An Efficient Algorithm for Computing Inbreeding Coefficients in Large Populations

As faults are unavoidable in large scale multiprocessor systems, it is important to be able to determine which units of the system are working and which are faulty. System-level diagnosis is a long-standing realistic approach to detect faults in multiprocessor systems. Diagnosis is based on the results of tests executed on the system units. In this work we evaluate the performance of evolutionary algorithms applied to the diagnosis problem. Experimental results are presented for both the traditional genetic algorithm (GA) and specialized versions of the GA. We then propose and evaluate specialized versions of Estimation of Distribution Algorithms (EDA) for system-level diagnosis: the compact GA and Population-Based Incremental Learning both with and without negative examples. The evaluation was performed using four metrics: the average number of generations needed to find the solution, the average fitness after up to 500 generations, the percentage of tests that got to the optimal solution and the average time until the solution was found. An analysis of experimental results shows that more sophisticated algorithms converge faster to the optimal solution.

La población antioqueña posee una estructura genética compleja que refleja su historia de mezclas entre poblaciones europeas, africanas y amerindias. En este estudio se determinó la composición genética de 849 individuos de la población antioqueña, utilizando un panel de 75 marcadores informativos de ancestralidad y 4 programas que implementan métodos bayesianos para inferir la composición de la mezcla ancestral. La contribución europea (60%) fue dos veces mayor que la contribución amerindia (28%), ambas con una distribución amplia, y fue poca la contribución africana (12%). Se estimó el tiempo transcurrido desde el primer evento de mezcla, que fue de 11 generaciones por cada 100 cM, lo cual hace de esta población un recurso útil para implementar estudios de mapeo por mezcla. Se evaluó la utilidad del panel de marcadores, así como la robustez y utilidad de los diferentes programas utilizados.

Traditional truth inference algorithms take multiple source labels as input and infer true labels for objects. Besides source labels, object features have been introduced in inference algorithms to achieve superior performance. A typical algorithm such as learning from crowds learns a classification model with the guide of inferred true labels where true labels are inferred from source labels. However, the main shortcoming exists in current algorithms and limits their inference performance: label noise. Since source labels from real-world data are noisy, a classifier is likely to be misguided to learn an imprecise decision boundary. In this paper, we propose a deep clustering-based aggregation model (DCAM) to overcome the shortcoming. DCAM introduces clustering for object features to form fine-grained clusters, where objects in the same cluster are supposed to have similar labels. DCAM exploits a cluster label distribution to represent the labeling information of all objects in the corresponding cluster to overcome the problem of label noise. To implement the idea of clustering-based truth inference, DCAM integrates source label generation and deep clustering in a unified framework by utilizing maximum a posteriori (MAP) estimation. Therefore, the proposed model is a novel approach for truth inference with object features. Experimental results on eight real-world inference tasks show that DCAM has a significant improvement of inference accuracy over the state-of-the-art truth inference algorithms. We further discuss the effect of cluster numbers, the quality of clustering, and illustrate the learned embeddings to support the effectiveness of DCAM.

In the modern supply chain system, large-scale transportation tasks require the collaborative work of multiple vehicles to be completed on time. Over the past few decades, multi-vehicle route planning was mainly implemented by heuristic algorithms. However, these algorithms face the dilemma of long computation time. In recent years, some machine learning-based methods are also proposed for vehicle route planning, but the existing algorithms can hardly solve multi-vehicle time-sensitive problems. To overcome this problem, we propose a novel multi-agent reinforcement learning model, which optimizes the route length and the vehicle's arrival time simultaneously. The model is based on the encoder-decoder framework. The encoder mines the relationship between the customer nodes in the problem, and the decoder generates the route of each vehicle iteratively. Specially, we design multiple route recorders to extract the route history information of vehicles and realize the communication between them. In the inferring phase, the model could immediately generate routes for all vehicles in a new instance. To further improve the performance of the model, we devise a multi-sampling strategy and obtain the balance boundary between computation time and performance improvement. In addition, we propose a simulation-based vehicle configuration method to select the optimal number of vehicles in real applications. For validation, we conduct a series of experiments on problems with different customer amounts and various vehicle numbers. The results show that the proposed model outperforms other typical algorithms in both performance and calculation time.

Path bridging: A novel local search methodology for optimizing scheduling in highly constrained continuous galvanizing line operations

Purpose:Intensity‐modulated radiation treatment (IMRT) plan optimization needs pre‐calculated beamlet dose distribution. Pencil‐beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC) methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU‐based MC dose engine in lung IMRT optimization using a novel two‐steps workflow.Methods:A GPU‐based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two‐steps optimization workflow is proposed for fast MC‐based optimization. At first step, rough beamlet dose calculations is conducted with only a small number of particles per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second‐round optimization is conducted, yielding the final Result.Results:For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion:A fast GPU‐based MC dose calculation method along with a novel two‐step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.

A grid based surface tracking algorithm is developed for the efficient computation and extraction of Lagrangian coherent structures (LCS) in 3D unsteady flows. To avoid the costs of mesh generation, a grid based “marching cubes” type algorithm is used. Large savings in computational time are achieved by performing computations only near the ridges in the finite time Lyapunov exponent (FTLE) field, which identify LCS. This algorithm improves the computational order of the LCS algorithm from O(1/dx 3 ) to O(1/dx 2.2 ) and typical applications may see more than an order of magnitude of speedup.

Leptokurtic and platykurtic class of robust symmetrical and asymmetrical time series models

Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer’s disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named “FastGGM”.

In current routing algorithms based on encounter history in Delay-Tolerant Networks (DTNs), packets are always forwarded to nodes with highest probability to reach destination node. However, to the best of our knowledge, no analytical node transient contact pattern detection to achieve high performance, is reported in the literature. In this letter, DTN-Knca - a novel routing which detects frequently encountered nodes' transient contact patterns by correlation analysis is proposed. The trace-driven simulations demonstrate the higher throughput of DTN-Knca in comparison to the state-of-the-art DTN typical routing algorithms based on the encounter history knowledge.

The means of distributing dense passive radio-frequency identification (RFID) tags has been widely utilized for accurate indoor localization. However, they suffer a disadvantage on low localization precision due to the increasing interference of RFID tag collisions and the variation of behavior of tags. Current localization algorithms used in passive RFID location systems are mostly deterministic and have a limited capability on improving localization precision in a dynamic environment with uncertain sensor measurement. This paper investigates the feasibility of using particle filter technique as an efficient localization approach to deliver both relatively good accuracy and precision in dense passive RFID tag distribution applications. A position feature-based system model is first built to apply the typical particle filter technique in passive RFID location applications. Then, a new particle filter algorithm by using a moving direction estimation-based feature improvement scheme is proposed to enhance localization precision in a dense passive RFID tag environment. Experimental results show that the proposed method can provide relatively good accuracy and precision for passive RFID location applications, with an improved performance over the typical particle filter algorithm and a state-of-the-art deterministic method.

Wireless integrated sensor networks, which include collecting, managing data and communication, are used more and more widely for their low cost and convenient deployment. Nowadays the research concerning each aspect of sensor networks is fairly active. Data Aggregation mechanism is one of the key problems in sensor networks. By considering the data transmission delay and overall network energy efficiency, this chapter develops a game-theoretic model of real-time reliable aggregation (RA-G) mechanism for wireless sensor networks. Based on the study of related literatures, first of all in this chapter, the research status of WSN, the system architecture, the characteristics, and the critical technologies are summarized, current typical routing algorithms of WSN are classified and introduced one by one. Taking the implicit collaborative imperative for sensors to achieve overall network objectives (accomplish real-time collection tasks effectively) subject to individual resource consumption into account, this paper proposes a game-theoretic model of reliable data aggregation architecture in wireless sensor networks, defines a multi-tier data aggregation architecture in which semantic based aggregation and average computation aggregation is performed in sensor-level and node-level aggregation respectively. All nodes that detect the same target join the same logic group. Each selected group leader uses game-theoretic model which tradeoffs between energy dissipation and data transmission delay to determine the degree of aggregation. To meet the real-time constraints and balance the energy consumption between nodes, a decision-making model based on game theory which takes delay compensation into account is proposed in the data-relaying stage. The simulation results show that the use of reliable data aggregation architecture can reduce the total transmission overhead of WSN, make the network more energy-efficient and prolong the lifetime of sensor network. On the other hand, the game-theoretic model used in group-level aggregation and data-relaying stage balance the tradeoffs between the energy dissipation and the timeliness of data transmission; therefore, also RA-G data aggregation mechanism is reliable.

Efficient implicit algorithm for the equations of 2-D viscous compressible flow: application to shock-boundary layer interaction

Real time simulation of MPPT algorithms for PV energy system

A neural network adaptive control scheme for robot manipulators is proposed which consists of one Adaline network to identify structured system dynamics and another one to compensate for both structured and unstructured dynamic uncertainties. The former is trained offline using an LMS (least mean square) type algorithm, while the latter uses an online stable weight updating mechanism determined using Lyapunov theory. Since Adaline nets match robot regressor dynamics perfectly, the training processes of the resulting simple neural networks are computationally efficient and the proposed adaptive control scheme has very high potential in real-time applications. The proposed control scheme is illustrated by simulation and comparison studies. >