For example during flight, an aircraft's modal properties are known to change with both altitude and velocity. It is thus important to quantify these changes given only a truncated set of modal data, which is usually the case experimentally. This procedure is formally known as the generalised inverse eigenvalue problem. In this paper we experimentally show that first-order gradient-based methods that optimise objective functions defined over a modal are prohibitive due to the required small step sizes. This in turn leads to the justification of using a non-gradient, black box optimiser in the form of particle swarm optimisation. We further show how it is possible to solve such inverse eigenvalue problems in a lower dimensional space by the use of random projections, which in many cases reduces the total dimensionality of the optimisation problem by 80% to 99%. Two example problems are explored involving a ten-dimensional mass-stiffness toy problem, and a one-dimensional finite element mass-stiffness approximation for a Boeing 737-300 aircraft.

Abstract-- In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world. I. INTRODUCTION He number of daily-generated images by websites and personal archives are constantly growing. Indeed, the effective management of the rapid expansion of visual information has become a major problem and a necessity for strengthening visual search technique based on visual content [3]. This necessity is behind the emergence of new visual search techniques based on visual content. It has been widely identified that the most efficient and intuitive way to research visual information is based on the properties that are extracted from the images themselves. Researchers from different communities ("Computer Vision" [4], "Database Management", "Man-machine Interface", "Information Retrieval") were attracted by this field. Since then, the search for images by content has developed quite rapidly. The intuitive idea of "any system that analyzes or automatically organizes a set of data or knowledge must use, in one form or another, a similarity operator whose purpose is to establish similarities or the relationships that exist between the manipulated information".

We propose a new sampler that integrates the protocol of parallel tempering with the Nos\'e-Hoover (NH) dynamics. The proposed method can efficiently draw representative samples from complex posterior distributions with multiple isolated modes in the presence of noise arising from stochastic gradient. It potentially facilitates deep Bayesian learning on large datasets where complex multimodal posteriors and mini-batch gradient are encountered.

This is an Inside Science story. When a song plays on the radio, there are invisible forces at work that go beyond the creative scope of the writing, performing and producing of the song. One of those ineffable qualities is audio mastering, a process that smooths out the song and optimizes the listening experience on any device. Now, artificial intelligence algorithms are starting to work their way into this undertaking. "Mastering is a bit of a black art," explained Thomas Birtchnell, a researcher at the University of Wollongong in Australia.

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.

The practice of transforming raw data to a feature space so that inference can be performed in that space has been popular for many years. Recently, rapid progress in deep neural networks has given both researchers and practitioners enhanced methods that increase the richness of feature representations, be it from images, text or speech. In this work we show how a constructed latent space can be explored in a controlled manner and argue that this complements well founded inference methods. For constructing the latent space a Variational Autoencoder is used. We present a novel controller module that allows for smooth traversal in the latent space and construct an end-to-end trainable framework. We explore the applicability of our method for performing spatial transformations as well as kinematics for predicting future latent vectors of a video sequence.

Neural networks can achieve extraordinary results on a wide variety of tasks. However, when they attempt to sequentially learn a number of tasks, they tend to learn the new task while destructively forgetting previous tasks. One solution to this problem is pseudo-rehearsal, which involves learning the new task while rehearsing generated items representative of previous task/s. We demonstrate that pairing pseudo-rehearsal methods with a generative network is an effective solution to this problem in reinforcement learning. Our method iteratively learns three Atari 2600 games while retaining above human level performance on all three games, performing similar to a network which rehearses real examples from all previously learnt tasks.

Node embedding is the task of extracting informative and descriptive features over the nodes of a graph. The importance of node embeddings for graph analytics, as well as learning tasks such as node classification, link prediction and community detection, has led to increased interest on the problem leading to a number of recent advances. Much like PCA in the feature domain, node embedding is an inherently \emph{unsupervised} task; in lack of metadata used for validation, practical methods may require standardization and limiting the use of tunable hyperparameters. Finally, node embedding methods are faced with maintaining scalability in the face of large-scale real-world graphs of ever-increasing sizes. In the present work, we propose an adaptive node embedding framework that adjusts the embedding process to a given underlying graph, in a fully unsupervised manner. To achieve this, we adopt the notion of a tunable node similarity matrix that assigns weights on paths of different length. The design of the multilength similarities ensures that the resulting embeddings also inherit interpretable spectral properties. The proposed model is carefully studied, interpreted, and numerically evaluated using stochastic block models. Moreover, an algorithmic scheme is proposed for training the model parameters effieciently and in an unsupervised manner. We perform extensive node classification, link prediction, and clustering experiments on many real world graphs from various domains, and compare with state-of-the-art scalable and unsupervised node embedding alternatives. The proposed method enjoys superior performance in many cases, while also yielding interpretable information on the underlying structure of the graph.

Artificial intelligence (AI) might be technology's Holy Grail, but Australia's Human Rights Commissioner Edward Santow has warned about the need for responsible innovation and an understanding of the challenges new technology poses for basic human rights. "AI is enabling breakthroughs right now: Healthcare, robotics, and manufacturing; pretty soon we're told AI will bring us everything from the perfect dating algorithm to interstellar travel -- it's easy in other words to get carried away, yet we should remember AI is still in its infancy," Santow told the Human Rights & Technology conference in Sydney in July. Santow was launching the Human Rights and Technology Issues Paper, which was described as the beginning of a major project by the Human Rights Commission to protect the rights of Australians in a new era of technological change. The paper [PDF] poses questions centred on what protections are needed when AI is used in decisions that affect the basic rights of people. It asks also what is required from lawmakers, governments, researchers, developers, and tech companies big and small. Pointing to Microsoft's AI Twitter bot Tay, which in March 2016 showed the ugly side of humanity -- at least as present on social media -- Santow said it is a key example of how AI must be right before it's unleashed onto humans.

Artificial intelligence (AI) – the science of making computers mimic humans using logic, decision trees, deep learning, and machine learning – is fast approaching the market opportunity around preventive and predictive maintenance. According to a recent GlobalData survey, the top two business challenges in Australia are in improving operational efficiency and reducing costs. Many businesses, such as manufacturers, producers of natural resources, through to the agriculture and health sectors, need ongoing reliability of machines and their constituent parts to keep the lights on in the business. Unplanned outages, for example, can cost an oil and gas company, on average $50 million dollars annually. In the case of a windfarm, in the event of one single fail, an entire turbine needs to come down, a technical crew with a crane needs to be on site costing $100,000 or more for each time a part fails.