PHASELESS AUXILIARY-FIELD QUANTUM MONTE CARLO WITH DIRECT PRODUCT MULTI-SLATER DETERMINANTS TRIAL

    公开(公告)号:US20240170103A1

    公开(公告)日:2024-05-23

    申请号:US18185760

    申请日:2023-03-17

    CPC classification number: G16C10/00 G06N10/70

    Abstract: Example embodiments of the present disclosure relate to a solution for ph-AFQMC with direct product multi-Slater determinants trial. Multiple active spaces of a molecular system may be obtained and multiple coefficient tensors may be determined respectively. A composite coefficient tensor may be determined based on a tensor product of the multiple coefficient tensors of the multiple active spaces, and a trial wave function may be further determined based on the composite coefficient tensor and a cutoff value. As such, the multiple coefficient tensors for the multiple active spaces may be determined, thus the computation can be reduced. Additionally, since a cutoff value is used, the composite coefficient tensor is a sparse tensor and the number of Slater determinants may be reduced. Further, the determined trial wave function may be further used in a ph-AFQMC algorithm, and a balance between accuracy and efficiency may be achieved.

    VIDEO GENERATION WITH LATENT DIFFUSION MODELS

    公开(公告)号:US20240169479A1

    公开(公告)日:2024-05-23

    申请号:US18056444

    申请日:2022-11-17

    Applicant: Lemon Inc.

    CPC classification number: G06T3/4007 G06T3/4053

    Abstract: The present disclosure provides systems and methods for video generation using latent diffusion machine learning models. Given a text input, video data relevant to the text input can be generated using a latent diffusion model. The process includes generating a predetermined number of key frames using text-to-image generation tasks performed within a latent space via a variational auto-encoder, enabling faster training and sampling times compared to pixel space-based diffusion models. The process further includes utilizing two-dimensional convolutions and associated adaptors to learn features for a given frame. Temporal information for the frames can be learned via a directed temporal attention module used to capture the relation among frames and to generate a temporally meaningful sequence of frames. Additional frames can be generated via a frame interpolation process for inserting one or more transition frames between two generated frames. The process can also include a super-resolution process for upsampling the frames.

    PRE-RELEASE OF RECORDINGS FOR DIGITAL STREAMING PLATFORMS

    公开(公告)号:US20240143152A1

    公开(公告)日:2024-05-02

    申请号:US18050409

    申请日:2022-10-27

    Applicant: Lemon Inc.

    CPC classification number: G06F3/04847 G06F3/0482 G06F3/165

    Abstract: A computer system is provided for implementing a pre-release of a recording at a distribution platform. The computer system includes a processor coupled to a storage system that stores instructions, which, upon execution by the processor, cause the processor to present a release interface for the recording. The release interface includes graphical controls element for confirming the pre-release of the recording, for selecting a release date, and for selecting a pre-release date. The instructions further cause the processor to receive information describing a confirmation of the pre-release of the recording, a selected release date, and a selected pre-release date. The instructions further cause the processor to provide a truncated version of the recording for the pre-release of the recording. The instructions further cause the processor to transmit the truncated version of the recording to a digital streaming platform.

    HIGH-ACCESS-RATE DATA OBJECT TRANSFER
    76.
    发明公开

    公开(公告)号:US20240137318A1

    公开(公告)日:2024-04-25

    申请号:US18518361

    申请日:2023-11-22

    Applicant: Lemon Inc.

    CPC classification number: H04L47/12 G06N20/00 H04L67/1097

    Abstract: A computing system including one or more processing devices configured to detect a congestion condition occurring at a first storage node located in a storage network of a distributed storage system. The one or more processing devices are further configured to obtain respective first access rate data for a first plurality of data objects stored at the first storage node. Based at least in part on the first access rate data, the one or more processing devices are further configured to flag a first data object as a high-access-rate data object. The one or more processing devices are further configured to compute a transfer path between the first storage node and a second storage node in the storage network. The one or more processing devices are further configured to transfer the high-access-rate data object from the first storage node to the second storage node along the transfer path.

    Display screen or portion thereof with a graphical user interface

    公开(公告)号:USD1024094S1

    公开(公告)日:2024-04-23

    申请号:US29815603

    申请日:2021-11-15

    Applicant: Lemon Inc.

    Designer: Ling Zhong

    Abstract: FIG. 1 is a front view of a first embodiment of a display screen or portion thereof with a graphical user interface showing our new design; and,
    FIG. 2 is a front view of a second embodiment thereof.
    The outermost broken line rectangle illustrates a display screen or portion thereof and forms no part of the claimed design. The remaining broken lines illustrate portions of the graphical user interface that form no part of the claimed design.

    METHOD, APPARATUS, DEVICE AND MEDIUM FOR PROTECTING SENSITIVE DATA

    公开(公告)号:US20240126899A1

    公开(公告)日:2024-04-18

    申请号:US18539851

    申请日:2023-12-14

    Applicant: Lemon Inc.

    CPC classification number: G06F21/62 G06N3/04 G06N3/098

    Abstract: There are proposed a method, device, apparatus, and medium for protecting sensitive data. In a method, to-be-processed data is received from a server device. A processing result of a user for the to-be-processed data is received, the processing result comprising sensitive data of the user for the processing of the to-be-processed data. A gradient for training a server model at the server device is determined based on a comparison between the processing result and a prediction result for the to-be-processed data. The gradient is updated in a change direction associated with the gradient so as to generate an updated gradient to be sent to the server device. Noise is added only in the change direction associated with the gradient. The corresponding overhead of processing noise in a plurality of directions can be reduced, and no excessive noise data interfering with training will be introduced to the updated gradient.

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