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Recently years, the attempts on distilling mobile data into useful knowledge has been led to the deployment of machine learning algorithms at the network edge. Principal component analysis (PCA) is a classic technique for extracting the…
Principal component analysis (PCA) is a commonly used pattern analysis method that maps high-dimensional data into a lower-dimensional space maximizing the data variance, that results in the promotion of separability of data. Inspired by…
Principal Component Analysis (PCA) is a widely used technique in exploratory data analysis, visualization, and data preprocessing, leveraging the concept of variance to identify key dimensions in datasets. In this study, we focus on the…
The CP decomposition for high dimensional non-orthogonal spiked tensors is an important problem with broad applications across many disciplines. However, previous works with theoretical guarantee typically assume restrictive incoherence…
The ATLAS experiment at the LHC has measured the centrality dependence of charged particle pseudorapidity distribution, charged particle spectra, and the two-particle correlations in p+Pb collisions at a nucleon-nucleon centre-of-mass…
The presence of $\alpha$ clustered structures in light nuclei can enhance the initial spatial anisotropies in relativistic nuclear collisions relative to those arising from nuclei with uniform density distributions. Thus, observables that…
The transverse momentum spectrum of $\eta$ meson in relativistic heavy-ion collisions is studied at the next-to-leading-order (NLO) within the perturbative QCD, where the jet quenching effect in the QGP is incorporated with the effectively…
Heavy-ion experiments provide a new opportunity to gain a deeper understanding of the structure of nuclei. To achieve this, it is crucial to identify observables under circumstances that are minimally affected by the process that leads to…
Principal Component Analysis (PCA) is an important tool of dimension reduction especially when the dimension (or the number of variables) is very high. Asymptotic studies where the sample size is fixed, and the dimension grows [i.e., High…
Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting…
Searching for new physics~(NP) is one of the areas of high-energy physics that requires the most processing of large amounts of data. At the same time, quantum computing has huge potential advantages when dealing with large amounts of data.…
The Color Glass Condensate (CGC), describing the physics of the nonlinear gluonic interactions of QCD at high energy, provides a consistent first-principles framework to understand the initial conditions of heavy ion collisions. This talk…
Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal processing, mechanical engineering, psychometrics, and other fields under different…
Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, in an intuitive and…
We discuss recent progress towards developing accurate initial state descriptions for heavy ion collisions focusing on weak coupling based approaches, that enable one to constrain the high-energy structure of nuclei from deep inelastic…
It is important to understand whether $\alpha$-clustering structures can leave traces in ultra-relativistic heavy ion collisions. Using the modified AMPT model, we simulate three $\alpha$ + core configurations of $^{44}$Ti in…
Recent studies reveal that at high energies, collisions of small system like $p+p$ gives signatures similar to that widely observed in heavy ion collisions hinting towards a possibility of forming a medium with collective behaviour. With…
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA…
We present a brief review of phenomenological applications of the gluon saturation approach to the proton-nucleus collisions at high energies.
We present an embedding approach to treat local electron correlation effects in periodic environments. In a single, consistent framework, our plane-wave based scheme embeds a local high-level correlation calculation (here Coupled Cluster…