Complete Algorithm Research Articles

Assessment of molecular diversity in doubled haploid lines of camelina (Camelina sativa(L.) Crantz), as a new emerging oil crop

AbstractCamelina (Camelina sativa(L.) Crantz), an oilseed crop, belongs to the Brassicaceae family. Two unique features of camelina in comparison with the main oil crops are an adaptation to different environments and also its unique oil composition. The development of doubled haploid plants is one of the essential methods for crop improvement. The study of genetic diversity is an important step in planning crop breeding programmes. This research was conducted to evaluate the genetic variation of 81 camelina doubled haploid lines obtained from 15 crosses by inter simple sequence repeat (ISSR) markers. The total number of amplified bands was 243, of which 239 bands (98.3%) showed polymorphism. The percentage of polymorphic bands varied between 93.75 and 100. The size of the bands ranged from 50 to 1700 base pairs. The informative ISSRs were identified by estimating marker features: polymorphism information content, effective multiplex ratio, marker index and resolving power. Three markers had higher resolving power values (9.88, 8.5 and 7.46) and were the most informative markers to identify the lines. Cluster analysis based on the complete algorithm divided the lines into five groups, indicating relatively clear configuration from the geographic distribution patterns of the parents of the doubled haploid lines. Principal coordinate analysis classified the 81 camelina doubled haploid lines into six groups. The ISSR markers detected high polymorphism to reveal the genetic variation of camelina lines. The findings of this research, along with the characterization of biochemical traits of the lines, can improve breeding programmes achieve high-yielding camelina varieties with higher and better oil content.

Decline in human T-cell lymphotropic virus seroprevalence in blood donors from Minas Gerais, Brazil over a12-year period (2006-2017).

To investigate a 12-year historical series (2006-2017) of human T-cell lymphotropic virus (HTLV)-positive blood donations from Fundação Hemominas, Minas Gerais, Brazil, an observational retrospective study was performed to evaluate data of blood donor candidates who were screened for HTLV-1/2 by enzyme-linked immunosorbent assay or chemiluminescence assays and confirmed by Western blot. We analyzed 3 309 716 blood donations covering 2006-2017 that were extracted from the institutional database. In a total of 3 308 738 donations that have complete algorithm tests, the global frequency of HTLV-positive donations was 0.012%. The seroprevalence in first-time blood donors was 28.82/100 000 donors; 0.95/100 000 donations were HTLV-positive in repeat blood donors. The frequency of HTLV-seropositive females was significantly higher than males(odds ratio = 1.85, p < 0.001) in first-time donors. The median age of HTLV-positive first-time and repeat donors was similar (36 and 32 years, respectively). First-time donors ≥41 years had higher odds to be infected. There was a clear tendency of decline in the HTLV-positive donations in the period analyzed, going from 19.26/100 000 donations to 8.50/100 000 donations. The increase in the proportion of repeat donors over the period analyzed (from 23% in 2006 to 67% in 2017) must be the principal factor that contributed to this drop. Our results showed a continuous decline in the frequency of HTLV-positive donations from Minas Gerais, Brazil throughout 12 years and emphasize the importance of having a high rate of repeat donors in blood centers to reduce the residual risk of transfusion-transmitted infections.

Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernels

Tensor computations are increasingly prevalent numerical techniques in data science, but pose unique challenges for high-performance implementation. We provide novel algorithms and systems infrastructure which enable efficient parallel implementation of algorithms for tensor completion with generalized loss functions. Specifically, we consider alternating minimization, coordinate minimization, and a quasi-Newton (generalized Gauss-Newton) method. By extending the Cyclops library, we implement all of these methods in high-level Python syntax. To make possible tensor completion for very sparse tensors, we introduce new multi-tensor primitives, for which we provide specialized parallel implementations. We compare these routines to pairwise contraction of sparse tensors by reduction to hypersparse matrix formats, and find that the multi-tensor routines are more efficient in theoretical cost and execution time in experiments. We provide microbenchmarking results on the Stampede2 supercomputer to demonstrate the efficiency of the new primitives and Cyclops functionality. We then study the performance of the tensor completion methods for a synthetic tensor with 10 billion nonzeros and the Netflix dataset, considering both least squares and Poisson loss functions.

Specification decomposition for reactive synthesis

Reactive synthesis is the task of automatically deriving a correct implementation from a specification. It is a promising technique for the development of verified programs and hardware. Despite recent advances in terms of algorithms and tools, however, reactive synthesis is still not practical when the specified systems reach a certain bound in size and complexity. In this paper, we present a sound and complete modular synthesis algorithm that automatically decomposes the specification into smaller subspecifications. For them, independent synthesis tasks are performed, significantly reducing the complexity of the individual tasks. Our decomposition algorithm guarantees that the subspecifications are independent in the sense that completely separate synthesis tasks can be performed for them. Moreover, the composition of the resulting implementations is guaranteed to satisfy the original specification. Our algorithm is a preprocessing technique that can be applied to a wide range of synthesis tools. We evaluate our approach with state-of-the-art synthesis tools on established benchmarks: the runtime decreases significantly when synthesizing implementations modularly.

A fast parallel max-flow algorithm

A new parallel algorithm for the max-flow problem on directed networks with single-source and single-sink is proposed. The algorithm is based on tree sub-networks and on efficient parallel algorithm to compute max-flows on the tree sub-networks. The latter algorithm is proved to be work-optimal and time-optimal. The parallel implementation of the complete algorithm is more efficient than the best known parallel algorithm for the max-flow problem in terms of time-complexity and the sequential implementation of the algorithm achieves the best known sequential time-complexity, without using any complex data-structures or complex manipulations on the network.

Contour information regularized tensor ring completion for realistic image restoration

Tensor completion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low-rank nature of images, implicitly requiring that the whole picture is of globally consistent features. As a result, existing tensor completion algorithms often give reasonably good performance if the target image has only random pixel-level missing. Unfortunately, pixel-level missing is very rare in practice and it is often wanted to restore an image with irregular hole-shaped missing, such as removing electricity poles from landscape photos or irrelevant people from tourist photos. This task is extremely difficult for traditional low-rank based tensor completion methods. To overcome this drawback, a Contour Information regularized Tensor RIng Completion (CITRIC) method is proposed for practical image restoration. Meanwhile, the contour information regularization is used to capture significant local features, whereas the low-rank tensor ring structure is utilized to capture as much global information as possible. The alternating direction method of multipliers (ADMM) is adopted to optimize the cost function. Extensive experimental results using real-world images show that CITRIC is more practical than existing methods and can restore real-world images with irregular hole-shaped missing.

Performance of radiophotoluminescence personal dosimeters in terms of the ICRU Report 95’s operational quantities

The objective of this work is to assess the photon energy and angle response of the radiophotoluminescence (RPL) personal dosimetry system used at the Paul Scherrer Institute (PSI) in terms of the operational quantities for external radiation exposure personal dose, Hp, and personal absorbed dose in local skin, Dlocal skin, defined in the Report 95 of the International Commission on Radiation Measurements and Units (ICRU). The RPL responses in terms of the “new” ICRU Report 95 quantities to a range of photon energies and irradiation angles were calculated using the RPL responses in terms of the personal dose equivalent Hp(10) and Hp(0.07) from the ICRU Report 51, previously obtained during commissioning of the RPL system, and the conversion coefficients from air kerma to the various operational quantities. The indicated value provided by the current dosimetry algorithm over-estimates the personal dose, Hp, in the low-energy range (¡ 33 keV), whereas the estimation for the personal absorbed dose in local skin, Dlocal skin, with the current system is satisfactory. A new dosimetry algorithm was developed making use of the five signals obtained from the RPL detectors, corresponding to the signal from regions of RPL glass under five different filters, to improve the Hp estimation by the RPL dosimeters. The results indicate that, in this case, the new algorithm may be sufficient to achieve satisfactory photon energy and angle response in terms of the ICRU Report 95 quantity Hp without a physical redesign of the dosimeter badges. A few photon mixed fields were also investigated, but a complete algorithm for photon-beta mixed field remains to be developed.

Fault Line Selection of Distribution Network Based on Modified CEEMDAN and GoogLeNet Neural Network

Aiming at the difficulty of single-phase grounding fault line selection in a small current grounding system, a distribution network fault line selection method based on modified CEEMDAN and convolutional neural network is proposed. Firstly, the random forest and multiscale permutation entropy are used to modify the Complete Ensemble Empirical Mode Decomposition Adaptive Noise algorithm (CEEMDAN), and the zero-sequence current of each line is decomposed into a series of intrinsic mode functions through the modified CEEMDAN (MCEEMDAN) algorithm. Secondly, the intrinsic mode function is transformed into the image formation by using the signal-image conversion method, and the generated image is upgraded to a three-dimensional color image by combining pseudo-color coding technology. Finally, the color images converted from the signals of each line are fused as the input of the GoogLeNet network, and the fault line selection of the distribution network is realized in the form of probability output by the Softmax function. The experimental results show that the proposed method has not only strong feature extraction ability and high recognition accuracy but also has good anti-noise and robustness.

Singular Spectrum Analysis and Circulant Maximum Variance Frames

Singular spectrum analysis (SSA) is a powerful method that is frequently used in dynamical systems theory and time series analysis. However, the algorithm itself is only partially understood. In this paper, we tackle the problem of a thorough interpretation of the complete basic SSA algorithm. We point out the relationship between SSA and Fourier analysis (FA), linear filtering (LF), principal component analysis (PCA), and power spectrum density estimation (PSDE). The main contribution is a reformulation and comprehensive interpretation of SSA by means of a novel class of data-adaptive circulant frames. Based on this class of frames, we introduce a formalism for time series decomposition and reconstruction. This mathematical framework generalizes principle component analysis and SSA. We show that SSA, PCA, FA, LF, and PSDE can be found from special cases of the proposed frame structures. This way we are able to provide a formal description of SSA and its relationship to the aforementioned methods. Whilst previous approaches to the interpretation of SSA relied on approximations, we provide exact mathematical descriptions of the relation between the above-mentioned techniques.

Chaining Value Functions for Off-Policy Learning

To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its experience. This is important to learn counterfactuals, or because the experience was generated out of its own control. However, off-policy learning is non-trivial, and standard reinforcement-learning algorithms can be unstable and divergent. In this paper we discuss a novel family of off-policy prediction algorithms which are convergent by construction. The idea is to first learn on-policy about the data-generating behaviour, and then bootstrap an off-policy value estimate on this on-policy estimate, thereby constructing a value estimate that is partially off-policy. This process can be repeated to build a chain of value functions, each time bootstrapping a new estimate on the previous estimate in the chain. Each step in the chain is stable and hence the complete algorithm is guaranteed to be stable. Under mild conditions this comes arbitrarily close to the off-policy TD solution when we increase the length of the chain. Hence it can compute the solution even in cases where off-policy TD diverges. We prove that the proposed scheme is convergent and corresponds to an iterative decomposition of the inverse key matrix. Furthermore it can be interpreted as estimating a novel objective -- that we call a `k-step expedition' -- of following the target policy for finitely many steps before continuing indefinitely with the behaviour policy. Empirically we evaluate the idea on challenging MDPs such as Baird's counter example and observe favourable results.

Internet of Things Task Migration Algorithm under Edge Computing in the Design of English Translation Theory and Teaching Practice Courses

This study aims to improve the quality of English teaching in contemporary colleges and universities, so as to cultivate more English translation talents. Taking corpus, English translation, and teaching practice as the main research horizons, this study analyzes the application of master of translation and interpreting (MTI) course design, English translation, and task migration by combining the task migration algorithm under Internet of Things (IoT) with the self-built corpus, so as to realize the cultivation of translation talents and design of teaching practice. A translation corpus is constructed, a two-way interactive online course is designed, and the experimental results of the complete local migration algorithm (CLM algorithm), random migration algorithm (RM algorithm), and greedy heuristic migration algorithm (GHM algorithm) used in English teaching practice courses are analyzed and compared. The experimental simulation results reveal that the GHM algorithm proposed in this study shows good system stability, its duration is 60% better than that of the CLM algorithm, and its system throughput is increased by 50% compared with the CLM algorithm. In addition, the maximum delay time has little effect on the system throughput. When the system time slot length is fixed at 20 ms, the user migration rate of the genetic algorithm is the highest under the different total numbers of users. In addition, in view of the wide application of neural network in English translation teaching, this study establishes an English translation evaluation model based on the combination of particle swarm optimization (PSO) algorithm and neural network and compares it with the traditional neural network model in simulation experiments. The results show that the addition of PSO algorithm can effectively improve the convergence speed of artificial neural network (ANN), reduce the training time of the model, and improve the accuracy of the ANN network. Using the PSO algorithm to train the neural network, the optimal solutions of different particle swarms can be obtained, and the error is small. The PSO-ANNs model can promote the quality of English translation teaching and improve the English translation ability of the students. Therefore, applying the task transfer algorithm and PSO algorithm to the practice of English translation teaching has greatly improved the efficiency of the English classroom. To sum up, this study provides new ideas for the curriculum design of contemporary college English teaching and has reference value for the cultivation of college English translation talents.

Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution

Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of Multi-Agent Path Finding (MAPF), is crucial for high-level coordination in multi-agent systems, e.g., pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances by centralized approaches with small computation time, and (2) online scenario: executing unlabeled-MAPF despite timing uncertainties of real robots. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm, which takes an arbitrary initial target assignment then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that Offline TSWAP is highly scalable; providing near-optimal solutions while reducing runtime by orders of magnitude compared to existing approaches. In addition, we present the benefits of Online TSWAP, such as delay tolerance, through real-robot demos.

Loopless Top-K Planning

In top-k planning, the objective is to determine a set of k cheapest plans that provide several good alternatives to choose from. Such a solution set often contains plans that visit at least one state more than once. Depending on the application, plans with such loops are of little importance because they are dominated by a loopless representative and can prevent more meaningful plans from being found. In this paper, we motivate and introduce loopless top-k planning. We show how to enhance the state-of-the-art symbolic top-k planner, symK, to obtain an efficient, sound and complete algorithm for loopless top-k planning. An empirical evaluation shows that our proposed approach has a higher k-coverage than a generate-and-test approach that uses an ordinary top-k planner, which we show to be incomplete in the presence of zero-cost loops.

An Algorithm for the Closed-Form Solution of Certain Classes of Volterra–Fredholm Integral Equations of Convolution Type

In this paper, a direct operator method is presented for the exact closed-form solution of certain classes of linear and nonlinear integral Volterra–Fredholm equations of the second kind. The method is based on the existence of the inverse of the relevant linear Volterra operator. In the case of convolution kernels, the inverse is constructed using the Laplace transform method. For linear integral equations, results for the existence and uniqueness are given. The solution of nonlinear integral equations depends on the existence and type of solutions ofthe corresponding nonlinear algebraic system. A complete algorithm for symbolic computations in a computer algebra system is also provided. The method finds many applications in science and engineering.

Transformation-based model checking temporal trust in multi-agent systems

Several formal trust frameworks have been introduced in the area of Multi-Agent Systems (MASs). However, the problem of model checking trust logics is still a challenging research topic. In this paper, we address this challenge by proposing a formal and fully automatic model checking technique for two temporal logics of trust. We start by reviewing TCTL, a Computation Tree Logic of Trust, which has been recently proposed. We also introduce TCTLC for conditional trust. Then, we introduce sound and complete transformation-based algorithms that automatically transform the problem of model checking TCTL and TCTLC into the problem of model checking CTL. Moreover, we prove that although TCTL and TCTLC extend CTL, their model checking algorithms still have the same time complexity for explicit models, which is P-complete with regard to the size of the model and length of the formula, and the same space complexity for concurrent programs, which is PSPACE-complete with regard to the size of the components of these programs. Finally, experiments conducted on a standard industrial case study of auto-insurance claim processing demonstrate the efficiency and scalability of our approach in verifying TCTL and TCTLC formulae.

Toward Efficient Direct Dynamics Studies of Chemical Reactions: A Novel Matrix Completion Algorithm.

This paper describes the development and testing of a polynomial variety-based matrix completion (PVMC) algorithm. Our goal is to reduce computational effort associated with reaction rate coefficient calculations using variational transition state theory with multidimensional tunneling (VTST-MT). The algorithm recovers eigenvalues of quantum mechanical Hessians constituting the minimum energy path (MEP) of a reaction using only a small sample of the information, by leveraging underlying properties of these eigenvalues. In addition to the low-rank property that constitutes the basis for most matrix completion (MC) algorithms, this work introduces a polynomial constraint in the objective function. This enables us to sample matrix columns unlike most conventional MC methods that can only sample elements, which makes PVMC readily compatible with quantum chemistry calculations as sampling a single column requires one Hessian calculation. For various types of reactions─SN2, hydrogen atom transfer, metal-ligand cooperative catalysis, and enzyme chemistry─we demonstrate that PVMC on average requires only six to seven Hessian calculations to accurately predict both quantum and variational effects.

SEMCM: A Self-Expressive Matrix Completion Model for Anti-cancer Drug Sensitivity Prediction

Background: Genomic data sets generated by several recent large scale high-throughput screening efforts pose a complex computational challenge for anticancer drug sensitivity prediction. Objective: We aimed to design an algorithm model that would predict missing elements in incomplete matrices and could be applicable to drug response prediction programs. Method: We developed a novel self-expressive matrix completion model to improve the predictive performance of drug response prediction problems. The model is based on the idea of subspace clustering and as a convex problem, it can be solved by alternating direction method of multipliers. The original incomplete matrix can be filled through model training and parameters updated iteratively. Results: We applied SEMCM to Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets to predict unknown response values. A large number of experiments have proved that the algorithm has good prediction results and stability, which are better than several existing advanced drug sensitivity prediction and matrix completion algorithms. Without modeling mutation information, SEMCM could correctly predict cell line-drug associations for mutated cell lines and wild cell lines. SEMCM can also be used for drug repositioning. The newly predicted drug responses of GDSC dataset suggest that TI-73 was sensitive to Erlotinib. Moreover, the sensitivity of A172 and NCIH1437 to Paclitaxel was roughly the same. Conclusion: We report an efficient anticancer drug sensitivity prediction algorithm which is opensource and can predict the unknown responses of cancer cell lines to drugs. Experimental results prove that our method can not only improve the prediction accuracy but also can be applied to drug repositioning.

Diverse image enhancer for complex underexposed image

The visual appearance of images changes in line with varying environmental light conditions. Adjusting the exposure of various distorted images is a highly complex process. Previous approaches have addressed this issue from different viewpoints and attained remarkable progress. However, they either failed to achieve visually pleasing results or were suitable for a single class of images (e.g., underexposed or nonuniform images). To fully consider the exposure in various distorted images, we proposed a diverse image enhancement model that improved the brightness and contrast, processed the colors, and eliminated the hazy effect. Accordingly, an input red green blue color image was transformed into a hue, saturation, value color image. The V component was inverted and enhanced using three steps. In the first step, the hyperbolic and statistical methods were applied, and then their results were combined using an adjusted logarithmic methodology. This method properly adjusted the high-contrast and low-contrast impact while preserving the vital image information. In the second step, the output of the first step was inverted back and fed into a complete optimization algorithm to estimate the illumination map. Then, the exposure ratio map was estimated using an illumination map, which was adjusted using the camera response function. In the third step, a nonlinear stretching function was introduced to control brightness and contrast. For instance, a lower value of α yielded maximum stretching, and a higher value of α eliminated haze in the image to a great extent. Finally, an empirical evaluation and comparison of the most recent state-of-the-art approaches on eight datasets revealed that the proposed model efficiently addressed the exposure in various degraded images.

Analysis and Prevention and Control System of Domino Accident Risk Data in Chemical Parks Based on Topological Neural Network.

A topologically based neural network algorithm is used to conduct an in-depth study and analysis of domino accident risk data in chemical parks, and this is used to construct a prevention and control system applied to the safety prediction of chemical parks. Firstly, the operating model of the flue gas turbine is expanded and analyzed according to the basic theory of topology, and the object element model is constructed to determine the feature vector and potential risk level. Then, the idea of differential evolution is introduced into the topological neural network to solve the problem that the learning rate and weighting coefficients are difficult to determine, and then the complete DE-ENN algorithm is proposed and tested with the UCI standard data set to verify the effectiveness of the algorithm. Finally, the algorithm is applied to the potential risk identification of the smoke machine operation model, and the experimental results show that the method not only has a simple structure, short running time, and high prediction accuracy but also has excellent generalization ability. For the inherent risk and domino effect risk of chemical equipment in chemical fiber enterprises, the accident risk assessment method based on the protection layer analysis method is proposed; combined with the probability of domino accident and personnel vulnerability model based on the comprehensive analysis of the research results of the allowable risk standard, the allowable risk standard applicable to chemical fiber production enterprises in China is proposed. Given the potential accident risk characteristics of chemical fiber production enterprises, the calculation method of firefighting demand and firefighting capacity of firefighting system is given; the index system of firefighting system emergency response capacity assessment is constructed from three aspects of firefighting system integrity, reliability, and effectiveness, and the assessment model and grade classification standard of firefighting emergency response capacity of chemical fiber production enterprises are determined.

New Riemannian Preconditioned Algorithms for Tensor Completion via Polyadic Decomposition

We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor. These algorithms exploit a non-Euclidean metric on the product space of the factor matrices of the low-rank tensor in the polyadic decomposition form. This new metric is designed using an approximation of the diagonal blocks of the Hessian of the tensor completion cost function, thus has a preconditioning effect on these algorithms. We prove that the proposed Riemannian gradient descent algorithm globally converges to a stationary point of the tensor completion problem, with convergence rate estimates using the $\L{}$ojasiewicz property. Numerical results on synthetic and real-world data suggest that the proposed algorithms are more efficient in memory and time compared to state-of-the-art algorithms. Moreover, the proposed algorithms display a greater tolerance for overestimated rank parameters in terms of the tensor recovery performance, thus enable a flexible choice of the rank parameter.