An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31 percent of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-of-the-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed an image at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming and prone to human error. We are building a system that, when complete, will provide interactive 3D visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk for thrombosis. In this paper, we describe our approach, focusing on machine learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.

Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay and DropOut) and a newly introduced regularization method based on function norms. Commonly used methods can fail when only a small database is available. The use of a function norm introduces a direct control over the complexity of the function with the aim of diminishing the risk of overfitting. Results: As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. Conclusion: The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function norm regularization yielded higher and more robust results than competing methods. Significance: We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a 58\% reduction. The method is computationally feasible for intraoperative application (less than 2 seconds per image).

Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as labeling is a difficult, costly and time consuming activity. On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms. The inefficacy of the unsupervised techniques stems from the exclusion of the characteristics of the learned classifier, from the detection process. In this paper, we propose the Margin Density Drift Detection (MD3) algorithm, which tracks the number of samples in the uncertainty region of a classifier, as a metric to detect drift. The MD3 algorithm is a distribution independent, application independent, model independent, unsupervised and incremental algorithm for reliably detecting drifts from data streams. Experimental evaluation on 6 drift induced datasets and 4 additional datasets from the cybersecurity domain demonstrates that the MD3 approach can reliably detect drifts, with significantly fewer false alarms compared to unsupervised feature based drift detectors. The reduced false alarms enables the signaling of drifts only when they are most likely to affect classification performance. As such, the MD3 approach leads to a detection scheme which is credible, label efficient and general in its applicability.

Will The Undertaker retire after WrestleMania 33? It's a question many WWE fans are asking ahead of the Deadman's match with Roman Reigns at the biggest wrestling event of 2017. The Undertaker, 52, made his WWE debut in 1990, and he's been the one constant in the company ever since. He has a 23-1 record at WrestleMania, and he's had a match at the PPV in every year since 2001. But questions surrounding the Deadman's age and health have a lot of people wondering if Sunday night could be the last time fans get to see The Undertaker compete in a WWE ring. "He just had hip surgery. He's beaten down real bad. Put it this way--last year when he went to the dressing room after his match, he told everyone he was done," Dave Meltzer of The Wrestling Observer told Richard Deitsch on last week's "Sports Illustrated Media Podcast."

Artificial neural networks (ANNs) have gained a well-deserved popularity among machine learning tools upon their recent successful applications in image- and sound processing and classification problems. ANNs have also been applied for predicting the family or function of a protein, knowing its residue sequence. Here we present two new ANNs with multi-label classification ability, showing impressive accuracy when classifying protein sequences into 698 UniProt families (AUC=99.99%) and 983 Gene Ontology classes (AUC=99.45%).

WrestleMania 33 is easily WWE's biggest event of 2017, and it will be reflected in the length of Sunday's show. Between the kickoff show and the actual pay-per-view, the 13 matches on the card could span more than six hours. In order to catch every second of the action, fans should tune into WWE Network at 5 p.m. EDT when the kickoff show begins. The second hour of the kickoff show moves over to TV on USA at 6 p.m. EDT, and the PPV has a start time of 7 p.m. EDT. WWE PPVs usually end before 11 p.m. EDT (most start at 8 p.m. EDT), but that might not be the case Sunday night at Camping World Stadium in Orlando.

After months of rumors regarding WWE's biggest event of 2017, the WrestleMania 33 card is finally set. Thirteen matches are scheduled for Sunday's pay-per-view at Camping World Stadium in Orlando, and eight titles will be on the line. The WWE Universal Championship Match between Brock Lesnar and Goldberg is expected to go on last, putting an end to their feud that began with Goldberg's return in November. It seems to be clear which WWE superstar is winning the main event, but predictions for other matches aren't as easy to make. The SmackDown Tag Team Championships are the only belts that won't be defended at WrestleMania 33.

AI is helping cybersecurity but researchers are warning about over-promising. Cybersecurity pros shouldn't rely on artificial intelligence and machine learning just yet, according to a new report. The report from security firm Carbon Black, which surveyed 410 cybersecurity researchers and 74 percent said that AI-driven security solutions are flawed, citing "high false-positive rates", while 70 percent claimed attackers can bypass machine learning techniques. The respondents did not write off AI or machine learning as unhelpful but rather said that they just aren't there yet and cannot be solely relied on to make big decisions when it comes to security. AI and machine learning should be used "primarily to assist and augment human decision making," said the report.

A new machine-learning based security solution from IBM could help businesses detect phishing sites up to 250% faster than other methods. Announced via a blog post on Monday, the cognitive phishing detection feature is part of the IBM Security Trusteer platform. When it comes to hacking, phishing is one of the oldest tricks in the book. It has stayed around for so long, in part, because it still works. According to IBM Security research cited in the post, some 30% of phishing emails are opened by targeted recipients. Phishing works well because it capitalizes on the fact that humans are typically the weakest link in an organization's cybersecurity.

Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times of notes and to recognize the corresponding instrument sources. The aim of this study is to explore the usefulness of multiscale scattering operators for these two tasks on plucked string instrument and piano music. After resuming the theoretical background and illustrating the key features of this sound representation method, we evaluate its performances comparatively to other classical sound representations. Using both MIDI-driven datasets with real instrument samples and real musical pieces, scattering is proved to outperform other sound representations for these AMT subtasks, putting forward its richer sound representation and invariance properties.