First-Principles Perturbative Computation of Phonon Properties of Insulators in Finite Electric Fields

We present a perturbative method for calculating phonon properties of an insulator in the presence of a finite electric field. The starting point is a variational total-energy functional with a field-coupling term that represents the effect of the electric field. This total-energy functional is expanded in small atomic displacements within the framework of density-functional perturbation theory. The linear response of field-polarized Bloch functions to atomic displacements is obtained by minimizing the second-order derivatives of the total-energy functional. In the general case of nonzero phonon wavevector, there is a subtle interplay between the couplings between neighboring k-points introduced by the presence of the electric field in the reference state, and further-neighbor k-point couplings determined by the wavevector of the phonon perturbation. As a result, terms arise in the perturbation expansion that take the form of four-sided loops in k-space. We implement the method in the {\tt ABINIT} code and perform illustrative calculations of the field-dependent phonon frequencies for III-V semiconductors

General amyloid inhibitors? A critical examination of the inhibition of IAPP amyloid formation by inositol stereoisomers.

Islet amyloid polypeptide (IAPP or amylin) forms amyloid deposits in the islets of Langerhans; a process that is believed to contribute to the progression of type 2 diabetes and to the failure of islet transplants. An emerging theme in amyloid research is the hypothesis that the toxic species produced during amyloid formation by different polypeptides share common features and exert their effects by common mechanisms. If correct, this suggests that inhibitors of amyloid formation by one polypeptide might be effective against other amyloidogenic sequences. IAPP and Aβ, the peptide responsible for amyloid formation in Alzheimers disease, are particularly interesting in this regard as they are both natively unfolded in their monomeric states and share some common characteristics. Comparatively little effort has been expended on the design of IAPP amyloid inhibitors, thus it is natural to inquire if Aβ inhibitors are effective against IAPP, especially since no IAPP inhibitors have been clinically approved. A range of compounds inhibit Aβ amyloid formation, including various stereoisomers of inositol. Myo-, scyllo-, and epi-inositol have been shown to induce conformational changes in Aβ and prevent Aβ amyloid fibril formation by stabilizing non-fibrillar β-sheet structures. We investigate the ability of inositol stereoisomers to inhibit amyloid formation by IAPP. The compounds do not induce a conformational change in IAPP and are ineffective inhibitors of IAPP amyloid formation, although some do lead to modest apparent changes in IAPP amyloid fibril morphology. Thus not all classes of Aβ inhibitors are effective against IAPP. This work provides a basis of comparison to work on polyphenol based inhibitors of IAPP amyloid formation and helps provide clues as to the features which render them effective. The study also helps provide information for further efforts in rational inhibitor design

Cross likelihood ratio based speaker clustering using eigenvoice models

This paper proposes the use of eigenvoice modeling techniques with the Cross Likelihood Ratio (CLR) as a criterion for speaker clustering within a speaker diarization system. The CLR has previously been shown to be a robust decision criterion for speaker clustering using Gaussian Mixture Models. Recently, eigenvoice modeling techniques have become increasingly popular, due to its ability to adequately represent a speaker based on sparse training data, as well as an improved capture of differences in speaker characteristics. This paper hence proposes that it would be beneficial to capitalize on the advantages of eigenvoice modeling in a CLR framework. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 35.1% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system

Variational Inference in Nonconjugate Models

Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate updates are easily derived and in closed form. However, many models of interest---like the correlated topic model and Bayesian logistic regression---are nonconjuate. In these models, mean-field methods cannot be directly applied and practitioners have had to develop variational algorithms on a case-by-case basis. In this paper, we develop two generic methods for nonconjugate models, Laplace variational inference and delta method variational inference. Our methods have several advantages: they allow for easily derived variational algorithms with a wide class of nonconjugate models; they extend and unify some of the existing algorithms that have been derived for specific models; and they work well on real-world datasets. We studied our methods on the correlated topic model, Bayesian logistic regression, and hierarchical Bayesian logistic regression

Linking Smartphone GPS Data with Transport Planning: A Framework of Data Aggregation and Anonymization for a Journey Planning App

With the proliferation of GPS tracking data provided by smartphone apps, it is desirable to develop a data processing and anonymizing framework to transform raw GPS data into a suitable format for transport planning models. The paper aims to describe the effort to address such issues by map matching and aggregating the GPS information derived from a journey planning app. The effectiveness and flexibility of such a framework is demonstrated by an analysis of speeding and waiting time patterns in England and Wales by tracking 120 users for a year