Accurate and Fast Compressed Video Captioning

Existing video captioning approaches typically require to first sample video frames from a decoded video and then conduct a subsequent process (e.g., feature extraction and/or captioning model learning). In this pipeline, manual frame sampling may ignore key information in videos and thus degrade performance. Additionally, redundant information in the sampled frames may result in low efficiency in the inference of video captioning. Addressing this, we study video captioning from a different perspective in compressed domain, which brings multi-fold advantages over the existing pipeline: 1) Compared to raw images from the decoded video, the compressed video, consisting of I-frames, motion vectors and residuals, is highly distinguishable, which allows us to leverage the entire video for learning without manual sampling through a specialized model design; 2) The captioning model is more efficient in inference as smaller and less redundant information is processed. We propose a simple yet effective end-to-end transformer in the compressed domain for video captioning that enables learning from the compressed video for captioning. We show that even with a simple design, our method can achieve state-of-the-art performance on different benchmarks while running almost 2x faster than existing approaches. Code is available at https://github.com/acherstyx/CoCap

MicroRNA-196a-5p targeting LRP1B modulates phenotype of thyroid carcinoma cells

Introduction: Thyroid cancer (TC) is a common endocrine malignancy, comprising nearly one-third of all head and neck malignancies worldwide. MicroRNAs (miRNAs) have been implicated in the malignant progression of multiple cancers; however, their contribution to thyroid diseases has not been fully explored. Material and methods: This study aimed to illustrate the regulatory mechanism of microRNA-196a-5p in TC progression and to investigate whether microRNA-196a-5p affects progression of TC cells by targeting low-density lipoprotein receptor-associated protein 1B (LRP1B). MicroRNA-196a-5p and LRP1B expression status in TC cells and normal human thyroid cells was detected by quantative reverse transcription polymerase chain reaction (qRT-PCR) and western blot. Dual-luciferase reporter assay, cell counting kit-8 (CCK-8) assay, scratch healing assay, and Transwell assay were also performed. Results: The results showed that microRNA-196a-5p expression was up-regulated and LRP1B expression was down regulated in TC cells. In addition, the upregulation of microRNA-196a-5p facilitated progression of TC cells. Silencing microRNA-196a-5p led to the opposite results. Dual-luciferase reporter assay offered evidence for microRNA-196a-5p targeting LRP1B in TC. MicroRNA-196a-5p could target LRP1B to facilitate proliferation, invasion, and migration of TC cells. Conclusion: Overall, this study revealed that microRNA-196a-5p may be a cancer-promoting microRNA that plays an important role in TC progression

Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals

Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals Authors: Xi Guo; Puying Zhang; Yaojie Yue This is the outcome data of our research which is under submission. Though the impact of climate change on potential crop distributions has been extensively explored, there are few studies on potential wheat distributions at specific global warming levels (GWLs), e.g., 1.5 °C and 2 °C. Here, a grided (0.5 degree × 0.5 degree) dataset of global potential wheat distribution under the 1.5 °C and 2 °C GWLs is proposed. This dataset is produced using the MaxEnt model with support of multi-model data(GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M). The predictive accuracy of the proposed dataset was carefully validated between the predicted global wheat distribution and multiple known datasets. For more details of the approach used to predict the global wheat distribution please refer to: Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153. The results indicate the regional differences in the potential suitability of wheat cultivation under different GWLs. Eastern Europe, Pakistan, Northern India, Russia, and Canada witnessed a significant increase in wheat planting suitability. In contrast, Central Eastern Africa, Southeastern Australia, Southeastern China, Southern Brazil, France, Spain, and Italy demonstrated a significant decrease in wheat suitability. Compared with 1.5 °C GWLs, wheat planting suitability decreases more evidently in 2 °C GWLs in Central and Eastern Africa, Central and Southern India, Southeastern China, Australia, Mexico, Southern Brazil, and Argentina. Simultaneously, regions such as Russia, Pakistan, Canada, and the Great Lakes area of the United States observed further increases in wheat planting suitability. To ensure favorable conditions for the cultivation of wheat, it is crucial to limit the global average temperature increase to less than 2 °C. Our findings demonstrate the influence of different GWLs on potential global wheat distribution, highlighting the regional differences in the potential suitability of wheat cultivation under different GWLs. We argue that the potential global wheat distribution datasets under different GWLs are a valuable complement to currently available products. This potential global wheat distribution is one of the few products to take into account 1.5 °C and 2 °C GWLs based on multi-modal data. We believe that it can provide more valuable information for policymakers to make decisions for the warming world. The data of the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals is stored in a zip package, that is Global Planting Suitability of Wheat.zip. This package consists of 1 folder, i.e., SR1.5&2.0. This subfolder contains GeoTIFF files for the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals. Correspondingly Wheat_SR15.tif and Wheat_SR20.tif. The grid value of each file ranges from 0 to 1, indicating the possibility of wheat planting in each grid, and the higher the value, the higher the possibility that wheat exists. Reference: Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153

Fine Root Length of Maize Decreases in Response to Elevated CO2 Levels in Soil

To assess the environmental risks of carbon capture and storage (CCS) due to underground CO2 leakage, many studies have examined the impact on plant growth; however, the effect of leaked CO2 on root morphology remains poorly understood. This study simulated the effects of CO2 leakage from CCS on maize (Zea mays L.) root systems through pot experiments—one control treatment (no added CO2) and two elevated soil CO2 treatments (1000 g m−2 d−1 and 2000 g m−2 d−1). Compared with the control, root length, root surface area, and root volume were reduced by 44.73%, 34.14%, and 19.16%, respectively, in response to CO2 treatments with a flux of 2000 g m−2 d−1. Meanwhile, the fine root length in CO2 treatments with a flux of 1000 g m−2 d−1 and 2000 g m−2 d−1 were reduced by 29.44% and 45.88%, respectively, whereas no obvious difference in regard to coarse roots was found. Understanding changes in plant root morphology in this experiment, especially the decrease in the fine root length, are essential for explaining plant responses to CO2 leakage from CCS

Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)

Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) Authors: Xi Guo; Puying Zhang; Yaojie Yue This is the outcome data of our research which is under submission. Socioeconomic and climate change are both essential factors affecting the global cultivation distributions of crops. However, the role of socioeconomic factors in the prediction of future crop cultivation distribution under climate change has been rarely explored. Here, we proposed the MaxEnt-SPAM approach assuming that environmental conditions are the fundamental factors determining whether land is suitable for cultivating wheat, and socioeconomic factors are the driving forces that influence farmers’ crop choices. In short, the distribution of wheat cultivation depends on the maximization of potential revenue as well as satisfying wheat planting suitability. The proposed MaxEnt-SPAM approach for estimating the cultivation distribution of wheat in three combined Representative concentration pathway (RCP) -Shared socioeconomic pathway (SSP) scenarios, i.e., RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3). The steps were as follows: (1) Estimate wheat planting suitability under future RCP scenarios by the MaxEnt model. (2) Estimate farmers' crop choices under future SSP scenarios using Time series- Backpropagation (TS-BP) models. (3) Estimate global wheat cultivation distribution based on the SPAM model. Based on major known datasets on the distribution of wheat cultivation, the proposed MaxEnt-SPAM approach was carefully validated by comparing its prediction results with those known datasets. Satisfactory accuracy was achieved. It indicates that the predictive accuracy of the proposed approach could be over 85%, with a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets. Based on the above idea and approach, a grid (0.5 degree × 0.5 degree) global wheat cultivation distribution under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios were predicted. The results indicate that RCP8.5-SSP3 might be the most favorable for wheat cultivation. Moreover, socioeconomic development significantly restricts the potential distribution of wheat cultivation. The estimated wheat cultivation areas considering the effects of socioeconomic development average account for 77% of the potential wheat distribution determined by climatic factors under the selected RCP-SSP scenarios. Socio-economic development seems to benefit wheat cultivation in Africa. Our findings demonstrate the influence of socioeconomic factors on crop distribution from the perspective of the market economy, highlighting the necessity of coupling socioeconomic factors and climate change to accurately predict crop cultivation distribution. We argue that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable complement to currently available products. This wheat cultivation distribution prediction data is one of the few products to take into account both climate change and the drive for socio-economic development. We believe that it can provide a product that is more consistent with the logic of crop cultivation distribution than those that only consider climate change impacts. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to allow us to better understand the dynamics and distribution of global wheat cultivation distribution under different climate change and socio-economic development paths in the future. These data can potentially provide support for relevant research. Such as but not limited to earth system simulation, and agricultural sciences. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) Datasets and the Maxent-SPAM approach code are stored in a zip package, that SPAM_MaxEnt.zip. This package consists of 2 folders (code, and data) shown as follows. code: This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information. The exampleData contains 1. h_pri.tif: prior data 2. h_res.tif: global C3 crop cultivation proportion Run the main programme: cross_entroy.m data: This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format. 1 Global distribution of the long-term wheat-cultivation area fraction: This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated. r2s1f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario r4s2f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario r8s3f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario 2 Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution: This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:{1, 2, 3}, 1 wheat cultivation existed but was predicted to be unsuitable to plant wheat; 2 presented a reduction in the wheat cultivation area compared to the land's suitability; 3 presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat. com_suit_fra126.tif: the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6 com_suit_fra245.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5 com_suit_fra385.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5 3 Differences in the proportion of long-term wheat cultivation: This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability. r2s1_f.tif: Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario r4s2_f.tif: Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario r8s3_f.tif: Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario References: For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014). Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026. You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003. You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.00

JoSDW: Combating Noisy Labels by Dynamic Weight

The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most advanced existing methods mainly adopt a small loss sample selection strategy, such as selecting the small loss part of the sample for network model training. However, the previous literature stopped here, neglecting the performance of the small loss sample selection strategy while training the DNNs, as well as the performance of different stages, and the performance of the collaborative learning of the two networks from disagreement to an agreement, and making a second classification based on this. We train the network using a comparative learning method. Specifically, a small loss sample selection strategy with dynamic weight is designed. This strategy increases the proportion of agreement based on network predictions, gradually reduces the weight of the complex sample, and increases the weight of the pure sample at the same time. A large number of experiments verify the superiority of our method