Experiences of and preparedness for Intercultural Teacherhood in Higher Education : non-specialist English teachers’ positioning, agency and sense of legitimacy in China

Publisher Copyright: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This article focuses on a case study of English language teachers, who are asked to teach intercultural communication to mixed classes of local and international students in Chinese Higher Education, although they do not specialize in this complex field. They were interviewed to find out about their experiences and perceptions of this ‘improvised’ Intercultural Teacherhood. The study shows that their engagement with intercultural communication differs while the presence of international students has a major impact on all the teachers’ identity and sense of legitimacy. The paper ends on recommendations for (research on) preparing teachers to teach IC.Peer reviewe

Colour alignment for relative colour constancy via non-standard references

Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching. The proposed model works with non-standard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-linear-distances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Two challenging image datasets collected by multiple cameras under various illumination and exposure conditions were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods.Comment: 14 pages, 8 figures, 2 tables, accepted by IEEE Transactions on Image Processin

Dual Discriminator Adversarial Distillation for Data-free Model Compression

Knowledge distillation has been widely used to produce portable and efficient neural networks which can be well applied on edge devices for computer vision tasks. However, almost all top-performing knowledge distillation methods need to access the original training data, which usually has a huge size and is often unavailable. To tackle this problem, we propose a novel data-free approach in this paper, named Dual Discriminator Adversarial Distillation (DDAD) to distill a neural network without any training data or meta-data. To be specific, we use a generator to create samples through dual discriminator adversarial distillation, which mimics the original training data. The generator not only uses the pre-trained teacher's intrinsic statistics in existing batch normalization layers but also obtains the maximum discrepancy from the student model. Then the generated samples are used to train the compact student network under the supervision of the teacher. The proposed method obtains an efficient student network which closely approximates its teacher network, despite using no original training data. Extensive experiments are conducted to to demonstrate the effectiveness of the proposed approach on CIFAR-10, CIFAR-100 and Caltech101 datasets for classification tasks. Moreover, we extend our method to semantic segmentation tasks on several public datasets such as CamVid and NYUv2. All experiments show that our method outperforms all baselines for data-free knowledge distillation

Spectral Illumination Correction: Achieving Relative Color Constancy Under the Spectral Domain

Achieving color constancy between and within images, i.e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition. Most current methods attempt to solve this by illumination correction on perceptual color spaces. In this paper, we proposed two pixel-wise algorithms to achieve relative color constancy by working under the spectral domain. That is, the proposed algorithms map each pixel to the reflectance ratio of objects appeared in the scene and perform illumination correction in this spectral domain. Also, we proposed a camera calibration technique that calculates the characteristics of a camera without the need of a standard reference. We show that both of the proposed algorithms achieved the best performance on nonuniform illumination correction and relative illumination matching respectively compared to the benchmarked algorithms.This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement No 720325, FoodSmartphone.Peer-reviewedPost-print2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Louisville, KY, USA, US

Biomarker study of symptomatic intracranial atherosclerotic stenosis in patients with acute ischemic stroke

ObjectiveAcute ischemic stroke (AIS) is characterized by high rates of morbidity, disability, mortality, and recurrence, often leaving patients with varying degrees of sequelae. Symptomatic intracranial atherosclerotic stenosis (sICAS) is a significant contributor to AIS pathogenesis and recurrence. The formation and progression of sICAS are influenced by pathways such as lipid metabolism and inflammatory response. Given its high risk of clinical recurrence, timely assessment of intracranial vascular stenosis in AIS is crucial for diagnosing sICAS, treating stroke, and preventing stroke recurrence.MethodsFourteen AIS patients were divided into stenosis and control groups based on the presence or absence of intracranial vessel stenosis. Initially, 4D Label-free proteome quantification technology was employed for mass spectrometry analysis to identify differential proteins between the groups. Subsequently, functional enrichment analysis, including GO classification, KEGG pathway, and Domain, revealed trends related to differential proteins. The STRING (v.11.5) protein interaction network database was used to identify differential protein interactions and target proteins. Finally, parallel reaction monitoring (PRM) validated the selected target proteins.ResultsMass spectrometry identified 1,096 proteins, with 991 being quantitatively comparable. Using a p-value <0.05 and differential expression change thresholds of >1.3 for significant up-regulation and < 1/1.3 for significant down-regulation, 46 differential proteins were identified: 24 significantly up-regulated and 22 significantly down-regulated. PRM experiments validated five proteins related to lipid metabolism and inflammatory response: namely alpha-2-macroglobulin (A2M), lipopolysaccharide-binding protein (LBP), cathepsin G (CTSG), cystatin (CST)3, and fatty acid-binding protein (FABP)1.ConclusionThe detection of changes in these five proteins in AIS patients can aid in the diagnosis of sICAS, inform stroke treatment, and assist in preventing stroke recurrence. Moreover, it can contribute to the development of drugs for preventing AIS recurrence by integrating traditional Chinese and Western medicine

Water analysis with the help of tensor canonical decompositions

Coopération universitaire et scientifique Franco-VietnamienneInternational audienceRaw data are collected in five measurement locations along the Var river. It is assumed that some locations interact with each other, whereas others do not. In such a context, we are interested in determining the contribution of each location and in better understanding the water exchanges that are involved. Organic components can also be identified thanks to methods such as Canonical Polyadic decompositions (CP) (sometimes known as Parafac), applied to 3D fluorescence spectra calculated from the collected samples. The expected impact is a more efficient detection of polluting matters in water

Ginseng Total Saponins Reverse Corticosterone-Induced Changes in Depression-Like Behavior and Hippocampal Plasticity-Related Proteins by Interfering with GSK-3 β

This study aimed to explore the antidepressant mechanisms of ginseng total saponins (GTS) in the corticosterone-induced mouse depression model. In Experiment 1, GTS (50, 25, and 12.5 mg kg−1 d−1, intragastrically) were given for 3 weeks. In Experiment 2, the same doses of GTS were administrated after each corticosterone (20 mg kg−1 d−1, subcutaneously) injection for 22 days. In both experiments, mice underwent a forced swimming test and a tail suspension test on day 20 and day 21, respectively, and were sacrificed on day 22. Results of Experiment 1 revealed that GTS (50 and 25 mg kg−1 d−1) exhibited antidepressant activity and not statistically altered hippocampal protein levels of brain-derived neurotrophic factor (BDNF) and neurofilament light chain (NF-L). Results of Experiment 2 showed that GTS (50 and 25 mg kg−1 d−1) ameliorated depression-like behavior without normalizing hypercortisolism. The GTS treatments reversed the corticosterone-induced changes in mRNA levels of BDNF and NF-L, and protein levels of BDNF NF-L, phosphor-cAMP response element-binding protein (Ser133), and phosphor-glycogen synthase kinase-3β (Ser9) in the hippocampus. These findings imply that the effect of GTS on corticosterone-induced depression-like behavior may be mediated partly through interfering with hippocampal GSK-3β-CREB signaling pathway and reversing decrease of some plasticity-related proteins

Context-aware Mouse Behaviour Recognition using Hidden Markov Models

Automated recognition of mouse behaviors is crucial in studying psychiatric and neurologic diseases. To achieve this objective, it is very important to analyze the temporal dynamics of mouse behaviors. In particular, the change between mouse neighboring actions is swift in a short period. In this paper, we develop and implement a novel hidden Markov model (HMM) algorithm to describe the temporal characteristics of mouse behaviors. In particular, we here propose a hybrid deep learning architecture, where the first unsupervised layer relies on an advanced spatial-temporal segment Fisher vector encoding both visual and contextual features. Subsequent supervised layers based on our segment aggregate network are trained to estimate the state-dependent observation probabilities of the HMM. The proposed architecture shows the ability to discriminate between visually similar behaviors and results in high recognition rates with the strength of processing imbalanced mouse behavior datasets. Finally, we evaluate our approach using JHuang's and our own datasets, and the results show that our method outperforms other state-of-the-art approaches