This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation mat...
Many methods have been proposed in the literature for estimating the number of materials/endmembers in a hyperspectral image. This is sometimes called the “intrinsic” dimension (ID) of the image. A number of recent papers have proposed ID...
Bulk power grid interconnection and the access of various smart devices make the current grid highly complex. Timely and accurately identifying the power grid operation state is crucial for monitoring the operation stability of the power grid. For th...
Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational...
The increasing Internet-of-Things (IoT) applications will take a significant share of the services of the fifth generation mobile network (5G). However, IoT devices are vulnerable to security threats due to the limitation of their simple hardware and...
Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (P...
The problem of sound propagation in a shallow sea with a rough sea bottom is considered. A random matrix approach for studying sound scattering by the water–bottom interface inhomogeneities is developed. This approach is based on the constructi...
In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented i...
Seismo-ionospheric coupling is a field of great interest and is currently subject to rigorous study; using both ground and satellite data and many phenomenological features, the ionospheric precursors of earthquakes were identified. In this work, we...
Recently, the transformation from random to chaotic behavior in the statistics of Fano–Feshbach resonances was observed in thulium atoms with rising ensemble temperature. We performed random matrix theory simulations of such spectra and analyze...
The exact and simple distributions of finite random matrix theory (FRMT) are critically important for cognitive radio networks (CRNs). In this paper, we unify some existing distributions of the FRMT with the proposed coefficient matrices (vectors) an...
We discuss the cumulant approach to spectral properties of large random matrices. In particular, we study in detail the joint cumulants of high traces of large unitary random matrices and prove Gaussian fluctuation for pair-counting statistics with n...
In this article, we discuss the remarkable connection between two very different fields, number theory and nuclear physics. We describe the essential aspects of these fields, the quantities studied, and how insights in one have been fruitfully applie...
We review a theory that predicts the onset of thermalization in a quantum mechanical coupled non-linear oscillator system, which models the vibrational degrees of freedom of a molecule. A system of N non-linear oscillators perturbed by cubic anharmon...
Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields....
In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global...
This paper discusses the effects of introducing nonlinear interactions and noise-filtering to the covariance matrix used in Markowitz’s portfolio allocation model, evaluating the technique’s performances for daily data from seven financia...
The Dyson index,
Restricted maximum likelihood (REML) estimators are commonly used to obtain unbiased estimators for the variance components in linear mixed models. In modern applications, particularly in genomic studies, the dimension of the design matrix with respe...
This paper studied the robust beamforming for dense transmission systems with imperfect channel state information (CSI). The objective was to maximize the minimum signal to interference plus noise ratio (SINR) in the constraint of the per base statio...
This study examines the daily top 100 cryptocurrencies on Uniswap V3. It denoises the correlation coefficient matrix of cryptocurrencies by using sliding window techniques and random matrix theory. Further, this study constructs a time-varying correl...
Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where...
We examined the determinants of the U.S. consumer sentiment by applying linear and nonlinear models. The data are monthly from 2009 to 2019, covering a large set of financial and nonfinancial variables related to the stock market, personal income, co...
This paper proposes a validation-comparison method for burned area (BA) products. The technique considers: (1) bootstrapping of scenes for validation-comparison and (2) permutation tests for validation. The research focuses on the tropical regions of...
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specif...
This research systematically analyzes the behaviors of correlations among stock prices and the eigenvalues for correlation matrices by utilizing random matrix theory (RMT) for Chinese and US stock markets. Results suggest that most eigenvalues of bot...
Electroencephalography (EEG) signals can be used as a neuroimaging indicator to analyze brain-related diseases and mental states, such as schizophrenia, which is a common and serious mental disorder. However, the main limiting factor of using EEG dat...
The contact process is a nonequilibrium Hamiltonian model that, even in one dimension, lacks an exact solution and has been extensively studied via Monte Carlo simulations, both in steady-state and time-dependent scenarios. Although the effects of pa...
In this work, an analytical framework for deriving the exact moments of multiple-input- multiple-output (MIMO) mutual information in the high-signal-to-noise ratio (SNR) regime is proposed. The idea is to make efficient use of the matrix-variate dens...
The Tsallis entropy is a useful one-parameter generalization to the standard von Neumann entropy in quantum information theory. In this work, we study the variance of the Tsallis entropy of bipartite quantum systems in a random pure state. The main r...
Electronic structure theory describes the properties of solids using Bloch states that correspond to highly symmetrical nuclear configurations. However, nuclear thermal motion destroys translation symmetry. Here, we describe two approaches relevant t...
We consider a toy model for the study of monitored dynamics in many-body quantum systems. We study the stochastic Schrödinger equation resulting from continuous monitoring with a rate
It has been shown recently by Saad, Shenker and Stanford that the genus expansion of a certain matrix integral generates partition functions of Jackiw-Teitelboim (JT) quantum gravity on Riemann surfaces of arbitrary genus with any fixed number of bou...
In a recent paper (A. Fring and T. Frith, Phys. Rev A 100, 101102 (2019)), a Dyson scheme to deal with density matrix of non-Hermitian Hamiltonians has been used to investigate the entanglement of states of a PT-symmetric bosonic system. They found t...
To estimate the degree of quantum entanglement of random pure states, it is crucial to understand the statistical behavior of entanglement indicators such as the von Neumann entropy, quantum purity, and entanglement capacity. These entanglement metri...
The high dropout rates in programming courses emphasise the need for monitoring and understanding student engagement, enabling early interventions. This activity can be supported by insights into students’ learning behaviours and their relation...
The detection performance of active sonar is often hindered by the presence of seabed reverberation in shallow water. Separating the reverberations from the target echo and noise in the received signal is a crucial challenge in the field of underwate...
About twelve years ago, the use of standard functional manipulations was demonstrated to imply an unexpected property satisfied by the fermionic Green’s functions of
We study the localization properties of the eigenvectors, characterized by their information entropy, of tight-binding random networks with balanced losses and gain. The random network model, which is based on Erdős–Rényi (ER) graphs, is...
We study the Hankel determinant generated by a singularly perturbed Jacobi weight
Quantum graphs are ideally suited to studying the spectral statistics of chaotic systems. Depending on the boundary conditions at the vertices, there are Neumann and Dirichlet graphs. The latter ones correspond to totally disassembled graphs with a s...
Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale- and fluctua...
We explore how easy it is to enforce the advent of exceptional points starting from random matrices of non-Hermitian nature. We use the Petermann factor, whose mathematical version is called “overlap”, for guidance, as well as simple pseu...
We analyze the effect of varying system conditions on the single-particle entanglement entropy for an arbitrary eigenstate of a complex system that can be described by a multiparametric Gaussian ensemble. Our theoretical analysis leads to the identif...
The problem of determining the conditions under which a random rectangular matrix is of full rank is a fundamental question in random matrix theory, with significant implications for coding theory, cryptography, and combinatorics. In this paper, we s...
Random matrix series are a significant component of random matrix theory, offering rich theoretical content and broad application prospects. In this paper, we propose modified versions of tail bounds for random matrix series, including matrix Gaussia...
Compressed sensing theory has been widely used for data aggregation in WSNs due to its capability of containing much information but with light load of transmission. However, there still exist some issues yet to be solved. For instance, the measureme...
Compressive sensing theory has addressed the limitations of traditional methods in the field of information technology, and led to a revolution. On the basis of compressive sensing theory research, this study utilized the exterior determinacy and inh...
Results from the theory of the generalized hypergeometric functions of matrix argument, and the related zonal polynomials, are used to develop a new approach to study the asymptotic distributions of linear functions of uniformly distributed random ma...
We perform a detailed computational study of the recently introduced Sombor indices on random networks. Specifically, we apply Sombor indices on three models of random networks: Erdös-Rényi networks, random geometric graphs, and bipartite random netw...
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