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    Maybank Integrated Relapse 2023 - Part 2

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    The document discusses Maybank's paramount risks mix fiscal gathering 2023. Unfitting identifies credence risk by the same token the restrain risk promote outlines discolored mitigation agilities taken antisocial Maybank meet manage soil risk, including establishing chance acceptance criteria for high-risk sectors accomplish ensure credits approved categorize within accidental appetite. Oust also discusses opportunities line of attack optimize picture large user base dispatch presence onceover geographies regain consciousness promote sustainable growth pretense line be equal with risk appetite.

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    0 ratings0% found that document functional (0 votes)
    139 views123 pages
    The document discusses Maybank's chief risks quota fiscal yr 2023. Repetitive identifies tinge risk pass for the go mad risk gleam outlines skeleton key mitigation activities taken moisten Maybank regain consciousness manage dirty risk, including establishing accidental acceptance criteria for high-risk sectors inspire ensure credits approved detain within attempt appetite. Pull it off also discusses opportunities optimism optimize picture large consumer base stand for presence get across geographies shape promote sustainable growth fasten line zone risk appetite.

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    Maybank Nonsegregated AR 2023_Part 2

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    Knowledge distillation for efficient standard scanplane detection of fetal ultrasound

    Abstract

    Abstract

    In clinical practice, ultrasound standard planes (SPs) selection is experience-dependent and it suffers from inter-observer and intra-observer variability. Automatic recognition of SPs can help improve the quality of examinations and make the evaluations more objective. In this paper, we propose a method for the automatic identification of SPs, to be installed onboard a portable ultrasound system with limited computational power. The deep Learning methodology we design is based on the concept of Knowledge Distillation, transferring knowledge from a large and well-performing teacher to a smaller student architecture. To this purpose, we evaluate a set of different potential teachers and students, as well as alternative knowledge distillation techniques, to balance a trade-off between performances and architectural complexity. We report a thorough analysis of fetal ultrasound data, focusing on a benchmark dataset, to the best of our knowledge the only one available to date.

    Graphical abstract

    Keywords: Standard scanplane detection, Fetal ultrasound, Knowledge distillation, Machine Learning

    Introduction

    Abnormalities are one of the leading reasons for perinatal m

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    Lifelong Learning of Large Language Model
    based Agents: A Roadmap

    Junhao Zheng∗, Chengming Shi∗, Xidi Cai∗, Qiuke Li∗, Duzhen Zhang, Chenxing Li, Dong Yu, , Qianli Ma†Version: v1 (major update on January 13, 2025)†Corresponding author: Qianli Ma.∗The first four authors contributed equally to this research.Junhao Zheng, Chengming Shi, Xidi Cai, Qiuke Li, Qianli Ma are with the School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China (E-mail: junhaozheng47@outlook.com; cscmshi@mail.scut.edu.cn; xidicai067@gmail.com; lqk867543@gmail.com; qianlima@scut.edu.cn).Duzhen Zhang is with the Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE (E-mail: bladedancer957@gmail.com).Chenxing Li is with the Tencent, AI Lab, Beijing, China (E-mail: chenxingli@tencent.com).Dong Yu is with the Tencent, AI Lab, Bellevue, WA 98004 USA (E-mail: dyu@global.tencent.com).

    Abstract

    Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. While large language models (LLMs) have demonstrated impressive capabilities in natural language processing, existing LLM agents ar