Subdwarf B stars from the common envelope ejection channel

From the canonical binary scenario, the majority of sdBs are produced from low-mass stars with degenerate cores where helium is ignited in a way of flashes. Due to numerical difficulties, the models of produced sdBs are generally constructed from more massive stars with non-degenerate cores, leaving several uncertainties on the exact characteristics of sdB stars. Employing MESA, we systematically studied the characteristics of sdBs produced from the common envelope (CE) ejection channel, and found that the sdB stars produced from the CE ejection channel appear to form two distinct groups on the effective temperature-gravity diagram. One group (the flash-mixing model) almost has no H-rich envelope and crows at the hottest temperature end of the extremely horizontal branch (EHB), while the other group has significant H-rich envelope and spreads over the whole canonical EHB region. The key factor for the dichotomy of the sdB properties is the development of convection during the first helium flash, which is determined by the interior structure of the star after the CE ejection. For a given initial stellar mass and a given core mass at the onset of the CE, if the CE ejection stops early, the star has a relatively massive H-rich envelope, resulting in a canonical sdB generally. The fact of only a few short-orbital-period sdB binaries being in the flash-mixing sdB region and the lack of He-rich sdBs in short-orbital-period binaries indicate that the flash mixing is not very often in the products of the CE ejection. A falling back process after the CE ejection, similar to that happened in nova, is an appropriate way of increasing the envelope mass, then prevents the flash mixing.Comment: accepted by A&A 12 pages, 11 figure

A note on modular forms and generalized anomaly cancellation formulas

By studying modular invariance properties of some characteristic forms, we prove some new anomaly cancellation formulas which generalize the Han-Zhang and Han-Liu-Zhang anomaly cancellation formula

Augmenting bug localization with part-of-speech and invocation

Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source-file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of the approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e. ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat

Scalable Compression of Deep Neural Networks

Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems. In this paper, we propose a scalable representation of the network parameters, so that different applications can select the most suitable bit rate of the network based on their own storage constraints. Moreover, when a device needs to upgrade to a high-rate network, the existing low-rate network can be reused, and only some incremental data are needed to be downloaded. We first hierarchically quantize the weights of a pre-trained deep neural network to enforce weight sharing. Next, we adaptively select the bits assigned to each layer given the total bit budget. After that, we retrain the network to fine-tune the quantized centroids. Experimental results show that our method can achieve scalable compression with graceful degradation in the performance.Comment: 5 pages, 4 figures, ACM Multimedia 201

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns

High-level expression, purification, polyclonal antibody preparation against recombinant OprD from Pseudomonas aeruginosa

OprD is a specific porin which can binds imipenem and carbapenems in Pseudomonas aeruginosa. OprD loss plays a central role in mediating carbapenem resistance. Therefore, purification of oprD protein lays a pavement for the study in vivo and in vitro. In our study, the oprD gene was cloned into pQE30 expression vector, in frame with a sequence coding an N-terminal hexahistidine tag to allow purification by Ni2+ column. The recombinant OprD-6His was overproduced in inclusion form in Escherichia coli M15. OprD-6His was purified under denatured conditions using Ni-NTA conjugates. Antiserum against this recombinant OprD-6His protein was prepared in rabbit. Western blot analysis and enzyme linked immunosorbent assay (ELISA) were carried out to identify the reaction abilities and sensitivity of anti-OprD-6His polyclonal antibody to purified OprD-6His. Our results indicated that it was induction of E. coli M15 cells with 0.5 mM of isopropylthio-d-galactoside (IPTG) at 33°C for 4 h that the predicted 48 kDa OprD-6His fusion protein was expressed as the form of inclusion bodies with about 55-65 mg of OprD-6His per liter of culture. Western blot showed that recombinant OprD-6His protein could be identified by self-developed  anti-OprD-6His polyclonal antibody. Anti-OprD-6His polyclonal antibody was detectable with 1000 times dilution of the original polyclonal antibody solution. In conclusion, higher level expression of OprD was available in the pQE30 expression system at optimal condition. Self-prepared polyclone antibody can effectively detect difference of porin expression in P. aeruginosa with higher sensitivity and specificity.Key words: Pseudomonas aeroginosa; OprD; polyclonal antibody