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The success behind effective project management lies in estimating the time for individual activities. In many cases, these activity times are non-deterministic. In such situations, the conventional method (project evaluation and review technique (PERT)) obtains three time estimates, which are then used to calculate the expected time. In practice, it is often difficult to get three accurate time estimates. A recent paper suggests using just two time estimates and an approximation of the normal distribution to obtain the expected time and variance for that activity. In this paper, we propose an alternate method that uses only two bits of information: the most-likely and either the optimistic or the pessimistic time. We use a lognormal approximation and experimental results to show that our method is not only better than the normal approximation, but also better than the conventional method when the underlying activity distributions are moderately or heavily right skewed.

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What does it mean, to see? The plain man's answer would be, to know what is where by looking." This famous quote by David Marr (Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, Freeman, New York, 1982) sums up the holy grail of vision: discovering what is present in the world, and where it is, from unlabeled images. In this paper we tackle this challenging problem by proposing a generative model of object formation and describe an efficient algorithm to automatically learn the parameters of the model from a collection of unlabeled images. Our algorithm discovers the objects and their spatial extents by clustering together images containing similar foregrounds. Our approach simultaneously solves for the image clusters, the foreground appearance models and the spatial regions containing the objects by optimizing a single likelihood function defined over the entire image collection. We describe two methods for efficient foreground localization: the first method does not require any bottom-up image segmentation and discovers the foreground region as a contiguous rectangular bounding box. The second method expresses the foreground as a collection of super-pixels generated through a bottom-up segmentation of the image. However, unlike previous methods, objects are not assumed to be encapsulated by a single segment. Evaluation on standard benchmarks and comparison with prior methods demonstrate that our approach achieves state-of-the-art results on the problem of unsupervised foreground localization and clustering.

Although brain circuits presumably carry out powerful perceptual algorithms, few instances of derived biological methods have been found to compete favorably against algorithms that have been engineered for specific applications. We forward a novel analysis of a subset of functions of cortical–subcortical loops, which constitute more than 80% of the human brain, thus likely underlying a broad range of cognitive functions. We describe a family of operations performed by the derived method, including a non-standard method for supervised classification, which may underlie some forms of cortically dependent associative learning. The novel supervised classifier is compared against widely used algorithms for classification, including support vector machines (SVM) and k-nearest neighbor methods, achieving corresponding classification rates – at a fraction of the time and space costs. This represents an instance of a biologically derived algorithm comparing favorably against widely used machine learning methods on well-studied tasks.