Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes

A 3D assessment and feedback tool for Ankylosing Spondylitis from the perspective of healthcare professionals

To investigate the utility of 3D visualization technology to augment assessment and feedback for Ankylosing Spondylitis (AS), a visualization prototype was developed, and both subjective and objective measures of current assessment instruments were compared. To verify and establish a base-line for the prototype’s effectiveness, motion data and measurement data from a healthy adult in a laboratory environment were collected. To validate the prototype, a qualitative evaluation was undertaken using multiple methods including a pilot study, focus groups, and individual interviews. Research subjects comprised physiotherapists in clinical practice and academia and content analysis of their responses was used to substantiate the findings. The prototype enhanced both assessment and feedback of AS from the physiotherapist’s perspective and they believed it to be superior to the current methods used in practice for assessing the condition and in documenting variations for subsequent treatment. The physiotherapists believed that such a system had potential to encourage multidisciplinary working, and to be patient-centric, both with respect to the process of treatment and with regard to the convenience it offered to patients in managing their own condition. 3D visualization of AS symptoms and its treatment via exercise is a valuable technique as demonstrated by the prototype system

Improved detection of small atom numbers through image processing

We demonstrate improved detection of small trapped atomic ensembles through advanced post-processing and optimal analysis of absorption images. A fringe removal algorithm reduces imaging noise to the fundamental photon-shot-noise level and proves beneficial even in the absence of fringes. A maximum-likelihood estimator is then derived for optimal atom-number estimation and is applied to real experimental data to measure the population differences and intrinsic atom shot-noise between spatially separated ensembles each comprising between 10 and 2000 atoms. The combined techniques improve our signal-to-noise by a factor of 3, to a minimum resolvable population difference of 17 atoms, close to our ultimate detection limit.Comment: 4 pages, 3 figure

CAutoCSD-evolutionary search and optimisation enabled computer automated control system design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of 'Computer-Aided Control System Design' (CACSD) to the novel 'Computer-Automated Control System Design' (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency-domains. Such performance-prioritised unification is aimed to relieve practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-committing to the adopted scheme. With the recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytically and practically, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, meets multiple objectives in designing an LTI controller for a non-minimum phase plant and offers a high-performing LTI controller network for a nonlinear chemical process

Evaluating the Task Generalization of Temporal Convolutional Networks for Surgical Gesture and Motion Recognition using Kinematic Data

Fine-grained activity recognition enables explainable analysis of procedures for skill assessment, autonomy, and error detection in robot-assisted surgery. However, existing recognition models suffer from the limited availability of annotated datasets with both kinematic and video data and an inability to generalize to unseen subjects and tasks. Kinematic data from the surgical robot is particularly critical for safety monitoring and autonomy, as it is unaffected by common camera issues such as occlusions and lens contamination. We leverage an aggregated dataset of six dry-lab surgical tasks from a total of 28 subjects to train activity recognition models at the gesture and motion primitive (MP) levels and for separate robotic arms using only kinematic data. The models are evaluated using the LOUO (Leave-One-User-Out) and our proposed LOTO (Leave-One-Task-Out) cross validation methods to assess their ability to generalize to unseen users and tasks respectively. Gesture recognition models achieve higher accuracies and edit scores than MP recognition models. But, using MPs enables the training of models that can generalize better to unseen tasks. Also, higher MP recognition accuracy can be achieved by training separate models for the left and right robot arms. For task-generalization, MP recognition models perform best if trained on similar tasks and/or tasks from the same dataset.Comment: 8 pages, 4 figures, 6 tables. To be published in IEEE Robotics and Automation Letters (RA-L

COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling

Purpose: We propose a formal framework for the modeling and segmentation of minimally-invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objective labeling and the aggregation of different datasets. Methods: We model dry-lab surgical tasks as finite state machines, representing how the execution of MPs as the basic surgical actions results in the change of surgical context, which characterizes the physical interactions among tools and objects in the surgical environment. We develop methods for labeling surgical context based on video data and for automatic translation of context to MP labels. We then use our framework to create the COntext and Motion Primitive Aggregate Surgical Set (COMPASS), including six dry-lab surgical tasks from three publicly-available datasets (JIGSAWS, DESK, and ROSMA), with kinematic and video data and context and MP labels. Results: Our context labeling method achieves near-perfect agreement between consensus labels from crowd-sourcing and expert surgeons. Segmentation of tasks to MPs results in the creation of the COMPASS dataset that nearly triples the amount of data for modeling and analysis and enables the generation of separate transcripts for the left and right tools. Conclusion: The proposed framework results in high quality labeling of surgical data based on context and fine-grained MPs. Modeling surgical tasks with MPs enables the aggregation of different datasets and the separate analysis of left and right hands for bimanual coordination assessment. Our formal framework and aggregate dataset can support the development of explainable and multi-granularity models for improved surgical process analysis, skill assessment, error detection, and autonomy.Comment: 22 pages, 6 figures, 12 table

Beam Splitter for Spin Waves in Quantum Spin Network

We theoretically design and analytically study a controllable beam splitter for the spin wave propagating in a star-shaped (e.g., a $Y$-shaped beam) spin network. Such a solid state beam splitter can display quantum interference and quantum entanglement by the well-aimed controls of interaction on nodes. It will enable an elementary interferometric device for scalable quantum information processing based on the solid system.Comment: 5 pages, 4 figures, derivation of formulae change

Analysis of the Degradation of Dye in the Silk Dyed with Natural Dye-Mordant Combination

The purpose of this research was to investigate the effect of different mordants on the degradation of dye in the silk dyed with five standard dyes, alizarin, purpurin, berberine, palmatine, and indigotin after treating the dyed silk with H2O2/UV treatment

Enabling internal electronic circuitry within additively manufactured metal structures - The effect and importance of inter-laminar topography

Purpose: This paper aims to explore the potential of ultrasonic additive manufacturing (UAM) to incorporate the direct printing of electrical materials and arrangements (conductors and insulators) at the interlaminar interface of parts during manufacture to allow the integration of functional and optimal electrical circuitries inside dense metallic objects without detrimental effect on the overall mechanical integrity. This holds promise to release transformative device functionality and applications of smart metallic devices and products. Design/methodology/approach: To ensure the proper electrical insulation between the printed conductors and metal matrices, an insulation layer with sufficient thickness is required to accommodate the rough interlaminar surface which is inherent to the UAM process. This in turn increases the total thickness of printed circuitries and thereby adversely affects the integrity of the UAM part. A specific solution is proposed to optimise the rough interlaminar surface through deforming the UAM substrates via sonotrode rolling or UAM processing. Findings: The surface roughness (Sa) could be reduced from 4.5 to 4.1 µm by sonotrode rolling and from 4.5 to 0.8 µm by ultrasonic deformation. Peel testing demonstrated that sonotrode-rolled substrates could maintain their mechanical strength, while the performance of UAM-deformed substrates degraded under same welding conditions ( approximately 12 per cent reduction compared with undeformed substrates). This was attributed to the work hardening of deformation process which was identified via dual-beam focussed ion beam–scanning electron microscope investigation. Originality/value: The sonotrode rolling was identified as a viable methodology in allowing printed electrical circuitries in UAM. It enabled a decrease in the thickness of printed electrical circuitries by ca. 25 per cent