Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation Learning

Recent years have witnessed significant advancements in self-supervised learning (SSL) methods for speech-processing tasks. Various speech-based SSL models have been developed and present promising performance on a range of downstream tasks including speech recognition. However, existing speech-based SSL models face a common dilemma in terms of computational cost, which might hinder their potential application and in-depth academic research. To address this issue, we first analyze the computational cost of different modules during HuBERT pre-training and then introduce a stack of efficiency optimizations, which is named Fast-HuBERT in this paper. The proposed Fast-HuBERT can be trained in 1.1 days with 8 V100 GPUs on the Librispeech 960h benchmark, without performance degradation, resulting in a 5.2x speedup, compared to the original implementation. Moreover, we explore two well-studied techniques in the Fast-HuBERT and demonstrate consistent improvements as reported in previous work

Unsupervised SAR Image Change Type Recognition Using Regionally Restricted PCA-Kmean and Lightweight MobileNet

Change detection using synthetic aperture radar (SAR) multi-temporal images only detects the change area and generates no information such as change type, which limits its development. This study proposed a new unsupervised application of SAR images that can recognize the change type of the area. First, a regionally restricted principal component analysis k-mean (RRPCA-Kmean) clustering algorithm, combining principal component analysis, k-mean clustering, and mathematical morphology composition, was designed to obtain pre-classification results in combination with change type vectors. Second, a lightweight MobileNet was designed based on the results of the first stage to perform the reclassification of the pre-classification results and obtain the change recognition results of the changed regions. The experimental results using SAR datasets with different resolutions show that the method can guarantee change recognition results with good change detection correctness

EEG Based Dynamic Functional Connectivity Analysis in Mental Workload Tasks With Different Types of Information

The accurate evaluation of operators’ mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper. Six microstate topographies labeled as Microstate A-F were obtained to describe the task-state EEG dynamics, which was highly consistent with previous studies. Dynamic brain network analysis revealed that 15 nodes and 68 pairs of connectivity from the Frontal-Parietal region were sensitive to mental workload in all four tasks, indicating that these nodal metrics had potential to effectively evaluate mental workload in the cross-task scenario. The characteristic path length of Microstate D brain network in both Theta and Alpha bands decreased whereas the global efficiency increased significantly when the mental workload became higher, suggesting that the cognitive control network of brain tended to have higher function integration property under high mental workload state. Furthermore, by using a SVM classifier, an averaged classification accuracy of 95.8% for within-task and 80.3% for cross-task mental workload discrimination were achieved. Results implies that it is feasible to evaluate the cross-task mental workload using the dynamic functional connectivity metrics under specific microstate, which provided a new insight for understanding the neural mechanism of mental workload with different types of information

Genome-wide association mapping of aluminum toxicity tolerance and fine mapping of a candidate gene for Nrat1 in rice.

Aluminum (Al) stress is becoming the major limiting factor in crop production in acidic soils. Rice has been reported as the most Al-tolerant crop and the capacity of Al toxicity tolerance is generally evaluated by comparing root growth under Al stress. Here, we performed an association mapping of Al toxicity tolerance using a core collection of 211 indica rice accessions with 700 K high quality SNP data. A total of 21 putative QTL affecting shoot height (SH), root length (RL), shoot fresh weight (SFW), shoot dry weight (SDW), root dry weight (RDW) and shoot water content (SWC) were identified at seedling stage, including three QTL detected only under control condition, eight detected only under Al stress condition, ten simultaneously detected in both control and Al stress conditions, and seven were identified by stress tolerance index of their corresponding traits. Total of 21 candidate genes for 7 important QTL regions associated with Al toxicity tolerance were identified based on combined haplotype analysis and functional annotation, and the most likely candidate gene(s) for each important QTL were also discussed. Also a candidate gene Nrat1 on chromosome 2 was further fine-mapped using BSA-seq and linkage analysis in the F2 population derived from the cross of Al tolerant accession CC105 and super susceptible accession CC180. A new non-synonymous SNP variation was observed at Nrat1 between CC105 and CC180, which resulted in an amino-acid substitution from Ala (A) in CC105 to Asp (D) in CC180. Haplotype analysis of Nrat1 using 327 3K RGP accessions indicated that minor allele variations in aus and indica subpopulations decreased Al toxicity tolerance in rice. The candidate genes identified in this study provide valuable information for improvement of Al toxicity tolerance in rice. Our research indicated that minor alleles are important for QTL mapping and its application in rice breeding when natural gene resources are used

Performance of the two parents and F₂ population after treatment of Al stress.

<p>(a) Phenotypic difference of two parents under control and Al stress conditions. (b-c) Frequency distribution of root length (RL) and shoot height (SH) in the F₂ population derived from the two parents under Al stress condition.</p

Validation of the major QTL for Al toxicity tolerance by linkage analysis with 11 KASP SNP markers (a), genotype of 93 extremely Al toxicity tolerance (b) and sensitive (c) individuals by SNP1661173, the last three sites were NTC, PR and PS.

<p>Validation of the major QTL for Al toxicity tolerance by linkage analysis with 11 KASP SNP markers (a), genotype of 93 extremely Al toxicity tolerance (b) and sensitive (c) individuals by SNP1661173, the last three sites were NTC, PR and PS.</p

Manhattan plots of QTL for aluminum toxicity tolerance in the whole genome.

<p>Significant SNPs from different conditions are displayed in different colors: control is green, aluminum stress is grey, the ratio of stress to control is red. The associated traits are represented by different symbols: shoot height = triangle up, root length = triangle down, shoot fresh weight = ×, shoot dry weight = square, root dry weight = circle, shoot water content = star.</p

Manhattan plot of important QTL and haplotype analysis of candidate genes related to QTL including <i>qSh1</i> (a), <i>qSh7</i> (b), <i>qSdw2</i> (c), <i>qSdw3a</i> (d), <i>qSdw5</i> (e), <i>qSwc3</i> (f) and <i>qSwc8</i> (g).

<p>Each point was a gene in the region of the QTL. Line and histogram in different colors indicated different conditions: green is control condition, grey is Al stress condition and red is the ratio of the stress to control conditions. Dash line showed the threshold to determine candidate genes. The ** and *** suggested significance of ANOVA at p < 0.01and p < 0.001, respectively. The letter on histogram (a and b) indicated multiple comparisons result at the significant level 0.01. The value in brackets was the number of individuals for each haplotype.</p

QTL identified with significant association to Al toxicity tolerance related traits.

<p>QTL identified with significant association to Al toxicity tolerance related traits.</p

Box plot of six measured traits for 211 <i>indica</i> accessions under normal and Al stress conditions.

<p>CK, control condition; Al, Al stress condition; SH, shoot height; RL, root length; SFW, shoot fresh weight; SDW, shoot dry weight; RDW, root dry weight; SWC, shoot water content. *** indicated significance of ANOVA at p < 0.001.</p