678 research outputs found
Community detection in complex networks using Extremal Optimization
We propose a novel method to find the community structure in complex networks
based on an extremal optimization of the value of modularity. The method
outperforms the optimal modularity found by the existing algorithms in the
literature. We present the results of the algorithm for computer simulated and
real networks and compare them with other approaches. The efficiency and
accuracy of the method make it feasible to be used for the accurate
identification of community structure in large complex networks.Comment: 4 pages, 4 figure
Why minds cannot be received, but are created by brains
There is no controversy in psychology or brain sciences that brains create mind and consciousness. Doubts and opinions to the contrary are quite frequently expressed in non-scientific publications. In particular the idea that conscious mind is received, rather than created by the brain, is quite often used against “materialistic” understanding of consciousness. I summarize here arguments against such position, show that neuroscience gives coherent view of mind and consciousness, and that this view is intrinsically non-materialistic
A novel Border Identification algorithm based on an “Anti-Bayesian” paradigm
Border Identification (BI) algorithms, a subset of Prototype Reduction Schemes (PRS) aim to reduce the number of training vectors so that the reduced set (the border set) contains only those patterns which lie near the border of the classes, and have sufficient information to perform a meaningful classification. However, one can see that the true border patterns (“near” border) are not able to perform the task independently as they are not able to always distinguish the testing samples. Thus, researchers have worked on this issue so as to find a way to strengthen the “border” set. A recent development in this field tries to add more border patterns, i.e., the “far” borders, to the border set, and this process continues until it reaches a stage at which the classification accuracy no longer increases. In this case, the cardinality of the border set is relatively high. In this paper, we aim to design a novel BI algorithm based on a new definition for the term “border”. We opt to select the patterns which lie at the border of the alternate class as the border patterns. Thus, those patterns which are neither on the true discriminant nor too close to the central position of the distributions, are added to the “border” set. The border patterns, which are very small in number (for example, five from both classes), selected in this manner, have the potential to perform a classification which is comparable to that obtained by well-known traditional classifiers like the SVM, and very close to the optimal Bayes’ bound
Architektury kognitywne, czyli jak zbudować sztuczny umysł
Architektury kognitywne (AK) są próbą stworzenia modeli komputerowych
integrujących wiedzę o działaniu umysłu. Ich zadaniem jest implementacja konkretnych
schematów działania funkcji poznawczych umożliwiająca testowanie tych funkcji na
szerokiej gamie zagadnień. Wiele architektur kognitywnych opracowano w celu
symulacji procesu komunikacji pomiędzy człowiekiem i złożonymi maszynami (HCI,
Human-Computer Interfaces), symulowania czasów reakcji oraz różnych
psychofizycznych zależności. Można to do pewnego stopnia osiągnąć budując modele
układu poznawczego na poziomie symbolicznym, z wiedzą w postaci reguł logicznych.
Istnieją też projekty, które próbują powiązać procesy poznawcze z aktywacją modułów
reprezentujących konkretne obszary mózgu, zgodnie z obserwacjami w eksperymentach
z funkcjonalnym rezonansem magnetycznym (fMRI). Dużą grupę stanowią architektury
oparte na podejściu logicznym, które mają na celu symulację wyższych czynności
poznawczych, przede wszystkim procesów myślenia i rozumowania. Niektóre z
projektów rozwoju architektur poznawczych skupiają większe grupy badawcze
działające od wielu dziesięcioleci.
Ogólnie architektury kognitywne podzielić można na 3 duże grupy: architektury
symboliczne (oparte na funkcjonalnym rozumieniu procesów poznawczych);
architektury emergentne, oparte na modelach koneksjonistycznych; oraz architektury
hybrydowe, wykorzystujące zarówno modele neuronowe jak i reguły symboliczne. W
ostatnich latach znacznie wzrosło zainteresowanie architekturami inspirowanymi przez
neurobiologię (BICA, Brain Inspired Cognitive Architectures). Jak sklasyfikować różne
architektury, jakie wyzwania należy przed nimi postawić, jak oceniać postępy w ich
rozwoju, czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd
istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na
sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd
istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na
sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju oraz
wysunięcie własnych propozycji budowy nowej architektury
Size reduction of complex networks preserving modularity
The ubiquity of modular structure in real-world complex networks is being the
focus of attention in many trials to understand the interplay between network
topology and functionality. The best approaches to the identification of
modular structure are based on the optimization of a quality function known as
modularity. However this optimization is a hard task provided that the
computational complexity of the problem is in the NP-hard class. Here we
propose an exact method for reducing the size of weighted (directed and
undirected) complex networks while maintaining invariant its modularity. This
size reduction allows the heuristic algorithms that optimize modularity for a
better exploration of the modularity landscape. We compare the modularity
obtained in several real complex-networks by using the Extremal Optimization
algorithm, before and after the size reduction, showing the improvement
obtained. We speculate that the proposed analytical size reduction could be
extended to an exact coarse graining of the network in the scope of real-space
renormalization.Comment: 14 pages, 2 figure
Towards Comprehensive Foundations of Computational Intelligence
Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.
Urban traffic from the perspective of dual graph
In this paper, urban traffic is modeled using dual graph representation of
urban transportation network where roads are mapped to nodes and intersections
are mapped to links. The proposed model considers both the navigation of
vehicles on the network and the motion of vehicles along roads. The road's
capacity and the vehicle-turning ability at intersections are naturally
incorporated in the model. The overall capacity of the system can be quantified
by a phase transition from free flow to congestion. Simulation results show
that the system's capacity depends greatly on the topology of transportation
networks. In general, a well-planned grid can hold more vehicles and its
overall capacity is much larger than that of a growing scale-free network.Comment: 7 pages, 10 figure
Polymerase chain reaction detection of avipox and avian papillomavirus in naturally infected wild birds: comparisons of blood, swab and tissue samples
Avian poxvirus (avipox) is widely reported from avian species, causing cutaneous or mucosal lesions. Mortality rates of up to 100% are recorded in some hosts. Three major avipox clades are recognized. Several diagnostic techniques have been reported, with molecular techniques used only recently. Avipox has been reported from 278 different avian species, but only 111 of these involved sequence and/or strain identification. Collecting samples from wild birds is challenging as only few wild bird individuals or species may be symptomatic. Also, sampling regimes are tightly regulated and the most efficient sampling method, whole bird collection, is ethically challenging. In this study, three alternative sampling techniques (blood, cutaneous swabs and tissue biopsies) from symptomatic wild birds were examined. Polymerase chain reaction was used to detect avipoxvirus and avian papillomavirus (which also induces cutaneous lesions in birds). Four out of 14 tissue samples were positive but all 29 blood samples and 22 swab samples were negative for papillomavirus. All 29 blood samples were negative but 6/22 swabs and 9/14 tissue samples were avipox-positive. The difference between the numbers of positives generated from tissue samples and from swabs was not significant. The difference in the avipox-positive specimens in paired swab (4/6) and tissue samples (6/6) was also not significant. These results therefore do not show the superiority of swab or tissue samples over each other. However, both swab (6/22) and tissue (8/9) samples yielded significantly more avipox-positive cases than blood samples, which are therefore not recommended for sampling these viruses.The authors thank bird ringers from Alula and Monticola, especially Alfredo Ortega and Chechu Aguirre, for help with the capture and ringing of birds, which made this project possible. Thanks to Alvaro Ramírez for samples. This research was funded by the Ministerio de Ciencia e Innovación, Spain (grant number: CGL2010-15734/BOS). R.A.J.W. was supported by the Programa Internacional de Captación de Talento (PICATA) de Moncloa Campus de Excelencia Internacional while writing the manuscript
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