Stochastic motion planning and applications to traffic

This paper presents a stochastic motion planning algorithm and its application to traffic navigation. The algorithm copes with the uncertainty of road traffic conditions by stochastic modeling of travel delay on road networks. The algorithm determines paths between two points that optimize a cost function of the delay probability distribution. It can be used to find paths that maximize the probability of reaching a destination within a particular travel deadline. For such problems, standard shortest-path algorithms don’t work because the optimal substructure property doesn’t hold. We evaluate our algorithm using both simulations and real-world drives, using delay data gathered from a set of taxis equipped with GPS sensors and a wireless network. Our algorithm can be integrated into on-board navigation systems as well as route-finding Web sites, providing drivers with good paths that meet their desired goals.National Science Foundation (U.S.) (grant EFRI-0710252)National Science Foundation (U.S.) (grant IIS-0426838

2D Action Recognition Serves 3D Human Pose Estimation

Abstract. 3D human pose estimation in multi-view settings benefits from embeddings of human actions in low-dimensional manifolds, but the complexity of the embeddings increases with the number of actions. Creating separate, action-specific manifolds seems to be a more practical solution. Using multiple manifolds for pose estimation, however, requires a joint optimization over the set of manifolds and the human pose embedded in the manifolds. In order to solve this problem, we propose a particle-based optimization algorithm that can efficiently estimate human pose even in challenging in-house scenarios. In addition, the algorithm can directly integrate the results of a 2D action recognition system as prior distribution for optimization. In our experiments, we demonstrate that the optimization handles an 84D search space and provides already competitive results on HumanEva with as few as 25 particles.

Approximation Algorithms for Reliable Stochastic Combinatorial Optimization

We consider optimization problems that can be formulated as minimizing the cost of a feasible solution wTx over an arbitrary combinatorial feasible set F ⊂ {0, 1} n. For these problems we describe a broad class of corresponding stochastic problems where the cost vector W has independent random components, unknown at the time of solution. A natural and important objective that incorporates risk in this stochastic setting is to look for a feasible solution whose stochastic cost has a small tail or a small convex combination of mean and standard deviation. Our models can be equivalently reformulated as nonconvex programs for which no efficient algorithms are known. In this paper, we make progress on these hard problems. Our results are several efficient general-purpose approximation schemes. They use as a black-box (exact or approximate) the solution to the underlying deterministic problem and thus immediately apply to arbitrary combinatorial problems. For example, from an available δ-approximation algorithm to the linear problem, we construct a δ(1 + ǫ)-approximation algorithm for the stochastic problem, which invokes the linear algorithm only a logarithmic number of times in the problem input (and polynomial in 1 ǫ), for any desired accuracy level ǫ> 0. The algorithms are based on a geometric analysis of the curvature and approximability of the nonlinear level sets of the objective functions

Pathological work-up of sentinel lymph nodes in breast cancer. Review of current data to be considered for the formulation of guidelines.

Controversies and inconsistencies regarding the pathological work-up of sentinel lymph nodes (SNs) led the European Working Group for Breast Screening Pathology (EWGBSP) to review published data and current evidence that can promote the formulation of European guidelines for the pathological work-up of SNs. After an evaluation of the accuracy of SN biopsy as a staging procedure, the yields of different sectioning methods and the immunohistochemical detection of metastatic cells are reviewed. Currently published data do not allow the significance of micrometastases or isolated tumour cells to be established, but it is suggested that approximately 18% of the cases may be associated with further nodal (non-SN) metastases, i.e. approximately 2% of all patients initially staged by SN biopsy. The methods for the intraoperative and molecular assessment of SNs are also surveyed