Generate to Understand for Representation

In recent years, a significant number of high-quality pretrained models have emerged, greatly impacting Natural Language Understanding (NLU), Natural Language Generation (NLG), and Text Representation tasks. Traditionally, these models are pretrained on custom domain corpora and finetuned for specific tasks, resulting in high costs related to GPU usage and labor. Unfortunately, recent trends in language modeling have shifted towards enhancing performance through scaling, further exacerbating the associated costs. Introducing GUR: a pretraining framework that combines language modeling and contrastive learning objectives in a single training step. We select similar text pairs based on their Longest Common Substring (LCS) from raw unlabeled documents and train the model using masked language modeling and unsupervised contrastive learning. The resulting model, GUR, achieves impressive results without any labeled training data, outperforming all other pretrained baselines as a retriever at the recall benchmark in a zero-shot setting. Additionally, GUR maintains its language modeling ability, as demonstrated in our ablation experiment. Our code is available at \url{https://github.com/laohur/GUR}

Advances in Sorptive Removal of Hexavalent Chromium (Cr(VI)) in Aqueous Solutions Using Polymeric Materials

Sorptive removal of hexavalent chromium (Cr(VI)) bears the advantages of simple operation and easy construction. Customized polymeric materials are the attracting adsorbents due to their selectivity, chemical and mechanical stabilities. The mostly investigated polymeric materials for removing Cr(VI) were reviewed in this work. Assembling of robust functional groups, reduction of self-aggregation, and enhancement of stability and mechanical strength, were the general strategies to improve the performance of polymeric adsorbents. The maximum adsorption capacities of these polymers toward Cr(VI) fitted by Langmuir isotherm model ranged from 3.2 to 1185 mg/g. Mechanisms of complexation, chelation, reduction, electrostatic attraction, anion exchange, and hydrogen bonding were involved in the Cr(VI) removal. Influence factors on Cr(VI) removal were itemized. Polymeric adsorbents performed much better in the strong acidic pH range (e.g., pH 2.0) and at higher initial Cr(VI) concentrations. The adsorption of Cr(VI) was an endothermic reaction, and higher reaction temperature favored more robust adsorption. Anions inhibited the removal of Cr(VI) through competitive adsorption, while that was barely affected by cations. Factors that affected the regeneration of these adsorbents were summarized. To realize the goal of industrial application and environmental protection, removal of the Cr(VI) accompanied by its detoxication through reduction is highly encouraged. Moreover, development of adsorbents with strong regeneration ability and low cost, which are robust for removing Cr(VI) at trace levels and a wider pH range, should also be an eternally immutable subject in the future. Work done will be helpful for developing more robust polymeric adsorbents and for promoting the treatment of Cr(VI)-containing wastewater