October 2024 arXiv papers — page 92

Ehsan Tavanfar

We show that any complete local (normal) domain admits a module-finite quasi-Gorenstein normal (complete local) domain extension. In the geometric vein, we show that any normal projective variety $X$ over a field admits a finite surjective morphism $Y\rightarrow X$ from a normal quasi-Gorenstein projective variety $Y$. Notably, our results resolve the previo

David Schulte, Felix Hamborg, Alan Akbik

Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly. But which task to choose for transfer learning? Prior methods producing useful task rankings are infeasible f

Hon Lung Ho, Yupeng Wang, An Wang, Long Bai

Neurosurgery requires exceptional precision and comprehensive preoperative planning to ensure optimal patient outcomes. Despite technological advancements, there remains a need for intuitive, accessible tools to enhance surgical preparation and medical education in this field. Traditional methods often lack the immersive experience necessary for surgeons to

Soham Chakraborty

We construct the first examples of genuine ergodic discrete measured groupoids that are not isomorphic to any equivalence relation or transformation groupoid. We use a construction due to B.H. Neumann of an uncountable family of pairwise non-isomorphic 2-generated groups for our result.

Rafael Vazquez, Jean Auriol, Federico Bribiesca-Argomedo, Miroslav Krstic

Systems modeled by partial differential equations (PDEs) are at least as ubiquitous as systems that are by nature finite-dimensional and modeled by ordinary differential equations (ODEs). And yet, systematic and readily usable methodologies, for such a significant portion of real systems, have been historically scarce. Around the year 2000, the backstepping

Pak Kin Lau, Stuart Michael McManus

This paper introduces a new task in Natural Language Processing (NLP) and Digital Humanities (DH): Mining Asymmetric Intertextuality. Asymmetric intertextuality refers to one-sided relationships between texts, where one text cites, quotes, or borrows from another without reciprocation. These relationships are common in literature and historical texts, where

Michaela Denisová, Pavel Rychlý

The importance of inducing bilingual dictionary components in many natural language processing (NLP) applications is indisputable. However, the dictionary compilation process requires extensive work and combines two disciplines, NLP and lexicography, while the former often omits the latter. In this paper, we present the most common approaches from NLP that e

Minhyuk Seo, Hyunseo Koh, Jonghyun Choi

The majority of online continual learning (CL) advocates single-epoch training and imposes restrictions on the size of replay memory. However, single-epoch training would incur a different amount of computations per CL algorithm, and the additional storage cost to store logit or model in addition to replay memory is largely ignored in calculating the storage

Duan She, Yi-Wei Qiu, Defu Hou

Building upon Zubarev's nonequilibrium statistical operator formalism, we derive a relativistic canonical-like second-order spin hydrodynamics for two power-counting schemes. We obtain comprehensive second-order expressions for dissipative fluxes, including the shear stress tensor, bulk viscous pressure, charge-diffusion currents, rotational stress tensor, b

Massimiliano Paesani, Arthur G. Goetzee, Sanne Abeln, Halima Mouhib

Mammalian odorant binding proteins (OBPs) have long been suggested to transport hydrophobic odorant molecules through the aqueous environment of the nasal mucus. While the function of OBPs as odorant transporters is supported by their hydrophobic beta-barrel structure, no rationale has been provided on why and how these proteins facilitate the uptake of odor

Adaeze L. Ibik, Maria R. Drout, Raffaela Margutti, David Matthews

Luminous interacting supernovae are a class of stellar explosions whose progenitors underwent vigorous mass loss in the years prior to core-collapse. While the mechanism by which this material is ejected is still debated, obtaining the full density profile of the circumstellar medium (CSM) could reveal more about this process. Here, we present an extensive m

Tomasz Martyn

We study invariant sets and measures generated by iterated function systems defined on countable discrete spaces that are uniform grids of a finite dimension. The discrete spaces of this type can be considered as models of spaces in which actual numerical computation takes place. In this context, we investigate the possibility of the application of the rando

Masahiro Kato

This study examines portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and a variety of methods have been developed to achieve this goal. For instance, the mean-variance approach constructs portfolios by balancing the trade-off between the mean and variance of asset returns, while the quan

Vishnu Iyer, Atanas G. Stefanov

We consider a semilinear Schr\"odinger equation, driven by the power degenerate second order differential operator $\nabla\cdot (|x|^{2a} \nabla), a\in (0,1)$. We construct the solitary waves, in the sharp range of parameters, as minimizers of the Caffarelli-Kohn-Nirenberg's inequality. Depending on the parameter $a$ and the nonlinearity, we establish a numb

Tianlong Zhou, Jun Shang, Weixiong Rao

In this paper, we study the collaborative state fusion problem in a multi-agent environment, where mobile agents collaborate to track movable targets. Due to the limited sensing range and potential errors of on-board sensors, it is necessary to aggregate individual observations to provide target state fusion for better target state estimation. Existing schem

Yanan Ma, Chenghao Xiao, Chenhan Yuan, Sabine N van der Veer

Topic modelling is a pivotal unsupervised machine learning technique for extracting valuable insights from large document collections. Existing neural topic modelling methods often encode contextual information of documents, while ignoring contextual details of candidate centroid words, leading to the inaccurate selection of topic words due to the contextual

Yu. M. Poluektov, A. A. Soroka

Thermodynamic functions of the ideal Fermi gas at arbitrary temperatures are calculated using the standard Fermi-Stoner functions. The properties of the Fermi-Stoner functions are analyzed. The limiting cases of low-temperature and classical limits with taking into account quantum corrections and the special case of zero chemical potential are considered.

Yevheniia Khyzhniak, Michael Annan Lisa

In non-central heavy-ion collisions, the particle-emitting source can be tilted away from the beam direction, an effect that becomes particularly significant at collision energies of a few GeV and lower. This phenomenon, manifest itself in many observables such as directed flow, polarization, and vorticity, is therefore important to investigate. In this pape

Le Hong Phong, Ho Ngoc Luat, Vo Nguyen Le Duy

Detecting the presence of anomalies in regression models is a crucial task in machine learning, as anomalies can significantly impact the accuracy and reliability of predictions. Random Sample Consensus (RANSAC) is one of the most popular robust regression methods for addressing this challenge. However, this method lacks the capability to guarantee the relia

H. Moon, S. Yamani, G. H. McKinley, J. Lee

Semi-flexible polyelectrolytes are a group of biopolymers with a wide range of applications from drag reducing agents in turbulent flows to thickening agents in food and cosmetics. In this study, we investigate the rheology of aqueous solutions of xanthan gum as a canonical semi-flexible polyelectrolyte in steady shear and transient extensional flows via tor

Kaushiki Mukherjee, Biswajit Paul

Entanglement and nonlocality are two important nonclassical features of quantum correlations. Recently the study of quantum correlations in networks has undergone remarkable progress owing to technological development towards scalable quantum networks. However, compared to standard Bell scenario, manifestation of the interplay between these two aspects has r

Sebastián Donoso, Andreas Koutsogiannis, Borys Kuca, Wenbo Sun

We develop a robust structure theory for multiple ergodic averages of commuting transformations along Hardy sequences of polynomial growth. We then apply it to derive a number of novel results on joint ergodicity, recurrence and convergence. In particular, we prove joint ergodicity for (a) pairwise independent Hardy sequences and weakly mixing transformation

Martin Plazanet, Thomas Ayral

In this work, we study the projective quantum eigensolver (PQE) approach to optimizing unitary coupled cluster wave functions on quantum hardware, as introduced in arXiv:2102.00345. The projective quantum eigensolver is a hybrid quantum-classical algorithm which, by optimizing a unitary coupled cluster wave function, aims at computing the ground state of man

Haibo Wang, Yuxuan Qiu, Yanze Wang, Rob Brekelmans

Simulating transition dynamics between metastable states is a fundamental challenge in dynamical systems and stochastic processes with wide real-world applications in understanding protein folding, chemical reactions and neural activities. However, the computational challenge often lies on sampling exponentially many paths in which only a small fraction ends

Zixuan Yang, Jiaqi Zheng, Guihai Chen

Ensuring verifiable and interpretable safety of deep reinforcement learning (DRL) is crucial for its deployment in real-world applications. Existing approaches like verification-in-the-loop training, however, face challenges such as difficulty in deployment, inefficient training, lack of interpretability, and suboptimal performance in property satisfaction a

Junho Kim, Yeachan Kim, Jun-Hyung Park, Yerim Oh

We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs) for materials science. Unlike previous adaptation strategies that solely focus on constructing domain-specific corpus, MELT comprehensively considers both the corpus and the train

Bhinva Ram, Tanmoy Samanta, Yajie Chen, Alphonse Sterling

Small-scale Brightenings (SBs) are commonly observed in the transition region that separates the solar chromosphere from the corona. These brightenings, omnipresent in active region patches known as "moss" regions, could potentially contribute to the heating of active region plasma. In this study, we investigate the properties of SB events in a moss region a

Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli, Daniele Fontanelli

An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly adapt to the workpiece geometry. In this paper, we propose a Learning from Demonstration approach that allows a robot manipulator to learn and generalise motions across complex surfa

Jenny List, Ulrich Einhaus, Andre Filipe Silva, Leonhard Reichenbach

Pair-production and single-production of $W$ bosons provide many opportunities to look for new physics via precision measurements, for instance via scrutinising the involved triple-gauge vertices or by measuring CKM matrix elements in an environment very complementary to $B$ hadron decays. This contribution presents the ongoing work based on full simulation

Jiangchuan You, Ran Chen, Wanshun Li, Hui-hui Miao

A basic model of hydrogen bonds related to $\rm{H}_2\rm{O}$, which is adapted from the Jaynes--Cummings model, is suggested, and its different dynamic features are studied theoretically. In this model, the making and breaking of hydrogen bonds happen alongside the creation and destruction of phonons in the surrounding medium. A number of simplifying assumpti

Julian Barra, Shayan Shahbazi, Anthony Birri, Rajni Chahal

Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is

Zhenhui Xu, Bing-Chang Wang, Tielong Shen

This paper presents a novel model-free method to solve linear quadratic Gaussian mean field social control problems in the presence of multiplicative noise. The objective is to achieve a social optimum by solving two algebraic Riccati equations (AREs) and determining a mean field (MF) state, both without requiring prior knowledge of individual system dynamic

Stanislaw Szarek, Pawel Wolff

The celebrated Dvoretzky theorem asserts that every $N$-dimensional convex body admits central sections of dimension $d = \Omega(\log N)$, which is nearly spherical. For many instances of convex bodies, typically unit balls with respect to some norm, much better lower bounds on $d$ have been obtained, with most research focusing on such lower bounds and on t

Andreas Lang, Erich Schubert

The k-means clustering algorithm is a popular algorithm that partitions data into k clusters. There are many improvements to accelerate the standard algorithm. Most current research employs upper and lower bounds on point-to-cluster distances and the triangle inequality to reduce the number of distance computations, with only arrays as underlying data struct

Qitan Lv, Jie Wang, Hanzhu Chen, Bin Li

Generation of plausible but incorrect factual information, often termed hallucination, has attracted significant research interest. Retrieval-augmented language model (RALM) -- which enhances models with up-to-date knowledge -- emerges as a promising method to reduce hallucination. However, existing RALMs may instead exacerbate hallucination when retrieving

Jiaxuan Gao, Shusheng Xu, Wenjie Ye, Weilin Liu

Reward models have been increasingly critical for improving the reasoning capability of LLMs. Existing research has shown that a well-trained reward model can substantially improve model performances at inference time via search. However, the potential of reward models during RL training time still remains largely under-explored. It is currently unclear whet

J. Zhao, J. Aichelin, P. B. Gossiaux, K. Werner

Charmed hadron observables from the RHIC to LHC energies have been very successfully described with the recently advanced EPOS4HQ event generator. Here we extend this investigation to the production of beauty hadrons in proton-proton (pp) and heavy ion (HI) collisions

Rômulo Damasclin Chaves dos Santos

This work extends the study of mean field equations arising in two-dimensional (2D) turbulence by introducing generalized weighted Sobolev operators. Employing variational methods, particularly the mountain pass theorem and a refined blow-up analysis, we establish the existence of nontrivial solutions under broader boundary conditions than those considered i

Santu Prasad Jana, S Sreesanker, Suraina Gupta, Anjan K. Gupta

A counterclockwise hysteresis is observed at room temperature in the transfer characteristics of SrTiO$_3$ (STO) gated MoS$_2$ field effect transistor (FET) and attributed to bistable dipoles on the STO surface. The hysteresis is expectedly found to increase with increasing range, as well as decreasing rate, of the gate-voltage sweep. The hysteresis peaks ne

Ping Huang, Yuxin He, Hao Wang, Jingjing Chen

Accurate short-term forecasts of passenger flow in metro systems under delay conditions are crucial for emergency response and service recovery, which pose significant challenges and are currently under-researched. Due to the rare occurrence of delay events, the limited sample size under delay condictions make it difficult for conventional models to effectiv

Glenda Hui En Tan, Goh Xin Ru Karin, Shen Bingquan

Colorectal cancer is the most common cancer in Singapore and the third most common cancer worldwide. Blood in a person's stool is a symptom of this disease, and it is usually detected by the faecal occult blood test (FOBT). However, the FOBT presents several limitations - the collection process for the stool samples is tedious and unpleasant, the waiting per

Gioni Mexi, Felipe Serrano, Timo Berthold, Ambros Gleixner

For almost two decades, mixed integer programming (MIP) solvers have used graph-based conflict analysis to learn from local infeasibilities during branch-and-bound search. In this paper, we improve MIP conflict analysis by instead using reasoning based on cuts, inspired by the development of conflict-driven solvers for pseudo-Boolean optimization. Phrased in

Valdir Barbosa da Silva Junior, Ana Laura Boscolo, Diogo Ricardo da Costa, Luiz Antonio Barreiro

This study explores the integration of a diffusion control parameter into the chaotic dynamics of a modified bouncing ball model. By extending beyond simple elastic collisions, the model introduces elements that affect the diffusive behavior of kinetic energy, offering insights into the interplay between deterministic chaos and stochastic diffusion. The rese

Yui Lo, Yuqian Chen, Dongnan Liu, Wan Liu

The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores t

Seong-Il Park, Jay-Yoon Lee

Retrieval Augmented Language Models (RALMs) have gained significant attention for their ability to generate accurate answer and improve efficiency. However, RALMs are inherently vulnerable to imperfect information due to their reliance on the imperfect retriever or knowledge source. We identify three common scenarios-unanswerable, adversarial, conflicting-wh

Reggie C. Pantig

In this paper, we investigate the properties of black holes within the framework of multi-fractional theories of gravity, focusing on the effects of q-derivatives and weighted derivatives. These modifications, which introduce scale-dependent spacetime geometries, alter black hole solutions in intriguing ways. Within these frameworks, we analyze two key obser

Bolin Chen, Yan Ye, Jie Chen, Ru-Ling Liao

This paper proposes a Generative Face Video Compression (GFVC) approach using Supplemental Enhancement Information (SEI), where a series of compact spatial and temporal representations of a face video signal (e.g., 2D/3D keypoints, facial semantics and compact features) can be coded using SEI messages and inserted into the coded video bitstream. At the time

Taehun Kim

We provide a set of conditions that is necessary and sufficient for the $L^{2}$-wellposedness of the Cauchy problem for fifth and sixth order variable-coefficient linear dispersive equations. The necessity of these conditions had been presented by Tarama, and we scrutinized their proof to split the conditions into several parts so that an inductive argument

Hongzhi Liu, Hang Wang, Zijing Wang, Shaocong Xiang

In this paper, we employ the framework of localization algebras to compute the equivariant K-homology class of the Euler characteristic operator, a central object in studying equivariant index theory on manifolds. This approach provides a powerful algebraic language for analyzing differential operators on equivariant structures and allows for the application

Shunichiro Orihara, Tomotaka Momozaki

Subclassification estimators are one of the methods used to estimate causal effects of interest using the propensity score. This method is more stable compared to other weighting methods, such as inverse probability weighting estimators, in terms of the variance of the estimators. In subclassification estimators, the number of strata is traditionally set at

Lina Grineviciute, Ignas Lukosiunas, Julianija Nikitina, Algirdas Selskis

One of the exceptional features of non-Hermitian systems is the unidirectional wave interactions. Simultaneous modulation of the real and the imaginary part of the interaction potentials (of the refractive index and the gain/loss in the case of optical systems) can result in unequal coupling coefficients between the fields of different parts of the system. T

Zhichao Wang, Xinhai Chen, Chunye Gong, Bo Yang

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavil

Chenjia He, Xiaqing Sun, Hao Zhong, Qingfeng Meng

We found that the specific dispersion of metal-insulator-metal (MIM) waveguide allows the hybridization of surface plasmon polaritons (SPPs) and the waveguide, which is not possible with dielectric waveguides. The SPP-MIM hybridization structure forms such a meta-film that integrates the previously incompatible respective merits of SPR and LSPR, including fl

Longfu Li, Lei Shi, Lingyong Zeng, Kuan Li

Chemical doping is a critical factor in the development of new superconductors or optimizing the superconducting transition temperature (Tc) of the parent superconducting materials. Herein, a new simple urea approach is developed to synthesize the N-doped alfa-Mo2C. Benefiting from the simple urea method, a broad superconducting dome is found in the Mo2C1-xN

Han Xu, Taoxing Pan, Zhiqiang Liu, Xiaoxiao Xu

User behavior modeling -- which aims to extract user interests from behavioral data -- has shown great power in Click-through rate (CTR) prediction, a key component in recommendation systems. Recently, attention-based algorithms have become a promising direction, as attention mechanisms emphasize the relevant interactions from rich behaviors. However, the me

Hongqi Chen, Ji Hyung Lee

This paper advances a variable screening approach to enhance conditional quantile forecasts using high-dimensional predictors. We have refined and augmented the quantile partial correlation (QPC)-based variable screening proposed by Ma et al. (2017) to accommodate $\beta$-mixing time-series data. Our approach is inclusive of i.i.d scenarios but introduces ne

Carlos Navia, Marcel Oliveira, B. Felicio, Andre Nepomuceno

We analyze a bright and rare burst, GRB20221009A, showing through NED data that the angular distance between GRB221009A and the supernova SN 2022xiw is almost the same angular separation with galactic objects, i.e., the SN-GRB connection at z = 0.151 is not robust. Gamma rays with up to 18 TeV detected in association with the GRB constrain the attenuation to

Oh Joon Kwon, Daiki E. Matsunaga, Kee-Eung Kim

A critical component of the current generation of language models is preference alignment, which aims to precisely control the model's behavior to meet human needs and values. The most notable among such methods is Reinforcement Learning with Human Feedback (RLHF) and its offline variant Direct Preference Optimization (DPO), both of which seek to maximize a

Charles Elbar

We prove $L^{\infty}_{t}W^{1,p}_{x}$ Sobolev estimates in the Keller-Segel system with linear diffusion in any dimensionby proving a functional inequality, inspired by the Brezis-Gallou\"et-Wainger inequality. These estimates are also valid at the discrete level in the Jordan-Kinderlehrer-Otto (JKO) scheme. By coupling this result with the diffusion properti

Rajeev Ranjan Dwivedi, Priyadarshini Kumari, Vinod K Kurmi

Deep neural networks trained on biased data often inadvertently learn unintended inference rules, particularly when labels are strongly correlated with biased features. Existing bias mitigation methods typically involve either a) predefining bias types and enforcing them as prior knowledge or b) reweighting training samples to emphasize bias-conflicting samp

Ketin Yin, Zonghao Guo, ZhengHan Qin

In the current era of artificial intelligence, federated learning has emerged as a novel approach to addressing data privacy concerns inherent in centralized learning paradigms. This decentralized learning model not only mitigates the risk of data breaches but also enhances the system's scalability and robustness. However, this approach introduces a new chal

Gleydson Fernandes de Jesus, Maria Heloísa Fraga da Silva, Otto Menegasso Pires, Lucas Cruz da Silva

Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this cont

Jingling Zhang, Qianjie Wei, Xiaoying Wei, Mingming Fan

Virtual Reality (VR) museums provide immersive visiting experiences. Despite growing efforts in VR museum design optimization, limited research addresses its efficacy for older adults. We sought to investigate the challenges of and preferences for VR museum visits among older adults through a user-centered participatory workshop. Our preliminary findings ill

Chaodong Xiao, Minghan Li, Zhengqiang Zhang, Deyu Meng

Selective state space models (SSMs), such as Mamba, highly excel at capturing long-range dependencies in 1D sequential data, while their applications to 2D vision tasks still face challenges. Current visual SSMs often convert images into 1D sequences and employ various scanning patterns to incorporate local spatial dependencies. However, these methods are li

Tomasz Woźniak

The R package bsvars provides a wide range of tools for empirical macroeconomic and financial analyses using Bayesian Structural Vector Autoregressions. It uses frontier econometric techniques and C++ code to ensure fast and efficient estimation of these multivariate dynamic structural models, possibly with many variables, complex identification strategies,

Shima Baharlouei, Jamie M. Taylor, Carlos Uriarte, David Pardo

Developing efficient methods for solving parametric partial differential equations is crucial for addressing inverse problems. This work introduces a Least-Squares-based Neural Network (LS-Net) method for solving linear parametric PDEs. It utilizes a separated representation form for the parametric PDE solution via a deep neural network and a least-squares s

Gurinder Singh, Rupam Barman

Let $t\geq2$ and $k\geq1$ be integers. A $t$-regular partition of a positive integer $n$ is a partition of $n$ such that none of its parts is divisible by $t$. Let $b_{t,k}(n)$ denote the number of hooks of length $k$ in all the $t$-regular partitions of $n$. In this article, we prove some inequalities for $b_{t,k}(n)$ for fixed values of $k$. We prove that

Noman Bashir, Varun Gohil, Anagha Belavadi, Mohammad Shahrad

The rapid increase in computing demand and its corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several lifecycle phases, including its supply chain, operation, and end-of-life. Industry uses these metrics to optimize

Pantea Karimi, Solal Pirelli, Siva Kesava Reddy Kakarla, Ryan Beckett

Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to

Alonso Beaumont

H. Gl\"ockner and G. A. Willis have recently shown that locally pro-p contraction groups are nilpotent. The proof hinges on a fixed-point result: if the local field $\mathbb{F}_{p}(\!(t)\!)$ acts on its $d$-th power $\mathbb{F}_{p}(\!(t)\!)^{d}$ additively, continuously, and in an appropriately equivariant manner, then the action has a non-zero fixed point.

Viktória Fröhlich, Zsolt Regály

Dozens of white dwarfs with anomalous metal polluted atmospheres (DZ WDs) are known to host dust and gas discs. The line profiles of the Ca II triplet emitted by the gas discs show significant asymmetry. Several minor planets have been discovered orbiting such WDs. The most challenging burden of modelling gas discs around DZ WDs is to simultaneously explain

Tobias K. S. Ritschel

Time delays are ubiquitous in industrial processes, and they must be accounted for when designing control algorithms because they have a significant effect on the process dynamics. Therefore, in this work, we propose a simultaneous approach for numerical optimal control of delay differential equations with distributed time delays. Specifically, we linearize

Prabhakar, Subhajyoti Pal, Umesh Kumar, Manoranjan Kumar

We theoretically investigate the resonant inelastic X-ray scattering (RIXS) spectra in a quasi-1D chain of weakly coupled frustrated spin-1/2 trimers, as realized in Na$_{2}$Cu$_{3}$Ge$_{4}$O$_{12}$, with Cu $d^{9}$ 1/2 spins. We compute multi-spin correlations contributing to spin-conserving (SC) and spin non-conserving (NSC) RIXS cross-sections using ultra

Germán Vidal

Probabilistic programming is becoming increasingly popular thanks to its ability to specify problems with a certain degree of uncertainty. In this work, we focus on term rewriting, a well-known computational formalism. In particular, we consider systems that combine traditional rewriting rules with probabilities. Then, we define a novel "distribution semanti

Nathaniel Tornow, Christian B. Mendl, Pramod Bhatotia

Circuit knitting offers a promising path to the scalable execution of large quantum circuits by breaking them into smaller sub-circuits whose output is recombined through classical postprocessing. However, current techniques face excessive overhead due to a naive postprocessing method that neglects potential optimizations in the circuit structure. To overcom

Jens M. Melenk, Jörg Nick

We present a method to rapidly approximate convolution quadrature (CQ) approximations, based on a piecewise polynomial interpolation of the Laplace domain operator, which we call the \emph{parsimonious} convolution quadrature method. For implicit Euler and second order backward difference formula based discretizations, we require $O(\sqrt{N}\log N)$ evaluati

Shitong Fan, Wenbo Wang, Feiyang Xiao, Shiheng Zhang

It is crucial for auditory attention decoding to classify matched and mismatched speech stimuli with corresponding EEG responses by exploring their relationship. However, existing methods often adopt two independent networks to encode speech stimulus and EEG response, which neglect the relationship between these signals from the two modalities. In this paper

Omar Alsaiari, Nilufar Baghaei, Hatim Lahza, Jason Lodge

This study investigates the impact of emotionally enriched AI feedback on student engagement and emotional responses in higher education. Leveraging the Control-Value Theory of Achievement Emotions, we conducted a randomized controlled experiment involving 425 participants where the experimental group received AI feedback enhanced with motivational elements,

Abdulhady Abas Abdullah, Shima Tabibian, Hadi Veisi, Aso Mahmudi

Automatic Speech Recognition (ASR) for low-resource languages remains a challenging task due to limited training data. This paper introduces a comprehensive study exploring the effectiveness of Whisper, a pre-trained ASR model, for Northern Kurdish (Kurmanji) an under-resourced language spoken in the Middle East. We investigate three fine-tuning strategies:

Roberto Molinaro, Jordan Dane Daubinet, Alexander Jakob Dautel, Andreas Schlueter

We announce the release of EPT-1.5, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI earth system models. EPT-1.5 demonstrates substantial improvements over its predecessor, EPT-1. Built specifically for the European energy industry, EPT-1.5 shows remarkable performance in predicting energy-relevant variables, particularly

Chen-Hsiu Huang, Ja-Ling Wu

The digital image manipulation and advancements in Generative AI, such as Deepfake, has raised significant concerns regarding the authenticity of images shared on social media. Traditional image forensic techniques, while helpful, are often passive and insufficient against sophisticated tampering methods. This paper introduces the Secure Learned Image Codec

Xuechen Guo, Wenhao Chai, Shi-Yan Li, Gaoang Wang

Multimodal Large Language Model (MLLM) has recently garnered attention as a prominent research focus. By harnessing powerful LLM, it facilitates a transition of conversational generative AI from unimodal text to performing multimodal tasks. This boom begins to significantly impact medical field. However, general visual language model (VLM) lacks sophisticate

Keting Yin, Jiayi Mao

Federated Learning(FL) is popular as a privacy-preserving machine learning paradigm for generating a single model on decentralized data. However, statistical heterogeneity poses a significant challenge for FL. As a subfield of FL, personalized FL (pFL) has attracted attention for its ability to achieve personalized models that perform well on non-independent

Li Shen, Yongpeng Wu, Yin Xu, Xiaohu You

The soft-output successive cancellation list (SOSCL) decoder provides a methodology for estimating the a-posteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder for polar codes. However, the sequential nature of SCL decoding leads to a high decoding latency for the SO-SCL decoder. In this paper, we propose a soft-output f

Haojie Xu, Xia Wu, Wei Lu, Xiwang Cao

In this paper, we present an infinite family of MDS codes over $\mathbb{F}_{2^s}$ and two infinite families of almost MDS codes over $\mathbb{F}_{p^s}$ for any prime $p$, by investigating the parameters of the dual codes of two families of BCH codes. Notably, these almost MDS codes include two infinite families of near MDS codes over $\mathbb{F}_{3^s}$, reso

Ian Heywood, Andrew P. V. Siemion, Daniel Czech, Steve Ertel

We have conducted observations of the nearby (11.46 ly) star system Procyon, using MeerKAT's UHF (544-1087 MHz) receivers. We produce full-Stokes time and frequency integrated continuum images, as well as total intensity time series imaging at 8 s cadence, and full-Stokes vector-averaged dynamic spectra from the visibilities in order to search for transient

Hrishav Bakul Barua, Kalin Stefanov, Lemuel Lai En Che, Abhinav Dhall

Reconstruction of High Dynamic Range (HDR) from Low Dynamic Range (LDR) images is an important computer vision task. There is a significant amount of research utilizing both conventional non-learning methods and modern data-driven approaches, focusing on using both single-exposed and multi-exposed LDR for HDR image reconstruction. However, most current state

Junjun Jiang, Zengyuan Zuo, Gang Wu, Kui Jiang

Image restoration (IR) seeks to recover high-quality images from degraded observations caused by a wide range of factors, including noise, blur, compression, and adverse weather. While traditional IR methods have made notable progress by targeting individual degradation types, their specialization often comes at the cost of generalization, leaving them ill-e

Rui Ding, Chao Ji, Patrizia Pucci

In this paper, we study {existence and multiplicity} of normalized solutions for the following $(2, q)$-Laplacian equation \begin{equation*}\label{Eq-Equation1} \left\{\begin{array}{l} -\Delta u-\Delta_q u+\lambda u=f(u) \quad x \in \mathbb{R}^N , \int_{\mathbb{R}^N}u^2 d x=c^2, \end{array}\right. \end{equation*} where $1<q<N$, $N\geq3$, $\Delta_q=\operatorn

Raúl Iranzo, Víctor M. Batlle, Juan D. Tardós, José M. M. Montiel

Geometric reconstruction and SLAM with endoscopic images have advanced significantly in recent years. In most medical fields, monocular endoscopes are employed, and the algorithms used are typically adaptations of those designed for external environments, resulting in 3D reconstructions with an unknown scale factor. For the first time, we propose a method to

George Hannah, Rita T. Sousa, Ioannis Dasoulas, Claudia d'Amato

With the recent surge in popularity of Large Language Models (LLMs), there is the rising risk of users blindly trusting the information in the response, even in cases where the LLM recommends actions that have potential legal implications and this may put the user in danger. We provide an empirical analysis on multiple existing LLMs showing the urgency of th

Ashish Seth, Ramaneswaran Selvakumar, Sonal Kumar, Sreyan Ghosh

Audio-Language Models (ALMs) have demonstrated remarkable performance in zero-shot audio classification. In this paper, we introduce PAT (Parameter-free Audio-Text aligner), a simple and training-free method aimed at boosting the zero-shot audio classification performance of CLAP-like ALMs. To achieve this, we propose to improve the cross-modal interaction b

Bohan Zheng, Siyu Zhu, Xingping Zhou, Tong Liu

Classification of quantum phases is one of the most important areas of research in condensed matter physics. In this work, we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning. Firstly, we choose two advanced unsupervised learning algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ord

Jiayue Dai, Yunya Wang, Yihan Fang, Yuetong Chen

To address the semantic inconsistency issue with SAM or other single-image segmentation models handling image sequences, we introduce BYOCL. This novel model outperforms SAM in extensive experiments, showcasing its Hierarchical prototype capabilities across CLIP and other representations. BYOCL significantly reduces time and space consumption by dividing inp

Dobrik Georgiev, JJ Wilson, Davide Buffelli, Pietro Liò

Neural Algorithmic Reasoning (NAR) research has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms. However, most previous approaches have always used a recurrent architecture, where each iteration of the GNN matches an iteration of the algorithm. In this paper we study neurally solving algorithms from a different pers

Nhat-Minh Dzoan, Thi-Thoa Mac, Hoang-Hiep Ly, Xuan-Thuan Nguyen

This paper presents a novel approach to designing a Hedge Algebra Controller named Hedge Algebra Controller with Recursive Semantic Values (RS-HAC). This approach incorporates several newly introduced concepts, including Semantically Quantifying Simplified Mapping (SQSM) featuring a recursive algorithm, Infinite General Semantization (IGS), and Infinite Gene

Chuhan Xie, Kaicheng Jin, Jiadong Liang, Zhihua Zhang

We study time-uniform statistical inference for parameters in stochastic approximation (SA), which encompasses a bunch of applications in optimization and machine learning. To that end, we analyze the almost-sure convergence rates of the averaged iterates to a scaled sum of Gaussians in both linear and nonlinear SA problems. We then construct three types of

Kyosuke Tomonari, Daniel Blixt

We investigate the degrees of freedom of new general relativity. This theory is a three-parameter theory and is classified into nine irreducible types according to the rotation symmetry of $SO(3)$ on each leaf of ADM-foliation. In this work, we focus on unveiling the degrees of freedom of the physically interesting types of NGR: Type 2, Type 3, Type 5, and T

Andrea Bondesan, Bao Quoc Tang

We consider hard-potential cutoff multi-species Boltzmann operators modeling microscopic binary elastic collisions and bimolecular reversible chemical reactions inside a gaseous mixture. We prove that the spectral gap estimate derived for the linearized elastic collision operator can be exploited to deduce an explicit negative upper bound for the Dirichlet f

Yuanhao Liu, Shuo Liu, Yimeng Liu, Jingwen Yang

Cognitive diagnosis model (CDM) is a fundamental and upstream component in intelligent education. It aims to infer students' mastery levels based on historical response logs. However, existing CDMs usually follow the ID-based embedding paradigm, which could often diminish the effectiveness of CDMs in open student learning environments. This is mainly because