Forecasted 21 cm constraints on compensated isocurvature perturbations

A "compensated" isocurvature perturbation consists of an overdensity (or underdensity) in the cold dark matter which is completely cancelled out by a corresponding underdensity (or overdensity) in the baryons. Such a configuration may be generated by a curvaton model of inflation if the cold dark matter is created before curvaton decay and the baryon number is created by the curvaton decay (or vice-versa). Compensated isocurvature perturbations, at the level producible by the curvaton model, have no observable effect on cosmic microwave background anisotropies or on galaxy surveys. They can be detected through their effect on the distribution of neutral hydrogen between redshifts 30 to 300 using 21 cm absorption observations. However, to obtain a good signal to noise ratio, very large observing arrays are needed. We estimate that a fast Fourier transform telescope would need a total collecting area of about 20 square kilometers to detect a curvaton generated compensated isocurvature perturbation at more than 5 sigma significance.Comment: 7 pages, v2: minor typos corrected, reflects PRD accepted versio

Global Research Report – South and East Asia

Global Research Report – South and East Asia by Jonathan Adams, David Pendlebury, Gordon Rogers & Martin Szomszor. Published by Institute for Scientific Information, Web of Science Group

An Investigation into the Pedagogical Features of Documents

Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the "pedagogical value" of the document to the learner. While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of "pedagogical roles" of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.Comment: 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA) at EMNLP 2017; 12 page

MapSnapper: Engineering an Efficient Algorithm for Matching Images of Maps from Mobile Phones

The MapSnapper project aimed to develop a system for robust matching of low-quality images of a paper map taken from a mobile phone against a high quality digital raster representation of the same map. The paper presents a novel methodology for performing content-based image retrieval and object recognition from query images that have been degraded by noise and subjected to transformations through the imaging system. In addition the paper also provides an insight into the evaluation-driven development process that was used to incrementally improve the matching performance until the design specifications were met

Advances in Probabilistic Meta-Learning and the Neural Process Family

A natural progression in machine learning research is to automate and learn from data increasingly many components of our learning agents.Meta-learning is a paradigm that fully embraces this perspective, and can be intuitively described as embodying the idea of learning to learn. A goal of meta-learning research is the development of models to assist users in navigating the intricate space of design choices associated with specifying machine learning solutions. This space is particularly formidable when considering deep learning approaches, which involve myriad design choices interacting in complex fashions to affect the performance of the resulting agents. Despite the impressive successes of deep learning in recent years, this challenge remains a significant bottleneck in deploying neural network based solutions in several important application domains. But how can we reason about and design solutions to this daunting task? This thesis is concerned with a particular perspective for meta-learning in supervised settings. We view supervised learning algorithms as mappings that take data sets to predictive models, and consider meta-learning as learning to approximate functions of this form. In particular, we are interested in meta-learners that (i) employ neural networks to approximate these functions in an end-to-end manner, and (ii) provide predictive distributions rather than single predictors. The former is motivated by the success of neural networks as function approximators, and the latter by our interest in the few-shot learning scenario. The introductory chapters of this thesis formalise this notion, and use it to provide a tutorial introducing the Neural Process Family (NPF), a class of models introduced by Garnelo et al (2018) satisfying the above-mentioned modelling desiderata. We then present our own technical contributions to the NPF. First, we focus on fundamental properties of the model-class, such as expressivity and limiting behaviours of the associated training procedures. Next, we study the role of translation equivariance in the NPF. Considering the intimate relationship between the NPF and the representation of functions operating on sets, we extend the underlying theory of DeepSets to include translation equivariance. We then develop novel members of the NPF endowed with this important inductive bias. Through extensive empirical evaluation, we demonstrate that, in many settings, they significantly outperform their non-equivariant counterparts. Finally, we turn our attention to the development of Neural Processes for few-shot image-classification. We introduce models that navigate the important tradeoffs associated with this setting, and describe the specification of their central components. We demonstrate that the resulting models---CNAPs---achieve state-of-the-art performance on a challenging benchmark called Meta-Dataset, while adapting faster and with less computational overhead than their best-performing competitors

Low-cost, multi-agent systems for planetary surface exploration

The use of off-the-shelf consumer electronics combined with top-down design methodologies have made small and inexpensive satellites, such as CubeSats, emerge as viable, low-cost and attractive space-based platforms that enable a range of new and exciting mission scenarios. In addition, to overcome some of the resource limitation issues encountered with these platforms, distributed architectures have emerged to enable complex tasks through the use of multiple low complexity units. The low-cost characteristics of such systems coupled with the distributed architecture allows for an increase in the size of the system beyond what would have been feasible with a monolithic system, hence widening the operational capabilities without significantly increasing the control complexity of the system. These ideas are not new for Earth orbiting devices, but excluding some distributed remote sensing architectures they are yet to be applied for the purpose of planetary exploration. Experience gained through large rovers demonstrates the value of in-situ exploration, which is however limited by the associated high-cost and risk. The loss of a rover can and has happened because of a number of possible failures: besides the hazards directly linked to the launch and journey to the target-body, hard landing and malfunctioning of parts are all threats to the success of the mission. To overcome these issues this paper introduces the concept of using off-the-shelf consumer electronics to deploy a low-cost multi-rover system for future planetary surface exploration. It is shown that such a system would significantly reduce the programmatic-risk of the mission (for example catastrophic failure of a single rover), while exploiting the inherent advantages of cooperative behaviour. These advantages are analysed with a particular emphasis put upon the guidance, navigation and control of such architectures using the method of artificial potential field. Laboratory tests on multi-agent robotic systems support the analysis. Principal features of the system are identified and the underlying advantages over a monolithic single-agent system highlighted

A first assessment of operator compliance and dolphin behavioural responses during swim-with-dolphin programs for three species of Delphinids in the Azores

The popularity of swim-with wild dolphin programs around the world is fast growing, with the studies required to investigate their impact lagging behind. In the Azores, species targeted include the short-beaked common (Delphinus delphis), the bottlenose (Tursiops truncatus) and the Atlantic spotted dolphin (Stenella frontalis). To evaluate the effects of this activity on local dolphin populations, and thus provide support for management decisions, dolphin response data were collected onboard commercial boats off São Miguel Island between 2013 and 2015. All three species revealed high degree of neutral and avoidance behaviours, and very low approach rates. Tursiops showed higher frequency of neutral responses than Delphinus, while Stenella both avoided and approached more frequently than the other species. When boats intersected the path of dolphin groups, avoidance responses were more likely and the duration of swims was shorter. Swims were also shorter when animals were resting and travelling, and when groups were smaller. The operators generally complied with the legislation, except in respect to the number of swim attempts per dolphin group, which was higher than the legal maximum. Improvement of the current legislation and concurrent reinforcement of controls is essential to avoid detrimental long-term effects of this activity on dolphin populations in the Azores.This research was partially supported by the European Regional Development Fund (ERDF) through the COMPETE – Operational Competitiveness Programme and national funds through FCT – Foundation for Science and Technology, under the project PEst-C/MAR/LA0015/2013, by the Strategic Funding UID/Multi/04423/2013 through national funds provided by FCT – Foundation for Science and Technology and European Regional Development Fund (ERDF), in the framework of the programme PT2020 and by cE3c funding (Ref:UID/BIA/003329/2013). It was also partly supported by CIRN (Centro de Investigação de Recursos Naturais, University of the Azores), and CIIMAR (Interdisciplinary Centre of Marine and Environmental Research, Porto, Portugal). A. Cecchetti was supported by the Regional Fund for Science through the scholarship M.3.1.2/F/036/2011. K.A. Stockin was supported by a Royal Society of New Zealand Te Aparangi Rutherford Discovery Fellowship.info:eu-repo/semantics/publishedVersio