BACKGROUND Correlated neuronal activity in the brain is hypothesized to contribute to information representation, and is important for gauging brain dynamics in health and disease. Due to high dimensional neural datasets, it is difficult to study temporal variations in correlation structure. NEW METHOD We developed a multiscale method, Population Coordination (PCo), to assess neural populatio...

Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporat...

Today many formalisms exist for specifying complex Markov chains. In contrast, formalism for specifying the quantitative properties to analyze have remained quite primitive. In this paper a new formalism of temporal rewards that allows complex quantitative properties (including delay type measures) to be expressed in the form of a temporal reward formula. Together, an initial (discrete-time) Ma...

Temporal Sentence Grounding in Videos (TSGV) , which aims to ground a natural language sentence that indicates complex human activities an untrimmed video, has drawn widespread attention over the past few years. However, recent studies have found current benchmark datasets may obvious moment annotation biases, enabling several simple baselines even without training achieve state-of-the-art (SOT...

PURPOSE Whole-brain high-resolution quantitative imaging is extremely encoding intensive, and its rapid and robust acquisition remains a challenge. Here we present a 3D MR fingerprinting (MRF) acquisition with a hybrid sliding-window (SW) and GRAPPA reconstruction strategy to obtain high-resolution T1, T2 and proton density (PD) maps with whole brain coverage in a clinically feasible timeframe....

Time series classification is an important task in robotics that is often solved using supervised machine learning. However, classifier models are typically not ‘readable’ in the sense that humans cannot intuitively learn useful information about the relationship between inputs and outputs. In this paper, we address the problem of rich time series classification where we propose a novel framewo...

We present an algorithm to estimate depth in dynamic video scenes. We propose to learn and infer depth in videos from appearance, motion, occlusion boundaries, and geometric context of the scene. Using our method, depth can be estimated from unconstrained videos with no requirement of camera pose estimation, and with significant background/foreground motions. We start by decomposing a video int...

In this paper, an improved methodology for the determination of missing values in a spatio-temporal database is presented. This methodology performs denoising projection in order to accurately fill the missing values in the database. The improved methodology is called EOF Pruning and it is based on an original linear projection method called Empirical Orthogonal Functions (EOF). The experiments...