This paper presents our contribution to the SemEval-2014 shared task on BroadCoverage Semantic Dependency Parsing. We employ a feature-rich linear model, including scores for first and second-order dependencies (arcs, siblings, grandparents and co-parents). Decoding is performed in a global manner by solving a linear relaxation with alternating directions dual decomposition (AD3). Our system ac...

A low-dimensional dynamical system is observed in an experiment as a high-dimensional signal, for example, video of chaotic pendulums system. Assuming that we know the model up to some unknown parameters, can estimate underlying system’s parameters by measuring its time-evolution only once? The key information performing this estimation lies temporal inter-dependencies between signal and model....

Abstract Existing integer-valued generalised autoregressive conditional heteroskedasticity (INGARCH) models for spatio-temporal counts do not allow negative parameter and autocorrelation values. Using approximately linear INGARCH models, the unified flexible (B)INGARCH framework modelling unbounded (bounded) is proposed. These combine dependencies with kinds of a long memory. They are easily ad...

The linear correlation coefficient is typically used to characterize and analyze dependencies of neural spike counts. Here, we show that the correlation coefficient is in general insufficient to characterize these dependencies. We construct two neuron spike count models with Poisson-like marginals and vary their dependence structure using copulas. To this end, we construct a copula that allows ...

Using statistical models one can estimate features from natural images, such as images that we see in everyday life. Such models can also be used in computional visual neuroscience by relating the estimated features to the response properties of neurons in the brain. A seminal model for natural images was linear sparse coding which, in fact, turned out to be equivalent to ICA. In these linear g...

Recently, the Open Multi-Processing (OpenMP) standard has incorporated task-based programming, where a function call with input and output data is treated as a task. At run time, OpenMP’s superscalar scheduler tracks the data dependencies among the tasks and executes the tasks as their dependencies are resolved. On a shared-memory architecture with multiple cores, the independent tasks are exec...