pwc-archive/papers-with-abstracts huggingface dataset:
Social Learning in Lung Transplant Decision
Authors: Laura Doval, Federico Echenique Wanying Huang, Yi Xin
Date: 2024-11-15 00:00:00

We study the allocation of deceased-donor lungs to patients in need of a transplant. Patients make sequential decisions in an order dictated by a priority policy. Using data from a prominent Organ Procurement Organization in the United States, we provide reduced-form evidence of social learning: because patients accept or reject organs in sequence, their decisions exhibit herding behavior, often rejecting an organ that would otherwise be accepted. We develop and estimate a structural model to quantify the impact of various policy proposals and informational regimes. Our results show that blinding patients to their position in the sequence\textemdash thereby eliminating social learning\textemdash boosts organ allocation but reduces average utility per patient. In contrast, prioritizing patients by their likelihood of acceptance exacerbates social learning, leading to fewer organ allocations. Nevertheless, it raises utility per accepted organ and expedites the allocation process.
Reinterpreting Delay and Procrastination
Authors: Conrad Kosowsky
Date: 2024-11-18 00:00:00

I model a rational agent who spends resources between the current time and some fixed future deadline. Opportunities to spend resources arise randomly according to a Poisson process, and the quality of each opportunity follows a uniform distribution. The agent values their current resource stock at exactly the sum of expected utility from all future spending opportunities. Unlike in traditional discounted expected utility models, the agent exhibits correlation aversion, static (but not dynamic) preference reversals, and monotonicity with respect to payment timing. Connecting the agent's risk and time preference is intuitive, and doing so leads to a new model of procrastination where the agent misperceives their general attitude toward spending resources.
Modeling the Modeler: A Normative Theory of Experimental Design
Authors: Fernando Payró, Evan Piermont
Date: 2024-11-18 00:00:00
Tasks: Experimental Design

We consider an analyst whose goal is to identify a subject's utility function through revealed preference analysis. We argue the analyst's preference about which experiments to run should adhere to three normative principles: The first, Structural Invariance, requires that the value of a choice experiment only depends on what the experiment may potentially reveal. The second, Identification Separability, demands that the value of identification is independent of what would have been counterfactually identified had the subject had a different utility. Finally, Information Monotonicity asks that more informative experiments are preferred. We provide a representation theorem, showing that these three principles characterize Expected Identification Value maximization, a functional form that unifies several theories of experimental design. We also study several special cases and discuss potential applications.
Competition, Persuasion, and Search
Authors: Teddy Mekonnen, Bobak Pakzad-Hurson
Date: 2024-11-17 00:00:00

An agent engages in sequential search. He does not directly observe the quality of the goods he samples, but he can purchase signals designed by profit maximizing principal(s). We formulate the principal-agent relationship as a repeated contracting problem within a stopping game, and characterize the set of equilibrium payoffs. We show that when the agent's search cost falls below a given threshold, competition does not impact how much surplus is generated in equilibrium nor how the surplus is divided. In contrast, competition benefits the agent at the expense of total surplus when the search cost exceeds that threshold. Our results challenge the view that monopoly decreases market efficiency, and moreover, suggest that it leads to more information provision than does competition.
Uncovering the role of semantic and acoustic cues in normal and dichotic listening
Authors: Akshara Soman, Sai Samrat Kankanala, Sriram Ganapathy
Date: 2024-11-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Despite extensive research, the precise role of acoustic and semantic cues in complex speech perception tasks remains unclear. In this study, we propose a paradigm to understand the encoding of these cues in electroencephalogram (EEG) data, using match-mismatch (MM) classification task. The MM task involves determining whether the stimulus and response correspond to each other or not. We design a multi-modal sequence model, based on long short term memory (LSTM) architecture, to perform the MM task. The model is input with acoustic stimulus (derived from the speech envelope), semantic stimulus (derived from textual representations of the speech content), and neural response (derived from the EEG data). Our experiments are performed on two separate conditions, i) natural passive listening condition and, ii) an auditory attention based dichotic listening condition. Using the MM task as the analysis framework, we observe that - a) speech perception is fragmented based on word boundaries, b) acoustic and semantic cues offer similar levels of MM task performance in natural listening conditions, and c) semantic cues offer significantly improved MM classification over acoustic cues in dichotic listening task. Further, the study provides evidence of right ear advantage in dichotic listening conditions.
Explainable DNN-based Beamformer with Postfilter
Authors: Adi Cohen, Daniel Wong, Jung-Suk Lee, Sharon Gannot
Date: 2024-11-16 00:00:00
Tasks: Speech Enhancement

This paper introduces an explainable DNN-based beamformer with a postfilter (ExNet-BF+PF) for multichannel signal processing. Our approach combines the U-Net network with a beamformer structure to address this problem. The method involves a two-stage processing pipeline. In the first stage, time-invariant weights are applied to construct a multichannel spatial filter, namely a beamformer. In the second stage, a time-varying single-channel post-filter is applied at the beamformer output. Additionally, we incorporate an attention mechanism inspired by its successful application in noisy and reverberant environments to improve speech enhancement further. Furthermore, our study fills a gap in the existing literature by conducting a thorough spatial analysis of the network's performance. Specifically, we examine how the network utilizes spatial information during processing. This analysis yields valuable insights into the network's functionality, thereby enhancing our understanding of its overall performance. Experimental results demonstrate that our approach is not only straightforward to train but also yields superior results, obviating the necessity for prior knowledge of the speaker's activity.
Additional Tests for TV 3.0
Authors: Eduardo Peixoto, Pedro Garcia Freitas, Mylene Christine Queiroz Farias, Edil Medeiros, Gabriel Correia Lima da Cunha e Menezes, André Henrique Macedo da Costa
Date: 2024-11-18 00:00:00
Tasks: 4k

In 2023 we have conducted extensive experiments on subjective video quality for the TV 3.0 project at University of Bras\'ilia. A full report on these tests is available at the F\'orum SBTVD website . These tests have evaluated the H.266/VVC codec and a hybrid codec formed by the H.266/VVC and the LCEVC (Low Complexity Enhancement Video Coding) with different resolutions, ranging from 720p to 4K. This report contains the results of additional tests performed for TV 3.0 performed at University of Bras\'ilia. This new experiment consists of two new Video Under Tests (VUTs), one with the H.266/VVC codec at 4K resolution, and the other with the H.266/VVC+LCEVC codec at 4K resolution. In this new test, both codecs have the same GOP size (120 frames) and use the same VVC encoder (MainConcept live encoder). This new experiment follows the same experimental protocol as the previous experiments, in order to be fully comparable to the reported results. This document details the results of the new experiments.
Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging
Authors: Jaykumar H. Patel, Brenden T. Kadota, Calder D. Sheagren, Mark Chiew, Graham A. Wright
Date: 2024-11-17 00:00:00
Tasks: Anatomy, compressed sensing, Prognosis

Cardiovascular diseases (CVDs) remain the leading cause of mortality and morbidity worldwide. Both diagnosis and prognosis of these diseases benefit from high-quality imaging, which cardiac magnetic resonance imaging provides. CMR imaging requires lengthy acquisition times and multiple breath-holds for a complete exam, which can lead to patient discomfort and frequently results in image artifacts. In this work, we present a Low-rank tensor U-Net method (LowRank-CGNet) that rapidly reconstructs highly undersampled data with a variety of anatomy, contrast, and undersampling artifacts. The model uses conjugate gradient data consistency to solve for the spatial and temporal bases and employs a U-Net to further regularize the basis vectors. Currently, model performance is superior to a standard U-Net, but inferior to conventional compressed sensing methods. In the future, we aim to further improve model performance by increasing the U-Net size, extending the training duration, and dynamically updating the tensor rank for different anatomies.
An All-in-one Approach for Accelerated Cardiac MRI Reconstruction
Authors: Kian Anvari Hamedani, Narges Razizadeh, Shahabedin Nabavi, Mohsen Ebrahimi Moghaddam
Date: 2024-11-16 00:00:00
Tasks: All, Image Reconstruction, MRI Reconstruction, SSIM

Cardiovascular magnetic resonance (CMR) imaging is the gold standard for diagnosing several heart diseases due to its non-invasive nature and proper contrast. MR imaging is time-consuming because of signal acquisition and image formation issues. Prolonging the imaging process can result in the appearance of artefacts in the final image, which can affect the diagnosis. It is possible to speed up CMR imaging using image reconstruction based on deep learning. For this purpose, the high-quality clinical interpretable images can be reconstructed by acquiring highly undersampled k-space data, that is only partially filled, and using a deep learning model. In this study, we proposed a stepwise reconstruction approach based on the Patch-GAN structure for highly undersampled k-space data compatible with the multi-contrast nature, various anatomical views and trajectories of CMR imaging. The proposed approach was validated using the CMRxRecon2024 challenge dataset and outperformed previous studies. The structural similarity index measure (SSIM) values for the first and second tasks of the challenge are 99.07 and 97.99, respectively. This approach can accelerate CMR imaging to obtain high-quality images, more accurate diagnosis and a pleasant patient experience.
Efficient Denoising Method to Improve The Resolution of Satellite Images
Authors: Jhanavi Hegde
Date: 2024-11-11 00:00:00
Tasks: Denoising, Super-Resolution

Satellites are widely used to estimate and monitor ground cover, providing critical information to address the challenges posed by climate change. High-resolution satellite images help to identify smaller features on the ground and classification of ground cover types. Small satellites have become very popular recently due to their cost-effectiveness. However, smaller satellites have weaker spatial resolution, and preprocessing using recent generative models made it possible to enhance the resolution of these satellite images. The objective of this paper is to propose computationally efficient guided or image-conditioned denoising diffusion models (DDMs) to perform super-resolution on low-quality images. Denoising based on stochastic ordinary differential equations (ODEs) typically takes hundreds of iterations and it can be reduced using deterministic ODEs. I propose Consistency Models (CM) that utilize deterministic ODEs for efficient denoising and perform super resolution on satellite images. The DOTA v2.0 image dataset that is used to develop object detectors needed for urban planning and ground cover estimation, is used in this project. The Stable Diffusion model is used as the base model, and the DDM in Stable Diffusion is converted into a Consistency Model (CM) using Teacher-Student Distillation to apply deterministic denoising. Stable diffusion with modified CM has successfully improved the resolution of satellite images by a factor of 16, and the computational time was reduced by a factor of 20 compared to stochastic denoising methods. The FID score of low-resolution images improved from 10.0 to 1.9 after increasing the image resolution using my algorithm for consistency models.
LiTformer: Efficient Modeling and Analysis of High-Speed Link Transmitters Using Non-Autoregressive Transformer
Authors: Songyu Sun, Xiao Dong, Yanliang Sha, Quan Chen, Cheng Zhuo
Date: 2024-11-18 00:00:00
Tasks: Decoder

High-speed serial links are fundamental to energy-efficient and high-performance computing systems such as artificial intelligence, 5G mobile and automotive, enabling low-latency and high-bandwidth communication. Transmitters (TXs) within these links are key to signal quality, while their modeling presents challenges due to nonlinear behavior and dynamic interactions with links. In this paper, we propose LiTformer: a Transformer-based model for high-speed link TXs, with a non-sequential encoder and a Transformer decoder to incorporate link parameters and capture long-range dependencies of output signals. We employ a non-autoregressive mechanism in model training and inference for parallel prediction of the signal sequence. LiTformer achieves precise TX modeling considering link impacts including crosstalk from multiple links, and provides fast prediction for various long-sequence signals with high data rates. Experimental results show that LiTformer achieves 148-456$\times$ speedup for 2-link TXs and 404-944$\times$ speedup for 16-link with mean relative errors of 0.68-1.25%, supporting 4-bit signals at Gbps data rates of single-ended and differential TXs, as well as PAM4 TXs.
Optimizing Clustered Cell-Free Networking for Sum Ergodic Capacity Maximization with Joint Processing Constraint
Authors: Funing Xia, Junyuan Wang, Lin Dai
Date: 2024-11-18 00:00:00

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking (BC^2F-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed BC^2F-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our BC^2F-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.
Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach
Authors: Yifan Yu, Shengjie Xiu, Daniel P. Palomar
Date: 2024-11-18 00:00:00
Tasks: Computational Efficiency, State Estimation, State Space Models

In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our algorithm addresses two significant limitations found in existing approaches: susceptibility to measurement noise outliers and difficulties in incorporating constraints into state estimation. By formulating constrained state estimation as an optimization problem and employing the Majorization-Minimization (MM) approach, our framework provides a unified solution that enhances the robustness of the Kalman filter. Experimental results demonstrate high accuracy and computational efficiency achieved by our proposed approach, establishing it as a promising solution for robust and constrained state estimation in real-world applications.
Non-Invasive Glucose Level Monitoring from PPG using a Hybrid CNN-GRU Deep Learning Network
Authors: Abdelrhman Y. Soliman, Ahmed M. Nor, Octavian Fratu, Simona Halunga, Osama A. Omer, Ahmed S. Mubark
Date: 2024-11-17 00:00:00

Every year, humanity loses about 1.5 million persons due to diabetic disease. Therefore continuous monitoring of diabetes is highly needed, but the conventional approach, i.e., fingertip pricking, causes mental and physical pain to the patient. This work introduces painless and cheaper non-invasive blood glucose level monitoring, Exploiting the advancement and huge progress in deep learning to develop a hybrid convolution neural network (CNN) - gate recurrent unit (GRU) network to hit the targeted system, The proposed system deploys CNN for extracting spatial patterns in the photoplethysmogram (PPG) signal and GRU is used for detecting the temporal patterns. The performance of the proposed system achieves a Mean Absolute Error (MAE) of 2.96 mg/dL, a mean square error (MSE) of 15.53 mg/dL, a root mean square Error (RMSE) of 3.94 mg/dL, and a coefficient of determination ($R^2$ score) of 0.97 on the test dataset. According to the Clarke Error Grid analysis, 100% of points fall within the clinically acceptable zone (Class A)
IREE Oriented Active RIS-Assisted Green communication System with Outdated CSI
Authors: Kai Cao, Tao Yu, Jihong Li, Xiaojing Chen, Yanzan Sun, Qingqing Wu, Wen Chen, Shunqing Zhang
Date: 2024-11-17 00:00:00

The rapid evolution of communication technologies has spurred a growing demand for energy-efficient network architectures and performance metrics. Active Reconfigurable Intelligent Surfaces (RIS) are emerging as a key component in green network architectures. Compared to passive RIS, active RIS are equipped with amplifiers on each reflecting element, allowing them to simultaneously reflect and amplify signals, thereby overcoming the double multiplicative fading in the phase response, and improving both system coverage and performance. Additionally, the Integrated Relative Energy Efficiency (IREE) metric, as introduced in [1], addresses the dynamic variations in traffic and capacity over time and space, enabling more energy-efficient wireless systems. Building on these advancements, this paper investigates the problem of maximizing IREE in active RIS-assisted green communication systems. However, acquiring perfect Channel State Information (CSI) in practical systems poses significant challenges and costs. To address this, we derive the average achievable rate based on outdated CSI and formulated the corresponding IREE maximization problem, which is solved by jointly optimizing beamforming at both the base station and RIS. Given the non-convex nature of the problem, we propose an Alternating Optimization Successive Approximation (AOSO) algorithm. By applying quadratic transform and relaxation techniques, we simplify the original problem and alternately optimize the beamforming matrices at the base station and RIS. Furthermore, to handle the discrete constraints of the RIS reflection coefficients, we develop a successive approximation method. Experimental results validate our theoretical analysis of the algorithm's convergence , demonstrating the effectiveness of the proposed algorithm and highlighting the superiority of IREE in enhancing the performance of green communication networks.
Rate Splitting Multiple Access for RIS-aided URLLC MIMO Broadcast Channels
Authors: Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Marco Di Renzo, Robert Schober, Lajos Hanzo
Date: 2024-11-17 00:00:00

The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interference is rate splitting multiple access (RSMA), which encompasses many multiple-access technologies like non-orthogonal multiple access (NOMA), spatial division multiple access (SDMA), and broadcasting. Another effective technology to enhance the performance of URLLC systems and mitigate interference is constituted by reconfigurable intelligent surfaces (RISs). This paper develops RSMA schemes for multi-user multiple-input multiple-output (MIMO) RIS-aided broadcast channels (BCs) based on finite block length (FBL) coding. We show that RSMA and RISs can substantially improve the spectral efficiency (SE) and energy efficiency (EE) of MIMO RIS-aided URLLC systems. Additionally, the gain of employing RSMA and RISs noticeably increases when the reliability and latency constraints are more stringent. Furthermore, RISs impact RSMA differently, depending on the user load. If the system is underloaded, RISs are able to manage the interference sufficiently well, making the gains of RSMA small. However, when the user load is high, RISs and RSMA become synergetic.
Modeling blockage in high directional wireless. systems
Authors: Evangelos Koutsonas, Alexandros-Apostolos A. Boulogeorgos, Stylianos E. Trevlakis, Tanweer Ali, Theodoros A. Tsiftis
Date: 2024-11-17 00:00:00

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker's shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage.