Charged pion contribution to the anomalous magnetic moment of the muon

The model dependence inherent in hadronic calculations is one of the dominant sources of uncertainty in the theoretical prediction of the anomalous magnetic moment of the muon. In this thesis, we focus on the charged pion contribution and turn a critical eye on the models employed in the few previous calculations of $a_\mu^{\pi^+\pi^-}$. Chiral perturbation theory provides a check on these models at low energies, and we therefore calculate the charged pion contribution to light-by-light (LBL) scattering to $\mathcal{O}(p^6)$. We show that the dominant corrections to the leading order (LO) result come from two low energy constants which show up in the form factors for the $\gamma\pi\pi$ and $\gamma\gamma\pi\pi$ vertices. Comparison with the existing models reveal a potentially significant omission - none include the pion polarizability corrections associated with the $\gamma\gamma\pi\pi$ vertex. We next consider alternative models where the pion polarizability is produced through exchange of the $a_1$ axial vector meson. These have poor UV behavior, however, making them unsuited for the $a_\mu^{\pi^+\pi^-}$ calculation. We turn to a simpler form factor modeling approach, generating two distinct models which reproduce the pion polarizability corrections at low energies, have the correct QCD scaling at high energies, and generate finite contributions to $a_\mu^{\pi^+\pi^-}$. With these two models, we calculate the charged pion contribution to the anomalous magnetic moment of the muon, finding values larger than those previously reported: $a_\mu^\mathrm{I} = -1.779(4)\times10^{-10}\,,\,a_\mu^\mathrm{II} = -4.892(3)\times10^{-10}$.

Contribution of the temporoammonic pathway to hippocampal processing

The temporoammonic (TA) pathway is the direct, monosynaptic projection from layer III of entorhinal cortex to the distal dendritic region of area CA1 of the hippo­ campus. Although this pathway has been implicated in various functions, such as memory encoding and retrieval, spatial navigation, generation of oscillatory activity, and control of hippocampal excitability, the details of its physiology are not well understood. In this thesis, I examine the contribution of the TA pathway to hippocampal processing. I find that, as has been previously reported, the TA pathway includes both excitatory, glutamatergic components and inhibitory, GABAergic components. Several new discoveries are reported in this thesis. I show that the TA pathway is subject to forms of short-term activity-dependent regulation, including paired-pulse and frequency­ dependent plasticity, similar to other hippocampal pathways such as the Schaffer collateral (SC) input from CA3 to CA1. The TA pathway provides a strongly excitatory input to stratum radiatum giant cells of CA1. The excitatory component of the TA pathway undergoes a long-lasting decrease in synaptic strength following low-frequency stimulation in a manner partially dependent on the activation of NMDA receptors. High­ frequency activation of the TA pathway recruits a feedforward inhibition that can prevent CA1 pyramidal cells from spiking in response to SC input; this spike-blocking effect shows that the TA pathway can act to regulate information flow through the hippocampal trisynaptic pathway.

Report of an investigation of a plan of elimination of grade crossings on the A.T. & S.F. Ry. in the city of Pasadena, California

The problem of the Atchison, Topeka, and Santa Fe railroad in Pasadena is a very dynamic one, as is readily recognized by engineers, city officials, and laymen. The route of the railroad was first laid out in the eighties and because of certain liberal concessions granted by the City of Pasadena, the right-of-way was located through Pasadena, despite the fact that the grade coming into the city either from Los Angeles or San Bernardino was enormous. Some years later, other transcontinental routes of the Santa Fe out of Los Angles were sought, and a right-of-way was obtained by way of Fullerton and Riverside to San Bernardino, where this route joins the one from Los Angeles through Pasadena. This route, however, is ten miles longer than the one through Pasadena, which means a considerable loss of time in a short diversion of approximately only sixty miles in length.

Distributed Optimal Control of Cyber-Physical Systems: Controller Synthesis, Architecture Design and System Identification

The centralized paradigm of a single controller and a single plant upon which modern control theory is built is no longer applicable to modern cyber-physical systems of interest, such as the power-grid, software defined networks or automated highways systems, as these are all large-scale and spatially distributed. Both the scale and the distributed nature of these systems has motivated the decentralization of control schemes into local sub-controllers that measure, exchange and act on locally available subsets of the globally available system information. This decentralization of control logic leads to different decision makers acting on asymmetric information sets, introduces the need for coordination between them, and perhaps not surprisingly makes the resulting optimal control problem much harder to solve. In fact, shortly after such questions were posed, it was realized that seemingly simple decentralized optimal control problems are computationally intractable to solve, with the Wistenhausen counterexample being a famous instance of this phenomenon. Spurred on by this perhaps discouraging result, a concerted 40 year effort to identify tractable classes of distributed optimal control problems culminated in the notion of quadratic invariance, which loosely states that if sub-controllers can exchange information with each other at least as quickly as the effect of their control actions propagates through the plant, then the resulting distributed optimal control problem admits a convex formulation.

The identification of quadratic invariance as an appropriate means of "convexifying" distributed optimal control problems led to a renewed enthusiasm in the controller synthesis community, resulting in a rich set of results over the past decade. The contributions of this thesis can be seen as being a part of this broader family of results, with a particular focus on closing the gap between theory and practice by relaxing or removing assumptions made in the traditional distributed optimal control framework. Our contributions are to the foundational theory of distributed optimal control, and fall under three broad categories, namely controller synthesis, architecture design and system identification.

We begin by providing two novel controller synthesis algorithms. The first is a solution to the distributed H-infinity optimal control problem subject to delay constraints, and provides the only known exact characterization of delay-constrained distributed controllers satisfying an H-infinity norm bound. The second is an explicit dynamic programming solution to a two player LQR state-feedback problem with varying delays. Accommodating varying delays represents an important first step in combining distributed optimal control theory with the area of Networked Control Systems that considers lossy channels in the feedback loop. Our next set of results are concerned with controller architecture design. When designing controllers for large-scale systems, the architectural aspects of the controller such as the placement of actuators, sensors, and the communication links between them can no longer be taken as given -- indeed the task of designing this architecture is now as important as the design of the control laws themselves. To address this task, we formulate the Regularization for Design (RFD) framework, which is a unifying computationally tractable approach, based on the model matching framework and atomic norm regularization, for the simultaneous co-design of a structured optimal controller and the architecture needed to implement it. Our final result is a contribution to distributed system identification. Traditional system identification techniques such as subspace identification are not computationally scalable, and destroy rather than leverage any a priori information about the system's interconnection structure. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. To that end we propose a promising heuristic for identifying the dynamics of a subsystem that is still connected to a large system. We exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization problem. Finally, we conclude with a brief discussion of future research directions, with a particular emphasis on how to incorporate the results of this thesis, and those of optimal control theory in general, into a broader theory of dynamics, control and optimization in layered architectures.