International co-authorship relations and universityindustry-government (Triple Helix) relations have hitherto been studied separately. Using Japanese publication data for the 1981-2004 period, we were able to study both kinds of relations in a single design. In the Japanese file, 1,277,030 articles with at least one Japanese address were attributed to the three sectors, and we know additionally whether these papers were coauthored internationally. Using the mutual information in three and four dimensions, respectively, we show that the Japanese Triple-Helix system has been continuously eroded at the national level. However, since the mid-1990s, international coauthorship relations have contributed to a reduction of the uncertainty at the national level. In other words, the national publication system of Japan has developed a capacity to retain surplus value generated internationally. In a final section, we compare these results with an analysis based on similar data for Canada. A relative uncoupling of national universityindustry-government relations because of international collaborations is indicated in both countries.

Cooperative Co-evolution (CC) is an evolutionary computation framework that can be used to solve high dimensional optimization problems via a 'divide-and-conquer' mechanism. However, the main challenge when using this framework lies in problem decomposition. That is, deciding how to allocate decision variables to a particular sub-problem, especially interacting decision variables. Existing decomposition methods are typically computationally expensive. In this paper, we propose a new decomposition method, which we call Recursive Differential Grouping (RDG), by considering the interaction between decision variables based on non-linearity detection. RDG recursively examines the interaction between a selected decision variable and the remaining variables, placing all interacting decision variables into the same sub-problem. We use analytical methods to show that RDG can be used to efficiently decompose a problem, without explicitly examining all pairwise variable interactions. We evaluated the efficacy of the RDG method using large scale benchmark optimization problems. Numerical simulation experiments showed that RDG greatly improved the efficiency of problem decomposition in terms of time complexity. Significantly, when RDG was embedded in a CC framework, the optimization results were better than results from seven other decomposition methods.

a b s t r a c tSelecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed.In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice's (1976) selection framework. We describe each of the four component spaces -problem, algorithm, performance and characteristic -in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions.

Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However, solution quality relies heavily on the decomposition method used. Ideally, the interacting decision variables should be assigned to the same sub-component and the interdependency between sub-components should be kept to a minimum. Differential grouping, a recently proposed method, has high decomposition accuracy across a suite of benchmark functions. However, we show that differential grouping can only identify decision variables that interact directly. Subsequently, we propose an extension of differential grouping that is able to correctly identify decision variables that also interact indirectly. Empirical studies show that our extended differential grouping method achieves perfect decomposition on all of the benchmark functions investigated. Significantly, when our decomposition method is embedded in the cooperative co-evolution framework, it achieves comparable or better solution quality than the differential grouping method.

The high-resolution micro traffic data (HRMTD) of all roadway users is important for serving the connected-vehicle system in mixed traffic situations. The roadside LiDAR sensor gives a solution to providing HRMTD from real-time 3D point clouds of its scanned objects. Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from roadside LiDAR data. It can significantly reduce the data processing time and improve the vehicle/pedestrian identification accuracy. An algorithm is proposed in this paper, based on the spatial distribution of laser points, which filters both static and moving background efficiently. Various thresholds of point density are applied in this algorithm to exclude background at different distances from the roadside sensor. The case study shows that the algorithm can filter background LiDAR points in different situations (different road geometries, different traffic demands, day/night time, different speed limits). Vehicle and pedestrian shape can be retained well after background filtering. The low computational load guarantees this method can be applied for real-time data processing such as vehicle monitoring and pedestrian tracking.

This is a repository copy of Decomposition for Large-scale Optimization Problems with Overlapping Components.

Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement , with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.

No abstract