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Events for January 19, 2016
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PhD Defense - Niloofar Montazeri
Tue, Jan 19, 2016 @ 04:45 AM - 06:45 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Building a Knowledgebase for Deep Lexical Semantics
PhD Candidate: Niloofar Montazeri
Date / Time: Jan 19th (Tuesday), 4:45-6:45 pm
Place: SAL 213
Abstract:
Words describe the world, so if we are going to draw the appropriate inferences in understanding a text, we must have a prior explication of how we view the world (world knowledge) and how words and phrases map to this view (lexical semantics knowledge).
Existing world knowledge and lexical semantics knowledge resources are not particularly suitable for deep reasoning, either due to lack of connection between their elements or due to their simple knowledge representation method (binary relations between natural language phrases).
To enable deep understanding and reasoning over natural language, (Hobbs 2008) has proposed the idea of "Deep Lexical Semantics". In Deep Lexical Semantics, principal and abstract domains of commonsense knowledge are encoded into "core theories" and words are linked to these theories through axioms that use predicates from these theories. This research is concerned with the second task: Axiomatizing words in terms of predicates in core theories.
We show that a large scale lexical semantics knowledgebase for a given domain can be developed by dividing the authoring task using the optimum mix of manual and automatic methods. We use concept relations in existing lexical semantics resources to systematically identify the optimum set of concepts that need to be axiomatized manually and axiomatize a large number of relevant concepts automatically. We have used this method to axiomatize concepts related to the domain of composite entities and evaluated the quality of the resulting axioms. Furthermore, we have evaluated the usefulness of these axioms on the well-studied task of extracting part-of relations from text.
Committee:
Prof. Jerry R. Hobbs (Chair)
Prof. Kevin Knight
Prof. Andrew Gordon
Prof. Elsi Kaiser (External Member)
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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NSF Overview and Advanced Manufacturing Funding Opportunities
Tue, Jan 19, 2016 @ 10:00 AM - 11:30 AM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Zhijian Pei, NSF - Program Director
Talk Title: NSF Overview and Advanced Manufacturing Funding Opportunities
Host: Qiang Huang
More Information: ZJ Pei Abstract and Bio for USC.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Michele ISE
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USC Stem Cell Seminar: James Wells, Cincinnati Children's Hospital Medical Center
Tue, Jan 19, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: James Wells, Professor, Developmental Biology; Director for Basic Research, Endocrinology; Director of the Pluripotent Stem Cell Facility/Cincinnati Children's Hospital Medical Center
Talk Title: Pluripotent stem cell-derived tissues to study development and disease of the GI tract
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: Research in the Wells Lab focuses on identifying the molecular mechanisms that control organogenesis and on using this information to direct the differentiation of pluripotent stem cells into human organ tissues (organoids) including pancreas, stomach and intestine. Organoids are being used to model diabetes and diseases of the gastrointestinal tract and are being studied for their therapeutic potential to restore function to damaged tissues.
Host: Senta Georgia
More Info: http://stemcell.usc.edu/events/details/?event_id=916786
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
Event Link: http://stemcell.usc.edu/events/details/?event_id=916786
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PhD- Defense - Gholamreza Safi
Tue, Jan 19, 2016 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Date and Location: Tuesday, January 19th, 11:00 am at SAL 322.
Title: Detecting Anomalies in Event-Based Systems Through Static Analysis
PhD Candidate: Gholamreza Safi
Committee: Nenad Medvidovic (chair), William GJ Halfond, Sandeep Gupta(outside member)
The event-based paradigm allows developers to design and build systems that are highly flexible and can be easily adapted. There are two main complications that can occur in the systems that are based on this paradigm. The first complication concerns inter-component interactions. Events that are used by components for this purpose are sent, received, and processed nondeterministically, due to the systems' reliance on implicit invocation and implicit concurrency. This nondeterminism can lead to event anomalies, which occur when an event-based system receives multiple events that lead to the write of a shared field or memory location. Event anomalies can lead to unreliable, error-prone, and hard-to-debug behavior in an event-based system. The second complication concerns intra-component interactions that usually occur through method calls. Each sequence of method calls introduces an execution path to the system. It is possible that there exist multiple execution paths that are not accessing the same memory locations or sharing data but are unnecessarily synchronized with each other despite the fact that they can be executed concurrently. I call these situation synchronization anomalies.
To detect event anomalies, this dissertation presents a new static analysis technique, DEvA, for automatically Detecting Event Anomalies. DEvA has been evaluated on a set of open-source event-based systems against a state-of-the-art technique for detecting data races in multi-threaded systems and a recent technique for solving a similar problem with event processing in Android applications. DEvA exhibited high precision with respect to manually constructed ground truths and was able to locate event anomalies that had not been detected by the existing solutions.
Also, this dissertation presents a new static analysis technique, DSA, for automatically Detecting Synchronization Anomalies. I have evaluated DSA both empirically and analytically. My empirical evaluation shows that synchronization anomalies are a common problem and can occur in any randomly chosen system. Also, DSA is efficient and scalable while exhibiting high precision, meaning that there were no false positives in its results after being applied to fourteen subject systems. The analytical evaluation of DSA provides guidelines for the situations where removing a synchronization anomaly can be more beneficial. By removing just one synchronization anomaly from two of our subject systems based on the provided guidelines, there was an enhancement of 10% in the performance of those systems.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Epstein Institute Seminar - ISE 651
Tue, Jan 19, 2016 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Michael N. Katehakis, Rutgers University
Talk Title: Asymptotically Optimal Policies for Mult Armed Bandit Models Under Generalized Ranking
Host: Sheldon Ross
More Information: January 19, 2016_Katehakis.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Michele ISE
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CS Colloquium & Yahoo! Labs Seminar: Jure Leskovec (Stanford) - Machine Learning for Human Decision Making
Tue, Jan 19, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jure Leskovec, Stanford University
Talk Title: Machine Learning for Human Decision Making
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
In many real-life settings human judges are making decisions and choosing among many alternatives in order to label or classify items: Medical doctor diagnosing a patient, criminal court judge making a decision, a crowd-worker labeling an image, and a student answering a multiple-choice question. Gaining insights into human decision making is important for determining the quality of individual decisions as well as identifying mistakes and biases. In this talk we discuss the question of developing machine learning methodology for estimating the quality of individual judges and obtaining diagnostic insights into how various judges decide on different kinds of items. We develop a series of increasingly powerful hierarchical Bayesian models which infer latent groups of judges and items with the goal of obtaining insights into the underlying decision process. We apply our framework to a wide range of real-world domains, and demonstrate that our approach can accurately predict judges decisions, diagnose types of mistakes judges tend to make, and infer true labels of items.
The lecture will be available to stream HERE. [For best quality, right click -> open in new tab]
Biography: Jure Leskovec is assistant professor of Computer Science at Stanford University and chief scientist at Pinterest. His research focuses on mining large social and information networks, their evolution, and the diffusion of information and influence over them. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, economics, marketing, and healthcare. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter @jure
Host: Yan Liu
More Info: http://www-bcf.usc.edu/~liu32/mlseminar.html
Webcast: https://bluejeans.com/469517570Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/469517570
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: http://www-bcf.usc.edu/~liu32/mlseminar.html
