Abstract: Embodiments of the disclosure relate generally to formulations, methods of treatment, kits, and dosage forms for treating inflammatory disorders, including topic dermatitis, cancer, the formulations comprising an active pharmaceutical ingredient. The formulation provided comprises granules, wherein the granules comprise: micronized active ingredient; one or more granulation binders; one or more fillers; one or more disintegrants; and one or more antioxidants. In one embodiment, the methods of treatment include orally administering the active ingredient to a subject suffering from atopic dermatitis, where the active ingredient, is in an amount of about 20 mg to about 80 mg.

Abstract: A system includes a memory and a processor. The processor is configured to determine that a user has ceased using a first device while the user was in an active user session with an application. The processor is further configured to determine a last user interface with which the user interacted on the first device and to determine user-supplied information from the last user interface. The processor is further configured to determine that the user is attempting to start a new user session with the application on a second device, and in response, display a message on the second device inquiring if the user wishes to continue with the last user interface. The processor is further configured to display a new user interface on the second device that corresponds to the last user interface and display the user-supplied information from the last user interface in the new user interface.

Abstract: Distributed firewalls in a network are disclosed. Example firewall controllers disclosed herein are to instruct a first network node of a software-defined network to implement a first firewall instance of a distributed firewall, the first network node to implement the first firewall instance with a first virtual machine. Disclosed example firewall controllers are also to configure a second network node of the software-defined network to route network traffic through the first firewall instance and, after at least some of the network traffic is dropped by the first firewall instance, instruct the second network node to implement a second firewall instance of the distributed firewall, the second network node to implement the second firewall instance with a second virtual machine.

Abstract: A cloud security system and method implements cloud activity threat detection using analysis of cloud usage user behavior. In particular, the cloud security system and method implements threat detection for users, cloud service providers, or tenants (enterprises) of the cloud security system who are new or unknown to the cloud security system and therefore lacking sufficient cloud activity data to generate an accurate behavior model for effective threat detection. In accordance with embodiments of the present invention, the cloud security system and method performs user behavior analysis to generate generalized user behavior models for user groups, where each user group includes users with similar cloud usage behavior. The user behavior models of the user groups are assigned to users with sparse cloud activity data. In this manner, the cloud security system and method of the present invention ensures effective threat detection by using accurate and reliable user behavior models.

Abstract: Calculating energy loss during an outage, including: determining that windspeed data indicating device windspeeds measured at an energy generating device are unavailable within a particular time duration; receiving meteorological data associated with a site location of the energy generating device, the meteorological data including meteorological windspeed data collected within the particular time duration; and predicting one or more estimated device windspeeds at the energy generating device during the particular time duration based on the meteorological data using a trained model for the energy generating device, the trained model being trained using a machine learning algorithm that utilizes historical meteorological windspeed data associated with the site location collected during a previous time duration and corresponding historical device windspeed data measured at the energy generating device during the previous time duration.

Abstract: Predicting energy production for energy generating assets, including: receiving current and forecasted meteorological data associated with a location of an energy generating asset; and predicting an energy production value produced by the energy generating asset at a predetermined time based on the current and forecasted meteorological data using a trained model for the energy generating asset, the trained model being trained using a machine learning algorithm that utilizes historical meteorological data associated with the location of the energy generating asset and historical production capability data associated with a historical production capability of the energy generating asset.

Abstract: The present invention relates to anti-SIRP? (Signal regulatory protein alpha) antibodies and antigen-binding fragments thereof for therapeutic and diagnostic methods and compositions using them.

Abstract: Distributed firewalls in a network are disclosed. Example firewall controllers disclosed herein are to instruct a first network node of a software-defined network to implement a first firewall instance of a distributed firewall, the first network node to implement the first firewall instance with a first virtual machine. Disclosed example firewall controllers are also to configure a second network node of the software-defined network to route network traffic through the first firewall instance and, after at least some of the network traffic is dropped by the first firewall instance, instruct the second network node to implement a second firewall instance of the distributed firewall, the second network node to implement the second firewall instance with a second virtual machine.

Abstract: The present disclosure discloses a data science driven system and associated methodology framework for mining relevant knowledge from input/output traces of a closed-loop control system for glycemic control to aid in actionable feedback for Type 1 diabetes management. The framework takes Continuous Glucose Monitor (CGM) records and insulin pump records from a closed loop control (CLC) system and interprets the CGM records and insulin pump data to provide medically useful information, wherein the closed-loop control system is operable for operation in an “auto mode” (AP) and a “manual mode” (SAP).

Abstract: A system examines components of gestures of a gesture-based language for evaluating proper execution of the gesture, and also examines components of new gestures for evaluating lexical similarity with existing gestures of similar meaning or theme.

Abstract: A cache may store critical cache lines and non-critical cache lines, and may attempt to retain critical cache lines in the cache by, for example, favoring the critical cache lines in replacement data updates, retaining the critical cache lines with a certain probability when victim cache blocks are being selected, etc. Criticality values may be retained at various levels of the cache hierarchy. Additionally, accelerated eviction may be employed if the threads previously accessing the critical cache blocks are viewed as dead.

Abstract: A cache may store critical cache lines and non-critical cache lines, and may attempt to retain critical cache lines in the cache by, for example, favoring the critical cache lines in replacement data updates, retaining the critical cache lines with a certain probability when victim cache blocks are being selected, etc. Criticality values may be retained at various levels of the cache hierarchy. Additionally, accelerated eviction may be employed if the threads previously accessing the critical cache blocks are viewed as dead.

Abstract: An apparatus includes a plurality of processor circuits, a cache memory circuit, and a trace control circuit. The trace control circuit may be configured, in response to activation of a mode to record information indicative of program execution of at least one processor circuit of the plurality of processor circuits, to monitor memory requests transmitted between ones of the plurality of processor circuits and the cache memory circuit, and then to select a particular memory request of monitored memory requests using an arbitration algorithm. The trace control circuit may be further configured to allocate space in a trace buffer to the particular memory request, and to store, in the trace buffer, information associated with the particular memory request.

Abstract: A workflow construction system for constructing automation workflows that automate user specific processes. The workflow construction system may include a template library including workflow templates and pre-configured attributes. The workflow template can accelerate the design and construction of custom automation workflows. An orchestration layer included in the workflow construction system will also improve the performance of systems that execute the automation workflows by dynamically scaling the processing capacity, memory, and storage of servers and other systems hosting the model file instances of the automation workflows to ensure the available resources meet the demands of users completing processes using the automation workflows.

Abstract: Complexities arising from interaction between multiple inter-connected components in an autonomous cyber physical system can potentially result in differences in intended and observed operations of a CPS. To perform fault analysis of a CPS it is imperative to explain this discrepancy in terms of the components of CPS control code. A system estimates a “mined” hybrid system representation of a CPS based on observed input/output traces and extracts a state machine representation of the CPS control code. The system compares the “mined” hybrid system representation with the extracted state machine representation simplification of the CPS code to identify discrepancies between expected and observed operation of the CPS. The system explains discrepancies in terms of call conditions as binary or unary operations on input and output variables and status of function call arguments.

Abstract: Safety certification of cyber-physical system (CPS) such as autonomous cars or medical control systems are complicated especially due to the lack of transparency between the manufacturer and certifying authority. The manufacturer has significant restrictions in sharing knowledge with the certification authority. Further, given time constraints, it may not be feasible for the certification authority to examine internal details of the CPS. A system models the safety certification of CPS as an agile iterative game, where a manufacturer agent acting on behalf of the manufacturer and a certifier agent acting on behalf of a third-party certification entity aims to find an optimal subset of operating data to share for accurate safety certification. The certification agent, armed with CPS model mining methods and safety assessment analysis tools, aims to accurately assess safety of the CPS with the information shared.

Abstract: Various embodiments of systems and methods for mobile crowd sensing applications are disclosed herein.

Abstract: Methods and systems for utilizing co-simulation of a physical model and a self-adaptive predictive controller using hybrid automata are described. For example, there is disclosed a self-adaptive predictive control system to generate an initial patient model with initial patient model settings (k1, k2, . . . , kn) and execute a program that takes as input, the initial patient model settings as configuration parameters that specify at least a plurality of initial states. Such a system further receives new patient inputs and generates hybrid automata as a variation of the initial states by applying the new patient inputs to the initial patient model. The system further derives reachable states from the hybrid automata and outputs all reachable states computed. The system further detects changes and responsively generates a new patient predictive model with new parameter settings (k?1, k?2, . . . , k?n), iteratively repeating until a termination criterion is satisfied. Other related embodiments are disclosed.

Abstract: Systems and methods are provided for controlling predictive medical monitoring. A non-linear-predictive-guide model estimates a patient parameter that is used as a guide in a deep neural network for improving accuracy of estimation by the deep neural network. The guide model generates a guiding first estimated patient parameter based on the guide model and patient input data. The deep neural network generates a second estimated patient parameter based on the deep neural network, the patient input data, and the guiding first estimated patient parameter. The deep neural network includes an input layer that receives the guiding first estimated patient parameter, and hidden layers including respective artificial neurons configured to perform a linear or nonlinear transformation on output of at least one artificial neuron from an adjacent layer in the deep neural network. An output layer receives at least one output from a hidden layer.

Abstract: A workflow construction system for constructing automation workflows that automate user specific processes. The workflow construction system may include a template library including workflow templates and pre-configured attributes. The workflow template can accelerate the design and construction of custom automation workflows. An orchestration layer included in the workflow construction system will also improve the performance of systems that execute the automation workflows by dynamically scaling the processing capacity, memory, and storage of servers and other systems hosting the model file instances of the automation workflows to ensure the available resources meet the demands of users completing processes using the automation workflows.