THE INFLUENCE OF SLOPE DAMAGE ON THE KINEMATICS OF LANDSLIDES

The stability of large rock slopes is controlled by geological, structural, geomorphic, and environmental factors, which define the location, size, and failure mechanism of landslides. However, the stability of a slope can change with time, as a result of the formation and accumulation of slope damage, which weakens the rock mass forming the slope or the rupture surface of the incipient landslide. In this paper, we review three landslide sites, analysing the characteristics of the slope damage, and highlighting its effects on the kinematics of the slope and the evolution of the landslide. We note that, despite the importance of slope damage in controlling the timing and evolution of a slope failure, no frameworks or guidelines currently exist for performing a consistent and systematic analysis. We also emphasize that interdisciplinary approaches should be developed to assist in the quantification and characterization of rock slope damage

Kinematic Analysis of the 2020 Elliot Creek Landslide, British Columbia, Using Remote Sensing Data

The 2020 Elliot Creek landslide-tsunami-flood cascade originated from an 18.3 Mm3 rock slope failure in quartz diorite bedrock in a valley undergoing rapid glacial retreat. We used airborne LiDAR and optical imagery to characterize the slope and its surroundings. Using the LiDAR, we determined that two rockslides (2020 and an older undated one) occurred on this slope and shared a common basal rupture surface. We mapped two main sets of lineaments that represent structures that controlled the orientation of the lateral and rear release surfaces. Analysis of the topographic profile indicates a wedge-shaped failure block and a stepped rupture surface. Further topographic profile analysis indicates the possibility of a structurally controlled geomorphic step in the valley that corresponds with a change in the orientation of the valley. The rapid retreat of the West Grenville Glacier and the positions of the rupture surfaces suggest glacial retreat played a role in the landslides

Are your students safe to learn? The role of lecturer’s authentic leadership in the creation of psychologically safe environments and their impact on academic performance:The role of teacher's authentic leadership on the creation of psychologically safe environments and their impact on academic performance

As the role of students and lecturers in higher education changes, several questions emerge about the role of each of them on students? academic performance. This includes questions regarding the impact of the relationships between students, lecturer?s characteristics and the social environment on students? performance. To address these questions, this article reports a study of the impact of lecturer authentic leadership, psychological safety and network density on academic performance. It explores the relationship between network density, psychological safety and lecturer authentic leadership. A questionnaire was distributed to undergraduate students. A positive impact of lecturer authentic leadership and psychological safety on academic performance was found. Students from high-density groups tended to show better academic performance, higher psychological safety and tended to see their lecturers as being more authentic. A reflection on the role of the lecturer in higher education settings is presented. It also presents some recommendations on how student academic performance can be improved by the adoption of specific behaviours by their lecturer

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Percolation theory applied to measures of fragmentation in social networks

We apply percolation theory to a recently proposed measure of fragmentation $F$ for social networks. The measure $F$ is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removing a fraction $q$ of nodes and the total number of pairs in the original fully connected network. We compare $F$ with the traditional measure used in percolation theory, $P_{\infty}$, the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods from percolation, we study Erd\H{o}s-R\'{e}nyi (ER) and scale-free (SF) networks under various types of node removal strategies. The removal strategies are: random removal, high degree removal and high betweenness centrality removal. We find that for a network obtained after removal (all strategies) of a fraction $q$ of nodes above percolation threshold, $P_{\infty}\approx (1-F)^{1/2}$. For fixed $P_{\infty}$ and close to percolation threshold ($q=q_c$), we show that $1-F$ better reflects the actual fragmentation. Close to $q_c$, for a given $P_{\infty}$, $1-F$ has a broad distribution and it is thus possible to improve the fragmentation of the network. We also study and compare the fragmentation measure $F$ and the percolation measure $P_{\infty}$ for a real social network of workplaces linked by the households of the employees and find similar results.Comment: submitted to PR

Virtual Reality Social Prediction Improvement and Rehabilitation Intensive Training (VR-SPIRIT) for paediatric patients with congenital cerebellar diseases: study protocol of a randomised controlled trial

Background: Patients with cerebellar malformations exhibit not only movement problems, but also important deficits in social cognition. Thus, rehabilitation approaches should not only involve the recovery of motor function but also of higher-order abilities such as processing of social stimuli. In keeping with the general role of the cerebellum in anticipating and predicting events, we used a VR-based rehabilitation system to implement a social cognition intensive training specifically tailored to improve predictive abilities in social scenarios (VR-Spirit). Methods/design: The study is an interventional randomised controlled trial that aims to recruit 42 children, adolescents and young adults with congenital cerebellar malformations, randomly allocated to the experimental group or the active control group. The experimental group is administered the VR-Spirit, requiring the participants to compete with different avatars in the reaching of recreational equipment and implicitly prompting them to form expectations about their playing preference. The active control group participates in a VR-training with standard games currently adopted for motor rehabilitation. Both trainings are composed by eight 45-min sessions and are administered in the GRAIL VR laboratory (Motekforce Link, Netherlands), an integrated platform that allows patients to move in natural and attractive VR environments. An evaluation session in VR with the same paradigm used in the VR-Spirit but implemented in a different scenario is administered at the beginning (T0) of the two trainings (T1) and at the end (T2). Moreover, a battery of neurocognitive tests spanning different domains is administered to all participants at T0, T2 and in a follow-up session after 2 months from the end of the two trainings (T3). Discussion: This study offers a novel approach for rehabilitation based on specific neural mechanisms of the cerebellum. We aim to investigate the feasibility and efficacy of a new, intensive, social cognition training in a sample of Italian patients aged 7-25 years with congenital cerebellar malformations. We expect that VR-Spirit could enhance social prediction ability and indirectly improve cognitive performance in diverse domains. Moreover, through the comparison with a VR-active control training we aim to verify the specificity of VR-Spirit in improving social perception skills. Trial registration: ISRCTN, ID: ISRCTN 22332873. Retrospectively registered on 12 March 2018

A Laboratory for the Integration of Geomatic and Geomechanical Data: The Rock Pinnacle “Campanile di Val Montanaia”

This work describes a procedure for building a high-quality 3D model of a rocky pinnacle in the Dolomites, Italy, using Structure from Motion (SfM) techniques. The pinnacle, known as “Campanile di Val Montanaia”, is challenging to survey due to its high elevation and sub-vertical cliffs. The construction of the 3D model is the first step in a multi-disciplinary approach to characterize the rock mass and understand its behavior and evolution. This paper discusses the surveying operations, which involved climbing the pinnacle to collect Ground Control Points (GCPs) and using a UAV to capture aerial imagery. The photographs were processed using SfM software to generate point clouds, mesh, and texture, which were then used for rock mass discontinuity mapping. The study compares models of different qualities and point densities to determine the optimal trade-off between processing time and accuracy in terms of discontinuity mapping. The results show that higher quality models allow for more detailed mapping of discontinuities, with some drawbacks due to noise in the case of the densest solution (e.g., increase in frequency of outliers across the point cloud). These pros and cons are also discussed in relation to the computational cost necessary to build the models. The study also examines the limitations and challenges of performing discontinuity mapping in the different models, including subjectivity in interpretation. A further element of interest is the publication of a high-quality 3D georeferenced model of the “Campanile di Val Montanaia” to be used for several potential further applications, such as stability analyses and numerical modeling

Convergence and divergence in gesture repertoires as an adaptive mechanism for social bonding in primates

A key challenge for primates living in large, stable social groups is managing social relationships. Chimpanzee gestures may act as a time-efficient social bonding mechanism, and the presence (homogeneity) and absence (heterogeneity) of overlap in repertoires in particular may play an important role in social bonding. However, how homogeneity and heterogeneity in the gestural repertoire of primates relate to social interaction is poorly understood. We used social network analysis and generalized linear mixed modelling to examine this question in wild chimpanzees. The repertoire size of both homogeneous and heterogeneous visual, tactile and auditory gestures was associated with the duration of time spent in social bonding behaviour, centrality in the social bonding network and demography. The audience size of partners who displayed similar or different characteristics to the signaller (e.g. same or opposite age or sex category) also influenced the use of homogeneous and heterogeneous gestures. Homogeneous and heterogeneous gestures were differentially associated with the presence of emotional reactions in response to the gesture and the presence of a change in the recipient’s behaviour. Homogeneity and heterogeneity of gestural communication play a key role in maintaining a differentiated set of strong and weak social relationships in complex, multilevel societies