Abstract

Abstract
Catalytic computation, defined by Buhrman, Cleve, Koucký, Loff and Speelman (STOC 2014), is a space-bounded computation where in addition to our working memory we have an exponentially larger auxiliary memory which is full; the auxiliary memory may be used throughout the computation, but it must be restored to its initial content by the end of the computation. Motivated by the surprising power of this model, we set out to study the non-deterministic version of catalytic computation. We establish that non-deterministic catalytic log-space is contained in ZPP, which is the same bound known for its deterministic counterpart, and we prove that non-deterministic catalytic space is closed under complement (under a standard derandomization assumption). Furthermore, we establish hierarchy theorems for non-deterministic and deterministic catalytic computation. © 2017, Springer Science+Business Media New York.

Abstract
Data mining and machine learning algorithms can be employed to perform a variety of tasks. However, since most of these problems may depend on environments that change over time, performing classification tasks in dynamic environments has been a challenge in data mining research domain in the last decades. Currently, in the literature, the most common strategies used to detect changes are based on accuracy monitoring, which relies on previous knowledge of the data in order to identify whether or not correct classifications are provided. However, such a feedback can be infeasible in practical problems. In this work, we present a comprehensive overview of current machine learning/data mining approaches proposed to deal with dynamic environments problems. The objective is to highlight the main drawbacks and open issues, as well as future directions and problems worthy of investigation. In addition, we provide the definitions of the main terms used to represent this problem in the literature, such as concept drift and novelty detection. WIREs Data Mining Knowl Discov 2016, 6:156-166. doi: 10.1002/widm.1184 For further resources related to this article, please visit the .

Abstract

Abstract
Body skin temperature is a useful parameter for diagnosing diseases and infrared thermography can, be a powerful tool in providing important information to detect body temperature changes in a noninvasive way. The aim of this work was to study the pattern of skin temperature during pregnancy, to establish skin temperature reference values and to find correlations between these and the pregnant population characteristics. Sixty-one healthy pregnant women (mean age 30.6 +/- 5.1 years) in the 8th-40th gestational week with normal pregnancies were examined in 31 regions of interest (ROI). The ROIs were defined all over the body in order to determine the most influenced by factors such as age or body mass index (BMI). The results obtained in this work highlight that in normal pregnant women the skin temperature is symmetrically distributed, with the symmetrical areas differing less than 0.5 degrees C, with a mean value of 0.25 +/- 0.23 degrees C. This study identified a significant negative correlation between the BMI and temperature. Age has been shown to have great influence on the skin temperature, with a significant increase of temperature observed with age. This work explores a novel medical application of infrared thermography and provides a characterization of thermal skin profile in human pregnancy for a large set of ROIs while also evaluating the effects of age and BMI.

Abstract
The purpose of this paper is to present the main results of the ongoing analysis of applying dynamic network access tariffs in Portugal. For the 2015-2017 regulatory period, the Portuguese National Regulatory Authority, ERSE, required the three main Portuguese DSOs to submit, until the end of June 2016, plans for the implementation of network dynamic tariff schemes targeting Medium, High and Extra High Voltage customers, as well as the respective cost-benefit analysis. EDP Distribuição, the main Portuguese DSO, is preparing a report regarding the implementation of pilot projects on a sample of these segments of customers, which are due to be on the field during 2017. These pilots should help electrical energy stakeholders understand how the Electric System can benefit from the use of dynamic tariffs focused on networks, allowing for the quantification of benefits in a more accurate way. The level of demand response that results from price signals is a key issue that both the regulator and EDP Distribuição will quantify. Other important issue to assess in this study is the efficiency of cost recovery under a dynamic tariff scheme. In conclusion, this paper will present some results obtained from the cost-benefit analysis regarding the implementation of a Critical Peak Pricing scheme, as well as the key learnings supporting the introduction of dynamic schemes in the future, not only for EHV, HV and MV customers but also eventually extending it to LV ones.