Metallic phase of the quantum Hall effect in four-dimensional space

We study the phase diagram of the quantum Hall effect in four-dimensional (4D) space. Unlike in 2D, in 4D there exists a metallic as well as an insulating phase, depending on the disorder strength. The critical exponent $\nu\approx 1.2$ of the diverging localization length at the quantum Hall insulator-to-metal transition differs from the semiclassical value $\nu=1$ of 4D Anderson transitions in the presence of time-reversal symmetry. Our numerical analysis is based on a mapping of the 4D Hamiltonian onto a 1D dynamical system, providing a route towards the experimental realization of the 4D quantum Hall effect.Comment: 4+epsilon pages, 3 figure

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201

Enterprise gamification systems and employment legislation: a systematic literature review

A recent innovation in employee motivation systems is the introduction of ‘gamification’, which refers to the use of game design mechanics and principles to influence behaviour to enhance staff motivation and engagement. Enterprise gamification systems aggravate the differences in information availability between employers and employees, and employees who may be forced to adopt such systems may be placed under stress, worsening employment relationships in the workplace. Therefore, this research examines the potential legal implications of gamified employee motivation systems. This study undertook a systematic review of enterprise gamification and then used thematic analysis coupled with a review of legislation to examine whether gamification in workplaces meets the legal obligations of employers under their ‘duty of good faith’ in the New Zealand context. We find that carefully designed enterprise gamification systems should provide sufficient information and clarity for employees and support positive employment relationships. Deployments of enterprise gamification systems should be carefully planned with employee consultation and feedback supporting the introduction of an enterprise gamification system. Future research should look beyond the ‘good faith’ obligation and examine the relationship between gamification systems and the law on personal grievances

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings

On the master equation approach to kinetic theory: linear and nonlinear Fokker--Planck equations

We discuss the relationship between kinetic equations of the Fokker-Planck type (two linear and one non-linear) and the Kolmogorov (a.k.a. master) equations of certain N-body diffusion processes, in the context of Kac's "propagation of chaos" limit. The linear Fokker-Planck equations are well-known, but here they are derived as a limit N->infty of a simple linear diffusion equation on (3N-C)-dimensional N-velocity spheres of radius sqrt(N) (with C=1 or 4 depending on whether the system conserves energy only or energy and momentum). In this case, a spectral gap separating the zero eigenvalue from the positive spectrum of the Laplacian remains as N->infty,so that the exponential approach to equilibrium of the master evolution is passed on to the limiting Fokker-Planck evolution in R^3. The non-linear Fokker-Planck equation is known as Landau's equation in the plasma physics literature. Its N-particle master equation, originally introduced (in the 1950s) by Balescu and Prigogine (BP), is studied here on the (3N-4)-dimensional N-velocity sphere. It is shown that the BP master equation represents a superposition of diffusion processes on certain two-dimensional sub-manifolds of R^{3N} determined by the conservation laws for two-particle collisions. The initial value problem for the BP master equation is proved to be well-posed and its solutions are shown to decay exponentially fast to equilibrium. However, the first non-zero eigenvalue of the BP operator is shown to vanish in the limit N->infty. This indicates that the exponentially fast approach to equilibrium may not be passed from the finite-N master equation on to Landau's nonlinear kinetic equation.Comment: 20 pages; based on talk at the 18th ICTT Conference. Some typos and a few minor technical fixes. Modified title slightl

Radical political unionism reassessed

Defections from European social-democratic parties and a resurgence of union militancy have prompted some to diagnose a new left-wing trade unionism across Europe. This comment on the article by Connolly and Darlington scrutinizes trends in France and Germany but primarily analyses recent developments in Britain. While there are some instances of disaffiliation from the Labour Party, support for electoral alternatives, growth in political militancy and emphasis on new forms of internationalism, these have been limited. There is insufficient evidence to suggest that we are witnessing the making of a new radical collectivism

Tracking Cooper Pairs in a Cuprate Superconductor by Ultrafast Angle-Resolved Photoemission

In high-temperature superconductivity, the process that leads to the formation of Cooper pairs, the fundamental charge carriers in any superconductor, remains mysterious. We use a femtosecond laser pump pulse to perturb superconducting Bi2Sr2CaCu2O8+{\delta}, and study subsequent dynamics using time- and angle-resolved photoemission and infrared reflectivity probes. Gap and quasiparticle population dynamics reveal marked dependencies on both excitation density and crystal momentum. Close to the d-wave nodes, the superconducting gap is sensitive to the pump intensity and Cooper pairs recombine slowly. Far from the nodes pumping affects the gap only weakly and recombination processes are faster. These results demonstrate a new window into the dynamical processes that govern quasiparticle recombination and gap formation in cuprates.Comment: 22 pages, 9 figure

Speaker recognition with hybrid features from a deep belief network

Learning representation from audio data has shown advantages over the handcrafted features such as mel-frequency cepstral coefficients (MFCCs) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different lengths. In particular, we study the use of features from different levels of deep belief network for quantizing the audio data into vectors of audio word counts. These vectors represent the audio scripts of different lengths that make them easier to train a classifier. We show in the experiment that the audio word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio word count vector and the MFCC features