pwc-archive/papers-with-abstracts huggingface dataset:
Representing Lanes as Arc-length-based Parametric Curves to Facilitate Estimation in Vehicle Control
Authors: Wubing B. Qin
Date: 2022-06-09 00:00:00
Tasks: ARC

This paper revisits the fundamental mathematics of Taylor series to approximate curves with function representation and arc-length-based parametric representation. Parametric representation is shown to preserve its form in coordinate transformation and parameter shifting. These preservations can significantly facilitate lane estimation in vehicle control since lanes perceived by cameras are typically represented in vehicle body-fixed frames which are translating and rotating. Then we derived the transformation from function representation to arc-length-based parametric representation and its inverse. We applied the transformation to lane estimation in vehicle control problem, and derived the evolution of coefficients for parametric representation that can be used for prediction. We come up with a procedure to simulate the whole process with perception, lane estimation and control for the path-following problem. Simulations are performed to demonstrate the efficacy of the proposed lane estimation algorithm using parametric representation. The results indicate that the proposed technique ensures that vehicle control can achieve reasonably good performance at very low perception updating rate.
From Dimension-Free Manifolds to Dimension-varying Control Systems
Authors: Daizhan Cheng, Zhengping Ji
Date: 2022-06-09 00:00:00

Starting from the vector multipliers, the inner product, norm, distance, as well as addition of two vectors of different dimensions are proposed, which makes the spaces into a topological vector space, called the Euclidean space of different dimension (ESDD). An equivalence is obtained via distance. As a quotient space of ESDDs w.r.t. equivalence, the dimension-free Euclidean spaces (DFESs) and dimension-free manifolds (DFMs) are obtained, which have bundled vector spaces as its tangent space at each point. Using the natural projection from a ESDD to a DFES, a fiber bundle structure is obtained, which has ESDD as its total space and DFES as its base space. Classical objects in differential geometry, such as smooth functions, (co-)vector fields, tensor fields, etc., have been extended to the case of DFMs with the help of projections among different dimensional Euclidean spaces. Then the dimension-varying dynamic systems (DVDSs) and dimension-varying control systems (DVCSs) are presented, which have DFM as their state space. The realization, which is a lifting of DVDSs or DVCSs from DFMs into ESDDs, and the projection of DVDSs or DVCSs from ESDDs onto DFMs are investigated.
Seamless Accurate Positioning in Deep Urban Area based on Mode Switching Between DGNSS and Multipath Mitigation Positioning
Authors: Yongjun Lee, Yoola Hwang, Jae Young Ahn, Jiwon Seo, Byungwoon Park
Date: 2022-06-09 00:00:00

Multipath and non-line-of-sight (NLOS) signals are the major causes of poor accuracy of a global navigation satellite system (GNSS) in urban areas. Despite the wide usage of the GNSS in populated urban areas, it is difficult to suggest a generalized method because multipath errors are user-specific errors that cannot be eliminated by the DGNSS or a real-time kinematic technique. This paper introduces a real-time multipath estimation and mitigation technique, which considers compensation for the time offset between constellations. It also presents a mode-switching algorithm between the DGNSS and multipath mitigating mode and shows that this technique can be effectively utilized for automobiles in a deep urban environment without any help from sensors other than GNSS. The availability is improved from 64% to 100% and the error RMS is reduced from 11.1 m to 1.2 m on Teheran-ro, Seoul, Korea. Because this method does not require prior information or additional sensor implementation for high-positioning performance in deep urban areas, it is expected to gain wide usage in not only the automotive industry but also future intelligent transportation systems.
Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment
Authors: Xin Li, Daqi Zhu, Bing Sun, Qi Chen, Wenyang Gan, Zhigang Li
Date: 2022-06-09 00:00:00
Tasks: Model Predictive Control

In this paper, formation tracking for a multi-AUV system (MAS) using an improved adaptive sliding mode control method is studied in the Three Dimensional (3-D) underwater environment. Firstly, the kinematics model and the dynamic model of the AUVs are given as the Six Dimensions of Freedom (6-DOF) considered. Then, control law based on the mathematical model of the AUVs is proposed based on the improved sliding mode method. A second order sliding mode control method is adopted to eliminate the chatting phenomenon of the controller. Thirdly, considering the water flow in the underwater working environment of the AUVs, an adaptive module is added to the controller. With the adaptive approach, the finite disturbances caused by water flow could be handled with the controller. The proposed method achieves stability by substituting an adaptive continuous term for the switching term in the controller. At last, a robust sliding mode controller with continuous model predictive control strategy for the multi-AUV system is developed to achieve leader-follower formation tracking under the presence of bounded flow disturbances, and simulations are implemented to confirm the effectiveness of the proposed method.
A Sparse Polynomial Chaos Expansion-Based Method for Probabilistic Transient Stability Assessment and Enhancement
Authors: Jingyu Liu, Xiaoting Wang, Xiaozhe Wang
Date: 2022-06-09 00:00:00
Tasks: Sensitivity

This paper proposes an adaptive sparse polynomial chaos expansion(PCE)-based method to quantify the impacts of uncertainties on critical clearing time (CCT) that is an important index in transient stability analysis. The proposed method can not only give fast and accurate estimations for the probabilistic characteristics (e.g., mean, variance, probability density function) of the probabilistic CCT (PCCT), but also provides crucial information about the sensitivity of random inputs with respect to the variance of PCCT. Utilizing the sensitivity information, mitigation measures can be developed for transient stability enhancement. Numerical studies on the WSCC 9-bus system demonstrate the high accuracy and efficiency of the proposed method compared to the Monte Carlo simulation method. The provided sensitivity information and the effectiveness of mitigation measures in transient stability enhancement are also verified.
Bounded Rationality and Animal Spirits: A Fluctuation-Response Approach to Slutsky Matrices
Authors: Jerome Garnier-Brun, Jean-Philippe Bouchaud, Michael Benzaquen
Date: 2022-06-09 00:00:00
Tasks: valid

The Slutsky equation, central in consumer choice theory, is derived from the usual hypotheses underlying most standard models in Economics, such as full rationality, homogeneity, and absence of interactions. We present a statistical physics framework that allows us to relax such assumptions. We first derive a general fluctuation-response formula that relates the Slutsky matrix to spontaneous fluctuations of consumption rather than to response to changing prices and budget. We then show that, within our hypotheses, the symmetry of the Slutsky matrix remains valid even when agents are only boundedly rational but non-interacting. We then propose a model where agents are influenced by the choice of others, leading to a phase transition beyond which consumption is dominated by herding (or `"fashion") effects. In this case, the individual Slutsky matrix is no longer symmetric, even for fully rational agents. The vicinity of the transition features a peak in asymmetry.
Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction
Authors: Kumi Nakai, Takayuki Nagata, Keigo Yamada, Yuji Saito, Taku Nonomura, Masayuki Kano, Shin-ichi Ito, Hiromichi Nagao
Date: 2022-06-09 00:00:00
Tasks: parameter estimation, Sensitivity

The ``big'' seismic data not only acquired by seismometers but also acquired by vibrometers installed in buildings and infrastructure and accelerometers installed in smartphones will be certainly utilized for seismic research in the near future. Since it is impractical to utilize all the seismic big data in terms of the computational cost, methods which can select observation sites depending on the purpose are indispensable. We propose an observation site selection method for the accurate reconstruction of the seismic wavefield by process-driven approaches. The proposed method selects observation sites suitable for accurately estimating physical model parameters such as subsurface structures and source information to be input into a numerical simulation of the seismic wavefield. The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method. The observation site selection in the proposed method is based on the sensitivity of each observation site candidate to the physical model parameters; the matrix corresponding to the sensitivity is constructed by approximately calculating the derivatives based on the simulations, and then, observation sites are selected by evaluating the quantity of the sensitivity matrix based on the D-optimality criterion proposed in the optimal design of experiments. In the present study, physical knowledge on the sensitivity to the parameters such as seismic velocity, layer thickness, and hypocenter location was obtained by investigating the characteristics of the sensitivity matrix. Furthermore, the effectiveness of the proposed method was shown by verifying the accuracy of seismic wavefield reconstruction using the observation sites selected by the proposed method.
Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification
Authors: Beomgu Kang, Hye-Young Heo, HyunWook Park
Date: 2022-06-09 00:00:00
Tasks: Magnetic Resonance Fingerprinting

Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF) is a novel quantitative imaging technique that simultaneously measures several tissue parameters of semisolid macromolecule and free bulk water. In this study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that estimates the free bulk water and MTC tissue parameters from MR fingerprints regardless of MRF schedule, thereby avoiding time-consuming process such as generation of training dataset and network training according to each MRF schedule. A recurrent neural network is designed to cope with two types of variants of MRF schedules: 1) various lengths and 2) various patterns. Experiments on digital phantoms and in vivo data demonstrate that our approach can achieve accurate quantification for the water and MTC parameters with multiple MRF schedules. Moreover, the proposed method is in excellent agreement with the conventional deep learning and fitting methods. The flexible OTOM framework could be an efficient tissue quantification tool for various MRF protocols.
On the Performance of the Neyman Allocation with Small Pilots
Authors: Yong Cai, Ahnaf Rafi
Date: 2022-06-09 00:00:00
Tasks: Experimental Design

The Neyman Allocation is used in many papers on experimental design, which typically assume that researchers have access to large pilot studies. This may be unrealistic. To understand the properties of the Neyman Allocation with small pilots, we study its behavior in an asymptotic framework that takes pilot size to be fixed even as the size of the main wave tends to infinity. Our analysis shows that the Neyman Allocation can lead to estimates of the ATE with higher asymptotic variance than with (non-adaptive) balanced randomization. In particular, this happens when the outcome variable is relatively homoskedastic with respect to treatment status or when it exhibits high kurtosis. We provide a series of empirical examples showing that such situations can arise in practice. Our results suggest that researchers with small pilots should not use the Neyman Allocation if they believe that outcomes are homoskedastic or heavy-tailed. Finally, we examine some potential methods for improving the finite sample performance of the FNA via simulations.
A Two-Ball Ellsberg Paradox: An Experiment
Authors: Brian Jabarian, Simon Lazarus
Date: 2022-06-09 00:00:00
Tasks: Experimental Design, Vocal Bursts Valence Prediction

We conduct an incentivized experiment on a nationally representative US sample \\ (N=708) to test whether people prefer to avoid ambiguity even when it means choosing dominated options. In contrast to the literature, we find that 55\% of subjects prefer a risky act to an ambiguous act that always provides a larger probability of winning. Our experimental design shows that such a preference is not mainly due to a lack of understanding. We conclude that subjects avoid ambiguity \textit{per se} rather than avoiding ambiguity because it may yield a worse outcome. Such behavior cannot be reconciled with existing models of ambiguity aversion in a straightforward manner.
Inference for Matched Tuples and Fully Blocked Factorial Designs
Authors: Yuehao Bai, Jizhou Liu, Max Tabord-Meehan
Date: 2022-06-08 00:00:00
Tasks: Experimental Design

This paper studies inference in randomized controlled trials with multiple treatments, where treatment status is determined according to a "matched tuples" design. Here, by a matched tuples design, we mean an experimental design where units are sampled i.i.d. from the population of interest, grouped into "homogeneous" blocks with cardinality equal to the number of treatments, and finally, within each block, each treatment is assigned exactly once uniformly at random. We first study estimation and inference for matched tuples designs in the general setting where the parameter of interest is a vector of linear contrasts over the collection of average potential outcomes for each treatment. Parameters of this form include standard average treatment effects used to compare one treatment relative to another, but also include parameters which may be of interest in the analysis of factorial designs. We first establish conditions under which a sample analogue estimator is asymptotically normal and construct a consistent estimator of its corresponding asymptotic variance. Combining these results establishes the asymptotic exactness of tests based on these estimators. In contrast, we show that, for two common testing procedures based on t-tests constructed from linear regressions, one test is generally conservative while the other generally invalid. We go on to apply our results to study the asymptotic properties of what we call "fully-blocked" 2^K factorial designs, which are simply matched tuples designs applied to a full factorial experiment. Leveraging our previous results, we establish that our estimator achieves a lower asymptotic variance under the fully-blocked design than that under any stratified factorial design which stratifies the experimental sample into a finite number of "large" strata. A simulation study and empirical application illustrate the practical relevance of our results.
The Economics of Automated Market Makers
Authors: Robin Fritsch, Samuel Käser, Roger Wattenhofer
Date: 2022-06-09 00:00:00

This paper studies the question whether automated market maker protocols such as Uniswap can sustainably retain a portion of their trading fees for the protocol. We approach the problem by modelling how to optimally choose a pool's take rate, i.e\ the fraction of fee revenue that remains with the protocol, in order to maximize the protocol's revenue. The model suggest that if AMMs have a portion of loyal trade volume, they can sustainably set a non-zero take rate, even without losing liquidity to competitors with a zero take rate. Furthermore, we determine the optimal take rate depending on a number of model parameters including how much loyal trade volume pools have and how high the competitors' take rates are.
Estimating the Gains (and Losses) of Revenue Management
Authors: Xavier D'Haultfœuille, Ao Wang, Philippe Février, Lionel Wilner
Date: 2022-06-09 00:00:00
Tasks: Management

Despite the wide adoption of revenue management in many industries such as airline, railway, and hospitality, there is still scarce empirical evidence on the gains or losses of such strategies compared to uniform pricing or fully flexible strategies. We quantify such gains and losses and identify their underlying sources in the context of French railway transportation. The identification of demand is complicated by censoring and the absence of exogenous price variations. We develop an original identification strategy combining temporal variations in relative prices, consumers' rationality and weak optimality conditions on the firm's pricing strategy. Our results suggest similar or better performance of the actual revenue management compared to optimal uniform pricing, but also substantial losses of up to 16.7% compared to the optimal pricing strategy. We also highlight the key role of revenue management in acquiring information when demand is uncertain.
Capital and Labor Income Pareto Exponents in the United States, 1916-2019
Authors: Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
Date: 2022-06-09 00:00:00

Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods. Using tabulated summaries of incomes from tax authorities and a recent estimation method, we estimate income Pareto exponents in U.S. for 1916-2019. We find that during the past three decades, the capital and labor income Pareto exponents have been stable at around 1.2 and 2. Our findings suggest that the top tail income and wealth inequality is higher and wealthy agents have twice as large an impact on the aggregate economy than previously thought but there is no clear trend post-1985.
Information Asymmetry and Search Intensity
Authors: Atabek Atayev
Date: 2022-06-09 00:00:00

The existing studies on consumer search agree that consumers are worse-off when they do not observe sellers' production marginal cost than when they do. In this paper we challenge this conclusion. Employing a canonical model of simultaneous search, we show that it may be favorable for consumer to not observe the production marginal cost. The reason is that, in expectation, consumer search more intensely when they do not know sellers' production marginal cost than when they know it. More intense search imposes higher competitive pressure on sellers, which in turn benefits consumers through lower prices.
Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing
Authors: Yuanhao Cui, Xiaojun Jing, Visa Koivunen
Date: 2022-06-09 00:00:00
Tasks: Decoder, Philosophy

Radar and modern communication systems are both evaluating towards higher frequency bands and massive antenna arrays, thus increasing their similarities in terms of hardware structure, channel characteristics, and signal processing pipelines. To suppress the cross-system interference caused by communications and radar systems with shared spectral and hardware resources, the co-design philosophy, wherein the communications and radar/sensing systems can operate in parallel with jointly optimized performance, has drawn substantial attention from both academia and industry. In this paper, we propose a nullspace-based joint precoder-decoder design for spectrum sharing between multicarrier radar and multiuser multicarrier communication systems, by employing the maximizing signal interference noise ratio (max-SINR) criterion and interference alignment (IA) constraints. By projecting the cross-system interference to the designed null spaces, a maximum degree of freedom upper bound for the $K+1$-radar-communication-user interference channel can be achieved. Our simulation studies demonstrate that interference can be practically fully canceled in both communication and radar systems. This leads to improved detection performance in radar and a higher rate in communication subsystems. A significant performance gain over a nullspace-based precoder-only design is also obtained.
Adaptive Frequency Band Selection for Accurate and Fast Positioning utilizing SOPs
Authors: Nicolas Souli, Panayiotis Kolios, Georgios Ellinas
Date: 2022-06-09 00:00:00
Tasks: Learning Theory, Position

Signals of opportunity (SOPs) are a promising technique that can be used for relative positioning in areas where global navigation satellite system (GNSS) information is unreliable or unavailable. This technique processes features of the various signals transmitted over a broad wireless spectrum to enable a receiver to position itself in space. This work examines the frequency selection problem in order to achieve fast and accurate positioning using only the received signal strength (RSS) of the surrounding signals. Starting with a prior belief, the problem of searching for a frequency band that best matches a predicted location trajectory is investigated. To maximize the accuracy of the position estimate, a ranking-and-selection problem is mathematically formulated. A knowledge-gradient (KG) algorithm from optimal learning theory is proposed that uses correlations in the Bayesian prior beliefs of the frequency band values to dramatically reduce the algorithm's processing time. The technique is experimentally tested for a practical scenario of an unmanned aerial vehicle (UAV) moving around a GPS-denied environment, with obtained results demonstrating its validity and practical applicability.
On the Performance of UAV Relaying with Reconfigurable Antenna and Media Based Modulation in the Presence of Shadowed Fading
Authors: Ayse Betul Buyuksar, Eylem Erdogan, Ibrahim Altunbas
Date: 2022-06-09 00:00:00
Tasks: Diversity

Unmanned aerial vehicles (UAVs) have attracted significant interest from the academia and industry most recently. Motivated by the wide usage of UAVs, this paper considers UAV communication with reconfigurable antenna (RA) in the presence of fading and shadowing effects which occur due to tall buildings and skyscrapers in urban areas. More precisely, RA offers to receive information through mirror activation patterns (MAPs) so that it can achieve a receive diversity with decreased error probability by using only one radio frequency (RF) chain. Also, media based modulation (MBM) technique for data transmission with MAPs can be exploited by using RAs with reduced cost. To quantify the performance of the proposed UAV system, we derive a tight upper bound for the overall error probability by considering approximated channel model to the standardization studies for UAVs. The results have shown that RAs can make the overall system more resilient to shadowing and fading effects in terms of error performance, and they are energy efficient.