CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called `CoaCor'), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201

Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we investigate interactive semantic parsing, where the agent can ask the user clarification questions to resolve ambiguities via a multi-turn dialogue, on an important type of programs called "If-Then recipes." We develop a hierarchical reinforcement learning (HRL) based agent that significantly improves the parsing performance with minimal questions to the user. Results under both simulation and human evaluation show that our agent substantially outperforms non-interactive semantic parsers and rule-based agents.Comment: 13 pages, 2 figures, accepted by AAAI 201

Global Relation Embedding for Relation Extraction

We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%.Comment: Accepted to NAACL HLT 201

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.Comment: Accepted to the thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), 9 pages, 5 figure

A study of aging effects of barrel Time-Of-Flight system in the BESIII experiment

The Time-Of-Flight system consisting of plastic scintillation counters plays an important role for particle identification in the BESIII experiment at the BEPCII double ring $e^+e^-$ collider. Degradation of the detection efficiency of the barrel TOF system has been observed since the start of physical data taking and this effect has triggered intensive and systematic studies about aging effects of the detector. The aging rates of the attenuation lengths and relative gains are obtained based on the data acquired in past several years. This study is essential for ensuring an extended operation of the barrel TOF system in optimal conditions.Comment: 14 pages, 8 figure

China and the Fifth Estate: Net Delusion or Democratic Potential?

Arguably, liberal democratic societies are seeing the emergence of a ‘Fifth Estate’ that is being enabled by the Internet. This new organizational form is comparable to, but potentially more powerful than, the Fourth Estate, which developed as a significant force in an earlier period with an independent press and other mass media. While the significance of the press and the Internet to democratic governance is questioned in all societies, there is particular skepticism of their relevance outside the most liberal democratic regimes, which have a relatively free press and more pluralistic political systems, such as in North America and West Europe. Nevertheless, there have been vivid examples of where networked individuals have appeared to assert greater communicative power in the politics of governance, the media and everyday life, even in non-liberal democratic regimes, such as Hong Kong, and in some cases, China. This potential points to the need for more systematic empirical research in a wider variety of economic and political settings worldwide, particularly in states in which the Internet might offer a potential for more democratic governance and greater accountability of government controlled media. This paper examines cases in which networked individuals in China used the Internet to hold governmental and press institutions more accountable. The cases provide support for the relevance of the Fifth Estate concept in China, and also illuminates the process – showing how the Internet can be used to empower networked individuals in more autocratic regimes

Representation Class and Geometrical Invariants of Quantum States under Local Unitary Transformations

We investigate the equivalence of bipartite quantum mixed states under local unitary transformations by introducing representation classes from a geometrical approach. It is shown that two bipartite mixed states are equivalent under local unitary transformations if and only if they have the same representation class. Detailed examples are given on calculating representation classes.Comment: 11 page