November 2024 arXiv papers — page 46

Peng Chen, Tianyi Qi, Ting Zhang

Let $S_{n}$ be a sum of independent identically distribution random variables with finite first moment and $h_{M}$ be a call function defined by $g_{M}(x)=\max\{x-M,0\}$ for $x\in\mathbb{R}$, $M>0$. In this paper, we assume the random variables are in the domain $\mathcal{R}_{\alpha}$ of normal attraction of a stable law of exponent $\alpha$, then for $\alph

Hossein Keshtkar, Nadine Aburumman

Creating realistic droplet simulations and animations has long been a formidable challenge for researchers and developers due to the inherent complexity of fluid dynamics. Achieving lifelike droplet splash simulations while managing computational resources has often resulted in sacrifices compromising the realism of visualizations. Nevertheless, significant

Gene Freudenburg

Working over a field $k$ of characteristic zero, we study the ring $\mathfrak{R}=\mathfrak{D}^{\mathbb{Z}_2}$ where $\mathfrak{D}=k[x_0,x_1,x_2]/(2x_0x_2-x_1^2-1)$ and $\mathbb{Z}_2$ acts by $x_i\to -x_i$. $\mathfrak{D}$ admits an algebraic $SL_2(k)$-action which restricts to $\mathfrak{R}$. Our results include the following. (1) If $k$ is algebraically clos

A Vinil, Aneesh Sreevallabh Chivukula, Pranav Chintareddy

Adversarial learning is critical for enhancing model robustness, aiming to defend against adversarial attacks that jeopardize machine learning systems. Traditional methods often lack efficient mechanisms to explore diverse adversarial perturbations, leading to limited model resilience. Inspired by game-theoretic principles, where adversarial dynamics are ana

Runze Li, Jintao Xu, Wenxun Xing

Root mean square propagation (abbreviated as RMSProp) is a first-order stochastic algorithm used in machine learning widely. In this paper, a stable gradient-adjusted RMSProp (abbreviated as SGA-RMSProp) with mini-batch stochastic gradient is proposed, and its properties are studied on the linear least squares problem. R-linear convergence of the algorithm i

Md. Saiful Bari Siddiqui, Md Mohaiminul Islam, Md. Golam Rabiul Alam

Overfitting remains a significant challenge in deep learning, often arising from data outliers, noise, and limited training data. To address this, the Divide2Conquer (D2C) method was previously proposed, which partitions training data into multiple subsets and trains identical models independently on each. This strategy enables learning more consistent patte

Ying Bao, Xusen Cheng, Linlin Su, Alex Zarifis

The transformation of technology and collaboration methods driven by the e-government system forces government employees to reconsider their daily workflow and collaboration with colleagues. Despite the extensive existing knowledge of technology usage and collaboration, there are limitations in explaining the synergy between technology usage and group collab

A. Perodi, L. Salasnich

Inspired by recent proposals for detecting gravitational waves by using Bose-Einstein condensates (BECs), we investigate the interplay between these two phenomena. A gravitational wave induces a phase shift in the fidelity amplitude of the many-body quantum state. We study the enhancement of the phase shift in the case of Bose condensates confined by an anis

Albert Visser

In this paper we examine two ways of coding sequences in arithmetical theories. We investigate under what conditions they work. To be more precise, we study the creation of objects of a data-type that we call ur-strings, roughly sequences where the components are ordered but where we do not have an explicitly given projection function. First, we have a brief

Sarim Hashmi, Juan Lugo, Abdelrahman Elsayed, Dinesh Saggurthi

Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients. However, manual segmentation is time-consuming, requiring expert intervention and is susceptible to human error. Therefore, significant research has been devoted to developing machine learning methods that can accurately segment tumors in 3D multimod

Haiquan Wang, Chaoyi Ruan, Jia He, Jiaqi Ruan

The growth of Large Language Models (LLMs) has necessitated large-scale distributed training. Highly optimized frameworks, however, still suffer significant losses in Model FLOPS utilization (often below 50%) due to large communication volumes. Meanwhile, our comprehensive profiling shows that the computation- and communication-intensive operators overlap we

Roberta Satariano, Anatoly Fjodorovich Volkov, Halima Giovanna Ahmad, Luigi Di Palma

Magnetic Josephson Junctions (MJJs) are a special class of hybrid systems where antagonistic correlations coexist, thus providing a key for advances in weak superconductivity, superconducting spintronics and quantum computation. So far, the memory properties of MJJs have been mostly investigated in view of digital electronics and for spintronic devices at li

Sule Bai, Yong Liu, Yifei Han, Haoji Zhang

Recent advancements in pre-trained vision-language models like CLIP have enabled the task of open-vocabulary segmentation. CLIP demonstrates impressive zero-shot capabilities in various downstream tasks that require holistic image understanding. However, due to the image-level contrastive learning and fully global feature interaction, ViT-based CLIP struggle

Sinya Aoki, Jorge Ovalle

We present two analytical models of gravitational collapse toward the Schwarzschild black hole, starting from the interior of the revisited Schwarzschild solution recently reported in [Phys. Rev. D 109, 104032 (2024)]. Both models satisfy some energy conditions at all times as long as the collapse is slower than some limit. While a singularity of the Schwarz

Ji Hyeok Jung, Eun Tae Kim, Seoyeon Kim, Joo Ho Lee

Multimodal large language models (MLLMs) act as essential interfaces, connecting humans with AI technologies in multimodal applications. However, current MLLMs face challenges in accurately interpreting object orientation in images due to inconsistent orientation annotations in training data, hindering the development of a coherent orientation understanding.

Teng Zhou, Xiaoyu Zhang, Yongchuan Tang

Panoramic Image Generation (PIG) aims to create coherent images of arbitrary lengths. Most existing methods fall in the joint diffusion paradigm, but their complex and heuristic crop connection designs often limit their ability to achieve multilevel coherence. By deconstructing this challenge into its core components, we find it naturally aligns with next-to

Faridoun Mehri, Mahdieh Soleymani Baghshah, Mohammad Taher Pilehvar

Why do gradient-based explanations struggle with Transformers, and how can we improve them? We identify gradient flow imbalances in Transformers that violate FullGrad-completeness, a critical property for attribution faithfulness that CNNs naturally possess. To address this issue, we introduce LibraGrad -- a theoretically grounded post-hoc approach that corr

V. N. Smirnov, K. M. Kazistova, I. A. Sudakov, V. Leplat

Black-box optimization, a rapidly growing field, faces challenges due to limited knowledge of the objective function's internal mechanisms. One promising approach to address this is the Stochastic Order Oracle Concept. This concept, similar to other Order Oracle Concepts, relies solely on relative comparisons of function values without requiring access to th

Biqing Qi, Zhouyi Qian, Yiang Luo, Junqi Gao

As multi-modal large language models (MLLMs) are increasingly applied to complex reasoning tasks, the diversity and quality of reasoning paths become crucial factors affecting their performance. Although current methods aim to enhance reasoning quality through path expansion, they often neglect the diversity of reasoning paths and effective information shari

Anand Choudhary, Ajay Wasan

The nature-inspired field of quantum communication has witnessed exciting developments over the past few years with countries all over the world working hard to scale their experimental quantum networks to larger sizes and increased coverage. Evidently, quantum network simulators are the need of the hour as they provide a framework for tuning hardware parame

Thomas Depian, Simon Dominik Fink, Alexander Firbas, Robert Ganian

We study the classical problem of computing geometric thickness, i.e., finding a straight-line drawing of an input graph and a partition of its edges into as few parts as possible so that each part is crossing-free. Since the problem is NP-hard, we investigate its tractability through the lens of parameterized complexity. As our first set of contributions, w

P. Roura-Bas, A. A. Aligia

It is well known that quantized topological charge pumping takes place in the half filled Rice-Mele chain performing a closed cycle in parameter space. We extend previous studies to the case of charge and heat transport at arbitrary filling and temperature using the corresponding continuity equation with focus in the non-interacting case. The amount of charg

Baiyun Wang, Yijia Cheng, Yujie Hua, Wenxuan Tang

A high-sensitivity wireless pressure sensor with active processing structure designed on the dielectric substrate has been present and evaluated in this paper. The sensor configuration has been optimized by computer-aided design to achieve highest sensitivity and maximum working range for a given dimension. With the average sensitivity of 187kHz/kPa, the pro

Biqing Qi, Fangyuan Li, Zhen Wang, Junqi Gao

As an effective approach to equip models with multi-task capabilities without additional training, model merging has garnered significant attention. However, existing methods face challenges of redundant parameter conflicts and the excessive storage burden of parameters. In this work, through controlled experiments, we reveal that for task vectors, only thos

Jobir Adashev, Artem Lopatin, Zafar Normatov, Shokhsanam Solijonova

This article is devoted to the classification of anti-dendriform algebras that are associated with associativity. They are characterized as algebras with two operations whose sum is associative. In the paper all four-dimensional complex anti-dendriform algebras associated to four-dimensional associative algebras with one-dimensional center are classified

Yijiong Yu

It has been well-known that Chain-of-Thought can remarkably enhance LLMs' performance on complex tasks. However, because it also introduces slower inference speeds and higher computational costs, many researches have attempted to use implicit CoT, which does not need LLMs to explicitly generate the intermediate steps. However, the invisible reasoning process

Hantao Chen, Cheng Wang

This paper is concerned with Spearman's correlation matrices under large dimensional regime, in which the data dimension diverges to infinity proportionally with the sample size. We establish the central limit theorem for the linear spectral statistics of Spearman's correlation matrices, which extends the results of [\emph{Ann. Statist.} 43(2015) 2588--2623]

Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma

In this paper, we present a novel generalizable object pose estimation method to determine the object pose using only one RGB image. Unlike traditional approaches that rely on instance-level object pose estimation and necessitate extensive training data, our method offers generalization to unseen objects without extensive training, operates with a single ref

Haiyan Wang

This paper analyzes the stationary distributions of populations governed by the discrete stochastic logistic and Ricker difference equations at equilibrium examines with the gamma distribution. We identify mathematical relationships between the intrinsic growth rate in the stochastic equations and the parameters of the gamma distribution with a small stochas

Yongkun Du, Zhineng Chen, Hongtao Xie, Caiyan Jia

Connectionist temporal classification (CTC)-based scene text recognition (STR) methods, e.g., SVTR, are widely employed in OCR applications, mainly due to their simple architecture, which only contains a visual model and a CTC-aligned linear classifier, and therefore fast inference. However, they generally exhibit worse accuracy than encoder-decoder-based me

Sheng-Qi Zhang, Cong-Feng Qiao

Recent experimental progress on baryonic rare decays has spurred a deeper investigation on flavor-changing neutral current transitions in the baryon sector. Within the framework of QCD sum rules, we derive a complete set of form factors for the $ \Lambda_c\to p $ process in the large recoil region and use the $z$-series parametrization to extrapolate them ac

Mohammad Amirul Hairol Aman, Nurul Fathinah Azrisham, Ahmad Fakhrurrazi Ahmad Noorden, Wan Hazman Danial

Low internal and external quantum efficiencies in high Aluminium content AlGaN-based deep-ultraviolet light-emitting diode (DUV-LED) occurred due to strong polarization effects, spontaneous and piezoelectric polarization, at the interface between two materials. It also leads to a low carrier confinement and Quantum Confined Stark Effect (QCSE), contributing

Yaobin Hua, Rong-Jia Yang

We obtain a new general solution for the gravitational field equations in quantum fluctuation modified gravity, which reduces to different classes of black holes surrounded by fluids, by taking some specific values of the parameter of the equation of state. We discuss the strong energy condition in a general way and also for some special cases of different f

Peter Lebedev-Stepanov

An alternative form of the general solution of the linearized stationary Navier-Stokes equations for an incompressible fluid in spherical coordinates is obtained by the vector potential method. A previously published solution to this problem, dating back to the paper by Sampson, is given in terms of a stream function, which leads to formulas that are difficu

Halil ibrahim Kurt

This paper deals with the long-term behavior of positive solutions for the following parabolic-elliptic chemotaxis competition system with weak singular sensitivity and logistic source \begin{equation} \label{abstract-eq} \begin{cases} u_t=\Delta u-\chi \nabla\cdot (\frac{u}{v^{\lambda}} \nabla v) +ru- \mu u^2, \quad &x\in \Omega,\cr 0=\Delta v- \alpha v +\b

A. De Rújula

Interesting data on Gamma Ray Burts (GRBs) and Cosmic Rays (CRs) have recently been made public. GRB221009A has a record ``peak energy". The CR electron spectrum has been measured to unprecedented high energies and exhibits a ``knee" akin to the ones in all-particle or individual-element CR nuclei. IceCube has not seen high-energy neutrinos associated with G

Anuja Vats, Ivar Farup, Marius Pedersen, Kiran Raja

The importance of quantifying uncertainty in deep networks has become paramount for reliable real-world applications. In this paper, we propose a method to improve uncertainty estimation in medical Image-to-Image (I2I) translation. Our model integrates aleatoric uncertainty and employs Uncertainty-Aware Regularization (UAR) inspired by simple priors to refin

Koki Tokeshi

The curvaton paradigm can realise a part of or all the observed curvature perturbation. Based on the stochastic formalism of inflation and closed-form exact distributions therein, the distribution of the curvature perturbation is presented in an analytical manner by identifying a test field with a curvaton. The parameter space consisting of the decay rate an

Po-Shen Hsin, Ryohei Kobayashi, Guanyu Zhu

We systematically construct and classify fault-tolerant logical gates implemented by constant-depth circuits for quantum codes using cohomology operations and symmetry. These logical gates are obtained from unitary operators given by symmetry-protected topological responses, which correspond to generators of group cohomology and can be expressed explicitly o

Jiawen Weng, Zeke Xia, Ran Li, Ming Hu

Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference performance caused by data heterogeneity. Specifically, due to heterogeneous data, the optimization directions of different local models vary greatly, making it difficult for the t

Yihan Shen, Yajuan Sun

In this paper, we study the Lagrangian functions for a class of second-order differential systems arising from physics. For such systems, we present necessary and sufficient conditions for the existence of Lagrangian functions. Based on the variational principle and the splitting technique, we construct variational integrators and prove their equivalence to

P. -M. Zhang, Q. -L. Zhao, M. Elbistan, P. A. Horvathy

Ehlers and Kundt [1] argued in favor of the velocity effect: particles initally at rest hit by a burst of gravitational waves should fly apart with constant velocity after the wave has passed. Zel'dovich and Polnarev [2] suggested instead that waves generated by flyby would be merely displaced. Their prediction is confirmed provided the wave parameters take

Qian Chen, Zhanwei Wang, Xianhao Chen, Juan Wen

Edge artificial intelligence (AI) and space-ground integrated networks (SGINs) are two main usage scenarios of the sixth-generation (6G) mobile networks. Edge AI supports pervasive low-latency AI services to users, whereas SGINs provide digital services to spatial, aerial, maritime, and ground users. This article advocates the integration of the two technolo

Fan Wang, Zhongyi Han, Xingbo Liu, Xin Gao

In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA), which leverages pre-trained source models without accessing source data. Evaluating the superiority of UDA versus SFDA is an open and timely question with significant implications

Pengcheng Xu, Boyuan Jiang, Xiaobin Hu, Donghao Luo

Leveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible invariance control mechanism to preserve non-target contents. However, the prevailing diffusion inversion performs deficiently in flow-based models, and the invariance control can

Zhaoxu Chen, Yuxin Yang, Jian-gang Guo

As a special material symmetry operation, crystal twins usually influence physical properties. Here, detailed electrical transport and magnetic measurements were performed to reveal twinning effect on properties of van der Waals ferromagnet FePd$_2$Te$_2$. Orthorhombic crystal domains were observed in polarized optical microscopy and fixed $\pi$/2 angle betw

Tingting Li, Yiming Zhao, Yong Wang, Yanping Liu

A control scheme is proposed that leverages reinforcement learning to enhance entanglement by modulating the two-photon-driven amplitude in a Rabi model. The quantum phase diagram versus the amplitude of the two-photon process and the coupling between the cavity field and the atom in the Rabi model, is indicated by the energy spectrum of the hybrid system, t

Dongze Fan, Hoi Chun Po, Xiangang Wan, Feng Tang

The outcome of conventional topological materials prediction scheme could sensitively depend on first-principles calculations parameters. Symmetry, as a powerful tool, has been exploited to enhance the reliability of predictions. Here, we establish the relationship between the Wyckoff positions (WYPOs) and the phonon wavefunctions at each high-symmetry point

Jiaqi Wang, Yifei Gao, Jitao Sang

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal task reasoning. However, they often generate responses that appear plausible yet do not accurately reflect the visual content, a phenomenon known as hallucination. Recent approaches have introduced training-free methods to mitigate hallucinations by adjusting the dec

Anning Gao, Jason X. Prochaska, Zheng Cai, Siwei Zou

The mean free path of ionizing photons ($\lambda_\mathrm{mfp}^{912}$) in the intergalactic medium (IGM) is a crucial quantity in modelling the ionization state of IGM and the extragalactic ultraviolet background (EUVB), and is widely used in hydrodynamical simulations of galaxies and reionization. We construct the largest quasar spectrum dataset to date -- 1

Yuting Ma, Shengeng Tang, Xiaohua Xu, Lechao Cheng

Federated learning (FL) has emerged as a powerful approach to safeguard data privacy by training models across distributed edge devices without centralizing local data. Despite advancements in homogeneous data scenarios, maintaining performance between the global and local clients in FL over heterogeneous data remains challenging due to data distribution var

Avinanda Chakraborty, Maitreya Kundu, Suchetana Chatterjee, Swayamtrupta Panda

We study the differences in physical properties of quasar-host galaxies using an optically selected sample of radio loud (RL) and radio quiet (RQ) quasars (in the redshift range 0.15 < z < 1.9) which we have further cross-matched with the VLA-FIRST survey catalog. The sources in our sample have broad Hbeta and MgII emission lines (1000 km/s < FWHM < 15000 km

Jinlong Hu, Tingfeng Qiu

Streaming computing effectively manages large-scale streaming data in real-time, making it ideal for applications such as real-time recommendations, anomaly detection, and monitoring, all of which require immediate processing. In this context, the multi-way stream join operator is crucial, as it combines multiple data streams into a single operator, providin

H. Witała, J. Golak, R. Skibiński, H. Sakai

We discuss new spin observables presently accessible to measurement in the proton-deuteron (pd) system, namely polarization transfer coefficients from doubly spin-polarized initial state to the outgoing nucleon in the elastic nucleon-deuteron (Nd) scattering and in the nucleon-induced deuteron breakup reactions. The sensitivity of these observables to three-

Qiang Zou, Guoyue Luo

Microstructures, characterized by intricate structures at the microscopic scale, hold the promise of important disruptions in the field of mechanical engineering due to the superior mechanical properties they offer. One fundamental technique of microstructure design and manufacturing is geometric modeling, which generates the 3D computer models required to r

Daniel A. Dollinger, Michael Singleton

Recent advancements in Artificial Intelligence (AI), particularly with Large Language Models (LLMs), have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed architectures, which are designed to handle onl

Olivia Ma, Jonathan Passerat-Palmbach, Dmitrii Usynin

Fine-tuning large language models (LLMs) for specific tasks introduces privacy risks, as models may inadvertently memorise and leak sensitive training data. While Differential Privacy (DP) offers a solution to mitigate these risks, it introduces significant computational and performance trade-offs, particularly with standard fine-tuning approaches. Previous

Tom Claeys, Guilherme L. F. Silva

We consider a large class of deformations of continuous and discrete biorthogonal ensembles and investigate their behavior in the limit of a large number of particles. We provide sufficient conditions to ensure that if a biorthogonal ensemble converges to a (universal) limiting process, then the deformed biorthogonal ensemble converges to a deformed version

Haimiao Chen

We give a monomial basis for the Kauffman bracket skein algebra of the $4$-holed disk, and find a presentation. This is based on an insight into the ${\rm SL}(2,\mathbb{C})$-character variety of the rank $4$ free group.

Jiantao Jiang, Yanli Wang, Yifan Wang, Hehu Xie

The aim of this paper is to introduce a FieldTNN-based machine learning method for solving the Maxwell eigenvalue problem in both 2D and 3D domains, including both tensor and non-tensor computational regions. First, we extend the existing TNN-based approach to address the Maxwell eigenvalue problem, a fundamental challenge in electromagnetic field theory. Se

Jinlong Hu, Tingfeng Qiu

Streaming computing enables the real-time processing of large volumes of data and offers significant advantages for various applications, including real-time recommendations, anomaly detection, and monitoring. The multi-way stream join operator facilitates the integration of multiple data streams into a single operator, allowing for a more comprehensive unde

Florence Bockting, Stefan T. Radev, Paul-Christian Bürkner

We propose an expert-elicitation method for learning non-parametric joint prior distributions using normalizing flows. Normalizing flows are a class of generative models that enable exact, single-step density evaluation and can capture complex density functions through specialized deep neural networks. Building on our previously introduced simulation-based f

A. S. Gusev, E. V. Shimanovskaya

Last years studies have shown that the spatial regularity in the distribution of young stellar population along the spiral arms and rings of galaxies, previously considered to be rare, is a fairly common phenomenon. Spatial regularity has been found in the spiral arms and rings of galaxies of various morphology, from lenticular to extremely late-type spiral.

Alessandro Gnutti, Fabrizio Guerrini, Riccardo Leonardi, Antonio Ortega

Modern compression systems use linear transformations in their encoding and decoding processes, with transforms providing compact signal representations. While multiple data-dependent transforms for image/video coding can adapt to diverse statistical characteristics, assembling large datasets to learn each transform is challenging. Also, the resulting transf

Wytze de Vries, Jorn van Kampen, Mauro Salazar

This paper presents a longitudinal slip control system for a rear-wheel-driven electric endurance race car. The control system integrates Model Predictive Control (MPC) with Extremum Seeking Control (ESC) to optimize the traction and regenerative braking performance of the powertrain. The MPC contains an analytical solution which results in a negligible comp

Surojit Biswas, Buddhananda Banerjee

This paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized M\"{o}bius transformation to define the regression curve. By mapping the real axis to the circle, the model effectively captures the relationship between linear and angular components. A key innovation is the introduction of

Aryan Sajith, Krishna Chaitanya Rao Kathala

This study investigates the relative impact of training data quality versus quantity on the performance of small language models (SLMs), utilizing the TinyStories dataset for empirical analysis. Analysis of dataset variations with respect to size (25% and 50% of the original size) and duplication (controlled rates of 25%, 50%, 75%, and 100%) were performed.

Sayyed Ahmad Abtahi, Mohsen Maddahali, Ahmad Bakhtafrouz

A new X-band leaky-wave antenna has been developed using spin photonic topological insulators. This antenna features a hexagonal unit cell arranged in an armchair configuration. This arrangement provides advantages over the zigzag configuration, particularly by offering a wider operational region and a more suitable pattern. To design the structure, a parame

Yifan Hu, Yanxi Hou

This paper introduces a copula-based model for independent but non-identically distributed data with heteroscedastic extremes marginal and changing tail dependence structures. We establish a unified framework for inference by proving the weak convergence of the bivariate sequential tail empirical process and its empirical bootstrap counterpart. We derive the

Lishai Shoham, Itai Silber, Gal Tuvia, Maria Baskin

The modulation of channel conductance in field-effect transistors (FETs) via metal-oxide-semiconductor (MOS) structures has revolutionized information processing and storage. However, the limitations of silicon-based FETs in electrical switching have driven the search for new materials capable of overcoming these constraints. Electrostatic gating of competin

Iryna Bodnarchuk, Yuliya Mishura, Kostiantyn Ralchenko

We calculate and analyze various entropy measures and their properties for selected probability distributions. The entropies considered include Shannon, R\'enyi, generalized R\'enyi, Tsallis, Sharma-Mittal, and modified Shannon entropy, along with the Kullback-Leibler divergence. These measures are examined for several distributions, including gamma, chi-squ

Abdulla Rakhimov, Mukhtorali Nishonov

Measurement of numerical values of the anomalous density, $\sigma$, which plays important role in Bose -- Einstein condensation, and, especially, determination of its sign, has been a long standing problem. We develop Hartree -- Fock -- Bogoliubov theory taking account arbitrary phase of the condensate wave function. We show that, the sign of $\sigma$ direct

Anna Kareer, Eralp Demir, Edmund Tarleton, Christopher Hardie

In this paper, we present a comprehensive analysis of the contact mechanics associated with a micron-sized sliding asperity, which plays a crucial role in the abrasive wear processes. Utilising nanoscratch testing, we experimentally investigate the deformation and employ High-Resolution Electron Backscatter Diffraction (HR-EBSD) to characterise the resulting

Giovanna Citti, Nicolas Dirr, Federica Dragoni, Raffaele Grande

We derive curvature flows in the Heisenberg group by formal asymptotic expansion of a nonlocal mean-field equation under the anisotropic rescaling of the Heisenberg group. This is motivated by the aim of connecting mechanisms at a microscopic (i.e. cellular) level to macroscopic models of image processing through a multiscale approach. The nonlocal equation,

Dimitrios Bachtis

We introduce a $\phi^{4}$ lattice field theory with frustrated dynamics as a multi-agent system to reproduce stylized facts of financial markets such as fat-tailed distributions of returns and clustered volatility. Each lattice site, represented by a continuous degree of freedom, corresponds to an agent experiencing a set of competing interactions which infl

Lingzheng Zhang, Lifeng Shen, Yimin Zheng, Shiyuan Piao

Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabilities. However, aligning time series into semantic spaces of LLMs comes with high computational costs and inference complexity, particularly for long-range time series generation.

Tomer Markovich, Tom C. Lubensky

Chiral active materials are those that break both time-reversal symmetry and parity microscopically, which results in average rotation of the material's complex molecules around their center-of-mass (CM). These materials are far from equilibrium due to their local non-vanishing spin angular momentum. In this paper we show that, unlike passive fluids, the non

Pan Liao, Feng Yang, Di Wu, Jinwen Yu

Transformer-based multi-object tracking (MOT) methods have captured the attention of many researchers in recent years. However, these models often suffer from slow inference speeds due to their structure or other issues. To address this problem, we revisited the Joint Detection and Tracking (JDT) method by looking back at past approaches. By integrating the

O. S. Balashov, A. V. Faminskii

Direct and inverse initial-boundary problems on a bounded interval for systems of quasilinear evolution equations with general nonlinearities are considered. In the case of inverse problems conditions of integral overdetermination are introduced and right-hand sides of equations of special types are chosen as controls. Results on well-posedness of such probl

Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin, Enrique Lara-Pezzi

In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. The HODMD algorithm is used first as feature extraction technique for the echocardiograph

Wuzheng Dong, Yujuan Zhu, Sheng Zhang

This paper presents a method for object recognition and automatic labeling in large-area remote sensing images called LRSAA. The method integrates YOLOv11 and MobileNetV3-SSD object detection algorithms through ensemble learning to enhance model performance. Furthermore, it employs Poisson disk sampling segmentation techniques and the EIOU metric to optimize

Chi Sun, Jacob Linder

We theoretically investigate the supercurrent flow in a Josephson junction consisting of two spin-split superconductors combined by a normal metal weak link. The normal metal may be driven out of equilibrium, thus modifying the electron and hole occupation and consequently the supercurrent through the system. Considering first an equilibrium normal metal, we

Shiron Thalagala, Pak Kin Wong, Xiaozheng Wang, Tianang Sun

In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for extensive data and increased computational cost. To address this issue, a novel hybrid actor-critic reinforcement learning (RL) framework is introduced. The proposed fra

Dhruv Patel, Ankita Kumari Jain, Haikoo Khandor, Xhitij Choudhary

Non-intrusive load monitoring (NILM) focuses on disaggregating total household power consumption into appliance-specific usage. Many advanced NILM methods are based on neural networks that typically require substantial amounts of labeled appliance data, which can be challenging and costly to collect in real-world settings. We hypothesize that appliance data

Ayush Singh, Rajdeep Aher, Shivank Garg

The rapid advancements in large language models (LLMs) have revolutionized natural language processing, creating an increased need for efficient, task-specific fine-tuning methods. Traditional fine-tuning of LLMs involves updating a large number of parameters, which is computationally expensive and memory-intensive. Low-Rank Adaptation (LoRA) has emerged as

Guodan Dong, Jianhua Qin, Chutian Wu, Chang Xu

A reinforcement learning-enhanced genetic algorithm (RLGA) is proposed for wind farm layout optimization (WFLO) problems. While genetic algorithms (GAs) are among the most effective and accessible methods for WFLO, their performance and convergence are highly sensitive to parameter selections. To address the issue, reinforcement learning (RL) is introduced t

Thang Pang Ern, Devandhira Wijaya Wangsa

In 1914, Ramanujan presented a collection of 17 elegant and rapidly converging formulae for $\pi$. Among these, one of the most celebrated is the following series: \[\frac{1}{\pi}=\frac{2\sqrt{2}}{9801}\sum_{n=0}^{\infty}\frac{26390n+1103}{\left(n!\right)^4} \frac{\left(4n\right)!}{396^{4n}}\] In this paper, we give a full proof of this classic formula using

Gustav Müller-Franzes, Firas Khader, Robert Siepmann, Tianyu Han

MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DINOv2 are encouraging for 2D image analysis, these methods have not been applied to 3D medical images. Furthermore, deep learning models often lack explainability

Monalisa Ghosh, Chetna Singhal

The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of multimedia content to heterogeneous mobile

Haoang Li, Xiangqi Meng, Xingxing Zuo, Zhe Liu

Simultaneous localization and mapping (SLAM) has achieved impressive performance in static environments. However, SLAM in dynamic environments remains an open question. Many methods directly filter out dynamic objects, resulting in incomplete scene reconstruction and limited accuracy of camera localization. The other works express dynamic objects by point cl

Xiaojia Zhu, Rui Chen, Xiaoqi Guo, Zhiwen Shao

Scoliosis is one of the most common diseases in adolescents. Traditional screening methods for the scoliosis usually use radiographic examination, which requires certified experts with medical instruments and brings the radiation risk. Considering such requirement and inconvenience, we propose to use natural images of the human back for wide-range scoliosis

Rui Wan, Qi Zheng, Yibo Fan

Traditional and neural video codecs commonly encounter limitations in controllability and generality under ultra-low-bitrate coding scenarios. To overcome these challenges, we propose M3-CVC, a controllable video compression framework incorporating multimodal generative models. The framework utilizes a semantic-motion composite strategy for keyframe selectio

Qi Li, Cheng-Long Wang, Yinzhi Cao, Di Wang

In this work, we systematically explore the data privacy issues of dataset pruning in machine learning systems. Our findings reveal, for the first time, that even if data in the redundant set is solely used before model training, its pruning-phase membership status can still be detected through attacks. Since this is a fully upstream process before model tra

Corrado Coppola, Lorenzo Papa, Irene Amerini, Laura Palagi

Adaptive gradient methods have been increasingly adopted by deep learning community due to their fast convergence and reduced sensitivity to hyper-parameters. However, these methods come with limitations, such as increased memory requirements for elements like moving averages and a poorly understood convergence theory. To overcome these challenges, we introd

Peter J. Cameron, G. Sivanesan, C. Selvaraj, T. Tamizh Chelvam

Let $G$ be a finite solvable group and let $\Delta(G)$ be the character degree graph of $G$. In this paper, we obtain the metric dimension of certain character degree graphs. Specifically, we calculate the metric dimension for a regular character degree graph, a character degree graph with a diameter of $2$ that is not a block, a character degree graph with

Dawid Hanrahan

We study the even and odd Jacobi heat kernels defined in the context of the multidimensional double cone and its surface, the multidimensional hyperboloid and its surface, and the multidimensional paraboloid and its surface. By integrating the framework of Jacobi polynomials on these domains, as analyzed by Xu, with contemporary methods developed by Nowak, S

Mourad Choulli, Hiroshi Takase

We establish stability inequalities of an inverse obstacle problem for the magnetic Schr\"odinger equation. We mainly study the problem of reconstructing an unknown function defined on the obstacle boundary from two measurements performed on the boundary of a domain surrounding the obstacle. We show for the inverse problem a Lipschitzian stability locally in

Vikas Arora, D. V. S. Muthu, R Sankar, A K Sood

Topological properties of a three-dimensional Dirac semimetal Cd3As2, protected by crystal rotation and time-reversal symmetry, can be tuned with the application of pressure. Ultrafast spectroscopy is a unique tool to investigate the character and time evolution of electronic states, emphasizing the signatures of transition. We designed an experimental setup

Seunghun Lee

We give a criterion for a functor \(F:C\rightarrow B\) between small categories to generate a small presentation of the universal model category \(U(B)\) in the sense of Dugger.

Chris Bowman, Maud De Visscher, Alice Dell'Arciprete, Amit Hazi

We show that the extended Khovanov algebra $K^m_n$ is an $(|n-m|-1)$-faithful cover of the Khovanov arc algebra $H^m_n$.