Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions

Takagi-Sugeno-Kang (TSK) fuzzy system with Gaussian membership functions (MFs) is one of the most widely used fuzzy systems in machine learning. However, it usually has difficulty handling high-dimensional datasets. This paper explores why TSK fuzzy systems with Gaussian MFs may fail on high-dimensional inputs. After transforming defuzzification to an equivalent form of softmax function, we find that the poor performance is due to the saturation of softmax. We show that two defuzzification operations, LogTSK and HTSK, the latter of which is first proposed in this paper, can avoid the saturation. Experimental results on datasets with various dimensionalities validated our analysis and demonstrated the effectiveness of LogTSK and HTSK

The Impact of Policy Intensity on Overcapacity in Low-Carbon Energy Industry: Evidence From Photovoltaic Firms

This study evaluates the impact of policy intensity on overcapacity using 55 listed photovoltaic (PV) firms from 2011 to 2019 in China. We divide PV industrial chain into three segments, which are upstream, midstream, and downstream. Results show that China’s PV industry is diminishing returns to scale with low level of capacity utilization (20%). The enhancement of policy intensity can significantly promote overcapacity, but its impact varies in different policies and different enterprises. Fiscal subsidy has the largest positive effect in promoting overcapacity, followed by tax preference and land support. For three segments of PV industrial chain, fiscal subsidy, land support, and tax preference play a significant role in promoting overcapacity in each segment; the increase in financial support exacerbates overcapacity in midstream. The present study also tests the effectiveness of an important PV policy posed by the Chinese government in 2013. The results show that the policy is inefficient in the short term. Nevertheless, it promotes the development of PV industry in the long term. It takes a long time to reduce positive effect of policies on overcapacity. This study provides a guide for the government to make comprehensive use of different policies

UniTabE: Pretraining a Unified Tabular Encoder for Heterogeneous Tabular Data

Recent advancements in Natural Language Processing (NLP) have witnessed the groundbreaking impact of pretrained models, yielding impressive outcomes across various tasks. This study seeks to extend the power of pretraining methodologies to tabular data, a domain traditionally overlooked, yet inherently challenging due to the plethora of table schemas intrinsic to different tasks. The primary research questions underpinning this work revolve around the adaptation to heterogeneous table structures, the establishment of a universal pretraining protocol for tabular data, the generalizability and transferability of learned knowledge across tasks, the adaptation to diverse downstream applications, and the incorporation of incremental columns over time. In response to these challenges, we introduce UniTabE, a pioneering method designed to process tables in a uniform manner, devoid of constraints imposed by specific table structures. UniTabE's core concept relies on representing each basic table element with a module, termed TabUnit. This is subsequently followed by a Transformer encoder to refine the representation. Moreover, our model is designed to facilitate pretraining and finetuning through the utilization of free-form prompts. In order to implement the pretraining phase, we curated an expansive tabular dataset comprising approximately 13 billion samples, meticulously gathered from the Kaggle platform. Rigorous experimental testing and analyses were performed under a myriad of scenarios to validate the effectiveness of our methodology. The experimental results demonstrate UniTabE's superior performance against several baseline models across a multitude of benchmark datasets. This, therefore, underscores UniTabE's potential to significantly enhance the semantic representation of tabular data, thereby marking a significant stride in the field of tabular data analysis.Comment: 9 page

A Comparison Study of Curriculum between TESOL in the United Kingdom and TCSOL in China

This research analyses 10 universities from the United Kingdom and China respectively to make a comparison between TESOL and TCSOL curriculum. Based on the analysis, the compulsory courses, and optional courses, some similarities and differences have been analyzed. By referring to the curriculum of TESOL, some suggestions have been put forward. This research aims to inject broader approaches to the study of Master of Teaching Chinese to Speakers of Other Language (TCSOL), which would result in an enhanced understanding and enlargement of the subject matter, provide new thinking direction, promote the development of TCSOL, and reduce the possible confusion on the future development