VictorJuiz/thesis huggingface dataset:
Assessment of physical function from patterns in routine physical activity measured by wearable activity monitors

Limitations in physical functioning can lead to dire health and financial consequences. However, the progression of physical function impairment interacts with many different factors, such as organ function, activities and the environment. It is suspected that the complexity of physical function evaluation methods contributes to the low prevalence of function assessments in routine care. Earlier studies have shown that a bidirectional and monotonic relationship exists between physical activity and function. Wearable activity monitors (WAM) -- devices equipped with one or more accelerometers, offer an opportunity to infer physical function from the observed physical activity objectively and at scale. However, efforts to infer function from such activity measurements have been limited. In this dissertation I present methods to infer physical function from daily activity data. Using free-living activity data from the physical activity studies in the Osteoarthritis Initiative, I show that activity measurements obtained by a WAM are sensitive to changes in physical function, if we use an appropriate representation for daily activity. Further, I show that a representation of physical activity that quantifies a subject's engagement in different classes of activity patterns, improves the performance in a physical function classification task. I also demonstrate how such an activity representation may be constructed in an unsupervised manner, obviating the need for time consuming annotation of activity data. Finally, I motivate the case for a composite score of physical function and present two methods for deriving such a score from free-living activity. I also present evaluations of the computed score on predicting key events in the natural history of knee osteoarthritis. Inferring physical function from wearable activity monitors will enable remote monitoring of function, which in turn will facilitate better delivery of care to patients, at a lower cost.

Advisor: Shah Nigam
Delp Scott 
Hastie Trevor 
Smuck Matthew
The asymmetric synthesis of acyclic tetrasubstituted stereocenters by palladium-catalyzed allylic alkylation

Herein are described strategies for the asymmetric synthesis of acyclic tetrasubstituted stereocenters using palladium-catalyzed allylic alkylation. In particular prochiral nucleophiles are exploited for the synthesis of acyclic alpha tertiary hydroxyketones, fully substituted nitroalkanes, and all-carbon quaternary stereocenters. The problem of O-alkylation in benzylic nitronate synthesis was overcome by the use of a decarboxylative asymmetric allylic alkylation of allyl alpha nitroesters. Extensive screening of reaction conditions revealed a unique ligand and solvent combination that proved crucial for achieving high chemo- and enantioselectivity in this challenging reaction. Substrates were readily synthesized via a combinatorial cross-Claisen / alpha arylation protocol, and the method was highlighted by chemoselective functional group interconversions of a highly elaborated substrate. Boronic acids were exploited as templates of ene-diolate systems to solve a longstanding problem of direct asymmetric C-alkylation of alpha hydroxyketones. This process was rendered chemo-, regio-, and enantioselective in allylation reactions, while point and axial chirality were efficiently set in allylic alkylations of racemic allene substrates via a dynamic kinetic asymmetric transformation. This method represents one of the first examples where point and axial chirality are effectively set in allylic alkylation. As a follow-up to this work, enol boranes were found to be effective pronucleophiles in palladium-catalyzed allylic alkylation reactions. A 1,4-hydroboration reaction was exploited for the thermal generation of regio-defined enol boranes, and a unique electron-deficient ligand was found to exhibit differential reactivity in the subsequent alkylation reaction. This chemistry was further extended to provide a room temperature alkylation of ester derived enol boranes, in particular, unactivated esters. A preparative application was demonstrated in the synthesis of acyclic all-carbon quaternary stereocenters, where the stereoselectivity was a function of the identities of a chiral auxiliary, a chiral ligand, and a designer leaving group. It is hoped that this chemistry may spur broader interest in metal-catalyzed reactions of enol boranes.

Keywords: Chemistry.
Advisor: Trost Barry M. 
Burns Noah
Kanan Matthew William
Attractors in Type IIB Calabi-Yau Compactifications

We study the properties of extremal black hole excitations of 4d supersymmetric string vacua arising from compactifications on Calabi-Yau threefolds. The values of the vector multiplet moduli near the black hole horizon are governed by the attractor mechanism. In this thesis, we discuss the following two questions: "what is the distribution of attractor points on moduli space?" and "what are mathematical properties of Calabi-Yau manifolds at the attractor points?" We employ tools developed in [1,2] to answer the first question. Then, we prove an important theorem due to Moore [3] on the arithmetic of attractors and discuss some recent developments on this topic.

Keywords:  Department of Physics
attractors
attractor mechanism
arithmetic and attractors
Thesis
Advisor: Kachru Shamit
Hartnoll Sean
Augmented multiscale linear solver for mechanics in fractured formations

High-resolution geo-models with millions of grid cells are becoming a standard for modern reservoir simulators in oil and gas industry. In many applications such as uncertainty qualification, well location optimization, hydraulic fracturing studies, history matching, etc dynamic modeling of the global domain requires repetitive computation of the discretized linear system, which oftentimes ends up being the bottleneck for fast simulation (taking over 50% of the total simulation time). Mechanical deformation of fractured formations significantly affect the flow pattern of a reservoir through fracture closure/opening and slip reactivation due to stress state and fluid pressure changes. Multilevel iterative approaches use restriction and prolongation operators to effectively transfer information across the scales, and coupled with a local smoother lead to a superior convergence rate. However, fractures cause additional challenges for iterative linear solvers since they introduce discontinuity, irregular grids and ill-conditioned matrices, slowing down the solver. An Augmented Two-Step Algebraic Multiscale (TAMS) preconditioner is formulated for mechanical deformation of reservoirs with explicitly defined fractures to aid an iterative solver to a faster convergence to the fine solution. In the core of the proposed Augmented TAMS preconditioner as a global stage preconditioner is an adaptive enrichment of the standard multiscale basis function space with the special, numerically computed, discontinuous shape functions honoring the physics of the fractures. These functions propagate additional fracture-related information to the coarse scale and thus able to better approximate the local variation of the solution on the fine scale. The method is incorporated into a 3D general purpose research simulator AD-GPRS on an iterative linear solver level. Demonstration of the performance of the method is done by a numerical error analysis on a series of test cases for fractured formations with an arbitrary fracture geometry and distribution. A significant speed-up of about 30-50% in the number of linear iterations in comparison with the standard TAMS methods is achieved. The approach is also tested against the state-of-the art SAMG solver and shows relative reduction in the number of iterations in the presence of fractures.

Keywords: Energy Resources Engineering.
Advisor: Tchelepi Hamdi 
Garipov Timur 
Mukerji Tapan
Augmenting human driving via steering wheel skin stretch haptics

Cars are growing smarter as they incorporate improvements in sensing, computing and communication. They increasingly are able to monitor the environment, detect impending events, and make decisions, sometimes with higher speed and accuracy than is possible for the human driver. While this helps drivers in many ways, it also reduces situational awareness and driver ability to respond to emergencies, and it remains an open question how best to communicate this information. This thesis examines haptic skin stretch at the steering wheel as a way to improve communication from car to driver and augment driving ability. A steering wheel with an embedded skin stretch display was designed that is compact, lightweight, and allows holding at any position. Rotational motion of the front surface of the rim produces lateral deformation of the skin of the hands, stimulating slowly- and rapidly-adapting mechanoreceptors. The resulting haptic stimuli communicate magnitude, direction, and speed information. This thesis describes a series of experiments with this device characterizing the abilities of humans to perceive the feedback and react appropriately in various driving scenarios. In the first, human ability to perceive directional information in a driving vehicle in suburban traffic was assessed, with results generally consistent with those in stationary applications; however, a higher and especially faster rate of stretch application was preferred. In the second experiment, tests are conducted to determine whether drivers can correctly perceive and react to skin stretch navigation cues, which are compared to audio navigation cues during an auditory distraction task simulating a phone call. Results showed haptic feedback was at least as accurate as audio feedback and was less distracting. In the third experiment, human ability to utilize skin stretch for obstacle avoidance is examined: in particular, whether they could predict the size and location of obstacles. Subjects were found to perform correct maneuvers with high accuracy, modifying both maneuver direction and size based on the stimulus. Finally, an experiment in a different application, medical needle guidance, provided general insight in how best to provide steering information through skin deformation. Particular attention is given to the ability of skin stretch to alert the driver about impending events, inform about direction, and communicate the magnitude of an event. Results show that all three types of information can be conveyed, with attention to details of how skin stretch is imparted in terms of stimulus pattern, frequency, and magnitude.

Keywords: Mechanical Engineering.
Advisor: Cutkosky Mark R. 
Gerdes J. Christian
Automated 3D surface reconstruction of bird flight

Birds are able to both takeoff from a standstill and fly efficiently in forward flight, and in order to understand the biomechanics that enable them to do so, we quantify the shape and movement of their wings. Whereas previous studies that quantify 3D wing kinematics usually track a few 3D points on the bird manually, here we present a custom computer vision method that reconstructs the 3D surface of flying birds using high-speed cameras and projectors. This new structured-light approach is automated for high throughput of data and robustly reconstruct rapidly deforming objects at tested speeds of up to 3200 Hz. In order for these cameras and projectors to work from multiple view angles, we also develop an automated multi-camera-projector calibration method. This user-friendly calibration method is based on an integrated calibration target, image acquisition procedure, and algorithmic solution that achieves high accuracy by simultaneously calibrating all of the cameras and projectors. Using these new methods, we examine how the angle of attack and wing shape changes of a flying parrotlet (Forpus coelestis) enable it to take off from a standstill. We also combine these computer vision techniques with synchronized high-speed measurements of the vertical and horizontal aerodynamic forces produced during flight and the strain of the pectoralis muscle of a flying dove (Streptopelia risoria) to examine how bird muscles power strenuous flight modes such as takeoff and slow flight.

Keywords: Mechanical Engineering.
Advisor: Lentink David
Collins Steve (Steven Hartley) 
Wetzstein Gordon
Automated analysis of neural calcium imaging datasets based on robust statistics

Calcium imaging is a prominent technique in systems neuroscience research that allows simultaneous recordings of large numbers of neurons in awake, behaving animals. Automated extraction of neurons and their temporal activity from imaging datasets is a key step in producing neuroscience results, but the diversity of the imaging setups and their associated noise statistics hinder the development of general purpose, high-fidelity tools for cell extraction. Here, we address the problem of inferring the neural calcium activity and its use in cell extraction through the lens of robust statistics. We first present a data generation model that aims to capture the generic nature of contamination in calcium imaging datasets, which includes high amplitude noise sources that are impractical to model with fixed probability distributions. We propose a robust M-estimator for our data model that achieves the minimum worst-case risk. Additionally, we propose a fast solver for robust estimation problem that enjoys fast convergence with small computational complexity. Next, we implement an automated cell extraction method that relies on our robust estimator for inferring both the spatial and the temporal components of identified cellular regions. We show that our overall method, termed EXTRACT, is suited to datasets having a broad range of signal and noise characteristics. The efficient implementation of our method, together with acceleration from graphical processing units (GPUs), allow processing of imaging datasets an order of magnitude faster than recording durations. We validate the quality of the output of EXTRACT on simulated datasets as well as real large-scale datasets. Furthermore, in real experimental setups, we show that the neural calcium activity reconstructed with EXTRACT leads to more accurate biological findings in subsequent analyses compared to alternative cell extraction methods.

Keywords: Electrical Engineering.
Advisor: Schnitzer Mark Jacob
El Gamal Abbas A. 
Ganguli Surya
Automated vehicle control beyond the stability limits

Automated vehicles can access a much wider range of maneuvers - and therefore, avoid accidents in a larger number of scenarios - by operating safely beyond the stability limits. This thesis presents a series of contributions towards this goal, supported throughout by fully autonomous experiments on MARTY, a heavily-modified 1981 DMC DeLorean. First, we develop a physically insightful controller structure for automated drifting along a general path. In stark contrast with conventional driving scenarios, the speed of the vehicle is not explicitly tracked. Instead, we show that the coupling between lateral and longitudinal dynamics, often regarded as a formidable challenge for human drivers, allows velocity to be regarded as a stable zero dynamic under the imposed control law. This approach is experimentally demonstrated on a slowly-changing trajectory. The thesis then builds upon this result to present the fully autonomous execution of highly dynamic drifting maneuvers. The planning of a 'Figure 8' trajectory is formulated as a nonlinear optimization problem. We show that the key challenge is the loss of independent control over the velocity vector and vehicle body rotation rates that occurs when operating at the limits of the achievable state derivatives. Experiments confirm the effectiveness of modifications to the controller that account for this effect. To confidently venture past the well-known stability limits and utilize these transient maneuvers, however, it is critical to know the bounds on what is achievable. This thesis proposes a novel and intuitive approach for defining such an envelope, namely that the relevant criteria is avoiding the loss of steering authority over the rotational dynamics. The utility of this approach is demonstrated through a pair of experiments with and without a 'supervisor' that modifies the underlying controller. While the vehicle successfully completes the maneuver with the supervisor active, it spins out otherwise. Finally, the effectiveness of this combined approach is showcased though a series of tests on a long sustained drifting course of almost 1 kilometer in length, that includes several highly dynamic transitions, and reaches 40 degrees of sideslip, 2.5 rad/s of yaw rate, and speeds of 50km/h.

Keywords: Mechanical Engineering.
Advisor: Gerdes J. Christian 
Bobier-Tiu Carrie 
Pavone Marco
Rock Stephen M.
Automation in the Transportation Manufacturing Industry: How the Emergence of High-Tech Manufacturing Plants Affects its Labor Market

This paper explores the relationship between highly automated manufacturing firms in the
transportation manufacturing industry and various labor market outcomes. Specifically, I perform a county and industry-level analysis where I evaluate the association between a “Million Dollar Plant” opening on local wages and employment outcomes across populations with different skill compositions. Using a difference in differences regression model with fixed effects, I find that the opening of a Million Dollar Plant slightly increases employment per capita in both counties with predominantly low-skilled populations and those with predominantly high-skilled populations. Further, I find no significant associations between plant openings and county-level SNAPS participation, poverty rates, the per capita wage bill, and income distributions. Taken within the context of existing research, these findings suggest that the more automated transportation manufacturing plants do not pose significant adverse effects to low-skilled labor populations.

Keywords: Automation
manufacturing
transportation
Million Dollar Plant
labor
skill
Thesis
Advisor: Oyer Paul
Autonomous navigation of a flexible surgical robot in the lungs

Lung cancer is the leading cause of cancer-related death worldwide, and early diagnosis is critical to improving patient outcomes. To diagnose cancer, a highly trained pulmonologist must navigate a flexible bronchoscope deep into the branched structure of the lung for biopsy. The biopsy fails to sample the target tissue in 26-33% of cases largely because of poor registration with the preoperative CT map. If the localization were sufficiently precise, a closed-loop control system could drive the bronchoscope without human intervention. Automation may de-skill standard bronchoscopies, potentially reducing the cost of the procedure with a single pulmonologist monitoring multiple simultaneous procedures. We sought to enable autonomous navigation of the airways by advancing the intraoperative registration methods and the control of the flexible surgical robots. To improve intraoperative registration, we develop three deep learning approaches to localize the bronchoscope in the preoperative CT map based on the bronchoscopic video in real-time, called OffsetNet, AirwayNet, and BifurcationNet. The networks are trained entirely on simulated images derived from the patient-specific CT. The networks are evaluated on recorded bronchoscopy videos in a phantom lung and recorded videos in human cadaver lungs. AirwayNet outperforms other deep learning localization algorithms with an area under the precision-recall curve of 0.97 in the phantom lung, and areas ranging from 0.82 to 0.997 in the human cadaver lungs. To improve the control of flexible surgical robots, we developed an state estimation algorithm that adapted to the unknown contacts of the robot with the lung's environment. Using AirwayNet and the motion controller, we demonstrate autonomous driving in the phantom lung based on video feedback alone. The robot reaches four targets in the left and right lungs in 95% of the trials.

Keywords: Bioengineering.
Advisor: Camarillo David 
Okamura Allison 
Schwager Mac
Avian strategies for arboreal bimodal locomotion

Arboreal birds are experts at hopping and flying between tree branches, performing up to 30 short flights a minute as they search for food. These foraging flights date back millions of years, and yet we still know remarkably little about them. To better understand how birds perform this bimodal locomotion so effectively, we developed a new aerodynamic force platform which enables the first direct and non-intrusive measurements of both leg and wing forces from takeoff to landing. The stiff structural frame and carbon fiber composite force plates of the setup enable us to fully resolve forces generated during voluntary flights made by Pacific parrotlets, which beat their wings over 20 times per second. By combining these force measurements with kinematics derived from synchronized high-speed video, we developed new models for assessing the energetic tradeoffs and strategies the birds use during these flights. These insights also apply to a wide range of other birds and even avian precursors, and may help inform the design of bimodal robots that can transition to and from the air more effectively.

Keywords: Mechanical Engineering.
Advisor: Lentink David
Cutkosky Mark R. 
Dabiri John O. (John Oluseun)
BAFFLES: Bayesian Ages For Field Lower-Mass Stars

Age is a fundamental parameter of stars, yet ages derived from spectroscopic indicators in field stars often are given without a derivation of the uncertainty of the measurement. We have developed a Bayesian framework, BAFFLES, to produce the age posterior for a star from its B-V color, calcium emission strength (log(R'HK)), and lithium abundance (log(EW Li)). Accurate stellar age prediction is essential for understanding directly imaged exoplanets since for self-luminous planets and brown dwarfs, the accuracy in the age of the host star sets the precision in the derived mass of the companion. Current methods to determine the ages of stars do not provide a statistically robust estimate for the uncertainty in the derived age, and do not often address physical priors like a constant star formation rate. We empirically determine the likelihood functions for calcium and lithium as functions of age from literature measurements of these indicators, which are of stars that are members of nearby moving groups and clusters with well-determined ages. In addition, combining lithium and calcium posteriors for a single star allows us to increase the precision on the inferred age. We are currently investigating extending our framework to additional age indicators including Hα emission, X-ray emission, rotation period, and vsin(i).

Keywords:  Department of Physics
Bayesian
stellar ages
R'HK
lithium equivalent width
Thesis
Advisor: Macintosh Bruce
Nielsen Eric
Behavioral and neural markers of self-regulation

The ability to manipulate behavior in service of long-term goals, self-regulation, has a long history of research and thought behind it. Yet despite its long history and the multitude of related concepts and measures self-regulation research suffers from a lack of transferable measures, models and interventions. One overlooked reason for this might be the focus on creating mechanistic stories while sacrificing measurement rigor. This dissertation explores the meaning of a 'marker' of self-regulation from a psychometric perspective. Our work highlights the importance of the required and varying statistical properties of a self-regulation marker depending on the research question. With respect to within-subjects analyses we present the largest comparison of behavioral self-regulation measures in their suitability for individual difference analyses using retest-reliability. Regarding between-subjects designs we show how extensive model comparisons can reveal conflicting narratives and how neural group differences might lie in properties other than the central tendency of distributions.

Keywords: Psychology.
Advisor: Poldrack Russell A.
Knutson Brian 
Wagner Anthony David