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Events for October 04, 2018

  • Six Sigma Green Belt for Process Improvement

    Thu, Oct 04, 2018

    Executive Education

    Conferences, Lectures, & Seminars

    Talk Title: Six Sigma Green Belt for Process Improvement

    Abstract: Abstract: Learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality.

    Host: USC Viterbi Executive Education

    More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

    Audiences: Registered Attendees

    Contact: Viterbi Professional Programs

    Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

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  • CS Colloquium: Mohammad Soleymani (USC-ICT) - What Do Machines Learn in Emotion Recognition from EEG Signals?

    Thu, Oct 04, 2018 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Mohammed Soleymani, USC-ICT

    Talk Title: What Do Machines Learn in Emotion Recognition from EEG Signals?

    Series: CS Colloquium

    Abstract: Machines that are able to read our emotions and cognitive states make better companions. Emotions are often sensed by their external manifestations such as facial and vocal expressions. Additionally, studies in affective neuroscience have identified a set of emotional neural activities that can be captured by eletroencephalogram (EEG) signals, including asymmetric frontal brain activity and increase in information transfer. Motivated by these findings, a growing number of studies report developing EEG-based emotion recognition systems with promising results. In this talk, I first present my work on recognizing emotions of people watching videos. I then present a follow up study in which we aimed to better understand what machine learns in such scenarios. In the follow up work, we recorded a dataset which includes spontaneous emotions and posed expressions. Our analysis on the data collected in the follow up study demonstrates that the performance of existing EEG-based emotion recognition methods significantly decreases when evaluated across different corpora. We also found that models trained on spontaneous emotions perform well on recognizing mimicked expressions. Our results provide evidence that stimuli-related sensory information and facial electromyogram activities are the main components learned by machine learning models for emotion recognition using EEG signals.

    This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.

    Biography: Mohammed Soleymani is a research scientist with the USC Institute of Creative Technologies. He received his PhD in computer science from the University of Geneva in 2011. From 2012 to 2014, he was a Marie Curie fellow at Imperial College London. Prior to joining ICT, he was a research scientist at the Swiss Center for Affective Sciences, University of Geneva. His main line of research involves developing automatic emotion recognition and behavior understanding methods using physiological signals and facial expressions. He is also interested in understanding subjective attributes in multimedia content, e.g, predicting whether an image is interesting from its pixels or automatic recognition of music mood from acoustic content. He is a recipient of the Swiss National Science Foundation Ambizione grant and the EU Marie Curie fellowship. He has served on multiple conference organization committees and editorial roles, most notably as associate editor for the IEEE Transactions on Affective Computing and technical program chair for ACM ICMI 2018 and ACII 2017. He is one of the founding organizers of the MediaEval multimedia retrieval benchmarking campaign and the president elect for the Association for Advancement of Affective Computing (AAAC).

    Host: David Traum

    Location: Ronald Tutor Hall of Engineering (RTH) - 115

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • NL Seminar- CoQA, A Conversational Question Answering Challenge

    Thu, Oct 04, 2018 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Siva Reddy , Stanford University

    Talk Title: CoQA: A Conversational Question Answering Challenge

    Series: Natural Language Seminar

    Abstract: Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. In this talk, I will present our work on CoQA, a novel dataset for building Conversational Question Answering systems. CoQA contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.1%, which is 23.7 points behind human performance 88.8 percent, indicating there is ample room for improvement. We launch CoQA as a challenge to the community. See link below.

    Biography: Siva Reddy is a postdoc in Computer Science at Stanford University working with Prof. Christopher Manning. His research focuses on enabling natural communication between humans and machines. Prior to the postdoc, he was a Google PhD Fellow at the University of Edinburgh under the supervision of Prof. Mirella Lapata and Prof. Mark Steedman.

    Host: Xusen Yin

    More Info: https://stanfordnlp.github.io/coqa/

    Webcast: https://bluejeans.com/s/iHu_F/

    Location: Information Science Institute (ISI) - 6th Floor Conf Rm-CR# 689

    WebCast Link: https://bluejeans.com/s/iHu_F/

    Audiences: Everyone Is Invited

    Contact: Peter Zamar

    Event Link: https://stanfordnlp.github.io/coqa/

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  • CENG Seminar Series

    Thu, Oct 04, 2018 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Yu Hua, Huazhong University of Science and Technology

    Talk Title: Encrypted Non-volatile Main Memory Systems

    Abstract: Non-volatile memory (NVM) technologies are considered as promising candidates of the next-generation main memory. However, the non-volatility of NVMs leads to new security vulnerabilities. Memory encryption can be employed to mitigate the security vulnerabilities, but it increases the number of bits written to NVMs due to the diffusion property and thereby aggravates the NVM wear-out induced by writes. To address these security and endurance challenges, we propose DeWrite, a secure and deduplication-aware scheme to enhance the performance and endurance of encrypted NVMs based on a new in-line deduplication technique and the synergistic integrations of deduplication and memory encryption. Specifically, it performs low-latency in-line deduplication to exploit the abundant cache-line-level duplications leveraging the intrinsic read/write asymmetry of NVMs and light-weight hashing. It also opportunistically parallelizes the operations of deduplication and encryption and allows them to co-locate the metadata for high efficiency. DeWrite was implemented on the gem5 with NVMain.

    Biography: Dr. Yu Hua is a professor in Huazhong University of Science and Technology. He was Postdoc Research Associate in McGill University in 2009, and Postdoc Research Fellow in University of Nebraska-Lincoln in 2010-2011. He obtained his B.E and Ph.D degrees from Wuhan University respectively in 2001 and 2005. His research interests include file systems, cloud storage systems, non-volatile memory, big data analytics, etc. He publishes multiple papers in conferences and journals, including OSDI, MICRO, FAST, USENIX ATC, ACM SoCC, SC, HPDC, etc. He serves for multiple international conferences, including USENIX ATC, ASPLOS (ERC), SC, ACM SoCC, RTSS, ICDCS, ICCD, INFOCOM, IPDPS, etc. He is the distinguished member of CCF, senior member of ACM and IEEE, and the member of USENIX. He has been appointed as the Distinguished Speaker of ACM and CCF. His homepage is at: https://csyhua.github.io

    Host: Xuehai Qian, xuehai.qian@usc.edu

    More Information: 18.10.04_Yu Hua_CENG Seminar.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Brienne Moore

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  • CS Tech Talk: Parisa Mansourifard (Facebook) - Infrastructure Data Science Team at Facebook

    Thu, Oct 04, 2018 @ 03:30 PM - 04:50 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Parisa Mansourifard, Facebook

    Talk Title: Infrastructure Data Science Team at Facebook

    Series: Computer Science Colloquium

    Abstract: In this talk, I will present what my team does at Facebook and what problems we aim to solve. Infrastructure Data Science partners with engineering teams to develop data-driven solutions for significant infrastructure challenges such as app and site performance, systems efficiency and reliability, resource allocation and long-term capacity forecasts. Infra Data Scientists use a range of tools, from A/B testing to machine learning, to help Facebook make decisions about operations and system design. The team contributes to all parts of a project's lifecycle, including scoping, data discovery, research, methodological design, code implementation, and reporting and interpreting final results. The teams' work varies, in line with the complex and diverse challenges of maintaining one of the largest and most advanced enterprise infrastructures in the world. We look for candidates with a wide range of backgrounds to join our team and help with this work.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Parisa Mansourifard is currently a data scientist at Infrastructure data science team at Facebook. Before joining Facebook, she was a data scientist at SupplyFrame Inc. and a part-time lecturer at CS department of University of Southern California teaching machine learning. She received the B.S. and M.S. in electrical engineering from Sharif university of technology, Tehran, Iran, in 2008 and 2010 respectively. She also got a M.S. in computer science and Ph.D. in electrical engineering from University of Southern California, Los Angeles, CA, USA, in 2015 and 2017, respectively. During her Ph.D. she held Viterbi Dean fellowships in 2011-2014 and AAUW dissertation completion fellowship in 2015-2016. She also got a best paper award for the operations research track at EU IEOM conference in Paris 2018.

    Host: Computer Science Department

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Pearl Harbor Naval Shipyard and Intermediate Maintenance Facility Info Session

    Thu, Oct 04, 2018 @ 05:30 PM - 06:30 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions

    Pearl Harbor Naval Shipyard and IMF (PHNSY & IMF), the largest industrial employer in the State of Hawaii with over 5500 civilian and military employees, is in the business of repairing and modernizing naval ships and submarines. Our engineers, from a variety of disciplines, are involved in the planning and supervision of the highly challenging work of providing technical guidance through written work procedures and on-the-job direction of the workforce. On a day-to-day basis, our engineers spend their time working onboard submarines or surface ships and working in an office setting. PHNSY & IMF is looking for energetic self-motivated engineers to join the select group of men and women in our various Engineering Departments. We offer competitive pay, generous vacation benefits, paid overtime, excellent retirement system and federal health benefits.

    Location: Seeley G. Mudd Building (SGM) - 101

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • CCI presents Blockfinity's "Bringing Cities on to the Blockchain - Los Angeles"

    Thu, Oct 04, 2018 @ 07:00 PM - 09:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Receptions & Special Events

    Blockchain is a fast-growing technology with the intent to decentralize bureaucratic processes, create transparent transactions between multiple parties, and optimize enormous databases. The applications are endless when it comes to government, and municipalities, but our goal is to bring the community together to discuss theory, implementation, and case studies of how cities may work with Blockchain.

    Let's imagine the changes in social, economic and environmental by ridding of bureaucracy in city infrastructure such as piles of papers, giant traffic jams, documentation errors and double transactions. Join our discussion to understand which cities around the world are implementing blockchain and how, what can be improved, and how would this help us here in Los Angeles.

    Please RSVP here:
    Bringing Cities on to the Blockchain - Los Angeles

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: Brienne Moore

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