Abstract: A method, computer program product and computer system to automatically generate augment reality-based guides for maintenance procedures is provided. A processor retrieves non-structured text instructions to perform a maintenance procedure on a device within a data center. A processor extracts at least one imperative statement from the non-structured text instructions. A processor identifies a named entity in the extracted at least one imperative statement from the non-structured text instructions. A processor generates a mapping of the named entity to be used by an augmented reality device, where the mapping indicates the position of the named entity on the device within a data center. A processor provides to the augmented reality device the extracted at least one imperative statement and the mapping of the named entity, where the augmented reality device displays the extracted imperative statement and the mapping of the named entity to a user of the augmented reality device.

Abstract: Embodiments relate to cold-worked steel alloys with improved strength, hardness and corrosion resistance useful for creating products, such as exterior vehicle body components. Processes for preparing cold-worked steel alloys are also described.

Abstract: A bean roasting system includes a roasting chamber, a blower, a variable diverter and a controller. The roasting chamber, the blower and the variable diverter each is disposed at least partially within a recirculating gas flow path. The blower is configured to provide a flow stream of gas through the recirculating gas flow path. The variable diverter is configured to split the gas flow path into at least two flow paths including a treated flow path and a bypass flow path. The treated flow path includes a series arrangement of a gas heater and a catalytic converter. The variable diverter is configured to control a percentage of a flow stream of gas that is diverted into the bypass flow path. The controller is configured to activate different predetermined operating modes for the bean roasting system by controlling a state of the variable diverter and a state of the heater.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: A bean roasting system includes a roasting chamber, a blower, a variable diverter and a controller. The roasting chamber, the blower and the variable diverter each is disposed at least partially within a recirculating gas flow path. The blower is configured to provide a flow stream of gas through the recirculating gas flow path. The variable diverter is configured to split the gas flow path into at least two flow paths including a treated flow path and a bypass flow path. The treated flow path includes a series arrangement of a gas heater and a catalytic converter. The variable diverter is configured to control a percentage of a flow stream of gas that is diverted into the bypass flow path. The controller is configured to activate different predetermined operating modes for the bean roasting system by controlling a state of the variable diverter and a state of the heater.

Abstract: A bean roasting system includes a roasting drum, an agitator, and a door. The roasting drum has an inside surface extending from a back end to a front end. The agitator has blades mounted to an axial shaft. The axial shaft has an anterior end portion. The door is mounted rotationally relative to the roasting drum and includes a glass plate and a bearing assembly. The glass plate provides visual access to contents inside the roasting drum when the door is closed. The bearing assembly includes an outer housing and an inner bearing. The outer housing is mounted to the glass plate. The inner bearing defines a receiving hole that receives and supports the anterior end portion of the axial shaft when the door closed.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic biometric identifiers. The method includes generating a scannable code upon receiving a success nonce from a registration server. The registration server can access a user identifier and a hash of at least a signature using the success nonce. The signature can be generated based at least in part upon a biometric identifier of a user. The method includes recreating the hash of the signature stored by the registration server. The method includes generating the scannable code by encrypting the success nonce and the recreated hash. The biometric identifier of the user is generated by feeding a plurality of non-deterministic biometric inputs to a trained machine learning model producing a plurality of feature vectors. The method includes projecting the plurality of feature vectors onto a surface of a unit hyper-sphere and computing a characteristic identity vector representing the user.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The present invention provides a conjugate of formula (I) and its use in methods of treatment, and also methods for delivering an active agent into a cell. The methods may be used to deliver an active agent into a nematode, flatworm, parasite or bacterium. The conjugate of formula (I) is: (formula (I)), wherein -D- is C1-4 alkylene or C2-4 alkenylene, preferably C2-4 alkenylene, where the alkylene or alkenylene is optionally substituted with alkyl or halo; A- is an active agent for delivery; and —RA, —RB, —RT1, —RT2, —R1, —R2, —R3, —X— and -L- are as defined herein.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: A bean roasting system includes a roasting chamber, a blower, a variable diverter and a controller. The roasting chamber, the blower and the variable diverter each is disposed at least partially within a recirculating gas flow path. The blower is configured to provide a flow stream of gas through the recirculating gas flow path. The variable diverter is configured to split the gas flow path into at least two flow paths including a treated flow path and a bypass flow path. The treated flow path includes a series arrangement of a gas heater and a catalytic converter. The variable diverter is configured to control a percentage of a flow stream of gas that is diverted into the bypass flow path. The controller is configured to activate different predetermined operating modes for the bean roasting system by controlling a state of the variable diverter and a state of the heater.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: A bean roasting system includes a roasting drum, an agitator, and a door. The roasting drum has an inside surface extending from a back end to a front end. The agitator has blades mounted to an axial shaft. The axial shaft has an anterior end portion. The door is mounted rotationally relative to the roasting drum and includes a glass plate and a bearing assembly. The glass plate provides visual access to contents inside the roasting drum when the door is closed. The bearing assembly includes an outer housing and an inner bearing. The outer housing is mounted to the glass plate. The inner bearing defines a receiving hole that receives and supports the anterior end portion of the axial shaft when the door closed.