257 research outputs found
Why Altered States Are Not Enough: A Perspective from Buddhism
Transpersonal psychology has at times employed Buddhist terminology in ways that do not
reflect distinctions that underlie these tightly defined terms. From a Buddhist perspective,
attempts to equate Buddhist terms with language from other traditions are misdirected, and
produce results that no longer represent Buddhism. For example, it is an error to translate
certain Buddhist terms as referring to a shared universal consciousness; Buddhism explicitly
rejects this idea. Nor is it appropriate to assume that the generic, cross-traditional altered
state of nondual awareness postulated in some transpersonally-related circles is in any way
related to nirvana or other advanced states described within Buddhism. Buddhist practices
are focused on the achievement of particular knowledge and capacities, not the attainment
of altered states
Analysis of relative influence of nodes in directed networks
Many complex networks are described by directed links; in such networks, a
link represents, for example, the control of one node over the other node or
unidirectional information flows. Some centrality measures are used to
determine the relative importance of nodes specifically in directed networks.
We analyze such a centrality measure called the influence. The influence
represents the importance of nodes in various dynamics such as synchronization,
evolutionary dynamics, random walk, and social dynamics. We analytically
calculate the influence in various networks, including directed multipartite
networks and a directed version of the Watts-Strogatz small-world network. The
global properties of networks such as hierarchy and position of shortcuts,
rather than local properties of the nodes, such as the degree, are shown to be
the chief determinants of the influence of nodes in many cases. The developed
method is also applicable to the calculation of the PageRank. We also
numerically show that in a coupled oscillator system, the threshold for
entrainment by a pacemaker is low when the pacemaker is placed on influential
nodes. For a type of random network, the analytically derived threshold is
approximately equal to the inverse of the influence. We numerically show that
this relationship also holds true in a random scale-free network and a neural
network.Comment: 9 figure
Copasetic analysis: a framework for the blind analysis of microarray imagery
The official published version can be found at the link below.From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePixÂź when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis
A Bayesian approach to the estimation of maps between riemannian manifolds
Let \Theta be a smooth compact oriented manifold without boundary, embedded
in a euclidean space and let \gamma be a smooth map \Theta into a riemannian
manifold \Lambda. An unknown state \theta \in \Theta is observed via
X=\theta+\epsilon \xi where \epsilon>0 is a small parameter and \xi is a white
Gaussian noise. For a given smooth prior on \Theta and smooth estimator g of
the map \gamma we derive a second-order asymptotic expansion for the related
Bayesian risk. The calculation involves the geometry of the underlying spaces
\Theta and \Lambda, in particular, the integration-by-parts formula. Using this
result, a second-order minimax estimator of \gamma is found based on the modern
theory of harmonic maps and hypo-elliptic differential operators.Comment: 20 pages, no figures published version includes correction to eq.s
31, 41, 4
Parameterized Algorithms for Graph Partitioning Problems
We study a broad class of graph partitioning problems, where each problem is
specified by a graph , and parameters and . We seek a subset
of size , such that is at most
(or at least) , where are constants
defining the problem, and are the cardinalities of the edge sets
having both endpoints, and exactly one endpoint, in , respectively. This
class of fixed cardinality graph partitioning problems (FGPP) encompasses Max
-Cut, Min -Vertex Cover, -Densest Subgraph, and -Sparsest
Subgraph.
Our main result is an algorithm for any problem in
this class, where is the maximum degree in the input graph.
This resolves an open question posed by Bonnet et al. [IPEC 2013]. We obtain
faster algorithms for certain subclasses of FGPPs, parameterized by , or by
. In particular, we give an time algorithm for Max
-Cut, thus improving significantly the best known time
algorithm
PageRank Optimization by Edge Selection
The importance of a node in a directed graph can be measured by its PageRank.
The PageRank of a node is used in a number of application contexts - including
ranking websites - and can be interpreted as the average portion of time spent
at the node by an infinite random walk. We consider the problem of maximizing
the PageRank of a node by selecting some of the edges from a set of edges that
are under our control. By applying results from Markov decision theory, we show
that an optimal solution to this problem can be found in polynomial time. Our
core solution results in a linear programming formulation, but we also provide
an alternative greedy algorithm, a variant of policy iteration, which runs in
polynomial time, as well. Finally, we show that, under the slight modification
for which we are given mutually exclusive pairs of edges, the problem of
PageRank optimization becomes NP-hard.Comment: 30 pages, 3 figure
Fast Distributed PageRank Computation
Over the last decade, PageRank has gained importance in a wide range of
applications and domains, ever since it first proved to be effective in
determining node importance in large graphs (and was a pioneering idea behind
Google's search engine). In distributed computing alone, PageRank vector, or
more generally random walk based quantities have been used for several
different applications ranging from determining important nodes, load
balancing, search, and identifying connectivity structures. Surprisingly,
however, there has been little work towards designing provably efficient
fully-distributed algorithms for computing PageRank. The difficulty is that
traditional matrix-vector multiplication style iterative methods may not always
adapt well to the distributed setting owing to communication bandwidth
restrictions and convergence rates.
In this paper, we present fast random walk-based distributed algorithms for
computing PageRanks in general graphs and prove strong bounds on the round
complexity. We first present a distributed algorithm that takes O\big(\log
n/\eps \big) rounds with high probability on any graph (directed or
undirected), where is the network size and \eps is the reset probability
used in the PageRank computation (typically \eps is a fixed constant). We
then present a faster algorithm that takes O\big(\sqrt{\log n}/\eps \big)
rounds in undirected graphs. Both of the above algorithms are scalable, as each
node sends only small (\polylog n) number of bits over each edge per round.
To the best of our knowledge, these are the first fully distributed algorithms
for computing PageRank vector with provably efficient running time.Comment: 14 page
Cluster Editing: Kernelization based on Edge Cuts
Kernelization algorithms for the {\sc cluster editing} problem have been a
popular topic in the recent research in parameterized computation. Thus far
most kernelization algorithms for this problem are based on the concept of {\it
critical cliques}. In this paper, we present new observations and new
techniques for the study of kernelization algorithms for the {\sc cluster
editing} problem. Our techniques are based on the study of the relationship
between {\sc cluster editing} and graph edge-cuts. As an application, we
present an -time algorithm that constructs a kernel for the
{\it weighted} version of the {\sc cluster editing} problem. Our result meets
the best kernel size for the unweighted version for the {\sc cluster editing}
problem, and significantly improves the previous best kernel of quadratic size
for the weighted version of the problem
A Novel Approach for Foreign Substances Detection in Injection Using Clustering and Frame Difference
This paper focuses on developing a novel technique based on machine vision for detection of foreign substances in injections. Mechanical control yields spin/stop movement of injections which helps to cause relative movement between foreign substances in liquid and an ampoule bottle. Foreign substances are classified into two categories: subsiding-slowly object and subsiding-fast object. A sequence of frames are captured by a camera and used to recognize foreign substances. After image preprocessing like noise reduction and motion detection, two different methods, Moving-object Clustering (MC) and Frame Difference, are proposed to detect the two categories respectively. MC is operated to cluster subsiding-slowly foreign substances, based on the invariant features of those objects. Frame Difference is defined to calculate the difference between two frames due to the change of subsiding-fast objects. 200 ampoule samples filled with injection are tested and the experimental result indicates that the approach can detect the visible foreign substances effectively
- âŠ