Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have litt...

We develop a novel methodology for the single-trial analysis of multichannel time-varying neuroimaging signals. We introduce the space-by-time M/EEG decomposition, based on Non-negative Matrix Factorization (NMF), which describes single-trial M/EEG signals using a set of non-negative spatial and temporal components that are linearly combined with signed scalar activation coefficients. We illust...

The representation of music structures is, from Musicology to Artificial Intelligence, a widely known research focus. It entails several generic Knowledge Representation problems like structured knowledge representation, time representation and causality. In this paper, we focus the problem of representing and reasoning about time in the framework of a structured music representation approach, ...

We advocate the use of an explicit time representation in syntactic pattern recognition because it can result in more succinct models and easier learning problems. We apply this approach to the real-world problem of learning models for the driving behavior of truck drivers. We discretize the values of onboard sensors into simple events. Instead of the common syntactic pattern recognition approa...

The paper presents our intermediate results in ontologizing a refined formal representation of environments, events, and happenings elaborated in PSI project. Our formal approach is based on the first order logic and is inspired by Discrete Event Calculus (DEC). In difference to DEC based on discrete linear time representation, it uses fuzzy time intervals [7]. Our framework also refines classi...

In this paper, we describe a method for performing SLAM and visualinertial calibration robustly using inexpensive sensors such as rolling shutter CMOS cameras and MEMS IMUs. We make use of a continuous-time model for the trajectory of the camera that naturally allows us to fuse information from many unsynchronized and potentially high-rate sensors whilst limiting state size. We model the rollin...

The problem addressed in this work is to develop a comprehensive mathematical programming model for the efficient scheduling of oil-refinery operations. Our approach is first to decompose the overall problem spatially into three domains: the crude-oil unloading and blending, the production-unit operations and the product blending and delivery. In particular, the first problem involves the crude...

In this article, we consider a new kind of temporal pattern where both interval and punctual time representation are considered. These patterns, which we call temporal point-interval patterns, aim at capturing how events taking place during different time periods or at different time instants relate to each other. The datasets where these kinds of patterns may appear are temporal relational dat...