A New Jersey woman is alive because her Apple Watch alerted her to an elevated heart rate. It turned out she had fluid around her heart from a viral infection. Medical alert systems have been around for some time. Often, they're wearable devices that can detect when you fall, and alert emergency personnel if it senses you aren't responding. But what happens if you aren't wearing a device, or if you aren't experiencing any triggering signs or symptoms of a medical emergency at all?

One of the primary concerns of product quality control in the automotive industry is an automated detection of defects of small sizes on specular car body surfaces. A new statistical learning approach is presented for surface finish defect detection based on spline smoothing method for feature extraction and $k$-nearest neighbour probabilistic classifier. Since the surfaces are specular, structured lightning reflection technique is applied for image acquisition. Reduced rank cubic regression splines are used to smooth the pixel values while the effective degrees of freedom of the obtained smooths serve as components of the feature vector. A key advantage of the approach is that it allows reaching near zero misclassification error rate when applying standard learning classifiers. We also propose probability based performance evaluation metrics as alternatives to the conventional metrics. The usage of those provides the means for uncertainty estimation of the predictive performance of a classifier. Experimental classification results on the images obtained from the pilot system located at Volvo GTO Cab plant in Ume{\aa}, Sweden, show that the proposed approach is much more efficient than the compared methods.

Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience history, making this biological semantic machinery noisy with respect to the overall semantics inherent to media artifacts, such as music and language excerpts. We propose to represent shared semantics using low-dimensional vector embeddings by jointly modeling several brains from human subjects. We show these unsupervised efficient representations outperform the original high-dimensional fMRI voxel spaces in proxy music genre and language topic classification tasks. We further show that joint modeling of several subjects increases the semantic richness of the learned latent vector spaces.

Detection of malicious behavior is a fundamental problem in security. One of the major challenges in using detection systems in practice is in dealing with an overwhelming number of alerts that are triggered by normal behavior (the so-called false positives), obscuring alerts resulting from actual malicious activity. While numerous methods for reducing the scope of this issue have been proposed, ultimately one must still decide how to prioritize which alerts to investigate, and most existing prioritization methods are heuristic, for example, based on suspiciousness or priority scores. We introduce a novel approach for computing a policy for prioritizing alerts using adversarial reinforcement learning. Our approach assumes that the attackers know the full state of the detection system and dynamically choose an optimal attack as a function of this state, as well as of the alert prioritization policy. The first step of our approach is to capture the interaction between the defender and attacker in a game theoretic model. To tackle the computational complexity of solving this game to obtain a dynamic stochastic alert prioritization policy, we propose an adversarial reinforcement learning framework. In this framework, we use neural reinforcement learning to compute best response policies for both the defender and the adversary to an arbitrary stochastic policy of the other. We then use these in a double-oracle framework to obtain an approximate equilibrium of the game, which in turn yields a robust stochastic policy for the defender. Extensive experiments using case studies in fraud and intrusion detection demonstrate that our approach is effective in creating robust alert prioritization policies.

The research was led by Justin Chan, a PhD student in the department of computer science and engineering. Almost 500,000 Americans die each year from a cardiac arrest, the researchers wrote in the journal npj Digital Medicine. And the condition kills 100,000 Britons annually, according to Arrhythmia Alliance. Study author Dr Jacob Sunshine, assistant professor of anesthesiology and pain medicine, said: 'Cardiac arrests are a very common way for people to die and right now many of them can go unwitnessed. 'Part of what makes this technology so compelling is that it could help us catch more patients in time for them to be treated.'

Published today in the peer-reviewed journal Radiology, an IBM Research team created a new artificial intelligence (AI) model that can predict breast cancer malignancy and identify normal digital mammography exams as accurately as radiologists. Mammography, a low-dose x-ray procedure to image breasts, is considered the best breast cancer screening test available according to the American Cancer Society. However, mammograms are not always accurate. According to a U.S. 10-year study published in the New England Journal of Medicine, 23.8 percent of study participants had at least one false positive mammogram where breast cancer was not actually present. Furthermore, the American Cancer Society estimates that one in five screening mammograms are false-negatives that fail to detect existing breast cancer.

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 128 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.

With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges. Existing works focusing on protecting against membership inference attacks under the rigorous notion of differential privacy are susceptible to attribute inference attacks. In this paper, we develop a theoretical framework for task-specific privacy under the attack of attribute inference. Under our framework, we propose a minimax optimization formulation with a practical algorithm to protect a given attribute and preserve utility. We also extend our formulation so that multiple attributes could be simultaneously protected. Theoretically, we prove an information-theoretic lower bound to characterize the inherent tradeoff between utility and privacy when they are correlated. Empirically, we conduct experiments with real-world tasks that demonstrate the effectiveness of our method compared with state-of-the-art baseline approaches.

In this paper, we introduce two new directed graphical models from Gaussian data: the Gaussian graphical interaction model (GGIM) and the Gaussian graphical conditional expectation model (GGCEM). The development of these models comes from considering stationary Gaussian processes on graphs, and leveraging the equations between the resulting steady-state covariance matrix and the Laplacian matrix representing the interaction graph. Through the presentation of conceptually straightforward theory, we develop the new models and provide interpretations of the edges in each graphical model in terms of statistical measures. We show that when restricted to undirected graphs, the Laplacian matrix representing a GGIM is equivalent to the standard inverse covariance matrix that encodes conditional dependence relationships. We demonstrate that the problem of learning sparse GGIMs and GGCEMs for a given observation set can be framed as a LASSO problem. By comparison with the problem of inverse covariance estimation, we prove a bound on the difference between the covariance matrix corresponding to a sparse GGIM and the covariance matrix corresponding to the $l_1$-norm penalized maximum log-likelihood estimate. In all, the new models present a novel perspective on directed relationships between variables and significantly expand on the state of the art in Gaussian graphical modeling.

Optimal transport (OT)-based methods have a wide range of applications and have attracted a tremendous amount of attention in recent years. However, most of the computational approaches of OT do not learn the underlying transport map. Although some algorithms have been proposed to learn this map, they rely on kernel-based methods, which makes them prohibitively slow when the number of samples increases. Here, we propose a way to learn an approximate transport map and a parametric approximation of the Wasserstein barycenter. We build an approximated transport mapping by leveraging the closed-form of Gaussian (Bures-Wasserstein) transport; we compute local transport plans between matched pairs of the Gaussian components of each density. The learned map generalizes to out-of-sample examples. We provide experimental results on simulated and real data, comparing our proposed method with other mapping estimation algorithms. Preliminary experiments suggest that our proposed method is not only faster, with a factor 80 overall running time, but it also requires fewer components than state-of-the-art methods to recover the support of the barycenter. From a practical standpoint, it is straightforward to implement and can be used with a conventional machine learning pipeline.