Enhanced surface transfer doping of diamond by V2O5 with improved thermal stability

Surface transfer doping of hydrogen-terminated diamond has been achieved utilising V2O5 as a surface electron accepting material. Contact between the oxide and diamondsurface promotes the transfer of electrons from the diamond into the V2O5 as revealed by the synchrotron-based high resolution photoemission spectroscopy. Electrical characterization by Hall measurement performed before and after V2O5 deposition shows an increase in hole carrier concentration in the diamond from 3.0 × 1012 to 1.8 × 1013 cm−2 at room temperature. High temperature Hall measurements performed up to 300 °C in atmosphere reveal greatly enhanced thermal stability of the hole channel produced using V2O5 in comparison with an air-induced surface conduction channel. Transfer doping of hydrogen-terminated diamond using high electron affinity oxides such as V2O5 is a promising approach for achieving thermally stable, high performance diamond based devices in comparison with air-induced surface transfer dopin

Open Design and 3D Printing of Face Shields: The Case Study of a UK-China Initiative

At the start of the COVID-19 outbreak, many countries lacked personal protective equipment (PPE) to protect healthcare workers. To address this problem, open design and 3D printing technologies were adopted to provide much-in-need PPEs for key workers. This paper reports an initiative by designers and engineers in the UK and China. The case study approach and content analysis method were used to study the stakeholders, the design process, and other relevant issues such as regulation. Good practice and lessons were summarised, and suggestions for using distributed 3D printing to supply PPEs were made. It concludes that 3D printing has played an important role in producing PPEs when there was a shortage of supply, and distributed manufacturing has the potential to quickly respond to local small-bench production needs. In the future, clearer specification, better match of demands and supply, and quicker evaluation against relevant regulations will provide efficiency and quality assurance for 3D printed PPE supplies

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A Neuromorphic Computational Primitive for Robust Context-Dependent Decision Making and Context-Dependent Stochastic Computation

The prefrontal cortex (PFC) plays an important role in complex cognitive computations, including planning and decision making. Although recurrent spiking neural network (SNN) software models of PFC have been successful in reproducing many of its cognitive computational aspects, little attention has been devoted to the question of how such systems can perform with low-resolution parameters and be robust to noise and variability in their input signals and state variables. Here, we present a mixed-signal analog/digital neuromorphic implementation of a state-dependent SNN architecture that addresses these issues by construction. The network relies on synaptic dis-inhibition to ensure robust decision making even in the face of very large variability. Depending on its connectivity, the network can either perform robustly in a deterministic way or exploit the device mismatch and noise to explore stochastically multiple states in constraint satisfaction problems (CSPs). We validate the architecture by mapping it onto a network of spiking neurons in a multi-core mixed-signal neuromorphic system and presenting experimental results for three different examples of CSPs

Robust state-dependent computation in neuromorphic electronic systems

State-dependent computation is one of the main signatures of cognition. Recently, it has been shown how it can be used as a computational primitive in spiking neural networks for constructing complex cognitive behaviors in neuromorphic agents. However, to achieve the desired computations and behaviors in mixed signal analog-digital neuromorphic electronic systems, these computational primitives should be able to cope with noisy and imprecise components, such as silicon neurons and synapses, with noisy and unreliable external signals, and with interference from the environment. Here we present a spiking neural network model that addresses all these issues while exhibiting both analog signal processing properties and digital symbolic computational abilities. We show how this Neural State Machine (NSM) model can be used for realizing robust state-dependent computation on neuromorphic hardware, and we validate it with experimental results obtained from a recently developed multi-neuron multi-core neuromorphic computing architecture

Neural State Machines for Robust Learning and Control of Neuromorphic Agents

Mixed-signal analog/digital neuromorphic circuits are characterized by ultra-low power consumption, real-time processing abilities, and low-latency response times. These features make them promising for robotic applications that require fast and power-efficient computing. However, due to the device mismatch and variability present in these circuits, developing architectures that can perform complex computations in a robust and reproducible manner is quite challenging. In this paper, we present a spiking neural network architecture implemented using these neuromorphic circuits, that enables reliable control of an autonomous agent as well as robust learning and recognition of visual patterns in a noisy real-world environment. While learning is implemented with a software algorithm running with a chip-in-the-loop setup, the inference and motor control processes are implemented exclusively by the neuromorphic processor, situated on the neuromorphic agent. In addition to this processor device, the agent comprises a dynamic vision sensor which produces spikes as it interacts with the environment in real-time. We show how the robust learning and reliable control properties of the system arise out of a recently proposed neural computational primitive denoted as Neural State Machine (NSM). We describe the features of the NSMs used in this context and demonstrate the agent’s real-time robust perception and action behavior with experimental results

Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach

We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphic hardware. We show how this network can learn simple visual patterns composed of horizontal and vertical bars sensed by a Dynamic Vision Sensor, using a local spike-based plasticity rule. During recognition, the network classifies the pattern's identity while at the same time estimating its location and scale. We build on previous work that used learning with neuromorphic hardware in the loop and demonstrate that the proposed network can properly operate with on-chip learning, demonstrating a complete neuromorphic pattern learning and recognition setup. Our results show that the network is robust against noise on the input (no accuracy drop when adding 130% noise) and against up to 20% noise in the neuron parameters