pwc-archive/papers-with-abstracts huggingface dataset:
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach
Date: 2020-06-17 00:00:00
Tasks: Autonomous Vehicles, Multi-agent Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces an energy-efficient, software-defined vehicular edge
network for the growing intelligent connected transportation system. A joint
user-centric virtual cell formation and resource allocation problem is
investigated to bring eco-solutions at the edge. This joint problem aims to
combat against the power-hungry edge nodes while maintaining assured
reliability and data rate. More specifically, by prioritizing the downlink
communication of dynamic eco-routing, highly mobile autonomous vehicles are
served with multiple low-powered access points (APs) simultaneously for
ubiquitous connectivity and guaranteed reliability of the network. The
formulated optimization is exceptionally troublesome to solve within a
polynomial time, due to its complicated combinatorial structure. Hence, a
distributed multi-agent reinforcement learning (D-MARL) algorithm is proposed
for eco-vehicular edges, where multiple agents cooperatively learn to receive
the best reward. First, the algorithm segments the centralized action space
into multiple smaller groups. Based on the model-free distributed Q learner,
each edge agent takes its actions from the respective group. Also, in each
learning state, a software-defined controller chooses the global best action
from individual bests of the distributed agents. Numerical results validate
that our learning solution achieves near-optimal performances within a small
number of training episodes as compared with existing baselines.
Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
Date: 2020-03-21 00:00:00

Dynamic mode decomposition (DMD) is a versatile approach that enables the
construction of low-order models from data. Controller design tasks based on
such models require estimates and guarantees on predictive accuracy. In this
work, we provide a theoretical analysis of DMD model errors that reveals impact
of model order and data availability. The analysis also establishes conditions
under which DMD models can be made asymptotically exact. We verify our results
using a 2D diffusion system.
Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines
Authors: Tiep M. Hoang, Trung Q. Duong, Hoang Duong Tuan, Sangarapillai Lambotharan, Emi Garcia-Palacios, Long D. Nguyen
Date: 2020-03-02 00:00:00

This paper presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of K legal users, one access point (AP) and one active eavesdropper, is considered. To cope with the eavesdropper who breaks into the system during the uplink phase, we first build structured datasets based on several different features. We then apply support vector machine (SVM) classifiers and one-class SVM classifiers to those structured datasets for detecting the presence of eavesdropper. Regarding the data, we first process received signals at the AP and then define three different features (i.e., MEAN, RATIO and SUM) based on the post-processing signals. Noticeably, our three defined features are formulated such that they have relevant statistical properties. Enabling the AP to simulate the entire process of transmission, we form the so-called artificial training data (ATD) that is used for training SVM (or one-class SVM) models. While SVM is preferred in the case of having perfect channel state information (CSI) of all channels, one-class SVM is preferred in the case of having only the CSI of legal users. We also evaluate the accuracy of the trained models in relation to the choice of kernel functions, the choice of features, and the change of eavesdropper's power. Numerical results show that the accuracy is relatively sensitive to adjusting parameters. Under some settings, SVM classifiers (or even one-class SVM) can bring about the accuracy of over 90%.
CMasher: Scientific colormaps for making accessible, informative and 'cmashing' plots
Date: 2020-02-26 00:00:00

CMasher is a Python package that provides a curated collection of scientific
colormaps, showcased in the online documentation
(https://cmasher.readthedocs.io). The colormaps in CMasher are all designed to
be perceptually uniform sequential using the 'viscm' package; most of them are
color-vision deficiency friendly; and they cover a wide range of different
color combinations to accommodate for most applications. It aims to provide
several alternatives to commonly used colormaps, like 'chroma' and 'rainforest'
for 'jet'; 'sunburst' for 'hot'; 'neutral' for 'binary'; and 'fusion' and
'redshift' for 'coolwarm'. With CMasher, I hope to help others with picking the
correct colormap for the job.
Remarks on Strong Stabilization and Stable H-infinity Controller Design
Date: 2020-03-11 00:00:00

A state space based design method is given to find strongly stabilizing
controllers for multiple-input-multiple-output plants (MIMO). A sufficient
condition is derived for the existence of suboptimal stable H-infinity
controller in terms of linear matrix inequalities (LMIs) and the controller
order is twice that of the plant. A new parameterization of strongly
stabilizing controllers is determined using linear fractional transformations
(LFTs).
Linear-Model-inspired Neural Network for Electromagnetic Inverse Scattering
Date: 2020-10-20 00:00:00
Tasks: parameter estimation

Electromagnetic inverse scattering problems (ISPs) aim to retrieve
permittivities of dielectric scatterers from the scattering measurement. It is
often highly nonlinear, caus-ing the problem to be very difficult to solve. To
alleviate the issue, this letter exploits a linear model-based network (LMN)
learning strategy, which benefits from both model complexity and data learning.
By introducing a linear model for ISPs, a new model with network-driven
regular-izer is proposed. For attaining efficient end-to-end learning, the
network architecture and hyper-parameter estimation are presented. Experimental
results validate its superiority to some state-of-the-arts.
Separable MSE-based design of two-way multiple-relay cooperative MIMO 5G networks
Authors: Donatella Darsena, Giacinto Gelli, Ivan Iudice, Francesco Verde
Date: 2020-03-03 00:00:00

While the combination of multi-antenna and relaying techniques has been extensively studied for Long Term Evolution Advanced (LTE-A) and Internet of Things (IoT) applications, it is expected to still play an important role in 5th Generation (5G) networks. However, the expected benefits of these technologies cannot be achieved without a proper system design. In this paper, we consider the problem of jointly optimizing terminal precoders/decoders and relay forwarding matrices on the basis of the sum mean square error (MSE) criterion in multiple-input multiple-output (MIMO) two-way relay systems, where two multi-antenna nodes mutually exchange information via multi-antenna amplify-and-forward relays. This problem is nonconvex and a local optimal solution is typically found by using iterative algorithms based on alternating optimization. We show how the constrained minimization of the sum-MSE can be relaxed to obtain two separated subproblems which, under mild conditions, admit a closed-form solution. Compared to iterative approaches, the proposed design is more suited to be integrated in 5G networks, since it is computationally more convenient and its performance exhibits a better scaling in the number of relays.
On the design of multiple-relay cooperative MIMO networks with partial channel state information
Authors: Donatella Darsena, Giacinto Gelli, Francesco Verde
Date: 2020-03-03 00:00:00

This paper deals with the problem of jointly designing the source precoder, the relaying matrices, and the destination equalizer in a multiple-relay amplify-and-forward (AF) cooperative multiple-input multiple-output (MIMO) wireless network, when partial channel-state information (CSI) is available. Specifically, the considered approaches are based on the knowledge of instantaneous CSI of the first-hop channel matrix, whereas only statistical CSI of the second-hop channels is assumed. In such a scenario, with respect to the case when instantaneous CSI of both the first- and second-hop MIMO channel matrices is exploited, existing network designs exhibit a significant performance degradation. Relying on a relaxed minimum-mean-square-error (MMSE) criterion, we show that the design based on the potential activation of all possible antennas for all available AF relays leads to a mathematically intractable optimization problem. Therefore, we develop a joint relay-and-antenna selection procedure that determines the best subset of the available antennas possibly belonging to different relays. Monte Carlo simulations show that, compared to designs based on the selection of the best relay, the proposed strategy offers a significant performance gain, by also outperforming other recently proposed relay/antenna selection schemes.
The Role of Uncertainty in Controlling Climate Change
Date: 2020-10-16 00:00:00

Integrated Assessment Models (IAMs) of the climate and economy aim to analyze
the impact and efficacy of policies that aim to control climate change, such as
carbon taxes and subsidies. A major characteristic of IAMs is that their
geophysical sector determines the mean surface temperature increase over the
preindustrial level, which in turn determines the damage function. Most of the
existing IAMs are perfect-foresight forward-looking models, assuming that we
know all of the future information. However, there are significant
uncertainties in the climate and economic system, including parameter
uncertainty, model uncertainty, climate tipping risks, economic risks, and
ambiguity. For example, climate damages are uncertain: some researchers assume
that climate damages are proportional to instantaneous output, while others
assume that climate damages have a more persistent impact on economic growth.
Climate tipping risks represent (nearly) irreversible climate events that may
lead to significant changes in the climate system, such as the Greenland ice
sheet collapse, while the conditions, probability of tipping, duration, and
associated damage are also uncertain. Technological progress in carbon capture
and storage, adaptation, renewable energy, and energy efficiency are uncertain
too. In the face of these uncertainties, policymakers have to provide a
decision that considers important factors such as risk aversion, inequality
aversion, and sustainability of the economy and ecosystem. Solving this problem
may require richer and more realistic models than standard IAMs, and advanced
computational methods. The recent literature has shown that these uncertainties
can be incorporated into IAMs and may change optimal climate policies
significantly.
Sensitivity Minimization by Strongly Stabilizing Controllers for a Class of Unstable Time-Delay Systems
Date: 2020-03-19 00:00:00
Tasks: Sensitivity

Weighted sensitivity minimization is studied within the framework of strongly
stabilizing (stable) H-infinity controller design for a class of infinite
dimensional systems. This problem has been solved by Ganesh and Pearson, [11],
for finite dimensional plants using Nevanlinna-Pick interpolation. We extend
their technique to a class of unstable time delay systems. Moreover, we
illustrate suboptimal solutions, and their robust implementation.
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness
Authors: Qiuling Yang, Alireza Sadeghi, Gang Wang, Georgios B. Giannakis, Jian Sun
Date: 2020-03-03 00:00:00
Tasks: Denoising, Graph Neural Network, Image Denoising, Rolling Shutter Correction, State Estimation

Distributed renewable generation, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today's power systems (PS). In this context, to maintain a comprehensive view of the system in real time, fast and robust state estimation (SE) methods are urgently needed. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares by e.g., the workhorse Gauss-Newton method. Those iterative solvers however, are sensitive to initialization and may get stuck in local minima. To overcome these hurdles and inspired by recent image denoising techniques, this paper advocates a learnable regularization term for PSSE that uses a deep neural network (DNN) prior. For the resultant regularized PSSE problem, a "Gauss-Newton-like" alternating minimization solver is first developed. To accommodate real-time monitoring, a novel end-to-end DNN is constructed by unrolling the proposed alternating minimization solver. Interestingly, the power network topology can be easily incorporated into the DNN by designing a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is discussed. Numerical tests using real load data on the IEEE $118$-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives.
Mortality and Healthcare: a Stochastic Control Analysis under Epstein-Zin Preferences
Authors: Joshua Aurand, Yu-Jui Huang
Date: 2020-03-03 00:00:00

This paper studies optimal consumption, investment, and healthcare spending under Epstein-Zin preferences. Given consumption and healthcare spending plans, Epstein-Zin utilities are defined over an agent's random lifetime, partially controllable by the agent as healthcare reduces mortality growth. To the best of our knowledge, this is the first time Epstein-Zin utilities are formulated on a controllable random horizon, via an infinite-horizon backward stochastic differential equation with superlinear growth. A new comparison result is established for the uniqueness of associated utility value processes. In a Black-Scholes market, the stochastic control problem is solved through the related Hamilton-Jacobi-Bellman (HJB) equation. The verification argument features a delicate containment of the growth of the controlled morality process, which is unique to our framework, relying on a combination of probabilistic arguments and analysis of the HJB equation. In contrast to prior work under time-separable utilities, Epstein-Zin preferences facilitate calibration. The model-generated mortality closely approximates actual mortality data in the US and UK; moreover, the efficacy of healthcare can be calibrated and compared between the two countries.
The case for algebraic biology: from research to education
Authors: Matthew Macauley, Nora Youngs
Date: 2020-02-29 00:00:00

Though it goes without saying that linear algebra is fundamental to mathematical biology, polynomial algebra is less visible. In this article, we will give a brief tour of four diverse biological problems where multivariate polynomials play a central role -- a subfield that is sometimes called "algebraic biology." Namely, these topics include biochemical reaction networks, Boolean models of gene regulatory networks, algebraic statistics and genomics, and place fields in neuroscience. After that, we will summarize the history of discrete and algebraic structures in mathematical biology, from their early appearances in the late 1960s to the current day. Finally, we will discuss the role of algebraic biology in the modern classroom and curriculum, including resources in the literature and relevant software. Our goal is to make this article widely accessible, reaching the mathematical biologist who knows no algebra, the algebraist who knows no biology, and especially the interested student who is curious about the synergy between these two seemingly unrelated fields.
Equity-Based Incentives, Production/Service Functions And Game Theory
Authors: Michael C. Nwogugu
Date: 2020-02-15 00:00:00

EBIs/ESOs substantially change the traditional production/service function because ESOs/EBIs can have different psychological effects(motivation or de-motivation), and can create intangible capital and different economic payoffs. Although Game Theory is flawed, it can be helpful in describing interactions in ESO/EBIs transactions. ESOs/EBIs involve two-stage games and there are no perfect Nash Equilibria for the two sub-games. The large number of actual and potential participants in these games significantly complicates resolution of equilibria and increases the dynamism of the games given that players are more sensitive to other peoples moves in such games. This article: a) analyzes how ESOs/EBIs affect traditional assumptions of production functions (in both the manufacturing and service sectors), b) analyzes ESOs/EBIs transactions using game theory concepts, c) illustrates some of the limitations of game theory.
Landscape allocation: stochastic generators and statistical inference
Date: 2020-02-20 00:00:00

In agricultural landscapes, the composition and spatial configuration of
cultivated and semi-natural elements strongly impact species dynamics, their
interactions and habitat connectivity. To allow for landscape structural
analysis and scenario generation, we here develop statistical tools for real
landscapes composed of geometric elements including 2D patches but also 1D
linear elements such as hedges. We design generative stochastic models that
combine a multiplex network representation and Gibbs energy terms to
characterize the distributional behavior of landscape descriptors for land-use
categories. We implement Metropolis-Hastings for this new class of models to
sample agricultural scenarios featuring parameter-controlled spatial and
temporal patterns (e.g., geometry, connectivity, crop-rotation).
Pseudolikelihood-based inference allows studying the relevance of model
components in real landscapes through statistical and functional validation,
the latter achieved by comparing commonly used landscape metrics between
observed and simulated landscapes. Models fitted to subregions of the Lower
Durance Valley (France) indicate strong deviation from random allocation, and
they realistically capture small-scale landscape patterns. In summary, our
approach of statistical modeling improves the understanding of structural and
functional aspects of agro-ecosystems, and it enables simulation-based
theoretical analysis of how landscape patterns shape biological and ecological
processes.
Estimating the Effect of Central Bank Independence on Inflation Using Longitudinal Targeted Maximum Likelihood Estimation
Authors: Philipp F. M. Baumann, Michael Schomaker, Enzo Rossi
Date: 2020-03-04 00:00:00

The notion that an independent central bank reduces a country's inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation.
Detection of False Data Injection Attacks Using the Autoencoder Approach
Authors: Chenguang Wang, Simon Tindemans, Kaikai Pan, Peter Palensky
Date: 2020-03-04 00:00:00
Tasks: State Estimation

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in 'normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids
Authors: Dongqi Wu, Dileep Kalathil, Miroslav Begovic, Le Xie
Date: 2020-03-05 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs). The performance of widely-used overcurrent protection scheme is hindered by the presence of distributed generation, power electronic interfaced devices and fault impedance. In this paper, a reinforcement learning-based approach is proposed to design and implement protective relays in the distribution grid. The particular algorithm used is an Long Short-Term Memory (LSTM) enhanced deep neural network that is highly accurate, communication-free and easy to implement. The proposed relay design is tested in OpenDSS simulation on the IEEE 34-node test feeder and demonstrated much more superior performance over traditional overcurrent protection from the aspect of failure rate, robustness and response speed.
Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks
Date: 2020-02-29 00:00:00
Tasks: Quantization

Convolutional neural networks (CNNs) achieve state-of-the-art performance at
the cost of becoming deeper and larger. Although quantization (both fixed-point
and floating-point) has proven effective for reducing storage and memory
access, two challenges -- 1) accuracy loss caused by quantization without
calibration, fine-tuning or re-training for deep CNNs and 2) hardware
inefficiency caused by floating-point quantization -- prevent processors from
completely leveraging the benefits.
  In this paper, we propose a low-precision floating-point quantization
oriented processor, named Phoenix, to address the above challenges. We
primarily have three key observations: 1) 8-bit floating-point quantization
incurs less error than 8-bit fixed-point quantization; 2) without using any
calibration, fine-tuning or re-training techniques, normalization before
quantization further reduces accuracy degradation; 3) 8-bit floating-point
multiplier achieves higher hardware efficiency than 8-bit fixed-point
multiplier if the full-precision product is applied. Based on these key
observations, we propose a normalization-oriented 8-bit floating-point
quantization method to reduce storage and memory access with negligible
accuracy loss (within 0.5%/0.3% for top-1/top-5 accuracy, respectively). We
further design a hardware processor to address the hardware inefficiency caused
by floating-point multiplier. Compared with a state-of-the-art accelerator,
Phoenix is 3.32x and 7.45x better in performance with the same core area for
AlexNet and VGG16, respectively.