2023
11.
Parayil, N.; You, A.; Grimm, C.; Davidson, J.r.
Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator Proceedings Article
In: Precision agriculture, pp. 167–174, Wageningen Academic Publishers, 2023, ISBN: 978-90-8686-393-8, (Section: 19).
Links | BibTeX | Tags: Pruning, Thinning
@inproceedings{parayil_19_2023,
title = {Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator},
author = {Parayil, N. and You, A. and Grimm, C. and Davidson, J.r.},
url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_19},
doi = {10.3920/978-90-8686-947-3_19},
isbn = {978-90-8686-393-8},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {Precision agriculture},
pages = {167\textendash174},
publisher = {Wageningen Academic Publishers},
note = {Section: 19},
keywords = {Pruning, Thinning},
pubstate = {published},
tppubtype = {inproceedings}
}
12.
Wang, T.; Sankari, P.; Brown, J.; Paudel, A.; He, L.; Karkee, M.; Thompson, A.; Grimm, C.; Davidson, J.r.; Todorovic, S.
Automatic estimation of trunk cross sectional area using deep learning Proceedings Article
In: Precision agriculture, pp. 491–498, Wageningen Academic Publishers, 2023, ISBN: 978-90-8686-393-8, (Section: 62).
Links | BibTeX | Tags: AI, Labor, Pruning
@inproceedings{wang_62_2023,
title = {Automatic estimation of trunk cross sectional area using deep learning},
author = {Wang, T. and Sankari, P. and Brown, J. and Paudel, A. and He, L. and Karkee, M. and Thompson, A. and Grimm, C. and Davidson, J.r. and Todorovic, S.},
url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_62},
doi = {10.3920/978-90-8686-947-3_62},
isbn = {978-90-8686-393-8},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {Precision agriculture},
pages = {491\textendash498},
publisher = {Wageningen Academic Publishers},
note = {Section: 62},
keywords = {AI, Labor, Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
13.
Mishra, Ritwick; Heavey, Jack; Kaur, Gursharn; Adiga, Abhijin; Vullikanti, Anil
Reconstructing an Epidemic Outbreak Using Steiner Connectivity Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 10, pp. 11613–11620, 2023, ISSN: 2374-3468, (Number: 10).
Abstract | Links | BibTeX | Tags: AI
@article{mishra_reconstructing_2023,
title = {Reconstructing an Epidemic Outbreak Using Steiner Connectivity},
author = {Mishra, Ritwick and Heavey, Jack and Kaur, Gursharn and Adiga, Abhijin and Vullikanti, Anil},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26372},
doi = {10.1609/aaai.v37i10.26372},
issn = {2374-3468},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {37},
number = {10},
pages = {11613\textendash11620},
abstract = {Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.},
note = {Number: 10},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.
14.
Belkhouja, Taha; Yan, Yan; Doppa, Janardhan Rao
Dynamic Time Warping Based Adversarial Framework for Time-Series Domain Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7353–7366, 2023, ISSN: 1939-3539, (Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence).
Abstract | Links | BibTeX | Tags: AI
@article{belkhouja_dynamic_2023,
title = {Dynamic Time Warping Based Adversarial Framework for Time-Series Domain},
author = {Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao},
url = {https://ieeexplore.ieee.org/document/9970291},
doi = {10.1109/TPAMI.2022.3224754},
issn = {1939-3539},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {45},
number = {6},
pages = {7353\textendash7366},
abstract = {Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.},
note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.
15.
Ghosh, Subhankar; Belkhouja, Taha; Yan, Yan; Doppa, Janardhan Rao
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 6, pp. 7722–7730, 2023, ISSN: 2374-3468, (Number: 6).
Abstract | Links | BibTeX | Tags: AI, Water
@article{ghosh_improving_2023,
title = {Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis},
author = {Ghosh, Subhankar and Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/25936},
doi = {10.1609/aaai.v37i6.25936},
issn = {2374-3468},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {37},
number = {6},
pages = {7722\textendash7730},
abstract = {Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.},
note = {Number: 6},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.
16.
Ghosh, Subhankar; Shi, Yuanjie; Belkhouja, Taha; Yan, Yan; Doppa, Jana; Jones, Brian
Probabilistically Robust Conformal Prediction Proceedings Article
In: 2023.
Abstract | Links | BibTeX | Tags: AI
@inproceedings{ghosh_probabilistically_2023,
title = {Probabilistically Robust Conformal Prediction},
author = {Ghosh, Subhankar and Shi, Yuanjie and Belkhouja, Taha and Yan, Yan and Doppa, Jana and Jones, Brian},
url = {https://openreview.net/forum?id=4xI4oaqIs2},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
abstract = {Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assumes clean testing data and there is not much known about the robustness of CP algorithms w.r.t natural/adversarial perturbations to testing examples. This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples. PRCP generalizes the standard CP (cannot handle perturbations) and adversarially robust CP (ensures robustness w.r.t worst-case perturbations) to achieve better trade-offs between nominal performance and robustness. We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. The key idea behind aPRCP is to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka "quantile-of-quantile'' design). We provide theoretical analysis to show that aPRCP algorithm achieves robust coverage. Our experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using deep neural networks demonstrate that aPRCP achieves better trade-offs than state-of-the-art CP and adversarially robust CP algorithms.},
keywords = {AI},
pubstate = {published},
tppubtype = {inproceedings}
}
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assumes clean testing data and there is not much known about the robustness of CP algorithms w.r.t natural/adversarial perturbations to testing examples. This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples. PRCP generalizes the standard CP (cannot handle perturbations) and adversarially robust CP (ensures robustness w.r.t worst-case perturbations) to achieve better trade-offs between nominal performance and robustness. We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. The key idea behind aPRCP is to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka "quantile-of-quantile'' design). We provide theoretical analysis to show that aPRCP algorithm achieves robust coverage. Our experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using deep neural networks demonstrate that aPRCP achieves better trade-offs than state-of-the-art CP and adversarially robust CP algorithms.
17.
He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic
Bidirectional alignment for domain adaptive detection with transformers Proceedings Article
In: Proceedings of International Conference on Computer Vision, 2023.
Abstract | Links | BibTeX | Tags: Pruning
@inproceedings{noauthor_bidirectional_nodate,
title = {Bidirectional alignment for domain adaptive detection with transformers},
author = {He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic},
url = {https://www.amazon.science/publications/bidirectional-alignment-for-domain-adaptive-detection-with-transformers},
year = {2023},
date = {2023-03-08},
urldate = {2023-03-08},
journal = {Amazon Science},
publisher = {Proceedings of International Conference on Computer Vision},
abstract = {We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…},
keywords = {Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…
18.
Abatzoglou, John T.; McEvoy, Daniel J.; Nauslar, Nicholas J.; Hegewisch, Katherine C.; Huntington, Justin L.
Downscaled subseasonal fire danger forecast skill across the contiguous United States Journal Article
In: Atmospheric Science Letters, vol. 24, no. 8, pp. e1165, 2023, ISSN: 1530-261X, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1165).
Abstract | Links | BibTeX | Tags: Fallow, Water
@article{abatzoglou_downscaled_2023,
title = {Downscaled subseasonal fire danger forecast skill across the contiguous United States},
author = {Abatzoglou, John T. and McEvoy, Daniel J. and Nauslar, Nicholas J. and Hegewisch, Katherine C. and Huntington, Justin L.},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asl.1165},
doi = {10.1002/asl.1165},
issn = {1530-261X},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Atmospheric Science Letters},
volume = {24},
number = {8},
pages = {e1165},
abstract = {The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead-times of several weeks can help in wildland fire resource allocation as well as complement short-term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble-based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly-to-monthly fire business decision making for suppression-based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1165},
keywords = {Fallow, Water},
pubstate = {published},
tppubtype = {article}
}
The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead-times of several weeks can help in wildland fire resource allocation as well as complement short-term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble-based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly-to-monthly fire business decision making for suppression-based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.
19.
Tanvir Ferdousi; Mingliang Liu; Kirti Rajagopalan; Jennifer Adam; Abhijin Adiga; Mandy Wilson; SS Ravi; Anil Vullikanti; Madhav V Marathe; Samarth Swarup
A Machine Learning Framework to Explain Complex Geospatial Simulations: A Climate Change Case Study Proceedings Article
In: 2023.
Abstract | Links | BibTeX | Tags: AI, Water
@inproceedings{ferdousi_machine_2023,
title = {A Machine Learning Framework to Explain Complex Geospatial Simulations: A Climate Change Case Study},
author = {Tanvir Ferdousi and Mingliang Liu and Kirti Rajagopalan and Jennifer Adam and Abhijin Adiga and Mandy Wilson and SS Ravi and Anil Vullikanti and Madhav V Marathe and Samarth Swarup},
url = {https://tanvir-ferdousi.github.io/assets/pdf/explainability_wsc23.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
abstract = {The explainability of large and complex simulation models is an open problem. We present a framework to analyze such models by processing multidimensional data through a pipeline of target variable computation, clustering, supervised classification, and feature importance analysis. As a use case, the well-known large-scale hydrology and crop systems simulator VIC-CropSyst is utilized to evaluate how climate change may affect water availability in Washington, United States. We study how snowmelt varies with climate variables (temperature, precipitation) to identify different response characteristics. Based on these characteristics, spatial units are clustered into six distinct classes. A random forest classifier is used with Shapley values to rank static soil and land parameters that help detect each class. The results also include an analysis of risk across different classes to identify areas vulnerable to climate change. This paper demonstrates the usefulness of the proposed framework in providing explainability for large and complex simulations.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
The explainability of large and complex simulation models is an open problem. We present a framework to analyze such models by processing multidimensional data through a pipeline of target variable computation, clustering, supervised classification, and feature importance analysis. As a use case, the well-known large-scale hydrology and crop systems simulator VIC-CropSyst is utilized to evaluate how climate change may affect water availability in Washington, United States. We study how snowmelt varies with climate variables (temperature, precipitation) to identify different response characteristics. Based on these characteristics, spatial units are clustered into six distinct classes. A random forest classifier is used with Shapley values to rank static soil and land parameters that help detect each class. The results also include an analysis of risk across different classes to identify areas vulnerable to climate change. This paper demonstrates the usefulness of the proposed framework in providing explainability for large and complex simulations.
20.
Mishra, Ritwick; Eubank, Stephen; Nath, Madhurima; Amundsen, Manu; Adiga, Abhijin
Community Detection Using Moore-Shannon Network Reliability: Application to Food Networks Proceedings Article
In: Cherifi, Hocine; Mantegna, Rosario Nunzio; Rocha, Luis M.; Cherifi, Chantal; Micciche, Salvatore (Ed.): Complex Networks and Their Applications XI, pp. 271–282, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-21131-7.
Abstract | Links | BibTeX | Tags: AI
@inproceedings{mishra_community_2023,
title = {Community Detection Using Moore-Shannon Network Reliability: Application to Food Networks},
author = {Mishra, Ritwick and Eubank, Stephen and Nath, Madhurima and Amundsen, Manu and Adiga, Abhijin},
editor = {Cherifi, Hocine and Mantegna, Rosario Nunzio and Rocha, Luis M. and Cherifi, Chantal and Micciche, Salvatore},
url = {https://link.springer.com/chapter/10.1007/978-3-031-21131-7_21},
doi = {10.1007/978-3-031-21131-7_21},
isbn = {978-3-031-21131-7},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Complex Networks and Their Applications XI},
pages = {271\textendash282},
publisher = {Springer International Publishing},
address = {Cham},
series = {Studies in Computational Intelligence},
abstract = {Community detection in networks is extensively studied from a structural perspective, but very few works characterize communities with respect to dynamics on networks. We propose a generic framework based on Moore-Shannon network reliability for defining and discovering communities with respect to a variety of dynamical processes. This approach extracts communities in directed edge-weighted networks which satisfy strong connectivity properties as well as strong mutual influence between pairs of nodes through the dynamical process. We apply this framework to food networks. We compare our results with modularity-based approach, and analyze community structure across commodities, evolution over time, and with regard to dynamical system properties.},
keywords = {AI},
pubstate = {published},
tppubtype = {inproceedings}
}
Community detection in networks is extensively studied from a structural perspective, but very few works characterize communities with respect to dynamics on networks. We propose a generic framework based on Moore-Shannon network reliability for defining and discovering communities with respect to a variety of dynamical processes. This approach extracts communities in directed edge-weighted networks which satisfy strong connectivity properties as well as strong mutual influence between pairs of nodes through the dynamical process. We apply this framework to food networks. We compare our results with modularity-based approach, and analyze community structure across commodities, evolution over time, and with regard to dynamical system properties.
