Abstract: Techniques described herein are directed towards dynamically adding new pickup orders to an existing route structure. In at least some embodiments, a service provider separates a number of vendors into separate clusters based on one or more attributes. For example, the vendors may be separated by geographic region. The service provider may then identify a set of routes that are associated with each of the vendor clusters and run optimization techniques on that set of routes. The set of routes may be filtered based on attributes of the route. In some embodiments, the service provider may make a route alteration that accommodates the new pickup order.

Abstract: Systems, methods, and computer-executable instructions for verifying a chat bot. Registration information for a chat bot is received and stored. A search query is received from a browser that includes a search keyword. A determination if a chat bot should be included in search engine results page based upon the search query is made. The search query is matched to keywords associated with the chat bot. Using the registration information, if the chat bot is launchable from the search engine results page is determined. The chat bot is integrated in the search engine results page. The search engine results page is provided to the browser.

Abstract: Systems, methods, and computer-executable instructions for verifying a chat bot. Registration information for the chat bot is received that includes keywords. Queries are submitted to the chat bot and responses to the queries are received. The responses are classified using a classifier. Verification that the responses are classified as approved responses are done. The registration information is stored and the registration of the chat bot is completed based upon the verified responses. A search query that includes a search keyword is received. The search keyword is matched to the keywords associated with the chat bot. Search results that include a link to the chat bot that are based upon the search query are provided.

Abstract: In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.

Abstract: The present invention relates to a novel process of isolating and purifying tissue plasminogen activator and its variants more specifically TNK-tPA from CHO cells and describes an industrially applicable, simple, cost effective, robust and highly efficient process of TNK-tPA purification.

Abstract: Examples provided herein describe a method for providing security for Internet of Things (IoT) devices. For example, a data packet from an IoT device may be received at an edge device. A signature associated with the IoT device may be accessed at the edge device, where the signature includes network layer information about the IoT device. A set of rules may be applied by the edge device to validate the IoT device based on the accessed signature. Responsive to the IoT device being validated based on the accessed signature, received data packet, and the applied set of rules, the edge device may process the data packet from the IoT device.

Abstract: A system for diagnosing problems experienced with a mobile device includes a processor and first, second, third and fourth mirrors arranged such that light travels from a mobile device display to strike each of the first, second, third and fourth mirrors in turn and is reflected into a camera of the mobile device. The processor is configured to analyze the light received by the camera of the mobile device to diagnose problems experienced with the camera, the mobile device display or both.

Abstract: In some embodiments, an energy absorber element can comprise: a first support wall and a second support wall, a crush wall joining the first and second support walls together to define a deformable zone; a connection mechanism configured to connect the first and/or second support wall to a vehicle. In one embodiment, a method for using an energy absorber element in a vehicle can comprise: detachably connecting an energy absorber element to a vehicle at a support location for a vehicle component, once the energy absorber element has absorbed energy, detaching the energy absorber element from the vehicle; and separately replacing the energy absorber element from the vehicle component. In some embodiments, the vehicle component is not replaced.

Abstract: Various embodiments include methods, systems, and apparatus for optically aligning multiple sensors in a multi-sensor electro-optical (EO) system. Various embodiments include ways of aligning these sensors using a combination of optical elements.

Abstract: The disclosed embodiments illustrate methods and systems for scheduling one or more first vehicles along a route in a transportation system. The method includes determining one or more demands pertaining to a commutation along a route, where the route comprises at least one pair of stations such that there is a unique path between the pair of stations. The method further includes determining a set of constraints associated with the transportation system. The set of constraints are based on at least a count of second vehicles plying on one or more routes in the transportation system at a time instance, a capacity of a first vehicle, and a performance metric of the transportation system. Further, the method includes determining a count of first vehicles, for plying along the route at the time instance, based on at least the set of constraints.

Abstract: According to various embodiments, a method for gesture recognition using a neural network is provided. The method comprises a training mode and an inference mode. In the training mode, the method includes: passing a dataset into the neural network; and training the neural network to recognize a gesture of interest, wherein the neural network includes a convolution-nonlinearity step and a recurrent step. The inference mode, the method includes: passing a series of images into the neural network, wherein the series of images is not part of the dataset; and recognizing the gesture of interest in the series of images.

Abstract: According to various embodiments, a method for controlling drone movement for object tracking is provided. The method comprises: receiving a position and a velocity of a target; receiving sensor input from a drone; determining an angular velocity and a linear velocity for the drone; and controlling movement of the drone to track the target using the determined angular velocity and linear velocity.

Abstract: According to various embodiments, a method for neural network dataset enhancement is provided. The method comprises taking a first picture using a fixed camera of just a set background, then taking a second picture with the fixed camera. The second picture is taken with the set background and an object of interest in the picture frame. The method further comprises extracting pixels of the image of the object of interest from the second picture, and superimposing the pixels of the image of the object of interest onto a plurality of different images.

Abstract: According to various embodiments, a method for deep-learning based object tracking by a neural network is provided. The method comprises a training mode and an inference mode. In the training mode, the method includes: passing a dataset into the neural network, the dataset including a first image frame and a second image frame; and training the neural network to accurately output a similarity measure for the first and second image frames. In the inference mode, the method includes: passing a plurality of image frames into the neural network, wherein the plurality of image frames is not part of the dataset, the plurality of image frames comprising a first image frame and a second image frame, the first image frame including a first bounding box around an object and the second image frame including a second bounding box around an object; and automatically determining whether the object bounded by the first bounding box is the same object as the object bounded by the second bounding box.

Abstract: According to various embodiments, a method for gesture recognition using a neural network is provided. The method comprises a training mode and an inference mode. In the training mode, the method includes: passing a dataset into the neural network; and training the neural network to recognize the fingers of a training user and a gesture of interest, wherein the neural network includes a convolution-nonlinearity step and a recurrent step. In the inference mode, the method includes: passing a series of images into the neural network, wherein the series of image is a virtual reality feed that includes the hands of a VR user; and recognizing the fingers of the VR user and gestures of interests from the series of images.

Abstract: According to various embodiments, a method for distance and velocity estimation of detected objects is provided. The method includes receiving an image that includes a minimal bounding box around an object of interest. The method also includes calculating a noisy estimate of the physical position of the object of interest relative to a source of the image. Last, the method includes producing a smooth estimate of the physical position of the object of interest using the noisy estimate.

Abstract: In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.

Abstract: The disclosed embodiments illustrate methods and systems for scheduling one or more first vehicles along a route in a transportation system. The method includes determining one or more demands pertaining to a commutation along a route, where the route comprises at least one pair of stations such that there is a unique path between the pair of stations. The method further includes determining a set of constraints associated with the transportation system. The set of constraints are based on at least a count of second vehicles plying on one or more routes in the transportation system at a time instance, a capacity of a first vehicle, and a performance metric of the transportation system. Further, the method includes determining a count of first vehicles, for plying along the route at the time instance, based on at least the set of constraints.

Abstract: Universal serial bus (USB) devices may be redirected to a server to create USB virtual devices. Each of the USB devices to be redirected may have one or more partitions but each partition may not be suitable for redirection. A user may select one or more partitions for redirection. The server may read the selected partitions. The server will create virtual volumes for only the selected partitions and will mount a file system for each of the selected partitions only.

Abstract: Techniques described herein are directed towards dynamically adding new pickup orders to an existing route structure. In at least some embodiments, a service provider separates a number of vendors into separate clusters based on one or more attributes. For example, the vendors may be separated by geographic region. The service provider may then identify a set of routes that are associated with each of the vendor clusters and run optimization techniques on that set of routes. The set of routes may be filtered based on attributes of the route. In some embodiments, the service provider may make a route alteration that accommodates the new pickup order.