Learning in the presence of sudden concept drift and measurement drift

The current availability of vast data storage and the computational power to enact algorithms for interpreting that data in real time leads to the possibility of real time adaptive systems. Because change is nearly always inevitable, companies must strive to increase the adaptability of their manufacturing or service systems. To accomplish this, the methods for correcting the system and determining the correct change point must be studied. The motivation of this thesis is advancing the ability of proper prediction and classification model learning on data streams containing change. This problem is known as concept drift. Motivation also stems from a study on a system with these properties, at an active manufacturing facility. After reviewing articles relating to the specific problem in the study, a similarity between the study and the studies performed in the research area of advanced process control became clear. The underlying cause for the change in the manufacturing system is identified as measurement drift. The identification of measurement drift is explained. A discussion of the mathematical model representing measurement drift is provided. Existing concept drift algorithms are adapted to fit the needs of the measurement drift problem. Their performance on the data from the study and synthetic data sets mimicking varying levels of drift magnitude and frequency is assessed. The results are compared to a popular advanced process control method, exponential weighted moving average adapting intercept (EWMA-I). The advanced process control literature inspired the development of two new methods for learning in the presence of concept drift. The methods, ADMEAN and CD-EWMA (ADaptive MEAN and Concept Drift Exponential Weighted Moving Average), make changes to the incoming stream of independent variables. The performance of these algorithms on the measurement drift datasets and synthetic concept drift datasets is provided

ApoE Risk Disclosure: A Review of Positive and Negative Outcome

Two of this century’s most significant healthcare challenges are Alzheimer’s disease and mild cognitive impairment, with 40 million people suffering from the diseases. In fact, a conservative estimate projects that both conditions will double every 20 years until 2050. Alzheimer’s disease involves memory impairment, disorientation, confusion, and various problematic behaviors. Presently, no prevention method or cure has been discovered for Alzheimer’s. Mild cognitive impairment typically includes problems with memory, language, thinking, and judgment beyond those typical of one’s age. Usually, these symptoms do not interfere with daily activities but do not improve and have been linked with a risk of developing Alzheimer’s as time goes on. As research in this area has evolved, genetic biomarkers have been discovered that determine the potential risk of Alzheimer’s disease. While there are no guarantees that individuals will develop Alzheimer’s, they can increase the likelihood of disease onset. Despite the potential for life changes and behaviors that could reduce disease risk, most health professionals are unwilling to disclose these biomarkers to their patients. Clinicians’ perceived risk and biases in believing that disclosing this biomarker will harm patients can result in patients receiving limited health information in this area. However, the debate surrounding this disclosure as harmful to patients should be informed by objective outcomes, rather than only perceived harm. This literature review examines the objective outcomes of genetic risk disclosure and ethical guidelines relevant to any disclosure(s)

A simple method for estimating the effective detection distance of camera traps

Estimates of animal abundance are essential for understanding animal ecology. Camera traps can be used to estimate the abundance of terrestrial mammals, including elusive species, provided that the sensitivity of the sensor, estimated as the effective detection distance (EDD), is quantified. Here, we show how the EDD can be inferred directly from camera trap images by placing markers at known distances along the midline of the camera field of view, and then fitting distance-sampling functions to the frequency of animal passage between markers. EDD estimates derived from simulated passages using binned detection distances approximated those obtained from continuous detection distance measurements if at least five intervals were used over the maximum detection distance. A field test of the method in two forest types with contrasting vegetation density, with five markers at 2.5 m intervals, produced credible EDD estimates for 13 forest-dwelling mammals. EDD estimates were positively correlated with species body mass, and were shorter for the denser vegetation, as expected. Our findings suggest that this simple method can produce reliable estimates of EDD. These estimates can be used to correct photographic capture rates for difference in sampling effort resulting from differences in sensor sensitivity between species and habitats. Simplifying the estimation of EDD will result in less biased indices of relative abundance, and will also facilitate the use of camera trap data for estimating animal density

Targeted management buffers negative impacts of climate change on the hihi, a threatened New Zealand passerine

In order to buffer the risks climate change poses to biodiversity, managers need to develop new strategies to cope with an increasingly dynamic environment. Supplementary Feeding (SF) is a commonly-used form of conservation management that may help buffer the impacts of climate change. However, the role of SF as an adaptation tool is yet to be fully understood. Here we used the program MARK to quantify the relationship between weather (average temperature and total precipitation) and vital rates (survival and recruitment) of an island bird population, the hihi Notiomystis cincta, for which long term demographic data are available under periods of little and ad libitum SF. We then used predictive population modelling to project this population’s dynamics under each management strategy and several climate change scenarios in accordance with the Intergovernmental Panel on Climate Change predictions. Our stochastic population projections revealed that ad libitum SF likely buffer the population against heavier rainfall and more stochastic precipitation patterns; no buffering effect on temperature was detected. While the current SF approach is unlikely to prevent local extinction of the population under increasing temperatures, SF still presents itself as a valuable climate change adaptation tool by delaying extinction. To the best of our knowledge, this is the first study to quantify the interaction between climate and SF intensity of a threatened population. We call for on-going critical evaluation of management measures, and suggest that novel adaptation solutions that combine current approaches are required for conserving species with limited opportunity for dispersal

Assessing the camera trap methodologies used to estimate density of unmarked populations

1. Population density estimations are essential for wildlife management and conservation. Camera traps have become a promising cost-effective tool, for which several methods have been described to estimate population density when individuals are unrecognizable (i.e. unmarked populations). However, comparative tests of their applicability and performance are scarce. 2. Here, we have compared three methods based on camera traps to estimate population density without individual recognition: Random Encounter Model (REM), Random Encounter and Staying Time (REST) and Distance Sampling with camera traps (CT-DS). Comparisons were carried out in terms of consistency with one another, precision and cost-effectiveness. We considered six natural populations with a wide range of densities, and three species with different behavioural traits (red deer Cervus elaphus, wild boar Sus scrofa and red fox Vulpes vulpes). In three of these populations, we obtained independent density estimates as a reference. 3. The densities estimated ranged from 0.23 individuals/km2 (fox) to 34.87 individuals/km2 (red deer). We did not find significant differences in terms of density values estimated by the three methods in five out of six populations, but REM has a tendency to generate higher average density values than REST and CT-DS. Regarding the independents’ densities, REM results were not significantly different in any population, and REST and CT-DS were significantly different in one population. The precision obtained was not significantly different between methods, with average coefficients of variation of 0.28 (REST), 0.36 (REM) and 0.42 (CT-DS). The REST method required the lowest human effort. 4. Synthesis and applications. Our results show that all of the methods examined can work well, with each having particular strengths and weaknesses. Broadly, Random Encounter and Staying Time (REST) could be recommended in scenarios of high abundance, Distance Sampling with camera traps (CT-DS) in those of low abundance while Random Encounter Model (REM) can be recommended when camera trap performance is not optimal, as it can be applied with less risk of bias. This broadens the applicability of camera trapping for estimating densities of unmarked populations using information exclusively obtained from camera traps. This strengthens the case for scientifically based camera trapping as a cost-effective method to provide reference estimates for wildlife managers, including within multi-species monitoring programmes