1,142,318 research outputs found
Random Walks: A Review of Algorithms and Applications
A random walk is known as a random process which describes a path including a
succession of random steps in the mathematical space. It has increasingly been
popular in various disciplines such as mathematics and computer science.
Furthermore, in quantum mechanics, quantum walks can be regarded as quantum
analogues of classical random walks. Classical random walks and quantum walks
can be used to calculate the proximity between nodes and extract the topology
in the network. Various random walk related models can be applied in different
fields, which is of great significance to downstream tasks such as link
prediction, recommendation, computer vision, semi-supervised learning, and
network embedding. In this paper, we aim to provide a comprehensive review of
classical random walks and quantum walks. We first review the knowledge of
classical random walks and quantum walks, including basic concepts and some
typical algorithms. We also compare the algorithms based on quantum walks and
classical random walks from the perspective of time complexity. Then we
introduce their applications in the field of computer science. Finally we
discuss the open issues from the perspectives of efficiency, main-memory
volume, and computing time of existing algorithms. This study aims to
contribute to this growing area of research by exploring random walks and
quantum walks together.Comment: 13 pages, 4 figure
Classification algorithms for Big Data with applications in the urban security domain
A classification algorithm is a versatile tool, that can serve as a predictor for the
future or as an analytical tool to understand the past. Several obstacles prevent
classification from scaling to a large Volume, Velocity, Variety or Value. The aim
of this thesis is to scale distributed classification algorithms beyond current limits,
assess the state-of-practice of Big Data machine learning frameworks and validate
the effectiveness of a data science process in improving urban safety.
We found in massive datasets with a number of large-domain categorical features
a difficult challenge for existing classification algorithms. We propose associative
classification as a possible answer, and develop several novel techniques to distribute
the training of an associative classifier among parallel workers and improve the final
quality of the model. The experiments, run on a real large-scale dataset with more
than 4 billion records, confirmed the quality of the approach.
To assess the state-of-practice of Big Data machine learning frameworks and
streamline the process of integration and fine-tuning of the building blocks, we
developed a generic, self-tuning tool to extract knowledge from network traffic
measurements. The result is a system that offers human-readable models of the data
with minimal user intervention, validated by experiments on large collections of
real-world passive network measurements.
A good portion of this dissertation is dedicated to the study of a data science
process to improve urban safety. First, we shed some light on the feasibility of a
system to monitor social messages from a city for emergency relief. We then propose
a methodology to mine temporal patterns in social issues, like crimes. Finally,
we propose a system to integrate the findings of Data Science on the citizenry’s
perception of safety and communicate its results to decision makers in a timely
manner. We applied and tested the system in a real Smart City scenario, set in Turin,
Italy
Computational Capacity and Energy Consumption of Complex Resistive Switch Networks
Resistive switches are a class of emerging nanoelectronics devices that
exhibit a wide variety of switching characteristics closely resembling
behaviors of biological synapses. Assembled into random networks, such
resistive switches produce emerging behaviors far more complex than that of
individual devices. This was previously demonstrated in simulations that
exploit information processing within these random networks to solve tasks that
require nonlinear computation as well as memory. Physical assemblies of such
networks manifest complex spatial structures and basic processing capabilities
often related to biologically-inspired computing. We model and simulate random
resistive switch networks and analyze their computational capacities. We
provide a detailed discussion of the relevant design parameters and establish
the link to the physical assemblies by relating the modeling parameters to
physical parameters. More globally connected networks and an increased network
switching activity are means to increase the computational capacity linearly at
the expense of exponentially growing energy consumption. We discuss a new
modular approach that exhibits higher computational capacities and energy
consumption growing linearly with the number of networks used. The results show
how to optimize the trade-off between computational capacity and energy
efficiency and are relevant for the design and fabrication of novel computing
architectures that harness random assemblies of emerging nanodevices
Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey
This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances
on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301,
61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Poroelastic indentation of mechanically confined hydrogel layers
We report on the poroelastic indentation response of hydrogel thin films
geometrically confined within contacts with rigid spherical probes of radii in
the millimeter range. Poly(PEGMA) (poly(ethylene glycol)) methyl ether
methacrylate), poly(DMA) (dimethylacrylamide) and poly(NIPAM)
(\textit{N}-isopropylacrylamide) gel films with thickness less than 15 m
were grafted onto glass substrates using a thiol-ene click chemistry route.
Changes in the indentation depth under constant applied load were monitored
over time as a function of the film thickness and the radius of curvature of
the probe using an interferometric method. In addition, shear properties of the
indented films were measured using a lateral contact method. In the case of
poly(PEGMA) films, we show that poroelastic indentation behavior is adequately
described within the framework of an approximate contact model derived within
the limits of confined contact geometries. This model provides simple scaling
laws for the characteristic poroelastic time and the equilibrium indentation
depth. Conversely, deviations from this model are evidenced for poly(DMA) and
poly(NIPAM) films. From lateral contact experiments, these deviations are found
to result from strong changes in the shear properties as a result of glass
transition (poly(DMA)) or phase separation (poly(NIPAM)) phenomena induced by
the drainage of the confined films squeezed between the rigid substrates
Foam-like compression behavior of fibrin networks
The rheological properties of fibrin networks have been of long-standing
interest. As such there is a wealth of studies of their shear and tensile
responses, but their compressive behavior remains unexplored. Here, by
characterization of the network structure with synchronous measurement of the
fibrin storage and loss moduli at increasing degrees of compression, we show
that the compressive behavior of fibrin networks is similar to that of cellular
solids. A non-linear stress-strain response of fibrin consists of three
regimes: 1) an initial linear regime, in which most fibers are straight, 2) a
plateau regime, in which more and more fibers buckle and collapse, and 3) a
markedly non-linear regime, in which network densification occurs {{by bending
of buckled fibers}} and inter-fiber contacts. Importantly, the spatially
non-uniform network deformation included formation of a moving "compression
front" along the axis of strain, which segregated the fibrin network into
compartments with different fiber densities and structure. The Young's modulus
of the linear phase depends quadratically on the fibrin volume fraction while
that in the densified phase depends cubically on it. The viscoelastic plateau
regime corresponds to a mixture of these two phases in which the fractions of
the two phases change during compression. We model this regime using a
continuum theory of phase transitions and analytically predict the storage and
loss moduli which are in good agreement with the experimental data. Our work
shows that fibrin networks are a member of a broad class of natural cellular
materials which includes cancellous bone, wood and cork
Analysis and implementation of the Large Scale Video-on-Demand System
Next Generation Network (NGN) provides multimedia services over broadband
based networks, which supports high definition TV (HDTV), and DVD quality
video-on-demand content. The video services are thus seen as merging mainly
three areas such as computing, communication, and broadcasting. It has numerous
advantages and more exploration for the large-scale deployment of
video-on-demand system is still needed. This is due to its economic and design
constraints. It's need significant initial investments for full service
provision. This paper presents different estimation for the different
topologies and it require efficient planning for a VOD system network. The
methodology investigates the network bandwidth requirements of a VOD system
based on centralized servers, and distributed local proxies. Network traffic
models are developed to evaluate the VOD system's operational bandwidth
requirements for these two network architectures. This paper present an
efficient estimation of the of the bandwidth requirement for the different
architectures.Comment: 9 pages, 8 figure
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