253 research outputs found
Visual Feature Learning
Categorization is a fundamental problem of many computer vision applications, e.g., image
classification, pedestrian detection and face recognition. The robustness of a categorization
system heavily relies on the quality of features, by which data are represented. The prior
arts of feature extraction can be concluded in different levels, which, in a bottom up order,
are low level features (e.g., pixels and gradients) and middle/high-level features (e.g., the
BoW model and sparse coding). Low level features can be directly extracted from images
or videos, while middle/high-level features are constructed upon low-level features, and are
designed to enhance the capability of categorization systems based on different considerations
(e.g., guaranteeing the domain-invariance and improving the discriminative power).
This thesis focuses on the study of visual feature learning. Challenges that remain in designing
visual features lie in intra-class variation, occlusions, illumination and view-point
changes and insufficient prior knowledge. To address these challenges, I present several
visual feature learning methods, where these methods cover the following sub-topics: (i)
I start by introducing a segmentation-based object recognition system. (ii) When training
data are insufficient, I seek data from other resources, which include images or videos in a
different domain, actions captured from a different viewpoint and information in a different
media form. In order to appropriately transfer such resources into the target categorization
system, four transfer learning-based feature learning methods are presented in this section,
where both cross-view, cross-domain and cross-modality scenarios are addressed accordingly.
(iii) Finally, I present a random-forest based feature fusion method for multi-view
action recognition
Neural Network Potential Simulations of Copper Supported on Zinc Oxide Surfaces
Heterogeneous catalysis is an area of active research, because many industrially relevant reactions
involve gaseous reactants and are accelerated by solid phase catalysts. In recent years, activity in the
field has become more intense due to the development of surface science and simulation techniques
that allow for acquiring deeper insight into these catalysts, with the goal of producing more active,
cheaper and less toxic catalytic materials.
One particularly crucial case study for heterogeneous catalysis is the synthesis of methanol from
synthesis gas, composed of H2, CO and CO2. The reaction is catalyzed by a mixture of Cu and ZnO
nanoparticles with Al2O3 as a support material. This process is important not only due to methanol’s
many uses as a solvent, raw material for organic synthesis, and possible energy and carbon capture material, but also as an example for many other metal/metal oxide catalysts. A plethora of experimental
studies are available for this catalyst, as well as for simpler model systems of Cu clusters supported on
ZnO surfaces. Unfortunately, there is still a lack of theoretical studies that can support these experi-
mental results by providing an atom-by-atom representation of the system.
This scarcity of atomic level simulations is due to the absence of fast but ab-initio level accurate
potentials that would allow for reaching larger systems and longer simulated time scales. A promising
possibility to bridge this gap in potentials is the rise of machine learning potentials, which utilize the
tools of machine learning to reproduce the potential energy surface of a system under study, as sampled
by an expensive electronic structure reference method of choice. One early and fruitful example of
such machine learning force fields are neural network potentials, as initially developed by Behler and
Parrinello.
In this thesis, a neural network potential of the Behler-Parrinello type has been constructed for
ternary Cu/Zn/O systems, focusing on supported Cu clusters on the ZnO(10-10) surface, as a model for
the industrial catalyst. This potential was subsequently utilized to perform a number of simulations.
Small supported Cu clusters between 4 and 10 atoms were optimized with a genetic algorithm, and
a number of structural trends observed. These clusters revealed the first hints of the structure of the
Cu/ZnO interface, where Cu prefers to interact with the support through configurations in the continuum between Cu(110) and Cu(111). Simulated annealing runs for Cu clusters between 200 and 500
atoms reinforced this observation, with these larger clusters also adopting this sort of interface with the
support. Additionally, in these simulations the effect of strain induced by the support can be observed,
with deviations from ideal lattice constants reaching the top of all of the clusters. To further investigate
the influence of strain in this system, large coincident surfaces of Cu were deposited on ZnO supports.
Due to the lattice mismatch present between the two materials, this requires straining the Cu overlayer.
This analysis confirmed once again that Cu(110) and Cu(111) are the most stable surfaces when de-
posited on ZnO(10-10). During this thesis a number of new algorithm and programs were developed.
Of particular interest is the bin and hash algorithm, which was designed to aid in the construction and
curating of reference sets for the neural network potential, and can also be used to evaluate the quality
of atomic descriptor sets.2021-10-0
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
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