Trying project-specific bib

Author: Kazi Sajeed Mehrab

_bibliography_imageomics/papers.bib

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+---
+
+
+@article{khurana2024hierarchical,
+author = {Khurana, Mridul and Daw, Arka and Maruf, M. and Uyeda, Josef C. and Dahdul, Wasila and Charpentier, Caleb and Bakis, Yasin and Bart, Henry L. and Mabee, Paula M. and Lapp, Hilmar and Balhoff, James P. and Chao, Wei-Lun and Stewart, Charles and Berger-Wolf, Tanya and Karpatne, Anuj},
+title = {Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution},
+year = {2024},
+journal = {ECCV},
+booktitle = {Proceedings of ECCV},
+keywords = {Evolution, Diffusion Models, Hierarchical Conditioning, Generative AI, Computer Vision},
+preview = {phylo_diffusion_architecture.jpg},
+website = {https://imageomics.github.io/phylo-diffusion},
+arxiv = {2408.00160v1},
+abs = {A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution.}
+}
+
+@article{mehrab2024fish,
+  title={Fish-Vista: A Multi-Purpose Dataset for Understanding \& Identification of Traits from Images},
+  author={Mehrab, Kazi Sajeed and Maruf, M and Daw, Arka and Manogaran, Harish Babu and Neog, Abhilash and Khurana, Mridul and Altintas, Bahadir and Bakis, Yasin and Campolongo, Elizabeth G and Thompson, Matthew J and others},
+  journal={arXiv preprint arXiv:2407.08027},
+  arxiv = {2407.08027},
+  year={2024},
+  preview = {fishvista.jpg},
+}
+
+@article{sawhney2024motion,
+  title={Motion Enhanced Multi-Level Tracker (MEMTrack): A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments},
+  author={Sawhney, Medha and Karmarkar, Bhas and Leaman, Eric J and Daw, Arka and Karpatne, Anuj and Behkam, Bahareh},
+  journal={Advanced Intelligent Systems},
+  volume={6},
+  number={4},
+  pages={2300590},
+  year={2024},
+  publisher={Wiley Online Library}
+}
+
+@article{elhamod2023neuro,
+  title={Neuro-Visualizer: An Auto-encoder-based Loss Landscape Visualization Method},
+  author={Elhamod, Mohannad and Karpatne, Anuj},
+  journal={ICML},
+  arxiv = {2309.14601},
+  year={2024}
+}
+
+@article{maruf2023beyond,
+  title={Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation},
+  author={Maruf, M and Daw, Arka and Dutta, Amartya and Bu, Jie and Karpatne, Anuj},
+  journal={arXiv preprint arXiv:2308.11052},
+  year={2023}
+}
+
+@inproceedings{elhamod2023discovering,
+  title={Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks},
+  author={Elhamod, Mohannad and Khurana, Mridul and Manogaran, Harish Babu and Uyeda, Josef C and Balk, Meghan A and Dahdul, Wasila and Bakis, Yasin and Bart Jr, Henry L and Mabee, Paula M and Lapp, Hilmar and others},
+  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
+  pages={3966--3978},
+  year={2023}
+}
+
+@inproceedings{sawhney2023detecting,
+  title={Detecting and Tracking Hard-to-Detect Bacteria in Dense Porous Backgrounds},
+  author={Sawhney, Medha and Karmarkar, Bhas and Leaman, Eric J and Daw, Arka and Karpatne, Anuj and Behkam, Bahareh},
+  year={2023},
+  booktitle={CVPR Workshop on CV4Animals 2023}
+}
+
+@article{daw2022mitigating,
+  title={Mitigating propagation failures in physics-informed neural networks using retain-resample-release (r3) sampling},
+  author={Daw, Arka and Bu, Jie and Wang, Sifan and Perdikaris, Paris and Karpatne, Anuj},
+  journal={arXiv preprint arXiv:2207.02338},
+  arxiv = {2207.02338},
+  booktitle= {ICML},
+  year={2023}
+}
+
+@article{bu2023let,
+  title={Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation},
+  author={Bu, Jie and Mehrab, Kazi Sajeed and Karpatne, Anuj},
+  journal={arXiv preprint arXiv:2305.15562},
+  year={2023}
+}
+
+@INPROCEEDINGS{10020603,
+  author={Daw, Arka and Yeo, Kyongmin and Karpatne, Anuj and Klein, Levente},
+  booktitle={2022 IEEE International Conference on Big Data (Big Data)}, 
+  title={Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring}, 
+  year={2022},
+  volume={},
+  number={},
+  pages={4835-4841},
+  keywords={Methane;Pollution;Stacking;Big Data;Multitasking;Mathematical models;Greenhouse gases;Climate change;Environmental monitoring;Physics-informed Machine Learning;Uncertainty Quantification;Climate and Sustainability},
+  doi={10.1109/BigData55660.2022.10020603}
+}
+
+@article{elhamod2022cophy,
+  title={CoPhy-PGNN: Learning physics-guided neural networks with competing loss functions for solving eigenvalue problems},
+  author={Elhamod, Mohannad and Bu, Jie and Singh, Christopher and Redell, Matthew and Ghosh, Abantika and Podolskiy, Viktor and Lee, Wei-Cheng and Karpatne, Anuj},
+  journal={ACM Transactions on Intelligent Systems and Technology},
+  volume={13},
+  number={6},
+  pages={1--23},
+  year={2022},
+  publisher={ACM New York, NY}
+}
+
+@incollection{daw2022physics,
+  title={Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling},
+  author={Daw, Arka and Thomas, R Quinn and Carey, Cayelan C and Read, Jordan S and Appling, Alison P and Karpatne, Anuj},
+  booktitle={Knowledge Guided Machine Learning},
+  pages={399--416},
+  year={2022},
+  publisher={Chapman and Hall/CRC}
+}
+
+@article{elhamod2022hierarchy,
+  author = {Elhamod, Mohannad and Diamond, Kelly M. and Maga, A. Murat and Bakis, Yasin and Bart, Henry L. and Mabee, Paula and Dahdul, Wasila and Leipzig, Jeremy and Greenberg, Jane and Avants, Brian and Karpatne, Anuj},
+  title = {Hierarchy-guided Neural Networks for Species Classification},
+  journal={Methods in Ecology and Evolution},
+  volume={13},
+  number={3},
+  pages={642--652},
+  year={2022},
+}
+
+@article{bu2021learning,
+  title={Learning compact representations of neural networks using discriminative masking (DAM)},
+  author={Bu, Jie and Daw, Arka and Maruf, M and Karpatne, Anuj},
+  journal={Advances in Neural Information Processing Systems},
+  volume={34},
+  pages={3491--3503},
+  year={2021}
+}
+
+@inproceedings{papakis2021graph,
+  title={A graph convolutional neural network based approach for traffic monitoring using augmented detections with optical flow},
+  author={Papakis, Ioannis and Sarkar, Abhijit and Karpatne, Anuj},
+  booktitle={2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},
+  pages={2980--2986},
+  year={2021},
+  organization={IEEE}
+}
+
+
+@inproceedings{yu2021physics,
+  title={Physics-guided ai for large-scale spatiotemporal data},
+  author={Yu, Rose and Perdikaris, Paris and Karpatne, Anuj},
+  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
+  pages={4088--4089},
+  year={2021}
+}
+
+@inproceedings{daw2021pid,
+  title={PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics},
+  author={Daw, Arka and Maruf, M and Karpatne, Anuj},
+  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
+  pages={237--247},
+  year={2021}
+}
+
+@inproceedings{elhamod2021biology,
+  title={Biology-guided neural network for species classification},
+  author={Elhamod, M and Maruf, MA and Mandke, PK and Karpatne, A},
+  booktitle={INTEGRATIVE AND COMPARATIVE BIOLOGY},
+  volume={61},
+  pages={E228--E229},
+  year={2021},
+  organization={OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA}
+}
+
+@inproceedings{maruf2021biology,
+  title={Biology-guided neural network for fish trait discovery},
+  author={Maruf, MA and Elhamod, M and Mandke, PK and Karpatne, A},
+  booktitle={INTEGRATIVE AND COMPARATIVE BIOLOGY},
+  volume={61},
+  pages={E577--E578},
+  year={2021},
+  organization={OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA}
+}
+
+
+
+## changed inbook to inproceedings
+
+
+
+
+
+@article{doi:10.1089/big.2020.0071,
+author = {Muralidhar, Nikhil and Bu, Jie and Cao, Ze and He, Long and Ramakrishnan, Naren and Tafti, Danesh and Karpatne, Anuj},
+title = {Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems},
+journal = {Big Data},
+volume = {8},
+number = {5},
+pages = {431-449},
+year = {2020},
+doi = {10.1089/big.2020.0071},
+note ={PMID: 33090021},
+URL = {https://doi.org/10.1089/big.2020.0071},
+eprint = {https://doi.org/10.1089/big.2020.0071},
+}
+
+@article{papakis2020gcnnmatch,
+  title={Gcnnmatch: Graph convolutional neural networks for multi-object tracking via sinkhorn normalization},
+  author={Papakis, Ioannis and Sarkar, Abhijit and Karpatne, Anuj},
+  journal={arXiv preprint arXiv:2010.00067},
+  year={2020}
+}
+
+
+
+
+
+
+
+
+@article{muralidhar2019physics,
+  title={Physics-guided design and learning of neural networks for predicting drag force on particle suspensions in moving fluids},
+  author={Muralidhar, Nikhil and Bu, Jie and Cao, Ze and He, Long and Ramakrishnan, Naren and Tafti, Danesh and Karpatne, Anuj},
+  journal={arXiv preprint arXiv:1911.04240},
+  year={2019}
+}
+
+@inproceedings{daw2019physics,
+  title={Physics-aware architecture of neural networks for uncertainty quantification: Application in lake temperature modeling},
+  author={Daw, Arka and Karpatne, Anuj},
+  year={2019},
+  organization={SIGKDD}
+}
+