Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Climate change patterns in Amazonia and biodiversity

Abstract: Precise characterization of hydroclimate variability in Amazonia on various timescales is critical to understanding the link between climate change and biodiversity. Here we present absolute-dated speleothem oxygen isotope records that characterize hydroclimate variation in western and eastern Amazonia over the past 250 and 20 ka, respectively. Although our records demonstrate the coherent millennial-scale precipitation variability across tropical– subtropical South America, the orbital-scale precipitation variability between western and eastern Amazonia exhibits a quasi-dipole pattern. During the last glacial period, our records imply a modest increase in precipitation amount in western Amazonia but a significant drying in eastern Amazonia, suggesting that higher biodiversity in western Amazonia, contrary to ‘Refugia Hypothesis’, is maintained under relatively stable climatic conditions. In contrast, the glacial–interglacial climatic perturbations might have been instances of loss rather than gain in biodiversity in eastern Amazonia, where forests may have been more susceptible to fragmentation in response to larger swings in hydroclimate.

Visual Detection of Copper(II) by Azide- and Alkyne-Functionalized Gold Nanoparticles Using Click Chemistry

Abstract: We report a method for the detection of Cu ions by azideand terminal alkyne-functionalized gold nanoparticles (Au NPs) in aqueous solutions using click chemistry. The catalyst, Cu(I), was conveniently derived from the reduction of Cu(II) in the presence of sodium ascorbate. This method allows the naked eye, without the aid of any advanced instrument, to assay for the presence of Cu ions by the aggregation of Au NPs as a result of the Cu(I)-catalyzed conjugation between the two functional groups. Copper is a transition metal essential for life but also highly toxic to organisms, such as certain algae, fungi and many bacteria and viruses. In recent years, copper has been suspected of causing liver damage in children. The analysis and measurement of copper in environmental and biological samples have become increasingly important. Several methods exist for the detection of Cu ions, for example, those based on organic fluorophores or chromogenic sensors, quantum dots, atomic absorption spectroscopy, inductively coupled plasma mass spectroscopy, absorbance spectro-photometry, peptides and voltammetry. The color changes associated with the aggregation of metal nanoparticles has led to the development of a number of assays for a variety of target species. Colorimetric methods can be convenient and attractive in many applications because they can be easily monitored with the naked eye, without the aid of any advanced instruments. The extinction coefficient of 13 nm-diameter gold nanoparticles is 2.7 4 10m 1 cm , several orders of magnitude more than those of traditional organic chromophores. As a result, colors arising from nanoparticles at nanomolar concentrations can be observed by the naked eye, allowing sensitive detection of small amounts of analytes. Since Cu(I) is used as a catalyst in the cycloaddition reaction between azides and alkynes in click chemistry based on Huisgen6s reaction, the amount of copper needed for its completion is typically small. Therefore, a method that can visualize the progress of the reaction using the aggregation of Au NPs might also be useful for the detection of trace amounts of Cu(II) (by detection of Cu(I)). Because the azide/alkyne functional groups and their conjugation are highly selective and are essentially inert to most biological molecules, oxygen, water, and the majority of common reaction conditions in chemical synthesis, and are tolerant of a wide range of solvents, temperatures, and pH values, we reasoned that an assay based on such chemistry may find myriad uses. Our method for the detection of Cu ions relies on the Cu(I)-catalyzed 1,3-dipolar cycloaddition of alkynes and azides on the surface of functionalized Au NPs, that results in the aggregation of Au NPs (Scheme 1). We synthesized azideand terminal alkyne-functionalized thiols, 1 and 2, and prepared gold NPs coated with these

Image hallucination with primal sketch priors

Abstract: We propose a Bayesian approach to image hallucination. Given a generic low resolution image, we hallucinate a high resolution image using a set of training images. Our work is inspired by recent progress on natural image statistics that the priors of image primitives can be well represented by examples. Specifically, primal sketch priors (e.g., edges, ridges and corners) are constructed and used to enhance the quality of the hallucinated high resolution image. Moreover, a contour smoothness constraint enforces consistency of primitives in the hallucinated image by a Markov-chain based inference algorithm. A reconstruction constraint is also applied to further improve the quality of the hallucinated image. Experiments demonstrate that our approach can hallucinate high quality super-resolution images.

Data Quality: Some Comments on the NASA Software Defect Datasets

Abstract: Background--Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets. In recent years, there has been much interest in using machine learners to classify software modules into defect-prone and not defect-prone categories. The publicly available NASA datasets have been extensively used as part of this research. Objective--This short note investigates the extent to which published analyses based on the NASA defect datasets are meaningful and comparable. Method--We analyze the five studies published in the IEEE Transactions on Software Engineering since 2007 that have utilized these datasets and compare the two versions of the datasets currently in use. Results--We find important differences between the two versions of the datasets, implausible values in one dataset and generally insufficient detail documented on dataset preprocessing. Conclusions--It is recommended that researchers 1) indicate the provenance of the datasets they use, 2) report any preprocessing in sufficient detail to enable meaningful replication, and 3) invest effort in understanding the data prior to applying machine learners.