44,363 research outputs found
Magnetic circular dichroism from the impurity band in III-V diluted magnetic semiconductors
The magnetic circular dichroism of III-V diluted magnetic semiconductors,
calculated within a theoretical framework suitable for highly disordered
materials, is shown to be dominated by optical transitions between the bulk
bands and an impurity band formed from magnetic dopant states. The theoretical
framework incorporates real-space Green's functions to properly incorporate
spatial correlations in the disordered conduction band and valence band
electronic structure, and includes extended and localized electronic states on
an equal basis. Our findings reconcile unusual trends in the experimental
magnetic circular dichroism in III-V DMSs with the antiferromagnetic p-d
exchange interaction between a magnetic dopant spin and its host.Comment: 5 pages, 4 figure
On the pathological behavior of adaptive differential evolution on hybrid objective functions
Most state-of-the-art Differential Evolution (DE) algorithms are adaptive DEs with online parameter adaptation. We investigate the behavior of adaptive DE on a class of hy-brid functions, where independent groups of variables are associated with different component objective functions. An experimental evaluation of 3 state-of-the-art adaptive DEs (JADE, SHADE, jDE) shows that hybrid functions are "ada-ptive-DE-hard". That is, adaptive DEs have signicant fail-ure rates on these new functions. In-depth analysis of the adaptive behavior of the DEs reveals that their parameter adaptation mechanisms behave in a pathological manner on this class of problems, resulting in over-adaptation for one of the components of the hybrids and poor overall performance. Thus, this class of deceptive benchmarks pose a signicant challenge for DE
The role of electron-electron interactions in two-dimensional Dirac fermions
The role of electron-electron interactions on two-dimensional Dirac fermions
remains enigmatic. Using a combination of nonperturbative numerical and
analytical techniques that incorporate both the contact and long-range parts of
the Coulomb interaction, we identify the two previously discussed regimes: a
Gross-Neveu transition to a strongly correlated Mott insulator, and a
semi-metallic state with a logarithmically diverging Fermi velocity accurately
described by the random phase approximation. Most interestingly, experimental
realizations of Dirac fermions span the crossover between these two regimes
providing the physical mechanism that masks this velocity divergence. We
explain several long-standing mysteries including why the observed Fermi
velocity in graphene is consistently about 20 percent larger than the best
values calculated using ab initio and why graphene on different substrates show
different behavior.Comment: 11 pages, 4 figure
Fatty-acid uptake in prostate cancer cells using dynamic microfluidic raman technology
It is known that intake of dietary fatty acid (FA) is strongly correlated with prostate cancer progression but is highly dependent on the type of FAs. High levels of palmitic acid (PA) or arachidonic acid (AA) can stimulate the progression of cancer. In this study, a unique experimental set-up consisting of a Raman microscope, coupled with a commercial shear-flow microfluidic system is used to monitor fatty acid uptake by prostate cancer (PC-3) cells in real-time at the single cell level. Uptake of deuterated PA, deuterated AA, and the omega-3 fatty acids docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were monitored using this new system, while complementary flow cytometry experiments using Nile red staining, were also conducted for the validation of the cellular lipid uptake. Using this novel experimental system, we show that DHA and EPA have inhibitory effects on the uptake of PA and AA by PC-3 cells
Photomechanical Investigation of Structural Behavior of Gyroscope Components. Task IV - Analysis of Initial Redesign of AB5-K8 GYROSCOPE
Photomechanics of structure and materials in redesigned AB5-K8 gyroscope component
Superconducting correlations in ultra-small metallic grains
To describe the crossover from the bulk BCS superconductivity to a
fluctuation-dominated regime in ultrasmall metallic grains, new order
parameters and correlation functions, such as ``parity gap'' and ``pair-mixing
correlation function'', have been recently introduced. In this paper, we
discuss the small-grain behaviour of the Penrose-Onsager-Yang off-diagonal
long-range order (ODLRO) parameter in a pseudo-spin representation. Relations
between the ODLRO parameter and those mentioned above are established through
analytical and numerical calculations.Comment: 7 pages, 1 figur
Interaction driven metal-insulator transition in strained graphene
The question of whether electron-electron interactions can drive a metal to
insulator transition in graphene under realistic experimental conditions is
addressed. Using three representative methods to calculate the effective
long-range Coulomb interaction between -electrons in graphene and solving
for the ground state using quantum Monte Carlo methods, we argue that without
strain, graphene remains metallic and changing the substrate from SiO to
suspended samples hardly makes any difference. In contrast, applying a rather
large -- but experimentally realistic -- uniform and isotropic strain of about
seems to be a promising route to making graphene an antiferromagnetic
Mott insulator.Comment: Updated version: 6 pages, 3 figure
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
The absence of large scale datasets with pixel-level supervisions is a
significant obstacle for the training of deep convolutional networks for scene
text segmentation. For this reason, synthetic data generation is normally
employed to enlarge the training dataset. Nonetheless, synthetic data cannot
reproduce the complexity and variability of natural images. In this paper, a
weakly supervised learning approach is used to reduce the shift between
training on real and synthetic data. Pixel-level supervisions for a text
detection dataset (i.e. where only bounding-box annotations are available) are
generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which
provides pixel-level supervisions for the COCO-Text dataset, is created and
released. The generated annotations are used to train a deep convolutional
neural network for semantic segmentation. Experiments show that the proposed
dataset can be used instead of synthetic data, allowing us to use only a
fraction of the training samples and significantly improving the performances
Enhancement of wear properties of a polyether ether ketone polymer by incorporation of carbon and glass fibers
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