Adjacency list

About: Adjacency list is a research topic. Over the lifetime, 4419 publications have been published within this topic receiving 78449 citations.

Business rules user interface for development of adaptable enterprise applications

Abstract: Methods and apparatus, including computer program products, for interacting with a user to define business rules in a declarative manner. A rule set is displayed (FIG. 6, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622) as an editable list of conditions (606) and an editable list of actions (608), the conditions and actions being linked to each other by the combination of an editable list of if-values (612) and an editable list of then-values (614). If-values and then-values are explicitly linked to each other, conditions and if-values are explicitly linked to each other, and then-values and actions are explicitly linked to each other in the displayed lists. Inputs can be received from a user editing one or more of the editable lists. In particular implementations, the editable lists are displayed in a matrix structure of four quadrants (606, 608, 612, 614). Adjacency of if-values and then-values is used to represent a rule.

Semi-supervised Learning by Sparse Representation.

Abstract: In this paper, we present a novel semi-supervised learning framework based on `1 graph. The `1 graph is motivated by that each datum can be reconstructed by the sparse linear superposition of the training data. The sparse reconstruction coefficients, used to deduce the weights of the directed `1 graph, are derived by solving an `1 optimization problem on sparse representation. Different from conventional graph construction processes which are generally divided into two independent steps, i.e., adjacency searching and weight selection, the graph adjacency structure as well as the graph weights of the `1 graph is derived simultaneously and in a parameter-free manner. Illuminated by the validated discriminating power of sparse representation in [16], we propose a semi-supervised learning framework based on `1 graph to utilize both labeled and unlabeled data for inference on a graph. Extensive experiments on semi-supervised face recognition and image classification demonstrate the superiority of our proposed semi-supervised learning framework based on `1 graph over the counterparts based on traditional graphs.

Topological Structures for Geometric Modeling

Abstract: Geometric modeling technology for representing three-dimensional objects has progressed from early wireframe representations, through surface representations, to the most recent representation, solid modeling. Each of these forms has many possible representations. The boundary representation technique, where the surfaces, edges, and vertices of objects are represented explicitly, has found particularly wide application. Many of the more sophisticated versions of boundary representations explicitly store topological information about the positional relationships among surfaces, edges, and vertices. This thesis places emphasis on the use of topological information about the shape being modeled to provide a framework for geometric modeling boundary representations and their implementations, while placing little constraint on the actual geometric surface representations used. The major thrusts of the thesis fall into two areas of geometric modeling. First, a theoretical basis for two-manifold solid modeling boundary topology representation is developed. The minimum theoretical and minimum practical topological adjacency information required for the unambiguous topological representation of manifold solid objects is determined. This provides a basis for checking the correctness of existing and proposed representations. The correctness of the winged edge structure is also explored, and several new representations which have advantages over existing techniques are described and their sufficiency verified. Second, a non-two-manifold boundary geometric modeling topology representation is developed which allows the unified and simultaneous representation of wireframe, surface, and solid modeling forms, while featuring a representable range beyond what is achievable in any of the previous modeling forms. In addition to exterior surface features, interior features can be modeled, and non-manifold features can be represented directly. A new data structure, the Radial Edge structure, which provides access to all topological adjacencies in a non-manifold boundary representation, is described and its completeness is verified. A general set of non-manifold topology manipulation operators is also described which is independent of a specific data structure and is useful for insulating higher levels of geometric modeling functionality from the specifics and complexities of underlying data structures. The coordination of geometric and topological information in a geometric modeling system is also discussed.

A Set-Membership Approach to Event-Triggered Filtering for General Nonlinear Systems Over Sensor Networks

Abstract: This paper is concerned with the distributed set-membership filtering problem for a class of general discrete-time nonlinear systems under event-triggered communication protocols over sensor networks. To mitigate the communication burden, each intelligent sensing node broadcasts its measurement to the neighboring nodes only when a predetermined event-based media-access condition is satisfied. According to the interval mathematics theory, a recursive distributed set-membership scheme is designed to obtain an ellipsoid set containing the target states of interest via adequately fusing the measurements from neighboring nodes, where both the accurate estimate on Lagrange remainder and the event-based media-access condition are skillfully utilized to improve the filter performance. Furthermore, such a scheme is only dependent on neighbor information and local adjacency weights, thereby fulfilling the scalability requirement of sensor networks. In addition, an optimization algorithm is developed to minimize the trace of the estimated ellipsoid set, and the effect from the adopted event-triggered threshold is thoroughly discussed as well. Finally, a simulation example is utilized to illustrate the usefulness of the proposed distributed set-membership filtering scheme.