Bigger Buffer k-d Trees on Multi-Many-Core Systems

A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search. While providing valuable speed-ups on modern many-core devices in case both a large number of reference and query points are given, buffer k-d trees are limited by the amount of points that can fit on a single device. In this work, we show how to modify the original data structure and the associated workflow to make the overall approach capable of dealing with massive data sets. We further provide a simple yet efficient way of using multiple devices given in a single workstation. The applicability of the modified framework is demonstrated in the context of astronomy, a field that is faced with huge amounts of data

Cluster Based Term Weighting Model for Web Document Clustering

The term weight is based on the frequency with which the term appears in that document. The term weighting scheme measures the importance of a term with respect to a document and a collection. A term with higher weight is more important than a term with lower weight. A document ranking model uses these term weights to find the rank of a document in a collection. We propose a cluster-based term weighting models based on the TF-IDF model. This term weighting model update the inter-cluster and intra-cluster frequency components uses the generated clusters as a reference in improving the retrieved relevant documents. These inter cluster and intra-cluster frequency components are used for weighting the importance of a term in addition to the term and document frequency components

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Flow responses alteration by geometrical effects of tubercles on plates under the maximal angle of attack

Plates with leading-edge tubercles experience beneficially more gradual aerodynamics stalling when entering the poststall regime. Little is known, however, about the corresponding aquatic flow responses when these tubercles-furnished plates are subjected to the maximal angle of attack, with the flow direction perpendicular to their planar area. Hence, this study presents numerically, by means of the flow behavior solver ANSYS, the flow responses alteration in terms of the geometrical effects of tubercles on plates through changes in amplitudes (5 mm, 10 mm, 15 mm) and wavelengths (50 mm, 100 mm, 150 mm) under the maximal angle of attack in comparison to a control case, i.e., without tubercles. Additional to the commonly examined flow velocity and pressure, characteristics such as wake (area, reattachment length, flow recirculation intensity) and newly defined downstream vortical parameters (area, perimeter, and Feret diameters) for the vortex region have been proposed and assessed. It is found that the drag increases with the tubercle wavelength but corresponds inversely with the tubercle amplitude. By correlating with the best beneficial velocity and pressure profiles, it has been characterized that the optimally performing plate is the one that generates the greatest flow recirculation intensity, wake area, and reattachment length, corresponding to the capability to produce also the highest vortical area, perimeter, and major Feret diameter. Compared to the control case, all plates with tubercles alter beneficially these flow behaviors. In conclusion, plates with tubercles contribute favorably to the flow behaviors under the maximal angle of attack compared to the control case while the newly proposed downstream parameters could serve capably as alternatives in corroborating the flow physics description in future studies

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Citrullination facilitates cross-reactivity of rheumatoid factor with non-IgG1 Fc epitopes in rheumatoid arthritis

Rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPAs) are the two most prevalent autoantibodies in rheumatoid arthritis (RA), and are thought to have distinct autoantigen targets. Whilst RF targets the Fc region of antibodies, ACPAs target a far broader spectrum of citrullinated peptides. Here we demonstrate significant sequence and structural homology between proposed RF target epitopes in IgG1 Fc and the ACPA target fibrinogen. Two of the three homologous sequences were susceptible to citrullination, and this modification, which occurs extensively in RA, permitted significant cross-reactivity of RF+ patient sera with fibrinogen in both western blots and ELISAs. Crucially, this reactivity was specific to RF as it was absent in RF− patient and healthy control sera, and could be inhibited by pre-incubation with IgG1 Fc. These studies establish fibrinogen as a common target for both RF and ACPAs, and suggest a new mechanism in RF-mediated autoimmune diseases wherein RF may act as a precursor from which the ACPA response evolves

Design and Evaluation of Meningococcal Vaccines through Structure-Based Modification of Host and Pathogen Molecules

Neisseria meningitis remains a leading cause of sepsis and meningitis, and vaccines are required to prevent infections by this important human pathogen. Factor H binding protein (fHbp) is a key antigen that elicits protective immunity against the meningococcus and recruits the host complement regulator, fH. As the high affinity interaction between fHbp and fH could impair immune responses, we sought to identify non-functional fHbps that could act as effective immunogens. This was achieved by alanine substitution of fHbps from all three variant groups (V1, V2 and V3 fHbp) of the protein; while some residues affected fH binding in each variant group, the distribution of key amino underlying the interaction with fH differed between the V1, V2 and V3 proteins. The atomic structure of V3 fHbp in complex with fH and of the C-terminal barrel of V2 fHbp provide explanations to the differences in the precise nature of their interactions with fH, and the instability of the V2 protein. To develop transgenic models to assess the efficacy of non-functional fHbps, we determined the structural basis of the low level of interaction between fHbp and murine fH; in addition to changes in amino acids in the fHbp binding site, murine fH has a distinct conformation compared with the human protein that would sterically inhibit binding to fHbp. Non-functional V1 fHbps were further characterised by binding and structural studies, and shown in non-transgenic and transgenic mice (expressing chimeric fH that binds fHbp and precisely regulates complement system) to retain their immunogenicity. Our findings provide a catalogue of non-functional fHbps from all variant groups that can be included in new generation meningococcal vaccines, and establish proof-in-principle for clinical studies to compare their efficacy with wild-type fHbps