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Events for October 11, 2018
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Multimodal Emotion Recognition: Quantifying Dynamics and Structure in Audio-Visual Expressive Speech
Thu, Oct 11, 2018 @ 02:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yelin (Lynn) Kim, Ph.D., Assistant Professor, University at Albany, SUNY
Talk Title: Multimodal Emotion Recognition: Quantifying Dynamics and Structure in Audio-Visual Expressive Speech
Abstract: The rise of AI assistant systems, including Google Home, Apple Siri, and Amazon Echo, brings the urgent need for increased and deeper understanding of users. In this talk, I will present algorithmic and statistical methods for analyzing audio-visual human behavior, particularly focusing on emotional and social signals inferred from speech and facial expressions. These methods can provide emotional intelligence to AI systems. However, developing automatic emotion recognition systems is challenging since emotional expressions are complex, dynamic, inherently multimodal, and are entangled with other factors of modulation (e.g. speech generation and emphasis). I will present several algorithms to address these fundamental challenges in emotion recognition: (i) cross-modal modeling methods that capture and control for interactions between individual facial regions and speech using the Minimum Description Length (MDL) principle-based segmentation; (ii) localization and prediction of events with salient emotional behaviors using a max-margin optimization and dynamic programming; and (iii) temporal modeling methods to learn co-occurrence patterns between emotional behaviors and emotion label noise. These algorithms have enabled advancements in the modeling of audio-visual emotion recognition systems and increased the understanding of the underlying dynamic and multimodal structure of affective communication (e.g., cross-modal interaction, temporal structure, and inherent perceptual ambiguity).
Biography: Yelin Kim [http://yelinkim.com] is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, State University of New York (SUNY). She received her M.S. and Ph.D. in Electrical and Computer Engineering from the University of Michigan, Ann Arbor in 2013 and 2016, respectively, and her B.S. in Electrical and Computer Engineering from Seoul National University, South Korea in 2011. Her main research interests are in human-centered and affective computing, multimodal (audio-visual) modeling, and computational behavior analysis. Her work was recognized by several awards, including a Google Faculty Research Award (2018), a SUNY-A Faculty Research Award (2017), and the Best Student Paper Award at ACM Multimedia (2014).
Host: Dr. Shrikanth Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
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Auto-Tuned Threading for OLDI Microservices
Thu, Oct 11, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Akshitha Sriraman, University of Michigan
Talk Title: Auto-Tuned Threading for OLDI Microservices
Abstract: Modern On-Line Data Intensive (OLDI) applications have evolved from monolithic systems to instead comprise numerous, distributed microservices interacting via Remote Procedure Calls (RPCs). Microservices face sub-ms RPC latency goals, much tighter than their monolithic ancestors that must meet >=100ms latency targets. Sub-ms-scale threading and on currency design effects as well as OS and network overheads that were once insignificant for such monoliths, can now come to dominate in the sub-ms-scale microservice regime. It is therefore vital to characterize the influence of threading design, OS, and network effects on microservices. Unfortunately, widely used academic data center benchmark suites are unsuitable to aid this characterization as they use monolithic rather than microservice architectures.
We first investigate how OS/network overheads impact microservice tail latency by developing a complete suite of microservices called mSuite that we use to facilitate our study. Our characterization reveals that the relationship between optimal OS/network parameters and service load is complex. Our primary finding is that non-optimal OS scheduler decisions can degrade microservice tail latency by up to ~87%.
Secondly, we investigate how threading design critically impacts microservice tail latency by developing a taxonomy of threading models -“ a structured understanding of the implications of how microservices manage concurrency and interact with RPC interfaces under wide-ranging loads. We develop mTune, a system that has two features: (1) a novel framework that abstracts threading model implementation from application code, and (2) a novel automatic load adaptation system that curtails microservice tail latency by exploiting inherent latency trade-offs revealed in our taxonomy to transition among threading models. We study mTune in the context of mSuite to demonstrate up to 1.9x tail latency improvements over static threading choices and state-of-the-art adaptation techniques.
Biography: Akshitha is a fourth year Ph.D. student at the University of Michigan, where she is advised by Dr. Thomas F. Wenisch. Her primary research interests are in software systems and computer architecture. Her research focuses on developing software and hardware optimizations to improve the performance of large-scale distributed data center system.
Host: Xuehai Qian, xuehai.qian@usc.edu
More Information: 18.10.11 Akshitha Sriraman_CENG.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Brienne Moore
