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In this work, we derive universal nonadiabatic passages in an $M+N$-dimensional discrete system, where $M$ and $N$ denote the degrees of freedom for the assistant and working subspaces, respectively, that could be separated due to rotation or energy. A systematic method is provided to construct parametric ancillary bases for the nonadiabatic passages, which can set up connection between arbitrary initial and target states. In applications, a transitionless dynamics determined by the von Neumann equation with the time-dependent system Hamiltonian can be formulated to entangle distant qubits, as a vital prerequisite for the practical quantum networks. Using tunable longitudinal interaction between two distant qubits, the superconducting system evolves to the single-excitation Bell state with a fidelity as high as $F=0.997$ from the ground state. When the longitudinal interaction is switched off, the single-excitation Bell state can be further converted to the double-excitation Bell state with $F=0.982$ by parametric driving. Moreover, our protocol can be adapted to generate the Greenberger-Horne-Zeilinger state for $N$ qubits with $N$ steps. Our work develops a full-fledged theory for nonadiabatic state engineering in quantum information processing, which is flexible in target selection and robust against the external noise., Comment: 14 pages, 7 figures

Grain boundaries have extensive influence on the performance of crystal materials. However, the atomic-scale structure and its relation with local and crystallographic symmetries remain elusive in low-symmetry crystals. Herein, we find that the local pseudo-mirror-symmetric atomic layer is the common physical origin of a series of highly coherent grain boundaries in the low-symmetry $\beta$-Ga2O3 crystal. These include the (100) twin boundary and an emerging series of $(h-1'0'2)/(h+1'0'\bar{2})$ coherent asymmetric grain boundaries (CAGBs). Owing to the local pseudo-mirror symmetry and the special geometric relation of the $\beta$-Ga2O3 conventional cell, these CAGBs place 80% of the boundary atoms in pseudo-coincident sites, exhibiting high coherence under the coincident-site lattice model. With a combination of density functional theory calculations, Czochralski growth experiment, and atomic-scale characterizations, the structure and stability of the $(002)/(20\bar{2})$-A CAGB are confirmed, with a boundary energy density as low as 0.36 J/m2. This CAGB is responsible for the spontaneous formation of a twinned defect facet at the surface steps during the epitaxy growth of $\beta$-Ga2O3, warranting a substrate orientation selection rule for $\beta$-Ga2O3. Through this study, we provide insights into the grain boundary physics in the low-symmetry $\beta$-Ga2O3 crystal while emphasizing the importance of the local pseudo-symmetries in the low-symmetry crystals.

A stable and fast path linking arbitrary pair of quantum states is commonly desired for the engineering protocols inspired by stimulated Raman adiabatic passage, such as shortcut to adiabaticity and holonomic transformation. It has immediate applications in quantum control and quantum computation and is fundamental about the exact solution of a time-dependent Schr\"odinger equation. We construct a universal control framework based on the system dynamics within an ancillary picture, in which the system Hamiltonian is diagonal so that no transition exists among the ancillary bases during the time evolution. Practically the desired evolution path can be obtained by the von Neumann equation for the parametric ancillary bases. We demonstrate that our control framework can be reduced to the nonadiabatic holonomic quantum transformation, the Lewis-Riesenfeld theory for invariant, and the counterdiabatic driving method under distinct scenarios and conditions. Also it can be used to achieve the cyclic transfer of system population that could be a hard or complex problem for the existing methods. For the state engineering over a finite-dimensional quantum system, our work can provide a full-rank nonadiabatic time-evolution operator.

A stable and high-quality single-magnon state is desired by the single-magnon source for quantum information application with a macroscopic spin system. We consider a hybrid system where a magnon mode is directly coupled to a nonresonant superconducting qubit via the exchange interaction. The magnon and qubit are under the driving and probing fields with the same frequency, respectively. We find that the single-magnon probability $P_1$ can be maximized when the product of the magnon-driving field detuning and the qubit-probing field detuning is equivalent to the square of the magnon-qubit coupling strength, $\Delta_q\Delta_m=J^2$. Then, the double-magnon probability $P_2$ can be minimized by tuning the ratio of the probing intensity to the driving intensity and the relative phase between the two fields. Under these optimized conditions with accessible strong driving intensity and low decay rate, strong magnon blockade gives rise to a stable single-magnon state with a high quality. It features a large brightness (the single-magnon probability) $P_1\approx0.40$ and a high purity (the equal-time second-order correlation function) $g^{(2)}(0)\sim10^{-5}$. The two indicators as a whole prevail over the existing results for photon, phonon, and magnon modes with respect to a stable single-quantum state. The optimized conditions with a scalable modification $\Delta_q\Delta_m\approx NJ^2$ apply to the situation when one focus on only one of the $N$ magnon modes that are simultaneously coupled to a common qubit., Comment: 10 pages, 7 figures

Nonadiabatic holonomic quantum gates are high-speed and robust. Nevertheless, they were found to be more fragile than the adiabatic gates when systematic errors become dominant. Inspired by the dark-path scheme that was used to partially relieve the systematic error in the absence of external noise, we construct a universal set of nonadiabatic holonomic $N$-qubit gates using the Rydberg-Rydberg interaction between atoms under off-resonant driving. Based on an effective four-level configuration in the Rydberg-atom system, the modified nonadiabatic holonomic geometric gates present a clear resilience to both systematic error in the whole parametric range and external noise. In our scheme, the conventional ultrastrong interaction between control atoms and the target atom for the nonadiabatic holonomic quantum computation is compensated by the detuning of the driving fields on the target atom. That idea yields a deeper understanding about the holonomic transformation. Moreover, our scheme is compact and scale-free with respect to $N$. It is interesting to find that the three-qubit gate is less susceptible to errors than the double-qubit one.

A hybrid system established by the direct interaction between a magnon mode and a superconducting transmon qubit is used to realize a high-degree blockade for magnon. It is a fundamental way toward quantum manipulation at the level of a single magnon and preparation of single magnon sources. Through weakly driving the magnon and probing the qubit, our magnon-blockade proposal can be optimized when the transversal coupling strength between the magnon and qubit is equivalent to the detuning of the qubit and the probing field or that of the magnon and the driving field. Under this condition, the equal-time second-order correlation function $g^{(2)}(0)$ can be analytically minimized when the probing intensity is about three times the driving intensity. Moreover, the magnon blockade could be further enhanced by proper driving intensity and system decay rate, whose magnitudes outrange the current systems of cavity QED and cavity optomechanics. In particular, the correlation function achieves $g^{(2)}(0)\sim10^{-7}$, about two orders lower than that for the photon blockade in cavity optomechanics. Also, we discuss the effects on $g^{(2)}(0)$ from thermal noise and the extra longitudinal interaction between the magnon and qubit. Our optimized conditions for blockade are found to persist in these nonideal situations.

Hierarchical Bayesian Poisson regression models (HBPRMs) provide a flexible modeling approach of the relationship between predictors and count response variables. The applications of HBPRMs to large-scale datasets require efficient inference algorithms due to the high computational cost of inferring many model parameters based on random sampling. Although Markov Chain Monte Carlo (MCMC) algorithms have been widely used for Bayesian inference, sampling using this class of algorithms is time-consuming for applications with large-scale data and time-sensitive decision-making, partially due to the non-conjugacy of many models. To overcome this limitation, this research develops an approximate Gibbs sampler (AGS) to efficiently learn the HBPRMs while maintaining the inference accuracy. In the proposed sampler, the data likelihood is approximated with Gaussian distribution such that the conditional posterior of the coefficients has a closed-form solution. Numerical experiments using real and synthetic datasets with small and large counts demonstrate the superior performance of AGS in comparison to the state-of-the-art sampling algorithm, especially for large datasets.

Analyzing the behavior of complex interdependent networks requires complete information about the network topology and the interdependent links across networks. For many applications such as critical infrastructure systems, understanding network interdependencies is crucial to anticipate cascading failures and plan for disruptions. However, data on the topology of individual networks are often publicly unavailable due to privacy and security concerns. Additionally, interdependent links are often only revealed in the aftermath of a disruption as a result of cascading failures. We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures. Metropolis-Hastings algorithm coupled with the infrastructure-dependent proposal are employed to increase the efficiency of sampling possible graphs. Results of reconstructing a synthetic system of interdependent infrastructure networks demonstrate that the proposed approach outperforms existing methods in both accuracy and computational time. We further apply this approach to reconstruct the topology of one synthetic and two real-world systems of interdependent infrastructure networks, including gas-power-water networks in Shelby County, TN, USA, and an interdependent system of power-water networks in Italy, to demonstrate the general applicability of the approach.

We propose a nuclear-spin-polarization protocol in a general evolution-and-measurement framework. The protocol works in a spin-star configuration, where the central spin is coupled to the surrounding bath (nuclear) spins by flip-flop interaction of equal strength and is subject to a sequence of projective measurements on its ground state. Then a nondeterministic nuclear spin polarization could be implemented by entropy reduction through measurement. The optimized measurement-interval $\tau_{\rm opt}$ is analytically obtained in the near-resonant condition, which is relevant to the nuclear spins' polarization degree of the last-round measurement, the number of nuclear spins, and the coupling strength between the central spin and nuclear spins. Hundreds and even thousands of randomly aligned nuclear spins at the thermal state could be almost fully polarized with an optimized sequence of less than $20$ unequal-time-spacing measurements. In comparison to the conventional approaches, our protocol is not sensitive to the magnetic-field intensity, and it is robust against the extra counterrotating interaction in the near-resonant situation.

The progressive amplification of fluctuations in demand as the demand travels upstream the supply chains is known as the bullwhip effect. We first analytically characterize the bullwhip effect in general supply chain networks in two cases: (i) all suppliers have a unique layer position, where our method is founded on the control-theoretic approach, and (ii) not all suppliers have a unique layer position due to the presence of intra-layer links or inter-layer links between suppliers that are not positioned in consecutive layers, where we use both the absorbing Markov chain and the control-theoretic approach. We then investigate how network structures impact the BWE of supply chain networks. In particular, we analytically show that (i) if the market demand is generated from the same stationary process, the structure of supply networks does not affect the layer-wise bullwhip effect of supply networks, and (ii) if the market demand is generated from different stationary or non-stationary market processes, wider supply networks lead to a lower level of layer-wise bullwhip effect. Finally, numerical simulations are used to validate our propositions.

Network structure provides critical information for understanding the dynamic behavior of networks. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.

Methylammonium lead iodide, as related organometal halide perovskites, emerged recently as a particularly attractive material for photovoltaic applications. The origin of its appealing properties is sometimes assigned to its potential ferroelectric character, which remains however a topic of intense debate. Here, we rationalize from first-principles calculations how the spatial arrangement of methylammonium polar molecules is progressively constrained by the subtle interplay between their tendency to bond with the inorganic framework and the appearance of iodine octahedra rotations inherent to the perovskite structure. The disordered tetragonal phase observed at room temperature is paraelectric. We show that it should a priori become ferroelectric but that iodine octahedra rotations drive the system toward an antipolar orthorhombic ground state, making it a missed ferroelectric.

The lack of data on critical infrastructure systems has hindered the research progress in modeling and optimizing the system performance. This work develops a method for generating Synthetic Interdependent Critical Infrastructure Networks (SICIN) using simulation and non-linear optimization techniques. SICIN consists of three components: (i) determining the location of facilities in individual networks via a modified simulated annealing algorithm, (ii) generating interdependent links based on a novel pseudo-tripartite graph algorithm, and (iii) simulating network flow using nonlinear optimization considering the operations of individual networks and their interdependencies. Two existing systems of interdependent infrastructure networks are used to validate the proposed method. The results demonstrate that SICIN outperforms state-of-the-art simulation methods according to multiple topological and flow measures of similarity between the simulated and real networks.

Multicast is an efficient way to realize one-to-many group communications in large-scale networks such as the Internet. However, the deployment of IP multicast services over the Internet has not been as rapid as expected and needed. Excepting the fatal defects in designing IPv4 address structure. Another main reason that contributes to this slow deployment is the lack of carrier-grade multicast-enabled switches and routers that can be as to scale as their unicast counterparts. Implementing a high-performance switch/router relies on a polynomial-time group membership query algorithm within the Packet Forwarding Engines (PFEs) to determine whether or not a packet is forwarded through an egress. Among these, Bloom filter (BF)-based and Residue Number System (RNS)-based are being considered as two representations of the membership query algorithms. However, both approaches suffer from some fatal weaknesses such as space and time inefficiencies, especially for a carrier-grade PFE with high port-density features. According to similar properties of the prime theorem, we propose a simplified forwarding scheme in this paper, named Per-Port Prime Filter Array (P3FA). The simulation results indicate that the P3FA can significantly improve space efficiencies under specific lower egress-diversities conditions. Under the same space constraints, compared with the SVRF, the multicast time efficiencies, the unicast time efficiency of the P3FA are respectively increased by 12x-17234x and 19x-2038x at a range of port-densities 16-1024, but at the expense of hardware cost, which increased by \r{ho}/2x. A PFE designer that attempts to adopt P3FA should trade-off between required performance and cost.

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatment.

Accurate force sensing is important for endoluminal intervention in terms of both safety and lesion targeting. This paper develops an FBG-based force sensor for robotic bronchoscopy by configuring three FBG sensors at the lateral side of a conical substrate. It allows a large and eccentric inner lumen for the interventional instrument, enabling a flexible imaging probe inside to perform optical biopsy. The force sensor is embodied with a laser-profiled continuum robot and thermo drift is fully compensated by three temperature sensors integrated on the circumference surface of the sensor substrate. Different decoupling approaches are investigated, and nonlinear decoupling is adopted based on the cross-validation SVM and a Gaussian kernel function, achieving an accuracy of 10.58 mN, 14.57 mN and 26.32 mN along X, Y and Z axis, respectively. The tissue test is also investigated to further demonstrate the feasibility of the developed triaxial force sensor

A novel framework to construct an efficient sensing (measurement) matrix, called mixed adaptive-random (MAR) matrix, is introduced for directly acquiring a compressed image representation. The mixed sampling (sensing) procedure hybridizes adaptive edge measurements extracted from a low-resolution image with uniform random measurements predefined for the high-resolution image to be recovered. The mixed sensing matrix seamlessly captures important information of an image, and meanwhile approximately satisfies the restricted isometry property. To recover the high-resolution image from MAR measurements, the total variation algorithm based on the compressive sensing theory is employed for solving the Lagrangian regularization problem. Both peak signal-to-noise ratio and structural similarity results demonstrate the MAR sensing framework shows much better recovery performance than the completely random sensing one. The work is particularly helpful for high-performance and lost-cost data acquisition., Comment: 16 pages, 8 figures

We demonstrate in this letter a unique approach for watching outside while hiding in a carpet cloaking based on transformation optics. Unlike conventional carpet cloaking, which screens all the incident electromagnetic waves, we break the cloak and allow incident light get into the carpet. Hence outside information is detected inside the cloak. To recover the invisible cloaking, complementary techniques are applied in the broken space. Consequently, a hiding-inside-and-watching-outside (HIWO) carpet cloak is sewed, which works as a perfectly invisible cloaking and allows surveillance of the outside at the same time. Our work provides a strategy for ideal cloak with "hiding" and "watching" functions simultaneously., Comment: 18 pages, 4 figures. This is the second version of arXiv:1009.2849, which was submitted on Sept.15, 2010