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Events for April 23, 2015
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CS Colloquium: Mark Zhandry (Stanford) - The Surprising Power of Modern Cryptography
Thu, Apr 23, 2015 @ 09:45 AM - 10:50 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mark Zhandry, Stanford
Talk Title: The Surprising Power of Modern Cryptography
Series: CS Colloquium
Abstract: Modern cryptography is surprisingly powerful, yielding capabilities such as secure multiparty computation, computing on encrypted data, and hiding secrets in code. Currently, however, some of these advanced abilities are still too inefficient for practical use. The goals of my research are two-fold: (1) continue expanding the capabilities of cryptography and its applications, and (2) bring these advanced capabilities closer to practice.
In this talk, I will focus on a particular contribution that addresses both of these objectives: establishing a shared secret key among a group of participants with only a single round of interaction. The first such protocols required a setup phase, where a central authority determines the parameters for the scheme; unfortunately, this authority can learn the shared group key and must therefore be trusted. I will discuss how to remove this setup phase using program obfuscation, though the scheme is very impractical due to the inefficiencies of current obfuscators. I will then describe a new technical tool called witness pseudorandom functions and show how to use this tool in place of obfuscation, resulting in a significantly more efficient protocol.
the lecture will be available to stream HERE.
Biography: Mark Zhandry is a Ph.D. candidate at Stanford University advised by Dan Boneh. He studies cryptography and computer science theory and is currently focusing on developing new cutting-edge cryptographic capabilities and improving the efficiency of these applications. He is visiting Microsoft Research New England and MIT for the 2014-15 academic year.
Host: Computer Science Department
More Info: https://bluejeans.com/638649971
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/638649971
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Distinguished Lecture: Teri Odom (Northwestern)
Thu, Apr 23, 2015 @ 12:45 PM - 02:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Teri Odom, Northwestern, Materials Science & Engineering
Talk Title: Light-Matter Interactions in Plasmonic Nanocavities
Series: Distinguished Lectures
Abstract: TBA
Host: Prof. Armani
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Ryan Choi
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Phd Defense - Weijun Wang
Thu, Apr 23, 2015 @ 02:00 PM - 03:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD defense: Weijun Wang
Title: Tracking Multiple Articulating Humans from a Single Camera
Time: 2:00PM -3:30PM
Location: Powell Hall of Engineering(PHE) 631
Dissertation Committee:
Chair: Professor Ram Nevatia
Suya You
C.-C.Jay Kuo
Abstract:
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Monocular multi-target tracking aims at locating multiple targets, maintaining their identities across frames and estimating their motion trajectories from a single camera view, which is an important problem with many applications such as automatic surveillance and video retrieval. In particular, humans are often the most concerned targets as daily activities and events in real scenes usually involve human participants. Even though some fairly significant advances have been made on pedestrian tracking in recent years, the problem of tracking multiple humans towards higher-level reasoning is still far from solved. For example , humans might move in groups in real scenes and important social context features have not been effectively explored by the usual simplification that targets' trajectories are independent. Most importantly, unlike well-studied pedestrian detection, articulated human detection remains a challenging task which makes the existing pedestrian tracking approaches less effective on videos with multiple articulating humans. In this work, we focus on exploring important online learned appearance and social context cues to improve tracking performance on pedestrians as well as articulated humans.
As pedestrian tracking is the foundation of the proposed approach, we first propose to improve its performance by considering social context. We propose a general quadratic formulation to incorporate social dependency into a global optimization problem to improve multi-target tracking accuracy. To ensure the tracking efficiency, we show an approach to convert the new binary quadratic programming formulation to a semidefinite programming problem under convex relaxation, which can be efficiently solved by off-the-shelf methods. With the new formulation, we propose to consider a few simple common trajectory dependency factors, which can be efficiently inferred online to improve tracking performance, especially in semi-crowded scenarios. In scenarios where no trajectory dependency can be explored, our solution is the same and as efficient as those classic linear optimization formulations. Experimental results on standard datasets show the advantages of our approach over state-of-the-art. Moreover, this new formulation provides a general framework to consider various useful high order information to improve multi-target tracking.
To address the problem of tracking multiple articulating humans from a single camera, we propose a hybrid framework. Our method incorporates offline learned category-level detector with online learned instance-specific detector as a hybrid system. To deal with humans in large pose articulation, which can not be reliably detected by off-line trained detectors, we propose an online learned instance-specific patch-based detector, consisting of layered patch classifiers. With extrapolated tracklets by online learned detectors, we use the discriminative color filters learned online to compute the appearance affinity score for further global association.
Experimental evaluation on both standard pedestrian datasets and articulated human datasets shows significant improvement compared to state-of-the-art multi-human tracking methods.
Location: Charles Lee Powell Hall (PHE) - 631
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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CS Student Colloquium Series: Kuan Liu & Alireza Bagheri Garakani
Thu, Apr 23, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Kuan Liu & Alireza Bagheri Garakani, USC Computer Science
Talk Title: Similarity Learning for High Dimensional Sparse Data; A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Series: Student Seminar Series
Abstract: Similarity Learning for High Dimensional Sparse Data
Kuan Liu
A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, but they do not scale well respect to the dimensionality of the data. In this talk, we describe a method that can learn efficiently similarity measure from high dimensional sparse data. The core idea is to parameterize the similarity measure as a convex combination of rank-one matrices with specific sparsity structures. The parameters are then optimized with an approximate Frank-Wolfe procedure to maximally satisfy relative similarity constraints on the training data. Our algorithm greedily incorporates one pair of features at a time into the similarity measure, providing an efficient way to control the number of active features and thus reduce overfitting. It enjoys very appealing convergence guarantees and its time and memory complexity depends on the sparsity of the data instead of the dimension of the feature space. Our experiments on real world high-dimensional datasets demonstrate its potential for classification, dimensionality reduction and data exploration
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Alireza Bagheri Garakani
Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We address the key challenges of balancing communication costs and optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW) algorithm. We obtain theoretical guarantees on the optimization error and communication cost that do not depend on the total number of combining elements. We further show that the communication cost of dFW is optimal by deriving a lower-bound on the communication cost required to construct an epsilon-approximate solution. We validate our theoretical analysis with empirical studies on synthetic and real-world data, which demonstrate that dFW outperforms both baselines and competing methods. We also study the performance of dFW when the conditions of our analysis are relaxed, and show that dFW is fairly robust.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
