Improvements of Hungarian Hidden Markov Model-based text-to-speech synthesis

Statistical parametric, especially Hidden Markov Model-based, text-to-speech (TTS) synthesis has received much attention recently. The quality of HMM-based speech synthesis approaches that of the state-of-the-art unit selection systems and possesses numerous favorable features, e.g. small runtime footprint, speaker interpolation, speaker adaptation. This paper presents the improvements of a Hungarian HMM-based speech synthesis system, including speaker dependent and adaptive training, speech synthesis with pulse-noise and mixed excitation. Listening tests and their evaluation are also described

Power-Law Distributions of Dynamic Cascade Failures in Power-Grid Models

Power-law distributed cascade failures are well known in power-grid systems. Understanding this phenomena has been done by various DC threshold models, self-tuned at their critical point. Here we attempt to describe it using an AC threshold model, with a second-order Kuramoto type equation of motion of the power-flow. We have focused on the exploration of network heterogeneity effects, starting from homogeneous 2D lattices to the US power-grid, possessing identical nodes and links, to a realistic electric power-grid obtained from the Hungarian electrical database. The last one exhibits node dependent parameters, topologically marginally on the verge of robust networks. We show that too weak quenched heterogeneity, coming solely from the probabilistic self-frequencies of nodes (2D lattice) is not sufficient to find power-law distributed cascades. On the other hand too strong heterogeneity destroys the synchronization of the system. We found agreement with the empirically observed power-law failure size distributions on the US grid, as well as on the Hungarian networks near the synchronization transition point. We have also investigated the consequence of replacing the usual Gaussian self-frequencies to exponential distributed ones, describing renewable energy sources. We found a drop in the steady state synchronization averages, but the cascade size distribution both for the US and Hungarian systems remained insensitive and have kept the universal tails, characterized by the exponent $\tau\simeq 1.8$. We have also investigated the effect of an instantaneous feedback mechanism in case of the Hungarian power-grid.Comment: Extended version with minor changes, accepted in Entropy 22 pages, 13 figure

Copper tolerance of Aegilops, Triticum, Secale and triticale seedlings and copper and iron content in their shoots

Twenty-seven different cereal accessions belonging to the Triticinae subtribe were screened for copper tolerance in hydroponic cultures. Based on the shoot dry mass reduction and the decreased value of the FJFm fluorescence induction parameter the Secale species were the most tolerant ones. Slightly tolerant and relatively sensitive common wheat cultivars were also identified. No significant correlation was found between the copper and iron concentration of the shoots and the degree of tolerance

Heterogeneity effects in power grid network models

We have compared the phase synchronization transition of the second order Kuramoto model on 2D lattices and on large, synthetic power grid networks, generated from real data. The latter are weighted, hierarchical modular networks. Due to the inertia the synchronization transitions are of first order type, characterized by fast relaxation and hysteresis by varying the global coupling parameter K. Finite size scaling analysis shows that there is no real phase transition in the thermodynamic limit, unlike in the mean-field model. The order parameter and its fluctuations depend on the network size without any real singular behavior. In case of power grids the phase synchronization breaks down at lower global couplings, than in case of 2D lattices of the same sizes, but the hysteresis is much narrower or negligible due to the low connectivity of the graphs. The temporal behavior of de-synchronization avalanches after a sudden quench to low K values, has been followed and duration distributions with power-law tails have been detected. This suggests rare region effects, caused by frozen disorder, resulting in heavy tailed distributions, even without a self organization mechanism as a consequence of a catastrophic drop event in the couplings.Comment: 10 pages, 10 Figures, accepted version in PR

A háziorvos szerepe a reumatológiai betegek fájdalmának csillapításában

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Revisiting and modeling power-law distributions in empirical outage data of power systems

The size distribution of planned and forced outages and following restoration times in power systems have been studied for almost two decades and has drawn great interest as they display heavy tails. Understanding of this phenomenon has been done by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, explanations are intuitive, and more empirical data is needed to support hypotheses. In this paper, the authors analyze outage data collected from various public sources to calculate the outage energy and outage duration exponents of possible power-law fits. Temporal thresholds are applied to identify crossovers from initial short-time behavior to power-law tails. We revisit and add to the possible explanations of the uniformness of these exponents. By performing power spectral analyses on the outage event time series and the outage duration time series, it is found that, on the one hand, while being overwhelmed by white noise, outage events show traits of self-organized criticality (SOC), which may be modeled by a crossover from random percolation to directed percolation branching process with dissipation, coupled to a conserved density. On the other hand, in responses to outages, the heavy tails in outage duration distributions could be a consequence of the highly optimized tolerance (HOT) mechanism, based on the optimized allocation of maintenance resources.Comment: 16 pages, 8 figure

Improving power-grid systems via topological changes, or how self-organized criticality can help stability

Cascade failures in power grids occur when the failure of one component or subsystem causes a chain reaction of failures in other components or subsystems, ultimately leading to a widespread blackout or outage. Controlling cascade failures on power grids is important for many reasons like economic impact, national security, public safety and even rippled effects like troubling transportation systems. Monitoring the networks on node level has been suggested by many, either controlling all nodes of a network or by subsets. This study identifies sensitive graph elements of the weighted European power-grids (from 2016, 2022) by two different methods. Bridges are determined between communities and "weak" nodes are selected by the lowest local synchronization of the swing equation. In the latter case we add bypasses of the same number as the bridges at weak nodes, and we compare the synchronization, cascade failure behavior by the dynamical improvement with the purely topological changes. The results are also compared if bridges are removed from networks, which results in a case similar to islanding, and with the addition of links at randomly selected places. Bypassing was found to improve synchronization the best, while the average cascade sizes are the lowest with bridge additions. However, for very large or small global couplings these network changes do not help, they seem to be useful near the synchronization transition region, where self-organization drives the power-grid. Thus, we provide a demonstration for the Braess' Paradox on continent-sized power grid simulations and uncover the limitations of this phenomenon. We also determine the cascade size distributions and justify the power-law tails near the transition point on these grids.Comment: 11 pages 12 figure

Rejtett Markov-modell alapú szövegfelolvasó adaptációja félig spontán magyar beszéddel

Napjainkban számos automatikus szövegfelolvasási módszer létezik, de az elmúlt években a legnagyobb figyelmet a statisztikai parametrikus beszédkeltési módszer, ezen belül is a rejtett Markov-modell (Hidden Markov Model, HMM) alapú szövegfelolvasás kapta. A HMM-alapú szövegfelolvasás minsége megközelíti a manapság legjobbnak számító elemkiválasztásos szintézisét, és ezen túl számos elnnyel rendelkezik: adatbázisa kevés helyet foglal el, lehetséges új hangokat külön felvételek nélkül létrehozni, érzelmeket kifejezni vele, és már néhány mondatnyi felvétel esetén is lehetséges az adott beszél hangkarakterét visszaadni. Jelen cikkben bemutatjuk a HMM-alapú beszédkeltés alapjait, a beszéladaptációjának lehetségeit, a magyar nyelvre elkészült beszélfüggetlen HMM adatbázist és a beszéladaptáció folyamatát félig spontán magyar beszéd esetén. Az eredmények kiértékelése céljából meghallgatásos tesztet végzünk négy különböz hang adaptációja esetén, melyeket szintén ismertetünk a cikkünkben