Quantum fluctuations of one-dimensional free fermions and Fisher-Hartwig formula for Toeplitz determinants
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This paper aims at a first theoretical step toward the probabilistic modeling of nutrient contents in catchment-scale soil states. To this end, we wish to characterize probabilistically the slow, leaching-prone component of the hydrologic response, which c ...
Most discriminative techniques for detecting instances from object categories in still images consist of looping over a partition of a pose space with dedicated binary classifiers. The efficiency of this strategy for a complex pose, i.e., for fine-grained ...
Given the availability of large speech corpora, as well as the increasing of memory and computational resources, the use of template matching approaches for automatic speech recognition (ASR) have recently attracted new attention. In such template-based ap ...
Given the availability of large speech corpora, as well as the increasing of memory and computational resources, the use of template matching approaches for automatic speech recognition (ASR) have recently attracted new attention. In such template-based ap ...
A novel uncertainty assessment methodology, based on a statistical non-parametric approach, is presented in this paper. It achieves quantification of code physical model uncertainty by making use of model performance information obtained from studies of ap ...
The paper develops a dynamic antenna scheduling strategy for downlink MIMO communications, where a subset of the receive antennas at certain users is selectively disabled. The proposed method improves the signal-to-leakage-plus-noise (SLNR) ratio performan ...
A longstanding problem in the study of sediment transport in gravel-bed rivers is related to the physical mechanisms governing bed resistance and particle motion. To study this problem, we investigated the motion of coarse spherical glass beads entrained b ...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distributions over actions in value-based reinforcement learning. While this approach is simi ...