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Synthesis of Pyrrole Functionalized Materials for Organic Electronic Applications
No full textPyrrole is a well-known class of blocks used for conductive polymers and semiconducting
materials applied in organic electronics. Pyrroles were initially involved in developing conducting
polymer-based materials and relevant applications due to their high electron-rich properties and
doping ability. Lately, pyrroles got involved in the synthesis of organic semiconducting materials.
However, due to their high electron richness and elevated highest occupied molecular orbitals
(HOMO), pyrrole compounds were prone to oxidize in the air; Thus, it was hard to work with
pyrrole chemistry initially. Recently, scientists started to demonstrate an effective way of using
electron-rich pyrrole moieties in organic semiconductors by synthesizing pyrrole-fused aromatic
heterocyclic ring systems. Fusing pyrrole moieties with other stable aromatic ring systems such as
thiophene has assisted pyrroles with air stability by lowering HOMO and providing an opportunity
to fine-tune the bandgap. Following this, several different pyrrole-fused heterocyclic aromatic
blocks such as thienopyrrole, dithienopyrrole, and thienodipyrrole were introduced and
incorporated in organic semiconductors to apply them in organic electronics later. Pyrrole moieties
of these blocks paved the path to perform various structural modifications through N-functionalization, leading to the development of solution-processable semiconducting materials
from insoluble fused blocks. Solubilizing unit modifications on pyrrole N atoms improved the
solvent compatibility of fused-ring semiconducting materials, leading to a low-cost solvent
processing of such organic semiconductors. Even though pyrrole-containing fused-ring blocks are
advantageous in developing hole-transporting semiconductors due to their electron richness, there
are still not many studies performed to identify various other potential fused-pyrrole blocks that
can be used in the hole transporting semiconductors. Thus, it is necessary to systematically design
various pyrrole-functionalized blocks and materials for applying them in organic electronics to
fully understand their structure-property relationship with regards to the development of hole
transporting solution processable materials. In this study, such pyrrole functionalized organic small
molecular materials were systematically investigated to reveal their structure-property relationship
and OFETs application potentials. In regards to this, Chapter 1 summarizes the evolution of
organic semiconductors, pyrrole-based semiconducting materials applied in organic electronics,
and other related potential donors & acceptor blocks. In Chapter 2, we have demonstrated synthesis
and OFETs applications of thiophene or furan spacers flanked siloxane side-chain modified
diketopyrrolopyrrole (DPP) acceptors and thienopyrrole donors containing small molecules. This
is the first-time report of DPP and thienopyrrole containing small molecules applied in OFETs. In
Chapter 3, understudied 1H-indole and pyrrolopyridine potential donor blocks were incorporated
in thiophene spacers flanked benzothiadiazole-based donor-acceptor small molecules for applying
them in OFETs to understand the potential of these pyrrole-based materials in OFETs. In Chapter
4, an extension to the study in chapter 3 was performed by systematically varying the
chalcogenophehene spacer flanked to benzothiadiazole in 1H-indole-benzothiadiazole-based donor-acceptor small molecular design to further investigate the structure-property relationship of
the fused-pyrrole containing small molecular systems and their OFETs application potentials
Ensuring Integrity, Privacy, and Fairness for Machine Learning Using Trusted Execution Environments
No full textIn this day and age, numerous decision-making systems increasingly rely on machine learning
(ML) and deep learning to deliver cutting-edge technologies to the members of society. Due
to potential security, privacy and bias issues with respect to these ML methods, currently, end
users cannot fully trust these systems with their private data, and their prediction outcome.
For instance, in many cases, it is not clear how an individual’s medical record is being used
for building tools for medical diagnosis? Is the data always encrypted at rest? When they
are decrypted, is there a guarantee that only a trusted application can have access to the
private data to eliminate potential misuse? Throughout this dissertation, solutions that
leverage various security and integrity capabilities provided by hardware assisted Trusted
Execution Environments (TEE) are proposed to make these ML based systems more reliable
and trustworthy so that end users can have a greater trust in these systems.
As a starting point, we first address the privacy and integrity issues in ML model learning
in the cloud setting. Training of a deep learning model that only relies on a TEE is not
very attractive to businesses that need to continuously train their models in a remote cloud
setting. This is due to the fact that special hardware such as Graphical Processing Units
(GPU) are much more efficient in training ML models compared to CPU based TEEs. In this
dissertation, we propose an integrity-preserving solution that combines TEEs, and GPUs in
order to provide an efficient solution. In this solution, we focus on the ML model training
task using the efficient GPU while ensuring the detect any deviation from the ML model
learning protocol with a high probability using the TEE capabilities. Using our solution, we
can ascertain (with high probability) the model is trained with the correct training dataset
using the correct training hyperparameters, and correct code execution flow.
Once we provide an integrity preserving ML model training solution, we focus on how to
use the learned ML model privately and securely in practice. To provide privacy-preserving
inference on sensitive data, wherein ML model owner and data owner do not trust each
other, the dissertation proposes a solution that the inference task is run inside a TEE and
the result is sent to the data owner(s). The most important benefit of our solution is that
the data owner can ensure their data will not be used for any other purposes in the future
and no information other than the agreed model inference result is disclosed. Furthermore,
we show the efficacy of our solution in the context of genomic data analysis.
Next we focus on the bias and unfairness embedded in certain ML models. It is has been
reported that the ML models can unfairly treat certain subgroups, and it is hard to test for
such issues in application deployment settings where both the ML model and the input data
to the ML model is sensitive (i.e., both the model and the data cannot be disclosed to public
for auditing directly). This dissertation proposes a privacy-preserving solution for fairness
analytics using TEEs. In this setting, the model owner and the fairness test set owner do not
trust each other, therefore they do not want their input to be disclosed. The end goal is for
the fairness analyst to conduct tests about the quality and fairness of the model’s outcome
with respect to a set of predefined minority groups or subgroups and compare and contrast
them with privileged group(s). This way, models can be analyzed, and the analyst can shed
light on the potential latent biases in the ML model in a privacy-preserving manner.
Even if the ML model is trained, and deployed securely, due to data poisoning, the final
model may still contain hidden backdoors (which in the literature is referred to as trojan
attacks). Finally, in this dissertation, we develop novel techniques to detect such attacks. We
design experiments that first creates a multitude of models that carry a trojan, and another
set that does not have any trojan. Then, we build classifiers to see if we can tell them apart.
Our results show that ML models could be used to detect trojan attacks against other ML
models
Talking Human Synthesis: Learning Photorealistic Co-speech Motions and Visual Appearances From Videos
No full textTalking video synthesis is a cutting-edge technology that enables the creation of highly realistic video sequences of individuals speaking. This technology has a wide range of applications
in fields such as film-making, advertising, gaming, entertainment, social media, and is likely
to continue to be an active area of research in the coming years. However, there are still
many open questions and challenges in the field of talking video synthesis. In 3D talking
face generation, most existing methods can only generate 3D faces with a static head pose,
which is inconsistent with how humans perceive faces. Only a few works focus on head
pose generation, but even these ignore the attribute of personality. In realistic talking face
generation, it is still very challenging to generate photo-realistic talking faces that are indistinguishable from real captured videos, which not only contain synchronized lip motions, but
also have personalized and natural head movements and eye blinks, etc. In full-body speech
video synthesis, although substantial progress has been made in audio-driven talking video
synthesis, there still remain two major difficulties: existing works 1) need a long sequence of
training dataset (>1h) to synthesize co-speech gestures, which causes a significant limitation
on their applicability; 2) usually fail to generate long sequences, or can only generate long
sequences without enough diversity.
To address those limitations, my research will be developed in a progressive manner, focusing
on three main aspects. Firstly, we will delve into the generation of personalized head poses
for 3D talking faces. Secondly, for realistic 2D talking faces, we propose a generation method
that takes an audio signal as input and a short target video clip as a reference to synthesize
a photo-realistic video of the target face with natural lip motions, head poses, and eye blinks
that are synchronized with the input audio signal. Lastly, we propose a data-efficient ReMix
learning method, which can be trained on monocular ”in-the-wild” short videos to synthesize
photo-realistic talking videos with full-body gestures.
To generate personalized head poses for 3D talking faces, we propose a unified audio-driven
approach to endow 3D talking faces with personalized pose dynamics. To achieve this goal,
we establish an original person-specific dataset, providing corresponding head poses and
face shapes for each video. To model implicit face attributes with input audio, we propose a
FACe Implicit Attribute Learning Generative Adversarial Network (FACIAL-GAN), which
integrates the phonetics-aware, context-aware, and identity-aware information to synthesize
the 3D face animation with realistic motions of lips, head poses, and eye blinks. Finally, we
make an audio-pose remixed latent space assumption to encourage unpaired audio and pose
combinations, which results in diverse “one-to-many” mappings in pose generation. We also
develop a dual-function inference scheme to regularize both the start pose and the general
appearance of the next sequence, enhancing the long-term video generation of full continuity
and diversity.
Experimental results indicate that our methods can generate 1) person-specific head pose
sequences that are in sync with the input audio and that best match with the human experience of talking heads, 2) realistic talking face videos with not only synchronized lip motions,
but also natural head movements and eye blinks, 3) realistic synchronized full-body talking
videos with training data efficiency with better qualities than the results of state-of-the-art
methods
Scalable and Efficient Methods for Hierarchical and Robust Nonlinear Model Predictive Control
No full textSince its inception, Model Predictive Control (MPC) has become a widely adopted and
studied control technique, both for practitioners and academics, finding application in diverse
fields. Some of the main reasons for its popularity are the ability to directly deal with state
and input constraints while simultaneously optimizing the system operation with respect to
some predefined criteria. This characteristic comes from the fact that an MPC controller
solves an optimization problem at every time step during the system operation. As such,
MPC formulations can be divided into two broad categories: Linear MPC and Nonlinear
MPC. In Linear MPC, the controller uses a linear model of the system dynamics as part of
the equality constraints in the optimization problem in addition to other linear constraints
and a linear or quadratic objective function. This setup results in the need to solve a Linear
Program (LP) or Quadratic Program (QP) at each time step. In Nonlinear MPC however,
the controller uses a nonlinear model of the system dynamics with potentially other nonlinear
constraints and a nonlinear objective function. This in turn requires a Nonlinear MPC
controller to solve a Nonlinear Program (NLP) at each time step.
In the last decades, there has been increased adoption of Linear MPC in a diverse range of
applications, fueled by the availability of increasing computational power, the availability
of robust and efficient convex solvers, and a large body of research that helped establish
conditions for important theoretical properties of MPC formulations such as stability and
recursive feasibility. While these improvements benefited the field of Nonlinear MPC as well,
due to the inherent difficulty in dealing with nonlinear formulations, the progress in the
adoption of Nonlinear MPC by industry has not been nearly as large as for Linear MPC.
There are however many applications in which Linear MPC does not perform well due to
strong nonlinear system behavior or because the system is not supposed to operate around
an equilibrium point, which limits the prediction capabilities of linear models. Therefore,
there is much potential for improved performance through the use of Nonlinear MPC in these
applications. The wider adoption of Nonlinear MPC though has been hampered by issues such
as a lack of theoretical guarantees, high computational cost in solving the underlying NLPs,
or the need for expert knowledge for deployment due to the complexity of the formulations.
Therefore, this dissertation aims to provide advances to the field of Nonlinear MPC to
help the research community unlock more of the potential of this technique for practical
applications that can benefit from its use. The contributions of this work focus on two
particular types of Nonlinear MPC formulations: hierarchical Nonlinear MPC, applicable
for systems with dynamics in multiple timescales, and robust Nonlinear MPC, where there
is a need to guarantee constraint satisfaction even when the system is subject to external
disturbances. The methods studied and proposed in this dissertation are tested through
numerical simulations using a few different dynamical systems as benchmarks with a particular
focus on thermal management systems.
The proposed methods are compared to Linear MPC techniques to highlight the potential
benefits of using nonlinear formulations
Personal Protective Equipment and Safety Policy Compliance: a Phenomenological Study of North Texas Fire and EMS Personnel
No full textIn the course of their profession, public safety personnel are exposed to a variety of potentially
dangerous conditions, including fire, toxic smoke and hazardous materials, environmental
exposure, biological pathogens, violence, and transportation accidents. Although public safety
personnel are aware of these inherent risks, many fail to utilize personal protective equipment or
follow department safety policies. This explanatory-sequential mixed methods study aimed to
identify any associations between policy noncompliance and the organizational culture,
including its safety culture, equipment, or products used in the fire service and current policy.
The culmination of the survey data indicates that the most likely individual to comply with safety
regulations are those who are above the age of 41, hold a bachelor’s degree or higher, and do not
have civil service protections. Additionally, three themes arose from this iterative, qualitative
analysis: safety policy compliance, non-compliance with safety protocols, and changes in
compliance. The data may help organizations understand and address features of their own safety
culture, particularly as these may encourage and normalize deviation, inspire new products and
redesign others, and lead to better policies to make the job for public safety personnel safer more
comfortable
The Role of Protease-activated Receptor 2 in Chemotherapy- Induced Peripheral Neuropathy
No full textChemotherapy-induced peripheral neuropathy (CIPN) is a common, dose-limiting side effect of
chemotherapy treatment that can severely affect patients’ quality of life and survival rate.
Currently, no effective therapeutics exist to either treat or prevent CIPN, partly due to an
incomplete understanding of the underlying etiology. Following chemotherapy treatment, immune
system activation and increased immune cell infiltration into the dorsal root ganglion (DRG) and
epidermis have been observed. Proteases released by these immune cells have been proposed as a
possible driver for CIPN pain by acting on protease-activated receptor 2 (PAR2) on nociceptors.
PAR2 is a G-protein coupled receptor (GPCR) that is activated by proteolytic cleavage of its N-
terminus and has been shown to be implicated in chronic pain. The central goal of our research
was to test the therapeutic potential of targeting PAR2 in the context of CIPN pain. Our data
revealed that PAR2-mechanical and spontaneous nociception is dependent on a small
subpopulation of sensory neurons and that this subpopulation of sensory neurons also mediate
CIPN nociception, changes in nerve fiber density in the epidermis, and satellite glial cell activation.
Blocking PAR2 with an antagonist, C781, transiently reversed mechanical allodynia in established
CIPN. These findings highlight PAR2 as a possible therapeutic target for the treatment of CIPN
pain and future research should focus on improving the pharmacokinetic properties of PAR2
antagonists
Building Resiliency Into 5G Open-source and Disaggregated Architecture
No full textToday in the Internet era, communication service providers face tremendous constraints
on increasing capital expenditures and operating expenses compared to the much less income growth. Cloud Radio Access Network (C-RAN) architecture has emerged as a potential candidate for the future wireless network that highlights the notion of service cloud,
service-oriented resource scheduling, and management, thereby facilitating the utilization
of both Network Functions Virtualization and Software-Defined Networking (NFV-SDN)
technologies. The transport network reliability of the disaggregated C-RAN components is
paramount to ensure reliable data communication. Our first contribution focuses on providing transport network resiliency support for the C-RAN architecture using a programmable
optical software-defined network testbed. The testbed supports C-RAN functionalities by
offering fronthaul, midhaul, and backhaul transport capabilities with increased reliability.
The C-RAN components are further disaggregated and run as either virtual machines (VMs)
or containers in a virtualized environment. To ensure load-balancing and fault-tolerance of
the C-RAN components, our Optical programmable testbed with SDN capabilities supports
live migration of C-RAN functions among data centers. OS container-based virtualization
enables faster application instantiation than the hypervisor-based VM because of its smaller
footprint size. However, in the context of the mobile network protocol stack, the open-
source container migration software has yet to be developed to the full extent. Our second
and third contributions focus on the live migration of containerized core network and RAN
central unit virtual functions. The live migration is made feasible through our proof-of-
concept implementation of the open-source container migration software.
In the C-RAN architecture, the Next Generation NodeB (gNB) functions are decoupled into
three entities, namely Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU).
These entities will likely be virtualized and distributed in micro and macro data centers.
The virtualized CUs (vCUs) are decoupled further into virtualized CU Control-Plane (vCU-
CP) and virtualized CU User-Plane (vCU-UP) to optimize the location of the RAN functions
for 5G vertical use case scenarios and performance requirements. The vCU-CP handles the
signaling functionality, such as connection establishment and hand-over. All the 5G Core
Network control plane modules have a single point of contact with vCU-CP. Therefore, a
study on resiliency on vCU-CP is important to avoid the single point of failure, which comes
under our fourth contribution.
Our proof-of-concept guaranteed the fronthaul network reliability of the 5G transport network and during VNF live migration, it ensured end-user service continuity without permanent UE interruption. In addition, the temporary downtime experienced during the
live-migration is significantly lowered by more than 50% when using our container migration
prototype compared to traditional VM solutions
Transfer Learning and Uncertainty Quantification in Natural Language Processing for Political Science and Cyber Security
No full textRecent advancements in Natural Language Processing (NLP) driven by pretrained language
models have revolutionized various fields reliant on large-scale text-based research through
transfer learning. This dissertation presents efficient, reliable computational NLP applications to address real-world challenges, with a focus on political science, cyber security, and uncertainty quantification.
The dissertation begins with interdisciplinary research in political science, where advanced
NLP models are developed to track and analyze dynamics related to global political conflict.
The creation of ConfliBERT, the first domain-specific sociological language model, enables
improved performance on 18 downstream tasks, particularly in scenarios with limited data
availability. Moreover, by leveraging transfer learning and existing expert knowledge, specific tasks such as political event extraction and classification are further optimized. One
approach called Confli-T5 is a text generation model that augments labeled data by in-
corporating achievable templates derived from political science knowledge bases. Another
technique introduced is the Zero-Shot fine-grained relation classification model for PLOVER
ontology (ZSP), which eliminates the need for labeled data by relying solely on an annotation
codebook to classify intricate interactions between political actors. These strategies combine
the power of transfer learning with domain-specific expertise to reduce the dependence on
extensive labeled data, making them valuable tools in the field.
In the field of cyber security, text generation techniques are employed for cyber deception, generating multiple fake versions of critical documents to deter malicious intrusion.
A context-aware model called Fake Document Infilling (FDI) addresses the limitations of
existing approaches by considering contextual awareness. FDI produces highly believable
fake documents, protecting critical information and deceiving adversaries effectively.
Finally, uncertainty quantification techniques are explored to enhance the reliability of NLP
models in such interdisciplinary or cross-domain applications. A novel model, BERT-ENN,
employees evidential theory to quantify multidimensional uncertainty in the data and calibrate uncertainty estimation in text classifiers. This approach achieves state-of-the-art
out-of-distribution detection performance, thereby improving the reliability of NLP models
A General Framework of Non-convex Models for Sparse Recovery With Applications
No full textThanks to latest developments of science and technology, large data sets are becoming increasingly popular that lead to an emerging field, called compressive sensing (CS), which
is about acquiring and processing sparse signals. In this thesis, we first propose a general
framework to estimate sparse coefficients of generalized polynomial chaos (gPC) used in
uncertainty quantification (UQ). In particular, we aim to identify a rotation matrix such
that the gPC expansion of a set of random variables after the rotation has a sparser representation. However, this rotational approach alters the underlying linear system to be
solved, which makes finding the sparse coefficients more difficult than the case without
rotation. To resolve this issue, we examine several popular non-convex regularizations in
CS that empirically perform better than the classic `1 approach. All these regularizations
can be minimized by the alternating direction method of multipliers (ADMM). Numerical examples show superior performance of the proposed combination of rotation and
non-convex sparsity promoting regularizations over the ones without rotation and with
rotation but using the `1 norm.
We observe through the UQ study that the `1 − `2 regularization often performs satisfactorily among the others. We then apply it to synthetic aperture radar (SAR) imaging based
on a mathematical model of how electromagnetic waves are scattered in the space using
Maxwell’s equations. Specifically we deduce an efficient sensing matrix for SAR and examine the efficiency of the `1 − `2 regularization to promote sparsity of scattered signals.
Experimental results demonstrate that `1 − `2 can enhance the resolution of reconstructed
image over the classic `1 approach.
Motivated by conjugate gradient and adaptive momentum in the optimization literature,
we propose a novel algorithmic improvement. The proposed algorithm works for general
minimization problems, though numerical experiments are limited to `1 and `1 − `2 with a
least-squares data fidelity term, showcasing faster convergence of the proposed algorithm
over the traditional methods. We also establish the convergence of our algorithm for a
quadratic problem
Expression of Diverse Streptococcal Multiple Peptide Resistance Factors and Lipid Hydrolase in Streptococcus Mitis
No full textStreptococcus agalactiae (Group B Streptococcus; GBS) is a gram-positive pathogen that
colonizes the gastrointestinal and lower genital tracts. In GBS, the multiple peptide resistance
factor (MprF) synthesizes a novel lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), and the
well-known lipid lysyl-phosphatidylglycerol (Lys-PG). Lys-PG reduces the negative charge of
the membrane, protecting bacteria from cationic antimicrobial peptides (CAMPs). Additionally,
GBS encodes a predicted alpha-beta hydrolase upstream of mprF. In Enterococcus faecium, this
hydrolase is responsible for the turnover of Lys-PG. This project has three aims: to determine
whether other streptococcal MprF proteins also synthesize Lys-Glc-DAG and/or Lys-PG; the
impact of Lys-Glc-DAG and Lys-PG production on Streptococcus mitis survival in low pH; and
whether the GBS hydrolase is responsible for the turnover of both Lys-Glc-DAG and Lys-PG. S.
mitis was chosen as a heterologous host for this study since it does not natively encode mprF and
does not natively synthesize Lys-Glc-DAG or Lys-PG. Candidate MprF proteins from other
streptococcal species (S. downei and S. ferus) with high identity to GBS MprF were identified by
BLASTp. These genes and the GBS hydrolase gene were inserted into a plasmid using Gibson
assembly and transformed into S. mitis. This study found that the expression of S. ferus mprF
and S. downei mprF in S. mitis conferred synthesis of Lys-Glc-DAG. Significantly, S. ferus
MprF synthesized Lys-Glc-DAG at a similar level to GBS MprF. Previous research found that
expression of S. salivarius mprF in S. mitis conferred synthesis of Lys-PG also at a similar level
to GBS MprF. The production of Lys-Glc-DAG and/or Lys-PG in S. mitis through the utilization
of plasmids expressing the different mprFs did not increase the survival of S. mitis in low pH.
Finally, preliminary investigation of the GBS hydrolase and E. faecium hydrolase through a cell
lysate assay did not show turnover of Lys-Glc-DAG and/or Lys-PG, demonstrating that methods
for investigating hydrolase activity require refinement