New Perspectives for Rashba Spin-Orbit Coupling

In 1984, Bychkov and Rashba introduced a simple form of spin-orbit coupling to explain certain peculiarities in the electron spin resonance of two-dimensional semiconductors. Over the past thirty years, similar ideas have been leading to a vast number of predictions, discoveries, and innovative concepts far beyond semiconductors. The past decade has been particularly creative with the realizations of means to manipulate spin orientation by moving electrons in space, controlling electron trajectories using spin as a steering wheel, and with the discovery of new topological classes of materials. These developments reinvigorated the interest of physicists and materials scientists in the development of inversion asymmetric structures ranging from layered graphene-like materials to cold atoms. This review presents the most remarkable recent and ongoing realizations of Rashba physics in condensed matter and beyond.Comment: 56 pages, 7 figures, 3 boxes; Accepted for publication in Nature Materials. The present version is the original one, before passing by the referee

Tensor Network States: Optimizations and Applications in Quantum Many-Body Physics and Machine Learning

Tensor network states are ubiquitous in the investigation of quantum many-body (QMB) physics. Their advantage over other state representations is evident from their reduction in the computational complexity required to obtain various quantities of interest, namely observables. Additionally, they provide a natural platform for investigating entanglement properties within a system. In this dissertation, we develop various novel algorithms and optimizations to tensor networks for the investigation of QMB systems, including classical and quantum circuits. Specifically, we study optimizations for the two-dimensional Ising model in a transverse field, we create an algorithm for the $k$-SAT problem, and we study the entanglement properties of random unitary circuits. In addition to these applications, we reinterpret renormalization group principles from QMB physics in the context of machine learning to develop a novel algorithm for the tasks of classification and regression, and then utilize machine learning architectures for the time evolution of operators in QMB systems

Universality in Non-Equilibrium Quantum Systems

The phenomenon of universality is one of the most striking in many-body physics. Despite having sometimes wildly different microscopic constituents, systems can nonetheless behave in precisely the same way, with only the variable names interchanged. The canonical examples are those of liquid boiling into vapor and quantum spins aligning into a ferromagnet; despite their obvious differences, they nonetheless both obey quantitatively the same scaling laws, and are thus in the same universality class. Remarkable though this is, universality is generally a phenomenon limited to thermodynamic equilibrium, most commonly present at transitions between different equilibrium phases. Once out of equilibrium, the fate of universality is much less clear. Can strongly non-equilibrium systems behave universally, and are their universality classes different from those familiar from equilibrium? How is quantum mechanics important? This dissertation attempts to address these questions, at least in a small way, by showing and analyzing universal phenomena in several classes of non-equilibrium quantum systems.Comment: PhD thesis in physics submitted to the University of California, Berkeley, Summer 2020; 132 pages; based upon prior works arXiv:1909.05251 [Phys. Rev. Lett. 123, 246603 (2019)], arXiv:1906.11253 [Phys. Rev. Lett. 123, 230604 (2019)], arXiv:1807.09767 [Phys. Rev. B 98, 174203 (2018)], arXiv:1803.00019 [PNAS 115 (38) 9491-9496 (2018)], arXiv:1701.05899 [Phys. Rev. Lett. 118, 260602 (2017)

Hierarchical dynamics of individual RNA helix base pair formation and disruption

This thesis explores the RNA folding problem using single-molecule field effect transistors (smFETs) to measure the lifetimes of individual RNA base-pairing rearrangements. In the course of this research, considerable computational, chemical, and engineering contributions were developed so that the single-molecule measurements could be conducted and quantified. These advancements have allowed, on the basis of the smFET data collected herein, the quantification of a kinetic model for RNA stem-loop structures which has been generalized to quantitatively explore the phenomenological observation that an RNA found in the bacillus subtilis strain acts as a metabolite-sensing switch, allowing RNA polymerase to transcribe the messenger RNA when the metabolite is present and preventing transcription when the metabolite is absent. Together, the data presented quantify a simple model for the base pairing rearrangements that underlie RNA folding

Virtual Runtime Application Partitions for Resource Management in Massively Parallel Architectures

This thesis presents a novel design paradigm, called Virtual Runtime Application Partitions (VRAP), to judiciously utilize the on-chip resources. As the dark silicon era approaches, where the power considerations will allow only a fraction chip to be powered on, judicious resource management will become a key consideration in future designs. Most of the works on resource management treat only the physical components (i.e. computation, communication, and memory blocks) as resources and manipulate the component to application mapping to optimize various parameters (e.g. energy efficiency). To further enhance the optimization potential, in addition to the physical resources we propose to manipulate abstract resources (i.e. voltage/frequency operating point, the fault-tolerance strength, the degree of parallelism, and the configuration architecture). The proposed framework (i.e. VRAP) encapsulates methods, algorithms, and hardware blocks to provide each application with the abstract resources tailored to its needs. To test the efficacy of this concept, we have developed three distinct self adaptive environments: (i) Private Operating Environment (POE), (ii) Private Reliability Environment (PRE), and (iii) Private Configuration Environment (PCE) that collectively ensure that each application meets its deadlines using minimal platform resources. In this work several novel architectural enhancements, algorithms and policies are presented to realize the virtual runtime application partitions efficiently. Considering the future design trends, we have chosen Coarse Grained Reconfigurable Architectures (CGRAs) and Network on Chips (NoCs) to test the feasibility of our approach. Specifically, we have chosen Dynamically Reconfigurable Resource Array (DRRA) and McNoC as the representative CGRA and NoC platforms. The proposed techniques are compared and evaluated using a variety of quantitative experiments. Synthesis and simulation results demonstrate VRAP significantly enhances the energy and power efficiency compared to state of the art.Siirretty Doriast

Quantifying the Organization and Dynamics of Excitable Signaling Networks

The transmission of extracellular information through intracellular signaling networks is ubiquitous in biology---from single-celled organisms to complex multicellular systems. Via signal-transduction machinery, cells of all types can detect and respond to biological, chemical, and physical stimuli. Although studies of signaling mechanisms and pathways traditionally involve arrays of biochemical assays, detailed quantification of physical information is becoming an increasingly important tool for understanding the complexities of signaling. With the rich datasets currently being collected in biological experiments, understanding the mechanisms that govern intracellular signaling networks is becoming a multidisciplinary problem at the intersection of biology, computer science, physics, and applied mathematics. In this dissertation, I focus on understanding and characterizing the physical behavior of signaling networks. Through analysis of experimental data, statistical modeling, and computational simulations, I explore a characteristic of signaling networks called excitability, and show that an excitable-systems framework is broadly applicable for explaining the connection between intracellular behaviors and cell functions. One way to connect the physical behavior of signaling networks to cell function is through the structural and spatial analyses of signaling proteins. In the first part of this dissertation, I employ an adaptive-immune-cell model with a key activation step that is both promoted and inhibited by a microns-long, filamentous protein complex. I introduce a novel image-based bootstrap-like resampling method and demonstrate that the spatial organization of signaling proteins is an important contributor to immune-cell self regulation. Furthermore, I use the bootstrap-like resampling to demonstrate that the location of contact points between signaling proteins can provide mechanistic insight into how signal regulation is accomplished on the single-cell level. Finally, I probe the excitable dynamics of the system with a Monte Carlo simulation of nucleation-limited growth and degradation. Using the simulations, I show that careful balance between simulation parameters can elicit a tunable response dynamic. The spatiotemporal dynamics of signaling components are also important mediators of cell function. One key readout of the connection between signaling dynamics and cell function is the behavior of the cytoskeleton. In the second part of this dissertation, I use innate-immune-cell and epithelial-cell models to understand how a key cytoskeletal component, actin, is influenced by topographical features in the extracellular environment. Engineered nanotopographic substrates similar in size to typical extracellular-matrix structures have been shown to bias the flow of actin, a concept known as esotaxis. To measure this bias, I introduce a generalizable optical-flow-based-analysis suite that can robustly and systematically quantify the spatiotemporal dynamics of actin in both model systems. Interestingly, despite having wildly different migratory phenotypes and physiological functions, both cell types exhibit quantitatively similar topography-guidance dynamics which suggests that sensing and responding to extracellular textures is an evolutionarily-conserved phenomena. The signaling mechanisms that enable actin responses to the physical environment are poorly understood. Despite experimental evidence for the enhancement of actin-nucleation-promoting factors (NPFs) on extracellular features, connecting texture-induced signaling to overall cell behavior is an ongoing challenge. In the third part of this dissertation, I study the topography-induced guidance of actin in amoeboid cells on nanotopographic textures of different spacings. Using optical-flow analysis and statistical modeling, I demonstrate that topography-induced guidance is strongest when the features are similar in size to typical actin-rich protrusions. To probe this mechanism further, I employ a dendritic-growth simulation of filament assembly and disassembly with realistic biochemical rates, NPFs, and filament-network-severing dynamics. These simulations demonstrate that topography-induced guidance is more likely the result of a redistribution, rather than an enhancement, of NPF components. Overall, this dissertation introduces quantitative tools for the analysis, modeling, and simulations of excitable systems. I use these tools to demonstrate that an excitable-systems framework can provide deep, phenomenological insights into the character, organization, and dynamics of a variety of biological systems

Proceedings of AUTOMATA 2010: 16th International workshop on cellular automata and discrete complex systems

International audienceThese local proceedings hold the papers of two catgeories: (a) Short, non-reviewed papers (b) Full paper