pwc-archive/papers-with-abstracts huggingface dataset:
FeruzaSpeech: A 60 Hour Uzbek Read Speech Corpus with Punctuation, Casing, and Context
Authors: Anna Povey, Katherine Povey
Date: 2024-09-23 00:00:00

This paper introduces FeruzaSpeech, a read speech corpus of the Uzbek language, containing transcripts in both Cyrillic and Latin alphabets, freely available for academic research purposes. This corpus includes 60 hours of high-quality recordings from a single native female speaker from Tashkent, Uzbekistan. These recordings consist of short excerpts from a book and BBC News. This paper discusses the enhancement of the Word Error Rates (WERs) on CommonVoice 16.1's Uzbek data, Uzbek Speech Corpus data, and FeruzaSpeech data upon integrating FeruzaSpeech.
Optimized Magnetic Resonance Fingerprinting Using Ziv-Zakai Bound
Authors: Chaoguang Gong, Yue Hu, Peng Li, Lixian Zou, Congcong Liu, Yihang Zhou, Yanjie Zhu, Dong Liang, Haifeng Wang
Date: 2024-10-09 00:00:00
Tasks: Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative imaging technique within the field of Magnetic Resonance Imaging (MRI), offers comprehensive insights into tissue properties by simultaneously acquiring multiple tissue parameter maps in a single acquisition. Sequence optimization is crucial for improving the accuracy and efficiency of MRF. In this work, a novel framework for MRF sequence optimization is proposed based on the Ziv-Zakai bound (ZZB). Unlike the Cram\'er-Rao bound (CRB), which aims to enhance the quality of a single fingerprint signal with deterministic parameters, ZZB provides insights into evaluating the minimum mismatch probability for pairs of fingerprint signals within the specified parameter range in MRF. Specifically, the explicit ZZB is derived to establish a lower bound for the discrimination error in the fingerprint signal matching process within MRF. This bound illuminates the intrinsic limitations of MRF sequences, thereby fostering a deeper understanding of existing sequence performance. Subsequently, an optimal experiment design problem based on ZZB was formulated to ascertain the optimal scheme of acquisition parameters, maximizing discrimination power of MRF between different tissue types. Preliminary numerical experiments show that the optimized ZZB scheme outperforms both the conventional and CRB schemes in terms of the reconstruction accuracy of multiple parameter maps.
On the Solution of Linearized Inverse Scattering Problems in Near-Field Microwave Imaging by Operator Inversion and Matched Filtering
Authors: Matthias M. Saurer, Han Na, Marius Brinkmann, Thomas F. Eibert
Date: 2024-10-09 00:00:00
Tasks: Image Generation

Microwave imaging is commonly based on the solution of linearized inverse scattering problems by matched filtering algorithms, i.e., by applying the adjoint of the forward scattering operator to the observation data. A more rigorous approach is the explicit inversion of the forward scattering operator, which is performed in this work for quasi-monostatic imaging scenarios based on a planar plane-wave representation according to the Weyl-identity and hierarchical acceleration algorithms. The inversion is achieved by a regularized iterative linear system of equations solver, where irregular observations as well as full probe correction are supported. In the spatial image generation low-pass filtering can be considered in order to reduce imaging artifacts. A corresponding spectral backprojection algorithm and a spatial back-projection algorithm together with improved focusing operators are also introduced and the resulting image generation algorithms are analyzed and compared for a variety of examples, comprising both simulated and measured observation data. It is found that the inverse source solution generally performs better in term of robustness, focusing capabilities, and image accuracy compared to the adjoint imaging algorithms either operating in the spatial or spectral domain. This is especially demonstrated in the context of irregular sampling grids with non-ideal or truncated observation data and by evaluating all reconstruction results based on a rigorous quantitative analysis.
RNR-Nav: A Real-World Visual Navigation System Using Renderable Neural Radiance Maps
Authors: Minsoo Kim, Obin Kwon, Howoong Jun, Songhwai Oh
Date: 2024-10-08 00:00:00
Tasks: Visual Localization, Visual Navigation

We propose a novel visual localization and navigation framework for real-world environments directly integrating observed visual information into the bird-eye-view map. While the renderable neural radiance map (RNR-Map) shows considerable promise in simulated settings, its deployment in real-world scenarios poses undiscovered challenges. RNR-Map utilizes projections of multiple vectors into a single latent code, resulting in information loss under suboptimal conditions. To address such issues, our enhanced RNR-Map for real-world robots, RNR-Map++, incorporates strategies to mitigate information loss, such as a weighted map and positional encoding. For robust real-time localization, we integrate a particle filter into the correlation-based localization framework using RNRMap++ without a rendering procedure. Consequently, we establish a real-world robot system for visual navigation utilizing RNR-Map++, which we call "RNR-Nav." Experimental results demonstrate that the proposed methods significantly enhance rendering quality and localization robustness compared to previous approaches. In real-world navigation tasks, RNR-Nav achieves a success rate of 84.4%, marking a 68.8% enhancement over the methods of the original RNR-Map paper.
NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping
Authors: Yamin Li, Ange Lou, Ziyuan Xu, Shengchao Zhang, Shiyu Wang, Dario J. Englot, Soheil Kolouri, Daniel Moyer, Roza G. Bayrak, Catie Chang
Date: 2024-10-07 00:00:00
Tasks: Brain Decoding, EEG, Representation Learning

Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advancements in cross-modality synthesis and brain decoding, the use of deep neural networks has emerged as a promising solution for inferring whole-brain, high-resolution fMRI features directly from electroencephalography (EEG), a more widely accessible and portable neuroimaging modality. Nonetheless, the complex projection from neural activity to fMRI hemodynamic responses and the spatial ambiguity of EEG pose substantial challenges both in modeling and interpretability. Relatively few studies to date have developed approaches for EEG-fMRI translation, and although they have made significant strides, the inference of fMRI signals in a given study has been limited to a small set of brain areas and to a single condition (i.e., either resting-state or a specific task). The capability to predict fMRI signals in other brain areas, as well as to generalize across conditions, remain critical gaps in the field. To tackle these challenges, we introduce a novel and generalizable framework: NeuroBOLT, i.e., Neuro-to-BOLD Transformer, which leverages multi-dimensional representation learning from temporal, spatial, and spectral domains to translate raw EEG data to the corresponding fMRI activity signals across the brain. Our experiments demonstrate that NeuroBOLT effectively reconstructs unseen resting-state fMRI signals from primary sensory, high-level cognitive areas, and deep subcortical brain regions, achieving state-of-the-art accuracy with the potential to generalize across varying conditions and sites, which significantly advances the integration of these two modalities.
Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction
Authors: Shijun Liang, Ismail Alkhouri, Qing Qu, Rongrong Wang, Saiprasad Ravishankar
Date: 2024-10-06 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Image Reconstruction

Deep learning (DL) methods have been extensively applied to various image recovery problems, including magnetic resonance imaging (MRI) and computed tomography (CT) reconstruction. Beyond supervised models, other approaches have been recently explored including two key recent schemes: Deep Image Prior (DIP) that is an unsupervised scan-adaptive method that leverages the network architecture as implicit regularization but can suffer from noise overfitting, and diffusion models (DMs), where the sampling procedure of a pre-trained generative model is modified to allow sampling from the measurement-conditioned distribution through approximations. In this paper, we propose combining DIP and DMs for MRI and CT reconstruction, motivated by (i) the impact of the DIP network input and (ii) the use of DMs as diffusion purifiers (DPs). Specifically, we propose a sequential procedure that iteratively optimizes the DIP network with a DM-refined adaptive input using a loss with data consistency and autoencoding terms. We term the approach Sequential Diffusion-Guided DIP (uDiG-DIP). Our experimental results demonstrate that uDiG-DIP achieves superior reconstruction results compared to leading DM-based baselines and the original DIP for MRI and CT tasks.
Shadow Augmentation for Handwashing Action Recognition: from Synthetic to Real Datasets
Authors: Shengtai Ju, Amy R. Reibman
Date: 2024-10-04 00:00:00
Tasks: Action Recognition

Video analytics systems designed for deployment in outdoor conditions can be vulnerable to many environmental changes, particularly changes in shadow. Existing works have shown that shadow and its introduced distribution shift can cause system performance to degrade sharply. In this paper, we explore mitigation strategies to shadow-induced breakdown points of an action recognition system, using the specific application of handwashing action recognition for improving food safety. Using synthetic data, we explore the optimal shadow attributes to be included when training an action recognition system in order to improve performance under different shadow conditions. Experimental results indicate that heavier and larger shadow is more effective at mitigating the breakdown points. Building upon this observation, we propose a shadow augmentation method to be applied to real-world data. Results demonstrate the effectiveness of the shadow augmentation method for model training and consistency of its effectiveness across different neural network architectures and datasets.
On the Rate-Distortion-Complexity Trade-offs of Neural Video Coding
Authors: Yi-Hsin Chen, Kuan-Wei Ho, Martin Benjak, Jörn Ostermann, Wen-Hsiao Peng
Date: 2024-10-04 00:00:00

This paper aims to delve into the rate-distortion-complexity trade-offs of modern neural video coding. Recent years have witnessed much research effort being focused on exploring the full potential of neural video coding. Conditional autoencoders have emerged as the mainstream approach to efficient neural video coding. The central theme of conditional autoencoders is to leverage both spatial and temporal information for better conditional coding. However, a recent study indicates that conditional coding may suffer from information bottlenecks, potentially performing worse than traditional residual coding. To address this issue, recent conditional coding methods incorporate a large number of high-resolution features as the condition signal, leading to a considerable increase in the number of multiply-accumulate operations, memory footprint, and model size. Taking DCVC as the common code base, we investigate how the newly proposed conditional residual coding, an emerging new school of thought, and its variants may strike a better balance among rate, distortion, and complexity.
Stochastic Deep Restoration Priors for Imaging Inverse Problems
Authors: Yuyang Hu, Albert Peng, Weijie Gan, Peyman Milanfar, Mauricio Delbracio, Ulugbek S. Kamilov
Date: 2024-10-02 00:00:00
Tasks: Denoising, Image Super-Resolution, Super-Resolution

Deep neural networks trained as image denoisers are widely used as priors for solving imaging inverse problems. While Gaussian denoising is thought sufficient for learning image priors, we show that priors from deep models pre-trained as more general restoration operators can perform better. We introduce Stochastic deep Restoration Priors (ShaRP), a novel method that leverages an ensemble of such restoration models to regularize inverse problems. ShaRP improves upon methods using Gaussian denoiser priors by better handling structured artifacts and enabling self-supervised training even without fully sampled data. We prove ShaRP minimizes an objective function involving a regularizer derived from the score functions of minimum mean square error (MMSE) restoration operators, and theoretically analyze its convergence. Empirically, ShaRP achieves state-of-the-art performance on tasks such as magnetic resonance imaging reconstruction and single-image super-resolution, surpassing both denoiser-and diffusion-model-based methods without requiring retraining.
Frequency-Based Federated Domain Generalization for Polyp Segmentation
Authors: Hongyi Pan, Debesh Jha, Koushik Biswas, Ulas Bagci
Date: 2024-10-02 00:00:00
Tasks: Domain Generalization, Federated Learning, Medical Image Analysis, Segmentation

Federated Learning (FL) offers a powerful strategy for training machine learning models across decentralized datasets while maintaining data privacy, yet domain shifts among clients can degrade performance, particularly in medical imaging tasks like polyp segmentation. This paper introduces a novel Frequency-Based Domain Generalization (FDG) framework, utilizing soft-thresholding and hard-thresholding in the Fourier domain to address these challenges. By applying soft-thresholding and hard-thresholding to Fourier coefficients, our method generates new images with reduced background noise and enhances the model's ability to generalize across diverse medical imaging domains. Extensive experiments demonstrate substantial improvements in segmentation accuracy and domain robustness over baseline methods. This innovation integrates frequency domain techniques into FL, presenting a resilient approach to overcoming domain variability in decentralized medical image analysis.
Conditional Optimal Filter Selection for Multispectral Object Classification
Authors: Katja Kossira, David Schön, Jürgen Seiler, André Kaup
Date: 2024-10-02 00:00:00
Tasks: Classification

Capturing images using multispectral camera arrays has gained importance in medical, agricultural and environmental processes. However, using all available spectral bands is infeasible and produces much data, while only a fraction is needed for a given task. Nearby bands may contain similar information, therefore redundant spectral bands should not be considered in the evaluation process to keep complexity and the data load low. In current methods, a restricted and pre-determined number of spectral bands is selected. Our approach improves this procedure by including preset conditions such as noise or the bandwidth of available filters, minimizing spectral redundancy. Furthermore, a minimal filter selection can be conducted, keeping the hardware setup at low costs, while still obtaining all important spectral information. In comparison to the fast binary search filter band selection method, we managed to reduce the amount of misclassified objects of the SMM dataset from 318 to 124 using a random forest classifier.
Design Space Exploration at Frame-Level for Joint Decoding Energy and Quality Optimization in VVC
Authors: Teresa Stürzenhofäcker, Matthias Kränzler, Christian Herglotz, André Kaup
Date: 2024-10-01 00:00:00

In the pursuit of a reduced energy demand of VVC decoders, it was found that the coding tool configuration has a substantial influence on the bit rate efficiency and the decoding energy demand. The Advanced Design Space Exploration algorithm as proposed in the literature, can derive coding tool configurations that provide optimal trade-offs between rate and energy efficiency. Yet, some trade-off points in the design space cannot be reached with the state-of-the-art methodology, which defines coding tools for an entire bitstream. This work proposes a novel, granular adjustment of the coding tool usage in VVC. Consequently, the optimization algorithm is adjusted to explore coding tool configurations that operate on frame-level. Moreover, new optimization criteria are introduced to focus the search on specific bit rates. As a result, coding tool configurations are obtained which yield so far inaccessible trade-offs between bit rate efficiency and decoding energy demand for VVC-coded sequences. The proposed methodology extends the design space and enhances the continuity of the Pareto front.
Demons registration for 2D empirical wavelet transforms
Authors: Charles-Gérard Lucas, Jérôme Gilles
Date: 2024-09-19 00:00:00

The empirical wavelet transform is a fully adaptive time-scale representation that has been widely used in the last decade. Inspired by the empirical mode decomposition, it consists of filter banks based on harmonic mode supports. Recently, it has been generalized to build the filter banks from any generating function using mappings. In practice, the harmonic mode supports can have low constrained shape in 2D, leading to numerical difficulties to compute the mappings and therefore the related wavelet filters. This work aims to propose an efficient numerical scheme to compute empirical wavelet coefficients using the demons registration algorithm. Results show that the proposed approach gives a numerically robust wavelet transform. An application to texture segmentation of scanning tunnelling microscope images is also presented.
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI
Authors: Xinyuan Zheng, Orren Ravid, Robert A. J. Barry, Yoojean Kim, Qian Wang, Young-geun Kim, Xi Zhu, Xiaofu He
Date: 2024-09-30 00:00:00
Tasks: Computational Efficiency, Denoising, Diagnostic, Dimensionality Reduction, Functional Connectivity

Autism spectrum disorders (ASDs) are developmental conditions characterized by restricted interests and difficulties in communication. The complexity of ASD has resulted in a deficiency of objective diagnostic biomarkers. Deep learning methods have gained recognition for addressing these challenges in neuroimaging analysis, but finding and interpreting such diagnostic biomarkers are still challenging computationally. Here, we propose a feature reduction pipeline using resting-state fMRI data. We used Craddock atlas and Power atlas to extract functional connectivity data from rs-fMRI, resulting in over 30 thousand features. By using a denoising variational autoencoder, our proposed pipeline further compresses the connectivity features into 5 latent Gaussian distributions, providing is a low-dimensional representation of the data to promote computational efficiency and interpretability. To test the method, we employed the extracted latent representations to classify ASD using traditional classifiers such as SVM on a large multi-site dataset. The 95% confidence interval for the prediction accuracy of SVM is [0.63, 0.76] after site harmonization using the extracted latent distributions. Without using DVAE for dimensionality reduction, the prediction accuracy is 0.70, which falls within the interval. The DVAE successfully encoded the diagnostic information from rs-fMRI data without sacrificing prediction performance. The runtime for training the DVAE and obtaining classification results from its extracted latent features was 7 times shorter compared to training classifiers directly on the raw data. Our findings suggest that the Power atlas provides more effective brain connectivity insights for diagnosing ASD than Craddock atlas. Additionally, we visualized the latent representations to gain insights into the brain networks contributing to the differences between ASD and neurotypical brains.
Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models
Authors: Aymane Khaldi, El Mostafa Kalmoun
Date: 2024-09-29 00:00:00
Tasks: Hyperparameter Optimization

Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.
Looking through the mind's eye via multimodal encoder-decoder networks
Authors: Arman Afrasiyabi, Erica Busch, Rahul Singh, Dhananjay Bhaskar, Laurent Caplette, Nicholas Turk-Browne, Smita Krishnaswamy
Date: 2024-09-27 00:00:00
Tasks: Decoder

In this work, we explore the decoding of mental imagery from subjects using their fMRI measurements. In order to achieve this decoding, we first created a mapping between a subject's fMRI signals elicited by the videos the subjects watched. This mapping associates the high dimensional fMRI activation states with visual imagery. Next, we prompted the subjects textually, primarily with emotion labels which had no direct reference to visual objects. Then to decode visual imagery that may have been in a person's mind's eye, we align a latent representation of these fMRI measurements with a corresponding video-fMRI based on textual labels given to the videos themselves. This alignment has the effect of overlapping the video fMRI embedding with the text-prompted fMRI embedding, thus allowing us to use our fMRI-to-video mapping to decode. Additionally, we enhance an existing fMRI dataset, initially consisting of data from five subjects, by including recordings from three more subjects gathered by our team. We demonstrate the efficacy of our model on this augmented dataset both in accurately creating a mapping, as well as in plausibly decoding mental imagery.