pwc-archive/papers-with-abstracts huggingface dataset:
Advanced accent/dialect identification and accentedness assessment with multi-embedding models and automatic speech recognition
Authors: Shahram Ghorbani, John H. L. Hansen
Date: 2023-10-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Dialect Identification, Language Identification, Speaker Identification, speech-recognition, Speech Recognition

Accurately classifying accents and assessing accentedness in non-native speakers are both challenging tasks due to the complexity and diversity of accent and dialect variations. In this study, embeddings from advanced pre-trained language identification (LID) and speaker identification (SID) models are leveraged to improve the accuracy of accent classification and non-native accentedness assessment. Findings demonstrate that employing pre-trained LID and SID models effectively encodes accent/dialect information in speech. Furthermore, the LID and SID encoded accent information complement an end-to-end accent identification (AID) model trained from scratch. By incorporating all three embeddings, the proposed multi-embedding AID system achieves superior accuracy in accent identification. Next, we investigate leveraging automatic speech recognition (ASR) and accent identification models to explore accentedness estimation. The ASR model is an end-to-end connectionist temporal classification (CTC) model trained exclusively with en-US utterances. The ASR error rate and en-US output of the AID model are leveraged as objective accentedness scores. Evaluation results demonstrate a strong correlation between the scores estimated by the two models. Additionally, a robust correlation between the objective accentedness scores and subjective scores based on human perception is demonstrated, providing evidence for the reliability and validity of utilizing AID-based and ASR-based systems for accentedness assessment in non-native speech.
Speaking rate attention-based duration prediction for speed control TTS
Authors: Jesuraj Bandekar, Sathvik Udupa, Abhayjeet Singh, Anjali Jayakumar, Deekshitha G, Sandhya Badiger, Saurabh Kumar, Pooja VH, Prasanta Kumar Ghosh
Date: 2023-10-13 00:00:00
Tasks: Attribute, Speech Synthesis

With the advent of high-quality speech synthesis, there is a lot of interest in controlling various prosodic attributes of speech. Speaking rate is an essential attribute towards modelling the expressivity of speech. In this work, we propose a novel approach to control the speaking rate for non-autoregressive TTS. We achieve this by conditioning the speaking rate inside the duration predictor, allowing implicit speaking rate control. We show the benefits of this approach by synthesising audio at various speaking rate factors and measuring the quality of speaking rate-controlled synthesised speech. Further, we study the effect of the speaking rate distribution of the training data towards effective rate control. Finally, we fine-tune a baseline pretrained TTS model to obtain speaking rate control TTS. We provide various analyses to showcase the benefits of using this proposed approach, along with objective as well as subjective metrics. We find that the proposed methods have higher subjective scores and lower speaker rate errors across many speaking rate factors over the baseline.
FixPix: Fixing Bad Pixels using Deep Learning
Authors: Sreetama Sarkar, Xinan Ye, Gourav Datta, Peter A. Beerel
Date: 2023-10-18 00:00:00
Tasks: Deep Learning, Image Reconstruction, Line Detection

Efficient and effective on-line detection and correction of bad-pixels can improve yield and increase the expected lifetime of image sensors. This paper presents a comprehensive Deep Learning (DL) based on-line detection and correction approach, suitable for a wide range of pixel corruption rates. A confidence calibrated segmentation approach is introduced, which achieves nearly perfect bad pixel detection, even with a few training samples. A computationally light-weight correction algorithm is proposed for low rates of pixel corruption, that surpasses the accuracy of traditional interpolation-based techniques. In addition, a vision transformer (ViT) auto-encoder based image reconstruction approach is presented which yields promising results for high rates of pixel corruption or clustered defects. Unlike previous methods, which use proprietary images, we demonstrate the efficacy of the proposed methods on the open-source Samsung S7 ISP and MIT-Adobe FiveK datasets. Our approaches yield up to 99.6% detection accuracy with <0.6% false positives and corrected images within 1.5% average pixel error from 70% corrupted images. We achieve correction error at par with the state-of-the-art (SoTA) DL methods for clustered defects with less than half the computational cost.
Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction using Mesh Priors
Authors: Rudolf L. M. van Herten, Nils Hampe, Richard A. P. Takx, Klaas Jan Franssen, Yining Wang, Dominika Suchá, José P. Henriques, Tim Leiner, R. Nils Planken, Ivana Išgum
Date: 2023-10-17 00:00:00

Coronary artery disease (CAD) remains the leading cause of death worldwide. Patients with suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of cardiovascular events and determine the treatment. Clinical analysis of coronary arteries in CCTA comprises the identification of atherosclerotic plaque, as well as the grading of any coronary artery stenosis typically obtained through the CAD-Reporting and Data System (CAD-RADS). This requires analysis of the coronary lumen and plaque. While voxel-wise segmentation is a commonly used approach in various segmentation tasks, it does not guarantee topologically plausible shapes. To address this, in this work, we propose to directly infer surface meshes for coronary artery lumen and plaque based on a centerline prior and use it in the downstream task of CAD-RADS scoring. The method is developed and evaluated using a total of 2407 CCTA scans. Our method achieved lesion-wise volume intraclass correlation coefficients of 0.98, 0.79, and 0.85 for calcified, non-calcified, and total plaque volume respectively. Patient-level CAD-RADS categorization was evaluated on a representative hold-out test set of 300 scans, for which the achieved linearly weighted kappa ($\kappa$) was 0.75. CAD-RADS categorization on the set of 658 scans from another hospital and scanner led to a $\kappa$ of 0.71. The results demonstrate that direct inference of coronary artery meshes for lumen and plaque is feasible, and allows for the automated prediction of routinely performed CAD-RADS categorization.
MorphFlow: Estimating Motion in In Situ Tests of Concrete
Authors: Tessa Nogatz, Claudia Redenbach, Katja Schladitz
Date: 2023-10-17 00:00:00
Tasks: Time Series

We present a novel algorithm explicitly tailored to estimate motion from time series of 3D images of concrete. Such volumetric images are usually acquired by Computed Tomography and can contain for example in situ tests, or more complex procedures like self-healing. Our algorithm is specifically designed to tackle the challenge of large scale in situ investigations of concrete. That means it cannot only cope with big images, but also with discontinuous displacement fields that often occur in in situ tests of concrete. We show the superior performance of our algorithm, especially regarding plausibility and time efficient processing. Core of the algorithm is a novel multiscale representation based on morphological wavelets. We use two examples for validation: A classical in situ test on refractory concrete and and a three point bending test on normal concrete. We show that for both applications structural changes like crack initiation can be already found at low scales -- a central achievement of our algorithm.
Impact of Data Synthesis Strategies for the Classification of Craniosynostosis
Authors: Matthias Schaufelberger, Reinald Peter Kühle, Andreas Wachter, Frederic Weichel, Niclas Hagen, Friedemann Ringwald, Urs Eisenmann, Jürgen Hoffmann, Michael Engel, Christian Freudlsperger, Werner Nahm
Date: 2023-10-16 00:00:00
Tasks: Classification, Generative Adversarial Network

Introduction: Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data is rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically. Methods: We test the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)-based classification of craniosynostosis. The CNN is trained only on synthetic data, but validated and tested on clinical data. Results: The combination of a SSM and a GAN achieved an accuracy of more than 0.96 and a F1-score of more than 0.95 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources. Conclusion: Without a single clinical training sample, a CNN was able to classify head deformities as accurate as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.
Joint Sparse Representations and Coupled Dictionary Learning in Multi-Source Heterogeneous Image Pseudo-color Fusion
Authors: Long Bai, Shilong Yao, Kun Gao, Yanjun Huang, Ruijie Tang, Hong Yan, Max Q. -H. Meng, Hongliang Ren
Date: 2023-10-15 00:00:00
Tasks: Dictionary Learning

Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method. Firstly, the traditional Brovey transform is employed as a pre-processing method on the paired SAR and multispectral images. Then, CDL is used to capture the correlation between the pre-processed image pairs based on the dictionaries generated from the source images via enforced joint sparse coding. Afterward, the joint sparse representation in the pair of dictionaries is utilized to construct an image mask via calculating the reconstruction errors, and therefore generate the final fusion image. The experimental verification results of the SAR images from the Sentinel-1 satellite and the multispectral images from the Landsat-8 satellite show that the proposed method can achieve superior visual effects, and excellent quantitative performance in terms of spectral distortion, correlation coefficient, MSE, NIQE, BRISQUE, and PIQE.
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos
Authors: Li Chen, Jonathan Rubin, Jiahong Ouyang, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W Gregory, Cynthia R Gregory, Meihua Zhu, David O Kessler, Laurie Malia, Almaz Dessie, Joni Rabiner, Di Coneybeare, Bo Shopsin, Andrew Hersh, Cristian Madar, Jeffrey Shupp, Laura S Johnson, Jacob Avila, Kristin Dwyer, Peter Weimersheimer, Balasundar Raju, Jochen Kruecker, Alvin Chen
Date: 2023-10-14 00:00:00
Tasks: Contrastive Learning, Self-Supervised Learning

Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is limited. Here, we extend state-of-the-art contrastive learning SSL methods to 2D+time medical ultrasound video data by introducing a modified encoder and augmentation method capable of learning meaningful spatio-temporal representations, without requiring constraints on the input data. We evaluate our method on the challenging clinical task of identifying lung consolidations (an important pathological feature) in ultrasound videos. Using a multi-center dataset of over 27k lung ultrasound videos acquired from over 500 patients, we show that our method can significantly improve performance on downstream localization and classification of lung consolidation. Comparisons against baseline models trained without SSL show that the proposed methods are particularly advantageous when the size of labeled training data is limited (e.g., as little as 5% of the training set).
PC-bzip2: a phase-space continuity enhanced lossless compression algorithm for light field microscopy data
Authors: Changqing Su, Zihan Lin, You Zhou, Shuai Wang, Yuhan Gao, Chenggang Yan, Bo Xiong
Date: 2023-10-14 00:00:00
Tasks: CPU, GPU

Light-field fluorescence microscopy (LFM) is a powerful elegant compact method for long-term high-speed imaging of complex biological systems, such as neuron activities and rapid movements of organelles. LFM experiments typically generate terabytes image data and require a huge number of storage space. Some lossy compression algorithms have been proposed recently with good compression performance. However, since the specimen usually only tolerates low power density illumination for long-term imaging with low phototoxicity, the image signal-to-noise ratio (SNR) is relative-ly low, which will cause the loss of some efficient position or intensity information by using such lossy compression al-gorithms. Here, we propose a phase-space continuity enhanced bzip2 (PC-bzip2) lossless compression method for LFM data as a high efficiency and open-source tool, which combines GPU-based fast entropy judgement and multi-core-CPU-based high-speed lossless compression. Our proposed method achieves almost 10% compression ratio improvement while keeping the capability of high-speed compression, compared with original bzip2. We evaluated our method on fluorescence beads data and fluorescence staining cells data with different SNRs. Moreover, by introducing the temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.
A study on the ideal magnitude and phase of reconstructed point targets in SAR imaging
Authors: Guanying Sun, Carey Rappaport
Date: 2023-10-13 00:00:00

In this paper, the magnitude and phase of the reconstructed point targets in SAR imaging are studied quantitatively by using inverse crime. Two scenarios, one with single point target in the imaging area and the other with two point targets, are considered. The theorems on the magnitude and phase are established and proved for each scenario. In addition, several numerical examples are presented and the numerical results show that they agree with the corresponding theorems. This study is useful for appreciating the limitations of formulating inversion algorithms based on simplistic point target building blocks.
Unlocking the capabilities of explainable fewshot learning in remote sensing
Authors: Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar, Vu N Duong
Date: 2023-10-12 00:00:00
Tasks: Scene Classification

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, fewshot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies. In this review, we provide an up to date overview of both existing and newly proposed fewshot classification techniques, along with appropriate datasets that are used for both satellite based and UAV based data. Our systematic approach demonstrates that fewshot learning can effectively adapt to the broader and more diverse perspectives that UAVbased platforms can provide. We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. We emphasize the importance of integrating XAI techniques like attention maps and prototype analysis to increase the transparency, accountability, and trustworthiness of fewshot models for remote sensing. Key challenges and future research directions are identified, including tailored fewshot methods for UAVs, extending to unseen tasks like segmentation, and developing optimized XAI techniques suited for fewshot remote sensing problems. This review aims to provide researchers and practitioners with an improved understanding of fewshot learnings capabilities and limitations in remote sensing, while highlighting open problems to guide future progress in efficient, reliable, and interpretable fewshot methods.
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Authors: Yuhan Yang, Youlong Wu, Yuning Jiang, Yuanming Shi
Date: 2023-10-18 00:00:00
Tasks: Autonomous Driving

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learning in wireless data center networks, which contain a central edge server and multiple edge workers to collaboratively train a shared global model and benefit from parallel computing. However, the distributed nature causes the vulnerability of the learning process to faults and adversarial attacks from Byzantine edge workers, as well as the severe communication and computation overhead induced by the periodical information exchange process. To achieve fast and reliable model aggregation in the presence of Byzantine attacks, we develop a signed stochastic gradient descent (SignSGD)-based Hierarchical Vote framework via over-the-air computation (AirComp), where one voting process is performed locally at the wireless edge by taking advantage of Bernoulli coding while the other is operated over-the-air at the central edge server by utilizing the waveform superposition property of the multiple-access channels. We comprehensively analyze the proposed framework on the impacts including Byzantine attacks and the wireless environment (channel fading and receiver noise), followed by characterizing the convergence behavior under non-convex settings. Simulation results validate our theoretical achievements and demonstrate the robustness of our proposed framework in the presence of Byzantine attacks and receiver noise.
Two Enhanced-rate Power Allocation Strategies for Active IRS-assisted Wireless Network
Authors: Qiankun Cheng, Rongen Dong, Wenlong Cai, Ruiqi Liu, Feng Shu, Jiangzhou Wang
Date: 2023-10-15 00:00:00

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Netwon's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial.Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and appoaches exhaustive search as the number of IRS reflecting elements goes to large-scale.
Deep Learning Based Detection on RIS Assisted RSM and RSSK Techniques
Authors: Onur Salan, Ferhat Bayar, Haci Ilhan, Erdogan Aydin
Date: 2023-10-18 00:00:00
Tasks: Deep Learning

The reconfigurable intelligent surface (RIS) is considered a crucial technology for the future of wireless communication. Recently, there has been significant interest in combining RIS with spatial modulation (SM) or space shift keying (SSK) to achieve a balance between spectral and energy efficiency. In this paper, we have investigated the use of deep learning techniques for detection in RIS-aided received SM (RSM)/received-SSK (RSSK) systems over Weibull fading channels, specifically by extending the RIS-aided SM/SSK system to a specific case of the conventional SM system. By employing the concept of neural networks, the study focuses on model-driven deep learning detection namely block deep neural networks (B-DNN) for RIS-aided SM systems and compares its performance against maximum likelihood (ML) and greedy detectors. Finally, it has been demonstrated by Monte Carlo simulation that while B-DNN achieved a bit error rate (BER) performance close to that of ML, it gave better results than the Greedy detector.
Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning
Authors: Ali Nauman, Haya Mesfer Alshahrani, Nadhem Nemri, Kamal M. Othman, Nojood O Aljehane, Mashael Maashi, Ashit Kumar Dutta, Mohammed Assiri, Wali Ullah Khan
Date: 2023-10-18 00:00:00
Tasks: Deep Reinforcement Learning, Management, Multi-agent Reinforcement Learning

This study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods.
Complex Number Assignment in the Topology Method for Heartbeat Interval Estimation Using Millimeter-Wave Radar
Authors: Yuji Tanaka, Kimitaka Sumi, Itsuki Iwata, Takuya Sakamoto
Date: 2023-10-17 00:00:00

The topology method is an algorithm for accurate estimation of instantaneous heartbeat intervals using millimeter-wave radar signals. In this model, feature points are extracted from the skin displacement waveforms generated by heartbeats and a complex number is assigned to each feature point. However, these numbers have been assigned empirically and without solid justification. This study used a simplified model of displacement waveforms to predict the optimal choice of the complex number assignments to feature points corresponding to inflection points, and the validity of these numbers was confirmed using analysis of a publicly available dataset.