pwc-archive/papers-with-abstracts huggingface dataset:
Security Constrained Optimal Power Shutoff
Authors: Noah Rhodes, Carleton Coffrin, Line Roald
Date: 2023-04-26 00:00:00

Electric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use pre-emptive deenergization of power lines, commonly referred to as Public Safety Power Shut-offs (PSPS). Besides raising challenging trade-offs between power outages and wildfire safety, PSPS removes redundancy from the network just at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of such outages. To address this gap, we propose the Security-Constrained Optimal Power Shutoff (SC-OPS) problem which uses post-contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This SC-OPS model enables, for the first time, the exploration of a wide range of trade-offs between both wildfire risk and pre- and post-contingency load shedding while designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS.We demonstrate the efficacy of our model using the EPRI 39-bus test system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans.
Convexity Not Required: Estimation of Smooth Moment Condition Models
Authors: Jean-Jacques Forneron, Liang Zhong
Date: 2023-04-27 00:00:00

Generalized and Simulated Method of Moments are often used to estimate structural Economic models. Yet, it is commonly reported that optimization is challenging because the corresponding objective function is non-convex. For smooth problems, this paper shows that convexity is not required: under a global rank condition involving the Jacobian of the sample moments, certain algorithms are globally convergent. These include a gradient-descent and a Gauss-Newton algorithm with appropriate choice of tuning parameters. The results are robust to 1) non-convexity, 2) one-to-one non-linear reparameterizations, and 3) moderate misspecification. In contrast, Newton-Raphson and quasi-Newton methods can fail to converge for the same estimation because of non-convexity. A simple example illustrates a non-convex GMM estimation problem that satisfies the aforementioned rank condition. Empirical applications to random coefficient demand estimation and impulse response matching further illustrate the results.
Difference-in-Differences with Compositional Changes
Authors: Pedro H. C. Sant'Anna, Qi Xu
Date: 2023-04-27 00:00:00

This paper studies difference-in-differences (DiD) setups with repeated cross-sectional data and potential compositional changes across time periods. We begin our analysis by deriving the efficient influence function and the semiparametric efficiency bound for the average treatment effect on the treated (ATT). We introduce nonparametric estimators that attain the semiparametric efficiency bound under mild rate conditions on the estimators of the nuisance functions, exhibiting a type of rate doubly robust (DR) property. Additionally, we document a trade-off related to compositional changes: We derive the asymptotic bias of DR DiD estimators that erroneously exclude compositional changes and the efficiency loss when one fails to correctly rule out compositional changes. We propose a nonparametric Hausman-type test for compositional changes based on these trade-offs. The finite sample performance of the proposed DiD tools is evaluated through Monte Carlo experiments and an empirical application. We consider extensions of our framework that accommodate double machine learning procedures with cross-fitting, and setups when some units are observed in both pre- and post-treatment periods. As a by-product of our analysis, we present a new uniform stochastic expansion of the local polynomial multinomial logit estimator, which may be of independent interest.
Monetary policy and the joint distribution of income and wealth: The heterogeneous case of the euro area
Authors: Anna Stelzer
Date: 2023-04-27 00:00:00

This papers aims to establish the empirical relationship between income, net wealth and their joint distribution in a selected group of euro area countries. I estimate measures of dependence between income and net wealth using a semiparametric copula approach and calculate a bivariate Gini coefficient. By combining structural inference from vector autoregressions on the macroeconomic level with a simulation using microeconomic data, I investigate how conventional and unconventional monetary policy measures affect the joint distribution. Results indicate that effects of monetary policy are highly heterogeneous across different countries, both in terms of the dependence of income and net wealth on each other, and in terms of inequality in both income and net wealth.
STraM: A strategic network design model for national freight transport decarbonization
Authors: Steffen Jaap Skotvoll Bakker, Jonas Martin, E. Ruben van Beesten, Ingvild Synnøve Brynildsen, Anette Sandvig, Marit Siqveland, Antonia Golab
Date: 2023-04-27 00:00:00

National freight transport models are valuable tools for assessing the impact of various policies and investments on achieving decarbonization targets under different future scenarios. However, these models struggle to address several critical elements necessary for strategic planning, such as the development and adoption of new fuel technologies over time, inertia in transport fleets, and uncertainty surrounding future transport costs. In this paper, we develop a strategic network design model, named STraM, that explicitly incorporates these key factors. STraM provides a network design plan that includes infrastructure investments and fuel technology decisions, aiming to achieve cost-effective decarbonization of the freight transport system. The model's output can be used as input for higher-resolution national freight transport models to yield results with greater operational detail. We demonstrate the application of STraM through a case study of Norway, offering valuable insights into the strategic planning of sustainable freight transport.
The Effects of High-frequency Anticipatory Trading: Small Informed Trader vs. Round-Tripper
Authors: Ziyi Xu, Xue Cheng
Date: 2023-04-27 00:00:00

In an extended Kyle's model, the interactions between a large informed trader and a high-frequency trader (HFT) who can anticipate the former's incoming order are studied. We find that, in equilibrium, HFT may play the role of Small-IT or Round-Tripper: both of them trade in the same direction as IT in advance, but when IT's order arrives, Small-IT continues to take liquidity away, while Round-Tripper supplies liquidity back. So Small-IT always harms IT, while Round-Tripper may benefit her. What's more, with an anticipatory HFT, normal-speed small uninformed traders suffer less and price discovery is accelerated.
Racial and income-based affirmative action in higher education admissions: lessons from the Brazilian experience
Authors: Rodrigo Zeidan, Silvio Luiz de Almeida, Inácio Bó, Neil Lewis Jr
Date: 2023-04-27 00:00:00

This survey article provides insights regarding the future of affirmative action by analyzing the implementation methods and the empirical evidence on the use of placement quotas in the Brazilian higher education system. All federal universities have required income and racial-based quotas in Brazil since 2012. Affirmative action in federal universities is uniformly applied across the country, which makes evaluating its effects particularly valuable. Affirmative action improves the outcomes of targeted students. Specifically, race-based quotas raise the share of black students in federal universities, an effect not observed with income-based quotas alone. Affirmative action has downstream positive consequences for labor market outcomes. The results suggest that income and race-based quotas beneficiaries experience substantial long-term welfare benefits. There is no evidence of mismatching or negative consequences for targeted students' peers.
Selecting Sustainable Optimal Stock by Using Multi-Criteria Fuzzy Decision-Making Approaches Based on the Development of the Gordon Model: A case study of the Toronto Stock Exchange
Authors: Mohsen Mortazavi
Date: 2023-04-26 00:00:00
Tasks: Decision Making

Choosing the right stock portfolio with the highest efficiencies has always concerned accurate and legal investors. Investors have always been concerned about the accuracy and legitimacy of choosing the right stock portfolio with high efficiency. Therefore, this paper aims to determine the criteria for selecting an optimal stock portfolio with a high-efficiency ratio in the Toronto Stock Exchange using the integrated evaluation and decision-making trial laboratory (DEMATEL) model and Multi-Criteria Fuzzy decision-making approaches regarding the development of the Gordon model. In the current study, results obtained using combined multi-criteria fuzzy decision-making approaches, the practical factors, the relative weight of dividends, discount rate, and dividend growth rate have been comprehensively illustrated using combined multi-criteria fuzzy decision-making approaches. A group of 10 experts with at least a ten-year of experience in the stock exchange field was formed to review the different and new aspects of the subject (portfolio selection) to decide the interaction between the group members and the exchange of attitudes and ideas regarding the criteria. The sequence of influence and effectiveness of the main criteria with DEMATEL has shown that the profitability criterion interacts most with other criteria. The criteria of managing methods and operations (MPO), market, risk, and growth criteria are ranked next in terms of interaction with other criteria. This study concludes that regarding the model's appropriate and reliable validity in choosing the optimal stock portfolio, it is recommended that portfolio managers in companies, investment funds, and capital owners use the model to select stocks in the Toronto Stock Exchange optimally.
Optimal tie-breaking rules
Authors: Sumit Goel, Amit Goyal
Date: 2023-04-26 00:00:00
Tasks: Form

We consider two-player contests with the possibility of ties and study the effect of different tie-breaking rules on effort. For ratio-form and difference-form contests that admit pure-strategy Nash equilibrium, we find that the effort of both players is monotone decreasing in the probability that ties are broken in favor of the stronger player. Thus, the effort-maximizing tie-breaking rule commits to breaking ties in favor of the weaker agent. With symmetric agents, we find that the equilibrium is generally symmetric and independent of the tie-breaking rule. We also study the design of random tie-breaking rules that are ex-ante fair and identify sufficient conditions under which breaking ties before the contest actually leads to greater expected effort than the more commonly observed practice of breaking ties after the contest.
Once and for All: Scheduling Multiple Users Using Statistical CSI under Fixed Wireless Access
Authors: Xin Guan, Zhixing Chen, Yibin Kang, Qingjiang Shi
Date: 2023-04-27 00:00:00
Tasks: All, Scheduling

Conventional multi-user scheduling schemes are designed based on instantaneous channel state information (CSI), indicating that decisions must be made every transmission time interval (TTI) which lasts at most several milliseconds. Only quite simple approaches can be exploited under this stringent time constraint, resulting in less than satisfactory scheduling performance. In this paper, we investigate the scheduling problem of a fixed wireless access (FWA) network using only statistical CSI. Thanks to their fixed positions, user terminals in FWA can easily provide reliable large-scale CSI lasting tens or even hundreds of TTIs. Inspired by this appealing fact, we propose an \emph{`once-and-for-all'} scheduling approach, i.e. given multiple TTIs sharing identical statistical CSI, only a single high-quality scheduling decision lasting across all TTIs shall be taken rather than repeatedly making low-quality decisions every TTI. The proposed scheduling design is essentially a mixed-integer non-smooth non-convex stochastic problem with the objective of maximizing the weighted sum rate as well as the number of active users. We firstly replace the indicator functions in the considered problem by well-chosen sigmoid functions to tackle the non-smoothness. Via leveraging deterministic equivalent technique, we then convert the original stochastic problem into an approximated deterministic one, followed by linear relaxation of the integer constraints. However, the converted problem is still highly non-convex due to implicit equation constraints introduced by deterministic equivalent. To address this issue, we employ implicit optimization technique so that the gradient can be derived explicitly, with which we propose an algorithm design based on accelerated Frank-Wolfe method. Numerical results verify the effectiveness of our proposed scheduling scheme over state-of-the-art.
Pre-processing Blood-Volume-Pulse for In-the-wild Applications
Authors: Laurits Fromberg, Sneha Das, Line Katrine Harder Clemmensen
Date: 2023-04-27 00:00:00

Blood-volume-pulse (BVP) is a biosignal commonly used in applications for non-invasive affect recognition and wearable technology. However, its predisposition to noise constitutes limitations for its application in real-life settings. This paper revisits BVP processing and proposes standard practices for feature extraction from empirical observations of BVP. We propose a method for improving the use of features in the presence of noise and compare it to a standard signal processing approach of a 4th order Butterworth bandpass filter with cut-off frequencies of 1 Hz and 8 Hz. Our method achieves better results for most time features as well as for a subset of the frequency features. We find that all but one time feature and around half of the frequency features perform better when the noisy parts are known (best case). When the noisy parts are unknown and estimated using a metric of skewness, the proposed method in general works better or similar to the Butterworth bandpass filter, but both methods also fail for a subset features. Our results can be used to select BVP features that are meaningful under different SNR conditions.
Ordinary Differential Equation-based MIMO Signal Detection
Authors: Ayano Nakai-Kasai, Tadashi Wadayama
Date: 2023-04-27 00:00:00

The required signal processing rate in future wireless communication systems exceeds the performance of the latest electronics-based processors. Introduction of analog optical computation is one promising direction for energy-efficient processing. This paper considers a continuous-time minimum mean squared error detection for multiple-input multiple-output systems to realize signal detection using analog optical devices. The proposed method is formulated by an ordinary differential equation (ODE) and its performance at any continuous time can be theoretically analyzed. Deriving and analyzing the continuous-time system is a meaningful step to verifying the feasibility of analog-domain signal processing in the future systems. In addition, considering such an ODE brings byproducts to discrete-time detection algorithms, which can be a novel methodology of algorithm construction and analysis.
Bias-Free Estimation of the Auto- and Cross-Covariance and the Corresponding Power Spectral Densities from Gappy Data
Authors: Nils Damaschke, Volker Kühn, Holger Nobach
Date: 2023-04-27 00:00:00
Tasks: valid

Signal processing of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Besides randomly and independently occurring outliers also correlated data gaps are investigated. Non-parametric estimators for the mean value, the signal variance, the autocovariance and cross-covariance functions and the corresponding power spectral densities are given, which are bias-free, independent of the spectral composition of the data gaps. Bias-free estimation is obtained by averaging over valid samples only from the data set. The procedures abstain from interpolation of missing samples. An appropriate bias correction is used for cases where the estimated mean value is subtracted out from the data. Spectral estimates are obtained from covariance functions using Wiener-Khinchin's theorem.
mmWave RIS Phase Shift Feedback Based on Knowledge Base Autoencoder Framework
Authors: Hao Feng, Yuting Xu, Yuping Zhao
Date: 2023-04-27 00:00:00

In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, adjusting the phase shift of RIS unit cells is crucial for improving communication performance. Due to massive RIS unit cells, the number of phase shift parameters fed back from the base station (BS) to the RIS is enormous, which occupies a large number of frequency resources. In this paper, we propose a feedback scheme for millimeter-wave RIS phase shift applying a knowledge base autoencoder framework, in which the learnable knowledge base is shared at the BS and the RIS. The encoder at the BS compresses the RIS phase shift matrix to multiple feature vectors. Then the knowledge base vectors index is obtained by calculating the similarity between feature vectors and knowledge base vectors and transmitted to the RIS. With utilizing the index at the RIS, the corresponding knowledge base vectors are extracted and used as the decoder's inputs to reconstruct the phase shift of the RIS. Simulation results show that the proposed scheme can significantly improve the accuracy of phase shift feedback and impressively reduce the amount of RIS phase shift feedback data. Moreover, the proposed scheme is easy to deploy in actual scenarios due to lower complexity and fewer parameters.
Patient Independent Interictal Epileptiform Discharge Detection
Authors: Matthew McDougall, Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta
Date: 2023-04-27 00:00:00
Tasks: Binary Classification, EEG, Electroencephalogram (EEG)

Epilepsy is a highly prevalent brain condition with many serious complications arising from it. The majority of patients which present to a clinic and undergo electroencephalogram (EEG) monitoring would be unlikely to experience seizures during the examination period, thus the presence of interictal epileptiform discharges (IEDs) become effective markers for the diagnosis of epilepsy. Furthermore, IED shapes and patterns are highly variable across individuals, yet trained experts are still able to identify them through EEG recordings - meaning that commonalities exist across IEDs that an algorithm can be trained on to detect and generalise to the larger population. This research proposes an IED detection system for the binary classification of epilepsy using scalp EEG recordings. The proposed system features an ensemble based deep learning method to boost the performance of a residual convolutional neural network, and a bidirectional long short-term memory network. This is implemented using raw EEG data, sourced from Temple University Hospital's EEG Epilepsy Corpus, and is found to outperform the current state of the art model for IED detection across the same dataset. The achieved accuracy and Area Under Curve (AUC) of 94.92% and 97.45% demonstrates the effectiveness of an ensemble method, and that IED detection can be achieved with high performance using raw scalp EEG data, thus showing promise for the proposed approach in clinical settings.
On Propagation Characteristics of Reconfigurable Surface Wave Platform: Simulation and Experimental Verification
Authors: Z. Chu, K. F. Tong, K. K. Wong, C. B. Chae, C. H. Chan
Date: 2023-04-27 00:00:00

Reconfigurable intelligent surface (RIS) as a smart reflector is revolutionizing research for next-generation wireless communications. Complementing this is a concept of using RIS as an efficient propagation medium for potentially superior path loss characteristics. Motivated by a recent porous surface architecture that facilitates reconfigurable pathways with cavities filled with fluid metal, this paper studies the propagation characteristics of different pathway configurations in different lossy materials on the reconfigurable surface wave platform by using a commercial full electromagnetic simulation software and S-parameters experiments. This paper also looks into the best scheme to switch between a straight pathway and a $90^\circ$-bend and attempts to quantify the additional path loss when making a turn. Our experimental results verify the simulation results, showing the effectiveness of the proposed reconfigurable surface wave platform for a wide-band, low path loss and highly programmable communications.
Cell behavior in the face of uncertainty
Authors: David Lacoste, Olivier Rivoire, David S. Tourigny
Date: 2023-04-25 00:00:00

Organisms that grow and survive in uncertain environments may need to change their physiological state as the environment changes. When the environment is uncertain, one strategy known as bet-hedging is to make these changes randomly and independently of the environment, to ensure that at least part of the population is well adapted. Organisms that collect information from their environment may also use this information to modulate their changes of physiological states. We review these different strategies and point out parallels with the theory of optimal financial investments.
Perceived community alignment increases information sharing
Authors: Elisa C. Baek, Ryan Hyon, Karina López, Mason A. Porter, Carolyn Parkinson
Date: 2023-04-26 00:00:00

It has been proposed that information sharing, which is a ubiquitous and consequential behavior, plays a critical role in cultivating and maintaining a sense of shared reality. Across three studies, we tested this theory by investigating whether or not people are especially likely to share information that they believe will be interpreted similarly by others in their social circles. Using neuroimaging while members of the same community viewed brief film clips, we found that more similar neural responding of participants was associated with a greater likelihood to share content. We then tested this relationship using two behavioral studies and found (1) that people were particularly likely to share content that they believed others in their social circles would interpret similarly and (2) that perceived similarity with others leads to increased sharing likelihood. In concert, our findings support the idea that people are driven to share information to create and reinforce shared understanding, which is critical to social connection.
Backpropagation and F-adjoint
Authors: Ahmed Boughammoura
Date: 2023-03-29 00:00:00

This paper presents a concise mathematical framework for investigating both feed-forward and backward process, during the training to learn model weights, of an artificial neural network (ANN). Inspired from the idea of the two-step rule for backpropagation, we define a notion of F-adjoint which is aimed at a better description of the backpropagation algorithm. In particular, by introducing the notions of F-propagation and F-adjoint through a deep neural network architecture, the backpropagation associated to a cost/loss function is proven to be completely characterized by the F-adjoint of the corresponding F-propagation relatively to the partial derivative, with respect to the inputs, of the cost function.