Exploring Small Language Models with Prompt-Learning Paradigm for Efficient Domain-Specific Text Classification

Domain-specific text classification faces the challenge of scarce labeled data due to the high cost of manual labeling. Prompt-learning, known for its efficiency in few-shot scenarios, is proposed as an alternative to traditional fine-tuning methods. And besides, although large language models (LLMs) have gained prominence, small language models (SLMs, with under 1B parameters) offer significant customizability, adaptability, and cost-effectiveness for domain-specific tasks, given industry constraints. In this study, we investigate the potential of SLMs combined with prompt-learning paradigm for domain-specific text classification, specifically within customer-agent interactions in retail. Our evaluations show that, in few-shot settings when prompt-based model fine-tuning is possible, T5-base, a typical SLM with 220M parameters, achieve approximately 75% accuracy with limited labeled data (up to 15% of full data), which shows great potentials of SLMs with prompt-learning. Based on this, We further validate the effectiveness of active few-shot sampling and the ensemble strategy in the prompt-learning pipeline that contribute to a remarkable performance gain. Besides, in zero-shot settings with a fixed model, we underscore a pivotal observation that, although the GPT-3.5-turbo equipped with around 154B parameters garners an accuracy of 55.16%, the power of well designed prompts becomes evident when the FLAN-T5-large, a model with a mere 0.5% of GPT-3.5-turbo's parameters, achieves an accuracy exceeding 31% with the optimized prompt, a leap from its sub-18% performance with an unoptimized one. Our findings underscore the promise of prompt-learning in classification tasks with SLMs, emphasizing the benefits of active few-shot sampling, and ensemble strategies in few-shot settings, and the importance of prompt engineering in zero-shot settings.Comment: 10 pages excluding appendix and referenc

Analysis and implementation of adaptive filtered-X LMS algorithm based on reference signal self-extraction

By comparing conventional FXLMS (filtered-X least mean square) control algorithms, the present paper introduces an improved adaptive vibration control FXLMS algorithm based on reference signal self-extraction. It overcomes the problem of reference signal which correlated with external excitation signal is needed to be predicted in advance, namely, the reference signal is extracted from structural vibration in real time in the process of control algorithm. Its theoretical basis is: get an original vibration signal estimation using the error signal of the system and the estimation value is taken as the reference signal of adaptive filtering. In addition, to verify the feasibility and advantage of the proposed algorithm, we simulate solar panels with piezoelectric smart flexible plate and construct the corresponding experimental platform. Finally, the results presented in this paper demonstrate that the proposed algorithm is feasible, effective and achieve improvement with significantly faster convergence speed and better control effect compared with other algorithms

Application Progress of Nursing Information System in China

This paper introduces and summarizes the development background and function, application status and development limitations of nursing informatization, aiming to provide reference for nursing staff to further understand the relevant content of nursing informatization, better apply nursing informatization to clinical practice, and provide reference for clinical nursing informatization in China

Efficient LLM Inference on CPUs

Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the astronomical amount of model parameters, which requires a demand for large memory capacity and high memory bandwidth. In this paper, we propose an effective approach that can make the deployment of LLMs more efficiently. We support an automatic INT4 weight-only quantization flow and design a special LLM runtime with highly-optimized kernels to accelerate the LLM inference on CPUs. We demonstrate the general applicability of our approach on popular LLMs including Llama2, Llama, GPT-NeoX, and showcase the extreme inference efficiency on CPUs. The code is publicly available at: https://github.com/intel/intel-extension-for-transformers.Comment: NeurIPS'2023 on Efficient Natural Language and Speech Processin

Construction of all-in-focus images assisted by depth sensing

Multi-focus image fusion is a technique for obtaining an all-in-focus image in which all objects are in focus to extend the limited depth of field (DoF) of an imaging system. Different from traditional RGB-based methods, this paper presents a new multi-focus image fusion method assisted by depth sensing. In this work, a depth sensor is used together with a color camera to capture images of a scene. A graph-based segmentation algorithm is used to segment the depth map from the depth sensor, and the segmented regions are used to guide a focus algorithm to locate in-focus image blocks from among multi-focus source images to construct the reference all-in-focus image. Five test scenes and six evaluation metrics were used to compare the proposed method and representative state-of-the-art algorithms. Experimental results quantitatively demonstrate that this method outperforms existing methods in both speed and quality (in terms of comprehensive fusion metrics). The generated images can potentially be used as reference all-in-focus images.Comment: 18 pages. This paper has been submitted to Computer Vision and Image Understandin

A Novel Method for Extrinsic Calibration of Multiple RGB-D Cameras Using Descriptor-Based Patterns

This letter presents a novel method to estimate the relative poses between RGB-D cameras with minimal overlapping fields of view in a panoramic RGB-D camera system. This calibration problem is relevant to applications such as indoor 3D mapping and robot navigation that can benefit from a 360$^\circ$ field of view using RGB-D cameras. The proposed approach relies on descriptor-based patterns to provide well-matched 2D keypoints in the case of a minimal overlapping field of view between cameras. Integrating the matched 2D keypoints with corresponding depth values, a set of 3D matched keypoints are constructed to calibrate multiple RGB-D cameras. Experiments validated the accuracy and efficiency of the proposed calibration approach, both superior to those of existing methods (800 ms vs. 5 seconds; rotation error of 0.56 degrees vs. 1.6 degrees; and translation error of 1.80 cm vs. 2.5 cm.Comment: 6 pages, 7 figures, under review by IEEE Robotics and Automation Letters & ICR

Fast and non-destructive detection on the EVA gel content in photovoltaic modules by optical reflection

Poly(ethylene-co-vinyl acetate) (EVA) has been the dominating material in the photovoltaic (PV) encapsulant market for decades, owing to its superior cost-performance balance. To achieve its desired material properties, EVA undergoes a curing reaction during the module encapsulation process. The resulting EVA gel content after encapsulation is an important criterion for the module encapsulation quality control. Normally, the determination of gel content is achieved using a tedious solvent extraction method. In this paper, a fast and nondestructive detection method on the EVA gel content based on the optical reflection is explored. First, the homogeneity of the EVA gel content distribution after the standard EVA encapsulation process is studied. Then, the feasibility of the proposed optical approach applied to transparent modules is investigated. After that, a method is developed to apply it to opaque modules by incorporating a mirror into the module construction. It was found that the haze factor of the reflected light correlates well with the EVA gel content in the opaque modules. This proof-of-concept work could lead to the development of a fast and nondestructive tool for detecting the EVA gel content in both transparent and opaque PV modules, which is promising for integration as an inline diagnostic tool in the module manufacturing line

Symmetric Kullback-Leibler Metric Based Tracking Behaviors for Bioinspired Robotic Eyes

A symmetric Kullback-Leibler metric based tracking system, capable of tracking moving targets, is presented for a bionic spherical parallel mechanism to minimize a tracking error function to simulate smooth pursuit of human eyes. More specifically, we propose a real-time moving target tracking algorithm which utilizes spatial histograms taking into account symmetric Kullback-Leibler metric. In the proposed algorithm, the key spatial histograms are extracted and taken into particle filtering framework. Once the target is identified, an image-based control scheme is implemented to drive bionic spherical parallel mechanism such that the identified target is to be tracked at the center of the captured images. Meanwhile, the robot motion information is fed forward to develop an adaptive smooth tracking controller inspired by the Vestibuloocular Reflex mechanism. The proposed tracking system is designed to make the robot track dynamic objects when the robot travels through transmittable terrains, especially bumpy environment. To perform bumpy-resist capability under the condition of violent attitude variation when the robot works in the bumpy environment mentioned, experimental results demonstrate the effectiveness and robustness of our bioinspired tracking system using bionic spherical parallel mechanism inspired by head-eye coordination

The effect of cooling press on the encapsulation properties of crystalline photovoltaic modules: residual stress and adhesion

A high-quality encapsulation process is crucial to ensuring the performance and long-term reliability of photovoltaic (PV) modules. In crystalline Si technology-based modules, poly (ethylene-co-vinyl acetate) (EVA) is the most widely used PV encapsulant. Its encapsulation process is usually performed in a flat-bed vacuum bag laminator. In certain types of laminators, cooling press can be applied to the module cooling process after the module encapsulation, leading to a much higher cooling rate (similar to 100 degrees C/min) than conventional natural cooling due to the application of water cooling circulation and mechanical pressure on the modules. In this work, the effect of the cooling press on the encapsulation properties of PV modules with EVA as the encapsulant are assessed on the aspects of residual stress in the modules, peeling strength between glass and EVA, and the resulting EVA gel content after encapsulation. The results show that the cooling press influences the encapsulation properties of PV modules. In particular by applying the cooling press after encapsulation, the residual normal stress in the Si solar cell in the encapsulated module after cooling can be reduced by as much as 22 +/- 2 to 27 +/- 3% depending on the EVA gel content, whereas the peeling strength between front glass and EVA is increased by similar to 10%. This work should help the further optimization of PV module encapsulation processes aimed at improving module encapsulation quality. Copyright (C) 2013 John Wiley & Sons, Ltd

Towards in-line determination of EVA Gel Content during PV modules Lamination Processes

Poly (ethylene-co-vinyl acetate) (EVA) is the major polymer used for photovoltaic (PV) modules encapsulation. Its degree of cross-linking (related to its gel content) is taken as a major quality reference. Differential Scanning Calorimetry (DSC) has been proven to be fast and effective but is to determine the gel content, however, destructive for the PV module. With the aim to develop a non-destructive quality assessment tool, a detailed discussion on the DSC thermogram of EVA PV encapsulant is presented here. A possible path towards a fast and non-destructive method for determing EVA gel content is proposed based on the DSC analysis