Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the am...

INTRODUCTION ..................................................................................... 306 I. ATTACKS ON TERRORIST WEBSITES .......................................... 307 A. Palestinian Islamic Jihad (PIJ) .......................................... 308 B. Al Qaeda ............................................................................. 309 II. WIKILEAKS ...........................

When participants search for a shape (e.g., a circle) among a set of homogenous shapes (e.g., triangles) they are subject to distraction by color singletons that are more salient than the target. However, when participants search for a shape among heterogeneous shapes, the presence of a non-target color singleton does not slow responses to the target. Attempts have been made to explain these re...

Small nucleolar RNAs (snoRNAs) are required for ribose 2'-O-methylation of eukaryotic ribosomal RNA. Many of the genes for this snoRNA family have remained unidentified in Saccharomyces cerevisiae, despite the availability of a complete genome sequence. Probabilistic modeling methods akin to those used in speech recognition and computational linguistics were used to computationally screen the y...

We propose a new mechanism to design risk-pooling contracts between operators improve service resilience during disruptions. formulate novel two-stage stochastic multicommodity flow model determine the cost savings of coalition under different disruption scenarios and solve it using L-shaped method along with sample average approximation. Computational tests are conducted for network instances ...

Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of networks, a focus testing (e.g., generating individual discriminatory instances) and training enhancing through augmented training). In this work, we propose an approach to formally verify against fairness, independence-...

In safety-critical deep learning applications robustness measurement is a vital pre-deployment phase. However, existing verification methods are not sufficiently practical for deploying machine systems in the real world. On one hand, these attempt to claim that no perturbations can “fool” neural networks (DNNs), which may be too stringent practice. other works rigorously consider $$L_p$$ bounde...

In robotic applications, a key requirement for safe and efficient motion planning is the ability to map obstacle-free space in unknown, cluttered 3D environments. However, commodity-grade RGB-D cameras commonly used sensing fail register valid depth values on shiny, glossy, bright, or distant surfaces, leading missing data map. To address this issue, we propose framework leveraging probabilisti...