VictorJuiz/thesis huggingface dataset:
Multiscale distributed estimation with applications to GPS augmentation and network spectra [electronic resource]
Keywords: Aeronautics and Astronautics

Distributed estimation uses a network of sensors to measure a set of variables. The computation tasks required for finding the optimal estimate can be divided among the sensor nodes in a way that can be implemented as an iterative process using nodes with little computational power. Most algorithms for distributed estimation work for small networks, but convergence rates decrease with network size, making them impractical for use in large networks. We present a consensus algorithm with a convergence rate that scales logarithmically with network size by arranging nodes in a multigrid network structure. The algorithm can adapt to changes in the network structure and allows for selection of several parameters, representing a trade-off between performance and robustness of the network. We also describe how the algorithm is adapted to account for time-varying measurements and measurement weights. We present two applications of these methods. Our first application is an algorithm that allows us to determine the spectral properties of a state transition matrix on the network. Since the convergence rate of a consensus algorithm is related to the spectral properties of the state transition matrix, we can use this information to evaluate the effects of changes to the network structure. Our second application is a distributed GPS augmentation system. Traditional GPS augmentation systems use reference receivers to find a set of error correction values, which is broadcast to surrounding mobile receivers. Our distributed augmentation system uses only mobile receivers with unknown locations, which are able to obtain a set of correction values by sharing and processing data in a distributed network. The resulting method can be used to improve GPS point positioning accuracy in areas where fixed augmentation systems are not available.
Nano C-apertures applied to near-field inspection and data storage [electronic resource]
Keywords: Applied Physics

The restriction of optical spot sizes to the diffraction limit of ~lambda/2 is a long standing problem in optical imaging and optical data storage. As the length scales of interest to scientists and engineers have decreased, the need for a way to efficiently focus light to spot sizes of < lambda/10 has become more pressing. Near-field optical techniques are the only solution to optical probing at the deeply sub-wavelength scale, but the existing methods of solid immersion lenses, sharp probe tips and sub-wavelength apertures (NSOM) are all hampered by significant deficiencies. The nano C-aperture is a superior near-field optical source and FDTD simulations have shown that it is able to produce very small and intense near-field optical spots. Unfortunately, conventional fabrication methods are incapable of producing C-apertures that fully realize the high, absolute field intensities promised by simulations. Using a new silicon nitride membrane based fabrication method, the performance of the C-aperture can be greatly enhanced both in the near and far-field. In this dissertation, I present data demonstrating the improvements in transmitted power and near-field spot size offered by this new fabrication technique and I apply the improved apertures to near-field inspection and optical data storage. I show that the near-field spot can be used for optical imaging at a resolution of lambda/10 with an optical intensity ~10x higher than conventionally-fabricated C-apertures. Finally, I show that the improved C-aperture can be used to record and read data bits in a phase change recording medium (Ge2Sb2Te5) with minimum bit sizes of 53.5x50.2 nm or ~lambda/20.
Nanoelectronics [electronic resource] : technology assessment and projection at the device, circuit, and system level
Keywords: Electrical Engineering

Nowadays, physical gate length can no longer be effectively scaled down and traditional boosters (e.g., strain, high-k/metal gate) are having diminishing return. Continued progress in nanoelectronics necessitates a holistic view across the boundaries of device, circuit and system. The best devices are those that are optimized for the circuits and systems of the target application. Device design and engineering must aim at improvement at circuit and system level, while the new applications in various areas, such as life-science, are enabled by emerging technology. In this dissertation, the design space is explored for future Si CMOS technology, and several promising technologies in the post-Si era. Compact models of transport properties and capacitive components of different device structures have been developed to facilitate circuit-level analysis and system-level optimization. Possible ways of extending technology roadmap are proposed. We propose scenarios of selective device structure scaling that will enable Si CMOS technology scaling for several generations beyond the currently perceived limits. Beyond Si CMOS scaling, carbon nanotube field effect transistors (CNFETs) are optimized and projected to achieve 5x chip-level speed up over PDSOI with the same power constraints at 11 nm technology node for a high-performance four-core processor with 1.5M logic gates and 5MB SRAM per core. A new simple device technology assessment methodology based on energy-delay optimization is also introduced and applied on emerging devices including CNFETs, III-V field effect transistors (IIIV), and tunneling field effect transistors (TFET). In conclusion, this thesis addresses the significance of a holistic view across the traditional boundaries of device, circuit, and system in technology assessment and projection. Guided by this concept, we chart a new path for Si CMOS technology scaling for future technology generations. We also accomplish for the first time chip-level performance assessment and optimization of new emerging transistors such as CNFETs. This establishes a new benchmark and design methodology for emerging nanoelectronics.
Network analysis and drug target identification in mast cells [electronic resource]
Keywords: Immunology.

Target identification is both a critical and difficult step in the drug discovery process. A novel approach is presented here that utilizes flow cytometry to: 1) identify small molecule signaling network probes, and 2) characterize the diverse network activation states they induce. Signaling events across the network are compared to functional responses to determine the way in which each signaling node influences a given function. Nodes that accurately predict cellular functions that drive disease are promising drug targets. The approach was applied to mast cells to determine which signaling molecules best predicted the degranulation, eicosanoid release, and cytokine secretion induced by stimulation of the cells with IgE and specific antigen crosslinking. Erk and Akt phosphorylation best predicted degranulation; Erk and cJun phosphorylation best predicted eicosanoid release; and Akt phosphorylation alone best predicted cytokine secretion. The results confirmed several known aspects of mast cell IgE-dependent signaling and revealed unknown associations to be tested in future work -- notably, that the cJun pathway plays a role in eicosanoid production and that the Akt pathway is a critical integration point for transcription of most or all cytokine genes. Overall, the results suggest that Erk and Akt are the most promising targets in the IgE signaling network for development of mast cell stabilizing drugs.
Network effects in alternative fuel adoption [electronic resource] : empirical analysis of the market for ethanol
Keywords: Business.

This paper investigates the importance of network effects in the demand for ethanol-compatible vehicles and the supply of ethanol fuel retailers. An indirect network effect, or positive feedback loop, arises in this context due to spatially-dependent complementarities in the availability of ethanol fuel and the installed base of ethanol-compatible vehicles. Marketers and social planners are interested in whether these effects exist, and if so, how policy might accelerate adoption of the ethanol fuel standard within a targeted population. To measure these feedback effects, I develop an econometric framework that considers the simultaneous determination of ethanol-compatible vehicle demand and ethanol fuel supply in local markets. The demand-side of the model considers the automobile purchase decisions of consumers and fleet operators, and the supply-side model considers the ethanol market entry decisions of competing fuel retailers. I propose new estimators that address the endogeneity induced by the co-determination of alternative fuel vehicle demand and alternative fuel supply. I estimate the model using zip code level panel data from six states over a six year period. I find the network effect to be highly significant, both statistically and economically. Under typical market conditions, entry of an additional ethanol fuel retailer leads to a 12% increase in consumer demand for ethanol-compatible vehicles. The entry model estimates imply that a monopolist requires a local installed base of at least 204 ethanol-compatible vehicles to be profitable. As an application, I demonstrate how the model estimates can inform the promotional strategy of a vehicle manufacturer. Counterfactual simulations indicate that subsidizing fuel retailers to offer ethanol can be an effective policy to indirectly increase ethanol-compatible vehicle sales.
Network services [electronic resource] : congestion, investment, and contracting
Keywords: Management Science and Engineering

This thesis addresses aspects of a problem at the core of information technology: how does congestion management influence the outcome of interactions between providers of services on networks and users of those services? The mitigation and management of congestion, whether through an application of or investment in technology, is an essential task for any large or growing service provider. The particular way in which congestion is managed can have surprising effects both on the health of a network and the economics of service provision. We make two main contributions. First, we explore the interaction between content distribution and traffic engineering. We use a game-theoretic framework in which users of a network select the source of content, and the traffic engineer decides how the traffic will route through the network. Second, we analyze the impact of contractual structure on market outcomes in service industries. Our results highlight how different contractual agreements change the intensity of price competition in service industries; broadly speaking, we show that competition is intensified when firms choose to offer congestion level guarantees.
The network structures of corrupt innovations [electronic resource] : the case of Enron
Keywords: Sociology.

The focus of this dissertation is the interplay of information and social network structure. I investigate how different types of information influence how individuals choose to communicate, and the implications of these choices on the entire social system. In particular, I look at how both overt and covert information is shared across an organization's communication network over a four-year period. The contrast between overt and covert activities affords the opportunity to examine whether different content types change the way individuals mobilize to accomplish goals within an organization. First, I analyze how different social network positions allow individuals to obtain different types of information within an organization's network. I demonstrate that individuals are more likely to participate in an overt communication network when they have large but sparse networks of informal ties through which to acquire information. This finding is contrasted with covert network involvement, where greater closeness centrality or shorter distances to other organizational members predicts participation. Second, the examination of individuals who participated in both covert and overt information networks highlights the divergent behaviors associated with the two activities. When sharing overt information, individuals are more likely to encourage network cohesion, practice structural egalitarianism, and engage in reciprocal communication. In contrast, when actors share covert information, they attempt to reduce network cohesion and reciprocity and foster a power structure. Finally, I present results that show the aggregate network effects of individual strategies for sharing either overt or covert information. I find that communication within overt networks is far more reciprocal than communication in covert networks, where information flow is asymmetrical. This study uses longitudinal email data taken from Enron Corporation between 1998 and 2002; these data provide a unique opportunity to study the communication network of the firm's employees and analyze the messages shared between employees. In contrast to previous studies, which assume transmission of information along network ties, this dataset allows for actual observation of information transfer between organizational members. I couple social network analysis and qualitative coding to explore how the content of information affects both communication patterns and the spread of information.
NMR spectroscopy for structural and dynamic studies of the beta2-adrenergic receptor [electronic resource]
Keywords: Biophysics.

G protein coupled receptors (GPCRs) are seven transmembrane proteins that are expressed in all eukaryotic cells and tissues. These receptors play key roles in human physiology and disease. The goal of my work is to understand the molecular detail of ligand recognition by GPCRs, and how this process leads to conformational changes that manifest as cellular signaling. Meeting this goal will advance our knowledge of membrane protein biology. It will also reveal structural targets and physicochemical logic to aid pharmaceutical design. The age of GPCR structural biology recently arrived with the first x-ray structures of rhodopsin and the beta2 adrenergic receptor (beta2AR). However, membrane proteins are constantly fluctuating entities. Dynamic behavior is intrinsic to their function. As such, static x-ray structures alone are inadequate. Herein, I develop biophysical techniques to study these dynamic receptors. Using NMR spectroscopy, I characterize conformational changes in the extracellular region of the beta2AR, a surface rich with potential for drug design. I also explore the signaling properties of monomeric GPCRs and conformational changes of other macromolecules using single-molecule fluorescence. While many questions about GPCRs remain, I hope this work is a small step towards understanding these important, fascinating, and beautiful molecules.
Nonlocal modulation and dispersion [electronic resource]
Keywords: Electrical Engineering

Quantum entanglement has been the subject of a growing field in quantum optics that aims to study the physical properties of entangled systems and find new applications for their use. Entangled photons have been proposed as a means to achieve long distance quantum communications and cryptography, perform computational tasks beyond the capabilities of classical computers, image beyond the classical limit, and provide measurement sensitivities unobtainable with analogous classical light sources. At the heart of these applications is the fact that entangled photons behave strikingly different from classical light or even pairs of independent single photons. This thesis reviews an interference effect known as nonlocal cancellation of dispersion and proposes and demonstrates a new, analogous effect which we term as nonlocal modulation. These techniques allow one to undo the effect of one dispersive medium or phase modulator with a similar system at a distant point in space, as observed by correlations between entangled photons. We explore the use of nonlocal dispersion compensation, combined with chirped quasi-phase-matched nonlinear crystals, to generate ultrashort entangled photons with high generation rates. To aid in the measurement of ultrashort entangled photons, we propose and demonstrate a resonance technique to increase the sensitivity of ultrafast sum-frequency-generation correlators. Principal accomplishments of this work include: the first observation of nonlocal modulation, the first observation of the radar-like "chirp and compress" technique with entangled photons, and the first experimental demonstration of resonant sum frequency generation using broadband biphotons.
A non-negative framework for joint modeling of spectral structure and temporal dynamics in sound mixtures [electronic resource]
Keywords: Music

Statistical modeling of audio is an ongoing pursuit. There is a great deal of structure in audio and good models need to make use of this structure. Audio is non-stationary but the statistics of the spectral structure are quite consistent over segments of time. Moreover, there is a structure to the non-stationarity itself in the form of temporal dynamics. Sound mixtures are commonly encountered in practice. Polyphonic music, multiple concurrent speakers, and most environmental sounds are mixtures. Moreover, most real world sound sources are actually a mixture of the source and noise. When dealing with mixtures, the structure of the individual sources becomes particularly important if we wish deal with the sources separately. In recent years, non-negative spectrogram factorization methods have become quite popular for modeling audio as they provide a rich representation of audio spectra and are amenable to high quality reconstructions. However, they disregard non-stationarity as they use a single dictionary to characterize the statistics of the spectral structure of an entire source. On the other hand, hidden Markov models (HMMs) cater well to non-stationarity and have been used successfully to model temporal dynamics. They can be powerful for audio analysis, as shown by their application to speech recognition. They can also be used for the reconstruction of sources but have certain limitations due to a rigid observation model. This can be an issue for high quality reconstructions. We propose a new model of single sound sources, the non-negative hidden Markov model (N-HMM), that jointly models the spectral structure and temporal dynamics of a given source. In the proposed model, rather than learning a single dictionary, we learn several small dictionaries that characterize the spectral structure of the source, catering well to non-stationarity. Moreover, we jointly learn a Markov chain that characterizes the temporal dynamics of the source. This is done with a flexible observation model that allows high quality reconstructions. We demonstrate this model on content-aware audio processing. We then propose a new model of sound mixtures, the non-negative factorial hidden Markov model (N-FHMM), that combines models of individual sources. This model incorporates the spectral structure and temporal dynamics of each individual source. We demonstrate the model on single channel source separation and show that it yields superior performance to non-negative spectrogram factorization. Although it is demonstrated on source separation, the N-FHMM is a general model of sound mixtures and can be used for various applications.
Novel capacitorless single-transistor DRAM technologies [electronic resource]
Keywords: Electrical Engineering

The dynamic random access memory (DRAM) industry has achieved miracles packing more and more memory bits onto ever smaller silicon die. But, the scaling of the conventional 1Transistor/1Capacitor (1T/1C) DRAM is becoming increasingly difficult, in particular due to the capacitor which has become harder to scale, as device geometries shrink. Recently the capacitorless single-transistor (1T) DRAMs have attracted attention, due to its ability to achieve higher memory cell density and to solve the problems associated with the scaling of the capacitor. The information is stored as different charge levels at a capacitor in conventional 1T/1C DRAM, whereas the 1T DRAM employs floating body effects within the transistor to store the information without the need of the capacitor. The absence of the capacitor is advantageous in terms of scalability, process and fabrication complexity, and compatibility with the logic processing steps, device density, yield and cost. Due to all these advantages of the capacitorless DRAM, and to solve the scaling problem of conventional 1T/1C DRAM, this work is focused on creating novel single transistor DRAM technologies. . The first device that is studied is vertical double-gate (DG) capacitorless single-transistor DRAM. This device has advantages of being vertical with a small footprint, it can be integrated on bulk Si and being a double-gate device allows better electrostatic control of the channel and higher intrinsic device retention. The second device that is investigated is a novel DRAM device: capacitorless single-transistor quantum well DRAM, which employs energy-band engineering approach within the body of the transistor in order to create a "hole storage pocket" and "carrier distribution control layer" which results in superior device characteristics in terms of scalability and memory sensing window. The third and the final device that is studied is also a novel DRAM device: capacitorless single-transistor charge trap DRAM. The body of this device is engineered with the intentional charge traps so as to obtain a memory effect. This novel DRAM relies on the existence and absence of electrons within its body and uses a charge-trapping mechanism in its memory operation, unlike conventional 1T DRAMs that rely on holes and employ floating-body effects in their memory operation.