Breaking Computational Barriers to Perform Time Series Pattern Mining at Scale and at the Edge

Uncovering repeated behavior in time series is an important problem in many domains such as medicine, geophysics, meteorology, and many more. With the continuing surge of smart/embedded devices generating time series data, there is an ever growing need to perform analysis on datasets of increasing size. Additionally, there is an increasing need for analysis at low power edge devices due to latency problems inherent to the speed of light and the sheer amount of data being recorded. The matrix profile has proven to be a tool highly suitable for pattern mining in time series; however, a naive approach to computing the matrix profile makes it impossible to use effectively in both the cloud and at the edge. This dissertation shows how, through the use of GPUs and machine learning, the matrix profile is computed more feasibly, both at cloud-scale and at sensor-scale. In addition, it illustrates why both of these types of computation are important and what new insights they can provide to practitioners working with time series data

Damped and zero-damped quasinormal modes of charged, nearly extremal black holes

Despite recent progress, the complete understanding of the perturbations of charged, rotating black holes as described by the Kerr-Newman metric remains an open and fundamental problem in relativity. In this study, we explore the existence of families of quasinormal modes of Kerr-Newman black holes whose decay rates limit to zero at extremality, called zero-damped modes in past studies. We review the nearly extremal and WKB approximation methods for spin-weighted scalar fields (governed by the Dudley-Finley equation) and give an accounting of the regimes where scalar zero-damped and damped modes exist. Using Leaver’s continued fraction method, we verify that these approximations give accurate predictions for the frequencies in their regimes of validity. In the nonrotating limit, we argue that gravito-electromagnetic perturbations of nearly extremal Reissner-Nordström black holes have zero-damped modes in addition to the well-known spectrum of damped modes. We provide an analytic formula for the frequencies of these modes, verify their existence using a numerical search, and demonstrate the accuracy of our formula. These results, along with recent numerical studies, point to the existence of a simple universal equation for the frequencies of zero-damped gravito-electromagnetic modes of Kerr-Newman black holes, whose precise form remains an open question

A recipe for echoes from exotic compact objects

Gravitational wave astronomy provides an unprecedented opportunity to test the nature of black holes and search for exotic, compact alternatives. Recent studies have shown that exotic compact objects (ECOs) can ring down in a manner similar to black holes, but can also produce a sequence of distinct pulses resembling the initial ringdown. These “echoes” would provide definite evidence for the existence of ECOs. In this work we study the generation of these echoes in a generic, parametrized model for the ECO, using Green’s functions. We show how to reprocess radiation in the near-horizon region of a Schwarzschild black hole into the asymptotic radiation from the corresponding source in an ECO spacetime. Our methods allow us to understand the connection between distinct echoes and ringing at the resonant frequencies of the compact object. We find that the quasinormal mode ringing in the black hole spacetime plays a central role in determining the shape of the first few echoes. We use this observation to develop a simple template for echo waveforms. This template preforms well over a variety of ECO parameters, and with improvements may prove useful in the analysis of gravitational waves

Engineered nanotherapeutics for pulmonary aerosol delivery

Despite centuries of use and widespread application, aerosol delivery of therapeutics remains limited to a small subset of diseases and active pharmaceutical ingredients, mainly restricted to small molecule delivery for asthma management. Respiratory diseases which would benefit from direct and localized treatment span a much larger landscape; chronic obstructive pulmonary disease (COPD), lower respiratory infections, and lung cancers alone globally contribute 7.8 million annual deaths, with a reported 117 million pulmonary cases (~37% of population, 2012) and over $88 billion in health care costs in the US[1, 2]. To expand the application of aerosol delivery, novel approaches are needed. To address this need, we have explored various applications of nanoparticle immune engineering for respiratory therapeutics[3]. Incorrect immune responses lie at the heart of most respiratory diseases and advances in these therapeutic areas requires consideration of the unique environment. Notably, the lung has an abundance of antigen presenting cells (APCs), such as macrophages and dendritic cells (DC), which phagocytose foreign materials at the air-lung interface. There are a number of lung-specific APC populations[4, 5]. Some subsets are well understood, however, other specialized subsets have only recently been identified due to historic challenges in differentiating these populations[6, 7]. Thus, there are many remaining questions as to the division of labor between these cells, their significance in different disease conditions, and their interactions with other adjacent cell populations at the mucosal interface[8]. Advancing this understanding is critical to develop new therapeutics; APCs are poised as the gatekeepers to lung regulation and lung DC-subset specifically are likely cellular targets for therapeutic intervention[9]. In order to better understand how these lung innate immune cells respond to inhaled particle therapeutics, we have developed sets of engineered particles with defined physical properties that originate at the molecular level. We have developed a series of metal organic framework (MOF) nanoparticle carriers with independently tunable particle size and internal porosity, enabling systematic investigation of the effect of particle pore structure on cellular interactions. These UIO-66 MOF derivatives have not only been optimized as pulmonary aerosol carriers but provide critical insight on the role of internal particle porosity following cellular internalization. To further modulate the lung immune environment and evaluate the role of ligand surface density on immune-modulation, we simultaneously developed a series of degradable polymeric nanoparticle carriers with controlled surface densities of two Toll-like receptor (TLR) ligands, lipopolysaccharide (LPS), corresponding to TLR-4, and CpG oligodeoxynucleotide, corresponding to TLR-9[10]. Our in vitro results with murine bone marrow derived macrophages and in vivo studies following a direct instillation to murine airways both support a trade-off between particle dosage and optimal surface density; proinflammatory cytokine production was driven by the distribution of the adjuvant dose to a maximal number of innate cells, whereas the upregulation of costimulatory molecules on individual cells required an optimal density of TLR ligand on the particle surface. Taken together, results from these two sets of particle types demonstrate that both particle porosity and ligand surface density are critical parameters for tight control of immune stimulation and association with lung APCs and provide a foundation to build pathogen mimicking particle (PMP) vaccines and immunostimulatory therapeutics. References: 1. WHO: World Health Organization 2012. 2. NIH: National Heart, Lung, and Blood Institute 2012. 3. Moon, J. J.; Huang, B.; Irvine, D. J., Advanced materials (Deerfield Beach, Fla.) 2012, 24 (28), 3724-46. 4. Guilliams, M.; Lambrecht, B. N.; Hammad, H., Mucosal Immunol 2013, 6 (3), 464-73. 5. Kopf, M.; Schneider, C.; Nobs, S. P., Nat Immunol 2015, 16 (1), 36-44. 6. Blank, F.; Stumbles, P. A.; Seydoux, E.; Holt, P. G.; Fink, A.; Rothen-Rutishauser, B.; Strickland, D. H.; von Garnier, C., Am J Respir Cell Mol Biol 2013, 49 (1), 67-77. 7. Fytianos, K.; Drasler, B.; Blank, F.; von Garnier, C.; Seydoux, E.; Rodriguez-Lorenzo, L.; Petri-Fink, A.; Rothen-Rutishauser, B., Nanomedicine (Lond) 2016, 11 (18), 2457-2469. 8. Hasenberg, M.; Stegemann-Koniszewski, S.; Gunzer, M., Immunol Rev 2013, 25 (1), 189-214. 9. Zhao, L.; Seth, A.; Wibowo, N.; Zhao, C. X.; Mitter, N.; Yu, C.; Middelberg, A. P., Vaccine 2014, 32 (3), 327-37. 10. Noble, J.; Zimmerman, A.; Fromen, C. A., ACS Biomater Sci Eng 2017, 3 (4), 560-571

Anomaly Detection and Accuracy Measurement for Categorical Data

The Department of Defense (DoD) recently initiated an effort to compile all inter-service maintenance data for equipment and infrastructure, requiring the consolidation of maintenance records from over 40 different data sources.  This research evaluates and improves the accuracy of this maintenance data warehouse by means of value modeling and statistical methods for anomaly detection. The first step in this work included the categorization of error-identifying metadata, which was then consolidated into a weighted scoring model. The most novel aspect of the work involved error identification processes using conditional probability combinations and likelihood measures. This analysis showed promising results, successfully identifying numerous invalid maintenance description labels through the use of conditional probability tests. This process has potential to both reduce the amount of manual labor necessary to clean the DoD maintenance data records and provide better fidelity on DoD maintenance activities

A gut-to-brain signal of fluid osmolarity controls thirst satiation.

Satiation is the process by which eating and drinking reduce appetite. For thirst, oropharyngeal cues have a critical role in driving satiation by reporting to the brain the volume of fluid that has been ingested1-12. By contrast, the mechanisms that relay the osmolarity of ingested fluids remain poorly understood. Here we show that the water and salt content of the gastrointestinal tract are precisely measured and then rapidly communicated to the brain to control drinking behaviour in mice. We demonstrate that this osmosensory signal is necessary and sufficient for satiation during normal drinking, involves the vagus nerve and is transmitted to key forebrain neurons that control thirst and vasopressin secretion. Using microendoscopic imaging, we show that individual neurons compute homeostatic need by integrating this gastrointestinal osmosensory information with oropharyngeal and blood-borne signals. These findings reveal how the fluid homeostasis system monitors the osmolarity of ingested fluids to dynamically control drinking behaviour