Critical behaviours of contact near phase transitions

A central quantity of importance for ultracold atoms is contact, which measures two-body correlations at short distances in dilute systems. It appears in universal relations among thermodynamic quantities, such as large momentum tails, energy, and dynamic structure factors, through the renowned Tan relations. However, a conceptual question remains open as to whether or not contact can signify phase transitions that are insensitive to short-range physics. Here we show that, near a continuous classical or quantum phase transition, contact exhibits a variety of critical behaviors, including scaling laws and critical exponents that are uniquely determined by the universality class of the phase transition and a constant contact per particle. We also use a prototypical exactly solvable model to demonstrate these critical behaviors in one-dimensional strongly interacting fermions. Our work establishes an intrinsic connection between the universality of dilute many-body systems and universal critical phenomena near a phase transition.Comment: Final version published in Nat. Commun. 5:5140 doi: 10.1038/ncomms6140 (2014

Entropy and Its Quantum Thermodynamical Implication for Anomalous Spectral Systems

The state function entropy and its quantum thermodynamical implication for two typical dissipative systems with anomalous spectral densities are studied by investigating on their low-temperature quantum behavior. In all cases it is found that the entropy decays quickly and vanishes as the temperature approaches zero. This reveals a good conformity with the third law of thermodynamics and provides another evidence for the validity of fundamental thermodynamical laws in the quantum dissipative region.Comment: 10 pages, 3 figure

The Political Economy of Low-Carbon Investment: the Role of Coalitions and Alignments of Interest in the Green Transformation in China

The primary motivation behind this research is the need to accelerate the supply of renewable energy because of the important role that it plays in mitigating climate change and in fostering sustainable development. Understanding past drivers for low-carbon investment can help us identify those for the future, and what could accelerate such investment. Investment in renewable energy can be modelled as a problem of technical asset allocation or optimisation at the firm or sectoral level, but is not entirely explained by this approach – the context in which actors are involved, their motivations and the wider systems in which they operate must also be taken into account. The interactions between actors may sometimes accelerate investment and sometimes prevent it; however, understanding the dynamics of these processes is crucial if we are to shape them. This report is part of a two-part study focusing on the wind and solar power sectors in China and India, which aims to find and compare drivers for investment in renewable energy. This report takes the example of China. This China-based research examines ten case studies: four wind power and six solar power. They have been selected according to investment data and industrial characteristics. Each study covers the period during which investment in renewable technologies was at its peak and all are representative of the corporate financing at that time. For each, we reviewed all of the available literature (an abundance of papers and reports) and conducted face-to-face interviews. Our interviewees ranged from wind turbine and solar photovoltaic (PV) manufacturers, to employees of state-owned enterprises and banks, to local and central government officials (particularly from the National Development and Reform Commission and the Ministry of Commerce), as well as other researchers.UK Department for International Developmen

A resource aware MapReduce based parallel SVM for large scale image classification

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them support vector machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents RASMO, a resource aware MapReduce based parallel SVM algorithm for large scale image classifications which partitions the training data set into smaller subsets and optimizes SVM training in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of RASMO in heterogeneous computing environments. RASMO is evaluated in both experimental and simulation environments. The results show that the parallel SVM algorithm reduces the training time significantly compared with the sequential SMO algorithm while maintaining a high level of accuracy in classification

Cu/Ag EAM Potential Optimized for Heteroepitaxial Diffusion from ab initio Data

A binary embedded-atom method (EAM) potential is optimized for Cu on Ag(111) by fitting to ab initio data. The fitting database consists of DFT calculations of Cu monomers and dimers on Ag(111), specifically their relative energies, adatom heights, and dimer separations. We start from the Mishin Cu-Ag EAM potential and first modify the Cu-Ag pair potential to match the FCC/HCP site energy difference then include Cu-Cu pair potential optimization for the entire database. The optimized EAM potential reproduce DFT monomer and dimer relative energies and geometries correctly. In trimer calculations, the potential produces the DFT relative energy between FCC and HCP trimers, though a different ground state is predicted. We use the optimized potential to calculate diffusion barriers for Cu monomers, dimers, and trimers. The predicted monomer barrier is the same as DFT, while experimental barriers for monomers and dimers are both lower than predicted here. We attribute the difference with experiment to the overestimation of surface adsorption energies by DFT and a simple correction is presented. Our results show that the optimized Cu-Ag EAM can be applied in the study of larger Cu islands on Ag(111).Comment: 15 pages, 7 figure

China's post-coal growth

Slowing GDP growth, a structural shift away from heavy industry, and more proactive policies on air pollution and clean energy have caused China's coal use to peak. It seems that economic growth has decoupled from growth in coal consumption