Near-optimal irrevocable sample selection for periodic data streams with applications to marine robotics

We consider the task of monitoring spatiotemporal phenomena in real-time by deploying limited sampling resources at locations of interest irrevocably and without knowledge of future observations. This task can be modeled as an instance of the classical secretary problem. Although this problem has been studied extensively in theoretical domains, existing algorithms require that data arrive in random order to provide performance guarantees. These algorithms will perform arbitrarily poorly on data streams such as those encountered in robotics and environmental monitoring domains, which tend to have spatiotemporal structure. We focus on the problem of selecting representative samples from phenomena with periodic structure and introduce a novel sample selection algorithm that recovers a near-optimal sample set according to any monotone submodular utility function. We evaluate our algorithm on a seven-year environmental dataset collected at the Martha's Vineyard Coastal Observatory and show that it selects phytoplankton sample locations that are nearly optimal in an information-theoretic sense for predicting phytoplankton concentrations in locations that were not directly sampled. The proposed periodic secretary algorithm can be used with theoretical performance guarantees in many real-time sensing and robotics applications for streaming, irrevocable sample selection from periodic data streams.Comment: 8 pages, accepted for presentation in IEEE Int. Conf. on Robotics and Automation, ICRA '18, Brisbane, Australia, May 201

Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models

The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.Comment: 8 page

Bird response to land use change in Northern Argentina

Land use change is responsible for changes in wildlife populations including declines for many species. When one land cover type is converted into another for forestry, agriculture or pasture, some bird species are harmed while others may be favored. Understanding how biodiversity responds to land use change is essential to allowing us to anticipate and respond to management options and economic drivers of change. We studied the response of a diverse bird community in an historically grassland portion of northern Argentina, where eucalyptus plantations are expanding rapidly to support timber, pulp and incipient bioenergy industries. Plantations contained the fewest bird species and distinct and largely unique (little overlap) bird communities were found in the alternative grassland, agricultural and remnant savanna ecosystems.https://digitalcommons.mtu.edu/techtalks/1025/thumbnail.jp

First-Year Composition and the Writing-Research Gap

Annmarie Singh’s 2005 article “A Report on Faculty Perceptions of Students’ Information Literacy Competencies in Journalism and Mass Communication Programs: The ACEJMC Survey” showed that faculty in her sample believed many of their undergraduate students did not meet ACRL’s information literacy standards. However, most of these faculty members reported improvement in their students’ research competencies following instruction. We present the results of a study that extends Singh’s work in two useful ways: 1) it isolates teacher perceptions of first-year student skills; and 2) it describes the effectiveness of employing a variety of pedagogical strategies to teach students about the research process. This project surveyed English teachers at three institutions, a private liberal arts college, a public liberal arts college, and a land grant university, concerning their perceptions of their students’ information literacy skills. While Singh’s survey focused exclusively on teacher perceptions of student skills, we also asked teachers about the variety of strategies they used to introduce and reinforce information literacy competency in their classrooms. These strategies ranged from assigning a research project with little classroom or library support, to using ten or more research-related activities to build on a project. We found that teachers who employed a variety of strategies for teaching information literacy competency were significantly more satisfied with their students’ abilities to successfully complete researched projects. In this session, we will report on the results of our study and engage our audience in a conversation about how these results might shape collaborations between librarians and first-year writing programs

Engaging Youth in Bullying Prevention Through Community-based Participatory Research

Few studies that engage youth in community-based participatory research (CBPR) focus on issues of safety/violence, include elementary school-aged youth, or quantitatively assess outcomes of the CBPR process. This article expands understanding of CBPR with youth by describing and evaluating the outcomes of a project that engaged fifth-grade students at 3 schools in bullying-focused CBPR. Results suggest that the project was associated with decreases in fear of bullying and increases in peer and teacher intervention to stop bullying. We conclude with implications for the engagement of elementary school-aged youth in CBPR to address bullying and other youth issues

Winter Territoriality of the American Redstart in Oil Palm Plantations

Aspects of territorial behavior of Nearctic-neotropical migratory birds during the nonbreeding period are poorly studied. Information about territoriality, site persistence, between-year site fidelity, and territory sizes are not available for most birds, especially in tropical agroecosystems. Given the rapid expansion of oil palm in the neotropics, determining how oil palm affects the territorial behaviors of overwintering migratory birds is an important line of inquiry with conservation implications. The American Redstart (Setophaga ruticilla) is considered a model species for the study of population dynamics in the neotropics; however, territory size for American Redstart has only been assessed in native habitats. In this study, we outfitted individual redstarts with radio tags, across two winter seasons, to determine variation in territory sizes across oil palm plantations and native forest patches in the State of Tabasco, Mexico. Average redstart territory size was 0.29 ha in oil palm plantations and 0.17 ha in native forest. Albeit presenting larger territories in oil palm plantations, which could indicate poorer habitat quality, the difference between both habitats was not statistically significant. Our results demonstrate, for the first time, that American Redstarts hold territories in oil palm plantations and that territory size may serve as an important indicator of relative habitat quality for redstart populations in tropical working landscapes

Balancing exploration and exploitation: task-targeted exploration for scientific decision-making

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2022.How do we collect observational data that reveal fundamental properties of scientific phenomena? This is a key challenge in modern scientific discovery. Scientific phenomena are complex—they have high-dimensional and continuous state, exhibit chaotic dynamics, and generate noisy sensor observations. Additionally, scientific experimentation often requires significant time, money, and human effort. In the face of these challenges, we propose to leverage autonomous decision-making to augment and accelerate human scientific discovery. Autonomous decision-making in scientific domains faces an important and classical challenge: balancing exploration and exploitation when making decisions under uncertainty. This thesis argues that efficient decision-making in real-world, scientific domains requires task-targeted exploration—exploration strategies that are tuned to a specific task. By quantifying the change in task performance due to exploratory actions, we enable decision-makers that can contend with highly uncertain real-world environments, performing exploration parsimoniously to improve task performance. The thesis presents three novel paradigms for task-targeted exploration that are motivated by and applied to real-world scientific problems. We first consider exploration in partially observable Markov decision processes (POMDPs) and present two novel planners that leverage task-driven information measures to balance exploration and exploitation. These planners drive robots in simulation and oceanographic field trials to robustly identify plume sources and track targets with stochastic dynamics. We next consider the exploration- exploitation trade-off in online learning paradigms, a robust alternative to POMDPs when the environment is adversarial or difficult to model. We present novel online learning algorithms that balance exploitative and exploratory plays optimally under real-world constraints, including delayed feedback, partial predictability, and short regret horizons. We use these algorithms to perform model selection for subseasonal temperature and precipitation forecasting, achieving state-of-the-art forecasting accuracy. The human scientific endeavor is poised to benefit from our emerging capacity to integrate observational data into the process of model development and validation. Realizing the full potential of these data requires autonomous decision-makers that can contend with the inherent uncertainty of real-world scientific domains. This thesis highlights the critical role that task-targeted exploration plays in efficient scientific decision-making and proposes three novel methods to achieve task-targeted exploration in real-world oceanographic and climate science applications.This material is based upon work supported by the NSF Graduate Research Fellowship Program and a Microsoft Research PhD Fellowship, as well as the Department of Energy / National Nuclear Security Administration under Award Number DE-NA0003921, the Office of Naval Research under Award Number N00014-17-1-2072, and DARPA under Award Number HR001120C0033

Statistical models and decision making for robotic scientific information gathering

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2018.Mobile robots and autonomous sensors have seen increasing use in scientific applications, from planetary rovers surveying for signs of life on Mars, to environmental buoys measuring and logging oceanographic conditions in coastal regions. This thesis makes contributions in both planning algorithms and model design for autonomous scientific information gathering, demonstrating how theory from machine learning, decision theory, theory of optimal experimental design, and statistical inference can be used to develop online algorithms for robotic information gathering that are robust to modeling errors, account for spatiotemporal structure in scientific data, and have probabilistic performance guarantees. This thesis first introduces a novel sample selection algorithm for online, irrevocable sampling in data streams that have spatiotemporal structure, such as those that commonly arise in robotics and environmental monitoring. Given a limited sampling capacity, the proposed periodic secretary algorithm uses an information-theoretic reward function to select samples in real-time that maximally reduce posterior uncertainty in a given scientific model. Additionally, we provide a lower bound on the quality of samples selected by the periodic secretary algorithm by leveraging the submodularity of the information-theoretic reward function. Finally, we demonstrate the robustness of the proposed approach by employing the periodic secretary algorithm to select samples irrevocably from a seven-year oceanographic data stream collected at the Martha’s Vineyard Coastal Observatory off the coast of Cape Cod, USA. Secondly, we consider how scientific models can be specified in environments – such as the deep sea or deep space – where domain scientists may not have enough a priori knowledge to formulate a formal scientific model and hypothesis. These domains require scientific models that start with very little prior information and construct a model of the environment online as observations are gathered. We propose unsupervised machine learning as a technique for science model-learning in these environments. To this end, we introduce a hybrid Bayesian-deep learning model that learns a nonparametric topic model of a visual environment. We use this semantic visual model to identify observations that are poorly explained in the current model, and show experimentally that these highly perplexing observations often correspond to scientifically interesting phenomena. On a marine dataset collected by the SeaBED AUV on the Hannibal Sea Mount, images of high perplexity in the learned model corresponded, for example, to a scientifically novel crab congregation in the deep sea. The approaches presented in this thesis capture the depth and breadth of the problems facing the field of autonomous science. Developing robust autonomous systems that enhance our ability to perform exploratory science in environments such as the oceans, deep space, agricultural and disaster-relief zones will require insight and techniques from classical areas of robotics, such as motion and path planning, mapping, and localization, and from other domains, including machine learning, spatial statistics, optimization, and theory of experimental design. This thesis demonstrates how theory and practice from these diverse disciplines can be unified to address problems in autonomous scientific information gathering

Statistical models and decision making for robotic scientific information gathering

Thesis: S.M., Joint Program in Applied Ocean Physics and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 97-107).Mobile robots and autonomous sensors have seen increasing use in scientific applications, from planetary rovers surveying for signs of life on Mars, to environmental buoys measuring and logging oceanographic conditions in coastal regions. This thesis makes contributions in both planning algorithms and model design for autonomous scientific information gathering, demonstrating how theory from machine learning, decision theory, theory of optimal experimental design, and statistical inference can be used to develop online algorithms for robotic information gathering that are robust to modeling errors, account for spatiotemporal structure in scientific data, and have probabilistic performance guarantees. This thesis first introduces a novel sample selection algorithm for online, irrevocable sampling in data streams that have spatiotemporal structure, such as those that commonly arise in robotics and environmental monitoring. Given a limited sampling capacity, the proposed periodic secretary algorithm uses an information-theoretic reward function to select samples in real-time that maximally reduce posterior uncertainty in a given scientific model. Additionally, we provide a lower bound on the quality of samples selected by the periodic secretary algorithm by leveraging the submodularity of the information-theoretic reward function. Finally, we demonstrate the robustness of the proposed approach by employing the periodic secretary algorithm to select samples irrevocably from a seven-year oceanographic data stream collected at the Martha's Vineyard Coastal Observatory off the coast of Cape Cod, USA. Secondly, we consider how scientific models can be specified in environments - such as the deep sea or deep space - where domain scientists may not have enough a priori knowledge to formulate a formal scientific model and hypothesis. These domains require scientific models that start with very little prior information and construct a model of the environment online as observations are gathered. We propose unsupervised machine learning as a technique for science model-learning in these environments. To this end, we introduce a hybrid Bayesian-deep learning model that learns a nonparametric topic model of a visual environment. We use this semantic visual model to identify observations that are poorly explained in the current model, and show experimentally that these highly perplexing observations often correspond to scientifically interesting phenomena. On a marine dataset collected by the SeaBED AUV on the Hannibal Sea Mount, images of high perplexity in the learned model corresponded, for example, to a scientifically novel crab congregation in the deep sea. The approaches presented in this thesis capture the depth and breadth of the problems facing the field of autonomous science. Developing robust autonomous systems that enhance our ability to perform exploratory science in environments such as the oceans, deep space, agricultural and disaster-relief zones will require insight and techniques from classical areas of robotics, such as motion and path planning, mapping, and localization, and from other domains, including machine learning, spatial statistics, optimization, and theory of experimental design. This thesis demonstrates how theory and practice from these diverse disciplines can be unified to address problems in autonomous scientific information gathering.by Genevieve Elaine Flaspohler.S.M

Functional Identification Of A Leishmania Gene Related To The Peroxin 2 Gene Reveals Common Ancestry Of Glycosomes And Peroxisomes

Glycosomes are membrane-bounded microbody organelles that compartmentalize glycolysis as well as other important metabolic processes in trypanosomatids, The compartmentalization of these enzymatic reactions is hypothesized to play a crucial role in parasite physiology, Although the metabolic role of glycosomes differs substantially from that of the peroxisomes that are found in other eukaryotes, similarities in signals targeting proteins to these organelles suggest that glycosomes and peroxisomes may have evolved from a common ancestor, To examine this hypothesis, as well as gain insights into the function of the glycosome, we used a positive genetic selection procedure to isolate the first Leishmania mutant (gim1-1 [glycosome import] mutant) with a defect in the import of glycosomal proteins, The mutant retains glycosomes but mislocalizes a subset glycosomal proteins to the cytoplasm, Unexpectedly, the gim1-1 mutant lacks lipid bodies, suggesting a heretofore unknown role of the glycosome. We used genetic approaches to identify a gene, GIM1, that is able to restore import and lipid bodies, A nonsense mutation was found in one allele of this gene in the mutant line, The predicted Gim1 protein is related the peroxin 2 family of integral membrane proteins, which are required for peroxisome biogenesis, The similarities in sequence and function provide strong support for the common origin model of glycosomes and peroxisomes. The novel phenotype of gim1-1 and distinctive role of Leishmania glycosomes suggest that future studies of this system will provide a new perspective on microbody biogenesis and function