1,777 research outputs found
An adaptive Metropolis-Hastings scheme: sampling and optimization
We propose an adaptive Metropolis-Hastings algorithm in which sampled data
are used to update the proposal distribution. We use the samples found by the
algorithm at a particular step to form the information-theoretically optimal
mean-field approximation to the target distribution, and update the proposal
distribution to be that approximatio. We employ our algorithm to sample the
energy distribution for several spin-glasses and we demonstrate the superiority
of our algorithm to the conventional MH algorithm in sampling and in annealing
optimization.Comment: To appear in Europhysics Letter
Collective Intelligence for Control of Distributed Dynamical Systems
We consider the El Farol bar problem, also known as the minority game (W. B.
Arthur, ``The American Economic Review'', 84(2): 406--411 (1994), D. Challet
and Y.C. Zhang, ``Physica A'', 256:514 (1998)). We view it as an instance of
the general problem of how to configure the nodal elements of a distributed
dynamical system so that they do not ``work at cross purposes'', in that their
collective dynamics avoids frustration and thereby achieves a provided global
goal. We summarize a mathematical theory for such configuration applicable when
(as in the bar problem) the global goal can be expressed as minimizing a global
energy function and the nodes can be expressed as minimizers of local free
energy functions. We show that a system designed with that theory performs
nearly optimally for the bar problem.Comment: 8 page
Nonlinear Information Bottleneck
Information bottleneck (IB) is a technique for extracting information in one
random variable that is relevant for predicting another random variable
. IB works by encoding in a compressed "bottleneck" random variable
from which can be accurately decoded. However, finding the optimal
bottleneck variable involves a difficult optimization problem, which until
recently has been considered for only two limited cases: discrete and
with small state spaces, and continuous and with a Gaussian joint
distribution (in which case optimal encoding and decoding maps are linear). We
propose a method for performing IB on arbitrarily-distributed discrete and/or
continuous and , while allowing for nonlinear encoding and decoding
maps. Our approach relies on a novel non-parametric upper bound for mutual
information. We describe how to implement our method using neural networks. We
then show that it achieves better performance than the recently-proposed
"variational IB" method on several real-world datasets
A low-energy solar cosmic ray experiment for OGO-F
Instrumentation data for low energy solar cosmic ray measurements using OGO-F satellit
Positional Information – a concept underpinning our understanding of developmental biology
© 2019 Wiley Periodicals, Inc.Peer reviewedPostprin
When is Sessional Monitoring More Likely in Child and Adolescent Mental Health Services?
Sessional monitoring of patient progress or experience of therapy is an evidence-based intervention recommended by healthcare systems internationally. It is being rolled out across child and adolescent mental health services (CAMHS) in England to inform clinical practice and service evaluation. We explored whether patient demographic and case characteristics were associated with the likelihood of using sessional monitoring. Multilevel regressions were conducted on N = 2609 youths from a routinely collected dataset from 10 CAMHS. Girls (odds ratio, OR 1.26), older youths (OR 1.10), White youths (OR 1.35), and youths presenting with mood (OR 1.46) or anxiety problems (OR 1.59) were more likely to have sessional monitoring. In contrast, youths under state care (OR 0.20) or in need of social service input (OR 0.39) were less likely to have sessional monitoring. Findings of the present research may suggest that sessional monitoring is more likely with common problems such as mood and anxiety problems but less likely with more complex cases, such as those involving youths under state care or those in need of social service input
Adaptive Anomaly Detection via Self-Calibration and Dynamic Updating
The deployment and use of Anomaly Detection (AD) sensors often requires the intervention of a human expert to manually calibrate and optimize their performance. Depending on the site and the type of traffic it receives, the operators might have to provide recent and sanitized training data sets, the characteristics of expected traffic (i.e. outlier ratio), and exceptions or even expected future modifications of system's behavior. In this paper, we study the potential performance issues that stem from fully automating the AD sensors' day-to-day maintenance and calibration. Our goal is to remove the dependence on human operator using an unlabeled, and thus potentially dirty, sample of incoming traffic. To that end, we propose to enhance the training phase of AD sensors with a self-calibration phase, leading to the automatic determination of the optimal AD parameters. We show how this novel calibration phase can be employed in conjunction with previously proposed methods for training data sanitization resulting in a fully automated AD maintenance cycle. Our approach is completely agnostic to the underlying AD sensor algorithm. Furthermore, the self-calibration can be applied in an online fashion to ensure that the resulting AD models reflect changes in the system's behavior which would otherwise render the sensor's internal state inconsistent. We verify the validity of our approach through a series of experiments where we compare the manually obtained optimal parameters with the ones computed from the self-calibration phase. Modeling traffic from two different sources, the fully automated calibration shows a 7.08% reduction in detection rate and a 0.06% increase in false positives, in the worst case, when compared to the optimal selection of parameters. Finally, our adaptive models outperform the statically generated ones retaining the gains in performance from the sanitization process over time
Quantitative analysis of cell types during growth and morphogenesis in Hydra
Tissue maceration was used to determine the absolute number and the distribution of cell types in Hydra. It was shown that the total number of cells per animal as well as the distribution of cells vary depending on temperature, feeding conditions, and state of growth. During head and foot regeneration and during budding the first detectable change in the cell distribution is an increase in the number of nerve cells at the site of morphogenesis. These results and the finding that nerve cells are most concentrated in the head region, diminishing in density down the body column, are discussed in relation to tissue polarity
Coarse-Graining and Self-Dissimilarity of Complex Networks
Can complex engineered and biological networks be coarse-grained into smaller
and more understandable versions in which each node represents an entire
pattern in the original network? To address this, we define coarse-graining
units (CGU) as connectivity patterns which can serve as the nodes of a
coarse-grained network, and present algorithms to detect them. We use this
approach to systematically reverse-engineer electronic circuits, forming
understandable high-level maps from incomprehensible transistor wiring: first,
a coarse-grained version in which each node is a gate made of several
transistors is established. Then, the coarse-grained network is itself
coarse-grained, resulting in a high-level blueprint in which each node is a
circuit-module made of multiple gates. We apply our approach also to a
mammalian protein-signaling network, to find a simplified coarse-grained
network with three main signaling channels that correspond to cross-interacting
MAP-kinase cascades. We find that both biological and electronic networks are
'self-dissimilar', with different network motifs found at each level. The
present approach can be used to simplify a wide variety of directed and
nondirected, natural and designed networks.Comment: 11 pages, 11 figure
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