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Discrete choice models are defined conditional to the knowledge of the actual choice set by the analyst. The common practice for is to assume that individual-based choice sets can be deterministically generated based on the choice context and the character ...
This paper presents a new paradigm for choice set generation in the context of route choice. We assume that the choice sets contain all paths connecting each origin-destination pair. These sets are in general impossible to generate explicitly. Therefore, w ...
The basic goal in combinatorial group testing is to identify a set of up to d defective items within a large population of size n >> d using a pooling strategy. Namely, the items can be grouped together in pools, and a single measurement would reveal wheth ...
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Motivated by applications in software verification, we explore automated reasoning about the non-disjoint combination of theories of infinitely many finite structures, where the theories share set variables and set operations. We prove a combination theore ...
Uncertain information is commonplace in real-world data management scenarios. The ability to represent large sets of possible instances (worlds) while supporting efficient storage and processing is an important challenge in this context. The recent formali ...
We present a new algorithm that performs demosaicing and super-resolution jointly from a set of raw images sampled with a color filter array. Such a combined approach allows us to compute the alignment parameters between the images on the raw camera data b ...
Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility o ...
Model-Driven Engineering (MDE) harbors the promise of developing software-based systems with little or no coding. Instead of coding, it is envisioned that software engineers build models that are automatically translated into code. The modeling notation of ...
We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow’s rule, is defined by two thresholds on posterior probabilities. From simple des ...
We consider the problem of binary classification where the classfier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow’s rule, is defined by two thresholds on posterior probabilities. From simple desi ...
