Complex operational environments require improved tactical mission command capabilities with a high level of interoperability among coalition control and command (C2) systems. This paper focuses on two areas of interest: decision support based on automated planning and Service Oriented Architecture (SOA) for rapid service development. Previous experiments were performed bilaterally by US, France and Germany to focus on collaborative mission planning using Web Services (WSs). The results reported herein were obtained from a unified experiment performed by US, France and Germany involving a common scenario. The operational benefit from the experimentation has been to improve mutual understanding among allied forces, to dynamically plan for assistance among ground support troops (logistics, MEDE- VAC, and other areas) as well as to improve their coordination. The effort addressed system design, and integration within an experimental framework. It enabled the evolution of the CERDEC Mission Command Gateway (MCG) architecture as well as a constraint based planner ”ORTAC”, developed by French DGA and Sagem. It takes into account near real-time multimodal Situation Awareness and readiness status from tactical edge units. The trilateral experiment, entitled From Data to Decision included Net-Centric manned and unmanned assets from all three nations (France - Germany - US) operating as a cohesive coalition force while preserving command and support relationships as required through their respective chains of command.

Hail causes billions of dollars in losses by damaging buildings, vehicles, and crops. Improving the spatial and temporal accuracy of hail forecasts would allow people to mitigate hail damage. We have developed an approach to forecasting hail that identifies potential hail storms in storm-scale numerical weather prediction models and matches them with observed hailstorms. Machine learning models, including random forests, gradient boosting trees, and linear regression, are used to predict the expected hail size from each forecast storm. The individual hail size forecasts are merged with a spatial neighborhood ensemble probability technique to produce a consensus probability of hail at least 25.4 mm in diameter. The system was evaluated during the 2014 National Oceanic and Atmospheric Administration Hazardous Weather Testbed Experimental Forecast Program and compared with a physics-based hail size model. The machine-learning-based technique shows advantages in producing smaller size errors and more reliable probability forecasts. The machine learning approaches correctly predicted the location and extent of a significant hail event in eastern Nebraska and a marginal severe hail event in Colorado.

We describe a tool, MatchMaps, that generates sameAs and partOf matches between spatial objects (such as shops, shopping centres, etc.) in crowd-sourced and authoritative geospatial datasets. MatchMaps uses reasoning in qualitative spatial logic, description logic and truth maintenance techniques, to produce a consistent set of matches. We report the results of an initial evaluation of MatchMaps by experts from Ordnance Survey (Great Britain's National Mapping Authority). In both the case studies considered, MatchMaps was able to correctly match spatial objects (high precision and recall) with minimal human intervention.

Identifying a patient’s important medical problems requires broad and deep medical expertise, as well as significant time to gather all the relevant facts from the patient’s medical record and assess the clinical importance of the facts in reaching the final conclusion. A patient’s medical problem list is by far the most critical information that a physician uses in treatment and care of a patient. In spite of its critical role, its curation, manual or automated, has been an unmet need in clinical practice. We developed a machine learning technique in IBM Watson to automatically generate a patient’s medical problem list. The machine learning model uses lexical and medical features extracted from a patient’s record using NLP techniques. We show that the automated method achieves 70% recall and 67% precision based on the gold standard that medical experts created on a set of de-identified patient records from a major hospital system in the US. To the best of our knowledge this is the first successful machine learning/NLP method of extracting an open-ended patient’s medical problems from an Electronic Medical Record (EMR). This paper also contributes a methodology for assessing accuracy of a medical problem list generation technique.

Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points. The objective is to produce a classifier of relatively low risk, as measured under the 0-1 loss, ideally using fewer label requests than the number of random labeled data points sufficient to achieve the same. This work investigates the potential uses of surrogate loss functions in the context of active learning. Specifically, it presents an active learning algorithm based on an arbitrary classification-calibrated surrogate loss function, along with an analysis of the number of label requests sufficient for the classifier returned by the algorithm to achieve a given risk under the 0-1 loss. Interestingly, these results cannot be obtained by simply optimizing the surrogate risk via active learning to an extent sufficient to provide a guarantee on the 0-1 loss, as is common practice in the analysis of surrogate losses for passive learning. Some of the results have additional implications for the use of surrogate losses in passive learning.

Many high dimensional sparse learning problems are formulated as nonconvex optimization. A popular approach to solve these nonconvex optimization problems is through convex relaxations such as linear and semidefinite programming. In this paper, we study the statistical limits of convex relaxations. Particularly, we consider two problems: Mean estimation for sparse principal submatrix and edge probability estimation for stochastic block model. We exploit the sum-of-squares relaxation hierarchy to sharply characterize the limits of a broad class of convex relaxations. Our result shows statistical optimality needs to be compromised for achieving computational tractability using convex relaxations. Compared with existing results on computational lower bounds for statistical problems, which consider general polynomial-time algorithms and rely on computational hardness hypotheses on problems like planted clique detection, our theory focuses on a broad class of convex relaxations and does not rely on unproven hypotheses.

We consider two closely related problems: planted clustering and submatrix localization. The planted clustering problem assumes that a random graph is generated based on some underlying clusters of the nodes; the task is to recover these clusters given the graph. The submatrix localization problem concerns locating hidden submatrices with elevated means inside a large real-valued random matrix. Of particular interest is the setting where the number of clusters/submatrices is allowed to grow unbounded with the problem size. These formulations cover several classical models such as planted clique, planted densest subgraph, planted partition, planted coloring, and stochastic block model, which are widely used for studying community detection and clustering/bi-clustering. For both problems, we show that the space of the model parameters (cluster/submatrix size, cluster density, and submatrix mean) can be partitioned into four disjoint regions corresponding to decreasing statistical and computational complexities: (1) the \emph{impossible} regime, where all algorithms fail; (2) the \emph{hard} regime, where the computationally expensive Maximum Likelihood Estimator (MLE) succeeds; (3) the \emph{easy} regime, where the polynomial-time convexified MLE succeeds; (4) the \emph{simple} regime, where a simple counting/thresholding procedure succeeds. Moreover, we show that each of these algorithms provably fails in the previous harder regimes. Our theorems establish the minimax recovery limit, which are tight up to constants and hold with a growing number of clusters/submatrices, and provide a stronger performance guarantee than previously known for polynomial-time algorithms. Our study demonstrates the tradeoffs between statistical and computational considerations, and suggests that the minimax recovery limit may not be achievable by polynomial-time algorithms.

We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability $p_{c}$ per round. The agents and player choose one of three options: (1) Exploit (a good bandit), (2) Innovate (asocial learning for a good bandit among $n_{I}$ randomly chosen bandits), and (3) Observe (social learning for a good bandit). Each agent has two parameters $(c,p_{obs})$ to specify the decision: (i) $c$, the threshold value for Exploit, and (ii) $p_{obs}$, the probability for Observe in learning. The parameters $(c,p_{obs})$ are uniformly distributed. We determine the optimal strategies for the player using complete knowledge about the rMAB. We show whether or not social or asocial learning is more optimal in the $(p_{c},n_{I})$ space and define the swarm intelligence effect. We conduct a laboratory experiment (67 subjects) and observe the swarm intelligence effect only if $(p_{c},n_{I})$ are chosen so that social learning is far more optimal than asocial learning.

The joint problem of time segmentation and recognition of sequential data into meaningful subsequences has attracted significant research in a variety of domains. The ability to automatically segment and classify data is a core technology for applications like speaker diarisation, finance, activity understanding, multimedia annotation and human-computer interaction. To date, the main proposed solutions have included sliding windows [1], the hidden Markov model (HMM) [2], conditional random fields [3] [4], and structural SVM [5], covering the spectrum of generative, discriminative and maximum-margin dynamic classifiers. Along with advancements in learning and inference, research has witnessed increasingly realistic datasets which are bridging the gap between lab and real applications [6] [7]. Nevertheless, important challenges such as model adaptation and dynamic class sets remain unresolved. We address both these limitations by an adaptive online model that can accommodate an unlimited (theoretically infinite) number of classes. In a nutshell, this is achieved by applying a Bayesian nonparametric model, the hierarchical Dirichlet process (HDP), as the prior for a hidden Markov model (a model known as HDP-HMM [8] [9]), and exploiting an adaptive learning rate for model adaptation. The proposed model provides an adaptive online learning approach for joint segmentation and recognition of sequential data 1 with incremental class sets and we refer to it as ADON HDP-HMM in the following.

We study how well one can recover sparse principal components of a data matrix using a sketch formed from a few of its elements. We show that for a wide class of optimization problems, if the sketch is close (in the spectral norm) to the original data matrix, then one can recover a near optimal solution to the optimization problem by using the sketch. In particular, we use this approach to obtain sparse principal components and show that for \math{m} data points in \math{n} dimensions, \math{O(\epsilon^{-2}\tilde k\max\{m,n\})} elements gives an \math{\epsilon}-additive approximation to the sparse PCA problem (\math{\tilde k} is the stable rank of the data matrix). We demonstrate our algorithms extensively on image, text, biological and financial data. The results show that not only are we able to recover the sparse PCAs from the incomplete data, but by using our sparse sketch, the running time drops by a factor of five or more.