Lagrangian approach to local symmetries and self-dual model in gauge invariant formulation

Taking the St\"uckelberg Lagrangian associated with the abelian self-dual model of P.K. Townsend et al as a starting point, we embed this mixed first- and second-class system into a pure first-class system by following systematically the generalized Hamiltonian approach of Batalin, Fradkin and Tyutin. The resulting Lagrangian possesses an extended gauge invariance and provides a non-trivial example for a general Lagrangian approach to unravelling the full set of local symmetries of a Lagrangian.Comment: LaTeX, 15 page

Quantization of spontaneously broken gauge theory based on the BFT-BFV Formalism

We quantize the spontaneously broken abelian U(1) Higgs model by using the improved BFT and BFV formalisms. We have constructed the BFT physical fields, and obtain the first class observables including the Hamiltonian in terms of these fields. We have also explicitly shown that there are exact form invariances between the second class and first class quantities. Then, according to the BFV formalism, we have derived the corresponding Lagrangian having U(1) gauge symmetry. We also discuss at the classical level how one easily gets the first class Lagrangian from the symmetry-broken second class Lagrangian.Comment: 16 pages, latex, final version published in Mod. Phys. Lett.

Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution

With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising

Effect of hot water pretreatment severity on the degradation and enzymatic hydrolysis of corn stover

The effects of hot water pretreatment on the degradation and enzymatic hydrolysis of corn stover were studied. Nearly 100% cellulose recovery in the solid fraction was obtained when corn stover was pretreated at 170 degrees C to 210 degrees C for 3 to 10 min. The highest pretreatment severity of 4.239 (210 degrees C, 10 min) resulted in the highest solid solubilization (37.0%) and xylan solubilization (90.5%). At this severity, inhibitors such as acetic acid, furfural, and hydroxymethyl furfural (HMF) also reached the highest levels of 7.1, 4.6, and 0.6 g L-1, respectively. When the pretreatment temperature was less than 190 degrees C, the furfural concentration was below 1.0 g L-1 and no significant levels of HMF were detected. Enzymatic hydrolysis results showed that increased glucose yields were obtained with increased pretreatment temperatures of corn stover The highest glucose yield of 89.2% was obtained at the pretreatment severity of 3.716 (210 degrees C, 3 min). Due to the degradation of sugars, a glucose yield of 85.9% was obtained at the highest pretreatment severity of 4.239 (210 degrees C, 10 min)

Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them

Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have certain percentage of overlaps, and so the overlap classification problem is an important part of audio classification. Nowadays, the work on overlapped audio event classification is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innovatively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic Latent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in classifying isolated audio events

A Particle Element Approach for Modelling the 3D Printing Process of Fibre Reinforced Polymer Composites

This paper presents a new numerical approach for modelling the 3D printing process of fibre reinforced polymer composites by fused deposition modelling (FDM). The approach is based on the coupling between two particle methods, namely smoothed particle hydrodynamics (SPH) and discrete element method (DEM). The coupled SPH-DEM model has distinctive advantages in dealing with the free surface flow, large deformation of fibres, and/or fibre-fibre interaction that are involved in the FDM process. A numerical feasibility study is carried out to demonstrate its capability for both short and continuous fibre reinforced polymer composites, with promising results achieved for the rheological flow and fibre orientation and deformation. View Full-Tex