Zeta measures and Thermodynamic Formalism for temperature zero

We address the analysis of the following problem: given a real H\"older potential $f$ defined on the Bernoulli space and $\mu_f$ its equilibrium state, it is known that this shift-invariant probability can be weakly approximated by probabilities in periodic orbits associated to certain zeta functions. Given a H\"older function $f>0$ and a value $s$ such that $0<s<1$, we can associate a shift-invariant probability $\nu_{s}$ such that for each continuous function $k$ we have $$\int k d\nu_{s}=\frac{\sum_{n=1}^{\infty}\sum_{x\in Fix_{n}}e^{sf^{n}(x)-nP(f)}\frac{k^{n}(x)}{n}}{\sum_{n=1}^{\infty}\sum_{x\in Fix_{n}}e^{sf^{n}(x)-nP(f)}},$$ where $P(f)$ is the pressure of $f$, $Fix_n$ is the set of solutions of $\sigma^n(x)=x$, for any $n\in \mathbb{N}$, and $f^{n}(x) = f(x) + f(\sigma(x)) + f(\sigma^2(x))+... + f(\sigma^{n-1} (x)).$ We call $\nu_{s}$ a zeta probability for $f$ and $s$. It is known that $\nu_s \to \mu_{f}$, when $s \to 1$. We consider for each value $c$ the potential $c f$ and the corresponding equilibrium state $\mu_{c f}$. What happens with $\nu_{s}$ when $c$ goes to infinity and $s$ goes to one? This question is related to the problem of how to approximate the maximizing probability for $f$ by probabilities on periodic orbits. We study this question and also present here the deviation function $I$ and Large Deviation Principle for this limit $c\to \infty, s\to 1$. We will make an assumption: $\lim_{c\to \infty, s\to 1} c(1-s)= L>0$. We do not assume here the maximizing probability for $f$ is unique

Pseudo-Supersymmetry and the Domain-Wall/Cosmology Correspondence

The correspondence between domain-wall and cosmological solutions of gravity coupled to scalar fields is explained. Any domain wall solution that admits a Killing spinor is shown to correspond to a cosmology that admits a pseudo-Killing spinor: whereas the Killing spinor obeys a Dirac-type equation with hermitian `mass'-matrix, the corresponding pseudo-Killing spinor obeys a Dirac-type equation with a anti-hermitian `mass'-matrix. We comment on some implications of (pseudo)supersymmetry.Comment: 11 pages, contribution to the proceedings of IRGAC 2006;v3: minor change

SourcererCC: Scaling Code Clone Detection to Big Code

Despite a decade of active research, there is a marked lack in clone detectors that scale to very large repositories of source code, in particular for detecting near-miss clones where significant editing activities may take place in the cloned code. We present SourcererCC, a token-based clone detector that targets three clone types, and exploits an index to achieve scalability to large inter-project repositories using a standard workstation. SourcererCC uses an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks, (1) a large benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (250MLOC) using a standard workstation.Comment: Accepted for publication at ICSE'16 (preprint, unrevised

Neural networks with dynamical synapses: from mixed-mode oscillations and spindles to chaos

Understanding of short-term synaptic depression (STSD) and other forms of synaptic plasticity is a topical problem in neuroscience. Here we study the role of STSD in the formation of complex patterns of brain rhythms. We use a cortical circuit model of neural networks composed of irregular spiking excitatory and inhibitory neurons having type 1 and 2 excitability and stochastic dynamics. In the model, neurons form a sparsely connected network and their spontaneous activity is driven by random spikes representing synaptic noise. Using simulations and analytical calculations, we found that if the STSD is absent, the neural network shows either asynchronous behavior or regular network oscillations depending on the noise level. In networks with STSD, changing parameters of synaptic plasticity and the noise level, we observed transitions to complex patters of collective activity: mixed-mode and spindle oscillations, bursts of collective activity, and chaotic behaviour. Interestingly, these patterns are stable in a certain range of the parameters and separated by critical boundaries. Thus, the parameters of synaptic plasticity can play a role of control parameters or switchers between different network states. However, changes of the parameters caused by a disease may lead to dramatic impairment of ongoing neural activity. We analyze the chaotic neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I., 2004) and show that it has a collective nature.Comment: 7 pages, Proceedings of 12th Granada Seminar, September 17-21, 201