175,379 research outputs found
Enumerating topological -configurations
An -configuration is a set of points and lines in the
projective plane such that their point-line incidence graph is -regular. The
configuration is geometric, topological, or combinatorial depending on whether
lines are considered to be straight lines, pseudolines, or just combinatorial
lines. We provide an algorithm for generating, for given and , all
topological -configurations up to combinatorial isomorphism, without
enumerating first all combinatorial -configurations. We apply this
algorithm to confirm efficiently a former result on topological
-configurations, from which we obtain a new geometric
-configuration. Preliminary results on -configurations are also
briefly reported.Comment: 18 pages, 11 figure
Defect Particle Kinematics in One-Dimensional Cellular Automata
Let A^Z be the Cantor space of bi-infinite sequences in a finite alphabet A,
and let sigma be the shift map on A^Z. A `cellular automaton' is a continuous,
sigma-commuting self-map Phi of A^Z, and a `Phi-invariant subshift' is a
closed, (Phi,sigma)-invariant subset X of A^Z. Suppose x is a sequence in A^Z
which is X-admissible everywhere except for some small region we call a
`defect'. It has been empirically observed that such defects persist under
iteration of Phi, and often propagate like `particles'. We characterize the
motion of these particles, and show that it falls into several regimes, ranging
from simple deterministic motion, to generalized random walks, to complex
motion emulating Turing machines or pushdown automata. One consequence is that
some questions about defect behaviour are formally undecidable.Comment: 37 pages, 9 figures, 3 table
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
We propose an attention mechanism for 3D medical image segmentation. The
method, named segmentation-by-detection, is a cascade of a detection module
followed by a segmentation module. The detection module enables a region of
interest to come to attention and produces a set of object region candidates
which are further used as an attention model. Rather than dealing with the
entire volume, the segmentation module distills the information from the
potential region. This scheme is an efficient solution for volumetric data as
it reduces the influence of the surrounding noise which is especially important
for medical data with low signal-to-noise ratio. Experimental results on 3D
ultrasound data of the femoral head shows superiority of the proposed method
when compared with a standard fully convolutional network like the U-Net
Improving the Asymmetric TSP by Considering Graph Structure
Recent works on cost based relaxations have improved Constraint Programming
(CP) models for the Traveling Salesman Problem (TSP). We provide a short survey
over solving asymmetric TSP with CP. Then, we suggest new implied propagators
based on general graph properties. We experimentally show that such implied
propagators bring robustness to pathological instances and highlight the fact
that graph structure can significantly improve search heuristics behavior.
Finally, we show that our approach outperforms current state of the art
results.Comment: Technical repor
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