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BACKGROUND & AIMS One of the most frequent complications in patients with cancer and malnutrition is the surgical wound healing delay or failure. Some studies have shown that arginine improves wound healing in rodents and in healthy human beings. The main objective of this study was to assess the effect of early postoperative enteral immunonutrition on the(More)
BACKGROUND AND PURPOSE There is a lack of agreement regarding measuring the effects of stroke treatment in clinical trials, which often relies on the dichotomized value of 1 outcome scale. Alternative analyses consist mainly of 2 strategies: use all the information from an ordinal scale and combine information from several outcome scales in a single(More)
In this paper, we propose two estimators for the autocorrelation sequence of a periodic signal in additive noise. Both estimators are formulated employing tables which contain all the possible products of sample pairs in a speech signal frame. The first estimator is based on a pitch-synchronous averaging. This estimator is statistically analyzed and we show(More)
This paper addresses the problem of feature compensation in the log-spectral domain by using the missing-data (MD) approach to noise robust speech recognition, that is, the log-spectral features can be either almost unaffected by noise or completely masked by it. First, a general MD framework based on minimum mean square error (MMSE) estimation is(More)
One of the main concerns of national statistical agencies (NSAs) is to publish tabular data. NSAs have to guarantee that no private information from specific respondents can be disclosed from the released tables. The purpose of the statistical disclosure control field is to avoid such a leak of private information. Most protection techniques for tabular(More)
This paper presents a feature compensation framework based on minimum mean square error (MMSE) estimation and stereo training data for robust speech recognition. In our proposal, we model the clean and noisy feature spaces in order to obtain clean feature estimates. However, unlike other well-known MMSE compensation methods such as SPLICE or MEMLIN, which(More)
Minimum-distance controlled tabular adjustment methods (CTA), and its restricted variants (RCTA), is a recent perturbative approach for tabular data protection. Given a table to be protected, the purpose of RCTA is to find the closest table that guarantees protection levels for the sensitive cells. This is achieved by adding slight adjustments to the(More)
Minimum distance controlled tabular adjustment (CTA) is a perturbative technique of statistical disclosure control for tabular data. Given a table to be protected, CTA looks for the closest safe table by solving an optimization problem using some particular distance in the objective function. CTA has shown to exhibit a low disclosure risk. The purpose of(More)
While VoIP (voice over IP) is gaining importance in comparison with other types of telephony, packet loss remains as the main source of degradation in VoIP systems. Traditional speech codecs, such as those based on the CELP (code excited linear prediction) paradigm, can achieve low bit-rates at the cost of introducing interframe dependencies. As a result,(More)