Cloning Algorithm Research Articles

Video cloning for paintings via artistic style transfer

In the past, visual arts usually represented the static art like paintings, photography and sculptures. In recent years, many museums, artwork galleries, and even art exhibitions demonstrated dynamic artworks for visitors to relish. The most famous dynamic artwork is “The moving painting of Along the River During the Qingming Festival”. Nevertheless, it took 2 years to complete this work. They had to plan each action for every character at first, then drew each video frame by animators. Finally, it could achieve seamless stitching by using lots of projectors to render scene on the screen. In our research, we develop a method for generating animated paintings. It only needs a number of videos on a network of existing databases and requires users to perform some simple auxiliary operations to achieve the effect of animation synthesis. First, our system lets users select an object with the same class from the first video frame. We then employ random forests as learning algorithm to retrieve from a video the object which users want to insert into an artwork. Second, we utilize style transferring, which enables the video frames to be consistent with the style of painting. At last, we use the seamless image cloning algorithm to yield seamless synthesizing result. Our approach allows different users to synthesize animating paintings up to their selected styled video frames. The resulting work not only maintains the original author’s painting style, but also generates a variety of artistic conception for people to enjoy.

Multi Agent Based Intrusion Detection System using Artificial Immune System for Distributed Network

To enhance the ability of intrusion detection system (IDS) with detection accuracy and low false positive rate, artificial immune system (AIS) based multi agent IDS is proposed that is inspired by human immune system (HIS). Presented IDS is designed four new algorithms these are mobile agent cloning algorithm, attribute selection algorithm based on danger theory which is filtering unnecessary attributes and giving most appropriate attributes for intrusion detection, detection algorithm based on negative selection technique is the prime algorithm which is detecting intrusions and rule algorithm is the simple and initial level algorithm to find normality or abnormality in the captured packet. Proposed IDS is the combination of misuse and anomaly IDS where architecture is the client server cum distributed IDS architecture. Proposed IDS reducing work load of network administrator and overcome network latency through automation and dynamic adaptation of mobile agent which is implement clonal selection concept of AIS. Proposed IDS is increasing efficiency and detection accuracy by using negative selection concept of AIS to design rule agent and informative agent. Another concept of AIS is danger theory is used to design packet capture & extract packet agent which is support to reduce execution time and improve efficiency. Database agent is used to maintain or update database (training, testing) dynamically during novel information identified. Finally alert agent responded to client host after intrusion detected for further action. These entire agents are the primary and most influential performer of the proposed IDS. Presented IDS identify and focused on most appropriate 13 attributes out of 41 of a packet to detect intrusion and proved that intrusion detection accuracy and intrusion detection rate increased with 0.2% and 0.1% respectively and reduce false positive rate with 0.5%.

Improved Hadoop Cluster Performance by Dynamic Load and Resource Aware Speculative Execution and Straggler Node Detection

The big data is one of the fastest growing technologies, which can to handle huge amounts of data from various sources, such as social media, web logs, banking and business sectors etc. In order to pace with the changes in the data patterns and to accommodate the requirements of big data analytics, the platform for storage and processing such as Hadoop, also requires great advancements. Hadoop, an open source project executes the big data processing job in map and reduce phases and follows master-slave architecture. A Hadoop MapReduce job can be delayed if one of its many tasks is being assigned to an unreliable or congested machine. To solve this straggler problem, a novel algorithm design of speculative execution schemes for parallel processing clusters, from an optimization perspective, under different loading conditions is proposed. For the lightly loaded case, a task cloning scheme, namely, the combined file task cloning algorithm, which is based on maximizing the overall system utility, a straggler detection algorithm is proposed based on a workload threshold. The detection and cloning of tasks assigned with the stragglers only will not be enough to enhance the performance unless cloning of tasks is allocated in a resource aware method. So, a method is proposed which identifies and optimizes the resource allocation by considering all possible aspects of cluster performance balancing. One main issue arises due to the pre configuration of distinct map and reduce slots based on the number of files in the input folder. This can cause severe under-utilization of slot as map slots might not be fully utilized with respect to the input splits. To solve this issue, an alternative technique of Hadoop Slot Allocation is introduced in this paper by keeping the efficient management of slots model. The combine file task cloning algorithm combines the files which are less than the size of a single data block and executes them in the highly performing data node. On implementing these efficient cloning and combining techniques on a heavily loaded cluster after detecting the straggler, machine is found to reduce the elapsed time of execution to an average of 40%. The detection algorithm improves the overall performance of the heavily loaded cluster by 20% of the total elapsed time in comparison with the native Hadoop algorithm.

A novel mixed reality in breast and constructive jaw surgical tele-presence

Background and aimSurgical telepresence has been implemented using Mixed reality (MR) but, MR is theory based and only used for investigating research. The Aim of this paper is to propose and implement a new solution by merging augmented video (generating in local site) and virtual expertise surgeon hand (remote site). This system is to improve the visualization of surgical area, overlay accuracy in the merged video without having any discoloured patterns on hand, smudging artefacts on surgeon hand boundary and occluded areas of surgical area. MethodologyThe Proposed system consists of an Enhanced Multi-Layer Mean Value Cloning (EMLMV) algorithm that improves the overlay accuracy, visualization accuracy and the processing time. This proposed algorithm includes trimap and alpha matting as a pre-processing stage of merging process, which helps to remove the smudging and discoloured artefacts surrounded by remote surgeon hand. ResultsResults showing that the proposed system improved the accuracy by reducing the overlay error of merging image from 1.3 mm (Millimeter) to 0.9 mm. Furthermore, it improves the visibility of surgeon hand in the final merged image from 98.4% (visibility of pixels) to 99.1% (visibility of pixels). Similarly, the processing time in our proposed solution is reduced, which is computed as 10 s to produce 50 frames, whilst, the state of art solution computes 11 s for the same number of frames. ConclusionThe proposed system focuses on the merging of augmented reality video (local site), and the virtual reality video (remote site) with the accurate visualization. we consider discoloured areas, smudging artefacts and occlusion as the main aspects to improve the accuracy of merged video in terms of overlay error and visualization error. So, the proposed system would produce the merged video with the removal of artefacts around the expert surgeon hand.

An Improved Immune Clone Algorithm Logistics Delivery Strategy

Logistics routing problem is a typical NP hard problem, which is very difficult to solve accurately. On the basis of establishing logistics path optimization model, an immune clone algorithm is proposed. To improve the accuracy of search algorithms, the clonal selection and high frequency variations in the immune algorithm method are introduced. Then the antibody encoding virtual distribution point algorithm is designed to improve search efficiency. The benchmark problem of logistics delivery path optimization is simulated and analyzed. Experimental results show that the proposed immune cloning algorithm expands the range of population search and it have obvious advantages in solving large-scale complex physical distribution optimization problems. Also, the proposed algorithm can solve the optimal distribution of logistics effectively.

Estimation of zero-inflated parameter-driven models via data cloning

ABSTRACTIn this paper, we propose the use of the Data Cloning (DC) approach to estimate parameter-driven zero-inflated Poisson and Negative Binomial models for time series of counts. The data cloning algorithm obtains the familiar maximum likelihood estimators and their standard errors via a fully Bayesian estimation. This provides some computational ease as well as inferential tools such as confidence intervals and diagnostic methods which, otherwise, are not readily available for parameter-driven models. To illustrate the performance of the proposed method, we use Monte Carlo Simulations and real data on asthma-related emergency department visits in the Canadian province of Ontario.

A geometry-based algorithm for cloning real grains 2.0

We introduce a computational algorithm to “clone” the grain morphologies of a sample of real grains that have been digitalized. This cloning algorithm allows us to generate an arbitrary number of cloned grains that satisfy the same distributions of morphological features displayed by their parents and can be included into a numerical Discrete Element Method simulation. This study is carried out in three steps. First, distributions of morphological parameters such as aspect ratio, roundness, principal geometric directions, and spherical radius, called the morphological DNA, are extracted from the parents. Second, the geometric stochastic cloning (GSC) algorithm, relying purely on statistical distributions of the aforementioned parameters, is explained, detailed, and used to generate a pool of clones from its parents’ morphological DNA. Third, morphological DNA is extracted from the pool of clones and compared to the one obtained from a similar pool of parents, and the distribution of volume-surface ratio is used to perform quality control. Then, from these results, the error (mutation) in the GSC process is analyzed and used to discuss the algorithm’s drawbacks, knobs (parameters) tuning, as well as potential improvements.

Breakdown of the finite-time and -population scalings of the large deviation function in the large-size limit of a contact process

In a recent study (Nemoto et al 2017 Phys. Rev. E 95 012102; Guevara Hidalgo et al 2017 Phys. Rev. E 95 062134), the finite-time (t) and -population size (Nc) scalings in the evaluation of a large deviation function (LDF) estimator were analyzed by means of the cloning algorithm. These scalings provide valuable information about the convergence of the LDF estimator in the infinite-t and infinite-Nc limits. For the cases analyzed in that study, the scalings of the systematic errors of the estimator were found to behave as t−1 and in the large-t and large-Nc asymptotics. Moreover, it was shown how this convergence speed can be used in order to extract an asymptotic limit which rendered a better LDF estimation in comparison to the standard estimator. However, the validity of these scaling laws, and thus the convergence of the estimator, was proved only in systems for which the number of sites L (where the dynamics occurs) was small. In this paper, the analysis is extended to a wider range of system sizes L. We show how the introduction of the exponents and allows us to characterize the behavior of the LDF estimator for any system size. From these generalized - and -scalings, we verify that in the large-L limit the t−1- and -scalings are no longer valid. Moreover, as the convergence of the estimator relies on the positivity of these exponents, we show how for some cases can be negative implying that the estimation provided by the cloning algorithm is no longer reliable.

A Cloud Removal Algorithm to Generate Cloud and Cloud Shadow Free Images Using Information Cloning

One of the main problems of optical remote sensing is clouds and cloud shadows caused by specific atmospheric conditions during data acquisition. These features limit the usage of acquired images and increase the difficulty in data analysis, such as normalized difference vegetation index values, misclassification, and atmospheric correction. Accurate detection and reliable cloning of cloud and cloud shadow features in satellite images are very useful processes for optical remote sensing applications. In this study, an automated cloud removal algorithm to generate cloud and cloud shadow free images from multitemporal Landsat-8 images is introduced. Cloud and cloud shadow areas are classified by using process-based rule set developed by using spectral and spatial features after applying simple linear iterative clustering superpixel segmentation algorithm to the image to find cloud pixel groups easily and correctly. Segmentation-based cloud detection method gives better results than pixel-based for detection of cloud and cloud shadow patches. After detection of clouds and cloud shadows, cloud-free images are created by cloning cloudless regions from multitemporal dataset. Spectral and structural consistency are preserved by considering spectral features and seasonal effects while cloning process. Statistical similarity tests are applied to find best cloud-free image to use for cloning process. Cloning results are tested with the structural similarity index metric to evaluate the performance of cloning algorithm.

Simulation of turbulent reactive flows using a FDF methodology – Advances in particle density control for normalized variables

The present work is devoted to the development and implementation of a computational framework to perform numerical simulations of low Mach number turbulent reactive flows. The numerical algorithm designed for solving the transport equations relies on a semi implicit predictor-corrector integration scheme. The final computational model relies on a hybrid Large Eddy Simulation / transported Filtered Probability Density Function (FDF) framework. The TVD method CUBISTA is used in the discretization of the advective terms of the transport equations. The dynamic Smagorinsky turbulence closure model if retained and the influence of residual sub-grid scale scalar fluctuations on the filtered chemical reaction rate is taken into account within the Lagrangian PDF framework. The corresponding PDF model makes use of a Monte Carlo technique: Stochastic Differential Equations (SDE) equivalent to the Fokker-Planck equations are solved for the progress variable of chemical reactions. Due to the stochastic nature of the Monte-Carlo method and to the turbulent motions, the number of particles present in a given cell may significantly change in time. We present an algorithm for conservation of statistical moments up to second and suggestion of how one may reduce drastically a notional particle population and yet maintain an acceptable PDF shape. The TVD algorithm capabilities are demonstrated in the one-dimensional advection of different pulses, the energy equation is validated through the simulation of a natural convection flow in a cavity. Finally, the new cloning and annihilation particles algorithm is tested on known (given) PDF functions and then applied to LES of reactive turbulent channel flows of methane/air mixtures.

Efficient characterisation of large deviations using population dynamics

We consider population dynamics as implemented by the cloning algorithm for analysis of large deviations of time-averaged quantities. We use the simple symmetric exclusion process with periodic boundary conditions as a prototypical example and investigate the convergence of the results with respect to the algorithmic parameters, focussing on the dynamical phase transition between homogeneous and inhomogeneous states, where convergence is relatively difficult to achieve. We discuss how the performance of the algorithm can be optimised, and how it can be efficiently exploited on parallel computing platforms.

Mitigating Service Variability in MapReduce Clusters via Task Cloning: A Competitive Analysis

Measurement traces from real-world production environment show that the execution time of tasks within a MapReduce job varies widely due to the variability in machine service capacity. This variability issue makes efficient job scheduling over large-scale MapReduce clusters extremely challenging. To tackle this problem, we adopt the task cloning approach to mitigate the effect of machine variability and design corresponding scheduling algorithms so as to minimize the overall job flowtime in different scenarios. For offline scheduling where all jobs arrive at the same time, we design an $O(1)$ -competitive algorithm, which gives priorities to jobs with small effective workload. We then extend this offline algorithm to yield the so-called Smallest Remaining Effective Workload based $\beta$ -fraction Sharing plus Cloning algorithm (SREW+C( $\beta$ )) for the online case. We also show that SREW+C( $\beta$ ) is $(1+ 2\beta + \epsilon)$ -speed $O(\frac{1}{\beta \epsilon })$ -competitive with respect to the sum of job flowtime within a cluster. We demonstrate via trace-driven simulations that SREW+C( $\beta$ ) can significantly reduce the overall job flowtime by cutting down the elapsed time of small jobs substantially. In particular, SREW+C( $\beta$ ) reduces the total job flowtime by 14, 10 and 11 percent respectively when comparing to Mantri, Dolly and Grass.

Optimum design and vibration control of a space structure with the hybrid semi-active control devices

Based on the super elastic properties of the shape memory alloy (SMA) and the inverse piezoelectric effect of piezoelectric (PZT) ceramics, a kind of hybrid semi-active control device was designed and made, its mechanical properties test was done under different frequency and different voltage. The local search ability of genetic algorithm is poor, which would fall into the defect of prematurity easily. A kind of adaptive immune memory cloning algorithm(AIMCA) was proposed based on the simulation of clone selection and immune memory process. It can adjust the mutation probability and clone scale adaptively through the way of introducing memory cell and antibody incentive degrees. And performance indicator based on the modal controllable degree was taken as antigen-antibody affinity function, the optimization analysis of damper layout in a space truss structure was done. The structural seismic response was analyzed by applying the neural network prediction model and T-S fuzzy logic. Results show that SMA and PZT friction composite damper has a good energy dissipation capacity and stable performance, the bigger voltage, the better energy dissipation ability. Compared with genetic algorithm, the adaptive immune memory clone algorithm overcomes the problem of prematurity effectively. Besides, it has stronger global searching ability, better population diversity and faster convergence speed, makes the damper has a better arrangement position in structural dampers optimization leading to the better damping effect.

A geometry-based algorithm for cloning real grains

We introduce a computational algorithm to “clone” the grain morphologies of a sample of real grains that have been digitalized. This cloning algorithm allows us to generate an arbitrary number of cloned grains that satisfy the same distributions of morphological features displayed by their parents and can be included into a numerical Discrete Element Method simulation. This study is carried out in three steps. First, distributions of morphological parameters such as aspect ratio, roundness, principal geometric directions, and spherical radius, called the morphological DNA, are extracted from the parents. Second, the geometric stochastic cloning (GSC) algorithm, relying purely on statistical distributions of the aforementioned parameters, is explained, detailed, and used to generate a pool of clones from its parents’ morphological DNA. Third, morphological DNA is extracted from the pool of clones and compared to the one obtained from a similar pool of parents, and the distribution of volume-surface ratio is used to perform quality control. Then, from these results, the error (mutation) in the GSC process is analyzed and used to discuss the algorithm’s drawbacks, knobs (parameters) tuning, as well as potential improvements.

Finite-Size Scaling of a First-Order Dynamical Phase Transition: Adaptive Population Dynamics and an Effective Model.

We analyze large deviations of the time-averaged activity in the one-dimensional Fredrickson-Andersen model, both numerically and analytically. The model exhibits a dynamical phase transition, which appears as a singularity in the large deviation function. We analyze the finite-size scaling of this phase transition numerically, by generalizing an existing cloning algorithm to include a multicanonical feedback control: this significantly improves the computational efficiency. Motivated by these numerical results, we formulate an effective theory for the model in the vicinity of the phase transition, which accounts quantitatively for the observed behavior. We discuss potential applications of the numerical method and the effective theory in a range of more general contexts.

Lower Current Large Deviations for Zero-Range Processes on a Ring

We study lower large deviations for the current of totally asymmetric zero-range processes on a ring with concave current-density relation. We use an approach by Jensen and Varadhan which has previously been applied to exclusion processes, to realize current fluctuations by travelling wave density profiles corresponding to non-entropic weak solutions of the hyperbolic scaling limit of the process. We further establish a dynamic transition, where large deviations of the current below a certain value are no longer typically attained by non-entropic weak solutions, but by condensed profiles, where a non-zero fraction of all the particles accumulates on a single fixed lattice site. This leads to a general characterization of the rate function, which is illustrated by providing detailed results for four generic examples of jump rates, including constant rates, decreasing rates, unbounded sublinear rates and asymptotically linear rates. Our results on the dynamic transition are supported by numerical simulations using a cloning algorithm.

CQICO and Multi-objective Thermal Optimization for High Speed PM Generator

This paper proposes a novel Continuous Quantum Immune Clonal Optimization (CQICO) algorithm for thermal optimization on an 117kW high speed permanent magnet generator (HSPMG). The proposed algorithm mixes the Quantum Computation into the Immune Cloning Algorithm and causes better population diversity, higher global searching ability, and faster convergence which approved by simulation results. Then, the improved algorithm is applied to seek an optimized slot groove and improve HSPMG thermal performance, in which the 3-D fluid-thermal coupling analyses are processed with a multi-objective optimal group composed of the highest temperature and the temperature difference. Both the proposed algorithm and the obtained conclusions are of significances in the design and optimization of the cooling system in electric machines.

Integrating population dynamics models and distance sampling data: a spatial hierarchical state-space approach.

Stochastic versions of Gompertz, Ricker, and various other dynamics models play a fundamental role in quantifying strength of density dependence and studying long-term dynamics of wildlife populations. These models are frequently estimated using time series of abundance estimates that are inevitably subject to observation error and missing data. This issue can be addressed with a state-space modeling framework that jointly estimates the observed data model and the underlying stochastic population dynamics (SPD) model. In cases where abundance data are from multiple locations with a smaller spatial resolution (e.g., from mark-recapture and distance sampling studies), models are conventionally fitted to spatially pooled estimates of yearly abundances. Here, we demonstrate that a spatial version of SPD models can be directly estimated from short time series of spatially referenced distance sampling data in a unified hierarchical state-space modeling framework that also allows for spatial variance (covariance) in population growth. We also show that a full range of likelihood based inference, including estimability diagnostics and model selection,is feasible in this class of models using a data cloning algorithm. We further show through simulation experiments that the hierarchical state-space framework introduced herein efficiently captures the underlying dynamical parameters and spatial abundance distribution. We apply our methodology by analyzing a time series of line-transect distance sampling data for fin whales (Balaenoptera physalus) off the U.S. west coast. Although there were only seven surveys conducted during the study time frame, 1991-2014, our analysis detected presence of strong density regulation and provided reliable estimates of fin whale densities. In summary, we show that the integrative framework developed herein allows ecologists to better infer key population characteristics such as presence of density regulation and spatial variability in a population's intrinsic growth potential.

Discreteness effects in population dynamics

We analyse numerically the effects of small population size in the initial transient regime of a simple example population dynamics. These effects play an important role for the numerical determination of large deviation functions of additive observables for stochastic processes. A method commonly used in order to determine such functions is the so-called cloning algorithm which in its non-constant population version essentially reduces to the determination of the growth rate of a population, averaged over many realizations of the dynamics. However, the averaging of populations is highly dependent not only on the number of realizations of the population dynamics, and on the initial population size but also on the cut-off time (or population) considered to stop their numerical evolution. This may result in an over-influence of discreteness effects at initial times, caused by small population size. We overcome these effects by introducing a (realization-dependent) time delay in the evolution of populations, additional to the discarding of the initial transient regime of the population growth where these discreteness effects are strong. We show that the improvement in the estimation of the large deviation function comes precisely from these two main contributions.

Toward fully quantum modelling of ultrafast photodissociation imaging experiments. Treating tunnelling in the ab initio multiple cloning approach.

We present an account of our recent effort to improve simulation of the photodissociation of small heteroaromatic molecules using the Ab Initio Multiple Cloning (AIMC) algorithm. The ultimate goal is to create a quantitative and converged technique for fully quantum simulations which treats both electrons and nuclei on a fully quantum level. We calculate and analyse the total kinetic energy release (TKER) spectra and Velocity Map Images (VMI), and compare the results directly with experimental measurements. In this work, we perform new extensive calculations using an improved AIMC algorithm that now takes into account the tunnelling of hydrogen atoms. This can play an extremely important role in photodissociation dynamics.