Advanced Computation Systems

Contains research objectives

Objective methods for reliable detection of concealed depression

Recent research has shown that it is possible to automatically detect clinical depression from audio-visual recordings. Before considering integration in a clinical pathway, a key question that must be asked is whether such systems can be easily fooled. This work explores the potential of acoustic features to detect clinical depression in adults both when acting normally and when asked to conceal their depression. Nine adults diagnosed with mild to moderate depression as per the Beck Depression Inventory (BDI-II) and Patient Health Questionnaire (PHQ-9) were asked a series of questions and to read a excerpt from a novel aloud under two different experimental conditions. In one, participants were asked to act naturally and in the other, to suppress anything that they felt would be indicative of their depression. Acoustic features were then extracted from this data and analysed using paired t-tests to determine any statistically significant differences between healthy and depressed participants. Most features that were found to be significantly different during normal behaviour remained so during concealed behaviour. In leave-one-subject-out automatic classification studies of the 9 depressed subjects and 8 matched healthy controls, an 88% classification accuracy and 89% sensitivity was achieved. Results remained relatively robust during concealed behaviour, with classifiers trained on only non-concealed data achieving 81% detection accuracy and 75% sensitivity when tested on concealed data. These results indicate there is good potential to build deception-proof automatic depression monitoring systems

Habitat composition and spatial response of white-tailed deer to forestry treatments in moose range, Grand Portage Indian Reservation, Minnesota

Moose (Alces alces L.) have long been an important subsistence species are of great cultural value to the Ojibwe peoples. White-tailed deer (Odocoileus virginianus Z.) have been implicated in negatively impacting ecosystems as distribution shifts northward with changes in climate and land cover. In response, moose populations have shown declines due to transmission of brainworm (Parelaphostrongylus tenuis D.) where ranges overlap spatially with deer. Brainworm was identified as a primary factor in moose mortalities in the Grand Portage Indian Reservation of northeastern Minnesota. Spatial data was collected over a nine-year period from GPS collared moose and deer captured within the reservation. Home and seasonal ranges were delineated from GPS locations using kernel density estimation (KDE). Outputs were evaluated in ArcMap to determine spatial and temporal overlap, habitat composition of moose relative to deer, and responses to forest harvest and silvicultural treatments. Hypotheses tested include 1) white-tailed deer habitats are different from those of moose, 2) deer range overlaps significantly with moose range during the summer months, 3) forest management treatments aimed at benefitting moose, do not encourage deer use, while 4) forest management treatments made in deer range are utilized by deer. Results for deer show larger ranges and seasonal movements, unlike moose. Deer habitat use differs from that of moose with the exception of cover types that are limiting or support browse communities that are used similarly by both species. Both moose and deer utilize all forestry treatments showing the same order of intensity in use, with the exception of Prescribed Fire treatments that are concentrated in core deer range and rarely visited by moose because they are not in moose range

Advanced Computation Systems

Contains research objectives.Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U.S. Air Force) under Contract DA 36-039-AMC-03200(E

Advanced Computation Research

Contains research objectives.Computation Center, M. I. T