Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences

Thermal analysis of FeCoCu pre-alloyed powders used for diamond tools

By simulating the pressureless sintering process, the thermal effects of FeCoCu pre-alloyed powders have been investigated. According to the notions of the Kissinger method, the activation energies in the expansion-shrinkage conversion stage are analyzed. Results show that with Fe content increasing, the specimens’ specific heat capacity values present the increasing trend. The 25 %Fe–15 %Co–60 %Cu specimens have negative enthalpy value at 10 and 20°C/min heating rate but positive values at 30 °С/min. For the specimens with lower Cu content, the enthalpies are always positive. It is established that both the specific heat capacity and enthalpy are larger when at higher heating rates. The activation energy of the 65 %Fe–15 %Co–20 %Cu specimens is 10 times higher than that of the 25 %Fe–15 %Co–60 %Cu specimens and the 45 %Fe–15 %Co–40 %Cu specimens.При моделюванні процесу спікання без тиску досліджено термічні ефекти в попередньо легованих порошках FeCoCu. З використанням методу Кіссінджера проаналізовано енергію активації на стадії розширення–усадка. Результати показують, що при збільшенні вмісту Fe значення питомої теплоємності демонструють тенденцію до зростання. Зразки 25 %Fe–15 %Co–60 %Cu мають негативні значення ентальпії при швидкості нагріву 10 ° і 20 °С/хв, але позитивні при 30 °С/хв. Для зразків з меншим вмістом Cu ентальпія завжди позитивна. Встановлено, що питома теплоємність і ентальпія більші при більш високіх швидкостях нагрівання. Енергія активації зразків 65 %Fe–15 %Co–20 %Cu у 10 разів вища, ніж зразків 25 %Fe–15 %Co–60 %Cu і 45 %Fe–15 %Co–40 %Cu.При моделировании процесса спекания без приложения давления исследованы термические эффекты в предварительно легированных порошках FeCoCu. С использованием метода Киссинджера проанализирована энергия активации на стадии расширение–усадка. Результаты показывают, что с увеличением содержания железа значения удельной теплоемкости образцов демонстрируют тенденцию к повышению. Образцы 25 %Fe–15 %Co–60 %Cu имеют отрицательные значения энтальпии при скорости нагрева 10 и 20 °С/мин, но положительные при 30 °С/мин. Для образцов с меньшим содержанием Cu энтальпия всегда положительна. Установлено, что удельная теплоемкость и энтальпия больше при более высоких скоростях нагрева. Энергия активации образцов 65 %Fe–15 %Co–20 %Cu в 10 раз выше, чем образцов 25 %Fe–15 %Co–60 %Cu и 45 %Fe–15 %Co–40 %Cu

ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet often constrained by the scarcity of high-quality image-text data. To address this bottleneck, we introduce the ShareGPT4V dataset, a pioneering large-scale resource featuring 1.2 million highly descriptive captions, which surpasses existing datasets in diversity and information content, covering world knowledge, object properties, spatial relationships, and aesthetic evaluations. Specifically, ShareGPT4V originates from a curated 100K high-quality captions collected from advanced GPT4-Vision and has been expanded to 1.2M with a superb caption model trained on this subset. ShareGPT4V first demonstrates its effectiveness for the Supervised Fine-Tuning (SFT) phase, by substituting an equivalent quantity of detailed captions in existing SFT datasets with a subset of our high-quality captions, significantly enhancing the LMMs like LLaVA-7B, LLaVA-1.5-13B, and Qwen-VL-Chat-7B on the MME and MMBench benchmarks, with respective gains of 222.8/22.0/22.3 and 2.7/1.3/1.5. We further incorporate ShareGPT4V data into both the pre-training and SFT phases, obtaining ShareGPT4V-7B, a superior LMM based on a simple architecture that has remarkable performance across a majority of the multi-modal benchmarks. This project is available at https://ShareGPT4V.github.io to serve as a pivotal resource for advancing the LMMs community.Comment: Project: https://ShareGPT4V.github.i

MLLM-DataEngine: An Iterative Refinement Approach for MLLM

Despite the great advance of Multimodal Large Language Models (MLLMs) in both instruction dataset building and benchmarking, the independence of training and evaluation makes current MLLMs hard to further improve their capability under the guidance of evaluation results with a relatively low human cost. In this paper, we propose MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop iteration, the MLLM-DataEngine first analyze the weakness of the model based on the evaluation results, then generate a proper incremental dataset for the next training iteration and enhance the model capability iteratively. Compared with previous data collection methods which are separate from the benchmarking, the data generated by MLLM-DataEngine shows better targeting, quality, and correctness. For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts the ratio of different types of data within each incremental dataset based on the benchmarking results. For quality, we resort to GPT-4 to generate high-quality data with each given data type. For correctness, prompt design is critical for the data generation results. Rather than previous hand-crafted prompt, we propose an Interactive Prompt Optimization strategy, which optimizes the prompt with the multi-round interaction between human and GPT, and improve the correctness of generated data greatly. Through extensive experiments, we find our MLLM-DataEngine could boost the MLLM capability in a targeted and automatic manner, with only a few human participation. We hope it could be a general solution for the following MLLMs building. The MLLM-DataEngine has been open-sourced and is now available at https://github.com/opendatalab/MLLM-DataEngine.Comment: Code and models are available at https://github.com/opendatalab/MLLM-DataEngin

Dose-related liver injury of Geniposide associated with the alteration in bile acid synthesis and transportation.

Fructus Gardenia (FG), containing the major active constituent Geniposide, is widely used in China for medicinal purposes. Currently, clinical reports of FG toxicity have not been published, however, animal studies have shown FG or Geniposide can cause hepatotoxicity in rats. We investigated Geniposide-induced hepatic injury in male Sprague-Dawley rats after 3-day intragastric administration of 100 mg/kg or 300 mg/kg Geniposide. Changes in hepatic histomorphology, serum liver enzyme, serum and hepatic bile acid profiles, and hepatic bile acid synthesis and transportation gene expression were measured. The 300 mg/kg Geniposide caused liver injury evidenced by pathological changes and increases in serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and γ-glutamytransferase (γ-GT). While liver, but not sera, total bile acids (TBAs) were increased 75% by this dose, dominated by increases in taurine-conjugated bile acids (t-CBAs). The 300 mg/kg Geniposide also down-regulated expression of Farnesoid X receptor (FXR), small heterodimer partner (SHP) and bile salt export pump (BSEP). In conclusion, 300 mg/kg Geniposide can induce liver injury with associated changes in bile acid regulating genes, leading to an accumulation of taurine conjugates in the rat liver. Taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA) as well as tauro-α-muricholic acid (T-α-MCA) are potential markers for Geniposide-induced hepatic damage

VIGC: Visual Instruction Generation and Correction

The integration of visual encoders and large language models (LLMs) has driven recent progress in multimodal large language models (MLLMs). However, the scarcity of high-quality instruction-tuning data for vision-language tasks remains a challenge. The current leading paradigm, such as LLaVA, relies on language-only GPT-4 to generate data, which requires pre-annotated image captions and detection bounding boxes, suffering from understanding image details. A practical solution to this problem would be to utilize the available multimodal large language models (MLLMs) to generate instruction data for vision-language tasks. However, it's worth noting that the currently accessible MLLMs are not as powerful as their LLM counterparts, as they tend to produce inadequate responses and generate false information. As a solution for addressing the current issue, this paper proposes the Visual Instruction Generation and Correction (VIGC) framework that enables multimodal large language models to generate instruction-tuning data and progressively enhance its quality on-the-fly. Specifically, Visual Instruction Generation (VIG) guides the vision-language model to generate diverse instruction-tuning data. To ensure generation quality, Visual Instruction Correction (VIC) adopts an iterative update mechanism to correct any inaccuracies in data produced by VIG, effectively reducing the risk of hallucination. Leveraging the diverse, high-quality data generated by VIGC, we finetune mainstream models and validate data quality based on various evaluations. Experimental results demonstrate that VIGC not only compensates for the shortcomings of language-only data generation methods, but also effectively enhances the benchmark performance. The models, datasets, and code will be made publicly available

Comparison of long-term pregnancy outcomes between neosalpingostomy and salpingectomy for infertile women with bilateral severe hydrosalpinx

Objective·To compare the pregnancy outcomes of infertile women with bilateral severe hydrosalpinx receiving neosalpingostomy or salpingectomy.Methods·The single-center prospective cohort study from 2005 to 2012 focused on pregnancy outcomes of infertile women aged 20‒40 years, with bilateral severe hydrosalpinx, undergoing bilateral neosalpingostomy or salpingectomy in International Peace Maternal and Child Health Hospital, Shanghai Jiao Tong University School of Medicine. The choice for treatment was based on a shared decision approach, and the participants were divided into the neosalpingostomy group and salpingectomy group. After registration of baseline characteristics, including age, birth place, reproductive history, preoperative hysterosalpingography results, surgical findings, and pregnancy outcomes, women were followed up on an annual basis until July 2020 for the occurrence of live birth by outpatient follow-up or telephone questionnaire. Intention-to-treat analysis and per-protocol analysis were applied to compare the pregnancy outcomes. Kaplan-Meier analysis and COX proportional hazard model were used to analyze the reproductive outcomes. In addition, subgroup analysis was performed based on age stratification. The main outcome measures were live birth rate, cumulative live birth rate, and factors affecting live birth. Secondary outcome measures included the mode of conception, time to live birth, biochemical pregnancy rate, clinical miscarriage rate, and ectopic pregnancy rate.Results·A total of 113 women were included in the analysis, 58 women underwent bilateral neosalpingostomy, and 55 women underwent bilateral salpingectomy. The study demonstrated that in infertile women with bilateral severe hydrosalpinx, bilateral salpingectomy achieved higher cumulative live birth rate than bilateral neosalpingostomy (76.36% vs 62.07, HR=2.18,95%CI 1.37‒3.45). In the neosalpingostomy group, 34.48% (20/58) live births were obtained after in vitro fertilization treatment, and 27.59% (16/58) live births were obtained through spontaneous conception which mainly occurred within 3 years after initial neosalpingostomy, while all live births in the salpingectomy group were obtained after assisted reproductive therapy. However, the risk of ectopic pregnancy was higher in the neosalpingostomy group than that in the salpingectomy group (20.69% vs 1.82%, P<0.001). No statistically significant differences regarding biochemical pregnancy and clinical miscarriage between the two groups were found. During the subgroup analysis, the cumulative live birth rate of the salpingectomy group (n=51) was significantly higher than that of the neosalpingostomy group (n=48) in women younger than 35 years old (HR=2.25, 95%CI 1.39‒3.66), while between two groups of women aged 35 years old or older, there was no statistically significant difference in the cumulative live birth rate (HR=1.60, 95%CI 0.36‒7.19). In addition, after adjustment for confounding factors including age, previous abortion history, fibroid, benign ovarian cyst, and endometriosis, COX proportional hazard analysis revealed that salpingectomy was positively correlated to live birth compared with neosalpingostomy (aHR=1.94, 95%CI 1.18‒3.18).Conclusion·For infertile women with bilateral severe hydrosalpinx, neosalpingostomy provides the possibility for spontaneous conception but also brings about certain risk of ectopic pregnancy. Bilateral salpingectomy can achieve higher cumulative live birth rate while receiving postoperative in vitro fertilization treatment