Economic benefit of the National Broadband Network

This paper argues that all regions benefit from the NBN but the economic effects are greater in the major cities because of their larger economic activity. Executive summary This paper is a partial summary of a study undertaken in the Centre for Energy-Efficient Telecommunications (CEET) at the University of Melbourne. The study focuses on the potential economic impact of Australia’s NBN. The NBN affects the economy by making online services more widely available. Taking a conservative approach, we have considered just six categories of online services (cloud computing, electronic commerce, online higher education, telehealth practice, teleworking, and entertainment) from which there are documented economic benefits. We have attributed to the NBN only the additional benefit derived from its deployment over and above what we estimate would have been the broadband situation in Australia without the NBN. That is, we have not assumed that broadband availability would have stagnated without the NBN. We do expect, however, that future services will require higher access speeds, generally in the range 10-25 Mb/s. With this assumption and using a well-attested model of the Australian economy, we show that, in the long term, real GDP can be boosted by about 1.8% and real household consumption (a measure of national welfare) by about 2.0%. When we take into account the need to repay the cost of the NBN, GDP increases slightly but the benefit to real household consumption is reduced to 1.4%. Most of the benefit comes from telehealth and teleworking. Because the access speeds (downstream and upstream) required for the services are quite uncertain, we have looked at the effects of access speeds. If all the services except entertainment can be provided with no more than 2.5 Mb/s down and up (typical of implementations today), then the costs of the NBN outweigh the benefits. Real GDP increases by less than 0.2% but real household consumption declines by 0.4%. That is, building an NBN just for entertainment is not economically viable. An analysis of the regional distribution of benefits shows that all regions benefit from the NBN but the economic effects are greater in the major cities because of their larger economic activity

Using Bad Learners to find Good Configurations

Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, building an accurate performance model can be very expensive (and is often infeasible in practice). The central insight of this paper is that exact performance values (e.g. the response time of a software system) are not required to rank configurations and to identify the optimal one. As shown by our experiments, models that are cheap to learn but inaccurate (with respect to the difference between actual and predicted performance) can still be used rank configurations and hence find the optimal configuration. This novel \emph{rank-based approach} allows us to significantly reduce the cost (in terms of number of measurements of sample configuration) as well as the time required to build models. We evaluate our approach with 21 scenarios based on 9 software systems and demonstrate that our approach is beneficial in 16 scenarios; for the remaining 5 scenarios, an accurate model can be built by using very few samples anyway, without the need for a rank-based approach.Comment: 11 pages, 11 figure

Strongly Refuting Random CSPs Below the Spectral Threshold

Random constraint satisfaction problems (CSPs) are known to exhibit threshold phenomena: given a uniformly random instance of a CSP with $n$ variables and $m$ clauses, there is a value of $m = \Omega(n)$ beyond which the CSP will be unsatisfiable with high probability. Strong refutation is the problem of certifying that no variable assignment satisfies more than a constant fraction of clauses; this is the natural algorithmic problem in the unsatisfiable regime (when $m/n = \omega(1)$). Intuitively, strong refutation should become easier as the clause density $m/n$ grows, because the contradictions introduced by the random clauses become more locally apparent. For CSPs such as $k$-SAT and $k$-XOR, there is a long-standing gap between the clause density at which efficient strong refutation algorithms are known, $m/n \ge \widetilde O(n^{k/2-1})$, and the clause density at which instances become unsatisfiable with high probability, $m/n = \omega (1)$. In this paper, we give spectral and sum-of-squares algorithms for strongly refuting random $k$-XOR instances with clause density $m/n \ge \widetilde O(n^{(k/2-1)(1-\delta)})$ in time $\exp(\widetilde O(n^{\delta}))$ or in $\widetilde O(n^{\delta})$ rounds of the sum-of-squares hierarchy, for any $\delta \in [0,1)$ and any integer $k \ge 3$. Our algorithms provide a smooth transition between the clause density at which polynomial-time algorithms are known at $\delta = 0$, and brute-force refutation at the satisfiability threshold when $\delta = 1$. We also leverage our $k$-XOR results to obtain strong refutation algorithms for SAT (or any other Boolean CSP) at similar clause densities. Our algorithms match the known sum-of-squares lower bounds due to Grigoriev and Schonebeck, up to logarithmic factors. Additionally, we extend our techniques to give new results for certifying upper bounds on the injective tensor norm of random tensors

Measured unsteady transonic aerodynamic characteristics of an elastic supercritical wing with an oscillating control surface

Transonic steady and unsteady aerodynamic data were measured on a large elastic wing in the NASA Langley Transonic Dynamics Tunnel. The wing had a supercritical airfoil shape and a leading-edge sweepback of 28.8 deg. The wing was heavily instrumented to measure both static and dynamic pressures and deflections. A hydraulically driven outboard control surface was oscillated to generate unsteady airloads on the wing. Representative results from the wind tunnel tests are presented and discussed, and the unexpected occurrence of an unusual dynamic wing instability, which was sensitive to angle of attack, is reported

Overview and future challenges of nearly zero energy buildings (nZEB) design in Southern Europe

In times of great transition of the European construction sector to energy efficient and nearly zero energy buildings (nZEB), a market observation containing qualitative and quantitative indications should help to fill out some of the current gaps concerning the EU 2020 carbon targets. Next to the economic challenges, there are equally important factors that hinder renovating the existing residential building stock and adding newly constructed high performance buildings. Under these circumstances this paper summarises the findings of a cross-comparative study of the societal and technical barriers of nZEB implementation in 7 Southern European countries. The study analyses the present situation and provides an overview on future prospects for nZEB in Southern Europe. The result presents an overview of challenges and provides recommendations based on available empirical evidence to further lower those barriers in the European construction sector. The paper finds that the most Southern European countries are poorly prepared for nZEB implementation and especially to the challenge opportunity of retrofitting existing buildings. Creating a common approach to further develop nZEB targets, concepts and definitions in synergy with the climatic, societal and technical state of progress in Southern Europe is essential. The paper provides recommendations for actions to shift the identified gaps into opportunities for future development of climate adaptive high performance buildings. (C) 2017 Elsevier B.V. All rights reserved.info:eu-repo/semantics/publishedVersio

A method of strategic evaluation of energy performance of Building Integrated Photovoltaic in the urban context

This paper presents an integrated bottom-up approach aimed at helping those dealing with strategical analysis of installation of Building Integrated Photo Voltaic (BIPV) to estimate the electricity production potential along with the energy needs of urban buildings at the district scale. On the demand side, hourly energy profiles are generated using dynamic building simulation taking into account actual urban morphologies. On the supply side, electricity generated from the system is predicted considering both the direct and indirect components of solar radiation as well as local climate variables. Python-based Algorithm editor Grasshopper is used to interlink four types of modelling and simulation tools as 1) generation of 3-D model, 2) solar radiation analysis, 3) formatting weather files (TMY data set) and 4) dynamic energy demand. The method has been demonstrated for a cluster of 20 buildings located in the Yasar University in Izmir (Turkey), for which it is found the BIPV system could achieve an annual renewable share of 23%, in line with the Renewable Energy Directive target of 20%. Quantitatively-compared demand and supply information at hourly time step shows that only some energy needs can be met by BIPV, so there is a need for an appropriate matching strategy to better exploit the renewable energy potential

CMU-Penn T-SET UTC Researcher Creates Smarter Parking in Pittsburgh

The Technologies for Safe and Efficient Transportation (T-SET) UTC, a partnership between Carnegie Mellon University (CMU) and the University of Pennsylvania, is working to increase both efficiency and safety in transportation using advanced intelligent transportation systems (ITS) technologies. One of T-SET's recent award-winning collaborations is the ParkPGH project\u2014a smart parking system that uses historical parking and event data to show the availability of parking in eight parking facilities operated by private (Alco Parking) and public (Pittsburgh Parking Authority) partners within the Pittsburgh cultural district