Semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions

This paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions. Our results extend those of Chamberlain (1992b) and Ai and Chen (2003) for semiparametric conditional moment restriction models with identical information sets to the case of nested information sets, and those of Chamberlain (1992a) and Brown and Newey (1998) for models of sequential moment restrictions without unknown functions to cases with unknown functions of possibly endogenous variables. Our bound results are applicable to semiparametric panel data models and semiparametric two stage plug-in problems. As an example, we compute the efficiency bound for a weighted average derivative of a nonparametric instrumental variables (IV) regression, and find that the simple plug-in estimator is not efficient. Finally, we present an optimally weighted, orthogonalized, sieve minimum distance estimator that achieves the semiparametric efficiency bound.

Controllable conditional quantum oscillations and synchronization in superconducting flux qubits

Conditional quantum oscillations are investigated for quantum gate operations in superconducting flux qubits. We present an effective Hamiltonian which describes a conditional quantum oscillation in two-qubit systems. Rabi-type quantum oscillations are discussed in implementing conditional quantum oscillations to quantum gate operations. Two conditional quantum oscillations depending on the states of control qubit can be synchronized to perform controlled-gate operations by varying system parameters. It is shown that the conditional quantum oscillations with their frequency synchronization make it possible to operate the controlled-NOT and -U gates with a very accurate gate performance rate in interacting qubit systems. Further, this scheme can be applicable to realize a controlled multi-qubit operation in various solid-state qubit systems.Comment: 11 page

Semiparametric Efficiency Bound for Models of Sequential Moment Restrictions Containing Unknown Functions

This paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions. Our results extend those of Chamberlain (1992b) and Ai and Chen (2003) for semiparametric conditional moment restriction models with identical information sets to the case of nested information sets, and those of Chamberlain (1992a) and Brown and Newey (1998) for models of sequential moment restrictions without unknown functions to cases with unknown functions of possibly endogenous variables. Our bound results are applicable to semiparametric panel data models and semiparametric two stage plug-in problems. As an example, we compute the efficiency bound for a weighted average derivative of a nonparametric instrumental variables (IV) regression, and find that the simple plug-in estimator is not efficient. Finally, we present an optimally weighted, orthogonalized, sieve minimum distance estimator that achieves the semiparametric efficiency bound.Sequential moment models, Semiparametric efficiency bounds, Optimally weighted orthogonalized sieve minimum distance, Nonparametric IV regression, Weighted average derivatives, Partially linear quantile IV

Thermal effects on bipartite and multipartite correlations in fiber coupled cavity arrays

We investigate the thermal influence of fibers on the dynamics of bipartite and multipartite correlations in fiber coupled cavity arrays where each cavity is resonantly coupled to a two-level atom. The atom-cavity systems connected by fibers can be considered as polaritonic qubits. We first derive a master equation to describe the evolution of the atom-cavity systems. The bipartite (multipartite) correlations is measured by concurrence and discord (spin squeezing). Then, we solve the master equation numerically and study the thermal effects on the concurrence, discord, and spin squeezing of qubits. On the one hand, at zero temperature, there are steady-state bipartite and multipartite correlations. One the other hand, the thermal fluctuations of a fiber may blockade the generation of entanglement of two qubits connected directly by the fiber while the discord can be generated and stored for a long time. This thermal-induced blockade effects of bipartite correlations may be useful for quantum information processing. The bipartite correlations of a longer chain of qubits is more robust than a shorter one in the presence of thermal fluctuations

The uneven price impact of energy efficiency ratings on housing segments and implications for public policy and private markets

In the literature, there is extensive, although in some cases inconclusive, evidence on the impact of Energy Performance Certificates (EPC) on housing prices. Nonetheless, the question of whether such an impact is homogenous across residential segments remains highly unexplored. This paper addresses this latter issue utilizing multifamily listing data in metropolitan Barcelona. In doing so, first the entire sample is analyzed using a hedonic model. Second, the sample is split on the basis of a multivariate segmentation. Finally, separated hedonic models are specified again. The results suggest that in general, there is a modest impact of EPC ratings on listing prices, nonetheless it is not homogeneous across housing segments: (1) for the most modern apartments, with state-of-the-art features and active environmental comfort, energy ratings seem to play a null role in the formation of prices; (2) conversely, for the cheapest apartments, apartments boasting the most basic features, and apartments located in low-income areas, the “brown discount” is enormously significant, potentially depreciating the equity of those who have the least resources to carry out an energy retrofit. These results have implications for the assessment of the EPBD and its Spanish transposition, since a very well-intentioned environmental policy could have potentially harmful social repercussions in the absence of corrective measures.Peer ReviewedPostprint (published version

A slave mode expansion for obtaining ab-initio interatomic potentials

Here we propose a new approach for performing a Taylor series expansion of the first-principles computed energy of a crystal as a function of the nuclear displacements. We enlarge the dimensionality of the existing displacement space and form new variables (ie. slave modes) which transform like irreducible representations of the space group and satisfy homogeneity of free space. Standard group theoretical techniques can then be applied to deduce the non-zero expansion coefficients a priori. At a given order, the translation group can be used to contract the products and eliminate terms which are not linearly independent, resulting in a final set of slave mode products. While the expansion coefficients can be computed in a variety of ways, we demonstrate that finite difference is effective up to fourth order. We demonstrate the power of the method in the strongly anharmonic system PbTe. All anharmonic terms within an octahedron are computed up to fourth order. A proper unitary transformation demonstrates that the vast majority of the anharmonicity can be attributed to just two terms, indicating that a minimal model of phonon interactions is achievable. The ability to straightforwardly generate polynomial potentials will allow precise simulations at length and time scales which were previously unrealizable

Reveal flocking of birds flying in fog by machine learning

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning approach called "learning by confusion" or a widely used supervised learning approach. We find that after the training via either the unsupervised learning approach or the supervised learning one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine learning can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.Comment: 7 pages, 3 figure

Resource Allocation for Device-to-Device Communications Underlaying Heterogeneous Cellular Networks Using Coalitional Games

Heterogeneous cellular networks (HCNs) with millimeter wave (mmWave) communications included are emerging as a promising candidate for the fifth generation mobile network. With highly directional antenna arrays, mmWave links are able to provide several-Gbps transmission rate. However, mmWave links are easily blocked without line of sight. On the other hand, D2D communications have been proposed to support many content based applications, and need to share resources with users in HCNs to improve spectral reuse and enhance system capacity. Consequently, an efficient resource allocation scheme for D2D pairs among both mmWave and the cellular carrier band is needed. In this paper, we first formulate the problem of the resource allocation among mmWave and the cellular band for multiple D2D pairs from the view point of game theory. Then, with the characteristics of cellular and mmWave communications considered, we propose a coalition formation game to maximize the system sum rate in statistical average sense. We also theoretically prove that our proposed game converges to a Nash-stable equilibrium and further reaches the near-optimal solution with fast convergence rate. Through extensive simulations under various system parameters, we demonstrate the superior performance of our scheme in terms of the system sum rate compared with several other practical schemes.Comment: 13 pages, 12 figure