Optimizing land-use zonation in coastal areas: revealing the spatio-temporal patterns and trade-off/synergy relationships among farmland functions

Under the interaction between natural ecosystems and human interferences, farmland extends to multi-functions such as production, ecological, social, and cultural functions. Despite the exponential increase in research on the multi-functional evaluation of farmland in recent years, little study has been conducted at fine spatial and long-time scales. Furthermore, the existing quantitative analyses of multifunctional synergies and trade-offs in farmland mainly consider static spatial patterns and neglect dynamic information. Selecting the Chinese coastal province of Zhejiang as the study area, this study thus evaluated the spatio-temporal patterns of farmland functions from 2000 to 2020 at the county scale and introduced the trade-off/synergy degree (TSD) model to quantify the intensity of the relationships among functions. The results showed that farmland functional values and their relationships were significantly heterogeneous in spatial and temporal distribution. In addition to social function, the other functions all exhibited an increasing trend. Furthermore, strong correlations were mainly observed between production, ecological and cultural functions. Ultimately, five farmland zones were determined by the k-means clustering algorithm and considering both functional values and their relationships, and targeted suggestions applicable to each zone were put forward in this study. This study contributes to the utilization and planning of farmland and its surrounding land, especially to the improvement of the policy of returning farmland to forests

SkyMath: Technical Report

Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning. In this work, we present SkyMath, a large language model for mathematics with 13 billion parameters. By applying self-compare fine-tuning, we have enhanced mathematical reasoning abilities of Skywork-13B-Base remarkably. On GSM8K, SkyMath outperforms all known open-source models of similar size and has established a new SOTA performance

Skywork: A More Open Bilingual Foundation Model

In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves \emph{state of the art} performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs

Semi-supervised nonlinear hashing using bootstrap sequential projection learning.

Abstract-In this paper, we study the effective semi-supervised hashing method under the framework of regularized learningbased hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not only independent from the dimensionality of the original data space but also much smaller than the one using linear hash function. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which effectively corrects the errors by taking into account of all the previous learned bits holistically without incurring the extra computational overhead. Experimental results on the six benchmark datasets demonstrate that the presented method outperforms the state-of-the-art hashing algorithms at a large margin. Index Terms-Hashing, semi-supervised hashing, nearest neighbor search

Revealing Spatial Patterns of Cultural Ecosystem Services in Four Agricultural Landscapes: A Case Study from Hangzhou, China

Monitoring and mapping agricultural cultural ecosystem services (CES) is essential, especially in areas with a sharp contradiction between agricultural land protection and urban development. Despite research assessing CES increasing exponentially in recent years, our knowledge of the CES of agricultural landscapes is still inadequate. This study used four types of agricultural landscapes in Hangzhou, China, as the study area, analyzed their CES spatial patterns, and explored their societal preferences by integrating the multi-sourced datasets, clustering algorithms, and Maxent model. The results indicated that hot spots of agricultural CES correspond to river valley plains, which were also easily vulnerable to urbanization. Moreover, we found that the CES level of paddy field and dry farmland were higher than tea garden and orchard. Based on the above spatial patterns of supply, demand, and flow of CES, we identified four groups of agricultural land by cluster analysis, distinguishing between significant, unimportant, little used, and potential CES. Further, our results showed that natural and human factors could explain societal preferences. This study can provide a valuable basis for stakeholders to develop balanced strategies by the aforementioned results

Coupling of Soil Moisture and Air Temperature from Multiyear Data During 1980–2013 over China

Soil moisture is an important parameter in land surface processes, which can control the surface energy and water budgets and thus affect the air temperature. Studying the coupling between soil moisture and air temperature is of vital importance for forecasting climate change. This study evaluates this coupling over China from 1980–2013 by using an energy-based diagnostic method, which represents the momentum, heat, and water conservation equations in the atmosphere, while the contributions of soil moisture are treated as external forcing. The results showed that the soil moisture–temperature coupling is strongest in the transitional climate zones between wet and dry climates, which here includes Northeast China and part of the Tibetan Plateau from a viewpoint of annual average. Furthermore, the soil moisture–temperature coupling was found to be stronger in spring than in the other seasons over China, and over different typical climatic zones, it also varied greatly in different seasons. We conducted two case studies (the heatwaves of 2013 in Southeast China and 2009 in North China) to understand the impact of soil moisture–temperature coupling during heatwaves. The results indicated that over areas with soil moisture deficit and temperature anomalies, the coupling strength intensified. This suggests that soil moisture deficits could lead to enhanced heat anomalies, and thus, result in enhanced soil moisture coupling with temperature. This demonstrates the importance of soil moisture and the need to thoroughly study it and its role within the land–atmosphere interaction and the climate on the whole

Evaluating the Utility of Five Gene Fragments for Genetic Diversity Analyses of <i>Mytella strigata</i> Populations

Mytella strigata (Hanley, 1843) is an invasive mussel species that has rapidly spread in China in recent years. Here, we tested the utility of three mitochondrial gene fragments, COI, 12S, and 16S, and two nuclear gene fragments, D1 28S and 18S-ITS1, for characterizing the levels of genetic diversity among and within populations using 191 M. strigata specimens collected in China to aid ongoing efforts to identify the origin of the invasion as well as molecular genetic studies. M. strigata exhibited two sex-associated haplogroups according to the COI and 12S sequences. The ratio of female-lineage to male-lineage COI and 12S sequences was 149:22 and 72:7, and the genetic distances between haplogroups were 6.56 and 9.17, respectively. Only one haplotype was detected among the 18S-ITS1 sequences (413 bp), and three haplotypes were detected among the D1 28S sequences (296 bp). The haplotype diversity of both the female-lineage COI and 12S sequences was greater than 0.5, and the nucleotide diversity of the 12S, 16S, D1 28S, and 18S-ITS1 sequences was less than 0.005 in all six populations in China. Our findings indicated that COI is the most useful gene fragment for genetic diversity studies of M. strigata populations; D1 28S and 18S-ITS1 sequences would be useful for species identification because of their low intraspecific diversity. Our genetic analysis of the COI sequences revealed Colombia as the most likely origin of M. strigata in China and showed that the invasive populations in China have recently experienced or are currently experiencing a population bottleneck