pwc-archive/papers-with-abstracts huggingface dataset:
Anonymity in sharing the revenues from broadcasting sports leagues
Authors: Gustavo Bergantinos, Juan D. Moreno-Ternero
Date: 2023-03-31 00:00:00

We study the problem of sharing the revenues from broadcasting sports leagues axiomatically. Our key axiom is anonymity, the classical impartiality axiom. Other impartiality axioms already studied in these problems are equal treatment of equals, weak equal treatment of equals and symmetry. We study the relationship between all impartiality axioms. Besides we combine anonymity with other existing axioms in the literature. Some combinations give rise to new characterizations of well-known rules. The family of generalized split rules is characterized with anonymity, additivity and null team. The concede-and-divide rule is characterized with anonymity, additivity and essential team. Others combinations characterize new rules that had not been considered before. We provide three characterizations in which three axioms are the same (anonymity, additivity, and order preservation) the fourth one is different (maximum aspirations, weak upper bound, and non-negativity). Depending on the fourth axiom we obtain three different families of rules. In all of them concede-and-divide plays a central role.
Self-Supervised Learning-Based Source Separation for Meeting Data
Authors: Yuang Li, Xianrui Zheng, Philip C. Woodland
Date: 2023-04-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

Source separation can improve automatic speech recognition (ASR) under multi-party meeting scenarios by extracting single-speaker signals from overlapped speech. Despite the success of self-supervised learning models in single-channel source separation, most studies have focused on simulated setups. In this paper, seven SSL models were compared on both simulated and real-world corpora. Then, we propose to integrate the best-performing model WavLM into an automatic transcription system through a novel iterative source selection method. To improve real-world performance, time-domain unsupervised mixture invariant training was adapted to the time-frequency domain. Experiments showed that in the transcription system when source separation was inserted before an ASR model fine-tuned on separated speech, absolute reductions of 1.9% and 1.5% in concatenated minimum-permutation word error rate for an unknown number of speakers (cpWER-us) were observed on the AMI dev and test sets.
Ensemble prosody prediction for expressive speech synthesis
Authors: Tian Huey Teh, Vivian Hu, Devang S Ram Mohan, Zack Hodari, Christopher G. R. Wallis, Tomás Gomez Ibarrondo, Alexandra Torresquintero, James Leoni, Mark Gales, Simon King
Date: 2023-04-03 00:00:00
Tasks: Diversity, Ensemble Learning, Expressive Speech Synthesis, Prediction, Prosody Prediction, Speech Synthesis, text-to-speech, Text to Speech

Generating expressive speech with rich and varied prosody continues to be a challenge for Text-to-Speech. Most efforts have focused on sophisticated neural architectures intended to better model the data distribution. Yet, in evaluations it is generally found that no single model is preferred for all input texts. This suggests an approach that has rarely been used before for Text-to-Speech: an ensemble of models. We apply ensemble learning to prosody prediction. We construct simple ensembles of prosody predictors by varying either model architecture or model parameter values. To automatically select amongst the models in the ensemble when performing Text-to-Speech, we propose a novel, and computationally trivial, variance-based criterion. We demonstrate that even a small ensemble of prosody predictors yields useful diversity, which, combined with the proposed selection criterion, outperforms any individual model from the ensemble.
Improving Meeting Inclusiveness using Speech Interruption Analysis
Authors: Szu-Wei Fu, Yaran Fan, Yasaman Hosseinkashi, Jayant Gupchup, Ross Cutler
Date: 2023-04-02 00:00:00

Meetings are a pervasive method of communication within all types of companies and organizations, and using remote collaboration systems to conduct meetings has increased dramatically since the COVID-19 pandemic. However, not all meetings are inclusive, especially in terms of the participation rates among attendees. In a recent large-scale survey conducted at Microsoft, the top suggestion given by meeting participants for improving inclusiveness is to improve the ability of remote participants to interrupt and acquire the floor during meetings. We show that the use of the virtual raise hand (VRH) feature can lead to an increase in predicted meeting inclusiveness at Microsoft. One challenge is that VRH is used in less than 1% of all meetings. In order to drive adoption of its usage to improve inclusiveness (and participation), we present a machine learning-based system that predicts when a meeting participant attempts to obtain the floor, but fails to interrupt (termed a `failed interruption'). This prediction can be used to nudge the user to raise their virtual hand within the meeting. We believe this is the first failed speech interruption detector, and the performance on a realistic test set has an area under curve (AUC) of 0.95 with a true positive rate (TPR) of 50% at a false positive rate (FPR) of <1%. To our knowledge, this is also the first dataset of interruption categories (including the failed interruption category) for remote meetings. Finally, we believe this is the first such system designed to improve meeting inclusiveness through speech interruption analysis and active intervention.
From Smart to Intelligent Utility Meters in Natural Gas Distribution Networks
Authors: Ghaith Matalkah, Edward J. Coyle
Date: 2023-04-01 00:00:00

We propose a novel method for monitoring gas distribution networks (GDNs) using intelligent sensor nodes that can be integrated with existing smart gas meters (intelligent meters). The method aims at detecting and locating gas leaks in GDNs in real time. The intelligent meters leverage wireless connectivity in existing smart meters to collaborate in implementing this method, which comprises an active acoustic probing phase and a passive linear imaging phase. In the active acoustic phase, the intelligent meters collaboratively discover the topology of the monitored pipeline network using a novel acoustic pulse reflectometry technique. In the passive linear imaging phase, the intelligent meters use their knowledge of the pipeline network topology to create a linear image of the pipeline using the Time-Exposure Acoustic (TEA) algorithm. The resulting image reveals the presence and locations of active gas leaks in the network. We present the theoretical basis of the method and show results of implementing it on experimental data collected in the lab.
Multiple Hankel matrix rank minimization for audio inpainting
Authors: Pavel Záviška, Pavel Rajmic, Ondřej Mokrý
Date: 2023-03-31 00:00:00
Tasks: Audio declipping, Audio inpainting

Sasaki et al. (2018) presented an efficient audio declipping algorithm, based on the properties of Hankel-structure matrices constructed from time-domain signal blocks. We adapt their approach to solving the audio inpainting problem, where samples are missing in the signal. We analyze the algorithm and provide modifications, some of them leading to an improved performance. Overall, it turns out that the new algorithms perform reasonably well for speech signals but they are not competitive in the case of music signals.
Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging in vivo
Authors: Biao Huang, Jipeng Yan, Megan Morris, Victoria Sinnett, Navita Somaiah, Meng-Xing Tang
Date: 2023-04-03 00:00:00
Tasks: Diagnostic, Super-Resolution

Super-resolution ultrasound can image microvascular structure and flow at sub-wave-diffraction resolution based on localising and tracking microbubbles. Currently, tracking microbubbles accurately under limited imaging frame rates and high microbubble concentrations remains a challenge, especially under the effect of cardiac pulsatility and in highly curved vessels. In this study, an acceleration-incorporated microbubble motion model is introduced into a Kalman tracking framework. The tracking performance was evaluated using simulated microvasculature with different microbubble motion parameters and acquisition frame rates, and in vivo human breast tumour ultrasound datasets. The simulation results show that the acceleration-based method outperformed the non-acceleration-based method at different levels of acceleration and acquisition frame rates and achieved significant improvement in true positive rate (up to 10.03%), false negative rate (up to 28.61%) and correctly pairing fraction (up to 170.14%). The proposed method can also reduce errors in vasculature reconstruction via the acceleration-based nonlinear interpolation, compared with linear interpolation (up to 19 um). The tracking results from temporally downsampled low frame rate in vivo datasets from human breast tumours show that the proposed method has better microbubble tracking performance than the baseline method, if using results from the initial high frame data as reference. Finally, the acceleration estimated from tracking results also provides a spatial speed gradient map that may contain extra valuable diagnostic information.
COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks
Authors: Chengxi Li, Gang Li, Zhuoyue Wang, Xueqian Wang, Pramod K. Varshney
Date: 2023-04-03 00:00:00
Tasks: Change Detection, Translation

In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods.
Rotating labeling of entropy coders for synthetic DNA data storage
Authors: Xavier Pic, Eva Gil San Antonio, Melpomeni Dimopoulou, Marc Antonini
Date: 2023-04-02 00:00:00

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are considered as potential candidates for a new storage paradigm. Because of this trend, several coding solutions have been proposed over the past years for the storage of digital information into DNA. Despite being a promising solution, DNA storage faces two major obstacles: the large cost of synthesis and the noise introduced during sequencing. Additionally, this noise increases when biochemically defined coding constraints are not respected: avoiding homopolymers and patterns, as well as balancing the GC content. This paper describes a novel entropy coder which can be embedded to any block-based image-coding schema and aims to robustify the decoded results. Our proposed solution introduces variability in the generated quaternary streams, reduces the amount of homopolymers and repeated patterns to reduce the probability of errors occurring. In this paper, we integrate the proposed entropy coder into four existing JPEG-inspired DNA coders. We then evaluate the quality -- in terms of biochemical constraints -- of the encoded data for all the different methods.
The 3D-DCT transform: didactic experiment and possible useful tool
Authors: Fernando Martin-Rodriguez, Fernando Isasi-de-Vicente, Mónica Fernandez-Barciela
Date: 2023-04-01 00:00:00

This article is about an image transform called 3D-DCT, or three-dimensional discrete cosine transform. This is an extension of the well-known 1D and 2D-DCT, which is extensively used, mostly in multimedia coding. A modification of 1D-DCT (MDCT) is used in audio coding, whereas 2D-DCT is the key concept for JPEG standard (still image coding) and also for very popular video coding standards: MPEG-1 and 2, H.261 & H.263. Other, more modern methods like MPEG-4/H.264 and the newest MPEG-H/H.265 use a modified version of 2D-DCT. Nevertheless, 3D-DCT is less known. In this work, a simple implementation for 3D-DCT is constructed and some applications are explored. This is designed as a material for class work but, as it will be shown in this paper, some practical applications can be interesting. 3D-DCT can be applied for creating a video coder much simpler than those following MPEG-x/H.26x and with only slightly less performance. 3D-DCT has also been tested for 3D medical imaging (CT studies) coding, yielding very interesting results.
DrDisco: Deep Registration for Distortion Correction of Diffusion MRI with single phase-encoding
Authors: Zhangxing Bian, Muhan Shao, Aaron Carass, Jerry L. Prince
Date: 2023-04-01 00:00:00
Tasks: Diffusion  MRI, distortion correction

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive way of imaging white matter tracts in the human brain. DW-MRIs are usually acquired using echo-planar imaging (EPI) with high gradient fields, which could introduce severe geometric distortions that interfere with further analyses. Most tools for correcting distortion require two minimally weighted DW-MRI images (B0) acquired with different phase-encoding directions, and they can take hours to process per subject. Since a great amount of diffusion data are only acquired with a single phase-encoding direction, the application of existing approaches is limited. We propose a deep learning-based registration approach to correct distortion using only the B0 acquired from a single phase-encoding direction. Specifically, we register undistorted T1-weighted images and distorted B0 to remove the distortion through a deep learning model. We apply a differentiable mutual information loss during training to improve inter-modality alignment. Experiments on the Human Connectome Project dataset show the proposed method outperforms SyN and VoxelMorph on several metrics, and only takes a few seconds to process one subject.
Regularized Shallow Image Prior for Electrical Impedance Tomography
Authors: Zhe Liu, Zhou Chen, Qi Wang, Sheng Zhang, Yunjie Yang
Date: 2023-03-30 00:00:00

Untrained Neural Network Prior (UNNP) based algorithms have gained increasing popularity in tomographic imaging, as they offer superior performance compared to hand-crafted priors and do not require training. UNNP-based methods usually rely on deep architectures which are known for their excellent feature extraction ability compared to shallow ones. Contrary to common UNNP-based approaches, we propose a regularized shallow image prior method that combines UNNP with hand-crafted prior for Electrical Impedance Tomography (EIT). Our approach employs a 3-layer Multi-Layer Perceptron (MLP) as the UNNP in regularizing 2D and 3D EIT inversion. We demonstrate the influence of two typical hand-crafted regularizations when representing the conductivity distribution with shallow MLPs. We show considerably improved EIT image quality compared to conventional regularization algorithms, especially in structure preservation. The results suggest that combining the shallow image prior and the hand-crafted regularization can achieve similar performance to the Deep Image Prior (DIP) but with less architectural dependency and complexity of the neural network.
Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations
Authors: Anqi Feng, Yuan Xue, Yuli Wang, Chang Yan, Zhangxing Bian, Muhan Shao, Jiachen Zhuo, Rao P. Gullapalli, Aaron Carass, Jerry L. Prince
Date: 2023-03-30 00:00:00

Data-driven thalamic nuclei parcellation depends on high-quality manual annotations. However, the small size and low contrast changes among thalamic nuclei, yield annotations that are often incomplete, noisy, or ambiguously labelled. To train a robust thalamic nuclei parcellation model with noisy annotations, we propose a label propagation algorithm based on random walker to refine the annotations before model training. A two-step model was trained to generate first the whole thalamus and then the nuclei masks. We conducted experiments on a mild traumatic brain injury~(mTBI) dataset with noisy thalamic nuclei annotations. Our model outperforms current state-of-the-art thalamic nuclei parcellations by a clear margin. We believe our method can also facilitate the training of other parcellation models with noisy labels.
Performance of Integrated IoT Network with Hybrid mmWave/FSO/THz Backhaul Link
Authors: Pranay Bhardwaj, Vedant Bansal, Nikhil Biyani, Shiv Shukla, S. M. Zafaruddin
Date: 2023-04-03 00:00:00

Establishing end-to-end connectivity of Internet of Things (IoT) network with the core for collecting sensing data from remote and hard-to-reach terrains is a challenging task. In this article, we analyze the performance of an IoT network integrated with wireless backhaul link for data collection. We propose a solution that involves a self-configuring protocol for aggregate node (AN) selection in an IoT network, which sends the data packet to an unmanned aerial vehicle (UAV) over radio frequency (RF) channels. We adopt a novel hybrid transmission technique for wireless backhaul employing opportunistic selections combining (OSC) and maximal ratio combining (MRC) that simultaneously transmits the data packet on mmWave (mW), free space optical (FSO), and terahertz (THz) technologies to take advantage of their complementary characteristics. We employ the decode-and-forward (DF) protocol to integrate the IoT and backhaul links and provide physical layer performance assessment using outage probability and average bit-error-rate (BER) under diverse channel conditions. We also develop simplified expressions to gain a better understanding of the system's performance at high signal-to-noise ratio (SNR). We provide computer simulations to compare different wireless backhaul technologies under various channel and SNR scenarios and demonstrate the performance of the data collection using the integrated link.
Holographic MIMO Communications exploiting the Orbital Angular Momentum
Authors: Giulia Torcolacci, Nicolò Decarli, Davide Dardari
Date: 2023-04-03 00:00:00

This study delves into the potential of harnessing the orbital angular momentum (OAM) property of electromagnetic waves in near-field and line-of-sight scenarios by utilizing large intelligent surfaces, in the context of holographic multiple-input multiple-output (MIMO) communications. The paper starts by characterizing OAM-based communications and investigating the connection between OAM-carrying waves and optimum communication modes recently analyzed for communicating with smart surfaces. Subsequently, it proposes implementable strategies for generating and detecting OAM-based communication signals using intelligent surfaces and optimization methods that leverage focusing techniques. Then, the performance of these strategies is quantitatively evaluated through simulations. The numerical results show that OAM waves while constituting a viable and more practical alternative to optimum communication modes are sub-optimal in terms of achievable capacity.
Demonstration of a Standalone, Descriptive, and Predictive Digital Twin of a Floating Offshore Wind Turbine
Authors: Florian Stadtmann, Henrik Gusdal Wassertheurer, Adil Rasheed
Date: 2023-04-03 00:00:00
Tasks: Decision Making, Descriptive, Diagnostic, Transfer Learning

Digital Twins bring several benefits for planning, operation, and maintenance of remote offshore assets. In this work, we explain the digital twin concept and the capability level scale in the context of wind energy. Furthermore, we demonstrate a standalone digital twin, a descriptive digital twin, and a prescriptive digital twin of an operational floating offshore wind turbine. The standalone digital twin consists of the virtual representation of the wind turbine and its operating environment. While at this level the digital twin does not evolve with the physical turbine, it can be used during the planning-, design-, and construction phases. At the next level, the descriptive digital twin is built upon the standalone digital twin by enhancing the latter with real data from the turbine. All the data is visualized in virtual reality for informed decision-making. Besides being used for data bundling and visualization, the descriptive digital twin forms the basis for diagnostic, predictive, prescriptive, and autonomous tools. A predictive digital twin is created through the use of weather forecasts, neural networks, and transfer learning. Finally, digital twin technology is discussed in a much wider context of ocean engineering.
An Experimental Study of NOMA for Connected Autonomous Vehicles
Authors: Eray Guven, Caner Goztepe, Mehmet Akif Durmaz, Semiha Tedik Basaran, Gunes Karabulut Kurt, Oguz Kucur
Date: 2023-04-03 00:00:00
Tasks: Autonomous Vehicles, Management

Connected autonomous vehicles (CAV) constitute an important application of future-oriented traffic management .A vehicular system dominated by fully autonomous vehicles requires a robust and efficient vehicle-to-everything (V2X) infrastructure that will provide sturdy connection of vehicles in both short and long distances for a large number of devices, requiring high spectral efficiency (SE). Power domain non-orthogonal multiple access (PD-NOMA) technique has the potential to provide the required high SE levels. In this paper, a vehicular PD-NOMA testbed is implemented using software defined radio (SDR) nodes. The main concerns and their corresponding solutions arising from the implementation are highlighted. The bit error rates(BER) of vehicles with different channel conditions are measured for mobile and stationary cases. The extent of the estimation errors on the success rate beyond the idealized theoretical analysis view is investigated and the approaches to alleviate these errors are discussed. Finally, our perspective on possible PD-NOMA based CAV deployment scenarios is presented in terms of performance constraints and expectancy along with the overlooked open issues.