Programming with heterogeneous structures: Manipulating XML data using bondi

Manipulating semistructured data, such as XML, does not fit well within conventional programming languages. A typical manipulation requires finding all occurrences of a structure matching a structured search pattern, whose context may be different in different places, and both aspects cause difficulty. If a special-purpose query language is used to manipulate XML, an interface to a more general programming environment is required, and this interface typically creates runtime overhead for type conversion. However, adding XML manipulation to a general-purpose programming language has proven difficult because of problems associated with expressiveness and typing. We show an alternative approach that handles many kinds of patterns within an existing strongly-typed general-purpose programming language called bondi. The key ideas are to express complex search patterns as structures of simple patterns, pass these complex patterns as parameters to generic data-processing functions and traverse heterogeneous data structures by a generalized form of pattern matching. These ideas are made possible by the language's support for pattern calculus, whose typing on structures and patterns enables path and pattern polymorphism. With this approach, adding a new kind of pattern is just a matter of programming, not language design. Copyright © 2006, Australian Computer Society, Inc

Dual function additives: A small molecule crosslinker for enhanced efficiency and stability in organic solar cells

A bis‐azide‐based small molecule cross­linker is synthesized and evaluated as both a stabilizing and efficiency‐boosting additive in bulk heterojunction organic photovoltaic cells. Activated by a non­invasive and scalable solution processing technique, polymer:fullerene blends exhibit improved thermal stability with suppressed polymer skin formation at the cathode and frustrated fullerene aggregation on ageing, with initial efficiency increased from 6% to 7%

The effect of glutamine supplement on small intestinal morphology and xylose absorptive ability of weaned piglets

The purpose of this study is to demonstrate the effects of glutamine (Gln) supplement on small intestinal  morphology, xylose absorptive and growth performance of weaned piglets. Forty eight piglets weaned at 28 ± 2 days of age were randomly allotted to three treatment groups. A basal corn-soybean diet was formulated to contain 20.3% protein and 3450 kcal DE/kg diet. Glutamine was supplemented to the basal diet at 0% (control), 1% (Gln 1%) and 2% (Gln 2%). Pigs were fed experimental diets for three weeks. The results  showed that the villous height of the Gln groups tended higher than the control group in duodenum and jejunum (P < 0.1). Glutamine supplementation increased plasma net xylose absorptive concentration from 0.78 to 1.20 and 0.95 to 1.23 in Gln 1% and Gln 2% group, respectively, which were better than the control group (0.86 to 0.97) in day 7 to 14 after weaning. Growth performance was not significantly affected by Gln supplement;  however, average daily gain was approximately improved from 21 to 28% by Gln supplement compared to the control group during 21 days of experimental period. In summary, the results suggested that dietary  supplementation of Gln could be beneficial in small intestinal villous morphology and xylose absorptive  capacity, and could have a slight contribution to the average daily gain of weaned piglets.Key words: Glutamine, growth performance, intestinal morphology, weaned piglets

Way Back Home: Reflection on the Relationship between Migrants and Home in Globalization Era

In a more and more globalized and mobilized world, romantic and complete ”home” in the traditional sense has been replaced by a more vibrant and split ”home”. As for Chinese migrants, ”ancestor's home”, ”emotional home” and ”functional home” have become the three basic dimensionalities of ”home”. From the very moment of migrating, ”home” and its meaning have been splitting in these three dimensions. As more and more people migrated, splitting was further deepened, which thus caused serious anxiety and unsecured feeling of the migrants. In order to overcome this anxiety, the migrants, by creatively borrowing the organizing principles and traditional culture of traditional society, tried hard to close the internal crack within ”home”, which finally reconstructed a complete ”home” in a symbolic sense and finished transcending the tradition. Therefore, in this era of globalization, ”home” is a transmigrating process. It starts from ”complete home”, through ”split home”, and ends with ”symbolic home”, or, to be more accurate, ”complete home in symbolic sense”, which means finding the end from the starting point of the journey. 在日益全球化和充滿流動性的世界當中，傳統意義上的羅曼蒂克、完整的“家”已經被更加變動和分裂的“家”所代替。對華人移民而言，“祖先的家”、“情感的家”和“功能的家”構成了“家”的三個基本維度。從移民開始遷徙的那一刻起，“家”及其含義就沿著這三個維度不斷發生裂變。隨著移民進程的持續，裂變也不斷加深，從而在移民心中造成了深刻的焦慮和不安全感。為了克服這種焦慮，移民通過對傳統社會組織原則和文化傳統的創造性借用以及對集體記憶的選擇性營造，努力彌合“家”的內部所出現的裂痕，最終在像徵的意義上，重構了一個完整的“家”，同時也完成了對於傳統的“家”的超越。因此，在全球化的時代，“家”是一個輪迴的過程：始於“完整的家”，經過“分裂的家”，最後達致“象徵的家”，更確切地說，“象徵意義上的完整的家”，從而在旅行的起點找到了終點。abstrac

Learning Shape Priors for Single-View 3D Completion and Reconstruction

The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://shapehd.csail.mit.edu

Genetically modified "obligate" anaerobic Salmonella typhimurium as a therapeutic strategy for neuroblastoma

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In silico Assessment of Drug-like Properties of Alkaloids from Areca catechu L Nut

Purpose: To investigate in silico the drug-like properties of alkaloids (arecoline, arecaidine, guvacine, guvacoline, isoguvacine, arecolidine and homoarecoline) obtained from the fruits of Areca catechu L (areca nut).Methods: All chemical structures were re-drawn using Chemdraw Ultra 11.0. Furthermore, software including Bio-Loom for Windows - version 1.5, Molinspiration Property Calculator and ACD/I-LAB service were used to predict the drug-like properties of the alkaloids, including relative molecular mass (MW), partition coefficient log P (cLog P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), number of rotatable bonds (NROTB), pKa, and aqueous solubility at a given pH (LogS). In addition, Lipinski’s rule was used to evaluate druglike properties.Results: From our research, MWs of the seven compounds were all < 500. HBD and cLog P values of the seven compounds were all < 5, and HBA values were all < 10. In addition, TPSA value of each compound was < 60 Å2, and NROTB value was < 10. Besides, pKa values of the seven alkaloids were > 7.5; furthermore, they possess good solubility at pH 1.0, 5.0, and 7.0.Conclusion: All the seven alkaloids possess good drug-like properties, and demonstrated good oral absorption and bioavailability. The results also suggest that these compounds can be further developed into new oral drugs for treating certain diseases.Keywords: Areca catechu L, Areca nut, Drug-like properties, Alkaloids, Arecoline, Arecaidine, Guvacine, Guvacoline, Isoguvacine, Arecolidine, Homoarecoline, In silic

Frequency-Shifted Low-Noise Sagnac Sensor for Ultrasonic Measurements

Laser generation of ultrasound and the subsequent detection of the ultrasonic waves using laser interferometry are areas of active research [1–6]. In earlier papers, the present authors have discussed an LBU system which employs a diffraction grating for illumination of a line-array to generate narrow-band surface waves and Lamb waves [4], and a fiberized heterodyne dual-probe laser interferometer to measure signals [3]. This paper reports progress towards the development of a robust low cost fiberized Sagnac laser interferometer suitable for field applications. Bowers first reported [7] the use of a Sagnac-type interferometer for surface acoustic wave detection, and the present authors have previously reported [8 QNDE 95] a variant of that scheme. In this paper, we present an alternative lower noise system that uses low cost, long coherence He-Ne lasers that have better intensity noise characteristics than typically used laser diodes. A scheme for elimination of a parasitic interference utilizing a frequency shifting technique has been developed. The primary advantage of the Sagnac interferometer is that it is exactly path matched and as such requires no heterodyning or static path compensation for sensor stabilization. The Sagnac interferometer described below is suitable for the measurement of ultrasonic surface waves arising from laser- or PZT-generated sources or from acoustic emissions. The laser-based ultrasonics (LBU) system can be used to detect and characterize discrete defects such as cracks.</p

Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression

In recent decades, the collection of visual art paintings is large, digitized, and available for public uses that are rapidly growing. The development of multi-media systems is needed due to the huge amount of digitized artwork collections for retrieving and archiving this large-scale data. This multimedia system benefits from high-level tasks and has an essential step for measuring the similarity of visual between the artistic items. For modeling the similarities between the artworks or paintings, it is essential to extract useful features of visual paintings and propose the best approach for learning these similarity metrics. The infield of visual arts education, knowing the similarities and features, makes education more attractive by enhancing cognitive development in students. In this paper, the detailed visual features are listed, and the similarity measurement between the paintings is optimized by the Sparse Metric Learning-based Kernel Regression (KR-SML). A classification model is developed using hybrid SVM-ANN for semantic-level understanding to predict painting’s genre, artist, and style. Furthermore, the Human-Computer Interaction (HCI) based formulation model is built to analyze the proposed technique. The simulation results show that the proposed model is better in terms of performance than other existing techniques