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  <title>URCS Seminars &amp; Talks</title>
  <link>http://www.cs.rochester.edu/dept/seminar</link>
  <description>Upcoming Seminars &amp; Talks from the Computer Science Department at the University of Rochester</description>
  <language>en-us</language>
  <image>
   <url>http://www.cs.rochester.edu/images/monalogo.jpg</url>
   <title>Computer Science Department, University of Rochester</title>
   <link>http://www.cs.rochester.edu/dept/seminar</link>
  </image>
  <lastBuildDate>Thu, 28 Aug 2008 14:44:58 -0400</lastBuildDate>
  
   <item>
    <title>Dr. Daniel Gildea: To Be Announced</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/345</link>
    <description>[Monday, September 15, 2008 at 11:00 AM in Computer Studies Bldg. Room 209] </description>
    <pubDate>Thu, 28 Aug 2008 14:44:58 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/345</guid>
   </item>
  
   <item>
    <title>Dr. Mitsunori Ogihara: To Be Announced</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/346</link>
    <description>[Monday, December 01, 2008 at 11:00 AM in Computer Studies Bldg. Room 209] </description>
    <pubDate>Thu, 28 Aug 2008 14:44:22 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/346</guid>
   </item>
  
   <item>
    <title>Chuanpeng Li: Operating System Enhancements for Data-Intensive Server Systems</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/343</link>
    <description>[Monday, August 11, 2008 at  2:00 PM in Computer Studies  Building Room 601] Recent studies on operating system support for concurrent server systems mostly target CPU-intensive workloads with light disk I/O activities. However, an important class of server systems that access a large amount of disk-resident data, such as the index searching server of large-scale Web search engines, has received limited attention. In this thesis work, we examine operating system techniques to improve the performance of data-intensive server systems under concurrent execution. We propose OS enhancements in three aspects of the operating system: file system prefetching, memory management, and disk I/O system. First, we propose a competitive prefetching strategy that can balance the overhead of disk I/O switching and the wasted I/O bandwidth on prefetching unnecessary data. Second, we explore a new memory management scheme for prefetched data, in order to reduce prefetching-incurred page thrashing at high execution concurrency. Third, we present five performance anomalies we identified in the Linux I/O system, and provide general discussions on operating system performance debugging.

We have implemented the proposed techniques in the Linux 2.6 kernel. Performance evaluation on microbenchmarks and real applications shows promising results.

</description>
    <pubDate>Tue, 29 Jul 2008 14:29:47 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/343</guid>
   </item>
  
   <item>
    <title>Virendra Marathe: Enhancing the Performance and Usability of Software Transactional Memory</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/344</link>
    <description>[Tuesday, August 12, 2008 at  2:00 PM in Computer Studies Building, Room 601] Transactional Memory (TM) is an emerging concurrency control technology
that promises to significantly ease the difficult task of programming on
multicore systems.  Foundational work on TM grew out of research in nonblocking concurrent algorithms.  Hence early software TM (STM) systems
were all nonblocking.  However they incurred high overheads, rendering them largely impractical.

More recently, there has been a shift toward blocking implementations of
STM.  This has been accompanied by several significant performance
optimizations, making blocking STMs far more practical.  A general hypothesis, based on informal arguments, has emerged that nonblocking STMs are fundamentally slower than blocking STMs.  In this talk I will show that this hypothesis is wrongly founded, and that nonblocking STMs can approach the performance of, and even outperform, state-of-the-art blocking STMs.  I will present several variants of our most recent nonblocking STM that are competitive with state-of-the-art blocking STMs.

</description>
    <pubDate>Tue, 29 Jul 2008 14:28:32 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/344</guid>
   </item>
  
   <item>
    <title>Ashwin Lall: Streaming Algorithms for Network Flow Data Analysis</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/342</link>
    <description>[Thursday, July 03, 2008 at 10:00 AM in Computer Studies Bldg. Room 601] The algorithmic field of Data Streaming was first proposed over a decade ago by Alon, Matias, and Szegedy. Since then, considerable work has been done in the streaming model of computation. The streaming model is precisely the one we want to use to perform real-time computation on network data because it allows only single pass access to the data and guarantees low memory consumption and processor usage per packet. However, the ideas from the streaming context have been slow to catch on in the networking literature. In this dissertation we will seek to catalyze this change by demonstrating both theoretically and empirically that the ideas from streaming algorithms will have a considerable impact on the performance of algorithms for online network analysis. In particular, we present novel algorithms for maintaining counts of large number of items and for computing the entropy of network traffic to demonstrate that streaming algorithms can solve these problems remarkably well in practice.

</description>
    <pubDate>Wed, 11 Jun 2008 09:11:36 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/342</guid>
   </item>
  
   <item>
    <title>Ding Liu: Improved Tree-to-string Transducer for Machine Translation</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/341</link>
    <description>[Friday, May 23, 2008 at 12:30 PM in CSB 703] We propose three enhancements to the tree-to-string (TTS) transducer for
machine translation:
first-level expansion-based normalization
for TTS templates, a syntactic alignment
framework integrating the insertion of
unaligned target words, and a subtree-based ngram
model addressing the tree decomposition
probability. Empirical results show that
these methods improve the performance of a
TTS transducer based on the standard BLEU-4 metric. We also experiment with
semantic
labels in a TTS transducer, and achieve improvement
over our baseline system.
</description>
    <pubDate>Tue, 20 May 2008 10:52:33 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/341</guid>
   </item>
  
   <item>
    <title>Hao Zhang: Handling Complexity of Synchronous Grammars for Machine Translation</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/340</link>
    <description>[Tuesday, May 27, 2008 at  2:30 PM in Computer Studies Bldg. Room 601] Synchronous grammars are grammars that model two languages and their translational equivalence. They are rewriting systems extended to two dimensions. Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine translation output, but are often very computationally intensive. We improve the efficiency of such systems, making both decoding and training fast and effective. 

We devise an algorithm for factoring syntactic re-orderings by binarizing synchronous rules when possible and show that the resulting rule set significantly improves the speed and accuracy of a state-of-the-art syntax-based machine translation system. 

We take a multi-pass approach to machine translation decoding when using synchronous context-free grammars as the translation model and n-gram language models: the first pass uses a bigram language model, and the resulting parse forest is used in the second pass to guide search with a trigram language model. An additional fast decoding pass maximizing the expected count of correct translation hypotheses increases the BLEU score significantly.

We combine the strengths of Bayesian modeling and synchronous grammar in unsupervised learning of basic translation phrase pairs. The structured space of a synchronous grammar is a natural fit for phrase pair probability estimation, though the search space can be prohibitively large. Therefore we explore efficient algorithms for pruning this space that lead to empirically effective results.

</description>
    <pubDate>Mon, 12 May 2008 08:37:24 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/340</guid>
   </item>
  
   <item>
    <title>Carlos Gómez Gallo: Syntactic Production across Clauses</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/338</link>
    <description>[Friday, May 09, 2008 at 12:30 PM in CSB 209] Language production research has focused on planning at the
lexical and syntactic levels. Consequently, little is known
about speakers&#39; strategies for distributing information across
multiple utterances. We present evidence that (a) the overall
amount of information in an intended message affects how
speakers distribute that information across clauses, and (b)
speakers have relatively early access to (at least an estimate of)
message complexity, specifically before word retrieval. The
observed effect is unexpected for competing theories of sentence production based on availability or macro-propositional
accounts. We discuss these findings in an information theoretic
context coupled with a sketch of a model of limited resources
at the level of macro-propositional planning.
</description>
    <pubDate>Tue, 06 May 2008 14:06:01 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/338</guid>
   </item>
  
   <item>
    <title>Jerod Weinman: Discriminative Probabilistic Models for Robust Scene Text Recognition</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/339</link>
    <description>[Monday, May 05, 2008 at  1:50 PM in Computer Studies Building, Room 703] A first-grader can still read better than the best character
recognition software. Why? The reasons for this are varied, but one limitation of many approaches is a feed-forward, bottom-up, pipelined architecture that isolates the many tasks and information involved in reading. The result is a system that commits errors from which it cannot recover and has components that lack access to relevant information. In this talk, we&#39;ll look at what it might take to develop a &quot;seeing eye computer&quot; that can read to the blind.</description>
    <pubDate>Mon, 05 May 2008 08:18:01 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/339</guid>
   </item>
  
   <item>
    <title>Tongxin Bai: Blended Analysis for Speculative Specialization and Coarse-Grained Redundancy Detection</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/331</link>
    <description>[Monday, April 28, 2008 at  9:30 AM in Computer Studies Bldg. Room 632] Program specialization finds additional optimization opportunities by
targeting some rather than all executions of a program. The narrower
input domain increases the the number of runtime invariants and hence
exposes more runtime redundancies. Program specialization improves
program performance by eliminating the runtime redundancies. However,
the types of invariants that traditional specialization methods can
make use of are extremely limited, typically scalar variables. I
propose a set of new techniques, spanning from compiler and profile
analysis to specialization transformation, to extend the capability of
program specialization so that it can utilize the invariance hidden in
complex data structures. In the second half of the proposal I intend
to measure the similarity between runtime function instances. With
this information the analysis helps programmers understand program
performance issues in terms of computation redundancies. Besides the
redundancy issues memory bandwidth bottleneck is another performance
concern, particularly in this multi-core era. We propose a technique
called {\it partial caching of working sets} (PAW) to reduce cache
misses in a shared-cache environment through intentionally pinning
down a small part of each large working set in the cache.</description>
    <pubDate>Fri, 25 Apr 2008 11:44:38 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/331</guid>
   </item>
  
   <item>
    <title>Nicholas Morsillo: Semi-Supervised Probabilistic Models for Large-Scale Dataset Construction</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/336</link>
    <description>[Tuesday, April 29, 2008 at  1:30 PM in Computer Studies Bldg. Room 632] Object recognition is a fundamental problem for computer vision with a wide range of practical applications.  Although the field of visual recognition continues to hold great research promise, state of the art recognition systems have had limited real-world success.  A primary drawback of current methods is the inability to scale with large amounts of training data and many visual categories.

Scalability is increasingly crucial as the availability of digital imagery increases.  The internet hosts billions of images which can potentially serve as rich sources of training data for recognition.  Our work will explore this option through the following contributions.   1.  A new method for collecting large, clean image sets of specific object categories from web image search.  Our approach will use metadata including web page text to reduce the need for supervision, and it will exploit properties of web images for automatic segmentation and discovery of contextual information.  2. Creation of the first large-scale image dataset with clean labels, segmentation masks, and context descriptors.  3. A novel probabilistic model for visual recognition to efficiently combine object and contextual information from large-scale datasets.  We will thoroughly evaluate our approach by testing recognition performance on known datasets, using our processed web images for training.  </description>
    <pubDate>Thu, 24 Apr 2008 14:54:56 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/336</guid>
   </item>
  
   <item>
    <title>Xiao Zhang: On-chip Shared Resource Management in the Multicore Era</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/333</link>
    <description>[Tuesday, April 29, 2008 at 12:30 PM in Computer Studies Bldg. Room 703] Because of overwhelming heat concerns, processor manufacturers have shifted to the use of multiple cores on a single chip.
One of the key features of this new architecture is that all cores on a single chip share some cache (usually the last level cache) and
off-chip bandwidth.
This sharing presents new challenges due to the subtle interactions
of simultaneously executing processes.
For example, processes that are simultaneously accessing the shared cache may conflict with each other and result in skewed performance.

Today&#39;s operating systems are oblivious to to the needs of individual process for on-chip resources.
Multi-core operating systems must be made aware of on-chip resource competition in order for effective utilization of chip resources.
Using hardware performance counter information, it is possible to provide consolidated operating system support to efficiently manage
shared on-chip resources, particularly the shared cache and memory bus
bandwidth. I will explore both hardware-assisted and OS-level software techniques to improve the performance, fairness, and scalability of
multicore systems.</description>
    <pubDate>Thu, 24 Apr 2008 11:27:34 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/333</guid>
   </item>
  
   <item>
    <title>Shaojun Zhao: Applying Word Alignment to Automatic Machine Translation Evaluation</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/332</link>
    <description>[Monday, April 28, 2008 at  3:30 PM in Computer Studies Bldg. Room 703] All statistical machine translation (MT) systems are built on the idea of word
alignment. The HMM based word alignment model and the IBM model 4 play
the central role in word alignment. The HMM model has an efficient algorithm,
while the IBM model 4 has to perform sampling. We propose a model that has a
simple, exact dynamic programming algorithm, and achieves better performance
than the HMM model.
Automatic machine translation evaluation has revolutionized the development
of MT systems. The BLEU and NIST metric are the de-facto standards. They
are both based on N-gram overlap. Many other metrics have been proposed based
on the longest common subsequence. We propose several metrics
including a multidimensional alignment
approach which is likely to solve many of the drawbacks of the current metrics.</description>
    <pubDate>Thu, 24 Apr 2008 11:25:48 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/332</guid>
   </item>
  
   <item>
    <title>Ross Messing: Activity Recognition using Augmented Feature Flow</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/334</link>
    <description>[Tuesday, April 29, 2008 at  4:00 PM in Computer Studies Bldg. Room 703] Activity recognition systems have made extensive use of local motion,
but have largely failed to use long-range motion information.  We
develop a new way to represent the motion information in a video
sequence that captures long-range motion of features.  We show that
this information can be used to effectively disambiguate activities,
particularly when it is augmented by information orthogonal to motion,
like position.  We propose a number of ways to extend this work.  We
will investigate new representations of feature motion, and how to
tractably characterize the relationship between a pair of moving
features.  We will also explore powerful ways to augment the motion
information with much stronger position information (relative location
of features to a visual landmark) and appearance information (object
recognition on image patches extracted from the feature).  Lastly, we
propose a number of ways to make our model more robust and
nonparametric.</description>
    <pubDate>Thu, 24 Apr 2008 11:18:24 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/334</guid>
   </item>
  
   <item>
    <title>Hemayet Hossain: Effective on-Chip Cache Utilization in Chip Multiprocessors</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/335</link>
    <description>[Wednesday, April 30, 2008 at 10:00 AM in Computer Studies Bldg. Room 601] CMOS scaling trends allow increasing numbers  of transistors on a single chip. In
order to take advantage of these trends with manageable design complexity
while staying within power budgets, processor designers are increasingly turning
toward multi-core architectures --- often chip multiprocessors (CMPs) of
simultaneous multithreaded (SMT) cores --- for all application domains.

Increasing the number of cores on a single chip leads to higher demand on
on-chip cache capacity and interconnect bandwidth (on/off-chip). Scalable cache
coherence protocols support a large number of cores by reducing coherence
bandwidth requirements (e.g., directory). However, they squander away the low
latency cache-cache transfers (possible due to on-chip interconnects) by
introducing a level of indirection on the critical path of cache misses. Many
commercial, scientific, and data-mining workloads exhibit fine-grain (both
temporal and spatial) data-sharing patterns (due to communication and
synchronization) and desire low latency core-core data transfers but are penalized
by these scalable protocols. Furthermore, their working sets well exceed the
private cache sizes and stress-test the shared cache levels.

In this work, we (1) exploit low latency on chip interconnects and (2) improve
the utilization of shared caches. First, we super-impose a direct access
substrate on an existing scalable protocol to take advantage of low latency
interconnects. We show how this mechanism can be used to efficiently support
fine-grain sharing in applications. Additional benefits include reduced power
consumption and reduced interconnect bandwidth. We address location and
placement of data via novel uses of prediction and an adaptive replication,
migration, and producer-consumer optimization(ARMCO) cache coherence protocol.
Second, to meet the cache capacity demands of multiple cores and to improve the
utilization efficiency of on-chip cache resources we propose a sharing-aware
adaptive replacement policy (ShARP) and decoupling of tag and data. Finally, we
will analyze the effects of technology on, and scaling implications of, the
proposed protocol as part of this work.</description>
    <pubDate>Thu, 24 Apr 2008 10:46:46 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/335</guid>
   </item>
  
   <item>
    <title>Liang Huang: Forest Reranking: Chart Parsing with Non-Local Features</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/337</link>
    <description>[Monday, May 05, 2008 at 11:00 AM in ECE 426] [Note that this talk will take place on Monday and in the ECE department]

Conventional n-best reranking techniques often suffer from the limited scope of the n-best list, which rules out many potentially good alternatives. We instead propose forest reranking, a method that reranks the packed forest of exponentially many parses. Although exact inference is intractable with non-local features, we present an approximate algorithm that makes discriminative training practical over the whole Treebank. Our &amp;#64257;nal result, an F-score of 91.7, outperforms both 50-best and 100-best reranking baselines, and is better than any previously reported systems trained on the Treebank. This method can be viewed as an integration of discriminative reranking with traditional chart parsing based on dynamic programming.

I will also discuss some on-going work on applying forest reranking to machine translation.

--------------------

References:

Liang Huang. Forest Reranking: Discriminative Parsing with Non-Local Features.
To Appear in Proceedings of ACL 2008.
http://www.cis.upenn.edu/~lhuang3/forest-rerank.pdf
</description>
    <pubDate>Mon, 21 Apr 2008 14:31:26 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/337</guid>
   </item>
  
   <item>
    <title>Bin Wei: Towards Building Knowledge System from Text</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/328</link>
    <description>[Friday, April 25, 2008 at  1:30 PM in Computer Studies Bldg. Room 601] With growing amount of text data on line, especially some well-organized corpus like 
the Wikipedia, to make a better use of the data, (e.g: extract useful knowledge ) becomes 
a more and more interesting problem. This proposal proposes the thesis work on building 
knowledge system from normal, non-structured texts. The proposed approach will contain 
two steps: transform texts into relational database (this transformation should be able to work 
under semi-supervised manner)and build Markov Logic Network, a knowledge representation 
which combines probablistic and logic. 

</description>
    <pubDate>Mon, 21 Apr 2008 13:08:35 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/328</guid>
   </item>
  
   <item>
    <title>Benjamin Van Durme: Finding Cars, Goddesses and Enzymes &amp; Open Knowledge Acquisition</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/327</link>
    <description>[Friday, April 25, 2008 at 12:30 PM in CSB 703] I will discuss a simple, TFxIDF like method for acquiring large
numbers of pairs of instances and class labels.  For example,
&quot;go-karting&quot; is-a &quot;outdoor activity&quot;,  or &quot;alsike clover&quot; is-a &quot;forage&quot;.
This method allows for the combination of clusters built using
distributional similarity, plus imperfect lists of labelings derived
via Hearst-like patterns, in a manner allowing for easy trade-off
between precision and recall.

This research was done with Marius Pasca, while an intern at Google.
Papers on this and related work will appear at this year&#39;s ACL and AAAI.

If there is time, I will follow with an overview of current work
showing that so-called &quot;old school&quot; methods of extraction based on
traditional NLP are competitive with state of the art systems recently
developed within the community (where competing systems are built
using shallow methods, giving results not useable for symbolic
inference). In addition, we&#39;ve we shown that our system, KNEXT, can
produce the same sorts of &quot;class attributes&quot; that have thus far been
best acquired through the use of commercial search engine query logs,
a propriety resource currently unavailable to the research community.

This later research was done with Lenhart Schubert, and Ting Qian,
with assistance in result evaluation provided by Daphne Liu and Matt
Post.</description>
    <pubDate>Mon, 21 Apr 2008 10:25:23 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/327</guid>
   </item>
  
   <item>
    <title>Paul Ardis: Video Inpainting Using Human Attention Modeling</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/329</link>
    <description>[Friday, April 25, 2008 at  3:30 PM in Computer Studies Bldg. Room 632] Inpainting techniques, for all that they have accomplished, continue to fail to satisfy.  By refusing to quantify result quality and approximating human visual expectation in an ad-hoc fashion, current methods remain innovationally stagnant.  Furthermore, modern methods that attempt to diverge from their traditional roots in data interpolation are faced with the risk of computational intractibility.  I propose to improve this situation over the next two or three years by making use of human attentional modeling and statistical learning techniques, ultimately providing a clear indication of advancement in the field. Specifically, I intend to:  1.) learn a decision boundary between patch-based methods and interpolation methods using a modified Conditional Random Field, 2.) compile a high-resolution video dataset for training and benchmark comparison of methods in the field, 3.) analyze the quality and efficiency of results produced using the learned decision boundary and simple existing methods as options for selection, 4.) adjust existing salience models and produce a novel method of computation learned from natural statistics in existing eyetrack datasets, 5.) produce a novel interpolation method based upon a salience-guided analysis of global and local image statistics, and 6.) compare my work against a number of leading algorithms in a series of double-blind human subject experiments as well as studying computational efficiency.

</description>
    <pubDate>Thu, 17 Apr 2008 11:17:59 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/329</guid>
   </item>
  
   <item>
    <title>Suzanne Stevenson: Bridging the Gap between Syntax and the Lexicon: Computational Models of Acquiring Multiword Lexemes</title>
    <link>http://www.cs.rochester.edu/dept/seminar/view/324</link>
    <description>[Friday, April 18, 2008 at 12:00 PM in CSB 601] (Note change of time and location)

As the study of language has shifted over the past 20 years from a focus on abstract knowledge of language to a focus on actual language use, there&#39;s been increasing awareness that much of what people say does not fall neatly into the traditional model of grammar that separates syntax and the lexicon. In response, there&#39;s been significant work on elements of language that used to be relegated to &quot;the periphery&quot;, including multiword lexemes (collocations, idioms, multiword verbs of various kinds) that exhibit properties of both single words and syntactic phrases. Moreover, theories of child language learning have explored the importance of multiword constructions in guiding the acquisition process. However, computational modeling of child language acquisition has continued to focus largely on word learning and grammar learning. In this talk, I will present our computational linguistic research on identifying and classifying multiword verbs, and discuss preliminary work relating these techniques to the process of child language acquisition.

This is joint work with Afsaneh Fazly, University of Toronto.</description>
    <pubDate>Sun, 13 Apr 2008 18:26:07 -0400</pubDate>
    <guid>http://www.cs.rochester.edu/dept/seminar/view/324</guid>
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