Abstract: Aspects of the present disclosure provide techniques for improving a communication range of an analyte sensor system. The analyte sensor system may include a analyte sensor configured to generate analyte data associated with analyte levels of a user of the analyte sensor system, an antenna system comprising a plurality of antennas, a transceiver circuit configured to transmit the analyte data to a communications device via one or more antennas of the plurality of antennas of the antenna system, a switching device configured to selectively couple the one or more antennas to the transceiver circuit, and a circuit board configured to operatively connect the transcutaneous analyte sensor with the transceiver circuit.

Abstract: A network device may identify a link aggregation group (LAG) of a plurality of links between the network device and another network device. The network device may identify link aggregation control protocol (LACP) parameters that were communicated by the network device and the other network device in association with the LAG. The network device may determine, based on the LACP parameters, a priority order of the plurality of links in the LAG. The network device may communicate with the other network device, and based on the priority order of the plurality of links of the LAG, one or more precision time protocol (PTP) messages via the LAG. For example, the network device may determine that a first link and a second link in the priority order are not available, and therefore may communicate the one or more PTP messages via a third link in the priority order.

Abstract: Aspects of the present disclosure provide techniques for improving a communication range of an analyte sensor system. The analyte sensor system may include an analyte sensor configured to generate analyte data associated with analyte levels of a user of the analyte sensor system, a first conductive portion configured to transmit the analyte data to a communications device, a circuit board configured to operatively connect the analyte sensor with the first conductive portion, and a second conductive portion configured to reflect, away from a body of the user, a portion of power radiated from the first conductive portion associated with transmission of at least the analyte data.

Abstract: A method and system for polymorphic pruning of neural networks are disclosed. The method involves training a neural network model on an input dataset for an initial predetermined number of iterations to gather weight information, including the strength of each weight and changes in strength over iterations. The weights are stored in an array accessible to a pruning algorithm. An objective function is compiled using the weight information, and an optimization tool solves the objective function to generate a solution vector. This solution vector is used to create a pruning mask, which is applied to the neural network model to prune certain weights by setting them to zero. The pruned weight vector updates the model, resulting in a neural network with fewer non-zero connections between neurons.

Abstract: The present disclosure relates to systems and methods for optimizing neural networks by strategically identifying and pruning critical neurons to reduce computational resources while maintaining high levels of accuracy. The method involves determining critical neurons within a neural network based on features collected during an initial phase of training. These critical neurons are then pruned from the network, resulting in a pruned neural network with the critical neurons removed. The training process continues using the pruned neural network, allowing for significant computational savings without substantially impacting the network's performance.

Abstract: A circuit includes: a first latch; a second latch coupled to the first latch; and a third latch coupled to the second latch at an input terminal of the second latch, wherein the third latch includes: a first inverter and a second inverter, the first inverter being coupled between the input terminal of the second latch and an input terminal of the second inverter and the second inverter being coupled between an output terminal of the first inverter and an input terminal of the first inverter; a first switch connecting the first inverter to a first voltage source; a second switch connecting the first inverter to ground voltage; a third switch connecting the second inverter to the first voltage source; a fourth switch connecting the second inverter to the ground voltage; and a fifth switch connecting the second latch and the first inverter.

Abstract: Disclosed are system, method and/or computer program product embodiments for improving the performance of a machine learning based algorithm used to provide a user experience to a user via a media device. An embodiment selects a first set of hyperparameter values, implements a first iteration of the algorithm based on the first set of hyperparameter values, utilizes the first iteration of the algorithm to provide a first user experience to the user, determines a response of the user to the first user experience, selects, by a hyperparameter tuning ML model implemented as a contextual multi-arm bandit model or a reinforcement learning model and based on at least the response of the user, a second set of hyperparameter values, implements a second iteration of the algorithm based on the second set of hyperparameter values, and utilizes the second iteration of the algorithm to provide a second user experience to the user.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy based on one or more of the following: an interest-exploration policy, an earlier pseudo-user response generated by the pseudo-user neural network model, content data, and pseudo-user interaction history.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content to a user. An embodiment identifies a first set of content items based at least on a first set of weights respectively associated with different user interests, causes the first set of content items to be presented to the user, determines a measure of user interaction with the first set of content items, provides the measure of user interaction to one of a multi-arm bandit (MAB), contextual MAB, or reinforcement learning model that selects, based at least on the state information and the measure of user interaction, a second set of weights respectively associated with the different user interests, identifies a second set of content items based at least on the second set of weights, and causes the second set of content items to be presented to the user.

Abstract: The lease-purchase system gives consumers access to numerous products at merchants through a flexible lease-purchase transaction. The lease-purchase system allows merchants to grow their customer bases and allows them to move inventory through another consumer payment method. A parsing engine is utilized to automatically determine which merchant items are eligible to transaction via the lease system. At any time throughout the life of the lease-purchase transaction, the consumer can purchase the leased product or return it.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for training a universal sensor model based on a combined dataset provided by sensor devices of a common device class. Once trained, the universal sensor model may be deployed for providing recommendations based on for performing object detection on datasets received from different types of sensor devices of the common device class. Embodiments include determining whether to generate the combined dataset from different datasets from sensor devices of the common device class and determining when the sensor model will perform better using the combined dataset from sensor device rather than a single dataset from a single sensor device. In some embodiments, the datasets are image datasets comprising image data provided by the sensor devices.

Abstract: The present invention relates to a system for data analytics in a network between one or more local device(s) (130) and a cloud computing platform (120), in which data collected and/or stored on the local device(s) (130) and/or stored on the cloud computing platform (120) are processed by an analytical algorithm (A) which is subdivided into at least two sub-algorithms (SA1, SA2), wherein one sub-algorithm (SA1) is executed on the local device(s) (130) and the other sub-algorithm (SA2) is executed on the cloud computing platform (120).

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for training a universal sensor model based on a combined dataset provided by sensor devices of a common device class. Once trained, the universal sensor model may be deployed for providing recommendations based on performing object detection on datasets received from different types of sensor devices of the common device class. Embodiments include determining whether to generate the combined dataset from different datasets from sensor devices of the common device class and determining when the sensor model will perform better using the combined dataset from sensor device rather than a single dataset from a single sensor device. In some embodiments, the datasets are image datasets comprising image data provided by the sensor devices.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for training a universal sensor model based on a combined dataset provided by sensor devices of a common device class. Once trained, the universal sensor model may be deployed for providing recommendations based on performing object detection on datasets received from different types of sensor devices of the common device class. Embodiments include determining whether to generate the combined dataset from different datasets from sensor devices of the common device class and determining when the sensor model will perform better using the combined dataset from sensor device rather than a single dataset from a single sensor device. In some embodiments, the datasets are image datasets comprising image data provided by the sensor devices.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for optimizing user experience/engagement and revenue. An example embodiment operates by a computer-implemented method for providing one or more advertisements to a media device. The method includes receiving, by at least one computer processor, a user state associated with a user of the media device, where the user state corresponds to a time step. The method further includes receiving a revenue value associated with the user of the media device, where the revenue value corresponds to the time step. The method also include determining an action associated with the user based on the user state and the revenue value. The action includes one or more parameters associated with the one or more advertisements. The method further includes providing the action to the user.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.

Abstract: Methods and systems for providing concise data for analyzing checker completeness, in the context of formal verification analysis of circuit designs. The methods and systems concisely report information useful to a human user (e.g., circuit designer or verification engineer) for efficiently determining what manual action should be taken next to resolve holes in verification coverage. The reported information can include lists of signals on which checkers can be written, which lists can be ranked, can be limited to a subset of interest signals, and can include corresponding cover items for each reported interest signal. The present systems and methods thereby improve on reporting provided to the user, permitting the user to more quickly advance a formal verification process toward full coverage of the relevant portions of a circuit design.

Abstract: In some implementations, a device may receive, via a universal serial bus (USB) interface, configuration information and a supply of power from a network device. The device may receive, via an antenna that is external to the device, a first signal indicating timing information. The device may generate, based on the first signal, a second signal and a third signal, wherein the second signal comprises a one pulse per second signal and the third signal comprises a ten-megahertz signal. The device may provide, to the network device, the second signal and the third signal. The device may receive, via an input port, a clock signal to provide an extended holdover functionality to the network device.

Abstract: Clock multiplexer circuitry outputs one of a first or second clock signal. First selection circuitry is connected in series with first counter circuitry. The first selection circuitry and the first counter circuitry receive a first clock signal and a first selection signal. A first control signal is generated based on the first clock signal and the first selection signal. Second selection circuitry is connected in series with second counter circuitry. The second selection circuitry and the second counter circuitry receive a second clock signal and a second selection signal. A second control signal is generated based on the second clock signal and the second selection signal. The output circuitry is connected to the first counter circuitry and the second counter circuitry. The output circuitry outputs one of the first clock signal and the second clock signal based on the first control signal and the second control signal.

Abstract: In some implementations, a device may receive, via a universal serial bus (USB) interface, configuration information and a supply of power from a network device. The device may receive, via an antenna that is external to the device, a first signal indicating timing information. The device may generate, based on the first signal, a second signal and a third signal, wherein the second signal comprises a one pulse per second signal and the third signal comprises a ten-megahertz signal. The device may provide, to the network device, the second signal and the third signal. The device may receive, via an input port, a clock signal to provide an extended holdover functionality to the network device.