Sustainability best practice in PPP: Case study of a hospital project in the UK

Globally, sustainable development has been given high priority for the Government agenda in order to achieve a balance of social, economic and environmental factors. The UK government realise the importance and criticality of sustainable development and they intend to use the public procurement power to demand more sustainable public building development to improve energy efficiency and reduce carbon emissions. Public Private Partnership (PPP) is an effective procurement tool for the government to deliver the provision of public services. In the UK, the most common PPP form is Private Finance Initiative (PFI). Up until March 2012, a total of 717 PFI projects have been delivered to sustain social and economic development in the UK (HM Treasury, 2012). There is potential to use PPP to incorporate the sustainability agenda and support low carbon economic development. However, little research has been conducted to demonstrate the benefits and advantages of the PPP procurement system incorporating sustainable development. This paper aims to demonstrate best practice in sustainable development through PPP (PFI) procurement system in the UK. It initially illustrates the relationship between PPP and sustainable development and then uses a case study of one of the largest PPP hospital projects in the UK, utilising interviews and secondary data to show evidence of how the sustainability issues have been addressed within the procurement process and the advantage and limitations of using the PPP procurement system in delivering sustainable development. The results show best practice across different strands of sustainability through contribution to local employment and the local economy, a high percentage of waste recycling, dust and noise reduction and technical innovations such as green roofs, natural ventilation and a focus on occupant comfort

Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Synfire chains are one of the main theoretical constructs that have been appealed to to describe coherent spiking phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited their ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by, then reads in streaming input, processes the input, then makes a decision based on the processed information and shuts itself down

Visual Chunking: A List Prediction Framework for Region-Based Object Detection

We consider detecting objects in an image by iteratively selecting from a set of arbitrarily shaped candidate regions. Our generic approach, which we term visual chunking, reasons about the locations of multiple object instances in an image while expressively describing object boundaries. We design an optimization criterion for measuring the performance of a list of such detections as a natural extension to a common per-instance metric. We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals. We also develop a simple class-specific algorithm to generate a candidate region instance in near-linear time in the number of low-level superpixels that outperforms other region generating methods. In order to make predictions on novel images at testing time without access to ground truth, we develop learning approaches to emulate these algorithms' behaviors. We demonstrate that our new approach outperforms sophisticated baselines on benchmark datasets.Comment: to appear at ICRA 201

The Λ2\Lambda_2 limit of massive gravity

Lorentz-invariant massive gravity is usually associated with a strong coupling scale $\Lambda_3$. By including non-trivial effects from the Stueckelberg modes, we show that about these vacua, one can push the strong coupling scale to higher values and evade the linear vDVZ-discontinuity. For generic parameters of the theory and generic vacua for the Stueckelberg fields, the $\Lambda_2$-decoupling limit of the theory is well-behaved and free of any ghost or gradient-like instabilities. We also discuss the implications for nonlinear sigma models with Lorentzian target spaces.Comment: 38 pages, 1 figure, JHEP version, minor change

Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables

Charged track multiplicity is among the most powerful observables for discriminating quark- from gluon-initiated jets. Despite its utility, it is not infrared and collinear (IRC) safe, so perturbative calculations are limited to studying the energy evolution of multiplicity moments. While IRC-safe observables, like jet mass, are perturbatively calculable, their distributions often exhibit Casimir scaling, such that their quark/gluon discrimination power is limited by the ratio of quark to gluon color factors. In this paper, we introduce new IRC-safe counting observables whose discrimination performance exceeds that of jet mass and approaches that of track multiplicity. The key observation is that track multiplicity is approximately Poisson distributed, with more suppressed tails than the Sudakov peak structure from jet mass. By using an iterated version of the soft drop jet grooming algorithm, we can define a "soft drop multiplicity" which is Poisson distributed at leading-logarithmic accuracy. In addition, we calculate the next-to-leading-logarithmic corrections to this Poisson structure. If we allow the soft drop groomer to proceed to the end of the jet branching history, we can define a collinear-unsafe (but still infrared-safe) counting observable. Exploiting the universality of the collinear limit, we define generalized fragmentation functions to study the perturbative energy evolution of collinear-unsafe multiplicity.Comment: 38+10 pages, 21 figures; v2: discussions added to match JHEP versio