pwc-archive/papers-with-abstracts huggingface dataset:
The Hamilton-Jacobi-Bellman Equation in Economic Dynamics with a Non-Smooth Fiscal Policy
Authors: Yuhki Hosoya
Date: 2024-05-26 00:00:00

We consider a class of economic growth models that includes the classical Ramsey--Cass--Koopmans capital accumulation model and verify that, under several assumptions, the value function of the model is the unique viscosity solution to the Hamilton--Jacobi--Bellman equation. Moreover, we discuss a solution method for these models using differential inclusion, where the subdifferential of the value function plays an important role. Next, we present an assumption under which the value function is a classical solution to the Hamilton--Jacobi--Bellman equation, and show that many economic models satisfy this assumption. In particular, our result still holds in an economic growth model in which the government takes a non-smooth Keynesian policy rule.
Comparing experiments in discounted problems
Authors: Ludovic Renou, Xavier Venel
Date: 2024-05-26 00:00:00

This paper compares statistical experiments in discounted problems, ranging from the simplest ones where the state is fixed and the flow of information exogenous to more complex ones, where the decision-maker controls the flow of information or the state changes over time.
Very Low Complexity Speech Synthesis Using Framewise Autoregressive GAN (FARGAN) with Pitch Prediction
Authors: Jean-Marc Valin, Ahmed Mustafa, Jan Büthe
Date: 2024-05-31 00:00:00
Tasks: Speech Synthesis

Neural vocoders are now being used in a wide range of speech processing applications. In many of those applications, the vocoder can be the most complex component, so finding lower complexity algorithms can lead to significant practical benefits. In this work, we propose FARGAN, an autoregressive vocoder that takes advantage of long-term pitch prediction to synthesize high-quality speech in small subframes, without the need for teacher-forcing. Experimental results show that the proposed 600~MFLOPS FARGAN vocoder can achieve both higher quality and lower complexity than existing low-complexity vocoders. The quality even matches that of existing higher-complexity vocoders.
Speech Loudness in Broadcasting and Streaming
Authors: Matteo Torcoli, Mhd Modar Halimeh, Thomas Leitz, Yannik Grewe, Michael Kratschmer, Bernhard Neugebauer, Adrian Murtaza, Harald Fuchs, Emanuël A. P. Habets
Date: 2024-05-27 00:00:00

The introduction and regulation of loudness in broadcasting and streaming brought clear benefits to the audience, e.g., a level of uniformity across programs and channels. Yet, speech loudness is frequently reported as being too low in certain passages, which can hinder the full understanding and enjoyment of movies and TV programs. This paper proposes expanding the set of loudness-based measures typically used in the industry. We focus on speech loudness, and we show that, when clean speech is not available, Deep Neural Networks (DNNs) can be used to isolate the speech signal and so to accurately estimate speech loudness, providing a more precise estimate compared to speech-gated loudness. Moreover, we define critical passages, i.e., passages in which speech is likely to be hard to understand. Critical passages are defined based on the local Speech Loudness Deviation (SLD) and the local Speech-to-Background Loudness Difference (SBLD), as SLD and SBLD significantly contribute to intelligibility and listening effort. In contrast to other more comprehensive measures of intelligibility and listening effort, SLD and SBLD can be straightforwardly measured, are intuitive, and, most importantly, can be easily controlled by adjusting the speech level in the mix or by enabling personalization at the user's end. Finally, examples are provided that show how the detection of critical passages can support the evaluation and control of the speech signal during and after content production.
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognition
Authors: Zilu Guo, Qing Wang, Jun Du, Jia Pan, Qing-Feng Liu, Chin-Hui
Date: 2024-05-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

In this paper, we propose a variance-preserving interpolation framework to improve diffusion models for single-channel speech enhancement (SE) and automatic speech recognition (ASR). This new variance-preserving interpolation diffusion model (VPIDM) approach requires only 25 iterative steps and obviates the need for a corrector, an essential element in the existing variance-exploding interpolation diffusion model (VEIDM). Two notable distinctions between VPIDM and VEIDM are the scaling function of the mean of state variables and the constraint imposed on the variance relative to the mean's scale. We conduct a systematic exploration of the theoretical mechanism underlying VPIDM and develop insights regarding VPIDM's applications in SE and ASR using VPIDM as a frontend. Our proposed approach, evaluated on two distinct data sets, demonstrates VPIDM's superior performances over conventional discriminative SE algorithms. Furthermore, we assess the performance of the proposed model under varying signal-to-noise ratio (SNR) levels. The investigation reveals VPIDM's improved robustness in target noise elimination when compared to VEIDM. Furthermore, utilizing the mid-outputs of both VPIDM and VEIDM results in enhanced ASR accuracies, thereby highlighting the practical efficacy of our proposed approach.
Speech enhancement deep-learning architecture for efficient edge processing
Authors: Monisankha Pal, Arvind Ramanathan, Ted Wada, Ashutosh Pandey
Date: 2024-05-27 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Speech Enhancement

Deep learning has become a de facto method of choice for speech enhancement tasks with significant improvements in speech quality. However, real-time processing with reduced size and computations for low-power edge devices drastically degrades speech quality. Recently, transformer-based architectures have greatly reduced the memory requirements and provided ways to improve the model performance through local and global contexts. However, the transformer operations remain computationally heavy. In this work, we introduce WaveUNet squeeze-excitation Res2 (WSR)-based metric generative adversarial network (WSR-MGAN) architecture that can be efficiently implemented on low-power edge devices for noise suppression tasks while maintaining speech quality. We utilize multi-scale features using Res2Net blocks that can be related to spectral content used in speech-processing tasks. In the generator, we integrate squeeze-excitation blocks (SEB) with multi-scale features for maintaining local and global contexts along with gated recurrent units (GRUs). The proposed approach is optimized through a combined loss function calculated over raw waveform, multi-resolution magnitude spectrogram, and objective metrics using a metric discriminator. Experimental results in terms of various objective metrics on VoiceBank+DEMAND and DNS-2020 challenge datasets demonstrate that the proposed speech enhancement (SE) approach outperforms the baselines and achieves state-of-the-art (SOTA) performance in the time domain.
Real-Time and Accurate: Zero-shot High-Fidelity Singing Voice Conversion with Multi-Condition Flow Synthesis
Authors: Hui Li, Hongyu Wang, Zhijin Chen, Bohan Sun, Bo Li
Date: 2024-05-23 00:00:00
Tasks: Attribute, Decoder, Voice Conversion

Singing voice conversion is to convert the source singing voice into the target singing voice except for the content. Currently, flow-based models can complete the task of voice conversion, but they struggle to effectively extract latent variables in the more rhythmically rich and emotionally expressive task of singing voice conversion, while also facing issues with low efficiency in speech processing. In this paper, we propose a high-fidelity flow-based model based on multi-decoupling feature constraints called RASVC, which enhances the capture of vocal details by integrating multiple latent attribute encoders. We also use Multi-stream inverse short-time Fourier transform(MS-iSTFT) to enhance the speed of speech processing by skipping some complicated decoder processing steps. We compare the synthesized singing voice with other models from multiple dimensions, and our proposed model is highly consistent with the current state-of-the-art, with the demo which is available at \url{https://lazycat1119.github.io/RASVC-demo/}.
Identifying Functional Brain Networks of Spatiotemporal Wide-Field Calcium Imaging Data via a Long Short-Term Memory Autoencoder
Authors: Xiaohui Zhang, Eric C Landsness, Lindsey M Brier, Wei Chen, Michelle J. Tang, Hanyang Miao, Jin-Moo Lee, Mark A. Anastasio, Joseph P. Culver
Date: 2024-05-30 00:00:00
Tasks: Representation Learning

Wide-field calcium imaging (WFCI) that records neural calcium dynamics allows for identification of functional brain networks (FBNs) in mice that express genetically encoded calcium indicators. Estimating FBNs from WFCI data is commonly achieved by use of seed-based correlation (SBC) analysis and independent component analysis (ICA). These two methods are conceptually distinct and each possesses limitations. Recent success of unsupervised representation learning in neuroimage analysis motivates the investigation of such methods to identify FBNs. In this work, a novel approach referred as LSTM-AER, is proposed in which a long short-term memory (LSTM) autoencoder (AE) is employed to learn spatial-temporal latent embeddings from WFCI data, followed by an ordinary least square regression (R) to estimate FBNs. The goal of this study is to elucidate and illustrate, qualitatively and quantitatively, the FBNs identified by use of the LSTM-AER method and compare them to those from traditional SBC and ICA. It was observed that spatial FBN maps produced from LSTM-AER resembled those derived by SBC and ICA while better accounting for intra-subject variation, data from a single hemisphere, shorter epoch lengths and tunable number of latent components. The results demonstrate the potential of unsupervised deep learning-based approaches to identifying and mapping FBNs.
A Landmark-aware Network for Automated Cobb Angle Estimation Using X-ray Images
Authors: Jie Yang, Jiankun Wang, Max Q. -H. Meng
Date: 2024-05-30 00:00:00

Automated Cobb angle estimation based on X-ray images plays an important role in scoliosis diagnosis, treatment, and progression surveillance. The inadequate feature extraction and the noise in X-ray images are the main difficulties of automated Cobb angle estimation, and it is challenging to ensure that the calculated Cobb angle meets clinical requirements. To address these problems, we propose a Landmark-aware Network named LaNet with three components, Feature Robustness Enhancement Module (FREM), Landmark-aware Objective Function (LOF), and Cobb Angle Calculation Method (CACM), for automated Cobb angle estimation in this paper. To enhance feature extraction, FREM is designed to explore geometric and semantic constraints among landmarks, thus geometric and semantic correlations between landmarks are globally modeled, and robust landmark-based features are extracted. Furthermore, to mitigate the effect of background noise on landmark localization, LOF is proposed to focus more on the foreground near the landmarks and ignore irrelevant background pixels by exploiting category prior information of landmarks. In addition, we also advance CACM to locate the bending segments first and then calculate the Cobb angle within the bending segment, which facilitates the calculation of the clinical standardized Cobb angle. The experiment results on the AASCE dataset demonstrate that our proposed LaNet can significantly improve the Cobb angle estimation performance and outperform other state-of-the-art methods.
Stochastic super-resolution for Gaussian microtextures
Authors: Emile Pierret, Bruno Galerne
Date: 2024-05-24 00:00:00
Tasks: Super-Resolution

Super-Resolution (SR) is the problem that consists in reconstructing images that have been degraded by a zoom-out operator. This is an ill-posed problem that does not have a unique solution, and numerical approaches rely on a prior on high-resolution images. While optimization-based methods are generally deterministic, with the rise of image generative models more and more interest has been given to stochastic SR, that is, sampling among all possible SR images associated with a given low-resolution input. In this paper, we construct an efficient, stable and provably exact sampler for the stochastic SR of Gaussian microtextures. Even though our approach is limited regarding the scope of images it encompasses, our algorithm is competitive with deep learning state-of-the-art methods both in terms of perceptual metric and execution time when applied to microtextures. The framework of Gaussian microtextures also allows us to rigorously discuss the limitations of various reconstruction metrics to evaluate the efficiency of SR routines.
Towards Video Codec Performance Evaluation: A Rate-Energy-Distortion Perspective
Authors: Geetha Ramasubbu, André Kaup, Christian Herglotz
Date: 2024-05-28 00:00:00

The Bj{\o}ntegaard Delta rate (BD-rate) objectively assesses the coding efficiency of video codecs using the rate-distortion (R-D) performance but overlooks encoding energy, which is crucial in practical applications, especially for those on handheld devices. Although R-D analysis can be extended to incorporate encoding energy as energy-distortion (E-D), it fails to integrate all three parameters seamlessly. This work proposes a novel approach to address this limitation by introducing a 3D representation of rate, encoding energy, and distortion through surface fitting. In addition, we evaluate various surface fitting techniques based on their accuracy and investigate the proposed 3D representation and its projections. The overlapping areas in projections help in encoder selection and recommend avoiding the slow presets of the older encoders (x264, x265), as the recent encoders (x265, VVenC) offer higher quality for the same bitrate-energy performance and provide a lower rate for the same energy-distortion performance.
UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation
Authors: Ting Yu Tsai, Li Lin, Shu Hu, Ming-Ching Chang, Hongtu Zhu, Xin Wang
Date: 2024-05-25 00:00:00
Tasks: Image Segmentation, Mamba, MRI segmentation, Segmentation, Semantic Segmentation

Biomedical image segmentation is critical for accurate identification and analysis of anatomical structures in medical imaging, particularly in cardiac MRI. Manual segmentation is labor-intensive, time-consuming, and prone to errors, highlighting the need for automated methods. However, current machine learning approaches face challenges like overfitting and data demands. To tackle these issues, we propose a new UU-Mamba model, integrating the U-Mamba model with the Sharpness-Aware Minimization (SAM) optimizer and an uncertainty-aware loss function. SAM enhances generalization by locating flat minima in the loss landscape, thus reducing overfitting. The uncertainty-aware loss combines region-based, distribution-based, and pixel-based loss designs to improve segmentation accuracy and robustness. Evaluation of our method is performed on the ACDC cardiac dataset, outperforming state-of-the-art models including TransUNet, Swin-Unet, nnUNet, and nnFormer. Our approach achieves Dice Similarity Coefficient (DSC) and Mean Squared Error (MSE) scores, demonstrating its effectiveness in cardiac MRI segmentation.
Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks
Authors: Oleh Berezsky, Petro Liashchynskyi, Oleh Pitsun, Grygoriy Melnyk
Date: 2024-05-25 00:00:00
Tasks: Image Generation

A wide variety of biomedical image data, as well as methods for generating training images using basic deep neural networks, were analyzed. Additionally, all platforms for creating images were analyzed, considering their characteristics. The article develops a method for generating artificial biomedical images based on GAN. GAN architecture has been developed for biomedical image synthesis. The data foundation and module for generating training images were designed and implemented in a software system. A comparison of the generated image database with known databases was made.
realSEUDO for real-time calcium imaging analysis
Authors: Iuliia Dmitrieva, Sergey Babkin, Adam S. Charles
Date: 2024-05-24 00:00:00

Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing experimental time. A critical step in creating a closed-loop experiment is real-time inference of neural activity from streaming recordings. One challenging modality for real-time processing is multi-photon calcium imaging (CI). CI enables the recording of activity in large populations of neurons however, often requires batch processing of the video data to extract single-neuron activity from the fluorescence videos. We use the recently proposed robust time-trace estimator-Sparse Emulation of Unused Dictionary Objects (SEUDO) algorithm-as a basis for a new on-line processing algorithm that simultaneously identifies neurons in the fluorescence video and infers their time traces in a way that is robust to as-yet unidentified neurons. To achieve real-time SEUDO (realSEUDO), we optimize the core estimator via both algorithmic improvements and an fast C-based implementation, and create a new cell finding loop to enable realSEUDO to also identify new cells. We demonstrate comparable performance to offline algorithms (e.g., CNMF), and improved performance over the current on-line approach (OnACID) at speeds of 120 Hz on average.
Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images
Authors: Rinat I. Dumaev, Sergei A. Molodyakov, Lev V. Utkin
Date: 2024-05-24 00:00:00
Tasks: Additive models, Attribute, Decision Making

To increase the transparency of modern computer-aided diagnosis (CAD) systems for assessing the malignancy of lung nodules, an interpretable model based on applying the generalized additive models and the concept-based learning is proposed. The model detects a set of clinically significant attributes in addition to the final malignancy regression score and learns the association between the lung nodule attributes and a final diagnosis decision as well as their contributions into the decision. The proposed concept-based learning framework provides human-readable explanations in terms of different concepts (numerical and categorical), their values, and their contribution to the final prediction. Numerical experiments with the LIDC-IDRI dataset demonstrate that the diagnosis results obtained using the proposed model, which explicitly explores internal relationships, are in line with similar patterns observed in clinical practice. Additionally, the proposed model shows the competitive classification and the nodule attribute scoring performance, highlighting its potential for effective decision-making in the lung nodule diagnosis.
Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model
Authors: Lang Zhang, Jinling He, Dong Liang, Hairong Zheng, Yanjie Zhu
Date: 2024-05-24 00:00:00

Magnetic resonance diffusion tensor imaging (DTI) is a critical tool for neural disease diagnosis. However, long scan time greatly hinders the widespread clinical use of DTI. To accelerate image acquisition, a feature-enhanced joint diffusion model (Diff-DTI) is proposed to obtain accurate DTI parameter maps from a limited number of diffusion-weighted images (DWIs). Diff-DTI introduces a joint diffusion model that directly learns the joint probability distribution of DWIs with DTI parametric maps for conditional generation. Additionally, a feature enhancement fusion mechanism (FEFM) is designed and incorporated into the generative process of Diff-DTI to preserve fine structures in the generated DTI maps. A comprehensive evaluation of the performance of Diff-DTI was conducted on the Human Connectome Project dataset. The results demonstrate that Diff-DTI outperforms existing state-of-the-art fast DTI imaging methods in terms of visual quality and quantitative metrics. Furthermore, Diff-DTI has shown the ability to produce high-fidelity DTI maps with only three DWIs, thus overcoming the requirement of a minimum of six DWIs for DTI.
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
Authors: Ling-Qi Zhang, Zahra Kadkhodaie, Eero P. Simoncelli, David H. Brainard
Date: 2024-05-22 00:00:00
Tasks: compressed sensing, Denoising, Image Generation, Image Reconstruction, SSIM

We examine the problem of selecting a small set of linear measurements for reconstructing high-dimensional signals. Well-established methods for optimizing such measurements include principal component analysis (PCA), independent component analysis (ICA) and compressed sensing (CS) based on random projections, all of which rely on axis- or subspace-aligned statistical characterization of the signal source. However, many naturally occurring signals, including photographic images, contain richer statistical structure. To exploit such structure, we introduce a general method for obtaining an optimized set of linear measurements for efficient image reconstruction, where the signal statistics are expressed by the prior implicit in a neural network trained to perform denoising (known as a ``diffusion model''). We demonstrate that the optimal measurements derived for two natural image datasets differ from those of PCA, ICA, or CS, and result in substantially lower mean squared reconstruction error. Interestingly, the marginal distributions of the measurement values are asymmetrical (skewed), substantially more so than those of previous methods. We also find that optimizing with respect to perceptual loss, as quantified by structural similarity (SSIM), leads to measurements different from those obtained when optimizing for MSE. Our results highlight the importance of incorporating the specific statistical regularities of natural signals when designing effective linear measurements.
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis
Authors: Kleanthis Marios Papadopoulos, Tania Stathaki
Date: 2024-05-20 00:00:00
Tasks: Multiple Instance Learning, Survival Analysis, whole slide images

The abundance of information present in Whole Slide Images (WSIs) renders them an essential tool for survival analysis. Several Multiple Instance Learning frameworks proposed for this task utilize a ResNet50 backbone pre-trained on natural images. By leveraging recenetly released histopathological foundation models such as UNI and Hibou, the predictive prowess of existing MIL networks can be enhanced. Furthermore, deploying an ensemble of digital pathology foundation models yields higher baseline accuracy, although the benefits appear to diminish with more complex MIL architectures. Our code will be made publicly available upon acceptance.
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach
Authors: Jayanthi Vajiram, Aishwarya Senthil
Date: 2024-05-12 00:00:00
Tasks: Segmentation, Transfer Learning

The paper discusses the use of MRI for segmentation techniques, specifically focusing on brain tumor detection. It discusses the use of convolutional neural networks (CNN) for automatic segmentation but also discusses challenges such as non-isotropic resolution, Rician noise, and bias field effects. The paper proposes models like VGG16, ResNet50, and ResU-net to predict MRI images based on original and predicted masks. ResNet50 is found to be a promising model with high accuracy and F1 score.