pwc-archive/papers-with-abstracts huggingface dataset:
Robust Difference-in-differences Models
Authors: Kyunghoon Ban, Désiré Kédagni
Date: 2022-11-12 00:00:00
Tasks: Selection bias

The difference-in-differences (DID) method identifies the average treatment effects on the treated (ATT) under mainly the so-called parallel trends (PT) assumption. The most common and widely used approach to justify the PT assumption is the pre-treatment period examination. If a null hypothesis of the same trend in the outcome means for both treatment and control groups in the pre-treatment periods is rejected, researchers believe less in PT and the DID results. This paper develops a robust generalized DID method that utilizes all the information available not only from the pre-treatment periods but also from multiple data sources. Our approach interprets PT in a different way using a notion of selection bias, which enables us to generalize the standard DID estimand by defining an information set that may contain multiple pre-treatment periods or other baseline covariates. Our main assumption states that the selection bias in the post-treatment period lies within the convex hull of all selection biases in the pre-treatment periods. We provide a sufficient condition for this assumption to hold. Based on the baseline information set we construct, we provide an identified set for the ATT that always contains the true ATT under our identifying assumption, and also the standard DID estimand. We extend our proposed approach to multiple treatment periods DID settings. We propose a flexible and easy way to implement the method. Finally, we illustrate our methodology through some numerical and empirical examples.
Multiple Structural Breaks in Interactive Effects Panel Data and the Impact of Quantitative Easing on Bank Lending
Authors: Jan Ditzen, Yiannis Karavias, Joakim Westerlund
Date: 2022-11-12 00:00:00

This paper develops a new toolbox for multiple structural break detection in panel data models with interactive effects. The toolbox includes tests for the presence of structural breaks, a break date estimator, and a break date confidence interval. The new toolbox is applied to a large panel of US banks for a period characterized by massive quantitative easing programs aimed at lessening the impact of the global financial crisis and the COVID--19 pandemic. The question we ask is: Have these programs been successful in spurring bank lending in the US economy? The short answer turns out to be: ``No''.
Collective models and the marriage market
Authors: Simon Weber
Date: 2022-11-14 00:00:00
Tasks: counterfactual

In this paper, I develop an integrated approach to collective models and matching models of the marriage market. In the collective framework, both household formation and the intra-household allocation of bargaining power are taken as given. This is no longer the case in the present contribution, where both are endogenous to the determination of equilibrium on the marriage market. I characterize a class of "proper" collective models which can be embedded into a general matching framework with imperfectly transferable utility. In such models, the bargaining sets are parametrized by an analytical device called distance function, which plays a key role both for writing down the usual stability conditions and for estimation. In general, however, distance functions are not known in closed-form. I provide an efficient method for computing distance functions, that works even with the most complex collective models. Finally, I provide a fully-fledged application using PSID data. I identify the sharing rule and its distribution and study the evolution of the sharing rule and housework time sharing in the United States since 1969. In a counterfactual experiment, I simulate the impact of closing the gender wage gap.
Optimal Pricing Schemes in the Presence of Social Learning and Costly Reporting
Authors: Kaiwei Zhang, Xi Weng, Xienan Cheng
Date: 2022-11-14 00:00:00

A monopoly platform sells either a risky product (with unknown utility) or a safe product (with known utility) to agents who sequentially arrive and learn the utility of the risky product by the reporting of previous agents. It is costly for agents to report utility; hence the platform has to design both the prices and the reporting bonus to motivate the agents to explore and generate new information. By allowing sellers to set bonuses, we are essentially enabling them to dynamically control the supply of learning signals without significantly affecting the demand for the product. We characterize the optimal bonus and pricing schemes offered by the profit-maximizing platform. It turns out that the optimal scheme falls into one of four types: Full Coverage, Partial Coverage, Immediate Revelation, and Non-Bonus. In a model of exponential bandit, we find that there is a dynamical switch of the types along the learning trajectory. Although learning stops efficiently, information is revealed too slowly compared with the planner's optimal solution.
Firm-worker hypergraphs
Authors: Chao Huang
Date: 2022-11-13 00:00:00

A firm-worker hypergraph consists of edges in which each edge joins a firm and its possible employees. We show that a stable matching exists in both many-to-one matching with transferable utilities and discrete many-to-one matching when the firm-worker hypergraph has no nontrivial odd-length cycle. Firms' preferences satisfying this condition arise in a problem of matching specialized firms with specialists.
Two-Person Bargaining when the Disagreement Point is Private Information
Authors: Eric van Damme, Xu Lang
Date: 2022-11-13 00:00:00

We consider two-person bargaining problems in which (only) the disagreement outcome is private (and possibly correlated) information and it is common knowledge that disagreement is inefficient. We show that if the Pareto frontier is linear, the outcome of an ex post efficient mechanism cannot depend on the disagreement payoffs. If the frontier is non-linear, the result continues to hold when the disagreement payoffs are independent or there is a player with at most two types. We discuss implications of these results for axiomatic bargaining theory and for full surplus extraction in mechanism design.
Enhancing Flood Forecasting with Dual State-Parameter Estimation and Ensemble-based SAR Data Assimilation
Authors: Thanh Huy Nguyen, Sophie Ricci, Andrea Piacentini, Raquel Rodriguez Suquet, Gwendoline Blanchet, Santiago Pena Luque, Peter Kettig
Date: 2022-11-14 00:00:00
Tasks: Friction, parameter estimation

Ensemble data assimilation in flood forecasting depends strongly on the density, frequency and statistics of errors associated with the observation network. This work focuses on the assimilation of 2D flood extent data, expressed in terms of wet surface ratio, in addition to the in-situ water level data. The objective is to improve the representation of the flood plain dynamics with a TELEMAC-2D model and an Ensemble Kalman Filter (EnKF). The EnKF control vector is composed of friction coefficients and corrective parameters to the input forcing. It is augmented with the water level state averaged over selected subdomains of the floodplain. This work focuses on the 2019 flood event that occurred over the Garonne Marmandaise catchment. The merits of assimilating SAR-derived flood plain data complementary to in-situ water level observations are shown in the control parameter and observation spaces with 1D and 2D assessment metrics. It was also shown that the assimilation of Wet surface Ratio in the flood plain complementary to in-situ data in the river bed brings significative improvement when a corrective term on flood plain hydraulic state is included in the control vector. Yet, it has barely no impact in the river bed that is sufficiently well described by in-situ data. We highlighted that the correction of the hydraulic state in the flood plain significantly improved the flood dynamics, especially during the recession. This proof-of-concept study paves the way towards near-real-time flood forecast, making the most of remote sensing-derived flood observations.
Learning Neural Optimal Interpolation Models and Solvers
Authors: Maxime Beauchamp, Joseph Thompson, Hugo Georgenthum, Quentin Febvre, Ronan Fablet
Date: 2022-11-14 00:00:00
Tasks: Earth Observation, Gaussian Processes

The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations. Optimal interpolation (OI), also referred to as kriging, provides a theoretical framework to solve interpolation problems for Gaussian processes (GP). The associated computational complexity being rapidly intractable for n-dimensional tensors and increasing numbers of observations, a rich literature has emerged to address this issue using ensemble methods, sparse schemes or iterative approaches. Here, we introduce a neural OI scheme. It exploits a variational formulation with convolutional auto-encoders and a trainable iterative gradient-based solver. Theoretically equivalent to the OI formulation, the trainable solver asymptotically converges to the OI solution when dealing with both stationary and non-stationary linear spatio-temporal GPs. Through a bi-level optimization formulation, we relate the learning step and the selection of the training loss to the theoretical properties of the OI, which is an unbiased estimator with minimal error variance. Numerical experiments for 2D+t synthetic GP datasets demonstrate the relevance of the proposed scheme to learn computationally-efficient and scalable OI models and solvers from data. As illustrated for a real-world interpolation problems for satellite-derived geophysical dynamics, the proposed framework also extends to non-linear and multimodal interpolation problems and significantly outperforms state-of-the-art interpolation methods, when dealing with very high missing data rates.
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming
Authors: Xinyu Huang, Mushu Li, Wen Wu, Conghao Zhou, Xuemin Sherman Shen
Date: 2022-11-13 00:00:00
Tasks: Deep Reinforcement Learning

In this paper, we propose a digital twin (DT)-assisted cloud-edge collaborative transcoding scheme to enhance user satisfaction in live streaming. We first present a DT-assisted transcoding workload estimation (TWE) model for the cloud-edge collaborative transcoding. Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively. Two light-weight Bayesian neural networks are adopted to fit the TWE models in DTs, respectively. We then formulate a transcoding-path selection problem to maximize long-term user satisfaction within an average service delay threshold, taking into account the dynamics of video arrivals and video requests. The problem is transformed into a standard Markov decision process by using the Lyapunov optimization and solved by a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed scheme can effectively enhance user satisfaction compared with benchmark schemes.
Towards reliable calcification detection: calibration of uncertainty in coronary optical coherence tomography images
Authors: Hongshan Liu, Xueshen Li, Abdul Latif Bamba, Xiaoyu Song, Brigitta C. Brott, Silvio H. Litovsky, Yu Gan
Date: 2022-11-12 00:00:00
Tasks: Object, object-detection, Object Detection

Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). Image-guided solutions such as Percutaneous Coronary Intervention (PCI) are extensively used during the treatment of CAD. However, unidentified calcified regions within a narrowed artery could impair the outcome of the PCI. Prior to treatments, object detection is paramount to automatically procure accurate readings on the location and thickness of calcifications within the artery. Deep learning-based object detection methods have been explored in a variety of applications. The quality of object detection predictions could lead to uncertain results, which are not desirable in safety-critical scenarios. In this work, we implement an object detection model, You-Only-Look-Once v5 (YOLO), on a calcification detection framework within coronary OCT images. We evaluate the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate confidence scores of predictions, we implement dependent logistic calibration using each detection result's confidence and center coordinates. With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection. Our results show that the YOLO achieves higher precision and recall in comparison with the other object detection model, meanwhile producing more reliable results. The calibrated confidence of prediction results in a confidence error of approximately 0.13, suggesting that the confidence calibration on calcification detection could provide a more trustworthy result, indicating a great potential to assist clinical evaluation of treating the CAD during the imaging-guided procedure.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
Authors: Andrey Ignatov, Radu Timofte, Jin Zhang, Feng Zhang, Gaocheng Yu, Zhe Ma, Hongbin Wang, Minsu Kwon, Haotian Qian, Wentao Tong, Pan Mu, Ziping Wang, Guangjing Yan, Brian Lee, Lei Fei, Huaijin Chen, Hyebin Cho, Byeongjun Kwon, Munchurl Kim, Mingyang Qian, Huixin Ma, Yanan Li, Xiaotao Wang, Lei Lei
Date: 2022-11-07 00:00:00
Tasks: Bokeh Effect Rendering, Deep Learning, GPU

As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The runtime of the resulting models was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops. A detailed description of all models developed in this challenge is provided in this paper.
Channel Estimation with Reduced Phase Allocations in RIS-Aided Systems
Authors: Benedikt Fesl, Andreas Faika, Nurettin Turan, Michael Joham, Wolfgang Utschick
Date: 2022-11-14 00:00:00

We consider channel estimation in systems equipped with a reconfigurable intelligent surface (RIS). In order to illuminate the additional cascaded channel as compared to systems without a RIS, commonly an unaffordable amount of pilot sequences has to be transmitted over different phase allocations at the RIS. However, for a given base station (BS) cell, there exist immanent structural characteristics of the environment which can be leveraged to reduce the necessary number of phase allocations. We verify this observation by a study on discrete Fourier transform (DFT)-based phase allocations where we exhaustively search for the best combination of DFT columns. Since this brute-force search is unaffordable in practice, we propose to learn a neural network (NN) for joint phase optimization and channel estimation because of the dependency of the optimal phase allocations on the channel estimator, and vice versa. We verify the effectiveness of the approach by numerical simulations where common choices for the phase allocations and the channel estimator are outperformed. By an ablation study, the learned phase allocations are shown to be beneficial in combination with a different state-of-the-art channel estimator as well.
Dual-based Online Learning of Dynamic Network Topologies
Authors: Seyed Saman Saboksayr, Gonzalo Mateos
Date: 2022-11-14 00:00:00

We investigate online network topology identification from smooth nodal observations acquired in a streaming fashion. Different from non-adaptive batch solutions, our distinctive goal is to track the (possibly) dynamic adjacency matrix with affordable memory and computational costs by processing signal snapshots online. To this end, we leverage and truncate dual-based proximal gradient (DPG) iterations to solve a composite smoothness-regularized, time-varying inverse problem. Numerical tests with synthetic and real electrocorticography data showcase the effectiveness of the novel lightweight iterations when it comes to tracking slowly-varying network connectivity. We also show that the online DPG algorithm converges faster than a primal-based baseline of comparable complexity. Aligned with reproducible research practices, we share the code developed to produce all figures included in this paper.
Low-Complexity Equalization and Detection for OTFS-NOMA
Authors: Stephen McWade, Mark F. Flanagan, Arman Farhang
Date: 2022-11-14 00:00:00

Orthogonal time frequency space (OTFS) modulation has recently emerged as a potential 6G candidate waveform which provides improved performance in high-mobility scenarios. In this paper we investigate the combination of OTFS with non-orthogonal multiple access (NOMA). Existing equalization and detection methods for OTFS-NOMA, such as minimum-mean-squared error with successive interference cancellation (MMSE-SIC), suffer from poor performance. Additionally, existing iterative methods for single-user OTFS based on low-complexity iterative least-squares solvers are not directly applicable to the NOMA scenario due to the presence of multi-user interference (MUI). Motivated by this, in this paper we propose a low-complexity method for equalization and detection for OTFS-NOMA. Our proposed method uses an iterative process where in each iteration, an equalizer based on the least-squares with QR factorization (LSQR) algorithm is followed by a novel reliability zone (RZ) detection scheme which estimates the reliable symbols of the users and then uses interference cancellation to remove MUI. We present numerical results which demonstrate the superiority of our proposed method, in terms of symbol error rate (SER), to the existing MMSE-SIC benchmark scheme. Additionally, we present results which illustrate that a judicious choice of RZ thresholds is important for optimizing the performance of the proposed algorithm.
Indoor Coverage Enhancement for RIS-Assisted Communication Systems: Practical Measurements and Efficient Grouping
Authors: Sefa Kayraklık, Ibrahim Yildirim, Yarkın Gevez, Ertugrul Basar, Ali Görçin
Date: 2022-11-14 00:00:00

Reconfigurable intelligent surface (RIS)-empowered communications represent exciting prospects as one of the promising technologies capable of meeting the requirements of the sixth generation networks such as low-latency, reliability, and dense connectivity. However, validation of test cases and real-world experiments of RISs are imperative to their practical viability. To this end, this paper presents a physical demonstration of an RIS-assisted communication system in an indoor environment in order to enhance the coverage by increasing the received signal power. We first analyze the performance of the RIS-assisted system for a set of different locations of the receiver and observe around 10 dB improvement in the received signal power by careful RIS phase adjustments. Then, we employ an efficient codebook design for RIS configurations to adjust the RIS states on the move without feedback channels. We also investigate the impact of an efficient grouping of RIS elements, whose objective is to reduce the training time needed to find the optimal RIS configuration. In our extensive experimental measurements, we demonstrate that with the proposed grouping scheme, training time is reduced from one-half to one-eighth by sacrificing only a few dBs in received signal power.
Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares
Authors: Liyang Lu, Wenbo Xu, Yue Wang, Zhi Tian
Date: 2022-11-14 00:00:00
Tasks: Compressive Sensing

Compressive sensing (CS) has recently emerged as an extremely efficient technology of the wideband spectrum sensing. In compressive spectrum sensing (CSS), it is necessary to know the sparsity or the noise information in advance for reliable reconstruction. However, such information is usually absent in practical applications. In this paper, we propose a blind-block orthogonal least squares-based compressive spectrum sensing (B-BOLS-CSS) algorithm, which utilizes a novel blind stopping rule to cut the cords to these prior information. Specifically, we first present both the noiseless and noisy recovery guarantees for the BOLS algorithm based on the mutual incoherence property (MIP). Motivated by them, we then formulate the blind stopping rule, which exploits an $\ell_{2,\infty}$ sufficient statistic to blindly test the support atoms in the remaining measurement matrix. We further evaluate the theoretical performance analysis of the holistic B-BOLS-CSS algorithm by developing a lower bound of the signal-to-noise ratio (SNR) to ensure that the probability of exact recovery is no lower than a given threshold. Simulations not only demonstrate the improvement of our derived theoretical results, but also illustrate that B-BOLS-CSS works well in both low and high SNR environments.
Lecture notes on the design of low-pass digital filters with wireless-communication applications
Authors: Hugh L. Kennedy
Date: 2022-11-14 00:00:00

The low-pass filter is a fundamental building block from which digital signal-processing systems (e.g. radio and radar) are built. Signals in the electromagnetic spectrum extend over all timescales/frequencies and are used to transmit and receive very long or very short pulses of very narrow or very wide bandwidth. Time/Frequency agility is the key for optimal spectrum utilization (i.e. to suppress interference and enhance propagation) and low-pass filtering is the low-level digital mechanism for manoeuvre in this domain. By increasing and decreasing the bandwidth of a low-pass filter, thus decreasing and increasing its pulse duration, the engineer may shift energy concentration between frequency and time. Simple processes for engineering such components are described and explained below. These lecture notes are part of a short course that is intended to help recent engineering graduates design low-pass digital filters for this purpose, who have had some exposure to the topic during their studies, and who are now interested in the sending and receiving signals over the electromagnetic spectrum, in wireless communication (i.e. radio) and remote sensing (e.g. radar) applications, for instance. The best way to understand the material is to interact with the spectrum using receivers and or transmitters and software-defined radio development-kits provide a convenient platform for experimentation. Fortunately, wireless communication in the radio-frequency spectrum is an ideal application for the illustration of waveform agility in the electromagnetic spectrum. In Parts I and II, the theoretical foundations of digital low-pass filters are presented, i.e. signals-and-systems theory, then in Part III they are applied to the problem of radio communication and used to concentrate energy in time or frequency.