14459246
On optimal embeddings into incomplete hypercubes
The authors show the embeddings of various types of n-node incomplete binary trees into n-node or (n+1)-node composite hypercubes with dilation of at most 2. They also present lower bound proofs showing optimality of the dilation. They characterize the class of incomplete binary trees which are subgraphs of composite hypercubes. They present dilation 1 embedding of a two-dimensional n-node mesh, where one dimension is a power of two, into its optimal n-node composite hypercube. When neither dimension is a power of two, it is shown that a dilation 1 embedding is not possible; thereby characterizing the class of two-dimensional meshes that can be embedded into composite hypercubes with dilation 1. All two-dimensional meshes are shown to be embeddable with dilation 1 if expansion greater than 1 but less than 2 is allowed. The authors also consider two types of incomplete meshes and their embeddings into their optimal composite hypercubes.<<ETX>>
10426404
Load balanced tree embeddings
Abstract When an n -processor architecture T is embedded into an m -processor architecture H with n > m and every processor of H is assigned at least ⌞n/m⌟ and at most [ n / m ] processors of T , the embedding has a balanced processor load. We present efficient embeddings with a balanced load for the case when both architectures are complete binary trees. We show that T can be embedded into H with a dilation of 1 and congestion of at most min{[ n / m ], 2 log n }. We also consider embeddings that achieve a balanced l/i load; i.e. every processor of H simulates at most [( n + 1)/2 m ] leaves and at most [( n − 1)/2 m ] interior processors of T . We present an embedding that achieves a balanced l/i load, a dilation of 2[log log m ] + 1 and a congestion of O(log n ). We show that every embedding strategy achieving a balanced l/i load must have a dilation of at least 3. We also consider the embedding problem when every edge of T has a weight associated with it.
17006088
An Efficient MAP Classifier for Sensornets
The classification phase is computationally intensive and frequently recurs in tracking applications in sensor networks. Most related work uses traditional signal processing classifiers, such as Maximum A Posterior (MAP) classifier. Naive formulations of MAP are not feasible for resource constraint sensornet nodes. In this paper, we study computationally efficient methods for classification. We propose to use one-sided Jacobi iterations for eigen value decomposition of the covariance matrices, the inverse of which are needed in MAP classifier. We show that this technique greatly simplifies the execution of MAP classifier and makes it a feasible and efficient choice for sensornet applications.
41859107
Preconditioned Conjugate Gradient Solver for Large Sparse Systems on MPI
195611449
Scheduling tasks in multiprocessor systems using evolutionary strategies
Scheduling tasks in multiprocessor systems is a difficult problem. The paper describes a method based upon evolutionary strategies (using genetic algorithms) to aid in finding good task assignments. The technique is illustrated by scheduling a digital signal processing algorithm on a two processor distributed system.<<ETX>>
35215015
Coarse Grain Preconditioned Conjugate Gradient Solver for Large Sparse Systems
We explore a coarse grain parallel algorithm for the solution of large sparse linear systems with symmetric positive definite matrix, based on a preconditioned conjugate gradient method.
13925350
Embedding k-outerplanar graphs into ℓ1
We show that the shortest-path metric of any k-outerplanar graph, for any fixed k, can be approximated by a probability distribution over tree metrics with constant distortion, and hence also embedded into l1 with constant distortion. These graphs play a central role in polynomial time approximation schemes for many NP-hard optimization problems on general planar graphs, and include the family of weighted k × n planar grids.This result implies a constant upper bound on the ratio between the sparsest cut and the maximum concurrent flow in multicommodity networks for k-outerplanar graphs, thus extending a classical theorem of Okamura and Seymour [26] for outerplanar graphs, and of Gupta et al. [17] for treewidth-2 graphs. In addition, we obtain improved approximation ratios for k-outerplanar graphs on various problems for which approximation algorithms are based on probabilistic tree embeddings. We also conjecture that our random tree embeddings for k-outerplanar graphs can serve as a building block for more powerful l1 embeddings in future.
8765967
A Dual-Pathway Neural Network Model of Control Relinquishment in Motor Skill Learning
Cognitive psychologists have long recognized that the acquisition of a motor skill involves a transition from attention-demanding controlled processing to more fluent automatic processing. Neuroscientific studies suggest that controlled and automatic processing rely on two largely distinct neural pathways. The controlled pathway, which includes the prefrontal cortex, is seen as acquiring declarative representations of skills. In comparison, the automatic pathway is thought to develop procedural representations. Automaticity in motor skill learning involves a reduction in dependence on frontal systems and an increased reliance on the automatic pathway. In this paper, we propose a biologically plausible computational model of motor skill automaticity. This model offers a dual-pathway neurocomputational account of the translation of declarative knowledge into procedural knowledge during motor learning. In support of the model, we review some previously reported human experimental results involving the learning of a sequential key pressing task, and we demonstrate, through simulation, howthe model provides a parsimonious explanation for these results.
7447998
Semiorder database for complex activity recognition in multi-sensory environments
A prototype semiorder database used for activity recognition in multi-sensory monitoring environments is described. Activities are spatio-temporal compositions of events, which are a type of atomic semantic units for such compositions. The focus is on the temporal composition of activities from events in the presence of bounded duration of temporal uncertainty in an event occurrence. Such temporal uncertainty forces the concurrency between event occurrences to be intransitive. Under certain assumptions, a subclass of partial orders, known as semiorders, models such intransitive concurrency appropriately. The semiorder database stores events and their semiorder temporal order of occurrences. A semiorder data model and the corresponding query language that embeds a semiorder pattern language are the main constituents of the semiorder database. We demonstrate this database and queries for activity recognition in a real time environment. The demonstration also includes a transducer subsystem for detection of events.
206873246
Fast error diagnosis for combinational verification
We address the problem of localizing error sites in a combinational circuit that has been shown to be inequivalent to its specification. In the typical case, it is not possible to identify the error location exactly. We propose a novel diagnosis strategy of gradually increasing the level of detail in the analysis algorithm to ultimately derive a small list of potential error sites in a short time. Our techniques combine the use of simulation, BDDs, and SAT in a novel way to achieve the goal. A limitation of many previous approaches has been that they have been constrained to a specific error model. No such assumption is made in our work. We show through experimental results that these techniques are successful in that the final set of error sites derived is small, contains the actual error sites and is derived in a reasonable amount of time.
7475027
Metric embeddings with relaxed guarantees
We consider the problem of embedding finite metrics with slack: we seek to produce embeddings with small dimension and distortion while allowing a (small) constant fraction of all distances to be arbitrarily distorted. This definition is motivated by recent research in the networking community, which achieved striking empirical success at embedding Internet latencies with low distortion into low-dimensional Euclidean space, provided that some small slack is allowed. Answering an open question of Kleinberg, Slivkins, and Wexler (2004), we show that provable guarantees of this type can in fact be achieved in general: any finite metric can be embedded, with constant slack and constant distortion, into constant-dimensional Euclidean space. We then show that there exist stronger embeddings into /spl lscr//sub 1/ which exhibit gracefully degrading distortion: these is a single embedding into /spl lscr//sub 1/ that achieves distortion at most O(log 1//spl epsi/) on all but at most an /spl epsi/ fraction of distances, simultaneously for all /spl epsi/ > 0. We extend this with distortion O(log 1//spl epsi/)/sup 1/p/ to maps into general /spl lscr//sub p/, p /spl ges/ 1 for several classes of metrics, including those with bounded doubling dimension and those arising from the shortest-path metric of a graph with an excluded minor. Finally, we show that many of our constructions are tight, and give a general technique to obtain lower bounds for /spl epsi/-slack embeddings from lower bounds for low-distortion embeddings.