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Its focus on dependencies and patterns in relational data makes network science a promising addition to the analytic toolbox in archaeology. Despite its tradition in a number of other fields, however, the methodology of network science is only in development and its scope and proper usage are subject to debate. We argue that the historical linkage with graph theory and limitations in commonly available software form an obstacle to leveraging the full potential of network methods. This is illustrated via replication of a study of Maya obsidian (Golitko et al. Antiquity, 2012), in which it seemed necessary to discard detailed information in order to represent data in networks suitable for further processing. We propose means to avoid such information loss by using methods capable of handling valued rather than binarized data. The resulting representations corroborate previous conclusions but are more reliable and thus justify a more detailed interpretation of shifting supply routes as an underlying process contributing to the collapse of Maya urban centers. Some general conclusions for the use of network science in archaeology are offered. published

Fine-grained opinion analysis methods often make use of linguistic features but typically do not take the interaction between opinions into account. This article describes a set of experiments that demonstrate that relational features, mainly derived from dependency-syntactic and semantic role structures, can significantly improve the performance of automatic systems for a number of fine-grained opinion analysis tasks: marking up opinion expressions, finding opinion holders, and determining the polarities of opinion expressions. These features make it possible to model the way opinions expressed in natural-language discourse interact in a sentence over arbitrary distances. The use of relations requires us to consider multiple opinions simultaneously, which makes the search for the optimal analysis intractable. However, a reranker can be used as a sufficiently accurate and efficient approximation. A number of feature sets and machine learning approaches for the rerankers are evaluated. For the task of opinion expression extraction, the best model shows a 10-point absolute improvement in soft recall on the MPQA corpus over a conventional sequence labeler based on local contextual features, while precision decreases only slightly. Significant improvements are also seen for the extended tasks where holders and polarities are considered: 10 and 7 points in recall, respectively. In addition, the systems outperform previously published results for unlabeled (6 F-measure points) and polarity-labeled (10–15 points) opinion expression extraction. Finally, as an extrinsic evaluation, the extracted MPQA-style opinion expressions are used in practical opinion mining tasks. In all scenarios considered, the machine learning features derived from the opinion expressions lead to statistically significant improvements.

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.

A modern burial experiment was devised to test microscopic residue survival in acidic peat and slightly acidic clay soils at the Early Mesolithic site of Star Carr (North Yorkshire, UK), and at nearby control location. The experiment addresses concerns regarding the applicability of residue analysis in varied burial environments, and particularly in highly acidic archaeological conditions. Flint flakes (n= 78, including blank controls) were used on twelve plant, animal, and mineral materials to create residues and then buried. The residues were examined 1 month and 11 months after burial. An unburied reference collection containing the same twelve residue types in a fresh state was compared to the buried residues to assess diagenesis. The residue types that survived across all burial conditions and time intervals were: softwood tissue, tree resin, bird feathers, squirrel hair, and red ochre.\ud \ud During microscopic analysis, it became clear that many residues lack diagnostic traits, and thus an assessment of the extent to which each residue can be identified was conducted. The degree to which residues were able to be identified was further investigated with a variable pressure scanning electron microscope (SEM). SEM images of the reference residues were compared to the reflected VLM micrographs of the same residues, which improved characterisation in some cases. Residues were grouped into three categories (diagnostic, distinctive, and non-distinctive) within a visual characterisation guide. Our in situ microscopic analyses indicated that few residue types have diagnostic traits that allow them to be identified unambiguously, and thus further characterisation techniques are often required.

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/

Semantic similarity has typically been measured across items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of linguistic item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of linguistic item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method's quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of items.

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabilistic reconstruction procedure based on approximate message-passing, Scampi, which operates in the compressive regime, where the inverse imaging problem is underdetermined. While the proposed method is related to the recently proposed GrAMPA algorithm of Borgerding, Schniter, and Rangan, we further develop the probabilistic approach to compressive imaging by introducing an expectation-maximization learning of model parameters, making the Scampi robust to model uncertainties. Additionally, our numerical experiments indicate that Scampi can provide reconstruction performance superior to both GrAMPA as well as convex approaches to TV reconstruction. Finally, through exhaustive best-case experiments, we show that in many cases the maximal performance of both Scampi and convex TV can be quite close, even though the approaches are a prori distinct. The theoretical reasons for this correspondence remain an open question. Nevertheless, the proposed algorithm remains more practical, as it requires far less parameter tuning to perform optimally. International audience