pwc-archive/papers-with-abstracts huggingface dataset:
Randomized Matrix Weighted Consensus
Authors: Nhat-Minh Le-Phan, Minh Hoang Trinh, Phuoc Doan Nguyen
Date: 2023-03-26 00:00:00

In this paper, randomized gossip-type matrix-weighted consensus algorithms are proposed for both leaderless and leader-follower topologies. First, we introduce the notion of expected matrix-weighted network, which captures the multi-dimensional interactions between any two agents in a probabilistic sense. Under some mild assumptions on the distribution of the expected matrix weights and the upper bound of the updating step size, the proposed asynchronous pairwise update algorithms drive the network to achieve a consensus in expectation. An upper bound of the $\epsilon$-convergence time of the algorithm is then derived. Furthermore, the proposed algorithms are applied to the bearing-based network localization and formation control problems. The theoretical results are supported by several numerical examples.
Relay-based identification of Aerodynamic and Delay Sensor Dynamics with applications for Unmanned Aerial Vehicles
Authors: Anees Peringal, Mohamad Chehadeh, Igor Boiko, Yahya Zweiri
Date: 2023-03-25 00:00:00

In this paper, we present a real-time system identification method based on relay feedback testing with applications to multirotor unmanned aerial vehicles. The proposed identification method provides an alternative to the expensive lab testing of certain UAV dynamic parameters. Moreover, it has the advantage of identifying the parameters that get changed throughout the operation of the UAV, which requires onboard identification methods. The modified relay feedback test (MRFT) is used to generate stable limit cycles at frequency points that reveal the underlying UAV dynamics. The locus of the perturbed relay system (LPRS) is used to predict the exact amplitude and frequency of these limit cycles. Real-time identification is achieved by using the homogeneity properties of the MRFT and the LPRS which are proven in this paper. The proposed identification method was tested experimentally to estimate the aerodynamic parameters as well as the onboard sensor's time delay parameters. The MRFT testing takes a few seconds to perform, and the identification computations take an average of 0.2 seconds to complete in modern embedded computers. The proposed identification method is compared against state-of-the-art alternatives. Advantages in identification accuracy and quantification of uncertainty in estimated parameters are shown.
Compositional Neural Certificates for Networked Dynamical Systems
Authors: Songyuan Zhang, Yumeng Xiu, Guannan Qu, Chuchu Fan
Date: 2023-03-25 00:00:00

Developing stable controllers for large-scale networked dynamical systems is crucial but has long been challenging due to two key obstacles: certifiability and scalability. In this paper, we present a general framework to solve these challenges using compositional neural certificates based on ISS (Input-to-State Stability) Lyapunov functions. Specifically, we treat a large networked dynamical system as an interconnection of smaller subsystems and develop methods that can find each subsystem a decentralized controller and an ISS Lyapunov function; the latter can be collectively composed to prove the global stability of the system. To ensure the scalability of our approach, we develop generalizable and robust ISS Lyapunov functions where a single function can be used across different subsystems and the certificates we produced for small systems can be generalized to be used on large systems with similar structures. We encode both ISS Lyapunov functions and controllers as neural networks and propose a novel training methodology to handle the logic in ISS Lyapunov conditions that encodes the interconnection with neighboring subsystems. We demonstrate our approach in systems including Platoon, Drone formation control, and Power systems. Experimental results show that our framework can reduce the tracking error up to 75% compared with RL algorithms when applied to large-scale networked systems.
Exploring the use of deep learning in task-flexible ILC
Authors: Anantha Sai Hariharan Vinjarapu, Yorick Broens, Hans Butler, Roland Tóth
Date: 2023-03-25 00:00:00
Tasks: Deep Learning, Imitation Learning

Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is {generally} not applicable, {despite of some recent promising advances}. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.
On Improved Commutation for Moving-Magnet Planar Actuators
Authors: Yorick Broens, Hans Butler, Roland Tóth
Date: 2023-03-25 00:00:00
Tasks: Position

The demand for high-precision and high-throughput motion control systems has increased significantly in recent years. The use of moving-magnet planar actuators (MMPAs) is gaining popularity due to their advantageous characteristics, such as complete environmental decoupling and reduction of stage mass. Nonetheless, model-based commutation techniques for MMPAs are compromised by misalignment between the mover and coil array and mismatch between the ideal electromagnetic model and the physical system, often leading to decreased system performance. To address this issue, a novel improved commutation approach is proposed in this paper\YB{, which is applicable for general planar motor applications,} by means of dynamic regulation of the position dependence of the ideal model-based commutation algorithm, which allows for attenuation of magnetic misalignment, manufacturing inaccuracies and other unmodelled phenomena. The effectiveness of the proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype.
Aggregated (Bi-)Simulation of Finite Valued Networks
Authors: Zhengping Ji, Xiao Zhang, Daizhan Cheng
Date: 2023-03-25 00:00:00

The paper provides a method to approximate a large-scale finite-valued network by a smaller model called the aggregated simulation, which is a combination of aggregation and (bi-)simulation. First, the algebraic state space representation (ASSR) of a transition system is presented. Under output equivalence, the quotient system is obtained, which is called the simulation of the original transition system. The ASSR of the quotient system is obtained. The aggregated (bi-)simulation is execueted in several steps: a large scale finite-valued network is firstly aggregated into several blocks, each of which is considered as a network where the in-degree nodes and out-degree nodes are considered as the block inputs and block outputs respectively. Then the dynamics of each block is converted into its quotient system, called its simulation. Then the overall network can be approximated by the quotient systems of each blocks, which is called the aggregated simulation. If the simulation of a block is a bi-simulation, the approximation becomes a lossless transformation. Otherwise, the quotient system is only a (non-deterministic) transition system, and it can be replaced by a probabilistic networks. Aggregated simulation can reduce the dimension of the original network, while a tradeoff between computation complexity and approximation error need to be decided.
Impulse excitation diagram as a tool to achieve high energy orbits
Authors: Damian Gaska, Jerzy Margielewicz, Grzegorz Litak, Piotr Wolszczak, Slawomir Bucki
Date: 2023-03-24 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The paper presents the application of a new impulse excitation diagram (IED) to help realize high-energy orbits in nonlinear energy harvesting systems. In the case of non-linearity, we can deal with the occurrence of coexisting solutions and the proposed diagram allows for the use of the impulse excitation method in order to change the solution. For this purpose, the author's IED diagram was presented to determine the characteristics, duration and moment of initiation of the external disturbing impulse in order to jump to another orbit. An application example is the quasizero energy harvester and two different impulse characteristics.
Resilient Output Consensus Control of Heterogeneous Multi-agent Systems against Byzantine Attacks: A Twin Layer Approach
Authors: Xin Gong, Yiwen Liang, Yukang Cui, Shi Liang, TingWen Huang
Date: 2023-03-22 00:00:00

This paper studies the problem of cooperative control of heterogeneous multi-agent systems (MASs) against Byzantine attacks. The agent affected by Byzantine attacks sends different wrong values to all neighbors while applying wrong input signals for itself, which is aggressive and difficult to be defended. Inspired by the concept of Digital Twin, a new hierarchical protocol equipped with a virtual twin layer (TL) is proposed, which decouples the above problems into the defense scheme against Byzantine edge attacks on the TL and the defense scheme against Byzantine node attacks on the cyber-physical layer (CPL). On the TL, we propose a resilient topology reconfiguration strategy by adding a minimum number of key edges to improve network resilience. It is strictly proved that the control strategy is sufficient to achieve asymptotic consensus in finite time with the topology on the TL satisfying strongly $(2f+1)$-robustness. On the CPL, decentralized chattering-free controllers are proposed to guarantee the resilient output consensus for the heterogeneous MASs against Byzantine node attacks. Moreover, the obtained controller shows exponential convergence. The effectiveness and practicality of the theoretical results are verified by numerical examples.
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models
Authors: Daniel T. Speckhard, Christian Carbogno, Luca Ghiringhelli, Sven Lubeck, Matthias Scheffler, Claudia Draxl
Date: 2023-03-26 00:00:00
Tasks: Prediction Intervals

The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached with an infinitely large basis set, i.e., in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, exciting and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model is used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions.
Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC
Authors: Bhargav Joshi, Taihui Li, Buyun Liang, Roger Rusack, Ju Sun
Date: 2023-03-23 00:00:00

The 75,848 lead tungstate crystals in CMS experiment at the CERN Large Hadron Collider are used to measure the energy of electrons and photons produced in the proton-proton collisions. The optical transparency of the crystals degrades slowly with radiation dose due to the beam-beam collisions. The transparency of each crystal is monitored with a laser monitoring system that tracks changes in the optical properties of the crystals due to radiation from the collision products. Predicting the optical transparency of the crystals, both in the short-term and in the long-term, is a critical task for the CMS experiment. We describe here the public data release, following FAIR principles, of the crystal monitoring data collected by the CMS Collaboration between 2016 and 2018. Besides describing the dataset and its access, the problems that can be addressed with it are described, as well as an example solution based on a Long Short-Term Memory neural network developed to predict future behavior of the crystals.
Noisy dynamical systems evolve error correcting codes and modularity
Authors: Trevor McCourt, Ila R. Fiete, Isaac L. Chuang
Date: 2023-03-25 00:00:00

Noise is a ubiquitous feature of the physical world. As a result, the first prerequisite of life is fault tolerance: maintaining integrity of state despite external bombardment. Recent experimental advances have revealed that biological systems achieve fault tolerance by implementing mathematically intricate error-correcting codes and by organizing in a modular fashion that physically separates functionally distinct subsystems. These elaborate structures represent a vanishing volume in the massive genetic configuration space. How is it possible that the primitive process of evolution, by which all biological systems evolved, achieved such unusual results? In this work, through experiments in Boolean networks, we show that the simultaneous presence of error correction and modularity in biological systems is no coincidence. Rather, it is a typical co-occurrence in noisy dynamic systems undergoing evolution. From this, we deduce the principle of error correction enhanced evolvability: systems possessing error-correcting codes are more effectively improved by evolution than those without.
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
Authors: Sonja Grönroos, Maurizio Pierini, Nadezda Chernyavskaya
Date: 2023-03-09 00:00:00
Tasks: Anomaly Detection

More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algorithm that pre-selects potentially anomalous images of the sensor surface in real time has been developed to automate the visual inspection. The anomaly detection is done by an ensemble of independent deep convolutional neural networks: an autoencoder and a classifier. The performance is evaluated on images acquired in production. The pre-selection reduces the number of images requiring human inspection by 85%, with recall of 97%. Data gathered in production can be used for continuous learning to improve the accuracy incrementally.
Under-Identification of Structural Models Based on Timing and Information Set Assumptions
Authors: Daniel Ackerberg, Garth Frazer, Kyoo il Kim, Yao Luo, Yingjun Su
Date: 2023-03-27 00:00:00

We revisit identification based on timing and information set assumptions in structural models, which have been used in the context of production functions, demand equations, and hedonic pricing models (e.g. Olley and Pakes (1996), Blundell and Bond (2000)). First, we demonstrate a general under-identification problem using these assumptions in a simple version of the Blundell-Bond dynamic panel model. In particular, the basic moment conditions can yield multiple discrete solutions: one at the persistence parameter in the main equation and another at the persistence parameter governing the regressor. We then show that the problem can persist in a broader set of models but disappears in models under stronger timing assumptions. We then propose possible solutions in the simple setting by enforcing an assumed sign restriction and conclude by using lessons from our basic identification approach to propose more general practical advice for empirical researchers.
Sensitivity Analysis in Unconditional Quantile Effects
Authors: Julian Martinez-Iriarte
Date: 2023-03-24 00:00:00
Tasks: counterfactual, Selection bias, Sensitivity

This paper proposes a framework to analyze the effects of counterfactual policies on the unconditional quantiles of an outcome variable. For a given counterfactual policy, we obtain identified sets for the effect of both marginal and global changes in the proportion of treated individuals. To conduct a sensitivity analysis, we introduce the quantile breakdown frontier, a curve that (i) indicates whether a sensitivity analysis is possible or not, and (ii) when a sensitivity analysis is possible, quantifies the amount of selection bias consistent with a given conclusion of interest across different quantiles. To illustrate our method, we perform a sensitivity analysis on the effect of unionizing low income workers on the quantiles of the distribution of (log) wages.
Inflation forecasting with attention based transformer neural networks
Authors: Maximilian Tschuchnig, Petra Tschuchnig, Cornelia Ferner, Michael Gadermayr
Date: 2023-03-13 00:00:00
Tasks: Time Series

Inflation is a major determinant for allocation decisions and its forecast is a fundamental aim of governments and central banks. However, forecasting inflation is not a trivial task, as its prediction relies on low frequency, highly fluctuating data with unclear explanatory variables. While classical models show some possibility of predicting inflation, reliably beating the random walk benchmark remains difficult. Recently, (deep) neural networks have shown impressive results in a multitude of applications, increasingly setting the new state-of-the-art. This paper investigates the potential of the transformer deep neural network architecture to forecast different inflation rates. The results are compared to a study on classical time series and machine learning models. We show that our adapted transformer, on average, outperforms the baseline in 6 out of 16 experiments, showing best scores in two out of four investigated inflation rates. Our results demonstrate that a transformer based neural network can outperform classical regression and machine learning models in certain inflation rates and forecasting horizons.
Measuring Self-Preferencing on Digital Platforms
Authors: Lukas Jürgensmeier, Bernd Skiera
Date: 2023-03-27 00:00:00
Tasks: Fairness

Digital platforms use recommendations to facilitate exchanges between platform actors, such as trade between buyers and sellers. Aiming to protect consumers and guarantee fair competition on platforms, legislators increasingly require that recommendations on market-dominating platforms be free from self-preferencing. That is, platforms that also act as sellers (e.g., Amazon) or information providers (e.g., Google) must not prefer their own offers over comparable third-party offers. Yet, successful enforcement of self-preferencing bans -- to the potential benefit of consumers and third-party actors -- requires defining and measuring self-preferencing across a platform. In the context of recommendations through search results, this research contributes by i) conceptualizing a "recommendation" as an offer's level of search engine visibility across an entire platform (instead of its position in specific search queries, as in previous research); ii) discussing two tests for self-preferencing, and iii) implementing them in two empirical studies across three international Amazon marketplaces. Contrary to consumer expectations and emerging literature, our analysis finds almost no evidence for self-preferencing. A survey reveals that even if Amazon were proven to engage in self-preferencing, most consumers would not change their shopping behavior on the platform -- highlighting Amazon's significant market power and suggesting the need for robust protections for sellers and consumers.
Economics-Inspired Neural Networks with Stabilizing Homotopies
Authors: Marlon Azinovic, Jan Žemlička
Date: 2023-03-26 00:00:00

Contemporary deep learning based solution methods used to compute approximate equilibria of high-dimensional dynamic stochastic economic models are often faced with two pain points. The first problem is that the loss function typically encodes a diverse set of equilibrium conditions, such as market clearing and households' or firms' optimality conditions. Hence the training algorithm trades off errors between those -- potentially very different -- equilibrium conditions. This renders the interpretation of the remaining errors challenging. The second problem is that portfolio choice in models with multiple assets is only pinned down for low errors in the corresponding equilibrium conditions. In the beginning of training, this can lead to fluctuating policies for different assets, which hampers the training process. To alleviate these issues, we propose two complementary innovations. First, we introduce Market Clearing Layers, a neural network architecture that automatically enforces all the market clearing conditions and borrowing constraints in the economy. Encoding economic constraints into the neural network architecture reduces the number of terms in the loss function and enhances the interpretability of the remaining equilibrium errors. Furthermore, we present a homotopy algorithm for solving portfolio choice problems with multiple assets, which ameliorates numerical instabilities arising in the context of deep learning. To illustrate our method we solve an overlapping generations model with two permanent risk aversion types, three distinct assets, and aggregate shocks.
Specific investments under negotiated transfer pricing: effects of different surplus sharing parameters on managerial performance: An agent-based simulation with fuzzy Q-learning agents
Authors: Christian Mitsch
Date: 2023-03-25 00:00:00
Tasks: Q-Learning

This paper focuses on a decentralized profit-center firm that uses negotiated transfer pricing as an instrument to coordinate the production process. Moreover, the firm's headquarters gives its divisions full authority over operating decisions and it is assumed that each division can additionally make an upfront investment decision that enhances the value of internal trade. On early works, the paper expands the number of divisions by one downstream division and relaxes basic assumptions, such as the assumption of common knowledge of rationality. Based on an agent-based simulation, it is examined whether cognitively bounded individuals modeled by fuzzy Q-learning achieve the same results as fully rational utility maximizers. In addition, the paper investigates different constellations of bargaining power to see whether a deviation from the recommended optimal bargaining power leads to a higher managerial performance. The simulation results show that fuzzy Q-learning agents perform at least as well or better than fully individual rational utility maximizers. The study also indicates that, in scenarios with different marginal costs of divisions, a deviation from the recommended optimal distribution ratio of the bargaining power of divisions can lead to higher investment levels and, thus, to an increase in the headquarters' profit.
Exposure to War and Its Labor Market Consequences over the Life Cycle
Authors: Sebastian T. Braun, Jan Stuhler
Date: 2023-03-25 00:00:00

With 70 million dead, World War II remains the most devastating conflict in history. Of the survivors, millions were displaced, returned maimed from the battlefield, or spent years in captivity. We examine the impact of such wartime experiences on labor market careers and show that they often become apparent only at certain life stages. While war injuries reduced employment in old age, former prisoners of war postponed their retirement. Many displaced workers, particularly women, never returned to employment. These responses are in line with standard life-cycle theory and thus likely extend to other conflicts.