Fast Implement Research Articles

Superadiabatic scheme for fast implement quantum phase gates and prepare cluster states

Superadiabatic scheme for fast implement quantum phase gates and prepare cluster states

Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach

Abstract. Floe size distribution (FSD) has become a parameter of great interest in observations of sea ice because of its importance in affecting climate change, marine ecosystems, and human activities in the polar ocean. A most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data, where the segmentation of individual ice floes is a challenge in obtaining FSD from remotely sensed images. In this study, we adopt a deep learning (DL) semantic segmentation network to fast and adaptive implement the task of ice floe instance segmentation. In order to alleviate the costly and time-consuming data annotation problem of model training, classical image processing technique is applied to automatically label ice floes in local-scale marginal ice zone (MIZ). Several state-of-the-art (SoA) semantic segmentation models are then trained on the labelled MIZ dataset and further applied to additional large-scale optical Sentinel-2 images to evaluate their performance in floe instance segmentation and to determine the best model. A post-processing algorithm is also proposed in our work to refine the segmentation. Our approach has been applied to both airborne and high-resolution optical (HRO) satellite images to derive acceptable FSDs at local and global scales.

Set algebra — based algebraic evolutionary algorithm for binary optimization problems

In order to design algebraic evolutionary algorithms by set algebra, the intersection, union, complement, difference and symmetric difference operations of 0-1 vectors on {0,1}n are firstly defined based on set operations. Then, the isomorphism between algebraic system defined on {0,1}n and set algebra defined on power set P(Ω) of set Ω is proved. Therefrom a simple and fast implement method of set algebra is proposed. Third, symmetric difference operator and asymmetric mutation operator are successively proposed based on set algebra, they have global exploration and local exploitation capabilities respectively. On this basis, a novel algebraic evolutionary algorithm, named set algebra-based heuristic algorithm (SAHA), is proposed based on the operations of 0-1 vectors on {0,1}n for solving binary optimization problems. For verifying the performance of SAHA, it is used to solve 0-1 knapsack problem (0-1KP) and knapsack problem with single continuous variable (KPC), respectively. The comparison with the state-of-the-art algorithms of solving 0-1KP and KPC shows that SAHA can not only obtain excellent calculation results, but also is faster speed, it is most competitive for solving binary optimization problems.

A numerical method for two-dimensional multi-term time-space fractional nonlinear diffusion-wave equations

Recently, numerous numerical schemes have been developed for solving single-term time-space fractional diffusion-wave equations. Among them, some popular methods were constructed by using the graded meshes due to the solution with low temporal regularity. In this paper, we present an efficient alternating direction implicit (ADI) scheme for two-dimensional multi-term time-space fractional nonlinear diffusion-wave equations. Firstly, the considered problem is equivalently transformed into its partial integro-differential form with the Riemann-Liouville integral and multi-term Caputo derivatives. Secondly, the ADI scheme is constructed by using the first-order approximations and L1 approximations to approximate the terms in time, and using the fractional centered differences to discretize the multi-term Riesz fractional derivatives in space. Furthermore, the fast implement of the proposed ADI scheme is discussed by the sum-of-exponentials technique for both Caputo derivatives and Riemann-Liouville integrals. Then, the solvability, unconditional stability and convergence of the proposed ADI scheme are strictly established. Finally, two numerical examples are given to support our theoretical results, and demonstrate the computational performances of the fast ADI scheme.

A superlinear convergence scheme for nonlinear fractional differential equations and its fast implement

In this paper, we first construct an efficient scheme for nonlinear Caputo fractional differential equations with the initial value and the fractional degree [Formula: see text]. Then, the unconditional stability and the superlinear convergence with the order [Formula: see text] of the proposed scheme are strictly proved and discussed. Due to the nonlocal property of fractional operators, the new scheme is time-consuming for long-time simulations. Thus, a fast implement of the proposed scheme is presented based on the sum-of-exponentials (SOE) approximation for the kernel [Formula: see text] on the interval [Formula: see text] in the Riemann–Liouville integral, where [Formula: see text] is the stepsize. Some numerical experiments are provided to support the theoretical results of the new scheme and demonstrate the computational performance of its fast implement.

Fast implementation of four-dimensional entangled state via transitionless quantum driving

In comparison to low-dimensional entangled states, multi-dimensional entangled states are more security for quantum communication and more efficient and error-tolerant for quantum computation. Using transitionless quantum driving to construct shortcuts to adiabatic passage, we propose an efficient scheme to fast implement a four-dimensional entangled state of two atoms. The rigorous numerical simulations are showed. The results represent that the proposed generation scheme takes less time than traditional adiabatic scheme, and more robust against atomic spontaneous emission. Furthermore, this scheme is insensitive to the instability of experiment parameters.

Fast Implementation of Quantum Phase Gates and Creation of Cluster States via Transitionless Quantum Driving

In this paper, combining transitionless quantum driving and quantum Zeno dynamics, we propose an efficient scheme to fast implement a two-qubit quantum phase gate which can be used to generate cluster state of atoms trapped in distant cavities. The influence of various of various error sources including spontaneous emission and photon loss on the fidelity is analyzed via numerical simulation. The results show that this scheme not only takes less time than adiabatic scheme but also is not sensitive to both error sources. Additionally, a creation of N-atom cluster states is put forward as a typical example of the applications of the phase gates.

快速实现基于单形体体积生长的端元提取算法

快速实现基于单形体体积生长的端元提取算法

Fast analysis of electromagnetic scattering characteristics in spatial and frequency domains based on compressive sensing

Method of moments (MOM) is a common numerical method for solving electromagnetics, which is used widely owing to its high accuracy. As the traditional MOM is applied to solving electromagnetic problem in both spatial and frequency domains, the matrix equation generated from the integral equation must be solved repeatedly since small spatial increments and frequency increments are both required. To resolve problems of this kind, the compressive sensing (CS) theory combined with the asymptotic waveform evaluation (AWE) is introduced to form a new efficient computational method. Firstly, a new incident source is constructed based on CS theory, in which much spatial information is included. Secondly, illuminated by the new incident source, the new equation is solved by AWE technique, and finally a fast implement is formed for solving electromagnetic scattering characteristics in both spatial and frequency domains.

Mapping the sex determination locus in the Atlantic halibut (Hippoglossus hippoglossus) using RAD sequencing

BackgroundAtlantic halibut (Hippoglossus hippoglossus) is a high-value, niche market species for cold-water marine aquaculture. Production of monosex female stocks is desirable in commercial production since females grow faster and mature later than males. Understanding the sex determination mechanism and developing sex-associated markers will shorten the time for the development of monosex female production, thus decreasing the costs of farming.ResultsHalibut juveniles were masculinised with 17 α-methyldihydrotestosterone (MDHT) and grown to maturity. Progeny groups from four treated males were reared and sexed. Two of these groups (n = 26 and 70) consisted of only females, while the other two (n = 30 and 71) contained balanced sex ratios (50% and 48% females respectively). DNA from parents and offspring from the two mixed-sex families were used as a template for Restriction-site Associated DNA (RAD) sequencing. The 648 million raw reads produced 90,105 unique RAD-tags. A linkage map was constructed based on 5703 Single Nucleotide Polymorphism (SNP) markers and 7 microsatellites consisting of 24 linkage groups, which corresponds to the number of chromosome pairs in this species. A major sex determining locus was mapped to linkage group 13 in both families. Assays for 10 SNPs with significant association with phenotypic sex were tested in both population data and in 3 additional families. Using a variety of machine-learning algorithms 97% correct classification could be obtained with the 3% of errors being phenotypic males predicted to be females.ConclusionAltogether our findings support the hypothesis that the Atlantic halibut has an XX/XY sex determination system. Assays are described for sex-associated DNA markers developed from the RAD sequencing analysis to fast track progeny testing and implement monosex female halibut production for an immediate improvement in productivity. These should also help to speed up the inclusion of neomales derived from many families to maintain a larger effective population size and ensure long-term improvement through selective breeding.

L-SkyDiv query: Effectively improve the usefulness of skylines

Skyline query processing has recently received a lot of attention in database community. Given a set of multi-dimensional objects, the skyline query finds the objects that are not dominated by others. To the best of our knowledge, the existing researches mainly focus on how to efficiently return the whole skyline set. However, as the cardinality and dimensionality of input dataset increase, the number of skylines grows exponentially, and hence this “huge” skyline set is completely useless to users. Motivated by the above fact, in this paper, we present a novel type of l-SkyDiv query, which only returns l skylines having maximum diversity, to improve the usefulness of skyline result. Also, we prove that the l-SkyDiv query belongs to the NP-Hard problem theoretically, and propose three efficient heuristic algorithms whose time complexities are polynomial to fast implement the proposed query. Furthermore, we present detailed theoretical analyses and extensive experiments, demonstrating that our algorithms are both efficient and effective.

An Efficient Blind Image Deblurring Algorithm

This paper presents a novel algorithm which concerns with the fast implement of blind image deblurring with a well-reconstructed original image. Firstly, we model both the original image and the blur utilizing the harmonic model in the Sobolev image space, based on which, the prior distributions of them are obtained; Secondly, the Gamma distribution is used as the prior distributions of the unknown parameters to incorporate more prior knowledge for blind image deblurring; Finally, we estimate the original image, the blur and the unknown parameters simultaneously and iteratively by the evidence analysis method. The experimental results show the efficiency and the competitive performance compared of the proposed algorithm with existing blind image deblurring methods.

Article Commentary: “R-Rating” for Fast Food: A Novel Weapon in Our Fight against the Expanding Teen Obesity Epidemic

Article Commentary: “R-Rating” for Fast Food: A Novel Weapon in Our Fight against the Expanding Teen Obesity Epidemic

Region Information-Based ROI Extraction by Multi-Initial Fast Marching Algorithm

Region of interest (ROI) plays an important role in medical image analysis. In this paper, a new approach to ROI extraction based on the curve evolution is proposed. Different from the existent method, the proposed approach is efficient both in segmentation results and computational cost. The deforming curve is modeled as a monotonically marching front under a positive speed field, where a region speed function is derived by minimizing the new defined ROI energy, and integrated with the edge-based speed function. The curve evolution model integrating the ROI information has a large propagation range and could even drive the front in low-contrast and narrow thin areas. Moreover, a multi-initial fast marching algorithm, which permits the user to plant several seed curves as the initial front and evolves them simultaneously, is developed to fast implement the numerical solution. Selective planting seed curves could help the local growth and thus may further improve the segmentation results and reduce the computational cost. Experiments by our approach are presented and compared with that of the other methods, which show that the proposed approach could fast extract low-contrast and narrow thin ROI precisely.