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Register a new userA Ray-Knight representation of up-down Chinese restaurants
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We study composition-valued continuous-time Markov chains that appear naturally in the framework of Chinese Restaurant Processes (CRPs). As time evolves, new customers arrive (up-step) and existing customers leave (down-step) at suitable rates derived from the ordered CRP of Pitman and Winkel (2009). We relate such up-down CRPs to the splitting trees of Lambert (2010) inducing spectrally positive L{e}vy processes. Conversely, we develop theorems of Ray-Knight type to recover more general up-down CRPs from the heights of L{e}vy processes with jumps marked by integer-valued paths. We further establish limit theorems for the L{e}vy process and the integer-valued paths to connect to work by Forman et al. (2018+) on interval partition diffusions and hence to some long-standing conjectures.
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We introduce a three-parameter family of up-down ordered Chinese restaurant processes ${rm PCRP}^{(alpha)}(theta_1,theta_2)$, $alphain(0,1)$, $theta_1,theta_2ge 0$, generalising the two-parameter family of Rogers and Winkel. Our main result establishes self-similar diffusion limits, ${rm SSIP}^{(alpha)}(theta_1,theta_2)$-evolutions generalising existing families of interval partition evolutions. We use the scaling limit approach to extend stationarity results to the full three-parameter family, identifying an extended family of Poisson--Dirichlet interval partitions. Their ranked sequence of interval lengths has Poisson--Dirichlet distribution with parameters $alphain(0,1)$ and $theta:=theta_1+theta_2-alphage-alpha$, including for the first time the usual range of $theta>-alpha$ rather than being restricted to $thetage 0$. This has applications to Fleming--Viot processes, nested interval partition evolutions and tree-valued Markov processes, notably relying on the extended parameter range.
Using a divergent Bass-Burdzy flow we construct a self-repelling one-dimensional diffusion. Heuristically, it can be interpreted as a solution to an SDE with a singular drift involving a derivative of the local time. We show that this self-repelling diffusion inverts the second Ray-Knight identity on the line. The proof goes through an approximation by a self-repelling jump processes that has been previously shown by the authors to invert the Ray-Knight identity in the discrete.
Studies of sparse representation of deterministic signals have been well developed. Amongst there exists one called adaptive Fourier decomposition (AFD) established through adaptive selections of the parameters defining a Takenaka-Malmquist system in one-complex variable. The AFD type algorithms give rise to sparse representations of signals of finite energy. The multivariate generalization of AFD is one called pre-orthogonal AFD (POAFD), the latter being established with the context Hilbert space possessing a dictionary. The purpose of the present study is to generalize both AFD and POAFD to random signals. We work on two types of random signals. One is those expressible as the sum of a deterministic signal with an error term such as a white noise; and the other is, in general, as mixture of several classes of random signals obeying certain distributive law. In the first part of the paper we develop an AFD type sparse representation for one-dimensional random signals by making use analysis of one complex variable. In the second part, without complex analysis, we treat multivariate random signals in the context of stochastic Hilbert space with a dictionary. Like in the deterministic signal case the established random sparse representations are powerful tools in practical signal analysis.
We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time --- rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Students t-test with p<1e-5. Check our demo at https://www.cs.dartmouth.edu/dsail/demos/c2p2
The quark and charged lepton masses and the angles and phase of the CKM mixing matrix are nicely reproduced in a model which assumes SU(3)xSU(3) flavour symmetry broken by the v.e.v.s of fields in its bi-fundamental representation. The relations among the quark mass eigenvalues, m_u/m_c approx m_c/m_t approx m^2_d/m^2_s approx m^2_s/m^2_b approx Lambda^2_{GUT}/M^2_{Pl}, follow from the broken flavour symmetry. Large tan(beta) is required which also provides the best fits to data for the obtained textures. Lepton-quark grandunification with a field that breaks both SU(5) and the flavour group correctly extends the predictions to the charged lepton masses. The seesaw extension of the model to the neutrino sector predicts a Majorana mass matrix quadratically hierarchical as compared to the neutrino Dirac mass matrix, naturally yielding large mixings and low mass hierarchy for neutrinos.
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