pwc-archive/papers-with-abstracts huggingface dataset:
Emission impossible: Balancing Environmental Concerns and Inflation
Authors: René Aïd, Maria Arduca, Sara Biagini, Luca Taschini
Date: 2025-01-28 00:00:00

We provide a theoretical framework to examine how carbon pricing policies influence inflation and to estimate the policy-driven impact on goods prices from achieving net-zero emissions. Firms control emissions by adjusting production, abating, or purchasing permits, and these strategies determine emissions reductions that affect the consumer price index. We first examine an emissions-regulated economy, solving the market equilibrium under any dynamic allocation of allowances set by the regulator. Next, we analyze a regulator balancing emission reduction and inflation targets, identifying the optimal allocation when accounting for both environmental and inflationary concerns. By adjusting penalties for deviations from these targets, we demonstrate how regulatory priorities shape equilibrium outcomes. Under reasonable model parameterisation, even when considerable emphasis is placed on maintaining inflation at acceptable levels or grant lower priority to emissions reduction targets, the costs associated with emission deviations still exceed any savings from marginally lower inflation. Emission reduction goals should remain the primary focus for policymakers.
The hardcore brokers: Core-periphery structure and political representation in Denmark's corporate elite network
Authors: Lasse F. Henriksen, Jacob Lunding, Christoph H. Ellersgaard, Anton G. Larsen
Date: 2025-01-28 00:00:00

Who represents the corporate elite in democratic governance? Prior studies find a tightly integrated "inner circle" network representing the corporate elite politically across varieties of capitalism, yet they all rely on data from a highly select sample of leaders from only the largest corporations. We cast a wider net. Analyzing new data on all members of corporate boards in the Danish economy (200k directors in 120k boards), we locate 1500 directors that operate as brokers between local corporate networks. We measure their network coreness using k-core detection and find a highly connected core of 275 directors, half of which are affiliated with smaller firms or subsidiaries. Analyses show a strong positive association between director coreness and the likelihood of joining one of the 650 government committees epitomizing Denmark's social-corporatist model of governance (net of firm and director characteristics). The political network premium is largest for directors of smaller firms or subsidiaries, indicating that network coreness is a key driver of business political representation, especially for directors without claims to market power or weight in formal interest organizations.
Financial constraints, risk sharing, and optimal monetary policy
Authors: Aliaksandr Zaretski
Date: 2025-01-27 00:00:00

I characterize optimal government policy in a sticky-price economy with different types of consumers and endogenous financial constraints in the banking and entrepreneurial sectors. The competitive equilibrium allocation is constrained inefficient due to a pecuniary externality implicit in the collateral constraint and other externalities arising from consumer type heterogeneity. These externalities can be corrected with appropriate fiscal instruments. Independently of the availability of such instruments, optimal monetary policy aims to achieve price stability in the long run and approximate price stability in the short run, as in the conventional New Keynesian environment. Compared to the competitive equilibrium, the constrained efficient allocation significantly improves between-agent risk sharing, approaching the unconstrained Pareto optimum and leading to sizable welfare gains. Such an allocation has lower leverage in the banking and entrepreneurial sectors and is less prone to the boom-bust financial crises and zero-lower-bound episodes observed occasionally in the decentralized economy.
Asset Prices with Overlapping Generations and Capital Accumulation: Tirole (1985) Revisited
Authors: Ngoc-Sang Pham, Alexis Akira Toda
Date: 2025-01-27 00:00:00

We revisit the classic paper of Tirole "Asset Bubbles and Overlapping Generations" (1985, Econometrica), which shows that the emergence of asset bubbles solves the capital over-accumulation problem. While Tirole's main insight holds with pure bubbles (assets without dividends), we argue that his original analysis with a dividend-paying asset contains some issues. We provide a fairly complete analysis of Tirole's model with general dividends such as equilibrium existence, uniqueness, and long-run behavior under weaker but explicit assumptions and complement with examples based on closed-form solutions. Some of the claims in Tirole (1985) require qualifications including (i) after the introduction of an asset with negligible dividends, the economy may collapse towards zero capital stock ("resource curse") and (ii) the necessity of bubbles is less clear-cut.
A computational loudness model for electrical stimulation with cochlear implants
Authors: Franklin Alvarez, Yixuan Zhang, Daniel Kipping, Waldo Nogueira
Date: 2025-01-29 00:00:00

Cochlear implants (CIs) are devices that restore the sense of hearing in people with severe sensorineural hearing loss. An electrode array inserted in the cochlea bypasses the natural transducer mechanism that transforms mechanical sound waves into neural activity by artificially stimulating the auditory nerve fibers with electrical pulses. The perception of sounds is possible because the brain extracts features from this neural activity, and loudness is among the most fundamental perceptual features. A computational model that uses a three-dimensional (3D) representation of the peripheral auditory system of CI users was developed to predict categorical loudness from the simulated peripheral neural activity. In contrast, current state-of-the-art computational loudness models predict loudness from the electrical pulses with minimal parametrization of the electrode-nerve interface. In the proposed model, the spikes produced in a population of auditory nerve fibers were grouped by cochlear places, a physiological representation of the auditory filters in psychoacoustics, to be transformed into loudness contribution. Then, a loudness index was obtained with a spatiotemporal integration over this loudness contribution. This index served to define the simulated threshold of hearing (THL) and most comfortable loudness (MCL) levels resembling the growth function in CI users. The performance of real CI users in loudness summation experiments was also used to validate the computational model. These experiments studied the effect of stimulation rate, electrode separation and amplitude modulation. The proposed model provides a new set of perceptual features that can be used in computational frameworks for CIs and narrows the gap between simulations and the human peripheral neural activity.
Cortical Temporal Mismatch Compensation in Bimodal Cochlear Implant Users: Selective Attention Decoding and Pupillometry Study
Authors: Hanna Dolhopiatenko, Waldo Nogueira
Date: 2025-01-28 00:00:00

Bimodal stimulation, combining cochlear implant (CI) and acoustic input from the opposite ear, typically enhances speech perception but varies due to factors like temporal mismatch. Previously, we used cortical auditory evoked potentials (CAEPs) to estimate this mismatch based on N1 latency differences. This study expands on that by assessing the impact of temporal mismatch compensation on speech perception. We tested bimodal CI users in three conditions: clinical, compensated temporal mismatch, and a 50 ms mismatch. Measures included speech understanding, pupillometry, CAEPs, selective attention decoding, and parietal alpha power. Despite stable speech understanding across conditions, neural measures showed stronger effects. CAEP N1P2 amplitudes were highest in the compensated condition. Phase-locking value (PLV) and selective attention decoding improved but lacked significance. Parietal alpha power increased under 50 ms mismatch, suggesting cognitive resource allocation. Pupillometry correlated with speech understanding but showed limited sensitivity. Findings highlight that neural metrics are more sensitive than behavioral tests for detecting interaural mismatch. While CAEP N1P2 amplitudes significantly improved with compensation, other neural measures showed limited effects, suggesting the need for combined temporal and spectral compensation strategies.
Depth Separable architecture for Sentinel-5P Super-Resolution
Authors: Hyam Omar Ali, Romain Abraham, Bruno Galerne
Date: 2025-01-28 00:00:00
Tasks: Super-Resolution

Sentinel-5P (S5P) satellite provides atmospheric measurements for air quality and climate monitoring. While the S5P satellite offers rich spectral resolution, it inherits physical limitations that restricts its spatial resolution. Super-resolution (SR) techniques can overcome these limitations and enhance the spatial resolution of S5P data. In this work, we introduce a novel SR model specifically designed for S5P data that have eight spectral bands with around 500 channels for each band. Our proposed S5-DSCR model relies on Depth Separable Convolution (DSC) architecture to effectively perform spatial SR by exploiting cross-channel correlations. Quantitative evaluation demonstrates that our model outperforms existing methods for the majority of the spectral bands. This work highlights the potential of leveraging DSC architecture to address the challenges of hyperspectral SR. Our model allows for capturing fine details necessary for precise analysis and paves the way for advancements in air quality monitoring as well as remote sensing applications.
Improved Encoding for Overfitted Video Codecs
Authors: Thomas Leguay, Théo Ladune, Pierrick Philippe, Olivier Deforges
Date: 2025-01-28 00:00:00
Tasks: Optical Flow Estimation

Overfitted neural video codecs offer a decoding complexity orders of magnitude smaller than their autoencoder counterparts. Yet, this low complexity comes at the cost of limited compression efficiency, in part due to their difficulty capturing accurate motion information. This paper proposes to guide motion information learning with an optical flow estimator. A joint rate-distortion optimization is also introduced to improve rate distribution across the different frames. These contributions maintain a low decoding complexity of 1300 multiplications per pixel while offering compression performance close to the conventional codec HEVC and outperforming other overfitted codecs. This work is made open-source at https://orange-opensource.github.io/Cool-Chic/
Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization
Authors: Bo-Yun Shi, Yi-Cheng Lo, An-Yeu, Wu, Yi-Min Tsai
Date: 2025-01-28 00:00:00
Tasks: Mamba, Quantization

The Mamba model, utilizing a structured state-space model (SSM), offers linear time complexity and demonstrates significant potential. Vision Mamba (ViM) extends this framework to vision tasks by incorporating a bidirectional SSM and patch embedding, surpassing Transformer-based models in performance. While model quantization is essential for efficient computing, existing works have focused solely on the original Mamba model and have not been applied to ViM. Additionally, they neglect quantizing the SSM layer, which is central to Mamba and can lead to substantial error propagation by naive quantization due to its inherent structure. In this paper, we focus on the post-training quantization (PTQ) of ViM. We address the issues with three core techniques: 1) a k-scaled token-wise quantization method for linear and convolutional layers, 2) a reparameterization technique to simplify hidden state quantization, and 3) a factor-determining method that reduces computational overhead by integrating operations. Through these methods, the error caused by PTQ can be mitigated. Experimental results on ImageNet-1k demonstrate only a 0.8-1.2\% accuracy degradation due to PTQ, highlighting the effectiveness of our approach.
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning
Authors: Sua Jung
Date: 2025-01-22 00:00:00
Tasks: Attribute, Compositional Zero-Shot Learning, Language Modeling, Language Modelling, Prompt Learning, Scene Understanding, Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize subtle differences in meaning or the combination of states and objects through the use of known and unknown concepts during training. Existing methods either focused on prompt configuration or on using prompts to tune the pre-trained Vision-Language model. However, these methods faced challenges in accurately identifying subtle differences in meaning or combining states with objects. To jointly eradicate the above issues and construct an efficient and effective CZSL technique, we suggest a method to improve attribute recognition performance by utilizing diverse Prompt Learning with an Inter/Intra-Modality Fusion Synthesizer in scene understanding involving subtle semantic differences and multiple objects.
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
Authors: Siyi Chen, Yixuan Jia, Qing Qu, He Sun, Jeffrey A Fessler
Date: 2025-01-13 00:00:00
Tasks: State Estimation, Super-Resolution, Weather Forecasting

Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems. Model-driven methods (e.g., Kalman, particle) presuppose full knowledge of the true dynamics, which is not always satisfied in practice, while purely data-driven solvers learn a deterministic mapping between observations and states and therefore miss the intrinsic stochasticity of real processes. Recently, score-based diffusion models learn a global diffusion prior and provide a good modeling of the stochastic dynamics, showing new potential for DA. However, their all-at-once generation rather than step-by-step transition limits their performance when dealing with highly complex stochastic processes and lacks physical interpretability. To tackle these drawbacks, we introduce FlowDAS, a generative DA framework that uses stochastic interpolants to directly learn state transition dynamics and achieve step-by-step transition to better model the real dynamics. We also improve the framework by combining the observation, better suiting the DA settings. Directly learning the underlying dynamics from collected data removes restrictive dynamical assumptions, and conditioning on observations at each interpolation step yields stable, measurement-consistent forecasts. Experiments on Lorenz-63, Navier-Stokes super-resolution/sparse-observation scenarios, and large-scale weather forecasting -- where dynamics are partly or wholly unknown -- show that FlowDAS surpasses model-driven methods, neural operators, and score-based baselines in accuracy and physical plausibility.
Low-Complexity Multi-Target Detection in ELAA ISAC
Authors: Diluka Galappaththige, Shayan Zargari, Chintha Tellambura, Geoffrey Ye Li
Date: 2025-01-29 00:00:00
Tasks: Integrated sensing and communication, ISAC

Multi-target detection and communication with extremely large-scale antenna arrays (ELAAs) operating at high frequencies necessitate generating multiple beams. However, conventional algorithms are slow and computationally intensive. For instance, they can simulate a \num{200}-antenna system over two weeks, and the time complexity grows exponentially with the number of antennas. Thus, this letter explores an ultra-low-complex solution for a multi-user, multi-target integrated sensing and communication (ISAC) system equipped with an ELAA base station (BS). It maximizes the communication sum rate while meeting sensing beampattern gain targets and transmit power constraints. As this problem is non-convex, a Riemannian stochastic gradient descent-based augmented Lagrangian manifold optimization (SGALM) algorithm is developed, which searches on a manifold to ensure constraint compliance. The algorithm achieves ultra-low complexity and superior runtime performance compared to conventional algorithms. For example, it is \num{56} times faster than the standard benchmark for \num{257} BS antennas.
Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression
Authors: Koya Sato, Koji Ishibashi
Date: 2025-01-29 00:00:00
Tasks: Federated Learning, GPR, regression, Spatial Interpolation

This paper introduces a noise-tolerant computing method for over-the-air computation (AirComp) aimed at weighted averaging, which is critical in various Internet of Things (IoT) applications such as environmental monitoring. Traditional AirComp approaches, while efficient, suffer significantly in accuracy due to noise enhancement in the normalization by the sum of weights. Our proposed method allows nodes to adaptively truncate their weights based on the channel conditions, thereby enhancing noise tolerance. Applied to distributed Gaussian process regression (D-GPR), the method facilitates low-latency, low-complexity, and high-accuracy distributed regression across a range of signal-to-noise ratios (SNRs). We evaluate the performance of the proposed method in a radio map construction problem, which involves visualizing the radio environment based on limited sensing information and spatial interpolation. Numerical results show that our approach maintains computational accuracy in low-SNR scenarios and achieves performance close to ideal conditions in high SNR environments. In addition, a case study targeting a federated learning (FL) system demonstrates the potential of our proposed method in improving model aggregation accuracy, not only for D-GPR but also for FL systems.
Low-Complexity Event Detection and Identification in Coherent Correlation OTDR Measurements
Authors: Jasper Müller, Ognjen Jovanovic, Florian Azendorf, André Sandmann, Roman Ermakov, Sai Kireet Patri, Jörg-Peter Elbers, Jim Zou, Darko Zibar, Carmen Mas-Machuca
Date: 2025-01-29 00:00:00
Tasks: Event Detection

Pairing coherent correlation OTDR with low-complexity analysis methods, we investigate the detection of fast temperature changes and vibrations in optical fibers. A localization accuracy of ~2 m and extraction of vibration amplitudes and frequencies is demonstrated.
Experimental Outdoor Performance Evaluation of TVWS Narrowband Data Communication using SDR Platform
Authors: Muneer M. AlZubi, Mohamed-Slim Alouini
Date: 2025-01-28 00:00:00

Large-scale deployment of Internet of Things (IoT) networks in the industrial, scientific, and medical (ISM) band leads to spectrum congestion and requires multiple gateways to cover wide areas. This will increase cost, complexity, and energy consumption. TV White Spaces (TVWS) provides an abundant spectrum that is sufficient for low data rate IoT applications. This low-frequency band offers coverage over larger areas due to the ability of wireless signals to penetrate obstacles and terrain. In this paper, we examine the performance of narrowband data communications in TVWS through an outdoor experiment in a suburban area with line-of-sight (LOS) and non-line-of-sight (NLOS) propagation scenarios. We implement a software-defined radio (SDR) testbed and develop a GNU radio benchmark tool to perform outdoor experiments for TVWS narrowband data communication between a gateway and wireless nodes at various locations. The results reveal that the system can achieve a throughput of up to 97 Kbps with a packet error rate (PER) and packet loss rate (PLR) under 1% over NLOS paths, making it suitable for low-data rate applications. This work offers valuable insights for designing the physical layer of narrowband white space devices (WSDs). The developed benchmark tool will also greatly assist other researchers in evaluating the performance of SDR-based communication systems.
Federated Learning Strategies for Coordinated Beamforming in Multicell ISAC
Authors: Lai Jiang, Kaitao Meng, Murat Temiz, Jiaming Hu, Christos Masouros
Date: 2025-01-28 00:00:00
Tasks: Federated Learning, ISAC, Vertical Federated Learning

We propose two cooperative beamforming frameworks based on federated learning (FL) for multi-cell integrated sensing and communications (ISAC) systems. Our objective is to address the following dilemma in multicell ISAC: 1) Beamforming strategies that rely solely on local channel information risk generating significant inter-cell interference (ICI), which degrades network performance for both communication users and sensing receivers in neighboring cells; 2) conversely centralized beamforming strategies can mitigate ICI by leveraging global channel information, but they come with substantial transmission overhead and latency that can be prohibitive for latency-sensitive and source-constrained applications. To tackle these challenges, we first propose a partially decentralized training framework motivated by the vertical federated learning (VFL) paradigm. In this framework, the participating base stations (BSs) collaboratively design beamforming matrices under the guidance of a central server. The central server aggregates local information from the BSs and provides feedback, allowing BSs to implicitly manage ICI without accessing the global channel information. To make the solution scalable for densely deployed wireless networks, we take further steps to reduce communication overhead by presenting a fully decentralized design based on the horizontal federated learning (HFL). Specifically, we develop a novel loss function to control the interference leakage power, enabling a more efficient training process by entirely eliminating local channel information exchange. Numerical results show that the proposed solutions can achieve significant performance improvements comparable to the benchmarks in terms of both communication and radar information rates.
From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Gain-Phase Uncertainty
Authors: He Xu, Tuo Wu, Wei Liu, Maged Elkashlan, Naofal Al-Dhahir, Merouane Debbah, Chau Yuen, Hing Cheung So
Date: 2025-01-28 00:00:00

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is essential in multipath wireless communication scenarios, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method attained improved estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical multipath environments.