Are foreign students in Australian universities disadvantaged when learning Japanese through the medium of English?

There is concern that international students studying Japanese in Australia are seriously disadvantaged by having to learn a foreign language through the medium of another, imperfectly-mastered, foreign language. This paper tests the validity of these concerns through comparative evaluation of the frequency and type of errors made in written texts by Australian and international students

A UV-transparent passive concentrator/spectrum deconvolution method for simultaneous detection of endocrine disrupting chemicals (EDCs) and related contaminants in natural waters

Suspected endocrine disrupting chemicals (EDCs) have been widely detected in the environment, and are a source of increasing concern. One of the major challenges in assessing the risk associated with EDCs in the environment is that their environmental concentrations are typically extremely low making them difficult to quantify without extensive pre-concentration procedures. Further complicating their detection is the fact that they are present in mixtures, sometimes with tens to hundreds of other compounds (pharmaceuticals, personal care products, detergents, natural organic matter). The objective of the work described here was to develop a method for rapid monitoring and detection of EDCs at trace concentrations in natural waters. The method makes use of a UV-transparent polymer-based concentrator to be used as a passive sampling device. The UV-transparent polymer-based concentrator serves both as a solid phase extraction medium to concentrate EDCs for analysis and exclude many compounds likely to interfere with detection (fines, macromolecules such as organic matter, ionic surfactants), and as an analytical optical cell, allowing rapid EDC quantification without labor-intensive preconcentration procedures. A full-spectrum deconvolution technique is used to determine EDC concentrations from measured UV absorbance spectra in the polymer. Experiments were conducted to measure partitioning rate behavior and partition coefficients between the selected polymer (a functional polydimethylsiloxane) and water for seven compounds known or suspected of being endocrine disruptors: estrone, progesterone, estradiol, 2,6-di-tert-butyl-1,4-benzoquinone, phenanthrene, triclosan, and 4-nonylphenol. The method was tested for its ability to detect and quantify individual compounds in mixtures containing up to six components. Results show the method to have selectivity suitable for rapid screening applications at many sites where multiple compounds are present

A UV-transparent passive concentrator/spectrum deconvolution method for simultaneous detection of endocrine disrupting chemicals (EDCs) and related contaminants in natural waters

Suspected endocrine disrupting chemicals (EDCs) have been widely detected in the environment, and are a source of increasing concern. One of the major challenges in assessing the risk associated with EDCs in the environment is that their environmental concentrations are typically extremely low making them difficult to quantify without extensive pre-concentration procedures. Further complicating their detection is the fact that they are present in mixtures, sometimes with tens to hundreds of other compounds (pharmaceuticals, personal care products, detergents, natural organic matter). The objective of the work described here was to develop a method for rapid monitoring and detection of EDCs at trace concentrations in natural waters. The method makes use of a UV-transparent polymer-based concentrator to be used as a passive sampling device. The UV-transparent polymer-based concentrator serves both as a solid phase extraction medium to concentrate EDCs for analysis and exclude many compounds likely to interfere with detection (fines, macromolecules such as organic matter, ionic surfactants), and as an analytical optical cell, allowing rapid EDC quantification without labor-intensive preconcentration procedures. A full-spectrum deconvolution technique is used to determine EDC concentrations from measured UV absorbance spectra in the polymer. Experiments were conducted to measure partitioning rate behavior and partition coefficients between the selected polymer (a functional polydimethylsiloxane) and water for seven compounds known or suspected of being endocrine disruptors: estrone, progesterone, estradiol, 2,6-di-tert-butyl-1,4-benzoquinone, phenanthrene, triclosan, and 4-nonylphenol. The method was tested for its ability to detect and quantify individual compounds in mixtures containing up to six components. Results show the method to have selectivity suitable for rapid screening applications at many sites where multiple compounds are present

Asymmetric filtering-based dense convolutional neural network for person re-identification combined with Joint Bayesian and re-ranking

Person re-identification aims at matching individuals across multiple camera views under surveillance systems. The major challenges lie in the lack of spatial and temporal cues, which makes it difficult to cope with large variations of lighting conditions, viewing angles, body poses and occlusions. How to extract multimodal features including facial features, physical features, behavioral features, color features, etc is still a fundamental problem in person re-identification. In this paper, we propose a novel Convolutional Neural Network, called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) to learn powerful features, which can extract different levels’ features and take advantage of identity information. Moreover, instead of using typical metric learning methods, we obtain the ranking lists by merging Joint Bayesian and re-ranking techniques which do not need dimensionality reduction. Finally, extensive experiments show that our proposed architecture performs well on four popular benchmark datasets (CUHK01, CUHK03, Market-1501, DukeMTMC-reID)

Underwater motion deblurring based on cascaded attention mechanism

The  images  captured  in  the  underwater  scene  frequently  suffer  from  blur  effects  due  to  the  insufficient light  and  the  relative  motion  between  the  captured  scenes  and  the  imaging  system,  which  severely  hinders  the visual-based  exploration  and  investigation  in  the  ocean.  In  this  paper,  we  propose  a  feature  pyramid  attention network  (FPAN)  to  remove  themotion  blur  and  restore  the  blurry  underwater  images.  FPAN  incorporates  the cascaded  attention  modules  into  the  feature  pyramid  network  (FPN)  that  enables  it  to  learn  more  discriminative information. To facilitate the training of FPAN, we construct a weighted loss function, which consists of a content loss, an adversarial loss, and a perceptual loss. The cascaded attention module and the weighted loss function enable our proposed FPAN to generate more realistic high-quality images from the blurry underwater images. In addition, to deal with the lack of publicly available datasets in underwater image deblurring, we built two specific underwater deblurring  datasets,  namely  Underwater  Convolutional  Deblurring  Dataset  (UCDD)  and  Underwater  Multi-frame AveragingDeblurring Dataset (UMADD), to train and examine different deep learning-based networks.Finally, we conduct  sea  trial  experiments  on  our  autonomous  underwater  vehicle  (AUV).  Experimental  results  on  two underwater  deblurring  datasets  demonstrate  our  proposed  method  achieves  satisfactory results, which  validates  the potential practical values of our proposed method in real-world applications.</p

Underwater Object Detection in Noisy Imbalanced Datasets

Class imbalance occurs in the datasets with a disproportionate ratio of observations. The class imbalance problem drives the detection and classification systems to be more biased towards the over-represented classes while the under-represented classes may not receive sufficient learning. Previous works often deploy distribution based re-balancing approaches to address this problem. However, these established techniques do not work properly for underwater object detection where label noise commonly exists. In our experiments, we observe that the imbalanced detection problem may be caused by imbalance data distributions or label noise. To deal with these challenges, we first propose a noise removal (NR) algorithm to remove label noise in the datasets, and then propose a factor-agnostic gradient re-weighting algorithm (FAGR) to address the imbalanced detection problem. FAGR provides a rebalanced gradient to each class, which encourages the detection network to treat all the classes equally whilst minimising the detection discrepancy. Our proposed NR+FAGR framework achieves state-of-the-art (SOAT) performance on three underwater object datasets due to its high capacity in handling the class imbalance and noise issues. The source code will be made available at: https://github.com/IanDragon.</p

Underwater object detection using Invert Multi-Class Adaboost with deep learning

Abstract—In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real applications are usually small and their images are blurry, and (2) images in the underwater datasets and real applications accompany heterogeneous noise. To address these two problems, we first propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. SWIPENet consists of high resolution and semantic-rich Hyper Feature Maps which can significantly improve small object detection accuracy. In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the in-fluence of noise on the proposed SWIPENet. Experiments on two underwater robot picking contest datasets URPC2017 andURPC2018 show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches.</p

Surface morphology evolution of a polycrystalline diamond by inductively coupled plasma reactive ion etching (ICP-RIE)

The needle-like surface morphology evolution in oxygen plasma in combination with a secondary gas (Cl2, CHF3 or CF4) by inductively coupled plasma reactive ion etching (ICP-RIE) on a free-standing polycrystalline diamond was investigated. The addition of CF4 can produce trans-polyacetylene (t-PA), which is similar to the result when the pure O2 etching takes place, and generate compact needle-tip particles. However, the t-PA disappears with the introduction of Cl or H ions. The optimised etching parameters for the needle-like structure formation are as following: Cl2/O2 ratio 20% and RF-power (RFP) 100 W, where more compact and even nano-needles are realised with an average etching rate of 2 μm/min. The Cl2/O2 plasma etching results indicate that the time-dependent etching mechanism of diamond nano-needles results from (1 1 1) crystal plane selective etching and preferential graphitisation at the twin-plane boundary and dislocation area

Manipulation of the internal structure of high amylose maize starch by high pressure treatment and its diverse influence on digestion

© 2017 Elsevier Ltd. In this study, high amylose maize starch was mixed with different moisture contents, followed by high hydrostatic pressure (HHP) at 200, 400, 600, 800 and 1000 MP, respectively. Changes in starch physicochemical and digestion properties associated with HHP treatment were analyzed in terms of starch granular morphology, lamellar structures and crystalline characteristics. Results showed that, under the same pressure treatments, the starches with different moisture contents exhibited a similar pattern of the changes in the properties. The erosion of digestive enzymes on starch granules was enhanced with increased HPP pressures. Treatment with 200 and 400 MP led to a reduction of digestibility compared to its native one. However, digestion was gradually promoted when the treatment pressure reached up to 600 MP. Structural data acquired from SAXS and WAX indicated the treatment of HHP up to 600 MP partly destroyed the starch granules internally, resulting in a decreased degree of organized structure. These results may reveal the importance of starch lamellar structure and crystalline order as being the key structural parameters for influencing starch digestion properties. Changes in the electron density following the digestion indicated that digestion characteristics of the starch are highly related to the changes in its corresponding internal structure of amylopectin amorphous layer, amylose amorphous and amylopectin crystal layer caused by HPP. Further analysis of the changes in the relative crystallinity of the starch may suggest that starch digestion characteristics are highly related to lamellar structure but not relative crystallinity

Novel splice variants of Rat CaV2.1 that lack much of the synaptic protein interaction site are expressed in neuroendocrine cells

Voltage-gated Ca(2+) channels are responsible for the activation of the Ca(2+) influx that triggers exocytotic secretion. The synaptic protein interaction (synprint) site found in the II-III loop of Ca(V)2.1 and Ca(V)2.2 mediates a physical association with synaptic proteins that may be crucial for fast neurotransmission and axonal targeting. We report here the use of nested PCR to identify two novel splice variants of rat Ca(V)2.1 that lack much of the synprint site. Furthermore, we compare immunofluorescence data derived from antibodies directed against sequences in the Ca(V)2.1 synprint site and carboxyl terminus to show that channel variants lacking a portion of the synprint site are expressed in two types of neuroendocrine cells. Immunofluorescence data also suggest that such variants are properly targeted to neuroendocrine terminals. When expressed in a mammalian cell line, both splice variants yielded Ca(2+) currents, but the variant containing the larger of the two deletions displayed a reduced current density and a marked shift in the voltage dependence of inactivation. These results have important implications for Ca(V)2.1 function and for the mechanisms of Ca(V)2.1 targeting in neurons and neuroendocrine cells