Augmented Kinesthetic Teaching: Enhancing Task Execution Efficiency through Intuitive Human Instructions

In this paper, we present a complete and efficient implementation of a knowledge-sharing augmented kinesthetic teaching approach for efficient task execution in robotics. Our augmented kinesthetic teaching method integrates intuitive human feedback, including verbal, gesture, gaze, and physical guidance, to facilitate the extraction of multiple layers of task information including control type, attention direction, input and output type, action state change trigger, etc., enhancing the adaptability and autonomy of robots during task execution. We propose an efficient Programming by Demonstration (PbD) framework for users with limited technical experience to teach the robot in an intuitive manner. The proposed framework provides an interface for such users to teach customized tasks using high-level commands, with the goal of achieving a smoother teaching experience and task execution. This is demonstrated with the sample task of pouring water

CIF-T: A Novel CIF-based Transducer Architecture for Automatic Speech Recognition

RNN-T models are widely used in ASR, which rely on the RNN-T loss to achieve length alignment between input audio and target sequence. However, the implementation complexity and the alignment-based optimization target of RNN-T loss lead to computational redundancy and a reduced role for predictor network, respectively. In this paper, we propose a novel model named CIF-Transducer (CIF-T) which incorporates the Continuous Integrate-and-Fire (CIF) mechanism with the RNN-T model to achieve efficient alignment. In this way, the RNN-T loss is abandoned, thus bringing a computational reduction and allowing the predictor network a more significant role. We also introduce Funnel-CIF, Context Blocks, Unified Gating and Bilinear Pooling joint network, and auxiliary training strategy to further improve performance. Experiments on the 178-hour AISHELL-1 and 10000-hour WenetSpeech datasets show that CIF-T achieves state-of-the-art results with lower computational overhead compared to RNN-T models.Comment: Accepted by ICASSP 202

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7

Variety detection provides technical support for selecting XinHui citrus for use in the production of XinHui dried tangerine peel. Simultaneously, the mutual occlusion between tree leaves and fruits is one of the challenges in object detection. In order to improve screening efficiency, this paper introduces a YOLO(You Only Look Once)v7-BiGS(BiFormer&GSConv) citrus variety detection method capable of identifying different citrus varieties efficiently. In the YOLOv7-BiGS network model, initially, the BiFormer attention mechanism in the backbone of the YOLOv7-based network strengthens the model’s ability to extract citrus’ features. In addition, the introduction of the lightweight GSConv convolution in place of the original convolution within the ELAN of the head component effectively streamlines model complexity while maintaining performance integrity. To environment challenge validate the effectiveness of the method, the proposed YOLOv7-BiGS was compared with YOLOv5, YOLOv7, and YOLOv8. In the comparison of YOLOv7-BiGS with YOLOv5, YOLOv7, and YOLOv8, the experimental results show that the precision, mAP and recell of YOLOv7-BiGS are 91%, 93.7% and 87.3% respectively. Notably, compared to baseline methods, the proposed approach exhibited significant enhancements in precision, mAP, and recall by 5.8%, 4.8%, and 5.2%, respectively. To evaluate the efficacy of the YOLOv7-BiGS in addressing challenges posed by complex environmental conditions, we collected occluded images of Xinhui citrus fruits from the Xinhui orchard base for model detection. This research aims to fulfill performance criteria for citrus variety identification, offering vital technical backing for variety detection endeavors

Optimized properties of innovative ElectroChromic Device using ITO / Ag / ITO electrodes

The “Dielectric/Metal/Dielectric” (DMD) stacked films being used as transparent and conductive (TC) electrodes, have demonstrated excellent application in the ElectroChromic (EC) process and devices. In this work, multilayers (IAI) made of 50 nm of Indium Tin Oxide (ITO)/5 nm of metallic silver (Ag)/30 nm of ITO that exhibit band-gap, low resistance of 7.4 Ω and the high figure of merit of 9.9 × 10−3 Ω−1 were introduced in a complete five-layer Glass/IAI/NiOx/LiClO4-PC-PMMA/WO3/IAI/Glass ElectroChromic Device (ECD). The single IAI electrode as well as the two actives EC layers Glass/IAI/NiOx and Glass/IAI/WO3 were firstly characterized for their TC and EC properties respectively. Then, the EC properties of the complete five-layer ECD were analyzed. Fast response time (2.02 s for the bleaching and 2.25 s for complete coloration), wide optical modulation in the visible light region (∼55% at 550 nm), long lifetime (more than 6000 s), large capacity and good stability as well as high coloration efficiency (31.7 cm2 C−1) were obtained. The improved EC performance of ECD were related to the good electrical and optical properties of IAI electrode

Genomic identification of two Phytobacter diazotrophicus isolates from a neonatal intensive care unit in Singapore

We report the draft genome sequences of two Phytobacter diazotrophicus isolates recovered from a swab specimen from the water faucet located in the Neonatal Intensive Care Unit (ICU), National University Hospital, Singapore. The isolates were misidentified as Cronobacter sakazakii and Klebsiella oxytoca using biochemical methods. Whole-genome sequencing (WGS) was performed to determine their identity

Genome-wide identification and analysis of monolignol biosynthesis genes in Salix matsudana Koidz and their relationship to accelerated growth

Lignin plays an important role in plant growth and development. It serves as a raw material for the manufacture of paper, animal feed, and chemical fertilizers. However, the regulation of lignin biosynthesis genes and the composition of the relevant gene families remain unclear in many plant species. Here, we identified and characterized 11 families of monolignol biosynthesis genes in Salix matsudana Koidz. Based on phylogenetic analysis of lignin biosynthesis genes from nine angiosperm species (Arabidopsis thaliana, Oryza sativa, Zea mays, Solanum lycopersicum, S. suchowensis, S. purpurea, Populus euphratica, P. trichocarpa, and S. matsudana), the 11 gene families could be divided into two classes that differed in their apparent evolutionary history. We compared the distribution of lignin biosynthesis genes between the two sub-genomes (At and Bt) of S. matsudana and found that more duplicated genes were present in the Bt sub-genome. We analyzed RNA sequencing data from two parents of contrasting height and two of their F1 progeny, and detected 23 differentially expressed genes (DEGs) that may regulate accelerated growth. We analyzed the promoter regions of the lignin-related DEGs and identified several hormone-related (auxin, ethylene, and cytokinin) transcription factor binding sites. These results provide an important foundation for future studies on the molecular mechanisms and genetic regulation of lignin biosynthesis and its relationship to accelerated growth in forest trees

Understanding LLMs: A Comprehensive Overview from Training to Inference

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.Comment: 30 pages,6 figure

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology