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The current study employed an eye-movement technique with an attempt to explore the reading patterns for the two types of Chinese relative clauses, subject-extracted relative clauses (SRCs) and object-extracted relative clauses (ORCs), by native speakers (L1), and Japanese learners (L2) of Chinese. The data were analyzed in terms of gaze duration, regression path duration, and regression rate on the two critical regions, head noun, and embedded verb. The results indicated that both the L1 and L2 participants spent less time on the head nouns in ORCs than in SRCs. Also, the L2 participants spent less time on the embedded verbs in ORCs than in SRCs and their regression rate for embedded verbs was generally lower in ORCs than in SRC. The findings showed that the participants experienced less processing difficulty in ORCs than SRCs. These results suggest an ORC preference in L1 and L2 speakers of Chinese, which provides evidence in support of linear distance hypothesis and implies that the syntactic nature of Chinese is at play in the RC processing.

It remains debatable whether slash-and-burn practices were adopted in rice cultivation by the Neolithic Kuahuqiao culture in the Ningshao Plain, one of the birthplaces of rice farming. Here, we established charcoal-based indices to reconstruct the history of fire and vegetation in the Ningshao Plain since the last glacial period. We collected representative modern vegetation and conducted combustion and fragmentation experiments to simulate fire and depositional processes, respectively. Charcoals from modern vegetation show clear morphological differences between herbaceous and woody plants. In particular, the length to width ratios (L/W) of herbaceous charcoals were systematically higher than those of woody charcoals, and the associated end-member values were 4.50 and 1.94, respectively. These values were then applied to sediment cores (KHQ-14/15) collected in proximity to the Kuahuqiao archaeological site. Results show that the amount of combusted herbaceous plants increased sharply after the Holocene, and the most remarkable rise occurred around 8550 yr B.P. This observation may reflect local environment (sedimentary and/or climatic) changes or small-scale early human activities. During the Kuahuqiao cultural period (8250-7450 yr B.P.), the relative abundance of woody charcoals increased, but the overall fire intensity decreased. This finding suggests that the Kuahuqiao farming was restricted to a small geographic area and large-scale slash-and-burn farming activities were not adopted.

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models.

Ocean-going ships were key to rising maritime economies of the Early Modern period, and understanding how they were built is critical to grasp the challenges faced by shipwrights and merchant seafarers. Shipwreck timbers hold material evidence of the dynamic interplay of wood supplies, craftmanship, and evolving ship designs that helped shape the Early Modern world. Here we present the results of dendroarchaeological research carried out on Batavia���s wreck timbers, currently on display at the Western Australian Shipwrecks Museum in Fremantle. Built in Amsterdam in 1628 CE and wrecked on its maiden voyage in June 1629 CE in Western Australian waters, Batavia epitomises Dutch East India Company (Verenigde Oostindische Compagnie, or VOC) shipbuilding. In the 17th century, the VOC grew to become the first multinational trading enterprise, prompting the rise of the stock market and modern capitalism. Oak (Quercus sp.) was the preferred material for shipbuilding in northern and western Europe, and maritime nations struggled to ensure sufficient supplies to meet their needs and sustain their ever-growing mercantile fleets and networks. Our research illustrates the compatibility of dendrochronological studies with musealisation of shipwreck assemblages, and the results demonstrate that the VOC successfully coped with timber shortages in the early 17th century through diversification of timber sources (mainly Baltic region, L��beck hinterland in northern Germany, and Lower Saxony in northwest Germany), allocation of sourcing regions to specific timber products (hull planks from the Baltic and L��beck, framing elements from Lower Saxony), and skillful woodworking craftmanship (sapwood was removed from all timber elements). These strategies, combined with an innovative hull design and the use of wind-powered sawmills, allowed the Dutch to produce unprecedented numbers of ocean-going ships for long-distance voyaging and interregional trade in Asia, proving key to their success in 17th-century world trade. Funding: WvD LP130100137 Australian Research Council https://www.arc.gov.au/ No MD-D 607545 FP7 People Programme (Marie Curie Actions) https://ec.europa.eu/research/mariecurieactions/ 016.Veni.195.502 Nederlandse Organisatie voor Wetenschappelijk Onderzoek https://www.nwo.nl/en AD 677152 European Research Council https://erc.europa.eu/

Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance. However, traditional fuzz testing faces many challenges, such as how to mutate input seed files, how to increase code coverage, and how to bypass the format verification effectively. Therefore machine learning techniques have been introduced as a new method into fuzz testing to alleviate these challenges. This paper reviews the research progress of using machine learning techniques for fuzz testing in recent years, analyzes how machine learning improves the fuzzing process and results, and sheds light on future work in fuzzing. Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies five different stages in which machine learning has been used. Then this paper systematically studies machine learning-based fuzzing models from five dimensions of selection of machine learning algorithms, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Secondly, this paper assesses the performance of the machine learning techniques in existing research for fuzz testing. The results of the evaluation prove that machine learning techniques have an acceptable capability of prediction for fuzzing. Finally, the capability of discovering vulnerabilities both traditional fuzzers and machine learning-based fuzzers is analyzed. The results depict that the introduction of machine learning techniques can improve the performance of fuzzing. We hope to provide researchers with a systematic and more in-depth understanding of fuzzing based on machine learning techniques and provide some references for this field through analysis and summarization of multiple dimensions.