Abstract: The present disclosure relates generally to small molecule modulators of NLR Family Pyrin Domain Containing 3 (NL-RP3), or a pharmaceutically acceptable salt, isotopically enriched analog, stereoisomer, mixture of stereoisomers, or prodrug thereof, methods of making and intermediates thereof, and methods of using thereof. Further disclosed a method for treating a disease or condition mediated, at least in part, by NLRP3, the method comprising administering an effective amount of the pharmaceutical composition, to a subject in need thereof.

Abstract: The present disclosure relates generally to eukaryotic initiation factor 2B modulators, or a pharmaceutically acceptable salt, stereoisomer, mixture of stereoisomers, or prodrug thereof, and methods of making and using thereof.

Abstract: Methods and devices for processing a signal. The methods include supplying to a first modulator a first RF signal and a first optical signal, wherein the first modulator is configured to output a first output signal; generating a first intensity signal that is based on the first output signal, wherein the first intensity signal is further based on a first biasing parameter; and providing a first intensity signal to a first analog-to-digital converter (ADC) to create a first digital signal processable by a signal processing unit.

Abstract: A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

Abstract: An energy plant includes a plurality of subplants, a high level optimizer, a low level optimizer, and a controller. The plurality of subplants include a cogeneration subplant configured to generate steam and electricity and a chiller subplant electrically coupled to the cogeneration subplant and configured to consume the electricity generated by the cogeneration subplant. The high level optimizer is configured to determine recommended subplant loads for each of the plurality of subplants. The recommended subplant loads include a rate of steam production and a rate of electricity production of the cogeneration subplant and a rate of electricity consumption of the chiller subplant. The low level optimizer is configured to determine recommended equipment setpoints for equipment of the plurality of subplants based on the recommended subplant loads. The controller is configured to operate the equipment of the plurality of subplants based on the recommended equipment setpoints.

Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

Abstract: The present disclosure relates generally to small molecule modulators of NLR Family Pyrin Domain Containing 3 (NL-RP3), or a pharmaceutically acceptable salt, isotopically enriched analog, stereoisomer, mixture of stereoisomers, or prodrug thereof, methods of making and intermediates thereof, and methods of using thereof.

Abstract: The present disclosure relates generally to eukaryotic initiation factor 2B modulators, or a pharmaceutically acceptable salt, stereoisomer, mixture of stereoisomers, or prodrug thereof, and methods of making and using thereof.

Abstract: Methods and systems are provided for road condition classification that include one or more sensors configured to obtain sensor data pertaining to operation of a vehicle along a roadway by a human; and one or more processors that are coupled to the one or more sensors and that are configured to at least facilitate analyzing the sensor data as to one or more threshold values pertaining to the operation of the vehicle; and identifying one or more conditions of the roadway, based on the analyzing of the sensor data. Further, learned input is provided to the vehicle control system to operate with lane position behavior similar to a human driven vehicle.

Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

Abstract: The present disclosure relates generally to eukaryotic initiation factor 2B modulators, or a pharmaceutically acceptable salt, stereoisomer, mixture of stereoisomers, or prodrug thereof, and methods of making and using thereof.

Abstract: In a computer-implemented method of facilitating detection of document-related fraud, fraudulent document detection rules may be generated or updated by training a machine learning program using image data corresponding to physical documents, and fraud determinations corresponding to the documents. The documents and fraudulent document detection rules may correspond to a first type of document. Image data corresponding to an image of one of the physical documents may be received, where the physical document corresponds to the first type of document. By applying the fraudulent document detection rules to the image data, it may be determined that the physical document is, or may be, fraudulent. An indication of whether the physical document is, or may be, fraudulent may be displayed to one or more people via one or more respective computing device user interfaces.

Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

Abstract: A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

Abstract: The present disclosure relates generally to small molecule modulators of NLR Family Pyrin Domain Containing 3 (NL-RP3), or a pharmaceutically acceptable salt, isotopically enriched analog, stereoisomer, mixture of stereoisomers, or prodrug thereof, methods of making and intermediates thereof, and methods of using thereof.

Abstract: In a computer-implemented method of using customer data to determine that geolocation-based fraud alerts are false positives, it may be determined that an electronic fraud alert is a geolocation-based alert generated based upon an unexpected or abnormal transaction location. In response, customer data may be obtained from two or more sources via radio frequency links. It may then be determined that the customer data from the sources indicates that a customer is traveling. In response, it may be determined that a customer location indicated by the customer data corresponds to the transaction location. In response to determining that the customer location corresponds to the transaction location, the electronic fraud alert may be marked as a false positive, and the electronic fraud alert may be prevented from being transmitted to a mobile device of the customer, in order to reduce an amount of false positives that are transmitted to customers.

Abstract: In a computer-implemented method of facilitating detection of document-related fraud, fraudulent document detection rules may be generated or updated by training a machine learning program using image data corresponding to physical documents, and fraud determinations corresponding to the documents. The documents and fraudulent document detection rules may correspond to a first type of document. Image data corresponding to an image of one of the physical documents may be received, where the physical document corresponds to the first type of document. By applying the fraudulent document detection rules to the image data, it may be determined that the physical document is, or may be, fraudulent. An indication of whether the physical document is, or may be, fraudulent may be displayed to one or more people via one or more respective computing device user interfaces.

Abstract: A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

Abstract: In a computer-implemented method of using customer data to determine that geolocation-based fraud alerts are false positives, it may be determined that an electronic fraud alert is a geolocation-based alert generated based upon an unexpected or abnormal transaction location. In response, customer data may be obtained from two or more sources via radio frequency links. It may then be determined that the customer data from the sources indicates that a customer is traveling. In response, it may be determined that a customer location indicated by the customer data corresponds to the transaction location. In response to determining that the customer location corresponds to the transaction location, the electronic fraud alert may be marked as a false positive, and the electronic fraud alert may be prevented from being transmitted to a mobile device of the customer, in order to reduce an amount of false positives that are transmitted to customers.