Abstract: A secure electronic messaging system includes processors coupled to a network, and constituent applications, services, and processes configured to securely exchange messages between participants such as hosts and recipients, and to protect personally identifying information (PII). The system and processor(s) are configured to generate a deanonymization probability (DP) for an anonymized dataset (ADS) responsive to a query received from the network, and to generate the ADS with pseudoidentities of recipients, when the DP does not exceed a predetermined DP threshold (DPT). The system thereby reduces the probability that the ADS and or pseudoidentities can be deanonymized, and thereby enables secure message exchanges between the participants, while protecting and without sharing the PII of participant identities. When DPs exceed the DPT, the system generates error messages, and or alternative queries that generate ADSs with DPs that do not exceed the DPT.

Abstract: Cyber threat defense systems and methods are provided. The system includes a network module, an analyzer module and a classifier. The network module ingests network data, which is provided to one or more machine learning models included in the analyzer module. Each machine learning model identifies metrics associated with the network data and outputs a score indicative of whether anomalous network data metrics are caused by a cyber threat. These output scores are provided to the classifier, which determines a probability that a cybersecurity breach has occurred.

Abstract: A composition comprising a polypeptide ligated to an oligonucleotide through a sterol linker. A method of ligating a polypeptide to an oligonucleotide, comprising a polypeptide having a hedgehog steroyl transferase catalytic domain at the C-terminal of the polypeptide with an electrophilic residue, e.g., glycine, between polypeptide and the hedgehog steroyl transferase catalytic domain, and a steroylated oligonucleotide in solution, and permitting a reaction to cleave the hedgehog steroyl transferase catalytic domain from the polypeptide while ligating the steroylated oligonucleotide to the glycine at the C-terminal of the polypeptide. The oligonucleotide may be, for example, a therapeutic, diagnostic, or affinity ligand.

Abstract: Cyber threat defense systems and methods are provided. The system includes a network module, an analyzer module and a classifier. The network module ingests network data, which is provided to one or more machine learning models included in the analyzer module. Each machine learning model identifies metrics associated with the network data and outputs a score indicative of whether anomalous network data metrics are caused by a cyber threat. These output scores are provided to the classifier, which determines a probability that a cybersecurity breach has occurred.

Abstract: A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.

Abstract: A secure electronic messaging system includes processors coupled to a network, and constituent applications, services, and processes configured to securely exchange messages between participants such as hosts and recipients, and to protect personally identifying information (PII). The system and processor(s) are configured to generate a deanonymization probability (DP) for an anonymized dataset (ADS) responsive to a query received from the network, and to generate the ADS with pseudoidentities of recipients, when the DP does not exceed a predetermined DP threshold (DPT). The system thereby reduces the probability that the ADS and or pseudoidentities can be deanonymized, and thereby enables secure message exchanges between the participants, while protecting and without sharing the PII of participant identities. When DPs exceed the DPT, the system generates error messages, and or alternative queries that generate ADS s with DPs that do not exceed the DPT.

Abstract: An interactive cyber security user interface is provided. The interactive cyber security user interface comprises a large language model, LLM, module configured to receive a natural language input from a user; analyze the natural language input to determine contextual information from the natural language input; determine one or more components of a cyber security system to query based on the contextual information and the natural language input; and generate a query in a software code format accepted by the one or more components of the cyber security system based on an analysis of the natural language input, the contextual information, and the determined one or more components to be queried. The interactive cyber security user interface is configured to query the determined one or more components of the cyber security system using the generated query and receive a response to the query from the one or more components of the cyber security system.

Abstract: A secure electronic messaging system includes processors coupled to a network, and constituent applications, services, and processes configured to securely exchange messages between participants such as hosts and recipients, and to protect personally identifying information (PII). The system and processor(s) are configured to generate a deanonymization probability (DP) for an anonymized dataset (ADS) responsive to a query received from the network, and to generate the ADS with pseudoidentities of recipients, when the DP does not exceed a predetermined DP threshold (DPT). The system thereby reduces the probability that the ADS and or pseudoidentities can be deanonymized, and thereby enables secure message exchanges between the participants, while protecting and without sharing the PII of participant identities. When DPs exceed the DPT, the system generates error messages, and or alternative queries that generate ADSs with DPs that do not exceed the DPT.

Abstract: A cyber security restoration engine prioritizes nodes in a graph of nodes in a computer network or system that are involved in a cyber attack for remediation actions. The cyber security restoration engine performs this prioritization by, for each node, determining one or more edges linking the node to other nodes in the graph, the edges representing interactions between two nodes; obtaining metadata indicative of a type of interaction between two nodes connected by the edge and the roles of the two nodes in that interaction; determining how severe the interaction represented by that edge is within the context of the cyber attack, based on the metadata of that edge; and determining a severity score for the node by combining the severity score for each of the one or more edges connected to the node. The cyber security restoration engine prioritizes nodes for remediation action based on the severity scores for the nodes.

Abstract: A method for a cyber threat defense system is provided. The method comprises receiving a first abnormal behavior pattern where the first abnormal behavior pattern represents behavior on a first network deviating from a normal benign behavior of that network; and receiving a second abnormal behavior pattern where the second abnormal behavior pattern representing either behavior on the first network or on a second network deviating from a normal benign behavior of that network. The method further comprises comparing the first and second abnormal behavior patterns to determine a similarity score between the first and second abnormal behavior patterns and determining, based on the comparison, that the first abnormal behavior pattern likely corresponds to malicious behavior when the similarity score is above a threshold. A corresponding non-transitory computer readable medium is also provided.

Abstract: An apparatus may include a set of modules and artificial intelligence models to detect a cyber incident, a simulator to simulate an actual cyber attack of the cyber incident on a network including physical devices being protected by the set of modules and artificial intelligence models; and a feedback loop between i) the set of modules and artificial intelligence models and ii) the simulator, during an ongoing detected cyber incident. An attack path modeling module is configured to feed details of the detected incident by a cyber threat module into an input module of the simulator, and to run one or more hypothetical simulations of that detected incident in order to predict and control an autonomous response to the detected incident. Any software instructions forming part of the set of modules, the artificial intelligence models, and the simulator are stored in an executable form in memories and executed by processors.

Abstract: A vehicle seating assembly includes a seatback. The seatback includes a rear portion and a substantially horizontal strap including first and second ends fixed to the rear portion. The substantially horizontal strap further includes an intermediate attachment area fixed to the rear portion between the first and second ends.

Abstract: An Artificial Intelligence AI-based cyber threat analyst protects a system from cyber threats. A cyber threat analyst module uses i) one or more AI models, ii) a set of scripts, and iii) any combination of both, to form and investigate hypotheses on what are a possible set of cyber threats that include abnormal behavior and/or a suspicious activity. An analyzer module uses one or more data analysis processes including i) an agent analyzer data analysis process; ii) an Ngram data analysis process; iii) an exfiltration data analysis process; and iv) a network scan data analysis process; in order to obtain any of the abnormal behavior and the suspicious activity to start the investigation on the possible set of cyber threats hypotheses, as well as, to obtain the collection of system data points to either support or refute the possible cyber threat hypotheses.

Abstract: Methods, systems, and apparatus are disclosed for an Artificial Intelligence based cyber security system. An Artificial Intelligence based cyber analyst can make use of a data structure containing multiple tags to assist in creating a consistent, expanding modeling of an ongoing cyber incident. The Artificial Intelligence based cyber analyst can make use of a cyber incident graph database when rendering that incident to an end user. The Artificial Intelligence based cyber analyst can also be used as a mechanism to evaluate the quality of the alerts coming from 3rd parties' security tools both when the system being protected by the cyber security appliance is not actually under attack by a cyber threat as well as during an attack by a cyber threat.

Abstract: A vehicle seating assembly includes a first part, a second part with a second foam part, and a barrier with a permeable portion and a non-permeable portion. The vehicle seating assembly also includes an air passageway extending through the first part, the second foam part, and the permeable portion of the barrier and an air mover fluidly coupled to the air passageway for moving air through the air passageway.

Abstract: A slot die shim for use with a slot die, the slot die shim including a first channel configured to receive a first coating, a second channel configured to receive a second coating, and a third channel configured to receive a third coating. The slot die shim simultaneously dispenses the first coating through the first channel, the second coating through the second channel, and the third coating through the third channel onto a substrate. The coatings can be applied at different flow rates and include different test reagents. A vacuum force is applied to the first, second, and third coatings to control a width and a thickness of each of the coatings and a gap or distance between the coatings. The first, second, and third coatings are placed near one another or directly in contact with one another to minimize the area required for the coatings.

Abstract: A miter saw assembly includes a cutting tool configured to perform a cut along a cutting axis. A stop mechanism is linearly translatable in a first direction. A rotary motor includes a selectively rotatable rotor and a motion conversion mechanism configured to transform rotary motion of the rotor to linear motion of the stop mechanism in the first direction. A controller is configured to selectively rotate the rotor to cause linear translation of the stop mechanism toward or away from the cutting axis. A position detection system includes a visual sensor coupled to the stop mechanism and a ruler strip. The ruler strip includes a plurality of spaced apart position indicators configured for detection by the visual sensor in order to determine a position of the stop mechanism relative to the cutting axis of the cutting tool via position correlating perform by the controller.

Abstract: Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.

Abstract: An apparatus comprising: one or more machine learning modules that are trained on a normal behavior of entities associated with a network and interactions between the entities; an interface configured to receive a signal from an external apparatus to request and trigger an artificial intelligence based analyst investigation; where the interface is configured to work with at least one of: artificial intelligence models trained on how to conduct an investigation; and scripts on how to conduct an investigation, in order to determine whether a chain of related low level abnormalities associated with one or more of the entities should be determined to be one or more incidents worthy of generating a notification to a human user for possible further investigation and/or worthy of being determined as an actual cyber-threat, and thus, trigger an autonomous response from an autonomous response module to mitigate the cyber-threat.

Abstract: Cyber threat defense systems and methods are provided. The system includes a network module, an analyzer module and a classifier. The network module ingests network data, which is provided to one or more machine learning models included in the analyzer module. Each machine learning model identifies metrics associated with the network data and outputs a score indicative of whether anomalous network data metrics are caused by a cyber threat. These output scores are provided to the classifier, which determines a probability that a cybersecurity breach has occurred.