November 2024 arXiv papers — page 8

L. Huang, P. Zong, Z. X. Chang, N. Chang

We use the star formation rate-stellar mass relation ($SFR - {M_ * }$) of galaxies to measure their luminosity distances as well as estimate cosmological parameters. We compile a sample of 341 high-redshift galaxies at $5 < z < 14$ and an additional 51 galaxies at $1.8 < z < 3.5$ from the JWST observations, which can be used to investigate the correlation be

Khoa Bang Pham

The integral identity conjecture of Kontsevich and Soibelman plays an important role in proving the existence of motivic Donaldson-Thomas invariants for three-dimensional noncommutative Calabi-Yau manifolds. There are a number of different formulations of this conjecture in different contexts, and accordingly, there are corresponding solutions to them. The m

Takaaki Dohi, Mona Bhukta, Fabian Kammerbauer, Venkata Krishna Bharadwaj

A hybrid chiral skyrmion tube is a well-known example of a 3D topological spin texture, exhibiting an intriguing chirality transition along the thickness direction. This transition progresses from left-handed to right-handed N\'eel-type chirality, passing through a Bloch-type intermediate state. Such an exotic spin configuration potentially exhibits distinct

Claudia Fevola, Saiei-Jaeyeong Matsubara-Heo

The Euler discriminant of a family of very affine varieties is defined as the locus where the Euler characteristic drops. In this work, we study the Euler discriminant of families of complements of hyperplanes. We prove that the Euler discriminant is a hypersurface in the space of coefficients, and provide its defining equation in two cases: (1) when the coe

Sergio Caucao, Paulo Zúñiga

We consider a mixed variational formulation recently proposed for the coupling of the Brinkman--Forchheimer and Darcy equations and develop the first reliable and efficient residual-based a posteriori error estimator for the 2D version of the associated conforming mixed finite element scheme. For the reliability analysis, due to the nonlinear nature of the p

Sarbani Pramanik

We investigate a perturbed Gelfand problem involving a mixed local-nonlocal $p$-Laplacian operator with singular nonlinearity: \begin{equation*} \begin{aligned} -\Delta_p u + (-\Delta_p)^s u = \lambda \frac{f(u)}{u^{\beta}}\ \text{in} \ \Omega\newline u >0\ \text{in} \ \Omega,\ u =0\ \text{in} \ \mathbb{R}^N \setminus \Omega \end{aligned} \end{equation*} whe

Zeng-Zhen Tan, Rong HU, Ya-Ping Fang

In a Hilbert framework, we consider an inertial Tikhonov regularized dynamical system governed by a maximally comonotone operator, where the damping coefficient is proportional to the square root of the Tikhonov regularization parameter. Under an appropriate setting of the parameters, we prove the strong convergence of the trajectory of the proposed system t

Juhan Raidal, Juan Urrutia, Ville Vaskonen, Hardi Veermäe

We study the statistical properties of the anisotropy in the gravitational wave (GW) background originating from supermassive black hole (SMBH) binaries. We derive the distribution of the GW anisotropy power spectrum coefficients, $C_{l\geq1}/C_0$, in scenarios including environmental effects and eccentricities of the SMBH binaries. Although the mean of $C_{

Maciej Błaszak, Krzysztof Marciniak, Błażej M. Szablikowski

In this article we investigate stationary systems of the AKNS hierarchy. We prove that all these systems are classical separable St\"ackel systems. The explicit transformation from jet variables to separation coordinates is obtained by comparing Lax representations in jet and separable coordinates.

Muhammad Umer Ramzan, Wahab Khaddim, Muhammad Ehsan Rana, Usman Ali

This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become outdated. To address these issues, we propose a state-of-the-art Convolutional Neural Network (CNN) architectu

John Francis, Saba Esnaashari, Anton Poletaev, Sukankana Chakraborty

Large language models (LLMs) have demonstrated remarkable capabilities in text analysis tasks, yet their evaluation on complex, real-world applications remains challenging. We define a set of tasks, Multi-Insight Multi-Document Extraction (MIMDE) tasks, which involves extracting an optimal set of insights from a document corpus and mapping these insights bac

Kim-Celine Kahl, Selen Erkan, Jeremias Traub, Carsten T. Lüth

Vision-Language Models (VLMs) have great potential in medical tasks, like Visual Question Answering (VQA), where they could act as interactive assistants for both patients and clinicians. Yet their robustness to distribution shifts on unseen data remains a key concern for safe deployment. Evaluating such robustness requires a controlled experimental setup th

Marco Di Renzo

Stacked intelligent metasurface (SIM) is an emerging technology that capitalizes on reconfigurable metasurfaces for several applications in wireless communications. SIM is considered an enabler for integrating communication, sensing and computing in a unique platform. In this paper, we offer a survey on the state of the art of SIM for wireless communications

Göran Östlin, Pablo G. Pérez-González, Jens Melinder, Steven Gillman

The recently launched James Webb Space Telescope (JWST) is opening new observing windows on the distant universe. Among JWST's instruments, the Mid Infrared Instrument (MIRI) offers the unique capability of imaging observations at wavelengths $\lambda > 5\mu$m. This enables unique access to the rest frame near infra-red (NIR, $\lambda \ge 1$\mum) emission fr

Marco Di Renzo, Philipp del Hougne

Multiport network theory (MNT) is a powerful analytical tool for modeling and optimizing complex systems based on circuit models. We present an overview of current research on the application of MNT to the development of electromagnetically consistent models for programmable metasurfaces, with focus on reconfigurable intelligent surfaces for wireless communi

Han Bao, Jonas Vogel, Ulrich Poschinger, Ferdinand Schmidt-Kaler

Fast entangling gate operations are a fundamental prerequisite for quantum simulation and computation. We propose an entangling scheme for arbitrary pairs of ions in a linear crystal, harnessing the high electric polarizability of highly excited Rydberg states. An all-to-all quantum gate connectivity is based on an initialization of a pair of ions to a super

Marwan Najjar, Leonardo Santilli, Yi-Nan Wang

We explore $(-1)$-form symmetries within the framework of geometric engineering in M-theory. By constructing the Symmetry Topological Field Theory (SymTFT) for selected 5d $\mathcal{N}=1$, 4d $\mathcal{N}=2$ and 4d $\mathcal{N}=1$ theories, we formalize the geometric origin of these symmetries and compute the mixed anomaly polynomials involving $(-1)$-form a

Jerzy Białkowski, Adam Skowyrski

This article provides the second part of the research initiated in arXiv:2411.17381, where we introduced and investigated so called periodicity shadows, which are special skew-symmetric matrices related to symmetric algebras with periodic simple modules. In arXiv:2411.17381 we focused on theoretical aspects, whereas here we present complementary cosideration

Kijung Kim, Seungwook Hong, Wonjun Jung, Wooseok Kim

Mechanical metamaterials enable precise control over structural properties, but their design method remains challenging due to their complex structure. Although additive manufacturing has expanded geometric freedom, navigating this vast and complex design space still requires computationally intensive simulations or expert-driven processes. Recently, artific

Fei Gao, Angel S. Miramontes, Joannis Papavassiliou, Jan M. Pawlowski

We introduce a new symmetry-preserving framework for the physics of heavy-light mesons, whose key element is the effective incorporation of flavour-dependent contributions into the corresponding bound-state and quark gap equations. These terms originate from the fully-dressed quark-gluon vertices appearing in the kernels of these equations, and provide a nat

Menglan Hu, Chenxin Wang, Bin Cao, Benkuan Zhou

Emerging Low Earth Orbit (LEO) broadband constellations hold significant potential to provide advanced Internet services due to inherent geometric features of the grid topology. However, high dynamics, unstable topology changes, and frequent route updates bring significant challenge to fast and adaptive routing policies. In addition, since computing, bandwid

Siqing Zhang, Yuchen Ding, Wei Tang, Wei Sun

Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a representative issue, we identify three phenomena in federated recommendation systems: performance difference, data imbalance, and preference disparity. We discover that the state-of-t

Rodrigo Piera, Jaideep Singh, Yury Kurochkin, James A. Grieve

Randomness is a critical resource of modern cryptosystems. Quantum mechanics offers the best properties of an entropy source in terms of unpredictability. However, these sources are often fragile and can fail silently. Therefore, statistical tests on their outputs should be performed continuously. Testing a sequence for randomness can be very resource-intens

Dmitry Zaborov

High energy neutrino astronomy has seen significant progress in the past few years. This includes the detection of neutrino flux from the Galactic plane, as well as strong evidence for neutrino emission from the active galaxy NGC 1068, both reported by IceCube. New results start coming from the two km$^3$-scale neutrino telescopes under construction in the N

Cong Chen, Kaikai Yang, Hua Wang

Let $m\in \mathbb{N}$ and $0<\alpha<mn$.In this paper, we will use the idea of Hedberg to reprove that the multilinear operators $\mathcal{T}_{\Omega,\alpha;m}$ and $\mathcal{M}_{\Omega,\alpha;m}$ are bounded from $L^{p_1}(\mathbb R^n)\times L^{p_2}(\mathbb R^n)\times\cdots\times L^{p_m}(\mathbb R^n)$ into $L^q(\mathbb R^n)$ provided that $\vec{\Omega}=(\Ome

Edward W. G. Skevington, Robert M. Dorrell

Gravity currents are a ubiquitous density driven flow occurring in both the natural environment and in industry. They include: seafloor turbidity currents, primary vectors of sediment, nutrient and pollutant transport; cold fronts; and hazardous gas spills. However, while the energetics are critical for their evolution and particle suspension, they are inclu

Noraiz Tahir, Martin Lopez Corredoira, Francesco De Paolis

The halo of our Galaxy is populated with a significant number of high-velocity clouds (HVCs) moving with a speed up to $500$ km/s. It is suggested that these HVCs might contain a non-negligible fraction of the missing baryons. The main aim of the current paper is to estimate the baryonic mass of the Milky Way halo in the form of HVCs in order to constrain a

Guillermo Ballesteros, Jesús Gambín Egea, Flavio Riccardi

We present a method for solving loop integrals in dimensional regularization that is particularly useful in the context of inflation. We apply this method to the calculation of the tensor power spectrum induced by scalar fluctuations in slow-roll inflation.

Wojciech Florkowski

The concept of spin hydrodynamics is reexamined and briefly characterized.

Gerd Niestegge

Symmetry postulates play a crucial role in various approaches to reconstruct quantum theory from a few basic principles. Discrete and continuous symmetries are under consideration. The continuous case better matches the physical needs for mathematical models of dynamical processes and is studied here. Applying the representation theory of the orthomodular la

Xianliang Li, Jun Luo, Zhiwei Zheng, Hanxiao Wang

Momentum-based optimizers are widely adopted for training neural networks. However, the optimal selection of momentum coefficients remains elusive. This uncertainty impedes a clear understanding of the role of momentum in stochastic gradient methods. In this paper, we present a frequency domain analysis framework that interprets the momentum method as a time

Nils Krane, Elia Turco, Annika Bernhardt, Michal Juríček

Inelastic electron tunneling spectroscopy (IETS) is a powerful measurement technique often used in scanning tunneling spectroscopy to probe excited states of various nanostructures, e.g., the magnetic properties of complex spin systems. The observed excited states can be of magnetic and vibrational origin and it is therefore necessary to differentiate betwee

Hao Zhang, Shiji Wang

Laser triangulation sensors are widely used in industry for surface inspection due to simple setup, micron precision and low cost. Conventional laser triangulation methods only enable axial distance measurement limiting further applications, and their lateral resolution is limited by surface microstructure. For overcoming these issues, based on the geometric

Wanyue Zhang, Ziyong Li, Wen Yang, Chunlin Leng

During the development of large language models (LLMs), pre-training data play a critical role in shaping LLMs' capabilities. In recent years several large-scale and high-quality pre-training datasets have been released to accelerate the research of LLMs, including ChineseWebText1.0, C4, Pile, WanJuan, MAPCC and others. However, as LLMs continue to evolve, f

Oscar Henriksson, Niko Jokela, Julia Junttila

We study the dynamics of a first-order phase transition in a strongly coupled gauge theory at non-zero temperature and chemical potential, computing nucleation rates and wall speeds from first principles. The gauge theory is the four-dimensional superconformal SU(N)xSU(N) Klebanov-Witten theory, which at low temperatures displays an instability to forming sc

Tong Ding, Sophia J. Wagner, Andrew H. Song, Richard J. Chen

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level rema

Disheng Xu, Jiesong Zhang

We introduce a novel approach linking fractal geometry to partially hyperbolic dynamics, revealing several new phenomena related to regularity jumps and rigidity. One key result demonstrates a sharp phase transition for partially hyperbolic diffeomorphisms $f \in \mathrm{Diff}^\infty_{\mathrm{vol}}(\mathbb{T}^3)$ with a contracting center direction: $f$ is $

Katiele V. P. Brito, Joana M. G. L. Silva, Claudio R. Mirasso, Fernanda S. Matias

Neuronal heterogeneity, characterized by the presence of a multitude of spiking neuronal patterns, is a widespread phenomenon throughout the nervous system. In particular, the brain exhibits strong variability among inhibitory neurons. Despite the huge neuronal heterogeneity across brain regions, which in principle could decrease synchronization, cortical ar

Dimitrios Kastritsis, Enrico Lia, Iain Mckenzie, Stavros Iezekiel

An optoelectronic oscillator (OEO) producing a signal at 45.86 GHz is demonstrated that may potentially be utilized in the local oscillator (LO) generation of Earth observation applications such as the microwave sounding (MWS), microwave imaging (MWI) and ice cloud imaging (ICI) missions of METOP 2 of ESA. Preliminary results show that the performance of thi

F. Lesjak, L. Nortmann, D. Cont, F. Yan

The extreme temperature gradients from day- to nightside in the atmospheres of hot Jupiters generate fast winds in the form of equatorial jets or day-to-night flows. Observations of blue-shifted and red-shifted signals in the transmission and dayside spectra of WASP-189b have sparked discussions about the nature of winds on this planet. To investigate the st

Wenhua Zhao, Álvaro Rodríguez Echarri, Alberto Eljarrat, Hannah C. Nerl

Electron microscopy techniques such as electron energy-loss spectroscopy (EELS) facilitate the spatio-spectral characterization of plasmonic nanostructures. In this work, a time-dependent perspective is presented, which significantly enhances the utility of EELS. Specifically, silver nanowires offer the material and geometric features for various high-qualit

M. Behnami, M. Gillig, A. G. Moghaddam, D. V. Efremov

Layered van der Waals tellurides reveal topologically non-trivial properties that give rise to unconventional magneto-transport phenomena. Additionally, their semimetallic character with high mobility makes them promising candidates for large magneto-thermoelectric effects. Remarkable studies on the very large and unconventional Nernst effect in WTe$_2$ have

N. Belousov, S. Khoroshkin

Spectral decomposition with respect to the wave functions of Ruijsenaars hyperbolic system defines an integral transform, which generalizes classical Fourier integral. For a certain class of analytical symmetric functions we prove inversion formula and orthogonality relations, valid for complex valued parameters of the system. Besides, we study four regimes

S. Hashim, D. Habibović, C. Figueira de Morisson Faria

We investigate laser-induced nonsequential double ionization with linearly polarized bichromatic fields, focusing on the recollision-excitation with subsequent ionization (RESI) mechanism. Using the strong-field approximation, we assess how the symmetries of the field influence the dominant events. Furthermore, we show that, by manipulating the field paramet

Sergei Merkulov, Thomas Willwacher, Vincent Wolff

We prove that the Kontsevich graph complex $GC_d^{2}$ and its oriented version $OGC_{d+1}^2$ are quasi-isomorphic as dg Lie algebras.

M. M. Botana, A. S. Viz, M. V. Ramallo

We calculate the effects of carrier-density structuration and patterning on thin films of the cuprate superconductor La$_{2-x}$Sr$_{x}$CuO$_{4}$, in order to optimize its functional characteristics as sensing material for resistive transition-edge bolometers at liquid-He temperature. We perform finite-element computations considering two major contributions

Alessandro Scirè, Andrei Stefan Bejgu, Simone Tedeschi, Karim Ghonim

After the introduction of Large Language Models (LLMs), there have been substantial improvements in the performance of Natural Language Generation (NLG) tasks, including Text Summarization and Machine Translation. However, LLMs still produce outputs containing hallucinations, that is, content not grounded in factual information. Therefore, developing methods

Bojun Xiong, Jialun Liu, Jiakui Hu, Chenming Wu

Physically Based Rendering (PBR) materials play a crucial role in modern graphics, enabling photorealistic rendering across diverse environment maps. Developing an effective and efficient algorithm that is capable of automatically generating high-quality PBR materials rather than RGB texture for 3D meshes can significantly streamline the 3D content creation.

Dimitri Meunier, Zhu Li, Tim Christensen, Arthur Gretton

We study the kernel instrumental variable (KIV) algorithm, a kernel-based two-stage least-squares method for nonparametric instrumental variable regression. We provide a convergence analysis covering both identified and non-identified regimes: when the structural function is not identified, we show that the KIV estimator converges to the minimum-norm IV solu

Bernd Finkbeiner, Felix Jahn, Julian Siber

MITL is a temporal logic that facilitates the verification of real-time systems by expressing the critical timing constraints placed on these systems. MITL specifications can be checked against system models expressed as networks of timed automata. A violation of an MITL specification is then witnessed by a timed trace of the network, i.e., an execution cons

Wenyi Mo, Tianyu Zhang, Yalong Bai, Bing Su

Text-guided image generation and editing using diffusion models have achieved remarkable advancements. Among these, tuning-free methods have gained attention for their ability to perform edits without extensive model adjustments, offering simplicity and efficiency. However, existing tuning-free approaches often struggle with balancing fidelity and editing pr

W. R. M. Rocha, M. K. McClure, J. A. Sturm, T. L. Beck

This work focuses on the ice features toward the binary protostellar system Ced 110 IRS 4A and 4B, and observed with JWST as part of the Early Release Science Ice Age collaboration. We aim to explore the JWST observations of the binary protostellar system Ced~110~IRS4A and IRS4B to unveil and quantify the ice inventories toward these sources. We compare the

Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo

The advancement of large Vision-Language-Action (VLA) models has significantly improved robotic manipulation in terms of language-guided task execution and generalization to unseen scenarios. While existing VLAs adapted from pretrained large Vision-Language-Models (VLM) have demonstrated promising generalizability, their task performance is still unsatisfact

Jiahao Zhu, Hengzhi Wu

This study explores the use of Transformer-based models to predict both covariance and semi-covariance matrices for ETF portfolio optimization. Traditional portfolio optimization techniques often rely on static covariance estimates or impose strict model assumptions, which may fail to capture the dynamic and non-linear nature of market fluctuations. Our appr

Shangzhi Xu, Jialiang Dong, Weiting Cai, Juanru Li

Nowadays, software development progresses rapidly to incorporate new features. To facilitate such growth and provide convenience for developers when creating and updating software, reusing open-source software (i.e., thirdparty library reuses) has become one of the most effective and efficient methods. Unfortunately, the practice of reusing third-party libra

Shaowen Wang, Anan Liu, Jian Xiao, Huan Liu

Modern recommendation systems frequently employ online learning to dynamically update their models with freshly collected data. The most commonly used optimizer for updating neural networks in these contexts is the Adam optimizer, which integrates momentum ($m_t$) and adaptive learning rate ($v_t$). However, the volatile nature of online learning data, chara

Paolo Sgarro, Roman Ovcharov, Roman Khymyn, Sambit Ghosh

We present a frequency tunable magneto-acoustic oscillator (MAO) operating in low-phase-noise and complex dynamical regimes based on a single composite YIG-GGG resonator. The magneto-acoustic resonator (MAR) is based on a YIG (yttrium iron garnet) layer epitaxially grown on a GGG (gadolinium gallium garnet) substrate. By optimizing the YIG thickness, we obta

V. D. Kochev, T. I. Mogilyuk, S. S. Kostenko, P. D. Grigoriev

A film of liquid helium on the surface of material traps for ultracold neutrons protects the neutrons from being absorbed by the trap walls. By using surface roughness and an electrostatic field, it is possible to maintain a helium film of sufficient thickness throughout the height of the trap. The field distribution near the tip of such wall roughness of th

Di Chen, Ruiguo Zhong, Kehua Chen, Zhiwei Shang

Planning safe and efficient trajectories through signal-free intersections presents significant challenges for autonomous vehicles (AVs), particularly in dynamic, multi-task environments with unpredictable interactions and an increased possibility of conflicts. This study aims to address these challenges by developing a unified, robust, adaptive framework to

Oleg Morzhin, Alexander Pechen

In this work, we adopt the Gradient Projection Method (GPM) to problems of quantum control. For general $N$-level closed and open quantum systems, we derive the corresponding adjoint systems and gradients of the objective functionals, and provide the projection versions of the Pontryagin maximum principle and the GPM, all directly in terms of quantum objects

Krzysztof Ptaszynski, Massimiliano Esposito

In some models of nonequilibrium phase transitions, fluctuations of the analyzed currents have been observed to diverge with system size. To assess whether this behavior is universal across phase transitions, we examined heat current fluctuations in the Curie-Weiss model, a paradigmatic model of the paramagnetic-ferromagnetic phase transition, coupled to two

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The inclusive cross sections of prompt $J/\psi$ and $\psi(3686)$ production are measured at center-of-mass energies from 3.808 to 4.951 GeV. The dataset used is 22 fb$^{-1}$ of $e^{+}e^{-}$ annihilation data collected with the BESIII detector operating at the BEPCII storage ring. The results obtained are in agreement with the previous BESIII measurements of

Haiyi Qiu, Minghe Gao, Long Qian, Kaihang Pan

Video Large Language Models (Video-LLMs) have recently shown strong performance in basic video understanding tasks, such as captioning and coarse-grained question answering, but struggle with compositional reasoning that requires multi-step spatio-temporal inference across object relations, interactions, and events. The hurdles to enhancing this capability i

Erika Nozawa, Tetsuo Deguchi

We present a theoretical model and simulation for the formation dynamics of diverse texture patterns that emerge spontaneously or self-organize during phase inversion processes of fresh cream by mechanical whipping. The results suggest that the model should be applied for theoretically designing the texture and quality of whipped cream and butter products. T

Marlon Becker, Benjamin Risse

We empirically investigate the impact of learning randomly generated labels in parallel to class labels in supervised learning on memorization, model complexity, and generalization in deep neural networks. To this end, we introduce a multi-head network architecture as an extension of standard CNN architectures. Inspired by methods used in fair AI, our approa

Zifeng Shi, Meiqin Liu, Senlin Zhang, Ronghao Zheng

In recent years, model-based reinforcement learning (MBRL) has emerged as a solution to address sample complexity in multi-agent reinforcement learning (MARL) by modeling agent-environment dynamics to improve sample efficiency. However, most MBRL methods assume complete and continuous observations from each agent during the inference stage, which can be over

Taja Kuzman, Nikola Ljubešić

With the ever-increasing number of news stories available online, classifying them by topic, regardless of the language they are written in, has become crucial for enhancing readers' access to relevant content. To address this challenge, we propose a teacher-student framework based on large language models (LLMs) for developing multilingual news topic classi

Emma Prevot, Cameron Shand, Neil Oxtoby, for Alzheimer's Disease Neuroimaging Initiative

Alzheimer's disease (AD) exhibits substantial clinical and biological heterogeneity, complicating efforts in treatment and intervention development. While new computational methods offer insights into AD progression, the reproducibility of these subtypes across datasets remains understudied, particularly concerning the robustness of subtype definitions when

Jialun Cao, David Šiška

We address the liquidation problem arising from the credit risk management in decentralised finance (DeFi) by formulating it as an ergodic optimal control problem. In decentralised derivatives exchanges, liquidation is triggered whenever the parties fail to maintain sufficient collateral for their open positions. Consequently, effectively managing and liquid

L. Asselle, M. Starostka

In the 1960s Arnold conjectured that a Hamiltonian diffeomorphism of a closed connected symplectic manifold $(M,\omega)$ should have at least as many contractible fixed points as a smooth function on $M$ has critical points. Such a conjecture can be seen as a natural generalization of Poincar\'e's last geometric theorem and is one of the most famous (and sti

Bailu Jin, Weisi Guo

Understanding the dynamics of public opinion evolution on online social platforms is crucial for understanding influence mechanisms and the provenance of information. Traditional influence analysis is typically divided into qualitative assessments of personal attributes (e.g., psychology of influence) and quantitative evaluations of influence power mechanism

Di Mo, Yumin Zhang, Tianquan Tang, Xiaochao Ji

We investigate fundamental constraints on passive linear time-invariant acoustic systems through the developing alternative linear sum rules for sound absorption and transmission. Our approach, based on the Herglotz function method, yields integral identities without non-linear logarithmic terms or frequency weightings, providing clearer physical insights in

Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin

Several hypothesis testing methods have been proposed to validate the assumption of isotropy in spatial point patterns. A majority of these methods are characterised by an unknown distribution of the test statistic under the null hypothesis of isotropy. Parametric approaches to approximating the distribution involve simulation of patterns from a user-specifi

Coen Visser, Alexander Heinlein, Bianca Giovanardi

Physics-Informed Neural Networks (PINNs) have emerged as a tool for approximating the solution of Partial Differential Equations (PDEs) in both forward and inverse problems. PINNs minimize a loss function which includes the PDE residual determined for a set of collocation points. Previous work has shown that the number and distribution of these collocation p

Rodrigo Fischer, Patrick Matalla, Sebastian Randel, Laurent Schmalen

We investigate Kolmogorov-Arnold networks (KANs) for non-linear equalization of 112 Gb/s PAM4 passive optical networks (PONs). Using pruning and extensive hyperparameter search, we outperform linear equalizers and convolutional neural networks at low computational complexity.

Daniel L. Clarkson, Eduard P. Kontar

The fine structures of solar radio bursts reveal complex dynamics in the corona, yet the observed characteristics of these sub-second bursts are additionally complicated by radio wave scattering in the turbulent solar corona. We examine the impact of anisotropic turbulence in radio-wave propagation simulations with non-radial magnetic field structures in sha

Cristian Gabellini, Nikhil Shenoy, Stephan Thaler, Semih Canturk

Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and force calculations. However, Quantum-Mechanical (QM) datasets, crucial for MLIPs, are fragmented across various repositories, hindering accessibility and model development. We introd

Qiong Wu, Wenhao Lin, Yiyi Zhou, Weihao Ye

The excessive use of visual tokens in existing Multimoal Large Language Models (MLLMs) often exhibits obvious redundancy and brings in prohibitively expensive computation. To gain insights into this problem, we first conduct extensive empirical studies on the attention behaviors of MLLMs, and summarize three main inference stages in MLLMs: (i) Early fusion b

Mathijs G. C. Mientjes, Xin Guan, Marcel A. Verheijen, Erik P. A. M. Bakkers

PbSnTe is a topological crystalline insulator (TCI), which holds promise for scattering-free transport channels and fault-tolerant quantum computing. As the topologically non-trivial states live on the surface, the nanowire geometry, with a high surface-to-volume ratio, is ideal for probing these states. The controlled growth of PbSnTe nanowires using molecu

Yawen Shao, Wei Zhai, Yuhang Yang, Hongchen Luo

Open-Vocabulary 3D object affordance grounding aims to anticipate ``action possibilities'' regions on 3D objects with arbitrary instructions, which is crucial for robots to generically perceive real scenarios and respond to operational changes. Existing methods focus on combining images or languages that depict interactions with 3D geometries to introduce ex

N. Garcia-Chan, L. J. Alvarez-Vazquez, A. Martinez, M. E. Vazquez-Mendez

In this paper we propose a novel traffic flow model based on understanding the city as a porous media, this is, streets and building-blocks characterizing the urban landscape are seen now as the fluid-phase and the solid-phase of a porous media, respectively. Moreover, based in the interchange of mass in the porous media models, we can model the interchange

Michele Bucelli

This article presents updates to lifex [Africa, SoftwareX (2022)], a C++ library for high-performance finite element simulations of multiphysics, multiscale and multidomain problems. In this release, we introduce an additional intergrid transfer method for non-matching multiphysics coupling on the same domain, significantly optimize nearest-neighbor point se

Qihang Zhou, Shenhao Fang, Shibo He, Wenchao Meng

Condensing large datasets into smaller synthetic counterparts has demonstrated its promise for image classification. However, previous research has overlooked a crucial concern in image recognition: ensuring that models trained on condensed datasets are unbiased towards protected attributes (PA), such as gender and race. Our investigation reveals that datase

Pere Munar-Vallespir, Janis Nötzel, Florian Seitz

We study the problem of joint communication and detection of wiretapping on an optical fiber from a quantum perspective. Our system model describes a communication link that is capable of transmitting data under normal operating conditions and raising a warning at the transmitter side in case of eavesdropping. It contributes to a unified modelling approach,

Kim L. Kreienkamp, Sabine H. L. Klapp

Many active matter systems consist of different particle types that interact via nonreciprocal couplings. Such nonreciprocal couplings can lead to the spontaneous emergence of time-dependent states that break parity-time symmetry. On the field-theoretical level, the transition to these states is marked by so-called exceptional points. However, their precise

Carles Roch I Carceller, Alexander Bernal

We investigate how local distinguishability can restrict global properties of bi-partite states. We begin exploring how non-locality becomes limited by optimal local state discrimination and observe a non-trivial trade-off between the Clauser-Horne-Shimony-Holt (CHSH) violation and success probability of local discrimination. We extend our findings to boundi

Thomas Robiglio, Leonardo Di Gaetano, Ada Altieri, Giovanni Petri

Non-dyadic higher-order interactions affect collective behavior in various networked dynamical systems. Here we discuss the properties of a novel Ising model with higher-order interactions and characterize its phase transitions between the ordered and the disordered phase. By a mean-field treatment, we show that the transition is continuous when only three-b

Lucas Lacasa, F. Javier Marín-Rodríguez, Naoki Masuda, Lluís Arola-Fernández

A temporal network -- a collection of snapshots recording the evolution of a network whose links appear and disappear dynamically -- can be interpreted as a trajectory in graph space. In order to characterize the complex dynamics of such trajectory via the tools of time series analysis and signal processing, it is sensible to preprocess the trajectory by emb

Attila Cangi, Lenz Fiedler, Bartosz Brzoza, Karan Shah

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states,

Johannes Figueiredo, Marten Richter, Mirco Troue, Jonas Kiemle

Heterostructures made from 2D transition-metal dichalcogenides are known as ideal platforms to explore excitonic phenomena ranging from correlated moir\'e excitons to degenerate interlayer exciton ensembles. So far, it is assumed that the atomic reconstruction appearing in some of the heterostructures gives rise to a dominating localization of the exciton st

L. J. Alvarez-Vazquez, A. Martinez, M. E. Vazquez-Mendez

In this work we present a novel methodology to deal with the optimal performance of raceways (open-channel ponds where the circulating wastewater, during its purification process, is used to grow algae that will be used as a source for the production of bioenergy). The maximization of algal productivity is addressed here within an optimal control framework f

Dilini Kolombage, Barbara Verfürth

In this paper, we consider an elliptic eigenvalue problem with multiscale, randomly perturbed coefficients. For an efficient and accurate approximation of the solutions for many different realizations of the coefficient, we propose a computational multiscale method in the spirit of the Localized Orthogonal Decomposition (LOD) method together with an offline-

Lukas Bovermann, Evgeny Epelbaum, Hermann Krebs, Dean Lee

We study the three-nucleon system at next-to-next-to-next-to-leading order ($\mathrm{N^3LO}$) in the framework of chiral effective field theory (EFT) on the lattice. Our calculations do not rely on a perturbative treatment of subleading contributions to the nuclear forces. For the two-nucleon potential, we apply the previously developed $\mathrm{N^3LO}$ latt

Massimo Bianchi, Donato Bini, Giorgio Di Russo

We study the radiated energy by a scalar particle moving on a circular orbit (smeared in the extra dimension) in the spacetime of a topological star, extending a previous study [Phys. Rev. D {\bf 110}, 084077 (2024)]. We discuss motion in the presence of self-force effects too.

Akshaya Rajesh, Pavithra Ananthasubramanian, Nagarajan Raghavan, Ankush Kumar

Efficient audio feature extraction is critical for low-latency, resource-constrained speech recognition. Conventional preprocessing techniques, such as Mel Spectrogram, Perceptual Linear Prediction (PLP), and Learnable Spectrogram, achieve high classification accuracy but require large feature sets and significant computation. The low-latency and power effic

Stefano Bonetti, Mattia Corti

We present and analyze a discontinuous Galerkin method for the numerical modeling of a Kelvin-Voigt thermo/poro-viscoelastic problem. We present the derivation of the model and we develop a stability analysis in the continuous setting that holds both for the full inertial and quasi-static problems and that is robust with respect to most of the physical param

Daniel Pranjic, Bharadwaj Chowdary Mummaneni, Christian Tutschku

Feature selection is crucial for enhancing the accuracy and efficiency of machine learning (ML) models. This work investigates the utility of quantum annealing for the feature selection process in an ML-pipeline, used for maximizing the mutual information (MI) or conditional mutual information (CMI) of the underlying feature space. Calculating the optimal se

M. A. Shpot

We extend the validity range of a Ramanujan's hypergeometric transformation formula proved by Berndt, Bhargava and Garvan, Trans. Amer. Math. Soc. 347, 4163 (1995) and study its implications. Relations to special values of complete elliptic integrals of the first kind in the singular value theory are established. Consequently, we derive several closed-form e

Lukas Lanza, Philipp Braun

Safe obstacle avoidance and target set stabilization for nonlinear systems using reactive feedback control is under consideration. Based only on local information and by considering virtual dynamics, a safe path is generated online. The control law for the virtual dynamics is combined with a feedback controller for the dynamics of interest, where Lyapunov ar

Sayan Goswami, Sourav Kanti Patra

Answering a conjecture of A. Sisto, J. Sahasrabudhe proved the exponential version of the Schur theorem: for every finite coloring of the naturals, there exists a monochromatic copy of $\{x,y,x^y:x\neq y\},$ which initiates the study of exponential Ramsey theory in arithmetic combinatorics. In this article, We first give two short proofs of the exponential S