Abstract: According to embodiments of the invention, methods, and a computer system for configuring navigational controls in a geometric environment are disclosed. The method may include obtaining a data set for geometric representation on a display, forming one or more reference surfaces, calculating a fit score and a confidence score using one or more of the reference surfaces, and configuring the navigational system to a control scheme when a computational operation on the fit score and the confidence score is outside of a threshold value. The control scheme may be a geometric control scheme, a planar control scheme, and a roaming control scheme.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: According to embodiments of the invention, methods, and a computer system for configuring navigational controls in a geometric environment are disclosed. The method may include obtaining a data set for geometric representation on a display, forming one or more reference surfaces, calculating a fit score and a confidence score using one or more of the reference surfaces, and configuring the navigational system to a control scheme when a computational operation on the fit score and the confidence score is outside of a threshold value. The control scheme may be a geometric control scheme, a planar control scheme, and a roaming control scheme.

Abstract: According to embodiments of the invention, methods, and a computer system for configuring navigational controls in a geometric environment are disclosed. The method may include obtaining a data set for geometric representation on a display, forming one or more reference surfaces, calculating a fit score and a confidence score using one or more of the reference surfaces, and configuring the navigational system to a control scheme when a computational operation on the fit score and the confidence score is outside of a threshold value. The control scheme may be a geometric control scheme, a planar control scheme, and a roaming control scheme.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: According to embodiments of the invention, methods, and a computer system for configuring navigational controls in a geometric environment are disclosed. The method may include obtaining a data set for geometric representation on a display, forming one or more reference surfaces, calculating a fit score and a confidence score using one or more of the reference surfaces, and configuring the navigational system to a control scheme when a computational operation on the fit score and the confidence score is outside of a threshold value. The control scheme may be a geometric control scheme, a planar control scheme, and a roaming control scheme.

Abstract: Embodiments of the present invention provide systems and methods for graphing networks. In one embodiment, a dampening heuristic is utilized to graph networks to increase their stability. Nodes are visualized by finding local and global minima and equilibrium positions. Factors such as the influence of neighboring nodes; attraction and repulsion phases; and dynamism of complex networks are addressed by embodiments of the present invention. The stability of nodes are measured quantitatively using vectors, degree of influence of neighboring nodes on other nodes, and updating dampening heuristics.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: One or more processors receive a dataset that includes a plurality of nodes. One or more processors identify relationships between a plurality of interacting nodes within the dataset. One or more processors determine relationship strength values between a plurality of interacting node pairs within the dataset. One or more processors generate a graphical representation that represents the relationship strength values between the plurality of interacting nodes within the dataset. Interacting node pairs are connected by edges and the edges have a length that correlates with the relationship strength value between the interacting node pairs.

Abstract: In an approach to analyzing a path on a graph, a computer receives a graph comprising a plurality of vertices and edges, each edge linking two vertices. The computer, for each one of said plurality of vertices, analyzes edges linked to said one of plurality of vertices to determine a number of outbound links from said one of plurality of vertices, orders said edges, and assigns a value to each ordered edge. The computer, for the graph, receives a path comprising a plurality of edges linking two of said plurality of vertices through at least one other of said plurality of vertices, encodes said path, the encoding using said number of outbound links and said assigned values of each of said one or more edges linking said two of said plurality of vertices, compresses the encoded path, and analyzes said path on said graph using said compressed, encoded path.

Abstract: One or more processors receive a dataset that includes a plurality of nodes. One or more processors identify relationships between a plurality of interacting nodes within the dataset. One or more processors determine relationship strength values between a plurality of interacting node pairs within the dataset. One or more processors generate a graphical representation that represents the relationship strength values between the plurality of interacting nodes within the dataset. Interacting node pairs are connected by edges and the edges have a length that correlates with the relationship strength value between the interacting node pairs.

Abstract: One or more processors receive a dataset that includes a plurality of nodes. One or more processors identify relationships between a plurality of interacting nodes within the dataset. One or more processors determine relationship strength values between a plurality of interacting node pairs within the dataset. One or more processors generate a graphical representation that represents the relationship strength values between the plurality of interacting nodes within the dataset. Interacting node pairs are connected by edges and the edges have a length that correlates with the relationship strength value between the interacting node pairs.

Abstract: In an approach to analyzing a path on a graph, a computer receives a graph comprising a plurality of vertices and edges, each edge linking two vertices. The computer, for each one of said plurality of vertices, analyzes edges linked to said one of plurality of vertices to determine a number of outbound links from said one of plurality of vertices, orders said edges, and assigns a value to each ordered edge. The computer, for the graph, receives a path comprising a plurality of edges linking two of said plurality of vertices through at least one other of said plurality of vertices, encodes said path, the encoding using said number of outbound links and said assigned values of each of said one or more edges linking said two of said plurality of vertices, compresses the encoded path, and analyzes said path on said graph using said compressed, encoded path.

Abstract: One or more processors receive a dataset that includes a plurality of nodes. One or more processors identify relationships between a plurality of interacting nodes within the dataset. One or more processors determine relationship strength values between a plurality of interacting node pairs within the dataset. One or more processors generate a graphical representation that represents the relationship strength values between the plurality of interacting nodes within the dataset. Interacting node pairs are connected by edges and the edges have a length that correlates with the relationship strength value between the interacting node pairs.

Abstract: One or more processors receive a dataset that includes a plurality of nodes. One or more processors identify relationships between a plurality of interacting nodes within the dataset. One or more processors determine relationship strength values between a plurality of interacting node pairs within the dataset. One or more processors generate a graphical representation that represents the relationship strength values between the plurality of interacting nodes within the dataset. Interacting node pairs are connected by edges and the edges have a length that correlates with the relationship strength value between the interacting node pairs.