In this paper we present a system capable of collecting and annotating, human performed, robot understandable, everyday activities from virtual environments. The human movements are mapped in the simulated world using off-the-shelf virtual reality devices with full body, and eye tracking capabilities. All the interactions in the virtual world are physically simulated, thus movements and their effects are closely relatable to the real world. During the activity execution, a subsymbolic data logger is recording the environment and the human gaze on a per-frame basis, enabling offline scene reproduction and replays. Coupled with the physics engine, online monitors (symbolic data loggers) are parsing (using various grammars) and recording events, actions, and their effects in the simulated world.

Medical image translation (e.g. CT to MR) is a challenging task as it requires I) faithful translation of domain-invariant features (e.g. shape information of anatomical structures) and II) realistic synthesis of target-domain features (e.g. tissue appearance in MR). In this work, we propose Manifold Disentanglement Generative Adversarial Network (MDGAN), a novel image translation framework that explicitly models these two types of features. It employs a fully convolutional generator to model domain-invariant features, and it uses style codes to separately model target-domain features as a manifold. This design aims to explicitly disentangle domain-invariant features and domain-specific features while gaining individual control of both. The image translation process is formulated as a stylisation task, where the input is "stylised" (translated) into diverse target-domain images based on style codes sampled from the learnt manifold. We test MDGAN for multi-modal medical image translation, where we create two domain-specific manifold clusters on the manifold to translate segmentation maps into pseudo-CT and pseudo-MR images, respectively. We show that by traversing a path across the MR manifold cluster, the target output can be manipulated while still retaining the shape information from the input.

How should computer science and social science collaborate to build a common model? How should they proceed to gather data that is really useful to the modelling? How can they design a survey that is tailored to the target model? This paper aims to answer those crucial questions in the framework of a multidisciplinary research project. This research addresses the issue of co-constructing a model when several disciplines are involved, and is applied to modelling human behaviour immediately after an earthquake. The main contribution of the work is to propose a tool dedicated to multidisciplinary dialogue. It also proposes a reflexive analysis of the enriching intellectual process carried out by the different disciplines involved. Finally, from working with an anthropologist, a complementary view of the multidisciplinary process is given.

Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does not exploit the structural information of the unlabeled data. This leads to a sampling bias in the batch active learning setting, which selects several samples at once. In this work, we demonstrate that the amount of labeled training data can be reduced using active learning when it incorporates both uncertainty and diversity in the sequence labeling task. We examined the effects of our sequence-based approach by selecting weighted diverse in the gradient embedding approach across multiple tasks, datasets, models, and consistently outperform classic uncertainty-based sampling and diversity-based sampling.

AIMS. This study compared the performance of deep learning extensions of survival analysis models with traditional Cox proportional hazards (CPH) models for deriving cardiovascular disease (CVD) risk prediction equations in national health administrative datasets. METHODS. Using individual person linkage of multiple administrative datasets, we constructed a cohort of all New Zealand residents aged 30-74 years who interacted with publicly funded health services during 2012, and identified hospitalisations and deaths from CVD over five years of follow-up. After excluding people with prior CVD or heart failure, sex-specific deep learning and CPH models were developed to estimate the risk of fatal or non-fatal CVD events within five years. The proportion of explained time-to-event occurrence, calibration, and discrimination were compared between models across the whole study population and in specific risk groups. FINDINGS. First CVD events occurred in 61,927 of 2,164,872 people. Among diagnoses and procedures, the largest 'local' hazard ratios were associated by the deep learning models with tobacco use in women (2.04, 95%CI: 1.99-2.10) and with chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95%CI: 1.50-1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk predictors. The deep learning models significantly outperformed the CPH models on the basis of proportion of explained time-to-event occurrence (Royston and Sauerbrei's R-squared: 0.468 vs. 0.425 in women and 0.383 vs. 0.348 in men), calibration, and discrimination (all p<0.0001). INTERPRETATION. Deep learning extensions of survival analysis models can be applied to large health administrative databases to derive interpretable CVD risk prediction equations that are more accurate than traditional CPH models.

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution.

In my first article on Time Series, I hope to introduce the basic ideas and definitions required to understand basic Time Series analysis. We will start with the essential and key mathematical definitions, which are required to implement more advanced models. The information will be introduced in a similar manner as it was in a McGill graduate course on the subject, and following the style of the textbook by Brockwell and Davis. A'Time Series' is a collection of observations indexed by time. The observations each occur at some time t, where t belongs to the set of allowed times, T. Note: T can be discrete in which case we have a discrete time series, or it could be continuous in the case of continuous time series.

Email security designed around machine-learning makes it possible to automatically prevent data breaches. Learn more about how it works.

When it comes to the construction industry machine learning means many things. However, at its core, it all comes back to one thing: data. The more data that is produced through telematics, the more advanced artificial intelligence (AI) becomes, due to it having more data to learn from. The more complex the data the better for AI, and as AI becomes more advanced its decision-making improves. This means that construction is becoming more efficient thanks to a loop where data and AI are feeding into each other.

COVID-19 pandemic has affected the entire world. Many people lost their jobs, kids stay at home, and the economic crisis is disastrous. The question of "how will the world be after COVID-19" is of high interest. Many futurists predict a different world, where we should rethink public spaces and believes that the memory of the COVID-19 lockdown will remain for a long time (Del Bello, 2020). This information presents a sad situation, where the COVID-19 continues to spreads with tragic death cases.