IS THERE A CLASSICAL ANALOG OF A QUANTUM TIME-TRANSLATION MACHINE?

In a recent article [D. Suter, Phys. Rev. {\bf A 51}, 45 (1995)] Suter has claimed to present an optical implementation of the quantum time-translation machine which ``shows all the features that the general concept predicts and also allows, besides the quantum mechanical, a classical description.'' It is argued that the experiment proposed and performed by Suter does not have the features of the quantum time-translation machine and that the latter has no classical analog.Comment: 7 pages, LaTe

Separation of quadrupolar and magnetic contributions to spin-lattice relaxation in the case of a single isotope

We present a NMR pulse double-irradiation method which allows one to separate magnetic from quadrupolar contributions in the spin-lattice relaxation. The pulse sequence fully saturates one transition while another is observed. In the presence of a Delta m = 2 quadrupolar contribution, the intensity of the observed line is altered compared to a standard spin-echo experiment. We calculated analytically this intensity change for spins I=1, 3/2, 5/2, thus providing a quantitative analysis of the experimental results. Since the pulse sequence we used takes care of the absorbed radio-frequency power, no problems due to heating arise. The method is especially suited when only one NMR sensitive isotope is available. Different cross-checks were performed to prove the reliability of the obtained results. The applicability of this method is demonstrated by a study of the plane oxygen 17O (I = 5/2) in the high-temperature superconductor YBa_2Cu_4O_8: the 17O spin-lattice relaxation rate consists of magnetic as well as quadrupolar contributions.Comment: 7 pages, 6 figure

"Context effects in a negative externality experiment"

This study investigates the degree to which framing and context influence observed rates of free-riding behavior in a negative externality laboratory experiment. Building on the work of Andreoni (1995a) and Messer et al. (2007) we frame the decision not to contribute to a public fund as generating a negative externality on other group members. The experimental treatments involving 252 subjects vary communication, voting, and the status quo of the initial endowment. Results indicate that allowing groups the opportunity to communicate and vote significantly reduces rates of free-riding, and this effect is especially pronounced when initial endowments are placed in the private as opposed to the public fund.Negative externality; voluntary contribution mechanism; cheap talk; voting; status quo bias; experimental economics

Multi-scale conditional random fields for over-segmented irregular 3D point clouds classification

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holderIn this paper, we propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data. We improved Lim and Suterpsilas methods by introducing regional edge potentials in addition to the local edge and node potentials in the multi-scale Conditional Random Fields, and only a relatively small amount of increment in the computation time is required to achieve the improved recognition rate. In the model, the raw data points are over-segmented into an improved mid-level representation, ldquosuper-voxelsrdquo. Local and regional features are then extracted from the super-voxel and parameters learnt by the multi-scale Conditional Random Fields. The classification accuracy is improved by 5% to 10% with our proposed model compared to labeling with Conditional Random Fields in (Lim and Suter, 2007). The overall computation time by labeling the super-voxels instead of individual points is lower than the previous 3D data labeling approaches.Ee Hui Lim, David Sute

A re-evaluation of mixture-of-Gaussian background modeling

© Copyright 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.The mixture of Gaussians (MOG) has been widely used for robustly modeling complicated backgrounds, especially those with small repetitive movements (such as leaves, bushes, rotating fan, ocean waves, rain). The performance of MOG can be greatly improved by tackling several practical issues. In this paper, we quantitatively evaluate (using the Wallflower benchmarks) the performance of the MOG with and without our modifications. The experimental results show that the MOG, with our modifications, can achieve much better results - even outperforming other state-of-the-art methods.Hanzi Wang and David Sute

Fast sparse gaussian processes learning for man-made structure classification

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Informative Vector Machine (IVM) is an efficient fast sparse Gaussian process's (GP) method previously suggested for active learning. It greatly reduces the computational cost of GP classification and makes the GP learning close to real time. We apply IVM for man-made structure classification (a two class problem). Our work includes the investigation of the performance of IVM with varied active data points as well as the effects of different choices of GP kernels. Satisfactory results have been obtained, showing that the approach keeps full GP classification performance and yet is significantly faster (by virtue if using a subset of the whole training data points).Hang Zhou, David Sute

Background Initialization with A New Robust Statistical Approach

© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Initializing a background model requires robust statistical methods as the task should be robust against random occurrences of foreground objects, as well as against general image noise. The median has been employed for the problem of background initialization. However, the median has only a breakdown point of 50%. In this paper, we propose a new robust method which can tolerate more than 50% of noise and foreground pixels in the background initialization process. We compare our new method with five others and give quantitative evaluations on background initialization. Experiments show that the proposed method achieves very promising results in background initialization.Hanzi Wang and D. Sute

Experimental generation of pseudo bound entanglement

We use Nuclear Magnetic Resonance (NMR) to experimentally generate a bound entangled (more precisely: pseudo bound entangled) state, i.e. a quantum state which is non-distillable but nevertheless entangled. Our quantum system consists of three qubits. We characterize the produced state via state tomography to show that the created state has a positive partial transposition with respect to any bipartite splitting, and we use a witness operator to prove its entanglement.Comment: 5 page