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 $\pi$-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$_2$ to suspended samples hardly makes any difference. In contrast, applying a rather large -- but experimentally realistic -- uniform and isotropic strain of about $15\%$ 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