Kinetics of Brownian Transport

The rate of progress of Brownian processes is not easily quantifiable. An importantmeasure of the ”speed” of Brownian motion is themean first-passage time (FPT) to a given distance. FPTs exist in various flavours including exit- and transition-path times, which, for instance, can be used to quantify the length of reaction paths in folding transitions inmolecules such as DNA. Due to their inherently stochastic nature, measurements of any FPTs require repeated experiments under controlled conditions. In my thesis, I systematically explore FPTs in various contexts using a custom-built automated holographic optical tweezers (HOT) setup. More precisely, I investigate transition- and exit-path-time symmetries in equilibrium systems and demonstrate the breakdown of the symmetry in out-of-equilibriumsystems. Experimental data from folding DNA-hairpins show that the principles established on the mesoscale extend well into the molecular regime. In Kramers escape problem, the reciprocal of the escape rate corresponds to the time of first-passage to leave the initial state. A lower bound for the achievable FPT, e.g. of the reaction coordinate of a folding molecule, therefore corresponds to a speed-limit of the ensemble reaction rate. Using my setup, I show that certain barrier shapes can substantially lower the escape time across the barrier without changing the overall energy balance. This result has deep implications for reaction kinetics, e.g. in protein folding. Furthermore, I investigate the role of entropic forces in Brownian transport, show that hydrodynamic drag plays a crucial role in Brownian motion in confined systems, and give an experimental realisation of Fick-Jacobs theory. The thermodynamic applications of HOTs considered here necessitate the creation of fine-tuned optical landscapes, which requires precise phase-retrieval to compute the necessary holograms. In order to address this problem, I explore novel algorithms based on deep conditional generative models and test whether such models can assist in finding holograms for a given desired light distribution. I compare several differentmodels, including conditional generative-adversarial networks and conditional variational autoencoders, which are trained on data sets sampled on the HOT setup. Furthermore, I propose a novel forward-loss-minimising architecture and demonstrate its excellent performance on both validation and artificially-created test data sets.European Training Network (ETN) Grant No. 674979-NANOTRANS Winton Programme for the Physics of Sustainabilit

Recent Advances in Single-Particle Tracking: Experiment and Analysis

This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications