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IDENTIFICATION OF CONVEYABLE PERCUSSION DUO REPERTOIRE
No full textPercussion duos have unique challenges when compared to other instrumental groups. Many percussion duos form during undergraduate studies at a university. After graduating, challenges for these percussion duos include access to equipment, transporting equipment, and planning rehearsals, especially when living a long distance from each other. This study will explore award-winning percussion duos who have successfully navigated these challenges. The author creates a resource for percussion duos which identifies pieces that can be transported in a 15-passenger van. The resource includes three performance recitals with a list of equipment for each recital and a document with repertoire which includes duration, publisher, and instrumentation
The Socio-Ideological Means Model of Radicalization
No full textThe present dissertation posits a novel theory of radicalization expanded from and complementary to the 3N model of radicalization (Kruglanski, Bélanger, & Gunaratna, 2019), holding that narrative and network, while conceptually distinct, are, in reality, operationally inextricable. The socio-ideological means model argues that radicalization is a consequence of the process by which a socio-ideological means is selected to attain the ultimate goal of meeting the universal, fundamental, human need for significance (Kruglanski et al., 2022). The “socio-ideology” is defined as a narrative shared by the network of which the individual is member and to which the individual refers. Without a network, real or imagined; online or in-person; past, present, or future, a narrative will not be considered valid and adherence to it will not be considered valuable. Without a shared narrative, however, a network will not wield influence over its members, who will not feel bonded to one another. The socio-ideological means model holds that significance is the core social motivation underlying political movements and political action, including political violence. The stronger the need for significance, the more susceptible one will be to significance-granting socio-ideologies, including those which advocate violent extremism. Moreover, as the need for significance becomes more strongly activated, the set of socio-ideologies perceived as available to meet the need expands, to include even those which contradict one another or might otherwise be considered extreme. The present dissertation illustrates the socio-ideological means model using examples from past research on terrorism and radicalization, decades of social psychological literature, and empirical support from correlational and experimental research results. Finally, directions for future research based on the socio-ideological means model and its derived hypotheses are suggested
Integrated Polymer Photonics: Thermo-Optic Properties and Low-Loss Fiber-to-Chip Couplers for Cryogenic and Broadband Applications
No full textIntegrated photonics consolidates multiple photonic functions onto a compact platform. It enables high-speed data transmission, advanced sensing technologies, and energy-efficient optical computing. While conventional photonic integrated circuits (PICs) rely on established semiconductor platforms, polymer-based photonics offer a low-cost, flexible alternative with tunable optical properties. This dissertation explores the role of polymer materials in integrated photonics, focusing on two key areas. The first involves the development of low-loss fiber-to-chip couplers for polymer-based photonic platforms, specifically SU-8 in the C and L bands, as well as III-V (AlGaAs) photonic devices in the visible range. The second focuses on the thermo-optic characterization of SU-8 at cryogenic temperatures, utilizing these fiber-to-chip couplers for efficient device integration and packaging, enabling precise optical measurements in cryogenic environments.
In the first part of this dissertation, a 3D low-loss, broadband fiber-to-chip coupler is developed for polymer-integrated photonics, incorporating custom-designed fiber receptacles that enable a self-aligning structure. The polymer coupler, fabricated via two-photon polymerization (TPP), facilitates seamless light transition between standard optical fibers and on-chip waveguides, significantly reducing coupling losses by integrating mode field adapters and a hybrid coupler-waveguide tapered structure. Custom-designed fiber receptacles ensure stable and consistent fiber positioning, eliminating the need for high-precision alignment and facilitating robust fiber-to-chip packaging.
In the second part of this dissertation, the thermo-optic coefficient (TOC) of SU-8 is characterized at cryogenic temperatures to better understand its behavior in superconducting and quantum photonic applications. SU-8 is widely used in photonic devices due to its excellent optical properties, low-loss characteristics, and ease of fabrication. However, its TOC at ultralow temperatures remains largely unexplored despite its critical importance in designing stable and efficient photonic circuits for cryogenic environments. To address this gap, the TOC of SU-8 is systematically measured down to 3 K using an integrated microring resonator approach. The results reveal a significant reduction in TOC, decreasing by nearly two orders of magnitude as the temperature drops from room temperature to cryogenic levels, providing key insights for future low-temperature photonic designs. A critical connection between the two parts of this dissertation is established through the integration of the same 3D fiber-to-chip couplers developed in Part 1. These couplers enable efficient and stable fiber-to-SU-8 microring resonator packaging for characterization inside the cryostat, which lacks real-time active fiber alignment capabilities. Their robust design ensures consistent optical coupling throughout multiple thermal cycles, demonstrating exceptional resilience in extreme temperatures.
This dissertation’s third and final part focuses on developing a fiber-to-chip coupler for III-V photonic integrated circuits (PICs) operating in the visible wavelength range. This work addresses fiber-to-chip coupling challenges in AlGaAs waveguides that contain embedded single-photon sources for quantum applications. Efficient optical pumping of these emitters is expected to generate a high flux of single photons propagating through the waveguides. However, existing grating-based coupling schemes suffer from extremely low collection efficiency, limiting the practical viability of these quantum photonic devices. To overcome this limitation, a novel coupler design is proposed to enhance photon extraction and fiber-to-chip coupling. A key challenge in this work is developing a fabrication process for suspended AlGaAs devices, which differ structurally from conventional planar waveguides. Moreover, the coupler operating at around 780 nm requires careful photonic design adaptation. While experimental validation is ongoing, the proposed design is designed to improve mode conversion between waveguide and fiber modes, with simulations predicting a coupling efficiency exceeding 80%. This dissertation also discusses the initial steps toward experimental realization, addressing fabrication constraints due to AlGaAs’s high reflectivity. Additional parameter tuning and fabrication steps, particularly related to the release of suspended structures after 3D nanoscale printing of couplers on AlGaAs are explored
PHYTOSCREENING FOR PER- AND POLYFLUOROALKYL SUBSTANCES (PFAS) NEAR FORT GEORGE G. MEADE
No full textPer- and polyfluoroalkyl substances (PFASs) are a class of contaminants with growing concerns regarding their toxicity to humans, their high persistence, and their ubiquity in the environment. Many U.S. military bases that historically used PFAS-based Aqueous Film-Forming Foams (AFFFs) are now being investigated by the U.S. Department of Defense (DOD) to address potential releases into the environment, especially into groundwater. This study explores a preliminary screening approach to delineate the plume of groundwater contamination—phytoscreening—which uses the analysis of plant tissue to detect subsurface contamination. For phytoscreening applications, this study evaluated three tree species: red maple (Acer rubrum), sweetgum (Liquidambar styraciflua), and willow oak (Quercus phellos). Sites were selected in proximity to a point source, Fort George G. Meade, in MD, USA. At each tree, samples were collected from the leaves, twigs, trunk (n=54, 9 trees), and soil surface (0-10 cm) (n=18) to characterize PFAS accumulation between each plant tissue. A linear mixed effects model with censored data (LMEC) was used to analyze the effects of species and plant tissue. Of the 15 compounds detected in groundwater at Fort Meade, red maple and willow oak captured 7 compounds each, while sweetgum captured 3. Red maple and willow oak accumulated higher ∑27PFAS concentrations than sweetgum. PFAS accumulation also varied by plant tissue, with twigs showing the highest concentrations, followed by the trunk, and then leaf. Results indicate that the plant species and tissue type play a critical role in the accumulation of PFAS in trees
THE CHANGING POLITICAL ORIENTATION OF A NATIONAL MINORITY: THE PALESTINIAN-ISRAELIS IN THE ISRAELI POLITICAL CRISIS, 2019-2022
No full textThis dissertation explores the background and events that led to the formation of Israel’s first binational government, comprising both Zionist parties and, for the first time ever, an Arab party that identifies as Palestinian, the “Ra’am” party. This first Jewish-Palestinian government was formed in June 2021, at the height of the Israeli political crisis of 2019-2022. Between April 2019 and November 2022, Israel held five general elections, two Knesset sessions that failed toform a majority, and two additional Knesset sessions that were unable to produce a functioning
government for more than a year.
This unforeseen crisis necessitated a recalibration of positions among a multitude of actors within the Israeli political sphere, with the overarching objective of mitigating the situation. The Ra’am party, a moderate Islamist party, surprised observers when it began campaigning ahead of the March 2021 elections in favor of joining a coalition, and subsequently succeeded in doing so.
In this dissertation, I examine the forces that led to the political crisis, the developments that occurred during each of the five elections of the crisis, and how these developments ultimately created an atmosphere conducive to Jewish-Arab cooperation in government. I also present a theoretical paradigm to explain the shift in behavior among key elements in the Zionist establishment, from completely ignoring Arab parties to supporting conditional cooperation with
them.
I refer to this paradigm as the “Incomplete Citizenship” framework, which I believe best explains the narrative, mechanisms, and apparatus behind forming a partnership between an Arab party and centrist-liberal-Zionist parties in the post-2019 world. The Incomplete Citizenship framework also includes a set of prerequisites that I have identified and argue are necessary for an Arab party to acquiesce to in order to be accepted by its liberal Zionist peers into a coalition.
This dissertation also provides novel data on public opinion in the Palestinian-Israeli community, as well as the views of Palestinian-Israeli elites who took part in setting the agenda, campaigning, and negotiating during the political crisis
BITES AND BEARDS: EXAMINING TICK-TURKEY DYNAMICS ACROSS AN URBAN- RURAL GRADIENT
No full textEastern wild turkey (M. gallopavo silvestris) sightings are becoming increasingly common in urban landscapes, potentially indicating a shift in turkey home ranges. Additionally, there are recent concerns that turkey and tick presence may be related, however the relationship remains poorly understood. In this study, I evaluated the occupancy and abundance of turkey populations across in eight parks in Montgomery County, MD. Additionally, I collected ticks in five out of the eight parks to assess local tick populations. I developed single-season occupancy models and hierarchical N-mixture abundance models for two seasons ecologically relevant to turkeys. This information enhances our understanding of turkey habitat preferences in county parks in Maryland, which is valuable for wildlife management in areas with significant human development. By better understanding turkey and tick distributions, we can mitigate human wildlife conflicts and contextualize the turkey-tick relationship
Cardiac Arrhythmia Prediction from Pediatric ECGs: A Novel Machine Learning Risk Model
No full textMachine Learning (ML) models are valuable tools in healthcare for early disease detection, risk stratification, and improved diagnostic accuracy, leading to more effective disease management. ML’s application in cardiology is especially promising, as the early detection and prediction of cardiac events can significantly improve clinical outcomes through timely intervention. However, the early detection of cardiovascular disease is limited in pediatric populations due to age-related physiological fluctuations in ECG metrics. This in-progress study addresses these challenges by developing a Pediatric Arrhythmia Risk Assessment Tool (PARAT) based on traditional ML models and deep learning approaches, including convolutional neural networks, to predict future arrhythmia onset in seemingly normal pediatric electrocardiograms (ECGs). The study first used Children’s National Hospital’s patient database to collect retrospective pediatric ECGs recorded between January 2000 and June 2025. The goal of data collection was to create a training dataset from two patient groups: a) control patients with no arrhythmia and b) patients with normal ECGs who later developed arrhythmia. For each ECG, information such as the reason for ECG, QT interval length, Bazett-corrected QT interval length, and heart rate was extracted. A total of 249,579 pediatric ECGs were collected, of which 54,216 were arrhythmogenic. The next steps involve training the ML models with the two datasets and evaluating their performance. Following its training and performance evaluation, PARAT aims to predict future arrhythmic events in pediatric patients, thereby improving diagnostic accuracy and disease management
A CRYOGENIC OPTICAL CAVITY FOR DIRECT ABSORPTION EXPERIMENTS: HIGH RESOLUTION SPECTROSCOPY OF BUFFER-GAS-COOLED MOLECULES
No full textOver 300 unique molecules have been detected in space, existing in extreme conditions that produce exotic chemistry. The work of this dissertation is to design and build an instrument that can reproduce some of these conditions and allow astrophysically relevant molecules to be studied in a controlled laboratory frame. The spectral signatures of two astrophysically relevant cyano species were probed using a custom-built instrument that combines buffer-gas cooling and cavity ringdown spectroscopy. The first portion of this dissertation (Chapter 2) details the construction and design of this instrument, as well as physics by which it operates. Chapter 3 discusses the use hydrogen cyanide as a initial probe molecule for first light experiments and benchmarking. Chapter 4 details the use of this instrument to probe the previously unassigned 2ν1 overtone of cyanoacetylene and assess the potential of the band to be used for observational exoplanet studies. Finally, Chapter 5 details the future directions for the instrument and the various chemical reactions it can explore
The City is Sick: Adaptive Transnational Care Between Cuba and the United States
No full textBetween 2022 and 2023, over a million people left Cuba, representing 10% of Cuba’s total population and the largest exodus in Cuban history. These numbers are staggering and highlight the dire conditions on the island that Cubans have been forced to navigate. In addition, they are troubling indicators of what is to come as Cuba contends with its infrastructural decline and increasingly aging population. While the majority of those who left Cuba have done so with the intention of relocating to the United States, others have moved to receiving countries throughout the world. This exodus is motivated by a series of acute crises endured within the context of years of chronic precarity that have made life barely livable for the Cuban people. It is further impacted by the need to fill in the gaps and manage the uncertainty with adaptive transnational care. This dissertation operationalizes the language of “sickness” to examine how interlocking systems of domination, namely legacies of imperialism and colonialism, have bound Cuba and the United States for centuries. These systems are entangled with the lives of the Cuban people, shaping the provision of transnational care. Therefore, this dissertation examines how the historical and contemporary political relationship of these two countries has shaped how people care for one another in profoundly life-altering ways
MULTI-DOMAIN BIOMETRIC RECOGNITION USING FACE AND BODY EMBEDDINGS
No full textAlthough image- or video-based biometric recognition boasts excellent performance in the visible spectrum even under unconstrained conditions with variations in pose, illumination, and resolution, biometric recognition in more challenging domains, such as infrared, surveillance imagery, or long-range imagery, remains a significant challenge due to domain shifts and limited labeled data. In this dissertation, we study the problem of multi-domain biometric recognition using face and body embeddings on the IARPA JANUS Benchmark Multi-domain Face (IJB-MDF) dataset.
While systems based on deep neural networks have produced remarkable performance on many tasks such as face/object detection and recognition, they also require large amounts of labeled training data. However, there are many applications where collecting a relatively large labeled training data may not be feasible due to time and/or financial constraints. Trying to train deep networks on these small datasets in the standard manner usually leads to serious over-fitting issues and poor generalization. We explore how a state-of-the-art deep learning pipeline for unconstrained visual face identification and verification can be adapted to domains with scarce data/label availability using a semi-supervised learning approach. The rationale for system adaptation and experiments are set in the following context - given a pretrained network (that was trained on a large training dataset in the source domain), adapt it to generalize onto a target domain using a relatively small labeled (typically hundred to ten thousand times smaller) and an unlabeled training dataset. We present algorithms and results of extensive experiments with varying training dataset sizes and composition, and model architectures, using the IJB-MDF dataset for training and evaluation with visible and short-wave infrared (SWIR) domains as the source and target domains respectively.
Next, we tackle some more challenging domains including visible surveillance, body-worn imagery, remote videos (captured at 300m, 400m and 500m) and short-wave infrared videos (captured at 15m and 30m). While significant research has been done in the fields of domain adaptation and domain generalization, in this dissertation we tackle scenarios in which these methods have limited applicability owing to the lack of training data from target domains. We focus on the problem of single-source (visible) and multi-target face recognition task. We demonstrate that the template generation algorithm plays a crucial role, especially as the complexity of the target domain increases. We propose a template generation algorithm called Norm Pooling (and a variant known as Sparse Pooling) and show that it outperforms traditional average pooling across different domains and network architectures using the IJB-MDF dataset.
Biometric recognition becomes increasingly challenging as we move away from the visible spectrum to infrared imagery, where domain discrepancies significantly impact identification performance. We show that body embeddings outperform face embeddings for cross-spectral person identification in the medium-wave infrared (MWIR) and long-wave infrared (LWIR) domains. Due to the lack of multi-domain datasets, previous research on cross-spectral body identification - also known as Visible-Infrared Person Re-Identification (VI-ReID) - has primarily focused on individual infrared bands, such as near-infrared (NIR) or LWIR, separately. We address the multi-domain body recognition problem using the IJB-MDF dataset, which enables matching of SWIR, MWIR, and LWIR images against RGB (VIS) images. We leverage a vision transformer architecture to establish benchmark results on the IJB-MDF dataset and, through extensive experiments, provide valuable insights into the interrelation of infrared domains, the adaptability of VIS-pretrained models, the role of local semantic features in body-embeddings, and effective training strategies for small datasets. Additionally, we show that finetuning a body model, pretrained exclusively on VIS data, with a simple combination of cross-entropy and triplet losses achieves state-of-the-art results on the LLCM dataset.
Finally, we integrate Side Information Embedding (SIE) to the ViT architecture and examine the impact of encoding domain and camera information to enhance cross-spectral matching. Surprisingly, our results show that encoding only the camera information, without explicitly incorporating domain information, achieves state-of-the-art performance on the LLCM dataset. While occlusion handling has been extensively studied in visible-spectrum person re-identification (Re-ID), occlusions in VI-ReID remain largely underexplored — primarily because existing VI-ReID datasets, such as LLCM, SYSU-MM01, and RegDB, predominantly feature full-body, unoccluded images. To address this gap, we analyze the impact of range-induced occlusions using the IJB-MDF dataset, which provides a diverse set of visible and infrared images captured at various distances, enabling cross-range, cross-spectral evaluations