6,299 research outputs found
Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise
This study presents the results of a series of simulation experiments that
evaluate and compare four different manifold alignment methods under the
influence of noise. The data was created by simulating the dynamics of two
slightly different double pendulums in three-dimensional space. The method of
semi-supervised feature-level manifold alignment using global distance resulted
in the most convincing visualisations. However, the semi-supervised
feature-level local alignment methods resulted in smaller alignment errors.
These local alignment methods were also more robust to noise and faster than
the other methods.Comment: The final version will appear in ICONIP 2018. A DOI identifier to the
final version will be added to the preprint, as soon as it is availabl
Single Image Super-Resolution Using Lightweight CNN with Maxout Units
Rectified linear units (ReLU) are well-known to be helpful in obtaining
faster convergence and thus higher performance for many deep-learning-based
applications. However, networks with ReLU tend to perform poorly when the
number of filter parameters is constrained to a small number. To overcome it,
in this paper, we propose a novel network utilizing maxout units (MU), and show
its effectiveness on super-resolution (SR) applications. In general, the MU has
been known to make the filter sizes doubled in generating the feature maps of
the same sizes in classification problems. In this paper, we first reveal that
the MU can even make the filter sizes halved in restoration problems thus
leading to compaction of the network sizes. To show this, our SR network is
designed without increasing the filter sizes with MU, which outperforms the
state of the art SR methods with a smaller number of filter parameters. To the
best of our knowledge, we are the first to incorporate MU into SR applications
and show promising performance results. In MU, feature maps from a previous
convolutional layer are divided into two parts along channels, which are then
compared element-wise and only their max values are passed to a next layer.
Along with some interesting properties of MU to be analyzed, we further
investigate other variants of MU and their effects. In addition, while ReLU
have a trouble for learning in networks with a very small number of
convolutional filter parameters, MU do not. For SR applications, our MU-based
network reconstructs high-resolution images with comparable quality compared to
previous deep-learning-based SR methods, with lower filter parameters.Comment: ACCV201
A simple and robust method for connecting small-molecule drugs using gene-expression signatures
Interaction of a drug or chemical with a biological system can result in a
gene-expression profile or signature characteristic of the event. Using a
suitably robust algorithm these signatures can potentially be used to connect
molecules with similar pharmacological or toxicological properties. The
Connectivity Map was a novel concept and innovative tool first introduced by
Lamb et al to connect small molecules, genes, and diseases using genomic
signatures [Lamb et al (2006), Science 313, 1929-1935]. However, the
Connectivity Map had some limitations, particularly there was no effective
safeguard against false connections if the observed connections were considered
on an individual-by-individual basis. Further when several connections to the
same small-molecule compound were viewed as a set, the implicit null hypothesis
tested was not the most relevant one for the discovery of real connections.
Here we propose a simple and robust method for constructing the reference
gene-expression profiles and a new connection scoring scheme, which importantly
allows the valuation of statistical significance of all the connections
observed. We tested the new method with the two example gene-signatures (HDAC
inhibitors and Estrogens) used by Lamb et al and also a new gene signature of
immunosuppressive drugs. Our testing with this new method shows that it
achieves a higher level of specificity and sensitivity than the original
method. For example, our method successfully identified raloxifene and
tamoxifen as having significant anti-estrogen effects, while Lamb et al's
Connectivity Map failed to identify these. With these properties our new method
has potential use in drug development for the recognition of pharmacological
and toxicological properties in new drug candidates.Comment: 8 pages, 2 figures, and 2 tables; supplementary data supplied as a
ZIP fil
Truncated Schwinger-Dyson Equations and Gauge Covariance in QED3
We study the Landau-Khalatnikov-Fradkin transformations (LKFT) in momentum
space for the dynamically generated mass function in QED3. Starting from the
Landau gauge results in the rainbow approximation, we construct solutions in
other covariant gauges. We confirm that the chiral condensate is gauge
invariant as the structure of the LKFT predicts. We also check that the gauge
dependence of the constituent fermion mass is considerably reduced as compared
to the one obtained directly by solving SDE.Comment: 17 pages, 11 figures. v3. Improved and Expanded. To appear in Few
Body System
A rapid, efficient, and facile solution for dental hypersensitivity: The tannin–iron complex
Dental hypersensitivity due to exposure of dentinal tubules under the enamel layer to saliva is a very popular and highly elusive technology priority in dentistry. Blocking water flow within exposed dentinal tubules is a key principle for curing dental hypersensitivity. Some salts used in "at home" solutions remineralize the tubules inside by concentrating saliva ingredients. An "in-office" option of applying dense resin sealants on the tubule entrance has only localized effects on well-defined sore spots. We report a self-assembled film that was formed by facile, rapid (4 min), and efficient (approximately 0.5 g/L concentration) dip-coating of teeth in an aqueous solution containing a tannic acid-iron(III) complex. It quickly and effectively occluded the dentinal tubules of human teeth. It withstood intense tooth brushing and induced hydroxyapatite remineralisation within the dentinal tubules. This strategy holds great promise for future applications as an effective and user-friendly desensitizer for managing dental hypersensitivity.111310Ysciescopu
Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in Internet gaming disorder
Literatures have shown that Internet gaming disorder (IGD) subjects show impaired executive control and enhanced reward sensitivities than healthy controls. However, how these two networks jointly affect the valuation process and drive IGD subjects' online-game-seeking behaviors remains unknown. Thirty-five IGD and 36 healthy controls underwent a resting-states scan in the MRI scanner. Functional connectivity (FC) was examined within control and reward network seeds regions, respectively. Nucleus accumbens (NAcc) was selected as the node to find the interactions between these two networks. IGD subjects show decreased FC in the executive control network and increased FC in the reward network when comparing with the healthy controls. When examining the correlations between the NAcc and the executive control/reward networks, the link between the NAcc - executive control network is negatively related with the link between NAcc - reward network. The changes (decrease/increase) in IGD subjects' brain synchrony in control/reward networks suggest the inefficient/overly processing within neural circuitry underlying these processes. The inverse proportion between control network and reward network in IGD suggest that impairments in executive control lead to inefficient inhibition of enhanced cravings to excessive online game playing. This might shed light on the mechanistic understanding of IGD
Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues
Smart grid (SG) networks are newly upgraded networks of connected objects that greatly improve reliability, efficiency and sustainability of the traditional energy infrastructure. In this respect, the smart metering infrastructure (SMI) plays an important role in controlling, monitoring and managing multiple domains in the SG. Despite the salient features of SMI, security and privacy issues have been under debate because of the large number of heterogeneous devices that are anticipated to be coordinated through public communication networks. This survey paper shows a brief overview of real cyber attack incidents in traditional energy networks and those targeting the smart metering network. Specifically, we present a threat taxonomy considering: (i) threats in system-level security, (ii) threats and/or theft of services, and (iii) threats to privacy. Based on the presented threats, we derive a set of security and privacy requirements for SG metering networks. Furthermore, we discuss various schemes that have been proposed to address these threats, considering the pros and cons of each. Finally, we investigate the open research issues to shed new light on future research directions in smart grid metering networks
Learning Control of Quantum Systems
This paper provides a brief introduction to learning control of quantum
systems. In particular, the following aspects are outlined, including
gradient-based learning for optimal control of quantum systems, evolutionary
computation for learning control of quantum systems, learning-based quantum
robust control, and reinforcement learning for quantum control.Comment: 9 page
Extremely long quasiparticle spin lifetimes in superconducting aluminium using MgO tunnel spin injectors
There has been an intense search in recent years for long-lived
spin-polarized carriers for spintronic and quantum-computing devices. Here we
report that spin polarized quasi-particles in superconducting aluminum layers
have surprisingly long spin-lifetimes, nearly a million times longer than in
their normal state. The lifetime is determined from the suppression of the
aluminum's superconductivity resulting from the accumulation of spin polarized
carriers in the aluminum layer using tunnel spin injectors. A Hanle effect,
observed in the presence of small in-plane orthogonal fields, is shown to be
quantitatively consistent with the presence of long-lived spin polarized
quasi-particles. Our experiments show that the superconducting state can be
significantly modified by small electric currents, much smaller than the
critical current, which is potentially useful for devices involving
superconducting qubits
In Situ Synthesis of Reduced Graphene Oxide and Gold Nanocomposites for Nanoelectronics and Biosensing
In this study, an in situ chemical synthesis approach has been developed to prepare graphene–Au nanocomposites from chemically reduced graphene oxide (rGO) in aqueous media. UV–Vis absorption, atomic force microscopy, scanning electron microscopy, transmission electron microscopy, and Raman spectroscopy were used to demonstrate the successful attachment of Au nanoparticles to graphene sheets. Configured as field-effect transistors (FETs), the as-synthesized single-layered rGO-Au nanocomposites exhibit higher hole mobility and conductance when compared to the rGO sheets, promising its applications in nanoelectronics. Furthermore, we demonstrate that the rGO-Au FETs are able to label-freely detect DNA hybridization with high sensitivity, indicating its potentials in nanoelectronic biosensing
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