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Mummy Issues: Representations of the Man of Science and the Monstrous Mummy in Bram Stoker’s the Jewel of Seven Stars
No full textIn this dissertation, I examine fictional representations of nineteenth-century men of science in
conflict with a monster that emerged from Western meddling in Egypt: the mummy. While my
primary focus is on Bram Stoker’s novel The Jewel of Seven Stars (1903, 1912), I begin by
tracing the development of the cultural and literary trends that ultimately led to the rise of the
monstrous mummy: the British obsession with ancient Egyptian culture, the public unrolling of
mummies as ostensibly scientific spectacles, the publication of the first British mummy novel in
the early 1800s, and the late-century popularity of the mummy romance, a genre featuring
beautiful female mummies as objects of romantic desire.
My analysis of Stoker’s Jewel consists of three parts. In the first part, I examine the novel’s
representations of men of science: male characters who work as amateurs or paid professionals in
fields of specialized knowledge and who attempt to reach conclusions using the faculty of
reason. Together, they represent multiple specialties in the areas of law, medicine, and
Egyptology. I examine each character from the perspectives of status, scientific methodology,
and character as revealed through physiognomy. I also provide historical context for the many
disciplines referred to in the novel: forensics, neurology, hypnotism, archeology, and linguistics.
The second part focuses on the mummy, Queen Tera. With reference to the theoretical works of
Noël Carroll and Jeffrey Cohen, I argue that Tera is a “monster” who possesses both forbidden
knowledge and supernatural power—specifically, the power of astral projection. In the third part,
I break down the “Great Experiment” that is intended to reanimate the mummy, from the
scientific space in which it is performed to the contradictory outcomes of the experiment in the
1903 and 1912 editions of the novel. Stoker creates a complex scientific narrative for the
experiment that reimagines a supernatural process in terms of contemporary science, which I
place in historical context. Ultimately, I argue that Jewel’s mummy is a monster created by a
man of science—the feminine Nemesis to masculine scientific Hubris
Computation of Cycle Bases in Surface Embedded Graphs
No full textWe study the problem of finding a cycle basis, a minimum weight set of independent cycles that form a basis of the cycle space for a given graph. We focus on finding
the minimum cycle basis of directed graphs. This is a more complicated problem compared to the undirected case as the underlying field is Q for directed graphs instead of Z2
for undirected, which causes problems in the speed of calculations. Previously the fastest
known deterministic algorithm to find the minimum cycle basis of a directed graph runs in
O(m3n + m2n
2
log n) time [11]. We concentrate on graphs embedded on a surface of genus
g. We modify algorithms for undirected graphs to work on directed graphs. We present an
O(mn2
g
2
log g + mω+1) time algorithm to find the minimum cycle basis of a directed graph
embedded on a surface of genus g. We also give an improvement on the minimum cycle basis
in the undirected cas
Essays on Pricing Strategy and Decentralization Mechanism of Two-sided Sharing Platforms
No full textIn recent years, two-sided sharing platforms have emerged and thrived in many industries.
For example, Uber and Lyft are two of the largest platforms in the transportation sharing
market. Different from a traditional firm, a two-sided sharing platform has indirect control
on its supply side, which consists of independent self-schedule agents. In contrast, a traditional firm has direct contract-based employment relationship with its suppliers. Although
the difference in the business model, both the two-sided sharing platform and traditional
firm face many of the same strategic and operational challenges. In my first two chapters,
we investigate the pricing strategies of a two-sided sharing platform when some of the challenges are present. My third chapter is motivated by the emerging blockchain technology,
which enables transactions and data storage in a decentralized system. We propose a new
mechanism to build a decentralized system for sharing platforms.
In Chapter 2, we examine a sharing platform’s quality differentiation strategy through priority matching to serve consumers with heterogeneous willingness-to-pay for match quality.
The platforms also differentiate wages to providers that are heterogeneous in their costs and
offerings. Different from a traditional firm who only faces strategic behavior from consumers,
a platform faces strategic behavior from both consumers and providers. On the demand side,
the strategic behavior from consumers enables demand-side cannibalization, which discourages both the platform and the traditional seller to offer priority matching. On the supply
side, we find that if providers are non-strategic in accepting service requests, it is optimal
for the platform to differentiate wages as well if it offers priority matching and vice versa.
However, if providers behave strategically then the platform faces potential supply-side cannibalization, a risk that a traditional seller does not face. In such cases, it is not necessarily
optimal for the platform to differentiate wages when it offers priority matching. We find
that an increase in the degree of supply-side cannibalization discourages the platform from
offering differentiated services (wages) to consumers (providers). On the other hand, an
increase in the degree of demand-side cannibalization could encourage the platform to offer
differentiated wages despite supply-side cannibalization.
In Chapter 3, we study a sharing platform’s decision to offer subscription payment option
to consumers, when it faces consumers and providers with uncertainty and heterogeneous
frequencies to visit the platform. We find that the sharing economy platform’s incentive to
offer subscription payment option is driven by the provider-to-consumer coverage ratio in a
market. The platform chooses not to offer subscription option when there are fewer providers
and more consumers but offer subscription option when there are more providers and fewer
consumers. We also identify the effects of demand and supply variability on the platform’s
incentive to offer subscription payment option; when the demand potential variability is
greater, the platform prefers to offer a subscription payment option to induce only frequent
consumers to subscribe. In contrast, when the supply potential variability is greater, the
platform prefers to offer a subscription payment option with fewer number of subscribers or
no subscription payment option at all. In addition, compared to when subscription payment
option is not offered, when subscription payment option is offered, the infrequent consumers
are worse off while the frequent consumers can be better off. On the other hand, providers
are always better off and the social welfare is improved.
In Chapter 4, we propose a new blockchain mechanism called Proof-of-Merit (PoM) and
demonstrate our design in the context of ridesharing. Many businesses such as online retailing
platforms, airline companies, and sharing economy platforms need to solve complex problems
to determine how their business transactions are to be generated. However, the current
popular blockchain mechanisms like Proof-of-Work (PoW) and Proof-of-Stake (PoS) can only
be applied to the situations where transactions already exist, but not in the situations where
the transactions need to be generated. To address this problem, we develop PoM mechanism
that finds the block owner who solves complex problems that generate transactions. We
replace the notion of miners in PoW with matchers who provide matching solutions that
match riders to drivers in each period. In PoM, a winner is selected from a group of matchers
to create the next block based on the quality (merit) of their matching solutions. We
demonstrate the viability and nuances of the approach using agent-based simulation. We
show how the intrinsic tradeoff between two performance aspects of PoM, efficiency and
equity, is affected by a key design parameter called DCP and how PoM is able to achieve a
desirable tradeoff by controlling this parameter
Designed Experience and the Logics of Environments
No full textFrom landscape paintings to digital art and games, the methods of meaning-making and visual
representation reveal how designed experience modifies “Nature” (with a capital N) as a concept.
Drawing on an ocular-centered worldview, the concept of “Nature” considers the differences in
relationships to be mechanisms that divide, classify, and control instead of than ones that
connect, collaborate, and activate. I argue that designed experience that thinks of environments
(plural), and not of singular concepts such as “the environment” or “Nature,” builds connections
with “response-ability” (Barad 2012) based on differentiation instead of division. I embrace
Timothy Morton’s “ecological thought” and emphasis on plural environments by expanding on
them and incorporating them into the field of visual culture studies, game studies, and design
thinking (2010). Environments can be understood as this idea of moving together to build, create,
and form various modes of cohabiting and collaborating. Environments are not stable sites but
constantly changing structures that evolve out of the behaviors and encounters between living
beings. The notion of environments happens on a horizontal plane, meaning they are not
hierarchically separated.
The cases that I analyzed in my chapters, teamLab’s two installations Flutter of Butterflies
Beyond Borders (2015) and The Void (2016), and Walden, a game (2014), aim to connect
“Nature” and humans by accepting a clear-cut separation. The designed experience that they
foster re-establishes a division between an urban site, a wilderness site, and a human appearing
as an outsider. However, the separations are not solid or sanitized but leaky, subtle, and
ambiguous. Using a paper prototype of the game-like experience Corridor (2023), which I
designed, I underline how urban, and transportation can be perceived as a matter of coexistence
through the logic of environments. I turn to transportation as a designed experience of movement
and a helpful metaphor for understanding environments through the idea of moving together
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems
No full textIncreasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and
terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, in-
dustrial packaging, medical imaging, and non-destructive testing. Traditional methods for
perception and imaging are challenged by novel data-driven algorithms that offer improved
resolution, localization, and detection rates. Over the past decade, deep learning technology
has garnered substantial popularity, particularly in perception and computer vision appli-
cations. Whereas conventional signal processing techniques are more easily generalized to
various applications, hybrid approaches where signal processing and learning-based algo-
rithms are interleaved pose a promising compromise between performance and generalizabil-
ity. Furthermore, such hybrid algorithms improve model training by leveraging the known
characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep
learning algorithms and offering higher performance than conventional methods.
This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging
systems applicable to a host of problems in perception and sensing. Various problem spaces
are explored, including static and dynamic gesture classification; precise hand localization
for human computer interaction; high-resolution near-field mmWave imaging using forward
synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image
super-resolution using deep neural network (DNN) and Vision Transformer (ViT) archi-
tectures; and data-level multiband radar fusion using a novel hybrid-learning architecture.
Furthermore, we introduce several novel approaches for deep learning model training and
dataset synthesis. Depending on the application, a varying balance of classical signal pro-
cessing techniques and deep learning is applied to optimally leverage the advantages of each
technique. To verify the proposed algorithms, we employ virtual prototyping via simula-
tion and develop custom-built imaging testbeds for empirical testing. Our custom tools for
algorithm development, dataset generation, system-level design, and deployment are made
public to promote further innovation in this arena. The simulation and experimental results
demonstrate the wide application space of hybrid-learning algorithms and the efficacy of
joint signal processing data-driven algorithms for radar sensing, perception, and imaging
Validating Business Problem Hypotheses: a Goal-oriented and Machine Learning-based Approach
No full textValidating a business problem hindering a business goal is often more important than finding
solutions to the problem, specifically during requirements engineering. For example, validating the impact of a client’s low account, high transactions, or high loan payment per
month for a client’s unpaid loan decreasing the loan revenue of one bank would be critical
as an information system can be designed for the bank to take some actions to mitigate
the loan default. However, many business organizations are struggling to confirm whether
some potential problems hidden in Big Data are against a business goal or not. In other
words, they face difficulties finding real business problems and then improving business value,
although the investment in Big Data and Machine Learning (ML) projects has increased.
One challenge might include a lack of understanding about relationships between business
problems and data. The other challenges might consist of determining a testable factor
associated with a potential business problem, preparing a relevant dataset corresponding
to the business problem, analyzing the impact on the business problem to other problems,
and reasoning about inter-connected problems. Information systems solving unconfirmed
problems frequently tackle an erroneous problem and give incorrect predictions, leading to
some dissatisfying systems, consequently not achieving business goals, even redeveloping the
systems and taking many business resources. This dissertation presents a Goal-Oriented and
M achine learning-based framework using the notion of a Problem HY pothesis, Gomphy, to
help validate potential business problems. We propose five main technical contributions: 1.
The domain-independent Gomphy ontology and process are presented explicitly and formally
for describing categories of essential concepts and relationships concerning business goals,
problems, problem hypotheses, ML, and a dataset. The ontology ensures that business goals
and the related business problem hypotheses are traceable to an ML dataset. 2. An entity modeling method of a problem hypothesis is elaborated to help capture business events
and determine testable factors. 3. A data preparation method is described to build an ML
dataset, mapping a concept of a problem hypothesis to a data feature. 4. A feature evaluation method is presented using ML and ML Explainability library to detect contribution
relationships among the business hypotheses and problems. 5. A set of formalized validation rules are described for reasoning about connected problem hypothesis validation in a
goal-oriented problem hypothesis model. To see the strength and weaknesses of the Gomphy framework, we have validated potential banking problems about an unpaid loan and
customer churn in one retail bank as empirical studies. We feel that at least the proposed
framework helps validate business events that negatively contribute to a goal, providing
insights about the validated problem
Sputtered Electrode Coatings for Neural Stimulation and Recording
No full textElectrode coatings form an integral part of implantable microelectrode arrays (MEAs) which are
the basis for chronic neural interfaces capable of providing feedback though neural recording and
eliciting functional response through electrical stimulation in patients with lost or impaired
neurological functionality. The aim of this dissertation was to develop and assess chronically stable
electrode materials with low-impedance and high charge-injection capacity for neural recording
and stimulation respectively. To this end, we have investigated three types of transition metal oxide
thin-films, iridium oxide, ruthenium oxide and a mixed ruthenium/titanium oxide formed by a DC
magnetron reactive sputtering process. We have characterized these films in terms of their
microstructure, composition and electrochemistry in order to establish the relationship between
film properties and electrochemical charge-injection capacities.
Sputtered iridium oxide films (SIROF) and ruthenium oxide films (RuOx) were deposited by
reactive sputtering in a DC magnetron sputtering system using water-vapor as a reactive plasma
constituent. The films deposited using a combination of oxygen and water-vapor showed lower
impedance and higher charge-injection capacity than the films deposited using oxygen alone. The
films deposited using only water-vapor as the reactive gas showed the presence of nano-sized
metallic iridium and ruthenium on the SIROF and RuOx films respectively.
Systematic investigation by plasma optical spectroscopy, surface characterization and
electrochemical measurements revealed that the incorporation of water-vapor as a reactive gas
constituent, along with oxygen, alters the reduction-oxidation (redox) state of the plasma as well
as the microstructure and the electrochemical characteristics for the SIROF and RuOx films. A 3:1
water-vapor to oxygen plasma condition formed a hydrous, low-crystallinity and nodular film
microstructure that enabled both electronic and ionic conduction producing an apparent optimal
electrode coating with low impedance for recording and high charge-injection capacity for
stimulation.
Electrochemical measurements on SIROF and RuOx electrodes, deposited using a ratio of watervapor to oxygen 3:1, demonstrated that these have minimal contribution towards oxygen reduction,
an unwanted side reaction and also showed rapid reversibility of their respective Faradaic
processes during stimulation current pulsing. These films were found to be electrochemically
stable for ~1 billion pulses at biphasic 8 nC/phase (0.4 mC/cm2
) constant current stimulation in an
inorganic model of interstitial fluid at 37o C and also showed no indication of cytotoxicity towards
primary cortical neurons in a cell viability assay.
Additionally, we investigated the deposition and characterization of sputtered mixed oxide films
consisting of ruthenium/titanium oxide (RuTiOx) as a neural stimulation and recording electrode.
This study was motivated by the observation that RuOx films deposited at optimal watervapor:oxygen gas flow ratios were more friable and thus less physically stable than SIROF
deposited under the same conditions. Incorporation of titanium resulted in an increased the
hardness of the film, by a factor of two with respect to RuOx films, while still maintaining an
adequately low enough impedance for recording and a charge-injection capacity suitable for
clinical neural stimulation
A Green Chemistry Approach to Designing Bio-based Resins for 3D Printing
No full textSustainability has become a great topic of interest in recent years. Most associate sustainability
with how a material is produced, consumed, and disposed of, and how this continuous cycle
effects the environment. However, sustainability is a much more complex concept with nuance
regarding its reach on societal issues such as resource inequality and access, as well as, economic
availability and convenience. The reliance on petroleum-based materials, such as plastics, in
modern day society has been discussed in great depth in relation to sustainability. This is due to
fossil fuels being main contributors to environmental pollution, resource degradation, and rising
global temperatures. On the other hand, the dependence on plastics in almost every industry is a
result of their ease of production, favorable mechanical properties, and cost effectiveness.
Because of this, it will be difficult to influence a shift from petroleum-based materials to more
bio-based materials if these new materials do not perform similarity to industrially used plastics.
It is also crucial to consider the manufacturing processed used to produce the materials.
Competitive manufacturing techniques such as 3DP (3D printing) has become a desired
technique due to its ability to fabricate complex, uniform, and flexible products without the use
of molds or machining, its ability to provide fast, on-site production, and its cost-effective nature
without wasting unnecessary materials or excessive waste production. The main goal of this
research was to develop bio-based resins that are compatible with digital light processing 3D
printing technologies (DLP 3DP) and optimize these materials such that they have comparable
properties to commonly utilized plastics. Not only that, but a focus on designing materials that
can be chemically recycled, thermally healed, and mechanically reprocessed while maintaining
their mechanical properties, will help to produce a movement toward the implementation of more
eco-friendly materials. Here, a background on the sustainability movement and its evolution, as
well as, the issues associated with plastics will be discussed. Additionally, the implementation of
bio-based feedstocks within the design of recyclable, self-healable, and reprocessable thermosets
will be investigated and looked at through a green chemistry lens. The concepts explaining the
various 3DP technologies, the pros of utilizing such techniques, and the fabrication of 3D
printable resins will be explored. Finally, applications of such concepts utilizing bio-based
feedstocks such as functionalized vanillin, guaiacol, and eugenol with scientific, peer-reviewed
research with the reported findings will be presented
IoT Data Discovery and Learning
No full textThe massive number of Internet-of-Things (IoT) creates a torrent of data. These data may
be stored and hosted by nodes dispersed over the edge of the Internet, forming peer-to-peer
(p2p) IoT database networks (IoT-DBNs) that can be dynamically discovered and used to
enhance daily operations and solve real-world problems. The issues toward making use of the
massive amount of IoT data include how to discover the IoT data streams from the IoT-DBN
and how to learn and extract useful knowledge from the discovered data to help cope with
dynamically arising tasks. In this dissertation, we consider these two problems and develop
solutions for them.
First, we consider the IoT data discovery problem in growing IoT-DBNs. We show the
benefits of p2p unstructured routing for IoT data discovery and point out the space efficiency
issue that has been overlooked in keyword-based routing algorithms. As the first in the field,
this work investigates routing table designs and various compression techniques to support
effective and space efficient IoT data discovery routing. Novel summarization algorithms
are proposed, including alphabetical-based, hash-based, and meaning-based summarization
and their corresponding coding schemes. We also consider routing table design to support
summarization without degrading lookup efficiency for discovery query routing. To evaluate
our approach, we collected 100K IoT data streams from various IoT resources and distributed
them over a simulated Internet. Then, our data discovery routing with the summarization
techniques is applied for handling discovery queries. The results show that our summarization
solutions can reduce the routing table size by 20 to 30 folds with a 2-5% increase in latency
compared with other peer-to-peer discovery routing algorithms. Our approach outperforms
DHT-based approaches by 2 to 6 folds in latency and communication cost.
After IoT data discovery and retrieval, a prominent problem is how to learn from the data to
address real-world tasks. Since different applications require different learning schemes, we
choose to focus on one example application, the estimated time of arrival (ETA) problem,
which is very important in intelligent transportation systems and has received a lot of attention
recently. Though many tools exist for ETA, ETA for special vehicles, such as ambulances,
fire engines, etc., is still challenging due to the scarcity or non-existence of data. To tackle
it, we propose a deep transfer learning framework TLETA for the ETA of special vehicles,
namely TLETA. TLETA constructs cellular level spatial-temporal knowledge for fine-grained
extraction of driving patterns. The learning network contains transferable layers to support
knowledge transfer between different categories of vehicles. Importantly, our transfer models
only train the last layers to map the transferred knowledge, significantly reducing the training
time to achieve real-time learning. We also introduce the inter-region transfer method to
build a mapping function between vehicle domains within a region. The mapping functions
of top-k region spatial-temporal similarity are then used to construct the predictor in regions
whose target data is unavailable. The experimental studies show that our model outperforms
many state-of-the-art approaches in accuracy and training time
Advances in Methodologies Using EEG to Characterize the Cortical Processing of Speech and Its Perceived Sound Quality
No full textSpeech perception is dependent on access to the amplitude, spectral and temporal information in
speech. This dissertation focuses on the temporal structure of speech, which consists of a slow-
varying amplitude (temporal envelope, ENV) and a rapid-varying frequency (temporal fine
structure, TFS). Past studies on speech perception [for review, see Lorenzi and Moore 2008]
suggest ENV alone is sufficient for speech perception in quiet and TFS alone is used to segregate
speech from the background noise (e.g., a competing talker scenario). It has been shown that the
reduction in subjective quality ratings obtained through behavioral quality assessment is correlated
to the degree of degradation in the temporal envelope. However, the neural correlates of sound
quality perception with continuous speech are still unclear. This dissertation explores two
complementary research goals proposed as studies which consider speech perception as it relates
to ENV and TFS and its sound quality perception.
The dissertation is comprised of two studies: Study 1 attempts to characterize the cortical
processing of speech and Study 2 attempts to characterize the perceived sound quality in normal-
hearing listeners. First, the overall introduction to both studies is provided in Chapter 1. Next, we
lay out the background of both studies in detail in Chapter 2. Chapter 3 presents Study 1 of this
dissertation and investigates the role and relative contribution of ENV and TFS to speech
perception in normal hearing listeners in quiet. The synchronization between brain oscillations at
different frequency bands is commonly used as a marker for the key mechanisms in coordinating
neural dynamics for different temporal and spatial domains [Canolty and Knight 2010]. When
neural oscillations of two different frequency bands synchronize, their “peak” frequencies usually
exhibit a harmonic relationship. A recent study [Rodriguez and Alaerts 2019] showed a prominent
occurrence of this 2:1 harmonic cross-frequency relationship between alpha (8-14 Hz) and theta
(4-8 Hz) rhythms when task-relevant efficient cognitive processing is engaged. Study 1 examined
this power-power cross-frequency coupling (CFC) between alpha (8-14 Hz) and theta (4-8 Hz)
and also between gamma (30-100 Hz) and theta frequency bands of cortical activity in normal-
hearing listeners using electroencephalography (EEG) signals when processing ENV and TFS of
speech. The results showed a relatively increased CFC when listening to ENV alone. This finding
may suggest more synchrony across different frequency bands of cortical activity in processing
ENV than TFS.
Recent studies have shown that cortical activity basically tracks the envelope of continuous natural
speech, which could potentially serve as a useful method to study the underlying processes for
speech perception. Study 2 of the dissertation, presented in Chapters 4 & 5) investigates the
differences in cortical entrainment to the envelope of speech spoken by cochlear implant (CI)
talkers (degraded speech) and normal-hearing (NH) talkers. Although, a CI may help individuals
with hearing loss to restore or improve the ability to hear and provide the auditory feedback
necessary for improved speech production, speech produced by CI users is mostly abnormal
compared to normal hearing individuals (Gautam et al., 2019). The motivation is to achieve a
metric to assess “how well” hard-of-hearing talkers have spoken and the auditory feedback they
received in their current aural compensation. The results showed higher perceived sound quality
and closer tracking of speech envelope in normal-hearing listeners when listening to a sample of
speech produced by NH talkers than that for CI talkers. Finally, Chapter 6 presents overall
conclusions with contributions of the dissertation, and a discussion of possible directions for future
work.
The two key research aims pertaining to Study 1 and Study 2 respectively were to: 1) examine the
brain electrical activity and brain networks underlying the perception of ENV and TFS information
as compared to processing the original speech itself and thereby investigating the relative role of
ENV and TFS in speech perception in normal-hearing listeners and 2) to determine how well the
envelope of speech is represented neurophysiologically by objectively quantifying the cortical
tracking of speech envelope and to show how this cortical tracking of speech envelope differentiate
between the sample of speech produced by CI talkers and NH talkers in relation to speech’s
perceived sound quality.
The findings together from Study 1 and Study 2 provide insight into the neural mechanisms
involved in the cortical processing of ENV and TFS of continuous speech and its perceived sound
quality in normal-hearing listeners