VictorJuiz/thesis huggingface dataset:
Demographic and selective inference in great apes [electronic resource]
Keywords: Biology.

Humans and other great apes have intricate evolutionary histories, shaped by numerous demographic and selective events. A wealth of new genomic data provides the foundation for key insights into some of this complexity. This thesis develops tools for population genetic analyses, provides genome-wide insights into gorilla population genetics, and delivers fine-scale understanding of a specific human disease resistance allele. The chapters of this thesis provide complementary genome-wide and localized insights into great ape evolutionary history. Chapter 2 details a genetic simulation software that generates large-scale genetic data in flexible scenarios. Chapter 3 describes a study on the demographic and selective history of gorillas; we find a significant population contraction during the last glacial maximum (20 kya) in western lowland gorillas and evidence of positive selection in genes related to heart function and bitter taste perception. Chapter 4 is an in-depth analysis of one of the most population differentiated loci in the human genome, the DARC gene, which is known to provide resistance to a malaria-causing protozoan. We explore this gene's population genetics, including its geographical distribution and signatures of natural selection, as well as infer the mode and magnitude of selection on the malaria resistance allele. These chapters emphasize the importance of population genetics at various scales. Genome-wide methods allow identification of global trends, while in-depth analyses of specific loci provide insights into specific events and selective pressures shaping our genomes, and complement our large-scale inferences. This dissertation develops generalizable methods, achieves novel biological insights, and broadens our understanding of great ape evolutionary history.
Design and development of data acquisition electronics for a 1 mm resolution clinical Positron Emission Tomography (PET) system [electronic resource]
Keywords: Electrical Engineering.

Positron Emission Tomography (PET) is a functional medical imaging modality often used for cancer imaging. A typical clinical PET scanner will have a resolution greater than 5 mm. This resolution cannot resolve small cancerous lesions and may cause some lesions to be missed. This dissertation describes the design of a 1 mm3 resolution breast-dedicated clinical PET scanner which will provide the ability to identify smaller lesions and improve breast cancer screening or treatment. The PET scanner achieves this resolution by decreasing the dimensions of the PET scintillation detection elements to 1x1x1 mm3, creating a 3D position sensitive scintillation detector system, and placing the panels up against the breast. The scanner design uses 4608 basic detector units, each of which comprise position sensitive avalanche photodiodes (PSAPDs) coupled to 8x8 arrays of 1x1x1 mm3 LYSO scintillation crystal elements, requiring 18,432 readout channels. This dissertation details and evaluates the electronic architecture required to synchronize and readout the signals from the densely packed detectors. It also describes the signal processing performed on the signals to maximize the capability of the system.
Design and fabrication of nanofluidic transistors beyond Debye screening limit [electronic resource]
Keywords: Electrical Engineering.

The development of microfluidic systems, which miniaturize complex laboratory pro- cedures onto small microchips, has dramatically reduced the cost and time of biologi- cal analyses. With fluidic devices scaling down to nanometer regime, new phenomena that were previously inaccessible at larger length scales arise. Particularly, electro- static force can now be used to control ionic and biomolecule transport in nanofluidic devices. Nanofluidic devices using an active gate to manipulate charged molecules called nanofluidic transistors by analogy to MOSFET transistors, have applications ranging from sample preparation in point-of-care devices to sample sorting, and sea- water desalination. In ionic solutions, the Debye Length is considered as the characteristic length scale for effective field-effect control. Hence, the state-of-the-art nanofluidic transis- tors presented in the literature have required at least one dimension comparable to or smaller than Debye Length (sub-10 nm--30 nm) to achieve sufficient electrostatic control. This limit imposes challenges and difficulties on device fabrication. Simu- lations based on coupled Poisson-Nernst-Plank-Stokes models show that strong ionic transport can overcome the Debye-screening limits, thus leading to field-effect control beyond Debye Length. This thesis therefore focuses on the design and fabrication of nanofluidic transistors with channel size 100 nm to 200 nm, far beyond Debye Length limits (typically 10 nm in 1 mM concentration solutions), while achieving efficient field effect control. Further, a novel dual-gate configuration is introduced to improve nanofluidic transistors' current modulation ratio. Two possible device structural configurations—vertical channel and lateral channel devices—are proposed and compared with regards to their fabrication feasibilities and performances; both fabrication flows are compatible with conventional semicon- ductor fabrication technologies. The measurement results of NFTs beyond Debye- screening limit demonstrate highly effective electrostatic control of ions. In vertical channel devices, current modulation rate is 6-fold by using 1 V bias; in lateral channel devices, 2.6X current modulation rate is achieved by ∼10 V bias. Both results are better than the conventional Debye Length limited nanofluidics transistors. The nanofluidic transistors beyond Debye-screening limit demonstrate a new op- erating regime. The relaxed constraint on channel sizes offers advantages in device manufacturing, testing, and reliability. Based on standard process flows for fabrica- tion, this work also opens up the possibilities of on-chip fluid system integration as well as new applications in biological sensing and sample preparation.
The design and implementation of large-scale graph analysis language [electronic resource]
Keywords: Electrical Engineering.

With the rise of worldwide social networking services, large-scale graph analysis has become important. Because of a lack of high-level programming models, MapReduce or Pregel are used for large-scale graph analysis, but require much effort to implement even simple analyses. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to imperative programming languages. With SociaLite, users can provide simple annotations to the data layout and evaluation order; they can also define recursive aggregate functions, which can be evaluated incrementally and efficiently. Moreover, SociaLite is extended to provide high-level abstractions for distributed computation. These extensions allow users to simply annotate how data is to be distributed; then SociaLite compiler automatically generates parallel code for a cluster of multi-core machines. The evaluation of recursive aggregate functions is optimized with a delta-stepping technique on a distributed cluster. In addition, approximate computation is supported in SociaLite, allowing users to trade off accuracy for less execution time and storage space. We evaluated SociaLite with core graph algorithms, including shortest paths and PageRank, that are commonly used in graph analyses. With its optimizations to Datalog, SociaLite is shown to be much faster than Datalog, and as fast as highly optimized Java. When evaluated on a multi-core machine, SociaLite programs scaled linearly up to 16 cores, except for the shortest paths program which showed a speedup of 10 on 16 cores. In our experiment with 64 Amazon EC2 instances, SociaLite programs tracked the ideal weak scaling curve within a factor of two. Compared to Giraph, an open-source version of Pregel, SociaLite programs are 4 to 12 times faster across benchmark algorithms and 22 times more succinct on average. We also extensively evaluated state-of-the-art graph systems, GraphLab, Combinatorial BLAS, as well as Giraph, and compared their programmability and performance with SociaLite. From the evaluation, we found that SociaLite provides the simplest programming model; for BFS (Breadth First Search) algorithm, the SociaLite program is 10 times more succinct than the equivalent programs in other systems. SociaLite also demonstrated competitive performance; among the compared systems, SociaLite is second fastest after Combinatorial BLAS. Most importantly, as a high-level query language, SociaLite makes it easy for users with little programming background to write many sophisticated graph applications, that are automatically optimized to run on a cluster of machines efficiently.
Design and implementation of The VLF wave and particle precipitation mapper sensor suite [electronic resource]
Keywords: Electrical Engineering.

The VLF Wave and Particle precipitation Mapper (VPM) Sensor Suite will serve as a complement to ongoing DoD programs aimed at qualification of controlled precipitation of energetic electron radiation by ELF/VLF whistler-mode waves. Two benefitting programs are the Air Force Demonstration and Science Experiment (DSX) satellite mission, and the High frequency Active Auroral Research Program (HAARP). New capabilities provided by VPM are afforded by a dual detector configuration that provides sensitive measurements of energetic electrons both just-inside and just-outside the bounce loss cone, and by wave receivers with GPS-based timing facilitating interferometric measurements of electron density irregularities. The VPM sensor suite will make use of novel VLF micro-receivers consisting of radiation-hardened (up to a 2 Mrad) Low Noise Amplifier (LNA), Anti-Aliasing Filter (AAF), and Analog-to-Digital Converter (ADC) application specific integrated circuits (ASICs) developed over the past 6 years in the context of three student PhD theses. These devices provide the capability to conduct ELF/VLF observations at extremely low power and weight, while maintaining the high degree of sensitivity, resolution and dynamic range required in such measurements. Research areas of VPM will focus on determining energy spectra, temporal signatures, and spatial extent of energetic electron precipitation bursts induced by discrete whistler-mode waves, launched by ground-based sources: (i) lightning discharges, (ii) VLF transmitter signals (e.g., NPM, NAA, etc.), and (iii) ELF/VLF waves injected by modulated HF heating (i.e., HAARP). VPM will measure waves in broadband fashion so as to identify those from different sources, with high time and frequency resolution. It will simultaneously measure energetic particles with high sensitivity (large geometric factors), high time and energy resolution, so as to assess cause and affect relationships and determine calibrated theoretical models (by determining the flux levels that result from given levels of wave power density). VPM will also measure transmitted VLF wave intensity from the DSX/WIPER transmitter and determine transmitter efficiency, effective plasma sheath impedance and confirm dynamics of transmission process.
Design of large scale nudge engines [electronic resource]
Keywords: Electrical Engineering.

Congestion is a widespread problem in modern urban transportation networks; hundreds of billions of dollars are lost each year due to wasted time, extra fuel consumption, traffic accidents, etc. A significant fraction of these losses is due to peak-hour congestion on road networks, which occurs when the demand for transport exceeds capacity over a short time period each day. In this thesis, we explore the feasibility of reducing peak-hour road traffic congestion using incentives to shift drivers' commute times. We first discuss a practical implementation of such an incentive program — CAPRI (Congestion And Parking Relief Incentives). This program aimed to reduce peak-hour vehicular traffic into and out of Stanford University. Commuters who sign up for CAPRI earn points for the "good trips" they make, and these points can be redeemed for rewards (both monetary and in-kind). CAPRI also includes the capability to personalize incentives based on users' historical behavior. To complement the implementation, we develop a theoretical model for optimally reducing the cost of peak-hour congestion with targeted incentives. We study the evolution of congestion on a highway under time-varying demands using fluid models of traffic flow. We then examine the effect of shifting users' commute times on congestion. We show that ideas from the theory of optimal transport of measures can be used to develop cost-effective incentive schemes to reduce congestion. Specifically, we show that the "cost of congestion" and the "cost of nudging" are closely related to the Wasserstein distance between measures. We use this relationship to formulate linear programming problems to compute personalized recommendations and incentives to nudge drivers to the off-peak hour. We find that the resultant reduction in the cost of congestion is significant and that it helps to prioritize commuters for nudging based on their origin and destination.
Design, simulation, and fabrication of nanostructures for optoelectronic applications [electronic resource]
Keywords: Materials Science and Engineering.

Improving the efficiency of semiconductor optoelectronic devices is a central pursuit on the path to affordable, universal access to solar power, energy-efficient lighting, sensitive photodetectors, and vibrant display technologies. In all of these applications, light must pass through an interface between the device and the outside world. Nanostructures on the scale of the wavelength of light offer a powerful way to control the flow of light through this interface, which could enable devices with new functionality and dramatically improved optical efficiency. However, nanostructured surfaces also present some interesting design challenges. Here, I will present three case studies illustrating the promise and challenges of nanostructured interfaces in optoelectronic applications. First, I will present a camera sensor architecture that is able to capture the full light field from a scene, enabling 3D image reconstruction and focusing after the photo is taken. By stacking polymer microspheres on silicon nanoshells, we can focus and confine light in a very small volume. Using this coupled system, we are able to resolve the angle of light incident at up to 40 degrees away from the normal. By contrast, microlenses on a planar substrate can only resolve angles up to 10 degrees away from the normal. Second, I will discuss vacuum photocathodes, which are currently used in low-light detectors and electron sources and have the potential to be used in solar energy generation systems. I will present a simulation suite that models the optical and electronic behavior of photocathodes, offering new insight into nanostructured architectures that should maximize electron yield. In particular, the anti-reflection properties of the surface must be carefully balanced against nanostructured profiles that block electron emission. I will also present measurements we made of photoemission from nanostructured photocathodes to highlight the importance of developing interfaces with low surface recombination and to motivate a study on the passivation of gallium arsenide using gallium nitride. Third, I will describe a new hybrid optoelectronic interface that offers both high light transmission and high electrical conductivity. We use metal-assisted chemical etching (MACE) to create a silicon substrate that is covered by a gold film with protruding silicon nanopillars. When coated with a silicon nitride anti-reflection layer, we observe up to 97% absorption in this structure, which is remarkable considering that metal covers 60% of the top surface. We use optical simulations to show that Mie-like resonances in the nanopillars funnel light around the metal layer and into the substrate, offering a general paradigm for creating ultra-transparent metal contacts for solar cells, displays, and more.
Detection and analysis of the electromagnetic pulse from hypervelocity impact plasma expansion [electronic resource]
Keywords: Aeronautics and Astronautics.

Hypervelocity dust particles, including meteoroids and space debris, routinely impact spacecraft and produce plasmas that are initially dense (10^25/m^3), but rapidly expand into the surrounding vacuum. This research was the first to detect radio frequency (RF) emission associated with electromagnetic pulses (EMPs) from hypervelocity dust particle impacts in ground-based experiments. These micro particles that produced RF are 15 orders of magnitude less massive than previously observed. We find that one mechanism for EMP production is the result of the growth of the unstable Weibel mode in the plasma and that the EMP detection rate is strongly dependent on impact speed and on the electrical charge conditions at the impact surface. This mode grows from a plasma temperature anisotropy driven by the impact ionization process combined with the external electric field. Upon expansion, the Weibel mode grows, producing RF emission within nanoseconds. In particular, impacts of the fastest particles (speed > 15 km/s) occurring under spacecraft charging conditions representative of high geomagnetic activity are the most likely to produce RF emission. The sensor design and analysis that led to this discovery is discussed. This phenomenon may provide a source for unexplained RF measurements and satellite electrical anomalies.
Detection of atypical patterns of occupancy and mobility in smart homes and offices with a network of motion detectors [electronic resource]
Keywords: Electrical Engineering.

Ambient sensors are a category of sensing technologies designed to be embedded in an environment for the purposes of monitoring changes to that environment from either internal or external sources, such as occupants or the weather, respectively. The goal of this dissertation is to study aspects of the behavior and wellbeing of occupants through unobtrusive and non-invasive ambient motion sensors. We focus on passive versions of motion sensors, such as Passive InfraRed (PIR) motion detectors, which have been utilized by researchers for the recognition of Activities of Daily Living (ADL) in homes. Motion sensors are also used in office settings, where they act as occupancy sensors to better control HVAC and lighting systems to save energy. We begin by discussing the types of information that can be extracted from a dense network of PIR sensors in a home, focusing on indicators of wellness. We then discuss methods for extracting occupancy based features from dense motion sensor deployments that represent an occupant's behavior. We demonstrate how these occupancy based features exhibit a strong correlation with features derived from human annotated activities for multiple long term smart home datasets. From this we reason that our derived features can be used as a proxy for human labeled activities for certain applications such as atypical behavior detection. We then present a method to use occupancy based features to find days that are atypical, and compare our results with a classifier based on human annotated activities. Lastly we discuss how low level mobility features can be extracted from a network of motion sensors to form a mobility graph, which can be used to summarize the aggregate mobility of the occupants of an environment. We show how this mobility graph can be visualized to represent how occupants move between sensors and how changes in the mobility graph over time can be quantified. In a home environment, we show how significant changes in this mobility graph correlate with changes in the occupant's underlying behaviors. In an office environment, we show how changes in the mobility graph can be related to global events, like fire alarms.
Developed through Social Processes? Chinese Women’s Beliefs about Education, Family, and Career
Keywords: gender beliefs
proximate processes
academic choices
women’s careers
education in China
mother’s family roles
social psychology
Stanford Graduate School of Education International Comparative Education
Thesis

In China, gender gaps in academic majors and career aspirations have persisted despite decades of institutional efforts to improve gender equality. While most studies attribute this phenomenon to external factors such as gender discrimination, others point to a need for exploring Chinese women’s beliefs towards education and career. Through 16 in-depth qualitative interviews conducted with women studying or staying in the United States, the present study looks into the possible proximate processes through which Chinese women internalize stereotypical gender beliefs. Two gender beliefs that emerged from the analysis are that boys perform better than girls in mathematics and science and that mothers should contribute more to their children’s education than fathers. By adopting a social psychological perspective, I explain those beliefs in terms of two corresponding social processes through which interviewees rationalized and internalized the beliefs. These beliefs and processes led many interviewees to lowered aspirations that might have contributed to the persistence of gender differences in academic major choices and career aspirations. This study contributes to the social psychological literature on the formation of gender beliefs, and informs the observed persistence of gender inequality in China through Chinese women’s narratives.