Entangled-photon decision maker

The competitive multi-armed bandit (CMAB) problem is related to social issues such as maximizing total social benefits while preserving equality among individuals by overcoming conflicts between individual decisions, which could seriously decrease social benefits. The study described herein provides experimental evidence that entangled photons physically resolve the CMAB in the 2-arms 2-players case, maximizing the social rewards while ensuring equality. Moreover, we demonstrated that deception, or outperforming the other player by receiving a greater reward, cannot be accomplished in a polarization-entangled-photon-based system, while deception is achievable in systems based on classical polarization-correlated photons with fixed polarizations. Besides, random polarization-correlated photons have been studied numerically and shown to ensure equality between players and deception prevention as well, although the CMAB maximum performance is reduced as compared with entangled photon experiments. Autonomous alignment schemes for polarization bases were also experimentally demonstrated based only on decision conflict information observed by an individual without communications between players. This study paves a way for collective decision making in uncertain dynamically changing environments based on entangled quantum states, a crucial step toward utilizing quantum systems for intelligent functionalities

The EPR paradox, Bell's inequality, and the question of locality

Most physicists agree that the Einstein-Podolsky-Rosen-Bell paradox exemplifies much of the strange behavior of quantum mechanics, but argument persists about what assumptions underlie the paradox. To clarify what the debate is about, we employ a simple and well-known thought experiment involving two correlated photons to help us focus on the logical assumptions needed to construct the EPR and Bell arguments. The view presented in this paper is that the minimal assumptions behind Bell's inequality are locality and counterfactual definiteness, but not scientific realism, determinism, or hidden variables, as is often suggested. We further examine the resulting constraints on physical theory with an illustration from the many-worlds interpretation of quantum mechanics -- an interpretation that we argue is deterministic, local, and realist, but that nonetheless violates the Bell inequality.Comment: 28 pages; change of title, minor wording changes, move to TeX forma

Visual Tactile Integration in Rats and Underlying Neuronal Mechanisms

Our experience of the world depends on integration of cues from multiple senses to form unified percepts. How the brain merges information across sensory modalities has been the object of debate. To measure how rats bring together information across sensory modalities, we devised an orientation categorization task that combines vision and touch. Rats encounter an object\u2013comprised of alternating black and white raised bars\u2013that looks and feels like a grating and can be explored by vision (V), touch (T), or both (VT). The grating is rotated to assume one orientation on each trial, spanning a range of 180 degrees. Rats learn to lick one spout for orientations of 0\ub145 degrees (\u201chorizontal\u201d) and the opposite spout for orientations of 90\ub145\ub0 (\u201cvertical\u201d). Though training was in VT condition, rats could recognize the object and apply the rules of the task on first exposure to V and to T conditions. This suggests that the multimodal percept corresponds to that of the single modalities. Quantifying their performance, we found that rats have good orientation acuity using their whiskers and snout (T condition); however under our default conditions, typically performance is superior by vision (V condition). Illumination could be adjusted to render V and T performance equivalent. Independently of whether V and T performance is made equivalent, performance is always highest in the VT condition, indicating multisensory enhancement. Is the enhancement optimal with respect to the best linear combination? To answer this, we computed the performance expected by optimal integration in the framework of Bayesian decision theory and found that most rats combine visual and tactile information better than predicted by the standard ideal\u2013observer model. To confirm these results, we interpreted the data in two additional frameworks: Summation of mutual information for each sensory channel and probabilities of independent events. All three analyses agree that rats combine vision and touch better than could be accounted for by a linear interaction. Electrophysiological recordings in the posterior parietal cortex (PPC) of behaving rats revealed that neuronal activity is modulated by decision of the rats as well as by categorical or graded modality-shared representations of the stimulus orientation. Because the population of PPC neurons expresses activity ranging from strongly stimulus-related (e.g. graded in relation to stimulus orientation) to strongly choice-related (e.g. modulated by stimulus category but not by orientation within a category) we suggest that this region is involved in the percept-to-choice transformation

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Fuzzy correlation and regression analysis.

The first half of the dissertation focuses on the motivation and concept of fuzzy correlation. Fuzzy data will be formulated in a mathematical way, and then we will build models of two types of fuzzy correlations, their computation methods are also presented in this dissertation. For the first type of fuzzy correlation problem we proposed an approximate bound as well as a number of computationally efficient algorithms. Monte Carlo sampling method is used to compute the second type of fuzzy correlation problem. The results provided by the second type of fuzzy correlation are more informative than the result of the classical correlation.Some application examples are given at the end. Fuzzy regression models could be applied in short term stock price prediction. Intel Corp. 2003 stock price data are used in this demo. The Dosage-film response is estimated with a fuzzy regression model, this procedure is presented in detail in the last section. It is found that fuzzy regression gives more consistent results than the conventional regression model since it successfully models the inherent vagueness which exists in the application by formulated form.In the second part of the dissertation, eight fuzzy regression models are discussed. In order to enhance the central tendency and remove outliers which have important impact on the regression result, different techniques are used to improve the original model. The fuzzy regression method presented in this dissertation also applies to crisp data regression cases. Numerical examples are given for all the fuzzy correlation and fuzzy regression models we explored in this dissertation for illustration and verification purpose.Correlation and regression analysis are widely used in all kinds of data mining applications. However many real world data have the characteristic of vagueness; the classical data analysis techniques have limitation in managing this vagueness systematically. Fuzzy sets theory can be applied to model this kind of data. New concepts and methods of correlation and regression analysis for data with uncertainty are presented in this dissertation. Recently, fuzzy correlation and regression have been applied to many applications. Successful examples include quality control, marketing, image processing, robot control, medical diagnosis, etc. The purpose of this dissertation is to revisit the ongoing research work that people have already done on this issue and to develop some new models related to fuzzy data correlation and regression. In this dissertation, we define and conceptualize the correlation and regression concepts within the fuzzy context. Then the presently available methods are explored in light of their limitations. Then new concepts and new models are presented. Throughout this dissertation, a number of test data sets are used to verify how our ideas are implemented. Suggestions for further research will be provided

Entrepreneurship

The entrepreneur has been neglected over the years in formal economic theorizing. Previously there has been only eclectic theories such as human capital theory and network dynamics which discuss certain perspectives of entrepreneurial behaviour. This insightful book closes this gap in entrepreneurship literature. Inspired by modern physics, author Thomas Grebel brings together an evolutionary methodology, along the way implicating quantum, graph, and percolation theory. Here, Grebel has provided a synthesis of all the main theories of entrepreneurship. Taking an interdisciplinary approach to the subject, this fascinating book opens up new ideas in modelling and the original thinking contained within will be of interest to all those working in the area of business and management as well as those in economics