Abstract: A method includes providing a first predetermined number of unstained tissue samples and a second predetermined number of stained tissue samples, and training a machine learning model with the first predetermined number of unstained tissue samples and the second predetermined number of stained tissue samples in order to create at least one virtual histological stained image.

Abstract: Certain embodiments provide a pharmaceutical composition comprising: a protein molecule comprising an ERT enzyme-Fc fusion polypeptide and a modified Fc polypeptide; a buffer; an isotonicity agent; a surfactant; and a stabilizer; wherein the pH of the pharmaceutical composition is about 5.5 to 7.0, as well as methods of use thereof.

Abstract: A method for analyzing a mixture includes obtaining a first spectrum of the mixture comprising a plurality of components; selecting at least one position on the spectrum; and estimating a mixing weight for each of the components using an algorithm based on an intensity of the at least one position. A system for analyzing a mixture includes a spectrometer configured to obtain a first spectrum of the mixture comprising a plurality of components; a processor configured to select at least one position on the spectrum and estimate a mixing weight for each of the components using an algorithm based on an intensity of the at least one position.

Abstract: Examples described herein relate to circuitry, when operational, configured to: store records of memory accesses to a memory device by at least one requester based on a configuration, wherein the configuration is to specify a duration of memory access capture. In some examples, the at least one requester comprises one or more workloads running on one or more processors. In some examples, the configuration is to specify collection of one or more of: physical address ranges or read or write access type.

Abstract: Systems and methods for using machine learning to determine product offers for individuals are disclosed. According to certain aspects, the systems and methods may determine, using a machine learning model and based on a set of products purchased by a user, a set of digital offers associated with an additional set of products to provide to the user. The user may redeem any of the digital offers, and the systems and methods may use any additional product purchase information to update the machine learning model for use in subsequent analyses.

Abstract: Systems and methods for using machine learning to determine product offers for individuals are disclosed. According to certain aspects, the systems and methods may determine, using a machine learning model and based on a set of products purchased by a user, a set of digital offers associated with an additional set of products to provide to the user. The user may redeem any of the digital offers, and the systems and methods may use any additional product purchase information to update the machine learning model for use in subsequent analyses.

Abstract: Systems and methods for using machine learning to determine product offers for individuals are disclosed. According to certain aspects, the systems and methods may determine, using a machine learning model and based on a set of products purchased by a user, a set of digital offers associated with an additional set of products to provide to the user. The user may redeem any of the digital offers, and the systems and methods may use any additional product purchase information to update the machine learning model for use in subsequent analyses.

Abstract: A method of trajectory planning for a medical robot system is disclosed herein, wherein a pose of a first component of the robot system may be changed independently of a pose of a second component of the robot system. The method includes determining an initial status, wherein, for each degree of freedom, an initial value is measured. Based on the initial status, items of position information of at least two discrete reference points of the first component in respect of one another are determined as well as further items of position information of at least two discrete further reference points of the second component in respect of one another. Depending on the initial status, the items of position information and the further items of position information, a trajectory for the robot system is planned, which transfers the robot system from the initial status into a target status.

Abstract: Chainsaws and methods of operating chainsaws are provided. A chainsaw includes a guide bar supporting a chain; a motor configured to drive the chain, the motor receiving power from an electrical energy source of the chainsaw; a sprocket operably coupled between the motor and the chain to impart driving force from the motor to the chain; and an operating device operably coupled between the motor and the sprocket and moveable between a driving state and a neutral state, wherein the chain is freely moveable in at least one direction relative to the motor when the operating device is in the neutral state.

Abstract: A method of trajectory planning for a medical robot system is disclosed herein, wherein a pose of a first component of the robot system may be changed independently of a pose of a second component of the robot system. The method includes determining an initial status, wherein, for each degree of freedom, an initial value is measured. Based on the initial status, items of position information of at least two discrete reference points of the first component in respect of one another are determined as well as further items of position information of at least two discrete further reference points of the second component in respect of one another. Depending on the initial status, the items of position information and the further items of position information, a trajectory for the robot system is planned, which transfers the robot system from the initial status into a target status.

Abstract: Examples described herein relate to circuitry, when operational, configured to: store records of memory accesses to a memory device by at least one requester based on a configuration, wherein the configuration is to specify a duration of memory access capture. In some examples, the at least one requester comprises one or more workloads running on one or more processors. In some examples, the configuration is to specify collection of one or more of: physical address ranges or read or write access type.

Abstract: The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning models using biological systems (e.g. brain data) to engineer machine-learning-algorithms that can generalize better. Particularly, aspects are directed to a computer implemented method that includes measuring a plurality of biological responses (e.g. neural responses to stimuli or other variables such body movements); generating data (e.g. responses to stimuli) using the predictive model which can denoise biological data and extract task relevant information; scaling and transforming these predictions (e.g. measure representational similarities between stimuli); and using the biologically derived data to regularize machine-learning-algorithms.

Abstract: Certain embodiments provide a pharmaceutical composition comprising: a protein molecule comprising an ERT enzyme-Fc fusion polypeptide and a modified Fc polypeptide; a buffer; an isotonicity agent; a surfactant; and a stabilizer; wherein the pH of the pharmaceutical composition is about 5.5 to 7.0, as well as methods of use thereof.

Abstract: Techniques are described herein that provide an interface for consolidating medical care tasks to increase efficiency associated with managing a medical office. The medical care tasks may include scheduling appointments, providing clinical assessment data for an appointment, renewing medications, submitting insurance claims, reviewing payment data, and the like. A service provider may access member data to determine that a member is due for an appointment, such as that associated with a clinical visit, a procedure, a screening, a medical test, or the like. In some examples, the service provider may determine that a medication associated with the member is due for renewal or modification. The service provider may generate a medical care task associated with the appointment and/or medication renewal or modification and send the medical care task to an application on a computing device associated with a medical provider to address the medical care task.

Abstract: A new management node associated with a new rack sends at least a public key of the new management node to a first management node associated with a first rack and a plurality of autonomous management nodes. The first management node sends the new management node an access token trusted by at least a portion of the plurality of autonomous management nodes and a set of public keys corresponding to at least a portion of the plurality of autonomous management nodes. The new management node can send its public key and the access token to at least a portion of the plurality of autonomous management nodes. In response, the new management node can establish a mutual trust relationship with at least a portion of the plurality of autonomous management nodes.

Abstract: A construct having a plurality of distributed resources can include a portion of a second rack having a plurality of computing devices controlled by a second management node. The second management node can determine it contains insufficient construct data such as user data, group data, resource data, or authorization policy data to execute an operation associated with the construct. The second management node can synchronize at least a portion of construct data with a first management node. The first management node can be associated with the construct and a mutual trust relationship can exist between the first management node and the second management node. The first management node and the second management node can comprise autonomous management nodes capable of functioning independent of the network.