pwc-archive/papers-with-abstracts huggingface dataset:
An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers
Authors: Yong-Bin Kang, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Natalie Wieland, Elizabeth Kollias, Hung Du, Steven Thomson, Yuan-Fang Li
Date: 2021-11-09 00:00:00
Tasks: Multiple-choice, Question Generation, Question-Generation

There has been a recent and rapid shift to digital learning hastened by the pandemic but also influenced by ubiquitous availability of digital tools and platforms now, making digital learning ever more accessible. An integral and one of the most difficult part of scaling digital learning and teaching is to be able to assess learner's knowledge and competency. An educator can record a lecture or create digital content that can be delivered to thousands of learners but assessing learners is extremely time consuming. In the paper, we propose an Artificial Intelligence (AI)-based solution namely VidVersityQG for generating questions automatically from pre-recorded video lectures. The solution can automatically generate different types of assessment questions (including short answer, multiple choice, true/false and fill in the blank questions) based on contextual and semantic information inferred from the videos. The proposed solution takes a human-centred approach, wherein teachers are provided the ability to modify/edit any AI generated questions. This approach encourages trust and engagement of teachers in the use and implementation of AI in education. The AI-based solution was evaluated for its accuracy in generating questions by 7 experienced teaching professionals and 117 education videos from multiple domains provided to us by our industry partner VidVersity. VidVersityQG solution showed promising results in generating high-quality questions automatically from video thereby significantly reducing the time and effort for educators in manual question generation.
Memory-efficient array redistribution through portable collective communication
Authors: Norman A. Rink, Adam Paszke, Dimitrios Vytiniotis, Georg Stefan Schmid
Date: 2021-12-02 00:00:00

Modern large-scale deep learning workloads highlight the need for parallel execution across many devices in order to fit model data into hardware accelerator memories. In these settings, array redistribution may be required during a computation, but can also become a bottleneck if not done efficiently. In this paper we address the problem of redistributing multi-dimensional array data in SPMD computations, the most prevalent form of parallelism in deep learning. We present a type-directed approach to synthesizing array redistributions as sequences of MPI-style collective operations. We prove formally that our synthesized redistributions are memory-efficient and perform no excessive data transfers. Array redistribution for SPMD computations using collective operations has also been implemented in the context of the XLA SPMD partitioner, a production-grade tool for partitioning programs across accelerator systems. We evaluate our approach against the XLA implementation and find that our approach delivers a geometric mean speedup of $1.22\times$, with maximum speedups as a high as $5.7\times$, while offering provable memory guarantees, making our system particularly appealing for large-scale models.
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Authors: Kai Cui, Heinz Koeppl
Date: 2021-11-29 00:00:00
Proceeding: ICLR 2022 4

Recent advances at the intersection of dense large graph limits and mean field games have begun to enable the scalable analysis of a broad class of dynamical sequential games with large numbers of agents. So far, results have been largely limited to graphon mean field systems with continuous-time diffusive or jump dynamics, typically without control and with little focus on computational methods. We propose a novel discrete-time formulation for graphon mean field games as the limit of non-linear dense graph Markov games with weak interaction. On the theoretical side, we give extensive and rigorous existence and approximation properties of the graphon mean field solution in sufficiently large systems. On the practical side, we provide general learning schemes for graphon mean field equilibria by either introducing agent equivalence classes or reformulating the graphon mean field system as a classical mean field system. By repeatedly finding a regularized optimal control solution and its generated mean field, we successfully obtain plausible approximate Nash equilibria in otherwise infeasible large dense graph games with many agents. Empirically, we are able to demonstrate on a number of examples that the finite-agent behavior comes increasingly close to the mean field behavior for our computed equilibria as the graph or system size grows, verifying our theory. More generally, we successfully apply policy gradient reinforcement learning in conjunction with sequential Monte Carlo methods.
Formal verification of a controller implementation in fixed-point arithmetic
Authors: Lars Flessing, Grigory Devadze, Stefan Streif
Date: 2021-12-02 00:00:00

For the implementations of controllers on digital processors, certain limitations, e.g. in the instruction set and register length, need to be taken into account, especially for safety-critical applications. This work aims to provide a computer-certified inductive definition for the control functions that are implemented on such processors accompanied with the fixed-point data type in a proof assistant. Using these inductive definitions we formally ensure correct realization of the controllers on a digital processor. Our results guarantee overflow-free computations of the implemented control algorithm. The method presented in this paper currently supports functions that are defined as polynomials within an arbitrary fixed-point structure. We demonstrate the verification process in the case study on an example with different scenarios of fixed-point type implementations.
A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits
Authors: Dimitrios S. Karachalios, Ion Victor Gosea, Athanasios C. Antoulas
Date: 2021-12-02 00:00:00

In this contribution, we propose a data-driven procedure to fit quadratic-bilinear surrogate models from data. Although the dynamics characterizing the original model are strongly nonlinear, we rely on lifting techniques to embed the original model into a quadratic-bilinear format. Here, data represent generalized transfer function values. This method is an extension of methods that do bilinear, or quadratic inference, separately. It is based on first fitting a linear model with the classical Loewner framework, and then on inferring the best supplementing nonlinear operators, in a least-squares sense. The application scope of this method is given by electrical circuits with nonlinear components (such as diodes). We propose various test cases to illustrate the performance of the method.
A higher order Minkowski loss for improved prediction ability of acoustic model in ASR
Authors: Vishwanath Pratap Singh, Shakti P. Rath, Abhishek Pandey
Date: 2021-12-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Conventional automatic speech recognition (ASR) system uses second-order minkowski loss during inference time which is suboptimal as it incorporates only first order statistics in posterior estimation [2]. In this paper we have proposed higher order minkowski loss (4th Order and 6th Order) during inference time, without any changes during training time. The main contribution of the paper is to show that higher order loss uses higher order statistics in posterior estimation, which improves the prediction ability of acoustic model in ASR system. We have shown mathematically that posterior probability obtained due to higher order loss is function of second order posterior and thus the method can be incorporated in standard ASR system in an easy manner. It is to be noted that all changes are proposed during test(inference) time, we do not make any change in any training pipeline. Multiple baseline systems namely, TDNN1, TDNN2, DNN and LSTM are developed to verify the improvement incurred due to higher order minkowski loss. All experiments are conducted on LibriSpeech dataset and performance metrics are word error rate (WER) on "dev-clean", "test-clean", "dev-other" and "test-other" datasets.
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows
Authors: Param Budhraja, Mayank Baranwal, Kunal Garg, Ashish Hota
Date: 2021-12-02 00:00:00

Accelerated gradient methods are the cornerstones of large-scale, data-driven optimization problems that arise naturally in machine learning and other fields concerning data analysis. We introduce a gradient-based optimization framework for achieving acceleration, based on the recently introduced notion of fixed-time stability of dynamical systems. The method presents itself as a generalization of simple gradient-based methods suitably scaled to achieve convergence to the optimizer in a fixed-time, independent of the initialization. We achieve this by first leveraging a continuous-time framework for designing fixed-time stable dynamical systems, and later providing a consistent discretization strategy, such that the equivalent discrete-time algorithm tracks the optimizer in a practically fixed number of iterations. We also provide a theoretical analysis of the convergence behavior of the proposed gradient flows, and their robustness to additive disturbances for a range of functions obeying strong convexity, strict convexity, and possibly nonconvexity but satisfying the Polyak-{\L}ojasiewicz inequality. We also show that the regret bound on the convergence rate is constant by virtue of the fixed-time convergence. The hyperparameters have intuitive interpretations and can be tuned to fit the requirements on the desired convergence rates. We validate the accelerated convergence properties of the proposed schemes on a range of numerical examples against the state-of-the-art optimization algorithms. Our work provides insights on developing novel optimization algorithms via discretization of continuous-time flows.
Equivalent Circuit Programming for Power Flow Analysis and Optimization
Authors: Marko Jereminov, Larry Pileggi
Date: 2021-12-02 00:00:00

The utility of domain-specific knowledge for modeling, simulation, and optimization has been demonstrated for various research problem domains, including power systems. The concept of Equivalent Circuit Programming was previously developed and facilitated for robust, efficient, and scalable solution of network simulation and optimization problems. This paper extends the theoretical foundation of Equivalent Circuit Programming to enable the fusion of optimization theory and algorithms with the numerical methods that utilize the domain-specific knowledge of power flow models. The generality, scalability, and numerical robustness of the resulting framework are demonstrated on realistic AC power flow (ACPF) models of up to 70k buses with proper enforcement of industry-required operational and security constraints.
A More Scalable Mixed-Integer Encoding for Metric Temporal Logic
Authors: Vince Kurtz, Hai Lin
Date: 2021-12-02 00:00:00

The state-of-the-art in optimal control from timed temporal logic specifications, including Metric Temporal Logic (MTL) and Signal Temporal Logic (STL), is based on Mixed-Integer Convex Programming (MICP). The standard MICP approach is sound and complete, but struggles to scale to long and complex specifications. Drawing on recent advances in trajectory optimization for piecewise-affine systems, we propose a new MICP encoding for finite transition systems that significantly improves scalability to long and complex MTL specifications. Rather than seeking to reduce the number of variables in the MICP, we focus instead on designing an encoding with a tight convex relaxation. This leads to a larger optimization problem, but significantly improves branch-and-bound solver performance. In simulation experiments involving a mobile robot in a grid-world, the proposed encoding can reduce computation times by several orders of magnitude.
Shallow geothermal energy potential for heating and cooling of buildings with regeneration under climate change scenarios
Authors: Alina Walch, Xiang Li, Jonathan Chambers, Nahid Mohajeri, Selin Yilmaz, Martin Patel, Jean-Louis Scartezzini
Date: 2021-12-02 00:00:00
Tasks: Decision Making

Shallow ground-source heat pumps (GSHPs) are a promising technology for contributing to the decarbonisation of the energy sector. In heating-dominated climates, the combined use of GSHPs for both heating and cooling increases their technical potential, defined as the maximum energy that can be exchanged with the ground, as the re-injection of excess heat from space cooling leads to a seasonal regeneration of the ground. This paper proposes a new approach to quantify the technical potential of GSHPs, accounting for effects of seasonal regeneration, and to estimate the useful energy to supply building energy demands at regional scale. The useful energy is obtained for direct heat exchange and for district heating and cooling (DHC) under several scenarios for climate change and market penetration levels of cooling systems. The case study in western Switzerland suggests that seasonal regeneration allows for annual maximum heat extraction densities above 300 kWh/m$^2$ at heat injection densities above 330 kWh/m$^2$. Results also show that GSHPs may cover up to 55% of heating demand while covering 57% of service-sector cooling demand for individual GSHPs in 2050, which increases to around 85% with DHC. The regional-scale results may serve to inform decision making on strategic areas for installing GSHPs.
Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs
Authors: Yiyue Chen, Abolfazl Hashemi, Haris Vikalo
Date: 2021-01-23 00:00:00
Tasks: Stochastic Optimization

We consider the problem of decentralized optimization over time-varying directed networks. The network nodes can access only their local objectives, and aim to collaboratively minimize a global function by exchanging messages with their neighbors. Leveraging sparsification, gradient tracking and variance-reduction, we propose a novel communication-efficient decentralized optimization scheme that is suitable for resource-constrained time-varying directed networks. We prove that in the case of smooth and strongly-convex objective functions, the proposed scheme achieves an accelerated linear convergence rate. To our knowledge, this is the first decentralized optimization framework for time-varying directed networks that achieves such a convergence rate and applies to settings requiring sparsified communication. Experimental results on both synthetic and real datasets verify the theoretical results and demonstrate efficacy of the proposed scheme.
Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements
Authors: Mingjian Tuo, Xingpeng Li
Date: 2021-12-02 00:00:00

Conventional synchronous machines are gradually replaced by converter-based renewable resources. As a result, synchronous inertia, an important time-varying quantity, has substantially more impact on modern power systems stability. The increasing integration of renewable energy resources imports different dynamics into traditional power systems; therefore, the estimation of system inertia using mathematical model becomes more difficult. In this paper, we propose a novel learning-assisted inertia estimation model based on long-term recurrent convolutional network (LRCN) that uses system wide frequency and phase voltage measurements. The proposed approach uses a non-intrusive probing signal to perturb the system and collects ambient measurements with phasor measurement units (PMU) to train the proposed LRCN model. Case studies are conducted on the IEEE 24-bus system. Under a signal-to-noise ratio (SNR) of 60dB condition, the proposed LRCN based inertia estimation model achieves an accuracy of 97.56% with a mean squared error (MSE) of 0.0552. Furthermore, with a low SNR of 45dB, the proposed learning-assisted inertia estimation model is still able to achieve a high accuracy of 93.07%.
Definition of scenarios for modern power systems with a high renewable energy share
Authors: Carlos Collados-Rodríguez, Eduard Antolí-Gil, Enric Sánchez-Sánchez, Jaume Girona-Badia, Vinícius Albernaz Lacerda, Marc Cheah-Mañe, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
Date: 2021-12-01 00:00:00

Recent environmental policies have led many academic, industrial and governmental stakeholders to design and plan scenarios with very high share of renewable energy sources (RES). New system elements such as High-voltage Direct Current (HVDC) transmission, Microgrids, Virtual Power Plants (VPP) and Dynamic Virtual Power Plants (DVPP) are being increasingly studied with respect to their contribution to integrate future RES plants in the main grid. Several future scenarios are being analysed for each system and region, to ensure that the future energy systems, composed mostly of RES, can remain stable, match the demand during the seasonal variations across the year and are economically feasible. In this article, different types of energy scenarios are considered to obtain a range of options in terms of size, renewable generation technologies, and electrical network configuration. The scenarios were studied in the context of the POSYTYF project and were quantified through an optimization-based algorithm, using specific locations in Europe, and real data related to the availability of different RES, as well as the demand. It has been shown that photovoltaic (PV) and wind generation can provide the renewable backbone but they lack the flexibility needed to achieve a very high share in the energy mix. Other technologies, such as solar thermal and pumped hydro, become important to cover the last range of integration, as they provide a high flexibility, which is crucial for high share.
Modelling Approaches of Power Systems Considering Grid-Connected Converters and Renewable Generation Dynamics
Authors: Vinícius Albernaz Lacerda, Jaume Girona-Badia, Stephan Kusche, Florian Pöschke, Eduardo Prieto-Araujo, Horst Schulte, Oriol Gomis-Bellmunt
Date: 2021-12-01 00:00:00

This paper presents a comparative analysis of several modelling approaches of key elements used in simulations of power systems with renewable energy sources. Different models of synchronous generators, transmission lines, converters, wind generators and photovoltaic (PV) power plants are compared to assess the most suitable models for grid-connection studies. It also analyses how the dynamics of PV power plants and the mechanical dynamics of wind generators affect the electrical variables on the grid side. The models were compared in terms of precision and computational time through simulations of load connection, short-circuits, disconnection of generators and lines in a benchmark system modelled in Simulink.
Modelling and Simulation of Power Systems with Grid-Connected Converters in OpenModelica
Authors: Lluc Figueras Llerins, Vinícius Albernaz Lacerda, Adrien Guironnet, Quentin Cossart, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
Date: 2021-12-01 00:00:00

This paper analyses the capabilities of the OpenModelica environment to perform electromagnetic transient (EMT) type simulations of power transmission systems with grid-connected converters. A power transmission system has been modelled and simulated in OpenModelica and Simulink to compare both tools in terms of accuracy, robustness, flexibility and computational performance. Power system transient studies such as faults and switching of capacitor banks have been performed. The results confirmed an excellent overall agreement between both software and demonstrated a remarkable potential for using OpenModelica for EMT-type modelling and simulation of future power electronic dominated grids.
Robust Resource-Aware Self-triggered Model Predictive Control
Authors: Yingzhao Lian, Yuning Jiang, Naomi Stricker, Lothar Thiele, Colin N. Jones
Date: 2021-12-01 00:00:00
Tasks: model, Model Predictive Control

The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, increases the difficultly of controller design. This paper proposes a robust self-triggered model predictive control approach to optimize a control objective while managing resource consumption. In particular, a novel zero-order-hold aperiodic discrete-time feedback control law is developed to ensure robust constraint satisfaction for continuous-time linear systems.
Phasor Modelling Approaches and Simulation Guidelines of Voltage-Source Converters in Grid-Integration Studies
Authors: Vinícius Albernaz Lacerda, Eduardo Prieto Araujo, Marc Cheah-Mañe, Oriol Gomis-Bellmunt
Date: 2021-12-01 00:00:00

This paper reviews Voltage-Source Converters (VSCs) EMT and Phasor models currently used to simulate converter-interfaced generation (CIG) and renewable energy resources integration to power systems. Several modelling guidelines and suitability analyses were provided based on a comprehensive comparative study among the models. Various studies were performed in a small system and a large system, modelled in Simulink. We address a gap related to the suitability of CIGs phasor models in studies where the boundary between electromagnetic and electromechanical transients overlap. An insightful analysis of the adequate simulation time step for each model and study is also provided, along with several simulation guidelines.
The Physics of Machine Learning: An Intuitive Introduction for the Physical Scientist
Authors: Stephon Alexander, Sarah Bawabe, Batia Friedman-Shaw, Michael W. Toomey
Date: 2021-11-27 00:00:00
Tasks: BIG-bench Machine Learning

This article is intended for physical scientists who wish to gain deeper insights into machine learning algorithms which we present via the domain they know best, physics. We begin with a review of two energy-based machine learning algorithms, Hopfield networks and Boltzmann machines, and their connection to the Ising model. This serves as a foundation to understand the phenomenon of learning more generally. Equipped with this intuition we then delve into additional, more "practical," machine learning architectures including feedforward neural networks, convolutional neural networks, and autoencoders. We also provide code that explicitly demonstrates training a neural network with gradient descent.