pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Design of Climate Disclosure Policies: Transparency versus Externality
Authors: Shangen Li
Date: 2024-02-19 00:00:00

Does a more transparent climate disclosure policy induce lower emissions? This paper examines the welfare implications of transparency in climate disclosure regulation. Increased disclosure transparency could result in a larger equilibrium externality, but never leaves the firm worse off. Consequently, mandating full disclosure is no different from maximizing the firm's private benefit while disregarding the ensuing externality. Transparency beyond binary disclosure is necessary only when the firm holds private information about its incentives for emission reduction. I provide conditions under which focusing on threshold disclosure policies entails no loss of generality.
Ironing allocations
Authors: Filip Tokarski
Date: 2024-02-19 00:00:00

I propose a new approach to solving standard screening problems when the monotonicity constraint binds. A simple geometric argument shows that when virtual values are quasi-concave, the optimal allocation can be found by appropriately truncating the solution to the relaxed problem. I provide a simple algorithm for finding this optimal truncation when virtual values are concave.
Attraction Via Prices and Information
Authors: Pak Hung Au, Mark Whitmeyer
Date: 2024-02-19 00:00:00

We study the ramifications of increased commitment power for information provision in an oligopolistic market with search frictions. Although prices are posted and, therefore, guide search, if firms cannot commit to information provision policies, there is no active search at equilibrium so consumers visit (and purchase from) at most one firm. If firms can guide search by both their prices and information policies, there exists a unique symmetric equilibrium exhibiting price dispersion and active search. Nevertheless, when the market is thin, consumers prefer the former case, which features intense price competition. Firms always prefer the latter.
Transformer-based Learned Image Compression for Joint Decoding and Denoising
Authors: Yi-Hsin Chen, Kuan-Wei Ho, Shiau-Rung Tsai, Guan-Hsun Lin, Alessandro Gnutti, Wen-Hsiao Peng, Riccardo Leonardi
Date: 2024-02-20 00:00:00
Tasks: Decoder, Denoising, Image Compression, Image Reconstruction

This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from a single compressed bitstream. Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the standard image reconstruction to joint decoding and denoising. Our scheme adopts a two-pronged approach. It features a latent refinement module to refine the latent representation of a noisy input image for reconstructing a noise-free image. Additionally, it incorporates an instance-specific prompt generator that adapts the decoding process to improve on the latent refinement. Experimental results show that our method achieves a similar level of denoising quality to training a separate decoder for joint decoding and denoising at the expense of only a modest increase in the decoder's model size and computational complexity.
Deep, convergent, unrolled half-quadratic splitting for image deconvolution
Authors: Yanan Zhao, Yuelong Li, Haichuan Zhang, Vishal Monga, Yonina C. Eldar
Date: 2024-02-20 00:00:00
Tasks: Deblurring, Image Deblurring, Image Deconvolution

In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling-based deep network design since many traditional model-based approaches rely on iterative optimization. Despite exciting progress, typical unrolling approaches heuristically design layer-specific convolution weights to improve performance. Crucially, convergence properties of the underlying iterative algorithm are lost once layer-specific parameters are learned from training data. We propose an unrolling technique that breaks the trade-off between retaining algorithm properties while simultaneously enhancing performance. We focus on image deblurring and unrolling the widely-applied Half-Quadratic Splitting (HQS) algorithm. We develop a new parametrization scheme which enforces layer-specific parameters to asymptotically approach certain fixed points. Through extensive experimental studies, we verify that our approach achieves competitive performance with state-of-the-art unrolled layer-specific learning and significantly improves over the traditional HQS algorithm. We further establish convergence of the proposed unrolled network as the number of layers approaches infinity, and characterize its convergence rate. Our experimental verification involves simulations that validate the analytical results as well as comparison with state-of-the-art non-blind deblurring techniques on benchmark datasets. The merits of the proposed convergent unrolled network are established over competing alternatives, especially in the regime of limited training.
OMRA: Online Motion Resolution Adaptation to Remedy Domain Shift in Learned Hierarchical B-frame Coding
Authors: Zong-Lin Gao, Sang NguyenQuang, Wen-Hsiao Peng, Xiem HoangVan
Date: 2024-02-20 00:00:00
Tasks: Motion Estimation

Learned hierarchical B-frame coding aims to leverage bi-directional reference frames for better coding efficiency. However, the domain shift between training and test scenarios due to dataset limitations poses a challenge. This issue arises from training the codec with small groups of pictures (GOP) but testing it on large GOPs. Specifically, the motion estimation network, when trained on small GOPs, is unable to handle large motion at test time, incurring a negative impact on compression performance. To mitigate the domain shift, we present an online motion resolution adaptation (OMRA) method. It adapts the spatial resolution of video frames on a per-frame basis to suit the capability of the motion estimation network in a pre-trained B-frame codec. Our OMRA is an online, inference technique. It need not re-train the codec and is readily applicable to existing B-frame codecs that adopt hierarchical bi-directional prediction. Experimental results show that OMRA significantly enhances the compression performance of two state-of-the-art learned B-frame codecs on commonly used datasets.
Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis
Authors: Savannah P. Hays, Lianrui Zuo, Yihao Liu, Anqi Feng, Jiachen Zhuo, Jerry L. Prince, Aaron Carass
Date: 2024-02-19 00:00:00
Tasks: Anatomy, Image Generation, Image Registration, Image-to-Image Translation

Deep learning (DL) has led to significant improvements in medical image synthesis, enabling advanced image-to-image translation to generate synthetic images. However, DL methods face challenges such as domain shift and high demands for training data, limiting their generalizability and applicability. Historically, image synthesis was also carried out using deformable image registration (DIR), a method that warps moving images of a desired modality to match the anatomy of a fixed image. However, concerns about its speed and accuracy led to its decline in popularity. With the recent advances of DL-based DIR, we now revisit and reinvigorate this line of research. In this paper, we propose a fast and accurate synthesis method based on DIR. We use the task of synthesizing a rare magnetic resonance (MR) sequence, white matter nulled (WMn) T1-weighted (T1-w) images, to demonstrate the potential of our approach. During training, our method learns a DIR model based on the widely available MPRAGE sequence, which is a cerebrospinal fluid nulled (CSFn) T1-w inversion recovery gradient echo pulse sequence. During testing, the trained DIR model is first applied to estimate the deformation between moving and fixed CSFn images. Subsequently, this estimated deformation is applied to align the paired WMn counterpart of the moving CSFn image, yielding a synthetic WMn image for the fixed CSFn image. Our experiments demonstrate promising results for unsupervised image synthesis using DIR. These findings highlight the potential of our technique in contexts where supervised synthesis methods are constrained by limited training data.
TinyLIC-High efficiency lossy image compression method
Authors: Gaocheng Ma, Yinfeng Chai, Tianhao Jiang, Ming Lu, Tong Chen
Date: 2024-02-17 00:00:00
Tasks: Image Compression

Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H.264/AVC. However, recent advancements in deep learning have led to the emergence of learned image compression methods that offer significant improvements in coding efficiency compared to traditional codecs. These learned compression techniques have shown noticeable gains and even outperformed traditional schemes
Multistatic OFDM Radar Fusion of MUSIC-based Angle Estimation
Authors: Martin Willame, Hasan Can Yildirim, Laurent Storrer, François Horlin, Jérôme Louveaux
Date: 2024-02-20 00:00:00
Tasks: parameter estimation

This study investigates the problem of angle-based localization of multiple targets using a multistatic OFDM radar. Although the maximum likelihood (ML) approach can be employed to merge data from different radar pairs, this method requires a high complexity multi-dimensional search process. The multiple signal classification (MUSIC) algorithm simplifies the complexity to a two-dimensional search, but no framework is derived for combining MUSIC pseudo-spectrums in a multistatic configuration. This paper exploits the relationship between MUSIC and ML estimators to approximate the multidimensional ML parameter estimation with a weighted combination of MUSIC pseudo-spectrum. This enables the computation of a likelihood map on which a peak selection is applied for target detection. In addition to reducing the computational complexity, the proposed method relies only on transmitting the estimated channel covariance matrices of each radar pair to the central processor. A numerical analysis is conducted to assess the benefits of the proposed fusion.
Towards Intelligent Communications: Large Model Empowered Semantic Communications
Authors: Huiqiang Xie, Zhijin Qin, Xiaoming Tao, Zhu Han
Date: 2024-02-20 00:00:00
Tasks: model, Semantic Communication

Deep learning enabled semantic communications have shown great potential to significantly improve transmission efficiency and alleviate spectrum scarcity, by effectively exchanging the semantics behind the data. Recently, the emergence of large models, boasting billions of parameters, has unveiled remarkable human-like intelligence, offering a promising avenue for advancing semantic communication by enhancing semantic understanding and contextual understanding. This article systematically investigates the large model-empowered semantic communication systems from potential applications to system design. First, we propose a new semantic communication architecture that seamlessly integrates large models into semantic communication through the introduction of a memory module. Then, the typical applications are illustrated to show the benefits of the new architecture. Besides, we discuss the key designs in implementing the new semantic communication systems from module design to system training. Finally, the potential research directions are identified to boost the large model-empowered semantic communications.
3D high-resolution imaging algorithm using 1D MIMO array for autonomous driving application
Authors: Sen Yuan, Francesco Fioranelli, Alexander Yarovoy
Date: 2024-02-20 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

The problem of 3D high-resolution imaging in automotive multiple-input multiple-output (MIMO) side-looking radar using a 1D array is considered. The concept of motion-enhanced snapshots is introduced for generating larger apertures in the azimuth dimension. For the first time, 3D imaging capabilities can be achieved with high angular resolution using a 1D MIMO antenna array, which can alleviate the requirement for large radar systems in autonomous vehicles. The robustness to variations in the vehicle's movement trajectory is also considered and addressed with relevant compensations in the steering vector. The available degrees of freedom as well as the Signal to Noise Ratio (SNR) are shown to increase with the proposed method compared to conventional imaging approaches. The performance of the algorithm has been studied in simulations, and validated with experimental data collected in a realistic driving scenario.
Ray Tracing Algorithm for Reconfigurable Intelligent Surfaces
Authors: Sara Sandh, Hamed Radpour, Benjamin Rainer, Markus Hofer, Thomas Zemen
Date: 2024-02-20 00:00:00
Tasks: GPU

Ray tracing accelerated with graphics processing units (GPUs) is an accurate and efficient simulation technique of wireless communication channels. In this paper, we extend a GPU-accelerated ray tracer (RT) to support the effects of reconfigurable intelligent surfaces (RISs). To evaluate the electric field, we derived a RIS path loss model that can be integrated into a RT and enables further extensions for the implementation of additional features and incorporation into complex reflective scenarios. We verify the derivation and implementation of our model by comparison with empirical measurements in a lab environment. We demonstrate the capabilities of our model to support higher-order reflections from the RIS to the receiver. We find that such components have a significant effect on the received signal strength, concluding that the extensions of advanced functionality enabled by our model play an important role in the accurate modeling of radio wave propagation in an environment including RISs.
See Further Than CFAR: a Data-Driven Radar Detector Trained by Lidar
Authors: Ignacio Roldan, Andras Palffy, Julian F. P. Kooij, Dariu M. Gavrila, Francesco Fioranelli, Alexander Yarovoy
Date: 2024-02-20 00:00:00

In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets. We propose a data-driven radar target detector exploiting a highly efficient 2D CNN backbone inspired by the computer vision domain. Our approach is distinguished by a unique cross sensor supervision pipeline, enabling it to learn exclusively from unlabeled synchronized radar and lidar data, thus eliminating the need for costly manual object annotations. Using a novel large-scale, real-life multi-sensor dataset recorded in various driving scenarios, we demonstrate that the proposed detector generates dense, lidar-like point clouds, achieving a lower Chamfer distance to the reference lidar point clouds than CFAR detectors. Overall, it significantly outperforms CFAR baselines detection accuracy.
Stochastic Graph Heat Modelling for Diffusion-based Connectivity Retrieval
Authors: Stephan Goerttler, Fei He, Min Wu
Date: 2024-02-20 00:00:00
Tasks: Retrieval

Heat diffusion describes the process by which heat flows from areas with higher temperatures to ones with lower temperatures. This concept was previously adapted to graph structures, whereby heat flows between nodes of a graph depending on the graph topology. Here, we combine the graph heat equation with the stochastic heat equation, which ultimately yields a model for multivariate time signals on a graph. We show theoretically how the model can be used to directly compute the diffusion-based connectivity structure from multivariate signals. Unlike other connectivity measures, our heat model-based approach is inherently multivariate and yields an absolute scaling factor, namely the graph thermal diffusivity, which captures the extent of heat-like graph propagation in the data. On two datasets, we show how the graph thermal diffusivity can be used to characterise Alzheimer's disease (AD). We find that the graph thermal diffusivity is lower for AD patients than healthy controls and correlates with mini mental state examination (MMSE) scores, suggesting structural impairment in patients in line with previous findings.
Coded Backscattering Communication with LTE Pilots as Ambient Signal
Authors: Jingyi Liao, Kalle Ruttik, Riku Jantti, Phan-Huy Dinh-Thuy
Date: 2024-02-20 00:00:00

The 3GPP has recently conducted a study on the Ambient Internet of Things (AIoT), with a particular emphasis on examining backscatter communications as one of the primary techniques under consideration. Previous investigations into Ambient Backscatter Communications (AmBC) within the long term evolution (LTE) downlink have shown that it is feasible to utilize the user equipment channel estimator as a receiver for demodulating frequency shift keyed (FSK) messages transmitted by the backscatter devices. In practical deployment scenarios, the backscattered link often experiences a low signal-to-noise ratio, leading to subpar bit error rate (BER) performance in the case of uncoded transmissions. In this paper, we propose the adoption of the same convolutional coding methodology for backscatter links that is already employed for LTE downlink control signals. This approach facilitates the reuse of identical demodulation functions at the modem for both control signals and backscattered AIoT messages. To assess the performance of the proposed scheme, we conducted experiments utilizing real LTE downlink signals generated by a mobile operator within an office environment. When compared to uncoded FSK, convolutional channel coding delivers a notable gain of approximately 6 dB at a BER of $10^{-3}$. Consequently, the AmBC system demonstrates a high level of reliability, achieving a BER of $10^{-3}$ at a Signal-to-Noise Ratio (SNR) of 5 dB.
Angularly Sparse Channel Estimation in Dual-Wideband Tera-Hertz (THz) Hybrid MIMO Systems Relying on Bayesian Learning
Authors: Abhisha Garg, Suraj Srivastava, Nimish Yadav, Aditya K. Jagannatham, Lajos Hanzo
Date: 2024-02-19 00:00:00

Bayesian learning aided massive antenna array based THz MIMO systems are designed for spatial-wideband and frequency-wideband scenarios, collectively termed as the dual-wideband channels. Essentially, numerous antenna modules of the THz system result in a significant delay in the transmission/ reception of signals in the time-domain across the antennas, which leads to spatial-selectivity. As a further phenomenon, the wide bandwidth of THz communication results in substantial variation of the effective angle of arrival/ departure (AoA/ AoD) with respect to the subcarrier frequency. This is termed as the beam squint effect, which renders the channel state information (CSI) estimation challenging in such systems. To address this problem, initially, a pilot-aided (PA) Bayesian learning (PA-BL) framework is derived for the estimation of the Terahertz (THz) MIMO channel that relies exclusively on the pilot beams transmitted. Since the framework designed can successfully operate in an ill-posed model, it can verifiably lead to reduced pilot transmissions in comparison to conventional methodologies. The above paradigm is subsequently extended to additionally incorporate data symbols to derive a Data-Aided (DA) BL approach that performs joint data detection and CSI estimation. We will demonstrate that it is capable of improving the dual-wideband channels estimate, despite further reducing the training overhead. The Bayesian Cramer-Rao bounds (BCRLBs) are also obtained for explicitly characterizing the lower bounds on the mean squared error (MSE) of the PA-BL and DA-BL frameworks. Our simulation results show the improved normalized MSE (NMSE) and bit-error rate (BER) performance of the proposed estimation schemes and confirm that they approach their respective BCRLB benchmarks.
Joint mode switching and resource allocation in wireless-powered RIS-aided multiuser communication systems
Authors: Mingang Yuan, Wenzhe Zhang, Gaofei Huang
Date: 2024-02-19 00:00:00
Tasks: Deep Reinforcement Learning

This paper investigates a wireless-powered hybrid reflecting intelligent surface (hybrid RIS)-assisted multiple access system, where the RIS can harvest energy from energy station (ES) transmitted radio frequency signal (RF), and each reflecting element can flexibly switch between active mode, passive mode, and idle mode. The objective is to minimize the maximum energy consumption of the users by jointly optimizing the operating modes of each reflecting element, the amplification factor of active elements, the transmit power, and transmission time allocation, subject to quality-of-service (QoS) of each user and the available energy constraint of RIS. In the formulated optimization problem, the operating modes of each reflecting element are highly coupled with the amplification coefficient of the active reflecting elements, making it a challenging mixed-integer programming problem. To solve this problem, a hierarchical optimization method based on deep reinforcement learning is proposed, where the operating modes of each reflecting element and the amplification coefficient of active elements are obtained by solving the outer sub-problem using proximal policy optimization (PPO), and the transmit power and transmission time allocation are obtained by solving the inner sub-problem using convex optimization methods. Simulation results show that compared to the baseline scheme, the proposed scheme can reduce user energy consumption by $70 \%$.
Interference Mitigation in LEO Constellations with Limited Radio Environment Information
Authors: Fernando Moya Caceres, Akram Al-Hourani, Saman Atapattu, Michael Aygur, Sithamparanathan Kandeepan, Jing Fu, Ke Wang, Wayne S. T. Rowe, Mark Bowyer, Zarko Krusevac, Edward Arbon
Date: 2024-02-19 00:00:00

This research paper delves into interference mitigation within Low Earth Orbit (LEO) satellite constellations, particularly when operating under constraints of limited radio environment information. Leveraging cognitive capabilities facilitated by the Radio Environment Map (REM), we explore strategies to mitigate the impact of both intentional and unintentional interference using planar antenna array (PAA) beamforming techniques. We address the complexities encountered in the design of beamforming weights, a challenge exacerbated by the array size and the increasing number of directions of interest and avoidance. Furthermore, we conduct an extensive analysis of beamforming performance from various perspectives associated with limited REM information: static versus dynamic, partial versus full, and perfect versus imperfect. To substantiate our findings, we provide simulation results and offer conclusions based on the outcomes of our investigation.
Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands
Authors: Deijany Rodriguez Linares, Håkan Johansson, Yinan Wang
Date: 2024-02-19 00:00:00
Tasks: Symbolic Regression

This letter considers the design of linear-phase finite-length impulse response (FIR) filters for equalization of the frequency responses of digital-to-analog converters (DACs). The letter derives estimates for the filter orders required, as functions of the bandwidth and equalization accuracy, for four DAC pulses that are used in DACs in multiple Nyquist bands. The estimates are derived through a large set of minimax-optimal equalizers and the use of symbolic regression followed by minimax-optimal curve fitting for further enhancement. Design examples included demonstrate the accuracy of the proposed estimates. In addition, the letter discusses the appropriateness of the four types of linear-phase FIR filters, for the different equalizer cases, as well as the corresponding properties of the equalized systems.