pwc-archive/papers-with-abstracts huggingface dataset:
A novel feed rate scheduling method based on Sigmoid function with chord error and kinematics constraints
Authors: Hexiong Li, Xin Jiang, Guanying Huo, Cheng Su, Bolun Wang, Yifei Hu, Zhiming Zheng
Date: 2021-05-12 00:00:00
Tasks: Scheduling

In high speed CNC (Compute Numerical Control) machining, the feed rate scheduling has played an important role to ensure machining quality and machining efficiency. In this paper, a novel feed rate scheduling method is proposed for generating smooth feed rate profile conveniently with the consideration of both geometric error and kinematic error. First, a relationship between feed rate value and chord error is applied to determine the feed rate curve. Then, breaking points, which can split whole curve into several blocks, can be found out using proposed two step screening method. For every block, a feed rate profile based on the Sigmoid function is generated. With the consideration of kinematic limitation and machining efficiency, a time-optimal feed rate adjustment algorithm is proposed to further adjust feed rate value at breaking points. After planning feed rate profile for each block, all blocks feed rate profile will be connected smoothly. The resulting feed rate profile is more concise compared with the polynomial profile and more efficient compared with the trigonometric profile. Finally, simulations with two free-form NURBS curves are conducted and comparison with the sine-curve method are carried out to verify the feasibility and applicability of the proposed method.
Fundamental Diagrams of Commercial Adaptive Cruise Control: Worldwide Experimental Evidence
Authors: Tienan Li, Danjue Chen, Hao Zhou, Yuanchang Xie, Jorge Laval
Date: 2021-05-12 00:00:00

Experimental measurements on commercial adaptive cruise control (ACC) vehicles \RoundTwo{are} becoming increasingly available from around the world, providing an unprecedented opportunity to study the traffic flow characteristics that arise from this technology. This paper adds new experimental evidence to this knowledge base and presents a comprehensive empirical study on the ACC equilibrium behaviors via the resulting fundamental diagrams. We find that like human-driven vehicles, ACC systems display a linear equilibrium spacing-speed relationship (within the range of available data) but the key parameters of these relationships can differ significantly from human-driven traffic depending on input settings: At the minimum headway setting, equilibrium capacities in excess of 3500 vehicles per hour are observed, together with an extremely fast equilibrium wave speed of 100 kilometers per hour on average. These fast waves are unfamiliar to human drivers, and may pose a safety risk. The results also suggest that ACC jam spacing can be much larger than in human traffic, which reduces the network storage capacity.
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes
Authors: Viet-Anh Le, Truong X. Nghiem
Date: 2021-01-25 00:00:00
Tasks: Active Learning, Autonomous Racing, Gaussian Processes

This paper proposes a receding horizon active learning and control problem for dynamical systems in which Gaussian Processes (GPs) are utilized to model the system dynamics. The active learning objective in the optimization problem is presented by the exact conditional differential entropy of GP predictions at multiple steps ahead, which is equivalent to the log determinant of the GP posterior covariance matrix. The resulting non-convex and complex optimization problem is solved by the Sequential Convex Programming algorithm that exploits the first-order approximations of non-convex functions. Simulation results of an autonomous racing car example verify that using the proposed method can significantly improve data quality for model learning while solving time is highly promising for real-time applications.
A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems with Power Reserves and Fast Frequency Response Capabilities
Authors: Victor Paduani, Hui Yu, Bei Xu, Ning Lu
Date: 2021-05-11 00:00:00
Tasks: Point Tracking

This paper presents a fast power-setpoint tracking algorithm to enable utility-scale photovoltaic (PV) systems to provide high quality grid services such as power reserves and fast frequency response. The algorithm unites maximum power-point estimation (MPPE) with flexible power-point tracking (FPPT) control to improve the performance of both algorithms, achieving fast and accurate PV power-setpoint tracking even under rapid solar irradiance changes. The MPPE is developed using a real-time, nonlinear curve-fitting approach based on the Levenberg-Marquardt algorithm. A modified adaptive FPPT based on the Perturb and Observe technique is developed for the power-setpoint tracking. By using MPPE to decouple the impact of irradiance changes on the measured PV output power, we develop a fast convergence technique for tracking power-reference changes within three FPPT iterations. Furthermore, to limit the maximum output power ripple, a new design is introduced for the steady-state voltage step size of the adaptive FPPT. The proposed algorithm is implemented on a testbed consisting of a 500 kVA three-phase, single-stage, utility-scale PV system on the OPAL-RT eMEGASIM platform. Results show that the proposed method outperforms the state-of-the-art.
Computational Simulation and Analysis of Major Control Parameters of Time-Dependent PV/T Collectors
Authors: Jimeng Shi, Cheng-Xian Lin
Date: 2021-05-01 00:00:00

In order to improve performance of photovoltaic/thermal (or PV/T for simplicity) collectors, this paper firstly validated a previous computational thermal model and then introduced an improved computational thermal model to investigate the effects of the major control parameters on the thermal performance of PV/T collectors, including solar cell temperature, back surface temperature, and outlet water temperature. Besides, a computational electrical model of PV/T system was also introduced to elaborate the relationship of voltage, current and power of a PV module (MSX60 polycrystalline solar cell) used in an experiment in the literature. Simulation results agree with the experimental data very well. The effects of the time-steps from 1 hour to minute, which is closed to the real time, were also reported. At last, several suggestions to improve the efficiency of PV/T system were illustrated.
Simulating short- and long-term evolutionary dynamics on rugged landscapes
Authors: Leonardo Trujillo, Paul Banse, Guillaume Beslon
Date: 2021-05-12 00:00:00

We propose a minimal model to simulate long waiting times followed by evolutionary bursts on rugged landscapes. It combines point and inversions-like mutations as sources of genetic variation. The inversions are intended to simulate one of the main chromosomal rearrangements. Using the well-known family of NK fitness landscapes, we simulate random adaptive walks, i.e. successive mutational events constrained to incremental fitness selection. We report the emergence of different time scales: a short-term dynamics mainly driven by point mutations, followed by a long-term (stasis-like) waiting period until a new mutation arises. This new mutation is an inversion which can trigger a burst of successive point mutations, and then drives the system to new short-term increasing-fitness period. We analyse the effect of genes epistatic interactions on the evolutionary time scales. We suggest that the present model mimics the process of evolutionary innovation and punctuated equilibrium.
Polygrammar: Grammar for Digital Polymer Representation and Generation
Authors: Minghao Guo, Wan Shou, Liane Makatura, Timothy Erps, Michael Foshey, Wojciech Matusik
Date: 2021-05-05 00:00:00
Tasks: valid

Polymers are widely-studied materials with diverse properties and applications determined by different molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches are unable to offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers. As a demonstrative example, we implement our grammar for polyurethanes. Using our symbolic hypergraph representation and 14 simple production rules, our PolyGrammar is able to represent and generate all valid polyurethane structures. We also present an algorithm to translate any polyurethane structure from the popular SMILES string format into our PolyGrammar representation. We test the representative power of PolyGrammar by translating a dataset of over 600 polyurethane samples collected from literature. Furthermore, we show that PolyGrammar can be easily extended to the other copolymers and homopolymers such as polyacrylates. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, our PolyGrammar takes an important step toward a more comprehensive and practical system for polymer discovery and exploration. As the first bridge between formal languages and chemistry, PolyGrammar also serves as a critical blueprint to inform the design of similar grammars for other chemistries, including organic and inorganic molecules.
Spontaneous mechanical and energetic state transitions during Caenorhabditis elegans gastrulation
Authors: Jiao Miao, Guoye Guan, Chao Tang
Date: 2021-05-12 00:00:00

Gastrulation, namely cell internalization, is a significant milestone during the development of metazoans from worm to human, which generates multiple embryonic layers with distinct cell fates and spatial organizations. Although many molecular activities are known to facilitate this process, in this paper, we focus on gastrulation of the nematode Caenorhabditis elegans and theoretically demonstrate that even a group of cells with only isotropic repulsive and attractive interactions can experience such internalization behavior when dividing within a confined space. As the cell number increases and cell size decreases, the cells contacted to the eggshell become closer to each other along with harder lateral compression, and a cell that internalizes could effectively increase the cell neighbor distance and lower the potential energy of the system. The multicellular structure transits from single- to double-layer spontaneously with bistable states existing from 15- to 44-cell stages, near the gastrulation timing in vivo. Specifically, the cells with a larger size or placed near a smaller-curvature boundary are easier to internalize. Actively regulating a few cells' internalizations can make the morphogenesis noise-resistant. Our work successfully recaptures the key characteristics in C. elegans gastrulation and provides a rational interpretation of how this phenomenon emerges and is optimally programmed.
Carbon Leakage in a European Power System with Inhomogeneous Carbon Prices
Authors: Markus Schlott, Omar El Sayed, Mariia Bilousova, Fabian Hofmann, Alexander Kies, Horst Stöcker
Date: 2021-05-12 00:00:00

Global warming is one of the main threats to the future of humanity and extensive emissions of greenhouse gases are found to be the main cause of global temperature rise as well as climate change. During the last decades international attention has focused on this issue, as well as on searching for viable solutions to mitigate global warming. In this context, the pricing of greenhouse gas emissions turned out to be the most prominent mechanism: First, to lower the emissions, and second, to capture their external costs. By now, various carbon dioxide taxes have been adopted by several countries in Europe and around the world; moreover, the list of these countries is growing. However, there is no standardized approach and the price for carbon varies significantly from one country to another. Regionally diversified carbon prices in turn lead to carbon leakage, which will offset the climate protection goals. In this paper, a simplified European power system with flexible carbon prices regarding the Gross Domestic Product (GDP) is investigated. A distribution parameter that quantifies carbon leakage is defined and varied together with the base carbon price, where the combination of both parameters describes the spatially resolved price distribution, i.e. the effective carbon pricing among the European regions. It is shown that inhomogeneous carbon prices will indeed lead to significant carbon leakage across the continent, and that coal-fired electricity generation will remain a cheap and therefore major source of power in Eastern and South-Eastern Europe - representing a potential risk for the long term decarbonization targets within the European Union.
PolSIRD: Modeling Epidemic Spread under Intervention Policies
Authors: Nitin Kamra, Yizhou Zhang, Sirisha Rambhatla, Chuizheng Meng, Yan Liu
Date: 2020-09-03 00:00:00
Tasks: counterfactual

Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies. In addition, these models assume full observability of disease cases and do not account for under-reporting. We present a mathematical model, namely PolSIRD, which accounts for the under-reporting by introducing an observation mechanism. It also captures the effects of intervention policies on the disease spread parameters by leveraging intervention policy data along with the reported disease cases. Furthermore, we allow our recurrent model to learn the initial hidden state of all compartments end-to-end along with other parameters via gradient-based training. We apply our model to the spread of the recent global outbreak of COVID-19 in the United States, where our model outperforms the methods employed by the CDC in predicting the spread. We also provide counterfactual simulations from our model to analyze the effect of lifting the intervention policies prematurely and our model correctly predicts the second wave of the epidemic.
Robust Inference on Income Inequality: $t$-Statistic Based Approaches
Authors: Rustam Ibragimov, Paul Kattuman, Anton Skrobotov
Date: 2021-05-11 00:00:00

Empirical analyses on income and wealth inequality and those in other fields in economics and finance often face the difficulty that the data is heterogeneous, heavy-tailed or correlated in some unknown fashion. The paper focuses on applications of the recently developed \textit{t}-statistic based robust inference approaches in the analysis of inequality measures and their comparisons under the above problems. Following the approaches, in particular, a robust large sample test on equality of two parameters of interest (e.g., a test of equality of inequality measures in two regions or countries considered) is conducted as follows: The data in the two samples dealt with is partitioned into fixed numbers $q_1, q_2\ge 2$ (e.g., $q_1=q_2=2, 4, 8$) of groups, the parameters (inequality measures dealt with) are estimated for each group, and inference is based on a standard two-sample $t-$test with the resulting $q_1, q_2$ group estimators. Robust $t-$statistic approaches result in valid inference under general conditions that group estimators of parameters (e.g., inequality measures) considered are asymptotically independent, unbiased and Gaussian of possibly different variances, or weakly converge, at an arbitrary rate, to independent scale mixtures of normal random variables. These conditions are typically satisfied in empirical applications even under pronounced heavy-tailedness and heterogeneity and possible dependence in observations. The methods dealt with in the paper complement and compare favorably with other inference approaches available in the literature. The use of robust inference approaches is illustrated by an empirical analysis of income inequality measures and their comparisons across different regions in Russia.
Emerging Platform Work in the Context of the Regulatory Loophole (The Uber Fiasco in Hungary)
Authors: Csaba Mako, Miklos Illessy, Jozsef Pap, Saeed Nosratabadi
Date: 2021-05-12 00:00:00

The study examines the essential features of the so-called platform-based work, which is rapidly evolving into a major, potentially game-changing force in the labor market. From low-skilled, low-paid services (such as passenger transport) to highly skilled and high-paying project-based work (such as the development of artificial intelligence algorithms), a broad range of tasks can be carried out through a variety of digital platforms. Our paper discusses the platform-based content, working conditions, employment status, and advocacy problems. Terminological and methodological problems are dealt with in-depth in the course of the literature review, together with the 'gray areas' of work and employment regulation. To examine some of the complex dynamics of this fast-evolving arena, we focus on the unsuccessful market entry of the digital platform company Uber in Hungary 2016 and the relationship to institutional-regulatory platform-based work standards. Dilemmas relevant to the enforcement of labor law regarding platform-based work are also paid special attention to the study. Employing a digital workforce is a significant challenge not only for labor law regulation but also for stakeholder advocacy.
Inflation -- who cares? Monetary Policy in Times of Low Attention
Authors: Oliver Pfäuti
Date: 2021-05-11 00:00:00

I propose an approach to quantify attention to inflation in the data and show that the decrease in the volatility and persistence of U.S. inflation after the Great Inflation period was accompanied by a decline in the public's attention to inflation. This decline in attention has important implications (positive and normative) for monetary policy as it renders managing inflation expectations more difficult and can lead to inflation-attention traps: prolonged periods of a binding lower bound and low inflation due to slowly-adjusting inflation expectations. As attention declines the optimal policy response is to increase the inflation target. Accounting for the lower bound fundamentally changes the normative implications of declining attention. While lower attention raises welfare absent the lower-bound constraint, it decreases welfare when accounting for the lower bound.
Random Double Auction: A Robust Bilateral Trading Mechanism
Authors: Wanchang Zhang
Date: 2021-05-12 00:00:00

I construct a novel random double auction as a robust bilateral trading mechanism for a profit-maximizing intermediary who facilitates trade between a buyer and a seller. It works as follows. The intermediary publicly commits to charging a fixed commission fee and randomly drawing a spread from a uniform distribution. Then the buyer submits a bid price and the seller submits an ask price simultaneously. If the difference between the bid price and the ask price is greater than the realized spread, then the asset is transacted at the midpoint price, and each pays the intermediary half of the fixed commission fee. Otherwise, no trade takes place, and no one pays or receives anything. I show that the random double auction maximizes the worst-case expected profit across all dominant-strategy incentive compatible and ex-post individually rational mechanisms for the symmetric case. I also construct a robust trading mechanism with similar properties for the asymmetric case.
A CNN-based Prediction-Aware Quality Enhancement Framework for VVC
Authors: Fatemeh Nasiri, Wassim Hamidouche, Luce Morin, Nicolas Dhollande, Gildas Cocherel
Date: 2021-05-12 00:00:00
Tasks: Quantization

This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal. The motivation is that normative decisions made by the encoder can significantly impact the type and strength of artifacts in the decoded images. In this paper, the main focus has been put on decisions defining the prediction signal in intra and inter frames. This information has been used in the training phase as well as input to help the process of learning artifacts that are specific to each coding type. Furthermore, to retain a low memory requirement for the proposed method, one model is used for all Quantization Parameters (QPs) with a QP-map, which is also shared between luma and chroma components. In addition to the Post Processing (PP) approach, the In-Loop Filtering (ILF) codec integration has also been considered, where the characteristics of the Group of Pictures (GoP) are taken into account to boost the performance. The proposed CNN-based Quality Enhancement(QE) framework has been implemented on top of the VVC Test Model (VTM-10). Experiments show that the prediction-aware aspect of the proposed method improves the coding efficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR, at the same network complexity compared to the default CNN-based QE filter.
Efficient Non-sequential Division for FPGAs
Authors: Michael Lunglmayr
Date: 2021-05-12 00:00:00

The division operation is important for many areas of data processing. Especially considering today's demand for hardware accelerators for machine learning algorithms, there is a high demand for an efficient calculation of the division function, e.g. for averaging operations or the online calculation of activation functions. For such algorithms, which are often iterative in nature, one would like to have a non-sequential way of calculating the division operation. The work presents such an approach especially tailored to FPGAs as processing platforms. It is based on an efficient way of calculating the reciprocal operation, based on a low complexity approximation combined with a correction function. The described approach allows approximating the division operation (with errors that can be made arbitrarily low), within one clock cycle using only low hardware requirements. These hardware requirements are scaleable depending on the desired precision. We show results obtained by synthesis and hardware simulations demonstrating the low complexity and high clock speeds achievable with the described method compared to other methods described in the literature.
Fault-Resilient PCIe Bus with Real-time Error Detection and Correction
Authors: Mostafa Darvishi
Date: 2021-05-12 00:00:00

This paper presents a novel IP design for real-time fault/error detection and recovery on a peripheral component interconnect express (PCIe) which interfaces a host system (here a PC) to a slave design including processing system and memory transaction implemented on a Zynq Ultrascale Xilinx Kintex FPGA board (KCU105). The proposed IP design is capable of detection and correction of different types of PCIe errors on-the-fly