research-article Share on Tributes to Julian F. Miller (1955 - 2022) Authors: Wolfgang Banzhaf Michigan State University Michigan State UniversitySearch about this author , James A. Foster University of Idaho, Moscow, Idaho University of Idaho, Moscow, IdahoSearch about this author , Simon Harding Machine Intelligence Ltd Machine Intelligence LtdSearch about this author , Roman Kalkreuth TU Dortmund University, Dortmund, Germany TU Dortmund University, Dortmund, GermanySearch about this author , Gul Muhammad Khan University of Engineering and Technology, Peshawar, Pakistan University of Engineering and Technology, Peshawar, PakistanSearch about this author , W. B. Langdon University College London, London, UK University College London, London, UKSearch about this author , Riccardo Poli University of Essex, Colchester, UK University of Essex, Colchester, UKSearch about this author , Stephen Smith University of York, York, UK University of York, York, UKSearch about this author , Susan Stepney University of York, York, UK University of York, York, UKSearch about this author , Martin Trefzer University of York, York, UK University of York, York, UKSearch about this author , Dennis G. Wilson University of Toulouse; Toulouse Mind & Brain Institute, Toulouse, France University of Toulouse; Toulouse Mind & Brain Institute, Toulouse, FranceSearch about this author Authors Info & Claims ACM SIGEVOlutionVolume 15Issue 1April 2022 Article No.: 1pp 1–7https://doi.org/10.1145/3532942.3532943Published:20 April 2022Publication History 0citation26DownloadsMetricsTotal Citations0Total Downloads26Last 12 Months26Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Brillouin spectroscopy, based on the inelastic scattering of light from thermally driven acoustic waves or phonons [1], holds great promise in the field of life sciences as it provides functionally relevant micromechanical information in a contactless all-optical manner [2]. Due to the complexity of biological systems such as cells and tissues, which present spatio-temporal heterogeneities, interpretation of Brillouin spectra can be difficult. The data presented here were collected from gelatin hydrogels, used as tissue-mimicking model systems for Brillouin microspectroscopy measurements conducted using a lab-built Brillouin microscope with a dual-stage VIPA spectrometer. By varying the solute concentration in the range 4–18% (w/w), the macroscopic mechanical properties of the hydrogels can be tuned and the corresponding evolution in the Brillouin-derived longitudinal elastic modulus measured. An increase in Brillouin frequency shift with increasing solute concentration was observed, which was found to correlate with an increase in acoustic wave velocity and longitudinal modulus. The gels used here provide a viable model system for benchmarking and standardisation, and the data will be useful for spectrometer development and validation.

Brillouin spectroscopy is an emerging analytical tool in biomedical and biophysical sciences. It probes viscoelasticity through the propagation of thermally induced acoustic waves at gigahertz frequencies. Brillouin light scattering (BLS) measurements have traditionally been performed using multipass Fabry-Pérot interferometers, which have high contrast and resolution, however, as they are scanning spectrometers they often require long acquisition times in poorly scattering media. In the last decade, a new concept of Brillouin spectrometer has emerged, making use of highly angle-dispersive virtually imaged phase array (VIPA) etalons, which enable fast acquisition times for minimally turbid materials, when high contrast is not imperative. The ability to acquire Brillouin spectra rapidly, together with long term system stability, make this system a viable candidate for use in biomedical applications, especially to probe live cells and tissues. While various methods are being developed to improve system contrast and speed, little work has been published discussing the details of imaging data analysis and spectral processing. Here we present a method that we developed for the automated retrieval of Brillouin line shape parameters from imaging data sets acquired with a dual-stage VIPA Brillouin microscope. We applied this method for the first time to BLS measurements of collagen gelatin hydrogels at different hydration levels and cross-linker concentrations. This work demonstrates that it is possible to obtain the relevant information from Brillouin spectra using software for real-time high-accuracy analysis.

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that usin...

We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, $\alpha$-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.

We describe our software system enabling a tight integration between vision and control modules on complex, high-DOF humanoid robots. This is demonstrated with the iCub humanoid robot performing visual object detection and reaching and grasping actions. A key capability of this system is reactive avoidance of obstacle objects detected from the video stream while carrying out reach-and-grasp tasks. The subsystems of our architecture can independently be improved and updated, for example, we show that by using machine learning techniques we can improve visual perception by collecting images during the robot's interaction with the environment. We describe the task and software design constraints that led to the layered modular system architecture.

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that using a purpose-built hardware platform called Mecobo, it is possible to solve computational problems by evolving voltages and signals applied to an electrode array covered with a carbon nanotube–polymer composite. We demonstrate for the first time that this methodology can be applied to function optimization and also to the tone discriminator problem (TDP). For function optimization, we evaluate the approach on a suite of optimization benchmarks and obtain results that in some cases come very close to the global optimum or are comparable with those obtained using well-known software-based evolutionary approach. We also obtain good results in comparison with prior work on the tone discriminator problem. In the case of the TDP we also investigated the relative merits of different mixtures of materials and organizations of electrode array.

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit the properties of physical matter to solve computational problems without requiring a detailed understanding of such properties. EIM has so far been applied to very few computational problems. We show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve machine learning classification problems. This is the first time that EIM has been applied to such problems. We evaluate the approach on two standard datasets: Lenses and Iris. Comparing our technique with a well-known software-based evolutionary method indicates that EIM performs reasonably well. We suggest that EIM offers a promising new direction for evolutionary computation.

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we describe experiments using a purpose-built EIM platform called Mecobo to classify whether an applied square wave signal is above or below a user-defined threshold. This is the first demonstration that electrical configurations of materials (carbon nanotubes and a polymer) can be evolved to act as frequency classifiers.

Evolution-in-materio (EIM) is the manipulation of a physical system by computer controlled evolution (CCE). It takes the position that to obtain useful functions from a physical system one needs to apply highly specific physical signals and place the system in a particular physical state. It argues that CCE is an effective methodology for doing this. One of the potential advantages of this is that artificial evolution can potentially exploit physical effects that are either too complex to understand or hitherto unknown. EIM is most commonly used as a methodology for implementing computation in physical systems. The method is a hybrid of analogue and classical computation in that it uses classical computers to program physical systems or analogue devices. Thus far EIM has only been attempted in a rather limited set of physical and chemical systems. This review paper examines past work related to EIM and discusses historical underpinnings behind such work. It describes latest developments, gives an analysis of the advantages and disadvantages of such work and the challenges that still remain.

Evolution in Materio (EIM) exploits properties of physical systems for computation. “Designs” are evolved instead of a traditional top down design approach. Computation is a product of the state(s) of the material and input data. Evolution manipulates physical processes by stimulating materials assessed in situ. A hardware-software platform designed for EIM experimentation is presented. The platform, with features designed especially for EIM, is described together with demonstration experiments using carbon nanotubes in a thick film placed on micro-electrode arrays.

Evolution-in-materio (EIM) is a form of intrinsic evolution in which evolutionary algorithms are allowed to manipulate physical variables that are applied to materials. This method aims to configure materials so that they solve computational problems without requiring a detailed understanding of the properties of the materials. The concept gained attention through the work of Adrian Thompson who in 1996 showed that evolution could be used to design circuits in FPGAS that exploited the physical properties of the underlying silicon [21]. In this paper, we show that using a purpose-built hardware platform called Mecobo, we can solve computational problems by evolving voltages, signals and the way they are applied to a microelectrode array with a chamber containing single-walled carbon nanotubes and a polymer. Here we demonstrate for the first time that this methodology can be applied to the well-known computational problem of bin packing. Results on benchmark problems show that the technique can obtain results reasonably close to the known global optima. This suggests that EIM is a promising method for configuring materials to carry out useful computation.

We present an easy-to-use, modular framework for performing computer vision related tasks in support of cognitive robotics research on the iCub humanoid robot. The aim of this biologically inspired, bottom-up architecture is to facilitate research towards visual perception and cognition processes, especially their influence on robotic object manipulation and environment interaction. The icVision framework described provides capabilities for detection of objects in the 2D image plane and locate those objects in 3D space to facilitate the creation of a world model.

Building artificial agents and robots that can act in an intelligent way is one of the main research goals in artificial intelligence and robotics. Yet it is still hard to integrate functional cognitive processes into these systems. We present a framework combining computer vision and machine learning for the learning of object recognition in humanoid robots. A biologically inspired, bottom-up architecture is introduced to facilitate visual perception and cognitive robotics research. It aims to mimic processes in the human brain performing visual cognition tasks. A number of experiments with this icVision framework are described. We showcase both detection and identification in the image plane (2D), using machine learning. In addition we show how a biologically inspired attention mechanism allows for fully autonomous learning of visual object representations. Furthermore localising the detected objects in 3D space is presented, which in turn can be used to create a model of the environment.

In this paper, we present our on-going research to allow humanoid robots to learn spatial perception. We are using artificial neural networks (ANN) to estimate the location of objects in the robot's environment. The method is using only the visual inputs and the joint encoder readings, no camera calibration and information is necessary, nor is a kinematic model. We find that these ANNs can be trained to allow spatial perception in Cartesian (3D) coordinates. These lightweight networks are providing estimates that are comparable to current state of the art approaches and can easily be used together with existing operational space controllers.

We describe our recent research and advances in building a framework enabling artifical life ( ALife ) systems on real robotic hardware. Our framework allows our iCub humanoid to build better visual perception, improve its motion capabilities and even provide a sense of proprioception. This paper presents how we can use various techniques, such as, e.g., genetic programming, to build subsystems for these specific areas. Our framework runs in parallel with the hardware system and is updated with new information from the real robot. We plan to use this framework in the future for developing higher cognitive tasks, such as, scene understanding, prediction of action outcomes, and reasoning on our robot.

Robust object manipulation is still a hard problem in robotics, even more so in high degree-of-freedom (DOF) humanoid robots. To improve performance a closer integration of visual and motor systems is needed. We herein present a novel method for a robot to learn robust detection of its own hands and fingers enabling sensorimotor coordination. It does so solely using its own camera images and does not require any external systems or markers. Our system based on Cartesian Genetic Programming (CGP) allows to evolve programs to perform this image segmentation task in real-time on the real hardware. We show results for a Nao and an iCub humanoid each detecting its own hands and fingers.

Combining domain knowledge about both imaging processing and machine learning techniques can expand the abilities of Genetic Programming when used for image processing. We successfully demonstrate our new approach on several different problem domains. We show that the approach is fast, scalable and robust. In addition, by virtue of using off-the-shelf image processing libraries we can generate human readable programs that incorporate sophisticated domain knowledge.

The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of Cartesian Genetic Programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks.