December 2024 arXiv papers — page 160

Zhengtong Zhu, Zhiyi Chi

Stratified digraphs are popular models for feedforward neural networks. However, computation of their path homologies has been limited to low dimensional ones due to high computational complexity. A recursive algorithm is proposed to compute certain high-dimensional (reduced) path homologies of stratified digraphs. By recursion on matrix representations of h

Md. Jalil Piran, Nguyen H. Tran

Following a brief introduction to research, research processes, research types, papers, reviews, and evaluations, this paper presents a structured framework for addressing inconsistencies in research methodology, technical writing, quality assessment, and publication standards across academic disciplines. Using a four-dimensional evaluation model that focuse

Amita, Mamta Amol Wagh, Suman Kumar, Akhil Prakash

The paper explores the concept of the rank of a bicomplex matrix, delving into four distinct types of ranks and investigating conditions under which these ranks are equivalent. It also defines and analyzes the concept of idempotent row space and idempotent column space of a bicomplex matrix. Some examples and counter examples have been presented to substanti

Ardiansyah Koeshidayatullah, Abdulrahman Al-Fakih, SanLinn Ismael Kaka

The rise in energy demand highlights the importance of suitable subsurface storage, requiring detailed and accurate subsurface characterization often reliant on high-quality borehole well log data. However, obtaining complete well-log data is costly and time-consuming, with missing data being common due to borehole conditions or tool errors. While machine le

Rory Conlin, Jonathan Schilling, Daniel W. Dudt, Dario Panici

In this work we consider the free boundary inverse equilibrium problem for 3D ideal MHD. We review boundary conditions for both fixed and free boundary solutions and under what circumstances a sheet current may exist at the plasma-vacuum interface. We develop an efficient and accurate algorithm for computing the residual of these boundary conditions and use

Xu Liu, Zhouhui Lian

Remote Sensing Vision-Language Models (RS VLMs) have made much progress in the tasks of remote sensing (RS) image comprehension. While performing well in multi-modal reasoning and multi-turn conversations, the existing models lack pixel-level understanding and struggle with multi-image inputs. In this work, we propose RSUniVLM, a unified, end-to-end RS VLM d

Yuval Rymon

AI is transforming human labor at an unprecedented pace - improving 10$\times$ per year in training effectiveness. This paper analyzes how society can adapt to AI-driven human-labor automation (HLA), using Bernardi et al.'s societal adaptation framework. Drawing on literature from general automation economics and recent AI developments, the paper develops a

Will Traves

A solution is provided to the Bruxelles Problem, a geometric decision problem originally posed in 1825, that asks for a synthetic construction to determine when ten points in 3-space lie on a quadric surface, a surface given by the vanishing of a degree-2 polynomial. The solution constructs four new points that are coplanar precisely when the ten original po

Mengrui Wang, Manming Shu, Jiajing Yan, Chang Liu

Three-dimensional (3D) fluorescence imaging provides a vital approach for study of biological tissues with intricate structures, and optical sectioning structured illumination microscopy (OS-SIM) stands out for its high imaging speed, low phototoxicity and high spatial resolution. However, OS-SIM faces the problem of low signal-to-noise ratio (SNR) when usin

Guy Levy, Nathan Liebmann

Deepfakes are on the rise, with increased sophistication and prevalence allowing for high-profile social engineering attacks. Detecting them in the wild is therefore important as ever, giving rise to new approaches breaking benchmark records in this task. In line with previous work, we show that recently developed state-of-the-art detectors are susceptible t

Kehan Wen, Yutong Hu, Yao Mu, Lei Ke

Recent work in Offline Reinforcement Learning (RL) has shown that a unified Transformer trained under a masked auto-encoding objective can effectively capture the relationships between different modalities (e.g., states, actions, rewards) within given trajectory datasets. However, this information has not been fully exploited during the inference phase, wher

Jing-Chuan Wu, Qi Ye, Dong-Ling Deng, Li-Wei Yu

Tensor network machine learning models have shown remarkable versatility in tackling complex data-driven tasks, ranging from quantum many-body problems to classical pattern recognitions. Despite their promising performance, a comprehensive understanding of the underlying assumptions and limitations of these models is still lacking. In this work, we focus on

Saptarshi Chakraborty, Kshitij Khare, George Michailidis

This paper introduces a loss-based generalized Bayesian methodology for high-dimensional robust regression with serially correlated errors and predictors. The proposed framework employs a novel scaled pseudo-Huber (SPH) loss function, which smooths the well-known Huber loss, effectively balancing quadratic ($\ell_2$) and absolute linear ($\ell_1$) loss behav

Junwei Yin, Min Gao, Kai Shu, Wentao Li

The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches have emerged, most rely on content analysis with limited semantic depth, leading to suboptimal comprehension of news content.To address this limitation, capturing broader-range semant

Laurent Mahy

Physical properties of stars such as luminosity, surface temperature, distance, or mass are measured from observations. These physical properties are of paramount importance to understand how stars are born, live, and die in the universe near and far. This chapter discusses the basic concepts used by astronomers to derive key information about stars from the

Yu-Heng Shu, Jia-Hui Huang

In this paper, we fist consider the shadow radius of a quantum corrected black hole proposed recently, and provide a bound on the correction parameter based on the observational data of Sgr A*. Then, the effects of the correction parameter on the energy, angular momenta and angular velocities of particles on circular orbits in the accretion disk are discusse

Qi Li, Shuliang Wang

Clustering and outlier detection are two important tasks in data mining. Outliers frequently interfere with clustering algorithms to determine the similarity between objects, resulting in unreliable clustering results. Currently, only a few clustering algorithms (e.g., DBSCAN) have the ability to detect outliers to eliminate interference. For other clusterin

Ciarán Rogers, Bernhard Brandl, Guido de Marchi

Hydrogen emission lines have been used to estimate the mass accretion rate of pre-main-sequence stars for over $25$ years, although the physical origin of these lines is still unclear. Magnetospheric accretion (MA) and magneto-centrifugal winds are the two most often invoked mechanisms. Using a combination of HST photometry and new JWST NIRSpec spectra in th

Deepak Kumar

Neural networks are a group of neurons stacked together in multiple layers to mimic the biological neurons in a human brain. Neural networks have been trained using the backpropagation algorithm based on gradient descent strategy for several decades. Several variants have been developed to improve the backpropagation algorithm. The loss function for the neur

Mozhgan Naderi, Maryam Rastgarpour, Amir Reza Takhsha

Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timely diagnosis is essential for effective treatment and management of this disease. In this study, we proposed two low-parameter Convolutional Neural Networks (CNNs), IR-BRAINNET and Modified-DEMNET, designed to det

Minseog Oh, Donggyu Kim

In financial applications, we often observe both global and local factors that are modeled by a multi-level factor model. When detecting unknown local group memberships under such a model, employing a covariance matrix as an adjacency matrix for local group memberships is inadequate due to the predominant effect of global factors. Thus, to detect a local gro

Guangwei Liu, Liu Liu, Yanli Wang

In this paper, we study the Vlasov-Poisson system with massless electrons (VPME) near quasineutrality and with uncertainties. Based on the idea of reformulation on the Poisson equation by [P. Degond et.al., $\textit{Journal of Computational Physics}$, 229 (16), 2010, pp. 5630--5652], we first consider the deterministic problem and develop an efficient asympt

Wei Liu, Liang Wu, Francisco Villaescusa-Navarro, Marco Baldi

Strong degeneracy exists between some modified gravity (MG) models and massive neutrinos because the enhanced structure growth produced by modified gravity can be suppressed due to the free-streaming massive neutrinos. Previous works showed this degeneracy can be broken with non-Gaussian or velocity information. Therefore in this work, we focus on the large-

Francesco Giovanni Celiberto

We report progress on the Heavy-Flavor Non-Relativistic Evolution (HF-NRevo) setup, a novel methodology to address quarkonium formation within the fragmentation approximation. Our study sheds light on the moderate to large transverse-momentum sector, where the leading-twist collinear fragmentation of a single parton prevails over the higher-twist fragmentati

Xue Xian Zheng, M. M. Ur Rahma, Bilal Taha, Mudassir Masood

Camera-based photoplethysmography (PPG) obtained from smartphones has shown great promise for personalized healthcare and secure authentication. This paper presents a multimodal biometric system that integrates PPG signals extracted from videos with fingerprint data to enhance the accuracy of user verification. The system requires users to place their finger

Gianluca Basso, Andy Zucker

A Polish group $G$ has the generic point property if any minimal $G$-flow admits a comeager orbit, or equivalently if the universal minimal flow (UMF) does. The class $\mathsf{GPP}$ of such Polish groups is a proper extension of the class $\sf{PCMD}$ of Polish groups with metrizable UMF. Motivated by analogous results for $\mathsf{PCMD}$, we define and explo

Suresh Chandra Baral, Dilip Sasmal, Mitali Hupele, Sradhanjali Lenka

Sulfide-based photocatalysts (PC) are promising materials for efficiently producing hydrogen (H2). This chapter aims to provide a detailed survey of the recent advancements in sulfide-based photocatalysts and emphasize their enhanced performance and pathways to efficient H2 production. A detailed summary has been given, including several metal sulfides, such

Sunwoong Yang, Ricardo Vinuesa, Namwoo Kang

This study addresses the critical challenge of error accumulation in spatio-temporal auto-regressive (AR) predictions within scientific machine learning models by exploring temporal integration schemes and adaptive multi-step rollout strategies. We introduce the first implementation of the two-step Adams-Bashforth method specifically tailored for data-driven

Filippos Ventirozos, Ioanna Nteka, Tania Nandy, Jozef Baca

This paper presents a case study on the development of Auto-AdvER, a specialised named entity recognition schema and dataset for text in the car advertisement genre. Developed with industry needs in mind, Auto-AdvER is designed to enhance text mining analytics in this domain and contributes a linguistically unique NER dataset. We present a schema consisting

Aleena Thomas, Abhijith Ajayakumar, Raju K. George

Various controllability conditions have been obtained by researchers for heterogeneous networked systems with linear dynamics. However, the literature for nonlinear, heterogeneous networked systems is comparatively less. In this paper we analyse the controllabiity aspect of a nonlinearly perturbed linear networked system. The basic assumption is that the lin

Jiaxin Li, Xiantao Hou, Junli Ma, Changzheng Yuan

The $\psi(3770)$ and $\Upsilon(4S)$ states predominantly decay into open-flavor meson pairs, whereas the decays of $\psi(3770) \to \mbox{\text{non}-}D\bar{D}$ and $\Upsilon(4S) \to \mbox{\text{non}-}B\bar{B}$ are rare but crucial for elucidating the inner structure and decay dynamics of heavy quarkonium states. To achieve precise branching fraction measureme

Miguel Vanvlasselaer

In this talk, we present a mechanism of Dark Matter production during first order phase transitions and happening via the collision of the bubble wall and plasma quanta. We will first study the possibility that the dark matter is produced via a renormalisable operator. We will observe that in this context the DM can be much heavier than the scale of the phas

Ali Hasan Ali, Zsolt Páles

The aim of this paper is to establish various factorization results and then to derive estimates for linear functionals through the use of a generalized Taylor theorem. Additionally, several error bounds are established including applications to the trapezoidal rule as well as to a Simpson formula-type rule.

Xue Xian Zheng, Tareq Al-Naffouri

This work introduces an error feedback approach for reducing quantization noise of distributed graph filters. It comes from error spectrum shaping techniques from state-space digital filters, and therefore establishes connections between quantized filtering processes over different domains. Quantization noise expression incorporating error feedback for finit

Ciarán Rogers, Guido de Marchi, Bernhard Brandl

NGC 3603 is the optically brightest massive star forming region (SFR) in the Milky Way, representing a small scale starburst region. Studying young stars in regions like this allows us to assess how star and planet formation proceeds in a dense clustered environment with high levels of UV radiation. JWST provides the sensitivity, unbroken wavelength coverage

Shourya Verma, Simran Kadadi, Swathi Jayaprakash, Arpan Kumar Mahapatra

Network performance modeling presents important challenges in modern computer networks due to increasing complexity, scale, and diverse traffic patterns. While traditional approaches like queuing theory and packet-level simulation have served as foundational tools, they face limitations in modeling complex traffic behaviors and scaling to large networks. Thi

Richárd Grünwald, Zsolt Páles

The aim of this paper is to investigate inequalities that are analogous to the Minkowski and H\"older inequalities by replacing the addition and the multiplication by a more general operation, and instead of using power means, generalized Bajraktarevi\'c means are considered, in particular, Gini means. A further aim is to introduce the concept of local and g

Chong Huang, Xuyang Chen, Gaojie Chen, Pei Xiao

In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image transmission within space-air-ground integrated networks (SAGINs). The proposed model comprises multiple low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users. Considering the limitatio

Fatemeh Khatamsaz, Mahdi Abdollahi, Hedieh Abdollahi, Atefeh Javadi

NGC 6822 is an isolated dwarf irregular galaxy in the local group at a distance of 490 kpc. In this paper, we present the star formation history (SFH) within a field with a radius of 3 kpc, beyond the optical body of the galaxy (1.2 kpc). We utilized a novel method based on evolved asymptotic giant branch (AGB) stars. We collected the Near infrared data of 3

Gábor Marcell Molnár, Zsolt Páles

In this paper functions $f:D\to\mathbb{R}$ satisfying the inequality \[ f\Big(\frac{x+y}{2}\Big)\leq\frac12f(x)+\frac12f(y) +\varphi\Big(\frac{x-y}{2}\Big) \qquad(x,y\in D) \] are studied, where $D$ is a nonempty convex subset of a real linear space $X$ and $\varphi:\{\frac12(x-y) : x,y \in D\}\to\mathbb{R}$ is a so-called error function. In this situation $

Yilong Chen, Junyuan Shang, Zhengyu Zhang, Jiawei Sheng

Transformer models encounter challenges in scaling hidden dimensions efficiently, as uniformly increasing them inflates computational and memory costs while failing to emphasize the most relevant features for each token. For further understanding, we study hidden dimension sparsity and observe that trained Transformers utilize only a small fraction of token

Andreas Nold, Benjamin D. Goddard, David N. Sibley, Serafim Kalliadasis

Understanding the nanoscale effects controlling the dynamics of a contact line -- defined as the line formed at the junction of two fluid phases and a solid -- has been a longstanding problem in fluid mechanics pushing experimental and numerical methods to their limits. A major challenge is the multiscale nature of the problem, whereby nanoscale phenomena ma

Kyle Higgins, Olga P. Nyssen, Joshua Southern, Ivan Laponogov

Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen worldwide. Infecting roughly 1 in 2 individuals globally, it is the leading cause of peptic ulcer disease, chronic gastritis, and gastric cancer. To investigate whether personalized treatments would be optimal for patients suffering from infection, we developed the H. pylori AI-clinicia

Mahdi Abdollahi, Sima Taefi Aghdam, Atefeh Javadi, Seyed Azim Hashemi

NGC 5128 (Centaurus A), the closest giant elliptical galaxy outside the Local Group to the Milky Way, is one of the brightest extragalactic radio sources. It is distinguished by a prominent dust lane and powerful jets, driven by a supermassive black hole at its core. Using previously identified long-period variable (LPV) stars from the literature, this study

Md. Tanvir Alam, Md. Mahmudur Rahman, Md. Fahim Arefin, Chowdhury Farhan Ahmed

Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications. Hypergraph learning algorithms have been well-studied for num

Fei Shang, Haocheng Jiang, Panlong Yang, Dawei Yan

This paper introduces the design and implementation of WiField, a WiFi sensing system deployed on COTS devices that can simultaneously identify multiple wavelength-level targets placed flexibly. Unlike traditional RF sensing schemes that focus on specific targets and RF links, WiField focuses on all media in the sensing area for the entire electric field. In

C. Evans Hedges

This paper is concerned with freezing phase transitions in general dynamical systems. A freezing phase transition is one in which, for a given potential $\phi$, there exists some inverse temperature $\beta_0 > 0$ such that for all $\alpha, \beta > \beta_0$, the collection of equilibrium states for $\alpha \phi$ and $\beta \phi$ coincide. In this sense, below

Luigi Fontana, Carlo Morpurgo, Liuyu Qin

We obtain sharp estimates for heat kernels and Green's functions on complete noncompact Riemannian manifolds with Euclidean volume growth and nonnegative Ricci curvature. We will then apply these estimates to obtain sharp Moser-Trudinger inequalities on such manifolds.

Suresh Chandra Baral, Dilip Sasmal, Sayak Datta, Mange Ram

Although hydrogen generation by water electrolysis is the cheapest of all other available sources, water splitting still occurs with sluggish kinetics. It is a challenging barrier for H2 production on a large scale. Moreover, research is still underway to understand the oxygen evolution reaction (OER) and design the catalysts with improved OER performance. H

Wenhao Yuan, Xuehe Wang

This paper aims to design a Privacy-aware Client Sampling framework in Federated learning, named FedPCS, to tackle the heterogeneous client sampling issues and improve model performance. First, we obtain a pioneering upper bound for the accuracy loss of the FL model with privacy-aware client sampling probabilities. Based on this, we model the interactions be

Sergi Liesegang, Antonio Pascual-Iserte, Olga Muñoz

This paper presents several analytic closed-form approximations of the aggregated interference statistics within the framework of uplink massive machine-type communications (mMTC), taking into account the random activity of the sensors. Given its discrete nature and the large number of devices involved, a continuous approximation based on the Gram--Charlier

B. Liu, J. S. Feng, H. J. Xiang, Z. Dai

Two-dimensional (2D) van der Waals (vdW) magnets present a promising platform for spintronic applications due to their unique structural and electronic properties. The ability to electrostatically control their interlayer magnetic coupling between ferromagnetic and antiferromagnetic phases is particularly advantageous for the development of energy-efficient

Gaurav Shrivastava, Abhinav Shrivastava

Multi-step prediction models, such as diffusion and rectified flow models, have emerged as state-of-the-art solutions for generation tasks. However, these models exhibit higher latency in sampling new frames compared to single-step methods. This latency issue becomes a significant bottleneck when adapting such methods for video prediction tasks, given that a

Abd Ur Rehman, Azka Rehman, Muhammad Usman, Abdullah Shahid

Brain aging involves structural and functional changes and therefore serves as a key biomarker for brain health. Combining structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) has the potential to improve brain age estimation by leveraging complementary data. However, fMRI data, being noisier than sMRI, complicates mu

Lei Wang, Jianxun Lian, Yi Huang, Yanqi Dai

Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, and emotional companions. Evaluating this capability in LLMs is challenging due to the complex dynamics involved in role-playing, such as maintaining character fidelity throughout a

Misato Suzuki, Mayu Muramatsu, Kazuyuki Shizawa

In this study, the effect of ferrite grain size on the mechanical properties and dislocation behavior of dual-phase (DP) steel is investigated using dislocation-based crystal plasticity finite element analysis. DP steel, composed of a soft ferritic phase and a hard martensitic phase, shows mechanical properties that are significantly influenced by ferrite gr

Sergi Liesegang, Antonio Pascual-Iserte, Olga Muñoz

Machine-type communications (MTC) are crucial in the evolution of mobile communication systems. Within this context, we distinguish the so-called massive MTC (mMTC), where a large number of devices coexist in the same geographical area. In the case of sensors, a high correlation in the collected information is expected. In this letter, we evaluate the impact

Andrew James Bruce

These are expanded notes for a short series of lectures, presented at the University of Luxembourg in 2017, giving an introduction to some of the ideas of supersymmetry and supergeometry. In particular, we start from some motivating facts in physics, pass to the theory of supermanifolds, then to spinors, ending up at super-Minkowski space-times. We examine s

Gongfan Fang, Xinyin Ma, Xinchao Wang

Transformer-based diffusion models have achieved significant advancements across a variety of generative tasks. However, producing high-quality outputs typically necessitates large transformer models, which result in substantial training and inference overhead. In this work, we investigate an alternative approach involving multiple experts for denoising, and

Masaaki Furusawa, Tomo Narahara

The cotangent zeta function is a very interesting object, which is related to partial zeta functions and Hecke $L$-functions of real quadratic fields. Its special values at odd integers greater than 1 are explicitly evaluated by Berndt in the real quadratic unit case. Later Arakawa generalized the formula to the arbitrary real quadratic number case. The purp

Sergi Liesegang, Olga Muñoz, Antonio Pascual-Iserte

This paper presents an estimation approach within the framework of uplink massive machine-type communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original obs

Xiangyu Robin Gan, Yuxin Ray Song, Nick Walker, Maya Cakmak

Finite state machines (FSMs) are widely used to manage robot behavior logic, particularly in real-world applications that require a high degree of reliability and structure. However, traditional manual FSM design and modification processes can be time-consuming and error-prone. We propose that large language models (LLMs) can assist developers in editing FSM

Wenceslao Arroyo-Machado, Nicolas Robinson-Garcia, Daniel Torres-Salinas

This study examines the shift in the scientific community from X (formerly Twitter) to Bluesky, its impact on scientific communication, and consequently on social metrics (altmetrics). Analyzing 10,174 publications from multidisciplinary and library and information science (LIS) journals in 2024, the results reveal a notable increase in Bluesky activity for

Yajun Wang, Jinghan Jiang, Xin Du, Zhuxian Lian

In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. Due to employing wideband transmissions, the frequency selectivity of IRSs has to

Weiwei Liu, Xiaolong Su, Chijun Li, Cheng Zeng

Chiral edge state is a hallmark of topological physics, which has drawn significant attention across quantum mechanics, condensed matter and optical systems. Recently, synthetic dimensions have emerged as ideal platforms for investigating chiral edge states in multiple dimensions, overcoming the limitations of real space. In this work, we demonstrate reconfi

Yanqin Fan, Hyeonseok Park

This paper proposes minimum sliced distance estimation in structural econometric models with possibly parameter-dependent supports. In contrast to likelihood-based estimation, we show that under mild regularity conditions, the minimum sliced distance estimator is asymptotically normally distributed leading to simple inference regardless of the presence/absen

Somnath Mondal, Lakshmi Kanta Patra

The present manuscript is concerned with component-wise estimation of the positive power of ordered restricted standard deviation of two normal populations with certain restrictions on the means. We propose several improved estimators under a general scale invariant bowl-shaped loss function. Also, we proposed a class of improved estimators. It has been show

Ming Tao, Bing-Kun Bao, Yaowei Wang, Changsheng Xu

Large pretrained diffusion models have demonstrated impressive generation capabilities and have been adapted to various downstream tasks. However, unlike Large Language Models (LLMs) that can learn multiple tasks in a single model based on instructed data, diffusion models always require additional branches, task-specific training strategies, and losses for

Sana Khan, Piotr J. Cegielski, Manuel Runkel, Thomas Riedl

Designing optoelectronic devices based on cesium lead chloride (CsPbCl${_3}$) perovskites requires accurate values of their optical constants. Unfortunately, experimental data for this material is very limited thus far. Therefore, here, we applied spectroscopic ellipsometry (SE) to measure the complex optical constants of thermally evaporated CsPbCl${_3}$ th

Renjith Ramachandran

In the current fast-paced digital environment, enterprises are striving to offer a seamless and integrated customer experience across multiple touchpoints. This improved experience often leads to higher conversion rates and increased customer loyalty. To deliver such an experience, enterprises must think beyond the traditional boundaries of their architectur

Rodolfo Carobene, Stefano Barison, Andrea Giachero, Jannes Nys

In this paper, we present a new set of local fermion-to-qudit mappings for simulating fermionic lattice systems. We focus on the use of multi-level qudits, specifically ququarts. Traditional mappings, such as the Jordan-Wigner transformation (JWT), while useful, often result in non-local operators that scale unfavorably with system size. To address these cha

Maksims Dimitrijevs, Mārtiņš Kālis, Iļja Repko

Time-series forecasting is essential for strategic planning and resource allocation. In this work, we explore two quantum-based approaches for time-series forecasting. The first approach utilizes a Parameterized Quantum Circuit (PQC) model. The second approach employs Variational Quantum Linear Regression (VQLS), enabling time-series forecasting by encoding

Mohammed Bachir, Rongzhen Lyu

We present a new alternative theorems for sequences of functions. As applications, we extend recent results in the literature related to first-order necessary conditions for optimality problems. Our contributions involve extending well-known results, previously established for a finite number of inequality constraints to a countable number of inequality cons

Vitalii Soldatov

In the paper we develop a general theory of solvability of linear inhomogeneous boundary-value problems for systems of first-order ordinary differential equations in spaces of smooth functions on a finite interval. This problems are set with boundary conditions in generic form, that covers overdetermined and underdetermined cases. They also may contain deriv

Fida El Chami, Nicolas Ginoux, Georges Habib, Ola Makhoul

We extend the buckling and clamped-plate problems to the context of differential forms on compact Riemannian manifolds with smooth boundary. We characterize their smallest eigenvalues and prove that, in the case of bounded Euclidean domains, their spectra without multiplicities on forms coincide with the spectra of the corresponding problems on functions. We

Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani

Detecting objects occupying only small areas in an image is difficult, even for humans. Therefore, annotating small-size object instances is hard and thus costly. This study questions common sense by asking the following: is annotating small-size instances worth its cost? We restate it as the following verifiable question: can we detect small-size instances

Emanuele Riva, Jacopo Marconi, Francesco Braghin

This paper investigates the dynamics of compact localized modes in one-dimensional flat-band elastic lattices. Flat dispersion arises from destructive interference between neighboring elements, resulting in a zero group velocity across all momenta. This unique condition enables the formation of wave modes that are not only highly localized in space and inher

Michaela Vítková, Rafael Brahm, Trifon Trifonov, Petr Kabáth

We present a joint analysis of TTVs and Doppler data for the transiting exoplanet system TOI-4504. TOI-4504 c is a warm Jupiter-mass planet that exhibits the largest known transit timing variations (TTVs), with a peak-to-node amplitude of $\sim$ 2 days, the largest value ever observed, and a super-period of $\sim$ 930 d. TOI-4504 b and c were identified in p

Bilol Banerjee, Anil K. Ghosh

We develop some graph-based tests for spherical symmetry of a multivariate distribution using a method based on data augmentation. These tests are constructed using a new notion of signs and ranks that are computed along a path obtained by optimizing an objective function based on pairwise dissimilarities among the observations in the augmented data set. The

Mengying Hu, Jing Lin, Kun Ding

The hyperbolic lattice (HBL) has emerged as a compelling platform for exploring matter in non-Euclidean space. Among its notable features, the breakdown of the conventional Bloch theorem stands out, prompting a reexamination of band theory, with the determination of spectra for non-Hermitian systems being a prominent example. Here, we develop an approach to

E. V. Vasinovich, A. S. Moskvin

In this work, we present an analysis of magnetic properties of weak ferrimagnets of the YFe$_{1-x}$Cr$_x$O$_3$ type. Taking into account the main spin interactions - isotropic Heisenberg superexchange, antisymmetric Dzyaloshinskii-Moriya exchange, single-ion spin anisotropy - calculations of the free energy, concentration and temperature dependences of magne

Vikrant V. Jadhav

This study examines the characterization of binary star systems using Spectral Energy Distributions (SEDs), a technique increasingly essential with the rise of large-scale astronomical surveys. Binaries can emit flux at different regions of the electromagnetic spectrum, making SEDs a valuable tool in identifying and characterising unresolved binary systems.

Xiang Gao, Kai Lu

The Segment Anything Model (SAM), originally built on a 2D Vision Transformer (ViT), excels at capturing global patterns in 2D natural images but struggles with 3D medical imaging modalities like CT and MRI. These modalities require capturing spatial information in volumetric space for tasks such as organ segmentation and tumor quantification. To address thi

Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan

This paper proposes a unified framework for the global optimization of a continuous function in a bounded rectangular domain. Specifically, we show that: (1) under the optimal strategy for a two-armed decision model, the sample mean converges to a global optimizer under the Strategic Law of Large Numbers, and (2) a sign-based strategy built upon the solution

Rayyan Al-Qaiwani, Mark Callaway, Martin Rasmussen

We prove that projectivised finite-dimensional linear random dynamical systems possess a unique finest weak Morse decomposition. Based on this result, we define the Morse spectrum and investigate its basic properties. In particular, we show that the Morse spectrum is given by a finite union of closed intervals. Furthermore we demonstrate that under a bounded

Lasha Otarashvili, Tamilselvan Subramanian, Jason Holmberg, J. J. Levenson

Recent work has established the ecological importance of developing algorithms for identifying animals individually from images. Typically, a separate algorithm is trained for each species, a natural step but one that creates significant barriers to wide-spread use: (1) each effort is expensive, requiring data collection, data curation, and model training, d

M. Berretti, M. Stangalini, S. Mestici, D. B. Jess

In this work, we present SoFT: Solar Feature Tracking, a novel feature-tracking tool developed in Python and designed to detect, identify, and track magnetic elements in the solar atmosphere. It relies on a watershed segmentation algorithm to effectively detect magnetic clumps within magnetograms, which are then associated across successive frames to follow

Mikolaj Czerkawski, Marcin Kluczek, Jędrzej S. Bojanowski

With the ever-increasing volumes of the Earth observation data present in the archives of large programmes such as Copernicus, there is a growing need for efficient vector representations of the underlying raw data. The approach of extracting feature representations from pretrained deep neural networks is a powerful approach that can provide semantic abstrac

Mário B. Amaro

We describe a short, reproducible workflow for applying finite differences on nonuniform grids determined by a positive weight function g. The grid is obtained by equidistribution, mapping uniform computational coordinates $\xi\in[0,1]$ to physical space by the cumulative integral $S(x)=\int_a^x\!1/g(s)\,ds$ and its inverse, and in multiple dimensions by the

Yuqian Jiang, Xinge Zhang, Haoyue Bai, Yuping Tian

Recently, altermagnetism (AM) in condensed matter systems has attracted much attention due to the physical properties arising from the alternating spins in both real space and reciprocal space. In our work, we propose a stable monolayer Janus Nb2SeTeO with altermagnetic ground state and a new type of spin-valley locking (SVL) effect. The monolayer Janus Nb2S

Wenting Xu, Viorela Ila, Luping Zhou, Craig T. Jin

The concept of function and affordance is a critical aspect of 3D scene understanding and supports task-oriented objectives. In this work, we develop a model that learns to structure and vary functional affordance across a 3D hierarchical scene graph representing the spatial organization of a scene. The varying functional affordance is designed to integrate

Miquel Albertí Binimelis

Quantum computing has long promised to revolutionize the way we solve complex problems. At the same time, tensor networks are widely used across various fields due to their computational efficiency and capacity to represent intricate systems. While both technologies can address similar problems, the primary aim of this thesis is not to compare them. Such com

Itay Krispin-Avraham, Roy Orfaig, Ben-Zion Bobrovsky

Object detection is a significant field in autonomous driving. Popular sensors for this task include cameras and LiDAR sensors. LiDAR sensors offer several advantages, such as insensitivity to light changes, like in a dark setting and the ability to provide 3D information in the form of point clouds, which include the ranges of objects. However, 3D detection

Patrick Stowell, Robert Foster, Almahdi Elhamri

GEANT4 is a particle physics simulation tool used to develop and optimize radiation detectors. While C++ based examples exist, Python's growing popularity necessitates the development of a more accessible Python bindings interface. This work demonstrates the use of cppyy, the automated C++-Python binding package, to provide an accessible interface for develo

Kristoffer Wickstrøm, Marina Marie-Claire Höhne, Anna Hedström

The lack of ground truth explanation labels is a fundamental challenge for quantitative evaluation in explainable artificial intelligence (XAI). This challenge becomes especially problematic when evaluation methods have numerous hyperparameters that must be specified by the user, as there is no ground truth to determine an optimal hyperparameter selection. I

Mohammadali Memari, Soghra Mikaeyl Nejad, Amir Parsa Rabiei, Mehrshad Eisaei

Multidomain sentiment analysis involves estimating the polarity of an unstructured text by exploiting domain specific information. One of the main issues common to the approaches discussed in the literature is their poor applicability to domains that differ from those used to construct opinion models.This paper aims to present a new method for Persian multid

Sam Griesemer, Defu Cao, Zijun Cui, Carolina Osorio

Computer simulations have long presented the exciting possibility of scientific insight into complex real-world processes. Despite the power of modern computing, however, it remains challenging to systematically perform inference under simulation models. This has led to the rise of simulation-based inference (SBI), a class of machine learning-enabled techniq

Pengcheng Guo, Xuankai Chang, Hang Lv, Shinji Watanabe

Benefiting from massive and diverse data sources, speech foundation models exhibit strong generalization and knowledge transfer capabilities to a wide range of downstream tasks. However, a limitation arises from their exclusive handling of single-speaker speech input, making them ineffective in recognizing multi-speaker overlapped speech, a common occurrence

Tsiu-Kwen Lee, Jheng-Huei Lin

Let $R$ be a prime ring with center $Z(R)$ and with involution $*$. Given an additive subgroup $A$ of $R$, let $T(A):=\{x+x^*\mid x\in A\}$ and $K_0(A):=\{x-x^*\mid x\in A\}$. Let $L$ be a non-abelian Lie ideal of $R$. It is proved that if $d$ is a nonzero derivation of $R$ satisfying $d(T(L))=0$ (resp. $d(K_0(L))=0$), then $T(R)^2\subseteq Z(R)$ (resp. $K_0

Shuyang Hou, Jianyuan Liang, Anqi Zhao, Huayi Wu

As the scale and complexity of spatiotemporal data continue to grow rapidly, the use of geospatial modeling on the Google Earth Engine (GEE) platform presents dual challenges: improving the coding efficiency of domain experts and enhancing the coding capabilities of interdisciplinary users. To address these challenges and improve the performance of large lan