The Recurrent Temporal Discriminative Restricted Boltzmann Machines

Classification of sequence data is the topic of interest for dynamic Bayesian models and Recurrent Neural Networks (RNNs). While the former can explicitly model the temporal dependencies between class variables, the latter have a capability of learning representations. Several attempts have been made to improve performance by combining these two approaches or increasing the processing capability of the hidden units in RNNs. This often results in complex models with a large number of learning parameters. In this paper, a compact model is proposed which offers both representation learning and temporal inference of class variables by rolling Restricted Boltzmann Machines (RBMs) and class variables over time. We address the key issue of intractability in this variant of RBMs by optimising a conditional distribution, instead of a joint distribution. Experiments reported in the paper on melody modelling and optical character recognition show that the proposed model can outperform the state-of-the-art. Also, the experimental results on optical character recognition, part-of-speech tagging and text chunking demonstrate that our model is comparable to recurrent neural networks with complex memory gates while requiring far fewer parameters

Inhibition of DNA ejection from bacteriophage by Mg+2 counterions

The problem of inhibiting viral DNA ejection from bacteriophages by multivalent counterions, specifically Mg$^{+2}$ counterions, is studied. Experimentally, it is known that MgSO$_4$ salt has a strong and non-monotonic effect on the amount of DNA ejected. There exists an optimal concentration at which the minimum amount of DNA is ejected from the virus. At lower or higher concentrations, more DNA is ejected from the capsid. We propose that this phenomenon is the result of DNA overcharging by Mg$^{+2}$ multivalent counterions. As Mg$^{+2}$ concentration increases from zero, the net charge of DNA changes from negative to positive. The optimal inhibition corresponds to the Mg$^{+2}$ concentration where DNA is neutral. At lower/higher concentrations, DNA genome is charged. It prefers to be in solution to lower its electrostatic self-energy, which consequently leads to an increase in DNA ejection. By fitting our theory to available experimental data, the strength of DNA$-$DNA short range attraction energies, mediated by Mg$^{+2}$, is found to be $-$0.004 $k_BT$ per nucleotide base. This and other fitted parameters agree well with known values from other experiments and computer simulations. The parameters are also in aggreement qualitatively with values for tri- and tetra-valent counterions.Comment: 17 pages, 4 figures, improved manuscript. Submitted to J. Chem. Phys (2010

Thermodynamic dislocation theory of high-temperature deformation in aluminum and steel

The statistical-thermodynamic dislocation theory developed in previous papers is used here in an analysis of high-temperature deformation of aluminum and steel. Using physics-based parameters that we expect theoretically to be independent of strain rate and temperature, we are able to fit experimental stress-strain curves for three different strain rates and three different temperatures for each of these two materials. Our theoretical curves include yielding transitions at zero strain in agreement with experiment. We find that thermal softening effects are important even at the lowest temperatures and smallest strain rates.Comment: 7 pages, 8 figure

Bounding film drainage in common thin films

A review of thin film drainage models is presented in which the predictions of thinning velocities and drainage times are compared to reported values on foam and emulsion films found in the literature. Free standing films with tangentially immobile interfaces and suppressed electrostatic repulsion are considered, such as those studied in capillary cells. The experimental thinning velocities and drainage times of foams and emulsions are shown to be bounded by predictions from the Reynolds and the theoretical MTsR equations. The semi-empirical MTsR and the surface wave equations were the most consistently accurate with all of the films considered. These results are used in an accompanying paper to develop scaling laws that bound the critical film thickness of foam and emulsion films

The Anticorrelated Nature of the Primary and Secondary Eclipse Timing Variations for the Kepler Contact Binaries

We report on a study of eclipse timing variations in contact binary systems, using long-cadence lightcurves in the Kepler archive. As a first step, 'observed minus calculated' (O-C) curves were produced for both the primary and secondary eclipses of some 2000 Kepler binaries. We find ~390 short-period binaries with O-C curves that exhibit (i) random-walk like variations or quasi-periodicities, with typical amplitudes of +/- 200-300 seconds, and (ii) anticorrelations between the primary and secondary eclipse timing variations. We present a detailed analysis and results for 32 of these binaries with orbital periods in the range of 0.35 +/- 0.05 days. The anticorrelations observed in their O-C curves cannot be explained by a model involving mass transfer, which among other things requires implausibly high rates of ~0.01 M_sun per year. We show that the anticorrelated behavior, the amplitude of the O-C delays, and the overall random-walk like behavior can be explained by the presence of a starspot that is continuously visible around the orbit and slowly changes its longitude on timescales of weeks to months. The quasi-periods of ~50-200 days observed in the O-C curves suggest values for k, the coefficient of the latitude dependence of the stellar differential rotation, of ~0.003-0.013.Comment: Published in The Astrophysical Journal, 2013, Vol. 774, p.81; 14 pages, 12 figures, and 2 table

Stochastic and deterministic models for age-structured populations with genetically variable traits

Understanding how stochastic and non-linear deterministic processes interact is a major challenge in population dynamics theory. After a short review, we introduce a stochastic individual-centered particle model to describe the evolution in continuous time of a population with (continuous) age and trait structures. The individuals reproduce asexually, age, interact and die. The 'trait' is an individual heritable property (d-dimensional vector) that may influence birth and death rates and interactions between individuals, and vary by mutation. In a large population limit, the random process converges to the solution of a Gurtin-McCamy type PDE. We show that the random model has a long time behavior that differs from its deterministic limit. However, the results on the limiting PDE and large deviation techniques \textit{\`a la} Freidlin-Wentzell provide estimates of the extinction time and a better understanding of the long time behavior of the stochastic process. This has applications to the theory of adaptive dynamics used in evolutionary biology. We present simulations for two biological problems involving life-history trait evolution when body size is plastic and individual growth is taken into account.Comment: This work is a proceeding of the CANUM 2008 conferenc

Protocol for an economic evaluation alongside a cluster randomised controlled trial: cost-effectiveness of Learning Clubs, a multicomponent intervention to improve women’s health and infant’s health and development in Vietnam

Introduction: Economic evaluations of complex interventions in early child development are required to guide policy and programme development, but a few are yet available. Methods and analysis: Although significant gains have been made in maternal and child health in resource- constrained environments, this has mainly been concentrated on improving physical health. The Learning Clubs programme addresses both physical and mental child and maternal health. This study is an economic evaluation of a cluster randomised controlled trial of the impact of the Learning Clubs programme in Vietnam. It will be conducted from a societal perspective and aims to identify the cost-effectiveness and the economic and social returns of the intervention. A total of 1008 pregnant women recruited from 84 communes in a rural province in Vietnam will be included in the evaluation. Health and cost data will be gathered at three stages of the trial and used to calculate incremental cost-effectiveness ratios per percentage point improvement of infant’s development, infant’s health and maternal common mental disorders expressed in quality-adjusted life years gained. The return on investment will be calculated based on improvements in productivity, the results being expressed as benefit–cost ratios. Ethics and dissemination: The trial was approved by Monash University Human Research Ethics Committee (Certificate Number 2016–0683), Australia, and approval was extended to include the economic evaluation (Amendment Review Number 2018-0683-23806); and the Institutional Review Board of the Hanoi School of Public Health (Certificate Number 017-377IDD- YTCC), Vietnam. Results will be disseminated through academic journals and conference presentations

Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)