15,597 research outputs found
Validating Sample Average Approximation Solutions with Negatively Dependent Batches
Sample-average approximations (SAA) are a practical means of finding
approximate solutions of stochastic programming problems involving an extremely
large (or infinite) number of scenarios. SAA can also be used to find estimates
of a lower bound on the optimal objective value of the true problem which, when
coupled with an upper bound, provides confidence intervals for the true optimal
objective value and valuable information about the quality of the approximate
solutions. Specifically, the lower bound can be estimated by solving multiple
SAA problems (each obtained using a particular sampling method) and averaging
the obtained objective values. State-of-the-art methods for lower-bound
estimation generate batches of scenarios for the SAA problems independently. In
this paper, we describe sampling methods that produce negatively dependent
batches, thus reducing the variance of the sample-averaged lower bound
estimator and increasing its usefulness in defining a confidence interval for
the optimal objective value. We provide conditions under which the new sampling
methods can reduce the variance of the lower bound estimator, and present
computational results to verify that our scheme can reduce the variance
significantly, by comparison with the traditional Latin hypercube approach
MMpred: functional miRNA – mRNA interaction analyses by miRNA expression prediction
Background: MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional
regulation. Comprehensive analyses of how microRNA influence biological processes requires paired
miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few
such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of
interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding
genes (host genes).
Results: We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two
miRNA expression prediction methods – a scaling function and a linear model. Additionally, miRNA-mRNA anticorrelation
analyses are used to determine the most probable miRNA gene targets (i.e. the differentially
expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy
of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted
targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the
predicted interactions.
Conclusions: The MMpred pipeline requires only mRNA expression data as input and is independent of third
party miRNA target prediction methods. The method passed extensive numerical validation based on the
binding energy between the mature miRNA and 3’ UTR region of the target gene. We report that MMpred is
capable of generating results similar to that obtained using paired datasets. For the reported test cases we
generated consistent output and predicted biological relationships that will help formulate further testable
hypotheses
A differential memristive synapse circuit for on-line learning in neuromorphic computing systems
Spike-based learning with memristive devices in neuromorphic computing
architectures typically uses learning circuits that require overlapping pulses
from pre- and post-synaptic nodes. This imposes severe constraints on the
length of the pulses transmitted in the network, and on the network's
throughput. Furthermore, most of these circuits do not decouple the currents
flowing through memristive devices from the one stimulating the target neuron.
This can be a problem when using devices with high conductance values, because
of the resulting large currents. In this paper we propose a novel circuit that
decouples the current produced by the memristive device from the one used to
stimulate the post-synaptic neuron, by using a novel differential scheme based
on the Gilbert normalizer circuit. We show how this circuit is useful for
reducing the effect of variability in the memristive devices, and how it is
ideally suited for spike-based learning mechanisms that do not require
overlapping pre- and post-synaptic pulses. We demonstrate the features of the
proposed synapse circuit with SPICE simulations, and validate its learning
properties with high-level behavioral network simulations which use a
stochastic gradient descent learning rule in two classification tasks.Comment: 18 Pages main text, 9 pages of supplementary text, 19 figures.
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Neural reactivations during sleep determine network credit assignment.
A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship
Having Fun in Learning Formal Specifications
There are many benefits in providing formal specifications for our software.
However, teaching students to do this is not always easy as courses on formal
methods are often experienced as dry by students. This paper presents a game
called FormalZ that teachers can use to introduce some variation in their
class. Students can have some fun in playing the game and, while doing so, also
learn the basics of writing formal specifications in the form of pre- and
post-conditions. Unlike existing software engineering themed education games
such as Pex and Code Defenders, FormalZ takes the deep gamification approach
where playing gets a more central role in order to generate more engagement.
This short paper presents our work in progress: the first implementation of
FormalZ along with the result of a preliminary users' evaluation. This
implementation is functionally complete and tested, but the polishing of its
user interface is still future work
Suppression of backscattered diffraction from sub-wavelength ‘moth-eye’ arrays
The eyes and wings of some species of moth are covered with arrays of nanoscale features that dramatically reduce reflection of light. There have been multiple examples where this approach has been adapted for use in antireflection and antiglare technologies with the fabrication of artificial moth-eye surfaces. In this work, the suppression of iridescence caused by the diffraction of light from such artificial regular moth-eye arrays at high angles of incidence is achieved with the use of a new tiled domain design, inspired by the arrangement of features on natural moth-eye surfaces. This bio-mimetic pillar architecture contains high optical rotational symmetry and can achieve high levels of diffraction order power reduction. For example, a tiled design fabricated in silicon and consisting of domains with 9 different orientations of the traditional hexagonal array exhibited a ~96% reduction in the intensity of the ?1 diffraction order. It is suggested natural moth-eye surfaces have evolved a tiled domain structure as it confers efficient antireflection whilst avoiding problems with high angle diffraction. This combination of antireflection and stealth properties increases chances of survival by reducing the risk of the insect being spotted by a predator. Furthermore, the tiled domain design could lead to more effective artificial moth-eye arrays for antiglare and stealth applications
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