Adaptive Anomaly Detection via Self-Calibration and Dynamic Updating

The deployment and use of Anomaly Detection (AD) sensors often requires the intervention of a human expert to manually calibrate and optimize their performance. Depending on the site and the type of traffic it receives, the operators might have to provide recent and sanitized training data sets, the characteristics of expected traffic (i.e. outlier ratio), and exceptions or even expected future modifications of system's behavior. In this paper, we study the potential performance issues that stem from fully automating the AD sensors' day-to-day maintenance and calibration. Our goal is to remove the dependence on human operator using an unlabeled, and thus potentially dirty, sample of incoming traffic. To that end, we propose to enhance the training phase of AD sensors with a self-calibration phase, leading to the automatic determination of the optimal AD parameters. We show how this novel calibration phase can be employed in conjunction with previously proposed methods for training data sanitization resulting in a fully automated AD maintenance cycle. Our approach is completely agnostic to the underlying AD sensor algorithm. Furthermore, the self-calibration can be applied in an online fashion to ensure that the resulting AD models reflect changes in the system's behavior which would otherwise render the sensor's internal state inconsistent. We verify the validity of our approach through a series of experiments where we compare the manually obtained optimal parameters with the ones computed from the self-calibration phase. Modeling traffic from two different sources, the fully automated calibration shows a 7.08% reduction in detection rate and a 0.06% increase in false positives, in the worst case, when compared to the optimal selection of parameters. Finally, our adaptive models outperform the statically generated ones retaining the gains in performance from the sanitization process over time

Overdiagnosis and overtreatment of breast cancer: Progression of ductal carcinoma in situ: the pathological perspective

Ductal carcinoma in situ (DCIS) is encountered much more frequently in the screening population compared to the symptomatic setting. The behaviour of DCIS is highly variable and this presents difficulties in choosing appropriate treatment strategies for individual cases. This review discusses the current data on the frequency and rate of progression of DCIS, the value and limitations of clinicopathological and biological variables in predicting disease behaviour and suggests strategies to develop more robust means of predicting progression of DCIS

The occurrence of invasive cancers following a diagnosis of breast carcinoma in situ

Approximately 1 in every 600 women attending breast-screening programmes in the United Kingdom is diagnosed with breast carcinoma in situ (BCIS). However, there is little information on the occurrence of subsequent cancers (other than second breast cancers) in these women. We investigated the occurrence of invasive cancers in 12 836 women diagnosed with BCIS in southeast England between 1971 and 2003, using data from the Thames Cancer Registry. A greater than expected number of subsequent cancers was found for two sites: breast (standardised incidence ratio (SIR) 1.96; 95% confidence interval (CI) 1.79–2.14) and corpus uteri (SIR 1.42; 95% CI 1.11–1.78). For subsequent ipsilateral breast cancer in those treated with breast conservation, the excess was independent of the time since diagnosis of BCIS, whereas for subsequent contralateral breast cancer, there was a steady decline in excess over time. For subsequent uterine cancer, the excess became statistically significant only at >5 years after BCIS diagnosis, consistent with a treatment effect. This was further supported by Cox regression anaysis: the risk of subsequent uterine cancer was significantly increased in women receiving hormonal therapy compared with those not receiving it, with a hazard ratio of 2.97 (95% CI 1.84–4.80)

Should the surgeon or the general practitioner (GP) follow up patients after surgery for colon cancer? A randomized controlled trial protocol focusing on quality of life, cost-effectiveness and serious clinical events

This trial has been registered at ClinicalTrials.gov. The trial registration number is: NCT00572143.© 2008 Augestad et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Coffee intake and CYP1A2*1F genotype predict breast volume in young women: implications for breast cancer

As breast volume may be associated with heart cancer risk, we studied the relationship between breast volume, CYP1A2*1F and coffee intake. Among healthy premenopausal non-hormone users, 3+ cups per day was associated with lower volume only in C-allele carriers (Pinteraction=0.02), which is consistent with reports that coffee protects only C-allele carriers against breast cancer

Toward a theory of repeat purchase drivers for consumer services

The marketing discipline’s knowledge about the drivers of service customers’ repeat purchase behavior is highly fragmented. This research attempts to overcome that fragmented state of knowledge by making major advances toward a theory of repeat purchase drivers for consumer services. Drawing on means–end theory, the authors develop a hierarchical classification scheme that organizes repeat purchase drivers into an integrative and comprehensive framework. They then identify drivers on the basis of 188 face-to-face laddering interviews in two countries (USA and Germany) and assess the drivers’ importance and interrelations through a national probability sample survey of 618 service customers. In addition to presenting an exhaustive and coherent set of hierarchical repeat-purchase drivers, the authors provide theoretical explanations for how and why drivers relate to one another and to repeat purchase behavior. This research also tests the boundary conditions of the proposed framework by accounting for different service types. In addition to its theoretical contribution, the framework provides companies with specific information about how to manage long-term customer relationships successfully