pwc-archive/papers-with-abstracts huggingface dataset:
Firm Heterogeneity, Market Power and Macroeconomic Fragility
Authors: Alessandro Ferrari, Francisco Queirós
Date: 2022-05-08 00:00:00

We study how firm heterogeneity and market power affect macroeconomic fragility, defined as the probability of long slumps. We propose a theory in which the positive interaction between firm entry, competition and factor supply can give rise to multiple steady-states. We show that when firms are highly heterogeneous in terms of productivities, even small temporary shocks can trigger firm exit and make the economy spiral in a competition-driven poverty trap. We calibrate our model to incorporate the well-documented trends on rising firm heterogeneity in the US economy, and show that they significantly increase the likelihood and length of slow recoveries. We use our framework to study the 2008-09 recession and show that the model can rationalize the persistent deviation of output and most macroeconomic aggregates from trend, including the behavior of net entry, markups and the labor share. Post-crisis cross-industry data corroborates our proposed mechanism. We conclude by showing that firm subsidies can be powerful in preventing long slumps and can lead to welfare gains between 10% and 50%.
Inventories, Demand Shocks Propagation and Amplification in Supply Chains
Authors: Alessandro Ferrari
Date: 2022-05-08 00:00:00
Tasks: Position

I study the role of industries' position in supply chains in shaping the transmission of final demand shocks. First, I use a novel shift-share design leveraging destination-specific final demand shocks and a new measure of destination exposure accounting for direct and indirect linkages. I find that demand shocks amplify significantly as they propagate upstream, with upstream industries experiencing output elasticities up to three times larger than final good producers, consistent with the bullwhip effect. To rationalize these empirical results, I develop a tractable production network model with inventories and study how the properties of the network and the cyclicality of inventories interact to determine whether final demand shocks amplify or dissipate upstream. I test the mechanism by directly estimating the model-implied relationship between output growth and demand shocks, mediated by network position and inventories. I find that the presence of inventories increases output elasticities by 18% on average, highlighting the macroeconomic significance of this channel. Finally, I use the model to quantitatively study the effects of long-run trends of lengthening supply chains and rising inventories on the volatility of the economy.
Information-Robust Optimal Auctions
Authors: Wanchang Zhang
Date: 2022-05-09 00:00:00

A single unit of a good is sold to one of two bidders. Each bidder has either a high prior valuation or a low prior valuation for the good. Their prior valuations are independently and identically distributed. Each bidder may observe an independently and identically distributed signal about her prior valuation. The seller knows the distribution of the prior valuation profile and knows that signals are independently and identically distributed, but does not know the signal distribution. In addition, the seller knows that bidders play undominated strategies. I find that a second-price auction with a random reserve maximizes the worst-case expected revenue over all possible signal distributions and all equilibria in undominated strategies.
International cooperation and competition in orbit-use management
Authors: Aditya Jain, Akhil Rao
Date: 2022-05-08 00:00:00
Tasks: Management

Orbit-use management efforts can be structured as binding national regulatory policies or as self-enforcing international treaties. New treaties to control space debris growth appear unlikely in the near future. Spacefaring nations can pursue national regulatory policies, though regulatory competition and open access to orbit make their effectiveness unclear. We develop a game-theoretic model of national regulatory policies and self-enforcing international treaties for orbit-use management in the face of open access, regulatory competition, and catastrophe. While open access limits the effectiveness of national policies, market-access control ensures the policies can improve environmental quality. A large enough stock of legacy debris ensures existence of a global regulatory equilibrium where all nations choose to levy environmental regulations on all satellites. The global regulatory equilibrium supports a self-enforcing treaty to avert catastrophe by making it costlier to leave the treaty and free ride.
Credible Persuasion
Authors: Xiao Lin, Ce Liu
Date: 2022-05-06 00:00:00

We propose a new notion of credibility for Bayesian persuasion problems. A disclosure policy is credible if the sender cannot profit from tampering with her messages while keeping the message distribution unchanged. We show that the credibility of a disclosure policy is equivalent to a cyclical monotonicity condition on its induced distribution over states and actions. We also characterize how credibility restricts the Sender's ability to persuade under different payoff structures. In particular, when the sender's payoff is state-independent, all disclosure policies are credible. We apply our results to the market for lemons, and show that no useful information can be credibly disclosed by the seller, even though a seller who can commit to her disclosure policy would perfectly reveal her private information to maximize profit.
TransEM:Residual Swin-Transformer based regularized PET image reconstruction
Authors: Rui Hu, Huafeng Liu
Date: 2022-05-09 00:00:00
Tasks: Image Reconstruction

Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received. Recently deep neural networks especially convolutional neural networks(CNN) have been successfully applied to PET image reconstruction. However, the local characteristics of the convolution operator potentially limit the image quality obtained by current CNN-based PET image reconstruction methods. In this paper, we propose a residual swin-transformer based regularizer(RSTR) to incorporate regularization into the iterative reconstruction framework. Specifically, a convolution layer is firstly adopted to extract shallow features, then the deep feature extraction is accomplished by the swin-transformer layer. At last, both deep and shallow features are fused with a residual operation and another convolution layer. Validations on the realistic 3D brain simulated low-count data show that our proposed method outperforms the state-of-the-art methods in both qualitative and quantitative measures.
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme
Authors: Xinyu Huang, Conghao Zhou, Wen Wu, Mushu Li, Huaqing Wu, Xuemin, Shen
Date: 2022-05-09 00:00:00
Tasks: Deep Reinforcement Learning, Management

In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and time-varying, existing schemes based on universal and time-invariant PQoE models may suffer from performance degradation. To address this issue, we first propose a DT-assisted PQoE model construction method to obtain accurate user-specific PQoE models. Specifically, user DTs (UDTs) are respectively constructed for individual users, which can acquire and utilize users' data to accurately tune PQoE model parameters in real time. Next, given the obtained PQoE models, we formulate a resource management problem to maximize the overall long-term PQoE by taking the dynamics of user' locations, video content requests, and buffer statuses into account. To solve this problem, a deep reinforcement learning algorithm is developed to jointly determine segment version selection, and communication and computing resource allocation. Simulation results on the real-world dataset demonstrate that the proposed scheme can effectively enhance PQoE compared with benchmark schemes.
An Interactive Annotation Tool for Perceptual Video Compression
Authors: Evgenya Pergament, Pulkit Tandon, Kedar Tatwawadi, Oren Rippel, Lubomir Bourdev, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Alexander G. Anderson
Date: 2022-05-08 00:00:00
Tasks: Video Compression

Human perception is at the core of lossy video compression and yet, it is challenging to collect data that is sufficiently dense to drive compression. In perceptual quality assessment, human feedback is typically collected as a single scalar quality score indicating preference of one distorted video over another. In reality, some videos may be better in some parts but not in others. We propose an approach to collecting finer-grained feedback by asking users to use an interactive tool to directly optimize for perceptual quality given a fixed bitrate. To this end, we built a novel web-tool which allows users to paint these spatio-temporal importance maps over videos. The tool allows for interactive successive refinement: we iteratively re-encode the original video according to the painted importance maps, while maintaining the same bitrate, thus allowing the user to visually see the trade-off of assigning higher importance to one spatio-temporal part of the video at the cost of others. We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study - and are 1.9 times more likely to be preferred by viewers. The code for the tool and dataset can be found at https://github.com/jenyap/video-annotation-tool.git
Distributed and Joint Optimization of Precoding and Power for User-Centric Cell-Free Massive MIMO
Authors: Hongkang Yu, Xinquan Ye, Yijian Chen
Date: 2022-05-09 00:00:00
Tasks: CPU

In the cell-free massive multiple-input multiple-output (CF mMIMO) system, the centralized transmission scheme is widely adopted to manage the inter-user interference. Unfortunately, its implementation is limited by the extensive signaling overhead between the central process unit (CPU) and the access points (APs). To solve this problem, we propose a distributed downlink transmission scheme in this letter. First, the null space-based precoding is used to cancel the interference to partial users, where only a portion of channel state information (CSI) needs to be shared among the AP cluster. Based on this, the dual decomposition method is adopted to jointly optimize the precoder and power control, where the calculation can be performed independently by each AP cluster with closed-form expression. With very few iterations, our distributed scheme achieves the same performance as the centralized one. Moreover, it significantly reduces the information exchange to the CPU.
LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications
Authors: Iftikhar Rasheed, Muhammad Asif, Asim Ihsan, Wali Ullah Khan, Manzoor Ahmed, Khaled Rabie
Date: 2022-05-09 00:00:00
Tasks: Federated Learning, Generative Adversarial Network

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.
Performance assessment of medical and non-medical CPAP interfaces used during the COVID-19 pandemic
Authors: Marco Marini, Lorenzo Capponi, Ilaria Battistoni, Tommaso Tocci, Giulio Tribbiani, Francesco Merante, Matteo Francioni, Maria Vittoria Matassini, Giulia Pongetti, Matilda Skhoza, Luca Angelini, Roberto Marsili, Marco Mazzanti, Gian Piero Perna, Gianluca Rossi, Alberto Gili
Date: 2022-05-09 00:00:00

Background: At the beginning of 2020, a high number of COVID-19 cases affected Italy in a short period, causing a difficult supply of medical equipment. To deal with the problem, many healthcare operators readapted different masks to medical devices, but no experiment was conducted to evaluate their performance. The aims of our study were: to assess the performances of three masks and a CPAP helmet in their original configuration and after modifications, in the maintenance of mean pressure and half-amplitude variations using different PEEP valves and to analyse the impact of antibacterial (AB) or antibacterial-viral (ABV) pre-valve PEEP filters on the effective PEEP delivered to the patients. Four pressure ports were installed on each mask (three on helmet), mean values and half amplitudes of pressure were recorded. Tests were performed with any, AB, ABV filter before the PEEP valve. CPAP helmet was the most efficient interface producing more stable mean pressure and less prominent half-amplitude variations but the non-medical masks, especially after the modifications, maintained a stable mean pressure value with only a moderate increase of half-amplitude. The use of AB and ABV filters, produced respectively an increase of 2,23% and 16.5% in mean pressure, compared to no filter condition. CPAP helmet is the most reliable interface in terms of detected performance, but readapted masks can assure almost a similar performance. The use of ABV filters before the PEEP valve significantly increases the detected mean pressure while the AB filters have almost a neutral effect.
Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems
Authors: Takayuki Nagata, Keigo Yamada, Kumi Nakai, Yuji Saito, Taku Nonomura
Date: 2022-05-09 00:00:00

The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied straightforwardly to the group-greedy method, and a customized method is also considered. In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods. This strategy compensates for the deterioration of the solution due to compressed sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and numerical experiments are conducted using randomly generated sensor candidate matrices with potential sensor locations of 10,000--1,000,000. The proposed method can provide better optimization results than those obtained by the original group-greedy method when a similar computational cost is spent as for the original group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the compressed sensor candidates by the randomized algorithm. Similar results were also obtained in the real dataset. The proposed method is effective for the E-optimality criterion, in which the objective function that the optimization by the common greedy method is difficult due to the absence of submodularity of the objective function. The idea of the present method can improve the performance of all optimizations using a greedy algorithm.
Towards a median signal detector through the total Bregman divergence and its robustness analysis
Authors: Yusuke Ono, Linyu Peng
Date: 2022-05-09 00:00:00

A novel family of geometric signal detectors are proposed through medians of the total Bregman divergence (TBD), which are shown advantageous over the conventional methods and their mean counterparts. By interpreting the observation data as Hermitian positive-definite matrices, their mean or median play an essential role in signal detection. As is difficult to be solved analytically, we propose numerical solutions through Riemannian gradient descent algorithms or fixed-point algorithms. Beside detection performance, robustness of a detector to outliers is also of vital importance, which can often be analyzed via the influence functions. Introducing an orthogonal basis for Hermitian matrices, we are able to compute the corresponding influence functions analytically and exactly by solving a linear system, which is transformed from the governing matrix equation. Numerical simulations show that the TBD medians are more robust than their mean counterparts.
A New Array Synthesizer Based on Slepian Functions
Authors: Hesham Sharkas
Date: 2022-05-08 00:00:00

This study introduces a new multi-antenna array synthesizer based on Slepian functions. The synthesizer concentrates beamforming (BF) gain within a spatial region (i.e., an angular sector), optimizing Shannon capacity of the targeted region, which is suitable for codebook-based analog BF. Starting with the mean capacity formula incorporating the effect of BF, Jensen inequality was used to set upper and lower bounds of the mean capacity. Then, a novel method was introduced by combining the two bounds into a new approximation of the mean capacity that outperform both bounds. Finally, the approximation was formulated to a solvable Slepian optimization problem that yielded the weights of the synthesizer. The properties of the synthesizer were listed, including a discussion on how it behaves by changing the width of the targeted region. The steering method was derived, and simulation results were presented.
Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks
Authors: Kuntal Panwar, Prabhu Babu
Date: 2022-05-08 00:00:00

This paper investigates the hybrid source localization problem using the four radio measurements - time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA). First, after invoking tractable approximations in the RSS and AOA models, the maximum likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model is derived. Then a weighted least-squares problem is formulated from the MLE, which is solved using the principle of the majorization-minimization (MM), resulting in an iterative algorithm with guaranteed convergence. The key feature of the proposed method is that it provides a unified framework where localization using any possible merger out of these four measurements can be implemented as per the requirement/application. Extensive numerical simulations are conducted to study the performance of the proposed method. The obtained results indicate that the hybrid localization model improves the localization accuracy compared to the heterogeneous measurements under different network scenarios, which also includes the presence of non-line of sight (NLOS) errors.
Hierarchical Dirichlet Process Based Gamma Mixture Modelling for Terahertz Band Wireless Communication Channels
Authors: Erhan Karakoca, Güneş Karabulut Kurt, Ali Görçin
Date: 2022-05-08 00:00:00

Due to the unique channel characteristics of Terahertz (THz), comprehensive propagation channel modeling is essential to understand the spectrum and develop reliable communication systems in these bands. In this work, we propose the utilization of the hierarchical Dirichlet process Gamma mixture model (DPGMM) to characterize THz channels statistically in the absence of any prior knowledge. DPGMM provides mixture component parameters and the required number of components. A revised expectation-maximization (EM) algorithm is also proposed as a pre-step for DPGMM. Kullback-Leibler Divergence (KL-divergence) is utilized as an error metric to examine the amount of inaccuracy of the EM algorithm and DPGMM when modeling the experimental probability density functions (PDFs). DPGMM and EM algorithm are implemented over the measurements taken at frequencies between 240 GHz and 300 GHz. By comparing the results of the DPGMM and EM algorithms for the measurement datasets, we demonstrate how well the DPGMM fits the target distribution. It is shown that the proposed DPGMM can accurately describe the various THz channels as well as the EM algorithm, and its flexibility allows it to represent more complex distributions better than the EM algorithm. We also demonstrated that DPGMM can be used to model any wireless channel due to its versatility.
Realizing Ultra-Fast and Energy-Efficient Baseband Processing Using Analogue Resistive Switching Memory
Authors: Qunsong Zeng, Jiawei Liu, Jun Lan, Yi Gong, Zhongrui Wang, Yida Li, Kaibin Huang
Date: 2022-05-07 00:00:00

To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient (UFEE) baseband processors. Traditional complementary metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges in transistor scaling and the von Neumann bottleneck. To address these challenges, in-memory computing-based baseband processors using resistive random-access memory (RRAM) present an attractive solution. In this paper, we propose and demonstrate RRAM-based in-memory baseband processing for the widely adopted multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key operations, including discrete Fourier transform (DFT) and MIMO detection using linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step. In addition, RRAM-based channel estimation as well as mapper/demapper modules are proposed. By prototyping and simulations, we demonstrate that the RRAM-based full-fledged communication system can significantly outperform its CMOS-based counterpart in terms of speed and energy efficiency by $10^3$ and $10^6$ times, respectively. The results pave a potential pathway for RRAM-based in-memory computing to be implemented in the era of the sixth generation (6G) mobile communications.