An influential position in lexical semantics holds that semantic representations for words can be derived through analysis of patterns of lexical co-occurrence in large language corpora. Firth (1957) famously summarised this principle as “you shall know a word by the company it keeps”. We explored whether the same principle could be applied to non-verbal patterns of object co-occurrence in natural scenes. We performed latent semantic analysis (LSA) on a set of photographed scenes in which all of the objects present had been manually labelled. This resulted in a representation of objects in a high-dimensional space in which similarity between two objects indicated the degree to which they appeared in similar scenes. These representations revealed similarities among objects belonging to the same taxonomic category (e.g., items of clothing) as well as cross-category associations (e.g., between fruits and kitchen utensils). We also compared representations generated from this scene dataset with two established methods for elucidating semantic representations: (a) a published database of semantic features generated verbally by participants and (b) LSA applied to a linguistic corpus in the usual fashion. Statistical comparisons of the three methods indicated significant association between the structures revealed by each method, with the scene dataset displaying greater convergence with feature-based representations than did LSA applied to linguistic data. The results indicate that information about the conceptual significance of objects can be extracted from their patterns of co-occurrence in natural environments, opening the possibility for such data to be incorporated into existing models of conceptual representation.

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Gas separation methods have received much attention, as the process plays a key role in various industries. Among the gas separation methods, membrane-based methods, particularly those employing mixed matrix membranes (MMMs), are important. MMMs are formed by modifying the properties of polymeric membranes by fabricating them with inorganic particles. This paper presents the gas transport behaviour in MMMs fabricated with porous particles, as described by two-phase (ideal) and three-phase (non-ideal) models. The effect of particle porosity on gas permeability was incorporated into existing models through the J parameter, which adjusts the particle loading percentage. J-modified models were verified against existing models and experimental data for gas permeability were obtained with various MMMs. It was found that the proposed modified models provide a better prediction of the gas transport behaviour in MMMs.

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In this paper, we provide coincidence point and fixed point theorems satisfying an implicit relation, which extends and generalizes the result of Gregori and Sapena, for set-valued mappings in complete partially ordered fuzzy metric spaces. Also we prove a fixed point theorem for set-valued mappings on complete partially ordered fuzzy metric spaces which generalizes results of Mihet and Tirado. MSC: 54E40; 54E35; 54H25 Keywords: fixed point; coincidence point; set-valued mapping; partially ordered set; fuzzy metric space PreliminariesThe concept of fuzzy metric space was introduced by Kramosil and Michalek [] and the modified concept by George and Veeramani [] (for other modifications see [, ]). Furthermore, the fixed point theory in this kind of spaces has been intensively studied (see [-]).The applications of fixed point theorems are remarkable in different disciplines of mathematics, engineering, and economics in dealing with problems in approximation theory, game theory, and many others (see [] and references therein).In  Rodríguez-López and Romaguera [] introduced the Hausdorff fuzzy metric of a given fuzzy metric space in the sense of George and Veeramani on the set of non-empty compact subsets.Some fixed point results for set-valued mappings on fuzzy metric space can be found in [, ] and references therein.The aim of this paper is to prove a coincidence point and fixed point theorem on a partially ordered fuzzy metric space satisfying an implicit relation and another fixed point theorem. Our result substantially generalizes and extends the result of Gregori and Sapena For the sake of completeness, we briefly recall some basic concepts used in the following.

Objectives: Anomia is one of the most common and persistent symptoms of aphasia. Although treatments of anomia usually focus on semantic and/or phonological levels, which both have been demonstrated to be effective, the relationship between the underlying functional deficit in naming and response to a particular treatment approach remains unclear. The aim of this study was to determine the relationship between the effects of specific treatments (Semantic feature Analysis and Phonological Components Analysis) and their underlying functional deficit patterns within the framework of a cognitive processing model. Methods:In an ABCB reversal control task design, four participants with aphasia were selected according to the criteria based on using a cognitive model of lexical processing. Each patient received two types of treatment. In SFA, features semantically associated to the target words were elicited from the patient, whereas in PCA treatment, the phonological components of the target words were the focus of treatment. Naming accuracy scores obtained in pretreatment baseline phase were compared to post-treatment accuracy scores. Here, both itemspecific effects and generalization of untrained items were analyzed.Results: Both SFA and PCA treatments have the potential to improve naming ability in participants; however, the treatment approach that corresponds exactly to the underlying deficit causing failure in word retrieval is more effective.Discussion: While PCA is more effective for participants with phonological impairments, SFA is more effective for participants with semantic impairments. Therefore, a direct relationship between underlying functional deficit and response to specific treatment was established for all participants.

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here we explore the applicability of deep learning approaches by adapting a pre-trained convolutional neural network (CNN) to detect deformation in a nationalscale velocity field. For our proof-of-concept, we focus on the UK where previously identified deformation is associated with coalmining, ground water withdrawal, landslides and tunnelling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exists to construct a balanced training data set, and the deformation signals are slower and more localised than in previous applications. We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of the real UK velocity map, and iii) enhanced over-wrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.