pwc-archive/papers-with-abstracts huggingface dataset:
Robust Monopoly Regulation
Authors: Yingni Guo, Eran Shmaya
Date: 2019-10-09 00:00:00
Tasks: Robust Design

We study the regulation of a monopolistic firm using a robust-design approach. We solve for the policy that minimizes the regulator's worst-case regret, where the regret is the difference between his complete-information payoff minus his realized payoff. When the regulator's payoff is consumers' surplus, it is optimal to impose a price cap. The optimal cap balances the benefit from more surplus for consumers and the loss from underproduction. When his payoff is consumers' surplus plus the firm's profit, he offers a piece-rate subsidy in order to mitigate underproduction, but caps the total subsidy so as not to incentivize severe overproduction.
Using Internal Bar Strength as a Key Indicator for Trading Country ETFs
Authors: Aditya Pandey, Kunal Joshi
Date: 2023-06-14 00:00:00

This report aims to investigate the effectiveness of using internal bar strength (IBS) as a key indicator for trading country exchange-traded funds (ETFs). The study uses a quantitative approach to analyze historical price data for a bucket of country ETFs over a period of 10 years and uses the idea of Mean Reversion to create a profitable trading strategy. Our findings suggest that IBS can be a useful technical indicator for predicting short-term price movements in this basket of ETFs.
MQ-Coder inspired arithmetic coder for synthetic DNA data storage
Authors: Xavier Pic, Melpomeni Dimopoulou, Eva Gil San Antonio, Marc Antonini
Date: 2023-06-22 00:00:00
Tasks: Image Compression

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder. The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take away of this work is our arithmetic coder and it's integration into a performant image codec.
Interferometric lensless imaging: rank-one projections of image frequencies with speckle illuminations
Authors: Olivier Leblanc, Mathias Hofer, Siddharth Sivankutty, Hervé Rigneault, Laurent Jacques
Date: 2023-06-22 00:00:00

Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of an interferometric matrix--a matrix encoding the spectral content of the sample image. In this model, each SROP is induced by the complex sketching vector shaping the incident light wavefront with a spatial light modulator (SLM), while the projected interferometric matrix collects up to $O(Q^2)$ image frequencies for a $Q$-core MCF. While this scheme subsumes previous sensing modalities, such as raster scanning (RS) imaging with beamformed illumination, we demonstrate that collecting the measurements of $M$ random SLM configurations--and thus acquiring $M$ SROPs--allows us to estimate an image of interest if $M$ and $Q$ scale log-linearly with the image sparsity level This demonstration is achieved both theoretically, with a specific restricted isometry analysis of the sensing scheme, and with extensive Monte Carlo experiments. On a practical side, we perform a single calibration of the sensing system robust to certain deviations to the theoretical model and independent of the sketching vectors used during the imaging phase. Experimental results made on an actual MCF system demonstrate the effectiveness of this imaging procedure on a benchmark image.
Conceptual Design and Analysis of No-Insulation High-Temperature Superconductor Tubular Wave Energy Converter
Authors: Kyoungmo Koo, Wonseok Jang, Jeonghwan Park, Jaemyung Cha, Seungyong Hahn
Date: 2023-06-22 00:00:00

So far, a number of wave energy converters (WEC) have been proposed to increase efficiency and economic feasibility. Particularly, tubular WEC with permanent magnets and coil winding packs is mostly used to convert the wave energy. Due to the demand for high magnetic flux density in WEC, research has been conducted on high-temperature superconductors (HTS) WEC. In this paper, the conceptual design of no-insulation (NI) HTS tubular WEC and its optimization process are proposed. Using NI technology, it has become possible to design WEC with high volumetric efficiency and cost-effectiveness. Furthermore, the design is analyzed in the aspect of electromagnetism, mechanical force, and cryogen. The performance of the proposed WEC is evaluated as a response to various waveforms and their amplitudes. A rectifying circuit of WEC connected in parallel with load resistance is used for the output power study.
Backstepping Control of Coupled General Hyperbolic-Parabolic PDE-PDE Systems
Authors: Joachim Deutscher, Nicole Gehring, Nick Jung
Date: 2023-06-22 00:00:00

This paper considers the backstepping state feedback and observer design for hyperbolic and parabolic PDEs, which are bidirectionally interconnected in a general coupling structure. Both PDE subsystems consist of coupled scalar PDEs with the heterodirectional hyperbolic PDE subsystem subject to actuation and sensing. By making use of a multi-step approach to construct the transformation into a stable target system, it is shown that a backstepping state feedback and observer design only requires to solve the well-known kernel equations for the hyperbolic and parabolic subsystems as well as additional decoupling equations. The latter are standard initial boundary value problems for parabolic PDEs. This significantly facilitates the well-posedness analysis and the numerical computation of the backstepping controller. Exponential stability is verified for the state feedback loop, the observer error dynamics, and the closed-loop system using an observer-based compensator. The proposed backstepping design procedures are demonstrated for numerical examples.
Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors
Authors: Daiwei Fan, Max Bolderman, Sjirk Koekebakker, Hans Butler, Mircea Lazar
Date: 2023-06-22 00:00:00

Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficiency without increasing the manufacturing costs calls for innovative control design. Feedforward control is typically used in tracking control problems, where the desired reference is known in advance. In most applications, this is the case for HSMs, which need to track a periodic angular velocity and angular position reference. Performance achieved by feedforward control is limited by the accuracy of the available model describing the inverse system dynamics. In this work, we develop a physics-guided neural network (PGNN) feedforward controller for HSMs, which can learn the effect of parasitic forces from data and compensate for it, resulting in improved accuracy. Indeed, experimental results on an HSM used in printing industry show that the PGNN outperforms conventional benchmarks in terms of the mean-absolute tracking error.
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees
Authors: Bowen Yi, Chi Jin, Lei Wang, Guodong Shi, Viorela Ila, Ian R. Manchester
Date: 2023-06-22 00:00:00
Tasks: parameter estimation, Simultaneous Localization and Mapping

This paper introduces a new linear parameterization to the problem of visual inertial simultaneous localization and mapping (VI-SLAM) -- without any approximation -- for the case only using information from a single monocular camera and an inertial measurement unit. In this problem set, the system state evolves on the nonlinear manifold $SE(3)\times \mathbb{R}^{3n}$, on which we design dynamic extensions carefully to generate invariant foliations, such that the problem can be reformulated into online \emph{constant parameter} identification, then interestingly with linear regression models obtained. It demonstrates that VI-SLAM can be translated into a linear least squares problem, in the deterministic sense, \emph{globally} and \emph{exactly}. Based on this observation, we propose a novel SLAM observer, following the recently established parameter estimation-based observer (PEBO) methodology. A notable merit is that the proposed observer enjoys almost global asymptotic stability, requiring neither persistency of excitation nor uniform complete observability, which, however, are widely adopted in most existing works with provable stability but can hardly be assured in many practical scenarios.
Network dynamical stability analysis reveals key "mallostatic" natural variables that erode homeostasis and drive age-related decline of health
Authors: Glen Pridham, Andrew D. Rutenberg
Date: 2023-06-20 00:00:00

Using longitudinal study data, we dynamically model how aging affects homeostasis in both mice and humans. We operationalize homeostasis as a multivariate mean-reverting stochastic process. We hypothesize that biomarkers have stable equilibrium values, but that deviations from equilibrium of each biomarker affects other biomarkers through an interaction network - this precludes univariate analysis. We therefore looked for age-related changes to homeostasis using dynamic network stability analysis, which transforms observed biomarker data into independent "natural" variables and determines their associated recovery rates. Most natural variables remained near equilibrium and were essentially constant in time. A small number of natural variables were unable to equilibrate due to a gradual drift with age in their homeostatic equilibrium, i.e. allostasis. This drift caused them to accumulate over the lifespan course and makes them natural aging variables. Their rate of accumulation was correlated with risk of adverse outcomes: death or dementia onset. We call this tendency for aging organisms to drift towards an equilibrium position of ever-worsening health "mallostasis". We demonstrate that the effects of mallostasis on observed biomarkers are spread out through the interaction network. This could provide a redundancy mechanism to preserve functioning until multi-system dysfunction emerges at advanced ages.
A Discrimination Report Card
Authors: Patrick Kline, Evan K. Rose, Christopher R. Walters
Date: 2023-06-22 00:00:00
Tasks: Informativeness

We develop an Empirical Bayes grading scheme that balances the informativeness of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the racial biases of 97 U.S. employers. A four-grade ranking limits the chances that a randomly selected pair of firms is mis-ranked to 5% while explaining nearly half of the variation in firms' racial contact gaps. The grades are presented alongside measures of uncertainty about each firm's contact gap in an accessible rubric that is easily adapted to other settings where ranks and levels are of simultaneous interest.
Price elasticity of electricity demand: Using instrumental variable regressions to address endogeneity and autocorrelation of high-frequency time series
Authors: Silvana Tiedemann, Raffaele Sgarlato, Lion Hirth
Date: 2023-06-22 00:00:00
Tasks: Time Series

This paper examines empirical methods for estimating the response of aggregated electricity demand to high-frequency price signals, the short-term elasticity of electricity demand. We investigate how the endogeneity of prices and the autocorrelation of the time series, which are particularly pronounced at hourly granularity, affect and distort common estimators. After developing a controlled test environment with synthetic data that replicate key statistical properties of electricity demand, we show that not only the ordinary least square (OLS) estimator is inconsistent (due to simultaneity), but so is a regular instrumental variable (IV) regression (due to autocorrelation). Using wind as an instrument, as it is commonly done, may result in an estimate of the demand elasticity that is inflated by an order of magnitude. We visualize the reason for the Thams bias using causal graphs and show that its magnitude depends on the autocorrelation of both the instrument, and the dependent variable. We further incorporate and adapt two extensions of the IV estimation, conditional IV and nuisance IV, which have recently been proposed by Thams et al. (2022). We show that these extensions can identify the true short-term elasticity in a synthetic setting and are thus particularly promising for future empirical research in this field.
Armed Conflict and Early Human Capital Accumulation: Evidence from Cameroon's Anglophone Conflict
Authors: Hector Galindo-Silva, Guy Tchuente
Date: 2023-06-22 00:00:00

This paper examines the impact of the Anglophone Conflict in Cameroon on human capital accumulation. Using high-quality individual-level data on test scores and information on conflict-related violent events, a difference-in-differences design is employed to estimate the conflict's causal effects. The results show that an increase in violent events and conflict-related deaths causes a significant decline in test scores in reading and mathematics. The conflict also leads to higher rates of teacher absenteeism and reduced access to electricity in schools. These findings highlight the adverse consequences of conflict-related violence on human capital accumulation, particularly within the Anglophone subsystem. The study emphasizes the disproportionate burden faced by Anglophone pupils due to language-rooted tensions and segregated educational systems.
The Impact of Parenthood on Labour Market Outcomes of Women and Men in Poland
Authors: Radost Waszkiewicz, Honorata Bogusz
Date: 2023-06-22 00:00:00
Tasks: counterfactual

We examine the gender gap in income in Poland in relation to parenthood status, employing the placebo event history method adapted to low-resolution data (Polish Generations and Gender Survey). Our analysis reveals anticipatory behavior in both women and men who expect to become parents. We observe a decrease of approximately 20 percent in mothers' income post-birth. In contrast, the income of fathers surpasses that of non-fathers both pre- and post-birth, suggesting that the fatherhood child premium may be primarily driven by selection. We note an increase (decrease) in hours worked for fathers (mothers). Finally, we compare the gender gaps in income and wages between women and men in the sample with those in a counterfactual scenario where the entire population is childless. Our findings indicate no statistically significant gender gaps in the counterfactual scenario, leading us to conclude that parenthood drives the gender gaps in income and wages in Poland.
Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders
Authors: Xavier Pic, Eva Gil San Antonio, Melpomeni Dimopoulou, Marc Antonini
Date: 2023-06-22 00:00:00
Tasks: Image Compression, Quantization

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (rarely accessed data), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper presents some results on lossy image compression methods based on convolutional autoencoders adapted to DNA data storage, with synthetic DNA-adapted entropic and fixed-length codes. The model architectures presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematics that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take aways of this kind of compressive autoencoder are our latent space quantization and the different DNA adapted entropy coders used to encode the quantized latent space, which are an improvement over the fixed length DNA adapted coders that were previously used.
Automatic Feature Detection in Lung Ultrasound Images using Wavelet and Radon Transforms
Authors: Maria Farahi, Joan Aranda, Hessam Habibian, Alicia Casals
Date: 2023-06-22 00:00:00

Objective: Lung ultrasonography is a significant advance toward a harmless lung imagery system. This work has investigated the automatic localization of diagnostically significant features in lung ultrasound pictures which are Pleural line, A-lines, and B-lines. Study Design: Wavelet and Radon transforms have been utilized in order to denoise and highlight the presence of clinically significant patterns. The proposed framework is developed and validated using three different lung ultrasound image datasets. Two of them contain synthetic data and the other one is taken from the publicly available POCUS dataset. The efficiency of the proposed method is evaluated using 200 real images. Results: The obtained results prove that the comparison between localized patterns and the baselines yields a promising F2-score of 62%, 86%, and 100% for B-lines, A-lines, and Pleural line, respectively. Conclusion: Finally, the high F-scores attained show that the developed technique is an effective way to automatically extract lung patterns from ultrasound images.
Mutual Information Based Pilot Design for ISAC
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2023-06-22 00:00:00
Tasks: ISAC

The following paper presents a novel orthogonal pilot design dedicated for \textcolor{black}{integrated sensing and communications (ISAC)} systems performing multi-user communications and target detection. After careful characterization of both sensing and communication metrics based on mutual information (MI), we propose a multi-objective optimization problem (MOOP) tailored for pilot design, dedicated for simultaneously maximizing both sensing and communication MIs. Moreover, the MOOP is further simplified to a single-objective optimization problem, which characterizes trade-offs between sensing and communication performances. Due to the non-convex nature of the optimization problem, we propose to solve it via the projected gradient descent method on the Stiefel manifold. Closed-form gradient expressions are derived, which enable execution of the projected gradient descent algorithm. Furthermore, we prove convergence to a fixed orthogonal pilot matrix. Finally, we demonstrate the capabilities and superiority of the proposed pilot design, and corroborate relevant trade-offs between sensing MI and communication MI. In particular, significant signal-to-noise ratio (SNR) gains for communication are reported, while re-using the same pilots for target detection with significant gains in terms of probability of detection for fixed false-alarm probability. Other interesting findings are reported through simulations, such as an \textit{information overlap} phenomenon, whereby the fruitful ISAC integration can be fully exploited.
Harnessing the Potential of Optical Communications for the Metaverse
Authors: Baha Eddine Youcef Belmekki, Abderrahmen Trichili, Boon S. Ooi, Mohamed-Slim Alouini
Date: 2023-06-22 00:00:00

The Metaverse is a digital world that offers an immersive virtual experience. However, the Metaverse applications are bandwidth-hungry and delay-sensitive that require ultrahigh data rates, ultra-low latency, and hyper-intensive computation. To cater for these requirements, optical communication arises as a key pillar in bringing this paradigm into reality. We highlight in this paper the potential of optical communications in the Metaverse. First, we set forth Metaverse requirements in terms of capacity and latency; then, we introduce ultra-high data rates requirements for various Metaverse experiences. Then, we put forward the potential of optical communications to achieve these data rate requirements in backbone, backhaul, fronthaul, and access segments. Both optical fiber and optical wireless communication (OWC) technologies, as well as their current and future expected data rates, are detailed. In addition, we propose a comprehensive set of configurations, connectivity, and equipment necessary for an immersive Metaverse experience. Finally, we identify a set of key enablers and research directions such as analog neuromorphic optical computing, optical intelligent reflective surfaces (IRS), hollow core fiber (HCF), and terahertz (THz).
Distribution Network Fault Prediction Utilising Protection Relay Disturbance Recordings And Machine Learning
Authors: Ebrahim Balouji, Karl Bäckström, Viktor Olsson, Petri Hovila, Henry Niveri, Anna Kulmala, Ari Salo
Date: 2023-06-22 00:00:00
Tasks: Fault localization

As society becomes increasingly reliant on electricity, the reliability requirements for electricity supply continue to rise. In response, transmission/distribution system operators (T/DSOs) must improve their networks and operational practices to reduce the number of interruptions and enhance their fault localization, isolation, and supply restoration processes to minimize fault duration. This paper proposes a machine learning based fault prediction method that aims to predict incipient faults, allowing T/DSOs to take action before the fault occurs and prevent customer outages.
STAR-RIS-Assisted Privacy Protection in Semantic Communication System
Authors: Yiru Wang, Wanting Yang, Pengxin Guan, Yuping Zhao, Zehui Xiong
Date: 2023-06-22 00:00:00
Tasks: Intelligent Communication, Semantic Communication

Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms. SemCom involves extracting and compressing the core information at the transmitter while enabling the receiver to interpret it based on established knowledge bases (KBs). This approach enhances communication efficiency greatly. However, the open nature of wireless transmission and the presence of homogeneous KBs among subscribers of identical data type pose a risk of privacy leakage in SemCom. To address this challenge, we propose to leverage the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to achieve privacy protection in a SemCom system. In this system, the STAR-RIS is utilized to enhance the signal transmission of the SemCom between a base station and a destination user, as well as to covert the signal to interference specifically for the eavesdropper (Eve). Simulation results demonstrate that our generated task-level disturbance outperforms other benchmarks in protecting SemCom privacy, as evidenced by the significantly lower task success rate achieved by Eve.