pwc-archive/papers-with-abstracts huggingface dataset:
Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows
Authors: Ahmad Peyvan, Varun Kumar, George Em Karniadakis
Date: 2025-01-03 00:00:00

Shape optimization is essential in aerospace vehicle design, including reentry systems, and propulsion system components, as it directly influences aerodynamic efficiency, structural integrity, and overall mission success. Rapid and accurate prediction of external and internal flows accelerates design iterations. To this end, we develop a new variant of DeepONet, called Fusion-DeepONet as a fast surrogate model for geometry-dependent hypersonic and supersonic flow fields. We evaluated Fusion-DeepONet in learning two external hypersonic flows and a supersonic shape-dependent internal flow problem. First, we compare the performance of Fusion-DeepONet with state-of-the-art neural operators to learn inviscid hypersonic flow around semi-elliptic blunt bodies for two grid types: uniform Cartesian and irregular grids. Fusion-DeepONet provides comparable accuracy to parameter-conditioned U-Net on uniform grids while outperforming MeshGraphNet and Vanilla-DeepONet on irregular grids. Fusion-DeepONet requires significantly fewer trainable parameters than U-Net, MeshGraphNet, and FNO. For the second hypersonic problem, we set up Fusion-DeepONet to map from geometry and free stream Mach number to the temperature field around a reentry capsule traveling at hypersonic speed. This fast surrogate is then improved to predict the spatial derivative of the temperature, resulting in an accurate prediction of heat flux at the surfaces of the capsule. To enhance the accuracy of spatial derivative prediction, we introduce a derivative-enhanced loss term with the least computation overhead. For the third problem, we show that Fusion-DeepONet outperforms MeshGraphNet in learning geometry-dependent supersonic flow in a converging-diverging nozzle configuration. For all the problems, we used high-fidelity simulations with a high-order entropy-stable DGSEM solver to generate training datasets with limited samples.
Improving Transducer-Based Spoken Language Understanding with Self-Conditioned CTC and Knowledge Transfer
Authors: Vishal Sunder, Eric Fosler-Lussier
Date: 2025-01-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Spoken Language Understanding, Transfer Learning

In this paper, we propose to improve end-to-end (E2E) spoken language understand (SLU) in an RNN transducer model (RNN-T) by incorporating a joint self-conditioned CTC automatic speech recognition (ASR) objective. Our proposed model is akin to an E2E differentiable cascaded model which performs ASR and SLU sequentially and we ensure that the SLU task is conditioned on the ASR task by having CTC self conditioning. This novel joint modeling of ASR and SLU improves SLU performance significantly over just using SLU optimization. We further improve the performance by aligning the acoustic embeddings of this model with the semantically richer BERT model. Our proposed knowledge transfer strategy makes use of a bag-of-entity prediction layer on the aligned embeddings and the output of this is used to condition the RNN-T based SLU decoding. These techniques show significant improvement over several strong baselines and can perform at par with large models like Whisper with significantly fewer parameters.
CSI Compression using Channel Charting
Authors: Baptiste Chatelier, Vincent Corlay, Matthieu Crussière, Luc Le Magoarou
Date: 2024-12-16 00:00:00
Tasks: Dimensionality Reduction

Reaping the benefits of multi-antenna communication systems in frequency division duplex (FDD) requires channel state information (CSI) reporting from mobile users to the base station (BS). Over the last decades, the amount of CSI to be collected has become very challenging owing to the dramatic increase of the number of antennas at BSs. To mitigate the overhead associated with CSI reporting, compressed CSI techniques have been proposed with the idea of recovering the original CSI at the BS from its compressed version sent by the mobile users. Channel charting is an unsupervised dimensionality reduction method that consists in building a radio-environment map from CSIs. Such a method can be considered in the context of the CSI compression problem, since a chart location is, by definition, a low-dimensional representation of the CSI. In this paper, the performance of channel charting for a task-based CSI compression application is studied. A comparison of the proposed method against baselines on realistic synthetic data is proposed, showing promising results.
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines
Authors: Sharika J. Hegde, Max T. M. Ng, Marcos Rios, Hani S. Mahmassani, Ying Chen, Karen Smilowitz
Date: 2024-12-30 00:00:00
Tasks: Interpretable Machine Learning

The COVID-19 vaccine development, manufacturing, transportation, and administration proved an extreme logistics operation of global magnitude. Global vaccination levels, however, remain a key concern in preventing the emergence of new strains and minimizing the impact of the pandemic's disruption of daily life. In this paper, country-level vaccination rates are analyzed through a queuing framework to extract service rates that represent the practical capacity of a country to administer vaccines. These rates are further characterized through regression and interpretable machine learning methods with country-level demographic, governmental, and socio-economic variates. Model results show that participation in multi-governmental collaborations such as COVAX may improve the ability to vaccinate. Similarly, improved transportation and accessibility variates such as roads per area for low-income countries and rail lines per area for high-income countries can improve rates. It was also found that for low-income countries specifically, improvements in basic and health infrastructure (as measured through spending on healthcare, number of doctors and hospital beds per 100k, population percent with access to electricity, life expectancy, and vehicles per 1000 people) resulted in higher vaccination rates. Of the high-income countries, those with larger 65-plus populations struggled to vaccinate at high rates, indicating potential accessibility issues for the elderly. This study finds that improving basic and health infrastructure, focusing on accessibility in the last mile, particularly for the elderly, and fostering global partnerships can improve logistical operations of such a scale. Such structural impediments and inequities in global health care must be addressed in preparation for future global public health crises.
SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction
Authors: Gaofeng Chen, Yaoduo Zhang, Li Huang, Pengfei Wang, Wenyu Zhang, Dong Zeng, Jianhua Ma, Ji He
Date: 2024-12-31 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction

Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In recent years, self-supervised deep-learning (SSDL) techniques have shown great potential for the studies of CT image reconstruction. In this work, we propose a self-supervised cross-task mutual learning (SS-CTML) framework for CT image reconstruction. Specifically, a sparse-view scanned and a limited-view scanned sinogram data are first extracted from a full-view scanned sinogram data, which results in three individual reconstruction tasks, i.e., the full-view CT (FVCT) reconstruction, the sparse-view CT (SVCT) reconstruction, and limited-view CT (LVCT) reconstruction. Then, three neural networks are constructed for the three reconstruction tasks. Considering that the ultimate goals of the three tasks are all to reconstruct high-quality CT images, we therefore construct a set of cross-task mutual learning objectives for the three tasks, in which way, the three neural networks can be self-supervised optimized by learning from each other. Clinical datasets are adopted to evaluate the effectiveness of the proposed framework. Experimental results demonstrate that the SS-CTML framework can obtain promising CT image reconstruction performance in terms of both quantitative and qualitative measurements.
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution
Authors: Qiwei Zhu, Kai Li, Guojing Zhang, Xiaoying Wang, Jianqiang Huang, Xilai Li
Date: 2024-12-31 00:00:00
Tasks: Image Super-Resolution, State Space Models, Super-Resolution

In recent years, deep neural networks, including Convolutional Neural Networks, Transformers, and State Space Models, have achieved significant progress in Remote Sensing Image (RSI) Super-Resolution (SR). However, existing SR methods typically overlook the complementary relationship between global and local dependencies. These methods either focus on capturing local information or prioritize global information, which results in models that are unable to effectively capture both global and local features simultaneously. Moreover, their computational cost becomes prohibitive when applied to large-scale RSIs. To address these challenges, we introduce the novel application of Receptance Weighted Key Value (RWKV) to RSI-SR, which captures long-range dependencies with linear complexity. To simultaneously model global and local features, we propose the Global-Detail dual-branch structure, GDSR, which performs SR reconstruction by paralleling RWKV and convolutional operations to handle large-scale RSIs. Furthermore, we introduce the Global-Detail Reconstruction Module (GDRM) as an intermediary between the two branches to bridge their complementary roles. In addition, we propose Wavelet Loss, a loss function that effectively captures high-frequency detail information in images, thereby enhancing the visual quality of SR, particularly in terms of detail reconstruction. Extensive experiments on several benchmarks, including AID, AID_CDM, RSSRD-QH, and RSSRD-QH_CDM, demonstrate that GSDR outperforms the state-of-the-art Transformer-based method HAT by an average of 0.05 dB in PSNR, while using only 63% of its parameters and 51% of its FLOPs, achieving an inference speed 2.9 times faster. Furthermore, the Wavelet Loss shows excellent generalization across various architectures, providing a novel perspective for RSI-SR enhancement.
Estimation of 3T MR images from 1.5T images regularized with Physics based Constraint
Authors: Prabhjot Kaur, Atul Singh Minhas, Chirag Kamal Ahuja, Anil Kumar Sao
Date: 2024-12-31 00:00:00
Tasks: Image Registration

Limited accessibility to high field MRI scanners (such as 7T, 11T) has motivated the development of post-processing methods to improve low field images. Several existing post-processing methods have shown the feasibility to improve 3T images to produce 7T-like images [3,18]. It has been observed that improving lower field (LF, <=1.5T) images comes with additional challenges due to poor image quality such as the function mapping 1.5T and higher field (HF, 3T) images is more complex than the function relating 3T and 7T images [10]. Except for [10], no method has been addressed to improve <=1.5T MRI images. Further, most of the existing methods [3,18] including [10] require example images, and also often rely on pixel to pixel correspondences between LF and HF images which are usually inaccurate for <=1.5T images. The focus of this paper is to address the unsupervised framework for quality improvement of 1.5T images and avoid the expensive requirements of example images and associated image registration. The LF and HF images are assumed to be related by a linear transformation (LT). The unknown HF image and unknown LT are estimated in alternate minimization framework. Further, a physics based constraint is proposed that provides an additional non-linear function relating LF and HF images in order to achieve the desired high contrast in estimated HF image. The experimental results demonstrate that the proposed approach provides processed 1.5T images, i.e., estimated 3T-like images with improved image quality, and is comparably better than the existing methods addressing similar problems. The improvement in image quality is also shown to provide better tissue segmentation and volume quantification as compared to scanner acquired 1.5T images.
An unsupervised method for MRI recovery: Deep image prior with structured sparsity
Authors: Muhammad Ahmad Sultan, Chong Chen, Yingmin Liu, Katarzyna Gil, Karolina Zareba, Rizwan Ahmad
Date: 2025-01-02 00:00:00
Tasks: compressed sensing, Image Reconstruction, MRI Reconstruction, SSIM

Objective: To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data. Materials and Methods: The proposed method, deep image prior with structured sparsity (DISCUS), extends the deep image prior (DIP) by introducing group sparsity to frame-specific code vectors, enabling the discovery of a low-dimensional manifold for capturing temporal variations. \discus was validated using four studies: (I) simulation of a dynamic Shepp-Logan phantom to demonstrate its manifold discovery capabilities, (II) comparison with compressed sensing and DIP-based methods using simulated single-shot late gadolinium enhancement (LGE) image series from six distinct digital cardiac phantoms in terms of normalized mean square error (NMSE) and structural similarity index measure (SSIM), (III) evaluation on retrospectively undersampled single-shot LGE data from eight patients, and (IV) evaluation on prospectively undersampled single-shot LGE data from eight patients, assessed via blind scoring from two expert readers. Results: DISCUS outperformed competing methods, demonstrating superior reconstruction quality in terms of NMSE and SSIM (Studies I--III) and expert reader scoring (Study IV). Discussion: An unsupervised image reconstruction method is presented and validated on simulated and measured data. These developments can benefit applications where acquiring fully sampled data is challenging.
Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering
Authors: Cicero X. Lu, Tushar Mittal, Christine H. Chen, Alexis Y. Li, Kadin Worthen, B. A. Sargent, Carey M. Lisse, G. C. Sloan, Dean C. Hines, Dan M. Watson, Isabel Rebollido, Bin B. Ren, Joel D. Green
Date: 2025-01-02 00:00:00
Tasks: Clustering, Diversity, Interpretable Machine Learning

Debris disks, which consist of dust, planetesimals, planets, and gas, offer a unique window into the mineralogical composition of their parent bodies, especially during the critical phase of terrestrial planet formation spanning 10 to a few hundred million years. Observations from the $\textit{Spitzer}$ Space Telescope have unveiled thousands of debris disks, yet systematic studies remain scarce, let alone those with unsupervised clustering techniques. This study introduces $\texttt{CLUES}$ (CLustering UnsupErvised with Sequencer), a novel, non-parametric, fully-interpretable machine-learning spectral analysis tool designed to analyze and classify the spectral data of debris disks. $\texttt{CLUES}$ combines multiple unsupervised clustering methods with multi-scale distance measures to discern new groupings and trends, offering insights into compositional diversity and geophysical processes within these disks. Our analysis allows us to explore a vast parameter space in debris disk mineralogy and also offers broader applications in fields such as protoplanetary disks and solar system objects. This paper details the methodology, implementation, and initial results of $\texttt{CLUES}$, setting the stage for more detailed follow-up studies focusing on debris disk mineralogy and demographics.
Unsupervised learning for anticipating critical transitions
Authors: Shirin Panahi, Ling-Wei Kong, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
Date: 2025-01-02 00:00:00
Tasks: Time Series

For anticipating critical transitions in complex dynamical systems, the recent approach of parameter-driven reservoir computing requires explicit knowledge of the bifurcation parameter. We articulate a framework combining a variational autoencoder (VAE) and reservoir computing to address this challenge. In particular, the driving factor is detected from time series using the VAE in an unsupervised-learning fashion and the extracted information is then used as the parameter input to the reservoir computer for anticipating the critical transition. We demonstrate the power of the unsupervised learning scheme using prototypical dynamical systems including the spatiotemporal Kuramoto-Sivashinsky system. The scheme can also be extended to scenarios where the target system is driven by several independent parameters or with partial state observations.
iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice Embeddings
Authors: Shuhei Tomoshige, Hayato Muraki, Kenichi Oishi, Hitoshi Iyatomi
Date: 2025-01-03 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Current methods for searching brain MR images rely on text-based approaches, highlighting a significant need for content-based image retrieval (CBIR) systems. Directly applying 3D brain MR images to machine learning models offers the benefit of effectively learning the brain's structure; however, building the generalized model necessitates a large amount of training data. While models that consider depth direction and utilize continuous 2D slices have demonstrated success in segmentation and classification tasks involving 3D data, concerns remain. Specifically, using general 2D slices may lead to the oversight of pathological features and discontinuities in depth direction information. Furthermore, to the best of the authors' knowledge, there have been no attempts to develop a practical CBIR system that preserves the entire brain's structural information. In this study, we propose an interpretable CBIR method for brain MR images, named iCBIR-Sli (Interpretable CBIR with 2D Slice Embedding), which, for the first time globally, utilizes a series of 2D slices. iCBIR-Sli addresses the challenges associated with using 2D slices by effectively aggregating slice information, thereby achieving low-dimensional representations with high completeness, usability, robustness, and interoperability, which are qualities essential for effective CBIR. In retrieval evaluation experiments utilizing five publicly available brain MR datasets (ADNI2/3, OASIS3/4, AIBL) for Alzheimer's disease and cognitively normal, iCBIR-Sli demonstrated top-1 retrieval performance (macro F1 = 0.859), comparable to existing deep learning models explicitly designed for classification, without the need for an external classifier. Additionally, the method provided high interpretability by clearly identifying the brain regions indicative of the searched-for disease.
Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
Authors: Tien Dang, Viet Thanh Duy Nguyen, Minh Tuan Le, Truong-Son Hy
Date: 2025-01-03 00:00:00
Tasks: Contrastive Learning, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Representation Learning

Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential novel drug-disease relations. We introduce a novel multimodal approach that unifies embeddings from specialized Language Models (LMs) with Graph Contrastive Learning (GCL) to enhance intra-entity relationships while employing a Knowledge Graph Embedding (KGE) model to capture inter-entity relationships for effective link prediction. To address limitations in existing BKGs, we present PrimeKG++, an enriched knowledge graph incorporating multimodal data, including biological sequences and textual descriptions for each entity type. By combining semantic and relational information in a unified representation, our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes. Experimental results on PrimeKG++ and the DrugBank drug-target interaction dataset demonstrate the effectiveness and robustness of our method across diverse biomedical datasets. Our source code, pre-trained models, and data are publicly available at https://github.com/HySonLab/BioMedKG
Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent
Authors: Tong Wu
Date: 2025-01-03 00:00:00

Tensors, which give a faithful and effective representation to deliver the intrinsic structure of multi-dimensional data, play a crucial role in an increasing number of signal processing and machine learning problems. However, tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. A fundamental challenge is to reliably extract the meaningful information from corrupted tensor data in a statistically and computationally efficient manner. This paper develops a scaled gradient descent (ScaledGD) algorithm to directly estimate the tensor factors with tailored spectral initializations under the tensor-tensor product (t-product) and tensor singular value decomposition (t-SVD) framework. In theory, ScaledGD achieves linear convergence at a constant rate that is independent of the condition number of the ground truth low-rank tensor for two canonical problems -- tensor robust principal component analysis and tensor completion -- as long as the level of corruptions is not too large and the sample size is sufficiently large, while maintaining the low per-iteration cost of gradient descent. To the best of our knowledge, ScaledGD is the first algorithm that provably has such properties for low-rank tensor estimation with the t-SVD decomposition. Finally, numerical examples are provided to demonstrate the efficacy of ScaledGD in accelerating the convergence rate of ill-conditioned low-rank tensor estimation in these two applications.
Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
Authors: Wei-Bin Kou, Qingfeng Lin, Ming Tang, Jingreng Lei, Shuai Wang, Rongguang Ye, Guangxu Zhu, Yik-Chung Wu
Date: 2025-01-03 00:00:00
Tasks: Autonomous Driving

To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progresses, the amount of data fitted by the AD model expands, which helps to improve the AD model generalization substantially. However, such ever-expanding data is a double-edged sword for the AD model. Specifically, as the fitted data volume grows to exceed the the AD model's fitting capacities, the AD model is prone to under-fitting. To address this issue, we propose to use a pretrained Large Vision Models (LVMs) as backbone coupled with downstream perception head to understand AD semantic information. This design can not only surmount the aforementioned under-fitting problem due to LVMs' powerful fitting capabilities, but also enhance the perception generalization thanks to LVMs' vast and diverse training data. On the other hand, to mitigate vehicles' computational burden of training the perception head while running LVM backbone, we introduce a Posterior Optimization Trajectory (POT)-Guided optimization scheme (POTGui) to accelerate the convergence. Concretely, we propose a POT Generator (POTGen) to generate posterior (future) optimization direction in advance to guide the current optimization iteration, through which the model can generally converge within 10 epochs. Extensive experiments demonstrate that the proposed method improves the performance by over 66.48\% and converges faster over 6 times, compared to the existing state-of-the-art approach.