ROI coding of volumetric medical images with application to visualisation

This paper presents region of interest (ROI) coding of volumetric medical images with the region itself being three dimensional. An extension to 3D-SPIHT which allows 3D ROI coding is proposed. ROI coding enables faster reconstruction of diagnostically useful regions in volumetric datasets by assigning higher priority to them in the bitstream. It also introduces the possibility for increased compression performance, by allowing certain parts of the volume to be coded in a lossy manner while others are coded losslessly. Results presented highlight the benefits of the ROI extension. Additionally, a visualisation specific ROI coding case is examined. Results show the advantages of ROI coding in terms of the quality of the visualised decoded volumeThis paper presents region of interest (ROI) coding of volumetric medical images with the region itself being three dimensional. An extension to 3D-SPIHT which allows 3D ROI coding is proposed. ROI coding enables faster reconstruction of diagnostically useful regions in volumetric datasets by assigning higher priority to them in the bitstream. It also introduces the possibility for increased compression performance, by allowing certain parts of the volume to be coded in a lossy manner while others are coded losslessly. Results presented highlight the benefits of the ROI extension. Additionally, a visualisation specific ROI coding case is examined. Results show the advantages of ROI coding in terms of the quality of the visualised decoded volume

STRATEGIC AND SOCIAL PREPLAY COMMUNICATION IN THE ULTIMATUM GAME

Pre-play face-to-face communication is known to facilitate cooperation. Various explanations exist for this effect, varying in their dependence on the strategic content of the communication. Previous studies have found similar communication effects regardless of whether strategic communication is available. These results were so far taken to support a social-preferences based explanation of the communication effects. The current experiment provides a replication and extension of previous results to show that different processes come into play, depending on the communication protocol. Specically, pre-play communication in an ultimatum game was either restricted to nongame- related content or unrestricted. The results show that strategic, but not social, communication affects responders' strategies. Thus, the existing results are cast in a new light. I conclude that pre-play communication effects may be mediated by qualitatively dierent processes, depending on the social context.

The Structured Weighted Violations Perceptron Algorithm

We present the Structured Weighted Violations Perceptron (SWVP) algorithm, a new structured prediction algorithm that generalizes the Collins Structured Perceptron (CSP). Unlike CSP, the update rule of SWVP explicitly exploits the internal structure of the predicted labels. We prove the convergence of SWVP for linearly separable training sets, provide mistake and generalization bounds, and show that in the general case these bounds are tighter than those of the CSP special case. In synthetic data experiments with data drawn from an HMM, various variants of SWVP substantially outperform its CSP special case. SWVP also provides encouraging initial dependency parsing results

Uniform Chernoff and Dvoretzky-Kiefer-Wolfowitz-type inequalities for Markov chains and related processes

We observe that the technique of Markov contraction can be used to establish measure concentration for a broad class of non-contracting chains. In particular, geometric ergodicity provides a simple and versatile framework. This leads to a short, elementary proof of a general concentration inequality for Markov and hidden Markov chains (HMM), which supercedes some of the known results and easily extends to other processes such as Markov trees. As applications, we give a Dvoretzky-Kiefer-Wolfowitz-type inequality and a uniform Chernoff bound. All of our bounds are dimension-free and hold for countably infinite state spaces

Maximum Margin Multiclass Nearest Neighbors

We develop a general framework for margin-based multicategory classification in metric spaces. The basic work-horse is a margin-regularized version of the nearest-neighbor classifier. We prove generalization bounds that match the state of the art in sample size $n$ and significantly improve the dependence on the number of classes $k$. Our point of departure is a nearly Bayes-optimal finite-sample risk bound independent of $k$. Although $k$-free, this bound is unregularized and non-adaptive, which motivates our main result: Rademacher and scale-sensitive margin bounds with a logarithmic dependence on $k$. As the best previous risk estimates in this setting were of order $\sqrt k$, our bound is exponentially sharper. From the algorithmic standpoint, in doubling metric spaces our classifier may be trained on $n$ examples in $O(n^2\log n)$ time and evaluated on new points in $O(\log n)$ time