This paper presents varifold learning, a learning framework based on the mathematical concept of varifolds. Different from manifold based methods, our varifold learning framework ...
The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures bu...
The paper analyzes peculiarities of preprocessing of learning data represented in object data bases constituted by multiple relational tables with ontology on top of it. Exactly s...
It is well known that LDPC decoding is computationally demanding and one of the hardest signal operations to parallelize. Beyond data dependencies that restrict the decoding of a ...
We present a framework for segmenting and storing filament networks from scalar volume data. Filament structures are commonly found in data generated using high-throughput microsc...