Men with long facial features and large eyes, and women with slim faces and small eyes are percieved as more promiscuous, a new study has revealed. However, this perception only rings true for men, and not for women, according to the researchers. In the study, experts in Australia asked heterosexual men and women about their levels of'sociosexuality' – the willingness to engage in sexual activity outside of a committed relationship, also known as casual sex. The participants also had their photos taken and shown to other participants of the opposite sex, so they could judge, based on looks alone, if they had an interest in sociosexuality. Men who were open to casual sex typically had longer faces, higher foreheads, longer noses and larger eyes, the team found.

These algorithms allow computers do things like automatically identify and harness useful data to help decision making, find hidden insights without being explicitly programmed where to look, and predict outcomes to help authorities design effective policies. You'll graduate with skills at the forefront of this massive growth area, as society looks for automated solutions to enhance business and our lives through the use of computing systems and data. These skills can be applied in government departments, consultancy or private sector organisations.

France has fined Google €500 million ($590 million) in the latest antitrust ruling against the company. Authorities say Google did not reach a fair agreement with publishers to use snippets of their content on Google News, despite a 2020 order for the company to do so. Google and French newspaper group Alliance de la presse d'information générale agreed on a payment framework for news previews in January, and it has been in discussions with Agence France-Presse and magazine publishers. However, regulators said Google's payment offers were "negligible," as Bloomberg reports. Isabelle de Silva, head of competition regulator Autorité de la concurrence, said Google offered to pay less for news than it does for weather data or dictionary definitions.

Vertigo is a common but under-treated medical condition that affects up to 40% of people at some point in their lives. Currently, the diagnosis and treatment of vertigo-causing conditions is done primarily by specialists who represent only 1% of the doctors in Australia, but AI could change this. Dr Allison Young has recently received a junior fellowship from The Garnett Passe and Rodney Williams Memorial Foundation to address this. Her project, in collaboration with clinicians, data scientists and statisticians, will use machine learning and AI techniques to develop a "virtual expert" diagnostic tool to assist the diagnosis of vertigo-causing conditions in the hospital emergency room, general practice, and in outpatient clinics.

Regression and interpolation with Gaussian processes, or kriging, is a popular statistical tool for non-parametric function estimation, originating from geostatistics and time series analysis, and later adopted in many other areas such as machine learning and the design and analysis of computer experiments (see, e.g., Stein, 1999; Santner et al., 2003; Rasmussen and Williams, 2006, and references therein). It is widely used for constructing fast approximations of time-consuming computer models, with applications to calibration and validation (Kennedy and O'Hagan, 2001; Bayarri et al., 2007), engineering design (Jones et al., 1998; Forrester et al., 2008), Bayesian inference (Calderhead et al., 2009; Wilkinson, 2014), and the optimization of machine learning algorithms (Bergstra et al., 2011)--to name but a few. A Gaussian process (GP) prior is characterized by its mean and covariance functions. They are usually chosen within parametric families (for instance, constant or linear mean functions, and Matérn covariance functions), which transfers the problem of choosing the mean and covariance functions to that of selecting parameters. The selection is most often carried out by optimization of a criterion that measures the goodness of fit of the predictive distributions, and a variety of such criteria--the likelihood function, the leave-one-out (LOO) squared-predictionerror criterion (hereafter denoted by LOO-SPE), and others--is available from the literature.

Background: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among a wide variety of decision-making models for determining the degree of disease deterioration at the bedside. AutoScore was proposed as a useful clinical score generator based on machine learning and a generalized linear model. Its current framework, however, still leaves room for improvement when addressing unbalanced data of rare events. Methods: Using machine intelligence approaches, we developed AutoScore-Imbalance, which comprises three components: training dataset optimization, sample weight optimization, and adjusted AutoScore. All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i.e., mean value of sensitivity and specificity). By utilizing a publicly accessible dataset from Beth Israel Deaconess Medical Center, we assessed the proposed model and baseline approaches in the prediction of inpatient mortality. Results: AutoScore-Imbalance outperformed baselines in terms of AUC and balanced accuracy. The nine-variable AutoScore-Imbalance sub-model achieved the highest AUC of 0.786 (0.732-0.839) while the eleven-variable original AutoScore obtained an AUC of 0.723 (0.663-0.783), and the logistic regression with 21 variables obtained an AUC of 0.743 (0.685-0.800). The AutoScore-Imbalance sub-model (using down-sampling algorithm) yielded an AUC of 0. 0.771 (0.718-0.823) with only five variables, demonstrating a good balance between performance and variable sparsity. Conclusions: The AutoScore-Imbalance tool has the potential to be applied to highly unbalanced datasets to gain further insight into rare medical events and to facilitate real-world clinical decision-making.

The ability to convey relevant and diverse information is critical in multi-document summarization and yet remains elusive for neural seq-to-seq models whose outputs are often redundant and fail to correctly cover important details. In this work, we propose an attention mechanism which encourages greater focus on relevance and diversity. Attention weights are computed based on (proportional) probabilities given by Determinantal Point Processes (DPPs) defined on the set of content units to be summarized. DPPs have been successfully used in extractive summarisation, here we use them to select relevant and diverse content for neural abstractive summarisation. We integrate DPP-based attention with various seq-to-seq architectures ranging from CNNs to LSTMs, and Transformers. Experimental evaluation shows that our attention mechanism consistently improves summarization and delivers performance comparable with the state-of-the-art on the MultiNews dataset.

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a recently developed method of adaptive ML for time-varying systems. Our approach is to map very high (N>100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N~2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly high parameter systems and to track their evolution in real time based on feedback without massive new data sets for re-training.

The divide-and-conquer method has been widely used for estimating large-scale kernel ridge regression estimates. Unfortunately, when the response variable is highly skewed, the divide-and-conquer kernel ridge regression (dacKRR) may overlook the underrepresented region and result in unacceptable results. We develop a novel response-adaptive partition strategy to overcome the limitation. In particular, we propose to allocate the replicates of some carefully identified informative observations to multiple nodes (local processors). The idea is analogous to the popular oversampling technique. Although such a technique has been widely used for addressing discrete label skewness, extending it to the dacKRR setting is nontrivial. We provide both theoretical and practical guidance on how to effectively over-sample the observations under the dacKRR setting. Furthermore, we show the proposed estimate has a smaller asymptotic mean squared error (AMSE) than that of the classical dacKRR estimate under mild conditions. Our theoretical findings are supported by both simulated and real-data analyses.

In this paper, we consider existential rules, an expressive formalism well suited to the representation of ontological knowledge and data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.