A study of heuristic guesses for adiabatic quantum computation

Adiabatic quantum computation (AQC) is a universal model for quantum computation which seeks to transform the initial ground state of a quantum system into a final ground state encoding the answer to a computational problem. AQC initial Hamiltonians conventionally have a uniform superposition as ground state. We diverge from this practice by introducing a simple form of heuristics: the ability to start the quantum evolution with a state which is a guess to the solution of the problem. With this goal in mind, we explain the viability of this approach and the needed modifications to the conventional AQC (CAQC) algorithm. By performing a numerical study on hard-to-satisfy 6 and 7 bit random instances of the satisfiability problem (3-SAT), we show how this heuristic approach is possible and we identify that the performance of the particular algorithm proposed is largely determined by the Hamming distance of the chosen initial guess state with respect to the solution. Besides the possibility of introducing educated guesses as initial states, the new strategy allows for the possibility of restarting a failed adiabatic process from the measured excited state as opposed to restarting from the full superposition of states as in CAQC. The outcome of the measurement can be used as a more refined guess state to restart the adiabatic evolution. This concatenated restart process is another heuristic that the CAQC strategy cannot capture.     

Predictors of Quality of Life in Old Age: A Multivariate Study in Chile

There have been few studies that examine quality of life among older adults in the Latin American region. Using a recent representative national survey on quality of life among older adults in Chile, we examine the effects of eight potentially important factors (nutrition, physical activity, going outside the home, reading, quality of family relations, conflict, social support, self-efficacy) on three major dimensions of quality of life (functional capacity, material conditions, general satisfaction with life). The regression models confirm that the major predictive factor of satisfactory functional capacity is the practice of physical activities, while for satisfaction, the major predictive factors are self-efficacy, good quality of social relations, and the performance of significant activities such as reading and going outside the home. Educational level is a variable that cuts across all these predictors, in a context of great unequal social opportunities in Latin America. These results have implications for the development and implementation of social policies and programs to achieve an improvement in living conditions of the growing elderly population in the region, and reduce costs that societies face as a product of this new demographic scenario.     

On-board and Ground Visual Pose Estimation Techniques for UAV Control

In this paper, two techniques to control UAVs (Unmanned Aerial Vehicles), based on visual information are presented. The first one is based on the detection and tracking of planar structures from an on-board camera, while the second one is based on the detection and 3D reconstruction of the position of the UAV based on an external camera system. Both strategies are tested with a VTOL (Vertical take-off and landing) UAV, and results show good behavior of the visual systems (precision in the estimation and frame rate) when estimating the helicopter??s position and using the extracted information to control the UAV.     

Bipolar spectrum–substance use co-occurrence: Behavioral approach system (BAS) sensitivity and impulsiveness as shared personality vulnerabilities.

Bipolar disorders and substance use disorders (SUDs) show high co-occurrence. One explanation for this co-occurrence may be common personality vulnerabilities involved in both. The authors tested whether high behavioral approach system (BAS) sensitivity and impulsiveness are shared personality vulnerabilities in bipolar spectrum disorders and substance use problems and their co-occurrence in a longitudinal study of 132 individuals on the bipolar spectrum and 153 control participants. At Time 1, participants completed the Behavioral Inhibition System/BAS Scales and the Impulsive Nonconformity Scale. Substance use problems were assessed via the Michigan Alcoholism Screening Test and the Drug Abuse Screening Test at 4-month intervals for 1 year. Participants with bipolar disorder had higher rates of lifetime SUDs and substance use problems during the follow-up, relative to control participants. In line with hypotheses, higher BAS sensitivity and impulsiveness predicted bipolar status and increased substance use problems prospectively. BAS total, BAS Fun Seeking, and impulsiveness mediated the association between bipolar spectrum status and prospective substance use problems, with impulsiveness as the most important mediator. High BAS sensitivity and impulsiveness may represent shared personality vulnerabilities for both disorders and may partially account for their co-occurrence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Vision safety system based on cellular neural networks

This work is dedicated to develop a safety measurement for human–machine cooperative system, in which the machine region and the human region cannot be separated due to the overlap and the movement both from human and from machines. Modern production processes become more and more flexible. Therefore, there is a need that devices used in workplace also support flexibility as much as possible. Such characteristics have vision-based protective devices. We present a neural system for the advanced recognition of danger situation for safety control. The sequence of the images from two cameras located above the robot is presented to the system of cellular neural networks (CNNs) realized in the PC computer. They detect a new object appearing in a safety field, define its position with respect to the robot arm and perform the feature extraction of its image. Experiments conducted using artificial images (virtual environment) and low-quality images (internet cameras) indicate that our system can work in a real time and detect successively dangerous situations. We have found that the CNN is unable to detect a new object properly in the presence of high level of noise as a result of percolation type phase transition. An example of possible application of the system is presented.     

Model selection in reinforcement learning

We consider the problem of model selection in the batch (offline, non-interactive) reinforcement learning setting when the goal is to find an action-value function with the smallest Bellman error among a countable set of candidates functions. We propose a complexity regularization-based model selection algorithm, $\ensuremath{\mbox{\textsc {BErMin}}}$ , and prove that it enjoys an oracle-like property: the estimator??s error differs from that of an oracle, who selects the candidate with the minimum Bellman error, by only a constant factor and a small remainder term that vanishes at a parametric rate as the number of samples increases. As an application, we consider a problem when the true action-value function belongs to an unknown member of a nested sequence of function spaces. We show that under some additional technical conditions $\ensuremath{\mbox{\textsc {BErMin}}}$ leads to a procedure whose rate of convergence, up to a constant factor, matches that of an oracle who knows which of the nested function spaces the true action-value function belongs to, i.e., the procedure achieves adaptivity.     

GPGPU computation and visualization of three-dimensional cellular automata

This paper presents a general-purpose simulation approach integrating a set of technological developments and algorithmic methods in cellular automata (CA) domain. The approach provides a general-purpose computing on graphics processor units (GPGPU) implementation for computing and multiple rendering of any direct-neighbor three-dimensional (3D) CA. The major contributions of this paper are: the CA processing and the visualization of large 3D matrices computed in real time; the proposal of an original method to encode and transmit large CA functions to the graphics processor units in real time; and clarification of the notion of top-down and bottom-up approaches to CA that non-CA experts often confuse. Additionally a practical technique to simplify the finding of CA functions is implemented using a 3D symmetric configuration on an interactive user interface with simultaneous inside and surface visualizations. The interactive user interface allows for testing the system with different project ideas and serves as a test bed for performance evaluation. To illustrate the flexibility of the proposed method, visual outputs from diverse areas are demonstrated. Computational performance data are also provided to demonstrate the method’s efficiency. Results indicate that when large matrices are processed, computations using GPU are two to three hundred times faster than the identical algorithms using CPU.     

A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

The newest surveillance applications is attempting more complex tasks such as the analysis of the behavior of individuals and crowds. These complex tasks may use a distributed visual sensor network in order to gain coverage and exploit the inherent redundancy of the overlapped field of views. This article, presents a Multi-agent architecture based on the Belief-Desire-Intention (BDI) model for processing the information and fusing the data in a distributed visual sensor network. Instead of exchanging raw images between the agents involved in the visual network, local signal processing is performed and only the key observed features are shared. After a registration or calibration phase, the proposed architecture performs tracking, data fusion and coordination. Using the proposed Multi-agent architecture, we focus on the means of fusing the estimated positions on the ground plane from different agents which are applied to the same object. This fusion process is used for two different purposes: (1) to obtain a continuity in the tracking along the field of view of the cameras involved in the distributed network, (2) to improve the quality of the tracking by means of data fusion techniques, and by discarding non reliable sensors. Experimental results on two different scenarios show that the designed architecture can successfully track an object even when occlusions or sensor??s errors take place. The sensor??s errors are reduced by exploiting the inherent redundancy of a visual sensor network with overlapped field of views.     

Meshing and Simplification of High Resolution Urban Surface Data for UAV Path Planning

This work presents an approach to utilize high resolution surface data as a-priori information for three dimensional path planning at very low altitude. The major challenge is preserve features while reducing the amount of data to a minimum. Non significant height points are eliminated by a neighbor search, performed within a data structure generated from pseudo 3D Delaunay meshing. A comparison to an alternatively implemented simplification shows which inherent building features can be preserved. For highlighting the feasibility of the approach, the processing results of real urban surface data from the inner city of Berlin is presented and used for sampling based path planning of an unmanned helicopter.     

On �� 2 ? �� 2-products of automata

Two equivalent sufficient conditions are given for the completeness of classes of finite automata with respect to the isomorphic simulation under the ?? ₂ ? ?? ₂-product. It is conjectured that these conditions are also necessary with respect to the isomorphic or homomorphic simulation too.