May 2023 arXiv papers — page 129

Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt

Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle. Despite its widespread use in machine learning (ML), OT problem still bears its computational burden, while at the same time suffering from the curse of dimensionality for measures supported on general high-dimensional spac

Tobias Morocutti, Florian Schmid, Khaled Koutini, Gerhard Widmer

The ability to generalize to a wide range of recording devices is a crucial performance factor for audio classification models. The characteristics of different types of microphones introduce distributional shifts in the digitized audio signals due to their varying frequency responses. If this domain shift is not taken into account during training, the model

Jianfeng Kuang, Wei Hua, Dingkang Liang, Mingkun Yang

Visual information extraction (VIE), which aims to simultaneously perform OCR and information extraction in a unified framework, has drawn increasing attention due to its essential role in various applications like understanding receipts, goods, and traffic signs. However, as existing benchmark datasets for VIE mainly consist of document images without the a

Weitao Zhou, Zhong Cao, Nanshan Deng, Xiaoyu Liu

Self-driving vehicles (SDVs) are becoming reality but still suffer from "long-tail" challenges during natural driving: the SDVs will continually encounter rare, safety-critical cases that may not be included in the dataset they were trained. Some safety-assurance planners solve this problem by being conservative in all possible cases, which may significantly

Mohammed Sabbah, Federico Belli, Christian Brahms, John C. Travers

We experimentally and numerically investigate flat supercontinuum generation in gas-filled anti-resonant guiding hollow-core photonic crystal fiber. By comparing results obtained with either argon or nitrogen we determine the role of the rotational Raman response on the supercontinuum formation. When using argon, a supercontinuum extending from 350 nm to 2 {

Myung-cheol Roh, Pyoung-gang Lim, Jongju Shin

Deep learning methods have been achieved brilliant results in face recognition. One of the important tasks to improve the performance is to collect and label images as many as possible. However, labeling identities and checking qualities of large image data are difficult task and mistakes cannot be avoided in processing large data. Previous works have been t

Davide Bilò, Sarel Cohen, Tobias Friedrich, Hans Gawendowicz

Most networks are not static objects, but instead they change over time. This observation has sparked rigorous research on temporal graphs within the last years. In temporal graphs, we have a fixed set of nodes and the connections between them are only available at certain time steps. This gives rise to a plethora of algorithmic problems on such graphs, most

Marco Moletta, Maciej K. Wozniak, Michael C. Welle, Danica Kragic

We present a virtual reality (VR) framework to automate the data collection process in cloth folding tasks. The framework uses skeleton representations to help the user define the folding plans for different classes of garments, allowing for replicating the folding on unseen items of the same class. We evaluate the framework in the context of automating garm

Paul Dubois, Romain Lhotte

Evaluating the performance of students in higher education is essential for gauging the effectiveness of teaching methods and achieving greater equality of opportunities for all. In this study, we investigate the correlation between two teachers' grading practices in a deep learning course at the master's level, offered at CentraleSup\'elec. The two teachers

Nandini Mundra, Sumanth Doddapaneni, Raj Dabre, Anoop Kunchukuttan

Adapters have been positioned as a parameter-efficient fine-tuning (PEFT) approach, whereby a minimal number of parameters are added to the model and fine-tuned. However, adapters have not been sufficiently analyzed to understand if PEFT translates to benefits in training/deployment efficiency and maintainability/extensibility. Through extensive experiments

Hongwen Hu, Miao Ye, Chenwei Zhao, Qiuxiang Jiang

Multicast communication technology is widely applied in wireless environments with a high device density. Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and cannot quickly respond to network state changes, thus affecting the throughput, delay, and other QoS requirements of existi

Zhengqing Yuan, Yunhong He, Kun Wang, Yanfang Ye

The success of large language models (LLMs) has inspired an emerging research field of multimodal learning. However, a grand challenge of exploiting LLMs for multimodal learning is the size of pre-trained LLMs which are always with billions of parameters. To tackle this challenge, models such as MiniGPT-4 and LLaVA have been developed to fine-tune the pre-tr

Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva

Music demixing is the task of separating different tracks from the given single audio signal into components, such as drums, bass, and vocals from the rest of the accompaniment. Separation of sources is useful for a range of areas, including entertainment and hearing aids. In this paper, we introduce two new benchmarks for the sound source separation tasks a

Andreas Evers, Shipra Malhotra, Vanita D. Sood

The recognition of the importance of drug-like properties beyond potency to reduce clinical attrition of biologics has driven significant progress in the development of in vitro and in silico tools for developability assessment of antibody sequences. It is now routine to identify and eliminate or optimize antibody hits with poor developability profiles. To f

Weitao Zhou, Zhong Cao, Nanshan Deng, Kun Jiang

Deep reinforcement learning (DRL) has emerged as a promising approach for developing more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a neural network-based driving policy. However, the black-box nature of neural networks can result in unpredictable decision failures, making such AVs unreliable. To this end, this work

Alex Gittens, Malik Magdon-Ismail

Given data ${\rm X}\in\mathbb{R}^{n\times d}$ and labels $\mathbf{y}\in\mathbb{R}^{n}$ the goal is find $\mathbf{w}\in\mathbb{R}^d$ to minimize $\Vert{\rm X}\mathbf{w}-\mathbf{y}\Vert^2$. We give a polynomial algorithm that, \emph{oblivious to $\mathbf{y}$}, throws out $n/(d+\sqrt{n})$ data points and is a $(1+d/n)$-approximation to optimal in expectation. T

S. A. Belov, D. I. Riashchikov, D. Y. Kolotkov, S. Vasheghani Farahani

Torsional Alfv\'en waves in coronal plasma loops are usually considered to be non-collective, i.e. consist of cylindrical surfaces evolving independently, which significantly complicates their detection in observations. This non-collective nature, however, can get modified in the nonlinear regime. To address this question, the propagation of nonlinear torsio

Min Gan, Xiang-xiang Su, Guang-yong Chen, Jing Chen

We propose an online learning algorithm for a class of machine learning models under a separable stochastic approximation framework. The essence of our idea lies in the observation that certain parameters in the models are easier to optimize than others. In this paper, we focus on models where some parameters have a linear nature, which is common in machine

Sabrino Barro, Chloé Beguin, Dylan Brouzet, Leo Charosky

Smartphones and tablets are an integral part of our daily lives, and their capabilities extend well beyond communication and entertainment. With a broad choice of built-in sensors, using these mobile devices as experimental tools (MDETs) allows for a many different measurements, covering several fields of physics (mechanics, acoustics and waves, magnetism, o

Jacob T. Crosser, Braden A. W. Brinkman

The space of possible behaviors complex biological systems may exhibit is unimaginably vast, and these systems often appear to be stochastic, whether due to variable noisy environmental inputs or intrinsically generated chaos. The brain is a prominent example of a biological system with complex behaviors. The number of possible patterns of spikes emitted by

Yongxin Liu, Peng Zeng

Quantile regression (QR) can be used to describe the comprehensive relationship between a response and predictors. Prior domain knowledge and assumptions in application are usually formulated as constraints of parameters to improve the estimation efficiency. This paper develops methods based on multi-block ADMM to fit general penalized QR with linear constra

David Andriot

As a quantum gravity cut-off, the species scale $\Lambda_s$ gets naturally compared to the energy scale of a scalar potential $V$ in an EFT. In this note, we compare the species scale, its rate $|\nabla \Lambda_s|/\Lambda_s$ and their field dependence, to those of a scalar potential. To that end, we first identify a string compactification leading to a scala

Zhengwu Miao, Yong Chen

The paper proposes a deep learning method specifically dealing with the forward and inverse problem of variable coefficient partial differential equations -- Variable Coefficient Physical Information Neural Network (VC-PINN). The shortcut connections (ResNet structure) introduced into the network alleviates the "Vanishing gradient" and unifies the linear and

Samuel Danzon-Chambaud, Marguerite Foissac

This paper deals with the importance of developing codes of conduct for practitioners--be it journalists, doctors, attorneys, or other professions--that are encountering ethical issues when using computation, but do not have access to any framework of reference as to how to address those. At the same time, legal and technological developments are calling for

Iain Mackie, Shubham Chatterjee, Jeffrey Dalton

Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance feedback (GRF) shows that query expansion models using text generated from large language models can improve sparse retrieval without depending on first-pass retrieval effectivene

Tai Terje Huu Nguyen

The following is a Ph.D. thesis. The thesis is submitted in partial fulfillment of the requirements for the degree of Philosophiae Doctor (Ph.D.) at the Norwegian University of Science and Technology.

Qianying Liu, Dongsheng Yang, Wenjie Zhong, Fei Cheng

Numerical reasoning over table-and-text hybrid passages, such as financial reports, poses significant challenges and has numerous potential applications. Noise and irrelevant variables in the model input have been a hindrance to its performance. Additionally, coarse-grained supervision of the whole solution program has impeded the model's ability to learn th

Wenhao Li, Preethi Nair, Kate Rowlands, Karen Masters

Post-starburst galaxies (PSBs) are transition galaxies showing evidence of recent rapid star formation quenching. To understand the role of galaxy mergers in triggering quenching, we investigate the incidence of PSBs and resolved PSB properties in post-merger galaxies using both SDSS single-fiber spectra and MaNGA resolved IFU spectra. We find post-mergers h

Valentin Moos, Ignazio Scimemi, Alexey Vladimirov, Pia Zurita

We present the extraction of unpolarized transverse momentum dependent parton distributions functions (TMDPDFs) and Collins-Soper kernel from the fit of Drell-Yan and weak-vector boson production data. The TMDPDF are parameterized, as commonly done, using their (large transverse momentum) asymptotic matching to PDF. The analysis is done at the next-to-next-t

Giacomo Rubbini

Is incentive compatibility still necessary for implementation if we relax the rational expectations assumption? This paper proposes a generalized model of implementation that does not assume agents hold rational expectations and characterizes the class of solution concepts requiring Bayesian Incentive Compatibility (BIC) for full implementation. Surprisingly

Pedro Haerter, Ricardo L. Viana

Many systems of physical and biological interest are characterized by assemblies of phase oscillators whose interaction is mediated by a diffusing chemical. The coupling effect results from the fact that the local concentration of the mediating chemical affects both its production and absorption by each oscillator. Since the chemical diffuses through the med

K. G. Strassmeier, T. A. Carroll, I. V. Ilyin

We present a magnetic-field surface map for both stellar components of the young visual binary ksi Boo AB (A: G8V, B: K5V). Employed are high resolution Stokes-V spectra obtained with the Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI) at the Large Binocular Telescope (LBT). Stokes V line profiles are inverted with our iMAP software and com

Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady

Extracting information from the electrocardiography (ECG) signal is an essential step in the design of digital health technologies in cardiology. In recent years, several machine learning (ML) algorithms for automatic extraction of information in ECG have been proposed. Supervised learning methods have successfully been used to identify specific aspects in t

Vincent Grandjean, Roger Oliveira

Let $\sigma_q : \mathbb{R}^q \to {\bf S}^q \setminus N_q$ be the inverse of the stereographic projection with centre the north pole $N_q$. Let $W_i$ be a closed subset of $\mathbb{R}^{q_i}$, for $i=1,2$. Let $\Phi:W_1 \to W_2$ be a bi-Lipschitz homeomorphism. The main result states that the homeomorphism $\sigma_{q_2}\circ \Phi \circ \sigma_{q_1}^{-1}$ is a

Chong-Qiang Ye, Jian Li, Xiu-Bo Chen, Yanyan Hou

Semi-quantum private comparison (SQPC) enables two classical users with limited quantum capabilities to compare confidential information using a semi-honest third party (TP) with full quantum power. However, entanglement swapping, as an important property of quantum mechanics in previously proposed SQPC protocols is usually neglected. In this paper, we propo

Eelco van Kampen, Mark Lacy, Duncan Farrah, Claudia del P. Lagos

We have measured the angular auto-correlation function of near-infrared galaxies in SERVS+DeepDrill, the Spitzer Extragalactic Representative Volume Survey and its follow-up survey of the Deep Drilling Fields, in three large fields totalling over 20 sq. deg on the sky, observed in two bands centred on 3.6 and 4.5 micron. We performed this analysis on the ful

Biagio Lucini, David Mason, Maurizio Piai, Enrico Rinaldi

When studied at finite temperature, Yang-Mills theories in $3+1$ dimensions display the presence of confinement/deconfinement phase transitions, which are known to be of first order -- the $SU(2)$ gauge theory being the exception. Theoretical as well as phenomenological considerations indicate that it is essential to establish a precise characterisation of t

Maria Grazia Concilio, Lucio Frydman

One of solution-state Nuclear Magnetic Resonance (NMR)'s main weaknesses, is its relative insensitivity. J-driven Dynamic Nuclear Polarization (JDNP) was recently proposed for enhancing the sensitivity of solution-state NMR, by bypassing the limitations faced by conventional Overhauser DNP (ODNP), at the high magnetic fields where most analytical research is

Hannah Earley

The field of molecular programming allows for the programming of the structure and behavior of matter at the molecular level, even to the point of encoding arbitrary computation. However, current approaches tend to be wasteful in terms of monomers, gate complexes, and free energy. In response, we present a novel abstract model of molecular programming, Rever

Anniina Kinnunen, Ivan V. Lomakin, Tero Mäkinen, Kim Widell

Fatigue failure of crystalline materials is a difficult problem in science and engineering, and recent results have shown that fatigue crack growth can occur in intermittent jumps which have fat-tailed distributions. As fatigue crack propagation is known to leave markings -- called striations -- on the fracture surface, the distances between these should als

Hongxia Guo, Guanghui Hu

This paper is concerned with the multi-frequency factorization method for imaging the support of a wave-number-dependent source function. It is supposed that the source function is given by the inverse Fourier transform of some time-dependent source with a priori given radiating period. Using the multi-frequency far-field data at a fixed observation directio

Enrico Perfetto, Gianluca Stefanucci

Quantum simulations of photoexcited low-dimensional systems are pivotal for understanding how to functionalize and integrate novel two-dimensional (2D) materials in next-generation optoelectronic devices. First principles predictions are extremely challenging due to the simultaneous interplay of light-matter, electron-electron and electron-nuclear interactio

Lisa Kühnel, Julian Schneider, Ines Perrar, Tim Adams

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generati

Tu Anh Dinh, Jan Niehues

Quality Estimation (QE) is the task of predicting the quality of Machine Translation (MT) system output, without using any gold-standard translation references. State-of-the-art QE models are supervised: they require human-labeled quality of some MT system output on some datasets for training, making them domain-dependent and MT-system-dependent. There has b

Jurriaan van Diggelen, Karel van den Bosch, Mark Neerincx, Marc Steen

We propose methods for analysis, design, and evaluation of Meaningful Human Control (MHC) for defense technologies from the perspective of military human-machine teaming (HMT). Our approach is based on three principles. Firstly, MHC should be regarded as a core objective that guides all phases of analysis, design and evaluation. Secondly, MHC affects all par

Agelos Georgakopoulos, Panos Papasoglu

We present problems and results that combine graph-minors and coarse geometry. For example, we ask whether every geodesic metric space (or graph) without a fat $H$ minor is quasi-isometric to a graph with no $H$ minor, for an arbitrary finite graph $H$. We answer this affirmatively for a few small $H$. We also present a metric analogue of Menger's theorem an

Yu-Kuan Fu, Liang-Hsuan Tseng, Jiatong Shi, Chen-An Li

Most of the speech translation models heavily rely on parallel data, which is hard to collect especially for low-resource languages. To tackle this issue, we propose to build a cascaded speech translation system without leveraging any kind of paired data. We use fully unpaired data to train our unsupervised systems and evaluate our results on CoVoST 2 and CV

Véronique Bruyère, Guillermo A. Pérez, Gaëtan Staquet, Frits W. Vaandrager

In this work, we study properties of deterministic finite-state automata with timers, a subclass of timed automata proposed by Vaandrager et al. as a candidate for an efficiently learnable timed model. We first study the complexity of the configuration reachability problem for such automata and establish that it is PSPACE-complete. Then, as simultaneous time

Franco Brezzi, L. Donatella Marini

We revisit classical Virtual Element approximations on polygonal and polyhedral decompositions. We also recall the treatment proposed for dealing with decompositions into polygons with curved edges. In the second part of the paper we introduce a couple of new ideas for the construction of VEM-approximations on domains with curved boundary, both in two and th

M. Sharif, Sana Manzoor

This paper investigates the viability and stability of anisotropic compact stars in the framework of $f(\mathcal{R},\mathrm{T}^{2})$ theory ($\mathcal{R}$ is the Ricci scalar and $\mathrm{T}^{2}=\mathrm{T}_{\tau\upsilon}\mathrm{T}^{\tau\upsilon}$). In this perspective, we use Finch-Skea symmetry and consider different $f(\mathcal{R},\mathrm{T}^{2})$ models t

Bolun Zhang, Nguyen Van Huynh

End-to-End (E2E) learning-based concept has been recently introduced to jointly optimize both the transmitter and the receiver in wireless communication systems. Unfortunately, this E2E learning architecture requires a prior differentiable channel model to jointly train the deep neural networks (DNNs) at the transceivers, which is hardly obtained in practice

Zhe Wang, Yongxiong Wang, Jiapeng Zhang, Yiheng Tang

Individual differences of Electroencephalogram (EEG) could cause the domain shift which would significantly degrade the performance of cross-subject strategy. The domain adversarial neural networks (DANN), where the classification loss and domain loss jointly update the parameters of feature extractor, are adopted to deal with the domain shift. However, limi

Kajol Paithankar, Sanved Kolekar

We explore an interesting connection between black hole shadow parameters and the acceleration bounds for radial linear uniformly accelerated (LUA) trajectories in static spherically symmetric black hole spacetime geometries of the Schwarzschild type. For an incoming radial LUA trajectory to escape back to infinity, there exists a bound on its magnitude of a

Anna Luashvili, Catherine Boisson, Andreas Zech, Maialen Arrieta-Lobo

$\gamma$-ray-emitting narrow-line Seyfert 1 galaxies ($\gamma$-NLS1) constitute an intriguing small population of Active Galactic Nuclei with $\gamma$-ray emission resembling low power flat-spectrum radio quasars (FSRQ), but with differing physical properties. They are jetted, $\gamma$/radio-loud Seyfert galaxies, with relatively low black hole masses, accre

Shahaf Yamin, Haim Permuter

We investigate the problem of wireless routing in integrated access backhaul (IAB) networks consisting of fiber-connected and wireless base stations and multiple users. The physical constraints of these networks prevent the use of a central controller, and base stations have limited access to real-time network conditions. We aim to maximize packet arrival ra

Matías Bender, Pierre-Jean Spaenlehauer

We study polynomial systems with prescribed monomial supports in the Cox rings of toric varieties built from complete polyhedral fans. We present combinatorial formulas for the dimensions of their associated subvarieties under genericity assumptions on the coefficients of the polynomials. Using these formulas, we identify at which degrees generic systems in

Alexander Dobrick, Julian Hölz

In a recent paper by Kurasov, L\"uck, Mugnolo and Wolf, a hybrid gene regulatory network was proposed to model gene expression dynamics by using a stochastic system of coupled partial differential equations. This approach approximates protein counts in cells by modelling them as distributions. In a follow-up paper, the existence and strong convergence of the

Alfred Kume, Tomonari Sei, Andrew T. A. Wood

The paper considers the distribution of a general linear combination of central and non-central chi-square random variables by exploring the branch cut regions that appear in the standard Laplace inversion process. Due to the original interest from the directional statistics, the focus of this paper is on the density function of such distributions and not on

Emir Fejzić, Taco Niet, Cameron Wade, Will Usher

Located in Southern Europe, the Drina River Basin is shared between Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have an exceptionally high dependence on coal for power generation. In this paper, we analyse different development pathways for achieving climate neutrality in these countries and explore the potential

Denis Belomestny, Frank van der Meulen, Peter Spreij

We present a survey of some of our recent results on Bayesian nonparametric inference for a multitude of stochastic processes. The common feature is that the prior distribution in the cases considered is on suitable sets of piecewise constant or piecewise linear functions, that differ for the specific situations at hand. Posterior consistency and in most cas

Rohan Ghanta, Lukas Junge, Leo Morin

In 1996 Erdoes showed that among planar domains of fixed area, the smallest principal eigenvalue of the Dirichlet Laplacian with a constant magnetic field is uniquely achieved on the disk. We establish a quantitative version of this inequality, with an explicit remainder term depending on the field strength that measures how much the domain deviates from the

M. Sharif, M. Zeeshan Gul

This paper explores exact cosmological solutions of anisotropic universe model through Noether symmetry technique in energy-momentum squared gravity. This theory resolves the primordial singularity and provides viable cosmological consequences in the early universe. We consider specific models of this theory and evaluate Noether equations, symmetry generator

Milene Regina dos Santos, Rafael Izbicki

Regression methods assume that accurate labels are available for training. However, in certain scenarios, obtaining accurate labels may not be feasible, and relying on multiple specialists with differing opinions becomes necessary. Existing approaches addressing noisy labels often impose restrictive assumptions on the regression function. In contrast, this p

Ayyub Alzahem, Shahid Latif, Wadii Boulila, Anis Koubaa

Medical imaging is an essential tool for diagnosing various healthcare diseases and conditions. However, analyzing medical images is a complex and time-consuming task that requires expertise and experience. This article aims to design a decision support system to assist healthcare providers and patients in making decisions about diagnosing, treating, and man

Gilles Carron, Ilaria Mondello, David Tewodrose

We prove that any complete Riemannian manifold with negative part of the Ricci curvature in a suitable Dynkin class is bi-Lipschitz equivalent to a finite-dimensional $\mathrm{RCD}$ space, by building upon the transformation rule of the Bakry-\'Emery condition under time change. We apply this result to show that our previous results on the limits of closed R

A. S. M. Muhasin Reza, S. H. Naqib

BaAgAs is a ternary Dirac semimetal which can be tuned across a number of topological orders. In this study we have investigated the bulk physical properties of BaAgAs using density functional theory based computations. Most of the results presented in this work are novel. The optimized structural parameters are in good agreement with previous results. The e

Payel Das, Yang Huang, Ioana Ciuca, Francesca Fragkoudi

Phase-space data, chemistry, and ages together reveal a complex structure in the outer low-${\alpha}$ disc of the Milky Way. The age-vertical velocity dispersion profiles beyond the Solar Neighbourhood show a significant jump at 6 Gyr for stars beyond the Galactic plane. Stars older than 6 Gyr are significantly hotter than younger stars. The chemistry and ag

Carolyn Abbott, Hoang Thanh Nguyen, Alexander J. Rasmussen

The set of equivalence classes of cobounded actions of a group G on different hyperbolic metric spaces carries a natural partial order. Following Abbott--Balasubramanya--Osin, the group G is H--accessible if the resulting poset has a largest element. In this paper, we prove that every non-geometric 3--manifold has a finite cover with H--inaccessible fundamen

Hongliang He, Junlei Zhang, Zhenzhong Lan, Yue Zhang

Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings. However, in previous studies, each embedding used for contrastive learning only derived from one sentence instance, and we call these embeddings instance-level embeddings. In other words, each embedding is

Katja Klobas, Cecilia De Fazio, Juan P. Garrahan

We study the dynamics of a classical circuit corresponding to a discrete-time kinetically constrained East model. We show that this classical "Floquet-East" model displays pre-transition behaviour which is a dynamical equivalent of the hydrophobic effect in water. For the deterministic version of the model we prove exactly: (i) a change in scaling with size

S. A. Tyul'bashev, I. V. Chashei, M. A. Kitaeva

The effect of interplanetary plasma on pulsed pulsar radiation passing through is considered. The pulses of two rotating radio transients (J0609+16, J1132+25) and a pulsar (B0320+39) detected on the Large Phased Array (Pushchino observatory) were analyzed. It is shown that in observations at the frequency of 111 MHz, on elongations of 20o-40o, both an increa

Max Taylor-Davies, Stephanie Droop, Christopher G. Lucas

Imitation is a key component of human social behavior, and is widely used by both children and adults as a way to navigate uncertain or unfamiliar situations. But in an environment populated by multiple heterogeneous agents pursuing different goals or objectives, indiscriminate imitation is unlikely to be an effective strategy -- the imitator must instead de

Menghua Zhao, Aktaruzzaman Al Hossain, Carlos E. Colosqui, Matthieu Roché

We report and rationalize the observation of a crossover from the classical Lucas-Washburn dynamics to a long-lived anomalously slow regime for capillary rise in simple glass tubes. We propose an analytical model considering the role of thermal motion and the nanoscale surface topography to account for the experimental observations. The proposed model indica

Kai Ouyang, Chen Tang, Zenghao Chai, Wenhao Zheng

A critical challenge in contemporary recommendation systems lies in effectively leveraging multimodal content to enhance recommendation personalization. Although various solutions have been proposed, most fail to account for discrepancies between knowledge extracted through isolated feature extraction and its application in recommendation tasks. Specifically

Fausto Ferrari, Claudia Lederman

We continue our study in \cite{FL} on viscosity solutions to a one-phase free boundary problem for the $p(x)$-Laplacian with non-zero right hand side. We first prove that viscosity solutions are locally Lipschitz continuous, which is the optimal regularity for the problem. Then we prove that Lipschitz free boundaries of viscosity solutions are $C^{1,\alpha}$

Marion Neumeier, Andreas Tollkühn, Michael Botsch, Wolfgang Utschick

This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GNNs), the GFTNN is a novel network architecture that operates on graph structures. While several GNNs lack discriminative power due to suboptimal aggregation schemes, the proposed m

G. Puglisi, N. M. Pugno

We propose a new conceptual approach to reach unattained dissipative properties based on the friction of slender concentric sliding columns. We begin by searching for the optimal topology in the simplest telescopic system of two concentric columns. Interestingly, we obtain that the optimal shape parameters are material independent and scale invariant. Based

Judith Sáinz-Pardo Díaz, Álvaro López García

Anonymization techniques based on obfuscating the quasi-identifiers by means of value generalization hierarchies are widely used to achieve preset levels of privacy. To prevent different types of attacks against database privacy it is necessary to apply several anonymization techniques beyond the classical k-anonymity or $\ell$-diversity. However, the applic

Muhammad Sohaib Ayub, Muhammad Adnan, Muhammad Yasir Shafi

Parallel I/O refers to the ability of scientific programs to concurrently read/write from/to a single file from multiple processes executing on distributed memory platforms like compute clusters. In the HPC world, I/O becomes a significant bottleneck for many real-world scientific applications. In the last two decades, there has been significant research in

Niklas Euler, Martin Gärttner

Quantum entanglement has been identified as a crucial concept underlying many intriguing phenomena in condensed matter systems, such as topological phases or many-body localization. Recently, instead of considering mere quantifiers of entanglement like entanglement entropy, the study of entanglement structure in terms of the entanglement spectrum has shifted

Babita, Abhash Kumar Jha, Abhishek Juyal, Bibekananda Maji

In 2000, Hafner and Stopple proved a conjecture of Zagier which states that the constant term of the automorphic function $|\Delta(x+iy)|^2$ i.e., the Lambert series $\sum_{n=1}^\infty \tau(n)^2 e^{-4 \pi n y}$ can be expressed in terms of the non-trivial zeros of the Riemann zeta function. In this article, we study a certain Lambert series associated to Sie

Reed Essick, Isaac Legred, Katerina Chatziioannou, Sophia Han

Astrophysical observations of neutron stars probe the structure of dense nuclear matter and have the potential to reveal phase transitions at high densities. Most recent analyses are based on parametrized models of the equation of state with a finite number of parameters and occasionally include extra parameters intended to capture phase transition phenomeno

Hyung Jun Choi, Seonghak Kim, Youngwoo Koh

We establish convergence results related to the operator splitting scheme on the Cauchy problem for the nonlinear Schr\"odinger equation with rough initial data in $L^2$, $$ \left\{ \begin{array}{ll} i\partial_t u +\Delta u = \lambda |u|^{p} u, & (x,t) \in \mathbb{R}^d \times \mathbb{R}_+, u (x,0) =\phi (x), & x\in\mathbb{R}^d, \end{array} \right. $$ where $

Jonas Deré, Thomas Witdouck

Anosov diffeomorphisms are an important class of dynamical systems with many peculiar properties. Ever since they were introduced in the sixties, it has been an open question which manifolds can admit such diffeomorphisms, where tori of dimension greater than or equal to two are the typical examples. It is conjectured that the only manifolds supporting an An

Zhan Yu, Jun Fan, Zhongjie Shi, Ding-Xuan Zhou

In recent years, different types of distributed and parallel learning schemes have received increasing attention for their strong advantages in handling large-scale data information. In the information era, to face the big data challenges {that} stem from functional data analysis very recently, we propose a novel distributed gradient descent functional learn

Mohammed M. Dawoud, Changxin Liu, Amr Alanwar, Karl H. Johansson

For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measurement signals to a cloud estimator that predicts the system state. To solve this issue, we propose a differentially private set-based estimation pr

Wai Man Si, Michael Backes, Yang Zhang, Ahmed Salem

Machine learning has progressed significantly in various applications ranging from face recognition to text generation. However, its success has been accompanied by different attacks. Recently a new attack has been proposed which raises both accountability and parasitic computing risks, namely the model hijacking attack. Nevertheless, this attack has only fo

David Dolžan

We calculate the Wiener index of the zero-divisor graph of a finite semisimple ring. We also calculate the Wiener complexity of the zero-divisor graph of a finite simple ring and find an upper bound for the Wiener complexity in the semisimple case.

David Anglada-Rotger, Ferran Marqués, Montse Pardàs

Breast cancer early detection is crucial for improving patient outcomes. The Institut Catal\`a de la Salut (ICS) has launched the DigiPatICS project to develop and implement artificial intelligence algorithms to assist with the diagnosis of cancer. In this paper, we propose a new approach for facing the color normalization problem in HER2-stained histopathol

David Sawall, Markus Schweighofer

With this article, we hope to launch the investigation of what we call the real zero amalgamation problem. Whenever a polynomial arises from another polynomial by substituting zero for some of its variables, we call the second polynomial an extension of the first one. The real zero amalgamation problem asks when two (multivariate real) polynomials have a com

Jiazhan Feng, Chongyang Tao, Xiubo Geng, Tao Shen

Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural langua

Philipp Weiss, Ali Younessi, Sebastian Steinhorst

Fail-operational systems are a prerequisite for autonomous driving. Without a driver who can act as a fallback solution in a critical failure scenario, the system has to be able to mitigate failures on its own and keep critical applications operational. To reduce redundancy cost, graceful degradation can be applied by repurposing hardware resources at run-ti

Shanh Su, Jincan Chen

It is found that without any additional assumptions, Nernst's equation can be re-deduced from the experimental data obtained from the thermodynamic systems at ultra-low temperatures, and consequently, the physical content included by Nernst's equation should not be referred to as Nernst's postulate or Nernst's theorem. It should be called the Nernst statemen

Franziska Haller, Max Schemmer, Niklas Kühl, Carsten Holtmann

Cost-effective and responsible use of cloud computing resources (CCR) is on the business agenda of many companies. Despite this strategic goal, two geopolitical strategy decisions mainly influence the continuous existence of overcapacity: Europe's General Data Protection Regulation and the US's Cloud Act. Given the circumstances, a typical data center produc

Alejandro Caicedo, Matthew Dickson

Here we prove critical exponents for Random Connections Models (RCMs) with random marks. The vertices are given by a marked Poisson point process on $\mathbb{R}^d$ and an edge exists between any pair of vertices independently with a probability depending upon their spatial displacement and on their respective marks. Given conditions on the edge probabilities

Zizhang Wu, Zhuozheng Li, Zhi-Gang Fan, Yunzhe Wu

The monocular depth estimation task has recently revealed encouraging prospects, especially for the autonomous driving task. To tackle the ill-posed problem of 3D geometric reasoning from 2D monocular images, multi-frame monocular methods are developed to leverage the perspective correlation information from sequential temporal frames. However, moving object

F. Mannucci, M. Scialpi, A. Ciurlo, S. Yeh

The Gaia-Multi-Peak (GMP) technique can be used to identify large numbers of dual or lensed AGN candidates at sub-arcsec separation, allowing us to study both multiple SMBHs in the same galaxy and rare, compact lensed systems. The observed samples can be used to test the predictions of the models of SMBH merging once 1) the selection function of the GMP tech

Axel Maas

Subtle, but long-known, field-theoretical aspects require a more refined treatment of gauge theories involving a Brout-Englert-Higgs effect. This refinement can be done analytically using the Fr\"ohlich-Morchio-Strocchi mechanism. In the standard model, this leads to slight, but in principle detectable, quantitative changes in observables. This can have sign

Bence Borda

The Diophantine sums $\sum_{n=1}^N \| n \alpha \|^{-1}$ and $\sum_{n=1}^N n^{-1} \| n \alpha \|^{-1}$ appear in many different areas including the ergodic theory of circle rotations, lattice point counting and random walks, often in connection with Fourier analytic methods. Beresnevich, Haynes and Velani gave estimates for these and related sums in terms of

Lei Liu, Jimmy Xiangji Huang

Dialogue systems for non-English languages have long been under-explored. In this paper, we take the first step to investigate few-shot cross-lingual transfer learning (FS-XLT) and multitask learning (MTL) in the context of open-domain dialogue generation for non-English languages with limited data. We observed catastrophic forgetting in both FS-XLT and MTL