pwc-archive/papers-with-abstracts huggingface dataset:
Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions
Authors: Ercument Cahan, Jushan Bai, Serena Ng
Date: 2021-03-04 00:00:00
Tasks: Imputation, Missing Values

Economists are blessed with a wealth of data for analysis, but more often than not, values in some entries of the data matrix are missing. Various methods have been proposed to handle missing observations in a few variables. We exploit the factor structure in panel data of large dimensions. Our \textsc{tall-project} algorithm first estimates the factors from a \textsc{tall} block in which data for all rows are observed, and projections of variable specific length are then used to estimate the factor loadings. A missing value is imputed as the estimated common component which we show is consistent and asymptotically normal without further iteration. Implications for using imputed data in factor augmented regressions are then discussed. To compensate for the downward bias in covariance matrices created by an omitted noise when the data point is not observed, we overlay the imputed data with re-sampled idiosyncratic residuals many times and use the average of the covariances to estimate the parameters of interest. Simulations show that the procedures have desirable finite sample properties.
Cognitive-Driven Optimization of Sparse Array Transceiver for MIMO Radar Beamforming
Authors: Weitong Zhai, Xiangrong Wang, Syed A. Hamza, Moeness G. Amin
Date: 2021-03-04 00:00:00

Cognitive multiple-input multiple-output (MIMO) radar is capable of adjusting system parameters adaptively by sensing and learning in complex dynamic environment. Beamforming performance of MIMO radar is guided by both beamforming weight coefficients and the transceiver configuration. We propose a cognitive-driven MIMO array design where both the beamforming weights and the transceiver configuration are adaptively and concurrently optimized under different environmental conditions. The perception-action cycle involves data collection of full virtual array, covariance reconstruction and joint design of the transmit and receive arrays by antenna selection.The optimal transceiver array design is realized by promoting two-dimensional group sparsity via iteratively minimizing reweighted mixed L21-norm, with constraints imposed on transceiver antenna spacing for proper transmit/receive isolation. Simulations are provided to demonstrate the "perception-action" capability of the proposed cognitive sparse MIMO array in achieving enhanced beamforming and anti-jamming in dynamic target and interference environment.
Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching
Authors: Xiangrong Wang, Maria Greco, Fulvio Gini
Date: 2021-03-04 00:00:00

In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), to swiftly adapt both array configuration and excitation weights in accordance to the dynamic environment for enhancing interference suppression. In order to achieve an implementable design of array reconfiguration, the RCAS is conducted in the framework of regularized antenna switching, whereby the full array aperture is collectively divided into separate groups and only one antenna in each group is switched on to connect with the processing channel. A set of deterministic complementary sparse arrays with good quiescent beampatterns is first designed by RCAS and full array data is collected by switching among them while maintaining resilient interference suppression. Subsequently, adaptive sparse array tailored for the specific environment is calculated and reconfigured based on the information extracted from the full array data. The RCAS is devised as an exclusive cardinality-constrained optimization, which is reformulated by introducing an auxiliary variable combined with a piece-wise linear function to approximate the $l_0$-norm function. A regularization formulation is proposed to solve the problem iteratively and eliminate the requirement of feasible initial search point. A rigorous theoretical analysis is conducted, which proves that the proposed algorithm is essentially an equivalent transformation of the original cardinality-constrained optimization. Simulation results validate the effectiveness of the proposed RCAS strategy.
End-to-End Mispronunciation Detection and Diagnosis From Raw Waveforms
Authors: Bi-Cheng Yan, Berlin Chen
Date: 2021-03-04 00:00:00

Mispronunciation detection and diagnosis (MDD) is designed to identify pronunciation errors and provide instructive feedback to guide non-native language learners, which is a core component in computer-assisted pronunciation training (CAPT) systems. However, MDD often suffers from the data-sparsity problem due to that collecting non-native data and the associated annotations is time-consuming and labor-intensive. To address this issue, we explore a fully end-to-end (E2E) neural model for MDD, which processes learners' speech directly based on raw waveforms. Compared to conventional hand-crafted acoustic features, raw waveforms retain more acoustic phenomena and potentially can help neural networks discover better and more customized representations. To this end, our MDD model adopts a co-called SincNet module to take input a raw waveform and covert it to a suitable vector representation sequence. SincNet employs the cardinal sine (sinc) function to implement learnable bandpass filters, drawing inspiration from the convolutional neural network (CNN). By comparison to CNN, SincNet has fewer parameters and is more amenable to human interpretation. Extensive experiments are conducted on the L2-ARCTIC dataset, which is a publicly-available non-native English speech corpus compiled for research on CAPT. We find that the sinc filters of SincNet can be adapted quickly for non-native language learners of different nationalities. Furthermore, our model can achieve comparable mispronunciation detection performance in relation to state-of-the-art E2E MDD models that take input the standard handcrafted acoustic features. Besides that, our model also provides considerable improvements on phone error rate (PER) and diagnosis accuracy.
Data-driven distributionally robust MPC for constrained stochastic systems
Authors: Peter Coppens, Panagiotis Patrinos
Date: 2021-03-04 00:00:00

In this paper we introduce a novel approach to distributionally robust optimal control that supports online learning of the ambiguity set, while guaranteeing recursive feasibility. We introduce conic representable risk, which is useful to derive tractable reformulations of distributionally robust optimization problems. Specifically, to illustrate the techniques introduced, we utilize risk measures constructed based on data-driven ambiguity sets, constraining the second moment of the random disturbance. In the optimal control setting, such moment-based risk measures lead to tractable optimal controllers when combined with affine disturbance feedback. Assumptions on the constraints are given that guarantee recursive feasibility. The resulting control scheme acts as a robust controller when little data is available and converges to the certainty equivalent controller when a large sample count implies high confidence in the estimated second moment. This is illustrated in a numerical experiment.
Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models
Authors: Alessandro Casini
Date: 2021-03-04 00:00:00
Tasks: Time Series Analysis

We develop a theory of evolutionary spectra for heteroskedasticity and autocorrelation robust (HAR) inference when the data may not satisfy second-order stationarity. Nonstationarity is a common feature of economic time series which may arise either from parameter variation or model misspecification. In such a context, the theories that support HAR inference are either not applicable or do not provide accurate approximations. HAR tests standardized by existing long-run variance estimators then may display size distortions and little or no power. This issue can be more severe for methods that use long bandwidths (i.e., fixed-b HAR tests). We introduce a class of nonstationary processes that have a time-varying spectral representation which evolves continuously except at a finite number of time points. We present an extension of the classical heteroskedasticity and autocorrelation consistent (HAC) estimators that applies two smoothing procedures. One is over the lagged autocovariances, akin to classical HAC estimators, and the other is over time. The latter element is important to flexibly account for nonstationarity. We name them double kernel HAC (DK-HAC) estimators. We show the consistency of the estimators and obtain an optimal DK-HAC estimator under the mean squared error (MSE) criterion. Overall, HAR tests standardized by the proposed DK-HAC estimators are competitive with fixed-b HAR tests, when the latter work well, with regards to size control even when there is strong dependence. Notably, in those empirically relevant situations in which previous HAR tests are undersized and have little or no power, the DK-HAC estimator leads to tests that have good size and power.
Pricing Perpetual American put options with asset-dependent discounting
Authors: Jonas Al-Hadad, Zbigniew Palmowski
Date: 2021-03-04 00:00:00

The main objective of this paper is to present an algorithm of pricing perpetual American put options with asset-dependent discounting. The value function of such an instrument can be described as \begin{equation*} V^{\omega}_{\text{A}^{\text{Put}}}(s) = \sup_{\tau\in\mathcal{T}} \mathbb{E}_{s}[e^{-\int_0^\tau \omega(S_w) dw} (K-S_\tau)^{+}], \end{equation*} where $\mathcal{T}$ is a family of stopping times, $\omega$ is a discount function and $\mathbb{E}$ is an expectation taken with respect to a martingale measure. Moreover, we assume that the asset price process $S_t$ is a geometric L\'evy process with negative exponential jumps, i.e. $S_t = s e^{\zeta t + \sigma B_t - \sum_{i=1}^{N_t} Y_i}$. The asset-dependent discounting is reflected in the $\omega$ function, so this approach is a generalisation of the classic case when $\omega$ is constant. It turns out that under certain conditions on the $\omega$ function, the value function $V^{\omega}_{\text{A}^{\text{Put}}}(s)$ is convex and can be represented in a closed form; see Al-Hadad and Palmowski (2021). We provide an option pricing algorithm in this scenario and we present exact calculations for the particular choices of $\omega$ such that $V^{\omega}_{\text{A}^{\text{Put}}}(s)$ takes a simplified form.
MDEmic in a use case for microscopy metadata harmonization: Facilitating FAIR principles in practical application with metadata annotation tools
Authors: Susanne Kunis, Sebastian Hänsch, Christian Schmidt, Frances Wong, Caterina Strambio-De-Castillia, Stefanie Weidtkamp-Peters
Date: 2021-03-04 00:00:00
Tasks: Management

While the FAIR principles are well accepted in the scientific community, the implementation of appropriate metadata editing and transfer to ensure FAIR research data in practice is significantly lagging behind. On the one hand, it strongly depends on the availability of tools that efficiently support this step in research data management. On the other hand, it depends on the available standards regarding the interpretability of metadata. Here, we introduce a tool, MDEmic, for editing metadata of microscopic imaging data in an easy and comfortable way that provides high flexibility in terms of adjustment of metadata sets. This functionality was in great demand by many researchers applying microscopic techniques. MDEmic has already become a part of the standard installation package of the image database OMERO as OMERO.mde. This database helps to organize and visualize microscopic image data and keep track of their further processing and linkage to other data sets. For this reason, many imaging core facilities provide OMERO to their users. We present a use case scenario for the tailored application of OMERO.mde to imaging data of an institutional OMERO-based Membrane Dye Database, which requires specific experimental metadata. Similar to public image data repositories like the Image Data Resource, IDR, this database facilitates image data storage including rich metadata which enables data mining and re-use, one of the major goals of the FAIR principles.
Robust market-adjusted systemic risk measures
Authors: Matteo Burzoni, Marco Frittelli, Federico Zorzi
Date: 2021-03-04 00:00:00
Tasks: Relation

In this note we consider a system of financial institutions and study systemic risk measures in the presence of a financial market and in a robust setting, namely, where no reference probability is assigned. We obtain a dual representation for convex robust systemic risk measures adjusted to the financial market and show its relation to some appropriate no-arbitrage conditions.
On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer
Authors: Osmel Martínez Rosabal, Onel L. Alcaraz López, Hirley Alves, Samuel Montejo-Sánchez, Matti Latva-aho
Date: 2020-12-08 00:00:00

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.
Model Reference Adaptive Control of Piecewise Affine Systems with State Tracking Performance Guarantees
Authors: Tong Liu, Martin Buss
Date: 2021-03-04 00:00:00

In this paper, we investigate the model reference adaptive control approach for uncertain piecewise affine systems with performance guarantees. The proposed approach ensures the error metric, defined as the weighted Euclidean norm of the state tracking error, to be confined within a user-defined time-varying performance bound. We introduce an auxiliary performance function to construct a barrier Lyapunov function. This auxiliary performance signal is reset at each switching instant, which prevents the transgression of the barriers caused by the jumps of the error metric at switching instants. The dwell time constraints are derived based on the parameters of the user-defined performance bound and the auxiliary performance function. We also prove that the Lyapunov function is non-increasing even at the switching instants and thus does not impose extra dwell time constraints. Furthermore, we propose the robust modification of the adaptive controller for the uncertain piecewise affine systems subject to unmatched disturbances. A Numerical example validates the correctness of the proposed approach.
Understanding Vaccine Hesitancy: Empirical Evidence from India
Authors: Pramod Kumar Sur
Date: 2021-03-04 00:00:00

Why do vaccination rates remain low even in countries where long-established immunization programs exist and vaccines are provided for free? We study this paradox in the context of India, which contributes to the world's largest pool of under-vaccinated children and about one-third of all vaccine-preventable deaths globally. Combining historical records with survey datasets, we examine the Indian government's forced sterilization policy, a short-term aggressive family planning program implemented between 1976 and 1977. Using multiple estimation methods, including an instrumental variable (IV) and a geographic regression discontinuity design (RDD) approach, we document that the current vaccination completion rate is low in places where forced sterilization was high. We also explore the heterogeneous effects, mechanisms, and reasons for the mechanism. Finally, we examine the enduring consequence and present evidence that places more exposed to forced sterilization have an average 60 percent higher child mortality rate today. Together, these findings suggest that government policies implemented in the past can have persistent adverse impacts on demand for health-seeking behavior, even if the burden is exceedingly high.
Understanding Species Abundance Distributions in Complex Ecosystems of Interacting Species
Authors: Jim Wu, Pankaj Mehta, David Schwab
Date: 2021-03-02 00:00:00

Niche and neutral theory are two prevailing, yet much debated, ideas in ecology proposed to explain the patterns of biodiversity. Whereas niche theory emphasizes selective differences between species and interspecific interactions in shaping the community, neutral theory supposes functional equivalence between species and points to stochasticity as the primary driver of ecological dynamics. In this work, we draw a bridge between these two opposing theories. Starting from a Lotka-Volterra (LV) model with demographic noise and random symmetric interactions, we analytically derive the stationary population statistics and species abundance distribution (SAD). Using these results, we demonstrate that the model can exhibit three classes of SADs commonly found in niche and neutral theories and found conditions that allow an ecosystem to transition between these various regimes. Thus, we reconcile how neutral-like statistics may arise from a diverse community with niche differentiation.
End-to-end acoustic modelling for phone recognition of young readers
Authors: Lucile Gelin, Morgane Daniel, Julien Pinquier, Thomas Pellegrini
Date: 2021-03-04 00:00:00
Tasks: Acoustic Modelling, Transfer Learning

Automatic recognition systems for child speech are lagging behind those dedicated to adult speech in the race of performance. This phenomenon is due to the high acoustic and linguistic variability present in child speech caused by their body development, as well as the lack of available child speech data. Young readers speech additionally displays peculiarities, such as slow reading rate and presence of reading mistakes, that hardens the task. This work attempts to tackle the main challenges in phone acoustic modelling for young child speech with limited data, and improve understanding of strengths and weaknesses of a wide selection of model architectures in this domain. We find that transfer learning techniques are highly efficient on end-to-end architectures for adult-to-child adaptation with a small amount of child speech data. Through transfer learning, a Transformer model complemented with a Connectionist Temporal Classification (CTC) objective function, reaches a phone error rate of 28.1%, outperforming a state-of-the-art DNN-HMM model by 6.6% relative, as well as other end-to-end architectures by more than 8.5% relative. An analysis of the models' performance on two specific reading tasks (isolated words and sentences) is provided, showing the influence of the utterance length on attention-based and CTC-based models. The Transformer+CTC model displays an ability to better detect reading mistakes made by children, that can be attributed to the CTC objective function effectively constraining the attention mechanisms to be monotonic.
Stability and $\mathcal{H}_{\infty}$ Performance Analysis of Stochastic Linear Networked and Quantized Control Systems
Authors: Wei Ren, Junlin Xiong
Date: 2021-03-04 00:00:00

This paper studies the stability and $\mathcal{H}_{\infty}$ performance analysis problem for linear networked and quantized control systems with both communication delays random packet losses. To deal with the network-induced uncertainties and random packet dropouts, a novel discrete-time stochastic system model is developed for continuous-time networked control systems, and further overapproximated via a polytopic system with norm-bounded uncertainty. Based on the overapproximated system model, sufficient conditions are established for linear networked and quantized control systems in different cases to guarantee input-to-state stability and $\mathcal{H}_{\infty}$ performance with respect to the network-induced errors. Finally, a numerical example is presented to illustrated the developed results.
Data-Based System Analysis and Control of Flat Nonlinear Systems
Authors: Mohammad Alsalti, Julian Berberich, Victor G. Lopez, Frank Allgöwer, Matthias A. Müller
Date: 2021-03-04 00:00:00

Willems et al. showed that all input-output trajectories of a discrete-time linear time-invariant system can be obtained using linear combinations of time shifts of a single, persistently exciting, input-output trajectory of that system. In this paper, we extend this result to the class of discrete-time single-input single-output flat nonlinear systems. We propose a data-based parametrization of all trajectories using only input-output data. Further, we use this parametrization to solve the data-based simulation and output-matching control problems for the unknown system without explicitly identifying a model. Finally, we illustrate the main results with numerical examples.
Low Complexity Method for Simulation of Epidemics Based on Dijkstra's Algorithm
Authors: Davide Zorzenon, Fabio Molinari, Joerg Raisch
Date: 2020-10-06 00:00:00

Models of epidemics over networks have become popular, as they describe the impact of individual behavior on infection spread. However, they come with high computational complexity, which constitutes a problem in case large-scale scenarios are considered. This paper presents a discrete-time multi-agent SIR (Susceptible, Infected, Recovered) model that extends known results in literature. Based on that, using the novel notion of Contagion Graph, it proposes a graphbased method derived from Dijkstra's algorithm that allows to decrease the computational complexity of a simulation. The Contagion Graph can be also employed as an approximation scheme describing the "mean behavior" of an epidemic over a network and requiring low computational power. Theoretical findings are confirmed by randomized large-scale simulation.
Transfer Function Analysis and Implementation of Active Disturbance Rejection Control
Authors: Gernot Herbst
Date: 2020-11-02 00:00:00
Tasks: Computational Efficiency, Sensitivity

To support the adoption of active disturbance rejection control (ADRC) in industrial practice, this article aims at improving both understanding and implementation of ADRC using traditional means, in particular via transfer functions and a frequency-domain view. Firstly, to enable an immediate comparability with existing classical control solutions, a realizable transfer function implementation of continous-time linear ADRC is introduced. Secondly, a frequency-domain analysis of ADRC components, performance, parameter sensitivity, and tuning method is performed. Finally, an exact implementation of discrete-time ADRC using transfer functions is introduced for the first time, with special emphasis on practical aspects such as computational efficiency, low parameter footprint, and windup protection.