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

2-Chloro­methyl-1-methyl-1,3-benzimidazole

The title compound, C9H9ClN2, was prepared from the reaction of N-methyl­benzene-1,2-diamine and 2-chloro­acetic acid in boiling 6 M hydro­chloric acid. The benzimidazole unit is approximately planar, the largest deviation from the mean plane being 0.008 (1) Å. The Cl atom is displaced by 1.667 (2) Å from this plane. The methyl group is statistically disordered with equal occupancy

Graphene Acoustic Devices

In 2011, Ren’s group has developed the first graphene sound source device in the world. This is the first time that the graphene applications have been extended into acoustic area. The graphene sound source can produce sound in a wide sound frequency range from 100 Hz to 50 kHz. After that, we have innovated the first graphene earphone, which can be used both for human and animals. In 2017, both the sound detection and sound emission have been integrated into one graphene device, which is called graphene artificial throat. In this book chapter, more details for developing those graphene acoustic devices will be introduced, which can help to boost the real applications of graphene devices