A one-dimensional spin-orbit interferometer

We demonstrate that the combination of an external magnetic field and the intrinsic spin-orbit interaction results in nonadiabatic precession of the electron spin after transmission through a quantum point contact (QPC). We suggest that this precession may be observed in a device consisting of two QPCs placed in series. The pattern of resonant peaks in the transmission is strongly influenced by the non-abelian phase resulting from this precession. Moreover, a novel type of resonance which is associated with suppressed, rather than enhanced, transmission emerges in the strongly nonadiabatic regime. The shift in the resonant transmission peaks is dependent on the spin-orbit interaction and therefore offers a novel way to directly measure these interactions in a ballistic 1D system.Comment: 8 pages, 5 figure

Digital evolution in time-dependent fitness landscapes

We study the response of populations of digital organisms that adapt to a time-varying (periodic) fitness landscape of two oscillating peaks. We corroborate in general predictions from quasi-species theory in dynamic landscapes, such as adaptation to the average fitness landscape at small periods (high frequency) and quasistatic adaptation at large periods (low frequency). We also observe adaptive phase shifts (time tags between a change in the fitness landscape and art adaptive change in the population) that indicate a low-pass filter effect, in agreement with existing theory,. Finally, we witness long-term adaptation to fluctuating environments not anticipated in previous theoretical work

Method for classifying multiqubit states via the rank of the coefficient matrix and its application to four-qubit states

We construct coefficient matrices of size 2^l by 2^{n-l} associated with pure n-qubit states and prove the invariance of the ranks of the coefficient matrices under stochastic local operations and classical communication (SLOCC). The ranks give rise to a simple way of partitioning pure n-qubit states into inequivalent families and distinguishing degenerate families from one another under SLOCC. Moreover, the classification scheme via the ranks of coefficient matrices can be combined with other schemes to build a more refined classification scheme. To exemplify we classify the nine families of four qubits introduced by Verstraete et al. [Phys. Rev. A 65, 052112 (2002)] further into inequivalent subfamilies via the ranks of coefficient matrices, and as a result, we find 28 genuinely entangled families and all the degenerate classes can be distinguished up to permutations of the four qubits. We also discuss the completeness of the classification of four qubits into nine families

Efficient Learning for Undirected Topic Models

Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient. This paper provides a novel estimator to speed up the learning based on Noise Contrastive Estimate, extended for documents of variant lengths and weighted inputs. Experiments on two benchmarks show that the new estimator achieves great learning efficiency and high accuracy on document retrieval and classification.Comment: Accepted by ACL-IJCNLP 2015 short paper. 6 page

Anisotropy and interaction effects of strongly strained SrIrO3 thin films

Magneto-transport properties of SrIrO$_3$ thin films epitaxially grown on SrTiO$_3$, using reactive RF sputtering, are investigated. A large anisotropy between the in-plane and the out-of-plane resistivities is found, as well as a signature of the substrate cubic to tetragonal transition. Both observations result from the structural distortion associated to the epitaxial strain. The low-temperature and field dependences of the Hall number are interpreted as due to the contribution of Coulomb interactions to weak localization, evidencing the strong correlations in this material. The introduction of a contribution from magnetic scatters, in the analysis of magnetoconductance in the weakly localized regime, is proposed as an alternative to an anomalously large temperature dependence of the Land\'{e} coefficient

Base Station Switching Problem for Green Cellular Networks with Social Spider Algorithm

With the recent explosion in mobile data, the energy consumption and carbon footprint of the mobile communications industry is rapidly increasing. It is critical to develop more energy-efficient systems in order to reduce the potential harmful effects to the environment. One potential strategy is to switch off some of the under-utilized base stations during off-peak hours. In this paper, we propose a binary Social Spider Algorithm to give guidelines for selecting base stations to switch off. In our implementation, we use a penalty function to formulate the problem and manage to bypass the large number of constraints in the original optimization problem. We adopt several randomly generated cellular networks for simulation and the results indicate that our algorithm can generate superior performance