Intensity correlations and entanglement by frequency doubling in a dual ported resonator

We show that non-classical intensity correlations and quadrature entanglement can be generated by frequency doubling in a resonator with two output ports. We predict twin-beam intensity correlations 6 dB below the coherent state limit, and that the product of the inference variances of the quadrature fluctuations gives an Einstein-Podolsky-Rosen (EPR) correlation coefficient of V_EPR = 0.6 < 1. Comparison with an entanglement source based on combining two frequency doublers with a beam splitter shows that the dual ported resonator provides stronger entanglement at lower levels of individual beam squeezing. Calculations are performed using a self-consistent propagation method that does not invoke a mean field approximation. Results are given for physically realistic parameters that account for the Gaussian shape of the intracavity beams, as well as intracavity losses.Comment: 12 pages, 9 figures, normalization corrected, fig 3 and fig 7 update

Resolving Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.Comment: Comments welcom

Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24) - each essential parameters in wavelet-based methods - on the estimated values of network diagnostics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of network diagnostics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of network diagnostics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.Comment: working pape

Sifat Mekanik Berkas Vaskular Batang Kelapa Sawit

The objective of the project is to investigate the relationship between the stress-strain characteristics of oil palm trunk vascular bundles and its moisture content. Based on the stress-strain graphs, the mechanical characteristics are then related to the microstructure of the vascular bundles. The results show that for all samples tested, 3 zones of deformation are found i.e an initial zone that is slightly curving, a plateau zone and finally a linear zone. The existence of the plateau zone is then explained in terms of the vascular bundle microstructure. AJ; a result of the presence of the plateau zone, the stress-strain curve is then described by 2 modulus of elasticity i.e the initial modulus and final modulus. The final modulus was found to be higher than the initial modulus thus indicating that the vascular bundles become stiffer after encountering the plateau zone. The maximum stress and final modulus of the vascular bundles were found to increase when the moisture content of the vascular bundles decreased

A continuing study on the feasibility of a Badal-type photoretinoscopic apparatus

This is an experimental study of a photoretinoscopic apparatus based on the Badal optometer principle. The experimental set-up consisted of a two-element object array, +13.375 D input lens, +1.875 D intermediate lens, and +2.00 D output lens. An emmetropic human eye analogue was used to test the accuracy of the apparatus. One of the image sizes in the eye analogue was 20% larger than the other showing some departure from the Badal configuration, possibly due to errors in setting up optics for confocal relations. There is also a need for greater flux uniformity of the light source, and better quality lenses, so as to reduce aberrations, thereby enhancing resolution

IMRT QA using machine learning: A multi-institutional validation.

PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process

Entanglement and statistics in Hong-Ou-Mandel interferometry

Hong-Ou-Mandel interferometry allows one to detect the presence of entanglement in two-photon input states. The same result holds for two-particles input states which obey to Fermionic statistics. In the latter case however anti-bouncing introduces qualitative differences in the interferometer response. This effect is analyzed in a Gedankenexperiment where the particles entering the interferometer are assumed to belong to a one-parameter family of quons which continuously interpolate between the Bosonic and Fermionic statistics.Comment: 7 pages, 3 figures; minor editorial changes and new references adde