April 2024 arXiv papers — page 122

Fei Xue, Ignas Budvytis, Roberto Cipolla

Visual localization is a key technique to a variety of applications, e.g., autonomous driving, AR/VR, and robotics. For these real applications, both efficiency and accuracy are important especially on edge devices with limited computing resources. However, previous frameworks, e.g., absolute pose regression (APR), scene coordinate regression (SCR), and the

Mahdi Zreik

Let $\Omega_+\subset\mathbb{R}^{3}$ be a fixed bounded domain with boundary $\Sigma = \partial\Omega_{+}$. We consider $\mathcal{U}^\varepsilon$ a tubular neighborhood of the surface $\Sigma$ with a thickness parameter $\varepsilon>0$, and we define the perturbed Dirac operator $\mathfrak{D}^{\varepsilon}_{M}=D_m +M\beta \mathbb{1}_{\mathcal{U}^{\varepsilon}

Javier Used, Jesús Seoane, Irina Bashkirtseva, Lev Ryashko

We investigate the synchronization between two neurons using the stochastic version of the map-based Chialvo model. To simulate non-identical neurons, a mismatch is introduced in one of the main parameters of the model. Subsequently, the synchronization of the neurons is studied as a function of this mismatch, the noise introduced in the stochastic model, an

Julius Streibert, Hector O. Silva, Miguel Zumalacárregui

Einstein-aether theory provides a model to test the validity of local Lorentz invariance in gravitational interactions. The speed of gravitational waves as measured from the binary neutron star event GW170817 sets stringent limits on Einstein-aether theory, but only on a combination of the theory's free parameters. For this reason, a significant part of the

Barry Gilhuly, Armin Sadeghi, Stephen L. Smith

Visibility is a crucial aspect of planning and control of autonomous vehicles (AV), particularly when navigating environments with occlusions. However, when an AV follows a trajectory with multiple occlusions, existing methods evaluate each occlusion individually, calculate a visibility cost for each, and rely on the planner to minimize the overall cost. Thi

Mi Chen, Rafael S. de Souza, Quanfeng Xu, Shiyin Shen

We introduce galmoss, a python-based, torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the LSST-era. It incorporates widely used profiles such as the S\'ersic, Exponential disk, Ferrer, Ki

Shuo Zou, Bo Zhou, Qi Xuan

Degree correlation is a crucial measure in networks, significantly impacting network topology and dynamical behavior. The degree sequence of a network is a significant characteristic, and altering network degree correlation through degree-preserving rewiring poses an interesting problem. In this paper, we define the problem of maximizing network degree corre

Soumita Modak

In this paper, we advocate a novel measure for the purpose of checking the quality of a cluster partition for a sample into several distinct classes, and thus, determine the unknown value for the true number of clusters prevailing the provided set of data. Our objective leads us to the development of an approach through applying the multinomial distribution

Guang-Yu Wang, Xing-Gang Wu, Xu-Chang Zheng, Jiang Yan

In this paper, we present an improved calculation for the decay rate of the rare $Z$-boson decay into $J/\psi + J/\psi$. This decay is dominated by the photon fragmentation mechanism, i.e., the transition $Z\to J/\psi + \gamma^{*}$ followed by the fragmentation $\gamma^{*}\to J/\psi$. In our calculation, the amplitude of $\gamma^{*}\to J/\psi$ is extracted f

Joanna Komorniczak, Paweł Ksieniewicz

Practical applications of artificial intelligence increasingly often have to deal with the streaming properties of real data, which, considering the time factor, are subject to phenomena such as periodicity and more or less chaotic degeneration - resulting directly in the concept drifts. The modern concept drift detectors almost always assume immediate acces

Akash Kumar Gautam, Lukas Lange, Jannik Strötgen

Temporal expression (TE) normalization is a well-studied problem. However, the predominately used rule-based systems are highly restricted to specific settings, and upcoming machine learning approaches suffer from a lack of labeled data. In this work, we explore the feasibility of proprietary and open-source large language models (LLMs) for TE normalization

Namasivayam Kalithasan, Sachit Sachdeva, Himanshu Gaurav Singh, Vishal Bindal

Effective human-robot collaboration requires the ability to learn personalized concepts from a limited number of demonstrations, while exhibiting inductive generalization, hierarchical composition, and adaptability to novel constraints. Existing approaches that use code generation capabilities of pre-trained large (vision) language models as well as purely n

Lifan Jiang, Zhihui Wang, Changmiao Wang, Ming Li

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed \textbf{Consist

David J. Abramovitch, Jernej Mravlje, Jin-Jian Zhou, Antoine Georges

The spectral and transport properties of strongly correlated metals, such as SrVO$_3$ (SVO), are widely attributed to electron-electron ($e$-$e$) interactions, with lattice vibrations (phonons) playing a secondary role. Here, using first-principles electron-phonon ($e$-ph) and dynamical mean field theory calculations, we show that $e$-ph interactions play an

Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang

Diffusion models, a powerful and universal generative AI technology, have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples under active guidance towards task-desired

Yufeng Yue, Meng Yu, Luojie Yang, Yi Yang

Image restoration is rather challenging in adverse weather conditions, especially when multiple degradations occur simultaneously. Blind image decomposition was proposed to tackle this issue, however, its effectiveness heavily relies on the accurate estimation of each component. Although diffusion-based models exhibit strong generative abilities in image res

Ualisson Donardelli Bellon, Wyn Williams, Ricardo Ivan Ferreira Trindade, Ana Diaz

The recording of planetary magnetic fields is often attributed to uniformly-magnetised nanoscopic iron oxides, called single-domain (SD). Yet, the main magnetic constituents of rocks are more complex, non-uniformly magnetised grains in single or multi-vortex states. We know little about their behaviour due to limitations in defining their precise shape and i

Michael Dalvean

Word complexity is defined in a number of different ways. Psycholinguistic, morphological and lexical proxies are often used. Human ratings are also used. The problem here is that these proxies do not measure complexity directly, and human ratings are susceptible to subjective bias. In this study we contend that some form of 'latent complexity' can be approx

Martin Cramer Pedersen, Sourav Mukherjee, Amin Doostmohammadi, Chandana Mondal

Colloidal gels are prime examples of functional materials exhibiting disordered, amorphous, yet meta-stable forms. They maintain stability through short-range attractive forces and their material properties are tunable by external forces. Combining persistent homology analyses and simulations of three-dimensional colloidal gels doped with active particles, w

Kai Luo, Yakun Ju, Lin Qi, Kaixuan Wang

Predicting accurate normal maps of objects from two-dimensional images in regions of complex structure and spatial material variations is challenging using photometric stereo methods due to the influence of surface reflection properties caused by variations in object geometry and surface materials. To address this issue, we propose a photometric stereo netwo

Lukas Lange, Marc Müller, Ghazaleh Haratinezhad Torbati, Dragan Milchevski

Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little

Daniel Kats, Evelin M. C. Christlmaier, Thomas Schraivogel, Ali Alavi

We present a combination of the bi-orthogonal orbital optimisation framework with the recently introduced xTC version of transcorrelation. This allows us to implement non-iterative perturbation based methods on top of the transcorrelated Hamiltonian. Besides, the orbital optimisation influences results of other truncated methods, such as the distinguishable

Jue Wang, Yujie Wang, Lei Zhang, Enxi Zheng

This paper addresses the reconstruction of periodic structures using phase or phaseless near-field measurements. We introduce a novel illumination strategy based on the quasi-periodic condition. Employing the Dirichlet-to-Neumann (DtN) map, we reformulate the scattering problem into a uniquely solvable boundary value problem. Our theoretical analysis shows t

William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar

We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfigurations and renderings of new, unseen scenarios. In this wo

Vincent Albert Wolff, Edmir Xhoxhi, Felix Tautz

Collective Perception will play a crucial role for ensuring vehicular safety in the near future, enabling the sharing of local perceived objects with other Intelligent Transport System Stations (ITS-Ss). However, at the beginning of the roll-out, low market penetration rates are expected. This paper proposes and evaluates an application layer multi-hop Colle

Charlie Mattschas, Marius Puplauskis, Chris Toebes, Violetta Sharoglazova

In paraxial approximation, the electromagnetic eigenmodes inside an optical microresonator can be derived from a Schr\"odinger-type eigenvalue problem. In this framework, tilting the cavity mirrors effectively introduces a linear potential to the system. In our work, we apply solution strategies for inverse problems to precisely determine and control the rel

Mohamad H. Dinan, Arman Farhang

In this paper, we explore the integration of two revolutionary technologies, reconfigurable intelligent surfaces (RISs) and orthogonal time frequency space (OTFS) modulation, to enhance high-speed wireless communications. We introduce a novel phase shift design algorithm for RIS-assisted OTFS, optimizing energy reception and channel gain in dynamic environme

Barbara Lavina, Enrique Zanardi, Andrés Mujica, Hyunchae Cynn

The pressure-induced polymorphism of binary octect compounds has long been considered a settled problem although the possible atomic disordering of some phases remains a puzzling observation. Taking GaP as a case study, we conclude, through x-ray microdiffraction and first-principles calculations, that its high-pressure phase II (previously reported as being

Leandro G. Gomes

In this short note, we define and characterize all the spacetimes admitting observers to whom the cosmic expansion is homogeneous and isotropic and interpret their Einstein's equations.

Begoña Barrios, Leandro M. Del Pezzo, Alexander Quaas, Julio D. Rossi

We study the behavior of the fractional convexity when the fractional parameter goes to 1. For any notion of convexity, the convex envelope of a datum prescribed on the boundary of a domain is defined as the largest possible convex function inside the domain that is below the datum on the boundary. Here we prove that the fractional convex envelope inside a s

Yihan Duan, Siqi Xu, Shan Cheng, Xingbo Zhao

We demonstrate, in the context of a scalar version of the chiral effective field theory, that the multi-sea quark contribution to the nucleon is significant and highly non-trivial in sharp contrast to the prediction of perturbation theory. The non-perturbative calculation is performed in the Fock sector dependent renormalization scheme on the light front, in

Tuong Vy Nguyen, Alexander Glaser, Felix Biessmann

Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data. These technologies have already been used to generate training data for Machine Learning (ML) models, and large text-to-image models like DALL-E 2, Imagen, and Stable Diffusion a

Vincent Albert Wolff, Edmir Xhoxhi

Recent reports from the World Health Organization highlight that Vulnerable Road Users (VRUs) have been involved in over half of the road fatalities in recent years, with occlusion risk - a scenario where VRUs are hidden from drivers' view by obstacles like parked vehicles - being a critical contributing factor. To address this, we present a novel algorithm

Gukyeong Bang, Taehyeong Kim, Seonhee Lim

In this paper, we consider singular systems of linear forms over global function fields of class number one and give an upper bound for the Hausdorff dimension of the set of singular systems of linear forms by constructing an appropriate Margulis height function on the space of lattices over global function fields.

Pavel Smirnov, Frank Joublin, Antonello Ceravola, Michael Gienger

Large Language Models (LLMs) are capable of transforming natural language domain descriptions into plausibly looking PDDL markup. However, ensuring that actions are consistent within domains still remains a challenging task. In this paper we present a novel concept to significantly improve the quality of LLM-generated PDDL models by performing automated cons

Ingrid Haegele

Addressing female underrepresentation in leadership positions has become a key policy objective. However, little is known about the extent to which leadership appeals differently to women. Collecting new data from a large firm, I document that women are substantially less likely to apply for early-career promotions. Realized application patterns and large-sc

Pablo Braz e Silva, Roberto de A. Capistrano-Filho, Jackellyny Dassy do Nascimento Carvalho, David dos Santos Ferreira

This article deals with the $H^{1}$--level local null controllability for the energy-critical nonlinear Schr\"{o}dinger equation in $\mathbb{R}^3$. Firstly, we demonstrate that the problem under consideration is well-posed using Strichartz estimates. Moreover, through the Hilbert uniqueness method, we prove the linear Schr\"{o}dinger equation to be controlla

Xuanming Cao, Chengyu Tao, Juan Du

The surface quality inspection of manufacturing parts based on 3D point cloud data has attracted increasing attention in recent years. The reason is that the 3D point cloud can capture the entire surface of manufacturing parts, unlike the previous practices that focus on some key product characteristics. However, achieving accurate 3D anomaly detection is ch

Vladimir Dzhunushaliev, Vladimir Folomeev

Flux tube solutions within non-Abelian SU(3) Proca theory with external sources are obtained. It is shown that such tubes have a longitudinal chromoelectric field possessing two components (nonlinear and gradient), as well as a transverse chromomagnetic field whose force lines create concentric circles with the center on the axis of the tube. The scenario of

Francesco Cordiano, Bart De Schutter

Scenario reduction algorithms can be an effective means to provide a tractable description of the uncertainty in optimal control problems. However, they might significantly compromise the performance of the controlled system. In this paper, we propose a method to compensate for the effect of scenario reduction on stochastic optimal control problems for chanc

Thomas Curtright, Gaurav Verma

We discuss the regions forbidden to classical scattering trajectories by repulsive potentials. We give explicit results for the asymptotic form of these regions, far from the scattering center, in terms of the scattering angle function.

Yaosong Yu, Qihang Chen, Liang Jing, Ciro Pappalardo

The star formation rate is a crucial astrophysical tracer for understanding the formation and evolution of galaxies, determining the interaction between interstellar medium properties and star formation, thereby inferring the evolutionary laws of cosmic star formation history and cosmic energy density. The mainstream approach to studying the stellar property

R. Terra

In this work we study the effects of the proton and $Pb$ internal structure on charmonium production at high multiplicities. To do this, we assume that the nucleon has an Y shape, which means that the effective quarks (quarks + antiquarks + gluons) are in the extremities of the Y, connected by gluon lines. Since the quarks are in the periphery and the gluons

Clarissa R. Do Ó, Ben Sappey, Quinn M. Konopacky, Jean-Baptiste Ruffio

The 1RXS J034231.8+121622 system consists of an M dwarf primary and a directly imaged low-mass stellar companion. We use high resolution spectroscopic data from Keck/KPIC to estimate the objects' atmospheric parameters and radial velocities (RVs). Using PHOENIX stellar models, we find that the primary has a temperature of 3460 $\pm$ 50 K a metallicity of 0.1

Robert Fuchs, Juanjuan Ren, Sebastian Franke, Stephen Hughes

A multi-cavity quantization scheme is developed using quasinormal modes (QNMs) of optical cavities embedded in a homogeneous background medium for cases where retardation is significant in the inter-cavity coupling. Using quantities that can be calculated in computational optics with numerical Maxwell solvers, we extend previous QNM quantization schemes and

Geoffrey M Vasil, Daniel Lecoanet, Kyle Augustson, Keaton J Burns

The Sun's magnetic dynamo cycle features a distinct pattern: a propagating region of sunspot emergence appears around 30 degrees latitude and vanishes near the equator every 11 years. Moreover, longitudinal flows called "torsional oscillations" closely shadow sunspot migration, undoubtedly sharing a common cause. Contrary to theories suggesting deep origins

Ricardo Pereira, Luís Garrote, Tiago Barros, Ana Lopes

Indoor scenes are usually characterized by scattered objects and their relationships, which turns the indoor scene classification task into a challenging computer vision task. Despite the significant performance boost in classification tasks achieved in recent years, provided by the use of deep-learning-based methods, limitations such as inter-category ambig

Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, Sung Ju Hwang

The pace of scientific research, vital for improving human life, is complex, slow, and needs specialized expertise. Meanwhile, novel, impactful research often stems from both a deep understanding of prior work, and a cross-pollination of ideas across domains and fields. To enhance the productivity of researchers, we propose ResearchAgent, which leverages the

Baoquan Yuan, Xinyuan Xu, Changhao Li

In this paper, we study the global regularity problem for the 2D Rayleigh-B\'{e}nard equations with logarithmic supercritical dissipation. By exploiting a combined quantity of the system, the technique of Littlewood-Paley decomposition and Besov spaces, and some commutator estimates, we establish the global regularity of a strong solution to this equations i

Y. -S. Chen, C. Huang, P. -Y. Liu, H. -W. Yen

Hydrogen embrittlement in metals (HE) is a serious challenge for the use of high strength materials in engineering practice and a major barrier to the use of hydrogen for global decarbonization. Here we describe the factors and variables that determine HE susceptibility and provide an overview of the latest understanding of HE mechanisms. We discuss hydrogen

Josua Spisak, Matthias Kerzel, Stefan Wermter

Humanoid robots can benefit from their similarity to the human shape by learning from humans. When humans teach other humans how to perform actions, they often demonstrate the actions, and the learning human imitates the demonstration to get an idea of how to perform the action. Being able to mentally transfer from a demonstration seen from a third-person pe

Juan Herrera Mateos, Leandro Tosi, Alessandro Braggio, Fabio Taddei

We study nonlocal thermoelectricity in a superconducting wire subject to spin-orbit coupling and a magnetic field with a relative orientation $\theta$ between them. We calculate the current flowing in a normal probe attached to the bulk of a superconducting wire, as a result of a temperature difference applied at the ends of the wire. We focus on the linear

H. Tsuchiya, K. Hibino, K. Kawata, M. Ohnishi

From 1998 to 2017, neutron monitors located at an altitude of 4300 m on the Tibetan plateau detected 127 long-duration bursts of high-energy radiation in association with thunderclouds. These bursts typically lasted for 10 to 40 minutes, and 89\% of them occurred between 10:00 and 24:00 local time. They were also found to be more likely to occur at night, es

Michael Painter, Mohamed Baioumy, Nick Hawes, Bruno Lacerda

Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques. However, UCT can be slow to explore an optimal action when it initially appears inferior to other actions. Maximum ENtropy Tree-Search (MENTS) incorporates the maximum entropy principle into an MCTS approach, utili

E. Caffau, P. Bonifacio L. Monaco, L. Sbordone, M. Spite

The Gaia satellite has provided the community with three releases containing astrometrical and photometric data as well as by products, such as stellar parameters and variability indicators. By selecting in the Gaia database, one can select stars with the requested characteristics, such as high speed. At present any selection is based on available Gaia relea

Lukas Kratochvila

Obstacle detection is one of the basic tasks of a robot movement in an unknown environment. The use of a LiDAR (Light Detection And Ranging) sensor allows one to obtain a point cloud in the vicinity of the sensor. After processing this data, obstacles can be found and recorded on a map. For this task, I present a pipeline capable of detecting obstacles even

Shubhayan Ghosal

This paper delves into recent advancements in Quantum Reinforcement Learning (QRL), particularly focusing on non-commutative environments, which represent uncharted territory in this field. Our research endeavors to redefine the boundaries of decision-making by introducing formulations and strategies that harness the inherent properties of quantum systems. A

Nadia Nasri, Carlos Gutiérrez-Álvarez, Sergio Lafuente-Arroyo, Saturnino Maldonado-Bascón

Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forgetting, where models lose proficiency in previously learned tasks as they acquire new ones. While numerous solutions have been proposed, existing experimental setups often rely on idealized class-i

Damiano De Gaspari, Levi Haunschmid-Sibitz

The Stochastic Burgers equation was introduced in [H. van Beijeren, R. Kutner and H. Spohn, Excess noise for driven diffusive systems, PRL, 1985] as a continuous approximation of the fluctuations of the asymmetric simple exclusion process. It is formally given by $$\partial_t\eta =\frac{1}{2}\Delta\eta+ \mathfrak w\cdot\nabla(\eta^2) + \nabla\cdot\xi,$$ wher

Oliver O'Brien, Laurens Lootens, Frank Verstraete

We present a locality preserving unitary mapping from fermions to qubits on a 2D torus whilst accounting for the mapping of topological sectors. Extending the work of Shukla et al. [Phys. Rev. B 101, 155105], an explicit intertwiner is constructed in the form of a projected entangled pair operator. By encoding the information about the charge sectors (and if

Takayuki J. Hayashi, Akihiro Doi, Hiroshi Nagai

A substantial fraction of quasars display broad absorption lines (BALs) in their rest-frame ultraviolet spectra. While the origin of BALs is thought to be related to the accretion disc wind, it remains unclear whether the observed ratio of BAL to non-BAL quasars is due to orientation. We conducted observations of 48 BAL quasars and the same number of non-BAL

Marvin Pafla, Kate Larson, Mark Hancock

The field of eXplainable artificial intelligence (XAI) has produced a plethora of methods (e.g., saliency-maps) to gain insight into artificial intelligence (AI) models, and has exploded with the rise of deep learning (DL). However, human-participant studies question the efficacy of these methods, particularly when the AI output is wrong. In this study, we c

Tuomas Kynkäänniemi, Miika Aittala, Tero Karras, Samuli Laine

Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance is clearly harmful toward the beginning of the chain (high noise levels), largely unnecessary toward the end (low noise levels

Lajos Diósi

We discuss the Hamiltonian hybrid coupling between a classical and a quantum subsystem. If applicable to classical gravity coupled to quantized matter, this hybrid theory might realize a captivating `postquantum' alternative to full quantum-gravity. We summarize the nonrelativistic hybrid dynamics in improved formalism adequate to Hamiltonian systems. The ma

Max-Olivier Hongler, Olivier Gallay, Fariba Hashemi

The renowned van der Waals (VDW) state equation quantifies the equilibrium relationship between pressure $P$, volume $V$ and temperature $k_{B}T$ of a real gas. We assign new variable interpretations adapted to the economic context: $P \rightarrow Y$, representing price; $V \rightarrow X$, representing demand; and $k_{B}T \rightarrow \kappa$, representing in

Yi Sun, Hong Shen, Bingqing Li, Wei Xu

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by informatio

Andreas Säuberli, Simon Clematide

Reading comprehension tests are used in a variety of applications, reaching from education to assessing the comprehensibility of simplified texts. However, creating such tests manually and ensuring their quality is difficult and time-consuming. In this paper, we explore how large language models (LLMs) can be used to generate and evaluate multiple-choice rea

Ryan Williams

This paper aims to show that a simple framework, utilizing basic formalisms from set theory and category theory, can clarify and inform our theories of the relation between mind and matter.

Matthew N. Crowe, Edward R. Johnson

Modons, or dipolar vortices, are common and long-lived features of the upper ocean, consisting of a pair of monopolar vortices moving through self-advection. Such structures remain stable over long times and may be important for fluid transport over large distances. Here we present a semi-analytical method for finding fully nonlinear modon solutions in a mul

Masashi Osada, Gustavo A. Garcia Ricardez, Yosuke Suzuki, Tadahiro Taniguchi

Large language models (LLMs) and vision-language models (VLMs) have been increasingly used in robotics for high-level cognition, but their use for low-level cognition, such as interpreting sensor information, remains underexplored. In robotic grasping, estimating the reflectance of objects is crucial for successful grasping, as it significantly impacts the d

Momoko Tsujimoto, Kenichi Kinugawa

We show that there can exist two liquid states in distinguishable helium-4 ($^4$He) obeying Boltzmann statistics by path integral centroid molecular dynamics (CMD) simulations. This is an indication of quantum liquid polyamorphism induced by nuclear quantum effect. For 0.08-3.3 K and 1-500 bar, we extensively conducted the isothermal-isobaric CMD simulations

Xiaoxu Han, Xin Mu, Jinghui Zhong

Planning the movement of the sink to maximize the lifetime in wireless sensor networks is an essential problem of great research challenge and practical value. Many existing mobile sink techniques based on mathematical programming or heuristics have demonstrated the feasibility of the task. Nevertheless, the huge computation consumption or the over-reliance

Anastasiya Krysl, Dmytro B. But, Kęstutis Ikamas, Jakob Holstein

This paper presents a study on performance optimization and resonant frequency modification of terahertz detectors by the use of hyper-hemispherical silicon superstrate lenses. The detectors are patch-TeraFETs, i.e., field-effect transistors with monolithically integrated patch antennas fabricated with a commercial 65-nm CMOS foundry process and designed for

Damiano F. G. Fiorillo, Edoardo Vitagliano

We revisit supernova (SN) bounds on a hidden sector consisting of millicharged particles $\chi$ and a massless dark photon. Unless the self-coupling is fine-tuned to be small, rather than exiting the SN core as a gas, the particles form a relativistic fluid and subsequent dark QED fireball, streaming out against the drag due to the interaction with matter. N

Shiming Chen, Wenjin Hou, Salman Khan, Fahad Shahbaz Khan

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn match

Stefano Lepri

We investigate the possibility of characterizing the different thermalization pathways through a large-deviation approach. Specifically, we consider clean, disordered and quasi-periodic harmonic chains under energy and momentum-conserving noise. For their associated master equations, describing the dynamics of normal modes energies, we compute the fluctuatio

Lasse H. Hansen, Simon B. Jensen, Mark P. Philipsen, Andreas Møgelmose

Identifying and classifying underground utilities is an important task for efficient and effective urban planning and infrastructure maintenance. We present OpenTrench3D, a novel and comprehensive 3D Semantic Segmentation point cloud dataset, designed to advance research and development in underground utility surveying and mapping. OpenTrench3D covers a comp

Giuseppe Sirianni, Alberto Guardone, Barbara Re, Rémi Abgrall

This work presents a procedure to solve the Euler equations by explicitly updating, in a conservative manner, a generic thermodynamic variable such as temperature, pressure or entropy instead of the total energy. The presented procedure is valid for any equation of state and spatial discretization. When using complex equations of state such as Span-Wagner, c

Georgios Gavrilopoulos, Guillaume Lecué, Zong Shang

We obtain upper bounds for the estimation error of Kernel Ridge Regression (KRR) for all non-negative regularization parameters, offering a geometric perspective on various phenomena in KRR. As applications: 1. We address the multiple descent problem, unifying the proofs of arxiv:1908.10292 and arxiv:1904.12191 for polynomial kernels and we establish multipl

Valery I. Kovalchuk

Thermal behaviors of hydroxypropyl cellulose dilute aqueous solutions with impurities of Group~I alkali metal ions (Li, Na, K, Rb and Cs chlorides) has been studied by means of static and dynamic light scattering measurements. From the experimental data, it follows that at temperatures above the LCST and in the presence of salts, there arise supramolecular a

Xiaowei Wu, Shengwei Zhou

We consider the problem of allocating $m$ indivisible items to a set of $n$ heterogeneous agents, aiming at computing a proportional allocation by introducing subsidy (money). It has been shown by Wu et al. (WINE 2023) that when agents are unweighted a total subsidy of $n/4$ suffices (assuming that each item has value/cost at most $1$ to every agent) to ensu

Neville Harnew

This paper remembers thirty years of the Beauty conference series and celebrates its 20th meeting. The conference highlights are reviewed.

Anwai Archit, Constantin Pape

CNNs, most notably the UNet, are the default architecture for biomedical segmentation. Transformer-based approaches, such as UNETR, have been proposed to replace them, benefiting from a global field of view, but suffering from larger runtimes and higher parameter counts. The recent Vision Mamba architecture offers a compelling alternative to transformers, al

Tobias Rindlisbacher

The Cayley-Hamilton theorem is used to implement an iterative process for the efficient numerical computation of matrix power series and their differentials. In addition to straight-forward applications in lattice gauge theory simulations e.g. to reduce the computational cost of smearing, the method can also be used to simplify the evaluation of SU(N) one-li

Torbjørn Smith, Olav Egeland

A method for learning Hamiltonian dynamics from a limited and noisy dataset is proposed. The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) of inherently Hamiltonian vector fields, and in particular, odd Hamiltonian vector fields. This is done with a symplectic kernel, and it is shown how the kernel can be modified to a

Ignacio Borsa, Barbara Jäger

We present a Monte-Carlo program for the simulation of polarized deep inelastic scattering at next-to-leading order in QCD matched to parton shower programs building on an existing implementation of the unpolarized case in the POWHEG BOX package. We discuss extensions of the POWHEG BOX framework necessary to account for polarized initial states and validate

Ranjan Patowary, Gautam Barua, Radhika Sukapuram

Network Functions (NFs) improve the safety and efficiency of networks. Flows traversing NFs may need to be migrated to balance load, conserve energy, etc. When NFs are stateful, the information stored on the NF per flow must be migrated before the flows are migrated, to avoid problems of consistency. We examine what it means to correctly migrate flows from a

H. Hadi, Amin Rezaei Akbarieh, David F. Mota

The investigation of non-vacuum cosmological backgrounds containing black holes is greatly enhanced by the Kiselev solution. This solution plays a crucial role in understanding the properties of the background and its relationship with the features of the black hole. Consequently, the gravitational memory effects at large distances from the black hole offer

Guillaume Bagan, Eric Duchêne, Florian Galliot, Valentin Gledel

We propose a generalization of positional games, supplementing them with a restriction on the order in which the elements of the board are allowed to be claimed. We introduce poset positional games, which are positional games with an additional structure -- a poset on the elements of the board. Throughout the game play, based on this poset and the set of the

Simon Elias Schrader, Håkon Emil Kristiansen, Thomas Bondo Pedersen, Simen Kvaal

Recent advances in attosecond science have made it increasingly important to develop stable, reliable and accurate algorithms and methods to model the time evolution of atoms and molecules in intense laser fields. A key process in attosecond science is high-harmonic generation, which is challenging to model with fixed Gaussian basis sets, as it produces high

Daniele Mari, André F. R. Guarda, Nuno M. M. Rodrigues, Simone Milani

The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single

Bruno Lévy, Yann Brenier, Roya Mohayaee

We study Monge-Amp\`ere gravity (MAG) as an effective theory of cosmological structure formation through optimal transport theory. MAG is based on the Monge-Amp\`ere equation, a nonlinear version of the Poisson equation, that relates the Hessian determinant of the potential to the density field. We explain how MAG emerges from a conditioned system of indepen

Rui Li, Martin Trapp, Marcus Klasson, Arno Solin

Deployment of deep neural networks in real-world settings typically requires adaptation to new tasks with few examples. Few-shot classification (FSC) provides a solution to this problem by leveraging pre-trained backbones for fast adaptation to new classes. However, approaches for multi-domain FSC typically result in complex pipelines aimed at information fu

Vladimir Voronin

The leptonic and semileptonic decays of mesons are investigated within the Domain model of QCD vacuum and hadronization. The Domain Model is the mean-field approach based on the statistical ensemble of almost everywhere homogeneous Abelian (anti-)self-dual gluon fields which reproduces main features of low-energy QCD and allows to deduce a nonlocal effective

Bernard Bercu, Stefano Favaro

The Ewens-Pitman model refers to a distribution for random partitions of $[n]=\{1,\ldots,n\}$, which is indexed by a pair of parameters $\alpha \in [0,1)$ and $\theta>-\alpha$, with $\alpha=0$ corresponding to the Ewens model in population genetics. The large $n$ asymptotic properties of the Ewens-Pitman model have been the subject of numerous studies, with

Feng-Wu Guo, Wen-Hao Liu, Zhi Wang, Shu-Shen Li

The insulator-to-metal transition (IMT) in vanadium dioxide (VO2) has garnered extensive attention for its potential applications in ultrafast switches, neuronal network architectures, and storage technologies. However, a significant controversy persists regarding the formation of the IMT, specifically concerning whether a complete structural phase transitio

Tapas Chatterjee, Sonam Garg

In this article, we aim to extend the research conducted by Chatterjee and Garg in 2024, particularly focusing on the $q$-analogue of the generalized Stieltjes constants. These constants constitute the coefficients in the Laurent series expansion of a $q$-analogue of the Hurwitz zeta function around $s=1$. Chatterjee and Garg previously established arithmeti

Tapas Chatterjee, Sonam Garg

In this article, our aim is to extend the research conducted by Kurokawa and Wakayama in 2003, particularly focusing on the $q$-analogue of the Hurwitz zeta function. Our specific emphasis lies in exploring the coefficients in the Laurent series expansion of a $q$-analogue of the Hurwitz zeta function around $s=1$. We establish the closed-form expressions fo

Antonino Pagano, Domenico Garlisi, Fabrizio Giuliano, Tiziana Cattai

This paper presents a comprehensive framework designed to facilitate the widespread deployment of the Internet of Things (IoT) for enhanced monitoring and optimization of Water Distribution Systems (WDSs). The framework aims to investigate the utilization of massive IoT in monitoring and optimizing WDSs, with a particular focus on leakage detection, energy c

Danushka Edirimanna, Hins Hu, Samitha Samaranayake

We are in the midst of a technology-driven transformation of the urban mobility landscape. However, unfortunately these new innovations are still dominated by car-centric personal mobility, which leads to concerns such as environmental sustainability, congestion, and equity. On the other hand, mass transit provides a means to move large amounts of travelers