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 $q\leq3$, 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 $\sim$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 $H(z)$ 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