Text summarization using concept hierarchy

This dissertation aims to create new sentences to summarize text documents. In addition to generating new sentences, this project also generates new concepts and extracts key sentences to summarize documents. This project is the first research work that can generate new key concepts and can create new sentences to summarize documents. Automatic document summarization is the process of creating a condensed version of the document. The condensed version extracts the key contents from the original document. Most related research uses statistical methods that generate a summary based on word distribution in the document. In this dissertation, we create a summary based on concept distributions and concept hierarchies. We use Stanford parser as our syntax parser and ResearchCyc (Cyc) as our knowledge base. Words and phrases of a document are mapped into Cyc concepts. We introduce a unique concept propagation method to generate abstract concepts and use those abstract concepts for the summarization. This method has two advantages over the existing methods. One advantage is the use of multi-level upward propagation to solve the word sense disambiguation problem. The other is that the propagation process provides a method to produce generalized concepts. In the first part of the project, we generate a summary by extracting key concepts and key sentences from documents. We use Stanford parser to segment a document to sentences and to parse each sentence to words or phrases tagged with their part-of-speeches. We use Cyc commands to map those words and phrases to their corresponding Cyc concepts and increase the weights of those concepts. To handle word sense disambiguation and to create summarized concepts, we propagate the weight of the concepts upward along the Cyc concept hierarchy. Then, we extract the concepts with some of the highest weights to be the key concepts. To extract key sentences from the document, we weigh each sentence in the document based on the concept weight associated with the sentence. Then, we extract the sentences with some of the highest weights to summarize the document. In the second part of the project, we generate new sentences to summarize a document based on the generalized concepts. First, we extract the subject, predicate, and object from each sentence. Then, we create compatible matrices based on the compatibility between the subjects, predicates, and objects among sentences. Two terms are considered to be compatible if the following three conditions hold: the two terms are the same concept, one concept is the other concept\u27s immediate super class, or two concepts share the same immediate super class. From the compatible matrices, we build compatible clusters and finally generate new sentences for each compatible cluster. These newly generated sentences serve as a summary for the document. We have implemented and tested our approaches. The test results show that our approaches are viable and have great potential for future research

Apply Hofstede’s national cultural dimension theory to analyze chinese tourist behaviors in Portugal tourism

With the globalization, the development of outbound travel experience rapidly grew in recent decades. International tourism has become the largest industry in the world. It is the new engine for economic development in many countries. Cross-cultural tourism has brought great benefit to the destination countries. On the other side, it also brings some negative effects between the tourists and the natives. Culture is one of the important factors on promoting the cross-cultural tourism but also becomes one of the barriers in its development. For Chinese tourists, Portugal is an emerging travel destination. Portugal tourism has natural advantages but also market disadvantages. It is very important for Portugal, which has limited resources, to use reasonable strategies on satisfying Chinese tourists’ demands and spreading local cultures. In this study, I use Hofstede’s national cultural dimension theory and some empirical studies to analyze the cultural differences between Portugal and China, and try to find out how cultures influence tourists behaviors. Finally, I try to provide some suggestions to help Portugal develop a sustainable tourism in order to attract more Chinese tourists and increase their satisfaction. In addition, it could be a model for the travel destinations to explore new tourist markets with different cultures.Com a globalização, a viagem transnacional também se desenvolve rapidamente. Além de ser a maior indústria do mundo, a indústria da viagem internacional também é um novo motor para o desenvolvimento económico de muitos países. Enquanto que a viagem transnacional traz vários lucros para os países de destino, as diferentes culturas entre os visitantes e os países de destino também causa muitos efeitos negativos. A cultura é um fator principal que promove a viagem transnacional, mas também é um obstáculo que impede o seu desenvolvimento. Para os visitantes chineses, Portugal é um novo destino de viagem, que tem as suas vantagens e desvantagens no desenvolvimento da indústria turística. Como aproveitar de forma razoável os recursos locais para atender os visitantes chineses, proteger e divulgar a cultura local é muito importante para Portugal, que é um país com recursos limitados. O texto combina a teoria transcultural de Hofstede e as práticas de outros estudiosos para analisar a diferença cultural entre Portugal e a China, a fim de estudar os efeitos de um contexto cultural diferente no comportamento de turistas. Com base nisso, propõem-se sugestões que possam atender as necessidades de turistas chineses, aumentar a sua satisfação da viagem a Portugal e ao mesmo tempo, divulgar o desenvolvimento sustentável da indústria turística de Portugal. Além disso, este estudo também serve como um modelo de referência para os mercados emergentes com culturas diferentes dos outros destinos de viagem

Migrant Farm Worker App – AgHelp!

Migrant and Seasonal Farmworkers (MSFWs) travel each year to help cultivate and harvest crops in various regions of the United States. They often struggle to find resources such as education, health care or legal services when they get to a new place. On the other side, agencies with limited outreach budget also struggle to connect with them. Meanwhile, growers pay thousands of dollars for recruiting farmworkers every harvest season but often fail in finding enough laborers, while lots of unemployed farmworkers are waiting. To estimate the potential of using a mobile app to address this problem within the farm worker community, we interviewed more than 70 farmworkers, agencies, and growers through Grand Valley State University’s Customer Discovery Program in September 2016. We found a strong demand for a convenient and portable way to connect farmworkers, agencies, and farmers to real-time information. This project is to build a native mobile application named “AgHelp!” for iOS phone users, written in Swift 3.x. It provides a platform for connecting migrant seasonal farmworkers, agencies, growers, and potentially Mexican stores. Bringing these groups together has never been done. “AgHelp!” will assist migrant farmworkers to locate resources more conveniently and help growers find and retain laborers. It also will help agencies locate seasonal workers who are not living in labor camps, and increase the number of MSFWs served. The notification feature of this app will alert users about the latest events, jobs, and news, so that it will help secure farmworkers’ jobs and lives when they are traveling

Axionlike-particle generation by laser-plasma interaction

Axion, a hypothetical particle that is crucial to quantum chromodynamics and dark matter theory, has not yet been found in any experiment. With the improvement of laser technique, much stronger quasi-static electric and magnetic fields can be created in laboratory using laser-plasma interaction. In this article, we discuss the feasibility of axion or axionlike-particle's exploring experiments using planar and cylindrically symmetric laser-plasma fields as backgrounds while probing with an ultrafast superstrong optical laser or x-ray free-electron laser with high photon number. Compared to classical magnet design, the axion source in laser-plasma interaction trades the accumulating length for the source's interacting strength. Besides, a structured field in the plasma creates a tunable transverse profile of the interaction and improves the signal-noise ratio via the mechanisms such as phase-matching. The mass of axion discussed in this article ranges from 1 \textmu eV to 1 eV. Some simple schemes and estimations of axion production and probe's polarization rotation are given, which reveals the possibility of future laser-plasma axion source in laboratory.Comment: 24 pages, 5 figure

A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix.spp.) Biomass Equations

Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values \u3c 0.001) for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R2 values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy

IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers

Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting. However, building deep neural networks in real-world scenarios requires not only high accuracy on clean data but also robustness when data distributions shift. While prior methods have proposed that there is a trade-off between accuracy and robustness, we propose IPMix, a simple data augmentation approach to improve robustness without hurting clean accuracy. IPMix integrates three levels of data augmentation (image-level, patch-level, and pixel-level) into a coherent and label-preserving technique to increase the diversity of training data with limited computational overhead. To further improve the robustness, IPMix introduces structural complexity at different levels to generate more diverse images and adopts the random mixing method for multi-scale information fusion. Experiments demonstrate that IPMix outperforms state-of-the-art corruption robustness on CIFAR-C and ImageNet-C. In addition, we show that IPMix also significantly improves the other safety measures, including robustness to adversarial perturbations, calibration, prediction consistency, and anomaly detection, achieving state-of-the-art or comparable results on several benchmarks, including ImageNet-R, ImageNet-A, and ImageNet-O.Comment: NeurIPS 202

Analysis of corrections to the eikonal approximation

Various corrections to the eikonal approximations are studied for two- and three-body nuclear collisions with the goal to extend the range of validity of this approximation to beam energies of 10 MeV/nucleon. Wallace's correction does not improve much the elastic-scattering cross sections obtained at the usual eikonal approximation. On the contrary, a semiclassical approximation that substitutes the impact parameter by a complex distance of closest approach computed with the projectile-target optical potential efficiently corrects the eikonal approximation. This opens the possibility to analyze data measured down to 10 MeV/nucleon within eikonal-like reaction models.Comment: 10 pages, 8 figure

Plasma n-3 and n-6 fatty acids and inflammatory markers in Chinese vegetarians

Background: Polyunsaturated fatty acid (PUFA) intake favorably affects chronic inflammatory-related diseases such as cardiovascular disease; however, the relationship between the PUFA and inflammatory factors in the healthy vegetarians were not clear. We aimed to investigate the plasma fatty acids status, and its association with plasma inflammatory factors in Chinese vegetarians and omnivores. Methods: A total of 89 male vegetarians and 106 male omnivores were participated the study. Plasma concentrations of inflammatory factors were detected by ELISA, and as standard methods fatty acids were extracted and determined by chromatography. Results: Compared with omnivores, vegetarians have significant higher interleukin-6 (IL-6), plasma n-6 PUFA, n-6/n-3, and 18:3n-3; while they have significant lower leukotriene B4 (LTB4), cyclo-oxygenase-2 (COX2) and prostaglandin E2 (PGE2), 20:5n-3, 22:5n-3, 22:6n-3, and n-3 PUFA. In vegetarians, plasma 20:4n-6 was significant positively related to TNF-α. LTB4 was significantly positively related to plasma 22:6n-3, and negatively associated with n-6 PUFA. Conclusion: Vegetarians have higher plasma n-6 PUFA and IL-6, but lower LTB4, n-3 PUFA, 22:6n-3, COX2 and PGE2 levels. It would seem appropriate for vegetarians to increase their dietary n-3 PUFA, while reduce dietary n-6 PUFA and thus reduce the risk of chronic inflammatory-related diseases