Regularization and Kernelization of the Maximin Correlation Approach

Robust classification becomes challenging when each class consists of multiple subclasses. Examples include multi-font optical character recognition and automated protein function prediction. In correlation-based nearest-neighbor classification, the maximin correlation approach (MCA) provides the worst-case optimal solution by minimizing the maximum misclassification risk through an iterative procedure. Despite the optimality, the original MCA has drawbacks that have limited its wide applicability in practice. That is, the MCA tends to be sensitive to outliers, cannot effectively handle nonlinearities in datasets, and suffers from having high computational complexity. To address these limitations, we propose an improved solution, named regularized maximin correlation approach (R-MCA). We first reformulate MCA as a quadratically constrained linear programming (QCLP) problem, incorporate regularization by introducing slack variables in the primal problem of the QCLP, and derive the corresponding Lagrangian dual. The dual formulation enables us to apply the kernel trick to R-MCA so that it can better handle nonlinearities. Our experimental results demonstrate that the regularization and kernelization make the proposed R-MCA more robust and accurate for various classification tasks than the original MCA. Furthermore, when the data size or dimensionality grows, R-MCA runs substantially faster by solving either the primal or dual (whichever has a smaller variable dimension) of the QCLP.Comment: Submitted to IEEE Acces

The Influence of Hydrophobic Mismatch on Structure and Dynamics of Transmembrane Helices and Lipid Bilayers

Membrane proteins with one or a few transmembrane (TM) helices are abundant and often involved in important TM-included signaling and regulation through formation of hetero- and homo-oligomers. Especially, solid-state NMR (SSNMR) is a powerful technique to describe the orientations of membrane proteins and peptides in their native membrane bilayer environments. However, it is still challenging to obtain the structural information of membrane protein. Since protein-lipid interaction and bilayer regulation of membrane protein functions are largely controlled by the hydrophobic match between the TM domain of membrane proteins and the surrounding lipid bilayer, the interplay between the structure and the energetics of lipid and protein components of biomembranes is one of long-standing interests in biophysics. Structural and dynamic changes of the TM helices in response to a hydrophobic mismatch as well as molecular forces governing such changes remain to be fully understood at the atomic level. In this dissertation, to systematically characterize responses of a TM helix and lipid adaptations to a hydrophobic mismatch, I have performed a total of 5.8-μs umbrella sampling simulations and calculated the potentials of mean force (PMFs) as a function of TM helix tilt angle under various mismatch conditions. Single-pass TM peptides called WALP were used in two lipid bilayers with different hydrophobic thicknesses to consider hydrophobic mismatch caused by either the TM length or the bilayer thickness. The deuterium (2H) quadrupolar splitting (DQS), one of the SSNMR observables, has been used to characterize the orientations of various single-pass TM helices using a semi-static rigid-body model such as the geometric analysis of labeled alanine (GALA) method. However, dynamic information of these TM helices, which could be related to important biological function, can be missing or misinterpreted with the semi-static model. The result in Chapter 3 demonstrates that SSNMR ensemble dynamics provides a means to extract orientational and dynamic information of TM helices from their SSNMR observables and to explain the discrepancy between molecular dynamics simulation and GALA-based interpretation of DQS data. Finally, this dissertation describes the influence of hydrophobic mismatch on structure and dynamics of TM helices and lipid bilayers through molecular dynamics simulation of Gramicidin A (gA) channel in various lipid bilayers. The structure and dynamics of the gA channel as well as important lipid properties were investigated to address the influence by various hydrophobic mismatch conditions

Randomized Adversarial Style Perturbations for Domain Generalization

We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP), which is motivated by the observation that the characteristics of each domain are captured by the feature statistics corresponding to style. The proposed algorithm perturbs the style of a feature in an adversarial direction towards a randomly selected class, and makes the model learn against being misled by the unexpected styles observed in unseen target domains. While RASP is effective to handle domain shifts, its naive integration into the training procedure might degrade the capability of learning knowledge from source domains because it has no restriction on the perturbations of representations. This challenge is alleviated by Normalized Feature Mixup (NFM), which facilitates the learning of the original features while achieving robustness to perturbed representations via their mixup during training. We evaluate the proposed algorithm via extensive experiments on various benchmarks and show that our approach improves domain generalization performance, especially in large-scale benchmarks

Recent advances in hydrogen storage technologies based on nanoporous carbon materials

AbstractHydrogen is a promising energy carrier that can potentially facilitate a transition from fossil fuels to sustainable energy sources without producing harmful by-products. Prior to realizing a hydrogen economy, however, viable hydrogen storage materials must be developed. Physical adsorption in porous solids provides an opportunity for hydrogen storage under low-stringency conditions. Physically adsorbed hydrogen molecules are weakly bound to a surface and, hence, are easily released. Among the various surface candidates, porous carbons appear to provide efficient hydrogen storage, with the advantages that porous carbon is relatively low-cost to produce and is easily prepared. In this review, we summarize the preparation methods, pore characteristics, and hydrogen storage capacities of representative nanoporous carbons, including activated carbons, zeolite-templated carbon, and carbide-derived carbon. We focus particularly on a series of nanoporous carbons developed recently: metal–organic framework-derived carbons, which exhibit promising properties for use in hydrogen storage applications