pwc-archive/papers-with-abstracts huggingface dataset:
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
Authors: Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, DaCheng Tao
Date: 2022-03-17 00:00:00
Tasks: Quantum Machine Learning

Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number. This result leads to a general standpoint that deep quantum circuits would not be feasible for practical tasks. In this work, we propose an initialization strategy with theoretical guarantees for the vanishing gradient problem in general deep quantum circuits. Specifically, we prove that under proper Gaussian initialized parameters, the norm of the gradient decays at most polynomially when the qubit number and the circuit depth increase. Our theoretical results hold for both the local and the global observable cases, where the latter was believed to have vanishing gradients even for very shallow circuits. Experimental results verify our theoretical findings in the quantum simulation and quantum chemistry.
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
Authors: Xufeng Cai, Chaobing Song, Cristóbal Guzmán, Jelena Diakonikolas
Date: 2022-03-17 00:00:00

We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel variants of stochastic Halpern iteration with recursive variance reduction. In the cocoercive -- and more generally Lipschitz-monotone -- setup, our algorithm attains $\epsilon$ norm of the operator with $\mathcal{O}(\frac{1}{\epsilon^3})$ stochastic operator evaluations, which significantly improves over state of the art $\mathcal{O}(\frac{1}{\epsilon^4})$ stochastic operator evaluations required for existing monotone inclusion solvers applied to the same problem classes. We further show how to couple one of the proposed variants of stochastic Halpern iteration with a scheduled restart scheme to solve stochastic monotone inclusion problems with ${\mathcal{O}}(\frac{\log(1/\epsilon)}{\epsilon^2})$ stochastic operator evaluations under additional sharpness or strong monotonicity assumptions.
A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition
Authors: Ruilin Li, Ruobin Gao, P. N. Suganthan
Date: 2022-03-14 00:00:00
Tasks: EEG, Eeg Decoding, Electroencephalogram (EEG), Ensemble Learning

Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four decomposition methods are employed to disassemble the EEG signals into components of different complexity. Instead of handcraft features, the CNNs in this framework directly learn from the decomposed components. In addition, a component-specific batch normalization layer is employed to reduce subject variability. Moreover, we employ two ensemble modes to integrate the outputs of all CNNs, comprehensively exploiting the diverse information of the decomposed components. Against the challenging cross-subject driver fatigue recognition task, the models under the framework all showed superior performance to the strong baselines. Specifically, the performance of different decomposition methods and ensemble modes was further compared. The results indicated that discrete wavelet transform-based ensemble CNN achieved the highest average classification accuracy of 83.48% among the compared methods. The proposed framework can be extended to any CNN architecture and be applied to any EEG-related tasks, opening the possibility of extracting more beneficial features from complex EEG data.
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
Authors: Jiahao Shao, Shijia Geng, Zhaoji Fu, Weilun Xu, Tong Liu, Shenda Hong
Date: 2022-03-14 00:00:00
Tasks: Adversarial Attack, Classification, ECG Classification

In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of their high accuracy rate. However, researchers have found that adversarial attacks can significantly reduce the accuracy of DNNs. Studies have been conducted to defend ECG-based DNNs against traditional adversarial attacks, such as projected gradient descent (PGD), and smooth adversarial perturbation (SAP) which targets ECG classification; however, to the best of our knowledge, no study has completely explored the defense against adversarial attacks targeting ECG classification. Thus, we did different experiments to explore the effects of defense methods against white-box adversarial attack and black-box adversarial attack targeting ECG classification, and we found that some common defense methods performed well against these attacks. Besides, we proposed a new defense method called Adversarial Distillation Training (ADT) which comes from defensive distillation and can effectively improve the generalization performance of DNNs. The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization. Furthermore, we found that our method performed better against PGD attacks with low noise levels, which means that our method has stronger robustness.
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
Authors: Peter D. Turney
Date: 2002-12-11 00:00:00
Tasks: Information Retrieval, Retrieval

This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR is empirically evaluated using 80 synonym test questions from the Test of English as a Foreign Language (TOEFL) and 50 synonym test questions from a collection of tests for students of English as a Second Language (ESL). On both tests, the algorithm obtains a score of 74%. PMI-IR is contrasted with Latent Semantic Analysis (LSA), which achieves a score of 64% on the same 80 TOEFL questions. The paper discusses potential applications of the new unsupervised learning algorithm and some implications of the results for LSA and LSI (Latent Semantic Indexing).
Distributed formation control of networked mechanical systems
Authors: N. Javanmardi, P. Borja, M. J. Yazdanpanah, J. M. A. Scherpen
Date: 2022-03-17 00:00:00

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.
A Pricing Mechanism for Balancing the Charging of Ride-Hailing Electric Vehicle Fleets
Authors: Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis
Date: 2022-03-17 00:00:00
Tasks: Management

Both ride-hailing services and electric vehicles are becoming increasingly popular and it is likely that charging management of the ride-hailing vehicles will be a significant part of the ride-hailing company's operation in the near future. Motivated by this, we propose a game theoretic model for charging management, where we assume that it is the fleet-operator that wants to minimize its operational cost, which among others include the price of charging. To avoid overcrowded charging stations, a central authority will design pricing policies to incentivize the vehicles to spread out among the charging stations, in a setting where several ride-hailing companies compete about the resources. We show that it is possible to construct pricing policies that make the Nash-equilibrium between the companies follow the central authority's target value when the desired load is feasible. Moreover, we provide a decentralized algorithm for computation of the equilibrium and conclude the paper with a numerical example illustrating the results.
Harmonic Pole Placement
Authors: Pierre Riedinger, Jamal Daafouz
Date: 2022-03-17 00:00:00

In this paper, we propose a method to design state feedback harmonic control laws that assign the closed loop poles of a linear harmonic model to some desired locations. The procedure is based on the solution of an infinite-dimensional harmonic Sylvester equation under an invertibility constraint. We provide a sufficient condition to ensure this invertibility and show how this infinite-dimensional Sylvester equation can be solved up to an arbitrary small error. The results are illustrated on an unstable linear periodic system. We also provide a counterexample to illustrate the fact that, unlike the classical finite dimensional case, the solution of the Sylvester equation may not be invertible in the infinite dimensional case even if an observability condition is satisfied.
Proactive Posturing of Large Power Grid for Mitigating Hurricane Impacts
Authors: Edward Quarm Jnr., Xiaoyuan Fan, Marcelo Elizondo, Ramtin Madani
Date: 2022-03-17 00:00:00

In the past decade, natural disasters such as hurricanes have challenged the operation and control of U.S. power grid. It is crucial to develop proactive strategies to assist grid operators for better emergency response and minimized electricity service interruptions; the better the grid may be preserved, the faster the grid can be restored. In this paper, we propose a proactive posturing of power system elements, and formulate a Security-Constrained Optimal Power Flow (SCOPF) informed by cross-domain hurricane modeling as well as its potential impacts on grid elements. Simulation results based on real-world power grid and historical hurricane event have verified the applicability of the proposed optimization formulation, which shows potential to enable grid operators and planners with interactive cross-domain data analytics for mitigating hurricane impacts.
Optimal Control of Partially Observable Markov Decision Processes with Finite Linear Temporal Logic Constraints
Authors: Krishna C. Kalagarla, Dhruva Kartik, Dongming Shen, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo
Date: 2022-03-17 00:00:00

Autonomous agents often operate in scenarios where the state is partially observed. In addition to maximizing their cumulative reward, agents must execute complex tasks with rich temporal and logical structures. These tasks can be expressed using temporal logic languages like finite linear temporal logic (LTL_f). This paper, for the first time, provides a structured framework for designing agent policies that maximize the reward while ensuring that the probability of satisfying the temporal logic specification is sufficiently high. We reformulate the problem as a constrained partially observable Markov decision process (POMDP) and provide a novel approach that can leverage off-the-shelf unconstrained POMDP solvers for solving it. Our approach guarantees approximate optimality and constraint satisfaction with high probability. We demonstrate its effectiveness by implementing it on several models of interest.
Robust Control Approaches for Minimizing the Bandwidth Ratio in Multi-Loop Control
Authors: Rahul Mallik
Date: 2022-03-17 00:00:00

Conventional dc-dc, dc-ac converter control entail an inner current and outer voltage (ICOV) control loop. Stability of multi-loop control is achieved by ensuring faster dynamics of inner loops, often separating the bandwidths by large factors. Heuristically a factor-of-ten has often been used to separate the bandwidths of two adjacent loops in the nested architecture. In this paper, we present a numerical method to optimally select the ratio of bandwidths for a nested controller. We first manipulate the robust framework to show that the optimal $H_\infty$ subsumes the classical inner current outer voltage control. We then use optimality of the proposed $H_\infty$ controller to inspect feasibility of different ratios of bandwidths of the inner and outer controllers. We finally select the numerically closest bandwidths of the two nested loops which the guarantee stability. Simulation models are used to verify the optimal bandwidth separation for a candidate grid-forming dc-ac converter with inner current-outer voltage control.
Structure-Preserving Model Reduction for Nonlinear Power Grid Network
Authors: Bita Safaee, Serkan Gugercin
Date: 2022-03-17 00:00:00

We develop a structure-preserving system-theoretic model reduction framework for nonlinear power grid networks. First, via a lifting transformation, we convert the original nonlinear system with trigonometric nonlinearities to an equivalent quadratic nonlinear model. This equivalent representation allows us to employ the $\mathcal{H}_2$-based model reduction approach, Quadratic Iterative Rational Krylov Algorithm (Q-IRKA), as an intermediate model reduction step. Exploiting the structure of the underlying power network model, we show that the model reduction bases resulting from Q-IRKA have a special subspace structure, which allows us to effectively construct the final model reduction basis. This final basis is applied on the original nonlinear structure to yield a reduced model that preserves the physically meaningful (second-order) structure of the original model. The effectiveness of our proposed framework is illustrated via two numerical examples.
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
Authors: Ethan King, Jan Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna Vrabie
Date: 2022-03-16 00:00:00
Tasks: Scheduling

The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than traditional economic dispatch (T-ED) that leads to reduced overall generation costs. However, the incorporation of differential equations that govern the system dynamics makes DED an optimization problem that is computationally prohibitive to solve. In this work, we present a new data-driven approach based on differentiable programming to efficiently obtain parametric solutions to the underlying DED problem. In particular, we employ the recently proposed differentiable predictive control (DPC) for offline learning of explicit neural control policies using an identified Koopman operator (KO) model of the power system dynamics. We demonstrate the high solution quality and five orders of magnitude computational-time savings of the DPC method over the original online optimization-based DED approach on a 9-bus test power grid network.
Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC
Authors: Ruitao Song, Zhixian Ye, Liyang Wang, Tianyi He, Liangjun Zhang
Date: 2022-03-16 00:00:00
Tasks: Computational Efficiency, Model Predictive Control, Trajectory Planning

In this paper, we present a systematic approach for high-performance and efficient trajectory tracking control of autonomous wheel loaders. With the nonlinear dynamic model of a wheel loader, nonlinear model predictive control (MPC) is used in offline trajectory planning to obtain a high-performance state-control trajectory while satisfying the state and control constraints. In tracking control, the nonlinear model is embedded into a Linear Parameter Varying (LPV) model and the LPV-MPC strategy is used to achieve fast online computation and good tracking performance. To demonstrate the effectiveness and the advantages of the LPV-MPC, we test and compare three model predictive control strategies in the high-fidelity simulation environment. With the planned trajectory, three tracking control strategies LPV-MPC, nonlinear MPC, and LTI-MPC are simulated and compared in the perspectives of computational burden and tracking performance. The LPV-MPC can achieve better performance than conventional LTI-MPC because more accurate nominal system dynamics are captured in the LPV model. In addition, LPV-MPC achieves slightly worse tracking performance but tremendously improved computational efficiency than nonlinear MPC. A video with loading cycles completed by our autonomous wheel loader in the simulation environment can be found here: https://youtu.be/QbNfS_wZKKA.
The Digital Divide in Canada and the Role of LEO Satellites in Bridging the Gap
Authors: Tuheen Ahmmed, Afsoon Alidadi, Zichao Zhang, Aizaz U. Chaudhry, Halim Yanikomeroglu
Date: 2022-03-16 00:00:00

Overcoming the digital divide in rural and remote areas has always been a big challenge for Canada with its huge geographical area. In 2016, the Canadian Radio-television and Telecommunications Commission announced broadband Internet as a basic service available for all Canadians. However, approximately one million Canadians still do not have access to broadband services as of 2020. The COVID-19 pandemic has made the situation more challenging, as social, economic, and educational activities have increasingly been transferred online. The condition is more unfavorable for Indigenous communities. A key challenge in deploying rural and remote broadband Internet is to plan and implement high-capacity backbones, which are now available only in denser urban areas. For any Internet provider, it is almost impossible to make a viable business proposal in these areas. For example, the vast land of the Northwest Territories, Yukon, and Nunavuts diverse geographical features present obstacles for broadband infrastructure. In this paper, we investigate the digital divide in Canada with a focus on rural and remote areas. In so doing, we highlight two potential solutions using low Earth orbit (LEO) constellations to deliver broadband Internet in rural and remote areas to address the access inequality and the digital divide. The first solution involves integrating LEO constellations as a backbone for the existing 4G/5G telecommunications network. This solution uses satellites in a LEO constellation to provide a backhaul network connecting the 4G/5G access network to its core network. The 3rd Generation Partnership Project already specifies how to integrate LEO satellite networks into the 4G/5G network, and the Canadian satellite operator Telesat has already showcased this solution with one terrestrial operator, TIM Brasil, in their 4G network.
Causality issue in the heat balance method for calculating the design heating and cooling load
Authors: Christian Ghiaus
Date: 2022-03-14 00:00:00

Heating and cooling load calculation based on dynamic models is widely used in simulation software and it is the method recommended by ASHRAE and CEN. The principle is to make the heat balance for the air volume of a room space considered at uniform temperature and to calculate from this equation the load, i.e. the power needed to obtain the required indoor temperature. The problem is that, by doing so, the physical causality is not respected. If the model is approximated by a piecewise linear dynamical system, this procedure results in an improper transfer function. In order to point out this problem, a method to obtain state space and transfer function models from thermal networks is introduced. Then, the transfer function representation is employed to show that changing the physical causality results in an improper transfer function. The practical consequence is that when the space temperature has a step variation, the calculated load tends to infinity if the simulation time step tends to zero. The issue of causality may be a problem in equation-based simulation software, such as MODELICA, in which the equations do not represent causal relations: a wrong choice of the causality in a balance equation may result in improper transfer functions.
Beyond the Limitation of Pulse Width in Optical Time-domain Reflectometry
Authors: Hao Wu, Ming Tang
Date: 2022-03-14 00:00:00

Optical time-domain reflectometry (OTDR) is the basis for distributed time-domain optical fiber sensing techniques. By injecting pulse light into an optical fiber, the distance information of an event can be obtained based on the time of light flight. The minimum distinguishable event separation along the fiber length is called the spatial resolution, which is determined by the optical pulse width. By reducing the pulse width, the spatial resolution can be improved. However, at the same time, the signal-to-noise ratio of the system is degraded, and higher speed equipment is required. To solve this problem, data processing methods such as iterative subdivision, deconvolution, and neural networks have been proposed. However, they all have some shortcomings and thus have not been widely applied. Here, we propose and experimentally demonstrate an OTDR deconvolution neural network based on deep convolutional neural networks. A simplified OTDR model is built to generate a large amount of training data. By optimizing the network structure and training data, an effective OTDR deconvolution is achieved. The simulation and experimental results show that the proposed neural network can achieve more accurate deconvolution than the conventional deconvolution algorithm with a higher signal-to-noise ratio.
Selection and parallel trends
Authors: Dalia Ghanem, Pedro H. C. Sant'Anna, Kaspar Wüthrich
Date: 2022-03-17 00:00:00
Tasks: Sensitivity

We study the role of selection into treatment in difference-in-differences (DiD) designs. We derive necessary and sufficient conditions for parallel trends assumptions under general classes of selection mechanisms. These conditions characterize the empirical content of parallel trends. For settings where the necessary conditions are questionable, we propose tools for selection-based sensitivity analysis. We also provide templates for justifying DiD in applications with and without covariates. A reanalysis of the causal effect of NSW training programs demonstrates the usefulness of our selection-based approach to sensitivity analysis.
Lorenz map, inequality ordering and curves based on multidimensional rearrangements
Authors: Yanqin Fan, Marc Henry, Brendan Pass, Jorge A. Rivero
Date: 2022-03-17 00:00:00
Tasks: Attribute

We propose a multivariate extension of the Lorenz curve based on multivariate rearrangements of optimal transport theory. We define a vector Lorenz map as the integral of the vector quantile map associated with a multivariate resource allocation. Each component of the Lorenz map is the cumulative share of each resource, as in the traditional univariate case. The pointwise ordering of such Lorenz maps defines a new multivariate majorization order, which is equivalent to preference by any social planner with inequality averse multivariate rank dependent social evaluation functional. We define a family of multi-attribute Gini index and complete ordering based on the Lorenz map. We propose the level sets of an Inverse Lorenz Function as a practical tool to visualize and compare inequality in two dimensions, and apply it to income-wealth inequality in the United States between 1989 and 2022.