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University Calendar
Events for May
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ASBME Presents Performance Capture Technology
Wed, May 01, 2013 @ 06:00 PM - 07:00 PM
Viterbi School of Engineering Student Organizations
University Calendar
Motion capture technology has been paramount in creating cinema blockbusters such as Avatar. Actors are strapped into motion capture suits as technology then renders the entire environment around them. However this technology has implications beyond the movie industry. Here at USC, motion capture technology is fused with improvisational acting in order to study and document human behavior. Join us, as we get a glimpse into this new research, and discuss the implications it can have in areas of biomedical engineering such as addiction treatment and cognitive and behavioral therapy. Panda will be served for dinner!
Location: Kaprielian Hall (KAP) - 140
Audiences: Everyone Is Invited
Contact: Associated Students of Biomedical Engineering
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PhD Defense - Manaschai Kunaseth
Mon, May 06, 2013 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Manaschai Kunaseth
Time: 10:00am-12:00pm, Monday, May 6, 2013
Location: SSL 104
Committee:
Robert F. Lucas
Aiichiro Nakano (Chair)
Katherine Shing
Title:
Metascalable Hybrid Message-Passing and Multithreading Algorithms for n-Tuple Computation
Abstract:
The emergence of the multicore era has granted unprecedented computing capabilities. Extensively available multicore clusters have influenced hybrid message-passing and multithreading parallel algorithms to become a standard parallelization for modern clusters. However, hybrid parallel applications of portable scalability on emerging high-end multicore clusters consisting of multimillion cores are yet to be accomplished. Achieving scalability on emerging multicore platforms is an enormous challenge, since we do not even know the architecture of future platforms, with new hardware features such as hardware transactional memory (HTM) constantly being deployed. Scalable implementation of molecular dynamics (MD) simulations on massively parallel computers has been one of the major driving forces of supercomputing technologies. Especially, recent advancements in reactive MD simulations based on many-body interatomic potentials have necessitated efficient dynamic n-tuple computation. Hence, it is of great significance now to develop scalable hybrid n-tuple computation algorithms to provide a viable foundation for high-performance parallel-computing software on forthcoming architectures.
This dissertation research develops a scalable hybrid message-passing and multithreading algorithm for n-tuple MD simulation, which will continue to scale on future architectures (i.e. achieving metascalability). The two major contributions of this dissertation research are: (1) design a scalable hybrid message-passing and multithreading parallel algorithmic framework on multicore architectures and evaluate it on most advanced parallel architectures; and (2) develop a computation-pattern algebraic framework to design scalable algorithms for general n-tuple computation and prove its optimality in a systematic and mathematically rigorous manner. We expect that the proposed hybrid algorithms and mathematical approaches will provide a generic framework to a broad range of applications on future extreme-scale computing platforms.
Location: Seaver Science Library (SSL) - 104
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Dirk Hovy
Mon, May 06, 2013 @ 01:30 PM - 03:30 PM
Thomas Lord Department of Computer Science
University Calendar
Learning Semantic Types and Relations from Text
Committee: Jerry Hobbs (chair), Elsi Kaiser (external), Dennis McLeod, Kevin Knight, David Chiang
NLP applications such as Question Answering (QA), Information Extraction (IE), or Machine Translation (MT) are incorporating increasing amounts of semantic information. A fundamental building block of semantic information is the relation between a predicate and its arguments, e.g. eat(John,burger). In order to reason at higher levels of abstraction, it is useful to group relation instances according to the types of their predicates and the types of their arguments. For example, while eat(Mary,burger) and devour(John,tofu) are two distinct relation instances, they share the underlying predicate and argument types INGEST(PERSON,FOOD).
A central question is: where do the types and relations come from?
The subfield of NLP concerned with this is relation extraction, which comprises two main tasks:
1. identifying and extracting relation instances from text
2. determining the types of their predicates and arguments
The first task is difficult for several reasons. Relations can express their predicate explicitly or implicitly. Furthermore, their elements can be far part, with unrelated words intervening. In this thesis, we restrict ourselves to relations that are explicitly expressed between syntactically related words. We harvest the relation instances from dependency parses.
The second task is the central focus of this thesis. Specifically, we will address these three problems: 1) determining argument types 2) determining predicate types 3) determining argument and predicate types. For each task, we model predicate and argument types as latent variables in a hidden Markov models. Depending on the type system available for each of these tasks, our approaches range from unsupervised to semi-supervised to fully supervised training methods.
The central contributions of this thesis are as follows:
1. Learning argument types (unsupervised): We present a novel approach that learns the type system along with the relation candidates when neither is given. In contrast to previous work on unsupervised relation extraction, it produces human-interpretable types rather than clusters. We also investigate its applicability to downstream tasks such as knowledge base population and construction of ontological structures. An auxiliary contribution, born from the necessity to evaluate the quality of human subjects, is MACE (Multi-Annotator Competence Estimation), a tool that helps estimate both annotator competence and the most likely answer.
2. Learning predicate types (unsupervised and supervised): Relations are ubiquitous in language, and many problems can be modeled as relation problems. We demonstrate this on a common NLP task, word sense disambiguation (WSD) for prepositions (PSD). We use selectional constraints between the preposition and its argument in order to determine the sense of the preposition. In contrast, previous approaches to PSD used n-gram context windows that do not capture the relation structure. We improve supervised state-of-the-art for two type systems.
3. Argument types and predicates types (semi-supervised): Previously, there was no work in jointly learning argument and predicate types because (as with many joint learning tasks) there is no jointly annotated data available. Instead, we have two partially annotated data sets, using two disjoint type systems: one with type annotations for the predicates, and one with type annotations for the arguments. We present a semisupervised approach to jointly learn argument types and predicate types, and demonstrate it for jointly solving PSD and supersense-tagging of their arguments. To the best of our knowledge, we are the first to address this joint learning task.
Our work opens up interesting avenues for both the typing of existing large collections of triple stores, using all available information, and for WSD of various word classes.
Location: Hedco Neurosciences Building (HNB) - 100
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Celebrating the Life Of Claire Nelson
Wed, May 08, 2013 @ 12:30 PM - 01:30 PM
Thomas Lord Department of Computer Science
University Calendar
Please Join Us In Celebrating the Life Of Claire Nelson Wednesday, May 8th @ 12:30 pm Kilgore Chapel 835 W. 34th Street, West of Trousdale Parkway. All are welcome to attend the memorial ceremony to share in a remembrance of Claire’s life.
Location: Kilgore Chapel
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Hossein Tajalli
Wed, May 15, 2013 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Integrated Self-adaptive Software Environments
PhD Candidate: Hossein Tajalli
Committee:
Nenad Medvidovic - Chair
William G.J. Halfond
Massoud Pedram - Outside member
Date: 5/15, Time: 10:00-12:00
Location: SAL 322
Abstract:
Modern software systems are increasingly expected to satisfy high reliability and high availability requirements. During their life-span, they need to constantly and seamlessly adapt and evolve in response to new requirements and changing circumstances. Software adaptation and evolution in modern software systems could not conflict with their availability. Consequently, self-adaptive software systems are desirable.
Adaptation tools in several recent self-adaptive software systems are implemented as development environment tools. This resulted in the tight integration of the development and run-time environments in these systems and several structural and quality shortcomings (e.g., availability and resource consumption). As a software system evolves during its life-span, adaptation activities that pertain to it also evolve. Consequently, a self-adaptive software system should also be able to autonomously change its adaptive behavior. New tools and approaches are demanded to support self-adaptation of the adaptation tools in self-adaptive software systems. Additionally, there is a disconnect between the modeling and the adaptation artifacts in the existing self-adaptive software systems, which limits the self-adaptability of those systems. New modeling techniques to link models and adaptation artifacts of self-adaptive systems are required.
This dissertation provides a reference architecture for integrated self-adaptive software environments that addresses several structural and quality shortcomings of the existing integrated environments. Moreover, it provides two model-driven approaches to support adaptation of both run-time application and adaptation tools in a self-adaptive software system. These approaches dynamically synthesize behavioral models of the run-time application and the adaptation tools from software models. The resulting synthesized behavioral models are used to guide the adaptation behavior of the system. The design of the reference architecture and the model-driven approaches that comes with it provides higher flexibility, separation of concerns, fault-tolerance, adaptability, and robustness compared to the existing self-adaptive systems.Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Ali Khodaei
Wed, May 29, 2013 @ 02:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate
Ali Khodaei
Committee
Elizabeth Currid-Halkett (Outside Member)
Cyrus Shahabi (Chair)
Gaurav S. Sukhatme
Title
COMBINING TEXTUAL WEB SEARCH WITH SPATIAL, TEMPORAL AND SOCIAL ASPECTS OF THE WEB
Abstract
Over the last few years,Web has changed significantly. Emergence
of Web 2.0 have enabled people to interact with web document in new ways not possible before.
It is now a common practice for many web documents to get geo-tagged, time-tagged or integrated with popular social networks.
With these new changes and the abundant usage of spatial, temporal and social
information in web documents as well as user search queries,
the necessity of integration of such non-textual aspects of the web
to the regular textual web search has grown rapidly over the past few years.
To integrate each of those non-textual dimensions to the textual web search and to enable spatial-textual, temporal-textual and social-textual web search,
in this dissertation we propose a set of new relevance models, index structures and algorithms specifically
designed for adding each non-textual dimension (spatial, temporal and social) to the current state of (textual) web search.
First, we propose a new ranking model and a hybrid index structure called
Spatial-Keyword Inverted File to handle location-based ranking and indexing of web
documents in an integrated/efficient manner. Second,
we propose a new indexing and ranking framework for temporal-textual
retrieval. The framework leverages the classical vector space model and provides a complete scheme for indexing,
query processing and ranking of the temporal-textual queries. Finally, we
show how to personalizes the search results based on users' social
actions. We propose a new relevance model called PerSocial relevance
model utilizing three levels of social signals to improve the web
search. Furthermore, We Develop Several Approaches To Integrate
PerSocial relevance model Into The Textual Web Search Process.
(the last part - adding social signals to web search- is the topic of my defense presentation).
Location: Charles Lee Powell Hall (PHE) - 333
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
