174 research outputs found
On the Error Resilience of Ordered Binary Decision Diagrams
Ordered Binary Decision Diagrams (OBDDs) are a data structure that is used in
an increasing number of fields of Computer Science (e.g., logic synthesis,
program verification, data mining, bioinformatics, and data protection) for
representing and manipulating discrete structures and Boolean functions. The
purpose of this paper is to study the error resilience of OBDDs and to design a
resilient version of this data structure, i.e., a self-repairing OBDD. In
particular, we describe some strategies that make reduced ordered OBDDs
resilient to errors in the indexes, that are associated to the input variables,
or in the pointers (i.e., OBDD edges) of the nodes. These strategies exploit
the inherent redundancy of the data structure, as well as the redundancy
introduced by its efficient implementations. The solutions we propose allow the
exact restoring of the original OBDD and are suitable to be applied to
classical software packages for the manipulation of OBDDs currently in use.
Another result of the paper is the definition of a new canonical OBDD model,
called {\em Index-resilient Reduced OBDD}, which guarantees that a node with a
faulty index has a reconstruction cost , where is the number of nodes
with corrupted index
Hillview:A trillion-cell spreadsheet for big data
Hillview is a distributed spreadsheet for browsing very large datasets that
cannot be handled by a single machine. As a spreadsheet, Hillview provides a
high degree of interactivity that permits data analysts to explore information
quickly along many dimensions while switching visualizations on a whim. To
provide the required responsiveness, Hillview introduces visualization
sketches, or vizketches, as a simple idea to produce compact data
visualizations. Vizketches combine algorithmic techniques for data
summarization with computer graphics principles for efficient rendering. While
simple, vizketches are effective at scaling the spreadsheet by parallelizing
computation, reducing communication, providing progressive visualizations, and
offering precise accuracy guarantees. Using Hillview running on eight servers,
we can navigate and visualize datasets of tens of billions of rows and
trillions of cells, much beyond the published capabilities of competing
systems
Allocation of Excitation Signals for Generic Identifiability of Linear Dynamic Networks
A recent research direction in data-driven modeling is the identification of
dynamic networks, in which measured vertex signals are interconnected by
dynamic edges represented by causal linear transfer functions. The major
question addressed in this paper is where to allocate external excitation
signals such that a network model set becomes generically identifiable when
measuring all vertex signals. To tackle this synthesis problem, a novel graph
structure, referred to as \textit{directed pseudotree}, is introduced, and the
generic identifiability of a network model set can be featured by a set of
disjoint directed pseudotrees that cover all the parameterized edges of an
\textit{extended graph}, which includes the correlation structure of the
process noises. Thereby, an algorithmic procedure is devised, aiming to
decompose the extended graph into a minimal number of disjoint pseudotrees,
whose roots then provide the appropriate locations for excitation signals.
Furthermore, the proposed approach can be adapted using the notion of
\textit{anti-pseudotrees} to solve a dual problem, that is to select a minimal
number of measurement signals for generic identifiability of the overall
network, under the assumption that all the vertices are excited
On the recognition of complex structures: Computer software using artificial intelligence applied to pattern recognition
An approach to simultaneous interpretation of objects in complex structures so as to maximize a combined utility function is presented. Results of the application of a computer software system to assign meaning to regions in a segmented image based on the principles described in this paper and on a special interactive sequential classification learning system, which is referenced, are demonstrated
A pointer-free data structure for merging heaps and min-max heaps
AbstractIn this paper a data structure for the representation of mergeable heaps and min-max heaps without using pointers is introduced. The supported operations are: Insert, DeleteMax, DeleteMin, FindMax, FindMin, Merge, NewHeap, DeleteHeap. The structure is analyzed in terms of amortized time complexity, resulting in a O(1) amortized time for each operation except for Insert, for which a O(lg n) bound holds
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