Red fluorescence and 3-12 micron emission in NGC 2023, HD 44179, M 82, and Lynds 1780

A red excess observed in the Red Rectangle (HD 44179), was attributed to a possible molecular fluorescence mechanism was discovered in NGC 2023 and analyzed in subsequent work in this and other nebulae. An unexpected red light excess was also noticed in a high latitude dark cloud L 1780. The fluorescence was attributed to hydrogenated amorphous carbon by Duley (1985), on the basis of laboratory work. Alternatively, transitions between electronic states of free polycyclic aromatic hydrocarbon molecules, by-passing the cascade along the vibrational states was considered. In L 1780, the red excess was related to the 12 micron emission detected by IRAS. A quantitative comparison of the intensity of the red fluorescence and that of the 3 to 12 micron features is thus warranted in helping assess the physical properties of large interstellar molecules. The red fluorescence radiation, F(R), appears as a bump on the spectra between 0.6 and 0.9 micron. Values were deduced from the spectra for HD 44179, and for the high latitude cloud L 1780. Corrections for the extinction, both interstellar and internal to the nebulae, were included. The 3 to 12 micron brightness, F(IR), was obtained through integration of the spectra for NGC 2023, and for HD 44179 after removal of a smooth continuum due to hot large grains. The values of the ratio of fluorescence flux to the infrared flux, F(R)/F(IR), are summarized. Red fluorescence and infrared radiation are two separate ways to access to the size of the molecules through observation, and it is rewarding that both approaches give similar results. These findings bring a striking coherence into the physical description of the particles, and add further support to the initial attribution of the infrared features to polycyclic aromatic hydrocarbons (PAHs)

Viscous analyses for flow through subsonic and supersonic intakes

A parabolized Navier-Stokes code was used to analyze a number of diffusers typical of a modern inlet design. The effect of curvature of the diffuser centerline and transitioning cross sections was evaluated to determine the primary cause of the flow distortion in the duct. Results are presented for S-shaped intakes with circular and transitioning cross sections. Special emphasis is placed on verification of the analysis to accurately predict distorted flow fields resulting from pressure-driven secondary flows. The effect of vortex generators on reducing the distortion of intakes is presented. Comparisons of the experimental and analytical total pressure contours at the exit of the intake exhibit good agreement. In the case of supersonic inlets, computations of the inlet flow field reveal that large secondary flow regions may be generated just inside of the intake. These strong flows may lead to separated flow regions and cause pronounced distortions upstream of the compressor

Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions

A person's face discloses important information about their affective state. Although there has been extensive research on recognition of facial expressions, the performance of existing approaches is challenged by facial occlusions. Facial occlusions are often treated as noise and discarded in recognition of affective states. However, hand over face occlusions can provide additional information for recognition of some affective states such as curiosity, frustration and boredom. One of the reasons that this problem has not gained attention is the lack of naturalistic occluded faces that contain hand over face occlusions as well as other types of occlusions. Traditional approaches for obtaining affective data are time demanding and expensive, which limits researchers in affective computing to work on small datasets. This limitation affects the generalizability of models and deprives researchers from taking advantage of recent advances in deep learning that have shown great success in many fields but require large volumes of data. In this paper, we first introduce a novel framework for synthesizing naturalistic facial occlusions from an initial dataset of non-occluded faces and separate images of hands, reducing the costly process of data collection and annotation. We then propose a model for facial occlusion type recognition to differentiate between hand over face occlusions and other types of occlusions such as scarves, hair, glasses and objects. Finally, we present a model to localize hand over face occlusions and identify the occluded regions of the face.Comment: Accepted to International Conference on Affective Computing and Intelligent Interaction (ACII), 201