Experts discuss a medical ecosystem where people, policies and technology interconnect, allowing doctors to administer precise treatment to one patient -- the N of 1 -- and then deliver it to everyone. The old "take two aspirin and call me in the morning" cure is antithetical to today's precision medicine approach to healthcare, which zeroes in on an individual's unique genetic makeup, environment and lifestyle to determine an exacting treatment. Traditional healthcare is broadly based on cohort studies that compare risk factors and outcomes for a large set of patients who share similar symptoms. Cohort studies are more credible when the "N" -- the number of patients included in the study -- is very large. That's diametrically at odds with the goal of precision medicine, which aims to find a treatment plan tailored for a particular person: "The N of 1."

Powell High School teacher Jimmy Waters stands in front of the San Jose Semaphore in California. Waters cracked a code broadcast by the building that had been unsolved for more than four years. Jimmy Waters, 31, teaches advanced algebra, geometry and trigonometry at Powell High School. Last summer, Waters devoted a month of his vacation time to solving the puzzle presented by the San Jose Semaphore, a project created by New York-based artist Ben Rubin in 2006. Adobe planned on rewarding Waters with a one-year subscription to its Creative Cloud software, but at his request, the company is donating 40 one-year subscriptions to Powell High School's computer lab, on top of a 3-D printer "to help the students push the boundaries of creativity even further," according to a company news release.

DeepMind, the London-based artificial intelligence company owned by Alphabet Inc., claims it overcame a key limitation affecting one of the most promising machine learning technologies: the software's inability to remember. The breakthrough, described in a paper published Tuesday in the academic journal Proceedings of the National Academy of Sciences, may open the way for artificial intelligence systems to be more easily applied to multiple tasks, instead of being narrowly trained for one purpose. It should also improve the ability of AI systems to transfer knowledge between tasks and to master a sequence of linked steps. Neural networks, software which is loosely based on the structure of synapses in the human brain, are considered the best machine learning technique for language translation, image classification and image generation. But these networks suffer from a major flaw scientists call "catastrophic forgetting." They exist in a kind of perpetual present: every time the network is given new data, it overwrites what it has previously learned.

Certain types of driverless vehicles may not be safe, peers have warned. Over-reliance on technology could mean drivers react slowly to taking back control of a semi-autonomous vehicle in an emergency, they said. However, the Lords Science and Technology Committee noted that some technology could reduce accidents caused by human error. The Department for Transport said driverless cars "have the potential to transform the way we travel." Vehicles can be split into different levels of automation, according to industry body the Society of Motor Manufacturers and Traders (SMMT).

There are three equivalent ways of representing two jointly observed real-valued signals: as a bivariate vector signal, as a single complex-valued signal, or as two analytic signals known as the rotary components. Each representation has unique advantages depending on the system of interest and the application goals. In this paper we provide a joint framework for all three representations in the context of frequency-domain stochastic modeling. This framework allows us to extend many established statistical procedures for bivariate vector time series to complex-valued and rotary representations. These include procedures for parametrically modeling signal coherence, estimating model parameters using the Whittle likelihood, performing semi-parametric modeling, and choosing between classes of nested models using model choice. We also provide a new method of testing for impropriety in complex-valued signals, which tests for noncircular or anisotropic second-order statistical structure when the signal is represented in the complex plane. Finally, we demonstrate the usefulness of our methodology in capturing the anisotropic structure of signals observed from fluid dynamic simulations of turbulence.

Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario suffers from a tunnel vision effect: without recourse to independent sampling, the urge to query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of AS methods and standard classifiers. We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried next. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. From these observations we propose D3TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, matching or exceeding the performance of competing methods on seven real network datasets in our evaluation.

Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate. COPD can be quantified by classifying patches of computed tomography images, and combining patch labels into an overall diagnosis for the image. As labeled patches are often not available, image labels are propagated to the patches, incorrectly labeling healthy patches in COPD patients as being affected by the disease. We approach quantification of COPD from lung images as a multiple instance learning (MIL) problem, which is more suitable for such weakly labeled data. We investigate various MIL assumptions in the context of COPD and show that although a concept region with COPD-related disease patterns is present, considering the whole distribution of lung tissue patches improves the performance. The best method is based on averaging instances and obtains an AUC of 0.742, which is higher than the previously reported best of 0.713 on the same dataset. Using the full training set further increases performance to 0.776, which is significantly higher (DeLong test) than previous results.

This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the statistical model is learned from the data. We show that this omission leads to a systematic underestimation of the prediction errors. Starting from a generalization of the Cram\'er-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes. The improved bound is easily computed and we illustrate it using synthetic and real data examples. of uncertainty for prediction of Gaussian processes and illustrate it using synthetic and real data examples.

In this study, we introduce a new approach to combine multi-classifiers in an ensemble system. Instead of using numeric membership values encountered in fixed combining rules, we construct interval membership values associated with each class prediction at the level of meta-data of observation by using concepts of information granules. In the proposed method, uncertainty (diversity) of findings produced by the base classifiers is quantified by interval-based information granules. The discriminative decision model is generated by considering both the bounds and the length of the obtained intervals. We select ten and then fifteen learning algorithms to build a heterogeneous ensemble system and then conducted the experiment on a number of UCI datasets. The experimental results demonstrate that the proposed approach performs better than the benchmark algorithms including six fixed combining methods, one trainable combining method, AdaBoost, Bagging, and Random Subspace.

Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated $k_0$ or noise variance $\sigma^2$, both of which are unavailable in many practical applications. In this article we develop a modified version of OMP called tuning free OMP or TF-OMP which does not require a SC. TF-OMP is proved to accomplish successful sparse recovery under the usual assumptions on restricted isometry constants (RIC) and mutual coherence of design matrix. TF-OMP is numerically shown to deliver a highly competitive performance in comparison with OMP having \textit{a priori} knowledge of $k_0$ or $\sigma^2$. Greedy algorithm for robust de-noising (GARD) is an OMP like algorithm proposed for efficient estimation in classical overdetermined linear regression models corrupted by sparse outliers. However, GARD requires the knowledge of inlier noise variance which is difficult to estimate. We also produce a tuning free algorithm (TF-GARD) for efficient estimation in the presence of sparse outliers by extending the operating principle of TF-OMP to GARD. TF-GARD is numerically shown to achieve a performance comparable to that of the existing implementation of GARD.