Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions

This dissertation is the accumulation of the application of adaptive, empirical learning-based methods in the study and characterization of the El Niño Southern Oscillation. In specific, it focuses on ENSO’s effects on rainfall and drought conditions in two major regions shown to be linked through the strength of the dependence of their climate on ENSO: 1) the southern Pacific Coast of the United States and 2) the Nile River Basin. In these cases, drought and rainfall are tied to deep economic and social factors within the region. The principal aim of this dissertation is to establish, with scientific rigor, an epistemological and foundational justification of adaptive learning models and their utility in the both the modeling and understanding of a wide-reaching climate phenomenon such as ENSO. This dissertation explores a scientific justification for their proven accuracy in prediction and utility as an aide in deriving a deeper understanding of climate phenomenon. In the application of drought forecasting for Southern California, adaptive learning methods were able to forecast the drought severity of the 2015-2016 winter with greater accuracy than established models. Expanding this analysis yields novel ways to analyze and understand the underlying processes driving California drought. The pursuit of adaptive learning as a guiding tool would also lead to the discovery of a significant extractable components of ENSO strength variation, which are used with in the analysis of Nile River Basin precipitation and flow of the Nile River, and in the prediction of Nile River yield to p=0.038. In this dissertation, the duality of modeling and understanding is explored, as well as a discussion on why adaptive learning methods are uniquely suited to the study of climate phenomenon like ENSO in the way that traditional methods lack. The main methods explored are 1) differentiable Programming, as a means of construction of novel self-learning models through which the meaningfulness of parameters arises from emergent phenomenon and 2) empirical decompositions, which are driven by an adaptive rather than rigid component extraction principle, are explored further as both a predictive tool and as a tool for gaining insight and the construction of models

Fundamental Tradeoffs in Estimation of Finite-State Hidden Markov Models

Hidden Markov models (HMMs) constitute a broad and flexible class of statistical models that are widely used in studying processes that evolve over time and are only observable through the collection of noisy data. Two problems are essential to the use of HMMs: state estimation and parameter estimation. In state estimation, an algorithm estimates the sequence of states of the process that most likely generated a certain sequence of observations in the data. In parameter estimation, an algorithm computes the probability distributions that govern the time-evolution of states and the sampling of data. Although algorithms for the two problems are widely researched, relatively little study has been devoted to understanding the tradeoffs between key design variables of these algorithms from a mathematically rigorous viewpoint. In this thesis, we provide such a study by establishing theorems regarding these tradeoffs. Furthermore, we illustrate the implications of these theorems in practice, highlighting the scope of their applicability and generality. We then suggest directions for future research in this area by bringing attention to the critical assumptions and tools used in the proofs of our theorems

Deep learning investigation for chess player attention prediction using eye-tracking and game data

This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels Using a skip-layer architecture of an autoencoder, with a unified decoder, we are able to use multiscale features to predict saliency of part of the board at different scales, showing multiple relations between pieces. We have used scan path and fixation data from players engaged in solving chess problems, to compute 6600 saliency maps associated to the corresponding chess piece configurations. This corpus is completed with synthetically generated data from actual games gathered from an online chess platform. Experiments realized using both scan-paths from chess players and the CAT2000 saliency dataset of natural images, highlights several results. Deep features, pretrained on natural images, were found to be helpful in training visual attention prediction for chess. The proposed neural network architecture is able to generate meaningful saliency maps on unseen chess configurations with good scores on standard metrics. This work provides a baseline for future work on visual attention prediction in similar contexts

Signal Integrity Optimization of RF/Microwave Transmission Lines in Multilayer PCBs

While allowing for flexible trace routing and device miniaturization, multilayer printed circuit boards (PCB) suffer from performance issues at high frequency due to the impedance mismatch caused by vertical transitions. In this paper, a process for optimizing the high-speed performance of microstrip to stripline transitions in multilayer PCBs is demonstrated. This includes strategic tuning of via dimensions using time-domain reflectometry and an analysis of the use of shielding vias to prevent parasitic cavity resonance. Simulations of optimized 2-layer, 4-layer, and 6-layer microstrip to stripline transitions show a return loss of 20 dB up to 7 GHz. To demonstrate a useful microwave application, a planar filter with a passband of 4 GHz to 6 GHz is submerged 6-layers. The simulation shows that when paired with the optimized vertical transitions, the filter can maintain performance

Phase transition of interacting disordered bosons in one dimension

Interacting bosons generically form a superfluid state. In the presence of disorder it can get converted into a compressible Bose glass state. Here we study such transition in one dimension at moderate interaction using bosonization and renormalization group techniques. We derive the two-loop scaling equations and discuss the phase diagram. We find that the correlation functions at the transition are characterized by universal exponents in a finite region around the fixed point.Comment: five pages and two pages and one figur

Smart Bottle Firmware and Wireless Charging Integration

In the past 3 decades, there has been a 60% increase of infants under the age of 2 who are considered obese [1]. The California Polytechnic State University, San Luis Obispo Child Development Department wants to research parents feeding habits and the Smart Bottle tool was built to aid this effort in finding the relationship between infant obesity and parent feeding habits. The Smart Bottle is a baby bottle meter attached to the bottom of the bottle. The goal of this device is to analyze the state of the feeding the parent is currently at and measure the amount of food the baby has consumed. The most recent iteration of the Smart Bottle (V5) is fully functional and attachable to infant feeding bottles to collect feeding data stored via SD card. V5 also implements a Bluetooth module for wireless data transfer, however, it does not have a User Interface. This project looks to build Smart Bottle (V6) which implements a wireless charging module integrated alongside a User Interface (UI). The wireless charging allows for a fully sealed enclosure reducing the risk of any electrical hazards whereas the UI allows for faster duplex communication. Wireless charging will be implemented through inductive charging, a wireless energy transfer that uses electromagnetic induction to generate power. This project allows for the Child Development researchers to have a convenient method of data extraction while also protecting the consumers

Observing Quantum Tunneling in Perturbation Series

We apply Borel resummation method to the conventional perturbation series of ground state energy in a metastable potential, $V(x)=x^2/2-gx^4/4$. We observe numerically that the discontinuity of Borel transform reproduces the imaginary part of energy eigenvalue, i.e., total decay width due to the quantum tunneling. The agreement with the exact numerical value is remarkable in the whole tunneling regime 0.Comment: 12 pages, 2 figures. Phyzzx, Tables.tex, The final version to appear in Phys. Lett.

Effect of Fluorine on Near-Liquidus Phase Equilibria of Basalts

Volatile species such as H2O, CO2, F, and Cl have significant impact in generation and differentiation of basaltic melts. Thus far experimental work has primarily focused on the effect of water and carbon dioxide on basalt crystallization, liquid-line of descent, and mantle melting [e.g., 1, 2] and the effects of halogens have received far less attention [3-4]. However, melts in the planetary interiors can have non-negligible chlorine and fluorine concentrations. Here, we explore the effects of fluorine on near-liquidus phase equilibria of basalt. We have conducted nominally anhydrous piston cylinder experiments using graphite capsules at 0.6 - 1.5 GPa on an Fe-rich model basalt composition. 1.75 wt% fluorine was added to the starting mix in the form of AgF2. Fluorine in the experimental glass was measured by SIMS and major elements of glass and minerals were analyzed by EPMA. Nominally volatile free experiments yield a liquidus temperature from 1330 C at 0.8GPa to 1400 at 1.6GPa and an olivine(Fo72)-pyroxene(En68)-liquid multiple saturation point at 1.25 GPa and 1375 C. The F-bearing experiments yield a liquiudus temperature from 1260 C at 0.6GPa to 1305 at 1.5GPa and an ol(Fo66)-pyx(En64)-MSP at 1 GPa and 1260 C. This shows that F depresses the basalt liquidus, extends the pyroxene stability field to lower pressure, and forces the liquidus phases to be more Fe-rich. KD(Fe-Mg/mineral-melt) calculated for both pyroxenes and olivines show an increase with increasing F content of the melt. Therefore, we infer that F complexes with Mg in the melt and thus increases the melt s silica activity, depressing the liquidus and changing the composition of the crystallizing minerals. Our study demonstrates that on a weight percent basis, the effect of fluorine is similar to the effect of H2O [1] and Cl [3] on freezing point depression of basalts. But on an atomic fraction basis, the effect of F on liquidus depression of basalts is xxxx compared to the effect of H. Future studies on kimberlitic and subduction zone magmas, which could have significant amount of fluorine, will need to consider the combined effects of F, Cl, and H on their stability and chemical evolution