24,778 research outputs found
Structure of the and the strong coupling constant with the light-cone QCD sum rules
In this article, we take the point of view that the charmed scalar meson
is the conventional meson and calculate the strong
coupling constant within the framework of the light-cone QCD
sum rules approach. The numerical values for the large scalar- coupling
constant support the hadronic dressing mechanism. Just like
the scalar mesons and , the may have small
scalar kernel of the typical meson size. The strong
coupling to the hadronic channels (or the virtual mesons loops) may result in
smaller mass than the conventional scalar meson in the constituent
quark models, and enrich the pure state with other components. The
may spend part (or most part) of its lifetime as virtual
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 CuBiSe
We propose three possible momentum-dependent pairing potentials for candidate
of topological superconductor (for example CuBiSe), 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 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
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