Although most brain-machine interface studies have focused on decoding kinematic parameters of motion such as hand position and velocity, it is known that motor cortical activity also correlates with kinetic signals, including active hand force and joint torque. Here, we attempted to reconstruct torque trajectories of the shoulder and elbow joints from the activity of simultaneously recorded units in primary motor cortex (MI) as monkeys (Macaca Mulatta) made reaching movements in the horizontal plane. Using a linear filter decoding approach that considers the history of neuronal activity up to one second in the past, we found reconstruction performance nearly equal to that of Cartesian hand position, despite the considerably greater bandwidth of the torque signals. The addition of delayed position and velocity feedback to the decoder generated consistently better torque reconstructions, suggesting that simple limb-state feedback may be useful to optimize brain-machine interface performance. 1

We propose semantic texton forests, efficient and powerful new low-level features. These are ensembles of decision trees that act directly on image pixels, and therefore do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train

algorithms by using kernel functions. The second one is: we can make accurate predictions by building an ensemble of relatively simple-minded models rather than carefully fine-tuning a single model. I will begin the course, however, with a few basic nonparametric techniques. This is partly because some

this parsing framework to both tracks of the CoNLL 2007 shared task, in each case taking advantage of multiple models trained with different learners. In the multilingual track, we train three LR models for each of the ten languages, and combine the analyses obtained with each individual model with a maximum

Three recently proposed and promising methods for postprocessing ensemble forecasts based on their historical error characteristics (i.e., ensemble-model output statistics methods) are compared using a multidecadal reforecast dataset. Logistic regressions and nonhomogeneous Gaussian regressions

workflow and the way that scientists share and access data and scientific information. Since 2000, the Ensembl web interface has had three major updates and numerous smaller updates. These have largely been in response to expanding data types and valuable representations of existing data types. In 2007

ABSTRACT A variant of a least squares ensemble (Kalman) filter that is suitable for implementation on parallel architectures is presented. This parallel ensemble filter produces results that are identical to those from sequential algorithms already described in the literature when forward

(Manuscript submitted on the 26/09/2007) High-resolution models can reproduce mesoscale dynamics and the variability in the Gulf of Mexico (GOM), but cannot provide accurate locations of currents without data assimilation (DA). We use the computationally cheap Ensemble Optimal Inter-polation (En

Abstract. Ensembles are often capable of greater prediction accuracy than any of their individual members. As a consequence of the diversity between individual base-learners, an ensemble will not suffer from overfitting. On the other hand, in many cases we are dealing with imbalanced data and a

ABSTRACT: The spatial characteristics of ensemble transform Kalman filter (ETKF) sensitive area predictions (SAPs) are explored using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts for the period of the 2003 North Atlantic THORPEX Regional Campaign. The ensemble size