Some Novel Contributions to Radiative B Decay in Supersymmetry without R-parity

We present a systematic analysis at the leading log order of the influence of combination of bilinear and trilinear R-parity violating couplings on the decay b-->s gamma. Such contributions have never been explored in the context of this decay. We show that influence of charged-slepton-Higgs mixing mediated loops can dominate the SM and MSSM contributions and hence can provide strong bounds on the combination of bilinear-trilinear R-parity violating couplings. Such contributions are also enhanced by large tan beta. With substantially extended basis of operators (28 operators), we provide illustrative analytical formulae of the major contributions to complement our complete numerical results which demonstrate the importance of QCD running effects.Comment: 4 pages, 2 figure

On Extended Electroweak Symmetries

We discuss extensions of the Standard Model through extending the electroweak gauge symmetry. An extended electroweak symmetry requires a list of extra fermionic and scalar states. The former is necessary to maintain cancellation of gauge anomalies, and largely fixed by the symmetry embedding itself. The latter is usually considered quite arbitrary, so long as a vacuum structure admitting the symmetry breaking is allowed. Anomaly cancellation may be used to link the three families of quarks and leptons together, given a perspective on flavor physics. It is illustrated lately that the kind of models may also have the so-called little Higgs mechanism incorporated. This more or less fixes the scalar sector and take care of the hierarchy problem, making such models of extended electroweak symmetries quite appealing candidates as TeV scale effective field theories.Comment: 1+8 pages of latex with ws-procs9x6.cls; talk presented at Coral Gables Conference 200

Little Higgs Model Completed with a Chiral Fermionic Sector

The implementation of the little Higgs mechanism to solve the hierarchy problem provides an interesting guiding principle to build particle physics models beyond the electroweak scale. Most model building works, however, pay not much attention to the fermionic sector. Through a case example, we illustrate how a complete and consistent fermionic sector of the TeV effective field theory may actually be largely dictated by the gauge structure of the model. The completed fermionic sector has specific flavor physics structure, and many phenomenological constraints on the model can thus be obtained beyond gauge, Higgs, and top physics. We take a first look on some of the quark sector constraints.Comment: 14 revtex pages with no figure, largely a re-written version of hep-ph/0307250 with elaboration on flavor sector FCNC constraints; accepted for publication in Phys.Rev.

Effect of carrot puree edible films on quality preservation of fresh-cut carrots

peer-reviewedFinancial support from the high level talent fund of Henan University of Technology Science and Technology (No. 2012BS024) is gratefully acknowledged.The effect of edible films based on carrot puree, chitosan, corn starch, gelatin, glycerol and cinnamaldehyde on fresh-cut carrots was studied during storage. Several parameters, such as firmness, colour, weight loss, total carotenoids, total phenols, polyphenol oxidase (PPO) activity and peroxidase (POD) activity in coated carrots were determined at regular intervals and then compared with the uncoated carrots throughout the storage period. Significant and expected changes were observed in all carrot samples that were compared. The coating treatment significantly (P < 0.05) delayed the senescence, reduced the deterioration of exterior quality and retained total carotenoids well compared with control (P < 0.05). In addition, significant inhibition of PPO activity (P < 0.05) and POD activity (P < 0.05) as well as reduced accumulation of polyphenols (P < 0.05) were observed for all coated samples. All of these favourable responses induced by coating treatment on minimally processed fresh-cut carrots showed beneficial physiological effects, which would give some useful references to the fresh-cut fruit and vegetable processing industry and satisfy people’s requirements allowing for extending product shelf life without negatively affecting the sensory quality or acceptability.Henan University of Technology Science and Technolog

Audio Set classification with attention model: A probabilistic perspective

This paper investigates the classification of the Audio Set dataset. Audio Set is a large scale weakly labelled dataset of sound clips. Previous work used multiple instance learning (MIL) to classify weakly labelled data. In MIL, a bag consists of several instances, and a bag is labelled positive if at least one instances in the audio clip is positive. A bag is labelled negative if all the instances in the bag are negative. We propose an attention model to tackle the MIL problem and explain this attention model from a novel probabilistic perspective. We define a probability space on each bag, where each instance in the bag has a trainable probability measure for each class. Then the classification of a bag is the expectation of the classification output of the instances in the bag with respect to the learned probability measure. Experimental results show that our proposed attention model modeled by fully connected deep neural network obtains mAP of 0.327 on Audio Set dataset, outperforming the Google's baseline of 0.314 and recurrent neural network of 0.325.Comment: Accepted by ICASSP 201

Surrey-cvssp system for DCASE2017 challenge task4

In this technique report, we present a bunch of methods for the task 4 of Detection and Classification of Acoustic Scenes and Events 2017 (DCASE2017) challenge. This task evaluates systems for the large-scale detection of sound events using weakly labeled training data. The data are YouTube video excerpts focusing on transportation and warnings due to their industry applications. There are two tasks, audio tagging and sound event detection from weakly labeled data. Convolutional neural network (CNN) and gated recurrent unit (GRU) based recurrent neural network (RNN) are adopted as our basic framework. We proposed a learnable gating activation function for selecting informative local features. Attention-based scheme is used for localizing the specific events in a weakly-supervised mode. A new batch-level balancing strategy is also proposed to tackle the data unbalancing problem. Fusion of posteriors from different systems are found effective to improve the performance. In a summary, we get 61% F-value for the audio tagging subtask and 0.73 error rate (ER) for the sound event detection subtask on the development set. While the official multilayer perceptron (MLP) based baseline just obtained 13.1% F-value for the audio tagging and 1.02 for the sound event detection.Comment: DCASE2017 challenge ranked 1st system, task4, tech repor