pwc-archive/papers-with-abstracts huggingface dataset:
Theorizing the Socio-Cultural Dynamics of Consumer Decision-Making for Participation in Community-Supported Agriculture
Authors: Sota Takagi, Yusuke Numazawa, Kentaro Katsube, Wataru Omukai, Miki Saijo, Takumi Ohashi
Date: 2023-12-29 00:00:00
Tasks: Decision Making

In the context of the urgent need to establish sustainable food systems, Community Supported Agriculture (CSA), in which consumers share risks with producers, has gained increasing attention. Understanding the factors that influence consumer participation in CSA is crucial, yet the complete picture and interrelations of these factors remain unclear in existing studies. This research adopts a scoping review and the KJ method to elucidate the factors influencing consumer participation in CSA and to theorize the consumer participation. In particular, we focus on the dynamics of individual decision-making for participation, under the premise that individuals are embedded in socio-cultural environments. We examine the decision-making process based on the seesaw of expected gains and losses from participation, along with the reflexivity to the individual and the process of updating decision-making post-participation. Our study highlights how individual decision-making for participation is influenced by relationships with others within the embedded socio-cultural environment, as well as by attachment and connection to the community. It also shows that discrepancies between expectations and experiences post-participation, and the transformation of the social capital, promote the updating of decision-making processes. In addition, among the factors identified in this study for participation in CSA, the decision to participate was heavily influenced by expectations of variety of ingredients, suggesting that other factors such as food education and learning opportunities, contribution to environmental and social issues, and connections with people and nature had little impact. Although there are limitations, the insights gained from this study offer profound implications for stakeholders and provide valuable insights for more sustainable and efficient CSA practices.
On the Parameter Estimation of Sinusoidal Models for Speech and Audio Signals
Authors: George P. Kafentzis
Date: 2024-01-02 00:00:00
Tasks: parameter estimation, Resynthesis

In this paper, we examine the parameter estimation performance of three well-known sinusoidal models for speech and audio. The first one is the standard Sinusoidal Model (SM), which is based on the Fast Fourier Transform (FFT). The second is the Exponentially Damped Sinusoidal Model (EDSM) which has been proposed in the last decade, and utilizes a subspace method for parameter estimation, and finally the extended adaptive Quasi-Harmonic Model (eaQHM), which has been recently proposed for AM-FM decomposition, and estimates the signal parameters using Least Squares on a set of basis function that are adaptive to the local characteristics of the signal. The parameter estimation of each model is briefly described and its performance is compared to the others in terms of signal reconstruction accuracy versus window size on a variety of synthetic signals and versus the number of sinusoids on real signals. The latter include highly non stationary signals, such as singing voices and guitar solos. The advantages and disadvantages of each model are presented via synthetic signals and then the application on real signals is discussed. Conclusively, eaQHM outperforms EDS in medium-to-large window size analysis, whereas EDSM yields higher reconstruction values for smaller analysis window sizes. Thus, a future research direction appears to be the merge of adaptivity of the eaQHM and parameter estimation robustness of the EDSM in a new paradigm for high-quality analysis and resynthesis of general audio signals.
Room impulse response reconstruction with physics-informed deep learning
Authors: Xenofon Karakonstantis, Diego Caviedes-Nozal, Antoine Richard, Efren Fernandez-Grande
Date: 2024-01-02 00:00:00
Tasks: Deep Learning

A method is presented for estimating and reconstructing the sound field within a room using physics-informed neural networks. By incorporating a limited set of experimental room impulse responses as training data, this approach combines neural network processing capabilities with the underlying physics of sound propagation, as articulated by the wave equation. The network's ability to estimate particle velocity and intensity, in addition to sound pressure, demonstrates its capacity to represent the flow of acoustic energy and completely characterise the sound field with only a few measurements. Additionally, an investigation into the potential of this network as a tool for improving acoustic simulations is conducted. This is due to its profficiency in offering grid-free sound field mappings with minimal inference time. Furthermore, a study is carried out which encompasses comparative analyses against current approaches for sound field reconstruction. Specifically, the proposed approach is evaluated against both data-driven techniques and elementary wave-based regression methods. The results demonstrate that the physics-informed neural network stands out when reconstructing the early part of the room impulse response, while simultaneously allowing for complete sound field characterisation in the time domain.
ODAQ: Open Dataset of Audio Quality
Authors: Matteo Torcoli, Chih-Wei Wu, Sascha Dick, Phillip A. Williams, Mhd Modar Halimeh, William Wolcott, Emanuel A. P. Habets
Date: 2023-12-30 00:00:00
Tasks: Audio Quality Assessment, Diversity, Music Quality Assessment

Research into the prediction and analysis of perceived audio quality is hampered by the scarcity of openly available datasets of audio signals accompanied by corresponding subjective quality scores. To address this problem, we present the Open Dataset of Audio Quality (ODAQ), a new dataset containing the results of a MUSHRA listening test conducted with expert listeners from 2 international laboratories. ODAQ contains 240 audio samples and corresponding quality scores. Each audio sample is rated by 26 listeners. The audio samples are stereo audio signals sampled at 44.1 or 48 kHz and are processed by a total of 6 method classes, each operating at different quality levels. The processing method classes are designed to generate quality degradations possibly encountered during audio coding and source separation, and the quality levels for each method class span the entire quality range. The diversity of the processing methods, the large span of quality levels, the high sampling frequency, and the pool of international listeners make ODAQ particularly suited for further research into subjective and objective audio quality. The dataset is released with permissive licenses, and the software used to conduct the listening test is also made publicly available.
Cool-Chic: Perceptually Tuned Low Complexity Overfitted Image Coder
Authors: Théo Ladune, Pierrick Philippe, Gordon Clare, Félix Henry, Thomas Leguay
Date: 2024-01-04 00:00:00
Tasks: Image Compression

This paper summarises the design of the Cool-Chic candidate for the Challenge on Learned Image Compression. This candidate attempts to demonstrate that neural coding methods can lead to low complexity and lightweight image decoders while still offering competitive performance. The approach is based on the already published overfitted lightweight neural networks Cool-Chic, further adapted to the human subjective viewing targeted in this challenge.
ED: Perceptually tuned Enhanced Compression Model
Authors: Pierrick Philippe, Théo Ladune, Stéphane Davenet, Thomas Leguay
Date: 2024-01-04 00:00:00
Tasks: Image Compression, model

This paper summarises the design of the candidate ED for the Challenge on Learned Image Compression 2024. This candidate aims at providing an anchor based on conventional coding technologies to the learning-based approaches mostly targeted in the challenge. The proposed candidate is based on the Enhanced Compression Model (ECM) developed at JVET, the Joint Video Experts Team of ITU-T VCEG and ISO/IEC MPEG. Here, ECM is adapted to the challenge objective: to maximise the perceived quality, the encoding is performed according to a perceptual metric, also the sequence selection is performed in a perceptual manner to fit the target bit per pixel objectives. The primary objective of this candidate is to assess the recent developments in video coding standardisation and in parallel to evaluate the progress made by learning-based techniques. To this end, this paper explains how to generate coded images fulfilling the challenge requirements, in a reproducible way, targeting the maximum performance.
USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis
Authors: Jing Jiao, Jin Zhou, Xiaokang Li, Menghua Xia, Yi Huang, Lihong Huang, Na Wang, Xiaofan Zhang, Shichong Zhou, Yuanyuan Wang, Yi Guo
Date: 2023-12-30 00:00:00
Tasks: Image Enhancement

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices.
SASA: Saliency-Aware Self-Adaptive Snapshot Compressive Imaging
Authors: Yaping Zhao, Edmund Y. Lam
Date: 2023-12-30 00:00:00
Tasks: Saliency Detection

The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data depends on the advent of novel optical designs to sample the HD data as two-dimensional (2D) compressed measurements. Nonetheless, the traditional SCI scheme is fundamentally limited, due to the complete disregard for high-level information in the sampling process. To tackle this issue, in this paper, we pave the first mile toward the advanced design of adaptive coding masks for SCI. Specifically, we propose an efficient and effective algorithm to generate coding masks with the assistance of saliency detection, in a low-cost and low-power fashion. Experiments demonstrate the effectiveness and efficiency of our approach. Code is available at: https://github.com/IndigoPurple/SASA
Multi-modal MRI sensitive to age: Focus on early brain development in infants
Authors: Jessica Dubois
Date: 2023-12-29 00:00:00

Exploring the developing brain is a major issue in understanding what enables children to acquire amazing abilities, and how early disruptions can lead to a wide range of neurodevelopmental disorders. MRI plays a key role here by providing a non-invasive way to link brain and behavioral changes. Several modalities are used in newborns and infants to characterize the properties of the developing brain, from growth, morphology to microstructure and functional specialization. Recent multi-modal studies have sought to couple complementary MRI markers to provide a more integrated view of brain development. In this chapter, we describe successively how these approaches have made it possible to assess the early maturation of brain tissues, to link different aspects of structural development, and to compare structural and functional brain development.
Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning
Authors: Ahmed Ayman
Date: 2023-12-24 00:00:00
Tasks: Few-Shot Learning, Image Segmentation, Metric Learning, One-Shot Learning, One-Shot Segmentation, Segmentation, Semantic Segmentation

The potential for augmenting the segmentation of brain tumors through the use of few-shot learning is vast. Although several deep learning networks (DNNs) demonstrate promising results in terms of segmentation, they require a substantial quantity of training data in order to produce suitable outcomes. Furthermore, a major issue faced by most of these models is their ability to perform well when faced with unseen classes. To address these challenges, we propose a one-shot learning model for segmenting brain tumors in magnetic resonance images (MRI) of the brain, based on a single prototype similarity score. Leveraging the recently developed techniques of few-shot learning, which involve the utilization of support and query sets of images for training and testing purposes, we strive to obtain a definitive tumor region by focusing on slices that contain foreground classes. This approach differs from other recent DNNs that utilize the entire set of images. The training process for this model is carried out iteratively, with each iteration involving the selection of random slices that contain foreground classes from randomly sampled data as the query set, along with a different random slice from the same sample as the support set. In order to distinguish the query images from the class prototypes, we employ a metric learning-based approach that relies on non-parametric thresholds. We employ the multimodal Brain Tumor Image Segmentation (BraTS) 2021 dataset, which comprises 60 training images and 350 testing images. The effectiveness of the model is assessed using the mean dice score and mean Intersection over Union (IoU) score.
Resource-Limited Automated Ki67 Index Estimation in Breast Cancer
Authors: J. Gliozzo, G. Marinò, A. Bonometti, M. Frasca, D. Malchiodi
Date: 2023-12-22 00:00:00

The prediction of tumor progression and chemotherapy response has been recently tackled exploiting Tumor Infiltrating Lymphocytes (TILs) and the nuclear protein Ki67 as prognostic factors. Recently, deep neural networks (DNNs) have been shown to achieve top results in estimating Ki67 expression and simultaneous determination of intratumoral TILs score in breast cancer cells. However, in the last ten years the extraordinary progress induced by deep models proliferated at least as much as their resource demand. The exorbitant computational costs required to query (and in some cases also to store) a deep model represent a strong limitation in resource-limited contexts, like that of IoT-based applications to support healthcare personnel. To this end, we propose a resource consumption-aware DNN for the effective estimate of the percentage of Ki67-positive cells in breast cancer screenings. Our approach reduced up to 75% and 89% the usage of memory and disk space respectively, up to 1.5x the energy consumption, and preserved or improved the overall accuracy of a benchmark state-of-the-art solution. Encouraged by such positive results, we developed and structured the adopted framework so as to allow its general purpose usage, along with a public software repository to support its usage.
Multispectral palmprint recognition based on three descriptors: LBP, Shift LBP, and Multi Shift LBP with LDA classifier
Authors: Salwua Aqreerah, Alhaam Alariyibi, Wafa El-Tarhouni
Date: 2023-12-18 00:00:00

Local Binary Patterns (LBP) are extensively used to analyze local texture features of an image. Several new extensions to LBP-based texture descriptors have been proposed, focusing on improving noise robustness by using different coding or thresholding schemes. In this paper we propose three algorithms (LBP), Shift Local Binary Pattern (SLBP), and Multi Shift Local Binary Pattern (MSLBP),to extract features for palmprint images that help to obtain the best unique and characteristic values of an image for identification. The Principal Component Analysis (PCA) algorithm has been applied to reduce the size of the extracted feature matrix in random space and in the matching process; the Linear Discriminant Analysis (LDA) algorithm is used. Several experiments were conducted on the large multispectral database (blue, green, red, and infrared) of the University of Hong Kong. As result, distinguished and high results were obtained where it was proved that, the blue spectrum is superior to all spectra perfectly.
Federated Multi-View Synthesizing for Metaverse
Authors: Yiyu Guo, Zhijin Qin, Xiaoming Tao, Geoffrey Ye Li
Date: 2023-12-18 00:00:00
Tasks: Domain Adaptation, Federated Learning, Transfer Learning

The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is data- and computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse. We propose a three-dimensional (3D)-aware generative model that uses collections of single-view images. These single-view images are transmitted to a group of users with overlapping fields of view, which avoids massive content transmission compared to transmitting tiles or whole 3D models. We then present a federated learning approach to guarantee an efficient learning process. The training performance can be improved by characterizing the vertical and horizontal data samples with a large latent feature space, while low-latency communication can be achieved with a reduced number of transmitted parameters during federated learning. We also propose a federated transfer learning framework to enable fast domain adaptation to different target domains. Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery.
Data Integration Framework for Virtual Reality Enabled Digital Twins
Authors: Florian Stadtmann, Hary Pirajan Mahalingam, Adil Rasheed
Date: 2024-01-04 00:00:00
Tasks: Data Integration, Unity

Digital twins are becoming increasingly popular across many industries for real-time data streaming, processing, and visualization. They allow stakeholders to monitor, diagnose, and optimize assets. Emerging technologies used for immersive visualization, such as virtual reality, open many new possibilities for intuitive access and monitoring of remote assets through digital twins. This is specifically relevant for floating wind farms, where access is often limited. However, the integration of data from multiple sources and access through different devices including virtual reality headsets can be challenging. In this work, a data integration framework for static and real-time data from various sources on the assets and their environment is presented that allows collecting and processing of data in Python and deploying the data in real-time through Unity on different devices, including virtual reality headsets. The integration of data from terrain, weather, and asset geometry is explained in detail. A real-time data stream from the asset to the clients is implemented and reviewed, and instructions are given on the code required to connect Python scripts to any Unity application across devices. The data integration framework is implemented for a digital twin of a floating wind turbine and an onshore wind farm, and the potential for future research is discussed.
OFDM-Based Digital Semantic Communication with Importance Awareness
Authors: Chuanhong Liu, Caili Guo, Yang Yang, Wanli Ni, Tony Q. S. Quek
Date: 2024-01-04 00:00:00
Tasks: Deep Reinforcement Learning, Semantic Communication

Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mainly focus on analog SemCom with simple channel models, which may limit its practical application. To reduce this gap, we propose an orthogonal frequency division multiplexing (OFDM)-based SemCom system that is compatible with existing digital communication infrastructures. In the considered system, the extracted semantics is quantized by scalar quantizers, transformed into OFDM signal, and then transmitted over the frequency-selective channel. Moreover, we propose a semantic importance measurement method to build the relationship between target task and semantic features. Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance. However, the optimization objective function cannot be accurately characterized using a mathematical expression due to the neural network-based semantic codec. Given the complex nature of the problem, we first propose a low-complexity sub-carrier allocation method that assigns sub-carriers with better channel conditions to more critical semantics. Then, we propose a deep reinforcement learning-based bit allocation algorithm with dynamic action space. Simulation results demonstrate that the proposed system achieves 9.7% and 28.7% performance gains compared to analog SemCom and conventional bit-based communication systems, respectively.
ICI-Free Channel Estimation and Wireless Gesture Recognition Based on Cellular Signals
Authors: Rui Peng, Yafei Tian, Shengqian Han
Date: 2024-01-04 00:00:00
Tasks: Gesture Recognition

Device-free wireless sensing attracts enormous attentions since it senses the environment without additional devices. While cellular signals are good opportunistic radio sources, the influence of inter-cell interference (ICI) on wireless sensing has not been adequately addressed. In this letter, we first investigate the cause of ICI and its impact on wireless sensing. Then we propose an ICI-free channel estimation method by reconstructing the broadcast signals of adjacent cells and solving simultaneous equations. Wireless gesture recognition can be greatly benefited by ICI mitigation. Finally, we build a prototype system to receive the commercial 4G-LTE signals, and demonstrate the accuracies of wireless gesture recognition under various conditions.