Local structure in nematic and isotropic liquid crystals

By computer simulations of systems of ellipsoids, we study the influence of the isotropic/nematic phase transition on the direct correlation functions (DCF) in anisotropic fluids. The DCF is determined from the pair distribution function by solving the full Ornstein-Zernike equation, without any approximations. Using a suitable molecular-fixed reference frame, we can distinguish between two qualitatively different contributions to the DCF: One which preserves rotational invariance, and one which breaks it and vanishes in the isotropic phase. We find that the symmetry preserving contribution is barely affected by the phase transition. However, symmetry breaking contributions emerge in the nematic phase and may become quite substantial. Thus the DCF in a nematic fluid is not rotationally invariant. In the isotropic fluid, the DCF is in good agreement with the prediction of the Percus-Yevick theory.Comment: to appear in J. Chem. Phy

Miniature High-Sensitivity High-Temperature Fiber Sensor with a Dispersion Compensation Fiber-Based Interferometer

A miniature high-sensitivity, high-temperature fiber sensor with an interferometer based on a bare small-core-diameter dispersion compensation fiber (DCF) is reported. The sensing head is a single-mode-fiber (SMF) DCF configuration formed by a 4 mm long bare DCF with one end connected to the SMF by a fusion splicing technique and the other end cleaved. Due to the large mode index difference and high thermo-optic coefficient induced by two dominative interference modes, a miniature high-temperature fiber sensor with a high sensitivity of 68.6 pm/°C is obtained by monitoring the wavelength shift of the interference spectrum. This type of sensor has the features of small size, high sensitivity, high stability, simple structure, and low cost

A density-functional theory study of the confined soft ellipsoid fluid

A system of soft ellipsoid molecules confined between two planar walls is studied using classical density-functional theory. Both the isotropic and nematic phases are considered. The excess free energy is evaluated using two different Ansätze and the intermolecular interaction is incorporated using two different direct correlation functions (DCF’s). The first is a numerical DCF obtained from simulations of bulk soft ellipsoid fluids and the second is taken from the Parsons–Lee theory. In both the isotropic and nematic phases the numerical DCF gives density and order parameter profiles in reasonable agreement with simulation. The Parsons–Lee DCF also gives reasonable agreement in the isotropic phase but poor agreement in the nematic phase

A GA-based simulation system for WMNs: comparison analysis for different number of flows, client distributions, DCF and EDCA functions

In this paper, we compare the performance of Distributed Coordination Function (DCF) and Enhanced Distributed Channel Access (EDCA) for normal and uniform distributions of mesh clients considering two Wireless Mesh Network (WMN) architectures. As evaluation metrics, we consider throughput, delay, jitter and fairness index metrics. For simulations, we used WMN-GA simulation system, ns-3 and Optimized Link State Routing. The simulation results show that for normal distribution, the throughput of I/B WMN is higher than Hybrid WMN architecture. For uniform distribution, in case of I/B WMN, the throughput of EDCA is a little bit higher than Hybrid WMN. However, for Hybrid WMN, the throughput of DCF is higher than EDCA. For normal distribution, the delay and jitter of Hybrid WMN are lower compared with I/B WMN. For uniform distribution, the delay and jitter of both architectures are almost the same. However, in the case of DCF for 20 flows, the delay and jitter of I/B WMN are lower compared with Hybrid WMN. For I/B architecture, in case of normal distribution the fairness index of DCF is higher than EDCA. However, for Hybrid WMN, the fairness index of EDCA is higher than DCF. For uniform distribution, the fairness index of few flows is higher than others for both WMN architectures.Peer ReviewedPostprint (author's final draft

Real Options: Applications in Public Economics

This paper illustrates the use of real options principles to value prototypical resource and industryinvestment projects. It captures important competitive/strategic dimensions in a step-by-stepanalysis of investment decisions (options) under uncertainty. It compares and contrasts staticdiscounted cash flow analysis (DCF) with real options analysis using three case studies. The initialexample values a resource extraction process using static DCF and then compares the projectvaluation when future information is valued and acted upon. The second example considers a coaldevelopment and uses the binomial valuation approach to capture the option value associated withhaving the right but not the obligation to exit the development. It contrasts this valuation approachagainst static DCF and highlights that future royalty payments could be underestimated if based onthe standard DCF valuation. The third example analyses the impact of providing a subsidy forhybrid vehicle production to accelerate potential uncertain environmental benefits. Lastly, thesuitability of the standard financial and economic evaluation tools used by treasury agencies isconsidered when projects contain real options.financial economics; investment decisions; public economics; externalities; subsidies; project evaluation

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201