pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories
Authors: Diego Vallarino
Date: 2024-04-03 00:00:00
Tasks: Survival Analysis

By integrating survival analysis, machine learning algorithms, and economic interpretation, this research examines the temporal dynamics associated with attaining a 5 percent rise in purchasing power parity-adjusted GDP per capita over a period of 120 months (2013-2022). A comparative investigation reveals that DeepSurv is proficient at capturing non-linear interactions, although standard models exhibit comparable performance under certain circumstances. The weight matrix evaluates the economic ramifications of vulnerabilities, risks, and capacities. In order to meet the GDPpc objective, the findings emphasize the need of a balanced approach to risk-taking, strategic vulnerability reduction, and investment in governmental capacities and social cohesiveness. Policy guidelines promote individualized approaches that take into account the complex dynamics at play while making decisions.
Robust Pricing for Quality Disclosure
Authors: Tan Gan, Hongcheng Li
Date: 2024-04-09 00:00:00

A platform charges a producer for disclosing quality evidence to consumers before trade. It aims to maximize its revenue guarantee across potentially multiple equilibria which arise from the interdependence of producer purchase decisions and consumer beliefs. The platform's optimal pricing strategy entrenches itself as a market gatekeeper: it induces a unique equilibrium in which non-disclosed products' perceived values are lower than the production cost. To achieve this goal, this pricing strategy iteratively destabilizes under-disclosure equilibria by luring producers to disclose slightly more. Higher-quality producers receive higher rents as their disclosure is prioritized. Despite losing rents, the platform optimally induces socially efficient information transmission for any given evidence structure, and it never benefits from garbling evidence. Compared to the non-robust benchmark, our framework generates more intuitive comparative statics: the platform's ability to extract surplus increases with its value as an information intermediary.
Information Sale on Network
Authors: Jihwan Do, Lining Han, Xiaoxi Li
Date: 2024-04-08 00:00:00

This paper studies a stylized model of a monopoly data seller when information-sharing network exists among data buyers. We show that, if the buyers' prior information is sufficiently noisy, the optimal selling strategy is characterized by a maximum independent set, which is the largest set of buyers who do not have information-sharing link at all. In addition, the precision of the seller's data decreases in the number of information-sharing links among buyers, but it is higher than the socially efficient level of precision.
Money Pumps and Bounded Rationality
Authors: Joshua Lanier, Matthew Polisson, John K. -H. Quah
Date: 2024-04-07 00:00:00

The standard criterion of rationality in economics is the maximization of a utility function that is stable across multiple observations of an agent's choice behavior. In this paper, we discuss two notions of the money pump that characterize two corresponding notions of utility-maximization. We explain the senses in which the amount of money that can be pumped from a consumer is a useful measure of the consumer's departure from utility-maximization.
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge
Authors: Yiwei Guo, Chenrun Wang, Yifan Yang, Hankun Wang, Ziyang Ma, Chenpeng Du, Shuai Wang, Hanzheng Li, Shuai Fan, HUI ZHANG, Xie Chen, Kai Yu
Date: 2024-04-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Singing Voice Synthesis, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Discrete speech tokens have been more and more popular in multiple speech processing fields, including automatic speech recognition (ASR), text-to-speech (TTS) and singing voice synthesis (SVS). In this paper, we describe the systems developed by the SJTU X-LANCE group for the TTS (acoustic + vocoder), SVS, and ASR tracks in the Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge. Notably, we achieved 1st rank on the leaderboard in the TTS track both with the whole training set and only 1h training data, with the highest UTMOS score and lowest bitrate among all submissions.
Uformer: A UNet-Transformer fused robust end-to-end deep learning framework for real-time denoising of lung sounds
Authors: Samiul Based Shuvo, Syed Samiul Alam, Taufiq Hasan
Date: 2024-04-05 00:00:00
Tasks: Denoising

Objective: Lung auscultation is a valuable tool in diagnosing and monitoring various respiratory diseases. However, lung sounds (LS) are significantly affected by numerous sources of contamination, especially when recorded in real-world clinical settings. Conventional denoising models prove impractical for LS denoising, primarily owing to spectral overlap complexities arising from diverse noise sources. To address this issue, we propose a specialized deep-learning model (Uformer) for lung sound denoising. Methods: The proposed Uformer model is constituted of three modules: a Convolutional Neural Network (CNN) encoder module, dedicated to extracting latent features; a Transformer encoder module, employed to further enhance the encoding of unique LS features and effectively capture intricate long-range dependencies; and a CNN decoder module, employed to generate the denoised signals. An ablation study was performed in order to find the most optimal architecture. Results: The performance of the proposed Uformer model was evaluated on lung sounds induced with different types of synthetic and real-world noises. Lung sound signals of -12 dB to 15 dB signal-to-noise ratio (SNR) were considered in testing experiments. The proposed model showed an average SNR improvement of 16.51 dB when evaluated with -12 dB LS signals. Our end-to-end model, with an average SNR improvement of 19.31 dB, outperforms the existing model when evaluated with ambient noise and fewer parameters. Conclusion: Based on the qualitative and quantitative findings in this study, it can be stated that Uformer is robust and generalized to be used in assisting the monitoring of respiratory conditions.
Image and Video Compression using Generative Sparse Representation with Fidelity Controls
Authors: Wei Jiang, Wei Wang
Date: 2024-04-09 00:00:00
Tasks: Decoder, Image Compression, Video Compression

We propose a framework for learned image and video compression using the generative sparse visual representation (SVR) guided by fidelity-preserving controls. By embedding inputs into a discrete latent space spanned by learned visual codebooks, SVR-based compression transmits integer codeword indices, which is efficient and cross-platform robust. However, high-quality (HQ) reconstruction in the decoder relies on intermediate feature inputs from the encoder via direct connections. Due to the prohibitively high transmission costs, previous SVR-based compression methods remove such feature links, resulting in largely degraded reconstruction quality. In this work, we treat the intermediate features as fidelity-preserving control signals that guide the conditioned generative reconstruction in the decoder. Instead of discarding or directly transferring such signals, we draw them from a low-quality (LQ) fidelity-preserving alternative input that is sent to the decoder with very low bitrate. These control signals provide complementary fidelity cues to improve reconstruction, and their quality is determined by the compression rate of the LQ alternative, which can be tuned to trade off bitrate, fidelity and perceptual quality. Our framework can be conveniently used for both learned image compression (LIC) and learned video compression (LVC). Since SVR is robust against input perturbations, a large portion of codeword indices between adjacent frames can be the same. By only transferring different indices, SVR-based LIC and LVC can share a similar processing pipeline. Experiments over standard image and video compression benchmarks demonstrate the effectiveness of our approach.
STAIC regularization for spatio-temporal image reconstruction
Authors: Deepak G Skariah, Muthuvel Arigovindan
Date: 2024-04-07 00:00:00
Tasks: Form, Image Reconstruction, Image Restoration

We propose a regularization-based image restoration scheme for 2D images recorded over time (2D+t). We design an infimal convolution-based regularization function which we call spatio-temporal Adaptive Infimal Convolution (STAIC) regularization. We formulate the infimal convolution in the form of an additive decomposition of the 2D+t image such that the extent of spatial and temporal smoothing is controlled in a spatially and temporally varying manner. This makes the regularization adaptable to the local characteristics of the motion leading to an improved ability to handle noise. We also develop a minimization method for image reconstruction by using the proposed form of regularization. We demonstrate the effectiveness of the proposed regularization using TIRF images recorded over time and compare with some selected existing regularizations.
Study of the effect of Sharpness on Blind Video Quality Assessment
Authors: Anantha Prabhu, David Pratap, Narayana Darapeni, Anwesh P R
Date: 2024-04-06 00:00:00
Tasks: SSIM, Video Quality Assessment, Visual Question Answering (VQA)

Introduction: Video Quality Assessment (VQA) is one of the important areas of study in this modern era, where video is a crucial component of communication with applications in every field. Rapid technology developments in mobile technology enabled anyone to create videos resulting in a varied range of video quality scenarios. Objectives: Though VQA was present for some time with the classical metrices like SSIM and PSNR, the advent of machine learning has brought in new techniques of VQAs which are built upon Convolutional Neural Networks (CNNs) or Deep Neural Networks (DNNs). Methods: Over the past years various research studies such as the BVQA which performed video quality assessment of nature-based videos using DNNs exposed the powerful capabilities of machine learning algorithms. BVQA using DNNs explored human visual system effects such as content dependency and time-related factors normally known as temporal effects. Results: This study explores the sharpness effect on models like BVQA. Sharpness is the measure of the clarity and details of the video image. Sharpness typically involves analyzing the edges and contrast of the image to determine the overall level of detail and sharpness. Conclusion: This study uses the existing video quality databases such as CVD2014. A comparative study of the various machine learning parameters such as SRCC and PLCC during the training and testing are presented along with the conclusion.
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
Authors: Suman Sourabh, Murugappan Valliappan, Narayana Darapaneni, Anwesh R P
Date: 2024-04-06 00:00:00
Tasks: Brain Image Segmentation, Brain Tumor Segmentation, Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Introduction: The present study on the development and evaluation of an automated brain tumor segmentation technique based on deep learning using the 3D U-Net model. Objectives: The objective is to leverage state-of-the-art convolutional neural networks (CNNs) on a large dataset of brain MRI scans for segmentation. Methods: The proposed methodology applies pre-processing techniques for enhanced performance and generalizability. Results: Extensive validation on an independent dataset confirms the model's robustness and potential for integration into clinical workflows. The study emphasizes the importance of data pre-processing and explores various hyperparameters to optimize the model's performance. The 3D U-Net, has given IoUs for training and validation dataset have been 0.8181 and 0.66 respectively. Conclusion: Ultimately, this comprehensive framework showcases the efficacy of deep learning in automating brain tumour detection, offering valuable support in clinical practice.
Translation-based Video-to-Video Synthesis
Authors: Pratim Saha, Chengcui Zhang
Date: 2024-04-03 00:00:00
Tasks: Colorization, Image-to-Image Translation, Super-Resolution, Translation, Video Super-Resolution, Video-to-Video Synthesis

Translation-based Video Synthesis (TVS) has emerged as a vital research area in computer vision, aiming to facilitate the transformation of videos between distinct domains while preserving both temporal continuity and underlying content features. This technique has found wide-ranging applications, encompassing video super-resolution, colorization, segmentation, and more, by extending the capabilities of traditional image-to-image translation to the temporal domain. One of the principal challenges faced in TVS is the inherent risk of introducing flickering artifacts and inconsistencies between frames during the synthesis process. This is particularly challenging due to the necessity of ensuring smooth and coherent transitions between video frames. Efforts to tackle this challenge have induced the creation of diverse strategies and algorithms aimed at mitigating these unwanted consequences. This comprehensive review extensively examines the latest progress in the realm of TVS. It thoroughly investigates emerging methodologies, shedding light on the fundamental concepts and mechanisms utilized for proficient video synthesis. This survey also illuminates their inherent strengths, limitations, appropriate applications, and potential avenues for future development.
JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
Authors: Woo Kyoung Han, Sunghoon Im, Jaedeok Kim, Kyong Hwan Jin
Date: 2024-04-03 00:00:00
Proceeding: CVPR 2024 1
Tasks: Decoder, JPEG Artifact Removal

We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation. The JPEG algorithm significantly quantizes discrete cosine transform (DCT) spectra to achieve a high compression rate, inevitably resulting in quality degradation while encoding an image. We have designed a continuous cosine spectrum estimator to address the quality degradation issue that restores the distorted spectrum. By leveraging local DCT formulations, our network has the privilege to exploit dequantization and upsampling simultaneously. Our proposed model enables decoding compressed images directly across different quality factors using a single pre-trained model without relying on a conventional JPEG decoder. As a result, our proposed network achieves state-of-the-art performance in flexible color image JPEG artifact removal tasks. Our source code is available at https://github.com/WooKyoungHan/JDEC.
Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection
Authors: Zihang Song, Osvaldo Simeone, Bipin Rajendran
Date: 2024-04-09 00:00:00
Tasks: In-Context Learning

In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mapping provided as context. ICL requires no explicit learning, i.e., no explicit updates of model weights, directly mapping context and new input to the new output. Prior work has proved the usefulness of ICL for detection in MIMO channels. In this setting, the context is given by pilot symbols, and ICL automatically adapts a detector, or equalizer, to apply to newly received signals. However, the implementation tested in prior art was based on conventional artificial neural networks (ANNs), which may prove too energy-demanding to be run on mobile devices. This paper evaluates a neuromorphic implementation of the transformer for ICL-based MIMO detection. This approach replaces ANNs with spiking neural networks (SNNs), and implements the attention mechanism via stochastic computing, requiring no multiplications, but only logical AND operations and counting. When using conventional digital CMOS hardware, the proposed implementation is shown to preserve accuracy, with a reduction in power consumption ranging from $5.4\times$ to $26.8\times$, depending on the model sizes, as compared to ANN-based implementations.
Reconfigurable Multiple-Valued Logic Function and Sequential Circuit Realizations via Threshold Logic Gates
Authors: Ahmet Unutulmaz, Cem Ünsalan
Date: 2024-04-09 00:00:00

In this paper, we present a general reconfigurable multiple-valued logic circuit. The proposed architecture is based on threshold logic gate and is compatible with binary logic, which allows a designer to easily integrate multiple valued logic with binary logic. We also present a methodology to design sequential circuits.
Sustainable and Precision Agriculture with the Internet of Everything (IoE)
Authors: Adil Z. Babar, Ozgur B. Akan
Date: 2024-04-09 00:00:00
Tasks: Management

Agriculture faces critical challenges from population growth, resource scarcity, and climate change, driving a shift toward advanced, technology-integrated farming. Mechanization has transformed agriculture, enhancing sustainability and crop productivity. Now, technologies like artificial intelligence (AI), robotics, biotechnology, blockchain, and the Internet of Things (IoT) are advancing precision agriculture. The concept of the Internet of Everything (IoE) has gained traction due to its holistic approach to integrating various IoT specializations, called IoXs with X referring to a specific domain. This paper explores the transformative role of IoE in agriculture, expanding beyond traditional IoT applications to integrate niche subdomains like molecular communication (MC), the Internet of Nano Things (IoNT), the Internet of Bio-Nano Things (IoBNT), designer phages, and the Internet of Fungus (IoF). Our study provides a detailed review of how these IoE subdomains, in conjunction with 6G, blockchain, and machine learning (ML), can enhance precision farming in areas like crop monitoring, resource management, and disease control. Unlike prior IoT centric reviews, this work uniquely focuses on IoEs potential to advance agriculture at molecular and biological scales, achieving more precise resource utilization and resilience. Key contributions include an exploration of these technologies applicability, associated challenges, and recommendations for future research directions within precision agriculture.
Flow Fusion, Exploiting Measurement Redundancy for Smarter Allocation
Authors: Christine Foss Sjulstad, Danielle Monteiro, Bjarne Grimstad
Date: 2024-04-09 00:00:00

In petroleum production systems, continuous multiphase flow rates are essential for efficient operation. They provide situational awareness, enable production optimization, improve reservoir management and planning, and form the basis for allocation. Furthermore, they can be crucial to ensure a fair revenue split between stakeholders for complex production systems where operators share the facilities. Yet, due to complex multiphase flow dynamics and uncertain subsurface fluid properties, the flow rates are challenging to obtain with high accuracy. Consequently, flow rate measurement and estimation solutions, such as multiphase flow meters and virtual flow meters, have different degrees of accuracy and suitability, and impact production decisions and production allocation accordingly. We propose a field-proven, data-driven framework for reconciliation and allocation. With data validation and reconciliation as the theoretical backbone, the solution exploits measurement redundancy to fuse together relevant flow rate information to infer the most likely flow rates in the production system based on quantifiable uncertainties. The framework consists of four modules: data-processing, uncertainty estimation, reconciliation, and gross error detection. The latter, being the focus of this paper, is a means to identify and mitigate the effect of measurements subject to systematic error, which can invalidate the reconciliation. In this paper, we highlight that a combination of statistical tests and supporting logic for gross error detection and elimination can be beneficial in obtaining a more justifiable production allocation. Using the maximum power measurement test, the module can be limited in its ability to pinpoint the erroneous measurement. Yet, it is demonstrated that the detections can be convenient indications of gross errors and where these might reside in the production system.
Pseudo MIMO (pMIMO): An Energy and Spectral Efficient MIMO-OFDM System
Authors: Sen Wang, Tianxiong Wang, Shulun Zhao, Zhen Feng, Guangyi Liu, Chunfeng Cui, Chih-Lin I, Jiangzhou Wang
Date: 2024-04-09 00:00:00

This article introduces an energy and spectral efficient multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission scheme designed for the future sixth generation (6G) wireless communication networks. The approach involves connecting each receiving radio frequency (RF) chain with multiple antenna elements and conducting sample-level adjustments for receiving beamforming patterns. The proposed system architecture and the dedicated signal processing methods enable the scheme to transmit a bigger number of parallel data streams than the number of receiving RF chains, achieving a spectral efficiency performance close to that of a fully digital (FD) MIMO system with the same number of antenna elements, each equipped with an RF chain. We refer to this system as a ''pseudo MIMO'' system due to its ability to mimic the functionality of additional invisible RF chains. The article begins with introducing the underlying principles of pseudo MIMO and discussing potential hardware architectures for its implementation. We then highlight several advantages of integrating pseudo MIMO into next-generation wireless networks. To demonstrate the superiority of our proposed pseudo MIMO transmission scheme to conventional MIMO systems, simulation results are presented. Additionally, we validate the feasibility of this new scheme by building the first pseudo MIMO prototype. Furthermore, we present some key challenges and outline potential directions for future research.
Interference Management for Full-Duplex ISAC in B5G/6G Networks: Architectures, Challenges, and Solutions
Authors: Aimin Tang, Xudong Wang, J. Andrew Zhang
Date: 2024-04-09 00:00:00
Tasks: ISAC, Management

Integrated sensing and communications (ISAC) has been visioned as a key technique for B5G/6G networks. To support monostatic sensing, a full-duplex radio is indispensable to extract echo signals from targets. Such a radio can also greatly improve network capacity via full-duplex communications. However, full-duplex radios in existing ISAC designs are mainly focused on wireless sensing, while the ability of full-duplex communications is usually ignored. In this article, we provide an overview of full-duplex ISAC (FD-ISAC), where a full-duplex radio is used for both wireless sensing and full-duplex communications in B5G/6G networks, with a focus on the fundamental interference management problem in such networks. First, different ISAC architectures are introduced, considering different full-duplex communication modes and wireless sensing modes. Next, the challenging issues of link-level interference and network-level interference are analyzed, illustrating a critical demand on interference management for FD-ISAC. Potential solutions to interference management are then reviewed from the perspective of radio architecture design, beamforming, mode selection, and resource allocation. The corresponding open problems are also highlighted.