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Numerous routing algorithms have been proposed for the overall network on the traditional Benes permutation networks. To achieve efficient system communication, a study of routing through a small number of information paths is needed from a practical viewpoint. In other words, it is desired that the requirement of permutation by a system should be realized so that for networks currently used, only a small part of the switch settings is affected. In this paper in the forementioned principle, a dynamic routing algorithm (basic design) based on permutation is proposed. Given a permutation t as a control input, a new permutation ... = P∘t can be realized through currently realized permutation by resetting only a part of the information paths in a network, which does not affect any other information path. Moreover, as an element of a permutation network, we propose a d-transposition block which performs a transposition operation efficiently, and show examples of d-transposition blocks for d = 3, 4. [ABSTRACT FROM AUTHOR]

Vehicle behavior of a PRT (Personal Rapid Transit system) multiberth station has already been analyzed as a continuous time system under the assumption of Poisson vehicle arrival and exponential passenger loading and unloading time. However, it is more realistic to model it as a discrete time system considering a widely used point-follower control. Thereby a general passenger loading and unloading time can be easily treated. An off-line station with two berth in PRT, in which vehicles move on a guideway under point-follower control, is modeled and analyzed as a discrete-time Markov chain in this paper. The state quantity is defined based on the physical state of two berths at the arrival instance of a cell. First it is shown that the inter-entrance time can be calculated numerically for a general passenger loading and unloading time. Then, several measures of the station behavior such as mean inter-entrance time, entrance frequently and entrance factor are analytically obtained for the case of fixed passenger loading and unloading time, the opposite of the exponential distribution. The differences between two berths and one berth are discussed by showing numerical examples. [ABSTRACT FROM AUTHOR]

A control algorithm and a probability-theoretical traffic analysis of a grade-separated intersection model of the personal rapid transit (PRT) systems under the quasi-synchronous scheme is presented. At the intersection, vehicles diverge from the less preferential subline and merge into the preferential mainline. To realize efficient cell-assignment, this algorithm makes a slot-slipping decision before the diverge point and performs the actual slot-slipping after it is on the off line. Even a diverge-minded vehicle is forced to go straight (be adopted) when the number of maneuvering cells exceeds its limit and passes the intersection without the maneuver. The behavior of this algorithm under the random arrival assumption is formulated as a Markov chain, the analysis of which leads to the explicit expressions for the delay distribution, the average delay, and the abort rate. [ABSTRACT FROM AUTHOR]

Random feature maps are a promising tool for large-scale kernel methods. Since most random feature maps generate dense random features causing memory explosion, it is hard to apply them to very-large-scale sparse datasets. The factorization machines and related models, which use feature combinations efficiently, scale well for large-scale sparse datasets and have been used in many applications. However, their optimization problems are typically non-convex. Therefore, although they are optimized by using gradient-based iterative methods, such methods cannot find global optimum solutions in general and require a large number of iterations for convergence. In this paper, we define the item-multiset kernel, which is a generalization of the itemset kernel and dot product kernels. Unfortunately, random feature maps for the itemset kernel and dot product kernels cannot approximate the item-multiset kernel. We thus develop a method that converts an item-multiset kernel into an itemset kernel, enabling the item-multiset kernel to be approximated by using a random feature map for the itemset kernel. We propose two random feature maps for the itemset kernel, which run faster and are more memory efficient than the existing feature map for the itemset kernel. They also generate sparse random features when the original (input) feature vector is sparse and thus linear models using proposed methods. Experiments using real-world datasets demonstrated the effectiveness of the proposed methodology: linear models using the proposed random feature maps ran from 10 to 100 times faster than ones based on existing methods. • Random feature maps are a promising tool for large-scale kernel methods. • We consider the item-multiset kernel, which uses feature interactions. • We propose new random feature maps for the itemset/item-multiset kernel. • Our methods run faster and require less memory than existing methods. • Our methods are applicable to large-scale sparse datasets. [ABSTRACT FROM AUTHOR]

Open data are becoming increasingly available in various domains, and many organizations rely on making decisions according to data. Such decision making requires care to distinguish between correlations and causal relationships. Among data analysis tasks, causal relationship analysis is especially complex because of unobserved confounders. For example, to correctly analyze the causal relationship between two variables, the possible confounding effect of a third variable should be considered. In the open-data environment, however, it is difficult to consider all possible confounders in advance. In this paper, we propose a framework for exploratory causal analysis of open data, in which possible confounding variables are collected and incrementally tested from a large volume of open data. To the extent of the authors' knowledge, no framework has been proposed to incorporate data for possible confounders in causal analysis process. This paper shows an original way to expand causal structures and generate reasonable causal relationships. The proposed framework accounts for the effect of possible confounding in causal analysis by first using a crowdsourcing platform to collect explanations of the correlation between variables. Keywords are then extracted using natural language processing methods. The framework searches the related open data according to the extracted keywords. Finally, the collected explanations are tested using several automated causal analysis methods. We conducted experiments using open data from the World Bank and the Japanese government. The experimental results confirmed that the proposed framework enables causal analysis while considering the effects of possible confounders. [ABSTRACT FROM AUTHOR]

• To the best of our knowledge, our work is the first attempt to apply Bayesian probabilistic tensor factorization to multicriteria recommendation. Our model, which we call "Bayesian probabilistic tensor factorization for multicriteria (BPTF-MC)," predicts the overall rating and the rating from each viewpoint simultaneously. It does this by using multicriteria latent features as additional factors. • The BPTF-MC model enables the prediction of ratings for items by each user and of aggregated ratings from the evaluations of a small number of users. • Experimental results for the Rakuten public datasets show that the BPTF-MC model achieves better performance than single-criterion models and low-rank tensor factorization models for both recommendation and rating aggregation. Ratings by users on various items such as products and services have become easily available on the Web. Also available in many cases, in addition to an overall rating for each item by each user, are multicriteria ratings from different viewpoints. Our previous study showed that multicriteria rating approaches performed better than single-criterion ones for both recommendation and rating aggregation. We have now formulated a Bayesian probabilistic model for multicriteria evaluation as an alternative to low-rank approximation. We evaluated the performance of this model, in which model capacity is controlled by integrating over all model parameters, and investigated whether it can be made to work more efficiently by using a Markov chain Monte Carlo method for both recommendation and rating aggregation. It performed better than low-rank approximation methods that obtain a maximum a posteriori estimate by fitting to the data. [ABSTRACT FROM AUTHOR]

A method that uses an adaptive learning rate is presented for training neural networks. Unlike most conventional updating methods in which the learning rate gradually decreases during training, the proposed method increases or decreases the learning rate adaptively so that the training loss (the sum of cross-entropy losses for all training samples) decreases as much as possible. It thus provides a wider search range for solutions and thus a lower test error rate. The experiments with some well-known datasets to train a multilayer perceptron show that the proposed method is effective for obtaining a better test accuracy under certain conditions. [ABSTRACT FROM AUTHOR]

Systems for aggregating illustrations require a function for automatically distinguishing illustrations from photographs as they crawl the network to collect images. A previous attempt to implement this functionality by designing basic features that were deemed useful for classification achieved an accuracy of only about 58%. On the other hand, deep neural networks had been successful in computer vision tasks, and convolutional neural networks (CNNs) had performed good at extracting such useful image features automatically. We evaluated alternative methods to implement this classification functionality with focus on deep neural networks. As the result of experiments, the method that fine-tuned deep convolutional neural network (DCNN) acquired 96.8% accuracy, outperforming the other models including the custom CNN models that were trained from scratch. We conclude that DCNN with fine-tuning is the best method for implementing a function for automatically distinguishing illustrations from photographs. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose estimating multiple true labels for multi-labeled instances. [•] We flexibly incorporate label dependency into the label-generation process. [•] It is effective to simultaneously estimate the states of strongly related labels. [•] Reliable results are estimated using the opinions of a few crowdsourcing workers. [•] Our models can reduce the cost of multi-label data collection for AI techniques. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents our research in text-based affect analysis (AA) of narratives. AA represents a task of estimating or recognizing emotions elicited by a certain semiotic modality. In text-based AA the modality in focus is the textual representation of language. In this research we study particularly one type of language realization, namely narratives (e.g., stories, fairy tales, etc.). Affect analysis within the context of narratives is a challenging task because narratives are created of different kinds of sentences (descriptions, dialogs, etc.). Moreover, different characters become subjects of different emotional expressions in different parts of narratives. In this research we address the problem of person/character related affect recognition in narratives. We propose a method for emotion subject extraction from a sentence based on analysis of anaphoric expressions and compare two methods for affect analysis. We evaluate the system and discuss its possible future improvements. [Copyright &y& Elsevier]