Graviton Signals in Central Production at the LHC

In this paper, we study central production, in the inclusive case, to look for graviton signals in large extra dimensional model using dilepton and diphoton channels. We carefully analyze signal and possible Standard Model background processes and study the feasibility of such new physics searches in a relatively clean environment as in central production where the proton fragments are mostly emitted in the forward direction, and there is a clear rapidity gap between them and the centrally produced system. Our analysis shows that the LHC with 14 TeV center of mass energy and 100 fb$^{-1}$ (300 fb$^{-1}$) of integrated luminosity can probe the effective gravity scale up to 3.6 TeV (4.4 TeV) in both the dilepton and diphoton channels.Comment: 25 pages, 15 figure

Privacy-preserving recommendation system using federated learning

Federated Learning is a form of distributed learning which leverages edge devices for training. It aims to preserve privacy by communicating users’ learning parameters and gradient updates to the global server during the training while keeping the actual data on the users’ devices. The training on global server is performed on these parameters instead of user data directly while fine tuning of the model can be done on client’s devices locally. However, federated learning is not without its shortcomings and in this thesis, we present an overview of the learning paradigm and propose a new federated recommender system framework that utilizes homomorphic encryption. This results in a slight decrease in accuracy metrics but leads to greatly increased user-privacy. We also show that performing computations on encrypted gradients barely affects the recommendation performance while ensuring a more secure means of communicating user gradients to and from the global server

Asessment of Rotor Critical Speeds*- A Note

In this paper, the evaluation of an aircraft. engine rotor critical speeds has been studied and the comparison of calculated results with computer and transfer matrix method is described