Fusion Based AER System Using Deep Learning Approach for Amplitude and Frequency Analysis

Automatic emotion recognition from Speech (AERS) systems based on acoustical analysis reveal that some emotional classes persist with ambiguity. This study employed an alternative method aimed at providing deep understanding into the amplitude–frequency, impacts of various emotions in order to aid in the advancement of near term, more effectively in classifying AER approaches. The study was undertaken by converting narrow 20 ms frames of speech into RGB or grey-scale spectrogram images. The features have been used to fine-tune a feature selection system that had previously been trained to recognise emotions. Two different Linear and Mel spectral scales are used to demonstrate a spectrogram. An inductive approach for in sighting the amplitude and frequency features of various emotional classes. We propose a two-channel profound combination of deep fusion network model for the efficient categorization of images. Linear and Mel- spectrogram is acquired from Speech-signal, which is prepared in the recurrence area to input Deep Neural Network. The proposed model Alex-Net with five convolutional layers and two fully connected layers acquire most vital features form spectrogram images plotted on the amplitude-frequency scale. The state-of-the-art is compared with benchmark dataset (EMO-DB). RGB and saliency images are fed to pre-trained Alex-Net tested both EMO-DB and Telugu dataset with an accuracy of 72.18% and fused image features less computations reaching to an accuracy 75.12%. The proposed model show that Transfer learning predict efficiently than Fine-tune network. When tested on Emo-DB dataset, the propȯsed system adequately learns discriminant features from speech spectrȯgrams and outperforms many stȧte-of-the-art techniques.

Low-Resource Language Discrimination toward Chinese Dialects with Transfer Learning and Data Augmentation

Chinese dialects discrimination is a challenging natural language processing task due to scarce annotation resource. In this article, we develop a novel Chinese dialects discrimination framework with transfer learning and data augmentation (CDDTLDA) in order to overcome the shortage of resources. To be more specific, we first use a relatively larger Chinese dialects corpus to train a source-side automatic speech recognition (ASR) model. Then, we adopt a simple but effective data augmentation method (i.e., speed, pitch, and noise disturbance) to augment the target-side low-resource Chinese dialects, and fine-tune another target ASR model based on the previous source-side ASR model. Meanwhile, the potential common semantic features between source-side and target-side ASR models can be captured by using self-attention mechanism. Finally, we extract the hidden semantic representation in the target ASR model to conduct Chinese dialects discrimination. Our extensive experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two benchmark Chinese dialects corpora.

Characterizing features of outbreak duration for novel SARS-CoV-2 variants of concern

Characterizing the dynamics of epidemic trajectories is critical to understanding the potential impacts of emerging outbreaks and to designing appropriate mitigation strategies. As the COVID-19 pandemic evolves, however, the emergence of SARS-CoV-2 variants of concern has complicated our ability to assess in real-time the potential effects of imminent outbreaks, such as those presently caused by the Omicron variant. Here, we report that SARS-CoV-2 outbreaks across regions exhibit strain-specific times from onset to peak, specifically for Delta and Omicron variants. Our findings may facilitate real-time identification of peak medical demand and may help fine-tune ongoing and future outbreak mitigation deployment efforts.

A New Approach for Accurate Identification of Allele State Based on Real-Time PCR for Biomedical Tasks

Genotyping of single nucleotide polymorphisms (SNPs) is an important task in medicine, veterinary medicine and biology. Precise differentiation of SNPs can be challenging. Methods based onTaqman can lead to false positive results due to nonspecific annealing of the probe. The aim of this research was to develop a new approach for the accurate differentiation of SNPs based on real-time PCR with Taqmanprobes and their rivals.The rivals competed with the Taqmanprobes for annealing to the site. The rivals blocked the nonspecific allele so that the Taqmanprobe could not anneal to it. Thus,the Taqmanprobe only detected specific alleles.This approach madeit possible to fine-tune the diagnostic system by selecting the ratio of Taqmanprobes and rivals (in non-equimolar amounts too).The new approach was tested on several diagonally significant SNPs in veterinary medicine.Using Taqman probes and rival probes showed a significantly greater specificity and efficiency in the determination of both homozygotes and heterozygotes than when conventional systems based only on Taqmanwere used. Keywords: SNP, allele identification, real-time PCR, fluorescent dye

A Liquid Democracy Enabled Blockchain-Based Electronic Voting System

Developing an electronic voting system that meets the practical needs of administrators has been a difficult task for a long time. Now, blockchain technologies solve this problem by providing a distributed ledger with immutable, encrypted, and secure transactions. Distributed ledger technologies are an interesting technological leap in the field of data innovation, transparency, and trustability. In public blockchain, distributed ledger technology is widely used. The blockchain technology can be used in an almost infinite number of ways to benefit from sharing economies. The purpose of this study is to assess how blockchain may be utilized to build electronic voting systems that can be used as a service. The purpose of electronic voting systems is explained in this article, as are the technological and legal limitations of employing blockchain as a service. Then, using blockchain as a foundation, we propose a new electronic voting system that fixes the flaws we observed. In general, this paper evaluates the capabilities of distributed ledger technologies by depicting a contextual investigation in order to fine-tune the process of political election decisions and employing a blockchain-based application that improves security and lowers the cost of conducting nationwide elections.

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem—classifying reactions into distinct families—and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels.

NCoR1 and SMRT Fine-Tune Inflammatory Versus Tolerogenic Balance in Dendritic Cells By Differentially Regulating STAT3 Signaling

Dendritic cell (DC) fine-tunes inflammatory versus tolerogenic responses to protect from immune-pathology. However, the role of co-regulators in maintaining this balance is unexplored. NCoR1-mediated repression of DC immune-tolerance has been recently reported. Here we found that depletion of NCoR1 paralog SMRT (NCoR2) enhanced cDC1 activation and expression of IL-6, IL-12 and IL-23 while concomitantly decreasing IL-10 expression/secretion. Consequently, co-cultured CD4+ and CD8+ T-cells depicted enhanced Th1/Th17 frequency and cytotoxicity, respectively. Comparative genomic and transcriptomic analysis demonstrated differential regulation of IL-10 by SMRT and NCoR1. SMRT depletion represses mTOR-STAT3-IL10 signaling in cDC1 by down-regulating NR4A1. Besides, Nfkbia and Socs3 were down-regulated in Ncor2 (Smrt) knockdown cDC1, supporting increased production of inflammatory cytokines. Moreover, studies in mice showed, adoptive transfer of SMRT knockdown cDC1 in OVA-DTH induced footpad inflammation led to increased Th1/Th17 and reduced tumor burden after B16 melanoma injection by enhancing oncolytic CD8+ T-cell frequency, respectively. We also depicted decreased Ncor2 expression in Rheumatoid Arthritis, a Th1/Th17 disease.

Fine-Tuning of the Grain Size by Alternative Splicing of GS3 in Rice

Grain size is subtly regulated by multiple signaling pathways in rice. Alternative splicing is a general mechanism that regulates gene expression at the post-transcriptional level. However, to our knowledge, the molecular mechanism underlying grain size regulation by alternative splicing is largely unknown. GS3, the first identified QTL for grain size in rice, is regulated at the transcriptional and post-translational level. In this study, we identified that GS3 is subject to alternative splicing. GS3.1 and GS3.2, two dominant isoforms, accounts for about 50% and 40% of total transcripts, respectively. GS3.1 encodes the full-length protein, while GS3.2 generated a truncated proteins only containing OSR domain due to a 14 bp intronic sequence retention. Genetic analysis revealed that GS3.1 overexpressors decreased grain size, but GS3.2 showed no significant effect on grain size. Furthermore, we demonstrated that GS3.2 disrupts GS3.1 signaling by competitive occupation of RGB1. Therefore, we draw a conclusion that the alternative splicing of GS3 decreases the amount of GS3.1 and GS3.2 disrupts the GS3.1 signaling to inhibit the negative effects of GS3.1 to fine-tune grain size. Moreover, the mechanism is conserved in cereals rather than in Cruciferae, which is associated with its effects on grain size. The results provide a novel, conserved and important mechanism underlying grain size regulation at the post-transcriptional level in cereals.

A smart and responsive crystalline porous organic cage membrane with switchable pore apertures for graded molecular sieving

Membranes with high selectivity offer an attractive route to molecular separations, where technologies such as distillation and chromatography are energy intensive. However, it remains challenging to fine tune the structure and porosity in membranes, particularly to separate molecules of similar size. Here, we report a process for producing composite membranes that comprise crystalline porous organic cage films fabricated by interfacial synthesis on a polyacrylonitrile support. These membranes exhibit ultrafast solvent permeance and high rejection of organic dyes with molecular weights over 600 g mol−1. The crystalline cage film is dynamic, and its pore aperture can be switched in methanol to generate larger pores that provide increased methanol permeance and higher molecular weight cut-offs (1,400 g mol−1). By varying the water/methanol ratio, the film can be switched between two phases that have different selectivities, such that a single, ‘smart’ crystalline membrane can perform graded molecular sieving. We exemplify this by separating three organic dyes in a single-stage, single-membrane process.