Efficient Non-deterministic Search in Structured Prediction: A Case Study on Syntactic Parsing

Non-determinism occurs naturally in many search-based machine learning and natural language processing (NLP) problems. For example, the goal of parsing is to construct the syntactic tree structure of a sentence given a grammar. Agenda-based parsing is a dynamic programming approach to find the most likely syntactic tree of a sentence according to a probabilistic grammar. A chart is used to maintain all the possible subtrees for different spans in the sentence and an agenda is used to rank all the constituents. The parser chooses only one constituent from the agenda per step. Non-determinism occurs naturally in agenda-based parsing since the new constituent is often built by combining items from a few steps earlier. Unfortunately, like most other problems in NLP, the size of the search space is huge and exhaustive search is impossible. However, users expect a fast and accurate system. In this dissertation, I focus on the question of ``Why, when, and how shall we take advantage of non-determinism?'' and show its efficacy to improve the parser in terms of speed and/or accuracy. Existing approaches like search-based imitation learning or reinforcement learning methods have different limitations when it comes to a large NLP system. The solution proposed in this dissertation is ``We should train the system non-deterministically and test it deterministically if possible.'' and I also show that ``it is better to learn with oracles than simple heuristics''. We start by solving a generic Markov Decision Process with a non-deterministic agent. We show its theoretical convergence guarantees and verify its efficiency on maze solving problems. Then we focus on agenda-based parsing. To re-prioritize the parser, we model a decoding problem as a Markov Decision Process with a large state/action space. We discuss the advantages/disadvantages of existing techniques and propose a hybrid reinforcement/apprenticeship learning algorithm to trade off speed and accuracy. We also propose to use a dynamic pruner with features that depend on the run-time status of the chart and agenda and analyze the importance of those features in the pruning classification. Our models show comparable results with respect to start-of-the-art strategies

Dynamic analysis and optimal control of a novel fractional-order 2I2SR rumor spreading model

In this paper, a novel fractional-order 2I2SR rumor spreading model is investigated. Firstly, the boundedness and uniqueness of solutions are proved. Then the next-generation matrix method is used to calculate the threshold. Furthermore, the stability of rumor-free/spreading equilibrium is discussed based on fractional-order Routh–Hurwitz stability criterion, Lyapunov function method, and invariance principle. Next, the necessary conditions for fractional optimal control are obtained. Finally, some numerical simulations are given to verify the results

Programming by Example Made Easy

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee, which leverages an "entity-action" model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee's effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers.Comment: Accepted by ACM Transactions on Software Engineering and Methodolog

A NOVEL CLUSTERING ALGORITHM BASED ON P SYSTEMS

Abstract. Membrane computing (known as P systems) is a novel class of distributed parallel computing models. In this paper, a partition-based clustering algorithm under the framework of membrane computing is proposed. The clustering algorithm is based on a tissue-like P system, which is used to exploit the optimal cluster centers for a data set. Each object in the tissue-like P system represents a group of candidate cluster center

Risk factors for recurrent IgA nephropathy after renal transplantation: A meta-analysis

Recurrent glomerulonephritis after renal transplantation is the third most common cause of allograft loss, the most frequent of which is associated with IgA nephropathy (IgAN). This study aims to provide a systematic review of the risk factors associated with recurrent IgAN after renal transplantation. We searched English and Chinese databases, including PubMed, Embase, Web of Science, CNKI, and others, and included all case-control studies involving risk factors for recurrent IgAN after renal transplantation from the databases’ establishment to March 2022. Data were analyzed using the Stata 12.0. A total of 20 case–control studies were included in the meta-analysis, with 542 patients with recurrent IgAN and 1385 patients without recurrent IgAN. The results showed that donor age (standardized mean difference [SMD] -0.13 [95% CI -0.26, -0.001]; P = 0.048), patient age at transplantation (SMD -0.41 [95% CI -0.53, -0.29]; P < 0.001), time from diagnosis to end-stage renal disease (SMD -0.42 [95% CI -0.74, -0.10]; P = 0.010), previous transplantation (odds ratio [OR] 1.73 [95% CI 1.06, 2.81]; P = 0.027), living donor (OR 1.86 [95% CI 1.34, 2.58]; P < 0.001), related donor (OR 2.64, [95% CI 1.84, 3.79]; P < 0.001), tacrolimus use (OR 0.71 [95% CI 0.52, 0.98]; P = 0.035), basiliximab use (OR 0.39 [95% CI 0.27, 0.55]; P < 0.001), proteinuria (SMD 0.42 [95% CI 0.13, 0.71]; P = 0.005) and serum IgA level (SMD 0.48 [95% CI 0.27, 0.69]; P < 0.001) were associated with recurrent IgAN after renal transplantation. In general, tacrolimus and basiliximab use were protective factors against recurrent IgAN after renal transplantation, whereas donor age, patient age at transplantation, time from diagnosis to end-stage renal disease, previous transplantation, living donor, related donor, proteinuria, and serum IgA level were risk factors for recurrent IgAN after renal transplantation. Clinical decision making should warrant further consideration of these risk factors