pwc-archive/papers-with-abstracts huggingface dataset:
Computer Vision Aided Beam Tracking in A Real-World Millimeter Wave Deployment
Authors: Shuaifeng Jiang, Ahmed Alkhateeb
Date: 2021-11-29 00:00:00
Tasks: Decoder, Management

Millimeter-wave (mmWave) and terahertz (THz) communications require beamforming to acquire adequate receive signal-to-noise ratio (SNR). To find the optimal beam, current beam management solutions perform beam training over a large number of beams in pre-defined codebooks. The beam training overhead increases the access latency and can become infeasible for high-mobility applications. To reduce or even eliminate this beam training overhead, we propose to utilize the visual data, captured for example by cameras at the base stations, to guide the beam tracking/refining process. We propose a machine learning (ML) framework, based on an encoder-decoder architecture, that can predict the future beams using the previously obtained visual sensing information. Our proposed approach is evaluated on a large-scale real-world dataset, where it achieves an accuracy of $64.47\%$ (and a normalized receive power of $97.66\%$) in predicting the future beam. This is achieved while requiring less than $1\%$ of the beam training overhead of a corresponding baseline solution that uses a sequence of previous beams to predict the future one. This high performance and low overhead obtained on the real-world dataset demonstrate the potential of the proposed vision-aided beam tracking approach in real-world applications.
Waveform Optimization for Wireless Power Transfer with Power Amplifier and Energy Harvester Non-Linearities
Authors: Yumeng Zhang, Bruno Clerckx
Date: 2021-11-28 00:00:00

Waveform optimization has recently been shown to be a key technique to boost the efficiency and range of far-field wireless power transfer (WPT). Current research has optimized transmit waveform adaptive to channel state information (CSI) and accounting for energy harvester (EH)'s non-linearity but under the assumption of linear high power amplifiers (HPA) at the transmitter. This paper proposes a channel-adaptive waveform design strategy that optimizes the transmitter's input waveform considering both HPA and EH non-linearities. Simulations demonstrate that HPA's non-linearity degrades the energy harvesting efficiency of WPT significantly, while the performance loss can be compensated by using the proposed optimal input waveform.
Data-independent Low-complexity KLT Approximations for Image and Video Coding
Authors: A. P. Radünz, T. L. T. da Silveira, F. M. Bayer, R. J. Cintra
Date: 2021-11-28 00:00:00
Tasks: Dimensionality Reduction, Video Compression

The Karhunen-Lo\`eve transform (KLT) is often used for data decorrelation and dimensionality reduction. The KLT is able to optimally retain the signal energy in only few transform components, being mathematically suitable for image and video compression. However, in practice, because of its high computational cost and dependence on the input signal, its application in real-time scenarios is precluded. This work proposes low-computational cost approximations for the KLT. We focus on the blocklengths $N \in \{4, 8, 16, 32 \}$ because they are widely employed in image and video coding standards such as JPEG and high efficiency video coding (HEVC). Extensive computational experiments demonstrate the suitability of the proposed low-complexity transforms for image and video compression.
Passive Indoor Localization with WiFi Fingerprints
Authors: Minh Tu Hoang, Brosnan Yuen, Kai Ren, Ahmed Elmoogy, Xiaodai Dong, Tao Lu, Robert Westendorp, Kishore Reddy Tarimala
Date: 2021-11-29 00:00:00
Tasks: Indoor Localization

This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of 0.8 m when the phone is actively transmitting data frames and 1.5 m when it is not transmitting data frames.
Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning
Authors: Adarsh Dave, Jared Mitchell, Sven Burke, Hongyi Lin, Jay Whitacre, Venkatasubramanian Viswanathan
Date: 2021-11-23 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.
Low-Latency Online Speaker Diarization with Graph-Based Label Generation
Authors: Yucong Zhang, Qinjian Lin, Weiqing Wang, Lin Yang, Xuyang Wang, Junjie Wang, Ming Li
Date: 2021-11-27 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

This paper introduces an online speaker diarization system that can handle long-time audio with low latency. We enable Agglomerative Hierarchy Clustering (AHC) to work in an online fashion by introducing a label matching algorithm. This algorithm solves the inconsistency between output labels and hidden labels that are generated each turn. To ensure the low latency in the online setting, we introduce a variant of AHC, namely chkpt-AHC, to cluster the speakers. In addition, we propose a speaker embedding graph to exploit a graph-based re-clustering method, further improving the performance. In the experiment, we evaluate our systems on both DIHARD3 and VoxConverse datasets. The experimental results show that our proposed online systems have better performance than our baseline online system and have comparable performance to our offline systems. We find out that the framework combining the chkpt-AHC method and the label matching algorithm works well in the online setting. Moreover, the chkpt-AHC method greatly reduces the time cost, while the graph-based re-clustering method helps improve the performance.
Stability Analysis of a Feedback-linearization-based Controller with Saturation: A Tilt Vehicle with the Penguin-inspired Gait Plan
Authors: Zhe Shen, Yudong Ma, Takeshi Tsuchiya
Date: 2021-11-29 00:00:00

Saturations in control signal can challenge the stability proof of a feedback-linearization-based controller, even leading the system unstable [1]. Thus, several approaches are established to avoid reaching the saturation bound [2,3]. Meanwhile, to help design the controller for a quad-tilt-rotor, [1] modeled a tilt vehicle with implementing the feedback-linearization-based controllers. In this article, we provide a gait plan for this tilt vehicle and control it utilizing the feedback linearization. Since saturations exist in the control signals, we study the stability based on Lyapunov theory.
Unknown Input Observer Design for Linear Time-Invariant Systems -- A Unifying Framework
Authors: Markus Tranninger, Helmut Niederwieser, Richard Seeber, Martin Horn
Date: 2021-11-29 00:00:00

This paper presents a new observer design approach for linear time invariant multivariable systems subject to unknown inputs. The design is based on a transformation to the so-called special coordinate basis. This form reveals important system properties like invertability or the finite and infinite zero structure. Depending on the system's strong observability properties, the special coordinate basis allows for a straightforward unknown input observer design utilizing linear or nonlinear observers design techniques. The chosen observer design technique does not only depend on the system properties, but also on the desired convergence behavior of the observer. Hence, the proposed design procedure can be seen as a unifying framework for unknown input observer design.
Distributed computation of fast consensus weights using ADMM
Authors: Kiran Rokade, Rachel Kalpana Kalaimani
Date: 2020-02-19 00:00:00

We consider the problem of achieving average consensus among multiple agents, where the inter-agent communication network is depicted by a graph. We consider the discrete-time consensus protocol where each agent updates its value as a weighted average of its own value and those of its neighbours. Given a graph, it is known that there exists a set of 'optimal weights' such that the agents reach average consensus asymptotically with an optimal rate of convergence. However, existing methods require the knowledge of the entire graph to compute these optimal weights. We propose a method for each agent to compute its set of optimal weights locally, i.e., each agent only has to know who are its neighbours. The method is derived by solving a matrix norm minimization problem subject to linear constraints in a distributed manner using the Alternating Direction Method of Multipliers (ADMM). We illustrate our results using numerical examples and compare our method with an existing method called the Metropolis weights, which are also computed locally.
Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation
Authors: Shengbo Wang, Bo Lyu, Shiping Wen, Kaibo Shi, Song Zhu, TingWen Huang
Date: 2021-11-27 00:00:00
Tasks: parameter estimation

Safety is always one of the most critical principles for a system to be controlled. This paper investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefited from the dynamic regressor extension and mixing (DREM), an extended element-wise parameter identification law is utilized to dismiss the uncertainty. On the one hand, it is shown that the proposed control scheme can always guarantee the safety in the identification process with noised signal injection excitation, which was not considered in the previous study. On the other hand, the element-wise estimation process in DREM can minimize conservatism of the safe adaptive process compared to other existing adaptive CBF algorithms. The stability as well as the forward invariance of the presented safe control-estimation scheme is proved. Furthermore, the robustness of the scheme under bounded disturbances is analyzed, where a robust CBF with modest conditions is used to ensure safety. The framework is illustrated by simulations on adaptive cruise control, where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances and the potential crash risk is avoided by the proposed safe control scheme.
Optimal Tracking Control for Unknown Linear Systems with Finite-Time Parameter Estimation
Authors: Shengbo Wang, Shiping Wen, Kaibo Shi, Song Zhu, TingWen Huang
Date: 2021-11-27 00:00:00
Tasks: parameter estimation

The optimal control input for linear systems can be solved from algebraic Riccati equation (ARE), from which it remains questionable to get the form of the exact solution. In engineering, the acceptable numerical solutions of ARE can be found by iteration or optimization. Recently, the gradient descent based numerical solutions has been proven effective to approximate the optimal ones. This paper introduces this method to tracking problem for heterogeneous linear systems. Differently, the parameters in the dynamics of the linear systems are all assumed to be unknown, which is intractable since the gradient as well as the allowable initialization needs the prior knowledge of system dynamics. To solve this problem, the method named dynamic regressor extension and mix (DREM) is improved to estimate the parameter matrices in finite time. Besides, a discounted factor is introduced to ensure the existence of optimal solutions for heterogeneous systems. Two simulation experiments are given to illustrate the effectiveness.
Dynamic Analysis of Nonlinear Civil Engineering Structures using Artificial Neural Network with Adaptive Training
Authors: Xiao Pan, Zhizhao Wen, T. Y. Yang
Date: 2021-11-21 00:00:00

Dynamic analysis of structures subjected to earthquake excitation is a time-consuming process, particularly in the case of extremely small time step required, or in the presence of high geometric and material nonlinearity. Performing parametric studies in such cases is even more tedious. The advancement of computer graphics hardware in recent years enables efficient training of artificial neural networks that are well-known to be capable of learning highly nonlinear mappings. In this study, artificial neural networks are developed with adaptive training algorithms, which enables automatic nodes generation and layers addition. The hyperbolic tangent function is selected as the activation function. Stochastic Gradient Descent and Back Propagation algorithms are adopted to train the networks. The neural networks initiate with a small number of hidden layers and nodes. During training, the performance of the network is continuously tracked, and new nodes or layers are added to the hidden layers if the neural network reaches its capacity. At the end of the training process, the network with appropriate architecture is automatically formed. The performance of the networks has been validated for inelastic shear frames, as well as rocking structures, of which both are first built in finite element program for dynamic analysis to generate training data. Results have shown the developed networks can successfully predict the time-history response of the shear frame and the rock structure subjected to real ground motion records. The efficiency of the proposed neural networks is also examined, which shows the computational time can be reduced by 43% by the neural networks method than FE models. This indicates the trained networks can be utilized to generate rocking spectrums of structures more efficiently which demands a large number of time-history analyses.
Speciation in a MacArthur model predicts growth, stability and adaptation in ecosystems dynamics
Authors: Elena Bellavere, Christian H. S. Hamster, Joshua A. Dijksman
Date: 2018-03-01 00:00:00
Tasks: Diversity

Ecosystems dynamics is often considered as driven by a coupling of species' resource consumption and its population size dynamics. Such resource-population dynamics is captured by MacArthur-type models. One biologically relevant feature that would also need to be captured by such models is the introduction of new and different species. Speciation introduces a stochastic component in the otherwise deterministic MacArthur theory. We describe here how speciation can be implemented to yield a model that is consistent with current theory on equilibrium resource-consumer models, but also displays readily observable rank diversity metric changes. The model also reproduces a priority effect. Adding speciation to a MacArthur-style model so provides an attractively simple extension to explore the rich dynamics in evolving ecosystems.
The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity
Authors: Alessandro Casini
Date: 2021-11-29 00:00:00
Tasks: ERP, Gaussian Processes, valid

We show that the nonstandard limiting distribution of HAR test statistics under fixed-b asymptotics is not pivotal (even after studentization) when the data are nonstationarity. It takes the form of a complicated function of Gaussian processes and depends on the integrated local long-run variance and on on the second moments of the relevant series (e.g., of the regressors and errors for the case of the linear regression model). Hence, existing fixed-b inference methods based on stationarity are not theoretically valid in general. The nuisance parameters entering the fixed-b limiting distribution can be consistently estimated under small-b asymptotics but only with nonparametric rate of convergence. Hence, We show that the error in rejection probability (ERP) is an order of magnitude larger than that under stationarity and is also larger than that of HAR tests based on HAC estimators under conventional asymptotics. These theoretical results reconcile with recent finite-sample evidence in Casini (2021) and Casini, Deng and Perron (2021) who showing that fixed-b HAR tests can perform poorly when the data are nonstationary. They can be conservative under the null hypothesis and have non-monotonic power under the alternative hypothesis irrespective of how large the sample size is.
Robust Permutation Tests in Linear Instrumental Variables Regression
Authors: Purevdorj Tuvaandorj
Date: 2021-11-26 00:00:00
Tasks: regression

This paper develops permutation versions of identification-robust tests in linear instrumental variables (IV) regression. Unlike the existing randomization and rank-based tests in which independence between the instruments and the error terms is assumed, the permutation Anderson- Rubin (AR), Lagrange Multiplier (LM) and Conditional Likelihood Ratio (CLR) tests are asymptotically similar and robust to conditional heteroskedasticity under standard exclusion restriction i.e. the orthogonality between the instruments and the error terms. Moreover, when the instruments are independent of the structural error term, the permutation AR tests are exact, hence robust to heavy tails. As such, these tests share the strengths of the rank-based tests and the wild bootstrap AR tests. Numerical illustrations corroborate the theoretical results.
Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching
Authors: Odran Bonnet, Alfred Galichon, Yu-Wei Hsieh, Keith O'Hara, Matt Shum
Date: 2021-11-26 00:00:00
Tasks: Discrete Choice Models

The problem of demand inversion - a crucial step in the estimation of random utility discrete-choice models - is equivalent to the determination of stable outcomes in two-sided matching models. This equivalence applies to random utility models that are not necessarily additive, smooth, nor even invertible. Based on this equivalence, algorithms for the determination of stable matchings provide effective computational methods for estimating these models. For non-invertible models, the identified set of utility vectors is a lattice, and the matching algorithms recover sharp upper and lower bounds on the utilities. Our matching approach facilitates estimation of models that were previously difficult to estimate, such as the pure characteristics model. An empirical application to voting data from the 1999 European Parliament elections illustrates the good performance of our matching-based demand inversion algorithms in practice.
Ranking of different of investment risk in high-tech projects using TOPSIS method in fuzzy environment based on linguistic variables
Authors: Mohammad Ebrahim Sadeghi, Hamed Nozari, Hadi Khajezadeh Dezfoli, Mehdi Khajezadeh
Date: 2021-11-29 00:00:00

Examining the trend of the global economy shows that global trade is moving towards high-tech products. Given that these products generate very high added value, countries that can produce and export these products will have high growth in the industrial sector. The importance of investing in advanced technologies for economic and social growth and development is so great that it is mentioned as one of the strong levers to achieve development. It should be noted that the policy of developing advanced technologies requires consideration of various performance aspects, risks and future risks in the investment phase. Risk related to high-tech investment projects has a meaning other than financial concepts only. In recent years, researchers have focused on identifying, analyzing, and prioritizing risk. There are two important components in measuring investment risk in high-tech industries, which include identifying the characteristics and criteria for measuring system risk and how to measure them. This study tries to evaluate and rank the investment risks in advanced industries using fuzzy TOPSIS technique based on verbal variables.
From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market outcomes
Authors: Selina Gangl, Martin Huber
Date: 2021-11-29 00:00:00

The majority of Swiss children attend mandatory and cost-free kindergarten at four. We examine the effect of this policy on maternal labour market outcomes. Using administrative data, we exploit the birthday cut-off for kindergarten entry in the same or in the following year and apply a non-parametric regression discontinuity design (RDD). We find that mandatory kindergarten has a statistically significant positive effect on the labour market attachment of previously non-employed mothers, increasing their employment probability by 4 percentage points. In contrast, there are no significant effects on other groups or in the total sample of mothers.
Do soda taxes affect the consumption and health of school-aged children? Evidence from France and Hungary
Authors: Selina Gangl
Date: 2021-11-29 00:00:00

This paper examines the effect of two different soda taxes on consumption behaviour and health of school-aged children in Europe: Hungary imposed a Public Health Product Tax (PHPT) on several unhealthy products in 2011. France introduced solely a soda tax, containing sugar or artificial sweeteners, in 2012. In order to exploit spatial variation, I use a semi-parametric Difference-in-Differences (DID) approach. Since the policies differ in Hungary and France, I analyse the effects separately by using a neighbouring country without a soda tax as a control group. The results suggest a counter-intuitive positive effect of the tax on soda consumption in Hungary. The reason for this finding could be the substitution of other unhealthy beverages, which are taxed at a higher rate, by sodas. The effect of the soda tax in France is as expected negative, but insignificant which might be caused by a low tax rate. The body mass index (BMI) is not affected by the tax in any country. Consequently, policy makers should think carefully about the design and the tax rate before implementing a soda tax.
Minimum Wages in Concentrated Labor Markets
Authors: Martin Popp
Date: 2021-11-26 00:00:00

Economists increasingly refer to monopsony power to reconcile the absence of negative employment effects of minimum wages with theory. However, systematic evidence for the monopsony argument is scarce. In this paper, I perform a comprehensive test of this argument by using labor market concentration as a proxy for monopsony power. Labor market concentration turns out substantial in Germany. Absent wage floors, higher concentration reduces wages and employment, reflecting monopsonistic conduct of firms. Sectoral minimum wages lead to negative employment effects in slightly concentrated or more competitive labor markets. This effect weakens with increasing concentration and, ultimately, becomes positive in highly concentrated or monopsonistic markets. Overall, the results lend empirical support to the monopsony argument, implying that conventional minimum wage effects on employment conceal heterogeneity across market forms.