Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation

Knowledge distillation(KD) is a common approach to improve model performance in automatic speech recognition (ASR), where a student model is trained to imitate the output behaviour of a teacher model. However, traditional KD methods suffer from teacher label storage issue, especially when the training corpora are large. Although on-the-fly teacher label generation tackles this issue, the training speed is significantly slower as the teacher model has to be evaluated every batch. In this paper, we reformulate the generation of teacher label as a codec problem. We propose a novel Multi-codebook Vector Quantization (MVQ) approach that compresses teacher embeddings to codebook indexes (CI). Based on this, a KD training framework (MVQ-KD) is proposed where a student model predicts the CI generated from the embeddings of a self-supervised pre-trained teacher model. Experiments on the LibriSpeech clean-100 hour show that MVQ-KD framework achieves comparable performance as traditional KD methods (l1, l2), while requiring 256 times less storage. When the full LibriSpeech dataset is used, MVQ-KD framework results in 13.8% and 8.2% relative word error rate reductions (WERRs) for non -streaming transducer on test-clean and test-other and 4.0% and 4.9% for streaming transducer. The implementation of this work is already released as a part of the open-source project icefall.Comment: Submitted to ICASSP 202

Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks

Facial expressions, verbal, behavioral, such as limb movements, and physiological features are vital ways for affective human interactions. Researchers have given machines the ability to recognize affective communication through the above modalities in the past decades. In addition to facial expressions, changes in the level of sound, strength, weakness, and turbulence will also convey affective. Extracting affective feature parameters from the acoustic signals have been widely applied in customer service, education, and the medical field. In this research, an improved AlexNet-based deep convolutional neural network (A-DCNN) is presented for acoustic signal recognition. Firstly, preprocessed on signals using simplified inverse filter tracking (SIFT) and short-time Fourier transform (STFT), Mel frequency Cepstrum (MFCC) and waveform-based segmentation were deployed to create the input for the deep neural network (DNN), which was applied widely in signals preprocess for most neural networks. Secondly, acoustic signals were acquired from the public Ryerson Audio-Visual Database of Affective Speech and Song (RAVDESS) affective speech audio system. Through the acoustic signal preprocessing tools, the basic features of the kind of sound signals were calculated and extracted. The proposed DNN based on improved AlexNet has a 95.88% accuracy on classifying eight affective of acoustic signals. By comparing with some linear classifications, such as decision table (DT) and Bayesian inference (BI) and other deep neural networks, such as AlexNet+SVM, recurrent convolutional neural network (R-CNN), etc., the proposed method achieves high effectiveness on the accuracy (A), sensitivity (S1), positive predictive (PP), and f1-score (F1). Acoustic signals affective recognition and classification can be potentially applied in industrial product design through measuring consumers’ affective responses to products; by collecting relevant affective sound data to understand the popularity of the product, and furthermore, to improve the product design and increase the market responsiveness

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075).S

A scientometric analysis of affordance research in the field of interaction design based on CiteSpace

A method of scientometrics analysis was adopted in this study to objectively visualize the research status quo, track the emerging trends and understand the intellectual structure of affordance research in the field of interaction design with 726 documents published in the ISI Web of Science Core Collection (WoSCC) database from 1995-2020. The results identified that annual trends of the papers will continue to grow. USA plays a significant role and is a pioneer in the field. There is no leading institution or author with high output or high influence. The study has formed 4 hot research topic: Theoretical study, Perception-level study, Behaviour-level study and Experience-level study. According to the references co-citation network, we can know that affordance research in the field of interaction design has produced a number of classic literature

A Review of Orbital Angular Momentum Vortex Beams Generation: From Traditional Methods to Metasurfaces

In this paper, we review the generation of vortex beams carrying orbital angular momentum in the microwave domain. We firstly present the theory of Laguerre–Gaussian beams where it is demonstrated that they carry such type of momentum. We further provide an overview of the classical methods used to generate orbital angular momentum vortex beams, which rely on two main methods; plane wave to vortex wave conversion and direct generation using radiating antennas. Then, we present recent progress in the physics of metasurfaces devoted to the generation of vortex beams with a discussion about reflective and transmissive metasurfaces for plane wave to vortex wave conversion as well as methods to reduce the intrinsic divergence characteristics of vortex beams. Finally, we conclude on this rapidly developing research field

A Study of 358 Cases of Locally Advanced Nasopharyngeal Carcinoma Receiving Intensity-Modulated Radiation Therapy: Improving the Seventh Edition of the American Joint Committee on Cancer T-Staging System

To evaluate the rationality and limitations of the seventh edition of the American Joint Committee on Cancer (the 7th AJCC edition) T-staging system for locally advanced nasopharyngeal carcinoma (NPC). The prognosis of 358 patients with stage T3/T4 NPC treated with intensity-modulated radiotherapy (IMRT) was analyzed with the Kaplan–Meier method or the log-rank test. The 7th AJCC staging system of NPC has some limitations in that the T category is neither the significant factor in OS/LRFS nor the independent prognostic factor in OS/LRFS/DMFS/DFS (P>0.05). After adjustment by anatomic structures, univariate analysis has shown that the adjusted-T category has statistical significance between T3 and T4 for OS (86.4% and 71.3%, P=0.002), LRFS (97% and 90.9%, P=0.048), DMFS (90.9% and 77.2%, P=0.001), and DFS (86.2% and 67.5%, P=0.000), and multivariate analysis has shown that the adjusted-T category is an independent prognostic factor for OS/DMFS/DFS (with the exception of LRFS). Then, GTV-P was taken into consideration. Multivariate analysis showed that these nT categories serve as suitable independent prognostic factors for OS/DMFS/DFS (P<0.001) and LRFS (HR = 3.131; 95% CI, 1.090–8.990; P=0.043). The 7th AJCC staging system has limitations and should be improved by including the modifications suggested, such as anatomic structures and tumor volume adjustment

Oxaloacetate Ameliorates Chemical Liver Injury via Oxidative Stress Reduction and Enhancement of Bioenergetic Fluxes

Chemical injury is partly due to free radical lipid peroxidation, which can induce oxidative stress and produce a large number of reactive oxygen species (ROS). Oxaloacetic acid is an important intermediary in the tricarboxylic acid cycle (TCA cycle) and participates in metabolism and energy production. In our study, we found that oxaloacetate (OA) effectively alleviated liver injury which was induced by hydrogen peroxide (H2O2) in vitro and carbon tetrachloride (CCl4) in vivo. OA scavenged ROS, prevented oxidative damage and maintained the normal structure of mitochondria. We further confirmed that OA increased adenosine triphosphate (ATP) by promoting the TCA production cycle and oxidative phosphorylation (OXPHOS). Finally, OA inhibited the mitogen-activated protein kinase (MAPK) and apoptotic pathways by suppressing tumor necrosis factor-&alpha; (TNF-&alpha;). Our findings reveal a mechanism for OA ameliorating chemical liver injury and suggest a possible implementation for preventing the chemical liver injury