Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.

Abstract: A technology is described for a system for identifying a colorimetric test result from a target nucleotide test performed on a solid phase substrate. The system can comprise a sensor configured to detect a spectrum of color wavelengths. The system can comprise one or more processors. The one or more processors can be configured to: receive color wavelength data; determine a wavelength threshold for providing a target nucleotide positive test result; identify whether the color wavelength data meets or exceeds the wavelength threshold for providing a target nucleotide positive test result; and generate a result indicator indicating either a target nucleotide positive or target nucleotide negative test result.

Abstract: A technology is described for a system for identifying a colorimetric test result from a pathogen test performed on a solid phase substrate. The system can comprise a sensor configured to detect a spectrum of color wavelengths. The system can comprise one or more processors. The one or more processors can be configured to: receive color wavelength data; determine a wavelength threshold for providing a pathogen positive test result; identify whether the color wavelength data meets or exceeds the wavelength threshold for providing a pathogen positive test result; and generate a result indicator indicating either a pathogen positive or pathogen negative test result.

Abstract: A set of incident records are received for a computing system. The incident records are analyzed to identify similar incident records which are then linked. Incident clusters are generated based upon the links and incident records in each cluster are ranked. A prompt is generated to an artificial intelligence (AI) model based on the ranked, related incidents and the AI model returns a response that identifies a root cause and mitigation steps corresponding to the ranked incidents.

Abstract: The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.

Abstract: The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.

Abstract: Loop-mediated isothermal amplification assays, kits, and methods that target and/or detect the presence of a fecal indicator bacteria in a sample. These assays, kits, and methods can be portable and capable of providing fast (within 60 minutes) results in the field, eliminating the need for a laboratory and other complex equipment.

Abstract: Devices, systems, and methods for analyzing nanoparticles and microparticles. An exemplary device for analyzing microparticles and nanoparticles is disclosed herein, comprising an elongated capillary tube having a square cross-section, and a substrate configured to receive the elongated capillary tube thereon, wherein the elongated capillary tube is affixed to the substrate using an adhesive.

Abstract: Disclosed herein are methods for inducing immunity against a severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV2) in a patient in need thereof. The method comprises administering a vaccine composition comprising a self-adjuvanted SARS-CoV2 Spike (S) RNA-based vaccine (AAHI-SC2), followed by administering a replication defective adenovirus (hAd5) vaccine composition, wherein the adenovirus comprises an E1 gene region deletion and an E2b gene region deletion.

Abstract: A set of incident records are received for a computing system. The incident records are analyzed to identify similar incident records which are then linked. Incident clusters are generated based upon the links and incident records in each cluster are ranked. A prompt is generated to an artificial intelligence (AI) model based on the ranked, related incidents and the AI model returns a response that identifies a root cause and mitigation steps corresponding to the ranked incidents.

Abstract: Systems and methods are provided for generating and updating a dependency graph that is used in combination with textual information about incidents to improve incident-linking suggestions. Systems and methods are also provided for generating, training, and using a machine learning model configured to perform incident linking using both graph data and text data. Beneficially, these systems and methods align the graph data and text data in order to more efficiently and accurately leverage information from the multi-modal data.

Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.

Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.

Abstract: Methods for site-specific risk evaluation of foodborne pathogens and/or fecal indicator bacteria contamination. Methods for generating a heat map of a field showing contamination concentrations integrated with environmental data are also provided.

Abstract: Devices, systems, kits, and methods for on-farm detection of foodborne pathogens and/or fecal indicator bacteria, wherein the devices and systems are particularly suitable for use in LAMP testing of crops in the field.

Abstract: The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.

Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.

Abstract: Assays, kits, and methods that target and/or detect the presence of bovine respiratory complex (BRD)-associated pathogens in a sample. These assays, kits and methods can be portable and capable of providing fast (within 45 minutes) and accurate (at least 96%) results in the field, eliminating the need for a laboratory and other complex equipment.

Abstract: Systems and methods are provided for interpreting a correlation model that predicts a correlation between a pair of data corresponding to a pair of incident tickets using an interpreter model. The correlation model includes a Siamese Network including a plurality of neural networks. The interpreter model, trained by using training data, represents a student model (a glass-box model) while the correlation model, trained using the training data, represents a more complex teacher model (a black-box mode) of a teacher-student model. The present disclosure generates global feature importance scores based on the trained interpreter model, which indicates a degree of influence of a feature compared to other features in incident data in determining correlations, to generate additional training data emphasizing influential features and to retrain the correlation model.

Abstract: Inter-device communication with a pulse-amplitude modulation (PAM) signal can have at least two data eyes with different Vref levels. A physical interface (PHY) can be trained for the PAM signal by training a first data eye separately from the second data eye. The training can include adjusting the first Vref level separately from the second Vref level to center each reference voltage on its respective data eye.