Formulation of Kirchhoff Rod Based on Quasi-coordinates

The quasi-coordinates are applied to formulate Kirchhoff\u27s rod. The potential energy of the rod expressed by the quasi-coordinates has a similar form as the kinetic energy and complementary kinetic energy in dynamics. The conjugate quasi-momentum is defined and the canonical equations due to the quasi-coordinates are given. Kirchhoff\u27s equations can be derived directly from Boltzman-Hamel\u27s equations or its canonical form with arc length s as independent variables. Lagrange\u27s theorem is extended to determine the stability of equilibrium con-figuration of the elastic rod, and is proved using the Lyapunov\u27s direct method. It is noticed that the condition of stability has a different physical explanation than in dynamics

Millicharged Atomic Dark Matter

We present a simplified version of the atomic dark matter scenario, in which charged dark constituents are bound into atoms analogous to hydrogen by a massless hidden sector U(1) gauge interaction. Previous studies have assumed that interactions between the dark sector and the standard model are mediated by a second, massive Z' gauge boson, but here we consider the case where only a massless gamma' kinetically mixes with the standard model hypercharge and thereby mediates direct detection. This is therefore the simplest atomic dark matter model that has direct interactions with the standard model, arising from the small electric charge for the dark constituents induced by the kinetic mixing. We map out the parameter space that is consistent with cosmological constraints and direct searches, assuming that some unspecified mechanism creates the asymmetry that gives the right abundance, since the dark matter cannot be a thermal relic in this scenario. In the special case where the dark "electron" and "proton" are degenerate in mass, inelastic hyperfine transitions can explain the CoGeNT excess events. In the more general case, elastic transitions dominate, and can be close to current direct detection limits over a wide range of masses.Comment: 5 pages, 2 figures; v2: added references, and formula for dark ionization fraction; published versio

Joint sparse model-based discriminative K-SVD for hyperspectral image classification

Sparse representation classification (SRC) is being widely investigated on hyperspectral images (HSI). For SRC methods to achieve high classification performance, not only is the development of sparse representation models essential, the designing and learning of quality dictionaries also plays an important role. That is, a redundant dictionary with well-designated atoms is required in order to ensure low reconstruction error, high discriminative power, and stable sparsity. In this paper, we propose a new method to learn such dictionaries for HSI classification. We borrow the concept of joint sparse model (JSM) from SRC to dictionary learning. JSM assumes local smoothness and joint sparsity and was initially proposed for classification of HSI. We leverage JSM to develop an extension of discriminative K-SVD for learning a promising discriminative dictionary for HSI. Through a semi-supervised strategy, the new dictionary learning method, termed JSM-DKSVD, utilises all spectrums over the local neighbourhoods of labelled training pixels for discriminative dictionary learning. It can produce a redundant dictionary with rich spectral and spatial information as well as high discriminative power. The learned dictionary can then be compatibly used in conjunction with the established SRC methods, and can significantly improve their performance for HSI classification

Dimension reduction for data with heterogeneous missingness

Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard dimension reduction techniques. As a large number of dimension reduction approaches are based on the Gram matrix, we first investigate the effects of missingness on dimension reduction by studying the statistical properties of the Gram matrix with or without missingness, and then we present a bias-corrected Gram matrix with nice statistical properties under heterogeneous missingness. Extensive empirical results, on both simulated and publicly available real datasets, show that the proposed unbiased Gram matrix can significantly improve a broad spectrum of representative dimension reduction approaches

Robust interface between flying and topological qubits

Hybrid architectures, consisting of conventional and topological qubits, have recently attracted much attention due to their capability in consolidating the robustness of topological qubits and the universality of conventional qubits. However, these two kinds of qubits are normally constructed in significantly different energy scales, and thus this energy mismatch is a major obstacle for their coupling that supports the exchange of quantum information between them. Here, we propose a microwave photonic quantum bus for a direct strong coupling between the topological and conventional qubits, in which the energy mismatch is compensated by the external driving field via the fractional ac Josephson effect. In the framework of tight-binding simulation and perturbation theory, we show that the energy splitting of the topological qubits in a finite length nanowire is still robust against local perturbations, which is ensured not only by topology, but also by the particle-hole symmetry. Therefore, the present scheme realizes a robust interface between the flying and topological qubits. Finally, we demonstrate that this quantum bus can also be used to generate multipartitie entangled states with the topological qubits.Comment: Accepted for publication in Scientific Report

Time To Live: Temporal Management of Large-Scale RFID Applications

In coming years, there will be billions of RFID tags living in the world tagging almost everything for tracking and identification purposes. This phenomenon will impose a new challenge not only to the network capacity but also to the scalability of event processing of RFID applications. Since most RFID applications are time sensitive, we propose a notion of Time To Live (TTL), representing the period of time that an RFID event can legally live in an RFID data management system, to manage various temporal event patterns. TTL is critical in the "Internet of Things" for handling a tremendous amount of partial event-tracking results. Also, TTL can be used to provide prompt responses to time-critical events so that the RFID data streams can be handled timely. We divide TTL into four categories according to the general event-handling patterns. Moreover, to extract event sequence from an unordered event stream correctly and handle TTL constrained event sequence effectively, we design a new data structure, namely Double Level Sequence Instance List (DLSIList), to record intermediate stages of event sequences. On the basis of this, an RFID data management system, namely Temporal Management System over RFID data streams (TMS-RFID), has been developed. This system can be constructed as a stand-alone middleware component to manage temporal event patterns. We demonstrate the effectiveness of TMS-RFID on extracting complex temporal event patterns through a detailed performance study using a range of high-speed data streams and various queries. The results show that TMS-RFID has a very high throughout, namely 170,000 - 870,000 events per second for different highly complex continuous queries. Moreover, the experiments also show that the main structure to record the intermediate stages in TMS-RFID does not increase exponentially with the number of events. These illustrate that TMS-RFID not only has a high processing speed, but also has a good scalability

Quantum simulation of exotic PT-invariant topological nodal loop bands with ultracold atoms in an optical lattice

Since the well-known PT symmetry has its fundamental significance and implication in physics, where PT denotes the combined operation of space-inversion P and time-reversal T, it is extremely important and intriguing to completely classify exotic PT-invariant topological metals and to physically realize them. Here we, for the first time, establish a rigorous classification of topological metals that are protected by the PT symmetry using KO-theory. As a physically realistic example, a PT-invariant nodal loop (NL) model in a 3D Brillouin zone is constructed, whose topological stability is revealed through its PT-symmetry-protected nontrivial Z2 topological charge. Based on these exact results, we propose an experimental scheme to realize and to detect tunable PT-invariant topological NL states with ultracold atoms in an optical lattice, in which atoms with two hyperfine spin states are loaded in a spin-dependent 3D OL and two pairs of Raman lasers are used to create out-of-plane spin-flip hopping with site-dependent phase. Such a realistic cold-atom setup can yield topological NL states, having a tunable ring-shaped band-touching line with the two-fold degeneracy in the bulk spectrum and non-trivial surface states. The states are actually protected by the combined PT symmetry even in the absence of both P and T symmetries, and are characterized by a Z2-type invariant (a quantized Berry phase). Remarkably, we demonstrate with numerical simulations that (i) the characteristic NL can be detected by measuring the atomic transfer fractions in a Bloch-Zener oscillation; (ii) the topological invariant may be measured based on the time-of-flight imaging; and (iii) the surface states may be probed through Bragg spectroscopy. The present proposal for realizing topological NL states in cold atom systems may provide a unique experimental platform for exploring exotic PT-invariant topological physics.Comment: 11 pages, 6 figures; accepted for publication in Phys. Rev.

Energy-Efficient Fault-Tolerant Scheduling Algorithm for Real-Time Tasks in Cloud-Based 5G Networks

© 2013 IEEE. Green computing has become a hot issue for both academia and industry. The fifth-generation (5G) mobile networks put forward a high request for energy efficiency and low latency. The cloud radio access network provides efficient resource use, high performance, and high availability for 5G systems. However, hardware and software faults of cloud systems may lead to failure in providing real-time services. Developing fault tolerance technique can efficiently enhance the reliability and availability of real-time cloud services. The core idea of fault-tolerant scheduling algorithm is introducing redundancy to ensure that the tasks can be finished in the case of permanent or transient system failure. Nevertheless, the redundancy incurs extra overhead for cloud systems, which results in considerable energy consumption. In this paper, we focus on the problem of how to reduce the energy consumption when providing fault tolerance. We first propose a novel primary-backup-based fault-tolerant scheduling architecture for real-time tasks in the cloud environment. Based on the architecture, we present an energy-efficient fault-tolerant scheduling algorithm for real-time tasks (EFTR). EFTR adopts a proactive strategy to increase the system processing capacity and employs a rearrangement mechanism to improve the resource utilization. Simulation experiments are conducted on the CloudSim platform to evaluate the feasibility and effectiveness of EFTR. Compared with the existing fault-tolerant scheduling algorithms, EFTR shows excellent performance in energy conservation and task schedulability