Application of Learning Strategies to Culture-Based Language Instruction

Learning strategy is one of the most important factors that determine the learning result. So, teaching learners to grasp certain kinds of strategies is a key factor which can promote the learning efficiency. This thesis discusses the learning strategies in the theoretical and pedagogical aspects, illustrates the significance of culture-based language instruction in second language teaching, and elaborates three ways to help students use appropriate strategies in their culture-based language learning

Self-Supervised Speaker Verification Using Dynamic Loss-Gate and Label Correction

For self-supervised speaker verification, the quality of pseudo labels decides the upper bound of the system due to the massive unreliable labels. In this work, we propose dynamic loss-gate and label correction (DLG-LC) to alleviate the performance degradation caused by unreliable estimated labels. In DLG, we adopt Gaussian Mixture Model (GMM) to dynamically model the loss distribution and use the estimated GMM to distinguish the reliable and unreliable labels automatically. Besides, to better utilize the unreliable data instead of dropping them directly, we correct the unreliable label with model predictions. Moreover, we apply the negative-pairs-free DINO framework in our experiments for further improvement. Compared to the best-known speaker verification system with self-supervised learning, our proposed DLG-LC converges faster and achieves 11.45%, 18.35% and 15.16% relative improvement on Vox-O, Vox-E and Vox-H trials of Voxceleb1 evaluation dataset.Comment: Accepted by Interspeech 202

Study on the Imprinting Status of Insulin-Like Growth Factor II (IGF-II) Gene in Villus during 6–10 Gestational Weeks

Objective. To compare the difference of imprinting status of insulin-like growth factor II (IGF-II) gene in villus between normal embryo development group and abnormal embryo development group and to investigate the relationship between karyotype and the imprinting status of IGF-II gene. Methods. A total of 85 pregnant women with singleton pregnancy were divided into two groups: one with abnormal embryo development (n = 38) and the other with normal embryo development (n = 47). Apa I polymorphism of IGF-II gene in chorionic villus was assayed with reverse transcriptase polymerase chain reaction (RT-PCR) and restriction fragment length polymorphism (RFLP). The relationship between chromosomal abnormal karyotype and IGF-II gene imprinting status was analyzed by primary cell culture and G-banding chromosomal karyotype analysis. Results. IGF-II imprinting loss rate was higher in the abnormal embryo development group than the normal embryo development group (44.7% versus 31.6%), but without significant difference (P > .05). The percentage of abnormal chromosomes of chorionic villus in the abnormal embryo development group was 42.5%, in which IGF-II imprinting loss rate reached 64.7%. No abnormal karyotypes were found in the normal embryo development group. However, there was significant difference in IGF-II imprinting loss rate between two groups (P > .05). Conclusion. During weeks 6–10 of gestation, abnormal embryonic development is correlated with chromosomal abnormalities. The imprinting status of IGF-II gene played important roles in embryonic development, and imprinting loss might be related to chromosomal abnormalities

Attention-based Encoder-Decoder End-to-End Neural Diarization with Embedding Enhancer

Deep neural network-based systems have significantly improved the performance of speaker diarization tasks. However, end-to-end neural diarization (EEND) systems often struggle to generalize to scenarios with an unseen number of speakers, while target speaker voice activity detection (TS-VAD) systems tend to be overly complex. In this paper, we propose a simple attention-based encoder-decoder network for end-to-end neural diarization (AED-EEND). In our training process, we introduce a teacher-forcing strategy to address the speaker permutation problem, leading to faster model convergence. For evaluation, we propose an iterative decoding method that outputs diarization results for each speaker sequentially. Additionally, we propose an Enhancer module to enhance the frame-level speaker embeddings, enabling the model to handle scenarios with an unseen number of speakers. We also explore replacing the transformer encoder with a Conformer architecture, which better models local information. Furthermore, we discovered that commonly used simulation datasets for speaker diarization have a much higher overlap ratio compared to real data. We found that using simulated training data that is more consistent with real data can achieve an improvement in consistency. Extensive experimental validation demonstrates the effectiveness of our proposed methodologies. Our best system achieved a new state-of-the-art diarization error rate (DER) performance on all the CALLHOME (10.08%), DIHARD II (24.64%), and AMI (13.00%) evaluation benchmarks, when no oracle voice activity detection (VAD) is used. Beyond speaker diarization, our AED-EEND system also shows remarkable competitiveness as a speech type detection model.Comment: IEEE/ACM Transactions on Audio Speech and Language Processing Under Revie

Plasma exosomes from children with juvenile dermatomyositis are taken up by human aortic endothelial cells and are associated with altered gene expression in those cells

BACKGROUND: The pathology of juvenile dermatomyositis (JDM) is characterized by prominent vessel wall and perivascular inflammation. This feature of the disease has remained unexplained and under-investigated. We have hypothesized that plasma exosomes, which play an important role in inter-cellular communication, may play a role in the vascular injury associated with JDM. OBJECTIVE: To characterize the circulating exosomes of children with JDM and determine whether the small RNA cargoes within those exosomes are capable of altering transcriptional programs within endothelial cells. DESIGN/METHODS: We purified exosomes from plasma samples of children with active, untreated JDM (n = 6) and healthy controls (n = 9). We characterized the small RNA cargoes in JDM and control exosomes by RNA sequencing using the Illumina HiSeq 2500 platform. We then incubated isolated exosomes from healthy controls and children with JDM with cultured human aortic endothelial cells (HAEC) for 24 h. Fluorescence microscopy was used to confirm that both control and JDM exosomes were taken up by HAEC. RNA was then purified from HAEC that had been incubated with either control or JDM exosomes and sequenced on the Illumina platform. Differential expression of mRNAs from HAEC incubated with control or JDM exosomes was ascertained using standard computational methods. Finally, we assessed the degree to which differential gene expression in HAEC could be attributed to the different small RNA cargoes in JDM vs control exosomes using conventional and novel analytic methods. RESULTS: We identified 10 small RNA molecules that showed differential abundance when we compared JDM and healthy control exosomes. Fluorescence microscopy of labeled exosomes confirmed that both JDM and control exosomes were taken up by HAEC. Differential gene expression analysis revealed 59 genes that showed differential expression between HAEC incubated with JDM exosomes vs HAEC incubated with exosomes from controls. Statistical analysis of gene expression data demonstrated that multiple miRNAs exerted transcriptional control on multiple genes with HAEC. CONCLUSIONS: Plasma exosomes from children with active, untreated JDM are taken up by HAEC and are associated with alterations in gene expression in those cells. These findings provide new insight into potential mechanisms leading to the targeting of vascular tissue by the immune system in JDM

Attention-based Encoder-Decoder Network for End-to-End Neural Speaker Diarization with Target Speaker Attractor

This paper proposes a novel Attention-based Encoder-Decoder network for End-to-End Neural speaker Diarization (AED-EEND). In AED-EEND system, we incorporate the target speaker enrollment information used in target speaker voice activity detection (TS-VAD) to calculate the attractor, which can mitigate the speaker permutation problem and facilitate easier model convergence. In the training process, we propose a teacher-forcing strategy to obtain the enrollment information using the ground-truth label. Furthermore, we propose three heuristic decoding methods to identify the enrollment area for each speaker during the evaluation process. Additionally, we enhance the attractor calculation network LSTM used in the end-to-end encoder-decoder based attractor calculation (EEND-EDA) system by incorporating an attention-based model. By utilizing such an attention-based attractor decoder, our proposed AED-EEND system outperforms both the EEND-EDA and TS-VAD systems with only 0.5s of enrollment data.Comment: Accepted by InterSpeech 202