Interpolatory methods for H∞\mathcal{H}_\infty model reduction of multi-input/multi-output systems

We develop here a computationally effective approach for producing high-quality $\mathcal{H}_\infty$-approximations to large scale linear dynamical systems having multiple inputs and multiple outputs (MIMO). We extend an approach for $\mathcal{H}_\infty$ model reduction introduced by Flagg, Beattie, and Gugercin for the single-input/single-output (SISO) setting, which combined ideas originating in interpolatory $\mathcal{H}_2$-optimal model reduction with complex Chebyshev approximation. Retaining this framework, our approach to the MIMO problem has its principal computational cost dominated by (sparse) linear solves, and so it can remain an effective strategy in many large-scale settings. We are able to avoid computationally demanding $\mathcal{H}_\infty$ norm calculations that are normally required to monitor progress within each optimization cycle through the use of "data-driven" rational approximations that are built upon previously computed function samples. Numerical examples are included that illustrate our approach. We produce high fidelity reduced models having consistently better $\mathcal{H}_\infty$ performance than models produced via balanced truncation; these models often are as good as (and occasionally better than) models produced using optimal Hankel norm approximation as well. In all cases considered, the method described here produces reduced models at far lower cost than is possible with either balanced truncation or optimal Hankel norm approximation

Spiral strand cables subjected to high velocity fragment impact

Structural cables are widely adopted around the world in offshore construction, sports stadia, large scale bridges, Ferris wheels and suspended canopy and fabric structures. However, the robustness of such structures to blast or impact is uncertain with a particular concern related to the loss of a primary structural cable when damaged by high velocity blast fragmentation. This paper presents the first ever numerical and experimental study on commonly used high-strength steel spiral strand cables subjected to high velocity fragment impact. Spiral strand cables were impacted by 20 mm fragment simulating projectiles travelling at velocities between 200 and 1400 m/s. Complex 3D non-linear finite element models were developed and carefully compared with experimental tests. The penetration resistance of the cables and resultant damage were studied with respect to fragment impact velocity. It was found that for all the impact velocities, the fragment penetration depth was less than half of the cable diameter demonstrating a considerable amount of resilience. Considering the damage caused, the residual cable breaking strengths were estimated and found to be still higher than the minimum breaking load of an un-damaged cable. The numerical models were also able to reproduce the main features of the impact tests, including the extent of localised damage area, the fragment penetration depth and mode of individual wire failures, thus demonstrating their potential to be widely used in industry for structural resilience and robustness assessments by structural engineers

A multi-agent reinforcement learning model of common-pool resource appropriation

Humanity faces numerous problems of common-pool resource appropriation. This class of multi-agent social dilemma includes the problems of ensuring sustainable use of fresh water, common fisheries, grazing pastures, and irrigation systems. Abstract models of common-pool resource appropriation based on non-cooperative game theory predict that self-interested agents will generally fail to find socially positive equilibria---a phenomenon called the tragedy of the commons. However, in reality, human societies are sometimes able to discover and implement stable cooperative solutions. Decades of behavioral game theory research have sought to uncover aspects of human behavior that make this possible. Most of that work was based on laboratory experiments where participants only make a single choice: how much to appropriate. Recognizing the importance of spatial and temporal resource dynamics, a recent trend has been toward experiments in more complex real-time video game-like environments. However, standard methods of non-cooperative game theory can no longer be used to generate predictions for this case. Here we show that deep reinforcement learning can be used instead. To that end, we study the emergent behavior of groups of independently learning agents in a partially observed Markov game modeling common-pool resource appropriation. Our experiments highlight the importance of trial-and-error learning in common-pool resource appropriation and shed light on the relationship between exclusion, sustainability, and inequality

Excitation spectrum of a two-component Bose-Einstein condensate in a ring potential

A mixture of two distinguishable Bose-Einstein condensates confined in a ring potential has numerous interesting properties under rotational and solitary-wave excitation. The lowest-energy states for a fixed angular momentum coincide with a family of solitary-wave solutions. In the limit of weak interactions, exact diagonalization of the many-body Hamiltonian is possible and permits evaluation of the complete excitation spectrum of the system.Comment: 4 pages, 1 figur

REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficient. We propose REST, a new method that simultaneously tackles both issues via 1) adversarial training and controlling the Lipschitz constant of the neural network through spectral regularization while 2) enabling neural network compression through sparsity regularization. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. For the sleep staging task over single-channel electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67 vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large, real-world EEG datasets. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference. We open-source the code repository with this paper: https://github.com/duggalrahul/REST.Comment: Accepted to WWW 202

Technology Contribution to Improve Autistic Children Life Quality

To review published literature on the use of technology and how it has improved autistic children life style. A systematic review of the English literature was performed using the PRISMA guideline. Papers indexed in WOS and Scopus databases were included, adjusted to a timeline between 2016 and 2020 and focused on mobile technology, interventions, improvement of social behavior and communication and autism, aimed to describe the most used mechanism to improve autistic life style. Thirty two (32) papers were included in the review. We obtained 14 papers on the Scopus database and 18 on the WOS database. The majority of studies evidenced the use of virtual reality, mobile devices, video modelling and robots as the most common applications for autism therapies. Technology has caused an improvement in autistic children life quality. The development of mobile applications, virtual reality applications and robots have showed a positive impact reflected in the performance of daily activities and a better understanding of how they feel, how to behave, how to express themselves and interact with others. Technology gives the opportunity to monitor children status; and offers adaptability, safety, and accuracy of the information