Learning generative texture models with extended Fields-of-Experts

We evaluate the ability of the popular Field-of-Experts (FoE) to model structure in images. As a test case we focus on modeling synthetic and natural textures. We find that even for modeling single textures, the FoE provides insufficient flexibility to learn good generative models – it does not perform any better than the much simpler Gaussian FoE. We propose an extended version of the FoE (allowing for bimodal potentials) and demonstrate that this novel formulation, when trained with a better approximation of the likelihood gradient, gives rise to a more powerful generative model of specific visual structure that produces significantly better results for the texture task

The H.E.S.S. View of the Central 200 Parsecs

The inner few hundred parsecs of our galaxy provide a laboratory for the study of the production and propagation of energetic particles. Very-high-energy gamma-rays provide an effective probe of these processes and, especially when combined with data from other wave-bands, gamma-rays observations are a powerful diagnostic tool. Within this central region, data from the H.E.S.S. instrument have revealed three discrete sources of very-high-energy gamma-rays and diffuse emission correlated with the distribution of molecular material. Here I provide an overview of these recent results from H.E.S.S.Comment: Proceedings of the Galactic Centre Workshop 200

Inducing Language Networks from Continuous Space Word Representations

Recent advancements in unsupervised feature learning have developed powerful latent representations of words. However, it is still not clear what makes one representation better than another and how we can learn the ideal representation. Understanding the structure of latent spaces attained is key to any future advancement in unsupervised learning. In this work, we introduce a new view of continuous space word representations as language networks. We explore two techniques to create language networks from learned features by inducing them for two popular word representation methods and examining the properties of their resulting networks. We find that the induced networks differ from other methods of creating language networks, and that they contain meaningful community structure.Comment: 14 page

Response of finite-time particle detectors in non-inertial frames and curved spacetime

The response of the Unruh-DeWitt type monopole detectors which were coupled to the quantum field only for a finite proper time interval is studied for inertial and accelerated trajectories, in the Minkowski vacuum in (3+1) dimensions. Such a detector will respond even while on an inertial trajctory due to the transient effects. Further the response will also depend on the manner in which the detector is switched on and off. We consider the response in the case of smooth as well as abrupt switching of the detector. The former case is achieved with the aid of smooth window functions whose width, $T$, determines the effective time scale for which the detector is coupled to the field. We obtain a general formula for the response of the detector when a window function is specified, and work out the response in detail for the case of gaussian and exponential window functions. A detailed discussion of both $T \rightarrow 0$ and $T \rightarrow \infty$ limits are given and several subtlities in the limiting procedure are clarified. The analysis is extended for detector responses in Schwarzschild and de-Sitter spacetimes in (1+1) dimensions.Comment: 29 pages, normal TeX, figures appended as postscript file, IUCAA Preprint # 23/9

Deep Neural Networks - A Brief History

Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure

Decarbonisation at home: The contingent politics of experimental domestic energy technologies

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordPolicy efforts to reduce the carbon intensity of domestic energy consumption have, over the last three decades, been dominated by an almost dichotomous reading of the relationship between technology and social change. On the one hand, there is a conception of personal responsibility that constructs domestic energy users as key actors in the adoption and (appropriate) use of low carbon energy technologies; from this perspective, environmental change becomes a matter of mobilising personal capacities such that individuals make better choices. On the other hand, decarbonising homes is conceived to be an outcome of top-down infrastructural interventions, with householders (or end users) positioned as relatively passive agents who will respond to engineered efficiency in linear and predictable ways. In practice, both positions have been found wanting in terms of accounting for how (and why) change happens and in turn delivering on ambitious policy goals. The argument we develop in this article goes beyond critiquing these problematic framings of technology and the locus of agency. Drawing on three contrasting low carbon energy technology projects in the UK, we present an alternative perspective which foregrounds a more experimental, ad hoc and ultimately provisional mode of governing with domestic energy technologies. We reflect on the meaning and political implications of this experimental turn in transforming (and decarbonising) domestic energy practices.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research on which this paper is based was funded by a grant from EON/EPSRC (EP/G000395/1)

Analysis of dropout learning regarded as ensemble learning

Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers, huge number of units, and connections. Therefore, overfitting is a serious problem. To avoid this problem, dropout learning is proposed. Dropout learning neglects some inputs and hidden units in the learning process with a probability, p, and then, the neglected inputs and hidden units are combined with the learned network to express the final output. We find that the process of combining the neglected hidden units with the learned network can be regarded as ensemble learning, so we analyze dropout learning from this point of view.Comment: 9 pages, 8 figures, submitted to Conferenc

Quantitative multi-objective verification for probabilistic systems

We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies