Does Climate Change Invoke Conditions that Create Conflict? Lessons Learned from Syria and Beyond

Senior Project submitted to The Division of Social Studies of Bard Colleg

Prenatal exposure to methadone or buprenorphine: Early childhood developmental outcomes.

BACKGROUND: Methadone and buprenorphine are recommended to treat opioid use disorders during pregnancy. However, the literature on the relationship between longer-term effects of prenatal exposure to these medications and childhood development is both spare and inconsistent. METHODS: Participants were 96 children and their mothers who participated in MOTHER, a randomized controlled trial of opioid-agonist pharmacotherapy during pregnancy. The present study examined child growth parameters, cognition, language abilities, sensory processing, and temperament from 0 to 36 months of the child\u27s life. Maternal perceptions of parenting stress, home environment, and addiction severity were also examined. RESULTS: Tests of mean differences between children prenatally exposed to methadone vs. buprenorphine over the three-year period yielded 2/37 significant findings for children. Similarly, tests of mean differences between children treated for NAS relative to those not treated for NAS yielded 1/37 significant finding. Changes over time occurred for 27/37 child outcomes including expected child increases in weight, head and height, and overall gains in cognitive development, language abilities, sensory processing, and temperament. For mothers, significant changes over time in parenting stress (9/17 scales) suggested increasing difficulties with their children, notably seen in increasing parenting stress, but also an increasingly enriched home environment (4/7 scales). CONCLUSIONS: Findings strongly suggest no deleterious effects of buprenorphine relative to methadone or of treatment for NAS severity relative to not-treated for NAS on growth, cognitive development, language abilities, sensory processing, and temperament. Moreover, findings suggest that prenatal opioid agonist exposure is not deleterious to normal physical and mental development

A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province

We propose a method for the analysis of functional data with complex dependencies, such as spatially dependent curves or time dependent surfaces, over highly textured domains. The models are based on the idea of regression with partial differential regularizations. In particular, we consider here two roughness penalties that account separately for the regularity of the field in space and in time. Among the various modelling features, the proposed method is able to deal with spatial domains featuring peninsulas, islands and other complex geometries. Space-time varying covariate information is included in the model via a semi-parametric framework. The proposed method is compared via simulation studies to other spatiotemporal techniques and it is applied to the analysis of the annual production of waste in the towns of Venice province

Uncovering Unique Concept Vectors through Latent Space Decomposition

Interpreting the inner workings of deep learning models is crucial for establishing trust and ensuring model safety. Concept-based explanations have emerged as a superior approach that is more interpretable than feature attribution estimates such as pixel saliency. However, defining the concepts for the interpretability analysis biases the explanations by the user's expectations on the concepts. To address this, we propose a novel post-hoc unsupervised method that automatically uncovers the concepts learned by deep models during training. By decomposing the latent space of a layer in singular vectors and refining them by unsupervised clustering, we uncover concept vectors aligned with directions of high variance that are relevant to the model prediction, and that point to semantically distinct concepts. Our extensive experiments reveal that the majority of our concepts are readily understandable to humans, exhibit coherency, and bear relevance to the task at hand. Moreover, we showcase the practical utility of our method in dataset exploration, where our concept vectors successfully identify outlier training samples affected by various confounding factors. This novel exploration technique has remarkable versatility to data types and model architectures and it will facilitate the identification of biases and the discovery of sources of error within training data

Updating existing emotional memories involves the frontopolar/orbitofrontal cortex in ways that acquiring new emotional memories does not

In life, we must often learn new associations to people, places, or things we already know. The current fMRI study investigated the neural mechanisms underlying emotional memory updating. Nineteen participants first viewed negative and neutral pictures and learned associations between those pictures and other neutral stimuli, such as neutral objects and encoding tasks. This initial learning phase was followed by a memory updating phase, during which participants learned picture-location associations for old pictures (i.e., pictures previously associated with other neutral stimuli) and new pictures (i.e., pictures not seen in the first phase). There was greater frontopolar/orbito-frontal (OFC) activity when people learned picture–location associations for old negative pictures than for new negative pictures, but frontopolar OFC activity did not significantly differ during learning locations of old versus new neutral pictures. In addition, frontopolar activity was more negatively correlated with the amygdala when participants learned picture–location associations for old negative pictures than for new negative or old neutral pictures. Past studies revealed that the frontopolar OFC allows for updating the affective values of stimuli in reversal learning or extinction of conditioning [e.g., Izquierdo, A., & Murray, E. A. Opposing effects of amygdala and orbital PFC lesions on the extinction of instrumental responding in macaque monkeys. European Journal of Neuroscience, 22, 2341–2346, 2005]; our findings suggest that it plays a more general role in updating associations to emotional stimuli

Building capacity in implementation science research training at the University of Nairobi.

BACKGROUND: Health care systems in sub-Saharan Africa, and globally, grapple with the problem of closing the gap between evidence-based health interventions and actual practice in health service settings. It is essential for health care systems, especially in low-resource settings, to increase capacity to implement evidence-based practices, by training professionals in implementation science. With support from the Medical Education Partnership Initiative, the University of Nairobi has developed a training program to build local capacity for implementation science. METHODS: This paper describes how the University of Nairobi leveraged resources from the Medical Education Partnership to develop an institutional program that provides training and mentoring in implementation science, builds relationships between researchers and implementers, and identifies local research priorities for implementation science. RESULTS: The curriculum content includes core material in implementation science theory, methods, and experiences. The program adopts a team mentoring and supervision approach, in which fellows are matched with mentors at the University of Nairobi and partnering institutions: University of Washington, Seattle, and University of Maryland, Baltimore. A survey of program participants showed a high degree satisfaction with most aspects of the program, including the content, duration, and attachment sites. A key strength of the fellowship program is the partnership approach, which leverages innovative use of information technology to offer diverse perspectives, and a team model for mentorship and supervision. CONCLUSIONS: As health care systems and training institutions seek new approaches to increase capacity in implementation science, the University of Nairobi Implementation Science Fellowship program can be a model for health educators and administrators who wish to develop their program and curricula

US Army Weapon Systems Human-Computer Interface (WSHCI) style guide, Version 1

A stated goal of the U.S. Army has been the standardization of the human computer interfaces (HCIS) of its system. Some of the tools being used to accomplish this standardization are HCI design guidelines and style guides. Currently, the Army is employing a number of style guides. While these style guides provide good guidance for the command, control, communications, computers, and intelligence (C4I) domain, they do not necessarily represent the more unique requirements of the Army`s real time and near-real time (RT/NRT) weapon systems. The Office of the Director of Information for Command, Control, Communications, and Computers (DISC4), in conjunction with the Weapon Systems Technical Architecture Working Group (WSTAWG), recognized this need as part of their activities to revise the Army Technical Architecture (ATA). To address this need, DISC4 tasked the Pacific Northwest National Laboratory (PNNL) to develop an Army weapon systems unique HCI style guide. This document, the U.S. Army Weapon Systems Human-Computer Interface (WSHCI) Style Guide, represents the first version of that style guide. The purpose of this document is to provide HCI design guidance for RT/NRT Army systems across the weapon systems domains of ground, aviation, missile, and soldier systems. Each domain should customize and extend this guidance by developing their domain-specific style guides, which will be used to guide the development of future systems within their domains

A route to minimally dissipative switching in magnets via THz phonon pumping

Advanced magnetic recording paradigms typically use large temperature changes to drive switching which is detrimental to device longevity, hence finding non-thermal routes is crucial for future applications. By employing atomistic spin-lattice dynamics simulations, we show efficient coherent magnetisation switching triggered by THz phonon excitation in insulating single species materials. The key ingredient is excitation near the $P$-point of the spectrum in conditions where spins typically cannot be excited and when manifold $k$ phonon modes are accessible at the same frequency. Our model predicts the necessary ingredients for low-dissipative switching and provides new insight into THz-excited spin dynamics.Comment: 8 pages including supplementary informatio