23,940 research outputs found
Constructive summation of the (2,2) quasi normal mode from a population of black holes
The quasi normal modes (QNMs) associated with gravitational-wave signals from
binary black hole (BBH) mergers can provide deep insight into the remnant's
properties. Once design sensitivity is achieved, present ground-based
gravitational wave interferometers could detect potentially hundreds of BBH
signals in the coming years. For most, the ringdown phase will have a very weak
signal-to-noise ratio (SNR). Signal summation techniques allow information
extraction from the weak SNR ringdowns.
We propose a method to constructively sum the (2,2) QNM from different BBH
signals by synchronizing and rescaling them. The parameter space adopted to
test the method is presently limited to mass ratio , initially
non-spinning black holes with face-on orientation. Moreover, since the
synchronisation procedure fails for the weakest signals, we select all
ringdowns with SNR above 2.6. Under these conditions, we show that for
different BBH populations, 40 to 70% of all the potential detections could be
used for the summation while still ensuring a summed SNR of 80% of the
maximal achievable SNR (i.e. for ideally synchronized signals).Comment: 7 pages, 10 figure
Comment on "Conservative force fields in nonextensive kinetic theory."
We discuss an improper application of the Maxwell-Boltzmann theory to a very
unrealistic model. The authors of the original paper claim that a generalized
theory would solve a problem that really does not exists. This Comment was
submited to Physica A and was not accepted as it is. Although tecnically
correct, we mantain ours view that such kind of "elementary exercises" would
not deserve publication in any serious journal as Physica A
On the Importance of Hubs in Hopfield Complex Neuronal Networks under Attack
The organizational principles behind the connectivity of a complex network
are known to influence its behavior. In this work we investigate, using the
Hopfield model, the influence of the network architecture on the performance
for associative recall while the network is under hub and edge attack. We show,
by using four different attack strategies, that although the importance of hubs
is more definite for Barab\'asi-Albert neuronal networks, the random removal of
the same amount of edges as in a hub may imply a greater reduction of memory
recall
Analytical procedure to determine the self-referred lacunarity function for simple shapes. Supplementary material
The analytical calculation of the self-referred lacunarity is used as a
validation standard of the computational algorithm. In this supplementary
material to our article (see cond-mat/0407079) we present a detailed
calculation for two simple shapes, namely a square box and a cross.Comment: 12 pages, 10 figures and 1 Tabl
Metastable Localization of Diseases in Complex Networks
We describe the phenomenon of localization in the epidemic SIS model on
highly heterogeneous networks in which strongly connected nodes (hubs) play the
role of centers of localization. We find that in this model the localized
states below the epidemic threshold are metastable. The longevity and scale of
the metastable outbreaks do not show a sharp localization transition, instead
there is a smooth crossover from localized to delocalized states as we approach
the epidemic threshold from below. Analyzing these long-lasting local outbreaks
for a random regular graph with a hub, we show how this localization can be
detected from the shape of the distribution of the number of infective nodes.Comment: 5 pages, 4 figures; Added Appendi
Two new tests to the distance duality relation with galaxy clusters
The cosmic distance duality relation is a milestone of cosmology involving
the luminosity and angular diameter distances. Any departure of the relation
points to new physics or systematic errors in the observations, therefore tests
of the relation are extremely important to build a consistent cosmological
framework. Here, two new tests are proposed based on galaxy clusters
observations (angular diameter distance and gas mass fraction) and
measurements. By applying Gaussian Processes, a non-parametric method, we are
able to derive constraints on departures of the relation where no evidence of
deviation is found in both methods, reinforcing the cosmological and
astrophysical hypotheses adopted so far.Comment: Accepted for publication in JCAP, 6 pages, 4 figure
CoBaR: Confidence-Based Recommender
Neighborhood-based collaborative filtering algorithms usually adopt a fixed
neighborhood size for every user or item, although groups of users or items may
have different lengths depending on users' preferences. In this paper, we
propose an extension to a non-personalized recommender based on confidence
intervals and hierarchical clustering to generate groups of users with optimal
sizes. The evaluation shows that the proposed technique outperformed the
traditional recommender algorithms in four publicly available datasets
On the practice of classification learning for clinical diagnosis and therapy advice in oncology
Artificial intelligence and medicine have a longstanding and proficuous
relationship. In the present work we develop a brief assessment of this
relationship with specific focus on machine learning, in which we highlight
some critical points which may hinder the use of machine learning techniques
for clinical diagnosis and therapy advice in practice. We then suggest a
conceptual framework to build successful systems to aid clinical diagnosis and
therapy advice, grounded on a novel concept we have coined drifting domains. We
focus on oncology to build our arguments, as this area of medicine furnishes
strong evidence for the critical points we take into account here.Comment: Submitted to Artificial Intelligence in Medicin
Critical exponents of the explosive percolation transition
In a new type of percolation phase transition, which was observed in a set of
non-equilibrium models, each new connection between vertices is chosen from a
number of possibilities by an Achlioptas-like algorithm. This causes
preferential merging of small components and delays the emergence of the
percolation cluster. First simulations led to a conclusion that a percolation
cluster in this irreversible process is born discontinuously, by a
discontinuous phase transition, which results in the term "explosive
percolation transition". We have shown that this transition is actually
continuous (second-order) though with an anomalously small critical exponent of
the percolation cluster. Here we propose an efficient numerical method enabling
us to find the critical exponents and other characteristics of this second
order transition for a representative set of explosive percolation models with
different number of choices. The method is based on gluing together the
numerical solutions of evolution equations for the cluster size distribution
and power-law asymptotics. For each of the models, with high precision, we
obtain critical exponents and the critical point.Comment: 7 pages, 4 figure
Inverting the Achlioptas rule for explosive percolation
In the usual Achlioptas processes the smallest clusters of a few randomly
chosen ones are selected to merge together at each step. The resulting
aggregation process leads to the delayed birth of a giant cluster and the
so-called explosive percolation transition showing a set of anomalous features.
We explore a process with the opposite selection rule, in which the biggest
clusters of the randomly chosen ones merge together. We develop a theory of
this kind of percolation based on the Smoluchowski equation, find the
percolation threshold, and describe the scaling properties of this continuous
transition, namely, the critical exponents and amplitudes, and scaling
functions. We show that, qualitatively, this transition is similar to the
ordinary percolation one, though occurring in less connected systems.Comment: 6 pages, 2 figure
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