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In 2001 excavation works undertaken in advance of quarrying at East Barns, East Lothian (NGR: NT 7121 7686), revealed the substantial remains of a robust Mesolithic house structure, securely dated to the late 9th millennium BC. The house was situated within a large, natural hollow whose gradual infilling had effectively sealed the archaeological deposits. The house consisted of a sub-circular sunken floor with the remains of a westfacing entrance and two concentric angled post rings, suggesting episodes of replacement if not actual rebuilding. The remains of interior furniture were also recorded in the form of post holes, a platform, and three probable hearth features. A charred deposit of occupation debris rich in lithics sealed many of the structural features around the internal perimeter of the house. The distribution of this deposit appeared to reflect informal refuse toss/drop zones formed during the occupation of the structure and suggests some form of internal spatial organisation. The house at East Barns joins an increasing group of substantial analogous sites related to Early Mesolithic activity in Scotland and northern England. These substantial house sites reflect increasing socio-economic, cultural and chronological complexity during the Mesolithic. As such the site allows provisional hypotheses to be formed about the scale and nature of Early Mesolithic social and economic adaptation around the North Sea Basin.

This paper proposes a novel limited activation matching pursuits (LAMP) method to monitor the number of eclectic vehicles (EVs) connected at a charging station and their charging activities. LAMP is able to reflect the two-dimensional characteristics of the number and charging time of the EVs. Constraints are entered on the activation coefficients of the matching pursuits to avoid over matching a particular type of EV. The method includes the development of the basis based on typical EV charging profiles and the improvement of matching pursuits. A case study is undertaken based on real EV data collected from London. The results show that 79.35% EVs can be accurately detected in charging station load profile duration of one week. The number of EVs connected within each half hour can be identified with a relative mean absolute deviation (RMAD) of 21.18%.

Metrology has been successfully used in the last decade to quantify use-wear on stone tools. Such techniques have been mostly applied to fine-grained rocks (chert), while studies on coarse-grained raw materials have been relatively infrequent. In this study, confocal microscopy was employed to investigate polished surfaces on a coarse-grained lithology, quartzite. Wear originating from contact with five different worked materials were classified in a data-driven approach using machine learning. Two different classifiers, a decision tree and a support-vector machine, were used to assign the different textures to a worked material based on a selected number of parameters (Mean density of furrows,Mean depth of furrows,Core material volume-Vmc). The method proved successful, presenting high scores for bone and hide (100%). The obtained classification rates are satisfactory for the other worked materials, with the only exception of cane, which shows overlaps with other materials. Although the results presented here are preliminary, they can be used to develop future studies on quartzite including enlarged sample sizes.

3D face recognition is an increasing popular modality for biometric authentication, for example in the iPhoneX. Landmarking plays a significant role in region based face recognition algorithms. The accuracy and consistency of the landmarking will directly determine the effectiveness of feature extraction and hence the overall recognition performance. While surface normals have been shown to provide high performing features for face recognition, their use in landmarking has not been widely explored. To this end, a new 3D facial landmarking algorithm based on thresholded surface normal maps is proposed, which is applicable to widely used 3D face databases. The benefits of employing surface normals are demonstrated for both facial roll and yaw rotation calibration and nasal landmarks localization. Results on the Bosphorus, FRGC and BU-3DFE databases show that the detected landmarks possess high within-class consistency and accuracy under different expressions. For several key landmarks the performance achieved surpasses that of state-of-the-art techniques and is also training free and computationally efficient. The use of surface normals therefore provides a useful representation of the 3D surface and the proposed landmarking algorithm provides an effective approach to localising the key nasal landmarks.