pwc-archive/papers-with-abstracts huggingface dataset:
Variational inference for large Bayesian vector autoregressions
Authors: Mauro Bernardi, Daniele Bianchi, Nicolas Bianco
Date: 2022-02-25 00:00:00
Tasks: regression, Variational Inference

We propose a novel variational Bayes approach to estimate high-dimensional vector autoregression (VAR) models with hierarchical shrinkage priors. Our approach does not rely on a conventional structural VAR representation of the parameter space for posterior inference. Instead, we elicit hierarchical shrinkage priors directly on the matrix of regression coefficients so that (1) the prior structure directly maps into posterior inference on the reduced-form transition matrix, and (2) posterior estimates are more robust to variables permutation. An extensive simulation study provides evidence that our approach compares favourably against existing linear and non-linear Markov Chain Monte Carlo and variational Bayes methods. We investigate both the statistical and economic value of the forecasts from our variational inference approach within the context of a mean-variance investor allocating her wealth in a large set of different industry portfolios. The results show that more accurate estimates translate into substantial statistical and economic out-of-sample gains. The results hold across different hierarchical shrinkage priors and model dimensions.
Fast variational Bayes methods for multinomial probit models
Authors: Rubén Loaiza-Maya, Didier Nibbering
Date: 2022-02-25 00:00:00
Tasks: Variational Inference

The multinomial probit model is often used to analyze choice behaviour. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice data sets. This paper proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for the unnormalized posterior density and an adequate choice of variational family. Both are challenging to specify in a multinomial probit, which has a posterior that requires identifying restrictions and is augmented with a large set of latent utilities. We employ a spherical transformation on the covariance matrix of the latent utilities to construct an unnormalized augmented posterior that identifies the parameters, and use the conditional posterior of the latent utilities as part of the variational family. The proposed method is faster than MCMC, and can be made scalable to both a large number of choice alternatives and a large number of observations. The accuracy and scalability of our method is illustrated in numerical experiments and real purchase data with one million observations.
Does an increase in the cost of imported inputs hurt exports? Evidence from firms' network of foreign suppliers
Authors: Santiago Camara
Date: 2022-02-25 00:00:00

This paper examines the relationship between changes in the cost of imported inputs and export performance using a novel dataset from Argentina which identifies domestic firms' network of foreign suppliers. To guide my empirical strategy, I construct a heterogeneous firm model subject to quality choice and frictions in the market for foreign supplier. The model predicts that the impact of an increase in the cost of imported inputs to be increasing in the adjustments costs of supplier linkages and in the quality of the product exported. I take the model to the data by constructing firm-specific shocks using a shift-share analysis which exploits firms' lagged exposure to foreign suppliers and finely defined import price shifts. Evidence suggests the presence of significant adjustment cost in firms' foreign supplier linkages and strong complementarities between imported inputs and export performance, particularly of high-quality products.
Measuring Shocks to Central Bank Independence using Legal Rulings
Authors: Stefan Griller, Florian Huber, Michael Pfarrhofer
Date: 2022-02-25 00:00:00

We investigate the consequences of legal rulings on the conduct of monetary policy. Several unconventional monetary policy measures of the European Central Bank have come under scrutiny before national courts and the European Court of Justice. These lawsuits have the potential to severely impact the scope and flexibility of central bank policies, and central bank independence in a wide sense, with important consequences for the real and financial economy. Since the number of relevant legal challenges is small, we develop an econometric approach that searches for minimum variance regimes which we use to isolate and measure the effects of these events. Our results suggest that legal rulings addressing central bank policies have a powerful effect on financial markets. Expansionary shocks ease financial conditions along various dimensions, and inflation swap reactions suggest inflationary pressures with stronger effects in the short term.
Policy choices and outcomes for offshore wind auctions globally
Authors: Malte Jansen, Philipp Beiter, Iegor Riepin, Felix Muesgens, Victor Juarez Guajardo-Fajardo, Iain Staffell, Bernard Bulder, Lena Kitzing
Date: 2022-02-25 00:00:00

Offshore wind energy is rapidly expanding, facilitated largely through auctions run by governments. We provide a detailed quantified overview of applied auction schemes, including geographical spread, volumes, results, and design specifications. Our comprehensive global dataset reveals heterogeneous designs. Although most remuneration designs provide some form of revenue stabilisation, their specific instrument choices vary and include feed-in tariffs, one-sided and two-sided contracts for difference, mandated power purchase agreements, and mandated renewable energy certificates. We review the schemes used in all eight major offshore wind jurisdictions across Europe, Asia, and North America and evaluate bids in their jurisdictional context. We analyse cost competitiveness, likelihood of timely construction, occurrence of strategic bidding, and identify jurisdictional aspects that might have influenced auction results. We find that auctions are embedded within their respective regulatory and market design context, and are remarkably diverse, though with regional similarities. Auctions in each jurisdiction have evolved and tend to become more exposed to market price risks over time. Less mature markets are more prone to make use of lower-risk designs. Still, some form of revenue stabilisation is employed for all auctioned offshore wind energy farms analysed here, regardless of the specific policy choices. Our data confirm a coincidence of declining costs and growing diffusion of auction regimes.
Crises and Political Polarization: Towards a Better Understanding of the Timing and Impact of Shocks and Media
Authors: Guglielmo Briscese, Maddalena Grignani, Stephen Stapleton
Date: 2022-02-24 00:00:00

We investigate how crises alter societies by analyzing the timing and channels of change using a longitudinal multi-wave survey of a representative sample of Americans throughout 2020. This methodology allows us to overcome some of the limitations of previous studies and uncover novel insights: (1) individuals with a negative personal experience during a crisis become more pro-welfare spending, in particular for policies they perceive will benefit them personally, and they become less trusting of institutions; (2) indirect shocks or the mere exposure to the crisis doesn't have a similar effect; (3) policy preferences and institutional trust can change quickly after a negative experience; and (4) consuming partisan media can mitigate or exacerbate these effects by distorting perceptions of reality. In an experiment, we find that exposing individuals to the same information can recalibrate distorted perceptions with lasting effects. Using a machine learning model to test for heterogeneous treatment effects, we find a negative personal experience did not make individuals more responsive to the information treatment, suggesting that lived and perceived experiences play an equally important role in changing preferences during a crisis.
Social Learning under Platform Influence: Consensus and Persistent Disagreement
Authors: Ozan Candogan, Nicole Immorlica, Bar Light, Jerry Anunrojwong
Date: 2022-02-25 00:00:00
Tasks: Stochastic Block Model

Individuals increasingly rely on social networking platforms to form opinions. However, these platforms typically aim to maximize engagement, which may not align with social good. In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on their neighbors' opinions and on the content shown to them by the platform. We focus on a stochastic block model with two blocks, where the initial opinions of the individuals in different blocks are different. We prove that for large and dense enough networks the trajectory of opinion dynamics in such networks can be approximated well by a simple two-agent system. The latter admits tractable analytical analysis, which we leverage to provide interesting insights into the platform's impact on the social learning outcome in our original two-block model. Specifically, by using our approximation result, we show that agents' opinions approximately converge to some limiting opinion, which is either: consensus, where all agents agree, or persistent disagreement, where agents' opinions differ. We find that when the platform is weak and there is a high number of connections between agents with different initial opinions, a consensus equilibrium is likely. In this case, even if a persistent disagreement equilibrium arises, the polarization in this equilibrium, i.e., the degree of disagreement, is low. When the platform is strong, a persistent disagreement equilibrium is likely and the equilibrium polarization is high. A moderate platform typically leads to a persistent disagreement equilibrium with moderate polarization. We analyze the effect of initial polarization on consensus and explore various extensions including a three block stochastic model and a correlation between initial opinions and agents' connection probabilities.
The outcome of the restabilization process in matching markets
Authors: Millán Guerra Beatriz Alejandra
Date: 2022-02-25 00:00:00

For a many-to-one matching model, we study the matchings obtained through the restabilization of stable matchings that had been disrupted by a change in the population. We include a simple representation of the stable matching obtained in terms of the initial stable matching (i.e., before being disrupted by changes in the population) and the firm-optimal stable matching. (We used Lattice Theory to characterize the outcome of the restabilization process.) We also describe the connection between the original stable matching and the one obtained after the restabilization process in the new market.
Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE
Authors: Tianrui Wang, Weibin Zhu, Yingying Gao, Yanan Chen, Junlan Feng, Shilei Zhang
Date: 2022-02-25 00:00:00

The harmonic structure of speech is resistant to noise, but the harmonics may still be partially masked by noise. Therefore, we previously proposed a harmonic gated compensation network (HGCN) to predict the full harmonic locations based on the unmasked harmonics and process the result of a coarse enhancement module to recover the masked harmonics. In addition, the auditory loudness loss function is used to train the network. For the DNS Challenge, we update HGCN with the following aspects, resulting in HGCN+. First, a high-band module is employed to help the model handle full-band signals. Second, cosine is used to model the harmonic structure more accurately. Then, the dual-path encoder and dual-path rnn (DPRNN) are introduced to take full advantage of the features. Finally, a gated residual linear structure replaces the gated convolution in the compensation module to increase the receptive field of frequency. The experimental results show that each updated module brings performance improvement to the model. HGCN+ also outperforms the referenced models on both wide-band and full-band test sets.
openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer
Authors: Kishan K C, Zhenning Tan, Long Chen, Minho Jin, Eunjung Han, Andreas Stolcke, Chul Lee
Date: 2022-02-24 00:00:00
Tasks: Open Set Learning, Speaker Identification

Household speaker identification with few enrollment utterances is an important yet challenging problem, especially when household members share similar voice characteristics and room acoustics. A common embedding space learned from a large number of speakers is not universally applicable for the optimal identification of every speaker in a household. In this work, we first formulate household speaker identification as a few-shot open-set recognition task and then propose a novel embedding adaptation framework to adapt speaker representations from the given universal embedding space to a household-specific embedding space using a set-to-set function, yielding better household speaker identification performance. With our algorithm, Open-set Few-shot Embedding Adaptation with Transformer (openFEAT), we observe that the speaker identification equal error rate (IEER) on simulated households with 2 to 7 hard-to-discriminate speakers is reduced by 23% to 31% relative.
A CNN-based Post-Processor for Perceptually-Optimized Immersive Media Compression
Authors: Angeliki Katsenou, Fan Zhang, David Bull
Date: 2022-02-25 00:00:00

In recent years, resolution adaptation based on deep neural networks has enabled significant performance gains for conventional (2D) video codecs. This paper investigates the effectiveness of spatial resolution resampling in the context of immersive content. The proposed approach reduces the spatial resolution of input multi-view videos before encoding, and reconstructs their original resolution after decoding. During the up-sampling process, an advanced CNN model is used to reduce potential re-sampling, compression, and synthesis artifacts. This work has been fully tested with the TMIV coding standard using a Versatile Video Coding (VVC) codec. The results demonstrate that the proposed method achieves a significant rate-quality performance improvement for the majority of the test sequences, with an average BD-VMAF improvement of 3.07 overall sequences.
Beyond Bjøntegaard: Limits of Video Compression Performance Comparisons
Authors: Christian Herglotz, Matthias Kränzler, Ruben Mons, André Kaup
Date: 2022-02-25 00:00:00
Tasks: valid, Video Compression

For 20 years, the gold standard to evaluate the performance of video codecs is to calculate average differences between ratedistortion curves, also called the "Bj{\o}ntegaard Delta". With the help of this tool, the compression performance of codecs can be compared. In the past years, we could observe that the calculus was also deployed for other metrics than bitrate and distortion in terms of peak signal-to-noise ratio, for example other quality metrics such as video multi-method assessment fusion or hardware-dependent metrics such as the decoding energy. However, it is unclear whether the Bj{\o}ntegaard Delta is a valid way to evaluate these metrics. To this end, this paper reviews several interpolation methods and evaluates their accuracy using different performancemetrics. As a result, we propose to use a novel approach based on Akima interpolation, which returns the most accurate results for a large variety of performance metrics. The approximation accuracy of this new method is determined to be below a bound of 1.5%.
PRNU Emphasis: a Generalization of the Multiplicative Model
Authors: Samuel Fernández-Menduiña, Fernando Pérez-González, Miguel Masciopinto
Date: 2022-02-24 00:00:00
Tasks: Denoising, model

The photoresponse non-uniformity (PRNU) is a camera-specific pattern, widely adopted to solve multimedia forensics problems such as device identification or forgery detection. The theoretical analysis of this fingerprint customarily relies on a multiplicative model for the denoising residuals. This setup assumes that the nonlinear mapping from the scene irradiance to the preprocessed luminance, that is, the composition of the Camera Response Function (CRF) with the optical and digital preprocessing pipelines, is a gamma correction. Yet, this assumption seldom holds in practice. In this letter, we improve the multiplicative model by including the influence of this nonlinear mapping on the denoising residuals. We also propose a method to estimate this effect. Results evidence that the response of typical cameras deviates from a gamma correction. Experimental device identification with our model increases the TPR by a $4.93\, \%$ on average for a fixed FPR of $0.01$.
On Digital Subcarrier Multiplexing under A Bandwidth Limitation and ASE Noise
Authors: Junho Cho, Xi Chen, Greg Raybon, Son Thai Le
Date: 2022-02-25 00:00:00

We show that digital subcarrier multiplexing (DSM) systems require much greater complexity for Nyquist pulse shaping than single-carrier (SC) systems, and it is a misconception that both systems use the same bandwidth when using the same pulse shaping. Through back-to-back (B2B) experiments with realistic transmitter (TX) modules and amplified spontaneous emission (ASE) noise loading, we show that even with optimized waterfilling and entropy loading, DSM does not achieve a larger net data rate (NDR) compared to SC when only ASE noise exists in the channel in long-haul transmission scenarios.
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck
Authors: Francesco Pezone, Sergio Barbarossa, Paolo Di Lorenzo
Date: 2022-02-25 00:00:00
Tasks: image-classification, Image Classification, Stochastic Optimization

Whenever communication takes place to fulfil a goal, an effective way to encode the source data to be transmitted is to use an encoding rule that allows the receiver to meet the requirements of the goal. A formal way to identify the relevant information with respect to a goal can be obtained exploiting the information bottleneck (IB) principle. In this paper, we propose a goal-oriented communication system, based on the combination of IB and stochastic optimization. The IB principle is used to design the encoder in order to find an optimal balance between representation complexity and relevance of the encoded data with respect to the goal. Stochastic optimization is then used to adapt the parameters of the IB to find an efficient resource allocation of communication and computation resources. Our goal is to minimize the average energy consumption under constraints on average service delay and accuracy of the learning task applied to the received data in a dynamic scenario. Numerical results assess the performance of the proposed strategy in two cases: regression from Gaussian random variables, where we can exploit closed-form solutions, and image classification using deep neural networks, with adaptive network splitting between transmit and receive sides.
NOMA Joint Channel Estimation and Signal Detection using Rotational Invariant Codes and GMM-based Clustering
Authors: Ayoob Salari, Mahyar Shirvanimoghaddam, Muhammad Basit Shahab, Yonghui Li, Sarah Johnson
Date: 2022-02-25 00:00:00
Tasks: Clustering

This paper studies the joint channel estimation and signal detection for the uplink power-domain non-orthogonal multiple access. The proposed technique performs both detection and estimation without the need of pilot symbols by using a clustering technique. We apply rotational-invariant coding to assist signal detection at the receiver without sending pilot symbols. We utilize Gaussian mixture model (GMM) to automatically cluster the received signals without supervision and optimize decision boundaries to improve the bit error rate (BER) performance. Simulation results show that the proposed scheme without using any pilot symbol achieves almost the same BER performance as that for the conventional maximum likelihood receiver with full channel state information.
MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems
Authors: Haobo Zhang, Hongliang Zhang, Boya Di, Kaigui Bian, Zhu Han, Lingyang Song
Date: 2022-02-25 00:00:00

As a widely used localization and sensing technique, radars will play an important role in future wireless networks. However, the wireless channels between the radar and the targets are passively adopted by traditional radars, which limits the performance of target detection. To address this issue, we propose to use the reconfigurable intelligent surface (RIS) to improve the detection accuracy of radar systems due to its capability to customize channel conditions by adjusting its phase shifts, which is referred to as MetaRadar. In such a system, it is challenging to jointly optimize both radar waveforms and RIS phase shifts in order to improve the multi-target detection performance. To tackle this challenge, we design a waveform and phase shift optimization (WPSO) algorithm to effectively solve the multi-target detection problem, and also analyze the performance of the proposed MetaRadar scheme theoretically. Simulation results show that the detection performance of the MetaRadar scheme is significantly better than that of the traditional radar schemes.
Delta family approach for the stochastic control problems of utility maximization
Authors: Jingtang Ma, Zhengyang Lu, Zhenyu Cui
Date: 2022-02-25 00:00:00
Tasks: Tensor Decomposition

In this paper, we propose a new approach for stochastic control problems arising from utility maximization. The main idea is to directly start from the dynamical programming equation and compute the conditional expectation using a novel representation of the conditional density function through the Dirac Delta function and the corresponding series representation. We obtain an explicit series representation of the value function, whose coefficients are expressed through integration of the value function at a later time point against a chosen basis function. Thus we are able to set up a recursive integration time-stepping scheme to compute the optimal value function given the known terminal condition, e.g. utility function. Due to tensor decomposition property of the Dirac Delta function in high dimensions, it is straightforward to extend our approach to solving high-dimensional stochastic control problems. The backward recursive nature of the method also allows for solving stochastic control and stopping problems, i.e. mixed control problems. We illustrate the method through solving some two-dimensional stochastic control (and stopping) problems, including the case under the classical and rough Heston stochastic volatility models, and stochastic local volatility models such as the stochastic alpha beta rho (SABR) model.