Model Completeness for Henselian Fields with finite ramification valued in a ZZ-Group

We prove that the theory of a Henselian valued field of characteristic zero, with finite ramification, and whose value group is a $Z$-group, is model-complete in the language of rings if the theory of its residue field is model-complete in the language of rings. We apply this to prove that every infinite algebraic extension of the field of $p$-adic numbers $\Bbb Q_p$ with finite ramification is model-complete in the language of rings. For this, we give a necessary and sufficient condition for model-completeness of the theory of a perfect pseudo-algebraically closed field with pro-cyclic absolute Galois group

Enrichments of Boolean Algebras: a uniform treatment of some classical and some novel examples

We give a unified treatment of the model theory of various enrichments of infinite atomic Boolean algebras, with special attention to quantifier-eliminations, complete axiomatizations and decidability. A classical example is the enrichment by a predicate for the ideal of finite sets, and a novel one involves predicates giving congruence conditions on the cardinality of finite sets. We focus on three examples, and classify them by expressive power

Model theory of finite-by-Presburger Abelian groups and finite extensions of pp-adic fields

We define a class of pre-ordered abelian groups that we call finite-by-Presburger groups, and prove that their theory is model-complete. We show that certain quotients of the multiplicative group of a local field of characteristic zero are finite-by-Presburger and interpret the higher residue rings of the local field. We apply these results to give a new proof of the model completeness in the ring language of a local field of characteristic zero (a result that follows also from work of Prestel-Roquette)

Chronic Post-Concussion Neurocognitive Deficits. I. Relationship with White Matter Integrity.

We previously identified visual tracking deficits and associated degradation of integrity in specific white matter tracts as characteristics of concussion. We re-explored these characteristics in adult patients with persistent post-concussive symptoms using independent new data acquired during 2009-2012. Thirty-two patients and 126 normal controls underwent cognitive assessments and MR-DTI. After data collection, a subset of control subjects was selected to be individually paired with patients based on gender and age. We identified patients' cognitive deficits through pairwise comparisons between patients and matched control subjects. Within the remaining 94 normal subjects, we identified white matter tracts whose integrity correlated with metrics that indicated performance degradation in patients. We then tested for reduced integrity in these white matter tracts in patients relative to matched controls. Most patients showed no abnormality in MR images unlike the previous study. Patients' visual tracking was generally normal. Patients' response times in an attention task were slowed, but could not be explained as reduced integrity of white matter tracts relating to normal response timing. In the present patient cohort, we did not observe behavioral or anatomical deficits that we previously identified as characteristic of concussion. The recent cohort likely represented those with milder injury compared to the earlier cohort. The discrepancy may be explained by a change in the patient recruitment pool circa 2007 associated with an increase in public awareness of concussion

Machine Learning For Planetary Mining Applications

Robotic mining could prove to be an efficient method of mining resources for extended missions on the Moon or Mars. One component of robotic mining is scouting an area for resources to be mined by other robotic systems. Writing controllers for scouting can be difficult due to the need for fault tolerance, inter-agent cooperation, and agent problem solving. Reinforcement learning could solve these problems by enabling the scouts to learn to improve their performance over time. This work is divided into two sections, with each section addressing the use of machine learning in this domain. The first contribution of this work focuses on the application of reinforcement learning to mining mission analysis. Various mission parameters were modified and control policies were learned. Then agent performance was used to assess the effect of the mission parameters on the performance of the mission. The second contribution of this work explores the potential use of reinforcement learning to learn a controller for the scouts. Through learning, these scouts would improve their ability to map their surroundings over time