A geometric and fractional entropy-based method for family photo classification

© 2019 Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Assessing The Biophysical Naturalness Of Grassland In Eastern North Dakota With Hyperspectral Imagery

Over the past two decades, non-native species within grassland communities have quickly developed due to human migration and commerce. Invasive species like Smooth Brome grass (Bromus inermis) and Kentucky Blue Grass (Poa pratensis), seriously threaten conservation of native grasslands. This study aims to discriminate between native grasslands and planted hayfields and conservation areas dominated by introduced grasses using hyperspectral imagery. Hyperspectral imageries from the Hyperion sensor on EO-1 were acquired in late spring and late summer on 2009 and 2010. Field spectra for widely distributed species as well as smooth brome grass and Kentucky blue grass were collected from the study sites throughout the growing season. Imagery was processed with an unmixing algorithm to estimate fractional cover of green and dry vegetation and bare soil. As the spectrum is significantly different through growing season, spectral libraries for the most common species are then built for both the early growing season and late growing season. After testing multiple methods, the Adaptive Coherence Estimator (ACE) was used for spectral matching analysis between the imagery and spectral libraries. Due in part to spectral similarity among key species, the results of spectral matching analysis were not definitive. Additional indexes, Level of Dominance and Band variance , were calculated to measure the predominance of spectral signatures in any area. A Texture co-occurrence analysis was also performed on both Level of Dominance and Band variance indexes to extract spatial characteristics. The results suggest that compared with disturbed area, native prairie tend to have generally lower Level of Dominance and Band variance as well as lower spatial dissimilarity. A final decision tree model was created to predict presence of native or introduced grassland. The model was more effective for identification of Mixed Native Grassland than for grassland dominated by a single species. The discrimination of native and introduced grassland was limited by the similarity of spectral signatures between forb-dominated native grasslands and brome-grass stands. However, saline native grasslands were distinguishable from brome grass

Nonlinear Dynamics

This volume covers a diverse collection of topics dealing with some of the fundamental concepts and applications embodied in the study of nonlinear dynamics. Each of the 15 chapters contained in this compendium generally fit into one of five topical areas: physics applications, nonlinear oscillators, electrical and mechanical systems, biological and behavioral applications or random processes. The authors of these chapters have contributed a stimulating cross section of new results, which provide a fertile spectrum of ideas that will inspire both seasoned researches and students

The classical-quantum boundary for correlations: discord and related measures

One of the best signatures of nonclassicality in a quantum system is the existence of correlations that have no classical counterpart. Different methods for quantifying the quantum and classical parts of correlations are amongst the more actively-studied topics of quantum information theory over the past decade. Entanglement is the most prominent of these correlations, but in many cases unentangled states exhibit nonclassical behavior too. Thus distinguishing quantum correlations other than entanglement provides a better division between the quantum and classical worlds, especially when considering mixed states. Here we review different notions of classical and quantum correlations quantified by quantum discord and other related measures. In the first half, we review the mathematical properties of the measures of quantum correlations, relate them to each other, and discuss the classical-quantum division that is common among them. In the second half, we show that the measures identify and quantify the deviation from classicality in various quantum-information-processing tasks, quantum thermodynamics, open-system dynamics, and many-body physics. We show that in many cases quantum correlations indicate an advantage of quantum methods over classical ones.Comment: Close to the published versio

Equivariance and Invariance for Robust Unsupervised and Semi-Supervised Learning

Although there is a great success of applying deep learning on a wide variety of tasks, it heavily relies on a large amount of labeled training data, which could be hard to obtain in many real scenarios. To address this problem, unsupervised and semi-supervised learning emerge to take advantage of the plenty of cheap unlabeled data to improve the model generalization. In this dissertation, we claim that equivariant and invariance are two critical criteria to approach robust unsupervised and semi-supervised learning. The idea is as follows: the features of a robust model ought to be sufficiently informative and equivariant to transformations on the input data, and the classifiers should be resilient and invariant to small perturbations on the data manifold and model parameters. Specifically, features are learnt via auto-encoding the transformations on the input data, and models are regularized through minimizing the effects of perturbations on features or model parameters. Experiments on several benchmarks show the proposed methods outperform many state-of-the-art approaches on unsupervised and semi-supervised learning, proving importance of the equivariance and invariance rules for robust feature representation learning

Multi-Photon Entanglement

Major efforts in quantum information science are devoted to the development of methods that are superior to the one of classical information processing, for example the quantum computer or quantum simulations. For these purposes, superposition and entangled states are considered a decisive resource. Furthermore, since the early days of quantum mechanics, entanglement has revealed the discrepancy between the quantum mechanical and the everyday life perception of the physical world. This combination of fundamental science and application-oriented research makes the realization, characterization, and application of entanglement a challenge pursued by many researchers. In this work, the observation of entangled states of polarization encoded photonic qubits is pushed forward in two directions: flexibility in state observation and increase in photon rate. To achieve flexibility two different schemes are developed: setup-based and entanglement-based observation of inequivalent multi-photon states. The setup-based scheme relies on multi-photon interference at a polarizing beam splitter with prior polarization manipulations. It allows the observation of a family of important four-photon entangled states. The entanglement-based scheme exploits the rich properties of Dicke states under particle projections or loss in order to obtain inequivalent multi-photon entangled states. The observed states are characterized using the fidelity and entanglement witnesses. An increase in photon rate is crucial to achieve entanglement of higher photon numbers. This holds especially, when photon sources are utilized that emit photons spontaneously. To this end, a new photon source is presented based on a femtosecond ultraviolet enhancement cavity and applied to the observation of the six-photon Dicke state with three excitations. The implemented schemes not only allow the observation of inequivalent types of entanglement, but also the realization of various quantum information tasks. In this work, the four-photon GHZ state has been used in a quantum simulation of a minimal instance of the toric code. This code enables the demonstration of basic properties of anyons, which are quasiparticles distinct from bosons and fermions. Further, the six-photon Dicke state has been applied for quantum metrology: It allows one to estimate a phase shift with a higher precision than by using only classical resources. Altogether, a whole series of experiments for observing inequivalent multi-photon entangled states can now be substituted by a single experimental setup based on the designs developed in this work. In addition to this new approach of photon processing, a novel photon source has been implemented, paving the way to realizations of applications requiring higher photon numbers.This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the Ludwig-Maximilians-Universität München's products or services. Internal or 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 must be obtained from the IEEE by writing to [email protected]. By choosing to view this material, you agree to all provisions of the copyright laws protecting it

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table