Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

In recent years, more and more people express their feelings through both images and texts, boosting the growth of multimodal data. Multimodal data contains richer semantics and is more conducive to judging the real emotions of people. To fully learn the features of every single modality and integrate modal information, this paper proposes a fine-grained multimodal sentiment analysis method FCLAG based on gating and attention mechanism. First, the method is carried out from the character level and the word level in the text aspect. CNN is used to extract more fine-grained emotional information from characters, and the attention mechanism is used to improve the expressiveness of the keywords. In terms of images, a gating mechanism is added to control the flow of image information between networks. The images and text vectors represent the original data collectively. Then the bidirectional LSTM is used to complete further learning, which enhances the information interaction capability between the modalities. Finally, put the multimodal feature expression into the classifier. This method is verified on a self-built image and text dataset. The experimental results show that compared with other sentiment classification models, this method has greater improvement in accuracy and F1 score and it can effectively improve the performance of multimodal sentiment analysis

Generative design in building information modelling (BIM) : approaches and requirements

The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies

Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition

We investigate the use of generative adversarial networks (GANs) in speech dereverberation for robust speech recognition. GANs have been recently studied for speech enhancement to remove additive noises, but there still lacks of a work to examine their ability in speech dereverberation and the advantages of using GANs have not been fully established. In this paper, we provide deep investigations in the use of GAN-based dereverberation front-end in ASR. First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset. Second, further adding residual connections in the deep LSTMs can boost the performance as well. Finally, we find that, for the success of GAN, it is important to update the generator and the discriminator using the same mini-batch data during training. Moreover, using reverberant spectrogram as a condition to discriminator, as suggested in previous studies, may degrade the performance. In summary, our GAN-based dereverberation front-end achieves 14%-19% relative CER reduction as compared to the baseline DNN dereverberation network when tested on a strong multi-condition training acoustic model.Comment: Interspeech 201

SCN5A Variants: Association With Cardiac Disorders

The SCN5A gene encodes the alpha subunit of the main cardiac sodium channel Nav1.5. This channel predominates inward sodium current (INa) and plays a critical role in regulation of cardiac electrophysiological function. Since 1995, SCN5A variants have been found to be causatively associated with Brugada syndrome, long QT syndrome, cardiac conduction system dysfunction, dilated cardiomyopathy, etc. Previous genetic, electrophysiological, and molecular studies have identified the arrhythmic and cardiac structural characteristics induced by SCN5A variants. However, due to the variation of disease manifestations and genetic background, impact of environmental factors, as well as the presence of mixed phenotypes, the detailed and individualized physiological mechanisms in various SCN5A-related syndromes are not fully elucidated. This review summarizes the current knowledge of SCN5A genetic variations in different SCN5A-related cardiac disorders and the newly developed therapy strategies potentially useful to prevent and treat these disorders in clinical setting

Impact of sphingomyelin levels on coronary heart disease and left ventricular systolic function in humans

Sphingomyelin (SM) is an abundant phospholipid in cell membranes and in lipoproteins. In human plasma, SM is mainly found in atherogenic lipoproteins; therefore, higher levels of SM may promote atherogenesis. We investigated the relations between plasma SM levels and the presence of angiographic coronary heart disease (CHD) and left ventricular systolic dysfunction. We studied 732 patients referred for coronary angiography. Median SM levels were higher among patients with CHD and in those with LV systolic dysfunction (LVEF<50%) than in patients without CHD or LV dysfunction. SM levels were significantly correlated with fibrinogen levels, diabetes, apoB, and triglyceride levels. On multivariate analyses, higher median SM levels were associated with a higher risk of CHD and lower LV ejection fraction. The pro-atherogenic property of plasma SM might be related to 1) CHD; 2) LV systolic dysfunction; and 3) metabolism of apoB-containing or triglyceride-rich lipoproteins

Isolation and Characterization of Microsatellite Loci in Pistacia weinmannifolia (Anacardiaceae)

Fourteen polymorphic microsatellite loci were isolated from the genomic DNA of Pistacia weinmannifolia, using the Fast Isolation by AFLP of Sequences Containing repeats (FIASCO) method, and screened on 12 individuals from each of two wild populations. The 14 polymorphic loci had an average of 4.1 alleles per locus varying from 1 to 9. The observed (Ho) and expected (He) heterozygosities across the two populations ranged from 0.000 to 0.933 and from 0.000 to 0.906, respectively. Tests for departure from Hardy-Weinberg equilibrium (HWE) and genotypic linkage disequilibrium (LD) were conducted for each of the two populations separately. It was found that no locus significantly deviated from HWE proportions and no significant LD was detected between loci (p < 0.001). In the test of cross-species utility, we successfully amplified nine (64.2%) of 14 loci in P. chinensis and four (28.6%) in P. mexicana. The relatively high level of polymorphism for these markers will facilitate further studies of gene flow, population structure and evolutionary history of P. weinmannifolia and its congeners