Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spotting

One difficult problem of keyword spotting is how to miniaturize its memory footprint while maintain a high precision. Although convolutional neural networks have shown to be effective to the small-footprint keyword spotting problem, they still need hundreds of thousands of parameters to achieve good performance. In this paper, we propose an efficient model based on depthwise separable convolution layers and squeeze-and-excitation blocks. Specifically, we replace the standard convolution by the depthwise separable convolution, which reduces the number of the parameters of the standard convolution without significant performance degradation. We further improve the performance of the depthwise separable convolution by reweighting the output feature maps of the first convolution layer with a so-called squeeze-and-excitation block. We compared the proposed method with five representative models on two experimental settings of the Google Speech Commands dataset. Experimental results show that the proposed method achieves the state-of-the-art performance. For example, it achieves a classification error rate of 3.29% with a number of parameters of 72K in the first experiment, which significantly outperforms the comparison methods given a similar model size. It achieves an error rate of 3.97% with a number of parameters of 10K, which is also slightly better than the state-of-the-art comparison method given a similar model size

A Noninterior Path following Algorithm for Solving a Class of Multiobjective Programming Problems

Multiobjective programming problems have been widely applied to various engineering areas which include optimal design of an automotive engine, economics, and military strategies. In this paper, we propose a noninterior path following algorithm to solve a class of multiobjective programming problems. Under suitable conditions, a smooth path will be proven to exist. This can give a constructive proof of the existence of solutions and lead to an implementable globally convergent algorithm. Several numerical examples are given to illustrate the results of this paper

Inhibition of TRPA1 Attenuates Doxorubicin-Induced Acute Cardiotoxicity by Suppressing Oxidative Stress, the Inflammatory Response, and Endoplasmic Reticulum Stress

The transient receptor potential ankyrin 1 (TRPA1) channel is expressed in cardiomyocytes and involved in many cardiovascular diseases. However, the expression and function of TRPA1 in doxorubicin- (Dox-) induced acute cardiotoxicity have not been elucidated. This study aimed at investigating whether blocking the TRPA1 channel with the specific inhibitor HC-030031 (HC) attenuates Dox-induced cardiac injury. The animals were randomly divided into four groups: control, HC, Dox, and Dox + HC. Echocardiography was used to evaluate cardiac function, and the heart was removed for molecular experiments. The results showed that the expression of TRPA1 was increased in the heart after Dox treatment. Cardiac dysfunction and increased serum CK-MB and LDH levels were induced by Dox, but these effects were attenuated by HC treatment. In addition, HC mitigated Dox-induced oxidative stress, as evidenced by the decreased MDA level and increased GSH level and SOD activity in the Dox + HC group. Meanwhile, HC treatment lowered the levels of the proinflammatory cytokines IL-1β, IL-6, IL-17, and TNF-α induced by Dox. Furthermore, HC treatment mitigated endoplasmic reticulum (ER) stress and cardiomyocyte apoptosis induced by Dox. These results indicated that inhibition of TRPA1 could prevent Dox-induced cardiomyocyte apoptosis in mice by inhibiting oxidative stress, inflammation, and ER stress

Data-driven model for divertor plasma detachment prediction

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks. An autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements, and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. Benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable to provide accurate detachment prediction (usually within a few percent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing to the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilizing this knowledge could open a new path for plasma control in magnetic fusion energy research.Comment: 24 pages, 15 figure

Major depressive disorder plays a vital role in the pathway from gastroesophageal reflux disease to chronic obstructive pulmonary disease: a Mendelian randomization study

Background: Observational studies have shown a bidirectional association between chronic obstructive pulmonary disease (COPD) and gastroesophageal reflux disease (GERD), but it is not clear whether this association is causal. In our previous study, we found that depression was a hot topic of research in the association between COPD and GERD. Is major depressive disorder (MDD) a mediator of the association between COPD and GERD? Here, we evaluated the causal association between COPD, MDD, and GERD using Mendelian randomization (MR) study.Methods: Based on the FinnGen, United Kingdom Biobank, and Psychiatric Genomics Consortium (PGC) databases, we obtained genome-wide association study (GWAS) summary statistics for the three phenotypes from 315,123 European participants (22,867 GERD cases and 292,256 controls), 462,933 European participants (1,605 COPD cases and 461,328 controls), and 173,005 European participants (59,851 MDD cases and 113,154 controls), respectively. To obtain more instrumental variables to reduce bias, we extracted relevant single-nucleotide polymorphisms (SNPs) for the three phenotypes from published meta-analysis studies. Bidirectional MR and expression quantitative trait loci (eQTL)-MR were performed using the inverse variance weighting method to assess the causal association between GERD, MDD, and COPD.Results: There was no evidence of a causal effect between GERD and COPD in the bidirectional MR analysis [forward MR for GERD on COPD: odds ratios (OR) = 1.001, p = 0.270; reverse MR for COPD on GERD: OR = 1.021, p = 0.303]. The causal effect between GERD and MDD appeared to be bidirectional (forward MR for GERD on MDD: OR = 1.309, p = 0.006; reverse MR for MDD on GERD: OR = 1.530, p < 0.001), while the causal effect between MDD and COPD was unidirectional (forward MR for MDD on COPD: OR = 1.004, p < 0.001; reverse MR for COPD on MDD: OR = 1.002, p = 0.925). MDD mediated the effect of GERD on COPD in a unidirectional manner (OR = 1.001). The results of the eQTL-MR were consistent with those of the bidirectional MR.Conclusion: MDD appears to play a vital role in the effect of GERD on COPD. However, we have no evidence of a direct causal association between GERD and COPD. There is a bidirectional causal association between MDD and GERD, which may accelerate the progression from GERD to COPD

Limitations of identical location SEM as a method of degradation studies on surfactant capped nanoparticle electrocatalysts

Identical location scanning electron microscopy (IL–SEM) has become an important tool for electrocatalysis research in the past few years. The method allows for the observation of the same site of an electrode, often down to the same nanoparticle, before and after electrochemical treatment. It is presumed that by IL–SEM, alterations in the surface morphology (the growth, shrinkage, or the disappearance of nanosized features) can be detected, and the thus visualized degradation can be linked to changes of the catalytic performance, observed during prolonged electrolyses. In the rare cases where no degradation is seen, IL–SEM may provide comfort that the studied catalyst is ready for up-scaling and can be moved towards industrial applications. However, although it is usually considered a non-invasive technique, the interpretation of IL–SEM measurements may get more complicated. When, for example, IL–SEM is used to study the degradation of surfactant-capped Ag nanocubes employed as electrocatalysts of CO2 electroreduction, nanoparticles subjected to the electron beam during pre-electrolysis imaging may lose some of their catalytic activity due to the under-beam formation of a passive organic contamination layer. Although the entirety of the catalyst obviously degrades, the spot mapped by IL–SEM reflects no or little changes during electrolysis. The aim of this paper is to shed light on an important limitation of IL–SEM: extreme care is necessary when applying this method for catalyst degradation studies, especially in case of nanoparticles with surface-adsorbed capping agents