3,287 research outputs found
Compositional competitiveness for distributed algorithms
We define a measure of competitive performance for distributed algorithms
based on throughput, the number of tasks that an algorithm can carry out in a
fixed amount of work. This new measure complements the latency measure of Ajtai
et al., which measures how quickly an algorithm can finish tasks that start at
specified times. The novel feature of the throughput measure, which
distinguishes it from the latency measure, is that it is compositional: it
supports a notion of algorithms that are competitive relative to a class of
subroutines, with the property that an algorithm that is k-competitive relative
to a class of subroutines, combined with an l-competitive member of that class,
gives a combined algorithm that is kl-competitive.
In particular, we prove the throughput-competitiveness of a class of
algorithms for collect operations, in which each of a group of n processes
obtains all values stored in an array of n registers. Collects are a
fundamental building block of a wide variety of shared-memory distributed
algorithms, and we show that several such algorithms are competitive relative
to collects. Inserting a competitive collect in these algorithms gives the
first examples of competitive distributed algorithms obtained by composition
using a general construction.Comment: 33 pages, 2 figures; full version of STOC 96 paper titled "Modular
competitiveness for distributed algorithms.
Software for Embedded Control Systems
The research of our team deals with the realization of control schemes on digital computers. As such the emphasis is on embedded control software implementation. Applications are in the field of mechatronic devices, using a mechatronic design approach (the integrated and optimal design of a mechanical system and its embedded control system). The ultimate goal is to support the application developer (i.e. mechatronic design engineer) such that implementing control software according to Ă°o it the first time rightÂż becomes business as usual
Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis
We present a novel unsupervised approach for multilingual sentiment analysis
driven by compositional syntax-based rules. On the one hand, we exploit some of
the main advantages of unsupervised algorithms: (1) the interpretability of
their output, in contrast with most supervised models, which behave as a black
box and (2) their robustness across different corpora and domains. On the other
hand, by introducing the concept of compositional operations and exploiting
syntactic information in the form of universal dependencies, we tackle one of
their main drawbacks: their rigidity on data that are structured differently
depending on the language concerned. Experiments show an improvement both over
existing unsupervised methods, and over state-of-the-art supervised models when
evaluating outside their corpus of origin. Experiments also show how the same
compositional operations can be shared across languages. The system is
available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work
that was accepted for publication in Knowledge-Based System
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
Improving Semantic Composition with Offset Inference
Count-based distributional semantic models suffer from sparsity due to
unobserved but plausible co-occurrences in any text collection. This problem is
amplified for models like Anchored Packed Trees (APTs), that take the
grammatical type of a co-occurrence into account. We therefore introduce a
novel form of distributional inference that exploits the rich type structure in
APTs and infers missing data by the same mechanism that is used for semantic
composition.Comment: to appear at ACL 2017 (short papers
Development of an automated aircraft subsystem architecture generation and analysis tool
Purpose â The purpose of this paper is to present a new computational framework to address future
preliminary design needs for aircraft subsystems. The ability to investigate multiple candidate
technologies forming subsystem architectures is enabled with the provision of automated architecture
generation, analysis and optimization. Main focus lies with a demonstration of the frameworks
workings, as well as the optimizers performance with a typical form of application problem.
Design/methodology/approach â The core aspects involve a functional decomposition, coupled
with a synergistic mission performance analysis on the aircraft, architecture and component levels.
This may be followed by a complete enumeration of architectures, combined with a user defined
technology filtering and concept ranking procedure. In addition, a hybrid heuristic optimizer, based on
ant systems optimization and a genetic algorithm, is employed to produce optimal architectures in both
component composition and design parameters. The optimizer is tested on a generic architecture
design problem combined with modified Griewank and parabolic functions for the continuous space.
Findings â Insights from the generalized application problem show consistent rediscovery of the
optimal architectures with the optimizer, as compared to a full problem enumeration. In addition
multi-objective optimization reveals a Pareto front with differences in component composition as well
as continuous parameters.
Research limitations/implications â This paper demonstrates the frameworks application on a
generalized test problem only. Further publication will consider real engineering design problems.
Originality/value â The paper addresses the need for future conceptual design methods of complex
systems to consider a mixed concept space of both discrete and continuous nature via automated methods
Towards an infrastructure for preparation and control of intelligent automation systems
In an attempt to handle some of the challenges of modern production, intelligent automation systems offer solutions that are flexible, adaptive, and collaborative. Contrary to traditional solutions, intelligent automation systems emerged just recently and thus lack the supporting tools and infrastructure that traditional systems nowadays take for granted. To support efficient development, commissioning, and control of such systems, this thesis summarizes various lessons learned during years of implementation. Based on what was learned, this thesis investigates key features of infrastructure for modern and flexible intelligent automation systems, as well as a number of important design solutions. For example, an important question is raised whether to decentralize the global state or to give complete access to the main controller.Moreover, in order to develop such systems, a framework for virtual preparation and commissioning is presented, with the main goal to offer support for engineers. As traditional virtual commissioning solutions are not intended for preparing highly flexible, collaborative, and dynamic systems, this framework aims to provide some of the groundwork and point to a direction for fast and integrated preparation and virtual commissioning of such systems.Finally, this thesis summarizes some of the investigations made on planning as satisfiability, in order to evaluate how different methods improve planning performance. Throughout the thesis, an industrial material kitting use case exemplifies presented perspectives, lessons learned, and frameworks
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