The Knowledge, Attitude, Practices, and Satisfaction of Non-Invasive Prenatal Testing among Chinese Pregnant Women under Different Payment Schemes: A Comparative Study

Non-invasive prenatal testing (NIPT) for aneuploidy screening has been widely applied across China, and costs can affect Chinese pregnant women's choices. This study aims to assess the knowledge, attitude, practices (KAP) and satisfaction regarding NIPT among pregnant women in China, and to further explore the relationship between payment schemes and women's acceptability of and satisfaction with NIPT. A questionnaire survey was performed in Shenzhen and Zhengzhou, China, which separately applied "insurance coverage" and "out-of-pocket" payment scheme for NIPT. The major differences between the two cities were compared using chi-square test, Wilcoxon rank sum test, and propensity score matched analysis. Logistic regression models were applied to explore predictors for women's acceptability and satisfaction. Compared with Zhengzhou participants, a higher proportion of Shenzhen women had heard of NIPT (87.30% vs. 64.03%), were willing to receive NIPT (91.80% vs. 80.43%) and had taken NIPT (83.12% vs. 54.54%), while their satisfaction level was lower. Having NIPT-related knowledge was associated with higher acceptability, and receiving genetic counseling helped to improve satisfaction. Besides, women with higher annual household incomes were more likely to take and be satisfied with NIPT. In conclusion, more attention should be paid to health education, subsidies for NIPT, and genetic counseling

PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

Grasping for novel objects is important for robot manipulation in unstructured environments. Most of current works require a grasp sampling process to obtain grasp candidates, combined with local feature extractor using deep learning. This pipeline is time-costly, expecially when grasp points are sparse such as at the edge of a bowl. In this paper, we propose an end-to-end approach to directly predict the poses, categories and scores (qualities) of all the grasps. It takes the whole sparse point clouds as the input and requires no sampling or search process. Moreover, to generate training data of multi-object scene, we propose a fast multi-object grasp detection algorithm based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB object set, 23.7k grasps) and a multi-object dataset (20k point clouds with annotations and masks) are generated. A PointNet++ based network combined with multi-mask loss is introduced to deal with different training points. The whole weight size of our network is only about 11.6M, which takes about 102ms for a whole prediction process using a GeForce 840M GPU. Our experiment shows our work get 71.43% success rate and 91.60% completion rate, which performs better than current state-of-art works.Comment: Accepted at the International Conference on Robotics and Automation (ICRA) 202

Automating Method Naming with Context-Aware Prompt-Tuning

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still sub-optimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract function of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency. In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of PL and NL through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous similarity-based consistency checking approaches. The experimental results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.Comment: Accepted by ICPC-202

Transsion TSUP's speech recognition system for ASRU 2023 MADASR Challenge

This paper presents a speech recognition system developed by the Transsion Speech Understanding Processing Team (TSUP) for the ASRU 2023 MADASR Challenge. The system focuses on adapting ASR models for low-resource Indian languages and covers all four tracks of the challenge. For tracks 1 and 2, the acoustic model utilized a squeezeformer encoder and bidirectional transformer decoder with joint CTC-Attention training loss. Additionally, an external KenLM language model was used during TLG beam search decoding. For tracks 3 and 4, pretrained IndicWhisper models were employed and finetuned on both the challenge dataset and publicly available datasets. The whisper beam search decoding was also modified to support an external KenLM language model, which enabled better utilization of the additional text provided by the challenge. The proposed method achieved word error rates (WER) of 24.17%, 24.43%, 15.97%, and 15.97% for Bengali language in the four tracks, and WER of 19.61%, 19.54%, 15.48%, and 15.48% for Bhojpuri language in the four tracks. These results demonstrate the effectiveness of the proposed method

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their usage in decision-critical contexts like healthcare. To bridge this gap, we propose an interpretable framework to analyze disorder-specific Regions of Interest (ROIs) and prominent connections. The proposed framework consists of two modules: a brain-network-oriented backbone model for disease prediction and a globally shared explanation generator that highlights disorder-specific biomarkers including salient ROIs and important connections. We conduct experiments on three real-world datasets of brain disorders. The results verify that our framework can obtain outstanding performance and also identify meaningful biomarkers. All code for this work is available at https://github.com/HennyJie/IBGNN.git.Comment: Previous version presented at icml-imlh 2021 (no proceedings, archived at 2107.05097), this version is accepted to miccai 202

Advances in Acute Myeloid Leukemia Stem Cells

As a common hematological malignant tumor, acute leukemia is believed to originate from a subpopulation of special cancer cells, named cancer stem cells. Cancer stem cells are recognized to be the main source of tumor origin, multidrug resistance, metastasis, and recurrence. Leukemic stem cells (LSCs) were first identified and confirmed to play an important role in the occurrence and development of leukemia. In this article, we summarize the following content: special markers and sorting methods for acute myeloid leukemia stem cells and the role of cancer stem cells in treatment resistance, metastasis and invasion, recurrence, and target treatment of acute leukemia

JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions

Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an essential cornerstone in building general AI systems. We propose a new commonsense reasoning dataset based on human's Interactive Fiction (IF) gameplay walkthroughs as human players demonstrate plentiful and diverse commonsense reasoning. The new dataset provides a natural mixture of various reasoning types and requires multi-hop reasoning. Moreover, the IF game-based construction procedure requires much less human interventions than previous ones. Experiments show that the introduced dataset is challenging to previous machine reading models with a significant 20% performance gap compared to human experts.Comment: arXiv admin note: text overlap with arXiv:2010.0978

Event-triggered tracking control for switched nonlinear systems

In this paper, we study the output tracking control problem based on the event-triggered mechanism for cascade switched nonlinear systems. Firstly, an integral controller based on event-triggered conditions is designed, and the output tracking error of the closed-loop system can converge to a bounded region under the switching signal satisfying the average dwell time. Secondly, it is proved that the proposed minimum inter-event interval always has a positive lower bound and the Zeno behavior is successfully avoided during the sampling process. Finally, the numerical simulation is given to verify the feasibility of the proposed method