Living in the Plantationocene

Preceding centuries of exploitation and commodification of all life and the natural world have led us to the crises we face today. To describe the dramatic changes our species has forced upon the planet, in 2000, scientists Eugene Stormer and Paul Crutzen dubbed the geological epoch we inhabit the Anthropocene (Global Change Newsletter). Although the term is useful to distinguish the altered composition of the atmosphere, soil, and oceans that human activity has produced from the ecological baseline of the Holocene, some scholars are critical of the term. Not all humans are equally to blame for the environmental degradation which surrounds us. Raj Patel and Jason W. Moore have proposed a more appropriate term for this era would be the Capitalocene, as it points to capitalism as the root of these problems which have been in the making since the fifteenth century. Alternatively, scholars Donna Haraway and Anna Tsing describe this period as the Plantationocene to better elucidate the history of racial oppression, violence and economic inequality which is inseparable from the history of ecological exhaustion and collapse (Moore et al. 6)

Examining relationships between Covid-19, investor personality traits, and investor risk tolerance.

In 2020, financial markets around the globe sharply declined as the threat of Covid-19 became severe. The financial market reactions were precedented. Due to the unprecedented reaction, conversations arose describing the influences that impacted investor behavior. Among these influences are behavioral influences such as personality traits and financial risk tolerance. The following research evaluates the relationships between Covid-19, Dark Triad Personality Traits (narcissism, Machiavellianism, and psychopathy), financial feelings, preference for consistency, long-term orientation, and the need to evaluate. A survey gauging individuals’ economic perception, personality traits, and risk behavior was distributed to 116 individuals. Quantitative analysis was done using t-tests to find significant differences between groups who exhibited high and low levels of any given personality trait. In addition to gauging significant differences in personality traits, the survey also gauged financial risk tolerance, financial feelings, and how investors perceive the threat of Covid-19. The study found that there are statistically significant relationships between Covid-19, Dark Triad Personality Traits, financial feelings, the need to evaluate, and preference for consistency. During a time of financial uncertainty such as Covid-19, individuals who exhibited higher levels of dark triad personality traits had lower financial feelings and a lesser preference for consistency. Individuals who displayed higher levels of dark triad personality traits also had a larger risk tolerance. Additionally, individuals who are more long-term oriented have more enhanced financial feelings. This research evaluated relationships to help better understand the relationships between Dark Triad Personality traits and risk among other factors

Arsenic Classification: Deep Learning Finding Toxin Exposure

Through our research we hope to demonstrate the efficiency and accuracy of deep learning technology in automating the classification of arsenic exposure at the cellular level. Classification of arsenic exposure is currently a human task that requires visual inspection of the cellular structure to find abnormalities. This is a task that can be successfully automated with computer vision. Current research shows that models can successfully recognize the cellular morphology caused by varying degrees of arsenic exposure, and we are working to isolate the model training practices that optimize accurate predictions. Our research involves comprehensive analysis of various model training methodologies, neural network architectures, and algorithms that can be applied towards automating this task. With a dataset of images provided by USM’s Biology department, we have worked with various crop sizes and preprocessing techniques to aid in the model’s learning process. This research is done to understand the techniques that promote the best learning at the cellular level and push the deep learning models further into toxin classification problems in more complex organisms. It is hoped this research can inform others of the potential deep learning has in cellular and toxicological classification problems, as well as to automate the process of classifying arsenic exposure

Coherent Diffractive Imaging Using Randomly Coded Masks

Coherent diffractive imaging (CDI) provides new opportunities for high resolution X-ray imaging with simultaneous amplitude and phase contrast. Extensions to CDI broaden the scope of the technique for use in a wide variety of experimental geometries and physical systems. Here, we experimentally demonstrate a new extension to CDI that encodes additional information through the use of a series of randomly coded masks. The information gained from the few additional diffraction measurements removes the need for typical object-domain constraints; the algorithm uses prior information about the masks instead. The experiment is performed using a laser diode at 532.2 nm, enabling rapid prototyping for future X-ray synchrotron and even free electron laser experiments. Diffraction patterns are collected with up to 15 different masks placed between a CCD detector and a single sample. Phase retrieval is performed using a convex relaxation routine known as "PhaseCut" followed by a variation on Fienup's input-output algorithm. The reconstruction quality is judged via calculation of phase retrieval transfer functions as well as by an object-space comparison between reconstructions and a lens-based image of the sample. The results of this analysis indicate that with enough masks (in this case 3 or 4) the diffraction phases converge reliably, implying stability and uniqueness of the retrieved solution