922017 research outputs found
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THE APPLICATION OF THE ARBITRARY-ORDER GALERKIN REDUCTION METHOD TO THE DYNAMIC ANALYSIS OF A ROTOR WITH A PRELOADED SINGLE-PAD FOIL-AIR BEARING
No full textEffect of Clustering in Federated Learning on Non-IID Electricity Consumption Prediction
No full textWhen applied to short-term energy consumption forecasting, the federated learning framework allows for the creation of a predictive model without sharing raw data. There is a limit to the accuracy achieved by standard federated learning due to the heterogeneity of the individual clients’ data, especially in the case of electricity data, where prediction of peak demand is a challenge. A set of clustering techniques has been explored in the literature to improve prediction quality while maintaining user privacy. These studies have mainly been conducted using sets of clients with similar attributes that may not reflect realworld consumer diversity. This paper explores, implements and compares these clustering techniques for privacy-preserving load forecasting on a representative electricity consumption dataset. The experimental results demonstrate the effects of electricity consumption heterogeneity on federated forecasting and a nonrepresentative sample’s impact on load forecasting
Speech-Driven Robot Face Action Generation with Deep Generative Model for Social Robots
No full textThe natural co-speech facial action as a kind of non-verbal behavior plays an essential role in human communication, which also leads to a natural and friendly human-robot interaction. However, a lot of previous works for robot speech-based behaviour generation are rule-based or handcrafted methods, which are time-consuming and with limited synchronization levels between the speech and the facial action. Based on the Generative Adversarial Networks (GAN) model, this paper developed an effective speech-driven facial action synthesizer, i.e., given an acoustic speech, a synchronous and realistic 3D facial action sequence is generated. In addition, a mapping between the 3D human facial action to the real robot facial action that regulates Zeno robot facial expressions is also completed. The evaluation results show the model has potential for natural human-robot interaction
A Petrological and Spectral Characterisation of the NU-LHT-2M Lunar Highlands Regolith Simulant in preparation for the PROSPECT payload ground testing campaign.
No full textIn preparation for the upcoming missions to the south polar region of the Moon, the Package for Resource Observation and in-Situ Prospecting for Exploration Commercial exploitation and Transportation (PROSPECT) underwent a series of tests to ensure its suitability for polar regolith and volatile analysis. A lunar regolith simulant, NU-LHT-2M, was used for geotechnical validation and volatile extraction testing. Therefore, the physical, chemical/mineralogical, and spectral properties of separate batches of this simulant have been characterised to better understand the results of the instrument laboratory testing phase. Here we compare measurements from two different batches of the simulant to Apollo bulk regolith samples in order to understand the suitability and representativeness of the simulant to the properties of surface highlands regolith. Based on our measurements, we recommend that the physical, mineralogical, and spectral properties of simulants be analysed both before and after space instrument testing campaigns. These bookended measurements would allow for a more detailed understanding of the test phase, including how the simulants have been altered by the test and, therefore, how the lunar surface may be affected by mission extraction and sampling processes
Antiracism CoaliTION in Pain Research (ACTION-PR): Guiding Principles for Equity in Reporting
No full textMulti-objective QUBO Solver: Bi-objective Quadratic Assignment Problem
No full textQuantum and quantum-inspired optimisation algorithms are designed to solve problems represented in binary, quadratic and unconstrained form. Combinatorial optimisation problems are therefore often formulated as Quadratic Unconstrained Binary Optimisation Problems (QUBO) to solve them with these algorithms. Moreover, these QUBO solvers are often implemented using specialised hardware to achieve enormous speedups, e.g. Fujitsu’s Digital Annealer (DA) and D-Wave’s Quantum Annealer. However, these are single-objective solvers, while many real-world problems feature multiple conflicting objectives. Thus, a common practice when using these QUBO solvers is to scalarise such multi-objective problems into a sequence of single-objective problems. Due to design trade-offs of these solvers, formulating each scalarisation may require more time than finding a local optimum.We present the first attempt to extend the algorithm supporting a commercial QUBO solver as a multi-objective solver that is not based on scalarisation. The proposed multi-objective DA algorithm is validated on the bi-objective Quadratic Assignment Problem. We observe that algorithm performance significantly depends on the archiving strategy adopted, and that combining DA with non-scalarisation methods to optimise multiple objectives outperforms the current scalarised version of the DA in terms of final solution quality
On Local Input-Output Stability of Nonlinear Feedback Systems via Local Graph Separation
No full textA new type of local input-output stability for nonlinear systems is defined, called M-local boundedness, which can be viewed as a local version of established definitions of global boundedness. This definition states that the system is bounded if the input Lebesgue signal has a norm smaller than M. Using graph separation concepts and a novel topological argument, which partitions the output space of the system into feasible and infeasible regions based on the restriction of the system input, sufficient conditions for M-local boundedness of a nonlinear feedback system are derived. Using this theorem, a new local nonlinear small gain condition is found for a closed-loop system with additive inputs. This small gain condition is then used in a numerical example, in which a differential equation with a quadratic element was partitioned into a feedback system and bounds on the norm of the input were found which ensured the system was M-locally stable
Race and Histories of Place: the racialisation of representational space in Govanhill and Butetown
No full textWe argue that the stories told about the histories and nature of places, are vehicles for narrating race. Drawing on interviews with professionals and community workers in Butetown in Cardiff and Govanhill in Glasgow, we explore how they negotiated – and contested - racialized histories of place, constructing different versions or claims to belong. Drawing on Henri Lefebvre’s spatial concepts we explore this conceptualisation through examination of the two areas which have distinct histories, and present experiences, of migration and racialization. In discussion of the accounts from the two distinct areas we show that narratives of the past have a political resonance which shape accounts of current experiences of migration. Accounts of place are often related in relationship to comparisons with and narratives of other places and to global processes of trade and migration. Whilst these racialised narratives are contested, they also shape responses to social problems faced by communities
Statistical Disclosure Control and Developments in Formal Privacy: In Memoriam to Chris Skinner
No full textI provide an overview of the evolution of Statistical Disclosure Control (SDC) research over the last decades and how it has evolved to handle the data revolution with more formal definitions of privacy. I emphasize the many contributions by Chris Skinner in the research areas of SDC. I review his seminal research, starting in the 1990’s with his work on the release of UK Census sample microdata. This led to a wide-range of research on measuring the risk of re-identification in survey microdata through probabilistic models. I also focus on other aspects of Chris’ research in SDC. Chris was the recipient of the 2019 Waksberg Award and sadly never got a chance to present his Waksberg Lecture at the Statistics Canada International Methodology Symposium. This paper follows the outline that Chris had prepared in preparation for that lecture
Stein's Method Meets Computational Statistics: A Review of Some Recent Developments
No full textStein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein's method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments and, in doing so, to stimulate further research into the successful field of Stein's method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-t testing.<br/