Realtime 3D graphics programming using the Quake3 engine

We present a lab assignment that accompanies a complete module called Real-time Graphics . The students task is to get familiar with content creation and programming a (previously) commercial 3D engine. In a first task, students have to create 3D content, which is integrated into the Quake3 engine. In a second task, the students have to implement a simple animation and finally add an impressive 3D graphics effect to the Quake3 engine. The lecture has been taught four times from 2004 to 2007. We present the assignment and report on experiences that we have gained

Age-related increase of kynurenic acid in human cerebrospinal fluid-IgG and beta(2)-microglobulin changes

Kynurenic acid (KYNA) is an endogenous metabolite in the kynurenine pathway of tryptophan degradation and is an antagonist at the glycine site of the N-methyl-D-aspartate as well as at the alpha 7 nicotinic cholinergic receptors. In the brain tissue KYNA is synthesised from L-kynurenine by kynurenine aminotransferases (KAT) I and II. A host of immune mediators influence tryptophan degradation. In the present study, the levels of KYNA in cerebrospinal fluid (CSF) and serum in a group of human subjects aged between 25 and 74 years were determined by using a high performance liquid chromatography method. In CSF and serum KAT I and II activities were investigated by radioenzymatic assay, and the levels of β2-microglobulin, a marker for cellular immune activation, were determined by ELISA. The correlations between neurochemical and biological parameters were evaluated. Two subject groups with significantly different ages, i.e. 50 years, p < 0.001, showed statistically significantly different CSF KYNA levels, i.e. 2.84 ± 0.16 fmol/μl vs. 4.09 ± 0.14 fmol/μl, p < 0.001, respectively; but this difference was not seen in serum samples. Interestingly, KYNA is synthesised in CSF principally by KAT I and not KAT II, however no relationship was found between enzyme activity and ageing. A positive relationship between CSF KYNA levels and age of subjects indicates a 95% probability of elevated CSF KYNA with ageing (R = 0.6639, p = 0.0001). KYNA levels significantly correlated with IgG and β2-microglobulin levels (R = 0.5244, p = 0.0049; R = 0.4253, p = 0.043, respectively). No correlation was found between other biological parameters in CSF or serum. In summary, a positive relationship between the CSF KYNA level and ageing was found, and the data would suggest age-dependent increase of kynurenine metabolism in the CNS. An enhancement of CSF IgG and β2-microglobulin levels would suggest an activation of the immune system during ageing. Increased KYNA metabolism may be involved in the hypofunction of the glutamatergic and/or nicotinic cholinergic neurotransmission in the ageing CNS

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, having brought exciting new models such as variational autoencoders and generative adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations. To address this issue we introduce Morpho-MNIST. We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of different models, identification of the roles of latent variables, and characterisation of sample diversity. We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation

Detecting outliers with foreign patch interpolation

In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model. Our code is available at https://github.com/jemtan/FP

Exact broken-symmetry states and Hartree-Fock solutions for quantum dots at high magnetic fields

Wigner molecules formed at high magnetic fields in circular and elliptic quantum dots are studied by exact diagonalization (ED) and unrestricted Hartree-Fock (UHF) methods with multicenter basis of displaced lowest Landau level wave functions. The broken symmetry states with semi-classical charge density constructed from superpositions of the ED solutions are compared to the UHF results. UHF overlooks the dependence of the few-electron wave function on the actual relative positions of electrons localized in different charge puddles and partially compensates for this neglect by an exaggerated separation of charge islands which are more strongly localized than in the exact broken-symmetry states.Comment: QD2004 proceedings under press in Physica

Volume Rendering with Advanced GPU Scheduling Strategies

Modern GPUs are powerful enough to enable interactive display of high-quality volume data even despite the fact that many volume rendering methods do not present a natural fit for current GPU hardware. However, there still is a vast amount of computational power that remains unused due to the inefficient use of the available hardware. In this work, we demonstrate how advanced scheduling methods can be employed to implement volume rendering algorithms in a way that better utilizes the GPU by example of three different state-of-the-art volume rendering techniques