A Class of Collisions of Plane Impulsive Light--Like Signals in General Relativity

We present a systematic study of collisions of homogeneous, plane--fronted, impulsive light--like signals which do not interact after head--on collision. For the head--on collision of two such signals, six real parameters are involved, three from each of the incoming signals. We find two necessary conditions to be satisfied by these six parameters for the signals to be non--interacting after collision. We then solve the collision problem in general when these necessary conditions hold. After collision the two signals focus each other at Weyl curvature singularities on each others signal front. Our family of solutions contains some known collision solutions as special cases.Comment: 14 pages, late

Histomorphological Aspects of SARS-CoV-2-Induced Hippocampal Damage: A Case Series

The direct healthcare side effects of the coronavirus disease identified in 2019 (COVID-19) caused by the severe acute respira-tory syndrome coronavirus 2 (SARS-CoV-2) are immeasurable. Furthermore, the chronic sequels of the disease are yet to be adequately studied and evaluated in the context of the post-infectious specter, referred to as a post-COVID syndrome. One of the most commonly reported such sequel is the so-called “brain fog” – loss of concentration, learning difficulties, and confu-sion. Herein, we analyzed a series of 50 autopsies of RT-PCR-proven COVID-19. Central nervous system (CNS) samples were obtained in 49 of the cases, with dentate gyrus samples acquired in 9 of them. Histopathological spectrums of hippocampal changes included vascular, degenerative, apoptotic, and necrotic on H&E stains with varying severity. The diffuse nature of the vascular changes, together with the epitheliotropic and especially the endotheliotropic nature of SARS-CoV-2, would suggest that the CNS damage is both hypoxic and vasculotropic. Infected endothelial cells bulge and desquamate (necrosis), disrupting the blood-brain barrier and secondary damage to the neurons by permeating plasma substances

Simulation and beyond – Principles of effective obstetric training

Simulation training provides a safe, non-judgmental environment where members of the multi-professional team can practice both their technical and non-technical skills. Poor teamwork and communication are recurring contributing factors to adverse maternal and neonatal outcomes. Simulation can improve outcomes and is now a compulsory part of the national training matrix. Components of successful training include involving the multi-professional team, high fidelity models, keeping training on-site, and focussing on human factors training; a key factor in adverse patient outcomes. The future of simulation training is an exciting field, with the advent of augmented reality devices and the use of artificial intelligence

Instabilities at vicinal crystal surfaces - competition between the electromigration of the adatoms and the kinetic memory effect

We studied the step dynamics during sublimation and growth in the presence of electromigration force acting on the adatoms. In the limit of fast surface diffusion and slow kinetics of atom attachment-detachment at the steps we formulate a model free of the quasi-static approximation in the calculation of the adatom concentration on the terraces. Numerical integration of the equations for the time evolution of the adatom concentrations and the equations of step motion reveals two different step bunching instabilities: 1) step density waves (small bunches which do not manifest any coarsening) induced by the kinetic memory effect and 2) step bunching with coarsening when the dynamics is dominated by the electromigration. The model developed in this paper also provides very instructive illustrations of the Popkov-Krug dynamical phase transition during sublimation and growth of a vicinal crystal surface.Comment: 15 pages, 6 figure

Gesture Recognition in Robotic Surgery: a Review

OBJECTIVE: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. METHODS: An article search was performed on 5 bibliographic databases with combinations of the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. RESULTS: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. CONCLUSION: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. SIGNIFICANCE: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field

Evaporation and growth of crystals - propagation of step density compression waves at vicinal surfaces

We studied the step dynamics during crystal sublimation and growth in the limit of fast surface diffusion and slow kinetics of atom attachment-detachment at the steps. For this limit we formulate a model free of the quasi-static approximation in the calculation of the adatom concentration on the terraces at the crystal surface. Such a model provides a relatively simple way to study the linear stability of a step train in a presence of step-step repulsion and an absence of destabilizing factors (as Schwoebel effect, surface electromigration etc.). The central result is that a critical velocity of the steps in the train exists which separates the stability and instability regimes. When the step velocity exceeds its critical value the plot of these trajectories manifests clear space and time periodicity (step density compression waves propagate on the vicinal surface). This ordered motion of the steps is preceded by a relatively short transition period of disordered step dynamics.Comment: 18 pages, 6 figure

MoBYv2AL: Self-supervised Active Learning for Image Classification

Active learning(AL) has recently gained popularity for deep learning(DL) models. This is due to efficient and informative sampling, especially when the learner requires large-scale labelled datasets. Commonly, the sampling and training happen in stages while more batches are added. One main bottleneck in this strategy is the narrow representation learned by the model that affects the overall AL selection. We present MoBYv2AL, a novel self-supervised active learning framework for image classification. Our contribution lies in lifting MoBY - one of the most successful self-supervised learning algorithms to the AL pipeline. Thus, we add the downstream task-aware objective function and optimize it jointly with contrastive loss. Further, we derive a data-distribution selection function from labelling the new examples. Finally, we test and study our pipeline robustness and performance for image classification tasks. We successfully achieved state-of-the-art results when compared to recent AL methods

3D Generative Model Latent Disentanglement via Local Eigenprojection

Designing realistic digital humans is extremely complex. Most data-driven generative models used to simplify the creation of their underlying geometric shape do not offer control over the generation of local shape attributes. In this paper, we overcome this limitation by introducing a novel loss function grounded in spectral geometry and applicable to different neural-network-based generative models of 3D head and body meshes. Encouraging the latent variables of mesh variational autoencoders (VAEs) or generative adversarial networks (GANs) to follow the local eigenprojections of identity attributes, we improve latent disentanglement and properly decouple the attribute creation. Experimental results show that our local eigenprojection disentangled (LED) models not only offer improved disentanglement with respect to the state-of-the-art, but also maintain good generation capabilities with training times comparable to the vanilla implementations of the models. Our code and pre-trained models are available at github.com/simofoti/LocalEigenprojDisentangled

Linking medical faculty stress/burnout to willingness to implement medical school curriculum change: a preliminary investigation

Rationale, aims and objectivesBalancing administrative demands from the medical school while providing patient support and seeking academic advancement can cause personal hardship that ranges from high stress to clinically recognizable conditions such as burnout. Regarding the importance of clinical faculties’ burnout and its effects on different aspects of their professional career, this study was conducted and aimed to evaluate the relationship between willingness to change teaching approaches as characterized by a modified stage‐of‐change model and measures of stress and burnout.MethodsThis descriptive analytic study was conducted on 143 clinical faculty members of Tehran University of Medical Sciences in Iran. Participants were asked to complete three questionnaires: a modified stages of change questionnaire the Maslach Burnout Inventory and the General Health Questionnaire. Data were analysed by SPSS: 16 using non‐parametric statistical tests such as multiple regression and ICC (intra‐class coefficient) and Spearman correlation coefficient test.ResultA significant relationship was found between faculty members’ readiness to change teaching approaches and the subscales of occupational burnout. Specifically, participants with low occupational burnout were more likely to be in the action stage, while those with high burnout were in the attitude or intention stage, which could be understood as not being ready to implement change. There was no significant correlation between general health scores and stage of change. ConclusionsWe found it feasible to measure stages of change as well as stress/burnout in academic doctors. Occupational burnout directly reduces the readiness to change. To have successful academic reform in medical schools, it therefore would be beneficial to assess and manage occupational burnout among clinical faculty members.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135679/1/jep12439.pd