Sharif Digital Repository

In this thesis, we studied the oscillation of neutrino flavors and the measurement of mixing parameters through the detection of cosmic neutrinos at neutrino telescopes. Most of the astrophysical explosions predict the emission of neutrino flux and kilometer-scale detectors are under construction for the observation of these neutrinos. Detection of astrophysical neutrinos opens a new window to understand the neutrino properties and also the properties of astrophysical sources.In this thesis first we review the phenomenology of neutrino flavor oscillation and characteristics of neutrino mass matrix. By introducing a new class of invariants (under general transformation of lepton fields bases)... 

Excitability is one of the most important characteristics of a neuron. In 1948, Hodgkin identified three different types of excitability of neurons. Excitability an all of its types can be observed in Hodgkin-Huxley model of neuronal dynamics (H-H model) as a four-dimensional system of differential equations and in at least two dimensional reductions of H-H type models. By applying the theory of dynamical systems (e.g. bifurcation theory), one can give a mathematical definition of excitability.Excitability of the neuron is equivalent to that the neuronal model is near a bifurcation through which the state of the system approaches to a stable limit cycle. In this thesis, a two-dimensional... 

Neutrino flavor oscillations are clear evidence of massive neutrinos. It has been for more than a decade that neutrino physics became one of active topics in the field of particle physics and various experiments started studying the flavor oscillation of neutrinos. Unfortunately, experiments studying the flavor oscillations are only sensitive to the mass square difference, and they are incapable of providing more information about the absolute mass scale of neutrinos. Additionally, sterile neutrinos having non-standard interactions are under vast investigations and studies. Determination of neutrino masses and their effective number is among the most challenging problems by which... 

Question Answering (QA) systems have reached human-level accuracy; however, these systems are vulnerable to adversarial examples. Recently, adversarial attacks have been widely investigated in text classification. However, there have been few research efforts on this topic in QA systems. In this thesis our approach is improving the robustness of QA systems using deep neural networks. In this thesis, as the first proposed approach, the knowledge distillation method is introduced to create a student model to improve the robustness of QA systems. In this regard, the pre-trained BERT model was used as a teacher, and its impact on the robustness of the student models on the Adversarial SQuAD...