Sign switch of Gaussian bending modulus for microemulsions; a self-consistent field analysis exploring scale invariant curvature energies

Bending rigidities of tensionless balanced liquid-liquid interfaces as occurring in microemulsions are predicted using self-consistent field theory for molecularly inhomogeneous systems. Considering geometries with scale invariant curvature energies gives unambiguous bending rigidities for systems with fixed chemical potentials: The minimal surface Im3m cubic phase is used to find the Gaussian bending rigidity, $\bar{\kappa}$, and a torus with Willmore energy $W=2 \pi^2$ allows for direct evaluation of the mean bending modulus, $\kappa$. Consistent with this, the spherical droplet gives access to $2 \kappa + \bar{\kappa}$. We observe that $\bar{\kappa}$ tends to be negative for strong segregation and positive for weak segregation; a finding which is instrumental for understanding phase transitions from a lamellar to a sponge-like microemulsion. Invariably, $\kappa$ remains positive and increases with increasing strength of segregation.Comment: 7 pages, 5 figure

Universal Dephasing Control During Quantum Computation

Dephasing is a ubiquitous phenomenon that leads to the loss of coherence in quantum systems and the corruption of quantum information. We present a universal dynamical control approach to combat dephasing during all stages of quantum computation, namely, storage, single- and two-qubit operators. We show that (a) tailoring multi-frequency gate pulses to the dephasing dynamics can increase fidelity; (b) cross-dephasing, introduced by entanglement, can be eliminated by appropriate control fields; (c) counter-intuitively and contrary to previous schemes, one can increase the gate duration, while simultaneously increasing the total gate fidelity.Comment: 4 pages,3 figure

Chain motion and viscoelasticity in highly entangled solutions of semiflexible rods

Brownian dynamics simulations are used to study highly entangled solutions of semiflexible polymers. Bending fluctuations of semiflexible rods are signficantly affected by entanglement only above a concentration $c^{**}$, where $c^{**}\sim 10^{3}L^{-3}$ for chains of similar length $L$ and persistence length. For $c > c^{**}$, the tube radius $R_{e}$ approaches a dependence $R_{e} \propto c^{-3/5}$, and the linear viscoelastic response develops an elastic contribution that is absent for $c < c^{**}$. Experiments on isotropic solutions of $F$-actin span concentrations near $c^{**}$ for which the predicted asymptotic scaling of the plateau modulus $G \propto c^{7/5}$ is not yet valid.Comment: 4 pages, 5 figures, submitted to PR

Influence of analysis and design models on minimum weight design

The results of numerical experiments designed to illustrate how the minimum weight design, accuracy, and cost can be influenced by: (1) refinement of the finite element analysis model and associated load path problems, and (2) refinement of the design variable linking model are examined. The numerical experiments range from simple structures where the modelling decisions are relatively obvious and less costly to the more complex structures where such decisions are less obvious and more costly. All numerical experiments used employ the dual formulation in ACCESS-3 computer program. Guidelines are suggested for creating analysis and design models that predict a minimum weight structure with greater accuracy and less cost. These guidelines can be useful in an interactive optimization environment and in the design of heuristic rules for the development of knowledge-based expert optimization systems

Experimental evidence on promotion of electric and improved biomass cookstoves.

Improved cookstoves (ICS) can deliver "triple wins" by improving household health, local environments, and global climate. Yet their potential is in doubt because of low and slow diffusion, likely because of constraints imposed by differences in culture, geography, institutions, and missing markets. We offer insights about this challenge based on a multiyear, multiphase study with nearly 1,000 households in the Indian Himalayas. In phase I, we combined desk reviews, simulations, and focus groups to diagnose barriers to ICS adoption. In phase II, we implemented a set of pilots to simulate a mature market and designed an intervention that upgraded the supply chain (combining marketing and home delivery), provided rebates and financing to lower income and liquidity constraints, and allowed households a choice among ICS. In phase III, we used findings from these pilots to implement a field experiment to rigorously test whether this combination of upgraded supply and demand promotion stimulates adoption. The experiment showed that, compared with zero purchase in control villages, over half of intervention households bought an ICS, although demand was highly price-sensitive. Demand was at least twice as high for electric stoves relative to biomass ICS. Even among households that received a negligible price discount, the upgraded supply chain alone induced a 28 percentage-point increase in ICS ownership. Although the bundled intervention is resource-intensive, the full costs are lower than the social benefits of ICS promotion. Our findings suggest that market analysis, robust supply chains, and price discounts are critical for ICS diffusion

ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks

A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive Synaptic Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for real time on-line learning in a dynamic environment. We incorporate an adaptive weight decay mechanism with the traditional Spike Timing Dependent Plasticity (STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic weights is modulated based on the temporal correlation between the spiking patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual forgetting of insignificant data while retaining significant, yet old, information. ASP, thus, maintains a balance between forgetting and immediate learning to construct a stable-plastic self-adaptive SNN for continuously changing inputs. We demonstrate that the proposed learning methodology addresses catastrophic forgetting while yielding significantly improved accuracy over the conventional STDP learning method for digit recognition applications. Additionally, we observe that the proposed learning model automatically encodes selective attention towards relevant features in the input data while eliminating the influence of background noise (or denoising) further improving the robustness of the ASP learning.Comment: 14 pages, 14 figure

Towards a Better Understanding of the Local Attractor in Particle Swarm Optimization: Speed and Solution Quality

Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimization problems. Although this technique is widely used, the understanding of the mechanisms that make swarms so successful is still limited. We present the first substantial experimental investigation of the influence of the local attractor on the quality of exploration and exploitation. We compare in detail classical PSO with the social-only variant where local attractors are ignored. To measure the exploration capabilities, we determine how frequently both variants return results in the neighborhood of the global optimum. We measure the quality of exploitation by considering only function values from runs that reached a search point sufficiently close to the global optimum and then comparing in how many digits such values still deviate from the global minimum value. It turns out that the local attractor significantly improves the exploration, but sometimes reduces the quality of the exploitation. As a compromise, we propose and evaluate a hybrid PSO which switches off its local attractors at a certain point in time. The effects mentioned can also be observed by measuring the potential of the swarm