Genetic algorithm and neural network hybrid approach for job-shop scheduling

Copyright @ 1998 ACTA PressThis paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.This research is supported by the National Nature Science Foundation and National High -Tech Program of P. R. China

Multi-view Regularized Gaussian Processes

Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view learning. In this paper, we present a new GP model for multi-view learning. Unlike existing methods, it combines multiple views by regularizing marginal likelihood with the consistency among the posterior distributions of latent functions from different views. Moreover, we give a general point selection scheme for multi-view learning and improve the proposed model by this criterion. Experimental results on multiple real world data sets have verified the effectiveness of the proposed model and witnessed the performance improvement through employing this novel point selection scheme

Coexistence of full which-path information and interference in Wheelers delayed choice experiment with photons

We present a computer simulation model that is a one-to-one copy of an experimental realization of Wheeler's delayed choice experiment that employs a single photon source and a Mach-Zehnder interferometer composed of a 50/50 input beam splitter and a variable output beam splitter with adjustable reflection coefficient $R$ (V. Jacques {\sl et al.}, Phys. Rev. Lett. 100, 220402 (2008)). For $0\le R\le 0.5$, experimentally measured values of the interference visibility $V$ and the path distinguishability $D$, a parameter quantifying the which-path information WPI, are found to fulfill the complementary relation $V^2+D^2\le 1$, thereby allowing to obtain partial WPI while keeping interference with limited visibility. The simulation model that is solely based on experimental facts, that satisfies Einstein's criterion of local causality and that does not rely on any concept of quantum theory or of probability theory, reproduces quantitatively the averages calculated from quantum theory. Our results prove that it is possible to give a particle-only description of the experiment, that one can have full WPI even if D=0, V=1 and therefore that the relation $V^2+D^2\le 1$ cannot be regarded as quantifying the notion of complementarity.Comment: Physica E, in press; see also http://www.compphys.ne

Modelling human behaviours and reactions under dangerous environment

This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools (TM) Dev. Programming within Virtools (TM) Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed

Spin dependent transport of ``nonmagnetic metal/zigzag nanotube encapsulating magnetic atoms/nonmagnetic metal'' junctions

Towards a novel magnetoresistance (MR) device with a carbon nanotube, we propose ``nonmagnetic metal/zigzag nanotube encapsulating magnetic atoms/nonmagnetic metal'' junctions. We theoretically investigate how spin-polarized edges of the nanotube and the encapsulated magnetic atoms influence on transport. When the on-site Coulomb energy divided by the magnitude of transfer integral, $U/|t|$, is larger than 0.8, large MR effect due to the direction of spins of magnetic atoms, which has the magnitude of the MR ratio of about 100%, appears reflecting such spin-polarized edges.Comment: 4 pages, 3 figures, accepted for publication in Synth. Metal

Learning-aided Stochastic Network Optimization with Imperfect State Prediction

We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve prediction model, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that requires zero a-prior system statistical information, and consists of three key components, namely sequential distribution estimation and change detection, dual learning, and online queue-based control. Specifically, we show that PLC simultaneously achieves good long-term performance, short-term queue size reduction, accurate change detection, and fast algorithm convergence. In particular, for stationary networks, PLC achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon), O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$ utility-backlog tradeoff for distributions that last $\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where $e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length for the same utility performance with a larger backlog). Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves an $O(\min(\epsilon^{-1+c/2}, e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate that state prediction (even imperfect) can help (i) achieve faster detection and convergence, and (ii) obtain better utility-delay tradeoffs

On several families of elliptic curves with arbitrary large Selmer groups

In this paper, we calculate the $\phi (\hat{\phi})-$Selmer groups S^{(\phi)} (E / \Q) and S^{(\hat{\varphi})} (E^{\prime} / \Q) of elliptic curves $y^{2} = x (x + \epsilon p D) (x + \epsilon q D)$ via descent theory (see [S, Chapter X]), in particular, we obtain that the Selmer groups of several families of such elliptic curves can be arbitrary large.Comment: 22 page

Thermomechanical Characterization And Modeling For TSV Structures

Continual scaling of devices and on-chip wiring has brought significant challenges for materials and processes beyond the 32-nm technology node in microelectronics. Recently, three-dimensional (3-D) integration with through-silicon vias (TSVs) has emerged as an effective solution to meet the future technology requirements. Among others, thermo-mechanical reliability is a key concern for the development of TSV structures used in die stacking as 3-D interconnects. This paper presents experimental measurements of the thermal stresses in TSV structures and analyses of interfacial reliability. The micro-Raman measurements were made to characterize the local distribution of the near-surface stresses in Si around TSVs. On the other hand, the precision wafer curvature technique was employed to measure the average stress and deformation in the TSV structures subject to thermal cycling. To understand the elastic and plastic behavior of TSVs, the microstructural evolution of the Cu vias was analyzed using focused ion beam (FIB) and electron backscattering diffraction (EBSD) techniques. Furthermore, the impact of thermal stresses on interfacial reliability of TSV structures was investigated by a shear-lag cohesive zone model that predicts the critical temperatures and critical via diameters.Microelectronics Research Cente