A Quantum Many-body Wave Function Inspired Language Modeling Approach

The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory. The latest development for a more effective QLM has adopted word embeddings as a kind of global dependency information and integrated the quantum-inspired idea in a neural network architecture. While these quantum-inspired LMs are theoretically more general and also practically effective, they have two major limitations. First, they have not taken into account the interaction among words with multiple meanings, which is common and important in understanding natural language text. Second, the integration of the quantum-inspired LM with the neural network was mainly for effective training of parameters, yet lacking a theoretical foundation accounting for such integration. To address these two issues, in this paper, we propose a Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The QMWF inspired LM can adopt the tensor product to model the aforesaid interaction among words. It also enables us to reveal the inherent necessity of using Convolutional Neural Network (CNN) in QMWF language modeling. Furthermore, our approach delivers a simple algorithm to represent and match text/sentence pairs. Systematic evaluation shows the effectiveness of the proposed QMWF-LM algorithm, in comparison with the state of the art quantum-inspired LMs and a couple of CNN-based methods, on three typical Question Answering (QA) datasets.Comment: 10 pages,4 figures,CIK

Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture

We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As opposed to the neural tensor layer that has been adopted recently, the holographic composition provides the benefits of scalable and rich representational learning approach without incurring huge parameter costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified architecture for both deep sentence modeling and semantic matching. Essentially, our model is trained end-to-end whereby the parameters of the LSTM are optimized in a way that best explains the correlation between question and answer representations. In addition, our proposed deep learning architecture requires no extensive feature engineering. Via extensive experiments, we show that HD-LSTM outperforms many other neural architectures on two popular benchmark QA datasets. Empirical studies confirm the effectiveness of holographic composition over the neural tensor layer.Comment: SIGIR 2017 Full Pape

Neighbourhood satisfaction in rural resettlement residential communities: the case of Suqian, China

Against the background of large-scale urbanisation and rural land expropriation, rural resettlement residential housing has been built to accommodate local rural residents in the peripheral areas of China. To explore the context-specific policy implications for improving neighbourhood satisfaction (NS) of residents in rural resettlement residential communities (RRRCs), this paper examines the determinants of NS, and their spatial effects, in rural resettlement residential neighbourhoods using Suqian, in Jiangsu Province, as a case study. This study contributes to the current literature in two ways: it constitutes the first attempt to examine NS among RRRCs; second, our spatial model helps to gain further understanding of horizontal and vertical spatial dependence effects. Our results indicate that income, gender, age, family structure, number of years living in a community, transport and architectural age all have significant effects on NS in RRRCs

Simulating transport pathways of pelagic Sargassum from the Equatorial Atlantic into the Caribbean Sea

Since 2011, beach inundation of massive amounts of pelagic Sargassum algae has occurred around the Caribbean nations and islands. Previous studies have applied satellite ocean color to determine the origins of this phenomenon. These techniques, combined with complementary approaches, suggest that, rather than blooms originating in the Caribbean, they arrive from the Equatorial Atlantic. However, oceanographic context for these occurrences remains limited. Here, we present results from synthetic particle tracking experiments that characterize the interannual and seasonal dynamics of ocean currents and winds likely to influence the transport of Sargassum from the Equatorial Atlantic into the Caribbean Sea. Our findings suggest that Sargassum present in the western Equatorial Atlantic (west of longitude 50°W) has a high probability of entering the Caribbean Sea within a year’s time. Transport routes include the Guiana Current, North Brazil Current Rings, and the North Equatorial Current north of the North Brazil Current Retroflection. The amount of Sargassum following each route varies seasonally. This has important implications for the amount of time it takes Sargassum to reach the Caribbean Sea. By weighting particle transport predictions with Sargassum concentrations at release sites in the western Equatorial Atlantic, our simulations explain close to 90% of the annual variation in observed Sargassum abundance entering the Caribbean Sea. Additionally, results from our numerical experiments are in good agreement with observations of variability in the timing of Sargassum movement from the Equatorial Atlantic to the Caribbean, and observations of the spatial extent of Sargassum occurrence throughout the Caribbean. However, this work also highlights some areas of uncertainty that should be examined, in particular the effect of “windage” and other surface transport processes on the movement of Sargassum. Our results provide a useful launching point to predict Sargassum beaching events along the Caribbean islands well in advance of their occurrence and, more generally, to understand the movement ecology of a floating ecosystem that is essential habitat to numerous marine speciesNFP, GJG, LJG, EJ and JT acknowledge support from the NOAA Atlantic Oceanographic and Meteorological Laboratory. JT was also supported by NOAA/OceanWatch. CH and MW acknowledge support from NASA (NNX14AL98G, NNX16AR74G, and NNX17AE57G) and the William and Elsie Knight Endowed Fellowship. Funding for the development of HYCOM has been provided by the National Ocean Partnership Program and the Office of Naval ResearchS