In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them. In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion. In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions. Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporallycoherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification. Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster. In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences. Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology. Experiments on both synthetic and real sequences of dense voxel-set data are shown. This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model construction.

Answer: Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

Question: Reactive (memoryless) policies are sufficient in completely observable Markov decision pro cesses (MDPs), but some kind of memory is usually necessary for optimal control of a par tially observable MDP. Policies with finite mem ory can be represented as finite-state automata. In this paper, we extend Baird and Moore's YAPS algorithm to the problem of learning gen eral finite-state automata. Because it performs stochastic gradient descent, this algorithm can be shown to converge to a locally optimal finite state controller. We provide the details of the algorithm and then consider the question of un der what conditions stochastic gradient descent will outperform exact gradient descent. We con clude with empirical results comparing the per formance of stochastic and exact gradient de scent, and showing the ability of our algorithm to extract the useful information contained in the sequence of past observations to compensate for the lack of observability at each time-step.

Answer: Learning Finite-State Controllers for Partially Observable Environments

Question: Distributed knowledge based applications in open domain rely on common sense infor mation which is bound to be uncertain and incomplete. To draw the useful conclusions from ambiguous data, one must address un certainties and conflicts incurred in a holis tic view. No integrated frameworks are vi able without an in-depth analysis of con flicts incurred by uncertainties. In this pa per, we give such an analysis and based on the result, propose an integrated framework. Our framework extends definite argumenta tion theory to model uncertainty. It sup ports three views over conflicting and uncer tain knowledge. Thus, knowledge engineers can draw different conclusions depending on the application context (i.e. view). We also give an illustrative example on strategical de cision support to show the practical useful ness of our framework.

Answer:
Resolving Conflicting Arguments under Uncertainties