Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task

With the increasing capabilities of large language models (LLMs), these high-performance models have achieved state-of-the-art results on a wide range of natural language processing (NLP) tasks. However, the models' performance on commonly-used benchmark datasets often fails to accurately reflect their reliability and robustness when applied to real-world noisy data. To address these challenges, we propose a unified robustness evaluation framework based on the slot-filling task to systematically evaluate the dialogue understanding capability of LLMs in diverse input perturbation scenarios. Specifically, we construct a input perturbation evaluation dataset, Noise-LLM, which contains five types of single perturbation and four types of mixed perturbation data. Furthermore, we utilize a multi-level data augmentation method (character, word, and sentence levels) to construct a candidate data pool, and carefully design two ways of automatic task demonstration construction strategies (instance-level and entity-level) with various prompt templates. Our aim is to assess how well various robustness methods of LLMs perform in real-world noisy scenarios. The experiments have demonstrated that the current open-source LLMs generally achieve limited perturbation robustness performance. Based on these experimental observations, we make some forward-looking suggestions to fuel the research in this direction.Comment: Accepted at NLPCC 2023 (Oral Presentation

Experimental Research on Liquid Desiccant Air-conditioning Unit

An experimental device of liquid desiccant air conditioning system is established. Experimental tests about the temperature difference between diluted solution of inlet and concentrated solution of exit in the solution heat exchanger are carried on, and CaCl2 solution is used as desiccant. Results show that: the fluctuation range in the day at different times of the basic difference of the measured temperature does not exceed 1°C, and the temperature difference between diluted solution of inlet and concentrated solution of exit in solution heat exchanger appears the minimum value of 2.7°C and the maximum value of 10.2°C. Also, the percent of the additional load and the ratio of additional load to the evaporator load are analyzed

Experimental Research on Liquid Desiccant Air-conditioning Unit

An experimental device of liquid desiccant air conditioning system is established. Experimental tests about the temperature difference between diluted solution of inlet and concentrated solution of exit in the solution heat exchanger are carried on, and CaCl2 solution is used as desiccant. Results show that: the fluctuation range in the day at different times of the basic difference of the measured temperature does not exceed 1°C, and the temperature difference between diluted solution of inlet and concentrated solution of exit in solution heat exchanger appears the minimum value of 2.7°C and the maximum value of 10.2°C. Also, the percent of the additional load and the ratio of additional load to the evaporator load are analyzed

Experimental Research on Liquid Desiccant Air-conditioning Unit

An experimental device of liquid desiccant air conditioning system is established. Experimental tests about the temperature difference between diluted solution of inlet and concentrated solution of exit in the solution heat exchanger are carried on, and CaCl2 solution is used as desiccant. Results show that: the fluctuation range in the day at different times of the basic difference of the measured temperature does not exceed 1°C, and the temperature difference between diluted solution of inlet and concentrated solution of exit in solution heat exchanger appears the minimum value of 2.7°C and the maximum value of 10.2°C. Also, the percent of the additional load and the ratio of additional load to the evaporator load are analyzed

Multiscale Feature Fusion Network Incorporating 3D Self-Attention for Hyperspectral Image Classification

In recent years, the deep learning-based hyperspectral image (HSI) classification method has achieved great success, and the convolutional neural network (CNN) method has achieved good classification performance in the HSI classification task. However, the convolutional operation only works with local neighborhoods, and is effective in extracting local features. It is difficult to capture interactive features over long distances, which affects the accuracy of classification to some extent. At the same time, the data from HSI have the characteristics of three-dimensionality, redundancy, and noise. To solve these problems, we propose a 3D self-attention multiscale feature fusion network (3DSA-MFN) that integrates 3D multi-head self-attention. 3DSA-MFN first uses different sized convolution kernels to extract multiscale features, samples the different granularities of the feature map, and effectively fuses the spatial and spectral features of the feature map. Then, we propose an improved 3D multi-head self-attention mechanism that provides local feature details for the self-attention branch, and fully exploits the context of the input matrix. To verify the performance of the proposed method, we compare it with six current methods on three public datasets. The experimental results show that the proposed 3DSA-MFN achieves competitive classification and highlights the HSI classification task

Multiscale Feature Fusion Network Incorporating 3D Self-Attention for Hyperspectral Image Classification

In recent years, the deep learning-based hyperspectral image (HSI) classification method has achieved great success, and the convolutional neural network (CNN) method has achieved good classification performance in the HSI classification task. However, the convolutional operation only works with local neighborhoods, and is effective in extracting local features. It is difficult to capture interactive features over long distances, which affects the accuracy of classification to some extent. At the same time, the data from HSI have the characteristics of three-dimensionality, redundancy, and noise. To solve these problems, we propose a 3D self-attention multiscale feature fusion network (3DSA-MFN) that integrates 3D multi-head self-attention. 3DSA-MFN first uses different sized convolution kernels to extract multiscale features, samples the different granularities of the feature map, and effectively fuses the spatial and spectral features of the feature map. Then, we propose an improved 3D multi-head self-attention mechanism that provides local feature details for the self-attention branch, and fully exploits the context of the input matrix. To verify the performance of the proposed method, we compare it with six current methods on three public datasets. The experimental results show that the proposed 3DSA-MFN achieves competitive classification and highlights the HSI classification task

Coordination-Driven Self-Assembly of a 2D Graphite-Like Framework Constructed from High-Nuclear Ce₁₀ Cluster Encapsulated Polyoxotungstates

It is challenging to explore and prepare high-nuclear lanthanide (Ln) cluster-encapsulated polyoxometalates (POMs). Herein, we fabricate an unprecedented Ce₁₀-cluster-embedded polyoxotungstate (POT) (TMA)₁₄H₂­[Ce^III(H₂O)₆]­{[Ce^IV₇Ce^III₃O₆­(OH)₆(CO₃)­(H₂O)₁₁]­[(P₂W₁₆O₅₉)]₃}·41H₂O (<b>1</b>) (TMA = tetramethyleneamine) by coordination-driven self-assembly strategy, which contains the largest Ce cluster [Ce^IV₇Ce^III₃O₆(OH)₆­(CO₃)­(H₂O)₁₁] (Ce₁₀) in all the Ln-containing POM chemistry to date. Self-assembly of the in situ dilacunary [P₂W₁₆O₅₉]^12– fragments and mixed Ce³⁺ and Ce⁴⁺ ions by means of coordination-driven force results in a novel 2D graphite-like framework constructed from mixed-valent cerium­(III/IV) cluster {Ce₁₀} encapsulated poly­(POT) units and Ce³⁺ ions. The most remarkable feature is that the skeleton of the centrosymmetric Ce₁₀-cluster-embedded POT trimer contains three dilacunary [P₂W₁₆O₅₉]^12– fragments trapping a novel {Ce₁₀} cluster via 18 terminal-oxo and three μ₄-oxo atoms

Integrated physiological, metabolomic, and transcriptomic analyses elucidate the regulation mechanisms of lignin synthesis under osmotic stress in alfalfa leaf (Medicago sativa L.)

Abstract Alfalfa, an essential forage crop known for its high yield, nutritional value, and strong adaptability, has been widely cultivated worldwide. The yield and quality of alfalfa are frequently jeopardized due to environmental degradation. Lignin, a constituent of the cell wall, enhances plant resistance to abiotic stress, which often causes osmotic stress in plant cells. However, how lignin responds to osmotic stress in leaves remains unclear. This study explored the effects of osmotic stress on lignin accumulation and the contents of intermediate metabolites involved in lignin synthesis in alfalfa leaves. Osmotic stress caused an increase in lignin accumulation and the alteration of core enzyme activities and gene expression in the phenylpropanoid pathway. We identified five hub genes (CSE, CCR, CADa, CADb, and POD) and thirty edge genes (including WRKYs, MYBs, and UBPs) by integrating transcriptome and metabolome analyses. In addition, ABA and ethylene signaling induced by osmotic stress regulated lignin biosynthesis in a contradictory way. These findings contribute to a new theoretical foundation for the breeding of high-quality and resistant alfalfa varieties