Sensory and motor variables are typically represented by a population of broadly tuned neurons. A coarser representation with broader tuning can often improve coding accuracy, but sometimes the accuracy may also improve with sharper tuning. The theoretical analysis here shows that the relationship between tuning width and accuracy depends crucially on the dimension of the encoded variable. A ge...

In the analysis of speech production, glottal inverse filtering has proved to be an effective yet non-invasive method for obtaining information about the voice source. One of the main challenges of the existing methods is blind estimation of the contribution of the lip radiation, which must often be manually determined. To obtain a fully automatic system, we propose an automatic method for dete...

Many different mechanisms have been developed to implement Distributed Transactional Memory (DTM). Unfortunately, there is no “one-size-fits-all” design that offers the desirable performance across all possible workloads and scales. In fact, the performance of these mechanisms is affected by a number of intertwined factors that make it hard, or even impossible, to statically configure a DTM pla...

This paper presents a general framework for time series clustering based on spectral decomposition of the affinity matrix. We use the Gaussian function to construct the affinity matrix and develop a gradient based method for self-tuning the variance of the Gaussian function. The feasibility of our method is guaranteed by the theoretical inference in this paper. And our approach can be used to c...

The Parsing and Learning System(PALS) is a massively parallel self-tuning context-free parser. It is capable of parsing sentences of unbounded length mainly due to its parse-tree representation scheme. The system is capable of improving its parsing performance through the presentation of training examples.

Computational terminology has notably evolved since the advent of computers. Regarding the extraction of terms in particular, a large number of resources has been developed: from very general tools to other much more specific acquis ition methodologies. Such acquisition methodologies range from using simple linguistic patterns or frequency counting methods to using much more evolved strategies ...

In this paper we present an alternative strategy for fine-tuning the parameters of a network. We named the technique Gradual Tuning. Once trained on a first task, the network is fine-tuned on a second task by modifying a progressively larger set of the network’s parameters. We test Gradual Tuning on different transfer learning tasks, using networks of different sizes trained with different regu...

Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally...

Restart is an application-level mechanism to speed up the completion of tasks that are subject to failures or unpredictable delays. In this paper we investigate if restart can be beneficial for Internet applications. For that reason we conduct and analyze a measurement study for restart applied to HTTP GET over TCP. Since application-level restart and TCP time-out mechanisms may interfere, we d...