17425751
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data
We address statistical classifier design given a mixed training set consisting of a small labelled feature set and a (generally larger) set of unlabelled features. This situation arises, e.g., for medical images, where although training features may be plentiful, expensive expertise is required to extract their class labels. We propose a classifier structure and learning algorithm that make effective use of unlabelled data to improve performance. The learning is based on maximization of the total data likelihood, i.e. over both the labelled and unlabelled data subsets. Two distinct EM learning algorithms are proposed, differing in the EM formalism applied for unlabelled data. The classifier, based on a joint probability model for features and labels, is a "mixture of experts" structure that is equivalent to the radial basis function (RBF) classifier, but unlike RBFs, is amenable to likelihood-based training. The scope of application for the new method is greatly extended by the observation that test data, or any new data to classify, is in fact additional, unlabelled data - thus, a combined learning/classification operation - much akin to what is done in image segmentation - can be invoked whenever there is new data to classify. Experiments with data sets from the UC Irvine database demonstrate that the new learning algorithms and structure achieve substantial performance gains over alternative approaches.
45307048
Image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh
Recently, we developed a sequence-based minimum mean-squared error (MMSE) estimator for decoding quantized data transmitted over noisy channels. The method effectively views the encoder and noisy channel tandem as a discrete hidden Markov model (HMM), with transmitted indices the unknown states and received indices the observable symbols. Here, we extend this 1D approach to images, using a Markov mesh random field to model the encoded image. Our decoder is based on an approximate forward/backward algorithm for calculating pixel "label probabilities" in Markov meshes which may also have application to image labeling and segmentation. For a DPCM-based image coding system and a high error-rate channel, the new decoder obtains significant performance gains, both objective and visually discernable, over the standard decoder, as well as over several other competing techniques.
16341198
Global Symbolic Maps from Local Navigation
In order to navigate autonomously, most robot systems are provided with some sort of global terrain map. To make storage practical, these maps usually have a high-level symbolic representation of the terrain. The robot's symbolic map is then used to plan a local path. This paper describes a system which uses the reverse (and perhaps more natural) process. This system processes local sensor data in such a way as to allow efficient, reactive local navigation. A byproduct of this navigation process is an abstraction of the terrain information which forms a global symbolic terrain map of the terrain through which the robot has passed. Since this map is in the same format as that used by the local navigation system, the map is easy for the system to use, augment, or correct. Compared with the data from which the maps are created, the maps are very space efficient, and can be modified, or used for navigation in real-time. Experiments with this system, both in simulation, and with a real robot operating in natural terrain, are described.
17886110
Using a single address space operating system for distributed computing and high performance
Recent 64-bit microprocessors have made a huge 18.4 quintillion byte address space potentially available to programs. This has led to the design of Operating Systems that provide a single virtual address space in which all code and data reside in and that spans all levels of storage and all nodes of a distributed system. These operating systems called SASOSs, have characteristics that can be used to support synchronization and coherency in a distributed system in ways that provide an improved program development environment and higher performance than that available from conventional operating systems. Sombrero, our SASOS design, makes use of its hardware support for object-grained protection, separate thread related protection domains and implicit protection domain crossing to provide synchronization and coherency support for distributed object copy set management not available in SASOSs built on stock processors. Its design, which provides direct system level support for object oriented programming includes a number of system architectural features targeted at modern distributed computing.
7600116
Cut-elimination for a logic with definitions and induction
Abstract In order to reason about specifications of computations that are given via the proof search or logic programming paradigm one needs to have at least some forms of induction and some principle for reasoning about the ways in which terms are built and the ways in which computations can progress. The literature contains many approaches to formally adding these reasoning principles with logic specifications. We choose an approach based on the sequent calculus and design an intuitionistic logic FOλ Δ N that includes natural number induction and a notion of definition. We have detailed elsewhere that this logic has a number of applications. In this paper we prove the cut-elimination theorem for FOλ Δ N , adapting a technique due to Tait and Martin-Lof. This cut-elimination proof is technically interesting and significantly extends previous results of this kind.
34355186
Achromatic numbers and graph operations
Abstract In this paper we investigate the achromatic number of the disjoint union ofgraphs and the achromatic number of the categorical product of graphs. We obtain the best possible upper and lower bounds for the union of graphs and the best possible lower bound for the product of graphs. Finally we show that the achromatic number of the product of graphs is bounded above.
5486748
Scaling internet routers using optics
Routers built around a single-stage crossbar and a centralized scheduler do not scale, and (in practice) do not provide the throughput guarantees that network operators need to make efficient use of their expensive long-haul links. In this paper we consider how optics can be used to scale capacity and reduce power in a router. We start with the promising load-balanced switch architecture proposed by C-S. Chang. This approach eliminates the scheduler, is scalable, and guarantees 100% throughput for a broad class of traffic. But several problems need to be solved to make this architecture practical: (1) Packets can be mis-sequenced, (2) Pathological periodic traffic patterns can make throughput arbitrarily small, (3) The architecture requires a rapidly configuring switch fabric, and (4) It does not work when linecards are missing or have failed. In this paper we solve each problem in turn, and describe new architectures that include our solutions. We motivate our work by designing a 100Tb/s packet-switched router arranged as 640 linecards, each operating at 160Gb/s. We describe two different implementations based on technology available within the next three years.