Static and free vibration analyses of plates on elastic foundation using a cell-based smoothed three-node mindlin plate element (CS-Min3)

A cell-based smoothed three-node Mindlin plate element (CS-MIN3) was recently proposed to improve the performance of the existing three-node Mindlin plate element (MIN3) for static and free vibration analyses of Mindlin plates. In this paper, the CSMIN3 is incorporated with spring systems for treating more complicated static and free vibration analyses of Mindlin plates on the elastic foundation. The plate-foundation system is modeled as a discretization of triangular plate elements supported by discrete springs at the nodal points representing the elastic foundation. The accuracy and reliability of the proposed method are verified by comparing with those of others available numerical results

Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations

Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach is through counterfactual explanations, which provide suggestions on what a user can do to alter an outcome. Not only must a counterfactual example counter the original prediction from the black-box classifier but it should also satisfy various constraints for practical applications. Diversity is one of the critical constraints that however remains less discussed. While diverse counterfactuals are ideal, it is computationally challenging to simultaneously address some other constraints. Furthermore, there is a growing privacy concern over the released counterfactual data. To this end, we propose a feature-based learning framework that effectively handles the counterfactual constraints and contributes itself to the limited pool of private explanation models. We demonstrate the flexibility and effectiveness of our method in generating diverse counterfactuals of actionability and plausibility. Our counterfactual engine is more efficient than counterparts of the same capacity while yielding the lowest re-identification risks

Frequency Attention for Knowledge Distillation

Knowledge distillation is an attractive approach for learning compact deep neural networks, which learns a lightweight student model by distilling knowledge from a complex teacher model. Attention-based knowledge distillation is a specific form of intermediate feature-based knowledge distillation that uses attention mechanisms to encourage the student to better mimic the teacher. However, most of the previous attention-based distillation approaches perform attention in the spatial domain, which primarily affects local regions in the input image. This may not be sufficient when we need to capture the broader context or global information necessary for effective knowledge transfer. In frequency domain, since each frequency is determined from all pixels of the image in spatial domain, it can contain global information about the image. Inspired by the benefits of the frequency domain, we propose a novel module that functions as an attention mechanism in the frequency domain. The module consists of a learnable global filter that can adjust the frequencies of student's features under the guidance of the teacher's features, which encourages the student's features to have patterns similar to the teacher's features. We then propose an enhanced knowledge review-based distillation model by leveraging the proposed frequency attention module. The extensive experiments with various teacher and student architectures on image classification and object detection benchmark datasets show that the proposed approach outperforms other knowledge distillation methods.Comment: Appear to WACV 202

Etiology and epidemiology of diarrhea in children in Hanoi, Vietnam

SummaryObjectivesThis paper provides a preliminary picture of diarrhea with regards to etiology, clinical symptoms, and some related epidemiologic factors in children less than five years of age living in Hanoi, Vietnam.MethodsThe study population included 587 children with diarrhea and 249 age-matched healthy controls. The identification of pathogens was carried out by the conventional methods in combination with ELISA, immunoseparation, and PCR. The antibiotic susceptibility was determined by MIC following the NCCLS recommendations.ResultsOf those with diarrhea, 40.9% were less than one year old and 71.0% were less than two years old. A potential pathogen was identified in 67.3% of children with diarrhea. They were group A rotavirus, diarrheagenic Escherichia coli, Shigella spp, and enterotoxigenic Bacteroides fragilis, with prevalences of 46.7%, 22.5%, 4.7%, and 7.3%, respectively. No Salmonella spp or Vibrio cholerae were isolated. Rotavirus and diarrheagenic E. coli were predominant in children less than two years of age, while Shigella spp, and enterotoxigenic B. fragilis were mostly seen in the older children. Diarrheagenic E. coli and Shigella spp showed high prevalence of resistance to ampicillin, chloramphenicol, and to trimethoprim/sulfamethoxazole. Children attending the hospitals had fever (43.6%), vomiting (53.8%), and dehydration (82.6%). Watery stool was predominant with a prevalence of 66.4%, followed by mucous stool (21.0%). The mean episodes of stools per day was seven, ranging from two to 23 episodes. Before attending hospitals, 162/587 (27.6%) children had been given antibiotics. Overall, more children got diarrhea in (i) poor families; (ii) families where piped water and a latrine were lacking; (iii) families where mothers washed their hands less often before feeding the children; (iv) families where mothers had a low level of education; (v) families where information on health and sanitation less often reached their households.ConclusionsGroup A rotavirus, diarrheagenic Escherichia coli, Shigella spp, and enterotoxigenic Bacteroides fragilis play an important role in causing diarrhea in children in Hanoi, Vietnam. Epidemiological factors such as lack of fresh water supply, unhygienic septic tank, low family income, lack of health information, and low educational level of parents could contribute to the morbidity of diarrhea in children

Flat Seeking Bayesian Neural Networks

Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior distribution based on observed data. The model sampled from the posterior distribution can be used for providing ensemble predictions and quantifying prediction uncertainty. It is well-known that deep learning models with lower sharpness have better generalization ability. However, existing posterior inferences are not aware of sharpness/flatness in terms of formulation, possibly leading to high sharpness for the models sampled from them. In this paper, we develop theories, the Bayesian setting, and the variational inference approach for the sharpness-aware posterior. Specifically, the models sampled from our sharpness-aware posterior, and the optimal approximate posterior estimating this sharpness-aware posterior, have better flatness, hence possibly possessing higher generalization ability. We conduct experiments by leveraging the sharpness-aware posterior with state-of-the-art Bayesian Neural Networks, showing that the flat-seeking counterparts outperform their baselines in all metrics of interest.Comment: Accepted at NeurIPS 202

Analyses of stiffened plates resting on the viscoelastic foundation subjected to a moving vehicle by a cell-based smoothed triangular plate element

Recently, a cell-based smoothed discrete shear gap method (CS-FEM-DSG3) based on the firstorder shear deformation theory (FSDT) was proposed for static and free vibration analyses of Mindlin plates. The CS-FEM-DSG3 uses three-node triangular elements that can be easily generated automatically for arbitrary complicated geometric domains. This paper further extends the CS-FEMDSG3 for static, free vibration, and dynamic response of the stiffened plate resting on viscoelastic foundation subjected to a moving vehicle. The viscoelastic foundation is modeled by discrete springs and dampers whereas the stiffened plate can be considered as the combination between the Mindlin plate and the Timoshenko beam elements. The moving vehicle is transformed into one concentrated load at its central point. Some numerical examples are investigated and numerical results show that the CS-FEMDSG3 overcomes shear-locking phenomena and has a fast convergence. The results also illustrate the good agreement of the CS-FEM-DSG3 for static and free vibration analyses of un-stiffened plate compared with the previous published methods. In addition, the numerical results for dynamic analysis of stiffened plates by the CS-FEM-DSG3 also show the expected property in which the deflection of the stiffened plate is much smaller than those of the un-stiffened plate

Synthesis and Characterization of RbxMn[Fe(CN)]6 and Mn3[Cr(CN)6]2

We present the synthesis and detailed characterization of RbxMn[Fe(CN)]6 and Mn3[Cr(CN)6]2 compounds. The composition of the materials significantly depends on the preparation conditions. Analysis of Raman spectroscopic results and X-ray powder diffraction data yielded a further assessment of the changes in structural features. The characteristic individual magnetic behavior, as well as the metal-to-metal charge-transfer capabilities of the various samples, could be related to significant changes within the structure that appear to be associated with the synthesis method used

A Measure of Smoothness in Synthesized Speech

The articulators typically move smoothly during speech production. Therefore, speech features of natural speech are generally smooth. However, over-smoothness causes "muffleness" and, hence, reduction in ability to identify emotions/expressions/styles in synthesized speech that can affect the perception of naturalness in synthesized speech. In the literature, statistical variances of static spectral features have been used as a measure of smoothness in synthesized speech but they are not sufficient enough. This paper proposes another measure of smoothness that can be efficiently applied to evaluate the smoothness of synthesized speech. Experiments showed that the proposed measure is reliable and efficient to measure the smoothness of different kinds of synthesized speech

Classification of cow’s behaviors based on 3-DoF accelerations from cow’s movements

Cow’s behavior classification helps people to monitor cow activities, thus the health and physiological periods of cows can be well tracked. To classify the behavior of cows, the data from the 3-axis acceleration sensor mounted on their neck is often used. Data acquisition and preprocessing of sensor data is required in this device. We acquire data from the 3-axis acceleration sensor mounted on the cows’neck and send to the microcontrollter. At the microcontroller, a proposed decision tree is applied in real-time manner to classify four important activities of the cows (standing, lying, feeding, and walking). Finally, the results can be sent to the server through the wireless transmission module. The test results confirm the reliability of the proposed device

A modern purification by accelerated solvent extraction and centrifugal partition chromatography and biological evaluation of capsaicin from Capsicum chinense

A special alkaloid compound known as capsaicin, which can only be found in the fruit of the Capsicum plant, was isolated and tested for its anti-inflammatory activity. The purpose of this work is to establish a simple and quick approach for capsaicin purification utilizing centrifugal partition chromatography (CPC) as well as an effective method - accelerated solvent extraction (ASE), for extracting capsaicin from Capsicum chinense. After purification, capsaicin was validated by HPLC-DAD at 281 nm to be > 90% purity. The in vivo anti-inflammatory activity of the isolated capsaicin was also investigated, and the IC50 value of the capsaicin was determined to be 57.61 µg/mL. The current work emphasizes how an ASE and CPC system may combine to extract high-purity capsaicin from Capsicum chinense, which have the anti-inflammatory activity, as we evaluated in the experiment