The dispersive contribution of ρ(1450,1700)\rho(1450,1700) decays and X(1576)

We study whether the broad enhancement X(1576) arises from the final state interaction (FSI) of $\rho(1450,1700)\to \rho^+\rho^-\to K^{+}K^{-}$ decays. We consider both the absorptive and dispersive contribution of the above amplitudes since the intermediate states are very close to $\rho(1450,1700)$. The same mechanism leads to a similar enhancement around 1580 MeV in the $\pi^{+}\pi^-$ spectrum in the $J/\psi\to \pi^{0}\pi^{+}\pi^{-}$ channel, which can be used to test whether X(1576) can be ascribed to the FSI effect of $\rho(1450,1700)\to \rho^+\rho^-$.Comment: 4 pages, 4 figure

The puzzle of excessive non-DDˉD\bar D component of the inclusive ψ(3770)\psi(3770) decay and the long-distant contribution

In this letter we suggest that the obvious discrepancy between theoretical prediction on the $\mathrm{non}-D\bar D$ decays of $\psi(3770)$ and data is to be alleviated by taking final state interaction (FSI) into account. By assuming that $\psi(3770)$ overwhelmingly dissociates into $D\bar D$, then the final state interaction induces a secondary process, we calculate the branching ratios of $\psi(3770)\to D\bar D\to J/\psi\eta, \rho\pi, \omega\eta, K^*K$. Our results show that the branching ratio of $\psi(3770)\to \mathrm{non}-D\bar{D}$ can reach up to $\mathcal{B}_{\mathrm{non}-D\bar{D}}^{FSI}=(0.2\sim1.1)$ while typical parameters $I=0.4$ GeV$^{-2}$ and $\alpha=0.8\sim 1.3$ are adopted. This indicates that the FSI is obviously non-negligible.Comment: 6 pages, 4 figures, 1 tables. More references and discussions added, typos corrected. Accepted by Phys. Lett.

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

Selective Weak Supervision for Neural Information Retrieval

This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available. We revisit the classic IR intuition that anchor-document relations approximate query-document relevance and propose a reinforcement weak supervision selection method, ReInfoSelect, which learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward. Iteratively, for a batch of anchor-document pairs, ReInfoSelect back propagates the gradients through the neural ranker, gathers its NDCG reward, and optimizes the data selection network using policy gradients, until the neural ranker's performance peaks on target relevance metrics (convergence). In our experiments on three TREC benchmarks, neural rankers trained by ReInfoSelect, with only publicly available anchor data, significantly outperform feature-based learning to rank methods and match the effectiveness of neural rankers trained with private commercial search logs. Our analyses show that ReInfoSelect effectively selects weak supervision signals based on the stage of the neural ranker training, and intuitively picks anchor-document pairs similar to query-document pairs.Comment: Accepted by WWW 202

Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer: The Phase III DUO-E Trial

PURPOSE Immunotherapy and chemotherapy combinations have shown activity in endometrial cancer, with greater benefit in mismatch repair (MMR)-deficient (dMMR) than MMR-proficient (pMMR) disease. Adding a poly(ADP-ribose) polymerase inhibitor may improve outcomes, especially in pMMR disease. METHODS This phase III, global, double-blind, placebo-controlled trial randomly assigned eligible patients with newly diagnosed advanced or recurrent endometrial cancer 1:1:1 to: carboplatin/paclitaxel plus durvalumab placebo followed by placebo maintenance (control arm); carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab plus olaparib placebo (durvalumab arm); or carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab plus olaparib (durvalumab + olaparib arm). The primary end points were progression-free survival (PFS) in the durvalumab arm versus control and the durvalumab + olaparib arm versus control. RESULTS Seven hundred eighteen patients were randomly assigned. In the intention-to-treat population, statistically significant PFS benefit was observed in the durvalumab (hazard ratio [HR], 0.71 [95% CI, 0.57 to 0.89]; P = .003) and durvalumab + olaparib arms (HR, 0.55 [95% CI, 0.43 to 0.69]; P < .0001) versus control. Prespecified, exploratory subgroup analyses showed PFS benefit in dMMR (HR [durvalumab v control], 0.42 [95% CI, 0.22 to 0.80]; HR [durvalumab + olaparib v control], 0.41 [95% CI, 0.21 to 0.75]) and pMMR subgroups (HR [durvalumab v control], 0.77 [95% CI, 0.60 to 0.97]; HR [durvalumab + olaparib v control] 0.57; [95% CI, 0.44 to 0.73]); and in PD-L1-positive subgroups (HR [durvalumab v control], 0.63 [95% CI, 0.48 to 0.83]; HR [durvalumab + olaparib v control], 0.42 [95% CI, 0.31 to 0.57]). Interim overall survival results (maturity approximately 28%) were supportive of the primary outcomes (durvalumab v control: HR, 0.77 [95% CI, 0.56 to 1.07]; P = .120; durvalumab + olaparib v control: HR, 0.59 [95% CI, 0.42 to 0.83]; P = .003). The safety profiles of the experimental arms were generally consistent with individual agents. CONCLUSION Carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab with or without olaparib demonstrated a statistically significant and clinically meaningful PFS benefit in patients with advanced or recurrent endometrial cancer

RPE and Stem Cell Therapy

acceptedVersionPeer reviewe

Relative P-impedance estimation using a dipole-based matching pursuit decomposition strategy

© 2015 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved. The P-impedance is one of the most important elastic parameters of rocks, and it is commonly used for reservoir characterization. Conventional P-impedance inversion merges a low-frequency log-based model with a high-frequency seismic-derived model. We have proposed a method to estimate the P-impedance by employing dipole-based matching pursuit (DMP) decomposition. The matching pursuit decomposes the seismic traces into a superposition of scaled wavelets, and the associated scalar information represents the reflectivity series, which can be integrated for P-impedance estimation. Unfortunately, DMP analysis is usually performed trace by trace, resulting in a poor lateral continuity. Applying conventional lateral smoothing through mean or median filtering improves the lateral continuity but typically decreases the vertical resolution. We have evaluated an adaptive smoothing strategy that required the filtering to follow bed boundaries in an automated manner, sharpening the boundaries while maintaining the high quality of inversion. We have determined the effectiveness of our algorithm by first applying it to a synthetic wedge model and then to a real seismic data set