Tomi Silander

A Dynamic Programming Algorithm for Learning Chain Event Graphs

Chain event graphs are a model family particularly suited for asymmetric causal discrete domains. This paper describes a dynamic programming algorithm for exact learning of chain event graphs from multivariate data. While the exact algorithm is slow, it allows reasonably fast approximations and provides clues for implementing more scalable heuristic algorithms.

Scalable transfer learning in heterogeneous, dynamic environments

Abstract Reinforcement learning is a plausible theoretical basis for developing self-learning, autonomous agents or robots that can effectively represent the world dynamics and efficiently learn the problem features to perform different tasks in different environments. The computational costs and complexities involved, however, are often prohibitive for real-world applications. This study introduces a scalable methodology to learn and transfer knowledge of the transition (and reward) models for model-based reinforcement learning in a complex world. We propose a variant formulation of Markov decision processes that supports efficient online-learning of the relevant problem features to approximate the world dynamics. We apply the new feature selection and dynamics approximation techniques in heterogeneous transfer learning, where the agent automatically maintains and adapts multiple representations of the world to cope with the different environments it encounters during its lifetime. We prove regret bounds for our approach, and empirically demonstrate its capability to quickly converge to a near optimal policy in both real and simulated environments.

Automated Prediction of Glasgow Outcome Scale for Traumatic Brain Injury

Clinical features found in brain CT scan images are widely used in traumatic brain injury (TBI) as indicators for Glasgow Outcome Scale (GOS) prediction. However, due to the lack of automated methods to measure and quantify the CT scan image features, the computerized prediction of GOS in TBI has not been well studied. This paper introduces an automated GOS prediction system for traumatic brain CT images. Different from most existing systems that perform the prognosis based on pre-processed data, our system directly works on brain CT scan images based on the image features. Our system can also be extended to large dataset with easy adaptation. For each new image of a CT scan series, our proposed system first makes use of sparse representation model that predicts the GOS of each CT image slice using Gabor features. Logistic regression, which integrates the GOS of each CT scan slice with a pre-trained model, is then applied to estimate the GOS score for the new case which contains multiple CT slices. Evaluation of the system has shown promising results in prediction of GOS of traumatic brain injury cases.

Unsupervised medical image classification by combining case-based classifiers

We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system. We propose a novel approach to combine sparse Gabor-feature based classifiers in an ensemble classification framework. The unsupervised nature of this non-parametric technique can significantly reduce the time and effort for system calibration. In particular, classification of medical images in this framework does not rely on segmentation, nor semantic-based or annotation-based feature selection. Our experiments show very promising results in classifying computer tomography image slices into pathological classes for traumatic brain injury patients.

To Demarcate or Not to Demarcate: Analysis of Marked Versus Unmarked On-Street Parking Efficiency

Curb space is a valuable asset for urban areas. The space is a finite resource with competing needs from various modes, land uses, and customers. In this context, when the curb space is used for parking, it is important that the space be used as efficiently as possible. There is no unanimous conclusion on whether a demarcated or undemarcated curbside configuration accommodates more vehicles. Most information on this subject is conflicting and anecdotal. This paper presents the results of an in-depth analysis with modeling and field data collection to determine whether a specific configuration is beneficial from a utilization standpoint. It also reviews the state-of-the-practice on demarcating on-street parking spaces and the results of a survey of local jurisdictions’ policies and practice and the logic behind the decision-making process. The authors conclude that factors other than efficiency might drive the decision to demarcate (or not).