Abstract: Disclosed herein are systems and methods for non-invasively predicting a hemodynamic state and/or an anesthetic depth of a patient, such as a pediatric patient. The method may include receiving a peripheral venous pressure (PVP) waveform from the patient, cleaning the PVP waveform, transforming the PVP waveform into the frequency domain, and automatically predicting the hemodynamic state and/or the anesthetic depth of the patient.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for providing digital content to users by applying a machine learning model based on composite utility scores reflecting multiple events categories. For example, the systems described herein can identify, from a digital content publisher, significance ratings of various event categories that a user can perform. The systems can analyze user activities to determine a composite utility score for user based on events that the users have performed. Furthermore, in one or more embodiments, the systems train a machine learning model based on training composite utility scores to identify additional users likely to have elevated composite utility scores. Moreover, the disclosed systems can utilize the trained machine learning model to provide targeted digital content to computing devices of these additional users.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for providing digital content to users by applying a machine learning model based on composite utility scores reflecting multiple events categories. For example, the systems described herein can identify, from a digital content publisher, significance ratings of various event categories that a user can perform. The systems can analyze user activities to determine a composite utility score for user based on events that the users have performed. Furthermore, in one or more embodiments, the systems train a machine learning model based on training composite utility scores to identify additional users likely to have elevated composite utility scores. Moreover, the disclosed systems can utilize the trained machine learning model to provide targeted digital content to computing devices of these additional users.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for providing digital content to users by applying a machine learning model based on composite utility scores reflecting multiple events categories. For example, the systems described herein can identify, from a digital content publisher, significance ratings of various event categories that a user can perform. The systems can analyze user activities to determine a composite utility score for user based on events that the users have performed. Furthermore, in one or more embodiments, the systems train a machine learning model based on training composite utility scores to identify additional users likely to have elevated composite utility scores. Moreover, the disclosed systems can utilize the trained machine learning model to provide targeted digital content to computing devices of these additional users.

Abstract: An online system estimates a long term value of various online system users to a publishing user who provides content to the online system for presentation to users. The publishing user may use the long term value when determining amounts of compensation to the online system for presenting content items from the publishing user. To estimate the long term value, the online system obtains retention data for a set of users describing user interaction with one or more objects during a time interval and determines a model describing user interaction with one or more objects over an additional time interval. From the model, the online system determines an average amount of time users interact with the one or more objects, which the online system uses along with an average revenue per daily active user to determine the long term value for the publishing user.

Abstract: A method analyzes a set of signal acquired from a physical system. A set of parameters characterizing the set of signals is measured. The parameters can include the mean and the variance of the power of the signal, and a Rician factor. A first point and a second point of a moment generating function for a combination of the set of signals are evaluated according to the set of parameters to obtain a first sample and a second sample, respectively. First and second equations are defined. The equations respectively have an approximation of a moment generation function of a lognormal random variable representing the combination of the set of signals, at the first point and the second point, on the left side, and the first and second sample on the right side. The two equations are solved to obtain a mean and a variance of the lognormal random variable representing a distribution of the combination of the set of the signals.

Abstract: A method analyzes a set of signal acquired from a physical system. A set of parameters characterizing the set of signals is measured. The parameters can include the mean and the variance of the power of the signal, and a Rician factor. A first point and a second point of a moment generating function for a combination of the set of signals are evaluated according to the set of parameters to obtain a first sample and a second sample, respectively. First and second equations are defined. The equations respectively have an approximation of a moment generation function of a lognormal random variable representing the combination of the set of signals, at the first point and the second point, on the left side, and the first and second sample on the right side. The two equations are solved to obtain a mean and a variance of the lognormal random variable representing a distribution of the combination of the set of the signals.

Abstract: A method increases transmit diversity gain in a wireless communication system. In the wireless network, a transmitter has multiple antennas and a receiver has one antenna. A phase of each of received signal is measured. One signal is selected as a reference signal. For each other signal, a phase, with respect to the phase of the reference signal, is measured independently to determine feedback information indicating a required rotation of each other signal so that a phase of each other rotated signal is within an identical quadrant as the phase of the reference signal. The feedback information for each other signal is sent to the transmitter. In the transmitter, each other signal is phase rotated according to the corresponding feedback information before the signal is transmitting to the receiver.

Abstract: A wireless communication system includes a transmitter and a receiver. The transmitter includes multiple groups of transmit antennas. Input symbols are generated and then orthogonal space-time block is encoded to produce a data stream for each group of transmit antennas. Each data stream is adaptively linear space encoded to produce an encoded signal for each transmit antenna of each group according to feedback information for the group. The receiver includes a single receive antenna, a module for measuring a phase of a channel impulse response for each transmit antenna. The feedback information is determined independently for each group of transmit antennas from the channel impulse responses. The feedback information for each group of transmit antennas is sent to the transmitter.

Abstract: A method increases transmit diversity gain in a wireless communication system. In the wireless network, a transmitter has multiple antennas and a receiver has one antenna. A phase of each of received signal is measured. One signal is selected as a reference signal. For each other signal, a phase, with respect to the phase of the reference signal, is measured independently to determine feedback information indicating a required rotation of each other signal so that a phase of each other rotated signal is within an identical quadrant as the phase of the reference signal. The feedback information for each other signal is sent to the transmitter. In the transmitter, each other signal is phase rotated according to the corresponding feedback information before the signal is transmitting to the receiver.