Computing Maximum Agreement Forests without Cluster Partitioning is Folly

Computing a maximum (acyclic) agreement forest (M(A)AF) of a pair of phylogenetic trees is known to be fixed-parameter tractable; the two main techniques are kernelization and depth-bounded search. In theory, kernelization-based algorithms for this problem are not competitive, but they perform remarkably well in practice. We shed light on why this is the case. Our results show that, probably unsurprisingly, the kernel is often much smaller in practice than the theoretical worst case, but not small enough to fully explain the good performance of these algorithms. The key to performance is cluster partitioning, a technique used in almost all fast M(A)AF algorithms. In theory, cluster partitioning does not help: some instances are highly clusterable, others not at all. However, our experiments show that cluster partitioning leads to substantial performance improvements for kernelization-based M(A)AF algorithms. In contrast, kernelizing the individual clusters before solving them using exponential search yields only very modest performance improvements or even hurts performance; for the vast majority of inputs, kernelization leads to no reduction in the maximal cluster size at all. The choice of the algorithm applied to solve individual clusters also significantly impacts performance, even though our limited experiment to evaluate this produced no clear winner; depth-bounded search, exponential search interleaved with kernelization, and an ILP-based algorithm all achieved competitive performance

Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems

We can leverage data and complex systems science to better understand society and human nature on a population scale through language --- utilizing tools that include sentiment analysis, machine learning, and data visualization. Data-driven science and the sociotechnical systems that we use every day are enabling a transformation from hypothesis-driven, reductionist methodology to complex systems sciences. Namely, the emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, with profound implications for our understanding of human behavior. Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture\u27s evolution through its texts using a big data lens. Given the growing assortment of sentiment measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts. Here, we perform detailed, quantitative tests and qualitative assessments of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts. Most importantly they can aid understanding of texts with reliable and meaningful word shift graphs if (1) the dictionary covers a sufficiently large enough portion of a given text\u27s lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories, forming patterns that are meaningful to us. By classifying the emotional arcs for a filtered subset of 4,803 stories from Project Gutenberg\u27s fiction collection, we find a set of six core trajectories which form the building blocks of complex narratives. We strengthen our findings by separately applying optimization, linear decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads. Within stories lie the core values of social behavior, rich with both strategies and proper protocol, which we can begin to study more broadly and systematically as a true reflection of culture. Of profound scientific interest will be the degree to which we can eventually understand the full landscape of human stories, and data driven approaches will play a crucial role. Finally, we utilize web-scale data from Twitter to study the limits of what social data can tell us about public health, mental illness, discourse around the protest movement of #BlackLivesMatter, discourse around climate change, and hidden networks. We conclude with a review of published works in complex systems that separately analyze charitable donations, the happiness of words in 10 languages, 100 years of daily temperature data across the United States, and Australian Rules Football games

Artificial Intelligence based multi-agent control system

Le metodologie di Intelligenza Artificiale (AI) si occupano della possibilità di rendere le macchine in grado di compiere azioni intelligenti con lo scopo di aiutare l’essere umano; quindi è possibile affermare che l’Intelligenza Artificiale consente di portare all’interno delle macchine, caratteristiche tipiche considerate come caratteristiche umane. Nello spazio dell’Intelligenza Artificiale ci sono molti compiti che potrebbero essere richiesti alla macchina come la percezione dell’ambiente, la percezione visiva, decisioni complesse. La recente evoluzione in questo campo ha prodotto notevoli scoperte, princi- palmente in sistemi ingegneristici come sistemi multi-agente, sistemi in rete, impianti, sistemi veicolari, sistemi sanitari; infatti una parte dei suddetti sistemi di ingegneria è presente in questa tesi di dottorato. Lo scopo principale di questo lavoro è presentare le mie recenti attività di ricerca nel campo di sistemi complessi che portano le metodologie di intelligenza artifi- ciale ad essere applicati in diversi ambienti, come nelle reti di telecomunicazione, nei sistemi di trasporto e nei sistemi sanitari per la Medicina Personalizzata. Gli approcci progettati e sviluppati nel campo delle reti di telecomunicazione sono presentati nel Capitolo 2, dove un algoritmo di Multi Agent Reinforcement Learning è stato progettato per implementare un approccio model-free al fine di controllare e aumentare il livello di soddisfazione degli utenti; le attività di ricerca nel campo dei sistemi di trasporto sono presentate alla fine del capitolo 2 e nel capitolo 3, in cui i due approcci riguardanti un algoritmo di Reinforcement Learning e un algoritmo di Deep Learning sono stati progettati e sviluppati per far fronte a soluzioni di viaggio personalizzate e all’identificazione automatica dei mezzi trasporto; le ricerche svolte nel campo della Medicina Personalizzata sono state presentate nel Capitolo 4 dove è stato presentato un approccio basato sul controllo Deep Learning e Model Predictive Control per affrontare il problema del controllo dei fattori biologici nei pazienti diabetici.Artificial Intelligence (AI) is a science that deals with the problem of having machines perform intelligent, complex, actions with the aim of helping the human being. It is then possible to assert that Artificial Intelligence permits to bring into machines, typical characteristics and abilities that were once limited to human intervention. In the field of AI there are several tasks that ideally could be delegated to machines, such as environment aware perception, visual perception and complex decisions in the various field. The recent research trends in this field have produced remarkable upgrades mainly on complex engineering systems such as multi-agent systems, networked systems, manufacturing, vehicular and transportation systems, health care; in fact, a portion of the mentioned engineering system is discussed in this PhD thesis, as most of them are typical field of application for traditional control systems. The main purpose if this work is to present my recent research activities in the field of complex systems, bringing artificial intelligent methodologies in different environments such as in telecommunication networks, transportation systems and health care for Personalized Medicine. The designed and developed approaches in the field of telecommunication net- works is presented in Chapter 2, where a multi-agent reinforcement learning algorithm was designed to implement a model-free control approach in order to regulate and improve the level of satisfaction of the users, while the research activities in the field of transportation systems are presented at the end of Chapter 2 and in Chapter 3, where two approaches regarding a Reinforcement Learning algorithm and a Deep Learning algorithm were designed and developed to cope with tailored travels and automatic identification of transportation moralities. Finally, the research activities performed in the field of Personalized Medicine have been presented in Chapter 4 where a Deep Learning and Model Predictive control based approach are presented to address the problem of controlling biological factors in diabetic patients

Long-term responses of populations and communities of trees to selective logging in tropical rain forests in Guyana

Since only a small area of Guyana's forest can be effectively protected and because timber harvesting is an important source of income, logged forests will play an important role in the conservation of biodiversity in Guyana. Selective logging, in which only a few trees per hectare are harvested and after which forest remains available, is potentially a good way to utilise the forest without destroying it. In Guyana hard wood from selective logging is an important source of income. As in other tropical countries, sustainable forest management should result in sustained timber yields over long periods of time to provide lasting revenues and to secure livelihoods, while on the other hand also diversity should be conserved as much as possible. To be able to define criteria for sustainable forest management, information on the long-term effects of logging is needed. Selective logging creates openings in the forest canopy, which results in increased light availability in the forest understorey. As a consequence of this increased light availability some tree species (the pioneers) are able to grow much faster. On the long term this may result in changes in species composition of the forest. The aim of the investigations described in this thesis was to determine the long-term effects of logging on tree population dynamics, forest composition and tree diversity and to evaluate the sustainability of alternative forest management scenarios for both future timber yields and biodiversity conservation. To investigate these long-term effects, a field study was done in logged and non-logged forests in Guyana and additionally a forest simulation model was developed to evaluate different management scenarios. This population dynamics model simulates growth, mortality and recruitment of trees and makes projections of forest composition and available hard wood in the course of decades. The results of the field study showed that increased light availability after logging is especially advantageous for pioneer species. The abundance of inherently slow growing tree species decreased, but recovered again in the course of years after logging. Model simulations showed, however, that selective logging did not severely affect forest composition. Even in simulations of the most intensive way of logging (12 trees ha-1, every 25 years) forest composition remained rather intact. This is probably due to the fact that in forests in central Guyana, pioneer species are very rare and thus will not easily dominate the forest after logging. After logging once using high harvest intensities of 12 trees ha-1, it took, however, more than 100 years before harvestable timber volumes were comparable again with a baseline (non-logging) situation. For slow growing tree species it even took more than 160 years after logging before the abundance of stems was comparable again with the baseline situation. Projected recovery periods were, however, substantially longer than the currently in Guyana advised length of felling cycle of 60 years. Highest total timber yields were achieved if trees were harvested every 25 years using high harvest intensities. At the same time this approach also resulted in a fast depletion of the available commercial timber volumes in the forest and thus reduced timber yields. The results of the investigations in this thesis can be used to determine criteria for sustainable forest management in Guyana

School of Marine Science Graduate Catalog 2004-2005

Catalog for the Graduate program from the School of Marine Science at the College of William and Mary for the listed academic year

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Twenty K.R. Narayanan Orations

"The Australia South Asia Research Centre (ASARC) was established in 1994 in one of the premier universities of the world—The Australian National University (ANU). Apart from its research and doctoral training activities, ASARC also needed a public forum with a global reach to involve the best minds working on economic development in India as well as to honour its founder, Dr K.R. Narayanan, President of the Republic of India. The K.R. Narayanan Oration series was developed in response to these twin needs. The first oration was held in 1994 and the latest (the 20th) was held in 2018. The first 10 orations were published by ANU Press in 2006. This new edition updates the volume to include all 20 orations delivered so far and provides an updated introduction. All these orations have been delivered by leading academics, scientists and policymakers deeply involved in the transformation of the Indian economy. This collection of the Narayanan Orations is thus at once both an expert account of key aspects of the economic development process in India and a peek into India's potential in the future. As such, the publication of this volume marks a watershed in the intellectual debate on India’s economic reforms program and should be welcomed by all those interested in the economic development of the country.

Monitoring climate for the effects of increasing greenhouse gas concentrations

Roger A. Pielke, Timothy G.F. Kittel, co-editors.Includes bibliographical references.A compendium of papers presented at a workshop sponsored on August 26-28, 1987 by the Cooperative Institute for Research in the Atmosphere

Conservation Genetics of a Declining Bumble Bee in Western North America; The Influence of Geography, Dispersal Limitation, and Anthopogenic Activity

Conservation biology addresses the problem of species loss by identifying species in need of protection. Conservation biology has subfields to address different aspects of biodiversity loss, including genetics and sociology. I used genetic approaches to assess the conservation status of western bumble bees, a bumble bee species of conservation concern. The western bumble bee is a bumble bee species that ranges from Alaska to New Mexico and as far east as Wyoming and Colorado. This species is disappearing in some places. It may soon be listed as endangered in the United States and is already listed as endangered in parts of its Canadian distribution. To complicate the problem further, the western bumble bee might really be two cryptic species. Recent genetic analyses indicate that there might be a northern species (Mckay’s bumble bee) and a southern species (the western bumble bee). I used DNA from specimens collected across the range and ran genetic analyses to estimate the relationships between western bumble bees and Mckay’s bumble bees. This study provided enough evidence to conclude that they are, in fact, two species. Next, I compared patterns of genetic diversity in the two species to environmental variables to determine how the environment influences how the bees to move across the landscape. I compared patterns of genetic diversity in bees that were collected between 1960 through 2020. Western bumble bees showed patterns of slightly decreasing genetic diversity through time from 1960 to 2019, but Mckay’s bumble bee did not. For both species, nighttime temperatures during the spring and proximity to a native fungal parasite were important predictors of differences in genetic diversity among samples. The distance from parasites is probably important because specimens that are near infections are more likely to be infected themselves. Although we found decreases in genetic diversity for western bumble bees, there is still enough genetic diversity in present-day populations for the species to recover if the effects of the drivers of the declines are managed. Finally, I surveyed 974 conservationists from diverse backgrounds to measure their understanding, trust, and motivation to action from conservation genetic studies. This is important because molecular methods provide important insight into the conservation status of at-risk species, but they are not used very often when land managers make conservation decisions. The results indicate that lack of understanding, but not trust, may be a barrier to increased use of molecular methods in conservation actions