214 research outputs found
Biomimetic Design for Efficient Robotic Performance in Dynamic Aquatic Environments - Survey
This manuscript is a review over the published articles on edge detection. At first, it provides theoretical background, and then reviews wide range of methods of edge detection in different categorizes. The review also studies the relationship between categories, and presents evaluations regarding to their application, performance, and implementation. It was stated that the edge detection methods structurally are a combination of image smoothing and image differentiation plus a post-processing for edge labelling. The image smoothing involves filters that reduce the noise, regularize the numerical computation, and provide a parametric representation of the image that works as a mathematical microscope to analyze it in different scales and increase the accuracy and reliability of edge detection. The image differentiation provides information of intensity transition in the image that is necessary to represent the position and strength of the edges and their orientation. The edge labelling calls for post-processing to suppress the false edges, link the dispread ones, and produce a uniform contour of objects
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
Automatic Initialization Of Contour For Level Set Algorithms Guided By Integration Of Multiple Views To Segment Abdominal CT Scans
This paper presents a new automatic initialization procedure for a level-set based segmentation algorithm that
works on all slices for a given CT dataset
Anomaly detection in the dynamics of web and social networks
In this work, we propose a new, fast and scalable method for anomaly
detection in large time-evolving graphs. It may be a static graph with dynamic
node attributes (e.g. time-series), or a graph evolving in time, such as a
temporal network. We define an anomaly as a localized increase in temporal
activity in a cluster of nodes. The algorithm is unsupervised. It is able to
detect and track anomalous activity in a dynamic network despite the noise from
multiple interfering sources. We use the Hopfield network model of memory to
combine the graph and time information. We show that anomalies can be spotted
with a good precision using a memory network. The presented approach is
scalable and we provide a distributed implementation of the algorithm. To
demonstrate its efficiency, we apply it to two datasets: Enron Email dataset
and Wikipedia page views. We show that the anomalous spikes are triggered by
the real-world events that impact the network dynamics. Besides, the structure
of the clusters and the analysis of the time evolution associated with the
detected events reveals interesting facts on how humans interact, exchange and
search for information, opening the door to new quantitative studies on
collective and social behavior on large and dynamic datasets.Comment: The Web Conference 2019, 10 pages, 7 figure
- …