183,309 research outputs found
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
A Diffie-Hellman based key management scheme for hierarchical access control
All organizations share data in a carefully managed fashion\ud
by using access control mechanisms. We focus on enforcing access control by encrypting the data and managing the encryption keys. We make the realistic assumption that the structure of any organization is a hierarchy of security classes. Data from a certain security class can only be accessed by another security class, if it is higher or at the same level in the hierarchy. Otherwise access is denied. Our solution is based on the Die-Hellman key exchange protocol. We show, that the theoretical worst case performance of our solution is slightly better than that of all other existing solutions. We also show, that our performance in practical cases is linear in the size of the hierarchy, whereas the best results from the literature are quadratic
Prospects and limitations of full-text index structures in genome analysis
The combination of incessant advances in sequencing technology producing large amounts of data and innovative bioinformatics approaches, designed to cope with this data flood, has led to new interesting results in the life sciences. Given the magnitude of sequence data to be processed, many bioinformatics tools rely on efficient solutions to a variety of complex string problems. These solutions include fast heuristic algorithms and advanced data structures, generally referred to as index structures. Although the importance of index structures is generally known to the bioinformatics community, the design and potency of these data structures, as well as their properties and limitations, are less understood. Moreover, the last decade has seen a boom in the number of variant index structures featuring complex and diverse memory-time trade-offs. This article brings a comprehensive state-of-the-art overview of the most popular index structures and their recently developed variants. Their features, interrelationships, the trade-offs they impose, but also their practical limitations, are explained and compared
Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation
A parallel algorithm for ``diffusion-limited aggregation'' (DLA) is described
and analyzed from the perspective of computational complexity. The dynamic
exponent z of the algorithm is defined with respect to the probabilistic
parallel random-access machine (PRAM) model of parallel computation according
to , where L is the cluster size, T is the running time, and the
algorithm uses a number of processors polynomial in L\@. It is argued that
z=D-D_2/2, where D is the fractal dimension and D_2 is the second generalized
dimension. Simulations of DLA are carried out to measure D_2 and to test
scaling assumptions employed in the complexity analysis of the parallel
algorithm. It is plausible that the parallel algorithm attains the minimum
possible value of the dynamic exponent in which case z characterizes the
intrinsic history dependence of DLA.Comment: 24 pages Revtex and 2 figures. A major improvement to the algorithm
and smaller dynamic exponent in this versio
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
The Parallel Complexity of Growth Models
This paper investigates the parallel complexity of several non-equilibrium
growth models. Invasion percolation, Eden growth, ballistic deposition and
solid-on-solid growth are all seemingly highly sequential processes that yield
self-similar or self-affine random clusters. Nonetheless, we present fast
parallel randomized algorithms for generating these clusters. The running times
of the algorithms scale as , where is the system size, and the
number of processors required scale as a polynomial in . The algorithms are
based on fast parallel procedures for finding minimum weight paths; they
illuminate the close connection between growth models and self-avoiding paths
in random environments. In addition to their potential practical value, our
algorithms serve to classify these growth models as less complex than other
growth models, such as diffusion-limited aggregation, for which fast parallel
algorithms probably do not exist.Comment: 20 pages, latex, submitted to J. Stat. Phys., UNH-TR94-0
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
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