Optical generation of hybrid entangled state via entangling single-photon-added coherent state

We propose a feasible scheme to realize the optical entanglement of single-photon-added coherent state (SPACS) and show that, besides the Sanders entangled coherent state, the entangled SPACS also leads to new forms of hybrid entanglement of quantum Fock state and classical coherent state. We probe the essential difference of two types of hybrid entangled state (HES). This HES provides a novel link between the discrete- and the continuous-variable entanglement in a natural way.Comment: 6 pages, 2 figure

Mobility-aware multi-user offloading optimization for Mobile Edge Computing

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordMobile Edge Computing (MEC) is a new computing paradigm with great potential to enhance the performance of user equipment (UE) by offloading resource-hungry computation tasks to lightweight and ubiquitously deployed MEC servers. In this paper, we investigate the problem of offloading decision and resource allocation among multiple users served by one base station to achieve the optimal system-wide user utility, which is defined as a trade-off between task latency and energy consumption. Mobility in the process of task offloading is considered in the optimization. We prove that the problem is NP-hard and propose a heuristic mobility-aware offloading algorithm (HMAOA) to obtain the approximate optimal offloading scheme. The original global optimization problem is converted into multiple local optimization problems. Each local optimization problem is then decomposed into two subproblems: a convex computation allocation subproblem and a non-linear integer programming (NLIP) offloading decision subproblem. The convex subproblem is solved with a numerical method to obtain the optimal computation allocation among multiple offloading users, and a partial order based heuristic approach is designed for the NLIP subproblem to determine the approximate optimal offloading decision. The proposed HMAOA is with polynomial complexity. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate its excellent performance

Computing the Loewner driving process of random curves in the half plane

We simulate several models of random curves in the half plane and numerically compute their stochastic driving process (as given by the Loewner equation). Our models include models whose scaling limit is the Schramm-Loewner evolution (SLE) and models for which it is not. We study several tests of whether the driving process is Brownian motion. We find that just testing the normality of the process at a fixed time is not effective at determining if the process is Brownian motion. Tests that involve the independence of the increments of Brownian motion are much more effective. We also study the zipper algorithm for numerically computing the driving function of a simple curve. We give an implementation of this algorithm which runs in a time O(N^1.35) rather than the usual O(N^2), where N is the number of points on the curve.Comment: 20 pages, 4 figures. Changes to second version: added new paragraph to conclusion section; improved figures cosmeticall

Tunable singlet-triplet splitting in a few-electron Si/SiGe quantum dot

We measure the excited-state spectrum of a Si/SiGe quantum dot as a function of in-plane magnetic field, and we identify the spin of the lowest three eigenstates in an effective two-electron regime. The singlet-triplet splitting is an essential parameter describing spin qubits, and we extract this splitting from the data. We find it to be tunable by lateral displacement of the dot, which is realized by changing two gate voltages on opposite sides of the device. We present calculations showing the data are consistent with a spectrum in which the first excited state of the dot is a valley-orbit state.Comment: 4 pages with 3 figure

Deep Reinforcement Learning-Based Offloading Scheduling for Vehicular Edge Computing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordVehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VT) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this paper, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a trade-off between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.European Commissio

Geometries for Possible Kinematics

The algebras for all possible Lorentzian and Euclidean kinematics with $\frak{so}(3)$ isotropy except static ones are re-classified. The geometries for algebras are presented by contraction approach. The relations among the geometries are revealed. Almost all geometries fall into pairs. There exists $t \leftrightarrow 1/(\nu^2t)$ correspondence in each pair. In the viewpoint of differential geometry, there are only 9 geometries, which have right signature and geometrical spatial isotropy. They are 3 relativistic geometries, 3 absolute-time geometries, and 3 absolute-space geometries.Comment: 40 pages, 7 figure

Modelo de arborización dendrítica basado en reconstrucciones de motoneuronas frénicas en ratas adultas

El área superficial de las dendritas en motoneuronas frénicas (PhrMNs) ha sido estimada anteriormente mediante técnicas estereológicas basadas en suposiciones geométricas, y medida en tres dimensiones (3D) utilizando microscopía confocal. Dado que el 97% del área receptora de una motoneurona corresponde a sus dendritas, la ramificación y extensión dendrítica son fisiológicamente importantes para determinar la salida de sus campos receptivos. Sin embargo, limitaciones inherentes a las estimaciones basadas en morfología neuronal y la tinción incompleta de los árboles dendríticos mediante técnicas retrógradas han dificultado los estudios sistemáticos de la morfología dendrítica en PhrMNs. En este estudio, se utilizó una nueva técnica que mejora la tinción dendrítica de las PhrMNs en preparaciones fijadas ligeramente. La reconstrucción dendrítica en 3D se logró con gran precisión utilizando microscopía confocal en PhrMNs de ratas adultas. Luego de una etapa de pre-procesamiento, la segmentación de los árboles dendríticos se realizó semi-automáticamente en 3D y usando mediciones directas del área superficial, se derivó un modelo cuadrático para estimar dicha área partiendo del diámetro de la dendrita primaria (r2 = 0.932; p<0.0001). Este método podría mejorar la evaluación de la plasticidad neuronal en respuesta a trauma u otras enfermedades permitiendo la estimación de la arborización dendrítica en PhrMNs, ya que el diámetro de la dendrita primaria puede obtenerse confiablemente de numerosas técnicas de tinción retrógrada.Stereological techniques that rely on morphological assumptions and direct three-dimensional (3D) confocal measurements have been previously used to estimate the dendritic surface areas of phrenic motoneurons (PhrMNs). Given that 97% of a motoneuron’s receptive area is provided by dendrites, dendritic branching and overall extension are physiologically important in determining the output of their synaptic receptive fields. However, limitations intrinsic to shape-based estimations and incomplete labeling of dendritic trees by retrograde techniques have hindered systematic approaches to examine dendritic morphology of PhrMNs. In this study, a novel method that improves dendritic filling of PhrMNs in lightly-fixed samples was used. Confocal microscopy allowed accurate 3D reconstruction of dendritic arbors from adult rat PhrMNs. Following pre-processing, segmentation was semi-automatically performed in 3D, and direct measurements of dendritic surface area were obtained. A quadratic model for estimating dendritic tree surface area based on measurements of primary dendrite diameter was derived (r2 = 0.932; p<0.0001). This method may enhance interpretation of motoneuron plasticity in response to injury or disease by permitting estimations of dendritic arborization of PhrMNs since measurements of primary dendrite diameter can be reliably obtained from a number of retrograde labeling techniques

Single-shot measurement of triplet-singlet relaxation in a Si/SiGe double quantum dot

We investigate the lifetime of two-electron spin states in a few-electron Si/SiGe double dot. At the transition between the (1,1) and (0,2) charge occupations, Pauli spin blockade provides a readout mechanism for the spin state. We use the statistics of repeated single-shot measurements to extract the lifetimes of multiple states simultaneously. At zero magnetic field, we find that all three triplet states have equal lifetimes, as expected, and this time is ~10 ms. At non-zero field, the T0 lifetime is unchanged, whereas the T- lifetime increases monotonically with field, reaching 3 seconds at 1 T.Comment: 4 pages, 3 figures, supplemental information. Typos fixed; updated to submitted versio

Economic Feedback Model Predictive Control of Wave Energy Converters

In this paper, we propose an economic feedback model predictive control (MPC) scheme to improve energy conversion efficiency of wave energy converters (WECs) and guarantee their safe operation over a wide range of sea conditions. The proposed MPC control law consists of two terms: one state feedback gain designed offline to maximize operating range and one term calculated online to maximize the energy output. Compared with the existing MPC strategies developed for the WEC control problem, the proposed feedback economic MPC strategy has the following distinguishing advantages: First, the satisfaction of safety constraints and the recursive feasibility can be guaranteed to ensure WEC's safe operation in a large range of sea states. Second, the novel MPC can notably improve energy production efficiency. Third, the controller design procedure is more convenient and straightforward compared with the existing MPC strategies. The efficacy of the proposed MPC strategy is demonstrated by numerical simulations with a point absorber as a case study. By comparison with a representative existing MPC strategy, the proposed economic MPC can significantly improve energy output