in this paper the conditional location problem is discussed. conditional location problems have a wide range of applications in location science. a new meta-heuristic algorithm for solving conditional p-median problems is proposed and results are compared to those of the previous studies. this algorithm produces much better results than the previous formulations.

Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might not be relevant. If a network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all cont...

This paper proposes a new algorithm for adaptive deep image compression (DIC) that can compress images different purposes or contexts at rates. The with semantic awareness, which means classification-related features are better protected in lossy compression. It builds on the existing conditional encoder-based DIC method and adds two features: model-based rate-distortion-classification-percepti...

The hippocampus is important for spatial navigation. Literature shows that allocentric visual contexts in the animal's background are critical for making conditional response selections during navigations. In a traditional maze task, however, it is difficult to identify exactly which subsets of visual contexts are critically used. In the current study, we tested in rats whether making condition...

Several researchers have suggested that Bayesian networks be used in web search and user profiling. One advantage of this approach is that Bayesian networks are more general than the probabilistic models previously used in information retrieval. In practice, experimental results demonstrate the effectiveness the modern Bayesian network approach. On the other hand, since Bayesian networks are de...

The state-space modeling of partially observed dynamic systems generally requires estimates of unknown parameters. From a practical point of view, it is relevant in such filtering contexts to simultaneously estimate the unknown states and parameters. Efficient simulation-based methods using convolution particle filters are proposed. The regularization properties of these filters is well suited,...

Economic choices depend on our predictions of the future. Yet, at times predictions are not based on all relevant information, but instead on the single most likely possibility, which is treated as though certainly the case— that is, digitally. Two sets of studies test whether this digitization bias would occur in higher-stakes economic contexts. When making predictions about the future asset p...

Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be solved effectively by a lightweight but powerful approach, consisting of: (1...

In this paper, we propose a Context-based Gustafson-Kessel (CGK) clustering that builds Information Granulation (IG) in the form of fuzzy set. The fundamental idea of this clustering is based on Conditional Fuzzy C-Means (CFCM) clustering introduced by Pedrycz. The proposed clustering develops clusters preserving homogeneity of the clustered patterns associated with the input and output space. ...

We present a novel approach to integrate transliteration into Hindi-to-Urdu statistical machine translation. We propose two probabilistic models, based on conditional and joint probability formulations, that are novel solutions to the problem. Our models consider both transliteration and translation when translating a particular Hindi word given the context whereas in previous work transliterat...