Retracts of vertex sets of trees and the almost stability theorem

Let G be a group, let T be an (oriented) G-tree with finite edge stabilizers, and let VT denote the vertex set of T. We show that, for each G-retract V' of the G-set VT, there exists a G-tree whose edge stabilizers are finite and whose vertex set is V'. This fact leads to various new consequences of the almost stability theorem. We also give an example of a group G, a G-tree T and a G-retract V' of VT such that no G-tree has vertex set V'.Comment: 15 pages, 0 figures. Formerly titled "Some refinements of the almost stability theorem". Version

Postcard: The Devine Brothers Clinic in Salina, Kansas

This color printed postcard is the exterior of a business building. The word DEVINE BROS. CLINIC is on the front of this three story building. There is a manicured lawn in front of the building. There is a border around the illustration that has print text at the bottom of the card. There is handwritten text on the back of the card.https://scholars.fhsu.edu/tj_postcards/1121/thumbnail.jp

Deep Learning Approach to Multi-phenomenological Nuclear Fuel Cycle Signals for Nonproliferation Applications

In order to reduce the time required for data analysis and decision-making relevant to nuclear proliferation detection, Artificial Intelligence (AI) techniques are applied to multi-phenomenological signals emitted from nuclear fuel cycle facilities to identify non-human readable characteristic signatures of operations for use in detecting proliferation activities. Seismic and magnetic emanations were collected in the vicinity of the High Flux Isotope Reactor (HFIR) and the McClellan Nuclear Research Center (MNRC). A novel bi-phenomenology DL network is designed to test the viability of transfer learning between nuclear reactor facilities. It is found that the network produces an 84.1% accuracy (99.4% without transient states) for predicting the operational state of the MNRC reactor when trained on the operational state of the HFIR reactor. In comparison, the best performing traditional ML single-phenomenology algorithm, K-Means, produces a 67.8% prediction accuracy (80.5% without transient states)

Determinants of Students’ First Impressions of Instructors and Courses

Students evaluated instructors and courses in the first two weeks of the fall semester to determine the factors that form impressions in the early stages of the semester. Results indicate differences exist between upper and lower division courses with presentation of material and perceived workload as key factors that students use to form first impressions.Teaching/Communication/Extension/Profession,

Spatial Differences of Land Use Change within Oklahoma's Wheat Belt

Farm Service Agency acreage data for the nine Oklahoma Agricultural Statistics Service districts is analyzed to determine the degree of price response in wheat acreage allocation decisions. Some critics have stated that land use after Freedom to Farm would change little, however these findings show acreage shifted greatly after the policy throughout the state.Land Economics/Use,

Quantifying the impact and relevance of scientific research

Qualitative and quantitative methods are being developed to measure the impacts of research on society, but they suffer from serious drawbacks associated with linking a piece of research to its subsequent impacts. We have developed a method to derive impact scores for individual research publications according to their contribution to answering questions of quantified importance to end users of research. To demonstrate the approach, here we evaluate the impacts of research into means of conserving wild bee populations in the UK. For published papers, there is a weak positive correlation between our impact score and the impact factor of the journal. The process identifies publications that provide high quality evidence relating to issues of strong concern. It can also be used to set future research agendas

Archetypal solution spaces for clustering gene expression datasets in identification of cancer subtypes

Gene expression profiles are essential in identifying different cancer phenotypes. Clustering gene expression datasets can provide accurate identification of cancerous cell lines, but this task is challenging due to the small sample size and high dimensionality. Using the $K$-means clustering algorithm we determine the organisation of the solution space for a variety of gene expression datasets using energy landscape theory. The solution space landscapes allow us to understand $K$-means performance, and guide more effective use when varying common dataset properties; number of features, number of clusters, and cluster distribution. We find that the landscapes have a single-funnelled structure for the appropriate number of clusters, which is lost when the number of clusters deviates from this. We quantify this landscape structure using a frustration metric and show that it may provide a novel diagnostic tool for the appropriate number of cancer subtypes.Comment: 24 pages, 8 figure

Clinical vampirism: a review and illustrative case report

This paper aims to review the phenomenology of vampirism and the various forms of its expression including its presentation in the psychopathology of psychotic disorders. We will explore in detail the case of an African vampire in a psychiatric clinical setting. Vampirism does not have roots in traditional African culture or folklore and thus this case is worth examining due to the unusual nature of the patient's clinical presentation. After a review of the literature, both lay and professional, a clinical case will be described. The discussion will suggest a biopsychosocial and contemporary psychoanalytic understanding of vampirism, and more specifically, of this patient. We also propose an additional type of vampirism be considered for inclusion in the classification of clinical vampirism. South African Psychiatry Review Vol. 9(3) 2006: 163-16