Cohomology of generalized restricted Lie algebras

AbstractIn this note, the generalized restricted Lie algebra, which was introduced by Shu Bin in [J. Algebra 194 (1997) 157–177], is studied. By generalizing the concept of restricted subalgebras and the concept of restricted homomorphism, we show that the second generalized restricted cohomology HϕL2(L,M) is isomorphic to the equivalence classes of those generalized restricted extension of M by L. For any generalized restricted Lie algebra (L,BL,ϕL) and any generalized restricted L-module M, we show that the sequence 0→HϕL1(L,M)→H1(L,M)→homFL,ML→HϕL2(L,M)→H2(L,M)→homFL,H1(L,M) is exact

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

Poverty Alleviation and Development Path Analysis: A Case Study on Rocky Desertification Area of in Yunnan Guangxi Guizhou Province in China

Yunnan Guangxi Guizhou provinces are concentrated areas of rocky desertification in China. In this paper, the poverty status of Yunnan Guangxi Guizhou provinces rocky desertification areas can be analysed in multidimension. We can improve many aspects such as the ecological environment, infrastructure, education skills training and others for poverty alleviation and development. According to the poverty theory and related theory in applying to poverty alleviation of the Yunnan Guangxi Guizhou rocky desertification area, combined with their self-conditions of Yunnan Guangxi Guizhou provinces rocky desertification area, strive to solve the bottleneck problems of the development and curb desertification expansion trend, fundamentally change the face of the Yunnan Guangxi Guizhou provinces rocky desertification area, promote the sustainable development. On the basis of these, we can build the model of poverty alleviation and development of Yunnan Guangxi Guizhou provinces rocky desertification area

The Three Gorges Dam Affects Regional Precipitation

Issues regarding building large-scale dams as a solution to power generation and flood control problems have been widely discussed by both natural and social scientists from various disciplines, as well as the policy-makers and public. Since the Chinese government officially approved the Three Gorges Dam (TGD) projects, this largest hydroelectric project in the world has drawn a lot of debates ranging from its social and economic to climatic impacts. The TGD has been partially in use since June 2003. The impact of the TGD is examined through analysis of the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) rainfall rate and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and high-resolution simulation using the Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) fifth-generation Mesoscale Model (MM5). The independent satellite data sets and numerical simulation clearly indicate that the land use change associated with the TGD construction has increased the precipitation in the region between Daba and Qinling mountains and reduced the precipitation in the vicinity of the TGD after the TGD water level abruptly rose from 66 to 135 m in June 2003. This study suggests that the climatic effect of the TGD is on the regional scale (approx.100 km) rather than on the local scale (approx.10 km) as projected in previous studies

Improved Performance of d₃₁-Mode Needle-actuating Transducer with PMN-PT Piezocrystal

Prototypes of a PZT-based ultrasound needle-actuating device have shown the ability to reduce needle penetration force and enhance needle visibility with color Doppler imaging during needle insertion for tissue biopsy and regional anesthesia. However, the demand for smaller, lighter devices and the need for high performance transducers have motivated investigation of a different configuration of needle-actuation transducer, utilizing the d 31 -mode of PZT4 piezoceramic, and exploration of further improvement in its performance using relaxor-type piezocrystal. This paper outlines the development of the d 31 -mode needle actuation transducer design from simulation to fabrication and demonstration. Full characterization was performed on transducers for performance comparison. The performance of the proposed smaller, lighter d 31 -mode transducer is comparable with that of previous d 33 -mode transducers. Furthermore, it has been found to be much more efficient when using PMN-PT piezocrystal rather than piezoceramic

Causal Reinforcement Learning: A Survey

Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. Despite many remarkable achievements in recent decades, applying reinforcement learning methods in the real world remains challenging. One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world and must therefore learn from scratch through numerous trial-and-error interactions. They may also face challenges in providing explanations for their decisions and generalizing the acquired knowledge. Causality, however, offers a notable advantage as it can formalize knowledge in a systematic manner and leverage invariance for effective knowledge transfer. This has led to the emergence of causal reinforcement learning, a subfield of reinforcement learning that seeks to enhance existing algorithms by incorporating causal relationships into the learning process. In this survey, we comprehensively review the literature on causal reinforcement learning. We first introduce the basic concepts of causality and reinforcement learning, and then explain how causality can address core challenges in non-causal reinforcement learning. We categorize and systematically review existing causal reinforcement learning approaches based on their target problems and methodologies. Finally, we outline open issues and future directions in this emerging field.Comment: 48 pages, 10 figure