Application of the ANNA neural network chip to high-speed character recognition

A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precisio

agINFRA - where agriculture, biodiversity and information technology meet

This poster will provide a brief introduction to and overview of the agINFRA project, a research infrastructure project funded by the EU. The project is developing a data infrastructure to support agricultural scientific communities through promoting data sharing and the development of trust in agricultural sciences. Members of the project are working to provide tools, hosted in a scientific gateway, for creating a linked open data environment for agricultural scientists. The project will try to remove existing obstacles concerning open access to scientific information (including discovery and use of the data) in agriculture. The project consortium also seeks to improve the preparedness of the agricultural scientific community to face, manage and exploit the abundance of relevant data that is (or will become) available to agricultural researchers as data becomes more openly available. It is intended that the project will promote research on food and agriculture, including research to adapt to, and mitigate climate change, and access to research results and technologies at national, regional and international levels. The overall aim of the project being to improve access to knowledge by creating a high level of interoperability between agricultural and other data resources

Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images

The Euclid telescope, due for launch in 2021, will perform an imaging and slitless spectroscopy survey over half the sky, to map baryon wiggles and weak lensing. During the survey Euclid is expected to resolve 100,000 strong gravitational lens systems. This is ideal to find rare lens configurations, provided they can be identified reliably and on a reasonable timescale. For this reason we have developed a Convolutional Neural Network (CNN) that can be used to identify images containing lensing systems. CNNs have already been used for image and digit classification as well as being used in astronomy for star-galaxy classification. Here our CNN is trained and tested on Euclid-like and KiDS-like simulations from the Euclid Strong Lensing Group, successfully classifying 77% of lenses, with an area under the ROC curve of up to 0.96. Our CNN also attempts to classify the lenses in COSMOS HST F814W-band images. After convolution to the Euclid resolution, we find we can recover most systems that are identifiable by eye. The Python code is available on Github

Systems, Networks and Policy

Systems theory is fundamental to understanding the dynamics of the complex social systems of concern to policy makers. A system is defined as: (1) an assembly of components, connected together in an organised way; (2) the components are affected by being in the system and the behaviour of the systems is changed if they leave it; (3) the organised assembly of components does something; and (4) the assembly has been identified as being of particular interest. Feedback is central to system behaviour at all levels, and can be responsible for systems behaving in complex and unpredictable ways. Systems can be represented by networks and there is a growing literature that shows how the behaviour of individuals is highly dependent on their social networks. This includes copying or following the advice of others when making decisions. Network theory gives insights into social phenomena such as the spread of information and the way people form social groups which then constrain their behaviour. It is emerging as a powerful way of examining the dynamics of social systems. Most systems relevant to policy have many levels, from the individual to local and national and international organisations and institutions. In many social systems the micro, meso and macrolevel dynamics are coupled, meaning that they cannot be studied or modified in isolation. Systems and network science allow computer simulations to be used to investigate possible system behaviour. This science can be made available to policy makers through policy informatics which involves computer-based simulation, data, visualisation, and interactive interfaces. The future of science-based policy making is seen to be through Global Systems Science which combines complex systems science and policy informatics to inform policy makers and facilitate citizen engagement. In this context, systems theory and network science are fundamental for modelling far-from-equilibrium systems for policy purposes

Measurement of Fragmentation Functions in DIS at HERA

The production of charged particles produced in Deep Inelastic Scattering (DIS) events at HERA has been studied using the ZEUS detector. Measurements have been made of the fragmentation variables xp and ln(1/xp)current region of the Breit frame of the interaction in the ranges 10 <Q2<5120 GeV2 and 0.6 10-3 < x < 0.25. Evidence is found for scaling violations in the scaled momentum, xp, as a function of Q2, and the data are shown to be well described by Next-to-Leading order calculations. The description of the ln(1/xp) distributions by the Modified Leading Log Approximation (MLLA) is studied, and its predictions for their evolution with energy are investigated. The data are compared with results from e+e- annihilation experiments

Functional mapping of stimulus colour in human subjects suffering a central visual defect

The multiple maps of the visual field found in the striate and the pre-striate cortex of the macaque exhibit selective responsiveness to different stimulus parameters (Zeki, 1978, 1980). Evidence for such organization in man is derived primarily from selective losses of visual function associated with disturbance of the central pathways. We present data for a single subject, M. W., who has normal achromatic vision but exhibits grossly abnormal responses to coloured and particularly red stumuli ..

Using cGANs for Anomaly Detection: Identifying Astronomical Anomalies in JWST NIRcam Imaging

We present a proof of concept for mining JWST imaging data for anomalous galaxy populations using a conditional Generative Adversarial Network (cGAN). We train our model to predict long wavelength NIRcam fluxes (LW: F277W, F356W, F444W between 2.4 to 5.0\mu m) from short wavelength fluxes (SW: F115W, F150W, F200W between 0.6 to 2.3\mu m) in approximately 2000 galaxies. We test the cGAN on a population of 37 Extremely Red Objects (EROs) discovered by the CEERS JWST Team arXiv:2305.14418. Despite their red long wavelength colours, the EROs have blue short wavelength colours (F150W \- F200W equivalently 0 mag) indicative of bimodal SEDs. Surprisingly, given their unusual SEDs, we find that the cGAN accurately predicts the LW NIRcam fluxes of the EROs. However, it fails to predict LW fluxes for other rare astronomical objects, such as a merger between two galaxies, suggesting that the cGAN can be used to detect some anomaliesComment: 4 pages, 1 figure with 5 sub-figures. Submitted, accepted and awaiting publication in AAS Journal

Hypernetwork-based peer marking for scalable certificated mass education

In the context of the need for massive free education for the Complex Systems Society and the UNESCO Complex Systems Digital Campus, scalable methods are essential for assessing tens of thousands of students’ work for certification. Automated marking is a partial solution but has many drawbacks. Peer marking, where students mark each others’ assignments, is a scalable solution since every extra student is an extra marker. However there are concerns about the quality of peer marking, since some students may not be competent to mark the work of others. Some students are better than others and often the best students are well qualified to assess the work of their peers. To make peer marking high quality we are using new hypernetwork-based methods to extend previous methods to discover which students are good markers and which students are less good as a course progresses

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201

Management of iron-deficiency anemia following acute gastrointestinal hemorrhage: A narrative analysis and review

Many patients experiencing acute gastrointestinal bleeding (GIB) require iron supplemen-tation to treat subsequent iron deficiency (ID) or iron-deficiency anemia (IDA). Guidelinesregarding management of these patients are lacking. We aimed to identify areas of unmetneed in patients with ID/IDA following acute GIB in terms of patient management andphysician guidance. We formed an international working group of gastroenterologists toconduct a narrative review based on PubMed and EMBASE database searches (fromJanuary 2000 to February 2021), integrated with observations from our own clinical expe-rience. Published data on this subject are limited and disparate, and those relating topost-discharge outcomes, such as persistent anemia and re-hospitalization, are particularlylacking. Often, there is no post-discharge follow-up of these patients by a gastroenterolo-gist. Acute GIB-related ID/IDA, however, is a prevalent condition both at the time of hos-pital admission and at hospital discharge and is likely underdiagnosed and undertreated.Despite limited data, there appears to be notable variation in the prescribing of intravenous(IV)/oral iron regimens. There is also some evidence suggesting that, compared with oraliron, IV iron may restore iron levels faster following acute GIB, have a better tolerabilityprofile, and be more beneficial in terms of quality of life. Gaps in patient care exist inthe management of acute GIB-related ID/IDA, yet further data from largepopulation-based studies are needed to confirm this. We advocate the formulation ofevidence-based guidance on the use of iron therapies in these patients, aiding a more stan-dardized best-practice approach to patient care