Abstract: Methods, circuits, apparatus, and systems for managing multi-phase clocking signals for integrated circuit devices are provided. In one aspect, an integrated circuit device includes: a clock signal generator configured to generate a reference clock signal and a plurality of processing units coupled to the clock signal generator. At least one of the plurality of processing units includes: a phase generator configured to selectively generate at least two sets of multi-phase clock signals based on the reference clock signal and corresponding control signals, the at least two sets of multi-phase clock signals having different respective frequencies; and a computation unit configured to perform at least one computing function based on a selected one of the at least two sets of multi-phase clock signals.

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

Abstract: Described herein is a system for enabling a user to perform complex goals using multiple skills/applications of an intelligent assistant device. Skills may register as consumers of an action or providers of an action, and the consumer skills may be configured to invoke provider skills to perform actions. The system receives a request to perform an action from a skill along with some action data. The system validates the action data, selects another skill to perform the action, and forwards the request to the selected skill to perform the action.

Abstract: Methods and apparatus for an accelerator controller hub (ACH). The ACH may be a stand-alone component or integrated on-die or on package in an accelerator such as a GPU. The ACH may include a host device link (HDL) interface, one or more Peripheral Component Interconnect Express (PCIe) interfaces, one or more high performance accelerator link (HPAL) interfaces, and a router, operatively coupled to each of the HDL interface, the one or more PCIe interfaces, and the one or more HPAL interfaces. The HDL interface is configured to be coupled to a host CPU via an HDL link and the one or more HPAL interfaces are configured to be coupled to one or more HPALs that are used to access high performance accelerator fabrics (HPAFs) such as NVlink fabrics and CCIX (Cache Coherent Interconnect for Accelerators) fabrics. Platforms including ACHs or accelerators with integrated ACHs support RDMA transfers using RDMA semantics to enable transfers between accelerator memory on initiators and targets without CPU involvement.

Abstract: Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state.

Abstract: Disclosed herein are methods for patient setup and patient target region localization for the irradiation of multiple patient target regions in a single treatment session. Virtual localization is a method that can be used to register a patient target region without requiring that the patient is physically moved using the patient platform. Instead, the planned fluence is updated to reflect the current location of the patient target region by selecting a localization reference in the localization image, calculating a localization function based on the localization reference point, and calculating the delivery fluence by convolving the localization function with a shift-invariant firing filter. Mosaic multi-target localization partitions a planned fluence map for multiple patient target regions into sub-regions that can be individually localized.

Abstract: The present disclosure relates to systems, devices and methods for receiving biosensor data acquired by a medical device, e.g., relating to glucose concentration values, and controlling the access and distribution of that data. In some embodiments, systems and methods are disclosed for monitoring glucose levels, displaying data relating to glucose values and metabolic health information, and controlling distribution of glucose data between applications executing on a computer, such as a smart phone. In some embodiments, systems and methods are disclosed for controlling access to medical data such as continuously monitored glucose levels, synchronizing health data relating to glucose levels between multiple applications executing on a computer, and/or encrypting data.

Abstract: An online system manages presentation of content items in various presentation contexts such as when the users are browsing pages or when the users have entered a search query. The online system trains a single unified machine learning model that predicts one or more likelihoods of a target event associated with presentation of a content item in the different presentation contexts. The learned model is applied to a set of candidate content items associated with a presentation opportunity in a specific context. Features that are inapplicable to the specific context may be masked when applying the model. The online system may select between the candidate content items based on the predicted likelihoods using the model trained across the multiple different contexts, such that the prediction for one context may be based in part on learned outcomes in other related contexts.

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

Abstract: One or more computing devices, systems, and/or methods for message classification are provided. For example, a set of messages is clustered into a set of clusters. A cluster comprises messages with similar features (e.g., similar subject lines, message body content, sender information, recipient information, structure, user action such as reading or deleting, spam vote information, etc.). Cluster features are computed for the clusters based upon features of messages within such clusters. A first table, comprising cluster entries corresponding cluster features of clusters, and a second table, comprising message entries corresponding to clusters to which messages are assigned, are created. Message features of a message are created, using the first table and second table, based upon features of the message and cluster features of clusters to which the message is assigned. A message classifier is used to classify the message (e.g., spam, safe, a threat, etc.) based upon the message features.

Abstract: One or more computing devices, systems, and/or methods for message classification are provided. For example, a set of messages is clustered into a set of clusters. A cluster comprises messages with similar features (e.g., similar subject lines, message body content, sender information, recipient information, structure, user action such as reading or deleting, spam vote information, etc.). Cluster features are computed for the clusters based upon features of messages within such clusters. A first table, comprising cluster entries corresponding cluster features of clusters, and a second table, comprising message entries corresponding to clusters to which messages are assigned, are created. Message features of a message are created, using the first table and second table, based upon features of the message and cluster features of clusters to which the message is assigned. A message classifier is used to classify the message (e.g., spam, safe, a threat, etc.) based upon the message features.

Abstract: Example systems may include: receiving a request for allocation of a content inventory unit; receiving two or more bids for the content inventory unit, each bid being subject to a respective sharing fraction; determining one or more adjusted bids, where each adjusted bid is determined based on one of the two or more bids and the respective sharing fraction for the bid; allocating the content inventory unit to a buyer that submitted a highest bid from among the two or more bids; determining a payment from the buyer as a maximum member of a first set of prices that comprises at least one of the two or more bids; determining a payment to a seller as a maximum member of a second set of prices that includes at least one of the adjusted bids; and transmitting data reflecting the allocation of the content inventory unit to the buyer.

Abstract: The present invention comprises an irrigation/aeration method and related device utilizing the optimal substrate air pocket properties of pea gravel and sub-pavement irrigation/aeration.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for allocating content. In one aspect, a method includes identifying bids for a plurality of eligible bidders that are eligible to provide a content item in response to a content item request. From among the eligible bidders, a set of passback bidders having a passback capability are identified. The passback capability provides passback bidders an option refuse to provide a content item. From among the bidders, a set of general bidders, for which the passback capability is not enabled, are identified. A bidder chain that includes a highest general bidder and passback bidders is generated. A highest bidder in the bidder chain that does not refuse to provide a content item after being identified as the winning bidder is selected as the final bidder.