First On-Sky High Contrast Imaging with an Apodizing Phase Plate

We present the first astronomical observations obtained with an Apodizing Phase Plate (APP). The plate is designed to suppress the stellar diffraction pattern by 5 magnitudes from 2-9 lambda/D over a 180 degree region. Stellar images were obtained in the M' band (4.85 microns) at the MMTO 6.5m telescope, with adaptive wavefront correction made with a deformable secondary mirror designed for low thermal background observations. The measured PSF shows a halo intensity of 0.1% of the stellar peak at 2 lambda/D (0.36 arcsec), tapering off as r^{-5/3} out to radius 9 lambda/D. Such a profile is consistent with residual errors predicted for servo lag in the AO system. We project a 5 sigma contrast limit, set by residual atmospheric fluctuations, of 10.2 magnitudes at 0.36 arcsec separation for a one hour exposure. This can be realised if static and quasi-static aberrations are removed by differential imaging, and is close to the sensitivity level set by thermal background photon noise for target stars with M'>3. The advantage of using the phase plate is the removal of speckle noise caused by the residuals in the diffraction pattern that remain after PSF subtraction. The APP gives higher sensitivity over the range 2-5 lambda/D compared to direct imaging techniques.Comment: 22 pages, 5 figures, 1 table, ApJ accepte

Accidental Inflation in String Theory

We show that inflation in type IIB string theory driven by the volume modulus can be realized in the context of the racetrack-based Kallosh-Linde model (KL) of moduli stabilization. Inflation here arises through the volume modulus slow-rolling down from a flat hill-top or inflection point of the scalar potential. This situation can be quite generic in the landscape, where by uplifting one of the two adjacent minima one can turn the barrier either to a flat saddle point or to an inflection point supporting eternal inflation. The resulting spectral index is tunable in the range of 0.93 < n_s < 1, and there is only negligible production of primordial gravitational waves r < 10^{-6}. The flatness of the potential in this scenario requires fine-tuning, which may be justified taking into account the exponential reward by volume factors preferring the regions of the universe with the maximal amount of slow-roll inflation. This consideration leads to a tentative prediction of the spectral index $n_s\approx 0.95$ or $n_s \approx 0.93$ depending on whether the potential has a symmetry phi -> - phi or not.Comment: 15 pages, 6 figures, LaTeX, uses RevTex

SUGRA chaotic inflation and moduli stabilisation

Chaotic inflation predicts a large gravitational wave signal which can be tested by the upcoming Planck satellite. We discuss a SUGRA implementation of chaotic inflation in the presence of moduli fields, and find that inflation does not work with a generic KKLT moduli stabilisation potential. A viable model can be constructed with a fine-tuned moduli sector, but only for a very specific choice of Kahler potential. Our analysis also shows that inflation models satisfying \partial_{i} W_{\rm inf}=0 for all inflation sector fields \phi_i can be combined successfully with a fine-tuned moduli sector.Comment: 20 pages, 4 figures, refs adde

Neuroinflammation, Mast Cells, and Glia: Dangerous Liaisons

The perspective of neuroinflammation as an epiphenomenon following neuron damage is being replaced by the awareness of glia and their importance in neural functions and disorders. Systemic inflammation generates signals that communicate with the brain and leads to changes in metabolism and behavior, with microglia assuming a pro-inflammatory phenotype. Identification of potential peripheral-to-central cellular links is thus a critical step in designing effective therapeutics. Mast cells may fulfill such a role. These resident immune cells are found close to and within peripheral nerves and in brain parenchyma/meninges, where they exercise a key role in orchestrating the inflammatory process from initiation through chronic activation. Mast cells and glia engage in crosstalk that contributes to accelerate disease progression; such interactions become exaggerated with aging and increased cell sensitivity to stress. Emerging evidence for oligodendrocytes, independent of myelin and support of axonal integrity, points to their having strong immune functions, innate immune receptor expression, and production/response to chemokines and cytokines that modulate immune responses in the central nervous system while engaging in crosstalk with microglia and astrocytes. In this review, we summarize the findings related to our understanding of the biology and cellular signaling mechanisms of neuroinflammation, with emphasis on mast cell-glia interactions

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods