Flame far too hot: William Empson's non-Euclidean predicament

William Empson’s poem ‘Letter I’ (1928–35) appears to anticipate the black hole, using the idea of a dying star from which no light escapes as a metaphor for unrequited passion. Closer inspection of the Cambridge undergraduate context in which the poem was written, along with the other source materials incorporated besides Arthur Eddington in the poem, reveals the motivation behind Empson’s playful engagement with the limits of what was possible under general relativity. Empson’s attempt to follow the metaphysical example of John Donne, using the new cosmology of the 1920s, led him to explore an extreme astro-physical condition that Eddington had dismissed as absurd, and that still had an uncertain scientific status in the 1930s

Research on Optimized Problem-solving Solutions: Selection of the Production Process

In manufacturing industries, various problems may occur during the production process. The problems are complexand involve the relevant context of working environments. A problem-solving process is often initiated to create asolution and achieve a desired status. In this process, determining how to obtain a solution from the variouscandidate solutions is an important issue. In such uncertain working environments, context information can providerich clues for problem-solving decision making. This work uses a selection approach to determine an optimizedproblem-solving process which will assist workers in choosing reasonable solutions. A context-based utility modelexplores the problem context information to obtain candidate solution actual utility values; a multi-criteria decisionanalysis uses the actual utility values to determine the optimal selection order for candidate solutions. Theselection order is presented to the worker as an adaptive knowledge recommendation. The worker chooses areasonable problem-solving solution based on the selection order. This paper uses a high-tech company’sknowledge base log as a source of analysis data. The experimental results show that the chosen approach to anoptimized problem-solving solution selection is effective. The contribution of this research is a method which iseasy to implement in a problem-solving decision support system

Measuring cosmic bulk flows with Type Ia Supernovae from the Nearby Supernova Factory

Context. Our Local Group of galaxies appears to be moving relative to the cosmic microwave background with the source of the peculiar motion still uncertain. While in the past this has been studied mostly using galaxies as distance indicators, the weight of type Ia supernovae (SNe Ia) has increased recently with the continuously improving statistics of available low-redshift supernovae. Aims. We measured the bulk flow in the nearby universe ($0.015 < z < 0.1$) using 117 SNe Ia observed by the Nearby Supernova Factory, as well as the Union2 compilation of SN Ia data already in the literature. Methods. The bulk flow velocity was determined from SN data binned in redshift shells by including a coherent motion (dipole) in a cosmological fit. Additionally, a method of spatially smoothing the Hubble residuals was used to verify the results of the dipole fit. To constrain the location and mass of a potential mass concentration (e.g., the Shapley supercluster) responsible for the peculiar motion, we fit a Hubble law modified by adding an additional mass concentration. Results. The analysis shows a bulk flow that is consistent with the direction of the CMB dipole up to $z \sim 0.06$, thereby doubling the volume over which conventional distance measures are sensitive to a bulk flow. We see no significant turnover behind the center of the Shapley supercluster. A simple attractor model in the proximity of the Shapley supercluster is only marginally consistent with our data, suggesting the need for another, more distant source. In the redshift shell $0.06 < z < 0.1$, we constrain the bulk flow velocity to $< 240~\textrm{km s}^{-1}$ (68% confidence level) for the direction of the CMB dipole, in contradiction to recent claims of the existence of a large-amplitude dark flow.Comment: 12 pages, 5 figures, added corrigendum (http://adsabs.harvard.edu/abs/2015A%26A...578C...1F

Long term planning for EFA and the MDGs: modes and mechanisms

This discussion paper provides an overview and analytic guide to long term planning of education systems in the context of Education for All and the Millennium Development Goals. Long term gains in educational access depend on anticipating future financial and non-financial constraints on growth and on successful implementation of plans which support growth that can be sustained. Some recent expansion of primary schooling has failed to take a sufficiently long term approach to growth and has risked the creation of resource bottlenecks, poor trade offs between quality and quantity, and dependence on uncertain financing. The paper first outlines three different styles of long term planning – Planning Lite, Framework National Planning, and Participatory Planning. It distinguishes between aspirational and target-generating approaches. It then describes the processes and tools that are needed to develop long term plans for expanded access that can reconcile goals and targets with realistic resource envelopes. These processes are designed to include mechanisms to promote consensus and build commitment. The nature of Medium Term Expenditure Frameworks (MTEF) is then explored as a necessary tool to manage implementation. Appendix 1 provides more detailed discussion of the three approaches to planning. Appendix 2 elaborates on aspirational planning and gradients of achievement. Appendix 3 explores issues concerned with targets and indicators of performance. Appendix 4 contains a selected list of source materials

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Analysing long-term interactions between demand response and different electricity markets using a stochastic market equilibrium model. ESRI WP585, February 2018

Power systems based on renewable energy sources (RES) are characterised by increasingly distributed, volatile and uncertain supply leading to growing requirements for flexibility. In this paper, we explore the role of demand response (DR) as a source of flexibility that is considered to become increasingly important in future. The majority of research in this context has focussed on the operation of power systems in energy only markets, mostly using deterministic optimisation models. In contrast, we explore the impact of DR on generator investments and profits from different markets, on costs for different consumers from different markets, and on CO2 emissions under consideration of the uncertainties associated with the RES generation. We also analyse the effect of the presence of a feed-in premium (FIP) for RES generation on these impacts. We therefore develop a novel stochastic mixed complementarity model in this paper that considers both operational and investment decisions, that considers interactions between an energy market, a capacity market and a feed-in premium and that takes into account the stochasticity of electricity generation by RES. We use a Benders decomposition algorithm to reduce the computational expenses of the model and apply the model to a case study based on the future Irish power system. We find that DR particularly increases renewable generator profits. While DR may reduce consumer costs from the energy market, these savings may be (over)compensated by increasing costs from the capacity market and the feed-in premium. This result highlights the importance of considering such interactions between different markets

Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement

When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other