pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
Authors: Goutam Bhat, Martin Danelljan, Fisher Yu, Luc van Gool, Radu Timofte
Date: 2021-08-18 00:00:00
Proceeding: ICCV 2021 10
Tasks: Burst Image Super-Resolution, Denoising, Image Restoration, Multi-Frame Super-Resolution, Super-Resolution

We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction. Our approach thereby leverages the advantages of deep learning, while also benefiting from the principled multi-frame fusion provided by the classical MAP formulation. We validate our approach through comprehensive experiments on burst denoising and burst super-resolution datasets. Our approach sets a new state-of-the-art for both tasks, demonstrating the generality and effectiveness of the proposed formulation.
Nonlinear Autoregression with Convergent Dynamics on Novel Computational Platforms
Authors: J. Chen, H. I. Nurdin
Date: 2021-08-18 00:00:00

Nonlinear stochastic modeling is useful for describing complex engineering systems. Meanwhile, neuromorphic (brain-inspired) computing paradigms are developing to tackle tasks that are challenging and resource intensive on digital computers. An emerging scheme is reservoir computing which exploits nonlinear dynamical systems for temporal information processing. This paper introduces reservoir computers with output feedback as stationary and ergodic infinite-order nonlinear autoregressive models. We highlight the versatility of this approach by employing classical and quantum reservoir computers to model synthetic and real data sets, further exploring their potential for control applications.
Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics
Authors: Benjamin J. Zhang, Youssef M. Marzouk, Konstantinos Spiliopoulos
Date: 2021-08-18 00:00:00

We introduce a novel geometry-informed irreversible perturbation that accelerates convergence of the Langevin algorithm for Bayesian computation. It is well documented that there exist perturbations to the Langevin dynamics that preserve its invariant measure while accelerating its convergence. Irreversible perturbations and reversible perturbations (such as Riemannian manifold Langevin dynamics (RMLD)) have separately been shown to improve the performance of Langevin samplers. We consider these two perturbations simultaneously by presenting a novel form of irreversible perturbation for RMLD that is informed by the underlying geometry. Through numerical examples, we show that this new irreversible perturbation can improve estimation performance over irreversible perturbations that do not take the geometry into account. Moreover we demonstrate that irreversible perturbations generally can be implemented in conjunction with the stochastic gradient version of the Langevin algorithm. Lastly, while continuous-time irreversible perturbations cannot impair the performance of a Langevin estimator, the situation can sometimes be more complicated when discretization is considered. To this end, we describe a discrete-time example in which irreversibility increases both the bias and variance of the resulting estimator.
One-shot conditional audio filtering of arbitrary sounds
Authors: Beat Gfeller, Dominik Roblek, Marco Tagliasacchi
Date: 2020-11-04 00:00:00

We consider the problem of separating a particular sound source from a single-channel mixture, based on only a short sample of the target source. Using SoundFilter, a wave-to-wave neural network architecture, we can train a model without using any sound class labels. Using a conditioning encoder model which is learned jointly with the source separation network, the trained model can be "configured" to filter arbitrary sound sources, even ones that it has not seen during training. Evaluated on the FSD50k dataset, our model obtains an SI-SDR improvement of 9.6 dB for mixtures of two sounds. When trained on Librispeech, our model achieves an SI-SDR improvement of 14.0 dB when separating one voice from a mixture of two speakers. Moreover, we show that the representation learned by the conditioning encoder clusters acoustically similar sounds together in the embedding space, even though it is trained without using any labels.
Analysis of pangolin metagenomic datasets reveals significant contamination, raising concerns for pangolin CoV host attribution
Authors: Adrian Jones, Daoyu Zhang, Yuri Deigin, Steven C. Quay
Date: 2021-08-18 00:00:00

Metagenomic datasets from pangolin tissue specimens have previously yielded SARS-related coronaviruses which show high homology in their receptor binding domain to SARS-CoV-2, suggesting a potential zoonotic source for this feature of the human virus, possibly via recombination (Liu et al. 2019, Lam et al. 2020, Xiao et al. 2020, Liu et al. 2020). Here we re-examine these published datasets. We report that only a few pangolin samples were found to contain coronavirus reads, and even then in low abundance, while other non-pangolin hosted viruses were present in higher abundance. We also discovered extensive contamination with human, rodent, and other mammalian gene sequences, which was a surprising finding. Furthermore, we uncovered a number of pangolin CoV sequences embedded in standard laboratory cloning vectors, which suggests the pangolin specimens could have been contaminated with sequences derived from synthetic biology experiments. Finally, we discover a third pangolin dataset (He et al. 2022) with low levels of SARSr-CoV sequences and unambiguous extensive contamination of several pangolin samples. For these reasons, we find it unlikely that the pangolins in question had a coronavirus infection while alive, and all current versions of the cited papers claiming a zoonotic infection of pangolins with a SARS-r CoV require substantial corrections and should be retracted until such corrections are made.
Tilted Platforms: Rental Housing Technology and the Rise of Urban Big Data Oligopolies
Authors: Geoff Boeing, Max Besbris, David Wachsmuth, Jake Wegmann
Date: 2021-08-18 00:00:00

This article interprets emerging scholarship on rental housing platforms -- particularly the most well-known and used short- and long-term rental housing platforms - and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators' own financial and political interests. The question of which data -- and people -- are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important differences in benefits and drawbacks exist between short-term and long-term rental housing platforms, but are underexplored in the literature: this article unpacks these differences and proposes policy recommendations.
Applying Intelligent Reflector Surfaces for Detecting Violent Expiratory Aerosol Cloud using Terahertz Signals
Authors: Harun Šiljak, Michael Taynnan Barros, Nathan D'Arcy, Daniel Perez Martins, Nicola Marchetti, Sasitharan Balasubramaniam
Date: 2021-08-17 00:00:00
Tasks: Cloud Detection

The recent COVID-19 pandemic has driven researchers from different spectrum to develop novel solutions that can improve detection and understanding of SARS-CoV-2 virus. In this article we propose the use of Intelligent Reflector Surface (IRS) emitting terahertz signals to detect airborne respiratory aerosol cloud that are secreted from people. Our proposed approach makes use of future IRS infrastructure to extend beyond communication functionality by adding environmental scanning for aerosol clouds. Simulations have also been conducted to analyze the accuracy of aerosol cloud detection based on a signal scanning and path optimization algorithm. Utilizing IRS for detecting respiratory aerosol cloud can lead to new added value of telecommunication infrastructures for sensor monitoring data that can be used for public health.
Simulation and estimation of an agent-based market-model with a matching engine
Authors: Ivan Jericevich, Patrick Chang, Tim Gebbie
Date: 2021-08-17 00:00:00

An agent-based model with interacting low frequency liquidity takers inter-mediated by high-frequency liquidity providers acting collectively as market makers can be used to provide realistic simulated price impact curves. This is possible when agent-based model interactions occur asynchronously via order matching using a matching engine in event time to replace sequential calendar time market clearing. Here the matching engine infrastructure has been modified to provide a continuous feed of order confirmations and updates as message streams in order to conform more closely to live trading environments. The resulting trade and quote message data from the simulations are then aggregated, calibrated and visualised. Various stylised facts are presented along with event visualisations and price impact curves. We argue that additional realism in modelling can be achieved with a small set of agent parameters and simple interaction rules once interactions are reactive, asynchronous and in event time. We argue that the reactive nature of market agents may be a fundamental property of financial markets and when accounted for can allow for parsimonious modelling without recourse to additional sources of noise.
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation
Authors: Zhongguo Li, Wen-Hua Chen, Jun Yang
Date: 2021-08-18 00:00:00
Tasks: Active Learning

In this paper, a concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors. Distinct from the existing solutions that require significant computational power for Bayesian estimation and path planning, the proposed solution is computationally affordable for onboard processors. A new concept of concurrent learning using multiple parallel estimators is proposed to learn the operational environment and quantify estimation uncertainty. The search agent is empowered with dual capability of exploiting current estimated parameters to track the source and probing the environment to reduce the impacts of uncertainty, namely Concurrent Learning based Dual Control for Exploration and Exploitation (CL-DCEE). In this setting, the control action not only minimises the tracking error between future agent's position and estimated source location, but also the uncertainty of predicted estimation. More importantly, the rigorous proven properties such as the convergence of CL-DCEE algorithm are established under mild assumptions on noises, and the impact of noises on the search performance is examined. Simulation results are provided to validate the effectiveness of the proposed CL-DCEE algorithm. Compared with the information-theoretic approach, CL-DCEE not only guarantees convergence, but produces better search performance and consumes much less computational time.
Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments
Authors: Yang Li, Bin Wang, Zhen Yang, Jiazheng Li, Guoqing Li
Date: 2021-08-18 00:00:00
Tasks: Scheduling

The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR)-enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity-gas-heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and micro-gas turbine (MT), as links of multi-energy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory (SOT) and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. Simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a trade-off between economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the Hybrid Intelligent Algorithm in terms of both optimization results and calculation efficiency.
Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon
Authors: Tim Brüdigam, Daniel Prader, Dirk Wollherr, Marion Leibold
Date: 2021-08-18 00:00:00
Tasks: Model Predictive Control, Prediction

Horizon length and model accuracy are defining factors when designing a Model Predictive Controller. While long horizons and detailed models have a positive effect on control performance, computational complexity increases. As predictions become less precise over the horizon length, it is worth investigating a combination of different models and varying time step size. Here, we propose a Model Predictive Control scheme that splits the prediction horizon into two segments. A detailed model is used for the short-term prediction horizon and a simplified model with an increased sampling time is employed for the long-term horizon. This approach combines the advantage of a long prediction horizon with a reduction of computational effort due to a simplified model and less decision variables. The presented Model Predictive Control is recursively feasible. A simulation study demonstrates the effectiveness of the proposed method: employing a long prediction horizon with advantages regarding computational complexity.
A Novel Formula Calculating the Dynamic State Error and Its Application in UAV Tracking Control Problem
Authors: Zhe Shen, Takeshi Tsuchiya
Date: 2021-08-18 00:00:00

This paper gives a novel formula (Copenhagen Limit) to calculate/estimate the dynamic state error of a system without a feedforward signal. Copenhagen Limit is in the form of a limit and finds the dynamic error in an analytical solution. It can be used to design the controller in a tracking control problem. A numerical example is displayed to illustrate the accuracy of the Copenhagen Limit. Besides, the controller of a UAV (quadrotor) is designed using the Copenhagen Limit in a trajectory-tracking problem. The result of it is also demonstrated and analyzed.
Modeling Degradation for Second-life Battery: Preliminary Results
Authors: Gabriele Pozzato, Seong Beom Lee, Simona Onori
Date: 2021-02-07 00:00:00

This paper presents a novel battery modeling framework based on the enhanced single particle model (ESPM) to account for degradation mechanisms of second-life batteries. While accounting for the transport and electrochemical phenomena in the battery solid and electrolyte phases, the dominant anode-related aging mechanisms, namely, solid electrolyte interphase (SEI) layer growth and lithium plating, are modeled. For the first time, the loss of active material (LAM), which describes the tendency of anode and cathode, over time, to reduce the electrode material available for intercalation and deintercalation, is introduced in the ESPM. Moreover, the coupling of the aging modes with the LAM dynamics provides a comprehensive framework that can be employed for the prediction of both linear and non-linear capacity fade crucial to assess second-life battery. Thus, relying on data borrowed from [18], a model parameter identification and a comprehensive sensitivity analysis are performed to prove the effectiveness of the modeling approach.
Compact Cooperative Adaptive Cruise Control for Energy Saving: Air Drag Modelling and Simulation
Authors: Yeojun Kim, Jacopo Guanetti, Francesco Borrelli
Date: 2021-08-18 00:00:00
Tasks: Model Predictive Control

This paper studies the value of communicated motion predictions in the longitudinal control of connected automated vehicles (CAVs). We focus on a safe cooperative adaptive cruise control (CACC) design and analyze the value of vehicle-to-vehicle (V2V) communication in the presence of uncertain front vehicle acceleration. The interest in CACC is motivated by the potential improvement in energy consumption and road throughput. In order to quantify this potential, we characterize experimentally the relationship between inter-vehicular gap, vehicle speed, and (reduction of) energy consumption for a compact plug-in hybrid electric vehicle. The resulting model is leveraged to show efficacy of our control design, which pursues small inter-vehicle gaps between consecutive CAVs and, therefore, improved energy efficiency. Our proposed control design is based on a robust model predictive control framework to systematically account for the system uncertainties. We present a set of thorough simulations aimed at quantifying energy efficiency improvement when vehicle states and predictions exchanged via V2V communication are used in the control law.
An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging
Authors: Peyman Mohtat, Sravan Pannala, Valentin Sulzer, Jason B. Siegel, Anna G. Stefanopoulou
Date: 2021-08-17 00:00:00

Fast charging of lithium-ion batteries is crucial to increase desirability for consumers and hence accelerate the adoption of electric vehicles. A major barrier to shorter charge times is the accelerated aging of the battery at higher charging rates, which can be driven by lithium plating, increased solid electrolyte interphase growth due to elevated temperatures, and particle cracking due to mechanical stress. Lithium plating depends on the overpotential of the negative electrode, and mechanical stress depends on the concentration gradient, both of which cannot be measured directly. Techniques based on physics-based models of the battery and optimal control algorithms have been developed to this end. While these methods show promise in reducing degradation, their optimization algorithms' complexity can limit their implementation. In this paper, we present a method based on the constant current constant voltage (CC-CV) charging scheme, called CC-CV$\eta \sigma$T (VEST). The new approach is simpler to implement and can be used with any model to impose varying levels of constraints on variables pertinent to degradation, such as plating potential and mechanical stress. We demonstrate the new CC-CV$\eta \sigma$T charging using an electrochemical model with mechanical and thermal effects included. Furthermore, we discuss how uncertainties can be accounted for by considering safety margins for the plating and stress constraints.
Wealth disparities and economic flow: Assessment using an asset exchange model with the surplus stock of the wealthy
Authors: Takeshi Kato, Yoshinori Hiroi
Date: 2021-08-17 00:00:00

How can we limit wealth disparities while stimulating economic flows in sustainable societies? To examine the link between these concepts, we propose an econophysics asset exchange model with the surplus stock of the wealthy. The wealthy are one of the two exchange agents and have more assets than the poor. Our simulation model converts the surplus contribution rate of the wealthy to a new variable parameter alongside the saving rate and introduces the total exchange (flow) and rank correlation coefficient (metabolism) as new evaluation indexes, adding to the Gini index (disparities), thereby assessing both wealth distribution and the relationships among the disparities, flow, and metabolism. We show that these result in a gamma-like wealth distribution, and our model reveals a trade-off between limiting disparities and vitalizing the market. To limit disparities and increase flow and metabolism, we also find the need to restrain savings and use the wealthy surplus stock. This relationship is explicitly expressed in the new equation introduced herein. The insights gained by uncovering the root of disparities may present a persuasive case for investments in social security measures or social businesses involving stock redistribution or sharing.
Why North Korean Refugees are Reluctant to Compete: The Roles of Cognitive Ability
Authors: Syngjoo Choi, Byung-Yeon Kim, Jungmin Lee, Sokbae Lee
Date: 2021-08-18 00:00:00

The study compares the competitiveness of three Korean groups raised in different institutional environments: South Korea, North Korea, and China. Laboratory experiments reveal that North Korean refugees are less likely to participate in competitive tournaments than South Koreans and Korean-Chinese immigrants. Analysis using a choice model with probability weighting suggests that lower cognitive ability may lead to lower expected performance, more pessimistic beliefs, and greater aversion to competition.
Economists' erroneous estimates of damages from climate change
Authors: Stephen Keen, Timothy M. Lenton, Antoine Godin, Devrim Yilmaz, Matheus Grasselli, Timothy J. Garrett
Date: 2021-08-17 00:00:00

Economists have predicted that damages from global warming will be as low as 2.1% of global economic production for a 3$^\circ$C rise in global average surface temperature, and 7.9% for a 6$^\circ$C rise. Such relatively trivial estimates of economic damages -- when these economists otherwise assume that human economic productivity will be an order of magnitude higher than today -- contrast strongly with predictions made by scientists of significantly reduced human habitability from climate change. Nonetheless, the coupled economic and climate models used to make such predictions have been influential in the international climate change debate and policy prescriptions. Here we review the empirical work done by economists and show that it severely underestimates damages from climate change by committing several methodological errors, including neglecting tipping points, and assuming that economic sectors not exposed to the weather are insulated from climate change. Most fundamentally, the influential Integrated Assessment Model DICE is shown to be incapable of generating an economic collapse, regardless of the level of damages. Given these flaws, economists' empirical estimates of economic damages from global warming should be rejected as unscientific, and models that have been calibrated to them, such as DICE, should not be used to evaluate economic risks from climate change, or in the development of policy to attenuate damages.