Nowadays, navigation is an unavoidable fact in military and civil aerial transportations. The Global Positioning System (GPS) is commonly used for computing the orientation or attitude of a moving platform. The relative positions of the GPS antennas are computed using the GPS code and/or phase measurements. To achieve a precise attitude determination, Carrier phase observations of GPS requiring...

Learning to Rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most existing work on learning to rank assumes that the training data is clean, which is, however, not always true. The ambiguity of query intent, the lack of domain knowledge, and the vague definition of relevance levels, all make it diffic...

We use this theorem to derive the same corollaries for the theories covered by the theorem as were derived for the strongly minimal case in [2]. We also note that the theorem is in some senses optimal. Specifically we can easily construct trivial Morley Rank 1 theories which are not categorical and for which the conclusion of the theorem fails. Also Marker in [3] constructs trivial totally cate...

We develop a tractable method to estimate multiple prior models of decision-making under ambiguity. In a representative sample of the U.S. population, we measure ambiguity attitudes in the gain and loss domains. We find that ambiguity aversion is common for uncertain events of moderate to high likelihood involving gains, but ambiguity seeking prevails for low likelihoods and for losses. We show...

This paper proposes a novel automatic pronunciation scoring framework using learning to rank. Human scores of the utterances are treated as ranks and are used as the ranking ground truths. Scores generated from various existing scoring methods are used as the features to train the learning to rank function. The output of the function is then segmented by the proposed DP-based method and hence b...