DetectA: abrupt concept drift detection in non-stationary environments

Almost all drift detection mechanisms designed for classification problems work reactively: after receiving the complete data set (input patterns and class labels) they apply a sequence of procedures to identify some change in the class-conditional distribution – a concept drift. However, detecting changes after its occurrence can be in some situations harmful to the process under analysis. This paper proposes a proactive approach for abrupt drift detection, called DetectA (Detect Abrupt Drift). Briefly, this method is composed of three steps: (i) label the patterns from the test set (an unlabelled data block), using an unsupervised method; (ii) compute some statistics from the train and test sets, conditioned to the given class labels for train set; and (iii) compare the training and testing statistics using a multivariate hypothesis test. Based on the results of the hypothesis tests, we attempt to detect the drift on the test set, before the real labels are obtained. A procedure for creating datasets with abrupt drift has been proposed to perform a sensitivity analysis of the DetectA model. The result of the sensitivity analysis suggests that the detector is efficient and suitable for datasets of high-dimensionality, blocks with any proportion of drifts, and datasets with class imbalance. The performance of the DetectA method, with different configurations, was also evaluated on real and artificial datasets, using an MLP as a classifier. The best results were obtained using one of the detection methods, being the proactive manner a top contender regarding improving the underlying base classifier accuracy

Neuroevolutionary learning in nonstationary environments

This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018 (GECCO '18

Advanced papillary serous carcinoma of the uterine cervix: a case with a remarkable response to paclitaxel and carboplatin combination chemotherapy

Papillary serous carcinoma of the uterine cervix (PSCC) is a very rare, recently described variant of cervical adenocarcinoma. This review, describes a case of stage IV PSCC whose main tumor existed in the uterine cervix and invaded one third of the inferior part of the anterior and posterior vaginal walls. Furthermore, it had metastasized from the para-aortic lymph nodes to bilateral neck lymph nodes. Immnoreactivity for CA125 was positive, whereas the staining for p53 and WT-1 were negative in both the original tumor and the metastatic lymph nodes. Six cycles of paclitaxel and carboplatin combination chemotherapy were administered and the PSCC dramatically decreased in size. The main tumor of the uterine cervix showed a complete response by magnetic resonance imaging (MRI), and on rebiopsy, more than 95% of the tumor cells in the cervix had microscopically disapperared. This is the first report of PSCC in which combination chemotherapy was used and showed a remarkable response

Overview and commentary of the CDEI's extended roadmap to an effective AI assurance ecosystem

In recent years, the field of ethical artificial intelligence (AI), or AI ethics, has gained traction and aims to develop guidelines and best practices for the responsible and ethical use of AI across sectors. As part of this, nations have proposed AI strategies, with the UK releasing both national AI and data strategies, as well as a transparency standard. Extending these efforts, the Centre for Data Ethics and Innovation (CDEI) has published an AI Assurance Roadmap, which is the first of its kind and provides guidance on how to manage the risks that come from the use of AI. In this article, we provide an overview of the document's vision for a “mature AI assurance ecosystem” and how the CDEI will work with other organizations for the development of regulation, industry standards, and the creation of AI assurance practitioners. We also provide a commentary of some key themes identified in the CDEI's roadmap in relation to (i) the complexities of building “justified trust”, (ii) the role of research in AI assurance, (iii) the current developments in the AI assurance industry, and (iv) convergence with international regulation

Small primary adenocarcinoma in adenomyosis with nodal metastasis: a case report

<p>Abstract</p> <p>Background</p> <p>Malignant transformation of adenomyosis is a very rare event. Only about 30 cases of this occurrence have been documented till now.</p> <p>Case presentation</p> <p>The patient was a 57-year-old woman with a slightly enlarged uterus, who underwent total hysterectomy and unilateral adnexectomy. On gross inspection, the uterine wall displayed a single nodule measuring 5 cm and several small gelatinous lesions. Microscopic examination revealed a common leiomyoma and multiple adenomyotic foci. A few of these glands were transformed into a moderately differentiated adenocarcinoma. The endometrium was completely examined and tumor free. The carcinoma was, therefore, considered to be an endometrioid adenocarcinoma arising from adenomyosis. Four months later, an ultrasound scan revealed enlarged pelvic lymph nodes: a cytological diagnosis of metastatic adenocarcinoma was made.</p> <p>Immunohistochemical studies showed an enhanced positivity of the tumor site together with the neighbouring adenomyotic foci for estrogen receptors, aromatase, p53 and COX-2 expression when compared to the distant adenomyotic glands and the endometrium. We therefore postulate that the neoplastic transformation of adenomyosis implies an early carcinogenic event involving p53 and COX-2; further tumor growth is sustained by an autocrine-paracrine loop, based on a modulation of hormone receptors as well as aromatase and COX-2 local expression.</p> <p>Conclusion</p> <p>Adenocarcinoma in adenomyosis may be affected by local hormonal influence and, despite its small size, may metastasize.</p

Estimation with Tc-99m tetrofosmin SPECT of salvaged myocardial mass after emergent reperfusion therapy in acute myocardial infarction.

The original publication is available at www.springerlink.com authorOBJECTIVES: The purpose of this study was to validate a new quantitative index of salvaged myocardial mass calculated from Tc-99m tetrofosmin SPECT for evaluating the therapeutic effect of emergent reperfusion therapy in acute myocardial infarction (AMI). METHODS: Tc-99m tetrofosmin SPECT was performed before and after emergent percutaneous transluminal coronary angioplasty (PTCA) in eight patients with AMI. In the pre-PTCA study, Tc-99m tetrofosmin was injected before emergent PTCA. Two weeks after the PTCA, post-PTCA study was performed. As a quantitative index of salvaged myocardial mass, salvaged myocardial volume (SMV) was defined as the difference of myocardial functional volume between the SPECT studies before and after the PTCA. To investigate the clinical significance of SMV, SMV was compared with the grade of therapeutic efficacy determined visually from pre- and post-PTCA SPECT images and clinical parameters, namely peak creatine phosphokinase level (pCK) and the time from the onset of the AMI to reperfusion (RPT). RESULTS: SMV showed a significant correlation with the visual grade of therapeutic efficacy (r = 0.737, p 6 hr) were 30.0 +/- 14.0 and -6.2 +/- 25.5 ml, showing a greater mean SMV value in the early-reperfusion group (p < 0.07). CONCLUSION: SMV could be used as a quantitative index of salvaged myocardial mass for evaluating the therapeutic effect of emergent reperfusion therapy

On the sui generis value capture of new digital technologies: the case of AI

Digital technologies are emerging at a fast rate, with applications ranging from farming to recruitment. Much of the research on these technologies has concerned optimization and applications, with less focus on the regulation and governance of these systems and how they might bring about foundational and theoretical shifts. Indeed, much of the literature is concerned with forwarding technical approaches, and the potential opportunities and harms, without offering theoretical or philosophical perspectives; few have asked what a digital thing is, what the ontological nature and state of phenomena produced and expressed by digital things are, and if there are distinctions between the conceptions of digital and non-digital technologies. We forward this discussion by investigating the question of what value is being expressed by an algorithm, which we conceptualize in terms of a digital asset, which we define as a valued digital thing that is derived from a particular digital technology

Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing

Cytogenetic alterations in ovarian clear cell carcinoma detected by comparative genomic hybridisation

Ovarian clear cell carcinoma (OCCC) accounts for a small but significant proportion of all ovarian cancers and is a distinct clinical and pathological entity. It tends to be associated with poorer response rates to chemotherapy and with a worse prognosis. Little is known about possible underlying genetic changes. DNA extracted from paraffin-embedded samples of 18 pure OCCC cases was analysed for genetic imbalances using comparative genomic hybridisation (CGH). All of the 18 cases showed genomic alterations. The mean number of alterations detected by CGH was 6 (range 1–15) indicating a moderate level of genetic instability. Chromosome deletions were more common than amplifications. The most prominent change involved chromosome 9 deletions in 10 cases (55%). This correlates with changes seen in other epithelial ovarian cancers. This deletion was confirmed using microsatellite markers to assess loss of heterozygosity (LOH) at four separate loci on chromosome 9. The most distinct region of loss detected was around the IFNA marker at 9p21 with 41% (11 out of 27cases) LOH. Other frequent deletions involved 1p (five out of 18; 28%); 11q (four out of 18; 22%) and 16 (five out of 18; 28%). Amplification was most common at chromosome 3 (six out of 18; 33%); 13q (four out of 18; 22%) and 15 (three out of 18; 17%). No high-level amplifications were identified. These features may serve as useful prognostic indicators in the management of OCCC