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    SYSTEMS, METHODS, AND APPARATUSES FOR ACCRUING AND REUSING KNOWLEDGE (ARK) FOR SUPERIOR AND ROBUST PERFORMANCE BY A TRAINED AI MODEL FOR USE WITH MEDICAL IMAGE CLASSIFICATION
    4.
    发明公开
    SYSTEMS, METHODS, AND APPARATUSES FOR ACCRUING AND REUSING KNOWLEDGE (ARK) FOR SUPERIOR AND ROBUST PERFORMANCE BY A TRAINED AI MODEL FOR USE WITH MEDICAL IMAGE CLASSIFICATION 审中-公开

    公开(公告)号：US20240339200A1

    公开(公告)日：2024-10-10

    申请号：US18627831

    申请日：2024-04-05

    申请人： Arizona Board of Regents on behalf of Arizona State University

    发明人： DongAo Ma ,  Jiaxuan Pang ,  Jianming Liang

    IPC分类号： G16H30/40 ,  G06V10/764 ,  G16H30/20

    CPC分类号： G16H30/40 ,  G06V10/764 ,  G16H30/20

    摘要： Exemplary systems include means for receiving medical image data at the system from a plurality of datasets provided via publicly available sources; evaluating the medical image data for the presence of expert notation embedded within the medical image data; determining the expert notations embedded within the medical image data are formatted using inconsistent and heterogeneous labeling across the plurality of datasets; generating an interim AI model by applying a task head classifier to learn the annotations of the expert notations embedded within the medical image data to generate an interim AI model; scaling the interim AI model having the learned annotations of the expert notations embedded therein to additional tasks by applying multi-task heads using cyclical pre-training of the interim AI model trained previously to generate task-specific AI models, with each respective task-specific AI model having differently configured task-specific learning objectives; training a pre-trained AI model specially configured for an application-specific target task by applying task re-visitation training forcing the pre-trained AI model being trained to re-visit all tasks in each round of training and forcing the pre-trained AI model being trained to re-use all accrued knowledge to improve learning by the pre-trained AI model being trained against the current application-specific target task for which the pre-trained AI model is being trained.

    Microbial desulfurization and surface activation of rubber
    6.
    发明授权
    Microbial desulfurization and surface activation of rubber 有权

    公开(公告)号：US12103985B2

    公开(公告)日：2024-10-01

    申请号：US17643377

    申请日：2021-12-08

    申请人： Anca Delgado ,  Elham Fini ,  Sk Faisal Kabir

    发明人： Anca Delgado ,  Elham Fini ,  Sk Faisal Kabir

    IPC分类号： C08C19/30 ,  C08L95/00

    CPC分类号： C08C19/30 ,  C08L95/00

    摘要： Preparing a microbially desulfurized crumb rubber includes combining microorganisms capable of breaking crosslinked sulfur bonds, sulfur-containing crumb rubber, and a salt solution to yield a mixture; combining a buffer with the mixture to yield a buffered mixture, thereby adjusting a pH of the mixture; providing oxygen to the buffered mixture; incubating the buffered mixture for a length of time to yield a microbially desulfurized mixture; combining the microbially desulfurized mixture with bitumen to yield a precursor; and heating the precursor to yield the microbially desulfurized crumb rubber. The microbially desulfurized crumb rubber can be combined with bitumen to yield a modified bitumen. The modified bitumen can be combined with asphalt to yield a modified asphalt.

    TARGETED ATTACKS ON DEEP REINFORCEMENT LEARNING-BASED AUTONOMOUS DRIVING WITH LEARNED VISUAL PATTERNS
    10.
    发明公开
    TARGETED ATTACKS ON DEEP REINFORCEMENT LEARNING-BASED AUTONOMOUS DRIVING WITH LEARNED VISUAL PATTERNS 审中-公开

    公开(公告)号：US20240303349A1

    公开(公告)日：2024-09-12

    申请号：US18599821

    申请日：2024-03-08

    申请人： Prasanth Buddareddygari ,  Travis Zhang ,  Yezhou Yang ,  Yi Ren

    发明人： Prasanth Buddareddygari ,  Travis Zhang ,  Yezhou Yang ,  Yi Ren

    IPC分类号： G06F21/57 ,  G06N20/00

    CPC分类号： G06F21/577 ,  G06N20/00 ,  G06F2221/033

    摘要： A system may be configured for implementing targeted attacks on deep reinforcement learning-based autonomous driving with learned visual patterns. In some examples, processing circuitry receives first input specifying an initial state for a driving environment and user configurable input specifying a target state. Processing circuitry may generate a representative dataset of the driving environment by performing multiple rollouts of the vehicle through the driving environment, including performing an action for the vehicle from the initial state with variable strength noise added to determine a next state for each rollout resulting from the action. Processing circuitry may train an artificial intelligence model to output a next predicted state based on the representative dataset as training input. In such an example, processing circuitry outputs from the artificial intelligence model, an attack plan against the autonomous driving agent to achieve the target state from the initial state.