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Creating consistency among project schedule data, BIM, and payment applications requires activities in a construction schedule to be mapped with the most relevant ASTM Uniformat classifications. To do so, we introduce UniformatBridge , a new transformer-based natural language processing model, that automatically labels activities in a project schedule with Uniformat classification. Our model introduces construction sequencing tokens that capture logistically-constrained predecessor and successor activities into BERT architecture. We also introduce a dataset of real-world construction project schedules with their ground-truth Uniformat classifications for validation. Experimental results using this dataset achieve F1-scores of 0.93 and 0.87 when matching unstructured schedule data to Uniformat Level 2 and 3 classifications, respectively. We share how our method unlocks development of new techniques to (1) automatically create 4D BIM, and (2) computer-vision progress monitoring to tie semantic segmentation of reality capture data based on Uniformat classes against schedule or payment application data structures, with/without BIM. • A new NLP transformer model to automatically map schedule activities to ASTM UniFormat classes. • Sequencing knowledge is embedded in the model via concatenating [pred] and [succ] tokens. • Method achieves 93% and 87% F1-Scores in mapping UniFormat II Level 2 & 3 classes with schedule activities. • Our method serves as a universal identifier enabling automated creation of 4D BIMs. • Our method streamlines mapping between schedule, cost and payment application data. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method to predict logical relationships between any pair of schedule activities with F1-Score of 88.3%. • A method to schedule a short list of input activities based on the learned logic. • Formalization of indirect logic dependencies between schedule activities. • A method for mining indirect dependencies from an input schedule. In construction, schedule mistakes causing delays beyond substantial completion dates cost contractors expensive liquidated damages. Hence, several industry guidelines, such as the DCMA's 14 point assessment, define schedule quality and offer systematic methods for ensuring it. These guidelines list "logic" as an essential control metric, and they require planners to ensure their schedules are free of missing or wrong logical dependencies. Checking the logic requires extensive construction domain knowledge, and planners perform it entirely manually as there are no available software solutions that support it. This paper offers a novel machine learning-based solution that learns construction scheduling domain knowledge from existing records completely automatically and applies it to validate the logic in input schedules achieving an F1 score of 88.3%. Furthermore, we tailor our method to use the learned knowledge to schedule a list of unordered activities. The details of the method, experimental results, benefits, and limitations are discussed. [ABSTRACT FROM AUTHOR]

This article presents an algorithm for translating the Penn Treebank into a corpus of Combinatory Categorial Grammar (CCG) derivations augmented with local and long-range word--word dependencies. The resulting corpus,CCGbank, includes 99.4% of the sentences in the Penn Treebank. It is available from the Linguistic Data Consortium,and has been used to train wide-coverage statistical parsers that obtain state-of-the-art rates of dependency recovery. In order to obtain linguistically adequate CCG analyses, and to eliminate noise and inconsistencies in the original annotation, an extensive analysis of the constructions and annotations in the Penn Treebank was called for, and a substantial number of changes to the Treebank were necessary. We discuss the implications of our findings for the extraction of other linguistically expressive grammars from the Treebank, and for the design of future treebanks. [ABSTRACT FROM AUTHOR]

Abstract: Unlike homopolymers, biopolymers are composed of specific sequences of different types of monomers. In proteins and RNA molecules, one-dimensional sequence information encodes a three-dimensional fold, leading to a corresponding molecular function. Such folded structures are not treated adequately through traditional methods of polymer statistical mechanics. A promising new way to solve problems of the statistical mechanics of biomolecules comes from computational linguistics, the field that uses computers to parse and understand the sentences in natural languages. Here, we give two examples. First, we show that a dynamic programming method of computational linguistics gives a fast way to search protein models for native structures. Interestingly, the computational search process closely resembles the physical folding process. Second, linguistics-based dynamic programming methods are also useful for computing partition functions and densities of states for some foldable biopolymers – helix-bundle proteins are reviewed here. In these ways, computational linguistics is helping to solve problems of the searching and counting of biopolymer conformations. [Copyright &y& Elsevier]

The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research. [ABSTRACT FROM AUTHOR]