Event-based simulation of neutron experiments: interference, entanglement and uncertainty relations

We discuss a discrete-event simulation approach, which has been shown to give a unified cause-and-effect description of many quantum optics and single-neutron interferometry experiments. The event-based simulation algorithm does not require the knowledge of the solution of a wave equation of the whole system, yet reproduces the corresponding statistical distributions by generating detection events one-by-one. It is showm that single-particle interference and entanglement, two important quantum phenomena, emerge via information exchange between individual particles and devices such as beam splitters, polarizers and detectors. We demonstrate this by reproducing the results of several single-neutron interferometry experiments, including one that demonstrates interference and one that demonstrates the violation of a Bell-type inequality. We also present event-based simulation results of a single neutron experiment designed to test the validity of Ozawa's universally valid error-disturbance relation, an uncertainty relation derived using the theory of general quantum measurements.Comment: Invited paper presented at the EmQM13 Workshop on Emergent Quantum Mechanics, Austrian Academy of Sciences (October 3-6, 2013, Vienna

Discrete-event simulation of uncertainty in single-neutron experiments

A discrete-event simulation approach which provides a cause-and-effect description of many experiments with photons and neutrons exhibiting interference and entanglement is applied to a recent single-neutron experiment that tests (generalizations of) Heisenberg's uncertainty relation. The event-based simulation algorithm reproduces the results of the quantum theoretical description of the experiment but does not require the knowledge of the solution of a wave equation nor does it rely on concepts of quantum theory. In particular, the data satisfies uncertainty relations derived in the context of quantum theory

Slovenian Virtual Gallery on the Internet

The Slovenian Virtual Gallery (SVG) is a World Wide Web based multimedia collection of pictures, text, clickable-maps and video clips presenting Slovenian fine art from the gothic period up to the present days. Part of SVG is a virtual gallery space where pictures hang on the walls while another part is devoted to current exhibitions of selected Slovenian art galleries. The first version of this application was developed in the first half of 1995. It was based on a file system for storing all the data and custom developed software for search, automatic generation of HTML documents, scaling of pictures and remote management of the system. Due to the fast development of Web related tools a new version of SVG was developed in 1997 based on object-oriented relational database server technology. Both implementations are presented and compared in this article with issues related to the transion between the two versions. At the end, we will also discuss some extensions to SVG. We will present the GUI (Graphical User Interface) developed specially for presentation of current exhibitions over the Web which is based on GlobalView panoramic navigation extension to developed Internet Video Server (IVS). And since SVG operates with a lot of image data, we will confront with the problem of Image Content Retrieval

An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation

An appearance-based framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.National Science Foundation (IIS-9912573, EIA-9809340

Zapping index: Using smile to measure advertisement zapping likelihood

In marketing and advertising research, 'zapping' is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers

Blind Multiclass Ensemble Classification

The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the "best" performing one, for a given dataset. Ensemble learning aims at such high-performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets.Comment: To appear in IEEE Transactions in Signal Processin