Structure of the Ds0(2317)D_{s0}(2317) and the strong coupling constant gDs0DKg_{D_{s0} D K} with the light-cone QCD sum rules

In this article, we take the point of view that the charmed scalar meson $D_{s0}(2317)$ is the conventional $c\bar{s}$ meson and calculate the strong coupling constant $g_{D_{s0} D K}$ within the framework of the light-cone QCD sum rules approach. The numerical values for the large scalar-$DK$ coupling constant $g_{D_{s0} D K}$ support the hadronic dressing mechanism. Just like the scalar mesons $f_0(980)$ and $a_0(980)$, the $D_{s0}(2317)$ may have small scalar $c\bar{s}$ kernel of the typical $c\bar{s}$ meson size. The strong coupling to the hadronic channels (or the virtual mesons loops) may result in smaller mass than the conventional scalar $c\bar{s}$ meson in the constituent quark models, and enrich the pure $c\bar{s}$ state with other components. The $D_{s0}(2317)$ may spend part (or most part) of its lifetime as virtual $D K$ state.Comment: 17 pages, 7 figure, revised version, add detailed error analysi

Surface Spectral Function of Momentum-dependent Pairing Potentials in a Topological Insulator: Application to Cux_xBi2_2Se3_3

We propose three possible momentum-dependent pairing potentials for candidate of topological superconductor (for example Cu$_x$Bi$_2$Se$_3$), and calculate the surface spectral function and surface density of state with these pairing potentials. We find that the first two can give the same spectral functions as the fully-gapped and node-contacted pairing potentials given in [Phys. Rev. Lett. 105, 097001], and that the third one can obtain topological non-trivial case which exists flat Andreev bound state and preserves the $C_3$ rotation symmetry. We hope our proposals and results be judged by future experiment.Comment: 5 pages, 3 figure

Repeating platinum/bevacizumab in recurrent or progressive cervical cancer yields marginal survival benefits

Our objective was to assess overall survival of cervical cancer patients following prior platinum/bevacizumab chemotherapy, comparing retreatment with platinum/bevacizumab with alternative therapies. A retrospective analysis was performed of women who received platinum/bevacizumab (PB) chemotherapy for cervical cancer at Washington University between July 1, 2005 and December 31, 2015. Wilcoxon rank-sum exact test and Fisher's exact test were used to compare the treatment groups, and Kaplan Meier curves were generated. Cox regression analyses were performed, with treatment free interval and prior therapy response included as covariates. Of 84 patients who received PB chemotherapy, 59 (70%) received no second line chemotherapy, as they did not recur, progressed without further chemotherapy, were lost to follow up, or expired. Of the remaining 25 patients, 9 were retreated with the combination of platinum/bevacizumab (PB), 6 were retreated with a platinum regimen without bevacizumab (P), and 10 were retreated with neither (not-P). The only long-term survivor was in the not-P group and was treated with an immunotherapy agent. Median overall survival of all patients was 7.1 months. There was a marginal difference in survival between women in the PB and not-PB groups (11.8 versus 5.7 months; HR 3.02, 95% CI, 0.98–9.28). There was no difference in survival based on platinum interval (HR 0.81; 95% CI, 0.27–2.45). Outcomes are grim for women retreated after platinum/bevacizumab therapy and are only marginally improved by retreatment with a platinum/bevacizumab regimen. Rather than additional PB therapy, women with cervical cancer who recur after platinum/bevacizumab should consider supportive care or clinical trials

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape