VictorJuiz/thesis huggingface dataset:
Knowing what follows [electronic resource] : epistemic closure and epistemic logic

The starting point of this dissertation is a simple but central question in epistemology and epistemic logic: roughly, if an agent knows that a proposition P follows from some other propositions, must she know P if she knows the others? In other words, must the set of propositions she knows be "closed under known implication"? This idea of full epistemic closure raises a tension with an attractive idea of fallibilism about knowledge. According to fallibilism, knowing a true proposition Q does not require ruling out every remote possibility of error or deception with respect to Q. If it did, we would know almost nothing. The tension between fallibilism and closure arises when a proposition S picks out a class of remote possibilities in which Q is false, so we know Q implies not-S. While fallibilism may say that we can know Q without ruling out the possibilities picked out by S, closure says that we know Q only if we know not-S. Although not a formal contradiction, this is a tension to say the least. In this dissertation, I explore the extent to which it is possible to make fallibilism compatible with closure. I begin by formalizing a family of fallibilist theories of knowledge in models for epistemic logic. Model-theoretic techniques are used to characterize the closure properties of knowledge according to different fallibilist pictures, identify the structural features of these pictures that correspond to closure properties, transform models of one theory into models of another, prove impossibility results, and ultimately find a middle way between full closure and no closure for fallibilism. I argue that the standard versions of "Fallibilism 1.0" each face one of three serious problems related to closure: the Problem of Vacuous Knowledge, the Problem of Containment, and the Problem of Knowledge Inflation. To solve these problems, I propose a new framework for Fallibilism 2.0: the Multipath Picture of Knowledge. This picture is based on taking seriously the idea that there can be multiple paths to knowing a complex claim about the world. An overlooked consequence of fallibilism is that these multiple paths to knowledge may involve ruling out different sets of alternatives, which should be represented in our picture of knowledge. I argue that the Multipath Picture of Knowledge is a better picture for all fallibilists, whether for or against full closure. Yet I also argue that only by accepting less than full closure can we solve the closure-related problems that plague previous versions of fallibilism.

Keywords: Philosophy
Advisor: Lawlor Krista primary advisor. Thesis advisor
Benthem Johan van
Longino Helen E. advisor. Thesis advisor
Landing and perching on vertical surfaces [electronic resource]

Perching combines the best of both climbing and flying platforms. While flying, the vehicle is agile and can cover long distances quickly. When perched, the same vehicle now has reduced energy expenditure, is quiet and can wait for better flying conditions. Perching could be used in search-and-rescue or surveillance operations, and opens the way to many new applications including reconfigurable sensor networks, close proximity inspection of structure or sample-and-return missions. In this thesis, we discuss an approach allowing small airplanes to land on rough vertical surfaces using micro-spines. The technique presented consists in flying toward the wall at cruise speed (10m/s), triggering a pitch up maneuver at the appropriate distance from the wall to rapidly slow down and absorb the remaining kinetic energy at impact with a suspension that also favors the engagements of the micro-spines. This suspension creates a landing envelope of successful touchdown conditions, which can be easily reached by the airplane assuming a properly designed airframe. This thesis proposes and validates an hybrid model of the suspension used to determine the size of the landing envelope. Furthermore, to create the largest landing envelope possible, an extension to current region of attraction estimation techniques is proposed. This extension uses sums-of-squares optimization, which combines polynomial approximations of barrier constraints with the traditional Lyapunov methods to achieve tight estimation in the presence of barriers. Finally, the flying dynamics are analyzed and a simple model is developed by approximating the maneuver in three distinct phases based on the angle of attack of the wing (AoA). The results presented provide a better understanding of the parameters determining the shape of the landing envelope created by the suspension, but also the parameters of the airframes shaping the funnel that brings the airplane from normal flying conditions to the landing envelope. Furthermore, the region of attraction extension was tested on a simple model of the airplane and showed a 12x improvement in the area size estimated. Ultimately, the airplane designed with the insights gained with that work was able to perform 20 consecutive autonomous landings in outdoor conditions using only onboard sensors (i.e. laser range finder, accelerometers and gyroscopes).

Keywords: Mechanical Engineering
Advisor: Cutkosky Mark R. primary advisor. Thesis advisor
Mitiguy Paul advisor. Thesis advisor
Rock Stephen M. advisor. Thesis advisor
Large data algorithmics [electronic resource]

In this thesis, we will explore the algorithmic aspect of large data applications on distributed frameworks. In the distributed batched processing setting, I will present highly scalable algorithms for the densest subgraph detection primitive in massive networks, as well as an efficient scalable algorithm called k-means [vertical line][vertical line] for the k-means clustering problem. In the distributed real-time processing setting, I will present algorithms for two important applications: incremental PageRank computation, and real-time social search.

Keywords: Electrical Engineering
Advisor: Goel Ashish primary advisor. Thesis advisor
Raghavan Prabhakar
Garcia-Molina Hector advisor. Thesis advisor
Large eddy simulation of soot evolution in turbulent reacting flows [electronic resource]

Soot particles are nanoparticles consisting primarily of carbon that are formed during the combustion of fuel-rich mixtures. Due to environmental and health concerns, soot emissions from combustion systems are tightly regulated, and this regulation will only become stricter in the future. To enable the design of the next generation of low-emission combustion systems, predictive numerical simulations will be required. However, soot is a particularly difficult modeling problem due to the needs for high-fidelity models for soot itself in addition to chemistry and turbulence. This dissertation seeks to develop an integrated modeling framework based on Large Eddy Simulation (LES) for soot evolution in turbulent reacting flows. The final objective is the demonstration and evaluation of the model in an actual aircraft combustor. In order to enable these high-fidelity simulations, three component models have been developed. First, a detailed soot model is developed within the framework of the Method of Moments. New models for soot aggregation and fragmentation are proposed, and closure of the moment source terms is achieved with the Hybrid Method of Moments (HMOM), an accurate yet computationally efficient method. Second, a new turbulent combustion model is developed based on the Radiation Flamelet/Progress Variable (RFPV) model that can account for the removal of precursors from the gas-phase to form soot particles. Third, a subfilter PDF model is developed to account for the unresolved small-scale interactions between soot, turbulence, and chemistry. The subfilter PDF approach is validated a priori against a recent DNS database of soot evolution in a turbulent nonpremixed flame. The integrated modeling approach is then validated against experimental measurements in two laboratory-scale turbulent nonpremixed flames: a natural gas piloted jet flame and an ethylene bluff body flame. Differences in soot evolution due to the differences in the large-scale mixing in the two flames are discussed. The validated model is then applied to the simulation of a Pratt & Whitney aircraft combustor. Two operating points are simulated to assess the ability of the integrated model to reproduce quantitative trends in soot emissions.

Keywords: Mechanical Engineering
Advisor: Pitsch Heinz
Bowman Craig T. (Craig Thomas)
Moin Parviz advisor. Thesis advisor
The large hadron collider and models of new physics [electronic resource]

The Large Hadron Collider (LHC) is pushing the boundaries of the Standard Model of particle physics. Quantum corrections push the mass of the Higgs to be very heavy; If the hints of the Higgs boson now seen at the LHC are in fact the Higgs, the lightness of the Higgs boson must be explained. Supersymmetry provides a natural explanation for the Higgs mass. This thesis discusses two aspects in Supersymmetric theories, how to build them and how to see them.

Keywords: Applied Physics
Advisor: Bucksbaum Philip H. primary advisor. Thesis advisor
Wacker Jay primary advisor. Thesis advisor
Kachru Shamit
Large-eddy simulation of multi-material mixing and over-expanded nozzle flow [electronic resource]

Turbulent flows involving the interaction of shock waves and density variations are present in diverse areas of science and engineering. Inertial confinement fusion (ICF), for example, is a theoretical technology which could lead to clean, inexpensive energy for thousands of years to come. The implications of ICF are enormous as are the engineering challenges associated with it. One primary obstacle is modeling and avoiding the hydrodynamic instabilities which grow on the surface of the ICF capsule and which can prevent fusion from occurring. The commercialization of space exploration and privately owned satellites has led to an unprecedented number of orbital launches, which is expected to double each year for the foreseeable future. Modern rocket propulsion systems must become more reliable and efficient to sustain this growth. In the design of the supersonic rocket nozzle, a primary consideration is the avoidance of large flow separation caused by shock waves and the resulting lateral forces or side loads generated. These side loads decrease the lifetime of the nozzle and destabilize the rocket. Due to the complexity of this physical process and the extreme conditions in which it occurs, limited experimental data are available and still much is not understood. Being able to accurately model turbulence and its interaction with shock waves and multi-species mixing has broad impact on these and other problems. In this thesis, numerical simulation is used to model these turbulent interactions in simplified configurations of the two aforementioned applications, ICF and rocket nozzles. Large-eddy Simulation (LES) is used to explore the multi-material mixing processes of the Rayleigh-Taylor instability and the Kelvin-Helmholtz instability. Analysis of the data reveal interesting physics which were not previously documented and which have implications to astrophysical phenomena (type 1a supernovae) and advanced energy technology (ICF). LES is used to simulate an over-expanded planar nozzle which contains a shock wave turbulent boundary layer interaction. A new numerical method for simulating this type of flow is described and tested on a variety of configurations to demonstrate its improved effectiveness and accuracy over previous methods. The LES approach is then used to study the source of shock wave instability in the nozzle. A mechanism is identified and verified by additional simulations and a parametric study of the nozzle configuration. A simple, reduced order model is proposed which captures the shock wave dynamics using a fraction of the computational cost of LES.

Keywords: Aeronautics and Astronautics
Advisor: Lele Sanjiva K. (Sanjiva Keshava)
Alonso Juan José
Cook Andrew
Jameson Antony
Large-eddy simulation of shock/turbulence interactions in hypersonic vehicle isolator systems [electronic resource]

Despite more than fifty years of research into the advancement of supersonic combustion ramjets, or scramjets, the technical challenges associated with the development of a reliable hypersonic, air-breathing propulsion platform continue to frustrate engineers to this day. Although scramjets offer the potential of substantially increased cycle efficiency over other types of chemical-propulsion systems, extreme failure modes such as engine unstart, in which the isolator shock system is ejected through the inlet, may lead to in-flight loss of thrust. Since it is well understood that shock-induced separation may affect the onset of unstart, it follows that improved understanding of the physics of shock / turbulence interaction may lead to more accurate prediction of design margins in physical scramjet engines. It is therefore the aim of the present research to explore with high-fidelity large-eddy simulation (LES) two unit problems of shock / turbulence interaction often found in scramjet isolator systems: (1) that of an oblique shock impinging on a turbulent boundary layer and (2) that of a normal shock train in a constant-area duct.

Keywords: Aeronautics and Astronautics
Advisor: Lele Sanjiva K. (Sanjiva Keshava)
Duraisamy Karthikeyan
Moin Parviz advisor. Thesis advisor
Large-scale sequencing and functional characterization of antibody repertoires [electronic resource]

Despite tremendous progress in understanding immune responses, the ability to characterize functional antibody responses remains limited. I developed a method that utilizes barcoding to enable high-throughput sequencing of paired heavy-chain and light-chain immunoglobulin sequences. This approach enables us to generate dendrograms of functional affinity-matured antibody responses, and express selected antibodies for functional characterization. This method was applied to a human who received an influenza vaccine, humans with Staphylocuccus aureus infection, a human with rhuematoid arthritis, an autoimmune disease, and a human who has lung adenocarcinoma, and is a long term non-progressor. These are examples in vaccination, infection, autoimmune disease and cancer, respectively. In each case, we show that we were able to obtain paired heavy and light chain sequences. We expressed selected antibodies for functional characterization and showed that we were able to obtain functional antibodies that bound to target antigens in each of the above indications. Furthermore, we also showed that large clonal families dominate in influenza vaccination, and that larger antibody clonal families have higher binding affinities. Taken together, the approach provides a method to directly characterize functional antibody responses that could advance our understanding of protective and pathogenic immune responses in health and disease.

Keywords: Immunology.
Advisor: Robinson William (William Hewitt) primary advisor. Thesis advisor
Fathman C. Garrison
Lewis David advisor. Thesis advisor
Miklos David (David B.) advisor. Thesis advisor
Nicolls Mark advisor. Thesis advisor
Laser induced fluorescence characterization of cusped field plasma thrusters [electronic resource]

Cusped field plasma thrusters are a recent development in electrostatic propulsion, aimed at improving operation in the low power regime. Several diagnostic techniques have been employed to characterize these discharges. The present study focuses on the use of laser induced fluorescence to map ion velocities throughout the plumes of several cusped field thruster variants. Laser induced fluorescence provides the opportunity to probe small plasma sources with high spatial resolution, and is particularly useful for studying plasma propulsion devices where hot, energetic and highly non-uniform environments can adversely affect intrusive probes. The first half of this thesis focuses on time-averaged laser induced fluorescence velocimetry for three cusped-field thrusters. While a time-averaged velocity diagnostic is adequate in determining overall performance, it cannot resolve the dynamics seen in oscillatory modes of thruster operation. The second half of this research focuses on the development of an advanced CW laser induced fluorescence diagnostic technique, capable of correlating high frequency current fluctuations to the resulting fluorescence excitation lineshapes. This method is validated using simulations and a 60 Hz xenon spectral lamp. It is then applied to an oscillatory mode of the diverging cusped field thruster. Results show that the positions of the ionization and peak acceleration regions in the thruster vary over the course of a discharge current oscillation, causing the ion velocities to fluctuate in time. This new method of time-synchronized CW laser-induced fluorescence will be invaluable for better understanding the dynamics of thruster operation as well as other quasi-periodic oscillatory discharges, in fields from combustion to materials processing, among others.

Keywords: Mechanical Engineering
Advisor: Cappelli Mark A. (Mark Antony) primary advisor. Thesis advisor
Hanson Ronald advisor. Thesis advisor
Hargus William A. advisor. Thesis advisor
A lasso for hierarchical interactions [electronic resource]

Building predictive interaction models is an important yet challenging problem, especially when the number of variables is large. Statisticians commonly demand that an interaction only be included in a model if both variables are marginally important. We study the problem of identifying hierarchical two-way interaction models from the viewpoint of the Lasso (i.e., L1-penalized regression). We show that we can produce sparse interaction models that honor the hierarchy restriction by adding a set of convex constraints to the Lasso problem. In contrast to stepwise procedures that are most commonly used for building interaction models, our formulation is convex, and so its solution is completely characterized by a set of optimality conditions. This makes it easier to study as a statistical estimator and gives a precise characterization of the effect of the hierarchy restriction. We prove under mild conditions that hierarchy holds with probability one and derive an unbiased estimate for the degrees of freedom of our estimator. A simple bound on this estimate gives a sense of the amount of fitting "saved" by the hierarchy constraint. We distinguish between two types of sparsity: parameter sparsity -- the number of nonzero coefficients in the model -- and practical sparsity -- the number of raw variables one needs to measure to make predictions in the future. While most statistical procedures focus on the former, the restriction to sparse hierarchical interactions gets at the latter, which is the quantity more closely tied to important data collection concerns such as cost, time, and effort. A simulation study reveals the relative statistical merits of the hierarchy assumption in different settings. We demonstrate our method in both linear and logistic regression settings on an HIV-1 drug resistance dataset and a classification problem involving olive oil. Our method has potential applications in genomewide association studies and other situations in which interactions may be important. Finally, we describe an algorithm that forms the basis of the hiernet package that we have created in R.

Keywords: Statistics
Advisor: Tibshirani Robert primary advisor. Thesis advisor
Hastie Trevor advisor. Thesis advisor
Taylor Jonathan E. advisor. Thesis advisor
Learning feature representations for music classification [electronic resource]

In the recent past music has become ubiquitous as digital data. The scale of music collections in some online music services surpasses ten million tracks. This significant growth and resulting changes in the music content industry pose challenges in terms of efficient and effective content search, retrieval and organization. The most common approach to these needs involves the use of text-based metadata or user data. However, limitations of these methods, such as popularity bias, have prompted research in content-based methods that use audio data directly. The content-based methods are generally composed of two processing modules--extracting features from audio and training a system using the features and ground truth. The audio features, the main interest of this thesis, are conventionally designed in a highly engineered manner based on acoustic knowledge, such as in mel-frequency cepstral coefficients (MFCCs) or chroma. As an alternative approach, there is increasing interest in learning features automatically from data without relying on domain knowledge or manual refinement. This feature representation approach has been studied primarily in the areas of computer vision or speech recognition. In this thesis, we investigate the learning-based feature representation with applications to content-based music information retrieval. Specifically, we suggest a data processing pipeline to effectively learn short-term acoustic dependencies from musical signals and build a song-level feature for music genre classification and music annotation/retrieval. While visualizing the learned acoustics patterns, we will attempt to interpret how they are associated with high-level musical semantics such as genre, emotion or song quality. Through a detailed analysis, we will show the effect of individual processing units in the pipeline and meta parameters of learning algorithms on performance. In addition to these tasks, we also examine the feature learning approach for classification-based piano transcriptions. Throughout experiments on popularly used datasets, we will show that the learned feature representations achieve results comparable to state-of-the-art algorithms or outperform them.

Keywords: Music
Advisor: Smith Julius O. (Julius Orion) primary advisor. Thesis advisor
Berger Jonathan
Slaney Malcolm advisor. Thesis advisor