Abstract: An automated Automatic Teller Machine (ATM) exception processing method, system, and recording medium for facilitating completion of an ATM deposit transaction upon an occurrence of an exception. The method includes remotely monitoring an ATM for the exception by an automated ATM monitoring system over a first communication network, and automatically reporting a detected exception resulting in a failed ATM deposit transaction, by a processor in the automated ATM monitoring system, to both a customer processing system and to a reconstruction transaction system over a second communication network. The process also includes processing a customer claim by the customer processing system based on the failed ATM deposit transaction, and reconstructing the failed ATM deposit transaction by the reconstruction transaction system. The method further incudes reconciling the customer claim with the reconstructed ATM deposit transaction.

Abstract: This disclosure provides compounds and compositions useful for treating spirochete diseases such as Lyme disease as well as baits containing the compounds.

Abstract: In one example in accordance with the present disclosure, a system is described. The system includes at least one directional antenna to 1) emit energy waves towards a mass in which an object is disposed and 2) receive reflected signals from a resonator disposed on the object as the mass is moved relative to the directional antenna. The system also includes a controller to, based on received reflected signals, determine a pose of the object within the mass.

Abstract: An apparatus may identify each object of a set of objects included in a first location, wherein the set of objects includes at least one IoT device and at least one other object. The apparatus may determine a first set of attributes for the at least one IoT device. The apparatus may store registration information associated with the at least one IoT device, the registration information including a first identifier associated with the at least one IoT device and including the first set of attributes for the at least one IoT device. The apparatus may obtain an input. The apparatus may determine whether the input corresponds to the at least one IoT device based on the registration information associated with the at least one IoT device. The apparatus may control the at least one IoT device when the input corresponds to the at least one IoT device.

Abstract: The invention provides compositions and methods for the breeding, production, processing and use of specialty cannabis.

Abstract: Described herein are apparatus and methods for performing inference into the identity of an object. For an object of a plurality of objects, the apparatus receives feature-location information identifying a feature at first location on a first object of the plurality and a feature at a second location on a second object of the plurality. The apparatus activates a first set of location cells that collectively represent the first location on the first object corresponding to a feature on an object of the plurality of objects and a second set of location cells that collectively represent the second location on the second object corresponding to a feature on an object of the plurality of objects. The apparatus activates a set of displacement cells representing displacement of the first set of location cells and the second set of location cells and identifies one or more objects by processing the displacement cells.

Abstract: An inference system performs inference, such as object recognition, based on sensory inputs generated by sensors and control information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The control information describes movement of the sensors or known locations of the sensors relative to a reference point. For a particular object, an inference system learns a set of object-location representations of the object. An object-location representation is a unique characterization of an object-centric location relative to the particular object. The inference system also learns a set of feature-location representations associated with the object-location representation that indicate presence of features at the corresponding object-location pair.

Abstract: An inference system performs inference, such as object recognition, based on sensory inputs generated by sensors and control information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The control information describes movement of the sensors or known locations of the sensors relative to a reference point. For a particular object, an inference system learns a set of object-location representations of the object. An object-location representation is a unique characterization of an object-centric location relative to the particular object. The inference system also learns a set of feature-location representations associated with the object-location representation that indicate presence of features at the corresponding object-location pair.

Abstract: According to aspects of the present disclosure there are provided methods and apparatus for providing a user-centric application comprising a plurality of service modules, the method comprising identifying available resources associated with each of a plurality of edge devices in the local network, identifying a first device of the local network, the first device offering a user input service, identifying a second device of the local network, the second device to receive an output event and present the output event for consumption by the user, for each service module, each service module to provide a portion of a functionality of the user-centric application and having an associated resource request, deploying the service module to an edge device based on the associated resource request and the identified available resources, and configuring data flows between the deployed service modules, the first device, and the second device to realize the user-centric application.

Abstract: Examples of methods for controlling machine learning model structures are described herein. In some examples, a method includes controlling a machine learning model structure. In some examples, the machine learning model structure may be controlled based on an environmental condition. In some examples, the machine learning model structure may be controlled to control apparatus power consumption associated with a processing load of the machine learning model structure.

Abstract: Aspects of the present application comprise an edge-based polymorphic network and associated agentless security solutions. Present systems and methods may effectively shapeshift the network through network topology management, node templates, link templates, and network packet manipulation. Using agentless techniques, the network may be reshaped when attacks are detected in a manner that impacts only the attacker, and an agentless color theory security solution may be used to dynamically protect network traffic.

Abstract: The invention provides compositions and methods for the breeding, production, processing and use of specialty Cannabis.

Abstract: The present disclosure provides a new and distinct hemp cultivar designated ‘05.09.24.S1’. The present disclosure relates to seeds of the hemp plant ‘05.09.24.S1,’ to plants and parts of the hemp plant ‘05.09.24.S1,’ and to methods for producing a hemp plant by crossing the hemp plant ‘05.09.24.S1’ with itself or other cannabis plants. The disclosure further relates to the morphological and physiological characteristics of the new and distinct hemp cultivar and its uses.

Abstract: A sparse neural network is trained such that weights or layer outputs of the neural network satisfy sparsity constraints. The sparsity is controlled by pruning one or more subsets of weights based on their signal-to-noise ratio (SNR). During the training process, an inference system generates outputs for a current layer by applying a set of weights for the current layer to a layer output of a previous layer. The set of weights for the current layer may be modeled as random variables sampled from probability distributions. The inference system determines a loss function and updates the set of weights by backpropagating error terms obtained from the loss function. This process is repeated until a convergence criterion is reached. One or more subsets of weights are then pruned based on their SNR depending on sparsity constraints for the weights of the neural network.

Abstract: The present disclosure provides a new and distinct hemp cultivar designated ‘095101’. The present disclosure relates to seeds of the hemp plant ‘095101,’ to plants and parts of the hemp plant ‘095101,’ and to methods for producing a hemp plant by crossing the hemp plant ‘095101’ with itself or other cannabis plants. The disclosure further relates to the morphological and physiological characteristics of the new and distinct hemp cultivar and its uses.

Abstract: Examples for global motion suppression in an event-driven camera are described herein. In some examples, global motion of an event-driven camera is detected based on a plurality of pixels of an event-driven image sensor detecting events. The global motion may be suppressed by event-driven image sensor circuitry in response to detecting the global motion.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and apparatus for operating a computational network are provided. The apparatus may obtain a set of navigational instructions describing a route to a destination. The apparatus may obtain first image data through a first camera that is oriented toward the route, the first image data depicting a first scene associated with the route. The apparatus may determine a first field of view associated with a user that is navigating the route to the destination based on a first sensor that is oriented toward the user. The apparatus may identify at least one salient object represented in the first scene based on the first field of view. The apparatus may output instructional information describing a first navigational instruction of the set of navigational instructions with reference to the at least one salient object.

Abstract: In one example in accordance with the present disclosure, a system is described. The system includes at least one directional antenna to 1) emit energy waves towards a mass in which an object is disposed and 2) receive reflected signals from a resonator disposed on the object as the mass is moved relative to the directional antenna. The system also includes a controller to, based on received reflected signals, determine a pose of the object within the mass.

Abstract: An example of an apparatus is provided. The apparatus includes an input device to receive camera data. The camera data is associated with a printing device. The apparatus further includes an analysis engine to analyze the camera data with a convolutional neural network model to identify an issue. The apparatus also includes a resolution engine to determine a solution for the issue.

Abstract: In one example in accordance with the present disclosure, a system is described. The system includes a movement device to move a mass with an object disposed therein. A receiver of the system receives accelerometer data from at least one accelerometer disposed within the mass in which the object is disposed. A controller of the system determines a pose of the object within the mass based on received accelerometer data.