Abstract: In one embodiment, a depth-first deep convolutional network (DCN) having a first convolutional layer having a first first-layer kernel and adapted to convolve a first input and a second convolutional layer having a first second-layer kernel and adapted to convolve a second-layer input. A method for the DCN includes initiating convolution in the first convolution layer of the first input tensor with the first first-layer kernel to generate a value strip for the second input tensor and, prior to completion of the convolution in the first convolution layer, initiating convolution in the second convolution layer of the second input with the first second-layer kernel to generate a value strip for a third layer.

Abstract: A cloud-based virtualization generation service collects industrial-automation-system-related data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component analyzes the data and generates a virtualized industrial automation system of the industrial automation system based on the analysis results. The virtualization management component updates the virtualized industrial automation system based on operation of, and user interactions with, the industrial automation system. The virtualization management component receives user interactions with the virtualized industrial automation system to facilitate remotely interacting with and/or controlling the industrial automation system and/or generates corresponding control signals that it sends to the industrial automation system to facilitate controlling operation of the industrial automation system.

Abstract: Among other things, a system and a method are provided for generating space-filling foam for treating vulvodynia with medication compounded of multiple reactants; the system comprises an aerosol surmounted on a vulva-enclosing container configured to seal the vulva; the actuator on the aerosol operates a one-way valve to dispense said space-filling foam; and the method includes the following steps: selecting the vulvar cap delivery dispenser, affixing the dispenser over the vulva, depressing the pressure-sensitive switch operating the valve to deliver the appropriate quantity of medication in a space-filling foam, removing the delivery dispenser, and cleansing the dispenser; repeating the procedure according to a treatment regimen.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: A diagnostic device to diagnose a motorized device with an on-board diagnostic system includes a housing, an electrical connector, and a wireless transmission element. The electrical connector is supported by the housing and is configured to connect to an electrical connector of the on-board diagnostic system. The wireless transmission element is electrically connected to the electrical connector and is supported by the housing.

Abstract: A method of constraining data represented in a deep neural network is described. The method includes determining an initial shifting specified to convert a fixed-point input value to a floating-point output value. The method also includes determining an additional shifting specified to constrain a dynamic range during converting of the fixed-point input value to the floating-point output value. The method further includes performing both the initial shifting and the additional shifting together to form a dynamic, range constrained, normalized floating-point output value.

Abstract: A computing device may allocate a plurality of blocks in the memory, wherein each of the plurality of blocks is of a uniform fixed size in the memory. The computing device may further store a plurality of bandwidth-compressed graphics data into the respective plurality of blocks in the memory, wherein one or more of the plurality of bandwidth-compressed graphics data each has a size that is smaller than the fixed size. The computing device may further store data associated with the plurality of bandwidth-compressed graphics data into unused space of one or more of the plurality of blocks that contains the respective one or more of the plurality of bandwidth-compressed graphics data.

Abstract: A layered industrial analytics architecture enables the flow of information from intelligent assets into tools and engines that perform analytics and enable decision-making in substantially real-time. The analytics architecture comprises analytic nodes that are distributed across multiple layers of an industrial enterprise, and includes system features that optimize movement of data across this layered architecture. Each analytic node includes base architectural constructs that host various analytic, data acquisition, and storage elements. These base constructs can operate autonomously, or in conjunction with other instances of base constructs or other elements of the control system. The system design uses a multi-platform compatible implementation that allows the base elements to be deployed on various different computing platforms.

Abstract: A method, apparatus, and system for an architecture for machine learning acceleration is presented. An apparatus includes a plurality of processing elements, each including a tightly-coupled memory, and a memory system coupled to the processing elements. A global synchronization manager is coupled to the plurality of the processing elements and to the memory system. The processing elements do not implement a coherency protocol with respect to the memory system. The processing elements implement direct memory access with respect to the memory system, and the global synchronization manager is configured to synchronize operations of the plurality of processing elements through the TCMs.

Abstract: Systems and methods are disclosed method for controlling instantaneous current changes in parallel processors with arrays of parallel computing elements, such as neural processors. An exemplary method comprises monitoring the array of computing elements and determining a transition from a first activity level of the array to a second activity level of the array, such as an idle-to-active or active-to-idle transition. Once a transition is determined, the array is selectively controlled to minimize the instantaneous current change from the transition from the first activity level to the second activity level.

Abstract: A cloud-based virtualization generation service collects industrial data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component (VMC) generates a virtualized industrial automation system of an industrial automation system based on data analysis results. The VMC facilitates remotely controlling the industrial automation system based on user interactions with the virtualized industrial automation system, and updates the virtualized industrial automation system based on collected data relating to the industrial automation system. The VMC customizes a user's view of the virtualized industrial automation system based on a user's role, authorization, location, or preferences, wherein different views of the virtualized industrial automation system with different data overlays are presented on different communication devices of different users.

Abstract: A layered industrial analytics architecture enables the flow of information from intelligent assets into tools and engines that perform analytics and enable decision-making in substantially real-time. The analytics architecture comprises analytic nodes that are distributed across multiple layers of an industrial enterprise, and includes system features that optimize movement of data across this layered architecture. Each analytic node includes base architectural constructs that host various analytic, data acquisition, and storage elements. These base constructs can operate autonomously, or in conjunction with other instances of base constructs or other elements of the control system. The system design uses a multi-platform compatible implementation that allows the base elements to be deployed on various different computing platforms.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: Among other things, compositions, kits, and methods for epilepsy therapy using a process for treating calcinosis manifested in Neurocysticercosis, Tuberous Sclerosis Complex, and Sturge-Weber Syndrome; an automated platform for real-time diagnosis and treatment of calcinosis comprising a computer-implemented method for using predictive analytics for administering a novel drug, Kamini™, to human patients generated at least in part from information that has been accumulated automatically from on-line resources.

Abstract: A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification.

Abstract: A diagnostic device to diagnose a motorized device with an on-board diagnostic system includes a housing, an electrical connector, and a wireless transmission element. The electrical connector is supported by the housing and is configured to connect to an electrical connector of the on-board diagnostic system. The wireless transmission element is electrically connected to the electrical connector and is supported by the housing.

Abstract: A cloud-based virtualization generation service collects industrial-automation-system-related data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component analyzes the data and generates a virtualized industrial automation system of the industrial automation system based on the analysis results. The virtualization management component updates the virtualized industrial automation system based on operation of, and user interactions with, the industrial automation system. The virtualization management component receives user interactions with the virtualized industrial automation system to facilitate remotely interacting with and/or controlling the industrial automation system and/or generates corresponding control signals that it sends to the industrial automation system to facilitate controlling operation of the industrial automation system.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.