Relation between directed polymers in random media and random bond dimer models

We reassess the relation between classical lattice dimer models and the continuum elastic description of a lattice of fluctuating polymers. In the absence of randomness we determine the density and line tension of the polymers in terms of the bond weights of hard-core dimers on the square and the hexagonal lattice. For the latter, we demonstrate the equivalence of the canonical ensemble for the dimer model and the grand-canonical description for polymers by performing explicitly the continuum limit. Using this equivalence for the random bond dimer model on a square lattice, we resolve a previously observed discrepancy between numerical results for the random dimer model and a replica approach for polymers in random media. Further potential applications of the equivalence are briefly discussed.Comment: 6 pages, 3 figure

Superfluid-Mott-Insulator Transition in a One-Dimensional Optical Lattice with Double-Well Potentials

We study the superfluid-Mott-insulator transition of ultracold bosonic atoms in a one-dimensional optical lattice with a double-well confining trap using the density-matrix renormalization group. At low density, the system behaves similarly as two separated ones inside harmonic traps. At high density, however, interesting features appear as the consequence of the quantum tunneling between the two wells and the competition between the "superfluid" and Mott regions. They are characterized by a rich step-plateau structure in the visibility and the satellite peaks in the momentum distribution function as a function of the on-site repulsion. These novel properties shed light on the understanding of the phase coherence between two coupled condensates and the off-diagonal correlations between the two wells.Comment: 5 pages, 7 figure

Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi

A micro-electro-mechanical-system-based thermal shear-stress sensor with self-frequency compensation

By applying the micro-electro-mechanical-system (MEMS) fabrication technology, we developed a micro-thermal sensor to measure surface shear stress. The heat transfer from a polysilicon heater depends on the normal velocity gradient and thus provides the surface shear stress. However, the sensitivity of the shear-stress measurements in air is less than desirable due to the low heat capacity of air. A unique feature of this micro-sensor is that the heating element, a film 1 µm thick, is separated from the substrate by a vacuum cavity 2 µm thick. The vacuum cavity prevents the conduction of heat to the substrate and therefore improves the sensitivity by an order of magnitude. Owing to the low thermal inertia of the miniature sensing element, this shear-stress micro-sensor can provide instantaneous measurements of small-scale turbulence. Furthermore, MEMS technology allows us make multiple sensors on a single chip so that we can perform distributed measurements. In this study, we use multiple polysilicon sensor elements to improve the dynamic performance of the sensor itself. It is demonstrated that the frequency-response range of a constant-current sensor can be extended from the order of 100 Hz to 100 kHz

Accurate determination of tensor network state of quantum lattice models in two dimensions

We have proposed a novel numerical method to calculate accurately the physical quantities of the ground state with the tensor-network wave function in two dimensions. We determine the tensor network wavefunction by a projection approach which applies iteratively the Trotter-Suzuki decomposition of the projection operator and the singular value decomposition of matrix. The norm of the wavefunction and the expectation value of a physical observable are evaluated by a coarse grain renormalization group approach. Our method allows a tensor-network wavefunction with a high bond degree of freedom (such as D=8) to be handled accurately and efficiently in the thermodynamic limit. For the Heisenberg model on a honeycomb lattice, our results for the ground state energy and the staggered magnetization agree well with those obtained by the quantum Monte Carlo and other approaches.Comment: 4 pages 5 figures 2 table

Macro aerodynamic devices controlled by micro systems

Micro-ElectroMechanical-Systems (MEMS) have emerged as a major enabling technology across the engineering disciplines. In this study, the possibility of applying MEMS to the aerodynamic field was explored. We have demonstrated that microtransducers can be used to control the motion of a delta wing in a wind tunnel and can even maneuver a scaled aircraft in flight tests. The main advantage of using micro actuators to replace the traditional control surface is the significant reduction of radar cross-sections. At a high angle of attack, a large portion of the suction loading on a delta wing is contributed by the leading edge separation vortices which originate from thin boundary layers at the leading edge. We used microactuators with a thickness comparable to that of the boundary layer in order to alter the separation process and thus achieved control of the global motion by minute perturbations

Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

What determine firms’ capital structure in China?

Purpose – This paper investigates the determinants of capital structure using a cross-section sample of 1481 non-financial firms listed on the Chinese stock exchanges in 2011. Design/methodology/approach – Employing four leverage measures (total leverage and long-term leverage in terms of both book value and market value, respectively), this study examines the effects of factors with proven influences on capital structure in literature, along with industry effect and ownership effect. Findings – We find that large firms favour debt financing while profitable firms rely more on internal capital accumulation. Intangibility and business risk increase the level of debt financing but tax has little impact on capital structure. We also observe strong industrial effect and ownership effect. Real estate firms borrow considerably more and firms from utility and manufacturing industries use more long-term debt despite compared with commercial firms. On the other hand, firms with state ownership tend to borrow more, while firms with foreign ownership choose more equity financing. Research limitations – The study uses cross-section data to avoid any potential time effects, which allows us to focus on our main research question – to identify the determinants of capital structure for Chinese firms. Future research may gain more insights using panel data and considering other factors such as crisis and financial reforms. Practical implications – These results may provide important implications to investors in making investment decision and to firms in making financing decisions. Originality/value – this paper uses by far the largest and latest cross-section sample from the Chinese stock markets, offering a more complete picture of the financing behaviours in the Chinese firms, with known characters and the impact of ownerships