489 research outputs found
Contextual Information Retrieval based on Algorithmic Information Theory and Statistical Outlier Detection
The main contribution of this paper is to design an Information Retrieval
(IR) technique based on Algorithmic Information Theory (using the Normalized
Compression Distance- NCD), statistical techniques (outliers), and novel
organization of data base structure. The paper shows how they can be integrated
to retrieve information from generic databases using long (text-based) queries.
Two important problems are analyzed in the paper. On the one hand, how to
detect "false positives" when the distance among the documents is very low and
there is actual similarity. On the other hand, we propose a way to structure a
document database which similarities distance estimation depends on the length
of the selected text. Finally, the experimental evaluations that have been
carried out to study previous problems are shown.Comment: Submitted to 2008 IEEE Information Theory Workshop (6 pages, 6
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Understanding the limits of LoRaWAN
The quick proliferation of LPWAN networks, being LoRaWAN one of the most
adopted, raised the interest of the industry, network operators and facilitated
the development of novel services based on large scale and simple network
structures. LoRaWAN brings the desired ubiquitous connectivity to enable most
of the outdoor IoT applications and its growth and quick adoption are real
proofs of that. Yet the technology has some limitations that need to be
understood in order to avoid over-use of the technology. In this article we aim
to provide an impartial overview of what are the limitations of such
technology, and in a comprehensive manner bring use case examples to show where
the limits are
Towards a Functional Explanation of the Connectivity LGN - V1
The principles behind the connectivity between LGN and V1 are not well understood. Models have to explain two basic experimental trends: (i) the combination of thalamic responses is local and it gives rise to a variety of oriented Gabor-like receptive felds in V1 [1], and (ii) these filters are spatially organized in orientation maps [2]. Competing explanations of orientation maps use purely geometrical arguments such as optimal wiring or packing from LGN [3-5], but they make no explicit reference to visual function. On the other hand, explanations based on func- tional arguments such as maximum information transference (infomax) [6,7] usually neglect a potential contribution from LGN local circuitry. In this work we explore the abil- ity of the conventional functional arguments (infomax and variants), to derive both trends simultaneously assuming a plausible sampling model linking the retina to the LGN [8], as opposed to previous attempts operating from the retina.
Consistently with other aspects of human vi- sion [14-16], additional constraints should be added to plain infomax to understand the second trend of the LGN-V1 con- nectivity. Possibilities include energy budget [11], wiring constraints [8], or error minimization in noisy systems, ei- ther linear [16] or nonlinear [14, 15]. In particular, consideration of high noise (neglected here) would favor the redundancy in the prediction (which would be required to match the relations between spatially neighbor neurons in the same orientation domain)
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