Embedded trees: estimation of Gaussian Processes on graphs with cycles

Graphical models provide a powerful general framework for encoding the structure of large-scale estimation problems. However, the graphs describing typical real-world phenomena contain many cycles, making direct estimation procedures prohibitively costly. In this paper, we develop an iterative inference algorithm for general Gaussian graphical models. It operates by exactly solving a series of modified estimation problems on spanning trees embedded within the original cyclic graph. When these subproblems are suitably chosen, the algorithm converges to the correct conditional means. Moreover, and in contrast to many other iterative methods, the tree-based procedures we propose can also be used to calculate exact error variances. Although the conditional mean iteration is effective for quite densely connected graphical models, the error variance computation is most efficient for sparser graphs. In this context, we present a modeling example suggesting that very sparsely connected graphs with cycles may provide significant advantages relative to their tree-structured counterparts, thanks both to the expressive power of these models and to the efficient inference algorithms developed herein. The convergence properties of the proposed tree-based iterations are characterized both analytically and experimentally. In addition, by using the basic tree-based iteration to precondition the conjugate gradient method, we develop an alternative, accelerated iteration that is finitely convergent. Simulation results are presented that demonstrate this algorithm's effectiveness on several inference problems, including a prototype distributed sensing application.     

Describing Visual Scenes Using Transformed Objects and Parts

We develop hierarchical, probabilistic models for objects, the parts composing them, and the visual scenes surrounding them. Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among object categories improves detection accuracy when learning from few examples. Turning to multiple object scenes, we propose nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. The resulting transformed Dirichlet process (TDP) leads to Monte Carlo algorithms which simultaneously segment and recognize objects in street and office scenes.     

Combining sulfate electrowinning with chloride leaching

Although the chloride leaching of copper sulfide concentrates has proved highly efficient, electrowinning from chloride solutions presents many difficulties, notably in cell design and the handling of the powder product. Sulfate electrowinning,on the other hand, continues to improve and has played a significant part in the widespread adoption of the solvent extraction-electrowinning process for copper recovery from low-grade ores. It has been found that the two steps can be combined by introducing a novel solvent extraction process after chloride leaching. This article presents the results of laboratory tests to prove the feasibility of this approach and discusses how it can be integrated into a commercially viable flow sheet.     

AI's 10 to Watch

The recipients of the 2008 IEEE Intelligent Systems 10 to Watch award—Philipp Cimiano, Dmitri Dolgov, Anat Levin, Peter Mika, Brian Milch, Louis-Philippe Morency, Boris Motik, Jennifer Neville, Erik Sudderth, and Luis von Ahn—discuss their current research and their visions of AI for the future.     

Calcifying odontogenic cyst (Gorlin's cyst) of the maxillary sinus

Occasionally the maxillary sinus is invaded by a cyst or tumor of odontogenic origin. Though usually benign, these lesions may be locally aggressive. A recent case involving bone destruction and sinus opacification is presented.