Electrically controllable RKKY interaction in semiconductor quantum wires

We demonstrate in theory that it is possible to all-electrically manipulate the RKKY interaction in a quasi-one-dimensional electron gas embedded in a semiconductor heterostructure, in the presence of Rashba and Dresselhaus spin-orbit interaction. In an undoped semiconductor quantum wire where intermediate excitations are gapped, the interaction becomes the short-ranged Bloembergen-Rowland superexchange interaction. Owing to the interplay of different types of spin-orbit interaction, the interaction can be controlled to realize various spin models, e.g., isotropic and anisotropic Heisenberg-like models, Ising-like models with additional Dzyaloshinsky-Moriya terms, by tuning the external electric field and designing the crystallographic directions. Such controllable interaction forms a basis for quantum computing with localized spins and quantum matters in spin lattices.

Hydrogenation and anisotropic expansions in R3Fe29-xTx (R = Y, Ce, Nd, Sm, Gd, Tb and Dy; T = V and Cr)

A systematic study of the phase formation, structure and magnetic properties of the R3Fe29-xTx compounds (R=Y, Ce, Nd, Sm, Gd, Tb, and Dy; T=V and Cr) has been performed upon hydrogenation. The lattice constants and the unit cell volume of R3Fe29-xTxHy decrease with increasing R atomic number from Nd to Dy, except for Ce, reflecting the lanthanide contraction. Regular anisotropic expansions mainly along the a- and b-axis rather than along the c-axis are observed for all of the compounds upon hydrogenation. Hydrogenation leads to an increase in the Curie temperature and a corresponding increase in the saturation magnetization at room temperature for each compound. First order magnetization processes (FOMP) occur in the external magnetic fields for Nd3Fe24.5Cr4.5H5.0, Tb3Fe27.0Cr2.0H2.8, and Gd3Fe28.0Cr1.0H4.2 compounds.

Triclinic deformation and anisotropic strain relaxation of an InAs film on a GaAs(001) substrate measured by a series of symmetric double crystal X-ray diffraction

We presented a series of symmetric double crystal X-ray diffraction (DCXD) measurements, (0 0 4), (2 2 0) and (2 - 2 0) diffraction, to investigate the strain relaxation in an InAs film grown on a GaAs(0 0 1) substrate. The strain tensor and rotation tensor were calculated according to the DCXD results. It is found that the misfit strain is relaxed nearly completely and the strain relaxation caused a triclinic deformation in the epilayer. The lattice parameter along the [1 1 0] direction is a little longer than that along the [1 - 1 0] direction. Furthermore, a significant tilt, 0.2 degrees, towards the [1 1 0] direction while a very slight one: 0.002 degrees, towards [1 - 1 0] direction were discussed. This anisotropic strain relaxation is attributed to the asymmetric distribution of misfit dislocations, which is also indicated by the variation of the full-width at half-maximum (FWHM) of (0 0 4) diffraction along four azimuth angles. (C) 1998 Elsevier Science B.V. All rights reserved.

地震波谱元法数值模拟和偏移研究

With the development of both seismic theory and computer technology, numerical modeling technology of seismic wave has achieved great advancement during the past half century. The current methods under development include finite differentiation method (FDM), finite element method (FEM), pseudospectral method (PSM), integral equation method (IEM) and spectral element method (SEM). They exert their very important roles in every corner of seismology and seismic prospecting. Large quantity of researches towards spectral element method in the end of last century bring this method to a new era, which results in perfect solution of many difficult problems. However, parts of posterior works such as seismic migration and inversion which base on spectral element method have never been studied widely at least up to the present whereas are of importance to seismic imaging and seismic wave propagation. Based on previous work, this paper uses spectral element method to investigate the characteristics and laws of the seismic wave propagation in isotropic and anisotropic media. By thoroughly studying this high-accuracy method, we implement a kind of reverse-time pre- and post-stack migration based on SEM. In order to verify the validity of the SEM method, we have simulated the propagation of seismic wave in several different models. The simulation results show that: (1) spectral element method can be used to model any complex models and the computational results are comparable with the expected results and the analytic results; (2) the optimum accuracy can be achieved when the rank is between 4 and 9. When it is below 4, the dispersion may occur; and when it is above 9, the time step-length will be changed accordingly with the reducing space step-length in order to keep the computation stability. This will exponentially increase the computation time and at the same time the memory even if simulating the same media. This paper also applies explosive reflection surface imaging technology, time constancy principle of wave-filed extrapolation and least travetime raytracing technology of surface source to SEM pre- and post-stack migration of isotropic and anisotropic media. All imaging results derived by the above methods agree well with the real geological models and the position of interface and inflexions can also return to their right location well. This indicates that the method proposed in this paper is a kind of technology with high accuracy and robust stability. It can serve as an alternative method in real seismic data processing. All these work can boost the development of high-accuracy seismic imaging, and therefore have significant inference value.

Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

We present the results of an implemented system for learning structural prototypes from grey-scale images. We show how to divide an object into subparts and how to encode the properties of these subparts and the relations between them. We discuss the importance of hierarchy and grouping in representing objects and show how a notion of visual similarities can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and uses these models to recognize new members of the class.

Description and Theoretical Analysis (Using Schemata) of Planner: A Language for Proving Theorems and Manipulating Models in a Robot

Planner is a formalism for proving theorems and manipulating models in a robot. The formalism is built out of a number of problem-solving primitives together with a hierarchical multiprocess backtrack control structure. Statements can be asserted and perhaps later withdrawn as the state of the world changes. Under BACKTRACK control structure, the hierarchy of activations of functions previously executed is maintained so that it is possible to revert to any previous state. Thus programs can easily manipulate elaborate hypothetical tentative states. In addition PLANNER uses multiprocessing so that there can be multiple loci of changes in state. Goals can be established and dismissed when they are satisfied. The deductive system of PLANNER is subordinate to the hierarchical control structure in order to maintain the desired degree of control. The use of a general-purpose matching language as the basis of the deductive system increases the flexibility of the system. Instead of explicitly naming procedures in calls, procedures can be invoked implicitly by patterns of what the procedure is supposed to accomplish. The language is being applied to solve problems faced by a robot, to write special purpose routines from goal oriented language, to express and prove properties of procedures, to abstract procedures from protocols of their actions, and as a semantic base for English.

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.

Vibrato in singing voice: the link between source-filter and sinusoidal models

The application of inverse filtering techniques for high-quality singing voice analysis/synthesis is discussed. In the context of source-filter models, inverse filtering provides a noninvasive method to extract the voice source, and thus to study voice quality. Although this approach is widely used in speech synthesis, this is not the case in singing voice. Several studies have proved that inverse filtering techniques fail in the case of singing voice, the reasons being unclear. In order to shed light on this problem, we will consider here an additional feature of singing voice, not present in speech: the vibrato. Vibrato has been traditionally studied by sinusoidal modeling. As an alternative, we will introduce here a novel noninteractive source filter model that incorporates the mechanisms of vibrato generation. This model will also allow the comparison of the results produced by inverse filtering techniques and by sinusoidal modeling, as they apply to singing voice and not to speech. In this way, the limitations of these conventional techniques, described in previous literature, will be explained. Both synthetic signals and singer recordings are used to validate and compare the techniques presented in the paper.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Differential and numerical models of hysteretic systems with stochastic and deterministic inputs

Many deterministic models with hysteresis have been developed in the areas of economics, finance, terrestrial hydrology and biology. These models lack any stochastic element which can often have a strong effect in these areas. In this work stochastically driven closed loop systems with hysteresis type memory are studied. This type of system is presented as a possible stochastic counterpart to deterministic models in the areas of economics, finance, terrestrial hydrology and biology. Some price dynamics models are presented as a motivation for the development of this type of model. Numerical schemes for solving this class of stochastic differential equation are developed in order to examine the prototype models presented. As a means of further testing the developed numerical schemes, numerical examination is made of the behaviour near equilibrium of coupled ordinary differential equations where the time derivative of the Preisach operator is included in one of the equations. A model of two phenotype bacteria is also presented. This model is examined to explore memory effects and related hysteresis effects in the area of biology. The memory effects found in this model are similar to that found in the non-ideal relay. This non-ideal relay type behaviour is used to model a colony of bacteria with multiple switching thresholds. This model contains a Preisach type memory with a variable Preisach weight function. Shown numerically for this multi-threshold model is a pattern formation for the distribution of the phenotypes among the available thresholds.

Integration of micro- and macroscopic models for pedestrian evacuation simulation

Simulation of pedestrian evacuations of smart buildings in emergency is a powerful tool for building analysis, dynamic evacuation planning and real-time response to the evolving state of evacuations. Macroscopic pedestrian models are low-complexity models that are and well suited to algorithmic analysis and planning, but are quite abstract. Microscopic simulation models allow for a high level of simulation detail but can be computationally intensive. By combining micro- and macro- models we can use each to overcome the shortcomings of the other and enable new capability and applications for pedestrian evacuation simulation that would not be possible with either alone. We develop the EvacSim multi-agent pedestrian simulator and procedurally generate macroscopic flow graph models of building space, integrating micro- and macroscopic approaches to simulation of the same emergency space. By “coupling” flow graph parameters to microscopic simulation results, the graph model captures some of the higher detail and fidelity of the complex microscopic simulation model. The coupled flow graph is used for analysis and prediction of the movement of pedestrians in the microscopic simulation, and investigate the performance of dynamic evacuation planning in simulated emergencies using a variety of strategies for allocation of macroscopic evacuation routes to microscopic pedestrian agents. The predictive capability of the coupled flow graph is exploited for the decomposition of microscopic simulation space into multiple future states in a scalable manner. By simulating multiple future states of the emergency in short time frames, this enables sensing strategy based on simulation scenario pattern matching which we show to achieve fast scenario matching, enabling rich, real-time feedback in emergencies in buildings with meagre sensing capabilities.