pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Controlled User Association and Resource Management for Energy-Efficient Green RANs with Limited Fronthaul
Authors: Li-Hsiang Shen, Chia-Lin Tsai, Chia-Yu Wang, Kai-Ten Feng
Date: 2021-12-07 00:00:00
Tasks: Management

To alleviate green house effect, high network energy efficiency (EE) has increasingly become an important research target in wireless green communications. Therefore, the investigation for resource management to mitigate the co-tier interference in the small cell network (SCN) is provided. Moreover, with the merits of cloud radio access network (C-RAN), small cell base stations (SBSs) can be decomposed of a central small cell (CSC) and remote small cells (RSCs). To achieve the coordination, the split medium access control (MAC) based functional splitting is adopted with scheduler deployed at CSCs and retransmission functions left at RSCs. However, limited fronthaul has a compelling impact at RSCs due to requirements of user quality-of-service (QoS). Accordingly, a traffic control-based user association and resource allocation (TURA) scheme is proposed for a centralized resource management. To deal with the infeasibility to control all RSCs by CSC, we propose a hybrid controlled user and resource management (HARM) scheme. A CSC performs TURA for RSCs to mitigate intra-group interference within localized C-RANs, whereas the CSCs among separate C-RANs conduct cooperative resource competition (CRC) game for alleviating inter-group interference. Based on regret-based learning algorithm, the proposed schemes are analytically proved to reach the correlated equilibrium (CE). Simulation results have validated the effect of traffic control in TURA scheme and the convergence of CRC. Moreover, the comparison of the proposed TURA, HARM, and CRC schemes with the benchmark is revealed. It is observed that the TURA scheme outperforms the other schemes under ideal fronthaul control, whilst the proposed HARM scheme can sustain EE performance considering feasible implementation.
Spiking Control Systems
Authors: Rodolphe Sepulchre
Date: 2021-12-07 00:00:00

Spikes and rhythms organize control and communication in the animal world, in contrast to the bits and clocks of digital technology. As continuous-time signals that can be counted, spikes have a mixed nature. This paper reviews ongoing efforts to develop a control theory of spiking systems. The central thesis is that the mixed nature of spiking results from a mixed feedback principle, and that a control theory of mixed feedback can be grounded in the operator theoretic concept of maximal monotonicity. As a nonlinear generalization of passivity, maximal monotonicity acknowledges at once the physics of electrical circuits, the algorithmic tractability of convex optimization, and the feedback control theory of incremental passivity. We discuss the relevance of a theory of spiking control systems in the emerging age of event-based technology.
Optimal Scheduling of Energy Storage for Power System with Capability of Sensing Short-term Future PV Power Production
Authors: Sarvar Hussain Nengroo, Sangkeum Lee, Hojun Jin, Dongsoo Har
Date: 2021-12-07 00:00:00
Tasks: energy management, Management, Scheduling

Constant rise in energy consumption that comes with the population growth and introduction of new technologies has posed critical issues such as efficient energy management on the consumer side. That has elevated the importance of the use of renewable energy sources, particularly photovoltaic (PV) system and wind turbine. This work models and discusses design options based on the hybrid power system of grid and battery storage. The effects of installed capacity on renewable penetration (RP) and cost of electricity (COE) are investigated for each modality. For successful operation of hybrid power system and electricity trading in power market, accurate predictions of PV power production and load demand are taken into account. A machine learning (ML) model is introduced for scheduling, and predicting variations of the PV power production and load demand. Fitness of the ML model shows, when employing a linear regression model, the mean squared error (MSE) of 0.000012, root mean square error (RMSE) of 0.003560 and R2 of 0.999379. Using predicted PV power production and load demand, reduction of electricity cost is 37.5 % when PV and utility grid are utilized, and is 43.06% when PV, utility grid, and storage system are utilized.
Distributed Containment Reference Signal for Nonholonomic Planar Vehicles
Authors: Lixia Yan
Date: 2021-12-07 00:00:00

Cooperative of multiple nonholonomic vehicles can be converted into tracking problems of a single-vehicle. The reference trajectory design within distributed features for each vehicle in the group is addressed in this note. The motivation is that nonholonomic vehicles cannot achieve asymptotical stabilization of non-feasible reference signals, and modifications about the virtual reference trajectory design are needed. Reduced-order design and time-varying technique, and some simple geometry tricks are applied to derive the dynamic reference trajectory.
$N$-Timescale Stochastic Approximation: Stability and Convergence
Authors: Rohan Deb, Shalabh Bhatnagar
Date: 2021-12-07 00:00:00

This paper presents the first sufficient conditions that guarantee the stability and almost sure convergence of $N$-timescale stochastic approximation (SA) iterates for any $N\geq1$. It extends the existing results on One-timescale and Two-timescale SA iterates to general $N$-timescale stochastic recursions using the ordinary differential equation (ODE) method. As an application of our results, we study SA algorithms with an added heavy ball momentum term in the context of Gradient Temporal Difference (GTD) algorithms. We show that, when the momentum parameters are chosen in a certain way, the schemes are stable and convergent to the same solution using our proposed results.
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning
Authors: Han Zhao, Lei Guo
Date: 2021-12-07 00:00:00
Tasks: Reinforcement Learning (RL)

In this paper a novel model-free algorithm is proposed. This algorithm can learn the nearly optimal control law of constrained-input systems from online data without requiring any a priori knowledge of system dynamics. Based on the concept of generalized policy iteration method, there are two neural networks (NNs), namely actor and critic NN to approximate the optimal value function and optimal policy. The stability of closed-loop systems and the convergence of weights are also guaranteed by Lyapunov analysis.
Computation of Feedback Control Laws Based on Switched Tracking of Demonstrations
Authors: Jiří Fejlek, Stefan Ratschan
Date: 2020-11-25 00:00:00
Tasks: Model Predictive Control

A common approach in robotics is to learn tasks by generalizing from special cases given by a so-called demonstrator. In this paper, we apply this paradigm and present an algorithm that uses a demonstrator (typically given by a trajectory optimizer) to automatically synthesize feedback controllers for steering a system described by ordinary differential equations into a goal set. The resulting feedback control law switches between the demonstrations that it uses as reference trajectories. In comparison to the direct use of trajectory optimization as a control law, for example, in the form of model predictive control, this allows for a much simpler and more efficient implementation of the controller. The synthesis algorithm comes with rigorous convergence and optimality results, and computational experiments confirm its efficiency.
Structured learning of safety guarantees for the control of uncertain dynamical systems
Authors: Marc-Antoine Beaudoin, Benoit Boulet
Date: 2021-12-06 00:00:00
Tasks: BIG-bench Machine Learning

Approaches to keeping a dynamical system within state constraints typically rely on a model-based safety condition to limit the control signals. In the face of significant modeling uncertainty, the system can suffer from important performance penalties due to the safety condition becoming overly conservative. Machine learning can be employed to reduce the uncertainty around the system dynamics, and allow for higher performance. In this article, we propose the safe uncertainty learning principle, and argue that the learning must be properly structured to preserve safety guarantees. For instance, robust safety conditions are necessary, and they must be initialized with conservative uncertainty bounds prior to learning. Also, the uncertainty bounds should only be tightened if the collected data sufficiently capture the future system behavior. To support the principle, two example problems are solved with control barrier functions: a lane-change controller for an autonomous vehicle, and an adaptive cruise controller. This work offers a way to evaluate if machine learning preserves safety guarantees during the control of uncertain dynamical systems. It also highlights challenging aspects of learning for control.
Learning-based synthesis of robust linear time-invariant controllers
Authors: Marc-Antoine Beaudoin, Benoit Boulet
Date: 2021-12-06 00:00:00

Recent advances in learning for control allow to synthesize vehicle controllers from learned system dynamics and maintain robust stability guarantees. However, no approach is well-suited for training linear time-invariant (LTI) controllers using arbitrary learned models of the dynamics. This article introduces a method to do so. It uses a robust control framework to derive robust stability criteria. It also uses simulated policy rollouts to obtain gradients on the controller parameters, which serve to improve the closed-loop performance. By formulating the stability criteria as penalties with computable gradients, they can be used to guide the controller parameters toward robust stability during gradient descent. The approach is flexible as it does not restrict the type of learned model for the simulated rollouts. The robust control framework ensures that the controller is already robustly stabilizing when first implemented on the actual system and no data is yet collected. It also ensures that the system stays stable in the event of a shift in dynamics, given the system behavior remains within assumed uncertainty bounds. We demonstrate the approach by synthesizing a controller for simulated autonomous lane change maneuvers. This work thus presents a flexible approach to learning robustly stabilizing LTI controllers that take advantage of modern machine learning techniques.
Neural Energy Casimir Control for Port-Hamiltonian Systems
Authors: Liang Xu, Muhammad Zakwan, Giancarlo Ferrari-Trecate
Date: 2021-12-06 00:00:00

The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium. However, its application relies on the availability of suitable Casimir and Lyapunov functions, whose computation are generally intractable. In this paper, we propose a neural network-based framework to learn these functions. We show how to achieve equilibrium assignment by adding suitable regularization terms in the training cost. We also propose a parameterization of Casimir functions for reducing the training complexity. Moreover, the distance between the equilibrium of the learned Lyapunov function and the desired equilibrium is analyzed, which indicates that for small suboptimality gaps, the distance decreases linearly with respect to the training loss. Our methods are backed up by simulations on a pendulum system.
Change of persistence in European electricity spot prices
Authors: Leonardo Rydin Gorjão, Dirk Witthaut, Pedro G. Lind, Wided Medjroubi
Date: 2021-12-07 00:00:00
Tasks: Time Series, Time Series Analysis

The European Power Exchange has introduced day-ahead auctions and continuous trading spot markets to facilitate the insertion of renewable electricity. These markets are designed to balance excess or lack of power in short time periods, which leads to a large stochastic variability of the electricity prices. Furthermore, the different markets show different stochastic memory in their electricity price time series, which seem to be the cause for the large volatility. In particular, we show the antithetical temporal correlation in the intraday 15 minutes spot markets in comparison to the day-ahead hourly market. We contrast the results from Detrended Fluctuation Analysis (DFA) to a new method based on the Kramers--Moyal equation in scale. For very short term ($<12$ hours), all price time series show positive temporal correlations (Hurst exponent $H>0.5$) except for the intraday 15 minute market, which shows strong negative correlations ($H<0.5$). For longer term periods covering up to two days, all price time series are anti-correlated ($H<0.5$).
Nonparametric Treatment Effect Identification in School Choice
Authors: Jiafeng Chen
Date: 2021-12-07 00:00:00
Tasks: Diagnostic, regression

This paper studies nonparametric identification and estimation of causal effects in centralized school assignment. In many centralized assignment settings, students are subjected to both lottery-driven variation and regression discontinuity (RD) driven variation. We characterize the full set of identified atomic treatment effects (aTEs), defined as the conditional average treatment effect between a pair of schools, given student characteristics. Atomic treatment effects are the building blocks of more aggregated notions of treatment contrasts, and common approaches estimating aggregations of aTEs can mask important heterogeneity. In particular, many aggregations of aTEs put zero weight on aTEs driven by RD variation, and estimators of such aggregations put asymptotically vanishing weight on the RD-driven aTEs. We develop a diagnostic tool for empirically assessing the weight put on aTEs driven by RD variation. Lastly, we provide estimators and accompanying asymptotic results for inference on aggregations of RD-driven aTEs.
A decomposition method to evaluate the `paradox of progress' with evidence for Argentina
Authors: Javier Alejo, Leonardo Gasparini, Gabriel Montes-Rojas, Walter Sosa-Escudero
Date: 2021-12-07 00:00:00
Tasks: quantile regression, regression

The `paradox of progress' is an empirical regularity that associates more education with larger income inequality. Two driving and competing factors behind this phenomenon are the convexity of the `Mincer equation' (that links wages and education) and the heterogeneity in its returns, as captured by quantile regressions. We propose a joint least-squares and quantile regression statistical framework to derive a decomposition in order to evaluate the relative contribution of each explanation. The estimators are based on the `functional derivative' approach. We apply the proposed decomposition strategy to the case of Argentina 1992 to 2015.
Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement
Authors: Igor Balnozan, Denzil G. Fiebig, Anthony Asher, Robert Kohn, Scott A. Sisson
Date: 2020-09-03 00:00:00

This article investigates retirement decumulation behaviours using the Grouped Fixed-Effects (GFE) estimator applied to Australian panel data on drawdowns from phased withdrawal retirement income products. Behaviours exhibited by the distinct latent groups identified suggest that retirees may adopt simple heuristics determining how they draw down their accumulated wealth. Two extensions to the original GFE methodology are proposed: a latent group label-matching procedure which broadens bootstrap inference to include the time profile estimates, and a modified estimation procedure for models with time-invariant additive fixed effects estimated using unbalanced data.
A Response to Economics as Gauge Theory
Authors: Timothy Nguyen
Date: 2021-12-07 00:00:00

We provide an analysis of the recent work by Malaney-Weinstein on "Economics as Gauge Theory" presented on November 10, 2021 at the Money and Banking Workshop hosted by University of Chicago. In particular, we distill the technical mathematics used in their work into a form more suitable to a wider audience. Furthermore, we resolve the conjectures posed by Malaney-Weinstein, revealing that they provide no discernible value for the calculation of index numbers or rates of inflation. Our conclusion is that the main contribution of the Malaney-Weinstein work is that it provides a striking example of how to obscure simple concepts through an uneconomical use of gauge theory.
Differentially Private $K$-means Clustering Applied to Meter Data Analysis and Synthesis
Authors: Nikhil Ravi, Anna Scaglione, Sachin Kadam, Reinhard Gentz, Sean Peisert, Brent Lunghino, Emmanuel Levijarvi, Aram Shumavon
Date: 2021-12-07 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called `15/15 rule', to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply $K$-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm's utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.
On the Kurtosis of Modulation Formats for Characterizing the Nonlinear Fiber Propagation
Authors: Junho Cho, Robert Tkach
Date: 2021-12-07 00:00:00

Knowing only two high-order statistical moments of modulation symbols, often represented by the fourth moment called "kurtosis", the overestimation of nonlinear interference (NLI) in a Gaussian noise (GN) model due to Gaussian signaling assumption can be corrected through an enhanced GN (EGN) model. However, in some modern optical communication systems where the transmitted modulation symbols are statistically correlated, such as in systems that use probabilistic constellation shaping (PCS) with finite-length sphere shaping, the kurtosis-based EGN model produces significant inaccuracies in analytical prediction of NLI. In this paper, we show that for correlated modulation symbols, the NLI can be more accurately estimated by substituting a statistical measure called windowed kurtosis into the EGN model, instead of the conventional kurtosis. Remarkably, the optimal window length for windowed kurtosis is found to be consistent with the self-phase modulation (SPM) and cross-phase modulation (XPM) characteristic times in various system configurations. The findings can be used in practice to analytically evaluate and design NLI-tolerant modulation formats.
VeHIF: An Accessible Vegetation High-Impedance Fault Data Set Format
Authors: Douglas P. S. Gomes, Cagil Ozansoy
Date: 2021-12-07 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository.
A theory of optimal convex regularization for low-dimensional recovery
Authors: Yann Traonmilin, Rémi Gribonval, Samuel Vaiter
Date: 2021-12-07 00:00:00

We consider the problem of recovering elements of a low-dimensional model from under-determined linear measurements. To perform recovery, we consider the minimization of a convex regularizer subject to a data fit constraint. Given a model, we ask ourselves what is the "best" convex regularizer to perform its recovery. To answer this question, we define an optimal regularizer as a function that maximizes a compliance measure with respect to the model. We introduce and study several notions of compliance. We give analytical expressions for compliance measures based on the best-known recovery guarantees with the restricted isometry property. These expressions permit to show the optimality of the ${\ell}$1-norm for sparse recovery and of the nuclear norm for low-rank matrix recovery for these compliance measures. We also investigate the construction of an optimal convex regularizer using the examples of sparsity in levels and of sparse plus low-rank models.
Force control of grinding process based on frequency analysis
Authors: Yuya Nogi, Sho Sakaino, Toshiaki Tsuji
Date: 2021-12-07 00:00:00

Hysteresis-induced drift is a major issue in the detection of force induced during grinding and cutting operations. In this paper, we propose an external force estimation method based on the Mel spectrogram of the force obtained from a force sensor. We focus on the frequent strong correlation between the vibration frequency and the external force in operations with periodic vibrations. The frequency information is found to be more effective for an accurate force estimation than the amplitude in cases with large noise caused by vibration. We experimentally demonstrate that the force estimation method that combines the Mel spectrogram with a neural network is robust against drift.
Self-exciting price impact via negative resilience in stochastic order books
Authors: Julia Ackermann, Thomas Kruse, Mikhail Urusov
Date: 2021-12-07 00:00:00

Most of the existing literature on optimal trade execution in limit order book models assumes that resilience is positive. But negative resilience also has a natural interpretation, as it models self-exciting behaviour of the price impact, where trading activities of the large investor stimulate other market participants to trade in the same direction. In the paper we discuss several new qualitative effects on optimal trade execution that arise when we allow resilience to take negative values. We do this in a framework where both market depth and resilience are stochastic processes.