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 5.0μm) from short wavelength fluxes (SW: F115W, F150W, F200W between 0.6 to 2.3μm) 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 anomalies

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

ABSTRACT Forthcoming large imaging surveys such as Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than 105 strong gravitational lens systems, including many rare and exotic populations such as compound lenses, but these 105 systems will be interspersed among much larger catalogues of ∼109 galaxies. This volume of data is too much for visual inspection by volunteers alone to be feasible and gravitational lenses will only appear in a small fraction of these data which could cause a large amount of false positives. Machine learning is the obvious alternative but the algorithms’ internal workings are not obviously interpretable, so their selection functions are opaque and it is not clear whether they would select against important rare populations. We design, build, and train several convolutional neural networks (CNNs) to identify strong gravitational lenses using VIS, Y, J, and H bands of simulated data, with F1 scores between 0.83 and 0.91 on 100 000 test set images. We demonstrate for the first time that such CNNs do not select against compound lenses, obtaining recall scores as high as 76 per cent for compound arcs and 52 per cent for double rings. We verify this performance using Hubble Space Telescope and Hyper Suprime-Cam data of all known compound lens systems. Finally, we explore for the first time the interpretability of these CNNs using Deep Dream, Guided Grad-CAM, and by exploring the kernels of the convolutional layers, to illuminate why CNNs succeed in compound lens selection.

Wide-field submillimetre surveys have driven many major advances in galaxy evolution in the past decade, but without extensive follow-up observations the coarse angular resolution of these surveys limits the science exploitation. This has driven the development of various analytical deconvolution methods. In the last half a decade Generative Adversarial Networks have been used to attempt deconvolutions on optical data. Here, we present an auto-encoder with a novel loss function to overcome this problem in the submillimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500 $\mu\mathrm{m}$ COSMOS data, with the superresolving target being the JCMT SCUBA-2 450 $\mu\mathrm{m}$ observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this auto-encoder. This is quantified through the point source fluxes and positions, the completeness, and the purity.

Governments across the world are formulating and implementing medical, social, economic and other policies to manage the COVID-19 pandemic and protect their citizens. Many governments claim that their policies follow the best available scientific advice. Much of that advice comes from computational modeling. Two of the main types of model are presented: the SIR (Susceptible, Infected, Recovered) model developed by Kermack and McKendrick in the 1920s and the more recent Agent Based Models. The SIR model gives a good intuition of how epidemics spread; including how mass vaccination can contain them. It is less useful than Agent Based Modeling for investigating the effects of policies such as social distancing, self-isolation, wearing facemasks, and test-trace-isolate. Politicians and the public have been perplexed to observe the lack of consensus in the scientific community and there being no single 'best science' to follow. The outcome of computational models depends on the assumptions made and the data used. Different assumptions will lead to different computational outcomes, especially when the available data are so poor. This leads some commentators to argue that the models are wrong and dangerous. Some may be, but computational modeling is one of the few ways available to explore and try to understand the space of possible futures. This lack of certainty means that computational modeling must be seen as just one of many inputs into the political decision making process. Politicians must balance all the competing inputs and make timely decisions based on their conclusions---be they right or wrong. In the same way that democracy is the least worst form of government, computational modeling may be the least worst way of trying to understand the future for policy making.

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 100000 strong gravitational lens systems. This is ideal to find rare lens configurations, provided they can be identified reliably and on a reasonable time-scale. 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 (Kilo-Degree Survey)-like simulations from the Euclid Strong Lensing Group, successfully classifying 77percent of lenses, with an area under the ROC curve of up to 0.96. Our CNN also attempts to classify the lenses in COSMOS Hubble Space Telescope 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.

Making multilevel systems well-defined is essential for the implementation of computer models to investigate the multilevel consequences of policy. This chapter shows that systems thinking can provide practical guidance to those building models of complex multilevel social systems in order to inform policymaking. Part–whole aggregation and taxonomic aggregation are described as methods of representing multilevel structure, and it is shown how they are interleaved in the construction of vocabulary to describe multilevel systems. This enables complex nested structures to be represented as a kind of backcloth that supports patterns of aggregate and disaggregate numbers that describe the day-to-day traffic of people, resources and responsibility that are essential for systems to function.

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.

The breadth of biodiversity literature available through the Biodiversity Heritage Library (BHL) is potentially of great use to agricultural research. It provides access to literature drawn from across the world, and its archives document the Earth as it was one hundred years ago and more. However, this strength of BHL is also its weakness: the breadth of coverage of BHL can complicate finding relevant literature. In this short paper, we will explore the practical issues arising from attempting to filter out relevant legacy literature to support agricultural research.

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.

We report on progress in handwriting recognition and signature verification. Our system, which uses pen-trajectory information, is suitable for use in pen-based computers. It has a multimodular architecture whose central trainable module is a time-delay neural network. Results comparing our system and a commercial recognizer are presented. Our best recognizer makes three times less errors on hand-printed word recognition than the commercial one.

May 01 1993 Improving Rejection Performance on Handwritten Digits by Training with “Rubbish” In Special Collection: CogNet Jane Bromley, Jane Bromley AT&T Bell Laboratories, Holmdel, NJ 07733, USA Search for other works by this author on: This Site Google Scholar John S. Denker John S. Denker AT&T Bell Laboratories, Holmdel, NJ 07733, USA Search for other works by this author on: This Site Google Scholar Author and Article Information Jane Bromley AT&T Bell Laboratories, Holmdel, NJ 07733, USA John S. Denker AT&T Bell Laboratories, Holmdel, NJ 07733, USA Received: July 27 1992 Accepted: October 20 1992 Online ISSN: 1530-888X Print ISSN: 0899-7667 © 1993 Massachusetts Institute of Technology1993 Neural Computation (1993) 5 (3): 367–370. https://doi.org/10.1162/neco.1993.5.3.367 Article history Received: July 27 1992 Accepted: October 20 1992 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Jane Bromley, John S. Denker; Improving Rejection Performance on Handwritten Digits by Training with “Rubbish”. Neural Comput 1993; 5 (3): 367–370. doi: https://doi.org/10.1162/neco.1993.5.3.367 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsNeural Computation Search Advanced Search This content is only available as a PDF. © 1993 Massachusetts Institute of Technology1993 Article PDF first page preview Close Modal You do not currently have access to this content.

This paper describes the development of an algorithm for verification of signatures written on a touch-sensitive pad. The signature verification algorithm is based on an artificial neural network. The novel network presented here, called a “Siamese” time delay neural network, consists of two identical networks joined at their output. During training the network learns to measure the similarity between pairs of signatures. When used for verification, only one half of the Siamese network is evaluated. The output of this half network is the feature vector for the input signature. Verification consists of comparing this feature vector with a stored feature vector for the signer. Signatures closer than a chosen threshold to this stored representation are accepted, all other signatures are rejected as forgeries. System performance is illustrated with experiments performed in the laboratory.

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 precision

A special-purpose chip, optimized for computational needs of neural networks and performing over 2000 multiplications and additions simultaneously, is described. Its data path is particularly suitable for the convolutional architectures typical in pattern classification networks but can also be configured for fully connected or feedback topologies. A development system permits rapid prototyping of new applications and analysis of the impact of the specialized hardware on system performance. The power and flexibility of the processor are demonstrated with a neural network for handwritten character recognition containing over 133000 connections.< >