Strong energy enhancement in a laser-driven plasma-based accelerator through stochastic friction

Conventionally, friction is understood as an efficient dissipation mechanism depleting a physical system of energy as an unavoidable feature of any realistic device involving moving parts, e.g., in mechanical brakes. In this work, we demonstrate that this intuitive picture loses validity in nonlinear quantum electrodynamics, exemplified in a scenario where spatially random friction counter-intuitively results in a highly directional energy flow. This peculiar behavior is caused by radiation friction, i.e., the energy loss of an accelerated charge due to the emission of radiation. We demonstrate analytically and numerically how radiation friction can enhance the performance of a specific class of laser-driven particle accelerators. We find the unexpected directional energy boost to be due to the particles' energy being reduced through friction whence the driving laser can accelerate them more efficiently. In a quantitative case we find the energy of the laser-accelerated particles to be enhanced by orders of magnitude.Comment: 14 pages, 3 figure

Mining Frequent Graph Patterns with Differential Privacy

Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. {\em Differential privacy} has recently emerged as the {\em de facto} standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns. We first show that previous techniques on differentially private discovery of frequent {\em itemsets} cannot apply in mining frequent graph patterns due to the inherent complexity of handling structural information in graphs. We then address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling based algorithm. Unlike previous work on frequent itemset mining, our techniques do not rely on the output of a non-private mining algorithm. Instead, we observe that both frequent graph pattern mining and the guarantee of differential privacy can be unified into an MCMC sampling framework. In addition, we establish the privacy and utility guarantee of our algorithm and propose an efficient neighboring pattern counting technique as well. Experimental results show that the proposed algorithm is able to output frequent patterns with good precision

Matrices with restricted entries and q-analogues of permutations

We study the functions that count matrices of given rank over a finite field with specified positions equal to zero. We show that these matrices are $q$-analogues of permutations with certain restricted values. We obtain a simple closed formula for the number of invertible matrices with zero diagonal, a $q$-analogue of derangements, and a curious relationship between invertible skew-symmetric matrices and invertible symmetric matrices with zero diagonal. In addition, we provide recursions to enumerate matrices and symmetric matrices with zero diagonal by rank, and we frame some of our results in the context of Lie theory. Finally, we provide a brief exposition of polynomiality results for enumeration questions related to those mentioned, and give several open questions.Comment: 29 pages, 2 figures, v2: one additional result, some formulas simplified, and a new reference; v3: corrected typo

Achievable efficiencies for probabilistically cloning the states

We present an example of quantum computational tasks whose performance is enhanced if we distribute quantum information using quantum cloning. Furthermore we give achievable efficiencies for probabilistic cloning the quantum states used in implemented tasks for which cloning provides some enhancement in performance.Comment: 9 pages, 8 figure

Sustainable energy planning for remote islands and the waste legacy from renewable energy infrastructure deployment

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record The transition towards a sustainable energy mix is required to achieve Sustainable Development Goal 7 for affordable and clean energy. Remote islands not connected to grid which depend on diesel generators may appear ideal because they can benefit from a variety of renewable energy sources. However, renewable energy deployment requires a lifetime perspective to not inherit waste and other problems to future generations. The aim of this paper is to present a life cycle sustainability framework developed and applied for the case of the island of Ushant off North West France. Seven renewable energy generation scenarios were examined and assessed using technoeconomic, social and environmental indicators utilising life cycle costing and life cycle assessment modelling. The results show that only three out of the seven examined renewable energy scenarios manage to cover the 6807 MWh per annum demand. These scenarios can improve all the indicators against the business-as-usual diesel generation scenario except the ones related to toxicity and reduce greenhouse gas emissions by more than 92%. The easy-to-use framework allows the users to adjust their scenarios and receive useful insight about the nature of the trade-offs between the various indicators. It can also be adapted and updated to include more technologies and support the investigation of more sustainable energy scenarios of other remote island cases in the future.France (Channel) England INTERREG VA programm

Using patterns position distribution for software failure detection

Pattern-based software failure detection is an important topic of research in recent years. In this method, a set of patterns from program execution traces are extracted, and represented as features, while their occurrence frequencies are treated as the corresponding feature values. But this conventional method has its limitation due to ignore the pattern’s position information, which is important for the classification of program traces. Patterns occurs in the different positions of the trace are likely to represent different meanings. In this paper, we present a novel approach for using pattern’s position distribution as features to detect software failure. The comparative experiments in both artificial and real datasets show the effectiveness of this method

Theory of Thermodynamic Magnetic Oscillations in Quasi-One-Dimensional Conductors

The second order correction to free energy due to the interaction between electrons is calculated for a quasi-one-dimensional conductor exposed to a magnetic field perpendicular to the chains. It is found that specific heat, magnetization and torque oscillate when the magnetic field is rotated in the plane perpendicular to the chains or when the magnitude of magnetic filed is changed. This new mechanism of thermodynamic magnetic oscillations in metals, which is not related to the presence of any closed electron orbits, is applied to explain behavior of the organic conductor (TMTSF)$_2$ClO$_4$.Comment: 11 pages + 5 figures (included

Performance and life cycle assessment of a small scale vertical axis wind turbine

This is the author accepted manuscriptWind energy is one of the most popular renewable energy technologies that is considered indispensable in any low carbon energy mix. Small scale wind technologies that occupy less space and can supply electricity directly to their owners are thought to be more environmental friendly than the large turbines and therefore attract less criticism. Based on these, smaller scale renewables especially micro wind turbines should be the ideal solution but this might be just a leap of logic. The aim of this paper is to investigate whether it is worth developing smaller scale vertical axis wind turbines (VAWT) as a solution towards mitigating climate change. A real case of a H-Rotor 5 kW Darrieus vertical axis wind turbine in Poland is investigated for its performance using actual generation data. More importantly, a life cycle assessment (LCA) is undertaken, by compiling a very detailed life cycle inventory based on primary data and two scenarios were examined for the end-of-life treatment, including recycling and incineration. The performance assessment results show that the actual performance is very poor mainly due to the low wind speed. For this reason a series of hypothetical capacity factors were used to facilitate comparison with other studies. Using the CML impact assessment methodology, eleven environmental impact categories are assessed. The results show that the majority of the impacts are accredited to the supporting infrastructure - especially the mast and the foundations - rather than the turbine itself, which in the case of the Global Warming Potential (GWP) accounts for only 30%. Although the specific VAWT cannot achieve a generation that could reduce the environmental impacts to the level of the existing wind energy in Poland, a feasible capacity factor of 1.4% could make the GWP lower than the average low voltage electricity mix in Poland. The environmental performance is very sensitive to the fluctuations of the capacity factor and recommendations are given for appropriate siting, recycling of the metals and integration of the turbine on existing building structure.France (Channel) England INTERREG IV

Local multiresolution order in community detection

Community detection algorithms attempt to find the best clusters of nodes in an arbitrary complex network. Multi-scale ("multiresolution") community detection extends the problem to identify the best network scale(s) for these clusters. The latter task is generally accomplished by analyzing community stability simultaneously for all clusters in the network. In the current work, we extend this general approach to define local multiresolution methods, which enable the extraction of well-defined local communities even if the global community structure is vaguely defined in an average sense. Toward this end, we propose measures analogous to variation of information and normalized mutual information that are used to quantitatively identify the best resolution(s) at the community level based on correlations between clusters in independently-solved systems. We demonstrate our method on two constructed networks as well as a real network and draw inferences about local community strength. Our approach is independent of the applied community detection algorithm save for the inherent requirement that the method be able to identify communities across different network scales, with appropriate changes to account for how different resolutions are evaluated or defined in a particular community detection method. It should, in principle, easily adapt to alternative community comparison measures.Comment: 19 pages, 11 figure