In this study, we propose a model which may assist in diagnosis of Alzheimer’s disease (AD) using T1 weighted MRI brain images. The proposed model involves construction of statistical features from multiple trans-axial slices from hippocampus and amygdala regions, which play a significant role in AD diagnosis. Features from multiple slices are then averaged, which resulted into a smaller set of relevant features. The reduced set of features enhances the performance of decision learning system, and takes less memory and computation time. Effectiveness of the proposed model is compared with recent voxel-based-morphometry work in terms of sensitivity, specificity and accuracy. Experimental results on a publicly available MRI dataset showed that the proposed method outperforms the recent voxel-based-morphometry model.

Author identification is a subfield of Natural Language Processing (NLP) that uses machine learning techniques to identify the author of a text. Most previous research focused on long texts with the assumption that a minimum text length threshold exists under which author identification would no longer be effective. This paper examines author identification in short texts far below this threshold, focusing on messages retrieved from Twitter (maximum length: 140 characters) to determine the most effective feature set for author identification. Both Bag-of-Words (BOW) and Style Marker feature sets were extracted and evaluated through a series of 15 experiments involving up to 12 authors with large and small dataset sizes. Support Vector Machines (SVM) were used for all experiments. Our results achieve classification accuracies approaching that of longer texts, even for small dataset sizes of 60 training instances per author. Style Marker feature sets were found to be significantly more useful than BOW feature sets as well as orders of magnitude faster, and are therefore suggested for potential applications in future research.

One scenario that commonly arises in computer games and military training simulations is predator-prey pursuit in which the goal of the non-player character agent is to successfully intercept a fleeing player. In this paper, we focus on a variant of the problem in which the agent does not have perfect information about the player’s location but has prior experience in combating the player. Effectively addressing this problem requires a combination of learning the opponent’s tactics while planning an interception strategy. Although for small maps, solving the problem with standard POMDP (Partially Observable Markov Decision Process) solvers is feasible, increasing the search area renders many standard techniques intractable due to the increase in the belief state size and required plan length. Here we introduce a new approach for solving the problem on large maps that exploits key events, high reward regions in the belief state discovered at the higher level of abstraction, to plan efficiently over the low-level map. We demonstrate that our hierarchical key-events planner can learn intercept policies from traces of previous pursuits significantly faster than a standard point-based POMDP solver, particularly as the maps scale in size.

There is an estimated 150 million children worldwide living with a disability. For many of these children in the U.S., physical therapy is provided as an intervention mechanism to support the child’s academic, developmental, and functional goals from birth and beyond. Typically, for a physical therapy intervention to be adopted, there must be sufficient evidence-based practices showing the efficacy of the given method in use with the target demographic. With the recent advances in robotics, therapeutic intervention protocols using robots is ideally positioned to make an impact in this domain. Unfortunately, there has not yet been sufficient evidence-based research focused on the use of robots in child-based therapy to result in a full systematic review of this area. As such, in this paper we provide a review of the emerging role of robotics in pediatric therapy, with the goal of summarizing the research that could possibly transition into providing evidence on the efficacy of robotic therapeutic interventions for children.

We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA), i.e., it is a parallel stochastic optimization technique. Existing approaches [Kurihara et al. UAI2009, Sato et al. UAI2009] and cannot be applied to the CRP because their QA framework is formulated using a fixed number of mixture components. The proposed QA algorithm can handle an unfixed number of classes in mixture models. We applied QA to a DPM model for clustering vertices in a network where a CRP seating arrangement indicates a network partition. A multi core processor was used for running QA in experiments, the results of which show that QA is better than SA, Markov chain Monte Carlo inference, and beam search at finding a maximum a posteriori estimation of a seating arrangement in the CRP. Since our QA algorithm is as easy as to implement the SA algorithm, it is suitable for a wide range of applications.

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining, etc. This article aims to provide a comprehensive survey and a structural understanding of published online portfolio selection techniques. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and "Meta-Learning Algorithms". In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the Capital Growth theory in order to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state-of-the-art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research directions.

Based on integrated infrastructure of resource sharing and computing in distributed environment, cloud computing involves the provision of dynamically scalable and provides virtualized resources as services over the Internet. These applications also bring a large scale heterogeneous and distributed information which pose a great challenge in terms of the semantic ambiguity. It is critical for application services in cloud computing environment to provide users intelligent service and precise information. Semantic information processing can help users deal with semantic ambiguity and information overload efficiently through appropriate semantic models and semantic information processing technology. The semantic information processing have been successfully employed in many fields such as the knowledge representation, natural language understanding, intelligent web search, etc. The purpose of this report is to give an overview of existing technologies for semantic information processing in cloud computing environment, to propose a research direction for addressing distributed semantic reasoning and parallel semantic computing by exploiting semantic information newly available in cloud computing environment.

The advent of high throughput technologies in biology has opened novel opportunities to investigate biological processes from a comprehensive point of view. At the same time, the noisy and high dimensional nature of these data sets gives rise to formidable statistical challenges, and has led to systems biology becoming a fertile area for machine learning applications, as well as a motivation for novel modelling methodologies. In this paper, we are interested in jointly modelling mRNA measurements (transcrip-tomics) together with metabolite measurements in order to provide a platform for understanding the chemical regulation of gene expression. From the statistical perspective, this is naturally addressed using a latent variables framework: mRNA transcription is known to be controlled by the activation state of a class of proteins, transcription factors (TFs), which mediate metabolic signals through fast conformational changes (Alon, 2006). However, due to their fast dynamic and often low concentrations, TFs are particularly difficult to assay experimentally, leading to the need for statistical inference methodologies (Asif & Sanguinetti, 2011; Shi et al., 2008). Here, we adopt a model of transcriptional regulation which is based on a binary representation of transcription factor states, a Factorial Hidden Markov Model (FHMM).

Many feature subset selection (FSS) algorithms have been proposed, but not all of them are appropriate for a given feature selection problem. At the same time, so far there is rarely a good way to choose appropriate FSS algorithms for the problem at hand. Thus, FSS algorithm automatic recommendation is very important and practically useful. In this paper, a meta learning based FSS algorithm automatic recommendation method is presented. The proposed method first identifies the data sets that are most similar to the one at hand by the k-nearest neighbor classification algorithm, and the distances among these data sets are calculated based on the commonly-used data set characteristics. Then, it ranks all the candidate FSS algorithms according to their performance on these similar data sets, and chooses the algorithms with best performance as the appropriate ones. The performance of the candidate FSS algorithms is evaluated by a multi-criteria metric that takes into account not only the classification accuracy over the selected features, but also the runtime of feature selection and the number of selected features. The proposed recommendation method is extensively tested on 115 real world data sets with 22 well-known and frequently-used different FSS algorithms for five representative classifiers. The results show the effectiveness of our proposed FSS algorithm recommendation method.

We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process.