A dynamic singular equation system of asset demand

The paper presents estimates of a dynamic demand system of the AIDS type for financial assets. The results suggest that dynamic behavior plays a major role in determining asset demand. Estimates on the basis of the equivalent static equilibrium models prove to be clearly inferior statistically. Also, the theoretical restrictions of homogeneity and symmetry are thoroughly rejected by the static model versions, however, not by the dynamic demand system. The cross rate elasticities between bonds and savings deposits and also between money and time deposits are found to be negligible for Germany. Time deposits turn out to be very sensitive to own and cross rates of return.

Direct method-based statistical limit analysis of wc-co composites

In this paper, a direct method-based prediction of load-bearing capacity of nonperiodic WC-Co composites is presented. The main goal is to generalize the methodology of limit analysis on periodic heterogeneous media to materials with random microstructures. For such materials, the admissible macroscopic loading domains demonstrate remarkable scatter among RVE models of identical size and constituents but different morphologies. Limit analysis is performed on samples of a group of RVE models converted automatically from scanning electron microscopy (SEM) images. The corresponding admissible loading domains are numerically determined and statistically interpreted. The obtained results for plastic limit loads by direct method are compared with those from conventional incremental analysis

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence.Comment: Presented at CVPR 201

An Efficient Multiway Mergesort for GPU Architectures

Sorting is a primitive operation that is a building block for countless algorithms. As such, it is important to design sorting algorithms that approach peak performance on a range of hardware architectures. Graphics Processing Units (GPUs) are particularly attractive architectures as they provides massive parallelism and computing power. However, the intricacies of their compute and memory hierarchies make designing GPU-efficient algorithms challenging. In this work we present GPU Multiway Mergesort (MMS), a new GPU-efficient multiway mergesort algorithm. MMS employs a new partitioning technique that exposes the parallelism needed by modern GPU architectures. To the best of our knowledge, MMS is the first sorting algorithm for the GPU that is asymptotically optimal in terms of global memory accesses and that is completely free of shared memory bank conflicts. We realize an initial implementation of MMS, evaluate its performance on three modern GPU architectures, and compare it to competitive implementations available in state-of-the-art GPU libraries. Despite these implementations being highly optimized, MMS compares favorably, achieving performance improvements for most random inputs. Furthermore, unlike MMS, state-of-the-art algorithms are susceptible to bank conflicts. We find that for certain inputs that cause these algorithms to incur large numbers of bank conflicts, MMS can achieve up to a 37.6% speedup over its fastest competitor. Overall, even though its current implementation is not fully optimized, due to its efficient use of the memory hierarchy, MMS outperforms the fastest comparison-based sorting implementations available to date

Acute Management of Cocaine-Associated Methaemoglobinaemia

Methaemoglobinaemia is a potentially life-threatening complication of problem drug use. This is a case report of a 29-year-old man who presented himself cyanosed after a cocaine binge. It highlights the diagnosis and management of this condition from an acute medical perspective

Effects of Caveolin Disruption on Signaling from the hFSH Receptor

Follicle stimulating hormone is produced by the pituitary gland, regulating the function of both the ovaries and the testes. The inability of this hormone to bind to its receptor can cause infertility in both men and women. Human follicle stimulating hormone receptor (hFSHR) is a GPCR (g protein-coupled receptor) that plays a crucial role in the development of sperm and egg cells in the gonads. The successful interaction of FSH with its receptor results in activation of several downstream intracellular pathways including the production of cAMP. Currently, hFSHR signaling is thought to rely on the protein caveolin. Caveolin is a protein found in the caveolae of the cell membrane. Caveolae, a type of lipid raft, support a diverse group of receptors that are needed at the cell membrane. The ability of hFSHR to bind to the lipid raft is believed to be accomplished through an interaction with the transmembrane domain IV of the hFSHR with the caveolin protein. An interruption in the caveolin binding motif of the hFSHR could have a large effect on the development of sperm and egg cells and subsequently cause infertility. In this study, we introduced wild type and mutant peptides corresponding to the 4th transmembrane domain of the hFSHR into cells expressing the receptor. We expect the wild type peptide to interfere with the interaction between caveolin and its binding motif. Additionally, we expect the mutant peptide to not interfere with this interaction. Currently, we are using a quantitative fluorescent assay to measure cAMP production in order to determine if the peptide is interfering with the activation of the hFSHR. I hypothesize that if caveolin is prevented from binding to hFSHR, the receptor will become less active. I anticipate a decrease in levels of cAMP in the cells that are treated with the wild-type (interfering) peptide. There should be no difference in the level of cAMP in cells treated with the mutant peptide and the cells with no peptide. Preliminary trials have suggested that there is a difference in levels of cAMP produced between the two peptides. In the future, we plan on using scrambled peptides to validate our current observations