The power of A/B testing under interference

In this paper, we address the fundamental statistical question: how can you assess the power of an A/B test when the units in the study are exposed to interference? This question is germane to many scientific and industrial practitioners that rely on A/B testing in environments where control over interference is limited. We begin by proving that interference has a measurable effect on its sensitivity, or power. We quantify the power of an A/B test of equality of means as a function of the number of exposed individuals under any interference mechanism. We further derive a central limit theorem for the number of exposed individuals under a simple Bernoulli switching interference mechanism. Based on these results, we develop a strategy to estimate the power of an A/B test when actors experience interference according to an observed network model. We demonstrate how to leverage this theory to estimate the power of an A/B test on units sharing any network relationship, and highlight the utility of our method on two applications - a Facebook friendship network as well as a large Twitter follower network. These results yield, for the first time, the capacity to understand how to design an A/B test to detect, with a specified confidence, a fixed measurable treatment effect when the A/B test is conducted under interference driven by networks.Comment: 14 page

Unveiling the Biometric Potential of Finger-Based ECG Signals

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications

Edad y resistencia al calor de las esporas de Bacillus coagulans y B. macerans en un puré ácido de plátano maduro

Decimal reduction times (D values) at 100° C in sterile acid ripe plantain puree were calculated for 2-, 10- and 16-month old spores of Bacillus coagulans and Bacillus macerans. Results indicated that the D100° C values obtained were, respectively, 8.4, 13.2 and 9.2 mm for B. coagulans and 9.7, 9.7 and 9.7 min for B. macerans. According to these data, spore age has no effect on heat resistance for B. macerans. For B. coagulans, however, spore age appears to have an effect on thermal resistance. When 10-month old B. coagulans spores were used, the D100° C values obtained were significantly higher (P<0.01) than the ones for either 2- or 16-month old spores. This possible fluctuation in heat resistance with age of spore may introduce an element of variability that should not be overlooked when B. coagulans is used to determine the adequacy of a thermal process in acid or acidified foods. Since under certain conditions the spores of B. macerans could be more, or at least equally, heat resistant than those of B. coagulans, the question arises whether B. macerans could or should be used in lieu of B. coagulans in determining the adequacy of thermal processes in acid or acidified foods.Se calculó el tiempo de reducción decimal (valor D) a 100° C. en puré ácido de plátano maduro estéril para esporas de Bacillus coagulans y Bacillus macerans de 2, 10 y 16 meses de edad. Los resultados indicaron que los valores D obtenidos fueron, respectivamente, de 8.4, 13.2 y 9.2 min, para B. coagulans y de 9.7, 9.7 y 9.7 m. para B. macerans. Según estos datos, en B. macerans la edad de la espora no tiene ningún efecto sobre su resistencia al calor. En el caso de B. coagulans, sin embargo, la edad de la espora parece tener un efecto sobre su resistencia térmica. Cuando se usaron esporas de B. coagulans de 10 meses de edad, los valores D 100° C obtiendos fueron mayores significativamente (P <0.01) que los de las esporas de 2 ó 16 meses de edad. Esta posible fluctuación en la resistencia térmica con la edad de la espora podría introducir un elemento de variabilidad que no se debería pasar por alto cuando se use B. coagulans para determinar la suficiencia de un proceso térmico para un alimento ácido o acidificado. Dado que bajo ciertas condiciones las esporas de B. macerans pueden ser más resistentes significativamente (P<0.05) al calor, o por lo menos no ser diferentes significativamente (P>0.05) a las de B. coagulans, surge la interrogante de que si se podría o debería usar a B. macerans en vez de a B. coagulans para determinar la suficiencia de procesos térmicos en aumentos ácidos o acidificados

REFRIGERATION BEFORE INCUBATION AND ITS EFFECT ON RECOVERY OF TWO TYPES OF BACTERIA FROM FOOD

REFRIGERATION BEFORE INCUBATION AND ITS EFFECT ON RECOVERY OF TWO TYPES OF BACTERIA FROM FOO

ECG-based biometrics: A real time classification approach

Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subject's hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer

Diseases of Eucalypts in Paraguay and First Report of Teratosphaeria zuluensis from South America

Background and objectives: The global forest economy is threatened by eucalypt pathogens which are often latent or cryptic species that escape common quarantine and detection methods. Plantation forestry using eucalypts is of considerable importance to Paraguay, but knowledge regarding the pests and diseases affecting these plantations is limited. This study identified fungal diseases present in these plantations. Materials and Methods: We surveyed eucalypt plantations in four provinces in Paraguay and collected material from diseased trees for identification of the causal agents. The samples were analyzed using a combination of morphological and molecular methods. Results: Diseases encountered included Botryosphaeria stem canker, Calonectria leaf blight, Chrysoporthe stem canker, myrtle/eucalypt rust, Coniella leaf spot, heartwood rot and Teratosphaeria stem canker. Contrary to expectations, the causal agent of Teratosphaeria stem canker was identified as Teratosphaeria zuluensis (M.J. Wingf., Crous & T.A. Cout.) M.J. Wingf. & Crous and not Teratosphaeria gauchensis (M.-N. Cortinas, Crous & M.J. Wingf.) M.J. Wingf. & Crous, that is commonly documented for the South American region. Conclusions: This study updates the knowledge on forest fungal pathogens in Paraguayan eucalypt plantations and is the first report of T. zuluensis in Paraguay and in South America

Ecg biometrics using deep learning and relative score threshold classification

PD/BDE/130216/2017The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.publishersversionpublishe