16332479
Preferential Urn Model on a Random Network
A preferential urn model on a random network and a complete graph is studied. The preferential urn model is based on the concept "the rich get richer", and has simple dynamics of ball-transfers. The model is considered as a simple socio-economic system. From analytical treatments, it has been revealed that the preferential urn model on the complete graph gives a fat-tailed occupation distribution in the case with randomness for fitness parameters. In the present paper, using numerical experiments, we show that the model on the random network gives a fat-tailed occupation distribution in the case without randomness for fitness parameters; hence, we would consider that the local ball-transfer process on the random network includes effects corresponding to the randomness for fitness parameters. This simple game would give a valuable insight into various phenomena from socio-economic point of view
120935286
Automatic hyperparameter estimation in probabilistic image restoration algorithm based on Gibbs microcanonical distribution
For an original image given as an 8-gray-level image, the framework for image restoration is composed of Bayes statistics for the a priori probability distribution based on the microcanonical distribution. In this process, the a priori probability distribution serving as the microcanonical distribution is formulated on the basis of the variables characterizing the spatial flatness and smoothness of the image in terms of the number of nearest pixel pairs with different gray levels and the number of nearest pixel pairs with gray levels differing by 1. In the present paper, we propose a new method of estimation, from a degraded image, of the number of nearest pixel pairs with different gray levels and the number of nearest pixel pairs with gray levels differing by 1 in the original image. Then, based on that formulation, an iterative computation algorithm for the restoration of a 256-gray-level image is presented from the viewpoint of statistical mechanics. Through some numerical experiments, we investigate how the proposed method can improve the quality of the restored image. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 36(1): 68–78, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.10365
11983347
Linear Response Formula in Generalized Belief Propagation for Probabilistic Inference
Linear response formulas for the generalized belief propagation in approximate inference are derived by using generalized belief propagation. The linear response formulas can give us the marginal probability between every pair of nodes and we obtain good accuracy in some examples of practical probabilistic inferences
2198276
Coupled Markov random field models with phases as line field and region field
We investigate a boundary-based and a region-based coupled Markov random field model, both of which are useful in image restorations of gray-level images. In the conventional boundary-based and the conventional region-based coupled Markov random field models, both a line field and a region field take only two discrete states, 0 and 1. In the boundary-based coupled random field model, existence and nonexistence of the edge at each nearest-neighbor pair of pixels are denoted by 1 and 0, respectively. In the region-based coupled random field model, some different regions at each pixel are labeled by discrete numbers. We propose a boundary-based coupled Markov random field model with continuous line field and a region-based coupled Markov random field model with continuous segmentation field from a standpoint of a plane rotator model in statistical mechanics. The iterative algorithms for image restoration are constructed by using a mean-field approximation. We investigate how the proposed models produce a better quality of restored images. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(7): 24–33, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10364
119404260
Probabilistic image processing based on the Q-Ising model by means of the mean-field method and loopy belief propagation
The framework is presented of Bayesian image restoration for multi-valued images by means of the Q-Ising model. Hyper parameters in the probabilistic model are determined so as to maximize the marginal likelihood. Practical algorithms are described based the conventional mean-field approximation and loopy belief propagation. We compare the results empirically with those provided by conventional filters and the new methods are found to be superior.
7133208
Statistical Learning Procedure in Loopy Belief Propagation for Probabilistic Image Processing
We give a fast and practical algorithm for statistical learning hyperparameters from observable data in probabilistic image processing, which is based on Gaussian graphical model and maximum likelihood estimation. Although hyperparameters in the probabilistic model are determined so as to maximize a marginal likelihood, a practical algorithm is described for the EM algorithm with the loopy belief propagation which is one of approximate inference algorithms in artificial intelligence
46335278
Tree-Structured Data Organization with Consecutive Retrieval Property
COMP2011 Aleksandar Ignjatovic 2005 c 2 Figure 1: A medieval copy of arguably the most influential textbook ever written, Eu-clid's Elements (Στoι χε´ια), from about 300 BC. It contains the oldest known algorithm recorded, Euclid's algorithm for computing the Greatest Common Divisor (GCD) of two natural numbers. Figure 2: First data structures: a Mesopotamian cuneiform tablet from about 3000 BC with an inventory list. Preface This booklet is not meant to be a textbook, but only a companion to the textbook that we use in this course, Cormen, Leiserson, Rivest and Stein's Introduction to Algorithms, the second edition. Its purpose is to help you read the appropriate sections of the textbook, and integrate it better with the rest of the course. It also adds a number of exercises not present in the textbook. Please read the textbook and do not rely on this supplement only.
22236580
OVID: Design and Implementation of a Video-Object Database System
A video-object data model and the design and implementation of a prototype video-object database system named OVID based on the model are described. Notable features of the video-object data model are a mechanism to share common descriptional data among video-objects, called interval-inclusion based inheritance, and operations to composite video-objects. The OVID system offers a browsing/inspection tool called VideoChart, and adhoc query facility called VideoSQL, and a video-object definition tool. >
29360261
Towards Abstracting Complex Database Objects: Generalization, Reduction and Unification of Set-type Objects (Extended Abstract)
Representing and manipulating complex (database) objects are important issues in the semantic database models and object-oriented database models, most of which have a rich expressive power for complex objects. In this paper, we will focus on the manipulation of complex objects, and introduce the notions and the operations for abstracting complex objects. We introduce the notion of "element-based" generalization relationships between complex objects, and two new abstraction operators: reduction and unification. Their algebraic properties are then formally investigated especially for "set-type" complex objects.
28304265
Uncertainty Management in Object-Oriented Database Systems
In this paper, we propose an approach and basic constructs to realize a fuzzy database processing capability on an Object-Oriented Database (OODB) system. The proposed approach can manage several types of uncertainties: (1) uncertain objects, (2) uncertain relationships between instance objects, (3) uncertain instance-of relationships, and (4) uncertain is-a (subclass/superclass) relationships. A notable feature is the usage of extensibility offered by OODB systems. That is, it is possible to add these several uncertainties without restructuring a database schema. We will describe our prototype fuzzy OODB system and briefly show the functionalities of the system by using an example movie database.
3247078
Individual Level Analysis Using Decision Making Features in Multiagent Based Simulation
We introduce a set of evaluation tools in the framework of individual level analysis for multiagent based simulations. These tools are intended to overcome the weak points of multiagent systems: that it is difficult to avoid mistakes in description and arbitrary modeling; and to find reliable explanations of causal relationships between the model and its result. We analyze some artificial market models using an evaluation process that consists of the evaluation of initial learning maturity, discrimination between models, and the visualization of attention tendency in a group. We conclude that these analytical methods are useful for the evaluation of multiagent based simulations in terms of validation and finding causal relations.