REVO-LION: Evaluating and Refining Vision-Language Instruction Tuning Datasets

There is an emerging line of research on multimodal instruction tuning, and a line of benchmarks have been proposed for evaluating these models recently. Instead of evaluating the models directly, in this paper we try to evaluate the Vision-Language Instruction-Tuning (VLIT) datasets themselves and further seek the way of building a dataset for developing an all-powerful VLIT model, which we believe could also be of utility for establishing a grounded protocol for benchmarking VLIT models. For effective analysis of VLIT datasets that remains an open question, we propose a tune-cross-evaluation paradigm: tuning on one dataset and evaluating on the others in turn. For each single tune-evaluation experiment set, we define the Meta Quality (MQ) as the mean score measured by a series of caption metrics including BLEU, METEOR, and ROUGE-L to quantify the quality of a certain dataset or a sample. On this basis, to evaluate the comprehensiveness of a dataset, we develop the Dataset Quality (DQ) covering all tune-evaluation sets. To lay the foundation for building a comprehensive dataset and developing an all-powerful model for practical applications, we further define the Sample Quality (SQ) to quantify the all-sided quality of each sample. Extensive experiments validate the rationality of the proposed evaluation paradigm. Based on the holistic evaluation, we build a new dataset, REVO-LION (REfining VisiOn-Language InstructiOn tuNing), by collecting samples with higher SQ from each dataset. With only half of the full data, the model trained on REVO-LION can achieve performance comparable to simply adding all VLIT datasets up. In addition to developing an all-powerful model, REVO-LION also includes an evaluation set, which is expected to serve as a convenient evaluation benchmark for future research

Polycyclic Aromatic Hydrocarbons Concentration in Straw Biochar with different Particle Size

AbstractBiochar, a carbon-rich material formed by a biomass pyrolyzed at relatively low temperatures (≤700°C), showed attractive sorption capacity on both organic pollutants and heavy metals and wildly used in various areas of environmental engineering. However, polycyclic aromatic hydrocarbons (PAHs) may also be assumed to be produced for the oxygen-limited pyrolysis condition in biochar production process. It is not well known about the affect of particle size in concentration and distributing characteristic of PAHs of biochar. In the current study, twenty-seven PAHs concentration in maize straw biochar produced with different powder particle size (9.31, 20.26, 60.77, 71.07, 101.9μm) were quantified, and the ∑27PAHs, total LMW PAHs, total MMW PAHs and total HMW PAHs concentration were analyzed. As the particle size increase, the ∑27PAHs concentrations show a trend of firstly increase and then decrease, and the maximum appears at 60.77μm (166.52 ng/g) and the minimum appears at 101.90μm (14.63 ng/g). LMW total PAHs and total MMW PAHs concentrations firstly increase and then decrease, with the particle size increasing from 9.31μm to 101.9μm. Meanwhile, the total HMW PAH concentrations decrease gradually when biochar particle size increasing. Compared to US, UK background soil concentrations and Canada standards, it is appropriate to conclude that PAHs in straw biochar have minimal effects after application to soil especially at 101.9μm

2-(5-Amino-2H-tetra­zol-2-yl)acetic acid

In the title mol­ecule, C3H5N5O2, the tetra­zole ring and carboxyl group form a dihedral angle of 82.25 (14)°. In the crystal, adjacent mol­ecules are linked through O—H⋯N, N—H⋯O and N—H⋯N hydrogen bonds into layers parallel to the bc plane

Structural integrity of deepwater composite pipes under combined thermal and mechanical loading

Publisher Copyright: © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer reviewedPublisher PD

Research on Carbon Emission Management of Electric Power Enterprises Based on Blockchain Technology

As a high energy-consuming industry, it is especially important for electric power companies to manage carbon emissions well. This study analyzes the main problems of electric power companies in carbon asset management, and investigates the methods of intelligent, digital and transparent management of carbon emission situation using intelligent Internet of Things and blockchain technology. Carbon emission management based on blockchain technology can improve the accuracy and openness of carbon asset data and promote electric power companies to move forward to the low-carbon ecological era