Abstract: A system (102) for assisting a patient to raise an alarm is disclosed. The system (102) may include a sensor (106) configured to be positioned at a head region of the patient and to detect movement of head of the patient. The system (102) may further include a patient assisting device (104) coupled to the sensor (106). The patient assisting device (104) may be configured to receive, from the sensor (106), a signal corresponding to the movement of the head of the patient, determine a state of alarm value from the signal, and generate an alarm based on the state of alarm value.

Abstract: Methods, systems, and devices that support a dynamic screen refresh rate are described. An electronic device may dynamically (e.g., autonomously, while operating) adjust the rate at which a screen is refreshed, such as to balance considerations such as user experience and power consumption by the electronic device. For example, the electronic device may use an increased refresh rate when executing applications for which user experience is enhanced by a higher refresh rate and may use a decreased refresh rate when executing other applications. As another example, the electronic device may use different refresh rates while executing different portions of the same application, as some aspects of an application (e.g., more intense portions of a video game) may benefit more than others from a higher refresh rate. The electronic device may also account of other factors, such as battery level, when setting or adjusting the refresh rate of the screen.

Abstract: Methods and apparatuses relating to processing neural networks are described. In one embodiment, an apparatus to process a neural network includes a plurality of fully connected layer chips coupled by an interconnect; a plurality of convolutional layer chips each coupled by an interconnect to a respective fully connected layer chip of the plurality of fully connected layer chips and each of the plurality of fully connected layer chips and the plurality of convolutional layer chips including an interconnect to couple each of a forward propagation compute intensive tile, a back propagation compute intensive tile, and a weight gradient compute intensive tile of a column of compute intensive tiles between a first memory intensive tile and a second memory intensive tile.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instruction that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a training procedure, labels based at least in part on price band activity data from a time period; training, using the training procedure, an affinity prediction model of a machine learning architecture; analyzing, using the affinity prediction model of the machine learning architecture, as trained, the price band activity data indicating interactions of a user with items; and generating, using the labels and the affinity prediction model of the machine learning architecture, as trained, one or more price affinity predictions for one or more items for the user. Other embodiments are disclosed herein.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: evaluating, using a price band determination model, degrees of expensiveness of items relative to each other in item type categories; generating, using the price band determination model, price bands associated with item type categories; assigning each of the items to a respective one of the price bands associated with a respective one of the item type categories; and presenting, to one or more end-user applications, at least one other item corresponding to at least one of the price bands associated with at least one of the item type categories. Other embodiments are disclosed.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital recommendations including discounted items with associated savings. In some examples, a computing device obtains savings data including savings items. In response to obtaining the savings data, the computing device obtains a plurality of clusters. Each cluster of the plurality of clusters includes a set of product types. The computing device obtains user data associated with a user, and based on the savings data and the plurality of clusters, determines a set of initial items for recommendation. The set of initial items includes a subset of the savings items. The computing device also determines final recommendation items for the user based on the set of initial items and the user data.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: determining price bands for an item type category; associating items included in the item type category with the price bands; analyzing, using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items included in each of the price bands for the item type category; and generating one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. Other embodiments are disclosed herein.

Abstract: The invention provides a programming environment configured to receive programming inputs through a first GUI based graphic block programming interface and through a second textual code programming interface that is configured to enable input of textual code for adding to or modifying the workflow program application, and configured for displaying within the second textual coding interface, textual program code comprising, at least the segments of textual program code associated with each of the identified graphic blocks, wherein the textual program code is expressed in a high level programming language. The integrated developer environment additionally provides for implementing fault tolerant program code by providing for state save event triggers that initiate state save events, such that saved states corresponding to the state save events can be used to restart execution of an interrupted workflow program application from the point of interruption.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing a machine learning architecture that is configured to evaluate expensiveness of items relative to each other, wherein the items are included in an item type category; receiving prices associated with the items included in the item type category; generating, using a price band determination model associated with the machine learning architecture, price bands based, at least in part, on the prices associated with the items, each of the price bands being associated with separate price range boundaries for the item type category; and assigning each of the items to one of the price bands. Other embodiments are disclosed herein.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Abstract: Systems, methods, and apparatuses relating to access synchronization in a shared memory are described. In one embodiment, a processor includes a decoder to decode an instruction into a decoded instruction, and an execution unit to execute the decoded instruction to: receive a first input operand of a memory address to be tracked and a second input operand of an allowed sequence of memory accesses to the memory address, and cause a block of a memory access that violates the allowed sequence of memory accesses to the memory address. In one embodiment, a circuit separate from the execution unit compares a memory address for a memory access request to one or more memory addresses in a tracking table, and blocks a memory access for the memory access request when a type of access violates a corresponding allowed sequence of memory accesses to the memory address for the memory access request.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital recommendations including discounted items with associated savings. In some examples, a computing device obtains savings data including savings items. In response to obtaining the savings data, the computing device obtains a plurality of clusters. Each cluster of the plurality of clusters includes a set of product types. The computing device obtains user data associated with a user, and based on the savings data and the plurality of clusters, determines a set of initial items for recommendation. The set of initial items includes a subset of the savings items. The computing device also determines final recommendation items for the user based on the set of initial items and the user data.

Abstract: Methods, systems, and devices that support a dynamic screen refresh rate are described. An electronic device may dynamically (e.g., autonomously, while operating) adjust the rate at which a screen is refreshed, such as to balance considerations such as user experience and power consumption by the electronic device. For example, the electronic device may use an increased refresh rate when executing applications for which user experience is enhanced by a higher refresh rate and may use a decreased refresh rate when executing other applications. As another example, the electronic device may use different refresh rates while executing different portions of the same application, as some aspects of an application (e.g., more intense portions of a video game) may benefit more than others from a higher refresh rate. The electronic device may also account of other factors, such as battery level, when setting or adjusting the refresh rate of the screen.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: determining price bands for an item type category; associating items included in the item type category with the price bands; analyzing, using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items included in each of the price bands for the item type category; and generating one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing a machine learning architecture that is configured to evaluate expensiveness of items relative to each other, wherein the items are included in an item type category; receiving prices associated with the items included in the item type category; generating, using a price band determination model associated with the machine learning architecture, price bands based, at least in part, on the prices associated with the items, each of the price bands being associated with separate price range boundaries for the item type category; and assigning each of the items to one of the price bands. Other embodiments are disclosed herein.

Abstract: An inertial vortex particle scrubber includes a housing having an inlet guide, twin vortex chambers and an outlet. A particle-laden air stream is accelerated as it passes through the inlet guide into the twin vortex chambers where particles are displaced by centrifugal forces toward a wall of the twin vortex chambers. A relatively particle-free air stream is then discharged from the outlet.

Abstract: Methods, systems, and computer-readable storage media for resource-efficient generation of a workover rig schedule by receiving well data and workover rig data for wells, each well producing oil, determining sets of schedules by, for each schedule in a set of schedules, incrementally: calculating a virtual gain for each well in a set of remaining wells, each virtual gain being a function of a production gain for a respective well, a travel time of a workover rig to travel from a current well to the respective well, a workover time for the respective well, a first parameter applied to the workover time, and a second parameter applied to the travel time, and selecting a well as a next well in a schedule based on a respective virtual gain; identifying, from the sets of schedules, a schedule having a maximum cumulative gain, and outputting the schedule as an optimal schedule.

Abstract: Methods, systems, and devices that support a dynamic screen refresh rate are described. An electronic device may dynamically (e.g., autonomously, while operating) adjust the rate at which a screen is refreshed, such as to balance considerations such as user experience and power consumption by the electronic device. For example, the electronic device may use an increased refresh rate when executing applications for which user experience is enhanced by a higher refresh rate and may use a decreased refresh rate when executing other applications. As another example, the electronic device may use different refresh rates while executing different portions of the same application, as some aspects of an application (e.g., more intense portions of a video game) may benefit more than others from a higher refresh rate. The electronic device may also account of rother factors, such as battery level, when setting or adjusting the refresh rate of the screen.