pwc-archive/papers-with-abstracts huggingface dataset:
Enabling Grid-Aware Market Participation of Aggregate Flexible Resources
Authors: Bai Cui, Ahmed Zamzam, Andrey Bernstein
Date: 2022-07-26 00:00:00

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. With the new FERC 2222 order, DER aggregations are allowed to participate in energy market. To enable market participation, these virtual power plants need to provide their generation cost curves. This paper proposes efficient optimization formulations and solution approaches for the characterization of hourly as well as multi-time-step generation cost curves for a distribution system with high penetration of DERs. Network and DER constraints are taken into account when deriving these cost curves, and they enable active distribution systems to bid into the electricity market. The problems of deriving linear and quadratic cost curves are formulated as robust optimization problems and tractable reformulation/solution algorithm are developed to facilitate efficient calculations. The proposed formulations and solution algorithm are validated on a realistic test feeder with high penetration of flexible resources.
Sizing Co-located Storage for Uncertain Renewable Energy Sold Through Forward Contracts
Authors: Tomas Valencia Zuluaga, Shmuel S. Oren
Date: 2022-07-26 00:00:00
Tasks: Sensitivity

In this paper, we propose a high-level Stochastic steady-state model to analyze the value of co-located energy storage systems for wind power producers that participate in an electricity market through Forward or Day Ahead contracts. In particular, we try to find optimal sizing and contracting and stationary operating policies for profit maximization in the long-run. We obtain a stylized model calibrated to actual wind power production and electricity wholesale price data that allows us to asses the value of storage size and perform sensitivity analysis on key parameters such as contract prices, storage cost and storage efficiency.
Simulation-based Probabilistic Risk Assessment
Authors: Tarannom Parhizkar
Date: 2022-07-25 00:00:00

Simulation-based probabilistic risk assessment (SPRA) is a systematic and comprehensive methodology that has been used and refined over the past few decades to evaluate the risks associated with complex systems. SPRA models are well established for cases with considerable data and system behavior information available. In this regard, multiple statistical and probabilistic tools can be used to provide a valuable assessment of dynamic probabilistic risk levels in different applications. This tutorial presents a comprehensive review of SPRA methodologies. Based on the reviewed literature, SPRA methods can be classified into three categories of dynamic probabilistic logic methods, dynamic stochastic analytical models, and hybrid discrete dynamic event and system simulation models. In this tutorial, the key strengths and weaknesses, and suggestions on ways to address real and perceived shortcomings of available SPRA methods are presented and discussed.
Distributed HVDC Emergency Power Control; case study Nordic Power System
Authors: Danilo Obradovic, Mehrdad Ghandhari, Robert Eriksson
Date: 2022-07-25 00:00:00

Frequency Containment Reserves might be insufficient to provide an appropriate response in the presence of large disturbances and low inertia scenarios. As a solution, this work assesses the supplementary droop frequency-based Emergency Power Control (EPC) from HVDC interconnections, applied in the detailed Nordic Power System model. EPC distribution and factors that determine the EPC performance of an HVDC link are the focus of interest. The main criteria are the maximum Instantaneous Frequency Deviation and used EPC power. The presented methodology is motivated based on the theoretical observation concerning linearized system representation. However, the assessed and proposed properties of interest, such as provided EPC active and reactive power, their ratio, and energy of total loads and losses in the system due to the EPC, concern highly nonlinear system behavior. Finally, based on the obtained study, remarks on the pragmatical importance of the EPC distribution to the frequency nadir limitation are provided.
Optimality and sustainability of hybrid limit cycles in the pollution control problem with regime shifts
Authors: Dmitry Gromov, Tadashi Shigoka, Anton Bondarev
Date: 2022-07-25 00:00:00

In this paper, we consider the problem of pollution control in a system that undergoes regular regime shifts. We first show that the optimal policy of pollution abatement is periodic as well, and is described by the unique hybrid limit cycle. We next introduce the notion of an environmentally sustainable solution, and demonstrate that such a policy is the only one that yields the best possible trade-off between steadily achieving profit and ensuring environmental preservation. In contrast to that, the policy that is not environmentally sustainable eventually enters stagnation. To further illustrate our findings, we compare the optimal periodic solution with a myopic one. Interestingly enough, the myopic solution yields higher overall payoff in the short-run, but completely fails in the long-run, while the environmentally sustainable policy yields maximal payoff and preserves the environment over the infinite time interval.
Knitting a Markov blanket is hard when you are out-of-equilibrium: two examples in canonical nonequilibrium models
Authors: Miguel Aguilera, Ángel Poc-López, Conor Heins, Christopher L. Buckley
Date: 2022-07-26 00:00:00

Bayesian theories of biological and brain function speculate that Markov blankets (a conditional independence separating a system from external states) play a key role for facilitating inference-like behaviour in living systems. Although it has been suggested that Markov blankets are commonplace in sparsely connected, nonequilibrium complex systems, this has not been studied in detail. Here, we show in two different examples (a pair of coupled Lorenz systems and a nonequilibrium Ising model) that sparse connectivity does not guarantee Markov blankets in the steady-state density of nonequilibrium systems. Conversely, in the nonequilibrium Ising model explored, the more distant from equilibrium the system appears to be correlated with the distance from displaying a Markov blanket. These result suggests that further assumptions might be needed in order to assume the presence of Markov blankets in the kind of nonequilibrium processes describing the activity of living systems.
Efficient Learning of Accurate Surrogates for Simulations of Complex Systems
Authors: A. Diaw, M. McKerns, I. Sagert, L. G. Stanton, M. S. Murillo
Date: 2022-07-11 00:00:00
Tasks: valid

Machine learning methods are increasingly used to build computationally inexpensive surrogates for complex physical models. The predictive capability of these surrogates suffers when data are noisy, sparse, or time-dependent. As we are interested in finding a surrogate that provides valid predictions of any potential future model evaluations, we introduce an online learning method empowered by optimizer-driven sampling. The method has two advantages over current approaches. First, it ensures that all turning points on the model response surface are included in the training data. Second, after any new model evaluations, surrogates are tested and "retrained" (updated) if the "score" drops below a validity threshold. Tests on benchmark functions reveal that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema, even when the scoring metric favors overall accuracy. We apply our method to simulations of nuclear matter to demonstrate that highly accurate surrogates for the nuclear equation of state can be reliably auto-generated from expensive calculations using a few model evaluations.
Non-trivial dynamics in a model of glial membrane voltage driven by open potassium pores
Authors: Predrag Janjic, Dimitar Solev, Ljupco Kocarev
Date: 2022-07-26 00:00:00

Despite the molecular evidence that close to linear steady state I-V relationship in mammalian astrocytes reflects a total current resulting from more than one differently regulated K+ conductances, detailed ODE models of membrane voltage Vm incorporating multiple conductances are lacking. Repeated results of deregulated expressions of major K+ channels in glia, Kir4.1, in models of disease, as well as their altered rectification when assembling heteromeric Kir4.1/Kir5.1 channels have motivated us to attempt a detailed model adding the weaker potassium K2P current, in addition to Kir4.1, and study the stability of the resting state Vr. We ask whether with a deregulated Kir conductivity the nominal resting state Vr remains stable, and the cell retains a potassium electrode behavior with Vm following E_K. The minimal 2-dimensional model near Vr showed that certain alterations of Kir4.1 current may result in multistability of Vm if the model incorporates the typically observed K+ currents: Kir, K2P, and non-specific potassium leak. More specifically, a decrease or loss of outward Kir4.1 conductance introduces instability of Vr, near E_K. That happens through a fold bifurcation giving birth to a much more depolarized second, stable resting state Vdr>-10 mV. Realistic timeseries were used to perturb the membrane model, from recordings at the glial membrane during electrographic seizures. Simulations of the perturbed system by constant current through GJCs and transient seizure-like discharges as local field potentials led to depolarization of the astrocyte and switching of Vm between the two stable states, in a down-state / up-state manner. If the prolonged depolarizations near Vdr prove experimentally plausible, such catastrophic instability would impact all aspects of the glial function, from metabolic support to membrane transport and practically all neuromodulatory roles assigned to glia.
An Axiomatic Framework for Cost-Benefit Analysis
Authors: Ganesh Karapakula
Date: 2022-07-26 00:00:00

In recent years, the Marginal Value of Public Funds (MVPF) has become a popular tool for conducting cost-benefit analysis; the MVPF relies on the ratio of willingness-to-pay for a policy divided by its net fiscal cost. The MVPF gives policymakers important information about the equity-efficiency trade-off that is not necessarily conveyed by absolute welfare measures. However, I show in this paper that the usefulness of MVPF for comparative welfare analysis is limited, because it suffers from several empirically important economic paradoxes and statistical irregularities. There are also several practical issues in using the MVPF to aggregate welfare across policies or across population subgroups. To address these problems, I develop a new axiomatic framework to construct a measure that quantifies the equity-efficiency trade-off in a better way. I do so without compromising on the core features of the MVPF: its unit-free property, and the main preference orderings underlying it. My axiomatic framework delivers a unique (econo)metric that I call the Relative Policy Value (RPV), which can be weighted to conduct both comparative and absolute welfare analyses (or a hybrid combination thereof) and to intuitively aggregate welfare (without encountering the issues in MVPF-based aggregation). I also propose computationally convenient methods to make uniformly valid statistical inferences on welfare measures. After reanalyzing several government policies using my new econometric methods, I conclude that there is substantial economic and statistical uncertainty about welfare of some policies that were previously reported to have very high or even "precisely estimated infinite" MVPF values.
Skill requirements in job advertisements: A comparison of skill-categorization methods based on explanatory power in wage regressions
Authors: Ziqiao Ao, Gergely Horvath, Chunyuan Sheng, YiFan Song, Yutong Sun
Date: 2022-07-26 00:00:00

In this paper, we compare different methods to extract skill requirements from job advertisements. We consider three top-down methods that are based on expert-created dictionaries of keywords, and a bottom-up method of unsupervised topic modeling, the Latent Dirichlet Allocation (LDA) model. We measure the skill requirements based on these methods using a U.K. dataset of job advertisements that contains over 1 million entries. We estimate the returns of the identified skills using wage regressions. Finally, we compare the different methods by the wage variation they can explain, assuming that better-identified skills will explain a higher fraction of the wage variation in the labor market. We find that the top-down methods perform worse than the LDA model, as they can explain only about 20% of the wage variation, while the LDA model explains about 45% of it.
Trading under the Proof-of-Stake Protocol -- a Continuous-Time Control Approach
Authors: Wenpin Tang, David D. Yao
Date: 2022-07-26 00:00:00
Tasks: POS

We develop a continuous-time control approach to optimal trading in a Proof-of-Stake (PoS) blockchain, formulated as a consumption-investment problem that aims to strike the optimal balance between a participant's (or agent's) utility from holding/trading stakes and utility from consumption. We present solutions via dynamic programming and the Hamilton-Jacobi-Bellman (HJB) equations. When the utility functions are linear or convex, we derive close-form solutions and show that the bang-bang strategy is optimal (i.e., always buy or sell at full capacity). Furthermore, we bring out the explicit connection between the rate of return in trading/holding stakes and the participant's risk-adjusted valuation of the stakes. In particular, we show when a participant is risk-neutral or risk-seeking, corresponding to the risk-adjusted valuation being a martingale or a sub-martingale, the optimal strategy must be to either buy all the time, sell all the time, or first buy then sell, and with both buying and selling executed at full capacity. We also propose a risk-control version of the consumption-investment problem; and for a special case, the ''stake-parity'' problem, we show a mean-reverting strategy is optimal.
Extending the Range of Robust PCE Inflation Measures
Authors: Sergio Ocampo, Raphael Schoenle, Dominic A. Smith
Date: 2022-07-25 00:00:00
Tasks: Time Series Analysis

Robust inflation measures gauge inflation behavior by excluding volatile expenditure categories from headline inflation. We evaluate the forecasting performance of a wide set of such measures between 1970 and 2024, including core, median, and trimmed mean personal-consumption-expenditure (PCE) inflation. Core inflation performs significantly worse than official median and trimmed mean inflation. Among a set of alternative trimmed mean measures, there is no single best trim based on forecasting performance: A wide set of trims generates statistically indistinguishable average errors. Nonetheless, different trims imply different predictions for trend inflation in any given month, within a range of 0.5 to 1 percentage points. In tracking trend inflation, this range and its midpoint outperform all trimmed mean inflation measures, suggesting the use of the range of inflation implied by the set of near-optimal trims as a valuable complement to any single inflation measure.
Natural Disasters, Entrepreneurship Activity, and the Moderating Role of Country Governance
Authors: Christopher Boudreaux, Anand Jha, Monica Escaleras
Date: 2022-07-25 00:00:00

The purpose of this paper is to investigate if a country quality of governance moderates the effect of natural disasters on startup activity within that country. We test our hypotheses using a panel of 95 countries from 2006 to 2016. Our findings suggest that natural disasters discourage startup activity in countries that have low quality governance but encourage startup activity in countries that have high quality governance. Moreover, our estimates reveal that natural disasters effects on startup activity persist for the short term (1-3 years) but not the long term. Our findings provide new insights into how natural disasters affect entrepreneurship activity and highlight the importance of country governance during these events.
On Exact and Robust Recovery for Plug-and-Play Compressed Sensing
Authors: Ruturaj G. Gavaskar, Chirayu D. Athalye, Kunal N. Chaudhury
Date: 2022-07-26 00:00:00
Tasks: compressed sensing

In Plug-and-Play (PnP) algorithms, an off-the-shelf denoiser is used for image regularization. PnP yields state-of-the-art results, but its theoretical aspects are not well understood. This work considers the question: Similar to classical compressed sensing (CS), can we theoretically recover the ground-truth via PnP under suitable conditions on the denoiser and the sensing matrix? One hurdle is that since PnP is an algorithmic framework, its solution need not be the minimizer of some objective function. It was recently shown that a convex regularizer $\Phi$ can be associated with a class of linear denoisers such that PnP amounts to solving a convex problem involving $\Phi$. Motivated by this, we consider the PnP analog of CS: minimize $\Phi(x)$ s.t. $Ax=A\xi$, where $A$ is a $m\times n$ random sensing matrix, $\Phi$ is the regularizer associated with a linear denoiser $W$, and $\xi$ is the ground-truth. We prove that if $A$ is Gaussian and $\xi$ is in the range of $W$, then the minimizer is almost surely $\xi$ if $rank(W)\leq m$, and almost never if $rank(W)> m$. Thus, the range of the PnP denoiser acts as a signal prior, and its dimension marks a sharp transition from failure to success of exact recovery. We extend the result to subgaussian sensing matrices, except that exact recovery holds only with high probability. For noisy measurements $b = A \xi + \eta$, we consider a robust formulation: minimize $\Phi(x)$ s.t. $\|Ax-b\|\leq\delta$. We prove that for an optimal solution $x^*$, with high probability the distortion $\|x^*-\xi\|$ is bounded by $\|\eta\|$ and $\delta$ if the number of measurements is large enough. In particular, we can derive the sample complexity of CS as a function of distortion error and success rate. We discuss the extension of these results to random Fourier measurements, perform numerical experiments, and discuss research directions stemming from this work.
Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
Authors: Cheng Ma, Jingyi Zhang, Jie zhou, Jiwen Lu
Date: 2022-07-26 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable characteristics of low computational cost and hardware independence. However, recent attempts to address the problem of single image super-resolution (SISR) with lookup tables are highly constrained by the small receptive field size. Besides, their frameworks of single-layer lookup tables limit the extension and generalization capacities of the model. In this paper, we propose a framework of series-parallel lookup tables (SPLUT) to alleviate the above issues and achieve efficient image super-resolution. On the one hand, we cascade multiple lookup tables to enlarge the receptive field of each extracted feature vector. On the other hand, we propose a parallel network which includes two branches of cascaded lookup tables which process different components of the input low-resolution images. By doing so, the two branches collaborate with each other and compensate for the precision loss of discretizing input pixels when establishing lookup tables. Compared to previous lookup table-based methods, our framework has stronger representation abilities with more flexible architectures. Furthermore, we no longer need interpolation methods which introduce redundant computations so that our method can achieve faster inference speed. Extensive experimental results on five popular benchmark datasets show that our method obtains superior SISR performance in a more efficient way. The code is available at https://github.com/zhjy2016/SPLUT.
Key frames assisted hybrid encoding for photorealistic compressive video sensing
Authors: Honghao Huang, Jiajie Teng, Yu Liang, Chengyang Hu, Minghua Chen, Sigang Yang, Hongwei Chen
Date: 2022-07-26 00:00:00
Tasks: Optical Flow Estimation

Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition. Numerous algorithms, ranging from regularization-based optimization and deep learning, are being investigated to improve reconstruction quality, but they are still limited by the ill-posed and information-deficient nature of the standard SCI paradigm. To overcome these drawbacks, we propose a new key frames assisted hybrid encoding paradigm for compressive video sensing, termed KH-CVS, that alternatively captures short-exposure key frames without coding and long-exposure encoded compressive frames to jointly reconstruct photorealistic video. With the use of optical flow and spatial warping, a deep convolutional neural network framework is constructed to integrate the benefits of these two types of frames. Extensive experiments on both simulations and real data from the prototype we developed verify the superiority of the proposed method.
Vision-Aided Blockage Avoidance in UAV-assisted V2X Communications
Authors: Ruijin Ding, Weihua Xu, Wanmai Yuan, Feifei Gao
Date: 2022-07-26 00:00:00
Tasks: Deep Reinforcement Learning

The blockage is a key challenge for millimeter wave communication systems, since these systems mainly work on line-of-sight (LOS) links, and the blockage can degrade the system performance significantly. It is recently found that visual information, easily obtained by cameras, can be utilized to extract the location and size information of the environmental objects, which can help to infer the communication parameters, such as blockage status. In this paper, we propose a novel vision-aided handover framework for UAV-assisted V2X system, which leverages the images taken by cameras at the mobile station (MS) to choose the direct link or UAV-assisted link to avoid blockage caused by the vehicles on the road. We propose a deep reinforcement learning algorithm to optimize the handover and UAV trajectory policy in order to improve the long-term throughput. Simulations results demonstrate the effectiveness of using visual information to deal with the blockage issues.
Joint Direction-of-Arrival and Time-of-Arrival Estimation with Ultra-wideband Elliptical Arrays
Authors: Alejandro Ramírez-Arroyo, Antonio Alex-Amor, Pablo Padilla, Juan F. Valenzuela-Valdés
Date: 2022-07-26 00:00:00

This paper presents a general technique for the joint Direction-of-Arrival (DoA) and Time-of-Arrival (ToA) estimation in multipath environments. The proposed ultra-wideband technique is based on phase-mode expansions and the use of nearly frequency-invariant elliptical arrays. New possibilities open with the present approach, as not only elliptical, but also circular and linear (highly flattened) arrays can be considered with the same implementation. Systematic selection/rejection of signals-of-interest/signals-not-of-interest in smart wireless environments is possible, unlike with previous approaches based on circular arrays. Concentric elliptical arrays of many sizes and eccentricities can be jointly considered, with the subsequent improvement that entails in DoA and ToA detection. This leads to the realization of pseudo-random array patterns; namely, quasi-arbitrary geometries created from the superposition of multiple elliptical arrays. Some simulation and experimental tests (measurements in an anechoic chamber) are carried out for several frequency bands to check the correct performance of the method. The method is proven to give accurate estimations in all tested scenarios, and to be robust against noise and position uncertainty in sensor placement.