Abstract: Disclosed in some examples are methods, systems, and machine readable mediums which provide summaries of topics determined within a corpus of documents. These summaries may be used by customer service associates, analysts, or other users to quickly determine both topics discussed and contexts of those topics over a large corpus of text. For example, a corpus of documents may be related to customer complaints and the topics may be summarized to produce summaries such as “credit report update due to stolen identity.” These summarizations may be used to efficiently spot trends and issues.

Abstract: Systems and methods for improving initial results of a reinforcement learning model are described herein. In an embodiment, a server computer initiates a reinforcement learning model for a modeled system. While executing the reinforcement learning model, the server computer computes a first result value for a particular action using the reinforcement learning model and a second result value for the particular action using a heuristic separate from the reinforcement model. Based, at least in part, on the first result value for the particular action and the second result value for the particular action, the server computer performs the particular action. The server computer determining a result from performing the particular action and updates the reinforcement learning model.

Abstract: Systems and methods for improving initial results of a reinforcement learning model are described herein. In an embodiment, a server computer initiates a reinforcement learning model for a modeled system. While executing the reinforcement learning model, the server computer computes a first result value for a particular action using the reinforcement learning model and a second result value for the particular action using a heuristic separate from the reinforcement model. Based, at least in part, on the first result value for the particular action and the second result value for the particular action, the server computer performs the particular action. The server computer determining a result from performing the particular action and updates the reinforcement learning model.

Abstract: An emulation system receives a swipe along a finger sensor to set a computer display in motion. After the swipe is completed, the display continues along its previous path. Depending on their direction, subsequent swipes can be used to accelerate or decelerate the motion. Gradually, the display decelerates. In one embodiment, this deceleration simulates an inertial decay, providing the user with a pleasing display that gradually rolls to a stop. The deceleration is modeled on a joystick return-to-home inertial decay, allowing the user greater control when navigating over the display. The finger sensor is used to emulate different electronic devices, such as a mouse, a scroll wheel, and a rotating wheel.

Abstract: An emulation system receives a swipe along a finger sensor to set a computer display in motion. After the swipe is completed, the display continues along its previous path. Depending on their direction, subsequent swipes can be used to accelerate or decelerate the motion. Gradually, the display decelerates. In one embodiment, this deceleration simulates an inertial decay, providing the user with a pleasing display that gradually rolls to a stop. The deceleration is modeled on a joystick return-to-home inertial decay, allowing the user greater control when navigating over the display. The finger sensor is used to emulate different electronic devices, such as a mouse, a scroll wheel, and a rotating wheel.

Abstract: Embodiments of the invention build models to predict the likelihood of entities that operate in a given identifier space also operating in a disjoined identifier space based on a source panel of entities that operate in one or both of the identifier spaces. In operation, a model building engine builds a model based on features associated with the source panel and features associated with standard populations in the given identifier space. The model is used to determine whether the target entity is more similar to those entities in the source panel that operate only in the given identifier space or those entities in the source panel that operate in both identifier spaces.

Abstract: A deformed dipole is suggested with trace elements configured for wideband LTE and GPS operation. The deformed dipole comprises a first dipole conductor disposed on a first surface and first side of the circuit board and a second dipole conductor disposed on an opposite surface and opposite side of the circuit board.

Abstract: Embodiments of the invention build models to predict the likelihood of entities that operate in a given identifier space also operating in a disjoined identifier space based on a source panel of entities that operate in one or both of the identifier spaces. In operation, a model building engine builds a model based on features associated with the source panel and features associated with standard populations in the given identifier space. The model used to determine whether the target entity is more similar to those entities in the source panel that operate only in the given identifier space or those entities in the source panel that operate in both identifier spaces.

Abstract: A method in accordance with the present invention is used to organize minutiae and other identifying characteristics of a patterned object to verify a user. In one embodiment, the method includes matching minutiae points of the patterned object in one more sets with minutiae points of the patterned object in a candidate set. The method also includes adjusting a confidence value of each minutia point in the one or more sets based on results of the matching. The method also includes organizing the one or more sets based on the confidence values, such as by ordering the minutiae in the one or more sets based on the confidence values, or by deleting from the one or more sets any identifying characteristics with a confidence value below a threshold.

Abstract: A method in accordance with the present invention is used to organize minutiae and other identifying characteristics of a patterned object to verify a user. In one embodiment, the method includes matching minutiae points of the patterned object in one more sets with minutiae points of the patterned object in a candidate set. The method also includes adjusting a confidence value of each minutia point in the one or more sets based on results of the matching. The method also includes organizing the one or more sets based on the confidence values, such as by ordering the minutiae in the one or more sets based on the confidence values, or by deleting from the one or more sets any identifying characteristics with a confidence value below a threshold.

Abstract: A snag-free and stress-free fish catcher includes a supporting frame, a fish pocket, having a fish cavity, supported by the supporting frame, and a tension resting bed, having a tension surface, supported within the fish cavity, wherein the tension surface of the tension resting bed has a predetermined tension larger than a weight of the fish. Therefore, when the fish is captured within the fish cavity of the fish pocket, the fish lies on the tension surface of the tension resting bed in a flatten manner.

Abstract: A snag-free and stress-free fish catcher includes a supporting frame, a fish pocket, having a fish cavity, supported by the supporting frame, and a tension resting bed, having a tension surface, supported within the fish cavity, wherein the tension surface of the tension resting bed has a predetermined tension larger than a weight of the fish. Therefore, when the fish is captured within the fish cavity of the fish pocket, the fish lies on the tension surface of the tension resting bed in a flatten manner.

Abstract: A snag-free and stress-free fish catcher includes a supporting frame, a fish pocket, having a fish cavity, supported by the supporting frame, and a tension resting bed, having a tension surface, supported within the fish cavity, wherein the tension surface of the tension resting bed has a predetermined tension larger than a weight of the fish. Therefore, when the fish is captured within the fish cavity of the fish pocket, the fish lies on the tension surface of the tension resting bed in a flatten manner.