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Events for April 18, 2024
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Aviation Safety Management Systems ASMS 24-4Thu, Apr 18, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
A Safety Management System (SMS) is now required for international commercial aircraft operators, airports, and air traffic services. The International Civil Aviation Organization (ICAO) established the standards and implementing procedures for SMS. All 191 countries that are members of ICAO have established or are establishing regulatory requirements for the implementation of SMS. This course teaches how organizations can establish an SMS within the context of their current safety system that meets the basic international standards of ICAO. The SMS Framework serves as a central foundation for this course.
SMS is a safety system by which an organization takes a more active role in identifying, analyzing, and mitigating safety issues that occur in the normal operation of their organization. SMS requires that organizational management take responsibility for the company’s safety program. The SMS approach requires the safety/quality team to be educated in their duties and responsibilities. This course will give you the essential skills to manage an organizational Safety Management System (SMS). The attendee will be able to manage a Safety Management System that includes risk management, audits, data collection, analysis, and incident investigations.
This course is designed for the individual planning or directing an aviation Safety Management System program. Fundamentals in systems organization and structure provide the individual with the skills and methodology to plan and manage an effective program. Emphasis is placed on understanding the principles of risk management, identifying program development strategies, audits, and applying the knowledge toward effective management systems and interoperability with Quality Assurance.Location: Online
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AASMS4
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Data for Safety Management DATA 24-2
Thu, Apr 18, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
The analysis of digital flight data collected from actual flights has resulted in tremendous steps forward in aviation safety. It is no longer necessary for an accident or incident to occur in order for safety hazards to be revealed. Flight Data Analysis provides critical safety information to identify trends, issues, and potentially dangerous practices. All modern commercial and business jet aircraft are equipped with flight data recorders that serve as the initial collection devices for flight data analysis. This course will present the basics of flight data analysis based on real-time flight information. It will present opportunities to analyze collective flight data as would be utilized by a commercial aircraft operator. The course will present animation software that depicts flight profiles and examines other sources of data, including video and air traffic control data, that may be used to create a data-based safety case.
Location: Century Boulevard Building (CBB) - 920
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24ADATA2
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ISSS - Dr. Shanthi Pavan, Thursday, April 18th at 10am in EEB 132
Thu, Apr 18, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Shanthi Pavan, IIT Madras
Talk Title: Continuous-Time Pipelined Analog-to-Digital Converters -“ Where Filtering Meets Analog-to-Digital Conversion
Series: Integrated Systems
Abstract: If someone told you that the power, noise, distortion, and area of a mixed-signal block could be reduced all at the same time, you'd probably think that this was a lie. It turns out that it is indeed possible sometimes - and this talk will present an example called the continuous-time pipeline (CTP) ADC. The CTP is an emerging technique that combines filtering with analog-to-digital conversion. Like a continuous-time delta-sigma modulator (CTDSM), a CTP has a "nice" input impedance that is easy to drive and has inherent anti-aliasing. However, unlike a CTDSM, a CTP does not require a high-speed feedback loop to be closed. As a result, it can achieve significantly higher bandwidth (like a Nyquist ADC). After discussing the operating principles behind the CTP, we describe the fundamental benefits of the CTP over a conventional signal chain that incorporates an anti-alias filter and a Nyquist-rate converter. We will then show design details and measurement results from a 100MHz 800MS/s CTP designed in a 65nm CMOS process.
Biography: Shanthi Pavan received the B.Tech. degree in electronics and communication engineering from IIT Madras, Chennai, India, in 1995, and the M.S. and D.Sc. degrees from Columbia University, New York, NY, USA, in 1997 and 1999, respectively. From 1997 to 2000, he was with Texas Instruments, Warren, NJ, USA, where he worked on high-speed analog filters and data converters. From 2000 to June 2002, he worked on microwave ICs for data communication at Bigbear Networks, Sunnyvale, CA, USA. Since July 2002, he has been with IIT Madras, where he is currently the NT Alexander Institute Chair Professor of Electrical Engineering. He is the author of Understanding Delta-Sigma Data Converters (second edition, with Richard Schreier and Gabor Temes), which received the Wiley-IEEE Press Professional Book Award for the year 2020. His research interests are in the areas of high-speed analog circuit design and signal processing. Dr. Pavan is a fellow of the Indian National Academy of Engineering, and the recipient of several awards, including the IEEE Circuits and Systems Society Darlington Best Paper Award in 2009. He has served as the Editor-in-Chief of the IEEE Transactions on Circuits and Systems-I: Regular Papers. He has been a Distinguished Lecturer of the Solid-State Circuits and Circuits-and-Systems Societies. He currently serves as the Vice-President of Publications of the IEEE Solid-State Circuits Society, on the Technical Program Committee of the International Solid-State Circuits Conference (ISSCC), and on the editorial board of the IEEE Journal of Solid-State Circuits. He is an IEEE Fellow.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Information: Shanthi Pavan Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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NL Seminar - DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models
Thu, Apr 18, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Ollie Liu, USC, USC
Talk Title: DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models
Abstract: REMINDER: Meeting hosts only admit on-line guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) to make us aware of your attendance so we can admit you. Specify if you will attend remotely or in person at least one business day prior to the event Provide your: full name, job title and professional affiliation and arrive at least 10 minutes before the seminar begins. If you do not have access to the 6th Floor for in-person attendance, please check in at the 10th floor main reception desk to register as a visitor and someone will escort you to the conference room location. Abstract: Large language models (LLMs) are increasingly used across society, including in domains like business, engineering, and medicine. These fields often grapple with decision-making under uncertainty, a critical yet challenging task. In this paper, we show that directly prompting LLMs on these types of decision-making problems yields poor results, especially as the problem complexity increases. To overcome this limitation, we propose DeLLMa (Decision-making Large Language Model assistant), a framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step scaffolding procedure, drawing upon principles from decision theory and utility theory, to provide an optimal and human-auditable decision-making process. We validate our framework on decision-making environments involving real agriculture and finance data. Our results show that DeLLMa can significantly improve LLM decision-making performance, achieving up to a 40% increase in accuracy over competing methods.
Biography: Ollie Liu is second-year Ph.D student in Computer Science at University of Southern California, co-advised by Prof. Dani Yogatama and Prof. Willie Neiswanger. In life, I usually go by Oliver. My current research interests lie in (multimodal) foundation models, especially their algorithmic reasoning capabilities and applications in sciences.
Host: Jonathan May and Justin Cho
More Info: https://www.isi.edu/research-groups-nlg/nlg-seminars/
Webcast: https://www.youtube.com/watch?v=XSTIFr9J0koLocation: Information Science Institute (ISI) - Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=XSTIFr9J0ko
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/research-groups-nlg/nlg-seminars/
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Thomas Lord Department of Computer Science: Distinguished Lecture Series feat. Dr. Mohit Bansal
Thu, Apr 18, 2024 @ 02:00 PM - 04:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Mohit Bansal, John R. & Louise S. Parker Distinguished Professor, UNC Chapel Hill
Talk Title: Multimodal Generative LLMs: Unification, Interpretability, Evaluation
Abstract: In this talk, I will present our journey of large-scale multimodal pretrained (generative) models across various modalities (text, images, videos, audio, layouts, etc.) and enhancing their important aspects such as unification (for generalizability, shared knowledge, and efficiency), interpretable programming/planning (for controllability and faithfulness), and evaluation (of fine-grained skills, faithfulness, and social biases). We will start by discussing early cross-modal vision-and-language pretraining models (LXMERT). We will then look at early unified models (VL-T5) to combine several multimodal tasks (such as visual QA, referring expression comprehension, visual entailment, visual commonsense reasoning, captioning, and multimodal translation) by treating all tasks as text generation. We will next look at recent, progressively more unified models (with joint objectives and architecture, as well as newer unified modalities during encoding and decoding) such as textless video-audio transformers (TVLT), vision-text-layout transformers for universal document processing (UDOP), and interactive, interleaved, composable any-to-any text-audio-image-video multimodal generation (CoDi, CoDi-2). Second, we will discuss interpretable and controllable multimodal generation (to improve faithfulness) via LLM-based planning and programming, such as layout-controllable image generation via visual programming (VPGen), consistent multi-scene video generation via LLM-guided planning (VideoDirectorGPT), open-domain, open-platform diagram generation (DiagrammerGPT), and LLM-based adaptive environment generation for training embodied agents (EnvGen). I will conclude with important faithfulness and bias evaluation aspects of multimodal generation models, based on fine-grained skill and social bias evaluation (DALL-Eval), interpretable and explainable visual programs (VPEval), as well as reliable fine-grained evaluation via Davidsonian semantics based scene graphs (DSG).
Please RSVP by Monday, April 15, 2024 (5:00 p.m., PST): https://forms.gle/shymnJc87y5fHFJaA
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Mohit Bansal is the John R. & Louise S. Parker Distinguished Professor and the Director of the MURGe-Lab (UNC-NLP Group) in the Computer Science department at UNC Chapel Hill. He received his PhD from UC Berkeley in 2013 and his BTech from IIT Kanpur in 2008. His research expertise is in natural language processing and multimodal machine learning, with a particular focus on multimodal generative models, grounded and embodied semantics, faithful language generation, and interpretable, efficient, and generalizable deep learning. He is a recipient of IIT Kanpur Young Alumnus Award, DARPA Director's Fellowship, NSF CAREER Award, Google Focused Research Award, Microsoft Investigator Fellowship, Army Young Investigator Award (YIP), DARPA Young Faculty Award (YFA), and outstanding paper awards at ACL, CVPR, EACL, COLING, and CoNLL. He has been a keynote speaker for the AACL 2023, CoNLL 2023, and INLG 2022 conferences. His service includes EMNLP and CoNLL Program Co-Chair, and ACL Executive Committee, ACM Doctoral Dissertation Award Committee, ACL Americas Sponsorship Co-Chair, and Associate/Action Editor for TACL, CL, IEEE/ACM TASLP, and CSL journals. Webpage: https://www.cs.unc.edu/~mbansal/
Host: USC Thomas Lord Department of Computer Science
More Info: https://forms.gle/shymnJc87y5fHFJaA
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
Event Link: https://forms.gle/shymnJc87y5fHFJaA
