pwc-archive/papers-with-abstracts huggingface dataset:
Differential voltage analysis for battery manufacturing process control
Authors: Andrew Weng, Jason B. Siegel, Anna Stefanopoulou
Date: 2023-03-13 00:00:00

Voltage-based battery metrics are ubiquitous and essential in battery manufacturing diagnostics. They enable electrochemical "fingerprinting" of batteries at the end of the manufacturing line and are naturally scalable, since voltage data is already collected as part of the formation process which is the last step in battery manufacturing. Yet, despite their prevalence, interpretations of voltage-based metrics are often ambiguous and require expert judgment. In this work, we present a method for collecting and analyzing full cell near-equilibrium voltage curves for end-of-line manufacturing process control. The method builds on existing literature on differential voltage analysis (DVA or dV/dQ) by expanding the method formalism through the lens of reproducibility, interpretability, and automation. Our model revisions introduce several new derived metrics relevant to manufacturing process control, including lithium consumed during formation and the practical negative-to-positive ratio, which complement standard metrics such as positive and negative electrode capacities. To facilitate method reproducibility, we reformulate the model to account for the "inaccessible lithium problem" which quantifies the numerical differences between modeled versus true values for electrode capacities and stoichiometries. We finally outline key data collection considerations, including C-rate and charging direction for both full cell and half cell datasets, which may impact method reproducibility. This work highlights the opportunities for leveraging voltage-based electrochemical metrics for online battery manufacturing process control.
Are consumers ready to pay extra for crowd-shipping e-groceries and why? A hybrid choice analysis for developing economies
Authors: Oleksandr Rossolov, Yusak O. Susilo
Date: 2023-03-13 00:00:00

This paper presents the behavioral study's results on willingness-to-pay the extra money by the customers for e-groceries deliveries based on crowd-shipping. The proposed methodology was tested for Ukraine, i.e., a developing country where the crowd-shipping services are under development conditions. To account for the behavior complexity of the consumers who have not faced the crowd-shipping services in the past, the choice model was enhanced with a latent variable. The findings indicate the revealed readiness of the e-shoppers to pay extra money for crowd-shipping delivery if it provides more flexible and consumer-oriented service. The expected environmental impact of the crowd-shipping delivery was not considered as important by the e-shoppers, which is explained by low concerns about the environment and car-oriented mobility in the considered case study.
Hybrid multi-observer for improving estimation performance
Authors: E. Petri, R. Postoyan, D. Astolfi, D. Nesic, V. Andrieu
Date: 2023-03-13 00:00:00

Various methods are nowadays available to design observers for broad classes of systems, where the primary focus is on establishing the convergence of the estimated states. Nevertheless, the question of the tuning of the observer to achieve satisfactory estimation performance remains largely open. In this context, we present a general design framework for the online tuning of the observer gains. Our starting point is a robust nominal observer designed for a general nonlinear system, for which an input-to-state stability property can be established. Our goal is then to improve the performance of this nominal observer. We present for this purpose a new hybrid multi-observer scheme, whose great flexibility can be exploited to enforce various desirable properties, e.g., fast convergence and good sensitivity to measurement noise. We prove that an input-to-state stability property also holds for the proposed scheme and, importantly, we ensure that the estimation performance in terms of a quadratic cost is (strictly) improved. We illustrate the efficiency of the approach in improving the performance of given nominal observers in two numerical examples (Van der Pol oscillator and Lithium-Ion (Li-Ion) battery model).
Adversarial Attacks to Direct Data-driven Control for Destabilization
Authors: Hampei Sasahara
Date: 2023-03-13 00:00:00
Tasks: image-classification, Image Classification

This study investigates the vulnerability of direct data-driven control to adversarial attacks in the form of a small but sophisticated perturbation added to the original data. The directed gradient sign method (DGSM) is developed as a specific attack method, based on the fast gradient sign method (FGSM), which has originally been considered in image classification. DGSM uses the gradient of the eigenvalues of the resulting closed-loop system and crafts a perturbation in the direction where the system becomes less stable. It is demonstrated that the system can be destabilized by the attack, even if the original closed-loop system with the clean data has a large margin of stability. To increase the robustness against the attack, regularization methods that have been developed to deal with random disturbances are considered. Their effectiveness is evaluated by numerical experiments using an inverted pendulum model.
Iterative Decoupling Method for High-Precision Imaging of Complex Surfaces
Authors: Eyal Baruch, Izhak Bucher
Date: 2023-03-12 00:00:00

Nonlinear systems and interaction forces are pervasive in many scientific fields, such as nanoscale metrology and materials science, but their accurate identification is challenging due to their complex behaviour and inaccessibility of measured domains. This problem intensifies for continuous systems undergoing distributed, coupled interactions, such as in the case of topography measurement systems, measuring narrow and deep grooves. Presented is a method to invert a set of nonlinear coupled equations, which can be functions of unknown distributed physical quantities. The method employs a successive approach to iteratively converge to the exact solution of the set of nonlinear equations. The latter utilizes an approximate yet invertible model providing an inexact solution, which is evaluated using the hard-to-invert exact model of the system. This method is applied to the problem of reconstructing the topography of surface contours using a thin and long vibrating fiber. In nanoscale metrology, measuring inaccessible deep and narrow grooves or steep walls becomes difficult and singular when attempting to extract distributed nonlinear interactions that depend on the topography. We verify our method numerically by simulating the Van der Waals (VdW) interaction forces between a nanofiber and a nanoscale deep groove, and experimentally by exploiting magnetic interactions between a magnetic topography and a vibrating, elastic beam. Our results validate the ability to accurately reconstruct the topography of normally inaccessible regions, making it a possible enhancement for traditional point based AFM measurements, as well as for other nonlinear inverse problems.
Semi-Tensor Product of Hypermatrices with Application to Compound Hypermatrices
Authors: Daizhan Cheng, Xiao Zhang, Zhengping Ji
Date: 2023-03-11 00:00:00

The semi-tensor product (STP) of matrices is extended to the STP of hypermatrices. Some basic properties of the STP of matrices are extended to the STP of hypermatrices. The hyperdeterminant of hypersquares is introduced. Some algebraic and geometric structures of matrices are extended to hypermatrices. Then the compound hypermatrix is proposed. The STP of hypermatrix is used to compound hypermatrix. Basic properties are proved to be available for compound hypermatrix.
Monte Carlo Grid Dynamic Programming: Almost Sure Convergence and Probability Constraints
Authors: Mohammad S. Ramadan, Ahmad Al-Tawaha, Mohamed Shouman, Ahmed Atallah, Ming Jin
Date: 2023-03-10 00:00:00

Dynamic Programming (DP) suffers from the well-known ``curse of dimensionality'', further exacerbated by the need to compute expectations over process noise in stochastic models. This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function, dependent on the process noise density, in a "self-approximating" fashion, eliminating the need for ordering or set-membership tests. We provide proof of almost sure convergence for the value iteration (and consequently, policy iteration) procedure. The proposed meshless sampling and interpolation algorithm alleviates the burden of gridding the state space, traditionally required in DP, and avoids constructing a piecewise constant value function over a grid. Moreover, we demonstrate that the proposed interpolation procedure is well-suited for handling probabilistic constraints by sampling both infeasible and feasible regions. The curse of dimensionality cannot be avoided, however, this approach offers a practical framework for addressing lower-order stochastic nonlinear systems with probabilistic constraints, while eliminating the need for linear interpolations and set membership tests. Numerical examples are presented to further explain and illustrate the convenience of the proposed algorithms.
Joint Optimization of Production and Maintenance in Offshore Wind Farms: Balancing the Short- and Long-Term Needs of Wind Energy Operation
Authors: Petros Papadopoulos, Farnaz Fallahi, Murat Yildirim, Ahmed Aziz Ezzat
Date: 2023-03-10 00:00:00
Tasks: Management, Stochastic Optimization

The rapid increase in scale and sophistication of offshore wind (OSW) farms poses a critical challenge related to the cost-effective operation and management of wind energy assets. A defining characteristic of this challenge is the economic trade-off between two concomitant processes: power production (the primary driver of short-term revenues), and asset degradation (the main determinant of long-term expenses). Traditionally, approaches to optimize production and maintenance in wind farms have been conducted in isolation. In this paper, we conjecture that a joint optimization of those two processes, achieved by rigorously modeling their short- and long-term dependencies, can unlock significant economic benefits for wind farm operators. In specific, we propose a decision-theoretic framework, rooted in stochastic optimization, which seeks a sensible balance of how wind loads are leveraged to harness short-term electricity generation revenues, versus alleviated to hedge against longer-term maintenance expenses. Extensive numerical experiments using real-world data confirm the superior performance of our approach, in terms of several operational performance metrics, relative to methods that tackle the two problems in isolation.
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
Authors: Sahara Ali, Omar Faruque, Yiyi Huang, Md. Osman Gani, Aneesh Subramanian, Nicole-Jienne Shchlegel, Jianwu Wang
Date: 2023-02-22 00:00:00
Tasks: Causal Inference, counterfactual, Time Series, Time Series Analysis

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are still unknown. Inferring the causal effects of atmospheric processes on sea ice melt using fixed treatment effect strategies leads to unrealistic counterfactual estimations. Such models are also prone to bias due to time-varying confoundedness. Further, the complex non-linearity in Earth science data makes it infeasible to perform causal inference using existing marginal structural techniques. In order to tackle these challenges, we propose TCINet - time-series causal inference model to infer causation under continuous treatment using recurrent neural networks and a novel probabilistic balancing technique. Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science.
Advanced Distributed Submarine Cable Monitoring and Environmental Sensing using Constant Power Probe Signals and Coherent Detection
Authors: Mikael Mazur, Nicolas K. Fontaine, Megan Kelleher, Valey Kamalov, Roland Ryf, Lauren Dallachiesa, Haoshuo Chen, David T. Neilson, Franklyn Quinlan
Date: 2023-03-12 00:00:00

In this work we demonstrate an FPGA-based coherent optical frequency domain reflectometry setup for cable monitoring. Using coherent detection for averaging and narrowband filtering, we significantly improve the signal-to-noise ratio (SNR) compared to traditional intensity-only techniques while also enabling continuous monitoring of phase and polarization. In addition, the probe signal has constant power, avoiding the nonlinear distortions and thereby enabling continuous use without interfering with data channels. We perform a field demonstration over a trans-oceanic cable using loopback configurations present in each repeater, demonstrating measurement SNR exceeding 30 dB for all about 80 repeaters with an averaging window of 1 second. We furthermore compare cable monitoring using coherent processing to today's solutions using power-only measurements. Our results show how transitioning to coherent technology for cable monitoring can improve sensitivity and enable new types of monitoring, exploring knowledge gained from transitioning from incoherent to coherent data transmission.
Counterfactual Copula and Its Application to the Effects of College Education on Intergenerational Mobility
Authors: Tsung-Chih Lai, Jiun-Hua Su
Date: 2023-03-12 00:00:00
Tasks: counterfactual

This paper proposes a nonparametric estimator of the counterfactual copula of two outcome variables that would be affected by a policy intervention. The proposed estimator allows policymakers to conduct ex-ante evaluations by comparing the estimated counterfactual and actual copulas as well as their corresponding measures of association. Asymptotic properties of the counterfactual copula estimator are established under regularity conditions. These conditions are also used to validate the nonparametric bootstrap for inference on counterfactual quantities. Simulation results indicate that our estimation and inference procedures perform well in moderately sized samples. Applying the proposed method to studying the effects of college education on intergenerational income mobility under two counterfactual scenarios, we find that while providing some college education to all children is unlikely to promote mobility, offering a college degree to children from less educated families can significantly reduce income persistence across generations.
Composite Sorting
Authors: Job Boerma, Aleh Tsyvinski, Ruodu Wang, Zhenyuan Zhang
Date: 2023-03-12 00:00:00

This paper introduces an assignment model with concave costs of skill gaps, which arise generally when firms mitigate costs of mismatch as in Stigler (1939) and Laffont and Tirole (1986, 1991). Concave costs of skill gaps imply that the output function is neither supermodular nor submodular. We thus introduce a tractable model that interpolates between the polar canonical cases of supermodularity and submodularity. We characterize sorting, wages, and comparative statics and show these substantively differ from traditional assignment models. Under composite sorting: (1) distinct worker types work in the same occupation, and (2) worker types are simultaneously part of both positive and negative sorting. Quantitatively, our model can generate and help explain earnings dispersion between and within occupations.
The Dynamics of Instability
Authors: César Barilla, Duarte Gonçalves
Date: 2023-03-13 00:00:00

We study a model in which two players with opposing interests try to alter a status quo through instability-generating actions. We show that instability can be used to secure longer-term durable changes, even if it is costly to generate and does not generate short-term gains. In equilibrium, instability generated by a player decreases when the status quo favors them more. Equilibrium always exhibits a region of stable states in which the status quo persists. As players' threat power increases, this region shrinks, ultimately collapsing to a single stable state that is supported via a deterrence mechanism. There is long-run path-dependency and inequity: although instability eventually leads to a stable state, it typically selects the least favorable one for the initially disadvantaged player.
Status substitution and conspicuous consumption
Authors: Alastair Langtry, Christian Ghinglino
Date: 2023-03-13 00:00:00

This paper adapts ideas from social identity theory to set out a new framework for modelling conspicuous consumption. Agents derive status from their own conspicuous consumption and from belonging to an identity group with high conspicuous consumption. Importantly, these two sources of status are substitutes. Agents also feel pressure to conform with their neighbours in a network. This framework can rationalise a set of seemingly conflicting stylised facts about conspicuous consumption that are currently explained by different families of models. In addition, our model delivers new testable predictions regarding the effect of network structure and income inequality on conspicuous consumption.
Redesigning the US Army's Branching Process: A Case Study in Minimalist Market Design
Authors: Kyle Greenberg, Parag A. Pathak, Tayfun Sönmez
Date: 2023-03-12 00:00:00

We present the proof-of-concept for minimalist market design (S\"{o}nmez, 2023) as an effective methodology to enhance an institution based on the desiderata of stakeholders with minimal interference. Four objectives-respecting merit, increasing retention, aligning talent, and enhancing trust-guided reforms to US Army's centralized branching process of cadets to military specialties since 2006. USMA's mechanism for the Class of 2020 exacerbated challenges implementing these objectives. Formulating the Army's desiderata as rigorous axioms, we analyze their implications. Under our minimalist approach to institution redesign, the Army's objectives uniquely identify a branching mechanism. Our design is now adopted at USMA and ROTC.
Inertial Updating
Authors: Adam Dominiak, Matthew Kovach, Gerelt Tserenjigmid
Date: 2023-03-11 00:00:00

We introduce and characterize inertial updating of beliefs. Under inertial updating, a decision maker (DM) chooses a belief that minimizes the subjective distance between their prior belief and the set of beliefs consistent with the observed event. Importantly, by varying the subjective notion of distance, inertial updating provides a unifying framework that nests three different types of belief updating: (i) Bayesian updating, (ii) non-Bayesian updating rules, and (iii) updating rules for events with zero probability, including the conditional probability system (CPS) of Myerson (1986a,b). We demonstrate that our model is behaviorally equivalent to the Hypothesis Testing model (HT) of Ortoleva (2012), clarifying the connection between HT and CPS. We apply our model to a persuasion game.
The Bounds of Mediated Communication
Authors: Roberto Corrao, Yifan Dai
Date: 2023-03-10 00:00:00

We study the bounds of mediated communication in sender-receiver games in which the sender's payoff is state-independent. We show that the feasible distributions over the receiver's beliefs under mediation are those that induce zero correlation, but not necessarily independence, between the sender's payoff and the receiver's belief. Mediation attains the upper bound on the sender's value, i.e., the Bayesian persuasion value, if and only if this value is attainable under unmediated communication, i.e., cheap talk. The lower bound is given by the cheap talk payoff. We provide a geometric characterization of when mediation strictly improves on this using the quasiconcave and quasiconvex envelopes of the sender's value function. In canonical environments, mediation is strictly valuable when the sender has countervailing incentives in the space of the receiver's belief. We apply our results to asymmetric-information settings such as bilateral trade and lobbying and explicitly construct mediation policies that increase the surplus of the informed and uninformed parties with respect to unmediated communication.
Can spoofing countermeasure and speaker verification systems be jointly optimised?
Authors: Wanying Ge, Hemlata Tak, Massimiliano Todisco, Nicholas Evans
Date: 2023-03-13 00:00:00
Tasks: Speaker Verification

Spoofing countermeasure (CM) and automatic speaker verification (ASV) sub-systems can be used in tandem with a backend classifier as a solution to the spoofing aware speaker verification (SASV) task. The two sub-systems are typically trained independently to solve different tasks. While our previous work demonstrated the potential of joint optimisation, it also showed a tendency to over-fit to speakers and a lack of sub-system complementarity. Using only a modest quantity of auxiliary data collected from new speakers, we show that joint optimisation degrades the performance of separate CM and ASV sub-systems, but that it nonetheless improves complementarity, thereby delivering superior SASV performance. Using standard SASV evaluation data and protocols, joint optimisation reduces the equal error rate by 27\% relative to performance obtained using fixed, independently-optimised sub-systems under like-for-like training conditions.
Adaptive Dereverberation, Noise and Interferer Reduction Using Sparse Weighted Linearly Constrained Minimum Power Beamforming
Authors: Henri Gode, Simon Doclo
Date: 2023-03-13 00:00:00
Tasks: Speech Enhancement

Interfering sources, background noise and reverberation degrade speech quality and intelligibility in hearing aid applications. In this paper, we present an adaptive algorithm aiming at dereverberation, noise and interferer reduction and preservation of binaural cues based on the wBLCMP beamformer. The wBLCMP beamformer unifies the multi-channel weighted prediction error method performing dereverberation and the linearly constrained minimum power beamformer performing noise and interferer reduction into a single convolutional beamformer. We propose to adaptively compute the optimal filter by incorporating an exponential window into a sparsity-promoting lp-norm cost function, which enables to track a moving target speaker. Simulation results with successive target speakers at different positions show that the proposed adaptive version of the wBLCMP beamformer outperforms a non-adaptive version in terms of objective speech enhancement performance measures.
Real-Time Audio-Visual End-to-End Speech Enhancement
Authors: Zirun Zhu, Hemin Yang, Min Tang, ZiYi Yang, Sefik Emre Eskimez, Huaming Wang
Date: 2023-03-13 00:00:00
Tasks: CPU, Speech Enhancement, Task 2

Audio-visual speech enhancement (AV-SE) methods utilize auxiliary visual cues to enhance speakers' voices. Therefore, technically they should be able to outperform the audio-only speech enhancement (SE) methods. However, there are few works in the literature on an AV-SE system that can work in real time on a CPU. In this paper, we propose a low-latency real-time audio-visual end-to-end enhancement (AV-E3Net) model based on the recently proposed end-to-end enhancement network (E3Net). Our main contribution includes two aspects: 1) We employ a dense connection module to solve the performance degradation caused by the deep model structure. This module significantly improves the model's performance on the AV-SE task. 2) We propose a multi-stage gating-and-summation (GS) fusion module to merge audio and visual cues. Our results show that the proposed model provides better perceptual quality and intelligibility than the baseline E3net model with a negligible computational cost increase.