The control room falls silent as multiple video streams from live bodycams fill its display monitors. The windowless building, its meter-thick walls baking in the late-afternoon heat, carries no signage, no identifying markers. As the men enter, the control room displays adjust from sunlight to show a near pitch-black corridor with doorways visible to the left. The audio is silent, save for the tread of rubber boots through pooled condensation and the hum of a generator somewhere inside. The four men had seen images of the hostage before they reached the building.

Scientists have created a strange chest-mounted robot that could feed the greediest of people when they're too full to move. The peculiar'Arm-A-Dine' robot arm attaches to the middle of someone's chest and takes food from their plate to their mouth. The robot, which is still just a prototype, is designed to augment the social experience of eating, researchers say. The robotic arm was created by Exertion Games Lab at RMIT University in Australia and the Indian Institute of Information Technology Design. 'Arm-A-Dine is our design exploration of a novel two-person playful eating system that focuses on a shared feeding experience', researchers told Spectrum.

Scientists have found a tiny new region of the brain that only humans have - and they believe it could be what makes our species unique. Researchers say the incredible find could help find a treatment for Parkinson's and motor neurone disease. For thirty years scientists - who have likened the discovery to finding a new star - suspected this region existed but were unable to see it. Scientists have found a new region of the brain that only humans have - and they believe it could be what makes our species unique. Professor George Paxinos from Neuroscience Research Australia (NeuRA) found the hidden region. He was able to find the region, called the Endorestiform Nucleus, thanks to better staining and imaging techniques.

Parallel tempering addresses some of the drawbacks of canonical Markov Chain Monte-Carlo methods for Bayesian neural learning with the ability to utilize high performance computing. However, certain challenges remain given the large range of network parameters and big data. Surrogate-assisted optimization considers the estimation of an objective function for models given computational inefficiency or difficulty to obtain clear results. We address the inefficiency of parallel tempering for large-scale problems by combining parallel computing features with surrogate assisted estimation of likelihood function that describes the plausibility of a model parameter value, given specific observed data. In this paper, we present surrogate-assisted parallel tempering for Bayesian neural learning where the surrogates are used to estimate the likelihood. The estimation via the surrogate becomes useful rather than evaluating computationally expensive models that feature large number of parameters and datasets. Our results demonstrate that the methodology significantly lowers the computational cost while maintaining quality in decision making using Bayesian neural learning. The method has applications for a Bayesian inversion and uncertainty quantification for a broad range of numerical models.

Eating food is an experience that tends to be about taste and texture and how the food looks and smells. Our focus goes from what's going on on the plate to what's going on in our mouths, without a lot of concern about what happens in between. Eating as a process doesn't get all that much attention; we tend to treat it as just a chore involving utensils. Which is fine, but are we missing out somehow? The Exertion Games Lab at RMIT University in Australia thinks that the answer to that is yes, and they're using chest-mounted social feeding robots to prove it.

Artificial Intelligence technologies and capabilities are driving digital transformation, growth, and opportunity in nearly every sector. In fact, a report by AlphaBeta urges Australia to double its pace of artificial intelligence and robotics automation to reap a $2.2 trillion market opportunity by 2030. So, why is it that AI can have such a massive impact, and should your organisation jump on the bandwagon? The short answer is that the window of competitive advantage will be small, and if you don't jump through it, one of your competitors will. There's much to be gained by using AI to improve business outcomes.

Eike Germann, Introduction to Reinforcement Learning What is Reinforcement Learning? How is it different from the machine learning we're familiar with? I'll present some foundational ideas (Markov decision processes, policy iteration, value iteration etc) and talk about their limitations. What algorithms are currently used to address those limitations and how do they do it? Based on these, I'll give a short overview of what RL is used currently used for - from training a machine to play space invaders to robotic movement in the real world.

Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is the lack of explainability. Extensive efforts have been made in the literature to incorporate explainability into LFMs. However, they either rely on auxiliary information which may not be available in practice, or fail to provide easy-to-understand explanations. In this paper, we propose a fast influence analysis method named FIA, which successfully enforces explicit neighbor-style explanations to LFMs with the technique of influence functions stemmed from robust statistics. We first describe how to employ influence functions to LFMs to deliver neighbor-style explanations. Then we develop a novel influence computation algorithm for matrix factorization with high efficiency. We further extend it to the more general neural collaborative filtering and introduce an approximation algorithm to accelerate influence analysis over neural network models. Experimental results on real datasets demonstrate the correctness, efficiency and usefulness of our proposed method.

A GPS-denied UAV (Agent B) is localised through INS alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several time instants. Agent B measures the signals' direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the Orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored.

Abstract--Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing. Most recent research in the field adopts supervised deep learning paradigms to automate extraction of intrinsic features from raw signal inputs and addresses HAR as a multi-class classification problem where detecting a single activity class within the duration of a sensory data segment suffices. However, due to the innate diversity of human activities and their corresponding duration, no data segment is guaranteed to contain sensor recordings of a single activity type. In this paper, we express HAR more naturally as a set prediction problem where the predictions are sets of ongoing activity elements with unfixed and unknown cardinality. For the first time, we address this problem by presenting a novel HAR approach that learns to output activity sets using deep neural networks. Moreover, motivated by the limited availability of annotated HAR datasets as well as the unfortunate immaturity of existing unsupervised systems, we complement our supervised set learning scheme with a prior unsupervised feature learning process that adopts convolutional auto-encoders to exploit unlabeled data.The empirical experiments on two widely adopted HAR datasets demonstrate the substantial improvement of our proposed methodology over the baseline models. I. INTRODUCTION With the proliferation of low-cost sensing technologies as well as the fast advancements in machine learning techniques, automatic human activity recognition (HAR) using wearable sensors has emerged as a key research area in ubiquitous computing [1]-[4].In this problem, high-level activity information is acquired through analyzing low-level sensor recordings with the goal of providing proactive assistance to users.