December 2024 arXiv papers — page 122

Yilei Zhao, Wentao Zhang, Tingran Yang, Yong Jiang

In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-based latent factor models, which learn latent factors self-adaptively. While these models focus on modeling overall market conditions, they often fail to effectively capture the tempo

Yangguang Feng

With the rapid development of artificial intelligence technology, the application of deepfake technology in the audio field has gradually increased, resulting in a wide range of security risks. Especially in the financial and social security fields, the misuse of deepfake audios has raised serious concerns. To address this challenge, this study proposes an a

Xi Lin, Paul Szenher, Yewei Huang, Brendan Englot

With the growing demands for Autonomous Surface Vehicles (ASVs) in recent years, the number of ASVs being deployed for various maritime missions is expected to increase rapidly in the near future. However, it is still challenging for ASVs to perform sensor-based autonomous navigation in obstacle-filled and congested waterways, where perception errors, closel

Yuanzhi Zhu, Ruiqing Wang, Shilin Lu, Junnan Li

Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to traditional deep learning approaches. However, these methods either require numerous sampling steps to generate high-quality images, resulting in significant computational overhead, o

Akbar Davlataliev, Bakhtiyor Narzilloev, Ibrar Hussain, Ahmadjon Abdujabbarov

We analyze the evolution of perturbations of a (charged) massive scalar field near a regular Simpson-Visser black hole, allowing for a non-zero external magnetic field. We show that the damping rate of the quasinormal frequencies is strongly suppressed by both the magnetic field and the mass term, with indications that arbitrarily long-lived modes, or quasi-

Rodrigo Alonso, Shakeel Ur Rahaman

Identifying a full basis of operators to a given order is key to the generality of Effective Field Theory (EFT) and is by now a problem of known solution in terms of the Hilbert series. The present work is concerned with hidden symmetry in general and Higgs EFT in particular and {\it(i)} connects the counting formula presented in [1] in the CCWZ formulation

Daniele Palaferri, Lorenzo Mancini, Chiara Vecchi, Leonardo Daga

Single photon detection is the underpinning technology for quantum communication and quantum sensing applications. At visible and near-infrared wavelengths, single-photon-detectors (SPDs) underwent a significant development in the past two decades, with the commercialization of SPADs and superconducting detectors. At longer wavelengths, in the mid-infrared r

Qin Tan, Zu Cheng Chen, You Wu, Lang Liu

We search for the stochastic gravitational-wave background (SGWB) predicted by pre-big-bang (PBB) cosmology using data from the first three observing runs of Advanced LIGO and Advanced Virgo. PBB cosmology proposes an alternative to cosmic inflation where the Universe evolves from a weak-coupling, low-curvature state to the hot Big Bang through a high-curvat

Javier de la Rosa, Vladislav Mikhailov, Lemei Zhang, Freddy Wetjen

The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of publisher-controlled copyrighted corpora on the performance of generative large language models (LLMs) for Norwegian. When evaluated on a diverse set of tasks, we

Kamal Aghazade, Ali Gholami

Full Waveform Inversion (FWI) stands as a nonlinear, high-resolution technology for subsurface imaging via surface-recorded data. This paper introduces an augmented Lagrangian dual formulation for FWI, rooted in the viewpoint that Lagrange multipliers serve as fundamental unknowns for the accurate linearization of the FWI problem. Once these multipliers are

Luka Medic, Anton Ramšak, Tomaž Rejec

We investigate a minimal model of a two-terminal Josephson junction with conventional superconducting (SC) leads and a pair of interconnected quantum dots in the presence of two Aharonov-Casher (AC) fluxes. The Andreev bound state spectrum features Weyl nodes within a three-dimensional synthetic Brillouin zone defined in the space of these AC fluxes and the

Maria Lucia Fania, Flaminio Flamini

We investigate on the existence of some "sporadic", rank-$r \geqslant 1$ Ulrich vector bundles on suitable $3$-fold scrolls $X$ over the Hirzebruch surface $\mathbb{F}_0$, which arise as tautological embeddings of projectivization of very-ample vector bundles on $\mathbb{F}_0$ that are uniform in the sense of Brosius and Aprodu--Brinzanescu. Such Ulrich bund

Matthew Houtput, Luigi Ranalli, Carla Verdi, Serghei Klimin

Electron-phonon interactions in solids are crucial for understanding many interesting phenomena, such as conventional superconductivity, temperature-dependent band-gap renormalization, and polarons. For harmonic materials, the linear interaction of one electron with one phonon is sufficient to quantitatively describe these properties. However, in anharmonic

Kenneth R. Davidson, Matthew Kennedy

We survey noncommutative Choquet theory and some of its applications.

Abel de Burgos

Massive stars are key contributors to the chemodynamical evolution of galaxies and the Universe. Despite their significance, discrepancies between observational data and theoretical models of massive stars challenge our understanding of these objects. A major uncertainty is the overdensity of B-type supergiants (BSGs) in the Hertzsprung-Russell diagram, wher

Haolin Li, Yuyang Miao, Zahra Sharif Khodaei, M. H. Aliabadi

PINN models have demonstrated capabilities in addressing fluid PDE problems, and their potential in solid mechanics is beginning to emerge. This study identifies two key challenges when using PINN to solve general solid mechanics problems. These challenges become evident when comparing the limitations of PINN with the well-established numerical methods commo

Tian-Rui Wang, Weishan Zhu, Xue-Fu Li, Wen-Sheng Hong

We revisit the launch of the galactic outflow in M82 using hydrodynamic simulations. Employing a sink-particle module, we self-consistently resolve star formation and feedback, avoiding reliance on simplified models. We investigate the effects of stellar feedback mechanisms, gas return from star-forming clouds, and disk mass on the starburst and outflow. Our

Rong Du, Luis C. Ho, Yuanze Ding, Ruancun Li

Models of active galactic nuclei often invoke a close physical association between the broad-line region and the accretion disk. We evaluate this theoretical expectation by investigating the relationship between the inclination angle of the BLR ($\theta_\mathrm{BLR}$) and the inclination angle of the inner accretion disk ($\theta_\mathrm{disk}$). For a sampl

Behnam Ghavami, Mani Sadati, Mohammad Shahidzadeh, Lesley Shannon

Despite the rising prevalence of deep neural networks (DNNs) in cyber-physical systems, their vulnerability to adversarial bit-flip attacks (BFAs) is a noteworthy concern. This paper proposes B3FA, a semi-black-box BFA-based parameter attack on DNNs, assuming the adversary has limited knowledge about the model. We consider practical scenarios often feature a

Suddhasattwa Brahma, Keshav Dasgupta, Bohdan Kulinich, Archana Maji

We devise a novel duality sequence to study late-time cosmology in the heterotic E_8 x E_8 setup of Horava and Witten with dynamical walls that are moving towards each other. Surprisingly, we find that the dimensionally reduced four-dimensional theory does not violate NEC and therefore we do not see either a bouncing or an ekpyrotic phase. Instead, our four-

Zeyu Wang, Qitong Wang, Peng Wang, Themis Palpanas

Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and approximate similarity search, with approximate search providing high-quality results within milliseconds, which makes it very attractive for certain modern applications. Reducing the

Runze Zhang

In this paper, we prove a $\partial\bar{\partial}$-type lemma on compact K\"ahler manifolds for logarithmic differential forms valued in the dual of a certain pseudo-effective line bundle, thereby confirming a conjecture proposed by X. Wan. We then derive several applications, including strengthened results by H. Esnault-E. Viehweg on the degeneracy of the s

Joel Kamnitzer

Associated to any unit interval graph, Syu Kato introduced a variety which gives (via the geometric Satake correspondence) a graded $GL_m$ representation whose character is the chromatic quasisymmetric polynomial of the graph. In this short note, we reprove Kato's result by analyzing the fixed points of his varieties.

Raj Hansini Khoiwal, Alan B. McMillan

Developing artificial intelligence (AI) and machine learning (ML) models for medical imaging typically involves extensive training and testing on large datasets, consuming significant computational time, energy, and resources. There is a need for more efficient methods that can achieve comparable or superior diagnostic performance without the associated reso

Jahid Chowdhury Choton, Lipsy Gupta, Pavithra Prabhakar

The Robot Operating System (ROS) is one of the most popular middleware for developing robot applications, but it is subject to major shortcomings when applied to real-time robotic systems in safety-critical environments. For this reason, ROS 2 was released in 2017 for implementing real-time capabilities in distributed robotic systems while supporting the mos

Gwen Maudet, Grégoire Danoy

Branch-and-Bound (B\&B) is an exact method in integer programming that recursively divides the search space into a tree. During the resolution process, determining the next subproblem to explore within the tree-known as the search strategy-is crucial. Hand-crafted heuristics are commonly used, but none are effective over all problem classes. Recent approache

Steven J. Chisolm, Emily Guo, Vignesh Subramaniam, Kyle D. Schulze

The elastic moduli of tissues are connected to their states of health and function. The epithelial monolayer is a simple, minimal, tissue model that is often used to gain understanding of mechanical behavior at the cellular or multi-cellular scale. Here we investigate how the elastic modulus of Madin Darby Canine Kidney (MDCK) cells depends on their packing

Zheng Li, Yibing Song, Ming-Ming Cheng, Xiang Li

Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text inputs, aiming to align image and text (category) spaces for downstream tasks. However, current training is restricted to aligning images with predefined known categories and cannot be associated with unknown categories.

Hai-Long Sun, Da-Wei Zhou, Hanbin Zhao, Le Gan

Class-Incremental Learning (CIL) requires models to continually acquire knowledge of new classes without forgetting old ones. Despite Pre-trained Models (PTMs) have shown excellent performance in CIL, catastrophic forgetting still occurs as the model learns new concepts. Existing work seeks to utilize lightweight components to adjust the PTM, while the forge

Joseph Humphreys, Chengxu Zhou

Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting adaptability to changes in terrain and dynamic state. Here we show that integrating three core principles of animal locomotion-

Thanh-Dat Truong

The dissertation presents four key contributions toward fairness and robustness in vision learning. First, to address the problem of large-scale data requirements, the dissertation presents a novel Fairness Domain Adaptation approach derived from two major novel research findings of Bijective Maximum Likelihood and Fairness Adaptation Learning. Second, to en

Sergey Masaev, Andrey Minkin, Evgeniy Troyak, Andrey Khrulkevich

A review of scientific papers has shown that digital twins are very common for modeling the states of physical objects. It is relevant to consider the creation of a digital twin of an enterprise to obtain various assessments in management, taking into account the human factor. In the research, the impact of the human factor is assessed by the digital twin an

Punit Gandhi, Yangyang Wang

Two identical van der Pol oscillators with mutual inhibition are considered as a conceptual framework for modeling a latching mechanism for cell cycle regulation. In particular, the oscillators are biased to a latched state in which there is a globally attracting steady-state equilibrium without coupling. The inhibitory coupling induces stable alternating la

Shagun Kaushal, Suprit Singh

We employ a self consistent framework to study the backreaction effects of particle creation in the coupled semiclassical dynamics of a quantum complex scalar field and a classical electric field in both (1 + 1) and (1 + 3) dimensional Minkowski and de Sitter spacetimes. Using a general Gaussian state formalism in the Schrodinger picture, we solve the result

Vyacheslav N. Shalyapin, Luis J. Goicoechea, Eleana Ruiz-Hinojosa

As there is a long-standing controversy over the time delay between the two images of the gravitationally lensed quasar FBQ 0951+2635, we combined early and new optical light curves to robustly measure a delay of 13.5 +/- 1.6 d (1sigma interval). The new optical records covering the last 17 yr were also used to trace the long-timescale evolution of the micro

Peter March

We propose a definition of the curl of a vector field X on a finite simple graph as the projection of X onto the orthogonal complement of circulation-free vector fields, where a vector field is circulation-free provided its line integral around every simple circuit vanishes. We justify the definition by observing that X and curl X have the same circulation a

Foivos Fioravantes, Dušan Knop, Jan Matyáš Křišťan, Nikolaos Melissinos

The Mutliagent Path Finding (MAPF) problem consists of identifying the trajectories that a set of agents should follow inside a given network in order to reach their desired destinations as soon as possible, but without colliding with each other. We aim to minimize the maximum time any agent takes to reach their goal, ensuring optimal path length. In this wo

Dafydd Cotoarbă, Daniel Straub, Ian FC Smith

The digital twin approach has gained recognition as a promising solution to the challenges faced by the Architecture, Engineering, Construction, Operations, and Management (AECOM) industries. However, its broader application across AECOM sectors remains limited. One significant obstacle is that traditional digital twins rely on deterministic models, which re

Qing Zhao, Yunyi Shen

Density dependence occurs at the individual level and thus is greatly influenced by spatial local heterogeneity in habitat conditions. However, density dependence is often evaluated at the population level, leading to difficulties or even controversies in detecting such a process. Bayesian individual-based models such as spatial capture-recapture (SCR) model

Yi Luo, Linghang Shi, Yihao Li, Aobo Zhuang

Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets. Artificial intelligence (AI), particularly Large Language Models (LLMs), has the potential to revolutionize this process by automating various steps. Still, significant challenges remain, including the need for multidisciplina

Baisen Wang, Le Zhuo, Zhaokai Wang, Chenxi Bao

Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fusion. Despite their effectiveness in other modalities, their application in multimodal music generation faces challenges of data scarcity, weak cross-modal alignment, and limited c

Alice Ruget, Lewis Wilson, Jonathan Leach, Rachael Tobin

Single-photon avalanche diodes (SPADs) are advanced sensors capable of detecting individual photons and recording their arrival times with picosecond resolution using time-correlated Single-Photon Counting detection techniques. They are used in various applications, such as LiDAR, and can capture high-speed sequences of binary single-photon images, offering

C. A. Mehta, D. M. Orlov, T. Feng, R. W. James

The detection of water molecules within the atmosphere of Jupiter, first by the Galileo Atmospheric Probe, and later by the Juno spacecraft, has given rise to the question of whether those molecules are sourced endogenously or exogenously. One hypothesis is that the ablation of meteoroids deposited the necessary oxygen into the atmosphere, which was subseque

Elena Apresyan, Gor Sarkissian

In this paper we derive new symmetry and new expression for $6j$-symbols of the unitary principal series representations of the $SL(2,\mathbb{C})$ group. This allowed us to derive for them the analogue of the Regge symmetry.

Zhaofeng Tian, Lichen Xia, Weisong Shi

Achieving energy-efficient trajectory planning for autonomous driving remains a challenge due to the limitations of model-agnostic approaches. This study addresses this gap by introducing an online nonlinear programming trajectory optimization framework that integrates a differentiable energy model into autonomous systems. By leveraging traffic and slope pro

Manuel Hagelüken, Marco F. Huber, Marco Roth

Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their ground state counterparts. We present a quantum machine learning model that predicts excited-state properties from the molecular ground stat

Deeksha M. Arya, Jin L. C. Guo, Martin P. Robillard

Software technologies are used by programmers with diverse backgrounds. To fulfill programmers' need for information, enthusiasts contribute numerous learning resources that vary in style and content, which act as documentation for the corresponding technology. We interviewed 26 volunteer documentation contributors, i.e. documentors, to understand why and ho

Oscar Jaramillo, Vighnesh Natarajan, Hamim Mahmud Rivy, Joshua Tensuan

Ultraviolet and visible integrated photonics are enabling for applications in quantum information, sensing, and spectroscopy, among others. Few materials support low-loss photonics into the UV, and the relatively low refractive index of known depositable materials limits the achievable functionality. Here we present a high-index integrated photonics platform

Axel Levy, Rishwanth Raghu, David Shustin, Adele Rui-Yang Peng

Cryo-electron microscopy (cryo-EM) is an experimental technique for protein structure determination that images an ensemble of macromolecules in near-physiological contexts. While recent advances enable the reconstruction of dynamic conformations of a single biomolecular complex, current methods do not adequately model samples with mixed conformational and c

Barry Loneck, Igor Zurbenko, Edward Valachovic

Given recent increases in ocean water levels brought on by climate change, this investigation decomposed changes in coastal water levels into its fundamental components to predict maximum water levels for a given coastal location. The study focused on Virginia Key, Florida, in the United States, located near the coast of Miami. Hourly mean lower low water (M

Naresh Kshetri, Mir Mehedi Rahman, Md Masud Rana, Omar Faruq Osama

The increasing integration of artificial intelligence (AI) within cybersecurity has necessitated stronger encryption methods to ensure data security. This paper presents a comparative analysis of symmetric (SE) and asymmetric encryption (AE) algorithms, focusing on their role in securing sensitive information in AI-driven environments. Through an in-depth st

Gabriele Dessena, Marco Civera, Ali Yousefi, Cecilia Surace

Operational Modal Analysis (OMA) is vital for identifying modal parameters under real-world conditions, yet existing methods often face challenges with noise sensitivity and stability. This work introduces NExT-LF, a novel method that combines the well-known Natural Excitation Technique (NExT) with the Loewner Framework (LF). NExT enables the extraction of I

Adam Labiosa, Zhihan Wang, Siddhant Agarwal, William Cong

Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge. Model-free reinforcement learning (RL) is a promising approach to learning decision-making in such domains, however, end-to-end RL in complex environments is often intractable. To address this challenge in the RoboCup St

Kaushal Kumar Maurya, KV Aditya Srivatsa, Kseniia Petukhova, Ekaterina Kochmar

In this paper, we investigate whether current state-of-the-art large language models (LLMs) are effective as AI tutors and whether they demonstrate pedagogical abilities necessary for good AI tutoring in educational dialogues. Previous efforts towards evaluation have been limited to subjective protocols and benchmarks. To bridge this gap, we propose a unifie

Arseny Mingajev

We consider the equation $P(Q(x_1,\ldots,x_\nu))=Q(P(x_1),\ldots,P(x_\nu))$ in polynomials over the field of complex numbers and prove that if ${\rm deg}(P)>1$, then it is only solvable in polynomials that are affinely conjugate to monomials.

Alistair Plum, Tharindu Ranasinghe, Christoph Purschke

This paper addresses the challenges in developing language models for less-represented languages, with a focus on Luxembourgish. Despite its active development, Luxembourgish faces a digital data scarcity, exacerbated by Luxembourg's multilingual context. We propose a novel text generation model based on the T5 architecture, combining limited Luxembourgish d

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A search for $D^0$ meson decays to the $\pi^+\pi^-e^+e^-$ and $K^+K^-e^+e^-$ final states is reported using a sample of proton-proton collisions collected by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb$^{-1}$. The decay $D^0 \rightarrow \pi^+\pi^-e^+e^-$ is observed for the first time when requi

Yingqian Min, Zhipeng Chen, Jinhao Jiang, Jie Chen

Recently, slow-thinking reasoning systems, such as o1, have demonstrated remarkable capabilities in solving complex reasoning tasks. These systems typically engage in an extended thinking process before responding to a query, allowing them to generate more thorough, accurate, and well-reasoned solutions. These systems are primarily developed and maintained b

Chiara Lionello, Matteo Becchi, Simone Martino, Giovanni M. Pavan

Extracting from trajectory data meaningful information to understand complex molecular systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable, if not required, to prevent losing important information. But to what extent such high-dimensionality is really needed/beneficial often remains unclear. Here we challenge such a

Antoine Amarilli, Wolfgang Gatterbauer, Neha Makhija, Mikaël Monet

The resilience problem for a query and an input set or bag database is to compute the minimum number of facts to remove from the database to make the query false. In this paper, we study how to compute the resilience of Regular Path Queries (RPQs) over graph databases. Our goal is to characterize the regular languages L for which it is tractable to compute t

Luke Postle, Evelyne Smith-Roberge, Massimo Vicenzo

A (vertex) colouring of graph is \emph{acyclic} if it contains no bicoloured cycle. In 1979, Borodin proved that planar graphs are acyclically 5-colourable. In 2010, Kawarabayashi and Mohar proved that locally planar graphs are acyclically 7-colourable. In 2002, Borodin, Fon-Der-Flaass, Kostochka, Raspaud, and Sopena proved that planar graphs are acyclically

Sergei D. Odintsov, Diego Sáez-Chillón Gómez, German S. Sharov

Over the last decades, tests on the standard model of cosmology, the so-called $\Lambda$CDM model, have been widely analysed and compared with many different models for describing dark energy. Modified gravities have played an important role in this sense as an alternative to $\Lambda$CDM model. Previous observational data has been always favouring $\Lambda$

Zi-Yue Bai, Bao-Jun Lai, Qin-Song Zou, Xiang Liu

As a $D$-wave partner of $\psi(3770)$ identified by the LHCb Collaboration, $\psi_3(3842)$ lies between the $D\bar{D}$ and $D\bar{D^*}$ thresholds. Its non-$D\bar{D}$ decay channels have attracted considerable interest. In this study, we investigate these allowed non-$D\bar{D}$ decays of $\psi_3(3842)$ into $PP$, $PV$, and $VV$ final states using the hadroni

Adam Freese

Gravitational form factors are often interpreted as providing access to stresses inside hadrons, in particular through Fourier transforms of the form factors $D$ and $\bar{c}$. Some researchers, however, have expressed skepticism of this interpretation. I revisit the question, and argue that it is indeed appropriate to interpret these quantities as stress di

Qi Shi, Pavel Naumov

Common knowledge/belief in rationality is the traditional standard assumption in analysing interaction among agents. This paper proposes a graph-based language for capturing significantly more complicated structures of higher-order beliefs that agents might have about the rationality of the other agents. The two main contributions are a solution concept that

Giovani Peruzzo

In this work, we investigate the renormalization of the gauge-invariant composite operators proposed in \cite{Dudal:2023jsu} to describe the $SU(2)\times U(1)$ Higgs model from a gauge-invariant perspective. To establish the relationship between the counterterms, we use the Algebraic Renormalization approach \cite{Piguet:1995er}. Therefore, we also derive th

Dan Jacobellis, Neeraja J. Yadwadkar

Modern sensors produce increasingly rich streams of high-resolution data. Due to resource constraints, machine learning systems discard the vast majority of this information via resolution reduction. Compressed-domain learning allows models to operate on compact latent representations, allowing higher effective resolution for the same budget. However, existi

Konstantinos Mylonas, Thrasyvoulos Spyropoulos

Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests that social media are highly polarized. A phenomenon that is commonly observed is the echo chamber structure, where users are organized in polarized communities and form connections only with similar-minded individuals,

Jeongsoo Kim, Shwetadwip Chowdhury

In optical diffraction tomography (ODT), a sample's 3D refractive-index (RI) is often reconstructed after illuminating it from multiple angles, with the assumption that the sample remains static throughout data collection. When the sample undergoes dynamic motion during this data-collection process, significant artifacts and distortions compromise the fideli

Lehan Wang, Chongchong Qi, Chubin Ou, Lin An

Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios. To expand the scope of clinical applications, we formulate a novel setting, "OCT-enhanced disease recognition from fundus images", that allows for the u

Yuzheng Liu, Siyan Dong, Shuzhe Wang, Yingda Yin

In this paper, we introduce SLAM3R, a novel and effective system for real-time, high-quality, dense 3D reconstruction using RGB videos. SLAM3R provides an end-to-end solution by seamlessly integrating local 3D reconstruction and global coordinate registration through feed-forward neural networks. Given an input video, the system first converts it into overla

Shun Li, Yan Jiang, Yingda Cheng

In this paper, we develop a low-rank method with high-order temporal accuracy using spectral deferred correction (SDC) to compute linear matrix differential equations. In [1], a low rank numerical method is proposed to correct the modeling error of the basis update and the Galerkin (BUG) method, which is a computational approach for DLRA. This method (merge-

Jacob Helwig, Xuan Zhang, Haiyang Yu, Shuiwang Ji

Computational modeling of aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils. Deep surrogate models have emerged as purely data-driven approaches that learn direct mappings from simulation conditions to solutions based on either simulation or experimental data. Here, we consider model

Soham Sane, Angus McLean

This paper presents a novel framework for enhancing reasoning capabilities in large language models (LLMs) by leveraging iterative reasoning and feedback-driven methodologies. Building on the limitations identified in the SimpleBench benchmark, a dataset designed to evaluate logical coherence and real-world reasoning, we propose a multi-step prompting strate

W. Zhang, T. Grismayer, L. O. Silva

We show that an anomalous pinch can occur in ultrarelativistic electron-electron or positron-positron beam interaction, caused by the combined interplay of collective beam motion (disruption) and strong-field quantum electrodynamics (SF-QED). The locally created electron-positron pairs, from SF-QED effects, screen the self-fields of the beams and can invert

J. F. van Diejen, E. Emsiz, I. N. Zurrián

We construct the basic representation of the double affine Hecke algebra at critical level $q=1$ associated to an irreducible reduced affine root system $R$ with a reduced gradient root system. For $R$ of untwisted type such a representation was studied by Oblomkov [O04] and further detailed by Gehles [G06] in the presence of minuscule weights.

Etienne Berriot, Pascal Démoulin, Olga Alexandrova, Arnaud Zaslavsky

This study focuses on a radial alignment between Parker Solar Probe (PSP) and Solar Orbiter (SolO) on the 29$^{\text{th}}$ of April 2021 (during a solar minimum), when the two spacecraft were respectively located at $\sim 0.075$ and $\sim 0.9$~au from the Sun. A previous study of this alignment allowed the identification of the same density enhancement (with

Sebastien Valeyre, Sofiane Aboura

Recently, LLMs (Large Language Models) have been adapted for time series prediction with significant success in pattern recognition. However, the common belief is that these models are not suitable for predicting financial market returns, which are known to be almost random. We aim to challenge this misconception through a counterexample. Specifically, we ut

Si-Pei Wang, Xin Li, Rui Wang, Jun-Ting Ye

The recently developed extended Skyrme effective interaction based on the so-called N3LO Skyrme pseudopotential is generalized to the general N$n$LO case by incorporating the derivative terms up to 2$n$th-order into the central term of the pseudopotential. The corresponding expressions of Hamiltonian density and single-nucleon potential are derived within th

Jiri Petracek, Vladimir Kuzmiak, Jiri Ctyroky, Ivan Richter

We introduce a new type of a bound state in the continuum (BIC) which appears in the photonic structure consisting of two coupled waveguides where one of them supports a discrete eigenmode spectrum embedded in the continuum of the other one. A BIC appears when the coupling is suppressed by suitable tuning of structural parameters. In contrast to the previous

Torgeir Aambø

We study the interplay between localizing subcategories in a stable $\infty$-category $\mathcal{C}$ with $t$-structure $(\mathcal{C}_{\geq 0}, \mathcal{C}_{\leq 0})$, the prestable $\infty$-category $\mathcal{C}_{\geq 0}$ and the abelian category $\mathcal{C}^{\heartsuit}$. We prove that weak localizing subcategories of $\mathcal{C}^{\heartsuit}$ are in bije

Delong Liu, Zhaohui Hou, Mingjie Zhan, Shihao Han

Recently, diffusion-based video generation models have achieved significant success. However, existing models often suffer from issues like weak consistency and declining image quality over time. To overcome these challenges, inspired by aesthetic principles, we propose a non-invasive plug-in called Uniform Frame Organizer (UFO), which is compatible with any

Junjie Zhou, Ke Zhu, Jianxin Wu

Knowledge Distillation (KD) is essential in transferring dark knowledge from a large teacher to a small student network, such that the student can be much more efficient than the teacher but with comparable accuracy. Existing KD methods, however, rely on a large teacher trained specifically for the target task, which is both very inflexible and inefficient.

Serhii Svystun, Oleksandr Melnychenko, Pavlo Radiuk, Oleg Savenko

With the rapid development of green energy, the efficiency and reliability of wind turbines are key to sustainable renewable energy production. For that reason, this paper presents a novel intelligent system architecture designed for the dynamic collection and real-time processing of visual data to detect defects in wind turbines. The system employs advanced

Vitalii Slobodzian, Pavlo Radiuk, Oleksander Barmak, Iurii Krak

The segmentation and classification of cardiac magnetic resonance imaging are critical for diagnosing heart conditions, yet current approaches face challenges in accuracy and generalizability. In this study, we aim to further advance the segmentation and classification of cardiac magnetic resonance images by introducing a novel deep learning-based approach.

Fabrizio Davide, Pietro Torre, Leonardo Ercolani, Andrea Gaggioli

We tasked 16 state-of-the-art large language models (LLMs) with estimating the likelihood of Artificial General Intelligence (AGI) emerging by 2030. To assess the quality of these forecasts, we implemented an automated peer review process (LLM-PR). The LLMs' estimates varied widely, ranging from 3% (Reka- Core) to 47.6% (GPT-4o), with a median of 12.5%. Thes

Xumin Gao

In order to promote agricultural automatic picking and yield estimation technology, this project designs a set of automatic detection, positioning and counting algorithms for grape bunches, and applies it to agricultural robots. The Yolov3 detection network is used to realize the accurate detection of grape bunches, and the local tracking algorithm is added

Mohammad Amin Rastkhadiv, Meysam Pazoki

Recent discoveries involving high-temperature superconductivity in H3S and LaH10 have sparked a renewed interest in exploring the potential for superconductivity within hydrides. These superconductors require extremely high-pressure condition ( 100 GP a), rendering them virtually impractical for industrial applications. In this study, we verify the occurrenc

Anne-Marie Lutgen, Alistair Plum, Christoph Purschke, Barbara Plank

Orthographic variation is very common in Luxembourgish texts due to the absence of a fully-fledged standard variety. Additionally, developing NLP tools for Luxembourgish is a difficult task given the lack of annotated and parallel data, which is exacerbated by ongoing standardization. In this paper, we propose the first sequence-to-sequence normalization mod

Caucher Birkar, Bingyi Chen

Given a Fano type log Calabi-Yau fibration $(X,B)\to Z$ with $(X,B)$ being $\epsilon$-lc, the first author in \cite{Bi23} proved that the generalised pair $(Z,B_Z+M_Z)$ given by the canonical bundle formula is generalised $\delta$-lc where $\delta>0$ depends only on $\epsilon$ and $\dim X-\dim Z$, which confirmed a conjecture of Shokurov. In this paper, we p

Francesco Grippa, Gaetano Lambiase, Tanmay Kumar Poddar

Neutron Stars (NSs), among the densest objects in the Universe, are exceptional laboratories for investigating Dark Matter (DM) properties. Recent theoretical and observational developments have heightened interest in exploring the impact of DM on NS structure, giving rise to the concept of Dark Matter Admixed Neutron Stars (DANSs). This review examines how

Chenglin Wang, Yucheng Zhou, Zijie Zhai, Jianbing Shen

Protein inverse folding is a fundamental problem in bioinformatics, aiming to recover the amino acid sequences from a given protein backbone structure. Despite the success of existing methods, they struggle to fully capture the intricate inter-residue relationships critical for accurate sequence prediction. We propose a novel method that leverages diffusion

Isha Jain, Shailesh Garg, Shaurya Shriyam, Souvik Chakraborty

Graph-based representations for samples of computational mechanics-related datasets can prove instrumental when dealing with problems like irregular domains or molecular structures of materials, etc. To effectively analyze and process such datasets, deep learning offers Graph Neural Networks (GNNs) that utilize techniques like message-passing within their ar

Tianyang Wang, Ming Liu, Benji Peng, Xinyuan Song

Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of the drug but also to explore its dosage, treatment regimens, and potential side effects. This review discusse

Fech Scen Khoo, Jose Luis Blázquez-Salcedo, Burkhard Kleihaus, Jutta Kunz

We employ a recently developed spectral method to obtain the spectrum of quasinormal modes of rapidly rotating black holes in alternative theories of gravity and apply it to the black holes of shift-symmetric Einstein-scalar-Gauss-Bonnet theory. In this theory the quasinormal modes were recently obtained by employing perturbation theory in quadratic order in

Maria Eleftheria Vlontzou, Maria Athanasiou, Kalliopi Dalakleidi, Ioanna Skampardoni

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals and patients with MCI/AD, obtained through

Bowen Chen, Hengyuan Zhang, Jingyuan Li, Deyuan Hu

The discovery of unconventional superconductivity around 80 K in perovskite nickelates under high pressure has furnished a new platform to explore high-temperature unconventional superconductivity in addition to cuprates. Understanding the normal state of nickelate superconductors is crucial to uncovering the origin of this unconventional superconductivity a

M. Leger, E. Giglio, S. Guillous, A. Houel

A proof of concept of an ion source using a molten salt mixture rich in Cs and O ions, able to emit an ion current both in positive and negative polarities, is presented. Similar to liquid metal ion sources, the ions of the salt are emitted by field evaporation from a Taylor cone formed under the influence of an intense electric field. In contrast to an "ope

L. Chergui, F. Brauneis, T. Arnone Cardinale, M. Schubert

The presence of a single attractive impurity in an ultracold repulsive bosonic system can drive a transition from a homogeneous to a localized state, as we here show for a one-dimensional ring system. In the few-body limit the localization of the bosons around the impurity, as seen in the pair correlations, is accompanied by low-lying modes that resemble fin

Marko Znidaric

Symmetries are important for understanding equilibrium as well as nonequilibrium properties like transport. In translationally invariant extended systems one might expect symmetry generators to also be homogeneous. Studying qubit circuits with nearest-neighbor U(1) gates we show that this needs not be the case. We find new inhomogeneous screw SU(2) and ${\rm