment. In a regressional framework, we assign a treatment and outcome model to estimate the average causal effect. Additionally, for

Przemysław Stokłosa, Janusz A. Starzyk, Paweł Raif, Adrian Horzyk

The paper addresses challenges in storing and retrieving sequences in contexts like anomaly detection, behavior prediction, and genetic information analysis. Associative Knowledge Graphs (AKGs) offer a promising approach by leveraging sparse graph structures to encode sequences. The objective was to develop a method for sequence storage and retrieval using A

Natalia Koliou, Tatiana Boura, Stasinos Konstantopoulos, George Meramveliotakis

What sets timeseries analysis apart from other machine learning exercises is that time representation becomes a primary aspect of the experiment setup, as it must adequately represent the temporal relations that are relevant for the application at hand. In the work described here we study wo different variations of the Transformer architecture: one where we

Mohamed Elhamdadi, Jan Kim

We prove that if a conjugation quandle is Hopfian, then its underlying group is also Hopfian. We also show that the converse does not hold by providing an example. This highlights a distinction between conjugation quandles and their underlying groups. While a recent result shows that every hyperbolic group is Hopfian, conjugation quandles of hyperbolic group

Sahar Sadrizadeh, César Descalzo, Ljiljana Dolamic, Pascal Frossard

Neural Machine Translation systems are used in diverse applications due to their impressive performance. However, recent studies have shown that these systems are vulnerable to carefully crafted small perturbations to their inputs, known as adversarial attacks. In this paper, we propose a new type of adversarial attack against NMT models. In this attack, we

George Contopoulos, Athanasios C. Tzemos

In the present paper we study the classical and the quantum H\'enon-Heiles systems. In particular we make a comparison between the classical and the quantum trajectories of the integrable and of the non integrable H\'enon Heiles Hamiltonian. From a classical standpoint, we study theoretically and numerically the form of the invariant curves in the Poincar\'e

Zixu Wang, Hao Yang, Yu Guo, Fei Wang

Snapshot Compressive Imaging (SCI) offers a possibility for capturing information in high-speed dynamic scenes, requiring efficient reconstruction method to recover scene information. Despite promising results, current deep learning-based and NeRF-based reconstruction methods face challenges: 1) deep learning-based reconstruction methods struggle to maintain

Haoyu Zhao, Hao Wang, Xingyue Zhao, Hao Fei

Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on video generation models to predict them, which is computational

Vinicius Gomes de Paula, Wanisson S. Santana, Clebson Cruz, Mario Reis

Quantum thermodynamics is a powerful theoretical tool for assessing the suitability of quantum materials as platforms for novel technologies. In particular, the modeling of quantum cycles allows us to investigate the heat changes and work extraction at the nanoscale, where quantum effects dominate over classical ones. In this Review, we cover the mathematica

Jiawei Shao, Xuelong Li

Recent advancements in large language models (LLMs) and their multimodal variants have led to remarkable progress across various domains, demonstrating impressive capabilities and unprecedented potential. In the era of ubiquitous connectivity, leveraging communication networks to distribute intelligence is a transformative concept, envisioning AI-powered ser

Li Huang, Haiyan Lu

PuCoGa5 has attracted significant attention due to its record-breaking superconducting transition temperature Tc=18.5 K among known f-electron superconductors. Here we systematically investigated the evolution of correlated electronic states in the plutonium-based unconventional superconductor PuCoGa5 upon temperature using the embedded dynamical mean-field

James Barker, Michael Griebel, Jan Hamaekers

Energy-based fragmentation methods approximate the potential energy of a molecular system as a sum of contribution terms built from the energies of particular subsystems. Some such methods reduce to truncations of the many-body expansion (MBE); others combine subsystem energies in a manner inspired by the principle of inclusion/exclusion (PIE). The combinato

Partha Sarathi Mondal, Pawan Kumar Mishra, Mitali Thorat, Ananya Verma

Phagocytosis is the process by which cells, which are 5 to 10 times larger than the particle size, engulf particles, holding substantial importance in various biological contexts ranging from the nutrient uptake of unicellular organisms to immune system of humans, animals etc. While the previous studies focused primarily on the mechanism of phagocytosis, in

Eemeli Tomberg

The It\^{o} and Stratonovich approaches are two ways to integrate stochastic differential equations. Detailed knowledge of the origin of the stochastic noise is needed to determine which approach suits a particular problem. I discuss this topic pedagogically in stochastic inflation, where the noise arises from a changing comoving coarse-graining scale or, eq

Charlotte Muth, Leon Schmidt, Shrinivas Chimmalgi, Laurent Schmalen

We investigate a monostatic orthogonal frequency-division multiplexing (OFDM)-based joint communication and sensing (JCAS) system for object tracking. Our setup consists of a transmitter and receiver equipped with an antenna array for fully digital beamforming. The native resolution of range and velocity in all radar-like sensing, including OFDM radar sensin

Viet Anh Nguyen, Linh Thi Dieu Nguyen, Thi Thu Ha Do, Ye Wu

Enhancing light emission from perovskite nanocrystal (NC) films is essential in light-emitting devices, as their conventional stacks often restrict the escape of emitted light. This work addresses this challenge by employing a TiO$_2$ grating to enhance light extraction and shape the emission of CsPbBr$_3$ nanocrystal films. Angle-resolved photoluminescence

Elliot M. Lynch, Guillaume Laibe

In the astrophysics community it is common practice to model collisionless dust, entrained in a gas flow, as a pressureless fluid. However a pressureless fluid is fundamentally different from a collisionless fluid - the latter of which generically possess a non-zero anisotropic pressure or stress tensor. In this paper we derive a fluid model for collisionles

Panchugopal Bikram

In this article, we extend the Bufetov pointwise ergodic theorem for spherical averages of even radius for free group actions on noncommutative $L\log L$-space. Indeed, we extend it to more general Orlicz space $L^\Phi(M, \tau)$ (noncommutative/classical), where $M$ is the semifinite von Neumann algebra with faithful normal semifinite trace $\tau$ and $\Phi:

Sangwon Ryu, Heejin Do, Daehee Kim, Hwanjo Yu

Large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., length or topic) remains underexplored, limiting their adaptability to specific user preferences. In this paper, we systematically explore the controllability of LLMs. To this end, we

V. A. Yerokhin, Z. Harman, C. H. Keitel

Calculations of the two-loop electron self-energy for the $1S$ Lamb shift are reported, performed to all orders in the nuclear binding strength parameter $Z\alpha$ (where $Z$ is the nuclear charge number and $\alpha$ is the fine structure constant). Our approach allows calculations to be extended to nuclear charges lower than previously possible and improves

Emilie Francis

'Fake News' continues to undermine trust in modern journalism and politics. Despite continued efforts to study fake news, results have been conflicting. Previous attempts to analyse and combat fake news have largely focused on distinguishing fake news from truth, or differentiating between its various sub-types (such as propaganda, satire, misinformation, et

Abdul Halim, Abdur Rohim

In this article, we propose a variational PDE model using $\ell_2-\ell_p$ regulariser for removing Poisson noise in presence of blur. The proposed minimization problem is solved using augmented Lagrangian method. The convergence of the sequence of minimizers have been carried out. Numerical simulations on some standard test images have been shown. The numeri

Kathleen West, Magnus Reid, Yehia Elkhatib, Lauritz Thamsen

Scientific workflows facilitate the automation of data analysis, and are used to process increasing amounts of data. Therefore, they tend to be resource-intensive and long-running, leading to significant energy consumption and carbon emissions. With ever-increasing emissions from the ICT sector, it is crucial to quantify and understand the carbon footprint o

Xavier Fernández-Real, Xavier Ros-Oton

This book aims to provide a self-contained introduction to the regularity theory for integro-differential elliptic equations, mostly developed in the 21st century. Such a class of equations often arises in analysis, probability theory, mathematical physics, and in several contexts in applied sciences. The authors give a detailed presentation of all the neces

Kaiyan He, Yikun Hu, Xuehui Li, Yunhao Song

Binary Code Similarity Detection (BCSD) is significant for software security as it can address binary tasks such as malicious code snippets identification and binary patch analysis by comparing code patterns. Recently, there has been a growing focus on artificial intelligence-based approaches in BCSD due to their scalability and generalization. Because binar

Arthur Offermans, Tjonnie G. F. Li

The presence of a massive body between the Earth and a gravitational-wave source will produce the so-called gravitational lensing effect. In the case of strong lensing, it leads to the observation of multiple deformed copies of the initial wave. Machine-learning (ML) models have been proposed for identifying these copies much faster than optimal Bayesian met

Shaoqing Xu, Fang Li, Shengyin Jiang, Ziying Song

Self-supervised learning has made substantial strides in image processing, while visual pre-training for autonomous driving is still in its infancy. Existing methods often focus on learning geometric scene information while neglecting texture or treating both aspects separately, hindering comprehensive scene understanding. In this context, we are excited to

Flavio Hafner, Chang Sun

Synthetic data generators, when trained using privacy-preserving techniques like differential privacy, promise to produce synthetic data with formal privacy guarantees, facilitating the sharing of sensitive data. However, it is crucial to empirically assess the privacy risks associated with the generated synthetic data before deploying generative technologie

Jun Xiao, Zihang Lyu, Hao Xie, Cong Zhang

Blind image restoration remains a significant challenge in low-level vision tasks. Recently, denoising diffusion models have shown remarkable performance in image synthesis. Guided diffusion models, leveraging the potent generative priors of pre-trained models along with a differential guidance loss, have achieved promising results in blind image restoration

Sneha Singhania, Silviu Cucerzan, Allen Herring, Sujay Kumar Jauhar

Capturing fresh information in near real-time and using it to augment existing large language models (LLMs) is essential to generate up-to-date, grounded, and reliable output. This problem becomes particularly challenging when LLMs are used for informational tasks in rapidly evolving fields, such as Web search related to recent or unfolding events involving

Joji Joseph, Bharadwaj Amrutur, Shalabh Bhatnagar

We introduce a training-free method for feature field rendering in Gaussian splatting. Our approach back-projects 2D features into pre-trained 3D Gaussians, using a weighted sum based on each Gaussian's influence in the final rendering. While most training-based feature field rendering methods excel at 2D segmentation but perform poorly at 3D segmentation wi

Kecheng Chen, Pingping Zhang, Hui Liu, Jie Liu

We have recently witnessed that ``Intelligence" and `` Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a general-purpose lossless compressor for various data modalities. This attribute particularly appeals to the lossless image compression community, given the increasing need to compres

Yagmur Ceren Alatas, Uzay Tefek, Sayedus Salehin, Hashim Alhmoud

Rapid identification of microparticles in liquid is an important problem in environmental and biomedical applications such as for microplastic detection in water sources and physiological fluids. Existing spectro-scopic techniques are usually slow and not compatible with flow-through systems. Here we analyze single microparticles in the 14 - 20 micrometer ra

Mohamed Rashad

We present Arabic-Nougat, a suite of OCR models for converting Arabic book pages into structured Markdown text. Based on Meta's Nougat architecture, Arabic-Nougat includes three specialized models: arabic-small-nougat, arabic-base-nougat, and arabic-large-nougat. These models are fine-tuned on a synthetic dataset, arabic-img2md, comprising 13.7k pairs of Ara

Tal Cohen, Itamar Vigdorovich

Given an epimorphism between topological groups $f:G\to H$, when can a generating set of $H$ be lifted to a generating set of $G$? We show that for connected Lie groups the problem is fundamentally abelian: generators can be lifted if and only if they can be lifted in the induced map between the abelianisations (assuming the number of generators is at least

George-Marios Fragkoulis, Nikos Karystinos, George Papadimitriou, Dimitris Gizopoulos

This paper presents the implementation and evaluation of the H (hypervisor) extension for the RISC-V instruction set architecture (ISA) on top of the gem5 microarchitectural simulator. The RISC-V ISA, known for its simplicity and modularity, has seen widespread adoption in various computing domains. The H extension aims to enhance RISC-V's capabilities for c

Tao Yu, Tailong Jin, Yanfeng Shen, Lei Zhang

Simulation of the acoustic wave equation plays an important role in various applications, including audio engineering, medical imaging, and fluid dynamics. However, the complexity of the propagation medium can pose challenges, such as the infinite computing region and the interface conditions between different media. In this paper, we construct a method for

M Jyothi Kiran, Venkatesh Chebolu, Goutam Das, Raja Datta

Routing and Spectrum Assignment (RSA) represents a significant challenge within Elastic Optical Networks (EONs), particularly in dynamic traffic scenarios where the network undergoes continuous changes. Integrating multiple modulation formats transforms it into Routing Modulation Level and Spectrum Assignment (RMLSA) problem, thereby making it more challengi

Yu Kang, Junwei Pan, Jipeng Jin, Shudong Huang

Modeling feature interactions plays a crucial role in accurately predicting click-through rates (CTR) in advertising systems. To capture the intricate patterns of interaction, many existing models employ matrix-factorization techniques to represent features as lower-dimensional embedding vectors, enabling the modeling of interactions as products between thes

Haodong Chen, Runnan Chen, Qiang Qu, Zhaoqing Wang

Recent advancements in 3D Gaussian Splatting (3DGS) have substantially improved novel view synthesis, enabling high-quality reconstruction and real-time rendering. However, blurring artifacts, such as floating primitives and over-reconstruction, remain challenging. Current methods address these issues by refining scene structure, enhancing geometric represen

Diego Diaz-Dominguez

We present a highly parallelizable text compression algorithm that scales efficiently to terabyte-sized datasets. Our method builds on locally consistent grammars, a lightweight form of compression, combined with simple recompression techniques to achieve further space reductions. Locally consistent grammar algorithms are particularly suitable for scaling, a

Xavier Allamigeon, Pascal Capetillo, Stephane Gaubert

Dynamical systems governed by priority rules appear in the modeling of emergency organizations and road traffic. These systems can be modeled by piecewise linear time-delay dynamics, specifically using Petri nets with priority rules. A central question is to show the existence of stationary regimes (i.e., steady state solutions) -- taking the form of invaria

Hongyu Yue, Xiaojin Xiong, Minyu Feng, Attila Szolnoki

While traditional game models often simplify interactions among agents as static, real-world social relationships are inherently dynamic, influenced by both immediate payoffs and alternative information. Motivated by this fact, we introduce a coevolutionary multiplex network model that incorporates the concepts of a relationship threshold and a recommendatio

Hicham Hammouchi, Narjisse Nejjari, Ghita Mezzour, Mounir Ghogho

Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose STRisk which is a predictive system where we expand the scope

Sayantika Bhowal, Andrea Urru, Sophie F. Weber, Nicola A. Spaldin

We show that the surface of a centrosymmetric, collinear, compensated antiferromagnet, which hosts bulk ferroically ordered magnetic octupoles, exhibits a linear magnetoelectric effect, a net magnetization, and a net electric dipole moment. Thus, the surface satisfies all the conditions of a multiferroic, in striking contrast to the bulk, which is neither po

Hannah Janmohamed, Maxence Faldor, Thomas Pierrot, Antoine Cully

In a variety of domains, from robotics to finance, Quality-Diversity algorithms have been used to generate collections of both diverse and high-performing solutions. Multi-Objective Quality-Diversity algorithms have emerged as a promising approach for applying these methods to complex, multi-objective problems. However, existing methods are limited by their

Yunpeng Jia, Chengqun Yang, Yuqin Chen, Cuihua Du

Previous research has established a relationship between radial action and scale height in Galactic disks, unveiling a correlation between radial and vertical heating. This finding poses a challenge to our existing comprehension of heating theories and consequently encodes crucial insights into the formation and heating history of Galactic disks. In this stu

Guangchi Fang, Bing Wang

Reconstructing 3D scenes with high fidelity and efficiency remains a central pursuit in computer vision and graphics. Recent advances in 3D Gaussian Splatting (3DGS) enable photorealistic rendering with Gaussian primitives, yet the modeling process remains governed predominantly by photometric supervision. This reliance often leads to irregular spatial distr

Gaoshuang Huang, Yang Zhou, Luying Zhao, Wenjian Gan

Cross-view geo-localization (CVGL), which involves matching and retrieving satellite images to determine the geographic location of a ground image, is crucial in GNSS-constrained scenarios. However, this task faces significant challenges due to substantial viewpoint discrepancies, the complexity of localization scenarios, and the need for global localization

Vitalii Aksenov, Martin Eigel

The possibility of using the Eulerian discretization for the problem of modelling high-dimensional distributions and sampling, is studied. The problem is posed as a minimization problem over the space of probability measures with respect to the Wasserstein distance and solved with entropy-regularized JKO scheme. Each proximal step can be formulated as a fixe

Gatien Polly, Alexis Mérigaud, Benjamin Thiria, Ramiro Godoy-Diana

This article explores how a submerged elastic plate, clamped at one edge, interacts with water waves. Submerged elastic plates have been considered as potentially effective design elements in the development of wave energy harvesters but their behavior in a wave field remains largely unexplored, especially experimentally. Positioned at a fixed depth in a wav

A. Kehagias, A. Riotto

The quasinormal mode spectrum of gravitational waves emitted during the black hole ringdown relaxation phase, following the merger of a black hole binary, is a crucial target of gravitational wave astronomy. By considering causality constraints on the on-shell graviton three-point couplings within a weakly coupled gravity theory, we present arguments indicat

Ossama Kullie

We present a high-precision solution of Dirac equation by numerically solving the minmax two-center Dirac equation with the finite element method (FEM). The minmax FEM provide a highly accurate benchmark result for systems with light or heavy atomic nuclear charge $Z$. A result is shown for the molecular ion ${\rm H}_2^+$ and the heavy quasi-molecular ion ${

Ziyang Chen, Yongjun Zhang, Wenting Li, Bingshu Wang

Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inherently suffer from the loss of geometric structures in certain feature channels, creating a bottleneck in achieving precise detail matching. Additionally, these methods lack interpret

Mercy Ranjit, Shaury Srivastav, Tanuja Ganu

LLM based copilot assistants are useful in everyday tasks. There is a proliferation in the exploration of AI assistant use cases to support radiology workflows in a reliable manner. In this work, we present RadPhi-3, a Small Language Model instruction tuned from Phi-3-mini-4k-instruct with 3.8B parameters to assist with various tasks in radiology workflows.

Emma Stensby Norstein, Kotaro Yasui, Takeshi Kano, Akio Ishiguro

Robot controllers are often optimised for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their

C. Yuce

We investigate a one-dimensional tight-binding lattice with asymmetrical couplings and various type of nonlinearities to study nonlinear non-Hermitian skin effect. Our focus is on the exploration of nonlinear skin modes through a fixed-point perspective. The nonlinear interactions are shown to have no impact on the spectral region in the semi-infinite system

Han Lin Shang, Yang Yang

We propose a nonstationary functional time series forecasting method with an application to age-specific mortality rates observed over the years. The method begins by taking the first-order differencing and estimates its long-run covariance function. Through eigen-decomposition, we obtain a set of estimated functional principal components and their associate

Lucas R. S. Santos, Murilo H. Oliveira, Luiz O. R. Solak, Daniel Z. Rossatto

Electromagnetically induced transparency (EIT) in cavities can narrow the transmission resonance below the empty-cavity linewidth. We investigate the limits of this narrowing from a single emitter to the few-atom regime. Using a Lindblad master equation for $N_{at}$ identical three-level atoms coupled to a cavity mode, driven by coherent probe and control fi

Santiago Salazar Jaramillo, Rishabh Jha, Stefan Kehrein

The question of thermalization of a closed quantum system is of central interest in non-equilibrium quantum many-body physics. Here we present one such study analyzing the dynamics of a closed coupled Majorana SYK system. We have a large-$q$ SYK model prepared initially at equilibrium quenched by introducing a random hopping term, thus leading to non-equilib

Tengzhou Lai, Ya-Wen Sun, Jia Tian

In this work, we propose a novel holographic method for computing correlation functions of operators in conformal field theories. This method refines previous approaches and is specifically aimed at being applied to heavy operators. For operators that correspond to particles in the bulk, we show that the correlation functions can be derived from the on-shell

Marina V. Yashina, Alexander G. Tatashev

A traffic model on an open one-dimensional lattice is considered. At any discrete time moment, with prescribed probability, a particle arrives to the leftmost cell of the lattice, and, with prescribed probability, the arriving particle belongs to one of the types characterized by the probabilities of particle attempts to move at the present time and the prob

Milou Temmink, Alice S. Booth, Margot Leemker, Nienke van der Marel

The ongoing physical and chemical processes in planet-forming disks set the stage for planet formation. The asymmetric disk around the young star Oph-IRS 48 has one of the most well-characterised chemical inventories, showing molecular emission from a wide variety of species at the dust trap. One of the explanations for the asymmetric structure is dust trapp

Hui Zhang, Chengran Yang, Wai-Keong Mok, Lingxiao Wan

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounte

Pengkun Jiao, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Efficient Visual Instruction Fine-Tuning (EVIT) seeks to adapt Multimodal Large Language Models (MLLMs) to downstream tasks with minimal computational overhead. However, as task diversity and complexity increase, EVIT faces significant challenges in resolving data conflicts. To address this limitation, we propose the Dual Low-Rank Adaptation (Dual-LoRA), a h

Rinaldo M. Colombo, Luca Giuzzi, Francesca Marcellini

Different populations of vehicles travel along a network. Each population has its origin, destination and travel costs - which may well be unbounded. Under the only requirement of the continuity of the travel costs, we prove the existence of a Nash equilibrium for all populations. Conditions for its uniqueness are also provided. A few cases are treated in de

Mustafa M. Abd Zaid, Ahmed Abed Mohammed, Putra Sumari

Satellite imagery has dramatically revolutionized the field of geography by giving academics, scientists, and policymakers unprecedented global access to spatial data. Manual methods typically require significant time and effort to detect the generic land structure in satellite images. This study can produce a set of applications such as urban planning and d

Brian D. Appelbe, Aidan J. Crilly

An analysis methodology is developed for the time-of-flight (TOF) signals recorded by two or more collinear neutron detectors located at different distances from a pulsed neutron source. It is based on taking central moments of the TOF signals and relating these to a set of co-moments of the distribution of production times and velocities of neutrons emitted

Ling Cheng, Qian Shao, Fengzhu Zeng, Feida Zhu

Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective. In this paper, by conducting a thorough analysis of the BTC transaction network, w

Yuze Liu, Tingjie Liu, Tiehua Zhang, Youhua Xia

Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world. Recent works discovered that the performance of LLMs is heavily dependent on the input prompt. However, prompt engineering is usually done manually in a trial-and-error fashion, wh

Neel Redkar

Low-Rank Adaptation (LoRA) layers have emerged as a promising approach for efficient model fine-tuning, but their capabilities and limitations have not been fully explored. This paper: 1) Investigates the fundamental question of whether LoRA layers are effective at increasing reasoning + planning abilities 2) We introduce HashChain Reasoning, a novel evaluat

Chiara Bellucci, Armando Rungi

This study investigates the causal impact of takeovers on firm-level financial accounts on a sample of 4,482 targets in the European Union in the period 2007- 2021. Findings suggest that horizontal integrations do not have a statistically significant impact, while vertical takeovers bring about a lower markup (0.7%), a larger market share (2.5%), a higher pr

Ivan Bizyaev

This paper investigates the trajectories of neutral particles in the Schwarzschild-Melvin spacetime. After reduction by cyclic coordinates this problem reduces to investigating a two-degree-of-freedom Hamiltonian system that has no additional integral. A classification of regions of possible motion of a particle is performed according to the values of the mo

Nasief Khlaif, Mohammad Saleh

Tight and essentially tight modules generalize weakly injective modules. Essential tightness requires embeddings to be essential. This restriction makes the two notions totally different. In this note, we investigate cases when those two notions are the same. Moreover, we look at the cases when essentiallity is imposed only on one of the embeddings rather th

Qingying Zhang, Lizhu Sun, Changjiang Bu

Let $G$ be a connected graph and let $F$ be a connected subgraph of $G$ with a given structure. We consider that the centrality of a vertex $i$ of $G$ is determined by the centrality of other vertices in all subgraphs contain $i$ and isomorphic to $F$. In this paper we propose an $F$-subgraph tensor and an $F$-subgraph eigenvector centrality of $G$. When the

Armengol Gasull, David Rojas

We prove that the period function of the center at the origin of the $\mathbb{Z}_k$-equivariant differential equation $\dot{z}=iz+a(z\overline{z})^nz^{k+1}, a\ne0,$ is monotonous decreasing for all $n$ and $k$ positive integers, solving a conjecture about them. We show this result as corollary of proving that the period function of the center at the origin o