Enhancing steganography for hiding pixels inside audio signals

Multimodal steganography consists of concealing a signal into another one of a different medium, such that the latter is only very slightly distorted and the hidden information can be later recovered. A previous work employed deep learning techniques to this end by hiding an image inside an audio signal's spectrogram in a way that the encoding of one is independent of the other. In this work we explore the way in which images were being encoded previously and present a collection of improvements that produce a significant increase in the quality of the system. These mainly consist in encoding the image in a smarter way such that more information is able to be transmitted in a container of the same size. We also explore the possibility of using the short-time Fourier transform phase as an alternative to the magnitude and to randomly permute the signal to break the structure of the noise. Finally, we report results when using a larger container signal and outline possible directions for future work

Wind energy forecasting with neural networks: a literature review

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version

“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

Go with the flow: Recurrent networks for wind time series multi-step forecasting

One of the ways of reducing the effects of Climate Change is to rely on renewable energy sources. Their intermittent nature makes necessary to obtain a mid-long term accurate forecasting. Wind Energy prediction is based on the ability to forecast wind speed. This has been a problem approached using different methods based on the statistical properties of the wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting obtaining a 12 hours ahead prediction using data from the National Renewable Energy Laboratory's WIND datasetPeer ReviewedPostprint (published version

Predicting wind energy generation with recurrent neural networks

Decarbonizing the energy supply requires extensive use of renewable generation. Their intermittent nature requires to obtain accurate forecasts of future generation, at short, mid and long term. Wind Energy generation prediction is based on the ability to forecast wind intensity. This problem has been approached using two families of methods one based on weather forecasting input (Numerical Weather Model Prediction) and the other based on past observations (time series forecasting). This work deals with the application of Deep Learning to wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting in a 12 h ahead prediction. For the Time Series input we used the US National Renewable Energy Laboratory’s WIND Dataset [3], (the largest available wind and energy dataset with over 120,000 physical wind sites), this dataset is evenly spread across all the North America geography which has allowed us to obtain conclusions on the relationship between physical site complexity and forecast accuracy. In the preliminary results of this work it can be seen a relationship between the error (measured as R2R2 ) and the complexity of the terrain, and a better accuracy score by some Recurrent Neural Network Architectures.Peer ReviewedPostprint (author's final draft

Survey on recent developments in semitoric systems

Semitoric systems are a special class of four-dimensional completely integrable systems where one of the first integrals generates an $\mathbb{S}^1$-action. They were classified by Pelayo & Vu Ngoc in terms of five symplectic invariants about a decade ago. We give a survey over the recent progress which has been mostly focused on the explicit computation of the symplectic invariants for families of semitoric systems depending on several parameters and the generation of new examples with certain properties, such as a specific number of singularities of lowest rank.Comment: 15 pages, 5 figure

Social support in social and educational resources

Aquest text és fonamentalment una reflexió global sobre l'acompanyament des de la pràctica quotidiana, en part terapèutica, en part educativa, en part social, d'un recurs d'atenció concebut, pensat i practicat justament amb l'acompanyament com a eix central de la seva organització i del seu sentit de ser. És escrit com una reflexió d'anada i tornada. D'anada, perque dóna compte del per que de l'acompanyament. De tornada, perque hom vol traspassar al leclor reflexions validades, criteris sobre l'acompanyament que han viscut diferents persones i professionals.Este texto es fundamentalmente una reflexión global sobre el acompañamiento hecha desde la práctica cotidiana, en parte terapéutica, en parte educativa, en parte social, de un recurso de atención concebido, pensado y practicado justamente con el acompañamiento como eje central de su organización y de su sentido de ser. Está escrito como una reflexión de ida y vuelta. De ida, porque da cuenta del "por qué" del acompañamiento. De vuelta, porque se quiere traspasar al lector reflexiones validadas, criterios sobre el acompañamiento que han vivido diferentes personas y profesionales.This lexl is basically a global refleclion on support from the point of view of everyday practice, parlty therapeulic, party educational, and party social, of a help resource Ihal has been conceived, designed and put ¡nto practice with support as the backbone of its organisalion and way ofbeing. It is written as thoughls on an oUlWard and return journey. An outward journey because il lakes in the "Why" of support, and return, beca use it wishes 10 pass on 10 the reader its validaled thougnts and criteria on supporl experienced by various people and professionals

The twisting index in semitoric systems

In 2009-2011 Pelayo and V\~{u} Ngoc classified semitoric integrable systems in terms of five invariants. Four of the invariants were already well-understood prior to the classification, but the fifth invariant, the so-called twisting index invariant, came as a surprise. Having a better understanding of the twisting index invariant of a semitoric system is a necessary step towards extending the symplectic classification result to more general situations, such as almost-toric systems, hypersemitoric systems, or higher dimensional systems which admit underlying complexity-one torus actions. The twisting index encodes how the structure in a neighborhood of a focus-focus fiber compares to the large-scale structure of the semitoric system. Pelayo and V\~{u} Ngoc originally defined the twisting index in terms of comparing certain momentum maps. The first half of the paper is devoted to giving an equivalent definition of the twisting index in terms of topological-geometric objects, such as homology cycles. The second half of the paper is concerned with computing the twisting index of a specific family of systems (the generalized coupled angular momenta) with two focus-focus singular points, which is the first time that the twisting index has been computed for a system with more than one focus-focus point. Along the way, we also compute the terms of the Taylor series invariant up to second order, completing the computation of all five semitoric invariants for this system. Thus there is now a fully classified third family of semitoric systems after the completion of the classification of spin oscillators and coupled angular momenta (Alonso $\&$ Hohloch $\&$ Dullin in 2019 and 2020).Comment: 46 page, 16 figure

Dynamical degrees of birational maps from indices of polynomials with respect to blow-ups II. 3D examples

The goal of this paper is the exact computation of the degrees $\text{deg}(f^n)$ of the iterates of birational maps $f: \mathbb{P}^N \dashrightarrow \mathbb{P}^N$. In the preceding companion paper, a new method has been proposed based on the use of indices of polynomials associated to the local blow-ups used to resolve contractions of hypersurfaces by $f$, and on the control of the factorization of pull-backs of polynomials. This leads to recurrence relations for the degrees and the indices. We apply this method to several illustrative examples in three dimensions. These examples demonstrate the flexibility of the method which, in particular, does not require the construction of an algebraically stable lift of $f$, unlike the previously known methods based on the Picard group.Comment: 39 pages, 2 figure