pwc-archive/papers-with-abstracts huggingface dataset:
Deep Task-Based Analog-to-Digital Conversion
Authors: Nir Shlezinger, Ariel Amar, Ben Luijten, Ruud J. G. van Sloun, Yonina C. Eldar
Date: 2022-01-29 00:00:00
Tasks: Meta-Learning, Quantization

Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. Their conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-amplitude quantities using a finite number of bits. ADCs typically implement generic uniform conversion mappings that are ignorant of the task for which the signal is acquired, and can be costly when operating in high rates and fine resolutions. In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out. We propose a model for sampling and quantization that facilitates the learning of non-uniform mappings from data. Based on this learnable ADC mapping, we present a mechanism for optimizing a hybrid acquisition system comprised of analog combining, tunable ADCs with fixed rates, and digital processing, by jointly learning its components end-to-end. Then, we show how one can exploit the representation of hybrid acquisition systems as deep network to optimize the sampling rate and quantization rate given the task by utilizing Bayesian meta-learning techniques. We evaluate the proposed deep task-based ADC in two case studies: the first considers symbol detection in multi-antenna digital receivers, where multiple analog signals are simultaneously acquired in order to recover a set of discrete information symbols. The second application is the beamforming of analog channel data acquired in ultrasound imaging. Our numerical results demonstrate that the proposed approach achieves performance which is comparable to operating with high sampling rates and fine resolution quantization, while operating with reduced overall bit rate.
Probabilistic load flow calculation of AC/DC hybrid system based on cumulant method
Authors: Yinfeng Sun, Dapeng Xia, Zichun Gao, Zhenhao Wang, Guoqing Li, Weihua Lu, Xueguang Wu, Yang Li
Date: 2022-01-29 00:00:00

The operating conditions of the power system have become more complex and changeable. This paper proposes a probabilistic load flow based on the cumulant method (PLF-CM) for the voltage sourced converter high voltage direct current (VSC-HVDC) hybrid system containing photovoltaic grid-connected systems. Firstly, the corresponding control mode is set for the converter, including droop control and master-slave control. The unified iterative method is used to calculate the conventional AC/DC flow. Secondly, on the basis of the probability model of load and photovoltaic output, based on the aforementioned flow results, use correlation coefficient matrix of this paper will change the relevant sample into independent sample, the cumulants of the load and photovoltaic output are obtained; then, the probability density function (PDF) and cumulative distribution function (CDF) of state variables are obtained by using Gram-Charlie series expansion method. Finally, the mean value and standard deviation of node voltage and line power are calculated on the modified IEEE 34-bus and IEEE 57-bus transmission systems. The algorithm can reflect the inherent uncertainty of new energy sources, and replace the complex convolution operation, greatly improving the calculation speed and the convergence.
Race Driver Evaluation at a Driving Simulator using a physical Model and a Machine Learning Approach
Authors: Julian von Schleinitz, Thomas Schwarzhuber, Lukas Wörle, Michael Graf, Arno Eichberger, Wolfgang Trutschnig, Andreas Schröder
Date: 2022-01-27 00:00:00

Professional race drivers are still superior to automated systems at controlling a vehicle at its dynamic limit. Gaining insight into race drivers' vehicle handling process might lead to further development in the areas of automated driving systems. We present a method to study and evaluate race drivers on a driver-in-the-loop simulator by analysing tire grip potential exploitation. Given initial data from a simulator run, two optimiser based on physical models maximise the horizontal vehicle acceleration or the tire forces, respectively. An overall performance score, a vehicle-trajectory score and a handling score are introduced to evaluate drivers. Our method is thereby completely track independent and can be used from one single corner up to a large data set. We apply the proposed method to a motorsport data set containing over 1200 laps from seven professional race drivers and two amateur drivers whose lap times are 10-20% slower. The difference to the professional drivers comes mainly from their inferior handling skills and not their choice of driving line. A downside of the presented method for certain applications is an extensive computation time. Therefore, we propose a Long-short-term memory (LSTM) neural network to estimate the driver evaluation scores. We show that the neural network is accurate and robust with a root-mean-square error between 2-5% and can replace the optimisation based method. The time for processing the data set considered in this work is reduced from 68 hours to 12 seconds, making the neural network suitable for real-time application.
Risk-based Design of Regular Plane Frames Subject to Damage by Abnormal Events: a Conceptual Study
Authors: Andre T. Beck, Lucas da Rosa Ribeiro, Marcos Valdebenito, Hector Jensen
Date: 2022-01-25 00:00:00

Constructed facilities should be robust with respect to the loss of load-bearing elements due to abnormal events. Yet, strengthening structures to withstand such damage has a significant impact on construction costs. Strengthening costs should be justified by the threat and should result in smaller expected costs of progressive collapse. In regular frame structures, beams and columns compete for the strengthening budget. In this paper, we present a risk-based formulation to address the optimal design of regular plane frames under element loss conditions. We address the threat probabilities for which strengthening has better cost-benefit than usual design, for different frame configurations, and study the impacts of strengthening extent and cost. The risk-based optimization reveals optimum points of compromise between competing failure modes: local bending of beams, local crushing of columns, and global pancake collapse, for frames of different aspect ratios. The conceptual study is based on a simple analytical model for progressive collapse, but it provides relevant insight for the design and strengthening of real structures.
Computational Scatter Correction for High-Resolution Flat-Panel CT Based on a Fast Monte Carlo Photon Transport Model
Authors: Ammar Alsaffar, Steffen Kieß, Kaicong Sun, Sven Simon
Date: 2022-01-31 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, GPU

In computed tomography (CT) reconstruction, scattering causes server quality degradation of the reconstructed CT images by introducing streaks and cupping artifacts which reduce the detectability of low contrast objects. Monte Carlo (MC) simulation is considered as the most accurate approach for scatter estimation. However, the existing MC estimators are computationally expensive especially for the considered high-resolution flat-panel CT. In this paper, we propose a fast and accurate photon transport model which describes the physics within the 1 keV to 1 MeV range using multiple controllable key parameters. Based on this model, scatter computation for a single projection can be completed within a range of few seconds under well-defined model parameters. Smoothing and interpolation are performed on the estimated scatter to accelerate the scatter calculation without compromising accuracy too much compared to measured near scatter-free projection images. Combining the scatter estimation with the filtered backprojection (FBP), scatter correction is performed effectively in an iterative manner. In order to evaluate the proposed MC model, we have conducted extensive experiments on the simulated data and real-world high-resolution flat-panel CT. Comparing to the state-of-the-art MC simulators, our photon transport model achieved a 202$\times$ speed-up on a four GPU system comparing to the multi-threaded state-of-the-art EGSnrc MC simulator. Besides, it is shown that for real-world high-resolution flat-panel CT, scatter correction with sufficient accuracy is accomplished within one to three iterations using a FBP and a forward projection computed with the proposed fast MC photon transport model.
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
Authors: Gaurav Arya, William F. Li, Charles Roques-Carmes, Marin Soljačić, Steven G. Johnson, Zin Lin
Date: 2022-01-28 00:00:00
Tasks: compressed sensing, Object, Super-Resolution

We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i.e. the object can be described by a small number of non-zero values, but the positions of these values are unknown). We nest an iterative, unapproximated compressed sensing reconstruction algorithm into our end-to-end optimization pipeline, resulting in an interpretable, data-efficient method for maximally leveraging metaoptics to exploit object sparsity. We apply our framework to super-resolution imaging and high-resolution depth imaging with a phase-change material. In both situations, our end-to-end framework computationally discovers optimal metasurface structures for compressed sensing recovery, automatically balancing a number of complicated design considerations to select an imaging measurement matrix from a complex, physically constrained manifold with millions ofdimensions. The optimized metasurface imaging systems are robust to noise, significantly improving over random scattering surfaces and approaching the ideal compressed sensing performance of a Gaussian matrix, showing how a physical metasurface system can demonstrably approach the mathematical limits of compressed sensing.
MicroVelocity: rethinking the Velocity of Money for digital currencies
Authors: Carlo Campajola, Marco D'Errico, Claudio J. Tessone
Date: 2022-01-31 00:00:00

We propose a novel framework to analyse the velocity of money in terms of the contribution (MicroVelocity) of each individual agent, and to uncover the distributional determinants of aggregate velocity. Leveraging on complete publicly available transactions data stored in blockchains from four cryptocurrencies, we empirically find that MicroVelocity i) is very heterogeneously distributed and ii) strongly correlates with agents' wealth. We further document the emergence of high-velocity intermediaries, thereby challenging the idea that these systems are fully decentralised. Further, our framework and results provide policy insights for the development and analysis of digital currencies.
Propagation of disruptions in supply networks of essential goods: A population-centered perspective of systemic risk
Authors: William Schueller, Christian Diem, Melanie Hinterplattner, Johannes Stangl, Beate Conrady, Markus Gerschberger, Stefan Thurner
Date: 2022-01-31 00:00:00
Tasks: Management

The Covid-19 pandemic drastically emphasized the fragility of national and international supply networks (SNs),leading to significant supply shortages of essential goods for people, such as food and medical equipment. Severe disruptions that propagate along complex SNs can expose the population of entire regions or even countries to these risks. A lack of both, data and quantitative methodology, has hitherto hindered us to empirically quantify the vulnerability of the population to disruptions. Here we develop a data-driven simulation methodology to locally quantify actual supply losses for the population that result from the cascading of supply disruptions. We demonstrate the method on a large food SN of a European country including 22,938 business premises, 44,355 supply links and 116 local administrative districts. We rank the business premises with respect to their criticality for the districts' population with the proposed systemic risk index, SRIcrit, to identify around 30 premises that -- in case of their failure -- are expected to cause critical supply shortages in sizable fractions of the population. The new methodology is immediately policy relevant as a fact-driven and generalizable crisis management tool. This work represents a starting point for quantitatively studying SN disruptions focused on the well-being of the population.
Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance
Authors: Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang
Date: 2022-01-31 00:00:00
Tasks: regression

We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probability of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.
A General Description of Growth Trends
Authors: Moshe Elitzur
Date: 2022-01-31 00:00:00
Tasks: Time Series, Time Series Analysis

Time series that display periodicity can be described with a Fourier expansion. In a similar vein, a recently developed formalism enables description of growth patterns with the optimal number of parameters (Elitzur et al, 2020). The method has been applied to the growth of national GDP, population and the COVID-19 pandemic; in all cases the deviations of long-term growth patterns from pure exponential required no more than two additional parameters, mostly only one. Here I utilize the new framework to develop a unified formulation for all functions that describe growth deceleration, wherein the growth rate decreases with time. The result offers the prospects for a new general tool for trend removal in time-series analysis.
On the Use of Instrumental Variables in Mediation Analysis
Authors: Bora Kim
Date: 2022-01-30 00:00:00

Empirical researchers are often interested in not only whether a treatment affects an outcome of interest, but also how the treatment effect arises. Causal mediation analysis provides a formal framework to identify causal mechanisms through which a treatment affects an outcome. The most popular identification strategy relies on so-called sequential ignorability (SI) assumption which requires that there is no unobserved confounder that lies in the causal paths between the treatment and the outcome. Despite its popularity, such assumption is deemed to be too strong in many settings as it excludes the existence of unobserved confounders. This limitation has inspired recent literature to consider an alternative identification strategy based on an instrumental variable (IV). This paper discusses the identification of causal mediation effects in a setting with a binary treatment and a binary instrumental variable that is both assumed to be random. We show that while IV methods allow for the possible existence of unobserved confounders, additional monotonicity assumptions are required unless the strong constant effect is assumed. Furthermore, even when such monotonicity assumptions are satisfied, IV estimands are not necessarily equivalent to target parameters.
Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network
Authors: Ihsan Chaoubi, Camille Besse, Hélène Cossette, Marie-Pier Côté
Date: 2022-01-27 00:00:00

Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments.
The effect of the pandemic on complex socio-economic systems: community detection induced by communicability
Authors: Gian Paolo Clemente, Rosanna Grassi, Giorgio Rizzini
Date: 2022-01-29 00:00:00
Tasks: Community Detection

The increasing complexity of interrelated systems has made the use of multiplex networks an important tool for explaining the nature of relations between elements in the system. In this paper, we aim at investigating various aspects of countries' behaviour during the coronavirus pandemic period. By means of a multiplex network we consider simultaneously stringency index values, COVID-19 infections and international trade data, in order to detect clusters of countries that showed a similar reaction to the pandemic. We propose a new methodological approach based on the Estrada communicability for identifying communities on a multiplex network, based on a two-step optimization. At first, we determine the optimal inter-layer intensity between levels by minimizing a distance function. Hence, the optimal inter-layer intensity is used to detect communities on each layer. Our findings show that the community detection on this multiplex network has greater information power than classical methods for single-layer networks. Our approach better reveals clusters on each layer with respect to the application of the same approach on each single-layer. Moreover, detected groups in the multiplex case benefit of a higher cohesion, leading to identifying on each layer a lower number of communities with respect to the ones obtained in the single-layer cases.
The China Trade Shock and the ESG Performances of US firms
Authors: Hui Xu, Yue Wu
Date: 2022-01-28 00:00:00

How does import competition from China affect engagement on ESG initiatives by US corporates? On the one hand, reduced profitability due to import competition and lagging ESG performance of Chinese exporters can disincentivize US firms to put more resources to ESG initiatives. On the other hand, the shift from labor-intensive production to capital/technology-intensive production along with offshoring may improve the US company's ESG performance. Moreover, US companies have incentives to actively pursue more ESG engagement to differentiate from Chinese imports. Exploiting a trade policy in which US congress granted China the Permanent Normal Trade Relations and the resulting change in expected tariff rates on Chinese imports, we find that greater import competition from China leads to an increase in the US company's ESG performance. The improvement primarily stems from "doing more positives" and from more involvement on environmental initiatives. Indirect and direct evidence shows that the improvement is not driven by the change in production process or offshoring, but is consistent with product differentiation. Our results suggest that the trade shock from China has significant impact on the US company's ESG performance.
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation
Authors: Ali K. Z. Tehrani, Mostafa Sharifzadeh, Emad Boctor, Hassan Rivaz
Date: 2022-01-31 00:00:00
Tasks: Optical Flow Estimation, Transfer Learning

The performance of ultrasound elastography (USE) heavily depends on the accuracy of displacement estimation. Recently, Convolutional Neural Networks (CNN) have shown promising performance in optical flow estimation and have been adopted for USE displacement estimation. Networks trained on computer vision images are not optimized for USE displacement estimation since there is a large gap between the computer vision images and the high-frequency Radio Frequency (RF) ultrasound data. Many researchers tried to adopt the optical flow CNNs to USE by applying transfer learning to improve the performance of CNNs for USE. However, the ground truth displacement in real ultrasound data is unknown, and simulated data exhibits a domain shift compared to the real data and is also computationally expensive to generate. To resolve this issue, semi-supervised methods have been proposed wherein the networks pre-trained on computer vision images are fine-tuned using real ultrasound data. In this paper, we employ a semi-supervised method by exploiting the first and second-order derivatives of the displacement field for the regularization. We also modify the network structure to estimate both forward and backward displacements, and propose to use consistency between the forward and backward strains as an additional regularizer to further enhance the performance. We validate our method using several experimental phantom and in vivo data. We also show that the network fine-tuned by our proposed method using experimental phantom data performs well on in vivo data similar to the network fine-tuned on in vivo data. Our results also show that the proposed method outperforms current deep learning methods and is comparable to computationally expensive optimization-based algorithms.
RC Filter Design for Wireless Power Transfer: A Fourier Series Approach
Authors: Constantinos Psomas, Ioannis Krikidis
Date: 2022-01-31 00:00:00

In this letter, we study the impact of the low-pass resistor-capacitor (RC) filter on radio frequency (RF) wireless power transfer (WPT). The RC filter influences both the RF bandwidth by removing the harmonics as well as the ripple voltage at the output of the rectifier. In particular, a large (small) RC time constant, reduces (increases) the ripple but decreases (enhances) the direct-current (DC) component. By following a Fourier series approach, we obtain closed-form expressions for the rectifier's output voltage, the RC filter's output as well as the DC voltage. Our analytical framework provides a complete characterization of the RC filter's impact on the WPT performance. We show that this complete and tractable analytical framework is suitable for the proper design of the RC filter in WPT systems.