Abstract: A computer system for parsing bulk message information into intelligent question retrieval models receives text-based data associated with a particular user. The system encodes the word into a context-dependent vector, wherein the context-dependent vector indicates the meaning of the word across a semantic space. The system also identifies within a context-independent database a context-independent vector that is associated with the word. Further, the system generates an objective output by combining the context-dependent vector and the context-independent vector. Further still, the system generates a sentence encoding representation by processing at least a portion of the text-based data through a high-level feature embedded convolutional semantic model to generate numerical representations of questions and answers within the text-based dataset. The sentence encoding representation is generated at least in part based upon the objective output.

Abstract: An air-cushioned vehicle or hovercraft includes a three-dimensional frame comprising a latticework of tubular members. A pressure plate is coupled to the underside of the frame and includes at least one aperture therein and a skirt attached adjacent a periphery thereof. At least one lifting fan is positioned relative to the at least one aperture to generate a current of air therethrough. The frame may be configured to generate a visual indication of damage thereto. Methods of making and air-cushion vehicle are disclosed. Methods of using an air-cushion vehicle are also disclosed.

Abstract: An air-cushioned vehicle or hovercraft includes a three-dimensional frame comprising a latticework of tubular members. A pressure plate is coupled to the underside of the frame and includes at least one aperture therein and a skirt attached adjacent a periphery thereof. At least one lifting fan is positioned relative to the at least one aperture to generate a current of air therethrough. The frame may be configured to generate a visual indication of damage thereto. Methods of making and air-cushion vehicle are disclosed. Methods of using an air-cushion vehicle are also disclosed.

Abstract: An apparatus (10) may implement a method (160) for creating (162) a data-domain sampled network (206). In certain embodiments, a method in accordance with the invention may define (166) a data domain, provide (172) points in a data-domain space or sub-space, followed by analyzing (174) data in the data domain (100). Analysis (174) may involve selecting (175) the dimensions or variables of interest, evaluating (176) or determining (176) the cycles per dynamic range, selecting (177) an interpolation method, and selecting (178) a number of sample points (223) in each respective dimension (227, 228). Providing (180) a data-domain network (206) typically includes applying (184) sampling theory to a native data domain (100). Calculating (188) waits for this by an interpolation module (212) specifies the data-domain network in its native domain (100).

Abstract: Dissolved selenium is removed from contaminated water by treating the water in a reactor containing selected endemic and other selenium reducing organisms. Microbes may be isolated from the specific water or imported from other selenium contaminated water, The microbes are then screened for ability to reduce selenium under the site specific environmental conditions. The selected microbes are optimized for selenium reduction, then established in a high density biofilm within a reactor. The selenium contaminated water is passed through the reactor with optimized nutrient mix added as needed. The elemental selenium is precipitated and removed from the water.

Abstract: A trainable, state-sampled, network controller (TSSNC) or state-sampled controller (SSC) requires little information regarding a plant (as with neural networks), but can use what information is available (as in classical controllers), and provides a linear network (as for CMAC) improving calculation speeds. A form of a governing differential equation characterizing a plant may include parameters and their derivatives of various orders as variables combined in linear and nonlinear terms. Classical control theory, and a method such as a Fourier transform of governing equations, may provide 8a form of a control law, linear in certain weights or coefficients. Knowledge of coefficients is not required for either the form of the governing equations or the form of the control law. An optimization method may be used to train the SSC, defining a table of weights (contributions to coefficients) to be used in the matrix equation representing the control law the solution yielding a control output to the plant.