Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian matrix factorization algorithm using stochastic gradient MCMC. Our algorithm, based on Distributed Stochastic Gradient Langevin Dynamics, can not only match the prediction accuracy of standard MCMC methods like Gibbs sampling, but at the same time is as fast and simple as stochastic gradient descent. In our experiments, we show that our algorithm can achieve the same level of prediction accuracy as Gibbs sampling an order of magnitude faster. We also show that our method reduces the prediction error as fast as distributed stochastic gradient descent, achieving a 4.1% improvement in RMSE for the Netflix dataset and an 1.8% for the Yahoo music dataset

Using differential reinforcement of high rates of behavior to improve work productivity : a replication and extension

Background: Due to deficits in adaptive and cognitive functioning, productivity may pose challenges for individuals with intellectual disability in the workplace.Method: Using a changing‐criterion embedded in a multiple baseline across partici‐pants design, we examined the effects of differential reinforcement of high rates of behaviour (DRH) on the rate of data entry (i.e., productivity) in four adults with intel‐lectual disability.Results: Although the DRH procedure increased the rate of correct data entry in all four participants, none of the participants achieved the criterion that we set with novice undergraduate students.Conclusions: Our results indicate that DRH is an effective intervention to increase rate of correct responding in individuals with intellectual disability, but that achiev‐ing the same productivity as workers without disability may not always be possible

Eta photoproduction on the neutron at GRAAL: Measurement of the differential cross section

In this contribution, we will present our first preliminary measurement of the differential cross section for the reaction gamma+n->eta+n. Comparison of the reactions gamma+p->eta+p for free and bound proton (D2 target) will also be discussed.Comment: 6 pages, 4 figures, Proceedings of the 10th International Symposium on Meson-Nucleon Physics and the Structure of the Nucleon, August 29-September 4 2004, Beijing, Chin

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution $\pi$ by simulating a Markov chain with transition kernel $P$ such that $\pi$ is invariant under $P$. However, there are many situations for which it is impractical or impossible to draw from the transition kernel $P$. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace $P$ by an approximation $\hat{P}$. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how 'close' the chain given by the transition kernel $\hat{P}$ is to the chain given by $P$. We apply these results to several examples from spatial statistics and network analysis.Comment: This version: results extended to non-uniformly ergodic Markov chain

Entanglement distribution and quantum discord

Establishing entanglement between distant parties is one of the most important problems of quantum technology, since long-distance entanglement is an essential part of such fundamental tasks as quantum cryptography or quantum teleportation. In this lecture we review basic properties of entanglement and quantum discord, and discuss recent results on entanglement distribution and the role of quantum discord therein. We also review entanglement distribution with separable states, and discuss important problems which still remain open. One such open problem is a possible advantage of indirect entanglement distribution, when compared to direct distribution protocols.Comment: 7 pages, 2 figures, contribution to "Lectures on general quantum correlations and their applications", edited by Felipe Fanchini, Diogo Soares-Pinto, and Gerardo Adess

Eta photoproduction off the neutron at GRAAL: Evidence for a resonant structure at W=1.67 GeV

New (preliminary) data on eta photoproduction off the neutron are presented. These data reveal a resonant structure at W=1.67 GeV.Comment: 8 pages, 4 figures. Published in Proceedings of Workshop on the Physics of Excited Nucleons NSTAR2004, Grenoble, France, March 24 - 27, pg.19

Lowering the Light Speed Isotropy Limit: European Synchrotron Radiation Facility Measurements

The measurement of the Compton edge of the scattered electrons in GRAAL facility in European Synchrotron Radiation Facility (ESRF) in Grenoble with respect to the Cosmic Microwave Background dipole reveals up to 10 sigma variations larger than the statistical errors. We now show that the variations are not due to the frequency variations of the accelerator. The nature of Compton edge variations remains unclear, thus outlining the imperative of dedicated studies of light speed anisotropy

Eta photoproduction off the neutron at GRAAL

The gamma n -> eta n quasi-free cross section reveals a resonant structure at W ~ 1.675 GeV. This structure may be a manifestation of a baryon resonance. A priori its properties, the possibly narrow width and the strong photocoupling to the neutron, look surprising. This structure may also signal the existence of a narrow state.Comment: To appear in Proceedings of Workshop on the Physics of Excited Nucleons NSTAR2005, 12 - 15 October 2005, Tallahassee, Florida, US

Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation"

This report is a collection of comments on the Read Paper of Fearnhead and Prangle (2011), to appear in the Journal of the Royal Statistical Society Series B, along with a reply from the authors