Prefix-Tuning Based Unsupervised Text Style Transfer

Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data. In this paper, we employ powerful pre-trained large language models and present a new prefix-tuning-based method for unsupervised text style transfer. We construct three different kinds of prefixes, i.e., \textit{shared prefix, style prefix}, and \textit{content prefix}, to encode task-specific information, target style, and the content information of the input sentence, respectively. Compared to embeddings used by previous works, the proposed prefixes can provide richer information for the model. Furthermore, we adopt a recursive way of using language models in the process of style transfer. This strategy provides a more effective way for the interactions between the input sentence and GPT-2, helps the model construct more informative prefixes, and thus, helps improve the performance. Evaluations on the well-known datasets show that our method outperforms the state-of-the-art baselines. Results, analysis of ablation studies, and subjective evaluations from humans are also provided for a deeper understanding of the proposed method

Silicone softener synthesis and application on knit and woven white cotton fabrics

Silicone Softener have wide spread applications in textile dyeing and finishing. In this paper three different samples of Silicone Softener softeners were synthesized on the basis of a new recipe with different chemical ratio and each of them have been used on knit and woven cotton white fabrics.  Several tests have been done on Silicone Softener like ionic surfactant test, solid content test, stability test etc. Although, several tests were done on the Silicone Softener treated knit and woven white cotton fabrics e.g. fabric whiteness test, Tensile strength test, absorbency test. From these test results, observation and comparison it has come out that, the best silicone softener among the three, increases the fabric softness, flexibility but imparts relatively less absorbency property. It can endow the textiles with soft, fluffy style. Proper stability and durability can reduce the problem of the roller sticky, floating oil. It is colorless and transparent thick liquid and easy to dilute. It has the excellent affinity to all kinds of fabrics including cotton knit fabrics, woven cotton fabrics, jute etc. The overall results come out with great prospect for Silicone Softener “Sample C” in textile finishing

Ultraviolet protection and antibacterial properties of silk fabric dyed with Cinnamomum camphora plant leaf extract

In this study, natural dyes have been extracted from camphor plant (Cinnamomum camphora) dry leaves in alkaline medium and are then applied on to silk fabric by exhaust dyeing process. The UV protection, antimicrobial property, color strength values and colorfastness of the silk dyed with plant extract are studied. The results show that the fabric samples treated with extract have excellent UV protection properties and are very effective in blocking UVA and UVB radiations. The fabric dyed with natural extract shows antimicrobial properties, which is proved by bacterial reduction in quantitative tests. The color fastness to washing and rubbing is found very good to excellent and color fastness to light is poor. Silk fabric dyed for less time shows golden color, while the increased dyeing time shows reddish-brown color. The UV protection and antimicrobial performance of camphor plant leaf extract are found excellent

Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics

This paper studies the problem of modeling interacting dynamical systems, which is critical for understanding physical dynamics and biological processes. Recent research predominantly uses geometric graphs to represent these interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem. The core of GOAT is to incorporate factorized prototypes from contextual knowledge into a continuous graph ODE framework. Specifically, GOAT employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of GOAT

New insights for the design of bionic robots:adaptive motion adjustment strategies during feline landings

Felines have significant advantages in terms of sports energy efficiency and flexibility compared with other animals, especially in terms of jumping and landing. The biomechanical characteristics of a feline (cat) landing from different heights can provide new insights into bionic robot design based on research results and the needs of bionic engineering. The purpose of this work was to investigate the adaptive motion adjustment strategy of the cat landing using a machine learning algorithm and finite element analysis (FEA). In a bionic robot, there are considerations in the design of the mechanical legs. (1) The coordination mechanism of each joint should be adjusted intelligently according to the force at the bottom of each mechanical leg. Specifically, with the increase in force at the bottom of the mechanical leg, the main joint bearing the impact load gradually shifts from the distal joint to the proximal joint; (2) the hardness of the materials located around the center of each joint of the bionic mechanical leg should be strengthened to increase service life; (3) the center of gravity of the robot should be lowered and the robot posture should be kept forward as far as possible to reduce machine wear and improve robot operational accuracy

A Multipopulation-Based Multiobjective Evolutionary Algorithm

Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multipopulation MOGA is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multiobjective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multipopulation method can improve the optimization ability of the MOGA. Also, the proposed multipopulation method is applied to other multiobjective evolutionary algorithms (MOEAs) for evaluating its performance against the IEEE Congress on Evolutionary Computation multiobjective benchmarks. The experimental results show that a single-population MOEA can be extended to a multipopulation version, while obtaining better optimization performance