pwc-archive/papers-with-abstracts huggingface dataset:
Resistant Inference in Instrumental Variable Models
Authors: Jens Klooster, Mikhail Zhelonkin
Date: 2024-03-25 00:00:00

The classical tests in the instrumental variable model can behave arbitrarily if the data is contaminated. For instance, one outlying observation can be enough to change the outcome of a test. We develop a framework to construct testing procedures that are robust to weak instruments, outliers and heavy-tailed errors in the instrumental variable model. The framework is constructed upon M-estimators. By deriving the influence functions of the classical weak instrument robust tests, such as the Anderson-Rubin test, K-test and the conditional likelihood ratio (CLR) test, we prove their unbounded sensitivity to infinitesimal contamination. Therefore, we construct contamination resistant/robust alternatives. In particular, we show how to construct a robust CLR statistic based on Mallows type M-estimators and show that its asymptotic distribution is the same as that of the (classical) CLR statistic. The theoretical results are corroborated by a simulation study. Finally, we revisit three empirical studies affected by outliers and demonstrate how the new robust tests can be used in practice.
The Devil is in the Details: Heterogeneous Effects of the German Minimum Wage on Working Hours and Minijobs
Authors: Mario Bossler, Ying Liang, Thorsten Schank
Date: 2024-03-25 00:00:00

In 2015, Germany introduced a national minimum wage. While the literature agrees on at most limited negative effects on the overall employment level, we go into detail and analyze the impact on the working hours dimension and on the subset of minijobs. Using data from the German Structure of Earnings Survey in 2010, 2014, and 2018, we find empirical evidence that the minimum wage significantly reduces inequality in hourly and monthly wages. While various theoretical mechanisms suggest a reduction in working hours, these remain unchanged on average. However, minijobbers experience a notable reduction in working hours which can be linked to the specific institutional framework. Regarding employment, the results show no effects for regular jobs, but there is a noteworthy decline in minijobs, driven by transitions to regular employment and non-employment. The transitions in non-employment imply a wage elasticity of employment of -0.1 for minijobs. Our findings highlight that the institutional setting leads to heterogeneous effects of the minimum wage.
Design Insights for Industrial CO2 Capture, Transport, and Storage Systems
Authors: Tubagus Aryandi Gunawan, Lilianna Gittoes, Cecelia Isaac, Chris Greig, Eric Larson
Date: 2024-03-25 00:00:00

We present design methods and insights for CO2 capture, transport, and storage systems for clusters of industrial facilities, with a case-study focus on the state of Louisiana. Our analytical framework includes: (1) evaluating the scale and concentration of capturable CO2 emissions at individual facilities for the purpose of estimating the cost of CO2 capture retrofits, (2) a screening method to identify potential CO2 storage sites and estimate their storage capacities, injectivities, and costs; and (3) an approach for cost-minimized design of pipeline infrastructure connecting CO2 capture plants with storage sites that considers land use patterns, existing rights-of-way, demographics, and a variety of social and environmental justice factors. In applying our framework to Louisiana, we estimate up to 50 million tCO2/y of industrial emissions (out of today's total emissions of 130 MtCO2/y) can be captured at under 100 USD/tCO2, and up to 100 MtCO2/y at under 120 USD/tCO2. We identified 98 potential storage sites with estimated aggregate total injectivity between 330 and 730 MtCO2/yr and storage costs ranging from 8 to 17 USD/tCO2. We find dramatic reductions in the aggregate pipeline length and CO2 transport cost per tonne when groups of capture plants share pipeline infrastructure rather than build dedicated single-user pipelines. Smaller facilities (emitting less than 1 MtCO2/y), which account for a quarter of Louisiana's industrial emissions, see the largest transport cost benefits from sharing of infrastructure. Pipeline routes designed to avoid disadvantaged communities (social and environmental justice) so as not to reinforce historical practices of disenfranchisement involve only modestly higher pipeline lengths and costs.
The Impact of Pradhan Mantri Ujjwala Yojana on Indian Households
Authors: Nabeel Asharaf, Richard S. J. Tol
Date: 2024-03-25 00:00:00

This study critically evaluates the impact of the Pradhan Mantri Ujjwala Yojana (PMUY) on LPG accessibility among poor households in India. Using Propensity Score Matching and Difference-in-Differences estimators and the National Family Health Survey (NFHS) dataset, the Average Treatment Effect on the interdedly Treated is a modest 2.1 percentage point increase in LPG consumption due to PMUY, with a parallel decrease in firewood consumption. Regional analysis reveals differential impacts, with significant progress in the North, West, and South but less pronounced effects in the East and North East. The study also underscores variance across social groups, with Schedule Caste households showing the most substantial benefits, while Scheduled Tribes households are hardly affected. Despite the PMUY's initial success in facilitating LPG access, sustaining its usage remains challenging. Policy should emphasise targeted interventions, income support, and address regional and community-specific disparities for the sustained usage of LPG.
The Costs of Competition in Distributing Scarce Research Funds
Authors: Gerald Schweiger, Adrian Barnett, Peter van den Besselaar, Lutz Bornmann, Andreas De Block, John P. A. Ioannidis, Ulf Sandström, Stijn Conix
Date: 2024-03-25 00:00:00

Research funding systems are not isolated systems - they are embedded in a larger scientific system with an enormous influence on the system. This paper aims to analyze the allocation of competitive research funding from different perspectives: How reliable are decision processes for funding? What are the economic costs of competitive funding? How does competition for funds affect doing risky research? How do competitive funding environments affect scientists themselves, and which ethical issues must be considered? We attempt to identify gaps in our knowledge of research funding systems; we propose recommendations for policymakers and funding agencies, including empirical experiments of decision processes and the collection of data on these processes. With our recommendations we hope to contribute to developing improved ways of organizing research funding.
Ethical considerations when planning, implementing and releasing health economic model software: a new proposal
Authors: Matthew P Hamilton, Caroline Gao, Jonathan Karnon, Luis Salvador-Carulla, Sue M Cotton, Cathrine Mihalopoulos
Date: 2024-03-25 00:00:00
Tasks: Ethics

Most health economic analyses are undertaken with the aid of computers. However, the research ethics of implementing health economic models as software (or computational health economic models (CHEMs)) are poorly understood. We propose that developers and funders of CHEMs should adhere to research ethics principles and pursue the goals of: (i) socially acceptable user requirements and design specifications; (ii) fit for purpose implementations; and (iii) socially beneficial post-release use. We further propose that a transparent (T), reusable (R) and updatable (U) CHEM is suggestive of a project team that has largely met these goals. We propose six criteria for assessing TRU CHEMs: (T1) software files are publicly available; (T2) developer contributions and judgments on appropriate use are easily identified; (R1) programming practices facilitate independent reuse of model components; (R2) licenses permit reuse and derivative works; (U1) maintenance infrastructure is in place; and (U2) releases are systematically retested and deprecated. Few existing CHEMs would meet all TRU criteria. Addressing these limitations will require the development of new and updated good practice guidelines and investments by governments and other research funders in enabling infrastructure and human capital.
A Dominance Argument Against Incompleteness
Authors: Christian Tarsney, Harvey Lederman, Dean Spears
Date: 2024-03-26 00:00:00

This article presents a new argument against many forms of moral and prudential value incompleteness. The argument relies on two central principles: (i) a weak "negative dominance" principle, to the effect that Lottery 1 is better than Lottery 2 only if some possible outcome of Lottery 1 is better than some possible outcome of Lottery 2, and (ii) a weak form of ex ante Pareto, to the effect that, if Lottery 1 gives an unambiguously better (stochastically dominant) prospect to some individuals than Lottery 2, and equally good prospects to everyone else, then Lottery 1 is better than Lottery 2. Given modest auxiliary assumptions, these two principles rule out incompleteness in the prudential ranking of individual lives, and many forms of incompleteness in the moral rankings of outcomes and lotteries.
On the Pettis Integral Approach to Large Population Games
Authors: Masaki Miyashita, Takashi Ui
Date: 2024-03-26 00:00:00

The analysis of large population economies with incomplete information often entails the integration of a continuum of random variables. We showcase the usefulness of the integral notion \`a la Pettis (1938) to study such models. We present several results on Pettis integrals, including convenient sufficient conditions for Pettis integrability and Fubini-like exchangeability formulae, illustrated through a running example. Building on these foundations, we conduct a unified analysis of Bayesian games with arbitrarily many heterogeneous agents. We provide a sufficient condition on payoff structures, under which the equilibrium uniqueness is guaranteed across all signal structures. Our condition is parsimonious, as it turns out necessary when strategic interactions are undirected. We further identify the moment restrictions, imposed on the equilibrium action-state joint distribution, which have crucial implications for information designer's problem of persuading a population of strategically interacting agents. To attain these results, we introduce and develop novel mathematical tools, built on the theory of integral kernels and reproducing kernel Hilbert spaces in functional analysis.
Enhancing Indoor and Outdoor THz Communications with Beyond Diagonal-IRS: Optimization and Performance Analysis
Authors: Asad Mahmood, Thang X. Vu, Symeon Chatzinotas, Björn Ottersten
Date: 2024-03-26 00:00:00

This work investigates the application of Beyond Diagonal Intelligent Reflective Surface (BD-IRS) to enhance THz downlink communication systems, operating in a hybrid: reflective and transmissive mode, to simultaneously provide services to indoor and outdoor users. We propose an optimization framework that jointly optimizes the beamforming vectors and phase shifts in the hybrid reflective/transmissive mode, aiming to maximize the system sum rate. To tackle the challenges in solving the joint design problem, we employ the conjugate gradient method and propose an iterative algorithm that successively optimizes the hybrid beamforming vectors and the phase shifts. Through comprehensive numerical simulations, our findings demonstrate a significant improvement in rate when compared to existing benchmark schemes, including time- and frequency-divided approaches, by approximately $30.5\%$ and $69.9\%$ respectively and even outperforms the STAR-IRS system by $76.99\%$. This underscores the significant influence of IRS elements on system performance relative to that of base station antennas, highlighting their pivotal role in advancing the communication system efficacy.
Environment Reconstruction based on Multi-User Selection and Multi-Modal Fusion in ISAC
Authors: Bo Lin, Chuanbin Zhao, Feifei Gao, Geoffrey Ye Li
Date: 2024-03-26 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems. In this paper, we explore the inherent clustered nature of wireless users and design a multi-user based environment reconstruction scheme. Specifically, we first select users based on the estimation precision of channel's multipath, including the line-of-sight (LOS) and the non-line-of-sight (NLOS) paths, to enhance the accuracy of environment reconstruction. Then, we develop a fusion strategy that merges communications signalling with camera image to increase the accuracy and robustness of environment reconstruction. The simulation results demonstrate that the proposed algorithm can achieve a remarkable sensing accuracy of centimeter level, which is about 17 times better than the scheme without user selection. Meanwhile, the fusion of communications data and vision data leads to a threefold accuracy improvement over the image only method, especially under challenging weather conditions like raining and snowing.
Improving the Spatial Correlation Characteristics of Antenna Arrays using Linear Operators and Wide-band Modelling
Authors: Marc Miranda, Sebastian Semper, Michael Döbereiner, Reiner Thomä
Date: 2024-03-26 00:00:00

The analysis of wireless communication channels at the mmWave, sub-THz and THz bands gives rise to difficulties in the construction of antenna arrays due to the small maximum inter-element spacing constraints at these frequencies. Arrays with uniform spacing greater than half the wavelength for a certain carrier frequency exhibit aliasing side-lobes in the angular domain, prohibiting non-ambiguous estimates of a propagating wave-front's angle of arrival. In this paper, we present how wide-band modelling of the array response is useful in mitigating this spatial aliasing effect. This approach aims to reduce the grating lobes by exploiting the angle- and frequency-dependent phase-shifts observed in the response of the array to a planar wave-front travelling across it. Furthermore, we propose a method by which the spatial correlation characteristics of an array operating at 33 GHz carrier frequency with an instantaneous bandwidth of 1 GHz can be improved such that the angular-domain side-lobes are reduced by 5-10 dB. This method, applicable to arbitrary antenna array manifolds, makes use of a linear operator that is applied to the base-band samples of the channel transfer function measured in space and frequency domains. By means of synthetically simulated arrays, we show that when operating with a bandwidth of 1 GHz, the use of a derived linear operator applied to the array output results in the spatial correlation characteristics approaching those of the array operating at a bandwidth of 12 GHz. Hence, non-ambiguous angle estimates can be obtained in the field without the use of expensive high-bandwidth RF front-end components.
Waveform Design for Joint Communication and SAR Imaging Under Random Signaling
Authors: Bowen Zheng, Fan Liu
Date: 2024-03-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

Conventional synthetic aperture radar (SAR) imaging systems typically employ deterministic signal designs, which lack the capability to convey communication information and are thus not suitable for integrated sensing and communication (ISAC) scenarios. In this letter, we propose a joint communication and SAR imaging (JCASAR) system based on orthogonal frequency-division multiplexing (OFDM) signal with cyclic prefix (CP), which is capable of reconstructing the target profile while serving a communication user. In contrast to traditional matched filters, we propose a least squares (LS) estimator for range profiling. Then the SAR image is obtained followed by range cell migration correction (RCMC) and azimuth processing. By minimizing the mean squared error (MSE) of the proposed LS estimator, we investigate the optimal waveform design for SAR imaging, and JCASAR under random signaling, where power allocation strategies are conceived for Gaussian-distributed ISAC signals, in an effort to strike a flexible performance tradeoff between the communication and SAR imaging tasks. Numerical results are provided to validate the effectiveness of the proposed ISAC waveform design for JCASAR systems.
Channel-Adaptive Pilot Design for FDD-MIMO Systems Utilizing Gaussian Mixture Models
Authors: Nurettin Turan, Benedikt Fesl, Benedikt Böck, Michael Joham, Wolfgang Utschick
Date: 2024-03-26 00:00:00

In this work, we propose to utilize Gaussian mixture models (GMMs) to design pilots for downlink (DL) channel estimation in frequency division duplex (FDD) systems. The GMM captures prior information during training that is leveraged to design a codebook of pilot matrices in an initial offline phase. Once shared with the mobile terminal (MT), the GMM is utilized to determine a feedback index at the MT in the online phase. This index selects a pilot matrix from a codebook, eliminating the need for online pilot optimization. The GMM is further used for DL channel estimation at the MT via observation-dependent linear minimum mean square error (LMMSE) filters, parametrized by the GMM. The analytic representation of the GMM allows adaptation to any signal-to-noise ratio (SNR) level and pilot configuration without re-training. With extensive simulations, we demonstrate the superior performance of the proposed GMM-based pilot scheme compared to state-of-the-art approaches.
A TDD Distributed MIMO Testbed Using a 1-Bit Radio-Over-Fiber Fronthaul Architecture
Authors: Lise Aabel, Sven Jacobsson, Mikael Coldrey, Frida Olofsson, Giuseppe Durisi, Christian Fager
Date: 2024-03-26 00:00:00

We present the uplink and downlink of a time-division duplex distributed multiple-input multiple-output (D-MIMO) testbed, based on a 1-bit radio-over-fiber architecture, which is low-cost and scalable. The proposed architecture involves a central unit (CU) that is equipped with 1-bit digital-to-analog and analog-to-digital converters, operating at 10 GS/s. The CU is connected to multiple single-antenna remote radio heads (RRHs) via optical fibers, over which a binary RF waveform is transmitted. In the uplink, a binary RF waveform is generated at the RRHs by a comparator, whose inputs are the received RF signal and a suitably designed dither signal. In the downlink, a binary RF waveform is generated at the CU via bandpass sigma-delta modulation. Our measurement results show that low error-vector magnitude (EVM) can be achieved in both the uplink and the downlink, despite 1-bit sampling at the CU. Specifically, for point-to-point over-cable transmission between a single user equipment (UE) and a CU equipped with a single RRH, we report, for a 10 MBd signal using single-carrier 16QAM modulation, an EVM of 3.3% in the downlink, and of 4.5% in the uplink. We then consider a CU connected to 3 RRHs serving over the air 2 UEs, and show that, after over-the-air reciprocity calibration, a downlink zero-forcing precoder designed on the basis of uplink channel estimates at the CU, achieves an EVM of 6.4% and 10.9% at UE 1 and UE 2, respectively. Finally, we investigate the ability of the proposed architecture to support orthogonal frequency-division multiplexing (OFDM) waveforms, and its robustness against both in-band and out-of-band interference.
On the Impact of Random Node Sampling on Adaptive Diffusion Networks
Authors: Daniel G. Tiglea, Renato Candido, Magno T. M. Silva
Date: 2024-03-26 00:00:00

In this paper, we analyze the effects of random sampling on adaptive diffusion networks. These networks consist in a collection of nodes that can measure and process data, and that can communicate with each other to pursue a common goal of estimating an unknown system. In particular, we consider in our theoretical analysis the diffusion least-mean-squares algorithm in a scenario in which the nodes are randomly sampled. Hence, each node may or may not adapt its local estimate at a certain iteration. Our model shows that, if the nodes cooperate, a reduction in the sampling probability leads to a slight decrease in the steady-state Network Mean-Square Deviation (NMSD), assuming that the environment is stationary and that all other parameters of the algorithm are kept fixed. Furthermore, under certain circumstances, this can also ensure the stability of the algorithm in situations in which it would otherwise be unstable. Although counter-intuitive, our findings are backed by simulation results, which match the theoretical curves well.
200Gb/s VCSEL transmission using 60m OM4 MMF and KP4 FEC for AI computing clusters
Authors: Tom Wettlin, Youxi Lin, Nebojsa Stojanovic, Stefano Calabrò, Ruoxu Wang, Lewei Zhang, Maxim Kuschnerov
Date: 2024-03-25 00:00:00

We show a beyond 200Gb/s VCSEL transmission experiment. Results are based on 35GHz VCSEL and advanced DSP. We show an AIR of 245Gb/s PAM-6 back-to-back, and 200Gb/s PAM-4 over 60m OM4 fiber assuming KP4-FEC.
Two-Dimensional Frequency-Difference-of-Arrival Varieties
Authors: Jeanne Duflot, Margaret Cheney, James A. Given
Date: 2024-03-25 00:00:00

This paper studies Frequency-Difference-of-Arrival (FDOA) curves for the 2-dimensional, 2-sensor case. The primary focus of this paper is to give a description of curves associated to the FDOA problem from the algebro-geometric point of view. To be more precise, the complex projective picture of the family of FDOA curves for all possible relative velocities is described.
RKFNet: A Novel Neural Network Aided Robust Kalman Filter
Authors: Pengcheng Hao, Oktay Karakus, Alin Achim
Date: 2024-03-25 00:00:00

Driven by the filtering challenges in linear systems disturbed by non-Gaussian heavy-tailed noise, the robust Kalman filters (RKFs) leveraging diverse heavy-tailed distributions have been introduced. However, the RKFs rely on precise noise models, and large model errors can degrade their filtering performance. Also, the posterior approximation by the employed variational Bayesian (VB) method can further decrease the estimation precision. Here, we introduce an innovative RKF method, the RKFNet, which combines the heavy-tailed-distribution-based RKF framework with the deep learning (DL) technique and eliminates the need for the precise parameters of the heavy-tailed distributions. To reduce the VB approximation error, the mixing-parameter-based function and the scale matrix are estimated by the incorporated neural network structures. Also, the stable training process is achieved by our proposed unsupervised scheduled sampling (USS) method, where a loss function based on the Student's t (ST) distribution is utilised to overcome the disturbance of the noise outliers and the filtering results of the traditional RKFs are employed as reference sequences. Furthermore, the RKFNet is evaluated against various RKFs and recurrent neural networks (RNNs) under three kinds of heavy-tailed measurement noises, and the simulation results showcase its efficacy in terms of estimation accuracy and efficiency.