Face processing: human perception and principal components analysis

Principal component analysis (PCA) of face images is here related to subjects' performance on the same images. In two experiments subjects were shown a set of faces and asked to rate them for distinctiveness. They were subsequently shown a superset of faces and asked to identify those which appeared originally. Replicating previous work, we found that hits and false positives (FPs) did not correlate: those faces easy to identify as being "seen" were unrelated to those faces easy to reject as being "unseen". PCA was performed on three data sets: (i) face images with eye-position standardised; (ii) face images morphed to a standard template to remove shape information; (iii) the shape information from faces only. Analyses based upon PCA of shape-free faces gave high predictions of FPs, while shape information itself contributed only to hits. Furthermore, while FPs were generally predictable from components early in the PCA, hits appear to be accounted for by later components. We conclude that shape and "texture" (the image-based information remaining after morphing) may be used separately by the human face processing system, and that PCA of images offers a useful tool for understanding this system

Metabolic clustering analysis as a strategy for compound selection in the drug discovery pipeline for leishmaniasis

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis–mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs

Electron-hadron shower discrimination in a liquid argon time projection chamber

By exploiting structural differences between electromagnetic and hadronic showers in a multivariate analysis we present an efficient Electron-Hadron discrimination algorithm for liquid argon time projection chambers, validated using Geant4 simulated data

First Operation of a Resistive Shell Liquid Argon Time Projection Chamber -- A new Approach to Electric-Field Shaping

We present a new technology for the shaping of the electric field in Time Projection Chambers (TPCs) using a carbon-loaded polyimide foil. This technology allows for the minimisation of passive material near the active volume of the TPC and thus is capable to reduce background events originating from radioactive decays or scattering on the material itself. Furthermore, the high and continuous electric resistivity of the foil limits the power dissipation per unit area and minimizes the risks of damages in the case of an electric field breakdown. Replacing the conventional field cage with a resistive plastic film structure called 'shell' decreases the number of components within the TPC and therefore reduces the potential points of failure when operating the detector. A prototype liquid argon (LAr) TPC with such a resistive shell and with a cathode made of the same material was successfully tested for long term operation with electric field values up to about 1.5 kV/cm. The experiment shows that it is feasible to successfully produce and shape the electric field in liquefied noble-gas detectors with this new technology.Comment: 13 page

Varimax rotation based on gradient projection needs between 10 and more than 500 random start loading matrices for optimal performance

Gradient projection rotation (GPR) is a promising method to rotate factor or component loadings by different criteria. Since the conditions for optimal performance of GPR-Varimax are widely unknown, this simulation study investigates GPR towards the Varimax criterion in principal component analysis. The conditions of the simulation study comprise two sample sizes (n = 100, n = 300), with orthogonal simple structure population models based on four numbers of components (3, 6, 9, 12), with- and without Kaiser-normalization, and six numbers of random start loading matrices for GPR-Varimax rotation (1, 10, 50, 100, 500, 1,000). GPR-Varimax rotation always performed better when at least 10 random matrices were used for start loadings instead of the identity matrix. GPR-Varimax worked better for a small number of components, larger (n = 300) as compared to smaller (n = 100) samples, and when loadings were Kaiser-normalized before rotation. To ensure optimal (stationary) performance of GPR-Varimax in recovering orthogonal simple structure, we recommend using at least 10 iterations of start loading matrices for the rotation of up to three components and 50 iterations for up to six components. For up to nine components, rotation should be based on a sample size of at least 300 cases, Kaiser-normalization, and more than 50 different start loading matrices. For more than nine components, GPR-Varimax rotation should be based on at least 300 cases, Kaiser-normalization, and at least 500 different start loading matrices.Comment: 19 pages, 8 figures, 2 tables, 4 figures in the Supplemen

A Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics

Numerous statistics have been proposed for the measure of offensive ability in major league baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue with a Bayesian hierarchical model for variable selection to capture which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distributions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of 50 different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that 33/50 metrics demonstrate signal. However, these metrics are highly correlated with one another and related to traditional notions of performance (e.g., plate discipline, power, and ability to make contact)

Modeling relation paths for knowledge base completion via joint adversarial training

Knowledge Base Completion (KBC), which aims at determining the missing relations between entity pairs, has received increasing attention in recent years. Most existing KBC methods focus on either embedding the Knowledge Base (KB) into a specific semantic space or leveraging the joint probability of Random Walks (RWs) on multi-hop paths. Only a few unified models take both semantic and path-related features into consideration with adequacy. In this paper, we propose a novel method to explore the intrinsic relationship between the single relation (i.e. 1-hop path) and multi-hop paths between paired entities. We use Hierarchical Attention Networks (HANs) to select important relations in multi-hop paths and encode them into low-dimensional vectors. By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i.e. relation classifier and source discriminator), to capture shared/similar information between them. By joint adversarial training, we encourage our model to extract features from the multi-hop paths which are representative for relation completion. We apply the trained model (except for the source discriminator) to several large-scale KBs for relation completion. Experimental results show that our method outperforms existing path information-based approaches. Since each sub-module of our model can be well interpreted, our model can be applied to a large number of relation learning tasks.Comment: Accepted by Knowledge-Based System