Enhancing the Unified Streaming and Non-streaming Model with Contrastive Learning

The unified streaming and non-streaming speech recognition model has achieved great success due to its comprehensive capabilities. In this paper, we propose to improve the accuracy of the unified model by bridging the inherent representation gap between the streaming and non-streaming modes with a contrastive objective. Specifically, the top-layer hidden representation at the same frame of the streaming and non-streaming modes are regarded as a positive pair, encouraging the representation of the streaming mode close to its non-streaming counterpart. The multiple negative samples are randomly selected from the rest frames of the same sample under the non-streaming mode. Experimental results demonstrate that the proposed method achieves consistent improvements toward the unified model in both streaming and non-streaming modes. Our method achieves CER of 4.66% in the streaming mode and CER of 4.31% in the non-streaming mode, which sets a new state-of-the-art on the AISHELL-1 benchmark.Comment: Accepted by INTERSPEECH 202

Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognition

Multilingual speech recognition for both monolingual and code-switching speech is a challenging task. Recently, based on the Mixture of Experts (MoE), many works have made good progress in multilingual and code-switching ASR, but present huge computational complexity with the increase of supported languages. In this work, we propose a computation-efficient network named Language-Routing Mixture of Experts (LR-MoE) for multilingual and code-switching ASR. LR-MoE extracts language-specific representations through the Mixture of Language Experts (MLE), which is guided to learn by a frame-wise language routing mechanism. The weight-shared frame-level language identification (LID) network is jointly trained as the shared pre-router of each MoE layer. Experiments show that the proposed method significantly improves multilingual and code-switching speech recognition performances over baseline with comparable computational efficiency.Comment: To appear in Proc. INTERSPEECH 2023, August 20-24, 2023, Dublin, Irelan

Online Camera-to-ground Calibration for Autonomous Driving

Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner. Existing solutions utilize static calibration, suffering from environmental variations such as tire pressure changes, vehicle loading volume variations, and road surface diversity. Other online solutions exploit the usage of road elements or photometric consistency between overlapping views across images, which require continuous detection of specific targets on the road or assistance with multiple cameras to facilitate calibration. In our work, we propose an online monocular camera-to-ground calibration solution that does not utilize any specific targets while driving. We perform a coarse-to-fine approach for ground feature extraction through wheel odometry and estimate the camera-to-ground calibration parameters through a sliding-window-based factor graph optimization. Considering the non-rigid transformation of camera-to-ground while driving, we provide metrics to quantify calibration performance and stopping criteria to report/broadcast our satisfying calibration results. Extensive experiments using real-world data demonstrate that our algorithm is effective and outperforms state-of-the-art techniques

A novel signature based on microvascular invasion predicts the recurrence of HCC.

BACKGROUND AND OBJECTIVES: In hepatocellular carcinoma (HCC) patients, microvascular invasion (MVI) is associated with worse outcomes regardless of treatment. No single reliable preoperative factor exists to predict MVI. The aim of the work described here was to develop a new MVI- based mRNA biomarker to differentiate between high and low risk patients. METHODS: Using The Cancer Genome Atlas (TCGA) database, we collected data from 315 HCC patients, including mRNA expression and complete clinical data. We generated a seven-mRNA signature to predict patient outcomes. The mRNA signature was validated using the GSE36376 cohort. Finally, we tested the formula in our own 53 HCC patients using qPCR for the seven mRNAs and analyzing the computed tomography (CT) features. RESULTS: This seven-mRNA signature significantly correlated with length of recurrence-free survival (RFS) and overall survival (OS) for both the training and validation groups. RFS and OS were briefer in high risk versus low risk patients. A Kaplan-Meier analysis also indicated that survival time was significantly shortened in the high risk group versus the low risk group. Time-dependent receiver operating characteristic analysis demonstrated good predictive performance for the seven-mRNA signature. The mRNA signature also acts as an independent factor according to a Multivariate analysis. Our results are consistent with the seven-mRNA formula risk score. CONCLUSION: Our research showed a novel seven-mRNA biomarker based on MVI predicting RFS and OS in HCC patients. This mRNA signature can stratify patients into subgroups based on their risk of recurrence to help guide individualized treatment and precision management in HCC

A case report of HER2-positive descending colon cancer with peritoneal metastasis and literature review

Human epidermal growth factor receptor 2 (HER2) is an anti-cancer drug target for colon cancer. Among patients with colorectal malignancy (colorectal cancer, CRC), those with HER2 mutations have a poor overall prognosis and a significantly increased drug resistance. In recent years, anti-HER2 therapeutic drugs have developed rapidly. According to several clinical studies and case reports, anti-HER2 therapy, as an emerging anti-cancer approach, plays a crucial role in the treatment of HER2-positive CRC patients. Here, we present a case of HER2-positive descending colon cancer with peritoneal metastasis. The patient is a 26-year-old male, diagnosed with malignant tumor of the descending colon with peritoneal metastasis in April 2020. After multiple treatment modalities, the disease progressed. After chemotherapy with Trastuzumab Deruxtecan (T-DXd/DS-8201), the metastatic foci significantly shrank, and after surgical resection, a tumor-free state (NED) was achieved. Up to now, the patient’s survival period has reached 56 months

The reduction of effective feedback reception due to negative emotions in appeals

Citizens’ daily appeals are generally accompanied by negative sentiment, yet little is known about the impact of negative emotions on official response behaviors in a closed online environment. This study analyzed over 2.6 million environmental appeals and their handling records from China’s closed complaint platform to explore how individual negative emotions affect department response behaviors. The results showed that negative emotions could cause departments to respond more rapidly and decrease the likelihood of the citizens receiving department assistance. Whether the appeal can be handled efficiently also depends on the oversight of the department and the respondent’s implementation. Negative emotion towards the department is more likely to lead to a failed handling of the appeal. In addition, when citizens face serious hazards, such as health risks, negative emotions are understandable. Negative emotional appeals concerning health risks receive more time and effective intervention by departments. This paper sheds light on the role of negative emotions in shaping feedback and provides suggestions for improving individual appeal expression and departmental response behavior

Ginsenoside Rh1 Improves the Effect of Dexamethasone on Autoantibodies Production and Lymphoproliferation in MRL/lpr Mice

Ginsenoside Rh1 is able to upregulate glucocorticoid receptor (GR) level, suggesting Rh1 may improve glucocorticoid efficacy in hormone-dependent diseases. Therefore, we investigated whether Rh1 could enhance the effect of dexamethasone (Dex) in the treatment of MRL/lpr mice. MRL/lpr mice were treated with vehicle, Dex, Rh1, or Dex + Rh1 for 4 weeks. Dex significantly reduced the proteinuria and anti-dsDNA and anti-ANA autoantibodies. The levels of proteinuria and anti-dsDNA and anti-ANA autoantibodies were further decreased in Dex + Rh1 group. Dex, Rh1, or Dex + Rh1 did not alter the proportion of CD4+ splenic lymphocytes, whereas the proportion of CD8+ splenic lymphocytes was significantly increased in Dex and Dex + Rh1 groups. Dex + Rh1 significantly decreased the ratio of CD4+/CD8+ splenic lymphocytes compared with control. Con A-induced CD4+ splenic lymphocytes proliferation was increased in Dex-treated mice and was inhibited in Dex + Rh1-treated mice. Th1 cytokine IFN-γ mRNA was suppressed and Th2 cytokine IL-4 mRNA was increased by Dex. The effect of Dex on IFN-γ and IL-4 mRNA was enhanced by Rh1. In conclusion, our data suggest that Rh1 may enhance the effect of Dex in the treatment of MRL/lpr mice through regulating CD4+ T cells activation and Th1/Th2 balance