The Multi-feature Beat tracker Essentia implementation uses five different onset detection functions to estimate the beats of a musical audio signal using only one beat tracker algorithm, where the beat tracker output is selected using a committee technique. This is a C++ implementation of the algorithm ZDG1 (five onset detection function), submitted to MIREX 2012 audio beat tracking task.

Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors’ results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures’ estimation. Monte Carlo simulation r...

Theoretical models suggest that the Quantum Chromo-Dynamics (QCD) phase diagram has a critical point demarcating the order of transition between the two phases: the hadron gas, in which the quarks are confined and the Quark-Gluon Plasma (QGP). The central goal of the experiments with relativistic heavy-ion collisions is to create and study such form of matter called the QGP and understand the Q...

The baryon-baryon continuum invariant mass spectrum generated from ultrarelativistic nucleus + nucleus collision data may reveal the existence of doubly-strange dibaryons not stable against strong decay if they lie within a few MeV of threshold. Furthermore, since the dominant component of these states is a superposition of two color-octet clusters which can be produced intermediately in a colo...

In this article, we modeled image target tracking into reinforcement learning framework, and we proposed a two-step reinforcement learning algorithm for target tracking. In this algorithm, we set multiple tracker agent to track the pixel of target, the intention of reinforcement learning is to achieve tracking strategy of every tracker agent, we divided each learning step of tracker into two pa...

For learning an object representation a huge amount of labeled data is needed. To minimize the labeling effort this paper proposes a new approach for learning from unlabeled data. The main idea is to combine a tracker and a learning method by directly feeding the learning algorithm with patches obtained by the tracker. In particular we apply an MSER based tracker and batch PCA for learning. But...