PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy.

Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. However, this raises privacy concerns, especially when face images collected for biometric recognition purposes are used for attribute analysis without the person's consent. To address this problem, we develop a technique for imparting soft biometric privacy to face images via an image perturbation methodology.

ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.

Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders.

Protein structure refinement via molecular-dynamics simulations: What works and what does not?

Protein structure refinement during CASP11 by the Feig group was described. Molecular dynamics simulations were used in combination with an improved selection and averaging protocol. On average, modest refinement was achieved with some targets improved significantly.

Interactions of amino acid side-chain analogs within membrane environments.

The interactions among four amino acid analog pairs (Asn, Ser, Phe, and Val) within the membrane environment were investigated using umbrella sampling molecular dynamics simulations. The results confirm generally expected qualitative trends of preferential association of polar compounds inside the membrane vs preferential interaction of hydrophobic compounds outside the membrane. Furthermore, correlations between amino acid interactions, membrane insertion, and membrane deformations are discussed and a detailed analysis of pair interaction energies is presented.

Density-biased sampling: a robust computational method for studying pore formation in membranes.

A new reaction coordinate to bias molecular dynamics simulation is described that allows enhanced sampling of density-driven processes, such as mixing and demixing two different molecular species. The methodology is validated by comparing the theoretical entropy of demixing two ideal gas species and then applied to induce deformation and pore formation in phospholipid membranes within an umbrella sampling framework. Comparison with previous biased simulations of membrane pore formation suggests overall quantitative agreement, but the density-based biasing potential results in a different, more realistic transition pathway than that in previous studies.

Physics-based protein structure refinement through multiple molecular dynamics trajectories and structure averaging.

We used molecular dynamics (MD) simulations for structure refinement of Critical Assessment of Techniques for Protein Structure Prediction 10 (CASP10) targets. Refinement was achieved by selecting structures from the MD-based ensembles followed by structural averaging. The overall performance of this method in CASP10 is described, and specific aspects are analyzed in detail to provide insight into key components.

Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles.

A molecular dynamics (MD) simulation based protocol for structure refinement of template-based model predictions is described. The protocol involves the application of restraints, ensemble averaging of selected subsets, interpolation between initial and refined structures, and assessment of refinement success. It is found that sub-microsecond MD-based sampling when combined with ensemble averaging can produce moderate but consistent refinement for most systems in the CASP targets considered here.

Dispersion stability in carbon nanotube modified polymers and its effect on the fracture toughness.

In this paper, the dispersion stability of multiwall carbon nanotubes (MWNTs) mixed with an epoxy resin is studied. An instrumented optical microscope with a hot stage was used to study the evolution of the carbon nanotubes (CNTs) dispersion during the cure of the resin. A new image processing approach is then introduced to quantify dispersion and identify the source of dispersion degradation during the cure.

Toughening of epoxy matrices with reduced single-walled carbon nanotubes.

Reduced single-walled carbon nanotubes (r-SWCNT) are shown to react readily at room temperature under inert atmosphere conditions with epoxide moieties, such as those in triglycidyl p-amino phenol (TGAP), to produce a soft covalently bonded interface around the SWCNT. The soft interface is compatible with the SWCNT-free cross-linked cured matrix and acts as a toughener for the composite. Incorporation of 0.