pwc-archive/papers-with-abstracts huggingface dataset:
Linear Matrix Inequality Design of Exponentially Stabilizing Observer-Based State Feedback Port-Hamiltonian Controllers
Authors: Jesus Toledo, Hector Ramirez, Yongxin Wu, Yann Le Gorrec
Date: 2020-10-13 00:00:00

The design of an observer-based state feedback (OBSF) controller with guaranteed passivity properties for port-Hamiltonian systems (PHS) is addressed using linear matrix inequalities (LMIs). The observer gain is freely chosen and the LMIs conditions such that the state feedback is equivalent to control by interconnection with an input strictly passive (ISP) and/or an output strictly passive (OSP) and zero state detectable (ZSD) port-Hamiltonian controller are established. It is shown that the proposed controller exponentially stabilizes a class of infinite-dimensional PHS and asymptotically stabilizes a class of finite-dimensional non-linear PHS. A Timoshenko beam model and a microelectromechanical system are used to illustrate the proposed approach.
Time efficiency analysis for undersampled quantitative MRI acquisitions
Authors: Riwaj Byanju, Stefan Klein, Alexandra Cristobal-Huerta, J. A. Hernandez-Tamames, Dirk H. J. Poot
Date: 2020-10-13 00:00:00
Tasks: Quantitative MRI

To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acceleration is possible using model-based reconstruction. We propose a theoretical metric called TEUSQA: Time Efficiency for UnderSampled QMRI Acquisitions to inform sequence design and sample pattern optimisation. TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation. TEUSQA can be used to evaluate undersampling patterns for multi-contrast QMRI sequences targeting any tissue parameter. To verify the time efficiency predicted by TEUSQA, we performed Monte Carlo simulations and an accelerated parameter mapping with two sequences (Inversion prepared fast spin echo for T1 and T2 mapping and 3D GRASE for T2 and B0 inhomogeneity mapping). Using TEUSQA, we assessed several ways to generate undersampling patterns in silico, providing insight into the relation between sample distribution and time efficiency for different acceleration factors. The time efficiency predicted by TEUSQA was within 15 % of that observed in the Monte Carlo simulations and the prospective acquisition experiment. The assessment of undersampling patterns showed that a class of good patterns could be obtained by low-discrepancy sampling. We believe that TEUSQA offers a valuable instrument for developers of novel QMRI sequences pushing the boundaries of acceleration to achieve clinically feasible protocols. Finally, we applied a time-efficient undersampling pattern selected using TEUSQA for a 32-fold accelerated scan to map T1 and T2 mapping of a healthy volunteer.
When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning
Authors: Megan Moore, William H. Clark IV, R. Michael Buehrer, William C. Headley
Date: 2020-10-13 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals. Additionally, recurrent neural networks typically process data on a sequential basis, enabling the potential for near real-time results. In this work, we investigate the novel usage of "just enough" decision making metrics for making decisions during inference based on a variable number of input symbols. Since some signals are more complex than others, due to channel conditions, transmitter/receiver effects, etc., being able to dynamically utilize just enough of the received symbols to make a reliable decision allows for more efficient decision making in applications such as electronic warfare and dynamic spectrum sharing. To demonstrate the validity of this concept, four approaches to making "just enough" decisions are considered in this work and each are analyzed for their applicability to wireless communication machine learning applications.
Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies
Authors: Jiahui Chen, Kaifu Gao, Rui Wang, GuoWei Wei
Date: 2020-10-13 00:00:00

Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic. They, however, are prone to over 5,000 mutations on the spike (S) protein uncovered by a Mutation Tracker based on over 200,000 genome isolates. It is imperative to understand how mutations would impact vaccines and antibodies in the development. In this work, we study the mechanism, frequency, and ratio of mutations on the S protein. Additionally, we use 56 antibody structures and analyze their 2D and 3D characteristics. Moreover, we predict the mutation-induced binding free energy (BFE) changes for the complexes of S protein and antibodies or ACE2. By integrating genetics, biophysics, deep learning, and algebraic topology, we reveal that most of 462 mutations on the receptor-binding domain (RBD) will weaken the binding of S protein and antibodies and disrupt the efficacy and reliability of antibody therapies and vaccines. A list of 31 vaccine escape mutants is identified, while many other disruptive mutations are detailed as well. We also unveil that about 65\% existing RBD mutations, including those variants recently found in the United Kingdom (UK) and South Africa, are binding-strengthen mutations, resulting in more infectious COVID-19 variants. We discover the disparity between the extreme values of RBD mutation-induced BFE strengthening and weakening of the bindings with antibodies and ACE2, suggesting that SARS-CoV-2 is at an advanced stage of evolution for human infection, while the human immune system is able to produce optimized antibodies. This discovery implies the vulnerability of current vaccines and antibody drugs to new mutations. Our predictions were validated by comparison with more than 1,400 deep mutations on the S protein RBD. Our results show the urgent need to develop new mutation-resistant vaccines and antibodies and to prepare for seasonal vaccinations.
A Simplified Formulation for the Backward/Forward Sweep Power Flow Method
Date: 2020-10-10 00:00:00

This paper describes a simplified formulation of the Backward/Forward (BW/FW)
Sweep Power Flow applied to radial distribution systems with distributed
generation under positive sequence modelling. Proposed formulation was applied
in an illustrative test system.
Machine Learning of Partial Differential Equations from Noise Data
Authors: Wenbo Cao, Weiwei Zhang
Date: 2020-09-28 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning of partial differential equations from data is a potential breakthrough to solve the lack of physical equations in complex dynamic systems, but because numerical differentiation is ill-posed to noise data, noise has become the biggest obstacle in the application of partial differential equation identification method. To overcome this problem, we propose Frequency Domain Identification method based on Fourier transforms, which effectively eliminates the influence of noise by using the low frequency component of frequency domain data to identify partial differential equations in frequency domain. We also propose a new sparse identification criterion, which can accurately identify the terms in the equation from low signal-to-noise ratio data. Through identifying a variety of canonical equations spanning a number of scientific domains, the proposed method is proved to have high accuracy and robustness for equation structure and parameters identification for low signal-to-noise ratio data. The method provides a promising technique to discover potential partial differential equations from noisy experimental data.
Tensor Train Factorization and Completion under Noisy Data with Prior Analysis and Rank Estimation
Authors: Le Xu, Lei Cheng, Ngai Wong, Yik-Chung Wu
Date: 2020-10-13 00:00:00
Tasks: image-classification, Image Classification, Variational Inference

Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many machine learning tasks. However, existing methods for TT decomposition either suffer from noise overfitting, or require extensive fine-tuning of the balance between model complexity and representation accuracy. In this paper, a fully Bayesian treatment of TT decomposition is employed to avoid noise overfitting, by endowing it with the ability of automatic rank determination. In particular, theoretical evidence is established for adopting a Gaussian-product-Gamma prior to induce sparsity on the slices of the TT cores, so that the model complexity is automatically determined even under incomplete and noisy observed data. Furthermore, based on the proposed probabilistic model, an efficient learning algorithm is derived under the variational inference framework. Simulation results on synthetic data show the success of the proposed model and algorithm in recovering the ground-truth TT structure from incomplete noisy data. Further experiments on real-world data demonstrate the proposed algorithm performs better in image completion and image classification, compared to other existing TT decomposition algorithms.
Frequency Domain Multi-user Detection for Single Carrier Modulation with Cyclic Prefix
Authors: Brent A. Kenney, Arslan J. Majid, Hussein Moradi, Behrouz Farhang-Boroujeny
Date: 2020-10-13 00:00:00

Frequency domain (FD) multi-user detection (MUD) has been shown to be an effective means of approaching the theoretical per-user capacity for single carrier modulation (SCM) schemes in massive MIMO scenarios with highly dispersive channels. When a cyclic prefix is added to the SCM waveform, the circulant structures of the resulting convolutional channel matrix allows for relatively simple expressions for the FD detection. In this paper, we develop a computationally efficient minimum mean squared error (MMSE) FD-MUD technique in a time-division duplexing (TDD) massive MIMO setup and show how processing steps can be shared between uplink and downlink.
Medial Injury/Dysfunction Induced Granulation Tissue Repair is the Pathogenesis of Atherosclerosis
Authors: Xinggang Wang, Aijun Sun, Junbo Ge
Date: 2020-10-13 00:00:00

Atherosclerosis, a chronic lesion of vascular wall, remains a leading cause of death and loss of life years. Classical hypotheses for atherosclerosis are long-standing mainly to explain atherogenesis. Unfortunately, these hypotheses may not explain the variation in the susceptibility to atherosclerosis. These issues are controversial over the past 150 years. Atherosclerosis from human coronary arteries was examined and triangle of media was found to be a true portraiture of cells injury in the media, and triangle of intima was a true portraiture of myofibroblast proliferation, extracellular matrix (ECM) secretion, collagen fiber formation and intimal thickening to repair media dysfunction. Myofibroblasts, ECM and lumen (intima)/vasa vasorum (VV) (adventitia) constitute granulation tissue repair. With granulation tissue hyperplasia, lots of collagen fibers (normal or denatured), foam cells and new capillaries formed. Thus, the following theory was postulated: Risk factors induce smooth muscle cells (SMCs) injury/loss, and fibrosis or structure destruction could be developed in the media, which lead to media dysfunction. Media dysfunction prompts disturbed mechanical properties of blood vessels, resulting in bigger pressure buildup in the intima and adventitia. Granulation tissues in the intima/adventitia develop to repair the injured media. Atherosclerosis, stiffening or aneurysm develops depending on media dysfunction severity and granulated tissue repair mode/degree. Nearly all characteristics of clinical atherosclerosis could be ideally interpreted with the theory. We believe that media dysfunction is a key initiator in the pathogenesis of atherosclerosis. It is expected that media dysfunction theory of atherosclerosis, should offer better understanding of the etiology for atherosclerosis.
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search
Date: 2020-10-13 00:00:00
Tasks: Deep Reinforcement Learning, Model Predictive Control, Reinforcement Learning (RL)

In this paper, we develop a grid-interactive multi-zone building controller
based on a deep reinforcement learning (RL) approach. The controller is
designed to facilitate building operation during normal conditions and demand
response events, while ensuring occupants comfort and energy efficiency. We
leverage a continuous action space RL formulation, and devise a two-stage
global-local RL training framework. In the first stage, a global fast policy
search is performed using a gradient-free RL algorithm. In the second stage, a
local fine-tuning is conducted using a policy gradient method. In contrast to
the state-of-the-art model predictive control (MPC) approach, the proposed RL
controller does not require complex computation during real-time operation and
can adapt to non-linear building models. We illustrate the controller
performance numerically using a five-zone commercial building.
The role of negative emissions in meeting China's 2060 carbon neutrality goal
Authors: Jay Fuhrman, Andres F. Clarens, Haewon McJeon, Pralit Patel, Scott C. Doney, William M. Shobe, Shreekar Pradhan
Date: 2020-10-13 00:00:00

China's pledge to reach carbon neutrality before 2060 is an ambitious goal and could provide the world with much-needed leadership on how to limit warming to +1.5C warming above pre-industrial levels by the end of the century. But the pathways that would achieve net zero by 2060 are still unclear, including the role of negative emissions technologies. We use the Global Change Analysis Model to simulate how negative emissions technologies, in general, and direct air capture (DAC) in particular, could contribute to China's meeting this target. Our results show that negative emissions could play a large role, offsetting on the order of 3 GtCO2 per year from difficult-to-mitigate sectors such as freight transportation and heavy industry. This includes up to a 1.6 GtCO2 per year contribution from DAC, constituting up to 60% of total projected negative emissions in China. But DAC, like bioenergy with carbon capture and storage and afforestation, has not yet been demonstrated at anywhere approaching the scales required to meaningfully contribute to climate mitigation. Deploying NETs at these scales will have widespread impacts on financial systems and natural resources such as water, land, and energy in China.
An Application of Dirac's Interaction Picture to Option Pricing
Authors: Mauricio Contreras G
Date: 2020-10-14 00:00:00
Tasks: Form, valid

In this paper, the Dirac's quantum mechanical interaction picture is applied to option pricing to obtain a solution of the Black-Scholes equation in the presence of a time-dependent arbitrage bubble. In particular, for the case of a call perturbed by a square bubble, an approximate solution (valid up third order in a perturbation series) is given in terms of the three first Greeks: Delta, Gamma, and Speed. Then an exact solution is constructed in terms of all higher order $S$-derivatives of the Black-Scholes formula. It is also shown that the interacting Black-Scholes equation is invariant under a discrete transformation that interchanges the interest rate with the mean of the underlying asset and vice versa. This implies that the interacting Black-Scholes equation can be written in a 'low energy' and a 'high energy' form, in such a way that the high-interaction limit of the low energy form corresponds to the weak-interaction limit of the high energy form. One can apply a perturbative analysis to the high energy form to study the high-interaction limit of the low energy form.
AlphaZero Based Post-Storm Repair Crew Dispatch for Distribution Grid Restoration
Authors: Hang Shuai, Fangxing, Li
Date: 2020-10-14 00:00:00
Tasks: Navigate, Stochastic Optimization

Natural disasters such as storms usually bring significant damages to distribution grids. This paper investigates the optimal routing of utility vehicles to restore outages in the distribution grid as fast as possible after a storm. First, the poststorm repair crew dispatch task with multiple utility vehicles is formulated as a sequential stochastic optimization problem. In the formulated optimization model, the belief state of the power grid is updated according to the phone calls from customers and the information collected by utility vehicles. Second, an AlphaZero[1] based utility vehicle routing (AlphaZero-UVR) approach is developed to achieve the real-time dispatching of the repair crews. The proposed AlphaZero-UVR approach combines deep neural networks with stochastic Monte-Carlo tree search (MCTS) to give a lookahead search decisions, which can learn to navigate repair crews without human guidance. Simulation results show that the proposed approach can efficiently navigate crews to repair all outages.
Designing Massive MIMO Detector via PS-ADMM approach
Authors: Quan Zhang, Yongchao Wang
Date: 2020-10-14 00:00:00

In this paper, we develop an efficient detector for massive multiple-input multiple-output (MIMO) communication systems via penalty-sharing alternating direction method of multipliers (PS-ADMM). Its main content are as follows: first, we formulate the MIMO detection as a maximum-likelihood optimization problem with bound relaxation constraints. Then, the higher modulation signals are decomposed into a sum of multiple binary variables through their inherent structures, by exploiting introduced binary variables as penalty functions, the detection optimization model is equivalent to a nonconvex sharing minimization problem. Second, a customized ADMM algorithm is presented to solve the formulated nonconvex optimization problem. In the implementation, all variables can be solved analytically and parallelly. Third, it is proved that the proposed PS-ADMM algorithm converges if proper parameters are chosen. Simulation results demonstrate the effectiveness of the proposed approach.
Rapid Generation of Stochastic Signals with Specified Statistics
Authors: Span Spanbauer, Ian Hunter
Date: 2020-10-14 00:00:00

We demonstrate a novel algorithm for generating stationary stochastic signals with a specified power spectral density (or equivalently, via the Wiener-Khinchin relation, a specified autocorrelation function) while satisfying constraints on the signal's probability density function. A tightly related problem has already been essentially solved by methods involving nonlinear filtering, however we use a fundamentally different approach involving optimization and stochastic interchange which immediately generalizes to generating signals with a broader range of statistics. This combination of optimization and stochastic interchange eliminates drawbacks associated with either method in isolation, improving the best-case scaling in runtime to generate a signal of length $n$ from $\mathcal{O}(n^2)$ for stochastic interchange on its own to $\mathcal{O}(n \: \text{log} \: n)$ without parallelization or $\mathcal{O}(n)$ with full parallelization. We demonstrate this speedup experimentally, and furthermore show that the signals we generate match the desired autocorrelation more accurately than those generated by stochastic interchange on its own. We observe that the signals we produce, unlike those generated by optimization on its own, are stationary.
Non-Additive Axiologies in Large Worlds
Authors: Christian Tarsney, Teruji Thomas
Date: 2020-10-14 00:00:00
Tasks: Ethics

Is the overall value of a world just the sum of values contributed by each value-bearing entity in that world? Additively separable axiologies (like total utilitarianism, prioritarianism, and critical level views) say 'yes', but non-additive axiologies (like average utilitarianism, rank-discounted utilitarianism, and variable value views) say 'no'. This distinction is practically important: additive axiologies support 'arguments from astronomical scale' which suggest (among other things) that it is overwhelmingly important for humanity to avoid premature extinction and ensure the existence of a large future population, while non-additive axiologies need not. We show, however, that when there is a large enough 'background population' unaffected by our choices, a wide range of non-additive axiologies converge in their implications with some additive axiology -- for instance, average utilitarianism converges to critical-level utilitarianism and various egalitarian theories converge to prioritiarianism. We further argue that real-world background populations may be large enough to make these limit results practically significant. This means that arguments from astronomical scale, and other arguments in practical ethics that seem to presuppose additive separability, may be truth-preserving in practice whether or not we accept additive separability as a basic axiological principle.
Transportation Internet: Concepts, Models, and Architectures
Authors: Hui Li
Date: 2020-10-14 00:00:00
Tasks: Autonomous Vehicles

Disruptive changes in vehicles and transportation have been triggered by automated, connected, electrified and shared mobility. Autonomous vehicles, like Internet data packets, are transported from one address to another through the road network. The Internet has become a general network transmission paradigm, and the Energy Internet is a successful application of this paradigm to the field of energy. By introducing the Internet paradigm to the field of transportation, this paper is the first to propose the Transportation Internet. Based on the concept of the Transportation Internet, fundamental models, such as the switching, routing, and hierarchical models, are established to form basic theories; new architectures, such as transportation routers and software defined transportation, are proposed to make transportation interconnected and open; system verifications, such as prototype and simulation, are also carried out to prove feasibility and advancement. The Transportation Internet, which is of far-reaching significance in science and industry, has brought systematic breakthroughs in theory, architecture, and technology, explored innovative research directions, and provided an Internet-like solution for the new generation of transportation.
Chance-constrained Stochastic MPC of Astlingen Urban Drainage Benchmark Network
Date: 2020-10-14 00:00:00
Tasks: Model Predictive Control

In urban drainage systems (UDS), a proven method for reducing the combined
sewer overflow (CSO) pollution is real-time control (RTC) based on model
predictive control (MPC). MPC methodologies for RTC of UDSs in the literature
rely on the computation of the optimal control strategies based on
deterministic rain forecast. However, in reality, uncertainties exist in
rainfall forecasts which affect severely accuracy of computing the optimal
control strategies. Under this context, this work aims to focus on the
uncertainty associated with the rainfall forecasting and its effects. One
option is to use stochastic information about the rain events in the
controller; in the case of using MPC methods, the class called stochastic MPC
is available, including several approaches such as the chance-constrained MPC
method. In this study, we apply stochastic MPC to the UDS using the
chance-constrained method. Moreover, we also compare the operational behavior
of both the classical MPC with perfect forecast and the chance-constrained MPC
based on different stochastic scenarios of the rain forecast. The application
and comparison have been based on simulations using a SWMM model of the
Astlingen urban drainage benchmark network.