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Article ; Online: New DRASTIC framework for groundwater vulnerability assessment: bivariate and multi-criteria decision-making approach coupled with metaheuristic algorithm

Lakshminarayanan, Balaji / Ramasamy, Saravanan / Anuthaman, Sreemanthrarupini Nariangadu / Karuppanan, Saravanan

Environ Sci Pollut Res. 2022 Jan., v. 29, no. 3 p.4474-4496

2022

Abstract: Unplanned anthropogenic activities and erratic climate events pose serious threats to groundwater contamination. Therefore, the vulnerability assessment model becomes an essential tool for proper planning and protection of this precious resource. DRASTIC ...

Abstract	Unplanned anthropogenic activities and erratic climate events pose serious threats to groundwater contamination. Therefore, the vulnerability assessment model becomes an essential tool for proper planning and protection of this precious resource. DRASTIC is an extensively adopted groundwater vulnerability assessment model that suffers from several shortcomings in its assessment due to the subjectivity of its rates and weights. In this paper, a new framework was developed to address the subjectivity of DRASTIC model using a bivariate, multi-criteria decision-making approach coupled with a metaheuristic algorithm. Shannon entropy (SE) and stepwise weight assessment ratio analysis (SWARA) methods were coupled with biogeography-based optimization (BBO) to modify rates and weights. The performance of developed models was assessed using area under the receiver operating characteristic (AU-ROC) curve and weighted F1 score. The Shannon-MH model yields better results with an AUC value of 0.8249, whereas other models resulted in an AUC value of 0.8186, 0.7714, 0.7672, and 0.7378 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. It is also evident from weighted F1 score that Shannon-MH model produced maximum accuracy with a value of 0.452 followed by 0.437, 0.419, 0.370, and 0.234 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. The results indicated that Shannon model coupled with metaheuristic algorithm outperforms other developed models in groundwater vulnerability assessment.
Keywords	algorithms ; climate ; entropy ; groundwater ; groundwater contamination ; models ; multi-criteria decision making ; risk assessment
Language	English
Dates of publication	2022-01
Size	p. 4474-4496.
Publishing place	Springer Berlin Heidelberg
Document type	Article ; Online
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-021-15966-0
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: New DRASTIC framework for groundwater vulnerability assessment: bivariate and multi-criteria decision-making approach coupled with metaheuristic algorithm.

Lakshminarayanan, Balaji / Ramasamy, Saravanan / Anuthaman, Sreemanthrarupini Nariangadu / Karuppanan, Saravanan

Environmental science and pollution research international

2021 Volume 29, Issue 3, Page(s) 4474–4496

Abstract	Unplanned anthropogenic activities and erratic climate events pose serious threats to groundwater contamination. Therefore, the vulnerability assessment model becomes an essential tool for proper planning and protection of this precious resource. DRASTIC is an extensively adopted groundwater vulnerability assessment model that suffers from several shortcomings in its assessment due to the subjectivity of its rates and weights. In this paper, a new framework was developed to address the subjectivity of DRASTIC model using a bivariate, multi-criteria decision-making approach coupled with a metaheuristic algorithm. Shannon entropy (SE) and stepwise weight assessment ratio analysis (SWARA) methods were coupled with biogeography-based optimization (BBO) to modify rates and weights. The performance of developed models was assessed using area under the receiver operating characteristic (AU-ROC) curve and weighted F1 score. The Shannon-MH model yields better results with an AUC value of 0.8249, whereas other models resulted in an AUC value of 0.8186, 0.7714, 0.7672, and 0.7378 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. It is also evident from weighted F1 score that Shannon-MH model produced maximum accuracy with a value of 0.452 followed by 0.437, 0.419, 0.370, and 0.234 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. The results indicated that Shannon model coupled with metaheuristic algorithm outperforms other developed models in groundwater vulnerability assessment.
MeSH term(s)	Algorithms ; Anthropogenic Effects ; Environmental Monitoring ; Groundwater ; Models, Theoretical
Language	English
Publishing date	2021-08-18
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-021-15966-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel

Qin, Yao / Wang, Xuezhi / Lakshminarayanan, Balaji / Chi, Ed H. / Beutel, Alex

2023

Abstract: A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can end up being far from the clean training data and what is the appropriate ... ...

Abstract	A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can end up being far from the clean training data and what is the appropriate label is less clear. Despite this, most existing work simply uses one-hot labels for augmented data. In this paper, we show re-using one-hot labels for highly distorted data might run the risk of adding noise and degrading accuracy and calibration. To mitigate this, we propose a generic method AutoLabel to automatically learn the confidence in the labels for augmented data, based on the transformation distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. We successfully apply AutoLabel to three different data augmentation techniques: the state-of-the-art RandAug, AugMix, and adversarial training. Experiments on CIFAR-10, CIFAR-100 and ImageNet show that AutoLabel significantly improves existing data augmentation techniques over models' calibration and accuracy, especially under distributional shift. Comment: Accepted to SaTML-2023
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-02-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Building One-class Detector for Anything

Ge, Yunhao / Ren, Jie / Zhao, Jiaping / Chen, Kaifeng / Gallagher, Andrew / Itti, Laurent / Lakshminarayanan, Balaji

Open-vocabulary Zero-shot OOD Detection Using Text-image Models

2023

Abstract: We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity ...

Abstract	We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector that leverages text-image pre-trained models in a zero-shot fashion and incorporates various descriptions of in-domain and OOD. Our approach is designed to detect anything not in-domain and offers the flexibility to detect a wide variety of OOD, defined via fine- or coarse-grained labels, or even in natural language. We evaluate our approach on challenging benchmarks including large-scale datasets containing fine-grained, semantically similar classes, distributionally shifted images, and multi-object images containing a mixture of in-domain and OOD objects. Our method shows superior performance over previous methods on all benchmarks. Code is available at https://github.com/gyhandy/One-Class-Anything Comment: 16 pages (including appendix and references), 3 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-05-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Morse Neural Networks for Uncertainty Quantification

Dherin, Benoit / Hu, Huiyi / Ren, Jie / Dusenberry, Michael W. / Lakshminarayanan, Balaji

2023

Abstract: We introduce a new deep generative model useful for uncertainty quantification: the Morse neural network, which generalizes the unnormalized Gaussian densities to have modes of high-dimensional submanifolds instead of just discrete points. Fitting the ... ...

Abstract	We introduce a new deep generative model useful for uncertainty quantification: the Morse neural network, which generalizes the unnormalized Gaussian densities to have modes of high-dimensional submanifolds instead of just discrete points. Fitting the Morse neural network via a KL-divergence loss yields 1) a (unnormalized) generative density, 2) an OOD detector, 3) a calibration temperature, 4) a generative sampler, along with in the supervised case 5) a distance aware-classifier. The Morse network can be used on top of a pre-trained network to bring distance-aware calibration w.r.t the training data. Because of its versatility, the Morse neural networks unifies many techniques: e.g., the Entropic Out-of-Distribution Detector of (Mac\^edo et al., 2021) in OOD detection, the one class Deep Support Vector Description method of (Ruff et al., 2018) in anomaly detection, or the Contrastive One Class classifier in continuous learning (Sun et al., 2021). The Morse neural network has connections to support vector machines, kernel methods, and Morse theory in topology. Comment: Accepted to ICML workshop on Structured Probabilistic Inference & Generative Modeling 2023
Keywords	Statistics - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-07-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Self-Evaluation Improves Selective Generation in Large Language Models

Ren, Jie / Zhao, Yao / Vu, Tu / Liu, Peter J. / Lakshminarayanan, Balaji

2023

Abstract: Safe deployment of large language models (LLMs) may benefit from a reliable method for assessing their generated content to determine when to abstain or to selectively generate. While likelihood-based metrics such as perplexity are widely employed, ... ...

Abstract	Safe deployment of large language models (LLMs) may benefit from a reliable method for assessing their generated content to determine when to abstain or to selectively generate. While likelihood-based metrics such as perplexity are widely employed, recent research has demonstrated the limitations of using sequence-level probability estimates given by LLMs as reliable indicators of generation quality. Conversely, LLMs have demonstrated strong calibration at the token level, particularly when it comes to choosing correct answers in multiple-choice questions or evaluating true/false statements. In this work, we reformulate open-ended generation tasks into token-level prediction tasks, and leverage LLMs' superior calibration at the token level. We instruct an LLM to self-evaluate its answers, employing either a multi-way comparison or a point-wise evaluation approach, with the option to include a ``None of the above'' option to express the model's uncertainty explicitly. We benchmark a range of scoring methods based on self-evaluation and evaluate their performance in selective generation using TruthfulQA and TL;DR. Through experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based scores not only improve accuracy, but also correlate better with the overall quality of generated content.
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	004
Publishing date	2023-12-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Exploring the Limits of Out-of-Distribution Detection

Fort, Stanislav / Ren, Jie / Lakshminarayanan, Balaji

2021

Abstract: Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data ... ...

Abstract	Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision Transformers pre-trained on ImageNet-21k. On a challenging genomics OOD detection benchmark, we improve the AUROC from 66% to 77% using transformers and unsupervised pre-training. To further improve performance, we explore the few-shot outlier exposure setting where a few examples from outlier classes may be available; we show that pre-trained transformers are particularly well-suited for outlier exposure, and that the AUROC of OOD detection on CIFAR-100 vs CIFAR-10 can be improved to 98.7% with just 1 image per OOD class, and 99.46% with 10 images per OOD class. For multi-modal image-text pre-trained transformers such as CLIP, we explore a new way of using just the names of outlier classes as a sole source of information without any accompanying images, and show that this outperforms previous SOTA on standard vision OOD benchmark tasks. Comment: S.F. and J.R. contributed equally
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2021-06-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Hybrid optimization model for conjunctive use of surface and groundwater resources in water deficit irrigation system.

Sampathkumar, Karthikeyan Moothampalayam / Ramasamy, Saravanan / Ramasubbu, Balamurugan / Karuppanan, Saravanan / Lakshminarayanan, Balaji

Water science and technology : a journal of the International Association on Water Pollution Research

2021 Volume 84, Issue 10-11, Page(s) 3055–3071

Abstract: The increasing demand for food production with limited available water resources poses a threat to agricultural activities. Conventional optimization algorithms increase the processing stage and perform in the space allocated from user. Therefore, the ... ...

Abstract	The increasing demand for food production with limited available water resources poses a threat to agricultural activities. Conventional optimization algorithms increase the processing stage and perform in the space allocated from user. Therefore, the proposed work was used to design better performance results. The conjunctive allocation of water resources maximizes the net benefit of farmers. In this study, the novel hybrid optimization model developed is the first of its kind and was designed to resolve the sharing of water resource conflict among different reaches based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of the water deficit in Sathanur reservoir command. The GA-based optimization model considered crop-related physical and economic parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reaches obtained from the BFO is considered as an input to the ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average productivity values, policy 3 (3.665 Rs/m
MeSH term(s)	Agricultural Irrigation ; Agriculture ; Groundwater ; Water ; Water Resources ; Water Supply
Chemical Substances	Water (059QF0KO0R)
Language	English
Publishing date	2021-12-01
Publishing country	England
Document type	Journal Article
ZDB-ID	764273-8
ISSN	1996-9732 ; 0273-1223
ISSN (online)	1996-9732
ISSN	0273-1223
DOI	10.2166/wst.2021.279
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Book ; Online: Bayesian Deep Ensembles via the Neural Tangent Kernel

He, Bobby / Lakshminarayanan, Balaji / Teh, Yee Whye

2020

Abstract: We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dynamics of wide neural networks (NNs). Previous work has shown that even in ... ...

Abstract	We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dynamics of wide neural networks (NNs). Previous work has shown that even in the infinite width limit, when NNs become GPs, there is no GP posterior interpretation to a deep ensemble trained with squared error loss. We introduce a simple modification to standard deep ensembles training, through addition of a computationally-tractable, randomised and untrainable function to each ensemble member, that enables a posterior interpretation in the infinite width limit. When ensembled together, our trained NNs give an approximation to a posterior predictive distribution, and we prove that our Bayesian deep ensembles make more conservative predictions than standard deep ensembles in the infinite width limit. Finally, using finite width NNs we demonstrate that our Bayesian deep ensembles faithfully emulate the analytic posterior predictive when available, and can outperform standard deep ensembles in various out-of-distribution settings, for both regression and classification tasks.
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning
Subject code	519
Publishing date	2020-07-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Test Sample Accuracy Scales with Training Sample Density in Neural Networks

Ji, Xu / Pascanu, Razvan / Hjelm, Devon / Lakshminarayanan, Balaji / Vedaldi, Andrea

2021

Abstract: Intuitively, one would expect accuracy of a trained neural network's prediction on test samples to correlate with how densely the samples are surrounded by seen training samples in representation space. We find that a bound on empirical training error ... ...

Abstract	Intuitively, one would expect accuracy of a trained neural network's prediction on test samples to correlate with how densely the samples are surrounded by seen training samples in representation space. We find that a bound on empirical training error smoothed across linear activation regions scales inversely with training sample density in representation space. Empirically, we verify this bound is a strong predictor of the inaccuracy of the network's prediction on test samples. For unseen test sets, including those with out-of-distribution samples, ranking test samples by their local region's error bound and discarding samples with the highest bounds raises prediction accuracy by up to 20% in absolute terms for image classification datasets, on average over thresholds. Comment: CoLLAs 2022 oral
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
Publishing date	2021-06-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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