35,603 research outputs found
A virtual workspace for hybrid multidimensional scaling algorithms
In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualization. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This suggests that hybrid combinations of appropriate algorithms might also successfully address other characteristics of data. This paper presents a system and framework in which a user can easily explore hybrid algorithms and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views
A visual workspace for constructing hybrid MDS algorithms and coordinating multiple views
Data can be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualisation. This has led to an abundance of often disparate algorithmic techniques. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This paper presents a system and framework in which a user can easily explore algorithms as well as their hybrid conjunctions and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views of algorithmic and visualisation components. We propose that our approach has two main benefits: significant improvements in run times of MDS algorithms can be achieved, and intermediate views of the data and the visualisation program structure can provide greater insight and control over the visualisation process
Discrete Morse theory for computing cellular sheaf cohomology
Sheaves and sheaf cohomology are powerful tools in computational topology,
greatly generalizing persistent homology. We develop an algorithm for
simplifying the computation of cellular sheaf cohomology via (discrete)
Morse-theoretic techniques. As a consequence, we derive efficient techniques
for distributed computation of (ordinary) cohomology of a cell complex.Comment: 19 pages, 1 Figure. Added Section 5.
An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks
Network Functions Virtualization (NFV) in Software Defined Networks (SDN)
emerged as a new technology for creating virtual instances for smooth execution
of multiple applications. Their amalgamation provides flexible and programmable
platforms to utilize the network resources for providing Quality of Service
(QoS) to various applications. In SDN-enabled NFV setups, the underlying
network services can be viewed as a series of virtual network functions (VNFs)
and their optimal deployment on physical/virtual nodes is considered a
challenging task to perform. However, SDNs have evolved from single-domain to
multi-domain setups in the recent era. Thus, the complexity of the underlying
VNF deployment problem in multi-domain setups has increased manifold. Moreover,
the energy utilization aspect is relatively unexplored with respect to an
optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the
VNF deployment problem in multi-domain SDN setup has been addressed with a
primary emphasis on reducing the overall energy consumption for deploying the
maximum number of VNFs with guaranteed QoS. The problem in hand is initially
formulated as a "Multi-objective Optimization Problem" based on Integer Linear
Programming (ILP) to obtain an optimal solution. However, the formulated ILP
becomes complex to solve with an increasing number of decision variables and
constraints with an increase in the size of the network. Thus, we leverage the
benefits of the popular evolutionary optimization algorithms to solve the
problem under consideration. In order to deduce the most appropriate
evolutionary optimization algorithm to solve the considered problem, it is
subjected to different variants of evolutionary algorithms on the widely used
MOEA framework (an open source java framework based on multi-objective
evolutionary algorithms).Comment: Accepted for publication in IEEE INFOCOM 2019 Workshop on Intelligent
Cloud Computing and Networking (ICCN 2019
Maximizing Protein Translation Rate in the Ribosome Flow Model: the Homogeneous Case
Gene translation is the process in which intracellular macro-molecules,
called ribosomes, decode genetic information in the mRNA chain into the
corresponding proteins. Gene translation includes several steps. During the
elongation step, ribosomes move along the mRNA in a sequential manner and link
amino-acids together in the corresponding order to produce the proteins.
The homogeneous ribosome flow model(HRFM) is a deterministic computational
model for translation-elongation under the assumption of constant elongation
rates along the mRNA chain. The HRFM is described by a set of n first-order
nonlinear ordinary differential equations, where n represents the number of
sites along the mRNA chain. The HRFM also includes two positive parameters:
ribosomal initiation rate and the (constant) elongation rate. In this paper, we
show that the steady-state translation rate in the HRFM is a concave function
of its parameters. This means that the problem of determining the parameter
values that maximize the translation rate is relatively simple. Our results may
contribute to a better understanding of the mechanisms and evolution of
translation-elongation. We demonstrate this by using the theoretical results to
estimate the initiation rate in M. musculus embryonic stem cell. The underlying
assumption is that evolution optimized the translation mechanism.
For the infinite-dimensional HRFM, we derive a closed-form solution to the
problem of determining the initiation and transition rates that maximize the
protein translation rate. We show that these expressions provide good
approximations for the optimal values in the n-dimensional HRFM already for
relatively small values of n. These results may have applications for synthetic
biology where an important problem is to re-engineer genomic systems in order
to maximize the protein production rate
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