Parameterized Complexity of Equitable Coloring

A graph on $n$ vertices is equitably $k$-colorable if it is $k$-colorable and every color is used either $\left\lfloor n/k \right\rfloor$ or $\left\lceil n/k \right\rceil$ times. Such a problem appears to be considerably harder than vertex coloring, being $\mathsf{NP\text{-}Complete}$ even for cographs and interval graphs. In this work, we prove that it is $\mathsf{W[1]\text{-}Hard}$ for block graphs and for disjoint union of split graphs when parameterized by the number of colors; and $\mathsf{W[1]\text{-}Hard}$ for $K_{1,4}$-free interval graphs when parameterized by treewidth, number of colors and maximum degree, generalizing a result by Fellows et al. (2014) through a much simpler reduction. Using a previous result due to Dominique de Werra (1985), we establish a dichotomy for the complexity of equitable coloring of chordal graphs based on the size of the largest induced star. Finally, we show that \textsc{equitable coloring} is $\mathsf{FPT}$ when parameterized by the treewidth of the complement graph

On Separating Environmental and Speaker Adaptation

This paper presents a maximum likelihood (ML) approach, concerned to the background model estimation, in noisy acoustic non-stationary environments. The external noise source is characterised by a time constant convolutional and a time varying additive components. The HMM composition technique, provides a mechanism for integrating parametric models of acoustic background with the signal model, so that noise compensation is tightly coupled with the background model estimation. However, the existing continuous adaptation algorithms usually do not take advantage of this approach, being essentially based on the MLLR algorithm. Consequently, a model for environmental mismatch is not available and, even under constrained conditions a significant number of model parameters have to be updated. From a theoretical point of view only the noise model parameters need to be updated, being the clean speech ones unchanged by the environment. So, it can be advantageous to have a model for environmental mismatch. Additionally separating the additive and convolutional components means a separation between the environmental mismatch and speaker mismatch when the channel does not change for long periods. This approach was followed in the development of the algorithm proposed in this paper. One drawback sometimes attributed to the continuous adaptation approach is that recognition failures originate poor background estimates. This paper also proposes a MAP-like method to deal with this situation

Blind source separation by independent component analysis applied to electroencephalographic signals

Independent Component Analysis (ICA) is a statistical based method, which goal is to find a linear transformation to apply to an observed multidimensional random vector such that its components become as statistically independent from each other as possible. Usually the Electroencephalographic (EEG) signal is hard to interpret and analyse since it is corrupted by some artifacts which originates the rejection of contaminated segments and perhaps in an unacceptable loss of data. The ICA filters trained on data collected during EEG sessions can identify statistically independent source channels which could then be further processed by using event-related potential (ERP), event-related spectral perturbation (ERSP) or other signal processing techniques. This paper describes, as a preliminary work, the application of ICA to EEG recordings of the human brain activity, showing its applicability

Spectral normalization MFCC derived features for robust speech recognition

This paper presents a method for extracting MFCC parameters from a normalised power spectrum density. The underlined spectral normalisation method is based on the fact that the speech regions with less energy need more robustness, since in these regions the noise is more dominant, thus the speech is more corrupted. Less energy speech regions contain usually sounds of unvoiced nature where are included nearly half of the consonants, and are by nature the least reliable ones due to the effective noise presence even when the speech is acquired under controlled conditions. This spectral normalisation was tested under additive artificial white noise in an Isolated Speech Recogniser and showed very promising results [1]. It is well known that concerned to speech representation, MFCC parameters appear to be more effective than power spectrum based features. This paper shows how the cepstral speech representation can take advantage of the above-referred spectral normalisation and shows some results in the continuous speech recognition paradigm in clean and artificial noise conditions

4,4′-(1,8-Naphthalene-1,8-di­yl)dibenzonitrile

In the title mol­ecule, C24H14N2, the exterior C—C—C angle of the naphthalene ring system involving the two phenyl-substituted C atoms is 126.06 (11)° and the dihedral angles between the mean plane of the naphthalene ring system and those of the benzene rings are 66.63 (5) and 67.89 (5)°. In the crystal, mol­ecules are linked into a ladders by four weak C—H⋯π inter­actions

Renormalization group and spectra of the generalized P\"oschl-Teller potential

We study the P\"oschl-Teller potential $V(x) = \alpha^2 g_s \sinh^{-2}(\alpha x) + \alpha^2 g_c \cosh^{-2}(\alpha x)$, for every value of the dimensionless parameters $g_s$ and $g_c$, including the less usual ranges for which the regular singularity at the origin prevents the Hamiltonian from being self-adjoint. We apply a renormalization procedure to obtain a family of well-defined energy eigenfunctions, and study the associated renormalization group (RG) flow. We find an anomalous length scale that appears by dimensional transmutation, and spontaneously breaks the asymptotic conformal symmetry near the singularity, which is also explicitly broken by the dimensionful parameter $\alpha$ in the potential. These two competing ways of breaking conformal symmetry give the RG flow a rich structure, with phenomena such as a possible region of walking coupling, massive phases, and non-trivial limits even when the anomalous dimension is absent. We show that supersymmetry of the potential, when present, is also spontaneously broken, along with asymptotic conformal symmetry. We use the family of eigenfunctions to compute the S-matrix in all regions of parameter space, for any value of anomalous scale, and systematically study the poles of the S-matrix to classify all bound, anti-bound and metastable states, including quasi-normal modes. The anomalous scale, as expected, changes the spectra in non-trivial ways.Comment: 42 pages, 16 figures. V2 - Improved version: new discussions added in Sect.4, introduction and conclusio

A new electromagnetic actuation system on an industrial sewing machine with on-line efficiency monitoring

This paper briefly reviews the study to evaluate the standard presser foot performance and a new actuation set-up to avoid the bouncing and the lack of fabric control. The compression force and displacement waveforms are also presented and widely discussed, as well as the seam’s quality analysis. A spectral (Fast Fourier Transform and Harmonic Distortion) analysis on the obtained waveforms, as well as an Admissible Displacement Limits (ADL) analysis are also described as important techniques to be used to supervise the presser foot force and to monitor the feeding efficiency. ADL have proved to be a better method. Further research should be undertaken in this area, in order to achieve, in a near future, a closed loop control of the presser foot.Fundação para a Ciência e a Tecnologia (FCT

Omni-directional catadioptric vision for soccer robots

This paper describes the design of a multi-part mirror catadioptric vision system and its use for self-localization and detection of relevant objects in soccer robots. The mirror and associated algorithms have been used in robots participating in the middle-size league of RoboCup — The World Cup of Soccer Robots.This work was supported by grant PRAXIS XXI BM/21091/99 of the Portuguese Foundation for Science and Technolog