Abstract: The present disclosure provides a method for preparing lutein ester, by providing a raw material selected from marigold flowers or marigold meal; subjecting the raw material to extraction using ethanol followed by acetone; and concentrating to obtain an extract which may then be crystallized to obtain lutein ester crystal of high purity. Further, the present disclosure provides a method for preparing lutein ester, by providing a raw material selected from marigold flowers or marigold meal; subjecting the raw material to extraction using isopropanol; and concentrating to obtain an extract which may then be crystallized to obtain lutein ester crystal of high purity.

Abstract: A method, apparatus, non-transitory computer readable medium, apparatus, and system for image processing include obtaining a text prompt and an input image depicting a person, generating a latent code based on the text prompt and the input image, wherein the latent code is optimized by an identity preserving loss, and generating, using an image generator of a machine learning model, a synthetic image based on the latent code, wherein the synthetic image includes an element of the text prompt and preserves an identity of the person in the input image.

Abstract: A non-transitory computer readable medium (107, 127) stores instructions readable and executable by at least one electronic processor (101, 113) to: retrieve, based on a query for a current service case, a plurality of the historical service case records (130) for historical service cases; rank the retrieved historical case service records to generate a ranked list (134) of historical service cases; extract service actions from the historical service case records for the ranked list of historical cases; recommend a plurality of recommended service actions for the current service case based on the service actions extracted from the historical service case records for the ranked list of historical service cases; and display, on a display device (105) of a remote electronic processing device (102) operable by a service engineer, a user interface (UI) (136) having user-selectable windows for displaying the ranked list of historical service cases and the recommended service actions.

Abstract: Techniques for predicting a wait time for a shopper based on a location the shopper's client device are presented. A system identifies a shopper's current location and uses a machine learning model to predict a wait time until the shopper will receive one or more orders. The machine learning model is trained to use input features including a number of orders received during a current time period for fulfillment near the current location, a number of other shoppers available for fulfilling orders during the current time period near the current location, historical information about a presentation of a plurality of orders to a plurality of shoppers near the current location, and historical information about the shopper and the other nearby available shoppers. The system then sends the predicted wait time to the client device for presentation to the shopper.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: This disclosure provides methods, components, devices and systems for communications for fast link adaptation (FLA). In some examples, a transmitter may transmit an FLA information request via a physical layer (PHY) protocol data unit (PPDU). The PPDU may include an FLA information request indicating that the receiver is to report one or more parameter values (such as proposed updates to current parameter values) for a currently used quantity of spatial streams. In some examples, the PPDU may include (such as in a media access control (MAC) header) a bit which can be turned on to request a second FLA element indicating one or more parameters for a proposed (such as, updated) quantity of spatial streams.

Abstract: Example solutions enhance security of bootable media images during bare metal restores. A boot image generation request and original image integrity data is received from a first computing device. An original image timestamp associated with the boot image generation request is stored. A message is received from a second computing device that includes current image integrity data generated by the second computing device using a current boot image. The original image integrity data is verified to match the current image integrity data. The message is determined to have been received within a length of time from the original image timestamp. A registration of the second computing device is performed within the device management system based on the verification and the determination.

Abstract: An online concierge system iteratively makes a batch of one or more orders available to an increasing number of shoppers to choose to fulfill. Each shopper may choose to accept or reject a batch for fulfillment. To improve batch acceptance and matching between batches and shoppers, the batches are scored with respect to expected resource costs, likelihood of acceptance by the shopper, and/or other quality metrics to iteratively offer the batch to an increasing number of shoppers (prioritizing the scoring factors) until a shopper accepts. The number of shoppers notified of the batch and the frequency that additional shoppers are selected may vary based on characteristics of the batch and likelihood the batch will be accepted by a shopper.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.

Abstract: The present application provides methods for preparing soluble lipidated ligand agents comprising a ligand entity and a lipid entity, and in some embodiments, provides relevant parameters of each of these components, thereby enabling appropriate selection of components to assemble active agents for any given target of interest.

Abstract: Systems and techniques are provided for performing spatial audio recording. For instance, a process can include detecting an occlusion for at least one audio frame of one or more audio frames associated with a spatial audio recording. During the spatial audio recording, the process can further include selecting, based on detection of the occlusion, at least one of an occluded spatial filter for the one or more audio frames or a non-occluded spatial filter for the one or more audio frames.

Abstract: An energy management system, for performing a local energy trading with several energy generation units in a local energy field, and performing an energy market trading with an energy market. The energy management system includes a local trading interface, a market trading interface, and a virtual power plant (VPP) platform. The VPP platform is operatively coupled to the local energy field through the local trading interface, and operatively coupled to the energy market through the market trading interface. In the local energy trading, the VPP platform estimates a set of local trading volumes and a set of local trading prices associated with the local energy field. In the energy market trading, the VPP platform provides a set of bids and a set of offerings into the energy market.

Abstract: Integrated circuit devices, methods, and circuitry for a timestamp engine to dynamically provision ports in accordance with PTP synchronization are provided. An integrated circuit device may include a number of ports configurable to communicate using timestamps corresponding to a number of Precision Time Protocol (PTP) clock domains. The integrated circuit may further include a timestamp engine configurable to receive timestamp requests and route a time of day (ToD) from a ToD circuit of a plurality of ToD circuits to different ports of the plurality of ports, where each ToD circuit is based on a PTP clock domain of the plurality of PTP clock domains.

Abstract: A fusion detection system includes the following elements. A data classification unit, for receiving several sensing data of several distributed devices, and classifying the sensing data as a first type, a second type, a third type and a fourth type. An anomaly detection unit, for performing an operation of an anomaly detection model based on the sensing data of the first type and the second type to generate an anomaly detection prediction result. A predictive maintenance unit, for performing an operation of a predictive maintenance model based on the sensing data of the first type, the second type, the third type and the fourth type and the abnormality detection prediction result to generate a predictive maintenance prediction result. A cost optimization unit, for performing an operation of a cost optimization model based on the predictive maintenance prediction result to generate a cost optimization decision.

Abstract: Integrated circuit devices, methods, and circuitry for a virtual time of day (ToD) engine to generate timestamps by using offsets associated with PTP clock domains. An integrated circuit device may include a number of ports configurable to communicate using timestamps corresponding to a number of Precision Time Protocol (PTP) clock domains. The integrated circuit device may also include a host data processing system to determine an offset for a first PTP clock domain of the plurality of PTP clock domains by calculating a difference between a first ToD provided by a clock source on the integrated circuit and one or more ToDs associated with the first PTP clock domain. The integrated circuit may further include a virtual ToD engine to generate a timestamp by adding the offset for the first PTP clock domain to a second ToD from the clock source and provide the timestamp to a port.

Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.