pwc-archive/papers-with-abstracts huggingface dataset:
Data-Driven Contract Design for Multi-Agent Systems with Collusion Detection
Authors: Nayara Aguiar, Parv Venkitasubramaniam, Vijay Gupta
Date: 2021-05-06 00:00:00

In applications such as participatory sensing and crowd sensing, self-interested agents exert costly effort towards achieving an objective for the system operator. We study such a setup where a principal incentivizes multiple agents of different types who can collude with each other to derive rent. The principal cannot observe the efforts exerted directly, but only the outcome of the task, which is a noisy function of the effort. The type of each agent influences the effort cost and task output. For a duopoly in which agents are coupled in their payments, we show that if the principal and the agents interact finitely many times, the agents can derive rent by colluding even if the principal knows the types of the agents. However, if the principal and the agents interact infinitely often, the principal can disincentivize agent collusion through a suitable data-driven contract.
Similarity Downselection: A Python implementation of a heuristic search algorithm for finding the set of the n most dissimilar items with an application in conformer sampling
Authors: Felicity F. Nielson, Sean M. Colby, Ryan S. Renslow, Thomas O. Metz
Date: 2021-05-06 00:00:00
Tasks: Heuristic Search

Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Finding the set of the n most dissimilar items is different than simply sorting an array of numbers because there exists a pairwise relationship between each item and all other items in the population. For instance, if you have a set of the most dissimilar n=4 items, one or more of the items from n=4 might not be in the set n=5. An exact solution would have to search all possible combinations of size n in the population, exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but is also more accurate than running the Monte Carlo for 1,000,000 iterations, each searching for set sizes n=3-7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing SDS produces a solution close to the exact solution in these instances.
On the Time Series Length for an Accurate Fractal Analysis in Network Systems
Authors: G. Millán
Date: 2021-05-06 00:00:00
Tasks: Time Series, Time Series Analysis

It is well-known that fractal signals appear in many fields of science. LAN and WWW traces, wireless traffic, VBR resources, etc. are among the ones with this behavior in computer networks traffic flows. An important question in these applications is how long a measured trace should be to obtain reliable estimates of de Hurst index (H). This paper addresses this question by first providing a thorough study of estimator for short series based on the behavior of bias, standard deviation (s), Root-Mean-Square Error (RMSE), and convergence when using Gaussian H-Self-Similar with Stationary Increments signals (H-sssi signals). Results show that Whittle-type estimators behave the best when estimating H for short signals. Based on the results, empirically derived the minimum trace length for the estimators is proposed. Finally for testing the results, the application of estimators to real traces is accomplished. Immediate applications from this can be found in the real-time estimation of H which is useful in agent-based control of Quality of Service (QoS) parameters in the high-speed computer network traffic flows.
Considerations for using reproduction data in toxicokinetic-toxicodynamic modelling
Authors: Tjalling Jager, Marie Trijau, Neil Sherborne, Benoit Goussen, Roman Ashauer
Date: 2021-05-04 00:00:00

Toxicokinetic-toxicodynamic (TKTD) modelling is essential to make sense of the time dependence of toxic effects, and to interpret and predict consequences of time-varying exposure. These advantages have been recognised in the regulatory arena, especially for environmental risk assessment (ERA) of pesticides, where time-varying exposure is the norm. We critically evaluate the link between the modelled variables in TKTD models and the observations from laboratory ecotoxicity tests. For the endpoint reproduction, this link is far from trivial. The relevant TKTD models for sub-lethal effects are based on Dynamic-Energy Budget (DEB) theory, which specifies a continuous investment flux into reproduction. In contrast, experimental tests score egg or offspring release by the mother. The link between model and data is particularly troublesome when a species reproduces in discrete clutches, and even more so when eggs are incubated in the mother's brood pouch (and release of neonates is scored in the test). This situation is quite common among aquatic invertebrates (e.g., cladocerans, amphipods, mysids), including many popular test species. We discuss these and other issues with reproduction data, reflect on their potential impact on DEB-TKTD analysis, and provide preliminary recommendations to correct them. Both modellers and users of model results need to be aware of these complications, as ignoring them could easily lead to unnecessary failure of DEB-TKTD models during calibration, or when validating them against independent data for other exposure scenarios.
Bring Your Own Codegen to Deep Learning Compiler
Authors: Zhi Chen, Cody Hao Yu, Trevor Morris, Jorn Tuyls, Yi-Hsiang Lai, Jared Roesch, Elliott Delaye, Vin Sharma, Yida Wang
Date: 2021-05-03 00:00:00
Tasks: Code Generation, Deep Learning

Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of these applications. However, to achieve high model coverage with high performance, each accelerator vendor has to develop a full compiler stack to ingest, optimize, and execute the DNNs. This poses significant challenges in the development and maintenance of the software stack. In addition, the vendors have to contiguously update their hardware and/or software to cope with the rapid evolution of the DNN model architectures and operators. To address these issues, this paper proposes an open source framework that enables users to only concentrate on the development of their proprietary code generation tools by reusing as many as possible components in the existing deep learning compilers. Our framework provides users flexible and easy-to-use interfaces to partition their models into segments that can be executed on "the best" processors to take advantage of the powerful computation capability of accelerators. Our case study shows that our framework has been deployed in multiple commercial vendors' compiler stacks with only a few thousand lines of code.
Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes
Authors: Aurora Cramer, Mark Cartwright, Fatemeh Pishdadian, Juan Pablo Bello
Date: 2021-05-06 00:00:00

While the estimation of what sound sources are, when they occur, and from where they originate has been well-studied, the estimation of how loud these sound sources are has been often overlooked. Current solutions to this task, which we refer to as source-specific sound level estimation (SSSLE), suffer from challenges due to the impracticality of acquiring realistic data and a lack of robustness to realistic recording conditions. Recently proposed weakly supervised source separation offer a means of leveraging clip-level source annotations to train source separation models, which we augment with modified loss functions to bridge the gap between source separation and SSSLE and to address the presence of background. We show that our approach improves SSSLE performance compared to baseline source separation models and provide an ablation analysis to explore our method's design choices, showing that SSSLE in practical recording and annotation scenarios is possible.
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases
Authors: Samyak Prajapati, Aman Swaraj, Ronak Lalwani, Akhil Narwal, Karan Verma
Date: 2021-05-05 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Time series forecasting methods play critical role in estimating the spread of an epidemic. The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread on. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases which is now being referred as the 2nd wave of the pandemic. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times. This aims of the study are three-fold: (a) To model the overall trend of the spread; (b) To generate a short-term forecast of 10 days in countries with the highest incidence of confirmed cases (USA, India and Brazil); (c) To quantitatively determine the algorithm that is best suited for precise modelling of the linear and non-linear features of the time series. The comparison of forecasting models for the total cumulative cases of each country is carried out by comparing the reported data and the predicted value, and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and ARIMA-NARNN) based on their RMSE, MAE and MAPE values. The hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best result among the selected models with a reduced RMSE, which proved to be almost 35.3% better than one of the most prevalent method of time-series prediction (ARIMA). The results demonstrated the efficacy of the hybrid implementation of the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt Winters, LSTM, and the ARIMA model in encapsulating the linear as well as non-linear patterns of the epidemical datasets.
Robust output feedback model predictive control using online estimation bounds
Authors: Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2021-05-07 00:00:00
Tasks: Model Predictive Control

We present a framework to design nonlinear robust output feedback model predictive control (MPC) schemes that ensure constraint satisfaction under noisy output measurements and disturbances. We provide novel estimation methods to bound the magnitude of the estimation error based on: stability properties of the observer; detectability; set-membership estimation; moving horizon estimation (MHE). Robust constraint satisfaction is guaranteed by suitably incorporating these online validated bounds on the estimation error in a homothetic tube based MPC formulation. In addition, we show how the performance can be further improved by combining MHE and MPC in a single optimization problem. The framework is applicable to a general class of detectable and (incrementally) stabilizable nonlinear systems. While standard output feedback MPC schemes use offline computed worst-case bounds on the estimation error, the proposed framework utilizes online validated bounds, thus reducing conservatism and improving performance. We demonstrate the reduced conservatism of the proposed framework using a nonlinear 10-state quadrotor example.
Integrated modelling of microvibrations induced by Solar Array Drive Mechanism for worst-case end-to-end analysis and robust disturbance estimation
Authors: Francesco Sanfedino, Daniel Alazard, Valentin Preda, Davide Oddenino
Date: 2021-05-07 00:00:00

Modern and future observation Space missions face increasingly more demanding pointing performance requirements. This is accompanied with the development of larger lightweight flexible structures. This paper outlines a methodology for modeling a generic multi-body flexible spacecraft in an end-to-end fashion within the Two-Inputs Two-Outputs Port framework: from disturbance all the way to pointing performance by tacking into account all the uncertainties of each sub-system. A particular focus is dedicated to a novel generic model for Solar Drive Array Mechanisms and its harmonic disturbances produced by the micro-stepping driver and the gearbox imperfections. The proposed model is validated with the on-board telemetries of a European spacecraft. Moreover this paper shows how the proposed model-based approach can be easily used for control design and closed-loop robust performance analysis. Worst-case scenarios of mechanical parameters combination are investigated to provide robust performance certificate of the reference study case. A Linear Parameter-Varying observer, scheduled by the solar array rotor angle, is finally proposed in order to estimate the disturbance torques induced by the gearbox imperfections just using the measurements coming from a classical attitude control system: a star tracker and a gyrometer.
Polytopic Input Constraints in Learning-Based Optimal Control Using Neural Networks
Authors: Lukas Markolf, Olaf Stursberg
Date: 2021-05-07 00:00:00
Tasks: Model Predictive Control

This work considers artificial feed-forward neural networks as parametric approximators in optimal control of discrete-time systems. Two different approaches are introduced to take polytopic input constraints into account. The first approach determines (sub-)optimal inputs by the application of gradient methods. Closed-form expressions for the gradient of general neural networks with respect to their inputs are derived. The approach allows to consider state-dependent input constraints, as well as to ensure the satisfaction of state constraints by exploiting recursive reachable set computations. The second approach makes use of neural networks with softmax output units to map states into parameters, which determine (sub-)optimal inputs by a convex combination of the vertices of the input constraint set. The application of both approaches in model predictive control is discussed, and results obtained for a numerical example are used for illustration.
Joint Subcarrier and Power Allocation in MU OFDM DCSK Systems with Noise Reduction
Authors: Majid Mobini, Mohammad Reza Zahabi
Date: 2021-05-07 00:00:00
Tasks: Form

This paper investigates the joint number of subcarriers and power optimization in Multi user Orthogonal Frequency Division Multiplexing based Differential Chaotic Shift Keying systems with noise reduction. We first find a closed-form expression for the optimal number of references in MU OFDM DCSK systems, while existing works only consider the numerical simulations. Moreover, this paper is the first to consider jointly subcarrier and power allocation in the MU OFDM DCSK system by defining a fractional problem. The BER expression of the MU OFDM DCSK system as an objective function is nonconvex and complex, which makes the analytical solution so difficult. We make it tractable by converting the fractional problem into a subtractive form, which leads to a near optimal solution. Simulation results show that our proposed approach provides better BER performance than the existing plans.
Self-Triggered Control for Near-Maximal Average Inter-Sample Time
Authors: Gabriel de Albuquerque Gleizer, Khushraj Madnani, Manuel Mazo Jr
Date: 2021-05-07 00:00:00

Self-triggered control (STC) is a sample-and-hold control method aimed at reducing communications within networked-control systems; however, existing STC mechanisms often maximize how late the next sample is, and as such they do not provide any sampling optimality in the long-term. In this work, we devise a method to construct self-triggered policies that provide near-maximal average inter-sample time (AIST) while respecting given control performance constraints. To achieve this, we rely on finite-state abstractions of a reference event-triggered control, in which early triggers are also allowed. These early triggers constitute controllable actions of the abstraction, for which an AIST-maximizing strategy can be computed by solving a mean-payoff game. We provide optimality bounds, and how to further improve them through abstraction refinement techniques.
Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems with LTL Specifications
Authors: Ryohei Oura, Toshimitsu Ushio, Ami Sakakibara
Date: 2021-05-07 00:00:00

In this paper, we consider supervisory control of stochastic discrete event systems (SDESs) under linear temporal logic specifications. Applying the bounded synthesis, we reduce the supervisor synthesis into a problem of satisfying a safety condition. First, we consider a synthesis problem of a directed controller using the safety condition. We assign a negative reward to the unsafe states and introduce an expected return with a state-dependent discount factor. We compute a winning region and a directed controller with the maximum satisfaction probability using a dynamic programming method, where the expected return is used as a value function. Next, we construct a permissive supervisor via the optimal value function. We show that the supervisor accomplishes the maximum satisfaction probability and maximizes the reachable set within the winning region. Finally, for an unknown SDES, we propose a two-stage model-free reinforcement learning method for efficient learning of the winning region and the directed controllers with the maximum satisfaction probability. We also demonstrate the effectiveness of the proposed method by simulation.
A Symbolic Regression Method for Dynamic Modeling and Control of Quadrotor UAVs
Authors: Wei Fang, Zhiyong Chen
Date: 2021-05-07 00:00:00
Tasks: Position, regression, Symbolic Regression

This paper presents a mathematic dynamic model of a quadrotor unmanned aerial vehicle (QUAV) by using the symbolic regression approach and then proposes a hierarchical control scheme for trajectory tracking. The symbolic regression approach is capable of constructing analytical quadrotor dynamic equations only through the collected data, which relieves the burden of first principle modeling. To facilitate position tracking control of a QUAV, the design of controller can be decomposed into two parts: a proportional-integral controller for the position subsystem is first designed to obtain the desired horizontal position and the backstepping method for the attitude subsystem is developed to ensure that the Euler angles and the altitude can fast converge to the reference values. The effectiveness is verified through experiments on a benchmark multicopter simulator.
Seizing Opportunity: Maintenance Optimization in Offshore Wind Farms Considering Accessibility, Production, and Crew Dispatch
Authors: Petros Papadopoulos, David Coit, Ahmed Aziz Ezzat
Date: 2020-12-01 00:00:00
Tasks: Scheduling

Operations and Maintenance (O&M) constitute a major contributor to offshore wind's cost of energy. Due to the harsh and remote environment in which offshore turbines operate, there has been a growing interest in opportunistic maintenance scheduling for offshore wind farms, wherein grouping maintenance tasks is incentivized at times of opportunity. Our survey of the literature, however, reveals that there is no unified consensus on what constitutes an "opportunity" for offshore maintenance. We therefore propose an opportunistic maintenance scheduling approach which defines an opportunity as either crew-dispatch-based (initiated by a maintenance crew already dispatched to a neighboring turbine), production-based (initiated by projected low production levels), or access-based (initiated by a provisionally open window of turbine access). We formulate the problem as a multi-staged rolling-horizon mixed integer linear program, and propose an iterative solution algorithm to identify the optimal hourly maintenance schedule, which is found to be drastically different, yet substantially better, than those obtained using offshore-agnostic strategies. Extensive numerical experiments on actual wind, wave, and power data demonstrate substantial margins of improvement achieved by our proposed approach, across a wide variety of key O&M metrics.
Morphological classification of astronomical images with limited labelling
Authors: Andrey Soroka, Alex Meshcheryakov, Sergey Gerasimov
Date: 2021-04-27 00:00:00
Tasks: Active Learning, Astronomy, Binary Classification, Classification, General Classification, Morphology classification

The task of morphological classification is complex for simple parameterization, but important for research in the galaxy evolution field. Future galaxy surveys (e.g. EUCLID) will collect data about more than a $10^9$ galaxies. To obtain morphological information one needs to involve people to mark up galaxy images, which requires either a considerable amount of money or a huge number of volunteers. We propose an effective semi-supervised approach for galaxy morphology classification task, based on active learning of adversarial autoencoder (AAE) model. For a binary classification problem (top level question of Galaxy Zoo 2 decision tree) we achieved accuracy 93.1% on the test part with only 0.86 millions markup actions, this model can easily scale up on any number of images. Our best model with additional markup achieves accuracy of 95.5%. To the best of our knowledge it is a first time AAE semi-supervised learning model used in astronomy.
A review on physical and data-driven based nowcasting methods using sky images
Authors: Ekanki Sharma, Wilfried Elmenreich
Date: 2021-04-28 00:00:00
Tasks: Solar Irradiance Forecasting

Amongst all the renewable energy resources (RES), solar is the most popular form of energy source and is of particular interest for its widely integration into the power grid. However, due to the intermittent nature of solar source, it is of the greatest significance to forecast solar irradiance to ensure uninterrupted and reliable power supply to serve the energy demand. There are several approaches to perform solar irradiance forecasting, for instance satellite-based methods, sky image-based methods, machine learning-based methods, and numerical weather prediction-based methods. In this paper, we present a review on short-term intra-hour solar prediction techniques known as nowcasting methods using sky images. Along with this, we also report and discuss which sky image features are significant for the nowcasting methods.