VictorJuiz/thesis huggingface dataset:
Multiclass learning for visual recognition [electronic resource]
Keywords: Electrical Engineering

Extracting semantic information from raw visual input is one of the long-term goals of computer vision. While humans can almost effortlessly recognize hundreds or thousands of object and scene categories, it poses great computational challenges to design machine learning algorithms that can effectively and efficiently learn and recognize a rich set of concepts. In this work, we consider three challenges when performing multiclass visual perception tasks such as object recognition/detection and scene categorization. First, we address the challenge of scaling up the classifier to a large number of classes in terms of both computational efficiency and statistical accuracy. We propose a learning algorithm that can automatically discover and utilize the hierarchical structure in the semantic label space from data. Empirically, the resulting hierarchical classifier achieves significant improvement over existing methods on both object recognition and scene categorization tasks with hundreds of classes. Second, while image training data is abundant for some object classes, recent studies confirm the heavy-tailed nature of their distribution over categories, fueling the need for learning algorithms that make efficient use of scarce training data. To this end, we study transfer learning applied to multiclass category-level object detection. Built on the HOG (histogram of gradient) feature and template matching detector, we propose a local, spatial and structured prior that can capture the correlation structures on the level of individual features and spatially neighboring pairs of features. We demonstrate improved average performance over all 20 Pascal classes compared to the state-of-the-art deformable part-based model. Last, the broadness of visual classes usually necessitates an ensemble of heterogeneous features to provide good discriminative power but at an expensive computational cost. We propose an active classification scheme to perform efficient inference with an ensemble of features/classifiers, where we view the classification inference task as a sensing problem. Observations, i.e., features/classifiers, are selected dynamically based on previous observations, using a value-theoretic computation that balances an estimate of the expected classification gain from each observation as well as its computational cost. We show that the active scheme can achieve comparable or even higher classification accuracy at a fraction of the computational costs of traditional methods.
Multifidelity optimization for supersonic aircraft design [electronic resource]
Keywords: Aeronautics and Astronautics

Numerical optimization techniques are often used in aircraft design to efficiently search a multidimensional space for improved designs. The optimization process can be excessively time consuming, however, due to the computationally expensive analyses that are typically required to accurately predict many aspects of vehicle performance throughout the design space. Multifidelity optimization techniques incorporate information from less expensive, but possibly less accurate (i.e., low-fidelity), analyses to reduce the time required to find an optimal configuration. The goal of this work was to develop an efficient multifidelity optimization technique and to demonstrate its effectiveness in the conceptual design of a small supersonic aircraft. The novel multifidelity optimization method combines local gradient information and 1-D search, in a manner similar to that used by some of the most efficient single-fidelity optimization strategies, with additional information from low cost, low-fidelity analyses. The 1-D search is performed along a curved path, generated by the optimization of a corrected low-fidelity model, to ensure improvement during each iteration. The effectiveness of this approach was demonstrated on a few simple problems, and then applied to supersonic aircraft design. This involved the integration of analyses of varying fidelity for supersonic inviscid drag predictions into an existing mission analysis routine. The low-fidelity analysis includes a new area rule method capable of providing reliable gradients. The high-fidelity analysis uses an Euler solver, Cart3D, to estimate inviscid drag. The new multifidelity optimization method and a traditional gradient-based optimizer were each used to design a 39 passenger supersonic transport. The multifidelity optimizer required significantly fewer high-fidelity analyses, while achieving similar design objectives, in each of the example problems.
A multi-regime combustion model for reactive flow in internal combustion engines [electronic resource]
Keywords: Mechanical Engineering

With the ever-rising need for better fuel efficiency and lower emissions, the development of better engine technologies is essential. Developing these technologies requires a good understanding of the interaction between the various coupled multi-physics phenomena present in the engine. Detailed simulations of the engine can potentially provide this. Such simulations are becoming tractable now with the increase in available computational power. Since the combustion process is the primary controlling feature in these engines, an accurate combustion model is essential for enabling these simulations. This model must be efficient and valid across different combustion regimes, since modern engines might operate in hybrid modes. In this work, a framework for studying combustion in engines is developed. In the first part of this work, the focus is on the premixed regime which is dominant in Spark Ignited (SI) engines. The canonical flamelet model used extensively to study constant pressure premixed combustion is extended to variable pressure conditions that are observed in an engine cylinder. An extrapolation procedure is also developed for ensuring a consistent enthalpy and temperature under strong compression. This model is validated against direct numerical simulations with detailed chemistry under different turbulence conditions. The performance of the isobaric model is also evaluated in high turbulence conditions by comparing against detailed experiments of a piloted premixed jet burner with finite rate chemistry and different turbulence levels. In the second part of this work, the autoignition regime is studied, which is the dominant regime in Homogeneous Charge Compression Ignition (HCCI) engines. The Representative Interactive Flamelet (RIF) combustion model has previously been used to describe ignition, combustion, and pollutant formation in direct-injected Diesel engines. It has also been applied successfully for HCCI type combustion. In this work, the RIF model is validated against direct numerical simulation data to evaluate model performance under mixture stratification in Diesel, HCCI, and hybrid regimes. A wide range of thermal stratification and concurrent stratification cases is considered using the model. Since mixed modes are present in a modern engine, a multi-regime combustion model is required. A model is developed in this work by integrating the models for autoignition and premixed regimes. This model is validated against direct numerical simulations with for cases both regimes are important. Finally, simulations of an actual engine are performed. First, the validity of the flow solution is tested by simulating flow bench configurations with no valve motion or reaction. Next, gas exchange simulations of a motored engine are performed. Last, simulation of mixture formation in an engine is performed.
Nanostructured materials for alternative energy devices [electronic resource] : supercapacitors, ultracapacitors, and lithium ion batteries
Keywords: Materials Science and Engineering

Energy storage and generation are the biggest technological challenges of our generation. Among various devices attracting attention are supercapacitors and ultracapacitors. These devices have higher power densities than rechargeable batteries, meaning that energy can be withdrawn and inserted more quickly, but they currently have insufficiently high energy densities to become widespread within commercial applications. In addition, ultracapacitors tend to use expensive organic electrolytes while supercapacitors tend to use either scarce or expensive metal oxides, and both devices in academia tend to report results based on extremely low mass loadings of active materials (microgram scale). To enhance the commercial viability of supercapacitors and ultracapacitors as feasible complementary energy storage devices to lithium ion batteries such as during the fast time scale regenerative braking process in electric or hybrid cars, these energy density and cost issues must be addressed. In this dissertation, porous 3D metallic nickel foam is used as a high surface area catalyst and substrate for carbon nanofiber growth and ultimately enables mass loadings of carbon nanofibers one or two orders of magnitude higher (tens of milligrams per square centimeter) than in other academically demonstrated devices to enable superior top-down, per-area capacitance, energy density, and power density ultracapacitor results. A different study is also presented that demonstrates that by way of lithium ion battery charging and discharging steps, in which lithium ions are inserted and extracted during each cycle and leave porous voids behind in the host structures, nanostructures such as silicon nanowires, zinc oxide nanorods, and silver nanowires can achieve controllable, tunable porosities. This general method of tuning porosities of nanostructures should provide a novel tool for researchers to control nanostructure morphologies.
Nanostructuring molybdenum disulfide to engineer its surface and bulk properties for solar hydrogen production [electronic resource]
Keywords: Chemical Engineering

The development of readily available and sustainable sources of energy is a key component to ensuring our long-term economic growth and prosperity. Solar technologies, in particular, hold the potential to address a significant portion of our energy demands for decades to come. However, large scale proliferation of solar energy requires further technological advancements to efficiently collect, store, and distribute the energy provided by the sun. Photoelectrochemical (PEC) water splitting is one approach that converts the energy available from solar photons directly into hydrogen (H2) in a manner that is sustainable and carbon-free. In this process, the solar energy absorbed by a PEC device oxidizes water via the oxygen evolution reaction (OER) to produce a sustainable supply of protons (H+) which are concurrently reduced into H2 via the hydrogen evolution reaction (HER). H2 production currently represents a
Neo-Aztec aesthetics and mytho-historical critique [electronic resource] : the return of Quetzalcóatl in the Mexican intellectual imagination [1925-1950]
Keywords: Iberian and Latin American Cultures

This work examines the appearance of the Mesoamerican god of civilization and humanity Quetzalcóatl in modern Mexican literature. In selected critical and philosophical writings of José Vasconcelos and Octavio Paz, the ancient Quetzalcóatl transforms into a proposed neo-Aztec mythology, which functions as a medium for these writers to express a distinct critique of Mexican history. This mythology reinforces an Indianist Vanguard aesthetic, based on primary accounts of the Aztec civilization, and revolves around Quetzalcóatl emerging as a post-revolutionary Mexican cultural and poetic hero. An analysis of this nature informs contemporary studies on pre-Hispanic myth and anthropology in modern Latin-American literature. The contribution of this work supplements the limited studies on the under-explored theme of pre-Hispanic myth in Mexican and Latin-American cultural and intellectual discourse as well. On a wider scale, this critical analysis offers a unique perspective of the trans-historical and heterogenous re-configuration of ancient myth systems and symbols in the modern literary imagination.
Network models and infectious disease control [electronic resource] : analysis and insights
Keywords: Electrical Engineering

Many models of infectious disease do not explicitly consider the underlying contact network through which the disease spreads. However, network structure can greatly influence the dynamics of an epidemic and has implications for infectious disease control policies. This dissertation explores two important themes of network science as it relates to infectious disease control policy: first, how to incorporate network structure into the modeling and evaluation of infectious disease control policies; and second, how to leverage network structure in the design of optimal control policies. Complete network information is rarely available. Furthermore, even when network data is available, it generally only represents a snapshot in time and does not capture population or network dynamics (e.g., the formation and dissolution of contacts). In Chapter 2, we address these data limitations and describe a general simulation framework for modeling the spread of infectious disease in a dynamic contact network. We provide a methodology for inferring important model parameters, such as those governing network structure and network dynamics, from readily available data sources. In Chapter 3, we present an application of this framework in evaluating the effectiveness and cost-effectiveness of mass media campaigns aimed at reducing concurrent sexual partnerships in sub-Saharan Africa for HIV prevention. In Chapter 4, we address the problem of leveraging network structure in designing infectious disease control policies. In particular, we consider the problem of identifying which links to remove from a contact network in order to maximize the number of individuals who are protected from infection. We show that this problem can be posed as a non-convex quadratically-constrained quadratic program (QCQP), from which a link removal algorithm can be derived. Evaluation of the QCQP algorithm on standard network models demonstrates that it exhibits near-optimal performance and outperforms other intuitive link removal algorithms, such as removing links in order of edge centrality.
Networked to the catalytic core [electronic resource] : structure-function studies of RNA enzymes reveal distinct connections from the periphery
Keywords: Chemistry

The past two decades of functional and structural studies on RNA enzymes have largely focused on revealing active site interactions between ribozymes and their substrates. More recently, active site interactions among ribozyme residues themselves and the substrate have revealed an intricate network of interactions in and around the active site that involves metal ions, metal ion ligands, hydrogen bonding, and stacking interactions. Does this network extend into the broader structure of the RNA? The current studies in this thesis are aimed at understanding how the overall structural scaffold of RNA is involved in catalysis. Peripheral elements that are brought into close proximity via long-range tertiary contacts surround the catalytic core of most group I introns. In the well-studied Tetrahymena group I intron, ablation of each of five long-range tertiary contacts destabilizes the folded ribozyme, indicating a role for these tertiary contacts in overall stability, as expected. But once folded, three of the five tertiary contact mutants exhibit distinct functional roles in catalysis. Structural changes distal from the mutation site, revealed by hydroxyl radical footprinting, suggest that that these contacts are coupled to the catalytic core from a distance. These structural data combined with X-ray crystal structures and phylogenetic data suggest a number of networks that structure functional sites by using long-range tertiary contacts to position rigid helices and their more local tertiary interactions relative to the rest of the RNA. Preliminary data that tests these hypotheses suggest that some, but not all, of these networks between the periphery and the functional sites may be conserved among group I introns and that long-range tertiary contacts as well as more local tertiary interactions such as base triples may act redundantly to maintain the structure of the same regions of RNA. This redundancy could allow RNAs that change conformation over their reaction cycles to rearrange only a few tertiary contacts while maintaining a global fold. With the studies described herein, we are poised to understand the structural networks that couple the peripheral regions of RNA to its catalytic sites. These insights may lead to a better understanding of how structural networks of communication are exploited to facilitate multi-step processes that are driven by more complex RNA enzymes like the ribosome and the spliceosome.
A new approach to predicting analyst forecast errors [electronic resource] : do investors overweight analyst forecasts?
Keywords: Business.

I provide evidence that investors overweight analyst forecasts by demonstrating that prices do not fully reflect predictable components of analyst errors, which conflicts with conclusions in prior research. I highlight estimation bias in traditional approaches and develop a new approach that reduces this bias. I estimate 'characteristic forecasts' that map current firm characteristics into forecasts of future earnings. Contrasting characteristic and analyst forecasts predicts analyst forecast errors and revisions. I document abnormal returns to strategies that sort firms by predicted forecast errors, consistent with investors overweighting analyst forecasts and predictable biases in analyst forecasts influencing the information content of prices.
New materials systems for aqueous batteries [electronic resource]
Keywords: Materials Science and Engineering

Existing battery technologies do not offer the cycle life, power, energy efficiency, or low cost needed for widespread adoption on the electric power grid. In the first part of this thesis, the development of aqueous lithium ion batteries is discussed. In the second part of this thesis, the use of Prussian Blue analogues as battery electrodes in aqueous potassium ion and sodium ion cells is presented. These materials have extremely fast kinetics and long cycle life when operated as battery electrodes in aqueous electrolytes. The effects of insertion species and electrode composition on the performance of Prussian Blue analogue electrodes were characterized in aqueous half-cells. Cycle lives of up to 40,000 cycles and high rate capability (over 80C) were observed for these electrode materials. Full cells were built using a copper hexacyanoferrate cathode, an activated carbon/conductive polymer anode, and an aqueous potassium ion electrolyte. These batteries batteries show promise for stationary storage applications including those on the electric grid.
New results on the singularity analysis of the Kaehler-Ricci flow [electronic resource]
Keywords: Mathematics

In this thesis, we study the singularity development of the Kaehler-Ricci flow on holomorphic fibrations, and classify the singularity models of some classes of fibrations using parabolic rescaling. We first study the collapsing behavior of Calabi-Yau fibrations under the Kaehler-Ricci flow. A compact Kaehler manifold with semi-ample canonical line bundle admits a fibration of Calabi-Yau manifolds with possibly singular fibers. The convergence behavior for this class of manifolds under the Kaehler-Ricci flow was first studied by Song and Tian who establish the metric convergence in the sense of currents. In this thesis, we obtain the optimal collapsing rate of the nonsingular Calabi-Yau fibers, thus improving Song and Tian's work in analytic and geometric aspects. Secondly, we focus on a specific type of holomorphic fibration, namely the CP^1-bundles over Kaehler-Einstein manifolds. Fiber collapsing in the sense of Gromov-Hausdorff convergence was shown to occur in this case by Song, Szekelyhidi and Weinkove. We study the finite-time singularities for these manifolds using parabolic rescaling and dilation procedures adapted from Hamilton and Perelman in their works of the Ricci flow on 3-manifolds. We prove that when the flow metric has cohomogeneity-1 symmetry the collapsing occurs as a Type I singularity and we show that the singularity is modelled by C^n X CP^1.
Noise coupling analysis for mixed signal systems [electronic resource]
Keywords: Electrical Engineering

Trends toward portable electronics are leading forces in developing new systems. Accordingly, two features have become crucial in implementing these integrated systems: wireless communication and efficiency in size and energy consumption. Efforts to facilitate both digital and analog features produce mixed signal System-On-Chip (SOC), which include both analog and digital functions on a single die. Noise coupling in such an environment is a key issue in improving system performance; in particular, the noise coupling through the common substrate (i.e. substrate noise) is unavoidable as physical separation between the functions shrinks with technology scaling. To prevent the system performance from degenerating due to the substrate noise, methodologies for analyzing, estimating and measuring the noise in both simulated and practical conditions are essential and worthy of full investigation. This dissertation has conducted such an investigation in mixed signal SOCs Overall, this dissertation presents methodologies regarding how to understand, estimate, and measure the substrate noise in the mixed-signal SOC. As long as mixed-signal systems prevail in the integrated electronics industry, the SOC as well as the SIP should become the main frames of system implementations, and the presented research can contribute to shaping these frames.