Comment on " a unified scheme for flavored mesons and baryons"

We would comment on the results of the paper "a unified scheme for flavored mesons and baryons" (P.C.Vinodkumar, J.N.Panandya, V.M.Bannur, and S.B.Khadkikar Eur. Phys. J. A4(1999)83), and point out some inconsistencies and mistakes in the work for solving the Dirac equation. In terms of an example for a single particle we investigate the reliability of the perturbative method for computing the Coulomb energy and discuss the contribution to the wavefunction at origin from the Coulomb potential. We conclude that the accuracy of their numerical results needs to be reconsidered.Comment: Latex file, 11page

Applicability of the Friedberg-Lee-Zhao method

Friedberg, Lee and Zhao proposed a method for effectively evaluating the eigenenergies and eigen wavefunctions of quantum systems. In this work, we study several special cases to investigate applicability of the method. Concretely, we calculate the ground-state eigenenergy of the Hellmann potential as well as the Cornell potential, and also evaluate the energies of the systems where linear term is added to the Coulomb and harmonic oscillator potentials as a perturbation. The results obtained in this method have a surprising agreement with the traditional method or the numerical results. Since the results in this method have obvious analyticity compared to that in other methods, and because of the simplicity for calculations this method can be applied to solving the Schr\"{o}dinger equation and provides us better understanding of the physical essence of the concerned systems. But meanwhile applications of the FLZ method are restricted at present, especially for certain potential forms, but due to its obvious advantages, it should be further developed.Comment: 14 pages,no figure

Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC