Principal component analysis of diffuse magnetic neutron scattering: a theoretical study.

We present a theoretical study of the potential of principal component (PC) analysis to analyse magnetic diffuse neutron scattering data on quantum materials. To address this question, we simulate the scattering functionSqfor a model describing a cluster magnet with anisotropic spin-spin interactions under different conditions of applied field and temperature. We find high dimensionality reduction and that the algorithm can be trained with surprisingly small numbers of simulated observations.

Breakthrough percepts of famous faces.

Recently, we showed that presenting salient names (i.e., a participant's first name) on the fringe of awareness (in rapid serial visual presentation, RSVP) breaks through into awareness, resulting in the generation of a P3, which (if concealed information is presented) could be used to differentiate between deceivers and nondeceivers.

Deep convolutional neural networks for Raman spectrum recognition: a unified solution.

Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an essential step. Here we describe our unified solution for the identification of chemical species in which a convolutional neural network is trained to automatically identify substances according to their Raman spectrum without the need for preprocessing.

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm.

The paradigm detailed in this manuscript describes an applied experimental method based on real police investigations during which an eyewitness or victim to a crime may create from memory a holistic facial composite of the culprit with the assistance of a police operator. The aim is that the composite is recognized by someone who believes that they know the culprit. For this paradigm, participants view a culprit actor on video and following a delay, participant-witnesses construct a holistic system facial composite.

Graphite furnace atomic absorption elemental analysis of ecstasy tablets.

Six metals (copper, magnesium, barium, nickel, chromium and lead) were determined in two separate batches of seized ecstasy tablets by graphite furnace atomic absorption spectroscopy (GFAAS) following digestion with nitric acid and hydrogen peroxide. Large intra-batch variations were found as expected for tablets produced in clandestine laboratories. For example, nickel in batch 1 was present in the range 0.

Nonlinear, near photo-realistic caricatures using a parametric facial appearance model.

A mathematical model previously developed for use in computer vision applications is presented as an empirical model for face space. The term appearance space is used to distinguish this from previous models. Appearance space is a linear vector space that is dimensionally optimal, enables us to model and describe any human facial appearance, and possesses characteristics that are plausible for the representation of psychological face space.

Human facial beauty: current theories and methodologies.

This article examines current theories of beauty and describes recent progress in the ability to generate photorealistic faces using a computer. First, we describe a novel experimental tool, FacePrints, that allows a user to "evolve" an attractive face using a computer. We discuss the use of this program for research on human beauty and review the main experimental studies that have led to our current theoretical perspective: beauty is a product of sexual selection.