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Ponderomotive Forces in Pilot-Wave Hydrodynamics
No full textDroplets bouncing on a vibrating bath may self-propel (or ‘walk’) via a resonant interaction with their self-induced pilot wave. In pilot-wave hydrodynamics (PWH), the spontaneous emergence of coherent, wave-like statistics from chaotic trajectories has been reported in several settings. Owing to the similarity of PWH to Louis de Broglie’s realist picture of quantum mechanics, the question of how such statistics emerge has received considerable recent attention.
A compelling setting where coherent statistics emerge in PWH is the hydrodynamic analog of the quantum corral. When walking droplets are confined to a circular cavity or ‘corral’, a coherent statistical pattern emerges, marked by peaks in the positional histogram coincident with extrema of the cavity eigenmode. Stroboscopic models that idealize the drop’s bouncing dynamics as being perfectly resonant with their Faraday wave field have proven incapable of capturing the emergent statistics.
In this thesis, we present new experimental and theoretical findings in a variety of pilotwave hydrodynamical settings where non-resonant bouncing plays a key role in the droplet dynamics and emergent statistics. First, we find that modulations to resonant bouncing influence the stability threshold of a Bravais lattice. Second, we demonstrate that resonant bouncing can be disrupted by the imposition of suboctave driving, which may be used to induce a rearrangement of bound states of bouncing droplets.
We then proceed to an integrated experimental and theoretical study of the hydrodynamic corral, highlighting the role of non-resonant bouncing in the emergent statistics. We first introduce a new experimental method for simultaneously measuring the drop position and pilot wave height. We then report new measurements of the pilot wave and vertical bouncing dynamics. We demonstrate that the complex pilot wave arising in corrals may play the same role as suboctave driving in disrupting resonant walking. Our experimental findings motivate a new theoretical framework that predicts that modulations in the histogram emerge as a consequence of ponderomotive effects induced by non-resonant bouncing. We then connect the ponderomotive drift observed in hydrodynamic corrals to extant theories of quantum mechanics.Ph.D
Burst Imaging with Learned Continuous Kernels
No full textBurst imaging is a technique that consists of taking multiple images in quick succession and merging them into one output image. By aligning and combining data from multiple frames, we can increase resolution, attenuate noise, reduce motion blur and expand the dynamic range to obtain a higher quality image. In this thesis, we propose a method that learns continuous kernels to process and merge burst frames. We show that the learned kernels adapt to local image information and take advantage of sub-pixel sample location information to demosaic, denoise and merge the burst into a high quality output.S.M
Thermally Hardened RF GaN HEMTs in Extreme Environments
No full textTraditional, room temperature electronics based on silicon has truly changed the world around us over the past 70+ years. However, many more applications still exist that are limited by the temperature performance of silicon devices (<250◦C). This area of high temperature (HT) electronics is an increasingly growing field with critical future applications in geothermal energy, space exploration, hypersonic aircraft, and deep gas/oil drilling, among others. Gallium Nitride (GaN) high electron mobility transistors (HEMTs) are especially well suited for high temperature electronic applications due to their low intrinsic carrier concentration and excellent electrical properties. Despite great progress in HT GaN technology, most demonstrations target logic or mixed-signal applications, and the performance of radio-frequency (RF) GaN devices remains lacking at high temperatures despite the critical need for wireless communication systems and high-speed electronics for these high-temperature applications. In this thesis, we investigate the physics of GaN HEMT devices at high temperatures and design RF transistors that demonstrate record performance at these temperatures.Ph.D
Mining Multifaceted Customer Opinions from Online Reviews
No full textOnline reviews are a valuable source for studying customer needs and preferences. Previous studies focus on extracting a set of a priori defined constructs such as product attribute perception or explicit customer needs from reviews. Such a priori focus circumvents the limitations of certain natural language processing algorithms but discards valuable information in reviews that are not in the scope of the predefined construct. This study proposes a new method of extracting customer opinions and opinion targets from reviews with the Aspect Sentiment Triplet Extraction (ASTE) algorithm and then identifying theoretical constructs critical for product development with a posteriori interpretation method. We demonstrate the value of our proposed method by identifying granular opinion targets and expressions to find infrequent but important phenomena such as user innovations and delights.S.M
Apparent algorithmic discrimination and real-time algorithmic learning in digital search advertising
No full textDigital algorithms try to display content that engages consumers. To do this, algorithms need to overcome a ‘cold-start problem’ by swiftly learning whether content engages users. This requires feedback from users. The algorithm targets segments of users. However, if there are fewer individuals in a targeted segment of users, simply because this group is rarer in the population, this could lead to uneven outcomes for minority relative to majority groups. This is because individuals in a minority segment are proportionately more likely to be test subjects for experimental content that may ultimately be rejected by the platform. We explore in the context of ads that are displayed following searches on Google whether this is indeed the case. Previous research has documented that searches for names associated in a US context with Black people on search engines were more likely to return ads that highlighted the need for a criminal background check than was the case for searches for white people. We implement search advertising campaigns that target ads to searches for Black and white names. Our ads are indeed more likely to be displayed following a search for a Black name, even though the likelihood of clicking was similar. Since Black names are less common, the algorithm learns about the quality of the underlying ad more slowly. As a result, an ad is more likely to persist for searches next to Black names than next to white names. Proportionally more Black name searches are likely to have a low-quality ad shown next to them, even though eventually the ad will be rejected. A second study where ads are placed following searches for terms related to religious discrimination confirms this empirical pattern. Our results suggest that as a practical matter, real-time algorithmic learning can lead minority segments to be more likely to see content that will ultimately be rejected by the algorithm
Assessing Blood-Based Laboratory Diagnostics for Alzheimers’s Disease: A Systems Approach
No full textThis thesis adopts a systems approach to analyze the complex network of stakeholders involved in adopting blood-based laboratory screening tests for Alzheimer’s disease (AD). Traditional diagnostic methods, including cerebrospinal fluid (CSF) testing and positron electron tomography (PET) brain imaging, are invasive, costly, and inaccessible to many. Blood-based tests offer a less invasive and more cost-effective alternative, yet they remain underutilized in clinical practice. By conducting a literature review, stakeholder interviews, and a Kano analysis, the thesis identifies and evaluates the key stakeholder needs to support the widespread adoption of these tests, such as the need for demonstrated clinical performance of these tests, reimbursement, broader education of patients and health care professionals, and safe, effective medicines to treat AD. The research highlights two emerging tests that have published studies demonstrating clinical validation, a key parameter of clinical performance. A stakeholder tension analysis is included with proposed tension resolutions using stakeholder saliency to guide prioritization. Addressing these stakeholder needs could facilitate broader implementation, improve early diagnosis, and support emerging therapeutic interventions for AD, thus reshaping the diagnostic landscape for this increasingly prevalent disease.S.M
On Dynamical Measures of Quantum Information
No full textIn this work, we use the theory of quantum states over time to define joint entropy for timelike-separated quantum systems. For timelike-separated systems that admit a dual description as being spacelike-separated, our notion of entropy recovers the usual von Neumann entropy for bipartite quantum states and thus may be viewed as a spacetime generalization of von Neumann entropy. Such an entropy is then used to define dynamical extensions of quantum joint entropy, quantum conditional entropy, and quantum mutual information for systems separated by the action of a quantum channel. We provide an in-depth mathematical analysis of such information measures and the properties they satisfy. We also use such a dynamical formulation of entropy to quantify the information loss/gain associated with the dynamical evolution of quantum systems, which enables us to formulate a precise notion of information conservation for quantum processes. Finally, we show how our dynamical entropy admits an operational interpretation in terms of quantifying the amount of state disturbance associated with a positive operator- valued measurement
Design of a cam and follower linear actuator for satellite optical systems
No full textOptical systems for satellites are used to image and track the physical environment of earth from space. Where the optical system images can be controlled through the rotation and movement of the optical system. Optical alignment is achieved though linear actuators, which constrain different degrees of freedom of the optical system. Optical systems require precise alignment, meaning the linear actuators that align them must have precise resolutions. During satellite launch, the satellite experiences both high acceleration and large magnitude vibrations,
which can damage equipment. Common precision actuation methods cannot meet the high stiffness required for these satellite linear actuators. A cam and follower linear actuator was
designed to fulfill these stiffness and precision requirements. Through modeling the dynamic and kinematic interactions between the cam and follower, a cam shape was designed, and necessary materials were chosen. Next through analysis of process capabilities of available
fabrication tools, manufacturing methods for different parts were selected. Finally, using components designed for testing, kinematic tests were conducted on the linear actuator. Testing
of the actuator demonstrated it was capable of actuating with a precision of 9.15 microns. More testing is needed to understand the stiffness of the device.S.B
A Model of Decadal Middle-Latitude Atmosphere–Ocean Coupled Modes
No full textAn analytical model of the mutual interaction of the middle-latitude atmosphere and ocean is formulated and studied. The model is found to support coupled modes in which oceanic baroclinic Rossby waves of decadal period grow through positive coupled feedback between the thermal forcing of the atmosphere induced by SST anomalies and the resulting wind stress forcing of the ocean. Growth only occurs if the atmospheric response to thermal forcing is equivalent barotropic, with a particular phase relationship with the underlying SST anomalies. The dependence of the growth rate and structure of the modes on the nature of the assumed physics of air-sea interaction is explored, and their possible relation to observed phenomena discussed
Causal Inference Under Privacy Constraints
No full textCausal inference is an important tool for learning the effects of interventions in observational or experimental settings. It is widely used in many fields such as epidemiology, economics, and political science to find answers like the average treatment effect of a medical procedure or the individual treatment effect of a personalized ad campaign. In commercial applications, the era of big data allows companies to increase their experiment volume, incentivizing them, in turn, to collect more user data. On one hand, large volumes of data are necessary to train generative models like ChatGPT. At the same time, companies’ increasing use of user data has drawn heavy criticism and consumer backlash, incurring legitimate concerns about privacy and consent. As concerns over user data safety and privacy grow, rules and regulations like GDPR change what kinds of data companies and researchers can acquire and how they can analyze the data. The necessity of now performing causal inference under a range of privacy constrants has carved new spaces for research at the intersection of causal inference and privacy. In my thesis, I will be exploring three paradigms for protecting user data — data minimization, differential privacy and synthetic data — and how to perform causal inference techniques under these new privacy regimes.Ph.D