Every "SmackDown Live" title will be defended Sunday night at WWE Clash of Champions 2017, the final pay-per-view of the calendar year. The wrestling year, however, is far from over, with a little less than four months remaining until WrestleMania 34. Clash of Champions is also the last WWE PPV before the 2018 Royal Rumble, which marks the unofficial beginning of the road to WrestleMania. It all starts at 7 p.m. EST with the pre-show, followed by the actual PPV at 8 p.m. EST. The entire event can be seen with a live stream online by using WWE Network, which costs $9.99 per month.

A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online Dec. 12 in the Journal of the American Medical Association. Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images.

I had a working, short script that took 3 1/2 minutes to run. While this may be fine if you only need to run it once, I needed to run it hundreds of time for simulations. My first attempt to do so ended about four hours after I started the code, with 400 simulations left to go, and I knew I needed to get some help. This post documents the iterative process of improving the performance of the function, culminating in a runtime of .64 seconds for 10,000 iterations, a speed-up of more than 100,000x. At Etsy I work a lot on our A/B Testing system.

We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error. Our approach builds off of techniques for distributionally robust optimization and Owen's empirical likelihood, and we provide a number of finite-sample and asymptotic results characterizing the theoretical performance of the estimator. In particular, we show that our procedure comes with certificates of optimality, achieving (in some scenarios) faster rates of convergence than empirical risk minimization by virtue of automatically balancing bias and variance. We give corroborating empirical evidence showing that in practice, the estimator indeed trades between variance and absolute performance on a training sample, improving out-of-sample (test) performance over standard empirical risk minimization for a number of classification problems.

Compliance organizations within banks and other financial institutions are turning to machine learning for improving their AML compliance programs. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. Automated machine learning provides a solution to address this challenge. In this webinar, Justin Dickerson, General Manager of Global Finance for DataRobot, and Dan Yelle, a Customer-Facing Data Scientist for DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.

The final pay-per-view of the year is set for Sunday night with WWE Clash of Champions 2017. The night's biggest matches feature the likes of AJ Styles, Kevin Owens and Randy Orton. Four championships will be defended on the card. Below are predictions for every match on the WWE Clash of Champions card, which features wrestlers from "SmackDown Live." The Jinder Mahal experiment is over.

Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images. The researchers found that the AUC of the DLS for referable diabetic retinopathy was 0.936, and sensitivity and specificity were 90.5 and 91.6 percent, respectively.

One of the toughest aspects of having epilepsy is not knowing when the next seizure will strike. A wearable warning system that detects pre-seizure brain activity and alerts people of its onset could alleviate some of that stress and make the disorder more manageable. To that end, IBM researchers say they have developed a portable chip that can do the job; they described their invention today in the Lancet's open access journal eBioMedicine. The scientists built the system on a mountain of brainwave data collected from epilepsy patients. The dataset, reported by a separate group in 2013, included over 16 years of continuous electroencephalography (EEG) recordings of brain activity, and thousands of seizures, from patients who had had electrodes surgically implanted in their brains.

Detecting outliers has become an increasing challenge in many areas, such as network intrusion detection, fraud detection, medical anomaly detection, and failure detection, as it was described by Chandola [1]. An outlier is basically a data that is significantly different from the normal behavior. In addition, several anomalies do not necessarily exhibit similar characteristics. Hence, detecting anomalies must be done by defining the normal behavior in the first place. Then, the deviation measured between an individual and the normal behavior gives good indications of anomalousness. However, as noticed in the same paper [1], defining a normal region that encompasses all the possible normal behaviors is sometimes really difficult. Moreover, an anomaly does not appear necessarily on all the explanatory variables, especially when the data is high-dimensional.

In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated when used in conjunction with the lasso and orthogonal matching pursuit algorithms. In this paper, we investigate the applicability of stability selection to structured selection algorithms: the group lasso and the structured input-output lasso. We find that using stability selection often increases the power of both algorithms, but that the presence of complex structure reduces the reliability of error control under stability selection. We give strategies for setting tuning parameters to obtain a good model size under stability selection, and highlight its strengths and weaknesses compared to competing methods screen and clean and cross-validation. We give guidelines about when to use which error control method.