Open-Ended Evolutionary Robotics: an Information Theoretic Approach

This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach

Ambient pressure x-ray photoelectron spectroscopy setup for synchrotron-based in situ and operando atomic layer deposition research

An ambient pressure cell is described for conducting synchrotron-based x-ray photoelectron spectroscopy (XPS) measurements during atomic layer deposition (ALD) processes. The instrument is capable of true in situ and operando experiments in which it is possible to directly obtain elemental and chemical information from the sample surface using XPS as the deposition process is ongoing. The setup is based on the ambient pressure XPS technique, in which sample environments with high pressure (several mbar) can be created without compromising the ultrahigh vacuum requirements needed for the operation of the spectrometer and the synchrotron beamline. The setup is intended for chemical characterization of the surface intermediates during the initial stages of the deposition processes. The SPECIES beamline and the ALD cell provide a unique experimental platform for obtaining new information on the surface chemistry during ALD half-cycles at high temporal resolution. Such information is valuable for understanding the ALD reaction mechanisms and crucial in further developing and improving ALD processes. We demonstrate the capabilities of the setup by studying the deposition of TiO2 on a SiO2 surface by using titanium(IV) tetraisopropoxide and water as precursors. Multiple core levels and the valence band of the substrate surface were followed during the film deposition using ambient pressure XPS.Peer reviewe

Intercalation of Lithium Ions from Gaseous Precursors into beta-MnO2 Thin Films Deposited by Atomic Layer Deposition

LiMn2O4 is a promising candidate for a cathode material in lithium-ion batteries because of its ability to intercalate lithium ions reversibly through its three-dimensional manganese oxide network. One of the promising techniques for depositing LiMn2O4 thin-film cathodes is atomic layer deposition (ALD). Because of its unparalleled film thickness control and film conformality, ALD helps to fulfill the industry demands for smaller devices, nanostructured electrodes, and all-solid-state batteries. In this work, the intercalation mechanism of Li+ ions into an ALD-grown beta-MnO2 thin film was studied. Samples were prepared by pulsing (LiOBu)-Bu-t and H2O for different cycle numbers onto about 100 nm thick MnO2 films at 225 degrees C and characterized with X-ray absorption spectroscopy, X-ray diffraction, X-ray reflectivity, time-of-flight elastic recoil detection analysis, and residual stress measurements. It is proposed that forPeer reviewe

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD '20), November 2-5, 2020, Virtual Event, US

Fabrication of a thin silicon detector with excellent thickness uniformity

We have fabricated and tested a thin silicon detector with the specific goal of having a very good thickness uniformity. SOI technology was used in the detector fabrication. The detector was designed to be used as a Delta E detector in a silicon telescope for measuring solar energetic particles in space. The detector thickness was specified to be 20 mu m with an rms thickness uniformity of +/- 0.5%. The active area consists of three separate elements, a round centre area and two surrounding annular segments. A new method was developed for measuring the thickness uniformity based on a modified Fizeau interferometer. The thickness uniformity specification was well met with the measured rms thickness variation of 43 nm. The detector was electrically characterized by measuring the I-V and C-V curves and the performance was verified using a Am-241 alpha source. (C) 2015 Elsevier B.V. All rights reserved.</p

Are new models needed to optimize the utilization of new medicines to sustain healthcare systems?

Medicines have made an appreciable contribution to improving health. However, even high-income countries are struggling to fund new premium-priced medicines. This will grow necessitating the development of new models to optimize their use. The objective is to review case histories among health authorities to improve the utilization and expenditure on new medicines. Subsequently, use these to develop exemplar models and outline their implications. A number of issues and challenges were identified from the case histories. These included the low number of new medicines seen as innovative alongside increasing requested prices for their reimbursement, especially for oncology, orphan diseases, diabetes and HCV. Proposed models center on the three pillars of pre-, peri- and post-launch including critical drug evaluation, as well as multi-criteria models for valuing medicines for orphan diseases alongside potentially capping pharmaceutical expenditure. In conclusion, the proposed models involving all key stakeholder groups are critical for the sustainability of healthcare systems or enhancing universal access. The models should help stimulate debate as well as restore trust between key stakeholder groups