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Geographic source estimation using airborne plant environmental DNA in dust

Summary

Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis, we developed a pipeline that utilizes the environmental DNA (eDNA) from plants in dust samples to estimate previous sample location(s). The species of plant-derived eDNA within dust samples were identified using metabarcoding and their geographic distributions were then derived from occurrence records in the USGS Biodiversity in Service of Our Nation (BISON) database. The distributions for all plant species identified in a sample were used to generate a probabilistic estimate of the sample source. With settled dust collected at four U.S. sites over a 15-month period, we demonstrated positive regional geolocation (within 600 km2 of the collection point) with 47.6% (20 of 42) of the samples analyzed. Attribution accuracy and resolution was dependent on the number of plant species identified in a dust sample, which was greatly affected by the season of collection. In dust samples that yielded a minimum of 20 identified plant species, positive regional attribution improved to 66.7% (16 of 24 samples). Using dust samples collected from 31 different U.S. sites, trace plant eDNA provided relevant regional attribution information on provenance in 32.2%. This demonstrated that analysis of plant eDNA in dust can provide an accurate estimate regional provenance within the U.S., and relevant forensic information, for a substantial fraction of samples analyzed.
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Summary

Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis, we developed a pipeline that utilizes the environmental DNA (eDNA) from plants in dust samples to estimate previous sample...

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Health-informed policy gradients for multi-agent reinforcement learning

Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on simple particle environments that have elements of system health, risk-taking, semi-expendable agents, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.
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Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then...

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Multimodal representation learning via maximization of local mutual information [e-print]

Published in:
Intl. Conf. on Medical Image Computing and Computer Assisted Intervention, MICCAI, 27 September-1 October 2021.

Summary

We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image. Our method learns image and text encoders by encouraging the resulting representations to exhibit high local mutual information. We make use of recent advances in mutual information estimation with neural network discriminators. We argue that, typically, the sum of local mutual information is a lower bound on the global mutual information. Our experimental results in the downstream image classification tasks demonstrate the advantages of using local features for image-text representation learning.
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Summary

We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image...

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Learning emergent discrete message communication for cooperative reinforcement learning

Published in:
37th Conf. on Uncertainty in Artificial Intelligence, UAI 2021, early access, 26-30 July 2021.

Summary

Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase the interpretability for human designers and other agents. This paper proposes a method to generate discrete messages analogous to human languages, and achieve communication by a broadcast-and-listen mechanism based on self-attention. We show that discrete message communication has performance comparable to continuous message communication but with much a much smaller vocabulary size. Furthermore, we propose an approach that allows humans to interactively send discrete messages to agents.
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Summary

Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase...

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Beyond expertise and roles: a framework to characterize the stakeholders of interpretable machine learning and their needs

Published in:
Proc. Conf. on Human Factors in Computing Systems, 8-13 May 2021, article no. 74.

Summary

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples stakeholders' knowledge from their interpretability needs. We characterize stakeholders by their formal, instrumental, and personal knowledge and how it manifests in the contexts of machine learning, the data domain, and the general milieu. We additionally distill a hierarchical typology of stakeholder needs that distinguishes higher-level domain goals from lower-level interpretability tasks. In assessing the descriptive, evaluative, and generative powers of our framework, we find our more nuanced treatment of stakeholders reveals gaps and opportunities in the interpretability literature, adds precision to the design and comparison of user studies, and facilitates a more reflexive approach to conducting this research.
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Summary

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples...

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Automated posterior interval evaluation for inference in probabilistic programming

Author:
Published in:
Intl. Conf. on Probabilistic Programming, PROBPROG, 22 October 2020.

Summary

In probabilistic inference, credible intervals constructed from posterior samples provide ranges of likely values for continuous parameters of interest. Intuitively, an inference procedure is optimal if it produces the most precise posterior intervals that cover the true parameter value with the expected frequency in repeated experiments. We present theories and methods for automating posterior interval evaluation of inference performance in probabilistic programming using two metrics: 1.) truth coverage, and 2.) ratio of the empirical over the ideal interval widths. Demonstrating with inference on popular regression and state-space models, we show how the metrics provide effective comparisons between different inference procedures, and capture the effects of collinearity and model misspecification. Overall, we claim such automated interval evaluation can accelerate the robust design and comparison of probabilistic inference programs by directly diagnosing how accurately and precisely they can estimate parameters of interest.
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Summary

In probabilistic inference, credible intervals constructed from posterior samples provide ranges of likely values for continuous parameters of interest. Intuitively, an inference procedure is optimal if it produces the most precise posterior intervals that cover the true parameter value with the expected frequency in repeated experiments. We present theories and...

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A multi-task LSTM framework for improved early sepsis prediction

Published in:
Proc. Artificial Intelligence in Medicine, AIME, 2020, pp. 49-58.

Summary

Early detection for sepsis, a high-mortality clinical condition, is important for improving patient outcomes. The performance of conventional deep learning methods degrades quickly as predictions are made several hours prior to the clinical definition. We adopt recurrent neural networks (RNNs) to improve early prediction of the onset of sepsis using times series of physiological measurements. Furthermore, physiological data is often missing and imputation is necessary. Absence of data might arise due to decisions made by clinical professionals which carries information. Using the missing data patterns into the learning process can further guide how much trust to place on imputed values. A new multi-task LSTM model is proposed that takes informative missingness into account during training that effectively attributes trust to temporal measurements. Experimental results demonstrate our method outperforms conventional CNN and LSTM models on the PhysioNet-2019 CiC early sepsis prediction challenge in terms of area under receiver-operating curve and precision-recall curve, and further improves upon calibration of prediction scores.
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Summary

Early detection for sepsis, a high-mortality clinical condition, is important for improving patient outcomes. The performance of conventional deep learning methods degrades quickly as predictions are made several hours prior to the clinical definition. We adopt recurrent neural networks (RNNs) to improve early prediction of the onset of sepsis using...

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GraphChallenge.org triangle counting performance [e-print]

Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of preparsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, Ttri, is a strong function of the number of edges in the graph, Ne, which improved significantly from 2017 (Ttri \approx (Ne/10^8)^4=3) to 2018 (Ttri \approx Ne/10^9) and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs
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Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems...

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Leveraging linear algebra to count and enumerate simple subgraphs

Published in:
2020 IEEE High Performance Extreme Computing Conf., HPEC, 22-24 September 2020.

Summary

Even though subgraph counting and subgraph matching are well-known NP-Hard problems, they are foundational building blocks for many scientific and commercial applications. In order to analyze graphs that contain millions to billions of edges, distributed systems can provide computational scalability through search parallelization. One recent approach for exposing graph algorithm parallelization is through a linear algebra formulation and the use of the matrix multiply operation, which conceptually is equivalent to a massively parallel graph traversal. This approach has several benefits, including 1) a mathematically-rigorous foundation, and 2) ability to leverage specialized linear algebra accelerators and high-performance libraries. In this paper, we explore and define a linear algebra methodology for performing exact subgraph counting and matching for 4-vertex subgraphs excluding the clique. Matches on these simple subgraphs can be joined as components for a larger subgraph. With thorough analysis, we demonstrate that the linear algebra formulation leverages path aggregation which allows it to be up 2x to 5x more efficient in traversing the search space and compressing the results as compared to tree-based subgraph matching techniques.
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Summary

Even though subgraph counting and subgraph matching are well-known NP-Hard problems, they are foundational building blocks for many scientific and commercial applications. In order to analyze graphs that contain millions to billions of edges, distributed systems can provide computational scalability through search parallelization. One recent approach for exposing graph algorithm...

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Towards a distributed framework for multi-agent reinforcement learning research

Summary

Some of the most important publications in deep reinforcement learning over the last few years have been fueled by access to massive amounts of computation through large scale distributed systems. The success of these approaches in achieving human-expert level performance on several complex video-game environments has motivated further exploration into the limits of these approaches as computation increases. In this paper, we present a distributed RL training framework designed for super computing infrastructures such as the MIT SuperCloud. We review a collection of challenging learning environments—such as Google Research Football, StarCraft II, and Multi-Agent Mujoco— which are at the frontier of reinforcement learning research. We provide results on these environments that illustrate the current state of the field on these problems. Finally, we also quantify and discuss the computational requirements needed for performing RL research by enumerating all experiments performed on these environments.
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Summary

Some of the most important publications in deep reinforcement learning over the last few years have been fueled by access to massive amounts of computation through large scale distributed systems. The success of these approaches in achieving human-expert level performance on several complex video-game environments has motivated further exploration into...

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