Enhanced ionization of acetylene in intense laser pulses is due to energy upshift and field coupling of multiple orbitals

Synopsis We describe a new enhanced ionization mechanism for polyatomic molecules. It works via a significant energy up-shift of valence orbitals for stretched bonds and a strong concomitant increase in the coupling between multiple molecular orbitals

Deep Learning for Prediction of AMD Progression: A Pilot Study

Purpose: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning. Methods: Seventy-one eyes of 71 patients with confirmed early/intermediate AMD with contralateral wet AMD were imaged with OCT three times over 2 years (baseline, year 1, year 2). These eyes were divided into two groups: eyes that had not converted to wet AMD (n = 40) at year 2 and those that had (n = 31). Two deep convolutional neural networks (CNN) were evaluated using 5-fold cross validation on the OCT data at baseline to attempt to predict which eyes would convert to advanced AMD at year 2: (1) VGG16, a popular CNN for image recognition was fine-tuned, and (2) a novel, simplified CNN architecture was trained from scratch. Preprocessing was added in the form of a segmentation-based normalization to reduce variance in the data and improve performance. Results: Our new architecture, AMDnet, with preprocessing, achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89 at the B-scan level and 0.91 for volumes. Results for VGG16, an established CNN architecture, with preprocessing were 0.82 for B-scans/0.87 for volumes versus 0.66 for B-scans/0.69 for volumes without preprocessing. Conclusions: A CNN with layer segmentation-based preprocessing shows strong predictive power for the progression of early/intermediate AMD to advanced AMD. Use of the preprocessing was shown to improve performance regardless of the network architecture

Real-time 2D/3D Deformable Registration Using Metric Learning

Abstract. We present a novel 2D/3D deformable registration method, called Registration Efficiency and Accuracy through Learning Metric on Shape (REALMS), that can support real-time Image-Guided Radiation Therapy (IGRT). The method consists of two stages: planning-time learning and registration. In the planning-time learning, it firstly models the patient’s 3D deformation space from the patient’s time-varying 3D planning images using a low-dimensional parametrization. Secondly, it samples deformation parameters within the deformation space and generates corresponding simulated projection images from the deformed 3D image. Finally, it learns a Riemannian metric in the projection space for each deformation parameter. The learned distance metric forms a Gaussian kernel of a kernel regression that minimizes the leave-one-out regression residual of the corresponding deformation parameter. In the registration, REALMS interpolates the patient’s 3D deformation parameters using the kernel regression with the learned distance metrics. Our test results showed that REALMS can localize the tumor in 10.89 ms (91.82 fps) with 2.56 ± 1.11 mm errors using a single projection image. These promising results show REALMS’s high potential to support realtime, accurate, and low-dose IGRT.

DESA1002 'Continuous City' <Casthuri Kamalaraj>

The city of Amsterdam is a densely packed area, using every space possible to boast its unique architectural identity. In my project, I have explored these core foundations in Amsterdam architecture, whilst tying it together with the function of my building. My design for an International Boarding School in Amsterdam is an exclusive school, offering both accommodation and education. I delved into the mindset of an international student, attempting to fit in whilst also maintaining pride in their own background. This concept of simultaneous conformity and individuality was the primary inspiration behind my design process this semester. The original block plan provided was from an area of Amsterdam which was primarily housing based. Due to the insertion of Ross Boulevard, I consequently chose a triangular form at the end of one blackened area on the site plan. This however, forced the issue of what limited space we had to work with in terms of building size. Here arose the newer concept of cantilevering over the pedestrian accessway, a form not often seen in the streets of Amsterdam. This created an almost “demented” shape of a triangle, with an additional right angled triangle attached for the upper floors, as a means of maximising what little space I had to begin with. From this point, the building took momentum, creating specific functions for each level, moving from a more public open area on the ground floor, to education on the first, and accommodation on the second to assure privacy. The building remained in the traditional cladding of brick, however windows were enlarged to not only allow more light to enter the building in Amsterdam’s dimly lit skies, but also to escape the image of an institution. Thus, being positioned tightly within a busy Amsterdam block, my project fits into its surroundings, whilst remaining true to its own individuality

A Neighborhood Incorporated Method in Image Registration

Abstract. Mutual information has been widely used in image registration as an effective similarity measure. It has attracted a lot of attention to the effective use of the spatial information. Here we propose a new measure that includes the mean of the neighborhood region of each pixel as one variable of the two-dimension normal distribution assumed in our method. The experimental results show that our method can not only improve the robustness of mutual information, but also reduce the affect of noise in image registration.