November 2024 arXiv papers — page 80

Flora Le, Dorothea Dumuid, Tyman E. Stanford, Joshua F. Wiley

Multilevel compositional data, such as data sampled over time that are non-negative and sum to a constant value, are common in various fields. However, there is currently no software specifically built to model compositional data in a multilevel framework. The R package multilevelcoda implements a collection of tools for modelling compositional data in a Bay

Markó Horváth, Tímea Tamási

In dynamic vehicle routing problems (DVRPs), some part of the information is revealed or changed on the fly, and the decision maker has the opportunity to re-plan the vehicle routes during their execution, reflecting on the changes. Accordingly, the solution to a DVRP is a flexible policy rather than a set of fixed routes. A policy is basically a problem-spe

Erik Miehling, Michael Desmond, Karthikeyan Natesan Ramamurthy, Elizabeth M. Daly

Building pluralistic AI requires designing models that are able to be shaped to represent a wide range of value systems and cultures. Achieving this requires first being able to evaluate the degree to which a given model is capable of reflecting various personas. To this end, we propose a benchmark for evaluating the steerability of model personas as a funct

Wansu Kim, Ki-Seng Tan, Fabien Trihan, Kwok-Wing Tsoi

Given an abelian variety $A$ over a global function field $K$ of characteristic $p>0$ and an irreducible complex continuous representation $\psi$ of the absolute Galois group of $K$, we obtain a BSD-type formula for the leading term of Hasse--Weil--Artin $L$-function for $(A,\psi)$ at $s=1$ under certain technical hypotheses. The formula we obtain can be app

Vaios Blatzios, Christopher H. Joyner, Sebastian Müller, Martin Sieber

We derive a Gutzwiller-type trace formula for quantum chaotic systems that accounts for both particle spin precession and discrete geometrical symmetries. This formula generalises previous results that were obtained either for systems with spin [1,2] or for systems with symmetries [3,4], but not for a combination of both. The derivation requires not only a c

Huan-Cong Liu, Chun-Xiao Xu, Wei-Xi Huang

Wall-modeled large-eddy simulation (WMLES) is widely recognized as a useful method for simulation of turbulent flows at high Reynolds numbers. Nevertheless, a continual issue in different wall models is the shift of the mean velocity profile from the wall-model/RANS (Reynolds-averaged Navier-Stokes) region to the LES region. This phenomenon, referred to as l

Xiaorang Guo, Jonas Winklmann, Dirk Stober, Amr Elsharkawy

Neutral atoms have emerged as a promising technology for implementing quantum computers due to their scalability and long coherence times. However, the execution frequency of neutral atom quantum computers is constrained by image processing procedures, particularly the assembly of defect-free atom arrays, which is a crucial step in preparing qubits (atoms) f

Isaac Ariza, Lorenzo J. Tardon, Ana M. Barbancho, Irene De-Torres

Electroencephalography (EEG) is a tool that allows us to analyze brain activity with high temporal resolution. These measures, combined with deep learning and digital signal processing, are widely used in neurological disorder detection and emotion and mental activity recognition. In this paper, a new method for mental activity recognition is presented; inst

Yong Jiao, Wenlong Lin, Sijie Luo, Dejian Zhou

In the present paper, we develop the random restriction method in the quantum framework. By applying this method, we establish the quantum Eldan-Gross inequality, the quantum Talagrand isoperimetric inequality, and related quantum KKL-type inequalities. Our results recover some recent results of Rouz\'e et al. \cite{RWZ2024} and Jiao et al. \cite{JLZ2025}, w

D. Esparza-Arredondo, C. Ramos Almeida, A. Audibert, M. Pereira-Santaella

Understanding the processes that drive the morphology and kinematics of molecular gas in galaxies is crucial for comprehending star formation and, ultimately, galaxy evolution. Using data obtained with the James Webb Space Telescope (JWST) and the Atacama Large Millimeter/submillimeter Array (ALMA), we study the behavior of the warm molecular gas at temperat

C. Quintero Noda, N. G. Shchukina, A. Asensio Ramos, M. J. Martínez González

Inferring the coupling of different atmospheric layers requires observing spectral lines sensitive to the atmospheric parameters, particularly the magnetic field vector, at various heights. The best way to tackle this goal is to perform multi-line observations simultaneously. For instance, the new version of the Gregor Infrared Spectrograph instrument offers

Shunya Yamada, Kansei Kanayama, Kazuaki Toyoura

In the present study, the nuclear quantum effects (NQEs) on proton diffusivity in oxides were evaluated by molecular dynamics (MD) simulations with the quantum thermal bath (QTB) based on the Langevin dynamics. We employed the proton diffusion in barium zirconate (BaZrO3) with the cubic perovskite structure as the model system, in which protons migrate by ro

Aryan Keluskar, Amrita Bhattacharjee, Huan Liu

Ambiguity in natural language poses significant challenges to Large Language Models (LLMs) used for open-domain question answering. LLMs often struggle with the inherent uncertainties of human communication, leading to misinterpretations, miscommunications, hallucinations, and biased responses. This significantly weakens their ability to be used for tasks li

Yuto Komichi, Yuri Aikawa, Kazunari Iwasaki, Kenji Furuya

We investigate chemistry in the compression layer behind the interstellar shock waves, where molecular cloud formation starts. We perform three-dimensional magnetohydrodynamics simulations of converging flows of atomic gas with shock parameters of inclination between the interstellar magnetic field and the shock wave, pre-shock density, and shock velocity. T

Fabio Leoni, Erdal C. Oğuz, Giancarlo Franzese

Confinement can significantly alter fluid properties, offering potential for specific technological applications. However, achieving precise control over the structural complexity of confined fluids and soft matter remains challenging, as it often requires careful tuning of system parameters. In this study, we perform large-scale molecular dynamics simulatio

Dongxue Liu, Tao Tao, Jun Li, Qing Jia

To evaluate and control bubble front penetration depth ${{h}_{B}}$ induced by ablative Rayleigh-Taylor instability (ARTI) from a weakly nonlinear phase to a self-similar phase, we first propose an improved buoyancy-drag (BD) model with a time-varying drag coefficient. The coefficient incorporates the influence of multiple physical mechanisms, including non-s

Kolja Joeris, Matthias Keulen, Jonathan E. Kollmer

We detail a platform for partial g environment and an experiment for simulated impacts on asteroid surfaces based on it. The partial g environment is created by a two stage approach: First, create microgravity using the ZARM drop tower. Second, convert microgravity to partial gravity by steady acceleration of experiment volume on linear drive inside microgra

Petar Jolakoski, Pece Trajanovski, Arnab Pal, Viktor Stojkoski

We study the effects of stochastic resetting on the Reallocating geometric Brownian motion (RGBM), an established model for resource redistribution relevant to systems such as population dynamics, evolutionary processes, economic activity, and even cosmology. The RGBM model is inherently non-stationary and non-ergodic, leading to complex resource redistribut

Wenbo Jiang, Jiaming He, Hongwei Li, Rui Zhang

Recently, Text-to-Image (T2I) synthesis technology has made tremendous strides. Numerous representative T2I models have emerged and achieved promising application outcomes, such as DALL-E, Stable Diffusion, Imagen, etc. In practice, it has become increasingly popular for model developers to selectively adopt personalized pre-trained text encoders and conditi

Jeonghwan Lee, Cong Ma

We consider estimation of a linear functional of the treatment effect using adaptively collected data. This task finds a variety of applications including the off-policy evaluation (\textsf{OPE}) in contextual bandits, and estimation of the average treatment effect (\textsf{ATE}) in causal inference. While a certain class of augmented inverse propensity weig

Maren Stratmann

The $t$-channel is the dominant production channel for single top-quarks at the LHC. The total cross section of this process is measured by ATLAS in proton-proton collisions at a center-of-mass energy $\sqrt{s}=13$ TeV. The production cross sections for single top-quarks and single top-antiquarks are measured to be $\sigma_{tq}=\text{137}^{+8}_{-8}$ pb and $

Alessandro Laneve, Giuseppe Ronco, Mattia Beccaceci, Paolo Barigelli

Photonic quantum information processing in metropolitan quantum networks lays the foundation for cloud quantum computing [1, 2], secure communication [3, 4], and the realization of a global quantum internet [5, 6]. This paradigm shift requires on-demand and high-rate generation of flying qubits and their quantum state teleportation over long distances [7]. D

Regina Rusch, Oleksandr Chepizhko, Thomas Franosch

We analyze gravitaxis of a Brownian circle swimmer by deriving and characterizing analytically the experimentally measurable intermediate scattering function (ISF). To solve the associated Fokker-Planck equation we use a spectral-theory approach and find formal expressions in terms of eigenfunctions and eigenvalues of the overdamped-noisy-driven-pendulum pro

P. H. M. van Spaendonck

Behavioral models are incredibly useful for understanding and validating software. However, the automatic extraction of such models from actual industrial code remains a largely unsolved problem with current solutions often not scaling well with the complexity and size of industrial systems or having to rely on approximations. To enable the extraction of use

Haoyu Zhang, Yangyang Guo, Mohan Kankanhalli

Vision-Language (V-L) pre-trained models such as CLIP show prominent capabilities in various downstream tasks. Despite this promise, V-L models are notoriously limited by their inherent social biases. A typical demonstration is that V-L models often produce biased predictions against specific groups of people, significantly undermining their real-world appli

Constantin Ickstadt, Thorsten Theobald, Bernhard von Stengel

Quint and Shubik (1997) conjectured that a non-degenerate n-by-n game has at most 2^n-1 Nash equilibria in mixed strategies. The conjecture is true for n at most 4 but false for n=6 or larger. We answer it positively for the remaining case n=5, which had been open since 1999. The problem can be translated to a combinatorial question about the vertices of a p

Søren Fournais, Léo Morin

In this paper we derive formulae for the semiclassical tunneling in the presence of a constant magnetic field in 2 dimensions. The `wells' in the problem are identical discs with Neumann boundary conditions, so we study the magnetic Neumann Laplacian in the complement of a set of discs. We provide a reduction method to an interaction matrix, which works for

Lorenzo J. Tardon, Isabel Barbancho, Ana M. Barbancho, Ichiro Fujinaga

The automatic analysis of scores has been a research topic of interest for the last few decades and still is since music databases that include musical scores are currently being created to make musical content available to the public, including scores of ancient music. For the correct analysis of music elements and their interpretation, the identification o

Dario Faro, Paola Frediani, Antonio Lacopo

In this paper we study higher Gaussian (or Wahl) maps for the canonical bundle of certain smooth projective curves. More precisely, we determine the rank of higher Gaussian maps of the canonical bundle for plane curves, for curves contained in certain linear systems in a surface given by a product of two curves and for curves contained in a sufficiently ampl

Decai Wu, Bowei Wu, Tingfeng Ma, Shuanghuizhi Li

Acoustic microfluidic is an important technology in particle manipulations in biomedical analyses and detections. However, the particle-movement manipulations achieved by the standing surface acoustic wave is suitable for particles in a thin layer of fluids, however it is difficult to manipulate particles in deeper solutions due to the energy loss of surface

Malte Hansen, Wilhelm Hasselbring

As software systems grow in complexity, data and tools that provide valuable insights for easier program comprehension become increasingly important. OpenTelemetry has become a standard for the collection of monitoring data. In this work we present our experiences with different ways how OpenTelemetry can be leveraged to automatically instrument software sys

Daniel Redondo-Sánchez, Miguel Rodríguez-Barranco, Alberto Ameijide, Francisco J. Alonso

We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio IMR, the IMR method. Using the previous 15 year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004 to 2013 for six cancer sites for eac

Yiming Shi, Xun Zhu, Kaiwen Wang, Ying Hu

3D medical image analysis is essential for modern healthcare, yet traditional task-specific models are inadequate due to limited generalizability across diverse clinical scenarios. Multimodal large language models (MLLMs) offer a promising solution to these challenges. However, existing MLLMs have limitations in fully leveraging the rich, hierarchical inform

Jiaju Zhang

We investigate the entanglement entropy in quantum states featuring repeated sequential excitations of unit patterns in momentum space. In the scaling limit, each unit pattern contributes independently and universally to the entanglement entropy, leading to a characteristic volume-law scaling. Crucially, this universal contribution remains identical for both

Milutin Obradović, Nikola Tuneski

Using some properties of the Grunsky coefficients we improve earlier results for upper bounds of the Hankel determinants of the second and third order for the class $\mathcal{S}$ of univalent functions.

Daniel M. Jimenez G., David Solans, Mikko Heikkila, Andrea Vitaletti

Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data. While this privacy-preserving method shows potential, it struggles when data across clients is not independent and identically dist

Christian Kanzow, Leo Lehmann

We consider the composite minimization problem with the objective function being the sum of a continuously differentiable and a merely lower semicontinuous and extended-valued function. The proximal gradient method is probably the most popular solver for this class of problems. Its convergence theory typically requires that either the gradient of the smooth

Liang Hou, Hao Yu, Guosong Xu

In this paper, we introduce a novel pricing model for Uniswap V3, built upon stochastic processes and the Martingale Stopping Theorem. This model innovatively frames the valuation of positions within Uniswap V3. We further conduct a numerical analysis and examine the sensitivities through Greek risk measures to elucidate the model's implications. The results

Huawei Liang, Yuanzhi Liu, Yu-Jia Zeng, Yangjian Cai

A theory based on the superposition principle is developed to uncover the basic physics of the wave behavior in a finite grating of N unit cells. The theory reveals that bound states in the continuum (BICs) of infinite quality factor (Q-factor) can be supported by such grating when the perfect reflection is introduced at its boundaries. If geometrical pertur

Jheng-Jie Chen, Jiun-Cheng Chen, Hung-Yi Wu

We show that the set $\mathcal{T}_{3, \mathrm{sm}}^{\mathrm{can}}$ of smooth threefold canonical thresholds coincides with $\mathcal{T}_{2, \mathrm{sm}}^{\mathrm{lc}}=\mathcal{HT}_{2}$, where $\mathcal{HT}_{2}$ is the $2$-dimensional hypersurface log canonical thresholds characterized by Kuwata \cite{K99a, K99b}. We classify the set $\mathcal{T}_{3}^{\mathrm

Laurence Aitchison

Here, we show that in the data-rich setting where you only train on each datapoint once (or equivalently, you only train for one epoch), standard "maximum likelihood" training optimizes the true data generating process (DGP) loss, which is equivalent to the test loss. Further, we show that the Bayesian model average optimizes the same objective, albeit while

Maurice Weber, Daniel Fu, Quentin Anthony, Yonatan Oren

Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-performing models lack transparency in their dataset curation and model development processes, posing an obstacle to

Marco Cattaneo, Louan Presse, Gazendra Shakya, Thomas Renggli

Understanding how substrate-attached bubbles respond to ultrasound is important for applications from industrial cleaning to biomedical therapy. Under ultrasonic excitation, bubbles can deform through Faraday instability and periodically emit high-speed jets. Although this behavior is increasingly well understood for free bubbles, the dynamics of wall-attach

JunJie Wee, Guo-Wei Wei

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies (mAbs) and vaccines, which are both time-consuming and costly. This underscores the need for efficient computational approaches. Recent advancements, like topo

Juan B. Roldán, Enrique Miranda, David Maldonado, Alexey N. Mikhaylov

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production.

Thomas Kiechl, Thomas Franosch, Michele Caraglio

We elaborate and validate a generalization of the renowned transition-path-sampling algorithm for a paradigmatic model of active particles, namely the Run-and-Tumble particles. Notwithstanding the non-equilibrium character of these particles, we show how the consequent lack of the microscopical reversibility property, which is usually required by transition-

Priyank Singh, András Gunyhó, Heikki Suominen, Giacomo Catto

Recently, ultrasensitive calorimeters have been proposed as a resource-efficient solution for multiplexed qubit readout in superconducting large-scale quantum processors. However, experiments demonstrating frequency multiplexing of these superconductor--normal--conductor--superconductor (SNS) sensors are are lacking in the literature. To this end, we present

Eric Scholz, Rafael Weißbach

Our model for the lifespan of an enterprise is the geometric distribution. We do not formulate a model for enterprise foundation, but assume that foundations and lifespans are independent. We aim to fit the model to information about foundation and closure of German enterprises in the AFiD panel. The lifespan for an enterprise that has been founded before th

C. Acal, A. M. Aguilera, F. J. Alonso, J. E. Ruiz-Castro

This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. As the data are by nature curves, functional principal component analysis is a suitable candidate to explain the main modes of variability. Taking into account this data-driven motivation, in this paper we propose two new fo

Matthias Becht, Hans-Peter Lehmann, Peter Sanders

A retrieval data structure stores a static function f : S -> {0,1}^r . For all x in S, it returns the r-bit value f(x), while for other inputs it may return an arbitrary result. The structure cannot answer membership queries, so it does not have to encode S. The information theoretic space lower bound for arbitrary inputs is r|S| bits. Retrieval data structu

Zihao Huang, Qiyang Min, Hongzhi Huang, Defa Zhu

It is widely acknowledged that the performance of Transformer models is logarithmically related to their number of parameters and computational complexity. While approaches like Mixture of Experts (MoE) decouple parameter count from computational complexity, they still face challenges in inference due to high memory access costs. This work introduces UltraMe

Zihao Chen, Zhentao Lin, Bi Zeng, Linyi Huang

To ensure the reliable operation of speech systems across diverse environments, noise addition methods have emerged as the standard solution.However, existing methods offer limited coverage of real-world scenes and depend on pre-existing noise libraries and scene metadata.This paper presents prompt-based Dynamic Generative Scene-based Noise Addition (DGSNA),

Catie Cuan, Tianshuang Qiu, Shreya Ganti, Ken Goldberg

This paper describes the robot technology behind an original performance that pairs a human dancer (Cuan) with an industrial robot arm for an eight-hour dance that unfolds over the timespan of an American workday. To control the robot arm, we combine a range of sinusoidal motions with varying amplitude, frequency and offset at each joint to evoke human motio

Xincheng Hu, Xiao Zeng, Zhaoqiang Liu, Guowu Yang

We investigate the affine equivalence (AE) problem of S-boxes. Given two S-boxes denoted as $S_1$ and $S_2$, we aim to seek two invertible AE transformations $A,B$ such that $S_1\circ A = B\circ S_2$ holds. Due to important applications in the analysis and design of block ciphers, the investigation of AE algorithms has performed growing significance. In this

Yifei Dong, Yimin Zhu, Lixian Zhang, Yihang Ding

To enhance the obstacle-crossing and endurance capabilities of vehicles operating in complex environments, this paper presents the design of a hybrid terrestrial/aerial coaxial tilt-rotor vehicle, TactV, which integrates advantages such as lightweight construction and high maneuverability. Unlike existing tandem dual-rotor vehicles, TactV employs a tiltable

M. F. Fauzi, H. S. Ramadhan, A. Sulaksono

A connection between regular black holes and horizonless ultracompact objects was proposed in~\cite{Carballo-Rubio:2022nuj}. In this paper, we construct a model of a horizonless compact object, specifically an anisotropic gravastar with continuous pressure, that corresponds to regular black hole spacetime in the appropriate limit. The construction begins by

Dawen Zhang, Xiwei Xu, Chen Wang, Zhenchang Xing

Significant efforts has been made to expand the use of Large Language Models (LLMs) beyond basic language tasks. While the generalizability and versatility of LLMs have enabled widespread adoption, evolving demands in application development often exceed their native capabilities. Meeting these demands may involve a diverse set of methods, such as enhancing

Ayon Sarker, Sajid Muhaimin Choudhury

This study introduces a dual-band plasmonic absorber designed for simultaneous sensing applications in the near-infrared (NIR) and mid-infrared (MIR) regions. The absorber, composed of silver nanostructures on a metal plate with a dielectric spacer, exhibits a combination of localized and gap surface plasmon resonances, resulting in two distinct absorption p

Dan Hudson, Jurgen van den Hoogen, Martin Atzmueller

State-of-the-art algorithms are reported to be almost perfect at distinguishing the vibrations arising from healthy and damaged machine bearings, according to benchmark datasets at least. However, what about their application to new data? In this paper, we confirm that neural networks for bearing fault detection can be crippled by incorrect hyperparameterisa

Yudong Han, Qingpei Guo, Liyuan Pan, Liu Liu

The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redundancy and correspondence in videos have hindered the performance potential of existing methods. Through statistical learning on current datasets, we observe that redundancy occurs in

Shilin Qu, Weiqing Wang, Yuan-Fang Li, Quoc Viet Hung Nguyen

Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commerce systems. Traditional methods often face difficulties in generating high-quality negative samples due to the imbalance between positive and negative instances. To address this, w

Wei Wang, Minwu Chen, Hongbin Wang, Gaoqiang Peng

Developing optimized restoration strategies for power distribution systems (PDSs) is critical to enhancing resilience. Prior knowledge of customer interruption cost (CIC) and load restoration behaviors, particularly cold load pickup (CLPU), is essential for effective decision-making. However, both CIC and CLPU are reciprocally influenced by the realized cust

Tengji Xu, Zeyu Luo, Shaojie Liu, Li Fan

AI models are essential in science and engineering, but recent advances are pushing the limits of traditional digital hardware. To address these limitations, physical neural networks (PNNs), which use physical substrates for computation, have gained increasing attention. However, developing effective training methods for PNNs remains a significant challenge.

Arun Kumar Das, Sandip Das, Sk Samim Islam, Ritam M Mitra

Here we study the multipacking problems for geometric point sets with respect to their Euclidean distances. We consider a set of $n$ points $P$ and define $N_s[v]$ as the subset of $P$ that includes the $s$ nearest points of $v \in P$ and the point $v$ itself. We assume that the \emph{$s$-th neighbor} of each point is unique, for every $s \in \{0, 1, 2, \dot

Bingli Wang, Houcheng Su, Nan Yin, Mengzhu Wang

As a technique to alleviate the pressure of data annotation, semi-supervised learning (SSL) has attracted widespread attention. In the specific domain of medical image segmentation, semi-supervised methods (SSMIS) have become a research hotspot due to their ability to reduce the need for large amounts of precisely annotated data. SSMIS focuses on enhancing t

Ming Zhang, Hongyi Sun, Yu-Bo Liu, Qihang Liu

We investigate the spin-density wave (SDW) behavior and the potential for superconductivity (SC) in La$_4$Ni$_3$O$_{10}$ under ambient pressure using a multi-orbital random-phase approximation (RPA). Starting with a twelve-orbital tight-binding model derived from density functional theory (DFT) calculations, we explore the influence of Hubbard interactions o

Masaaki Inaba, Eizo Akiyama

The evolutionary process that led to the emergence of modern human behaviors during the Middle Stone Age in Africa remains enigmatic. While various hypotheses have been proposed, we offer a new perspective that integrates the variability selection hypothesis (VSH) with the evolution of cooperation among human groups. The VSH suggests that human adaptability

Abraham Itzhak Weinberg, Alessio Faccia

Triple Entry (TE) is an accounting method that utilizes three accounts or 'entries' to record each transaction, rather than the conventional double-entry bookkeeping system. Existing studies have found that TE accounting, with its additional layer of verification and disclosure of inter-organizational relationships, could help improve transparency in complex

Zhixian Zhou, Bin Chen, Chen Sun, Peichang Zhang

Dynamic Spectrum Sharing can enhance spectrum resource utilization by promoting the dynamic distribution of spectrum resources. However, to effectively implement dynamic spectrum resource allocation, certain mechanisms are needed to incentivize primary users to proactively share their spectrum resources. This paper, based on the ERC404 standard and integrati

Lorenzo Fiore, Andrea Piccolroaz

This work aims to provide a computational model that can describe the complex behaviour of refractory industrial components under working conditions. Special attention is given to the asymmetric tension-compression behaviour and its evolution in the full range of working temperatures. The model accounts for inelastic flow in compression and brittle fracture

Jang Soo Kim, Minho Song

In 1995, Ismail and Masson introduced orthogonal polynomials of types \( R_I \) and \( R_{II} \), which are defined by specific three-term recurrence relations with additional conditions. Recently, Kim and Stanton found a combinatorial interpretation for the moments of orthogonal polynomials of type \( R_I \) in the spirit of the combinatorial theory of orth

César Jesús-Valls, Serguey T. Petcov, Junjie Xia

Using PREM as a reference model for the Earth density distribution we investigate the sensitivity of the Hyper-Kamiokande (HK) detector to deviations of the Earth i) core average density $\bar{\rho}_C$, ii) lower mantle average density $\bar{\rho}_{lman}$) and iii) upper mantle average density $\bar{\rho}_{uman}$, from their respective PREM densities. The an

C. Guillaume, G. Buldgen, A. M. Amarsi, M. A. Dupret

Context. HD140283, or the Methuselah star, is a well-known reference object in stellar evolution. Its peculiar chemical composition, proximity and absence of reddening makes it an interesting case-study of Pop II stars. Thanks to recent observational efforts, we now have precise interferometric and spectroscopic constraints, as well as revised astrometric pa

Mohamadreza Delbari, Bowu Wang, Nairy Moghadas Gholian, Arash Asadi

Liquid crystal (LC) technology enables low-power and cost-effective solutions for implementing the reconfigurable intelligent surface (RIS). However, the phase-shift response of LC-RISs is temperature-dependent, which, if unaddressed, can degrade the performance. This issue is particularly critical in applications such as secure communications, where variati

L. Eek, Z. F. Osseweijer, C. Morais Smith

Fractal geometries, characterized by self-similar patterns and non-integer dimensions, provide an intriguing platform for exploring topological phases of matter. In this work, we introduce a theoretical framework that leverages isospectral reduction to effectively simplify complex fractal structures, revealing the presence of topologically protected boundary

Guanlin Lyu, Yuguo Sun, Ping Qian, Panpan Gao

Yttrium and its alloys are promising materials for high-tech applications, particularly in aerospace and nuclear reactors. The doping of metallic elements at grain boundaries can significantly influence the stability, strength, and mechanical properties of these materials; however, studies on solute segregation effects in Y-based alloys remain scarce. To add

Yves Aubry

We prove that polynomials of degree 10 over finite fields of even characteristic with some conditions on theirs coefficients have a differential uniformity greater than or equal to 6 over $\mathbb{F}_{2^n}$ for all $n$ sufficiently large.

Xiaoteng Zhou, Yusheng Wang, Katsunori Mizuno

This letter proposes a novel approach for compensating target height data in 2D seabed mosaicking for low-visibility underwater perception. Acoustic cameras are effective sensors for sensing the marine environments due to their high-resolution imaging capabilities and robustness to darkness and turbidity. However, the loss of elevation angle during the imagi

Qingsong Lv, Jiasheng Sun, Sheng Zhou, Xu Zhang

To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolutional channels or layers, significantly enhances computational efficiency and is compatible with hardware acceleration. However, existing pruning methods that rely solely on image fea

Clara Derand

We obtain a Second Main Theorem type inequality for holomorphic maps $f : M \to X$, where $M$ is a parabolic manifold and $X$ is smooth projective with dim $M$ $\le$ dim $X$. We also derive a parabolic Tautological inequality for smooth logarithmic pairs.

Zhongjie Ma, Miao Jiang, Aile Sun, Shengzhu Yi

In this study, we demonstrate that when a ferroelectric nematic is confined between two glass plates coated with ionic polymers, a modulated phase emerges in a narrow temperature range between the nematic and ferroelectric nematic phases. This modulated phase emerges from the nematic phase in a continuous manner and then transforms into the ferroelectric nem

Dan Hill, Matteo Bruno, Hygor Piaget Monteiro Melo, Yuichiro Takeuchi

The concept of `proximity-based cities' has gained attention as a new urban organizational model. Most prominently, the 15-minute city contends that cities can function more effectively, equitably and sustainably if essential, everyday services and key amenities are within a 15-minute walk or cycle. However, focusing solely on travel time risks overlooking d

Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar, Rishi Saket

In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only an aggregate label derived from the instance-labels in the bag. In learning from label proportions (LLP) the aggregate label is the average of the instance-labels in a bag, and a

Samuel Humeau, Daniela Petrisan, Jurriaan Rot

The Kantorovich distance is a widely used metric between probability distributions. The Kantorovich-Rubinstein duality states that it can be defined in two equivalent ways: as a supremum, based on non-expansive functions into [0, 1], and as an infimum, based on probabilistic couplings. Orthogonally, there are categorical generalisations of both presentations

Melissa Kosovari, Thierry Buffeteau, Laurent Thomas, Andrée-Anne Guay Bégin

Biomaterial surface engineering and integrating cell-adhesive ligands are crucial in biological research and biotechnological applications. The interplay between cells and their microenvironment, influenced by chemical and physical cues, impacts cellular behavior. Surface modification of biomaterials profoundly affects cellular responses, especially at the c

Yongyu Wang

Graph learning plays a central role in many data mining and machine learning tasks, such as manifold learning, data representation and analysis, dimensionality reduction, clustering, and visualization. In this work, we propose a highly scalable, adversarial-robustness-guided graph pruning framework for learning graph topologies from data. By performing a spe

Simon Delarue, Thomas Bonald, Tiphaine Viard

Graph Neural Networks (GNNs) and their message passing framework that leverages both structural and feature information, have become a standard method for solving graph-based machine learning problems. However, these approaches still struggle to generalise well beyond datasets that exhibit strong homophily, where nodes of the same class tend to connect. This

Rui Zhang, Xiaoyang Hou, Zhihua Tian, Yan he

Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both attributed and structured data information, graph clustering methods often face practical challenges related to data isolation. Moreover, the absence of collaborative methods for gra

Daniel Mimouni, Paul Malisani, Jiamin Zhu, Welington de Oliveira

Scenario tree reduction techniques are essential for achieving a balance between an accurate representation of uncertainties and computational complexity when solving multistage stochastic programming problems. In the realm of available techniques, the Kovacevic and Pichler algorithm (Ann. Oper. Res., 2015 [1]) stands out for employing the nested distance, a

Maximilian Schöffel, Hiandra Tomasi, Norbert Wehn

Multi-Party Computation in the Head (MPCitH) algorithms are appealing candidates in the additional US NIST standardization rounds for Post-Quantum Cryptography (PQC) with respect to key sizes and mathematical hardness assumptions. However, their complexity presents a significant challenge for platforms with limited computational capabilities. To address this

Yin-Zhen Xu, Khépani Raya

Employing a unified Dyson-Schwinger/Bethe-Salpeter equations approach, we calculate the strong decay couplings $D^* D \pi$ and $B^* B \pi$ within the so-called impulse-approximation in the moving frame. The $B^* B \pi$ estimation is reported for the first time based on a Poincar\'e invariant computation of the associated Bethe-Salpeter amplitudes. Our predic

P Cardaliaguet

We show that any continuous semi-group on $L^1$ which is (i) $L^1-$contractive, (ii) satisfies the conservation law $\partial_t \rho+\partial_x(H(x,\rho))=0$ in $\mathbb{R}_+\times (\mathbb{R}\backslash\{0\})$ (for a space discontinuous flux $H(x,p)= H^l(p) {\bf 1}_{x<0}+ H^r(p) {\bf 1}_{x>0}$), and (iii) satisfies natural continuity and scaling properties,

Shashank Ravichandir, Bhavesh Valecha, Pietro Luigi Muzzeddu, Jens-Uwe Sommer

The transport of molecules for chemical reactions is critically important in various cellular biological processes. Despite thermal diffusion being prevalent in many biochemical processes, it is unreliable for any sort of directed transport or preferential accumulation of molecules. In this paper we propose a strategy for directed motion in which the molecul

O. Scholten, T. N. G. Trinh, S. Buitink, A. Corstanje

For high-energy cosmic-ray physics, it is imperative to determine the mass and energy of the cosmic ray that initiated the air shower in the atmosphere. This information can be extracted from the longitudinal profile of the air shower. In radio-metric observations, this profile is customarily determined through an extensive fitting procedure where calculated

Martin Arnaiz Iglesias, Adil Rengim Cetingoz, Noufel Frikha

This paper introduces and examines numerical approximation schemes for computing risk budgeting portfolios associated to positive homogeneous and sub-additive risk measures. We employ Mirror Descent algorithms to determine the optimal risk budgeting weights in both deterministic and stochastic settings, establishing convergence along with an explicit non-asy

Xia Huang, Dong Ye

Recently, Yanyan Li and Xukai Yan showed the following interesting Hardy inequalities with anisotropic weights: Let $n\geq 2$, $p \geq 1$, $p\alpha > 1-n$, $p(\alpha + \beta)> -n$, then there exists $C > 0$ such that $$\||x|^{\beta}|x'|^{\alpha+1} \nabla u\|_{L^p(\mathbb{R}^n)} \geq C\||x|^\beta|x'|^\alpha u\|_{L^p(\mathbb{R}^n)}, \quad \forall\; u\in C_c^1(

Ivan Lopez Paz, Celeste Fleta, Joan Marc Rafí, Gemma Rius

To cope with environments with high levels of radiation, non-silicon semiconductors such as silicon carbide detectors are being proposed for instrumentation. 4H-SiC diodes for radiation detection have been fabricated in the IMB-CNM Clean Room, for which different strategies to define the electrical contact of the implants had been implemented, in an attempt

Nhan T. Luu

Face recognition is a core task in computer vision designed to identify and authenticate individuals by analyzing facial patterns and features. This field intersects with artificial intelligence image processing and machine learning with applications in security authentication and personalization. Traditional approaches in facial recognition focus on capturi

Peter F. Faul, Amartya Goswami, Gideo Joubert, Graham Manuell

A semiring generalises the notion of a ring, replacing the additive abelian group structure with that of a commutative monoid. In this paper, we study a notion positioned between a ring and a semiring -- a semiring whose additive monoid is a commutative inverse semigroup. These inverse semirings include some important classes of semirings, as well as some ne

Yiqun Zhang, Mingjie Zhao, Hong Jia, Yang Lu

Clustering is a popular machine learning technique for data mining that can process and analyze datasets to automatically reveal sample distribution patterns. Since the ubiquitous categorical data naturally lack a well-defined metric space such as the Euclidean distance space of numerical data, the distribution of categorical data is usually under-represente