167 research outputs found
Proceedings of the NASA Conference on Space Telerobotics, volume 5
Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center
Protein structure prediction: improving and automating knowledge-based approaches
This work presents a computational approach to improve the automatic prediction of protein structures from sequence. Its main focus was twofold. An automated method for guiding the modeling process was first developed. This was tested and found to be state of the art in the CASP4 structure prediction contest in 2000. The second focus was the development of a novel divide and conquer algorithm for modeling flexible loops in proteins. Implementation of the search procedure and subsequent ranking is presented. The results are again compared with state of the art methods
Graph based pattern discovery in protein structures
The rapidly growing body of 3D protein structure data provides new opportunities to study the relation between protein structure and protein function. Local structure pattern of proteins has been the focus of recent efforts to link structural features found in proteins to protein function. In addition, structure patterns have demonstrated values in applications such as predicting protein-protein interaction, engineering proteins, and designing novel medicines. My thesis introduces graph-based representations of protein structure and new subgraph mining algorithms to identify recurring structure patterns common to a set of proteins. These techniques enable families of proteins exhibiting similar function to be analyzed for structural similarity. Previous approaches to protein local structure pattern discovery operate in a pairwise fashion and have prohibitive computational cost when scaled to families of proteins. The graph mining strategy is robust in the face of errors in the structure, and errors in the set of proteins thought to share a function. Two collaborations with domain experts at the UNC School of Pharmacy and the UNC Medical School demonstrate the utility of these techniques. The first is to predict the function of several newly characterized protein structures. The second is to identify conserved structural features in evolutionarily related proteins
Learning domain abstractions for long lived robots
Recent trends in robotics have seen more general purpose robots being deployed in
unstructured environments for prolonged periods of time. Such robots are expected to
adapt to different environmental conditions, and ultimately take on a broader range of
responsibilities, the specifications of which may change online after the robot has been
deployed.
We propose that in order for a robot to be generally capable in an online sense
when it encounters a range of unknown tasks, it must have the ability to continually
learn from a lifetime of experience. Key to this is the ability to generalise from experiences
and form representations which facilitate faster learning of new tasks, as well as
the transfer of knowledge between different situations. However, experience cannot be
managed na¨ıvely: one does not want constantly expanding tables of data, but instead
continually refined abstractions of the data – much like humans seem to abstract and
organise knowledge. If this agent is active in the same, or similar, classes of environments
for a prolonged period of time, it is provided with the opportunity to build
abstract representations in order to simplify the learning of future tasks. The domain
is a common structure underlying large families of tasks, and exploiting this affords
the agent the potential to not only minimise relearning from scratch, but over time to
build better models of the environment. We propose to learn such regularities from the
environment, and extract the commonalities between tasks.
This thesis aims to address the major question: what are the domain invariances
which should be learnt by a long lived agent which encounters a range of different
tasks? This question can be decomposed into three dimensions for learning invariances,
based on perception, action and interaction. We present novel algorithms for
dealing with each of these three factors.
Firstly, how does the agent learn to represent the structure of the world? We focus
here on learning inter-object relationships from depth information as a concise
representation of the structure of the domain. To this end we introduce contact point
networks as a topological abstraction of a scene, and present an algorithm based on
support vector machine decision boundaries for extracting these from three dimensional
point clouds obtained from the agent’s experience of a domain. By reducing the
specific geometry of an environment into general skeletons based on contact between
different objects, we can autonomously learn predicates describing spatial relationships.
Secondly, how does the agent learn to acquire general domain knowledge? While
the agent attempts new tasks, it requires a mechanism to control exploration, particularly
when it has many courses of action available to it. To this end we draw on the fact
that many local behaviours are common to different tasks. Identifying these amounts
to learning “common sense” behavioural invariances across multiple tasks. This principle
leads to our concept of action priors, which are defined as Dirichlet distributions
over the action set of the agent. These are learnt from previous behaviours, and expressed
as the prior probability of selecting each action in a state, and are used to guide
the learning of novel tasks as an exploration policy within a reinforcement learning
framework.
Finally, how can the agent react online with sparse information? There are times
when an agent is required to respond fast to some interactive setting, when it may have
encountered similar tasks previously. To address this problem, we introduce the notion
of types, being a latent class variable describing related problem instances. The agent
is required to learn, identify and respond to these different types in online interactive
scenarios. We then introduce Bayesian policy reuse as an algorithm that involves maintaining
beliefs over the current task instance, updating these from sparse signals, and
selecting and instantiating an optimal response from a behaviour library.
This thesis therefore makes the following contributions. We provide the first algorithm
for autonomously learning spatial relationships between objects from point
cloud data. We then provide an algorithm for extracting action priors from a set of
policies, and show that considerable gains in speed can be achieved in learning subsequent
tasks over learning from scratch, particularly in reducing the initial losses associated
with unguided exploration. Additionally, we demonstrate how these action priors
allow for safe exploration, feature selection, and a method for analysing and advising
other agents’ movement through a domain. Finally, we introduce Bayesian policy
reuse which allows an agent to quickly draw on a library of policies and instantiate the
correct one, enabling rapid online responses to adversarial conditions
Towards Automating Protein Structure Determination from NMR Data
Nuclear magnetic resonance (NMR) spectroscopy technique is becoming exceedingly significant due to its capability of studying protein structures in solution. However, NMR protein structure determination has remained a laborious and costly process until now, even with the help of currently available computer programs. After the NMR spectra are collected, the main road blocks to the fully automated NMR
protein structure determination are peak picking from noisy spectra, resonance assignment from imperfect peak lists, and structure calculation from incomplete assignment and ambiguous nuclear
Overhauser enhancements (NOE) constraints.
The goal of this dissertation is to propose error-tolerant and highly-efficient methods that work well on real and noisy data sets of NMR protein structure determination and the closely related protein structure prediction problems.
One major contribution of this dissertation is to propose a fully automated NMR protein structure determination system, AMR, with emphasis on the parts that I contributed. AMR only requires an input set with six NMR spectra. We develop a novel peak picking method, PICKY, to solve the crucial
but tricky peak picking problem. PICKY consists of a noise level estimation step, a component forming step, a singular value decomposition-based initial peak picking step, and a peak refinement step. The first systematic study on peak picking problem is conducted to test the performance of
PICKY. An integer linear programming (ILP)-based resonance assignment method, IPASS, is then developed to handle the imperfect peak lists generated by PICKY. IPASS contains an error-tolerant spin system forming method and an ILP-based assignment method. The assignment generated by IPASS is fed into the structure calculation step, FALCON-NMR. FALCON-NMR has a threading module, an ab
initio module, an all-atom refinement module, and an NOE constraints-based decoy selection module. The entire system, AMR, is successfully tested on four out of five real proteins with practical NMR spectra, and generates 1.25A, 1.49A, 0.67A, and 0.88A to the native reference structures, respectively.
Another contribution of this dissertation is to propose novel ideas and methods to solve three protein structure prediction problems which are closely related to NMR protein structure determination. We develop a novel consensus contact prediction method, which is able to eliminate server correlations, to solve the protein inter-residue contact prediction problem. We also propose
an ultra-fast side chain packing method, which only uses local backbone information, to solve the protein side chain packing problem. Finally, two complementary local quality assessment methods are
proposed to solve the local quality prediction problem for comparative modeling-based protein structure prediction methods
Remote Sensing of Plant Biodiversity
This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale
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