11,230 research outputs found
Heuristic algorithms for the min-max edge 2-coloring problem
In multi-channel Wireless Mesh Networks (WMN), each node is able to use
multiple non-overlapping frequency channels. Raniwala et al. (MC2R 2004,
INFOCOM 2005) propose and study several such architectures in which a computer
can have multiple network interface cards. These architectures are modeled as a
graph problem named \emph{maximum edge -coloring} and studied in several
papers by Feng et. al (TAMC 2007), Adamaszek and Popa (ISAAC 2010, JDA 2016).
Later on Larjomaa and Popa (IWOCA 2014, JGAA 2015) define and study an
alternative variant, named the \emph{min-max edge -coloring}.
The above mentioned graph problems, namely the maximum edge -coloring and
the min-max edge -coloring are studied mainly from the theoretical
perspective. In this paper, we study the min-max edge 2-coloring problem from a
practical perspective. More precisely, we introduce, implement and test four
heuristic approximation algorithms for the min-max edge -coloring problem.
These algorithms are based on a \emph{Breadth First Search} (BFS)-based
heuristic and on \emph{local search} methods like basic \emph{hill climbing},
\emph{simulated annealing} and \emph{tabu search} techniques, respectively.
Although several algorithms for particular graph classes were proposed by
Larjomaa and Popa (e.g., trees, planar graphs, cliques, bi-cliques,
hypergraphs), we design the first algorithms for general graphs.
We study and compare the running data for all algorithms on Unit Disk Graphs,
as well as some graphs from the DIMACS vertex coloring benchmark dataset.Comment: This is a post-peer-review, pre-copyedit version of an article
published in International Computing and Combinatorics Conference
(COCOON'18). The final authenticated version is available online at:
http://www.doi.org/10.1007/978-3-319-94776-1_5
Random on-board pixel sampling (ROPS) X-ray Camera
Recent advances in compressed sensing theory and algorithms offer new
possibilities for high-speed X-ray camera design. In many CMOS cameras, each
pixel has an independent on-board circuit that includes an amplifier, noise
rejection, signal shaper, an analog-to-digital converter (ADC), and optional
in-pixel storage. When X-ray images are sparse, i.e., when one of the following
cases is true: (a.) The number of pixels with true X-ray hits is much smaller
than the total number of pixels; (b.) The X-ray information is redundant; or
(c.) Some prior knowledge about the X-ray images exists, sparse sampling may be
allowed. Here we first illustrate the feasibility of random on-board pixel
sampling (ROPS) using an existing set of X-ray images, followed by a discussion
about signal to noise as a function of pixel size. Next, we describe a possible
circuit architecture to achieve random pixel access and in-pixel storage. The
combination of a multilayer architecture, sparse on-chip sampling, and
computational image techniques, is expected to facilitate the development and
applications of high-speed X-ray camera technology.Comment: 9 pages, 6 figures, Presented in 19th iWoRI
Verification of Hierarchical Artifact Systems
Data-driven workflows, of which IBM's Business Artifacts are a prime
exponent, have been successfully deployed in practice, adopted in industrial
standards, and have spawned a rich body of research in academia, focused
primarily on static analysis. The present work represents a significant advance
on the problem of artifact verification, by considering a much richer and more
realistic model than in previous work, incorporating core elements of IBM's
successful Guard-Stage-Milestone model. In particular, the model features task
hierarchy, concurrency, and richer artifact data. It also allows database key
and foreign key dependencies, as well as arithmetic constraints. The results
show decidability of verification and establish its complexity, making use of
novel techniques including a hierarchy of Vector Addition Systems and a variant
of quantifier elimination tailored to our context.Comment: Full version of the accepted PODS pape
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
Average precision (AP), the area under the recall-precision (RP) curve, is
the standard performance measure for object detection. Despite its wide
acceptance, it has a number of shortcomings, the most important of which are
(i) the inability to distinguish very different RP curves, and (ii) the lack of
directly measuring bounding box localization accuracy. In this paper, we
propose 'Localization Recall Precision (LRP) Error', a new metric which we
specifically designed for object detection. LRP Error is composed of three
components related to localization, false negative (FN) rate and false positive
(FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable
LRP error representing the best achievable configuration of the detector in
terms of recall-precision and the tightness of the boxes. In contrast to AP,
which considers precisions over the entire recall domain, Optimal LRP
determines the 'best' confidence score threshold for a class, which balances
the trade-off between localization and recall-precision. In our experiments, we
show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides
richer and more discriminative information than AP. We also demonstrate that
the best confidence score thresholds vary significantly among classes and
detectors. Moreover, we present LRP results of a simple online video object
detector which uses a SOTA still image object detector and show that the
class-specific optimized thresholds increase the accuracy against the common
approach of using a general threshold for all classes. At
https://github.com/cancam/LRP we provide the source code that can compute LRP
for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted
to other datasets as well.Comment: to appear in ECCV 201
Stability of central finite difference schemes for the Heston PDE
This paper deals with stability in the numerical solution of the prominent
Heston partial differential equation from mathematical finance. We study the
well-known central second-order finite difference discretization, which leads
to large semi-discrete systems with non-normal matrices A. By employing the
logarithmic spectral norm we prove practical, rigorous stability bounds. Our
theoretical stability results are illustrated by ample numerical experiments
Optimization and characterization of a new lipopeptide biosurfactant produced by marine Brevibacterium aureum MSA 13 in solid state culture
The biosurfactant production of a marine actinobacterium Brevibacterium aureum MSA 13 was optimized using industrial and agroindustrial solid waste residues as substrates in solid state culture
Fuzzy splicing systems
In this paper we introduce a new variant of splicing systems, called fuzzy splicing systems, and establish some basic properties of language families generated by this type of splicing systems. We study the “fuzzy effect” on splicing operations, and show that the “fuzzification” of splicing systems can increase and decrease the computational power of splicing systems with finite components with respect to fuzzy operations and cut-points chosen for threshold languages
An Analysis of the Causes Behind the Business Combination Frenzy, 1979-1986
For the past eight years, the American economy has experienced an increase in both the number and size of mergers in business. The magnitude and far-reaching effects of this phenomenon merit a serious study
- …