271,759 research outputs found

    Exploiting Causal Independence in Bayesian Network Inference

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    A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as ``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.Comment: See http://www.jair.org/ for any accompanying file

    Observation of Terahertz Radiation via the Two-Color Laser Scheme with Uncommon Frequency Ratios

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    In the widely-studied two-color laser scheme for terahertz (THz) radiation from a gas, the frequency ratio of the two lasers is usually fixed at ω2/ω1=\omega_2/\omega_1=1:2. We investigate THz generation with uncommon frequency ratios. Our experiments show, for the first time, efficient THz generation with new ratios of ω2/ω1=\omega_2/\omega_1=1:4 and 2:3. We observe that the THz polarization can be adjusted by rotating the longer-wavelength laser polarization and the polarization adjustment becomes inefficient by rotating the other laser polarization; the THz energy shows similar scaling laws with different frequency ratios. These observations are inconsistent with multi-wave mixing theory, but support the gas-ionization model. This study pushes the development of the two-color scheme and provides a new dimension to explore the long-standing problem of the THz generation mechanism.Comment: 6 pages, 3 figure

    Human gait recognition with matrix representation

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    Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most representative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database

    Graphical description of local Gaussian operations for continuous-variable weighted graph states

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    The form of a local Clifford (LC, also called local Gaussian (LG)) operation for the continuous-variable (CV) weighted graph states is presented in this paper, which is the counterpart of the LC operation of local complementation for qubit graph states. The novel property of the CV weighted graph states is shown, which can be expressed by the stabilizer formalism. It is distinctively different from the qubit weighted graph states, which can not be expressed by the stabilizer formalism. The corresponding graph rule, stated in purely graph theoretical terms, is described, which completely characterizes the evolution of CV weighted graph states under this LC operation. This LC operation may be applied repeatedly on a CV weighted graph state, which can generate the infinite LC equivalent graph states of this graph state. This work is an important step to characterize the LC equivalence class of CV weighted graph states.Comment: 5 pages, 6 figure

    Scaling in the time-dependent failure of a fiber bundle with local load sharing

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    We study the scaling behaviors of a time-dependent fiber-bundle model with local load sharing. Upon approaching the complete failure of the bundle, the breaking rate of fibers diverges according to r(t)(Tft)ξr(t)\propto (T_f-t)^{-\xi}, where TfT_f is the lifetime of the bundle, and ξ1.0\xi \approx 1.0 is a quite universal scaling exponent. The average lifetime of the bundle scales with the system size as NδN^{-\delta}, where δ\delta depends on the distribution of individual fiber as well as the breakdown rule.Comment: 5 pages, 4 eps figures; to appear in Phys. Rev.

    Single-particle subband structure of Quantum Cables

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    We proposed a model of Quantum Cable in analogy to the recently synthesized coaxial nanocable structure [Suenaga et al. Science, 278, 653 (1997); Zhang et al. ibid, 281, 973 (1998)], and studied its single-electron subband structure. Our results show that the subband spectrum of Quantum Cable is different from either double-quantum-wire (DQW) structure in two-dimensional electron gas (2DEG) or single quantum cylinder. Besides the double degeneracy of subbands arisen from the non-abelian mirrow reflection symmetry, interesting quasicrossings (accidental degeneracies), anticrossings and bundlings of Quantum Cable energy subbands are observed for some structure parameters. In the extreme limit (barrier width tends to infinity), the normal degeneracy of subbands different from the DQW structure is independent on the other structure parameters.Comment: 12 pages, 9 figure
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