Fatal diseases, as Critical Health Episodes (CHEs), represent real dangers for patients hospitalized in Intensive Care Units. These episodes can lead to irreversible organ damage and death. Nevertheless, diagnosing them in time would greatly reduce their inconvenience. This study therefore focused on building a highly effective early warning system for CHEs such as Acute Hypotensive Episodes and Tachycardia Episodes. To facilitate the precocity of the prediction, a gap of one hour was considered between the observation periods (Observation Windows) and the periods during which a critical event can occur (Target Windows). The MIMIC II dataset was used to evaluate the performance of the proposed system. This system first includes extracting additional features using three different modes. Then, the feature selection process allowing the selection of the most relevant features was performed using the Mutual Information Gain feature importance. Finally, the high-performance predictive model LightGBM was used to perform episode classification. This approach called MIG-LightGBM was evaluated using five different metrics: Event Recall (ER), Reduced Precision (RP), average Anticipation Time (aveAT), average False Alarms (aveFA), and Event F1-score (EF1-score). A method is therefore considered highly efficient for the early prediction of CHEs if it exhibits not only a large aveAT but also a large EF1-score and a low aveFA. Compared to systems using Extreme Gradient Boosting, Support Vector Classification or Naive Bayes as a predictive model, the proposed system was found to be highly dominant. It also confirmed its superiority over the Layered Learning approach.

The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unseen columns or tables when generating SQLs. In this work, we propose a novel framework to elicit relational structures from large-scale pre-trained language models (PLMs) via a probing procedure based on Poincar\'e distance metric, and use the induced relations to augment current graph-based parsers for better schema linking. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences, even when surface forms of mentions and entities differ. Moreover, our probing procedure is entirely unsupervised and requires no additional parameters. Extensive experiments show that our framework sets new state-of-the-art performance on three benchmarks. We empirically verify that our probing procedure can indeed find desired relational structures through qualitative analysis. Our code can be found at https://github.com/AlibabaResearch/DAMO-ConvAI.

There is strong interest in the generation of synthetic video imagery of people talking for various purposes, including entertainment, communication, training, and advertisement. With the development of deep fake generation models, synthetic video imagery will soon be visually indistinguishable to the naked eye from a naturally capture video. In addition, many methods are continuing to improve to avoid more careful, forensic visual analysis. Some deep fake videos are produced through the use of facial puppetry, which directly controls the head and face of the synthetic image through the movements of the actor, allow the actor to 'puppet' the image of another. In this paper, we address the question of whether one person's movements can be distinguished from the original speaker by controlling the visual appearance of the speaker but transferring the behavior signals from another source. We conduct a study by comparing synthetic imagery that: 1) originates from a different person speaking a different utterance, 2) originates from the same person speaking a different utterance, and 3) originates from a different person speaking the same utterance. Our study shows that synthetic videos in all three cases are seen as less real and less engaging than the original source video. Our results indicate that there could be a behavioral signature that is detectable from a person's movements that is separate from their visual appearance, and that this behavioral signature could be used to distinguish a deep fake from a properly captured video.

Releasing a new artificial intelligence system that learns from people on the internet can be a risky proposition. Just ask Microsoft's "teen" chatbot Tay--except you can't, because Tay was taken down when it started reproducing sexist and racist remarks shortly after it launched in 2016. Meta apparently believes that AI can learn to do better. The company just announced BlenderBot 3, a much more advanced chatbot designed to learn through conversation without getting into the kind of trouble that derailed Tay. And the company has made it available for anyone to try out.

Bonobos are our closest relatives in the animal kingdom, sharing about 98.7 per cent of our DNA - and it seem they have picked up a few human-like characteristics along the way. A new study has revealed that the apes produce high-pitched'baby-like' cries when they are attacked, in order to attract comfort from others. These displays of distress are strategic, increasing their chances of receiving consolation from bonobo bystanders, scientists claim. They resemble those typically used by infants - such as pouting, whimpering and showing tantrums. The study by psychologists at Durham University reveals that adult bonobos are also less likely to be re-attacked by their former opponent when they display these'baby-like' signals following a conflict.

There are thousands of species on Earth that we still don't know much about -- but we now know that they are already teetering on the edge of extinction. A new study used machine learning to figure out just how threatened these lesser-known species are, and the results were grim. Some species of animals and plants are labeled "data deficient" because conservationists haven't been able to gather enough information about them to understand how they live or how many of them are left. It turns out that those "data deficient" species are unfortunately even more threatened than other species that are more well known (to scientists, at least). The data from this study came from the International Union for Conservation of Nature (IUCN), which maintains a global "Red List" that ranks species based on how threatened they are.

ATLANTA, GA - AUGUST 1: Michael Johnson of the US (L) poses for the press next to the clock after ... [ ] the men's Olympic 400m race at the Olympic Stadium in Atlanta, Georgia, 01 Aug. Johnson hurtled into history books in world record time as he completed an unprecedented Olympic double with the 400m and the 200m. Johnson clocked 19.32 sec to destroy Frankie Fredericks of Namibia (19.68) and Ato Boldon of Trinidad (19.80). Not just in terms of gadget miniaturization, medical nanotechnology, increasingly sophisticated industrial electromechanical units and the process of so-called shrinkflation that leads our candy bars be thinner or shorter at the same price, but also data - data is getting smaller too. Data is getting smaller in two key senses: a) we are breaking down the component parts of application data flows into smaller containerized elements to work inside similarly compartmentalized and containerized application services – and b) the time windows within which business needs to react to data events is reducing. This latter time constraint on data of course leads us to the reality of real-time data and the need to be able to work with it.

Sharjah Taxi, a subsidiary of Sharjah Asset Management Company, investment arm of the government of Sharjah, has launched the first-of-its-kind smart taxi in the Middle East, with the chief goal being to employ artificial intelligence services to be utilised in vehicle operations and safety. The smart vehicles have been fitted with sensors, cameras, a mobile data unit and other devices connected to an'integrated system for control mechanisms'. Intelligent transport systems use contemporary technology in the areas of surveillance, data collection, control and means of communication. The arrangement helps regulate the flow of traffic, simplify access to key places, oversee driver behaviour, lessen wrong practices, and reduce metre manipulations. As a result, the number of trips and total squandered kilometres are reduced, ultimately decreasing the rate of road accidents and pollution, whilst raising operational competence.

We develop a Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e.. multidimensional tensor) structure. As a motivating example we consider molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model. We use a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that our model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multi-way structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for our motivating application. Software in the form of R code is available at https://github.com/BiostatsKim/BayesMSMW .

In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts.