ppxscal/embedding_network huggingface dataset:
Random SAT Instances à la Carte
Authors: Carlos Ansótegui, Maria Luisa Bonet, Jordi Levy
Venue: Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (2008)

Many studies focus on the generation of hard SAT instances. The hardness is usually measured by the time it takes SAT solvers to solve the instances. In this preliminary study, we focus on the generation of instances that have computational properties that are more similar to real-world instances. In particular, instances with the same degree of difficulty, measured in terms of the tree-like resolution space complexity. It is known that industrial instances, even with a great number of variables, can be solved by a clever solver in a reasonable amount of time. One of the reasons may be their relatively small space complexity, compared with randomly generated instances. We provide two generation methods of k-SAT instances, called geometrical and the geo-regular, as generalizations of the uniform and regular k-CNF generators. Both are based on the use of a geometric probability distribution to select variables. We study the phase transition phenomena and the hardness of the generated instances as a function of the number of variables and the base of the geometric distribution. We prove that, with these two parameters we can adjust the difficulty of the problems in the phase transition point. We conjecture that this will allow us to generate random instances more similar to industrial instances, of interest for testing purposes.
Temporal aggregation of Markov-switching financial return models
Authors: Wai-Sum Chan, Li-Xin Zhang, Siu Hung Cheung
Venue: Applied Stochastic Models in Business and Industry (2009)

In this paper we investigate the effects of temporal aggregation of a class of Markov-switching models known as Markov-switching normal (MSN) models. The growing popularity of the MSN processes in modelling financial returns can be attributed to their inherited flexibility characteristics, allowing for heteroscedasticity, asymmetry and excess kurtosis. The distributions of the process described by the basic MSN model and the model of the corresponding temporal aggregate data are derived. They belong to a general class of mixture normal distributions. The limiting behaviour of the aggregated MSN model, as the order of aggregation tends to infinity, is studied. We provide explicit formulae for the volatility, autocovariance, skewness and kurtosis of the aggregated processes. An application of measuring solvency risk with MSN models for horizons larger than 1 year and up to 10 years from the baseline U.S. S&P 500 stock market total return time series spanning about 50 years is given. Copyright &copy; 2008 John Wiley & Sons, Ltd.
A Distributed Architecture for Collaborative Design
Authors: Moisés Dutra, Kamel Slimani, Parisa Ghodous
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

Due to current trends in the design field towards virtual teams that collaborate over computer networks to achieve global optima in design, there is an increasing need for design teams to share knowledge and to establish and maintain a cooperative work through effective communication, coordination and collaboration at the knowledge level. As problems become more complex, teamwork is becoming increasingly important. This paper proposes a web-based multi-agent architecture to support multidisciplinary design teams that collaborate in a distributed design environment. Using ontologies and multi-agent system, the proposed framework addresses communication problem at knowledge level and aims to optimize constraints and conflicts management in concurrent engineering environment. A prototype, based on the Function-Behavior-Structure design framework, was built up to validate this approach.
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
Authors: Svetlana Lazebnik, Maxim Raginsky
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

This paper proposes a technique for jointly quantizing continuous features and the posterior distributions of their class labels based on minimizing empirical information loss such that the quantizer index of a given feature vector approximates a sufficient statistic for its class label. Informally, the quantized representation retains as much information as possible for classifying the feature vector correctly. We derive an alternating minimization procedure for simultaneously learning codebooks in the euclidean feature space and in the simplex of posterior class distributions. The resulting quantizer can be used to encode unlabeled points outside the training set and to predict their posterior class distributions, and has an elegant interpretation in terms of lossless source coding. The proposed method is validated on synthetic and real data sets and is applied to two diverse problems: learning discriminative visual vocabularies for bag-of-features image classification and image segmentation.
Workflow simulation for operational decision support
Authors: A. Rozinat, M. T. Wynn, W. M. P. van der Aalst, A. H. M. ter Hofstede, C. J. Fidge
Venue: Data Knowledge Engineering (2009)

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
Approximation of min coloring by moderately exponential algorithms
Authors: Nicolas Bourgeois, Bruno Escoffier, Vangelis Th. Paschos
Venue: Information Processing Letters (2009)

We study in this note approximation algorithms for the problem of coloring the vertices of a graph with as few colors as possible, with moderately exponential running times (and using either polynomial or exponential space), better than those of exact computation. Study of approximation is performed with respect to optimality measures, the minimum number of used colors and the maximum number of unused colors.
Medical nanobots
Authors: Kirk L. Kroeker
Venue: Communications of the ACM (2009)

Researchers working in medical nanorobotics are creating technologies that could lead to novel health-care applications, such as new ways of accessing areas of the human body that would otherwise be unreachable without invasive surgery.
A Meta-Level Knowledge Base System for Discovering Personal Career Opportunities by Connecting and Analyzing Occupational and Educational Databases
Authors: Yusuke Takahashi, Yasushi Kiyoki
Venue: Proceeding of the 2009 conference on Information Modelling and Knowledge Bases XX (2009)

This paper presents an implementation method of a meta-level knowledge base system for analyzing personal career opportunities by connecting occupational and educational databases. We have designed and implemented several functions to analyze information on personal career development, on the meta-level database system for connecting occupational and educational databases. This method is used to create dynamic relationships among heterogeneous databases on occupations, educational contents, social and educational issues and personal career information. In this method, several functions are defined for analyzing relationships among heterogeneous databases. (1) Educational contents represent, for instance, lectures in educational institutes, (2) occupations represent descriptions of concrete occupations, (3) social and educational issues represent academic industrial fields, and (4) personal career information represents histories, objectives and interests of an individual user's career. These databases are connected and analyzed in a dynamic way, according to users' contexts and situations. By using our method, individual career development becomes to be effectively supported when discovering personal career opportunities and designing personalized career development plans.