The efficiency of deep machine learning for automatic delineation of tumor areas has been demonstrated for intraoperative neuronavigation using active IR-mapping with the use of the cold test. The proposed approach employs a matrix IR-imager to remotely register the space-time distribution of surface temperature pattern, which is determined by the dynamics of local cerebral blood flow. The advantages of this technique are non-invasiveness, zero risks for the health of patients and medical staff, low implementation and operational costs, ease and speed of use. Traditional IR-diagnostic technique has a crucial limitation - it involves a diagnostician who determines the boundaries of tumor areas, which gives rise to considerable uncertainty, which can lead to diagnosis errors that are difficult to control. The current study demonstrates that implementing deep learning algorithms allows to eliminate the explained drawback.

--Owing to the rapid growth of touchscreen mobile terminals and pen-based interfa ces, handwriting-based writer identification systems are attracting increasing attention for personal authentication and digital forensics. However, most studies on writer identification have not been satisfying because of the insufficiency of data and th e difficulty of designing good features for various conditions of handwriting samples. Hence, we introduce an end-to-end system called DeepWriterID that employs a deep convolutional neural network (CNN) to address these problems. A key feature of DeepWriterID is a new method we are proposing, called DropSegment. It is designed to achieve data augmentation and to improve the generalized applicability of CNN. For sufficient feature representation, we further introduce path-signature feature maps to impr ove performance. Experiments were conducted on the NLPR handwriting database. Even though we only use pen-position information in the pen-down state of the given handwriting samples, we achieved new state-of-the-art identification rates of 95.72% for Chinese text and 98.51% for English text.

Gaussian latent tree models, or more generally, Gaussian latent forest models have Fisher-information matrices that become singular along interesting submodels, namely, models that correspond to subforests. For these singularities, we compute the real log-canonical thresholds (also known as stochastic complexities or learning coefficients) that quantify the large-sample behavior of the marginal likelihood in Bayesian inference. This provides the information needed for a recently introduced generalization of the Bayesian information criterion. Our mathematical developments treat the general setting of Laplace integrals whose phase functions are sums of squared differences between monomials and constants. We clarify how in this case real log-canonical thresholds can be computed using polyhedral geometry, and we show how to apply the general theory to the Laplace integrals associated with Gaussian latent tree and forest models. In simulations and a data example, we demonstrate how the mathematical knowledge can be applied in model selection.

Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes. We provide several enhancements to mode clustering: (i) a soft variant of cluster assignment, (ii) a measure of connectivity between clusters, (iii) a technique for choosing the bandwidth, (iv) a method for denoising small clusters, and (v) an approach to visualizing the clusters. Combining all these enhancements gives us a complete procedure for clustering in multivariate problems. We also compare mode clustering to other clustering methods in several examples

Specifying and implementing flexible human-computer dialogs, such as those used in kiosks and smart phone apps, is challenging because of the numerous and varied directions in which each user might steer a dialog. The objective of this research is to improve dialog specification and implementation. To do so we enriched a notation based on concepts from programming languages, especially partial evaluation, for specifying a variety of unsolicited reporting, mixed-initiative dialogs in a concise representation that serves as a design for dialog implementation. We also built a dialog mining system that extracts a specification in this notation from requirements. To demonstrate that such a specification provides a design for dialog implementation, we built a system that automatically generates an implementation of the dialog, called a stager, from it. These two components constitute a dialog modeling toolkit that automates dialog specification and implementation. These results provide a proof of concept and demonstrate the study of dialog specification and implementation from a programming languages perspective. The ubiquity of dialogs in domains such as travel, education, and health care combined with the demand for smart phone apps provide a landscape for further investigation of these results.

Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics. This paper offers a consolidated presentation of a theory of bounded rationality based on information-theoretic ideas. We provide a conceptual justification for using the free energy functional as the objective function for characterizing bounded-rational decisions. This functional possesses three crucial properties: it controls the size of the solution space; it has Monte Carlo planners that are exact, yet bypass the need for exhaustive search; and it captures model uncertainty arising from lack of evidence or from interacting with other agents having unknown intentions. We discuss the single-step decision-making case, and show how to extend it to sequential decisions using equivalence transformations. This extension yields a very general class of decision problems that encompass classical decision rules (e.g.

ABSTRACT In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images. We assume that the data comes from a union of multilinear subspaces (UOMS) model, which is a specific structured case of the much studied union of subspaces (UOS) model. For segmentation under this model, we develop Multilinear Subspace Clustering (MSC) algorithm and evaluate its performance on the YaleB and Olivietti image data sets. We show that MSC is highly competitive with existing algorithms employing the UOS model in terms of clustering performance while enjoying improvement in computational complexity. Index Terms - subspace clustering, multilinear algebra, spectral clustering 1. INTRODUCTION Most clustering algorithms seek to detect disjoint clouds of data.

Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We consider a framework of ordinal classification which pools the results from binary classifiers together. An inherent difficulty of this framework is that the class prediction can be ambiguous due to boundary crossing. To fix this issue, we propose a noncrossing ordinal classification method which materializes the framework by imposing noncrossing constraints. An asymptotic study of the proposed method is conducted. We show by simulated and data examples that the proposed method can improve the classification performance for ordinal data without the ambiguity caused by boundary crossings.

University of South Carolina, Department of Mechanical Engineering, Nuclear Engineering Program, Columbia, SC 29201 Send proofs to: Anthony M. Scopatz scopatz@cec.sc.edu 541 Main Street, Columbia, SC 29208 Number of Pages: 35 Number of Tables: 0 Number of Figures: 11 Keywords: nuclear fuel cycle, gaussian process, dynamic time warping Abstract A method for quickly determining deployment schedules that meet a given fuel cycle demand is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of deployed facilities. Each of these predictions is measured against the demand curve using the dynamic time warping distance. The minimum distance deployment schedule is evaluated in a full fuel cycle simulation, whose generated production curve then informs the model on the next optimization iteration. The method converges within five to ten iterations to a distance that is less than one percent of the total deployable production. A representative once-through fuel cycle is used to demonstrate the methodology for reactor deployment. I INTRODUCTION With the recent advent of agent-based nuclear fuel cycle simulators, such as Cyclus [1, 2], there comes the possibility to make in situ, dynamic facility deployment decisions. This would more fully model real-world fuel cycles where institutions (such as utility companies) predict future demand and choose their future deployment schedules appropriately. However, one of the major challenges to making in situ deployment decisions is the speed at which "good enough" decisions can be made. This paper proposes three related deployment-specific optimization algorithms that can be used for any demand curve and facility type. The demands of a fuel cycle scenario can often be simply stated, e.g. Here, the dynamic time warping (DTW) [3] distance is minimized between the demand curve and the regression of a Gaussian Process model (GP) [4] of prior simulations. This minimization produces a guess for a deployment schedule which is subsequently tested using an actual simulator. This process is repeated until an optimal deployment schedule for the given demand is found. Importantly, by using the Gaussian process surrogates, the number of simulation realizations that must be executed as part of the optimization may be reduced to only a handful. Furthermore, it is at least two orders-of-magnitude faster to test the model than it is to run a single low-fidelity fuel cycle simulation. Because of the relative computational cheapness, it is suitable to be used inside of a fuel cycle simulation.

Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future (image-)frames are dependent on control variables or actions as well as previous frames. While not composed of natural scenes, frames in Atari games are high-dimensional in size, can involve tens of objects with one or more objects being controlled by the actions directly and many other objects being influenced indirectly, can involve entry and departure of objects, and can involve deep partial observability. We propose and evaluate two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks. Experimental results show that the proposed architectures are able to generate visually-realistic frames that are also useful for control over approximately 100-step action-conditional futures in some games. To the best of our knowledge, this paper is the first to make and evaluate long-term predictions on high-dimensional video conditioned by control inputs.