December 2024 arXiv papers — page 44

Marija Stojkovic, Edward Linscott, Nicola Marzari

Photocatalytic water splitting has attracted considerable attention for renewable energy production. Since the first reported photocatalytic water splitting by titanium dioxide, this material remains one of the most promising photocatalysts, due to its suitable band gap and band-edge positions. However, predicting both of these properties is a challenging ta

Yizhe Li, Linrui Zhang, Xueqian Wang, Houde Liu

Safety-critical traffic scenarios are of great practical relevance to evaluating the robustness of autonomous driving (AD) systems. Given that these long-tail events are extremely rare in real-world traffic data, there is a growing body of work dedicated to the automatic traffic scenario generation. However, nearly all existing algorithms for generating safe

Jérémie Sublime, Ilaria Renna

The rapid adoption of Generative AI (GenAI) based on Large Language Models (LLMs) such as ChatGPT has recently and profoundly impacted education, offering transformative opportunities while raising significant concerns. In this study we present the results of a survey that investigates how 395 students aged 13 to 25 years old in France and Italy integrate LL

Youngmin Kim, Enhyeok Jang, Hyungseok Kim, Seungwoo Choi

Variational quantum algorithms (VQAs) have attracted remarkable interest over the past few years because of their potential computational advantages on near-term quantum devices. They leverage a hybrid approach that integrates classical and quantum computing resources to solve high-dimensional problems that are challenging for classical approaches alone. In

Chenlong Deng, Zhisong Zhang, Kelong Mao, Shuaiyi Li

In this work, we provide a thorough investigation of gist-based context compression methods to improve long-context processing in large language models. We focus on two key questions: (1) How well can these methods replace full attention models? and (2) What potential failure patterns arise due to compression? Through extensive experiments, we show that whil

Christian Hirsch, Nikolaj Nyvold Lundbye, Moritz Otto

In topological data analysis, the notions of persistent homology, birthtime, lifetime, and deathtime are used to assign and capture relevant cycles (i.e., topological features) of a point cloud, such as loops and cavities. In particular, cycles with a large lifetime are of special interest. In this paper, we study such large-lifetime cycles when the point cl

Xi He, Kai Huang, Qingqing Ye, Haibo Hu

Graph analysis has become increasingly popular with the prevalence of big data and machine learning. Traditional graph data analysis methods often assume the existence of a trusted third party to collect and store the graph data, which does not align with real-world situations. To address this, some research has proposed utilizing Local Differential Privacy

Shuaihang Chen, Yuanxing Liu, Wei Han, Weinan Zhang

LLM-based Multi-Agent Systems ( LLM-MAS ) have become a research hotspot since the rise of large language models (LLMs). However, with the continuous influx of new related works, the existing reviews struggle to capture them comprehensively. This paper presents a comprehensive survey of these studies. We first discuss the definition of LLM-MAS, a framework e

Charlotte Kristjansen, Xin Qian, Chenliang Su

We determine the spectrum of quantum fluctuations in a 1/2-BPS domain wall version of ABJM theory, thereby enabling the perturbative exploration of the corresponding defect CFT. As expected, the spectrum reflects the supersymmetry of the model.

Sunil Kumar Kopparapu

Electroencephalography (EEG) is an non-invasive method to record the electrical activity of the brain. The EEG signals are low bandwidth and recorded from multiple electrodes simultaneously in a time synchronized manner. Typical EEG signal processing involves extracting features from all the individual channels separately and then fusing these features for d

Qi Zhang, Shanshe Wang, Xinfeng Zhang, Siwei Ma

Nowadays, high-quality images are pursued by both humans for better viewing experience and by machines for more accurate visual analysis. However, images are usually compressed before being consumed, decreasing their quality. It is meaningful to predict the perceptual quality of compressed images for both humans and machines, which guides the optimization fo

Qiang Luo, Jize Zhao, Xiaoqun Wang

Aligning with the everlasting search for quantum spin liquids (QSLs), identifying the QSL in Kitaev magnets has garnered great research interest during the past decade and remains nevertheless an enormous challenge. One of the major difficulties lies in that Kitaev QSL is typically fragile against competing interactions like off-diagonal exchanges, which are

Zakhar Kabluchko, Mathias Sonnleitner

We prove a large deviations principle for orthogonal projections of the unit ball $\mathbb{B}_p^n$ of $\ell_p^n$ onto a random $k$-dimensional linear subspace of $\mathbb{R}^n$ as $n\to\infty$ in the case $2<p\le \infty$ and for the intersection of $\mathbb{B}_p^n$ with a random $k$-dimensional subspace in the case $1\le p <2$. The corresponding rate functio

R. Ya. Leshko, I. V. Bilynskyi, O. V. Leshko, M. Yu. Popov

A model of the non-concentric spherical core-shell quantum dot under the influence of an externally applied electric field was proposed. It was established that the energy spectrum of both the electron and the hole depends on the intensity of the electric field as well as on the specific location of the core within the quantum dot. The phenomenon of energy l

Y. M. Assimiou, S. T. Daniel, G. Issoufou, D. F. Anselme

In this paper, we present the solutions of the Schr\"{o}dinger equation and the thermodynamic properties of generalized hyperbolic Hulthen and Woods-Saxon potential. The eigenvalues and eigenfunctions were found using the parametric Nikiforov-Uvarov method (PNUM). The clean energies of the molecules HCl, NiC, CO, I$_2$, H$_2$, LiH, CuLi and CrH are calculate

Tadeusz Wosinski

Extensive studies on the impact of bismuth incorporation into the (Ga,Mn)As prototype dilute ferromagnetic semiconductor (DFS) on its structural, magnetic and magnetotransport properties are summarized in this review. Thin epitaxial layers of the quaternary (Ga,Mn)(Bi,As) compound, containing up to 1% Bi and 6% Mn atoms, and the reference ternary (Ga,Mn)As c

Manuel S. Mariani, Federico Battiston, Emőke-Ágnes Horvát, Giacomo Livan

Understanding the collective dynamics behind the success of ideas, products, behaviors, and social actors is critical for decision-making across diverse contexts, including hiring, funding, career choices, and the design of interventions for social change. Methodological advances and the increasing availability of big data now allow for a broader and deeper

Junki Tanaka, Martha Liliana Cortés, Hongna Liu, Ryo Taniuchi

The advent of high-intensity radioactive ion beams has opened new avenues for nuclear structure research. By studying exotic ions, phenomena such as shell evolution, halos, and the limits of stability have been studied. In particular, Quasi-Free Scattering (QFS) experiments on hydrogen targets have proven to be a valuable tool to investigate the structure of

Ao Sun, Yanting Li, Xiao-Hua Zhou

In clinical practice, multiple biomarkers are often measured on the same subject for disease diagnosis, and combining them can improve diagnostic accuracy. Existing studies typically combine multiple biomarkers by maximizing the Area Under the ROC Curve (AUC), assuming a gold standard exists or that biomarkers follow a multivariate normal distribution. Howev

Dipayan Chakraborty, Annegret K. Wagler

In the literature, several different identification problems in graphs have been studied, the most widely studied such problems are the ones based on dominating sets as a tool of identification. Hereby, the objective is to separate any two vertices of a graph by their unique neighborhoods in a suitably chosen dominating or total-dominating set. Such a (total

Tytti Rintamaki, Harshvardhan J. Pandit

The AI Act introduces the obligation to conduct a Fundamental Rights Impact Assessment (FRIA), with the possibility to reuse a Data Protection Impact Assessment (DPIA), and requires the EU Commission to create of an automated tool to support the FRIA process. In this article, we provide our novel exploration of the DPIA and FRIA as information processes to e

Jaesun Shin, Eunjoo Jeon, Taewon Cho, Namkyeong Cho

While message passing graph neural networks result in informative node embeddings, they may suffer from describing the topological properties of graphs. To this end, node filtration has been widely used as an attempt to obtain the topological information of a graph using persistence diagrams. However, these attempts have faced the problem of losing node embe

Rafael Granero-Belinchón

In this note we derive a new nonlocal and nonlinear dispersive equations capturing the main dynamics of a circular interface separating a light, viscous fluid rising buoyantly through a heavy, more viscous, miscible fluid at small Reynolds numbers. This equation that we termed the $g-$model shares some common structure with the Camassa-Holm equation but has

François Clément, Stefan Steinerberger

The purpose of this note is to prove estimates for $$ \left| \sum_{k=1}^{n} \mbox{sign} \left( \cos \left( \frac{2\pi a}{n} k \right) \right) \mbox{sign} \left( \cos \left( \frac{2\pi b}{n} k \right) \right)\right|,$$ when $n$ is prime and $a,b \in \mathbb{N}$. We show that the expression can only be large if $a^{-1}b \in \mathbb{F}_n$ (or a small multiple t

Aleksandar Mikovic

We review the approach to quantum gravity which is based on the assumption that the short-distance structure of the spacetime is given by a piecewise flat manifold corresponding to a triangulation of a smooth manifold. We then describe the coupling of the Standard Model to this quantum gravity theory and show that the corresponding path integral is finite wh

Daxin Li, Yuanchao Bai, Kai Wang, Junjun Jiang

Learned lossless image compression has achieved significant advancements in recent years. However, existing methods often rely on training amortized generative models on massive datasets, resulting in sub-optimal probability distribution estimation for specific testing images during encoding process. To address this challenge, we explore the connection betwe

Wenyi Wang, Kun Hu, Taishi Katsuragawa

We study the Solar System constraints on covariant $f(Q)$ gravity. The covariant $f(Q)$ theory is described by the metric and affine connection, where both the torsion and curvature vanish. Considering a model including a higher nonmetricity-scalar correction, $f(Q)= Q +\alpha Q^{n} - 2\Lambda$, we derive static and spherically symmetric solutions, which rep

Amirreza Razmjoo, Teng Xue, Suhan Shetty, Sylvain Calinon

We present a novel approach to enhance the performance of sampling-based Model Predictive Control (MPC) in constrained optimization by leveraging products of experts. Our methodology divides the main problem into two components: one focused on optimality and the other on feasibility. By combining the solutions from each component, represented as distribution

Jakub Hesoun, Petr Stehlík

In this paper we consider a simple two patch reaction diffusion model with strong Allee effect, sufficiently distinct carrying capacities, similar reaction strengths, and strong diffusion. In the homogeneous case, i.e., in in the case of equal or similar capacities and reaction strengths, it is well known that the number of stationary solutions ranges from t

Thomas Strasser, Marios Christodoulou, Richard Howl, Caslav Brukner

Proposed experiments for obtaining empirical evidence for a quantum description of gravity in a table-top setting focus on detecting quantum information signatures, such as entanglement or non-Gaussianity production, in gravitationally interacting quantum systems. Here, we explore an alternative approach where the quantization of gravity could be inferred th

Steven Charlton

Almost nothing is known about the parity of the partition function $p(n)$, which is conjectured to be random. Despite this expectation, Ono surprisingly proved the existence of infinitely many linear dependence congruence relations modulo 4 for $p(n)$, indicating that the parity of the partition function cannot be truly random. Answering a question of Ono, w

Qiyu Chen, Huiyuan Luo, Han Gao, Chengkan Lv

Unsupervised anomaly detection methods can identify surface defects in industrial images by leveraging only normal samples for training. Due to the risk of overfitting when learning from a single class, anomaly synthesis strategies are introduced to enhance detection capability by generating artificial anomalies. However, existing strategies heavily rely on

Ignacio Chacon, Andrea Echeverri, Carlos Cardenas, Francisco Munoz

Substitutional carbon defects in hexagonal boron nitride (hBN) are prominent single photon emitters (SPEs), and their potential for spin activity ($S\geq1$) is particularly intriguing. While studies have largely focused on intra-layer defects, we employ density functional theory (DFT) to investigate inter-layer dimers of identical carbon species (C$_X$C$_X$)

Xiaodan Chen, Alexandre Pitti, Mathias Quoy, Nancy F Chen

Understanding how infants perceive speech sounds and language structures is still an open problem. Previous research in artificial neural networks has mainly focused on large dataset-dependent generative models, aiming to replicate language-related phenomena such as ''perceptual narrowing''. In this paper, we propose a novel approach using a small-sized gene

Yuta Shikuri

Gaussian process regression is a powerful Bayesian nonlinear regression method. Recent research has enabled the capture of many types of observations using non-Gaussian likelihoods. To deal with various tasks in spatial modeling, we benefit from this development. Difficulties still arise when we can only access summarized data consisting of representative fe

Niklas J. Glaser, Federico A. Roy, Ivan Tsitsilin, Leon Koch

Achieving fast and high-fidelity qubit operations is crucial for unlocking the potential of quantum computers. In particular, reaching low gate errors in two-qubit gates has been a long-standing challenge in the field of superconducting qubits due to their typically long duration relative to coherence times. To realize fast gates, we utilize the hybridizatio

Yang Tian, Yan Chen

Motivated by the recently discovered bilayer nickelate superconductor, the pressurized $\rm{La}_{3}Ni_{2}O_{7}$, we present a renormalized mean-field theory of a bilayer single-band $t$-$J$ model, highlighting the interplay between magnetism and superconductivity. We analyze the pairing symmetry and magnetic properties of the system, predicting two distinct

Rukmini Nazre, Rujuta Budke, Omkar Oak, Suraj Sawant

Network intrusion detection is critical for securing modern networks, yet the complexity of network traffic poses significant challenges to traditional methods. This study proposes a Temporal Convolutional Network(TCN) model featuring a residual block architecture with dilated convolutions to capture dependencies in network traffic data while ensuring traini

Wei Liu, Junlong Li, Xiwen Zhang, Fan Zhou

Self-evolving trainin--where models iteratively learn from their own outputs--has emerged as a key approach for complex reasoning tasks, addressing the scarcity of high-quality chain-of-thought data. However, its effectiveness in multimodal reasoning, a domain more intricate than text-only reasoning, remains underexplored, and the understanding of critical f

Jung-Wan Ryu, Chang-Hwan Yi, Jae-Ho Han

We investigate the topological properties of multiple exceptional points in non-Hermitian two-level systems, emphasizing vorticity as a topological invariant arising from complex energy structures. We categorize EP pairs as fundamental building blocks of larger EP assemblies, distinguishing two types: type-I pairs with opposite vorticities and type-II pairs

Alexander Vanin, Vadim Bolshev, Anastasia Panfilova

This study explores the use of Large language models to analyze therapist remarks in a psychotherapeutic setting. The paper focuses on the application of BERTopic, a machine learning-based topic modeling tool, to the dialogue of two different groups of therapists (classical and modern), which makes it possible to identify and describe a set of topics that co

Clotilde Fermanian Kammerer, Véronique Fischer, Steven Flynn

In this article, we develop a pseudodifferential calculus on a general filtered manifold M . The symbols are fields of operators $\sigma$(x, $\pi$) parametrised by x $\in$ M and the unitary dual G x M of the osculating Lie group G x M . We define classes of symbols and a local quantization formula associated to a local frame adapted to the filtration. We pro

W. J. Huang, Pak-Hin Thomas Tam, L. L. Ren, J. M. Lin

Extremely low-mass white dwarfs (ELM WDs) are helium-core white dwarfs with masses less than 0.3 $M_{\odot}$. Short-period ELM WD binaries that exhibit ellipsoidal variations may harbor heavier companions, either massive white dwarfs or millisecond pulsars (MSPs). In this study, we selected $\sim$ 12,000 ELM WDs or their candidates, and searched for ellipsoi

Ashraf Ayubinia, Jong-hak Woo, Fatemeh Hafezianzadeh, Taehwan Kim

In this study, we develop an artificial neural network to estimate the infrared (IR) luminosity and star formation rates (SFR) of galaxies. Our network is trained using 'true' IR luminosity values derived from modeling the IR spectral energy distributions (SEDs) of FIR-detected galaxies. We explore five different sets of input features, each incorporating op

Pedro Aranda, Javier Segurado

Modeling the propagation of cracks at the microscopic level is fundamental to understand the effect of the microstructure on the fracture process. Nevertheless, microscopic propagation is often unstable and when using phase field fracture poor convergence is found or, in the case of using staggered algorithms, leads to the presence of jumps in the evolution

Kenny De Commer, Jacek Krajczok

We introduce a definition of braided tensor product $\operatorname{M}\overline{\boxtimes}\operatorname{N}$ of von Neumann algebras equipped with an action of a quasi-triangular quantum group $\mathbb{G}$ (this includes the case when $\mathbb{G}$ is a Drinfeld double). It is a new von Neumann algebra which comes together with embeddings of $\operatorname{M},\

Hoshu Hiyane, Giedrius Žlabys, Thomas Busch, Shohei Watabe

We study the exact nonequilibrium spectral function of a gas of strongly correlated Tonks-Girardeau bosons subjected to a strong periodic drive. Utilizing the theory of Floquet spectral function in conjunction with the Bose-Fermi mapping theorem, we show that nonequilibrium Lieb modes emerge if the underlying mapped fermions form a Floquet-Fermi sea. In the

Andreas Hackl, Christoph Lehner

We present a study using the distillation method to analyze the spectra of nucleon, nucleon-pion, and nucleon-pion-pion states in the positive-parity sector, as well as nucleon and nucleon-pion states in the negative-parity sector. The study uses seven domain-wall fermion ensembles with varying pion masses ($m_\pi = 139 - 279~\text{MeV}$), lattice spacings (

Daniel Špale, Petr Stehlík

We describe various types of traveling fronts of bistable reaction-diffusion cellular automata. These dynamical systems with discrete time, space, and state spaces can be seen as fully discrete versions of widely studied bistable reaction-diffusion equations. We show that moving traveling waves for high diffusion parameters are restricted to slow speeds and

Francisco Javier Cantero Zorita, Mikel Galafate, Javier M. Moguerza, Isaac Martín de Diego

Recent advancements in Artificial Intelligence (AI) have transformed decision-making in aeronautics and aerospace. These advancements in AI have brought with them the need to understand the reasons behind the predictions generated by AI systems and models, particularly by professionals in these sectors. In this context, the emergence of eXplainable Artificia

Khalil Remini, Leonie Schmeller, Dirk Peschka, Barbara Wagner

The shapes of liquid polystyrene (PS) droplets on viscoelastic polydimethylsiloxane (PDMS) substrates are investigated experimentally using atomic force microscopy for a range of droplet sizes and substrate elasticities. These shapes, which comprise the PS-air, PS-PDMS, and PDMS-air interfaces as well as the three-phase contact line, are compared to theoreti

Johannes Mäkelburg, Yiwen Peng, Mehwish Alam, Tobias Weller

Despite the vast amount of information encoded in Knowledge Graphs (KGs), information about the class affiliation of entities remains often incomplete. Graph Convolutional Networks (GCNs) have been shown to be effective predictors of complete information about the class affiliation of entities in KGs. However, these models do not learn the class affiliation

Yi Huang, Zhenan Sui, Mingyu Xie

We solve the modified Gursky-Streets equation, which is a fully nonlinear equation arising in conformal geometry with uniform $C^{1, 1}$ estimates when (i) $\gamma > 0$ and $1 \leq k \leq n$ or (ii) $r > 0$ and $2 s k \leq r n$. We also prove the existence of a Lipschitz continuous viscosity solution when $r \neq 0$.

Petar Mitrić

We investigate the transport properties of the Holstein model using the numerically exact quantum typicality (QT) approach. Roughly speaking, QT exploits the fact that even a single, randomly chosen pure state can effectively represent the full statistical ensemble in a high-dimensional Hilbert space. This allows us to compute frequency-dependent mobilities,

M. Weyland, L. Sanchez, P. Ruksasakchai, M. F. Andersen

We present a method for determining the atom number distribution of few atoms in a tight optical tweezer from their fluorescence distributions. In the tight tweezer regime, the detection light causes rapid atom loss due to light-assisted collisions. This in turn leads to non-Poissonian and overlapping fluorescence distributions for different initial atom num

Vladislav Egorov, Boris Kryzhanovsky

We have investigated a weighted chi-square distribution of the variable $\xi$ which is a weighted sum of squared normally distributed independent variables whose weights are cosines of angles $\phi_k=2\pi k/N$, where $k \in \{0,1,...,N-1\}$ and $N$ is the number of the freedom degrees. We have found the exact expression for the density function of this distr

Grigory Neustroev, Mirco Giacobbe, Anna Lukina

We introduce for the first time a neural-certificate framework for continuous-time stochastic dynamical systems. Autonomous learning systems in the physical world demand continuous-time reasoning, yet existing learnable certificates for probabilistic verification assume discretization of the time continuum. Inspired by the success of training neural Lyapunov

Julie Meunier, Basile Gallet

The scale-invariant inverse energy cascade is a hallmark of 2D turbulence, with its theoretical energy spectrum observed in both direct numerical simulations (DNS) and laboratory experiments. Under this scale-invariance assumption, the effective diffusivity of a 2D turbulent flow is dimensionally controlled by the energy flux and the friction coefficient onl

Pratsiovytyi Mykola, Makarchuk Oleg, Karvatskyi Dmytro

In this paper, we study the Lebesgue structure of the distribution of a random variable given in terms of a continued fraction with a two-symbol alphabet $\{\frac{1}{2}, 1\}$, also known as $A_2$-fractions. We establish necessary and sufficient conditions for the distribution to be discrete and provide some sufficient conditions for its singularity. We also

Qingyuan Liang, Zhao Zhang, Chen Liu, Zeyu Sun

LLMs demonstrate significant potential across various software engineering tasks. However, they still face challenges in generating correct code on the first attempt when addressing complex requirements. Introducing a discriminator to select reliable outputs from multiple generated results is an effective way to enhance their reliability and stability. Curre

Jiahao Diao, Rebecca H. Chisholm, Nicholas Geard, James M. McCaw

During the COVID-19 pandemic, case isolation emerged as a key non-pharmaceutical intervention in pandemic response. Its effectiveness hinges on the timing of isolation, which is often dictated by the onset of contagiousness. While minimizing isolation delays is crucial, the impact of evolving epidemic dynamics, such as changes in viral load distributions amo

Maria Valentini, Téa Wright, Ali Marashian, Jennifer Weber

To address an important gap in creating children's stories for vocabulary enrichment, we investigate the automatic evaluation of how well stories convey the semantics of target vocabulary words, a task with substantial implications for generating educational content. We motivate this task, which we call measuring contextual informativeness in children's stor

François Dubois, Paulo Cesar Philippi

We propose to extend the multiresolution relaxation times lattice Boltzmann schemes with an additional projection step. For the explicit example of the D2Q9 scheme, we define this extended method. We prove that in general the projection step does not change the asymptotic partial differential equations at second order. We present four numerical test cases. O

Boyuan Zhao

Given a topologically transitive system on the unit interval, one can investigate the cover time, i.e. time for an orbit to reach certain level of resolution in the repeller. We introduce a new notion of dimension, namely the stretched Minkowski dimension, and show that under mixing conditions, the asymptotics of typical cover times are determined by Minkows

Manjunath Mulimani, Annamaria Mesaros

In this work, we propose a method for domain-incremental learning for audio classification from a sequence of datasets recorded in different acoustic conditions. Fine-tuning a model on a sequence of evolving domains or datasets leads to forgetting of previously learned knowledge. On the other hand, freezing all the layers of the model leads to the model not

Louise Friot--Giroux, Françoise Peyrin, Voichiţa Maxim

Cone-beam tomography enables rapid 3D acquisitions, making it a suitable imaging modality for dental imaging. However, as with all X-ray techniques, the main challenge is to reduce the dose while maintaining good image quality. Moreover, dental reconstructions face a series of issues stemming from truncated projections as well as metal and cone beam artifact

Sourav Ghosh

In this survey article we discuss about possible generalizations of Anosov representations in the affine setting and their consequences.

Evgeniy A. Petrov

A characterization of finite homogeneous ultrametric spaces and finite ultrametric spaces generated by unrooted labeled trees is found in terms of representing trees. A characterization of finite ultrametric spaces having perfect strictly $n$-ary trees is found in terms of special graphs connected with the space. Further, we give a detailed survey of some sp

Sebastian Bechtel, Fabian Germ, Mark Veraar

The critical variational setting was recently introduced and shown to be applicable to many important SPDEs not covered by the classical variational setting. In this paper, we extend the critical variational setting in several ways. We introduce a flexibility in the range space for the nonlinear drift term, due to which certain borderline cases can now also

Théophile Le Gall

This paper studies the numerical simulation of the solution to the McKean-Vlasov equation with common noise. We begin by discretizing the solution in time using the Euler scheme, followed by spatial discretization through the particle method, inspired by the propagation of chaos property. Assuming H{\"o}lder continuity in time, as well as Lipschitz continuit

Robert Wijaya, Ngoc-Bao Nguyen, Ngai-Man Cheung

Large Vision-Language Models (LVLMs) have shown promising capabilities in understanding and generating information by integrating both visual and textual data. However, current models are still prone to hallucinations, which degrade the performance and greatly harm the user experience in real-world applications. Post-training alignment, particularly preferen

Evgeniy Petrov

It is shown that a minimum weight spanning tree of a finite ultrametric space can be always found in the form of path. As a canonical representing tree such path uniquely defines the whole space and, moreover, it has much more simple structure. Thus, minimum spanning paths are a convenient tool for studying finite ultrametric spaces. To demonstrate this we u

Andreas Goulas, Vasileios Mezaris, Ioannis Patras

To address computational and memory limitations of Large Multimodal Models in the Video Question-Answering task, several recent methods extract textual representations per frame (e.g., by captioning) and feed them to a Large Language Model (LLM) that processes them to produce the final response. However, in this way, the LLM does not have access to visual in

Jinke Li, Jieao Zhu, Linglong Dai

Accurate channel prediction is essential for addressing channel aging caused by user mobility. However, the actual channel variations over time are highly complex in high-mobility scenarios, which makes it difficult for existing predictors to obtain future channels accurately. The low accuracy of channel predictors leads to difficulties in supporting reliabl

Jüri Engelbrecht, Kert Tamm, Tanel Peets

This paper attempts to review our studies on the propagation of signals in nerves over the past decade. The need for interdisciplinary studies is stressed that helps to understand the physical mechanisms of coupling the electrical, mechanical, and thermal effects in nerves. Based on the analysis of structural properties of axons and possible mechanisms of in

Kai Wu, Ziang Xie, Jing Liu

Real-world networks carry all kinds of noise, resulting in numerous challenges for community detection. Further improving the performance and robustness of community detection has attracted significant attention. This paper considers edge noise, which causes edges in the network to be added or removed. Existing methods achieve graph denoising through link pr

Jeonghwan Cheon, Se-Bum Paik

Uncertainty calibration is crucial for various machine learning applications, yet it remains challenging. Many models exhibit hallucinations - confident yet inaccurate responses - due to miscalibrated confidence. Here, we show that the common practice of random initialization in deep learning, often considered a standard technique, is an underlying cause of

Shanze Gao

In the Minkowski space, we consider a compact, spacelike hypersurface with boundary, which can be written as a graph on a spacelike hyperplane. We prove that, if its $k$-th mean curvature is constant, and its boundary is on the hyperplane with constant intersection angles, then the hypersurface must be a part of a hyperboloid, unless it is entirely contained

Zongrui Hu, Xiao Ma, Leiye Xu, Xiaomin Zhou

In this paper, we investigate the discrete spectrum of probability measures for actions of locally compact groups. We establish that a probability measure has a discrete spectrum if and only if it has bounded measure-max-mean-complexity. As applications: 1) An invariant measure for a locally compact amenable group action has a discrete spectrum if and only i

Muhammad Reza Qorib, Qisheng Hu, Hwee Tou Ng

Given news articles about an entity, such as a public figure or organization, timeline summarization (TLS) involves generating a timeline that summarizes the key events about the entity. However, the TLS task is too underspecified, since what is of interest to each reader may vary, and hence there is not a single ideal or optimal timeline. In this paper, we

Shahin Mamedov, Minaya Allahverdiyeva, Narmin Akbarova

We study the deuteron gravitational form factors (GFFs) and generalized parton distributions (GPDs) within the soft-wall AdS/QCD model, where deuteron is described by the bulk vector field with twist $\tau=6$. For the finite-temperature studies, we apply the soft-wall model, which is thermalized by introducing a thermal dilaton field. GPDs and charge density

Anastasia Boushmelev, Matthew Black, Oliver Witzel

Semileptonic $B_{(s)}$ decays are of great phenomenological interest because they allow to extract CKM matrix elements or test lepton flavour universality. Taking advantage of existing data, we explore extracting form factors for vector final states using the narrow width approximation. Based on RBC/UKQCD's set of 2+1 flavour gauge field ensembles with Shami

M. Tahasanul Ibrahim, Rifshu Hussain Shaik, Andreas Schwung

This paper investigates the use of Evidence Theory to enhance the training efficiency of object detection models by incorporating uncertainty into the feedback loop. In each training iteration, during the validation phase, Evidence Theory is applied to establish a relationship between ground truth labels and predictions. The Dempster-Shafer rule of combinati

Song Wang, Zhenyu Lei, Zhen Tan, Jiaqi Ding

Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and human behavior. To enhance analysis and comprehension of brain activity, Graph Neural Networks (GNNs) have been widely applied to the analysis of functional connectivities (FC) derive

S. Sivaprasad Kumar, Arya Tripathi, Snehal Pannu

Logarithmic and inverse logarithmic coefficients play a crucial role in the theory of univalent functions. In this study, we focus on the class of starlike functions \(\mathcal{S}^*_\rho\), defined as \[ \mathcal{S}^*_\rho = \left\{ f \in \mathcal{A} : \frac{z f'(z)}{f(z)} \prec \rho(z), \; z \in \mathbb{D} \right\}, \] where \(\rho(z) := 1 + \sinh^{-1}(z)\)

Axel Brandenburg, Ethan T. Vishniac

We analyze direct numerical simulations of large-scale dynamos in inhomogeneous nonhelically driven rotating turbulence with and without shear. The forcing is modulated so that the turbulent intensity peaks in the middle of the computational domain and drops to nearly zero at the two ends above and below the midplane. A large-scale dynamo is driven by an $\a

Guoyi Zhang, Guangsheng Xu, Han Wang, Siyang Chen

Infrared small target detection (ISTD) is challenging due to complex backgrounds, low signal-to-clutter ratios, and varying target sizes and shapes. Effective detection relies on capturing local contextual information at the appropriate scale. However, small-kernel CNNs have limited receptive fields, leading to false alarms, while transformer models, with gl

Martina Rovelli

This note is a contribution for a proceedings volume of the workshop "Higher Segal Spaces and their Applications to Algebraic K-Theory, Hall Algebras, and Combinatorics". The content is a streamlined exposition based on a talk about a result by Bergner-Osorno-Ozornova-Rovelli-Scheimbauer from WITII. We discuss how a generalized version of Waldhausen's S-cons

Zhengguang Guo, Matthieu Hillairet

In this paper, we continue the analysis of the stationary exterior Navier-Stokes problem with interior boundary data and vanishing condition at infinity. We first show an existence result that extends a previous contribution of the second author by considering boundary data prescribing a non-trivial flux on the internal boundary. We obtain in particular that

Viktoriya Ozornova, Martina Rovelli

This note is an expository contribution for a proceedings volume of the workshop "Higher Segal Spaces and their Applications to Algebraic K-theory, Hall Algebras, and Combinatorics". We survey various versions of Waldhausen's S-construction and the role they play in defining K-theory, and we discuss their 2-Segality properties.

Huchen Jiang, Yangyang Ma, Chaofan Ding, Kexin Luan

With current state-of-the-art approaches aimed at enhancing the reasoning capabilities of Large Language Models(LLMs) through iterative preference learning inspired by AlphaZero, we propose to further enhance the step-wise reasoning capabilities through intrinsic self-correction to some extent. Our work leverages step-wise preference learning to enhance self

Nadine Große, Alejandro Uribe, Hanne van den Bosch

We consider Dirac-type operators on manifolds with boundary, and set out to determine all local smooth boundary conditions that give rise to (strongly) regular self-adjoint operators. By combining the general theory of boundary value problems for Dirac operators as in [BB12] and pointwise considerations, for local smooth boundary conditions the question of b

Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu

Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges for data collection and annotation. To address this, current methods often design various data flywheels to collect complex code instructions, enabling models to handle more intrica

Chunyang Wang, Biyue Pan, Zhibo Dai, Yudi Cai

Real-time and accurate prediction of aerodynamic flow fields around airfoils is crucial for flow control and aerodynamic optimization. However, achieving this remains challenging due to the high computational costs and the non-linear nature of flow physics. Traditional Computational Fluid Dynamics (CFD) methods face limitations in balancing computational eff

Zehao Zhang, Biwei Jiang, Yi Ren, He Zhao

Red supergiants (RSGs) are essential to understanding the evolution and the contribution to the interstellar medium of massive stars. However, the number of identified RSGs within the Milky Way is still limited mainly due to the difficulty of measuring stellar extinction and distance. The release of approximately one million RVS spectra in Gaia DR3 presents

Guosong Wang, Min Hou, Mingyue Qin, Xinrong Wu

Ocean forecasting is critical for various applications and is essential for understanding air-sea interactions, which contribute to mitigating the impacts of extreme events. State-of-the-art ocean numerical forecasting systems can offer lead times of up to 10 days with a spatial resolution of 10 kilometers, although they are computationally expensive. While

Karsten Keller, Evgeniy Petrov

Ordinal data analysis is an interesting direction in machine learning. It mainly deals with data for which only the relationships `$<$', `$=$', `$>$' between pairs of points are known. We do an attempt of formalizing structures behind ordinal data analysis by introducing the notion of ordinal spaces on the base of a strict axiomatic approach. For these space

Mattias Paul Heinrich

Point clouds are a very efficient way to represent volumetric data in medical imaging. First, they do not occupy resources for empty spaces and therefore can avoid trade-offs between resolution and field-of-view for voxel-based 3D convolutional networks (CNNs) - leading to smaller and robust models. Second, they provide a modality agnostic representation of

Xuan Wu

We develop a new method based on Caffarelli's contraction theorem in optimal transport to obtain sharp and uniform modulus of continuity estimates for $\beta$-Dyson Brownian motions with $\beta \geq 2$. Our method extends to a large class of random curve collections, which can be viewed as log-concave perturbations of Brownian motions, including the $\beta$-

Mikhail I. Gomoyunov

We consider a Cauchy problem for a (first-order) path-dependent Hamilton--Jacobi equation with coinvariant derivatives and a right-end boundary condition. Such problems arise naturally in the study of properties of the value functional in (deterministic) optimal control problems and differential games for time-delay systems. We prove that, under certain assu