Related papers: Machine Learning Power Week 2023: Clustering in Ha…
The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power…
Federated learning has attracted significant attention as a privacy-preserving framework for training personalised models on multi-source heterogeneous data. However, most existing approaches are unable to handle scenarios where subgroup…
Neutrinos, elusive and abundant, interact weakly with matter, making detection challenging. This thesis explores neutrino physics, focusing on mass hierarchy and oscillations, with emphasis on India's upcoming INO facility. The Iron…
The new proposed experiment CLEO-c in the Wilson Laboratory will explore the physics potential of the CLEO detector and the CESR storage ring operation in the center-of-mass energy range 3 - 5 GeV. The Physics program of CLEO-c can be…
The proposed high luminosity high energy Electron-Ion Collider (EIC) will explore the proton/nuclear structure in a wide Bjorken-x ($x_{BJ}$) and $Q^{2}$ phase space. Heavy flavor products are generated in initial collisions and have their…
In recent times, federated machine learning has been very useful in building intelligent intrusion detection systems for IoT devices. As IoT devices are equipped with a security architecture vulnerable to various attacks, these security…
The convergence of HPC and data-intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction between traditional HPC and ML approaches and motivates the…
We propose a method to measure the hadronic matter compressibility by means of the event-by-event analysis of heavy-ion collisions at high energies. The method, which utilizes the thermodynamical relation between the compressibility and the…
Spectral clustering is one of the most important algorithms in data mining and machine intelligence; however, its computational complexity limits its application to truly large scale data analysis. The computational bottleneck in spectral…
Clustering is an important research topic for wireless sensor networks (WSNs). A large variety of approaches has been presented focusing on different performance metrics. Even though all of them have many practical applications, an…
The FCC integrated programme comprises an $\rm e^+e^-$ high-luminosity circular collider that will produce very large samples of data in an energy range $88 \le \sqrt{s} \le 365$ GeV, followed by a high-energy $\rm pp$ machine that, with…
Many data mining tasks cannot be completely addressed by auto- mated processes, such as sentiment analysis and image classification. Crowdsourcing is an effective way to harness the human cognitive ability to process these machine-hard…
We derive a new method to improve the statistics of identified particles at high transverse momentum (\pt) using online-triggered events by the Barrel Electro-Magnetic-Calorimeter (BEMC) detector. The BEMC is used to select hadronic…
Exciting scientific results such as the discovery of the Higgs boson offer a great opportunity to engage young people in particle physics. International Particle Physics Masterclasses highlight how high school students across the world can…
Security-Constrained Unit Commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via Mixed-Integer Linear Programming, sometimes multiple times per day, with…
Since its inception, the Large Hadron Collider (LHC) has significantly advanced particle physics and will continue to do so in the context of the High Luminosity LHC (HL-LHC) program to collect $3000$ fb$^{-1}$ by the end of 2041. The…
Although an important goal of introductory physics labs is to train students in scientific reasoning and critical thinking, currently there are no standard tests in physics designed to assess such skills. We are in the process of developing…
This paper introduces mathematical frameworks that address the challenges of multi-view clustering in federated learning environments. The objective is to integrate optimization techniques based on new objective functions employing…
Estimating power consumption in modern Cloud environments is essential for carbon quantification toward green computing. Specifically, it is important to properly account for the power consumed by each of the running applications, which are…
Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges…