Table-to-text Generation by Structure-aware Seq2seq Learning

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.Comment: Accepted by AAAI201

Order-Planning Neural Text Generation From Structured Data

Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches do not model the order of contents during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In a biography, for example, the nationality of a person is typically mentioned before occupation in a biography. In this paper, we propose an order-planning text generation model to capture the relationship between different fields and use such relationship to make the generated text more fluent and smooth. We conducted experiments on the WikiBio dataset and achieve significantly higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores

Small Towns in Transitions, an Exploratory Study in Collingwood, Ontario

Collingwood, Ontario is experiencing an economic and social transition away from resource-dependent orient toward place-based development trajectory, after its economic breakdown of the traditional industries (e.g., shipbuilding) in the 1980s. Boom and bust cycles in single industry towns have been common not only in Canada but throughout the world. The transition in Collingwood is an alternative development strategy that leverages the local economic, social and environmental capitals, while it brings some new development challenges. This thesis offers insights into the characteristics of the economic and social transitions, and their interlinkages by employing Collingwood as a case study. The data on which this thesis is based includes 30 semi-structured interviews with key informants representing in-migrants and residents, economic and political representatives, 43 survey questionnaires, field observations and secondary statistics. Research findings indicate although economic and social transitions contain relative independence, multifaceted correlations exist between the two. Collingwood’s economic base changes from shipbuilding toward the tertiary sector through a focus on natural, social and cultural amenities. The transition is a neo-endogenous approach driven by place-based municipal actions, implemented by entrepreneurs, and enabled by amenity-seeking in-migrants and the expansion of the mountain resort in the neighboring town. The place-based development draws counter-urbanites in, and their urban consumptive behaviours reinforce ongoing economic and social transitions. Gentrification, economic and social polarization emerge resulting in livelihood uneasiness for the local residents. The research concludes that to create a more restorative, liveable and equitable society through establishing a shared place-identity among the heterogeneous stakeholder groups in the ongoing process of place-making could lead to positive and sustainable integration between economic and social development

Should Peak Dose Be Used to Prescribe Spatially Fractionated Radiation Therapy?-A Review of Preclinical Studies.

Spatially fractionated radiotherapy (SFRT) is characterized by the coexistence of multiple hot and cold dose subregions throughout the treatment volume. In preclinical studies using single-fraction treatment, SFRT can achieve a significantly higher therapeutic index than conventional radiotherapy (RT). Published clinical studies of SFRT followed by RT have reported promising results for bulky tumors. Several clinical trials are currently underway to further explore the clinical benefits of SFRT. However, we lack the important understanding of the correlation between dosimetric parameters and treatment response that we have in RT. In this work, we reviewed and analyzed this important correlation from previous preclinical SFRT studies. We reviewed studies prior to 2022 that treated animal-bearing tumors with minibeam radiotherapy (MBRT) or microbeam radiotherapy (MRT). Eighteen studies met our selection criteria. Increased lifespan (ILS) relative to control was used as the treatment response. The preclinical SFRT dosimetric parameters analyzed were peak dose, valley dose, average dose, beam width, and beam spacing. We found that valley dose was the dosimetric parameter with the strongest correlation with ILS (p-value < 0.01). For studies using MRT, average dose and peak dose were also significantly correlated with ILS (p-value < 0.05). This first comprehensive review of preclinical SFRT studies shows that the valley dose (rather than the peak dose) correlates best with treatment outcome (ILS)

Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that the ρ2 values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness

Mobile ICT and Knowledge Sharing in Underserved Communities

Organizing principles, exchange relationships, and technology affordance of underserved communities in emerging markets are different from privileged communities, which have been the focus in traditional information systems literature. This paper investigates mobile ICT and knowledge sharing in a rural farming community in India. Our qualitative field study reveals that value creating and value claiming norms are key enablers of knowledge sharing in underserved communities. The findings also identify the communication mechanisms and challenges of mobile ICT innovations that foster knowledge sharing among dispersed underserved communities. We discuss the implications for theory and suggest a practical guide to enhance knowledge sharing in underserved communities

Root disease of cereals

Effects of nitrogen source on take-all, 82N34, 77E4. Take-all, cultivar, fertilizer, fungicide interactions, 87MT47, 87MT48, 87MT49. Take –all decline, 86MT7. Rhizoctonia root rot. Rhizoctonia patch and soil compaction, 87E28. Rhizoctonia patch and short chemical fallow, 87E3, 87E38. Rhizoctonia strains and paddock history, 87E31. Rhizoctonia root rot - host effects on strains, 87BA4, 86E30. Fusarium crown rot. Fusarium crown rot and cultivars, 87ME2, 87ME4, 87ME3 ( abandoned )

FFTPL: An Analytic Placement Algorithm Using Fast Fourier Transform for Density Equalization

We propose a flat nonlinear placement algorithm FFTPL using fast Fourier transform for density equalization. The placement instance is modeled as an electrostatic system with the analogy of density cost to the potential energy. A well-defined Poisson's equation is proposed for gradient and cost computation. Our placer outperforms state-of-the-art placers with better solution quality and efficiency

Supervised clustering of high dimensional data using regularized mixture modeling

Identifying relationships between molecular variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the relationship between the high dimensional molecular manifestations and the clinical presentations, while taking into account the possible heterogeneity of the study subjects. We proposed a novel supervised clustering algorithm using penalized mixture regression model, called CSMR, to deal with the challenges in studying the heterogeneous relationships between high dimensional molecular features to a phenotype. The algorithm was adapted from the classification expectation maximization algorithm, which offers a novel supervised solution to the clustering problem, with substantial improvement on both the computational efficiency and biological interpretability. Experimental evaluation on simulated benchmark datasets demonstrated that the CSMR can accurately identify the subspaces on which subset of features are explanatory to the response variables, and it outperformed the baseline methods. Application of CSMR on a drug sensitivity dataset again demonstrated the superior performance of CSMR over the others, where CSMR is powerful in recapitulating the distinct subgroups hidden in the pool of cell lines with regards to their coping mechanisms to different drugs. CSMR represents a big data analysis tool with the potential to resolve the complexity of translating the clinical manifestations of the disease to the real causes underpinning it. We believe that it will bring new understanding to the molecular basis of a disease, and could be of special relevance in the growing field of personalized medicine