A unified analysis of likelihood-based estimators in the Plackett--Luce model

The Plackett--Luce model is a popular approach for ranking data analysis, where a utility vector is employed to determine the probability of each outcome based on Luce's choice axiom. In this paper, we investigate the asymptotic theory of utility vector estimation by maximizing different types of likelihood, such as the full-, marginal-, and quasi-likelihood. We provide a rank-matching interpretation for the estimating equations of these estimators and analyze their asymptotic behavior as the number of items being compared tends to infinity. In particular, we establish the uniform consistency of these estimators under conditions characterized by the topology of the underlying comparison graph sequence and demonstrate that the proposed conditions are sharp for common sampling scenarios such as the nonuniform random hypergraph model and the hypergraph stochastic block model; we also obtain the asymptotic normality of these estimators and discuss the trade-off between statistical efficiency and computational complexity for practical uncertainty quantification. Both results allow for nonuniform and inhomogeneous comparison graphs with varying edge sizes and different asymptotic orders of edge probabilities. We verify our theoretical findings by conducting detailed numerical experiments.Comment: 42 pages, corrected typos, added the supplementary file containing all remaining proof

Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is a great challenging task. Existing studies focus on building a context-response matching model with various neural architectures or PLMs and typically learning with a single response prediction task. These approaches overlook many potential training signals contained in dialogue data, which might be beneficial for context understanding and produce better features for response prediction. Besides, the response retrieved from existing dialogue systems supervised by the conventional way still faces some critical challenges, including incoherence and inconsistency. To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models. Specifically, we introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination, and jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. By this means, the auxiliary tasks can guide the learning of the matching model to achieve a better local optimum and select a more proper response. Experiment results on two benchmarks indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection in retrieval-based dialogues, and our model achieves new state-of-the-art results on both datasets.Comment: 10 page

Ectopic tissue engineered ligament with silk collagen scaffold for ACL regeneration: A preliminary study

Anterior cruciate ligament (ACL) reconstruction remains a formidable clinical challenge because of the lack of vascularization and adequate cell numbers in the joint cavity. In this study, we developed a novel strategy to mimic the early stage of repair in vivo, which recapitulated extra-articular inflammatory response to facilitate the early ingrowth of blood vessels and cells. A vascularized ectopic tissue engineered ligament (ETEL) with silk collagen scaffold was developed and then transferred to reconstruct the ACL in rabbits without interruption of perfusion. At 2 weeks after ACL reconstruction, more well-perfused cells and vessels were found in the regenerated ACL with ETEL, which decreased dramatically at the 4 and 12 week time points with collagen deposition and maturation. ACL treated with ETEL exhibited more mature ligament structure and enhanced ligament-bone healing post-reconstructive surgery at 4 and 12 weeks, as compared with the control group. In addition, the ETEL group was demonstrated to have higher modulus and stiffness than the control group significantly at 12 weeks post-reconstructive surgery. In conclusion, our results demonstrated that the ETEL can provide sufficient vascularity and cellularity during the early stages of healing, and subsequently promote ACL regeneration and ligament-bone healing, suggesting its clinic use as a promising therapeutic modality. Statement of Significance Early inflammatory cell infiltration, tissue and vessels ingrowth were significantly higher in the extra articular implanted scaffolds than theses in the joint cavity. By mimicking the early stages of wound repair, which provided extra-articular inflammatory stimulation to facilitate the early ingrowth of blood vessels and cells, a vascularized ectopic tissue engineered ligament (ETEL) with silk collagen scaffold was constructed by subcutaneous implantation for 2 weeks. The fully vascularized TE ligament was then transferred to rebuild ACL without blood perfusion interruption, and was demonstrated to exhibit improved ACL regeneration, bone tunnel healing and mechanical properties. (C) 2017 Published by Elsevier Ltd on behalf of Acta Materialia Inc