Improving GAN with neighbors embedding and gradient matching

We propose two new techniques for training Generative Adversarial Networks (GANs). Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of $30.80$ for the STL-10 dataset. Our code is available at: https://github.com/tntrung/ganComment: Published as a conference paper at AAAI 201

Effect of shell thickness on heterostructure of CdSe/CdS core/shell nanocrystals

Core/shell hetero-nanostructures are promising materials for fabricating optoelectronic devices, photodetectors, bioimaging, and biosensing. The CdSe/CdS core/shell nanocrystals (NCs) were synthesized in a wet chemical reaction. The shell thickness was modified by varying reaction times. The structure and optical properties as a function of the CdS shell thickness were investigated. A systematic redshift of the first exciton absorption peaks and photoluminescent (PL) spectra occurred after coating with CdS confirmed the shell growth over the CdSe core. The PL's intensity increased compared with that of bare NCs. The formation of high-quality NCs with uniform size distribution was shown in the transmission electron microscopy (TEM) image and confirmed by the narrow PL band and small FWHM

Prevalence of Salmonella in retail whole chicken carcasses in Hanoi, Vietnam

Au Vietnam, les informations sur la contamination de la viande de volaille par les salmonelles sont presque limitées. L’étude cherche à comparer la prévalence des salmonelles entre les marchés traditionnels et les supermarchés ainsi qu’entre les carcasses fraîches et congelées en plus de mesurer la température interne au moment de l’achat. Deux cent quarante-cinq carcasses de poulets entiers ont été achetées des marchés et des supermarchés dans sept arrondissements de la ville de Hanoi au Vietnam de juin à juillet 2011. L’échantillonnage a inclu 110 carcasses fraîches de marchés traditionnels (F/M), 109 carcasses fraîches des supermarchés (F/SM) et 26 carcasses congelées des supermarchés (FZ/SM). La température intérieure des carcasses a été évalué au moment de l’achat des carcasses. Salmonella a été isolé à partir de rinçage de carcasses et les isolats ont été sérotypés. La prévalence de carcasses positives pour Salmonella était de 66,5% (163/245) et variait entre les trois catégories : 84,55% (93/110) de F/M, 59,63% (65/109) de F/SM et 19,23% (5/26) de FZ/SM (P<0.05). Pour un total de 25 sérovars détectés, le sérovar principal fut Agona (24,78%) suivi de Albany (20,43%) et enfin Corvallis (10%). Deux des sérovars repérés se retrouvaient sur les mêmes carcasses pour 66 échantillons (26,9%). La température interne des carcasses des marchés traditionnels et des supermarchés était associé une différence significative (P < 0.05) avec une température moyenne de 27,3°C et 15,8°C respectivement. Cette étude dévoile une prévalence élevée de Salmonellaspp.des carcasses de poulets à Hanoi et démontre une difficulté partagée par tous les types de marchés à maintenir une température adéquate des carcasses.In Vietnam, the data on the prevalence of Salmonella contamination in retail chicken meat is limited. We wanted to compare that prevalence at traditional and modern supermarkets, as well as in fresh versus frozen carcasses, and to verify the inner carcass temperatures at time of purchase. A collection of 245 whole chicken carcasses were purchased from traditional markets and supermarkets, in seven urban district areas of Hanoi in June and July, 2011. Sampling plan included 110 fresh chickens from traditional markets (F/M), 109 fresh chickens from supermarkets (F/SM) and 26 frozen chickens from supermarkets (FZ/SM). The inner carcass temperature was measured at the time of purchase. Salmonella was isolated from carcass rinses and isolates were serotyped. The overall prevalence of Salmonella-positive carcasses was 66.5% (163/245). The Salmonella prevalence in the three types of chickens varied significantly, 84.55% (93/110) from F/M, 59.63% (65/109) from F/SM and 19.23% (5/26) from FZ/SM (P< 0.05). A total of 25 serovars were recovered. The predominant serovars were Agona (24.78%), Albany (20.43%) and Corvallis (10%). Two different serovars were isolated and coexisted on the same carcass in 66 samples (26.9%). The inner carcass temperatures of fresh samples from traditional markets and supermarkets were significantly different (P <0.05) with a mean inner carcass temperature of 27.3oC and 15.8oC respectively. This study revealed a high prevalence of Salmonella sp. from retail chickens in Hanoi and uncovered the difficulty encountered by all market types to store broiler chicken carcasses at a safe temperature

Coverage-Validity-Aware Algorithmic Recourse

Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency and hence ethics of machine learning models. Existing algorithmic recourse approaches often assume an invariant predictive model; however, the predictive model is usually updated upon the arrival of new data. Thus, a recourse that is valid respective to the present model may become invalid for the future model. To resolve this issue, we propose a novel framework to generate a model-agnostic recourse that exhibits robustness to model shifts. Our framework first builds a coverage-validity-aware linear surrogate of the nonlinear (black-box) model; then, the recourse is generated with respect to the linear surrogate. We establish a theoretical connection between our coverage-validity-aware linear surrogate and the minimax probability machines (MPM). We then prove that by prescribing different covariance robustness, the proposed framework recovers popular regularizations for MPM, including the $\ell_2$-regularization and class-reweighting. Furthermore, we show that our surrogate pushes the approximate hyperplane intuitively, facilitating not only robust but also interpretable recourses. The numerical results demonstrate the usefulness and robustness of our framework

STUDY ON LEARNING AUTONOMY STRATEGIES FOR ENGLISH SPEAKING SKILLS OF HIGH-QUALITY FIRST-YEAR STUDENTS, SCHOOL OF FOREIGN LANGUAGES, CAN THO UNIVERSITY, VIETNAM

This study examined the challenges of self-studying English-speaking skills for first-year English studies majors at Can Tho University. The study aimed to answer questions such as: What self-study strategies can first-year students majoring in high-quality English at School of Foreign Languages at Can Tho University apply to develop speaking skills? The target audience was 96 English studies majors who have studied speaking and listening at Can Tho University. A questionnaire and an interview with 13 students were used to collect data for this study. In order to support the quantitative data, a questionnaire was used, while an interview was used for the qualitative data. This study aimed to find out the difficulties encountered by students, thereby proposing solutions to overcome them and improve the quality of their learning autonomy or self-studying English-speaking skills.  Article visualizations

Optimising the Use of TRIzol-extracted Proteins in Surface Enhanced Laser Desorption/ Ionization (SELDI) Analysis

BACKGROUND: Research with clinical specimens is always hampered by the limited availability of relevant samples, necessitating the use of a single sample for multiple assays. TRIzol is a common reagent for RNA extraction, but DNA and protein fractions can also be used for other studies. However, little is known about using TRIzol-extracted proteins in proteomic research, partly because proteins extracted from TRIzol are very resistant to solubilization. RESULTS: To facilitate the use of TRIzol-extracted proteins, we first compared the ability of four different common solubilizing reagents to solubilize the TRIzol-extracted proteins from an osteosarcoma cell line, U2-OS. Then we analyzed the solubilized proteins by Surface Enhanced Laser Desorption/ Ionization technique (SELDI). The results showed that solubilization of TRIzol-extracted proteins with 9.5 M Urea and 2% CHAPS ([3-[(3-cholamidopropyl)-dimethylammonio]propanesulfonate]) (UREA-CHAPS) was significantly better than the standard 1% SDS in terms of solubilization efficiency and the number of detectable ion peaks. Using three different types of SELDI arrays (CM10, H50, and IMAC-Cu), we demonstrated that peak detection with proteins solubilized by UREA-CHAPS was reproducible (r > 0.9). Further SELDI analysis indicated that the number of ion peaks detected in TRIzol-extracted proteins was comparable to a direct extraction method, suggesting many proteins still remain in the TRIzol protein fraction. CONCLUSION: Our results suggest that UREA-CHAPS performed very well in solubilizing TRIzol-extracted proteins for SELDI applications. Protein fractions left over after TRIzol RNA extraction could be a valuable but neglected source for proteomic or biochemical analysis when additional samples are not available