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Interference effects in finite sections of one-dimensional moir\'e crystals are investigated using a Landauer-B\"uttiker formalism within the tight-binding approximation. We explain interlayer transport in double-wall carbon nanotubes and…
We apply a Gaussian variational approximation to model reduction in large biochemical networks of unary and binary reactions. We focus on a small subset of variables (subnetwork) of interest, e.g. because they are accessible experimentally,…
Supersymmetric Grand Unified Theories have a rich spectrum of particles barely heavier than the intermediate vector bosons. As their non-supersymmetric counterparts, they lead to many relations among low energy observables. But the precise…
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters…
Infinite sets of inequalities which generalize all the known inequalities that can be used in the majorization step of the Approximating Hamiltonian method are derived. They provide upper bounds on the difference between the quadratic…
In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general. We specifically perform a controlled…
Semiparametric efficient estimation of various multi-valued causal effects, including quantile treatment effects, is important in economic, biomedical, and other social sciences. Under the unconfoundedness condition, adjustment for…
Weak identification arises in many statistical problems when key variables exhibit weak correlations-for example, when instrumental variables correlate weakly with treatment, or when proxy variables correlate weakly with unmeasured…
The publicly available spectrum generators for the NMSSM often lead to different predictions for the mass of the standard model-like Higgs boson even if using the same renormalization scheme and two-loop accuracy. Depending on the parameter…
The Next-to-Minimal Supersymmetric Standard Model (NMSSM) offers a rich framework embedding physics beyond the Standard Model as well as consistent interpretations of the results about the Higgs signal detected at the LHC. We investigate…
This article presents a number of technical tools and results that may be instrumental to discern the nature of the Higgs particle. In scenarios where an additional strongly interacting sector is present in the electroweak theory resulting…
Bayesian neural networks (BNNs) estimate the posterior distribution of model parameters and utilize posterior samples for Bayesian Model Averaging (BMA) in prediction. However, despite the crucial role of flatness in the loss landscape in…
Linear mixed effects models are widely used in statistical modelling. We consider a mixed effects model with Bayesian variable selection in the random effects using spike-and-slab priors and developed a variational Bayes inference scheme…
We optimize the matrix representation of the nucleon-pair approximation (NPA) of the nuclear shell model. The NPA is a widely adopted truncation approach of the nuclear shell model and proves to be effective in describing low-lying states…
In this thesis we study a number of aspects about warped five-dimensional models. We first discuss on the construction of soft-wall models. We provide recipes for constructing consistent models of this kind and address the issue of how the…
We study the effect of interference between the Standard Model Higgs boson resonance and the continuum background in the process gamma gamma -> H -> b b-bar at a photon collider. Taking into account virtual gluon exchange between the…
$N$-body non efimovian bound or quasi-bound states for particles with short range interactions are considered in arbitrary dimensions. The different resonance regimes near the threshold are depicted by using a generalization of the…
Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm…
Spectral data is routinely broadened in order to improve appearance, approximate a higher sampling level or model experimental measurement effects. While there has been extensive work in the signal processing field to develop efficient…
Shrinkage estimation usually reduces variance at the cost of bias. But when we care only about some parameters of a model, I show that we can reduce variance without incurring bias if we have additional information about the distribution of…