Do Better Neighborhoods for MTO Families Mean Better Schools?

Explores the factors that kept children who moved to safer, lower-poverty neighborhoods through the Moving to Opportunity program from accessing better schools, such as lack of change in school district, lack of parental choice, and lack of information

Structural combination of neural network models

Forecasts combinations normally use point forecasts that were obtained from different models or sources ([1], [2], [3]). This paper explores the incorporation of internal structure parameters of feed-forward neural network (NN) models as an approach to combine their forecasts via ensembles. First, the generated NN models that could be part of the ensembles are subject to a clustering algorithm that uses the structure parameters and, from each of the clusters obtained, a small set of models is selected and their forecasts are combined in a two-stage procedure. Secondly, in an alternative and simpler implementation, a subset of the generated NN models is selected by using several reference points in the model structure parameter space. The choice of the reference points is optimised through a genetic algorithm and the models selected are averaged. Hourly electricity demand time series is used to assess multi-step ahead forecasting performance for up to a 12 hours horizon. Results are compared against several statistical benchmarks, the average of the individual forecasts and the best models in the ensembles. Results show that the clusterbased (CB) structural combinations do better than the genetic algorithm (GA) structural combinations in outperforming the average forecast, which is the traditional point forecast from an ensemble

The Deaf Gay/Lesbian Client: Some Perspectives

The role of the gay/lesbian person in the deaf culture and communication problems which contribute to or interfere with self acceptance as well as family or community acceptance are the focal points for this article. Changes that have been made in the deaf culture and in the culture at large in respect to sexual identity issues and the comparison of communities (deaf to gay/lesbian) are also outlined. Although not addressing very aspect of the issues surrounding the deaf, gay/lesbian client, this article provides suggestions for counselors and others who may be confronted with these issues

Non‐invasive recordings of fetal electrocardiogram during pregnancy using electric potential sensors

In this letter, we report the early detection of fetal cardiac electrical activity recorded from the maternal abdomen non-invasively. We developed a portable and non-invasive, prototype based on electric potential sensing technology to monitor both: the mother and fetal heart activity during pregnancy. In this proof of principle demonstration, we show the suitability of our technology to monitor the fetal heart development starting at week twenty, when the fetus heart is approximately one-tenth the size of an adult’s heart. The study was conducted for ten weeks to demonstrate how the maturation of the fetus leads to a change on the heart rate dynamics as it approaches birth. Importantly, electrocardiogram information is presented without any post processing given that our device eliminates the requirement of signal conditioning algorithms such as having to un-mix both, the maternal and fetal cardiac waveforms. The provided ECG trace allows extracting the heart rate and other heart activity parameters useful for further diagnostics. Finally, our device does not require any gels to be applied so movement induced potential is eliminated. This technology has the potential to be used for determining possible heart related congenital disorders during pregnancy

A novel non-invasive biosensor based on electric field detection for cardio-electrophysiology in zebrafish embryos

In this paper we report a novel biosensor based on electric field detection for recording cardiac electrical activity in zebrafish embryos. Using Sussex patented Electric Potential Sensing technology, a portable, non-invasive and cost-effective platform is developed to monitor in vivo electrocardiogram activity from the zebrafish heart. Cardiac activity signals were successfully detected from living zebrafish embryos starting at 3 days-post-fertilizatio

BacFITBase: A database to assess the relevance of bacterial genes during host infection

Bacterial infections have been on the rise world-wide in recent years and have a considerable impact on human well-being in terms of attributable deaths and disability-adjusted life years. Yet many mechanisms underlying bacterial pathogenesis are still poorly understood. Here, we introduce the BacFITBase database for the systematic characterization of bacterial proteins relevant for host infection aimed to enable the identification of new antibiotic targets. BacFITBase is manually curated and contains more than 90 000 entries with information on the contribution of individual genes to bacterial fitness under in vivo infection conditions in a range of host species. The data were collected from 15 different studies in which transposon mutagenesis was performed, including top-priority pathogens such as Acinetobacter baumannii and Campylobacter jejuni, for both of which increasing antibiotic resistance has been reported. Overall, BacFITBase includes information on 15 pathogenic bacteria and 5 host vertebrates across 10 different tissues. It is freely available at www.tartaglialab.com/bacfitbase

Structure combination of forecasting models with application in the energy sector

This dissertation proposes and implements the inclusion of model structure in combining forecasts. Empirical investigations are conducted with an emphasis on neural networks and seasonal exponential smoothing models using synthetic data and real time series, from the electricity sector. It starts with a literature review on combining forecasts and ensembles of neural networks, and highlights their use in forecasting within the energy sector. Research gaps are identified and the questions to be addressed in this research are set, thus leading to three empirical studies. The first study provides a detailed sensitivity analysis of the goodness-of-fit and forecasting performance of feed-forward neural networks on time series with different characteristics. It expands existing literature by increasing the number and variety of time series and by using graphical and statistical diagnostics to objectively judge the influence of model specification on forecasting performance. Having identified conditions for achieving stable model performance, this study facilitated the identification of suitable models for different time series characteristics, which are then useful in developing combinations (ensembles) of feed forward neural networks. The second study proposes structural combination methods based on clustering (CB) and genetic algorithms (GA) for forecasting time series. Clustering of neural networks using their parameter space is performed to identify a pool of forecasts to be combined. Three synthetic time series and two real time series (electricity demand and wind power production) were used to assess the performance of the two proposals against several benchmarks in univariate and multivariate forecasting problems. Structural combinations with GA were more competitive than those with CB for non-seasonal time series and the multivariate wind power forecasting application, whereas for the seasonal series, the CB tended to be more competitive. The third study focused on forecasting univariate time series with seasonality, by structurally combining, in separate applications, multiplicative Holt-Winters and multiplicative Holt-Winters-Taylor models. Noise addition and block swapping were applied to the original time series in order to generate structurally diverse individual models. Applications were conducted using a seasonal daily peak electricity demand time series, an hourly double-seasonal electricity demand series and a half-hourly double-seasonal electricity demand series. Structural combinations worked better for the peak electricity demand and half-hourly demand time series when model variation was induced via noise addition. For the double-seasonal hourly electricity demand, block swapping, as a means for diversity in models, resulted in better forecasts. Finally, in the last chapter of this dissertation, conclusions are drawn from this research. The contribution to the literature is assessed and a future research agenda is proposed