pwc-archive/papers-with-abstracts huggingface dataset:
Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment
Authors: Rotem Rousso, Eyal Cohen, Joseph Keshet, Eleanor Chodroff
Date: 2024-06-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Word Alignment

Forced alignment (FA) plays a key role in speech research through the automatic time alignment of speech signals with corresponding text transcriptions. Despite the move towards end-to-end architectures for speech technology, FA is still dominantly achieved through a classic GMM-HMM acoustic model. This work directly compares alignment performance from leading automatic speech recognition (ASR) methods, WhisperX and Massively Multilingual Speech Recognition (MMS), against a Kaldi-based GMM-HMM system, the Montreal Forced Aligner (MFA). Performance was assessed on the manually aligned TIMIT and Buckeye datasets, with comparisons conducted only on words correctly recognized by WhisperX and MMS. The MFA outperformed both WhisperX and MMS, revealing a shortcoming of modern ASR systems. These findings highlight the need for advancements in forced alignment and emphasize the importance of integrating traditional expertise with modern innovation to foster progress. Index Terms: forced alignment, phoneme alignment, word alignment
High Fidelity Text-to-Speech Via Discrete Tokens Using Token Transducer and Group Masked Language Model
Authors: Joun Yeop Lee, Myeonghun Jeong, Minchan Kim, Ji-Hyun Lee, Hoon-Young Cho, Nam Soo Kim
Date: 2024-06-25 00:00:00
Tasks: Computational Efficiency, Language Modeling, Language Modelling, Speech Synthesis, text-to-speech, Text to Speech

We propose a novel two-stage text-to-speech (TTS) framework with two types of discrete tokens, i.e., semantic and acoustic tokens, for high-fidelity speech synthesis. It features two core components: the Interpreting module, which processes text and a speech prompt into semantic tokens focusing on linguistic contents and alignment, and the Speaking module, which captures the timbre of the target voice to generate acoustic tokens from semantic tokens, enriching speech reconstruction. The Interpreting stage employs a transducer for its robustness in aligning text to speech. In contrast, the Speaking stage utilizes a Conformer-based architecture integrated with a Grouped Masked Language Model (G-MLM) to boost computational efficiency. Our experiments verify that this innovative structure surpasses the conventional models in the zero-shot scenario in terms of speech quality and speaker similarity.
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views
Authors: Zongyu Li, Yixuan Jia, Xiaojian Xu, Jason Hu, Jeffrey A. Fessler, Yuni K. Dewaraja
Date: 2024-06-27 00:00:00
Tasks: NeRF, Self-Supervised Learning

Purpose: This study addresses the challenge of extended SPECT imaging duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by developing a self-supervised learning approach to synthesize skipped SPECT projection views, thus shortening scan times in clinical settings. Methods: We employed a self-supervised coordinate-based learning technique, adapting the neural radiance field (NeRF) concept in computer vision to synthesize under-sampled SPECT projection views. For each single scan, we used self-supervised coordinate learning to estimate skipped SPECT projection views. The method was tested with various down-sampling factors (DFs=2, 4, 8) on both Lu-177 phantom SPECT/CT measurements and clinical SPECT/CT datasets, from 11 patients undergoing Lu-177 DOTATATE and 6 patients undergoing Lu-177 PSMA-617 radiopharmaceutical therapy. Results: For SPECT reconstructions, our method outperformed the use of linearly interpolated projections and partial projection views in relative contrast-to-noise-ratios (RCNR) averaged across different downsampling factors: 1) DOTATATE: 83% vs. 65% vs. 67% for lesions and 86% vs. 70% vs. 67% for kidney, 2) PSMA: 76% vs. 69% vs. 68% for lesions and 75% vs. 55% vs. 66% for organs, including kidneys, lacrimal glands, parotid glands, and submandibular glands. Conclusion: The proposed method enables reduction in acquisition time (by factors of 2, 4, or 8) while maintaining quantitative accuracy in clinical SPECT protocols by allowing for the collection of fewer projections. Importantly, the self-supervised nature of this NeRF-based approach eliminates the need for extensive training data, instead learning from each patient's projection data alone. The reduction in acquisition time is particularly relevant for imaging under low-count conditions and for protocols that require multiple-bed positions such as whole-body imaging.
Assessment of Clonal Hematopoiesis of Indeterminate Potential from Cardiac Magnetic Resonance Imaging using Deep Learning in a Cardio-oncology Population
Authors: Sangeon Ryu, Shawn Ahn, Jeacy Espinoza, Alokkumar Jha, Stephanie Halene, James S. Duncan, Jennifer M Kwan, Nicha C. Dvornek
Date: 2024-06-26 00:00:00
Tasks: Deep Learning, Diagnostic

Background: We propose a novel method to identify who may likely have clonal hematopoiesis of indeterminate potential (CHIP), a condition characterized by the presence of somatic mutations in hematopoietic stem cells without detectable hematologic malignancy, using deep learning techniques. Methods: We developed a convolutional neural network (CNN) to predict CHIP status using 4 different views from standard delayed gadolinium-enhanced cardiac magnetic resonance imaging (CMR). We used 5-fold cross validation on 82 cardio-oncology patients to assess the performance of our model. Different algorithms were compared to find the optimal patient-level prediction method using the image-level CNN predictions. Results: We found that the best model had an area under the receiver operating characteristic curve of 0.85 and an accuracy of 82%. Conclusions: We conclude that a deep learning-based diagnostic approach for CHIP using CMR is promising.
A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset
Authors: Muwei Jian, Haoran Zhang, Mingju Shao, Hongyu Chen, Huihui Huang, Yanjie Zhong, Changlei Zhang, Bin Wang, Penghui Gao
Date: 2024-06-26 00:00:00
Tasks: Computed Tomography (CT), Lung Cancer Diagnosis

Recently, intelligent analysis of lung nodules with the assistant of computer aided detection (CAD) techniques can improve the accuracy rate of lung cancer diagnosis. However, existing CAD systems and pulmonary datasets mainly focus on Computed Tomography (CT) images from one single period, while ignoring the cross spatio-temporal features associated with the progression of nodules contained in imaging data from various captured periods of lung cancer. If the evolution patterns of nodules across various periods in the patients' CT sequences can be explored, it will play a crucial role in guiding the precise screening identification of lung cancer. Therefore, a cross spatio-temporal lung nodule dataset with pathological information for nodule identification and diagnosis is constructed, which contains 328 CT sequences and 362 annotated nodules from 109 patients. This comprehensive database is intended to drive research in the field of CAD towards more practical and robust methods, and also contribute to the further exploration of precision medicine related field. To ensure patient confidentiality, we have removed sensitive information from the dataset.
Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects
Authors: Maliha Hossain, Charles A. Bouman, Brendt Wohlberg
Date: 2024-06-25 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

Flash X-ray computed tomography (CT) is an important imaging modality for characterization of high-speed dynamic events, such as Kolsky bar impact experiments for the study of mechanical properties of materials subjected to impulsive forces. Due to experimental constraints, the number of X-ray views that can be obtained is typically very sparse in both space and time, requiring strong priors in order to enable a CT reconstruction. In this paper, we propose an effective method for exploiting the cylindrical symmetry inherent in the experiment via a variant of total variation (TV) regularization that operates in cylindrical coordinates, and demonstrate that it outperforms competing approaches.
Pixel-weighted Multi-pose Fusion for Metal Artifact Reduction in X-ray Computed Tomography
Authors: Diyu Yang, Craig A. J. Kemp, Soumendu Majee, Gregery T. Buzzard, Charles A. Bouman
Date: 2024-06-25 00:00:00
Tasks: Computed Tomography (CT), Metal Artifact Reduction, Object

X-ray computed tomography (CT) reconstructs the internal morphology of a three dimensional object from a collection of projection images, most commonly using a single rotation axis. However, for objects containing dense materials like metal, the use of a single rotation axis may leave some regions of the object obscured by the metal, even though projections from other rotation axes (or poses) might contain complementary information that would better resolve these obscured regions. In this paper, we propose pixel-weighted Multi-pose Fusion to reduce metal artifacts by fusing the information from complementary measurement poses into a single reconstruction. Our method uses Multi-Agent Consensus Equilibrium (MACE), an extension of Plug-and-Play, as a framework for integrating projection data from different poses. A primary novelty of the proposed method is that the output of different MACE agents are fused in a pixel-weighted manner to minimize the effects of metal throughout the reconstruction. Using real CT data on an object with and without metal inserts, we demonstrate that the proposed pixel-weighted Multi-pose Fusion method significantly reduces metal artifacts relative to single-pose reconstructions.
Improving ovarian cancer segmentation accuracy with transformers through AI-guided labeling
Authors: Aneesh Rangnekar, Kevin M. Boehm, Emily A. Aherne, Ines Nikolovski, Natalie Gangai, Ying Liu, Dimitry Zamarin, Kara L. Roche, Sohrab P. Shah, Yulia Lakhman, Harini Veeraraghavan
Date: 2024-06-25 00:00:00

Transformer models have demonstrated the capability to produce highly accurate segmentation of organs and tumors. However, model training requires high-quality curated datasets to ensure robust generalization to unseen datasets. Hence, we developed an artificial intelligence (AI) guided approach to assist with radiologist tumor delineation of partially segmented computed tomography datasets containing primary (adnexa) tumors and metastatic (omental) implants. AI guidance was implemented by training a 2D multiple resolution residual network trained with a dataset of 245 contrast-enhanced CTs with partially segmented examples. The same dataset curated through AI guidance was then used to refine two pretrained transformer models called SMIT and Swin UNETR. The models were independently tested on 71 publicly available multi-institutional 3D CT datasets. Segmentation accuracy was computed using the Dice similarity coefficient metric (DSC), average symmetric surface distance (ASSD), and the relative volume difference (RVD) metrics. Radiomic features reproducibility was assessed using the concordance correlation coefficient (CCC). Training with AI-guided segmentations significantly improved the accuracy of both SMIT (p = 6.2e-5) and Swin UNETR (p = 2e-4) models compared with using a partially delineated training dataset. Furthermore, SMIT-generated segmentations resulted in more reproducible features compared to Swin UNETR under multiple feature categories. Our results show that AI-guided data curation provides a more efficient approach to train AI models and that AI-generated segmentations can provide reproducible radiomics features.
Sparse-view Signal-domain Photoacoustic Tomography Reconstruction Method Based on Neural Representation
Authors: Bowei Yao, Yi Zeng, Haizhao Dai, Qing Wu, Youshen Xiao, Fei Gao, Yuyao Zhang, Jingyi Yu, Xiran Cai
Date: 2024-06-25 00:00:00
Tasks: Image Reconstruction

Photoacoustic tomography is a hybrid biomedical technology, which combines the advantages of acoustic and optical imaging. However, for the conventional image reconstruction method, the image quality is affected obviously by artifacts under the condition of sparse sampling. in this paper, a novel model-based sparse reconstruction method via implicit neural representation was proposed for improving the image quality reconstructed from sparse data. Specially, the initial acoustic pressure distribution was modeled as a continuous function of spatial coordinates, and parameterized by a multi-layer perceptron. The weights of multi-layer perceptron were determined by training the network in self-supervised manner. And the total variation regularization term was used to offer the prior knowledge. We compared our result with some ablation studies, and the results show that out method outperforms existing methods on simulation and experimental data. Under the sparse sampling condition, our method can suppress the artifacts and avoid the ill-posed problem effectively, which reconstruct images with higher signal-to-noise ratio and contrast-to-noise ratio than traditional methods. The high-quality results for sparse data make the proposed method hold the potential for further decreasing the hardware cost of photoacoustic tomography system.
Joint Channel and Data Estimation for Multiuser Extremely Large-Scale MIMO Systems
Authors: Kabuto Arai, Koji Ishibashi, Hiroki Iimori, Paulo Valente Klaine, Szabolcs Malomsoky
Date: 2024-06-27 00:00:00
Tasks: Bayesian Inference, compressed sensing

This paper proposes a joint channel and data estimation (JCDE) algorithm for uplink multiuser extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. The initial channel estimation is formulated as a sparse reconstruction problem based on the angle and distance sparsity under the near-field propagation condition. This problem is solved using non-orthogonal pilots through an efficient low complexity two-stage compressed sensing algorithm. Furthermore, the initial channel estimates are refined by employing a JCDE framework driven by both non-orthogonal pilots and estimated data. The JCDE problem is solved by sequential expectation propagation (EP) algorithms, where the channel and data are alternately updated in an iterative manner. In the channel estimation phase, integrating Bayesian inference with a model-based deterministic approach provides precise estimations to effectively exploit the near-field characteristics in the beam-domain. In the data estimation phase, a linear minimum mean square error (LMMSE)-based filter is designed at each sub-array to address the correlation due to energy leakage in the beam-domain arising from the near-field effects. Numerical simulations reveal that the proposed initial channel estimation and JCDE algorithm outperforms the state-ofthe-art approaches in terms of channel estimation, data detection, and computational complexity.
An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNet
Authors: Shengwei Guo
Date: 2024-06-27 00:00:00
Tasks: Computational Efficiency, EEG, Sleep Quality, Sleep Staging

Sleep quality directly impacts human health and quality of life, so accurate sleep staging is essential for assessing sleep quality. However, most traditional methods are inefficient and time-consuming due to segmenting different sleep cycles by manual labeling. In contrast, automated sleep staging technology not only directly assesses sleep quality but also helps sleep specialists analyze sleep status, significantly improving efficiency and reducing the cost of sleep monitoring, especially for continuous sleep monitoring. Most of the existing models, however, are deficient in computational efficiency, lightweight design, and model interpretability. In this paper, we propose a neural network architecture based on the prior knowledge of sleep experts. Specifically, 1) Propose an end-to-end model named DetectsleepNet that uses single-channel EEG signals without additional data processing, which has achieved an impressive 80.9% accuracy on the SHHS dataset and an outstanding 88.0% accuracy on the Physio2018 dataset. 2) Constructure an efficient lightweight sleep staging model named DetectsleepNet-tiny based on DetectsleepNet, which has just 6% of the parameter numbers of existing models, but its accuracy exceeds 99% of state-of-the-art models, 3) Introducing a specific inference header to assess the attention given to a specific EEG segment in each sleep frame, enhancing the transparency in the decisions of models. Our model comprises fewer parameters compared to existing ones and ulteriorly explores the interpretability of the model to facilitate its application in healthcare. The code is available at https://github.com/komdec/DetectSleepNet.git.
OTFS-NOMA System for MIMO Communication Networks with Spatial Diversity
Authors: Wafa Hedhly, Leila Musavian, Nikolaos Thomos
Date: 2024-06-27 00:00:00
Tasks: Diversity

In this work, we study the use of non-orthogonal multiple access (NOMA) and orthogonal time frequency space (OTFS) modulation in a multiple-input multiple-output (MIMO) communication network where mobile users (MUs) with different mobility profiles are grouped into clusters. We consider a downlink scenario where a base station (BS) communicates with multiple users that have diverse mobility profiles. High-mobility (HM) users' signals are placed in the delay-Doppler (DD) domain using OTFS modulation in order to transform their time-varying channel into a sparse static channel, while low-mobility (LM) users signals are placed in the time-frequency (TF) domain. Precoding is adopted at the BS to direct focused beams towards each cluster of users. Moreover, NOMA spectrum sharing is used in each cluster to allow the coexistence of a single HM user and multiple LM users within the same resource block. LM users access disjoint subchannels to ensure their orthogonality. All users within the same cluster first detect the HM user's signal. Afterward, LM users suppress the interference from the HM user and detect their own signals. Closed-form expressions of the detection signal-to-noise ratios (SNRs) are derived. The numerical results showed that the performance of the proposed system highly depends on the number of LM users, the number of clusters and the power allocation factors between HM and LM users.
Coordinated RSMA for Integrated Sensing and Communication in Emergency UAV Systems
Authors: Binghan Yao, Ruoguang Li, Yingyang Chen, Li Wang
Date: 2024-06-27 00:00:00
Tasks: Integrated sensing and communication, ISAC

Recently, unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is emerging as a promising technique for achieving robust and rapid emergency response capabilities. Such a novel framework offers high-quality and cost-efficient C\&S services due to the intrinsic flexibility and mobility of UAVs. In parallel, rate-splitting multiple access (RSMA) is able to achieve a tailor-made communication by splitting the messages into private and common parts with adjustable rates, making it suitable for on-demand data transmission in disaster scenarios. In this paper, we propose a coordinated RSMA for integrated sensing and communication (CoRSMA-ISAC) scheme in emergency UAV system to facilitate search and rescue operations, where a number of ISAC UAVs simultaneously communicate with multiple communication survivors (CSs) and detect a potentially trapped survivor (TS) in a coordinated manner. Towards this end, an optimization problem is formulated to maximize the weighted sum rate (WSR) of the system, subject to the sensing signal-to-noise ratio (SNR) requirement. In order to solve the formulated non-convex problem, we first decompose it into three subproblems, i.e., UAV-CS association, UAV deployment, as well as beamforming optimization and rate allocation. Subsequently, we introduce an iterative optimization approach leveraging K-Means, successive convex approximation (SCA), and semi-definite relaxation (SDR) algorithms to reframe the subproblems into a more tractable form and efficiently solve them. Simulation results demonstrate that the proposed CoRSMA-ISAC scheme is superior to conventional space division multiple access (SDMA), non-orthogonal multiple access (NOMA), and orthogonal multiple access (OMA) in terms of both communication and sensing performance.
Fronthaul Quantization-Aware MU-MIMO Precoding for Sum Rate Maximization
Authors: Yasaman Khorsandmanesh, Emil Björnson, Joakim Jaldén
Date: 2024-06-27 00:00:00
Tasks: Quantization

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the precoding matrix is selected in a baseband unit (BBU) and then sent over a digital fronthaul to the transmitting antenna array. The fronthaul has a limited bit resolution with a known quantization behavior. We formulate a new sum rate maximization problem where the precoding matrix elements must comply with the quantizer. We solve this non-convex mixed-integer problem to local optimality by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. The precoding optimization subproblem becomes an integer least-squares problem, which we solve with a new algorithm using a sphere decoding (SD) approach. We show numerically that the proposed precoding technique vastly outperforms the baseline of optimizing an infinite-resolution precoder and then quantizing it. We also develop a heuristic quantization-aware precoding that outperforms the baseline while having comparable complexity.
Distributed MIMO Networks with Rotary ULAs for Indoor Scenarios under Rician Fading
Authors: Eduardo Noboro Tominaga, Onel Luis Alcaraz López, Tommy Svensson, Richard Demo Souza, Hirley Alves
Date: 2024-06-27 00:00:00
Tasks: Position

The Fifth-Generation (5G) wireless communications networks introduced native support for Machine-Type Communications (MTC) use cases. Nevertheless, current 5G networks cannot fully meet the very stringent requirements regarding latency, reliability, and number of connected devices of most MTC use cases. Industry and academia have been working on the evolution from 5G to Sixth Generation (6G) networks. One of the main novelties is adopting Distributed Multiple-Input Multiple-Output (D-MIMO) networks. However, most works studying D-MIMO consider antenna arrays with no movement capabilities, even though some recent works have shown that this could bring substantial performance improvements. In this work, we propose the utilization of Access Points (APs) equipped with Rotary Uniform Linear Arrays (RULAs) for this purpose. Considering a spatially correlated Rician fading model, the optimal angular position of the RULAs is jointly computed by the central processing unit using particle swarm optimization as a function of the location of the active devices. Considering the impact of imperfect location estimates, our numerical results show that the RULAs's optimal rotation brings substantial performance gains in terms of mean per-user spectral efficiency. The improvement grows with the strength of the line-of-sight components of the channel vectors. Given the total number of antenna elements, we study the trade-off between the number of APs and the number of antenna elements per AP, revealing an optimal number of APs for the cases of APs equipped with static ULAs and RULAs.