VictorJuiz/thesis huggingface dataset:
Germanium-Source Tunnel Field Effect Transistors for Ultra-Low Power Digital Logic

Driven by a strong demand for mobile and portable electronics, the chip market will undoubtedly impose "low power" as the key metric for microprocessor design. Although circuit and system level methods can be employed to reduce power, the fundamental limit in the overall energy efficiency of a system is still rooted in the Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) operating principle and its immutable physics: an injection of thermally distributed carriers will not allow for switching characteristics better than 60 mV/dec at room temperature. This constraint ultimately defines the lowest energy consumed per digital operation attainable with current Complementary-Metal-Oxide-Semiconductor (CMOS) technology./n/nIn this work, Tunnel Field Effect Transistor (TFET) based on Band-to-Band Tunneling (BTBT) will be proposed and investigated as an alternative logic switch which can achieve steeper switching characteristics than the MOSFET to permit for lower threshold (V TH ) and/nsupply voltage (V DD ) operation. It will be experimentally demonstrated that by employing Germanium (Ge) only in the source region of the device, a record high on to off current ratio (I ON /I OFF ) can be obtained for 0.5 V operation. Technology Computer Aided Design (TCAD) calibrated to the measured data will be used to perform design optimization study. The performance of the optimized Ge-source TFET will be benchmarked against CMOS technology to show greater than 10x improvement in the overall energy efficiency for frequency range up to 500 MHz. The fundamental challenges associated with TFET-based digital logic design will be addressed. In order to mitigate these constraints, a circuit-level solution based on n-channel TFET Pass-Transistor Logic (PTL) will be proposed and demonstrated through mixed-mode simulations. The accompanying design modifications required at the device level will be discussed.

Advisor: King Liu, Tsu-Jae
Using lidar in wildfire ecology of the California Sierra-Nevada forests

California's fire suppression policy has dramatically changed Sierra Nevada forests over the last century. Forests are becoming more dense and homogenous, leading to fire regime changes that increase the potential of stand-replacing wildfires over large, continuous areas. To mitigate this problem on public lands, the US Forest Service has proposed to implement strategically placed forest fuel reduction treatments. These treatments have been proved effective in modeled and simulated environments, but their efficacy and impact in real forests is not known. The research described in this dissertation is part of a large multidisciplinary project, known as the Sierra Nevada Adaptive Management Project (SNAMP), that aims to evaluate strategically placed landscape area treatments (SPLATs) in two forests of the Sierra Nevada mountains. Specifically, in this thesis, I investigate the feasibility of using an airborne light detection and ranging (lidar) system to gain accurate information about forest structure to inform wildfire behavior models, forest management, and habitat mapping./nFirst, I investigate the use of lidar data in predicting metrics at the landscape level, specifically to derive surface fuel models and continuous canopy metrics at the plot scale. My results in Chapter 2 indicate that using lidar to predict specific fuel models for FARSITE wildfire behavior model is challenging. However, the prediction of more general fuel models and continuous canopy metrics is feasible and reliable, especially for metrics near the top of the canopy./nIt is also possible to derive canopy parameters at the individual tree level. In Chapter 3, I compare the ability of two processing methods--object-based image analysis (OBIA) and 3D segmentation of the lidar point cloud--to detect and delineate individual trees. I find that while both methods delineate dominant trees and accurately predict their heights, the lidar-derived polygons more closely resemble the shape of realistic individual tree crowns./nAcquiring remotely sensed data at high resolution and over large areas can be expensive, especially in the case of lidar. In Chapter 4, I investigate the ability of lidar data to reliably predict forest canopy metrics at the plot level as the data resolution declines. I show that canopy metrics can be predicted at a reasonable accuracy with data resolutions as low as one pulse per squared meter. These findings will be useful to land managers making cost benefit decisions when acquiring new lidar data./nCollectively, the results of this dissertation suggest that remote sensing, and in particular lidar, can reliably and cost-effectively provide forest information across scales--from the individual tree level to the landscape level. These results will be useful for the fire and forest management community in general, as well as being key to the goals of the SNAMP program.

Advisor: Kelly, Maggi
Thermodynamics, Entropy, Information and the Efficiency of Solar Cells

For well over 50 years, the limits to photovoltaic energy conversion have been known and codified, and have played a vital role in the push for technological breakthroughs to reach - and even attempt to surpass - those limits. This limit, known as the Shockley-Queisser detailed-balance limit, was found by using only the most basic of thermodynamic assumptions, and therefore provides an upper bound that is difficult to contest without violating the laws of thermodynamics. Many different schemes have been devised to improve a solar cell's efficiency beyond this limit, with various benefits and drawbacks for each method./n/nSince the field of solar cell research has been analyzed and dissected for so long by a large variety of researchers, it is quite hard to say or discover anything new without repeating the work of the past. The approach taken in this work is to analyze solar cells from the joint perspective of thermodynamics and information theory. These two subjects have recently been appreciated to be highly interrelated, and using the formalism of Missing Information, we can differentiate between different novel technologies, as well as devise new limits for new and existing methodologies./nIn this dissertation, the fundamentals of photovoltaic conversion are analyzed from the most basic of principles, emphasizing the thermodynamic parameters of the photovoltaic process. In particular, an emphasis is made on the voltage of the device, as opposed to the current. This emphasis is made since there is a direct relation between the open-circuit voltage of a solar cell and the fundamental equations of thermodynamics and the Free Energy of the system. Moreover, this relation extends to the entropy of the system, which subsequently relates to the field of Information Theory. By focusing on the voltage instead of the current, realizations are made that are not obvious to the majority or researchers in the field, and in particular to efforts of surpassing the Shockley-Queisser limit, known as "3rd generation" concepts./nAfter analyzing the standard single-junction cell, other forms of surpassing the detailed-balance limit are presented and discussed, from the viewpoint of entropy and its relation to the amount of information lost or produced in the photovoltaic conversion process. In addition to the well-known 3rd generation methods: up- and down-conversion, carrier multiplication and intermediate band solar cells, other ideas are discussed such as using Feedback to shift the optimal bandgap of the cell, and the use of spectral splitting to completely utilize the solar spectrum. The focus on entropy (and the open-circuit voltage) as the primary variable of interest uncovers new limitations to these processes, and denotes preferences of certain technologies over others./nUsing this parallel approach provides insights into the field that were either neglected or not realized. This work thus provides a new set of guidelines for searching for and analyzing innovative techniques to maximize the power conversion efficiency from solar cells.

Advisor: Zhang, Xiang
Traffic Signal Optimization with Transit Priority: A Person-based Approach

Traffic responsive signal control with Transit Signal Priority (TSP) is a strategy that is increasingly used to improve transit operations in urban networks. However, none of the existing real-time signal control systems have explicitly incorporated the passenger occupancy of transit vehicles in granting priority or have effectively addressed issues such as the provision of priority to transit vehicles traveling in conflicting directions at signalized intersections. The contribution of this dissertation is the development of a person-based traffic responsive signal control system with TSP that minimizes total person delay in a network by explicitly considering all vehicles' passenger occupancy and transit schedule delay. By using such conditions, the issue of assigning priority to transit vehicles traveling in conflicting directions is also addressed in an efficient way. In addition, the impact of these priority strategies on auto traffic is addressed by minimizing the total person delay in the network under consideration and assigning penalties for interrupting the progression of platoons on arterials. The system is first developed for isolated intersections, and then extended to arterial signalized networks. Evaluation tests for a wide range of traffic and transit operating characteristics show that significant reductions in transit passenger delay can be achieved without substantially increasing auto passenger delay. Furthermore, the system achieves lower vehicle delays compared to signal settings obtained by state-of-the-art signal optimization software. Finally, it utilizes readily deployable technologies, which provide real-time information such as sensors, Automated Vehicle Location and Automated Passenger Counter systems and can be implemented on existing infrastructure in urban multimodal networks.

Advisor: Skabardonis, Alexander
Between Mission and Market: The Contested Commodification of Hospital Care

While hospital care has become more market-driven over the last thirty years, the commodification of hospital care remains uneven and incomplete. There are three persistent problems with the commodification of hospital care: our understanding of hospital care as a right; our understanding of hospital care as connected to moral values and emotional commitments; and our uncertainty about the value of care. Using participant observation and in-depth interviews across three not-for-profit hospitals in the same California city, I demonstrate how the commodification of hospital care is contested but done so differently, and with different effects, in different organizational contexts.

Advisor: Burawoy, Michael
Making Computer Vision Computationally Efficient

Computational requirements for computer vision algorithms have been increasing dramatically at a rate of several orders of magnitude per decade. In fact, the growth in the size of datasets and computational demands for computer vision algorithms are outpacing Moore's law scaling. Meanwhile, parallelism has become the major driver of improvements in hardware performance. Even in such a scenario, there has been a lack of interest in parallelizing computer vision applications from vision domain experts whose main concern has been productivity. We believe that ignoring parallelism is no longer an option. Numerical optimization and parallelization are essential for current and future generation of vision applications to run on efficiently on parallel hardware./nIn this thesis, we describe the computational characteristics of computer vision workloads using patterns. We show examples of how careful numerical optimization has led to increased speedups on many computer vision and machine learning algorithms including support vector machines, optical flow, point tracking, image contour detection and video segmentation. Together, these application kernels appear in about 50\% of computer vision applications, making them excellent targets for focusing our attention. We focus our efforts on GPUs, as they are the most parallel commodity hardware available today. GPUs also have high memory bandwidth which is useful as most computer vision algorithms are bandwidth bound. In conjunction with the advantages of GPUs for parallel processing, our optimizations (both numeric and low-level) have made the above mentioned algorithms practical to run on large data sets. We will also describe how these optimizations have enabled new, more accurate algorithms that previously would have been considered impractical. In addition to implementing computer vision algorithms on parallel platforms (GPUs and multicore CPUs), we propose tools to optimize the movement of data between the CPU and GPU./nWe have achieved speedups of 4-76x for support vector machine training, 4-372x for SVM classification, 37x for large displacement optical flow and 130x for image contour detection compared to serial implementations. A significant portion of the speedups in each case was obtained from algorithmic changes and numerical optimization. Better algorithms have improved performance not only on manycore platforms like GPUs, but also on multicore CPUs. In addition to achieving speedups on existing applications, our tool for helping manage data movement between CPU and GPU has reduced runtime by a factor of 1.7-7.8x and the amount of data transferred by over 100x. Taken together, these tools and techniques serve as important guidelines for analyzing and parallelizing computer vision algorithms.

Advisor: Keutzer, Kurt W
Human Remains and the Construction of Race and History, 1897-1945

This dissertation examines the use of human remains as tools for research and display over the course of a fifty-year span in the United States. It explores the shift away from racial classification toward emerging ideas regarding human prehistory and evolution. This project serves as both an intellectual history of the discourse surrounding these remains and a cultural history of the exhibitions that millions of visitors encountered at museums and fairs throughout much of the nineteenth and twentieth century.

Advisor: Candida Smith, Richard
Is the California Special Education Achievement Gap Really Closing?

The achievement gains of California students with disabilities (SWDs) on the California Standards Test (CST)-English Language Arts have surpassed the achievement gains of students with no reported disabilities in recent years, and so the special education achievement gap seemed to be closing. However, the reported achievement for SWDs has not factored in changes in the tested student populations that resulted from the introduction of modified assessments, suggesting that the achievement gains of SWDs are misleading. In this descriptive study I identified population characteristics of California students taking the various assessments and described how achievement trends were influenced as alternative assessment tests were implemented and as the numbers of students taking the various Standardized Testing and Reporting (STAR) tests fluctuated. Using published data from the California Department of Education STAR and DataQuest systems, I developed scenarios to evaluate the achievement of SWDs by exploring how the reported results were affected by changes in tested populations. I found that the achievement of all SWDs has improved only marginally and that the achievement gap has continued to increase, contradicting reports that the gap is closing. I also examined whether tests have been administered to appropriate students, according to NCLB regulations, and identified weaknesses in the federal regulations and state guidelines associated with assessing SWDs. I anticipate that these findings will provide policymakers, administrators, and special educators with a better assessment of the progress made by the State of California toward closing the special education achievement gap and of the consequences of current regulations, guidelines, and practices.

Advisor: Worrell, Frank C
The CRISPR endoribonuclease Csy4 utilizes unusual sequence- and structure-specific mechanisms to recognize and process crRNAs

Many prokaryotes contain clustered regularly interspaced short palindromic repeats (CRISPRs) that together with CRISPR-associated (cas) genes confer resistance to invasive genetic elements. Central to this immune system is the production of CRISPR-derived RNAs (crRNAs) via enzymatic cleavage of CRISPR locus transcripts. These crRNAs serve as guides for foreign nucleic acid targeting and degradation./nHere we identify Csy4 as the endoribonuclease responsible for CRISPR transcript processing in Pseudomonas aeruginosa UCBPP-PA14. Biochemical assays and six co-crystal structures of Csy4 bound to substrate and product crRNAs reveal the complex mechanisms Csy4 utilizes to recognize, position, and cleave its cognate RNA substrate in order to generate mature crRNAs. Csy4 makes sequence-specific contacts to the major groove of its cognate RNA stem-loop and makes extensive electrostatic interactions with the phosphate backbone that are highly sensitive to the helical geometry of the substrate, resulting in an extremely high affinity binding interaction (Kd ≈ 50 pM). Csy4 has equally tight affinity for both its substrate and product RNAs and therefore functions in vivo as a single turnover catalyst. Phylogenetically conserved serine and histidine residues constitute a catalytic dyad in which the serine pins the ribosyl 2′-hydroxyl nucleophile in place, allowing the histidine to deprotonate the active site 2′-hydroxyl, leading to nucleophilic attack on the scissile phosphate. The Csy4 active site lacks a general acid to protonate the leaving group and positively charged residues to stabilize the transition state, explaining why the observed catalytic rate constant is ~104-fold slower than that of RNase A. The RNA cleavage step carried out by Csy4 is essential for assembly of the Csy protein-crRNA complex that facilitates target recognition. Considering that Csy4 recognizes a single cellular substrate and subsequently sequesters the cleavage product, evolutionary pressure has likely selected for substrate specificity and high-affinity crRNA interactions at the expense of rapid cleavage kinetics./nA major goal of synthetic biology is to construct reliable and predictable genetic circuits. However, synthetic genetic systems often perform unpredictably due to structural interactions between DNA, RNA, and protein components. Here we present a novel synthetic RNA processing platform utilizing Csy4 and its cognate target RNA to physically separate otherwise linked genetic elements such as promoters, ribosome binding sites, cis regulatory elements, and riboregulators. Implementation of this platform provides a general approach for creating context-free standard genetic elements that can be readily applied to the bottom-up construction of increasingly complex biological systems in a plug-and-play manner.

Advisor: Doudna, Jennifer A
Computational Trade-offs in Statistical Learning

The last several years have seen the emergence of datasets of an unprecedented scale, and solving various inference problems on such datasets has received much focus in the modern machine learning and statistics literature. Understanding the statistical and computational aspects of learning from these large and high-dimensional datasets is the key focus of this thesis. One source of such data is the virtually unbounded amounts of unstructured information on the internet, which can often be compiled into large datasets for training machine learning algorithms using automated techniques, computer vision, natural language tasks, and web search and ranking being prime examples. Computational biology, computational astronomy and collaborative filtering are some other examples of problem domains where vast amounts of training data for learning algorithms are often readily available./n/nIt might be expected that the large number of samples make the statistical problem easier, and subsampling a smaller dataset should be an effective computational strategy. However, this is not always the case. With access to more and more data, the goal is often to understand higher order dependencies in the data, leading to an explosion in the number of parameters to be estimated. Indeed a large body of literature in modern machine learning and statistics is focusing on problems where the number of parameters grows with, and is often larger than the number of samples./nWhile the number of training samples and parameters seems to grow almost unboundedly, computation is struggling to keep up. The last few years have seen a clear tapering off in the rate of growth of computational power on individual cores. As a result, the algorithmic focus in machine learning has shifted to online and stochastic optimization algorithms, budgeted learning algorithms and parallel and distributed algorithms fit for multicore, cluster or cloud environments./nThough it might seem that computation is the main bottleneck for such massive data problems, it is not appropriate to consider these problems as purely computational and ignore the underlying statistical structure. In fact, in many cases the statistical nature of the data can make the computational problem easier, and make the goals of computation simpler than needed without this structure. As a result, a fundamental question in modern statistics and machine learning is to understand the error of statistical estimators, as a function of the sample size, number of parameters and the computational budget available./nThe goal of this thesis is to study these trade-offs between the computational and statistical aspects of learning problems. This line of research results in several natural questions, some of which are partially addressed in this thesis and others present interesting challenges for future work.

Advisor: Bartlett, Peter L
Wainwright, Martin J
Band-to-Band Tunneling Transistors: Scalability and Circuit Performance

Continuing scaling of transistors as density approaches the terascale regime (1012 devices/cm2) requires evaluating new devices that can perform on several metrics beyond density scaling, such as cost savings, performance improvements, and energy efficiency. A comprehensive review and evaluation of potential new devices is performed. Metrics such as processing cost, plan-view area scaling, and stage delay are benchmarked. One of the most promising devices, tunneling field effect transistors, is also the most confounding, as simulation and experimental results are orders of magnitude apart./nTo better understand and evaluate tunnel field effect transistors (TFETs), a new TCAD analysis tool with dynamic nonlocal tunneling path determination is calibrated to experimental data. From this calibrated model, an optimal source design for TFETs is found where a moderate doping concentration (~1019 cm-3) is found to be preferable to the higher doping concentrations more commonly used. Following this optimization, a study is performed to find the minimum device size, or the ultimate scalability, of TFETs. Using a raised source design allows TFETs to have a minimum device pitch (including contacts) of 29 nm./nA higher level of analysis is performed at the circuit level, where a Verilog-A based lookup table approach is used to evaluate the circuit performance of TFETs. Inverters, ring oscillators, SRAM, and Register Files are benchmarked and compared to UTB and FinFET technologies. TFETs are found to have advantages over standard CMOS for stage delays slower than 100ps in logic and for the 0.25V - 0.4V range in memory cells.

Advisor: King Liu, Tsu-Jae