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Events for May 17, 2024

  • SeMS Aviation Security Management Systems AVSEC 24-2

    Fri, May 17, 2024 @ 08:00 AM - 12:00 PM

    Aviation Safety and Security Program

    University Calendar

    This course is designed for individuals responsible for managing and implementing aviation security measures at medium to small-size aircraft operators, all airports, and Indirect Air Carriers (IACs). The course applies the fundamentals of SMS (hazard identification, risk assessment, and mitigation of risk) to aviation security. It demonstrates how to conduct a risk-based security program that builds upon national and international standards and requirements. The course presents the PRIFISE operational risk assessment tool as a framework for meeting emerging security threats. As cyber security has become a more important issue, this course has been extended to include a half-day on cyber security. Note: This is a non-SSI course.

    Location: Century Boulevard Building (CBB) - 920

    Audiences: Everyone Is Invited

    Contact: Daniel Scalese

    Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AAVSEC2

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  • PhD Defense- Hongkuan Zhou

    Fri, May 17, 2024 @ 10:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Student Activity

    PhD Defense- Hongkuan Zhou
    Title: Scaling up Temporal Graph Learning: Powerful Models, Efficient Algorithms, and Optimized Systems
    Committee Members: Prof. Keith Michael Chugg, Prof. Rajgopal Kannan, Prof Viktor K. Prasanna (Chair), Prof. Mukund Raghothaman
    Abstract: Recently, Temporal Graph Neural Networks (TGNNs) have extended the scope of Graph Representation Learning (GRL) to dynamic graphs. TGNNs generate high-quality and versatile dynamic node embeddings by simultaneously encoding the graph structures, node and edge contexts, and their temporal dependencies. TGNNs are shown to demonstrably outperform traditional dynamic graph analytic algorithms in impactful applications that address critical real-world challenges, such as social network analysis, healthcare applications, and traffic prediction and management. However, due to the challenges of the prevalent noise in real-world data, irregular memory accesses, complex temporal dependencies, and high computation complexity, current TGNNs face the following problems when scaling to large dynamic graphs: (1) Unpowerful models. Current TGNN models struggle to capture high-frequency information and handle the diverse and dynamic noise. (2) Ineffcieint algorithms. Current training algorithms cannot leverage the massive parallel processing architecture of modern hardware, while current inference algorithms cannot meet the requirements in different scenarios. And (3) Unoptimized systems. Current TGNN systems suffer from inefficient designs that hinder overall performance. In this dissertation, we address the above issues via model-algorithm-system co-design. For model improvements, we propose a static node-memory-enhanced TGNN model and a temporal adaptive sampling technique. For algorithm improvements, we propose a scalable distributed training algorithm with heuristic guidelines to achieve the optimal configuration, and a versatile inference algorithm. For system improvements, we propose techniques of dynamic feature caching, simplified temporal attention, etc., to compose optimized training and inference systems. We demonstrate significant improvements in accuracy, training time, inference latency, and throughput compared with state-of-the-art TGNN solutions.

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

    Audiences: Everyone Is Invited

    Contact: Hongkuan Zhou

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  • Join us to learn about the Advancements in Research Ultrasound from Verasonics

    Fri, May 17, 2024 @ 10:00 AM - 11:00 AM

    Alfred E. Mann Department of Biomedical Engineering

    Conferences, Lectures, & Seminars

    Speaker: Christian Coviello, PhD and Miguel Bernal, Phd, Verasonics

    Talk Title: Join us to learn about the Advancements in Research Ultrasound from Verasonics

    Host: Qifa Zhou

    Location: Corwin D. Denney Research Center (DRB) - 145

    Audiences: Everyone Is Invited

    Contact: Stephanie Perales

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  • Quantum Science & Technology Seminar - Z.Y. Jeff Ou, Friday, May 17th at 10:30am in EEB 248

    Fri, May 17, 2024 @ 10:30 AM - 11:45 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Z.Y. Jeff Ou, Physics, City University of Hong Kong

    Talk Title: Quantum Entangled Interferometers and Their Applications

    Series: Quantum Science & Technology Seminar Series

    Abstract: A new type of quantum interferometer utilizes nonlinear parametric processes as the wave splitting and recombination elements. Because of the nonlinear interaction, the fields inside the interferometer are intrinsically entangled and quantum mechanically correlated. This type of quantum correlated interferometer exhibits some unique properties that we will review in this talk. Because of these properties, this type of interferometer is superior to traditional beam splitter-based interferometers in many aspects. We will present its various forms and its realizations with different types of waves such as microwave, atomic waves (both internal and external degrees), and sound waves. We will discuss its applications in quantum metrology, quantum imaging, quantum spectroscopy, and quantum state engineering.

    Biography: Professor Ou obtained his BS in 1984 from Peking University and his Ph.D. in 1990 from University of Rochester. He is now a chair professor in City University of Hong Kong. Professor Ou is an expert in quantum optics, especially in quantum interference, for which he is famous for the Hong-Ou-Mandel interferometer. His current research focuses on quantum metrology, quantum sensing, quantum state engineering, and the fundamental quantum interference effects. Professor Ou is a fellow of American Physical Society and of Optica (formerly Optical Society of America).

    Host: Quntao Zhang, Wade Hsu, Mengjie Yu, Jonathan Habif & Eli Levenson-Falk

    More Information: Z.Y. Jeff Ou Flyer.pdf

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

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • AI Seminar- AI for Fostering Constructive Online Conversations

    Fri, May 17, 2024 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Kristina Gligoric, Stanford University

    Talk Title: AI for Fostering Constructive Online Conversations

    Abstract: REMINDER:  Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you are an outside visitor, please inform us at aiseminars DASH poc AT isi DOT edu beforehand so we will be aware of your attendance and let you in.  Zoom meeting ID: 704 285 0182Passcode: 832239  Abstract: NLP systems promise to positively impact society in high-stakes social domains. However, current evaluation and development focus on tasks that are not grounded in specific societal implications, which can lead to societal harms. In this talk, I will present recent work addressing these issues in the domain of online content moderation. In the first part, I will discuss online content moderation to enable constructive conversations about race. Content moderation practices on social media risk silencing the voices of historically marginalized groups. Both the most recent models and humans disproportionately flag posts in which users share personal experiences of racism. Not only does this censorship hinder the potential of social media to give voice to marginalized communities, but we also find that witnessing such censorship exacerbates feelings of isolation. A psychologically informed reframing intervention offers a path to reduce censorship through. In the second part, I will discuss how identified biases in models can be traced to the use-mention distinction, which is the difference between the use of words to convey a speaker’s intent and the mention of words for quoting what someone said or pointing out properties of a word. Computationally modeling the use-mention distinction is crucial for enabling counterspeech to hate and misinformation. Counterspeech that refutes problematic content mentions harmful language but is not harmful itself. Even recent language models fail at distinguishing use from mention. This failure propagates to downstream tasks but can be reduced through introduced mitigations. Finally, I discuss the big picture and other recent efforts to address these issues in different domains beyond content moderation, including education, emotional support, sustainability, and public discourse about AI. I will reflect on how, by doing so, we can minimize the harms and develop and apply NLP systems for social good.

    Biography: Kristina Gligoric is a Postdoctoral Scholar at Stanford University Computer Science Department, advised by Dan Jurafsky at the NLP group. Previously she obtained her Ph.D. in Computer Science at EPFL, where she was advised by Robert West. Her research focuses on developing computational approaches to address societal issues, drawing methods from NLP and causal inference. Her work has been published in top computer science conferences focused on computational social science and social media (CSCW, ICWSM, TheWebConf), natural language processing (EACL, NAACL, EMNLP), and broad audience journals (Nature Communications and Nature Medicine). She is a Swiss National Science Foundation Fellow and University of Chicago Rising star in Data Science. She received awards for her work, including EPFL Thesis Distinction and CSCW Best Paper Honorable Mention Award. This event will be recorded. It will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.

    Host: Myrl Marmarelis and Maura Covaci

    More Info: https://www.isi.edu/events/4952/ai-for-fostering-constructive-online-conversations/

    Location: Information Science Institute (ISI) - Conf Rm#1014

    Audiences: Everyone Is Invited

    Contact: Pete Zamar

    Event Link: https://www.isi.edu/events/4952/ai-for-fostering-constructive-online-conversations/

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  • PhD Dissertation Defense - Binh Vu

    Fri, May 17, 2024 @ 03:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Title: Exploiting Web Tables and Knowledge Graphs for Creating Semantic Descriptions of Data Sources  
    Committee: Craig Knoblock (Chair), Sven Koenig, Daniel Edmund O'Leary, Yolanda Gil, Jay Pujara  
    Date and Time: Friday, May 17th - 3:00p - 5:00p
    Location: SAL 322
    Abstract: There is an enormous number of tables available on the web, and they can provide valuable information for diverse applications. To harvest information from the tables, we need precise mappings, called semantic descriptions, of concepts and relationships in the data to classes and properties in a target ontology. However, creating semantic descriptions, or semantic modeling, is a complex task requiring considerable manual effort and expertise. Much research has focused on automating this problem. However, existing supervised and unsupervised approaches both face various difficulties. The supervised approaches require lots of known semantic descriptions for training and, thus, are hard to apply to a new or large domain ontology. On the other hand, the unsupervised approaches exploit the overlapping data between tables and knowledge graphs; hence, they perform poorly on tables with lots of ambiguity or little overlapping data. To address the aforementioned weaknesses, we present novel approaches for two main cases: tables that have overlapping data with a knowledge graph (KG) and tables that do not have overlapping data. Exploiting web tables that have links to entities in a KG, we automatically create a labeled dataset to learn to combine table data, metadata, and overlapping background knowledge (if available) to find accurate semantic descriptions. Our methods for the two cases together provide a comprehensive solution to the semantic modeling problem. In the evaluation, our approach in the overlapping setting yields an improvement of approximately 5\% in F$_1$ scores compared to the state-of-the-art methods. In the non-overlapping setting, our approach outperforms strong baselines by  10\% to 30\% in F$_1$ scores.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Contact: Felante' Charlemagne

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