VictorJuiz/thesis huggingface dataset:
Effects of content variety and tracking level on user presence [electronic resource]

A large sample, mixed design experiment explored the effects of tracking level and content variance on user presence. One hundred and ninety two participants experienced the same piece of virtual reality content—either CG content or spherical video—in 3 (rotation only) and 6 (rotation and translation) degrees of freedom. Each participant was also asked to respond to 5 randomized levels of external haptic stimuli during their period of immersion, designed to test the validity of measuring psychological presence as absence from the physical world. Higher levels of tracking (degrees of freedom 6) and CG content induced more presence than degrees of freedom 3 or spherical video, and participants were more distracted in degrees of freedom 6 and 3 than in the control condition. This inaugural work provides a foundation for future large-scale exploration of the relationship between tracking level, content type, and immersion.

Keywords: Communication.
Advisor: Bailenson Jeremy primary advisor. Thesis advisor
Hancock Jeff advisor. Thesis advisor
Monin Benoît
Reeves Byron
The effects of digitally mediated empathy interventions on bias against marginalized groups [electronic resource]

This two-part mixed methods study applies a dual processing model to explore the effects of digitally mediated empathy interventions on bias against marginalized groups, justice sensitivity, and helping behavior. Previous findings in the empathy literature suggest that while affective and cognitive empathy may be utilized to alter someone's perception of a marginalized group in a beneficial manner, the complex relationship between an individual and the technology they use may modify these effects and render previous findings incomplete. Results revealed that prompting affective or cognitive empathy in response to video stimuli did not elicit positive perspective-taking or decrease bias against these groups. For scales of homophobia that experienced significant interaction effects, the distraction and cued comment condition had the least amount of bias. Similarly, for the illegal alien scale, the distraction condition produced the least biased results. Findings from the justice sensitivity scales show that the empathy condition increased personal justice sensitivity, while those in the distraction condition experienced comparatively higher levels of "other-oriented" concern. These findings suggest that instead of eliciting positive feelings about the marginalized groups, the empathy-specific prompts activated biases against the individual. Additional findings for the effects on helping behavior suggest a more nuanced outcome that does not correlate with outcomes expected from previous psychological research. All of the results suggest that digitally mediated video stimuli designed to elicit empathy for marginalized groups may have the opposite effect due to the complex nature of the interaction between online media and culture.

Keywords: Communication.
Advisor: Glasser Theodore Lewis
Turner Fred primary advisor. Thesis advisor
Reeves Byron
Willinsky John
Effects of Transfer Income on Migration and Rural Unemployment: Evidence from the Alaska Permanent Fund Dividend

Cash transfer payments may affect migration pattens and unemployment in rural
areas where poverty is widespread. To characterize those effects, I examine trends in
migration and unemployment in Alaska in relation to variation in the amount of the
Alaska Permanent Fund Dividend (PFD). The PFD is an annual government cash
transfer guaranteed to nearly all Alaska residents; its amount is based on a five-year
average of the investment earnings of a public endowment. Using the identification
assumption that the PFD amount is not well-correlated with other determinants of
migration or unemployment in Alaska, I show that larger cash transfers are positively
related to migration rates from remote rural areas to urban areas like Anchorage,
Alaska’s largest city. I also show that larger cash transfers are negatively related to the unemployment rate in remote rural areas. These results are statistically significant and robust. Excluding the portion of the PFD that is attributable to the investment earnings of the Alaska Permanent Fund in the most recent fiscal years does not produce significantly different results, suggesting that the identification assumption is wellfounded. I discuss consequences of the migration effect and propose policy responses.

Keywords:  Department of Economics
cash transfers
equality of opportunity
migration
liquidity constraints
rural poverty
rural welfare
transfer income
unemployment
universal basic income
Stanford Department of Economics
Thesis
Advisor: Abramitzky Ran
Effects on surrounding tissue radiographic features in diagnosis of breast cancer [electronic resource]

Breast cancer is the most deadly cancer for women in developing countries and the second most deadly cancer for women in developed nations. Mammograms are an essential component to early detection of breast cancer; however, only 20% of biopsied breast lesions are cancerous. Researchers have spent the last two decades developing computer-aided diagnosis (CADx) systems to aid in the screening and diagnosis process, yet no CADx system has progressed to clinical use. I believe that the research in CADx of breast cancer in mammography could be improved by incorporating imaging features from the tissue surrounding masses. It has been shown that breast density is related to cancer risk, and the environment of a mass seen on a cellular level can aid in predicting survival. This knowledge has not yet been utilized in our design of CADx systems. I hypothesize that the utilization of surrounding breast tissue features will improve the performance of CADx systems in mammography. In order to test my hypothesis, I have proceeded through three steps. First, I have developed a standard data set for use in CADx system evaluation. Second, I have developed methods of unsupervised feature learning for quantitative analysis. Finally, I have tested the developed feature learning methods on the mass and on the tissue surrounding a mass. The CADx system I built including the features from surrounding tissue achieved an A_Z of 0.80. This result exceeded that of all other methods tested on this data set. As such, the result supports my hypothesis that radiographic imaging features from the tissue surrounding masses are discriminatory for diagnosis and can be used to improve the performance of CADx systems.

Keywords: Biomedical Informatics.
Advisor: Rubin Daniel
Lipson Jafi advisor. Thesis advisor
Musen Mark A. advisor. Thesis advisor
Napel Sandy advisor. Thesis advisor
Efficacy of antimicrobial peptide mimics against staphylococcus infections [electronic resource]

Staphylococcus aureus is one of the most common pathogens isolated from infected wounds, and significantly increases the cost of chronic wound treatment. Treatment with conventional antibiotics has been gradually rendered ineffective by the emergence of resistant pathogenic strains, e.g. methicillin-resistant Staphylococcus aureus (MRSA). Therefore, creating new antibiotics that use novel mechanisms and are challenging for pathogens to develop resistance to, is urgently required. One of the most promising candidates for the treatment of resistant infections are non-natural AMP mimics, formed by poly-n-substituted glycines, also called "peptoids". Antimicrobial peptoids developed in the Barron laboratory are mimics of naturally occurring host defence molecules, and have shown significant activity against a broad spectrum of bacteria, and therefore, were studied as a new generation of antimicrobial therapeutic agents against S. aureus infections. The peptoids showed highly significant activity against planktonic growth, impairment of existing biofilms, and prevention of biofilm formation for three strains of S. aureus - two non-resistant strains of S. aureus (Xen 36 and Xen 29), and one strain of MRSA. Compared to conventional antibiotics, the peptoids were particularly effective against MRSA. Among the peptoids studied, Peptoid 1, showed the highest tendency to form oligomers, possibly contributing to its high antimicrobial activity. Peptoids that are delivered as oligomers probably form a local accumulation of peptoids and improve their bioavailability onto the bacterial cell surface. Furthermore, the peptoids preferentially formed multimeric structures when in PBS solution, rather than water solution, results confirmed by analytical ultracentrifugation experiments. In addition, synergistic effects in antimicrobial activity against S. aureus were discovered when using combinations of peptoids, peptides and conventional antibiotics. Finally, the selected peptoids were tested for efficacy in a bioluminescent S. aureus mouse incision wound model, where it was observed that the peptoid-treated wounds completely cleared the infection within eight days, while the control wounds were still partially infected. It was discovered that Peptoid 1 delivered in PBS buffer in vivo gave better than an 8-fold better response than when delivered in water (as PBS was shown to facilitate multimerization). These observations demonstrate the promising potential of antimicrobial peptoids in the treatment of S. aureus wound infections.

Keywords: Bioengineering.
Advisor: Cochran Jennifer R. primary advisor. Thesis advisor
Contag Christopher H. primary advisor. Thesis advisor
Blevins Nikolas H.
Covert Markus advisor. Thesis advisor
Efficient methods and hardware for deep learning [electronic resource]

The future will be populated with intelligent devices that require inexpensive, low-power hardware platforms. Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks. However, these algorithms are computationally intensive, which makes it difficult to deploy on embedded devices with limited hardware resources and a tight power budget. Since Moore's law and technology scaling are slowing down, technology alone will not address this issue. To solve this problem, we focus on efficient algorithms and domain-specific architectures specially designed for the algorithm. By performing optimizations across the full stack from application through hardware, we improved the efficiency of deep learning through smaller model size, higher prediction accuracy, faster prediction speed, and lower power consumption. Our approach starts by changing the algorithm, using "Deep Compression" that significantly reduces the number of parameters and computation requirements of deep learning models by pruning, trained quantization, and variable length coding. "Deep Compression" can reduce the model size by 18x to 49x without hurting the prediction accuracy. We also discovered that pruning and the sparsity constraint not only applies to model compression but also applies to regularization, and we proposed dense-sparse-dense training (DSD), which can improve the prediction accuracy for a wide range of deep learning models. To efficiently implement "Deep Compression" in hardware, we developed EIE, the "Efficient Inference Engine", a domain-specific hardware accelerator that performs inference directly on the compressed model which significantly saves memory bandwidth. Taking advantage of the compressed model, and being able to deal with the irregular computation pattern efficiently, EIE improves the speed by 13x and energy efficiency by 3,400x over GPU.

Keywords: Electrical Engineering.
Advisor: Dally William J. primary advisor. Thesis advisor
Horowitz Mark
Li Fei Fei
Efficient optimization via approximation [electronic resource]

Optimization in nonrigid registration and image processing is often non-convex and computationally expensive. To address these challenges, I will present approaches for efficient optimization via approximation. First, I will show that classical models can achieve state-of-the-art performance with approximate global optimization, for the problems of nonrigid 3D registration and optical flow. We cast the objective as discrete Markov random field optimization and apply efficient global optimization algorithms based on linear programming relaxation. For nonrigid 3D registration, our approach outperforms a large body of prior work by a significant margin, increasing registration precision on real data by a factor of 3. For optical flow, we show that one-shot global optimization of a classical Horn-Schunck-type objective over regular grids at a single resolution is sufficient to initialize continuous interpolation and achieve state-of-the-art performance on challenging modern benchmarks. Second, I will present an approximation approach to accelerating a wide variety of image processing operators. We show that convolutional networks can model the action of many image processing operators more accurately than even sophisticated recent downsample-evaluate-upsample schemes while being up to an order of magnitude faster.

Advisor: James Doug
Koltun Vladlen
Ermon Stefano advisor. Thesis advisor
Elastic full waveform inversion of multicomponent data [electronic resource]

Subsurface seismic imaging has relied on the acoustic wave-propagation model for many decades. This choice has been justified by the greater availability of acoustic only data, i.e., ocean streamers, higher computational cost of shear-wave processing, and challenges in wave-mode separation methods. However, in the last few years, seismic exploration has moved to more complex subsurface targets, such as sub-salt and sub-basalt. In these scenarios, including a greater range of physical processes is advantageous. Elastic modeling and inversion achieves that by accounting for both pressure and shear wave propagations. Therefore, a greater understanding of elastic wave-equation methods in seismic imaging becomes fundamental. I formulate the imaging condition for the elastic wave-equation using the stress-velocity set of first-order partial differential equations. I show that the elastic imaging condition can be obtained similarly for density-Lame or density-velocity parameterizations of the model space. I demonstrate that these conditions are different than the acoustic case and can be obtained by calculating the adjoint Born approximation of the nonlinear problem. I discuss how elastic wave-equation modeling and imaging is computationally more intensive than acoustic methods. I propose solutions for memory cost and computational time optimizations and show performance gains in a simple synthetic example. Using the proposed formulation and computational improvements, I apply the elastic imaging condition to the Marmousi 2 synthetic model. I show an elastic reverse time migration (ERTM) result with model components in the density-Lame parameterization. I also show how this image can qualitatively indicate anomalies in a Lame parameter ratio. Finally, I combine all methodologies presented into an elastic full waveform inversion (EFWI) workflow. I apply this workflow to a 2D field data set acquired using four-component ocean-bottom nodes (4C OBNs). I obtain inversion results for density, P- and S-velocities up to 10 Hertz (Hz) frequency data. Finally, I combine P- and S-velocities to calculate a Vp/Vs model. The calculated model is composed of layers with Vp/Vs values between 1.5 and 2.3, which is consistent with the expected geology of the basin.

Keywords: Geophysics.
Advisor: Biondi Biondo
Clapp Robert G. (Robert Graham)
Mavko Gary
Electrochemical energy transformation processes [electronic resource] : an atomistic perspective

The upward trend in worldwide energy demand, combined with an increasingly urgent need to attenuate climate change brought about by rising anthropogenic CO2 levels in the atmosphere, drives the search for renewable sources of energy. As the price of electricity from photovoltaics and wind turbines continues to plummet, the prospects of using electrocatalysis to produce fuels and chemicals in a sustainable way become more and more interesting. Unfortunately, current state-of-the-art electrocatalysts are far from efficient enough to make such processes economically feasible, such that the design of new electrocatalysts for energy transformation is a major challenge to science. Thus, a fundamental understanding is likely to be essential to improving the effi- ciency of electrocatalytic processes, which is where density functional theory (DFT) has been invaluable in granting insights into e.g. reaction mechanisms and materials properties. With the recent advances in computing power and understanding of the electrochemical double layer, a fully atomistic, ab initio model of electrochemical reactions is beginning to be tractable. In this Thesis, I employ DFT calculations to study two different directions on the Energy Cycle, which connects low-energy materials to high-energy fuels. The key to a sustainable future is to convert between the two ends efficiently with the input and output of electrical energy. On the right-hand side of the Cycle, I study the fundamental mechanisms involved in discharging a battery, specifically on the anode. As for the left-hand side of the Cycle, I examine CO2 electroreduction to CO and in particular the effects of solvated cations on the reaction kinetics. Finally, I address the challenges in modeling the electrochemical interface with explicit solvent and charged species while capturing the applied potential accurately.

Keywords: Chemistry.
Advisor: Nørskov Jens K.
Kanan Matthew William
Martinez Todd J. (Todd Joseph)
Electrolyte gating of nanoscale complex oxide devices [electronic resource]

Electrolyte gating is a powerful method to induce large modulations in charge carrier density at a surface. This technique enables access to unexplored regions of the phase diagram in a variety of materials, and allows for devices at charge carrier densities beyond the dielectric breakdown limit. I will show how this technique can be combined with conventional nanofabrication to create novel gate-tunable nanoscale devices. I will show that electrolyte gating can be used in combination with nanopatterned conventional gates to realize a mesoscopic 2-dimensional electron system (2DES) in strontium titanate. Universal conductance fluctuations in the strontium titanate enable analysis of the rate and the sources of electron dephasing in this system. I will also demonstrate the use of electrolyte gating to reversibly tune the critical current of Josephson junction in a high-temperature superconductor fabricated with a Helium-ion beam. Finally, I will introduce a method to prevent electrochemistry in the device channel, improving the mobility of electrolyte-gated systems and the clarity of electrolyte gating results.

Keywords: Applied Physics.
Advisor: Goldhaber-Gordon David
Hwang Harold Yoonsung
Suzuki Yuri (Applied physicist) advisor. Thesis advisor
Electron and spin transport in disordered nanoscale contacts [electronic resource]

The continued scaling of semiconductor nanotechnology below 10 nanometer dimensions requires efficient injection of electrons across interfaces between devices and contacts. In this thesis we analyze transport in metal-semiconductor and metal-insulator junctions to quantify the conductance of these structures. This enables us to assess strategies for controlling contact resistivity and spin polarization for nanoelectronics and spintronics. First, we use density functional theory and Green's functions to decompose the resistance of metal-semiconductor junctions into the loss due to tunneling and the loss due to band structure mismatch. Second, we use atomistic Monte Carlo simulations to construct probability distributions of contact resistivity in the sub-10 nm regime, where the disorder due to discreteness of dopants is consequential. Third, we assess the effectiveness of shifting the pinning of Fermi levels using interfacial layers inserted between the metal and the semiconductor. Finally, we consider the case of spin-polarized transport in tunnel junctions where the impact of disorder varies by the symmetry of the participating state. The key finding of this work are that (1) semiconductor barrier thinning with dopants is not robust to atomistic variation, (2) the addition of barrier lowering with interlayers provides the necessary contact resistivity reduction to meet targets for transistor scaling, and (3) disorder at metal-oxide interfaces does not account for the observed loss of magnetoresistance in half-metallic tunnel junctions.

Keywords: Electrical Engineering.
Advisor: Saraswat Krishna primary advisor. Thesis advisor
Reed Evan J. advisor. Thesis advisor
Wong Hon-Sum Philip
Electronic control of organs [electronic resource] : secretory glands and blood vessels

Electro-neural stimulation can control organ function and help treat disease. Bioelectronic medicine and electrical control of organs are poised to become major new alternatives to traditional pharmacological treatments of disease. This work presents two examples of electronic control of organs: secretion from ocular glands and constriction of blood vessels. Dry eye disease afflicts 100 million people world-wide and originates from a deficiency of tear volume, lipid or protein in the corneal tear film. This work describes the pathways for enhanced secretion of tears by electrical stimulation of the lacrimal gland and sensory nerves, the composition of electrically enhanced tears and the optimal electrical parameters for aqueous secretion. This field has advanced from our basic animal work to clinical trials showing improved symptoms, tear volume, and ocular surface health in dry eye patients. Secondly, we describe electrical vasoconstriction to treat non-compressible hemorrhage—a leading cause of death in military and in civilian trauma. We show that electrical stimulation can reduce the rate of bleeding between 3- and 10-fold through neural and non-neural pathways of vasoconstriction. We designed a prototype of a battery-powered, miniature neurostimulator that could provide hours of vessel control.

Keywords: Electrical Engineering.
Advisor: Palanker Daniel primary advisor. Thesis advisor
Arbabian Amin advisor. Thesis advisor
Poon Ada Shuk Yan