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We present a novel approach to fully automated reconstruction of tree structures in noisy 2D images. Unlike in earlier approaches, we explicitly handle crossovers and bifurcation points, and impose geometric constraints while optimizing a global cost funct ...
The peer-to-peer (P2P) paradigm has become very popular for storing and sharing information in a totally decentralized manner. At first, research focused on P2P systems that host 1D data. Nowadays, the need for P2P applications with multidimensional data h ...
Institute of Electrical and Electronics Engineers2009
The field of neuroscience is witnessing a huge influx of experimental data thanks to the improvements in the data acquisition tools and techniques. Most of this data is being collected by thousands of experimenters located in various institutions around th ...
Scaling the performance of shared-everything on-line transaction processing to highly-parallel multicore hardware remains a great challenge for database system designers. Developments in OLTP technology remove locking and logging from being scalability bot ...
Experimental techniques in molecular biology have led to the production of enormous amounts of data on the dynamics of cellular processes. The availability of time series data characterizing genomic, proteomic and metabolic systems must be complemented wit ...
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinite ...
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper intr ...
We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets ...
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinite ...
Due to daylight variability, a design cannot be thoroughly assessed using single-moment simulations, which is why we need dynamic performance metrics like Daylight Autonomy and Useful Daylight Illuminance. Going one step further, the annual variation in pe ...
