pwc-archive/papers-with-abstracts huggingface dataset:
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes
Authors: Thomas Beckers, Sandra Hirche, Leonardo Colombo
Date: 2021-03-31 00:00:00
Tasks: Gaussian Processes

Formation control algorithms for multi-agent systems have gained much attention in the recent years due to the increasing amount of mobile and aerial robotic swarms. The design of safe controllers for these vehicles is a substantial aspect for an increasing range of application domains. However, parts of the vehicle's dynamics and external disturbances are often unknown or very time-consuming to model. To overcome this issue, we present a safe formation control law for multiagent systems based on double integrator dynamics by using Gaussian Processes for an online learning of the unknown dynamics. The presented approach guarantees a bounded error to desired formations with high probability, where the bound is explicitly given. A numerical example highlights the effectiveness of the learning-based formation control law.
Generalized Reinforcement Learning for Building Control using Behavioral Cloning
Authors: Zachary E. Lee, K. Max Zhang
Date: 2021-03-31 00:00:00
Tasks: Cloud Computing, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advanced building control methods such as model predictive control (MPC) offer significant potential benefits to both consumers and grid operators, but the high computational requirements have acted as barriers to more widespread adoption. Local control computation requires installation of expensive computational hardware, while cloud computing introduces data security and privacy concerns. In this paper, we drastically reduce the local computational requirements of advanced building control through a reinforcement learning (RL)-based approach called Behavioral Cloning, which represents the MPC policy as a neural network that can be locally implemented and quickly computed on a low-cost programmable logic controller. While previous RL and approximate MPC methods must be specifically trained for each building, our key improvement is that our controller can generalize to many buildings, electricity rates, and thermostat setpoint schedules without additional, effort-intensive retraining. To provide this versatility, we have adapted the traditional Behavioral Cloning approach through (1) a constraint-informed parameter grouping (CIPG) method that provides a more efficient representation of the training data; (2) an MPC-Guided training data generation method using the DAgger algorithm that improves stability and constraint satisfaction; and (3) a new deep learning model-structure called reverse-time recurrent neural networks (RT-RNN) that allows future information to flow backward in time to more effectively interpret the temporal information in disturbance predictions. The result is an easy-to-deploy, generalized behavioral clone of MPC that can be implemented on a programmable logic controller and requires little building-specific controller tuning, reducing the effort and costs associated with implementing smart residential heat pump control.
High-order Barrier Functions: Robustness, Safety and Performance-Critical Control
Authors: Xiao Tan, Wenceslao Shaw Cortez, Dimos V. Dimarogonas
Date: 2021-03-31 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we propose a notion of high-order (zeroing) barrier functions that generalizes the concept of zeroing barrier functions and guarantees set forward invariance by checking their higher order derivatives. The proposed formulation guarantees asymptotic stability of the forward invariant set, which is highly favorable for robustness with respect to model perturbations. No forward completeness assumption is needed in our setting in contrast to existing high order barrier function methods. For the case of controlled dynamical systems, we relax the requirement of uniform relative degree and propose a singularity-free control scheme that yields a locally Lipschitz control signal and guarantees safety. Furthermore, the proposed formulation accounts for "performance-critical" control: it guarantees that a subset of the forward invariant set will admit any existing, bounded control law, while still ensuring forward invariance of the set. Finally, a non-trivial case study with rigid-body attitude dynamics and interconnected cell regions as the safe region is investigated.
Prediction of Wind Speed Using Artificial Neural Networks and ANFIS Methods (Observation Buoy Example)
Authors: Inan Timur, Baba Ahmet Fevzi
Date: 2021-03-29 00:00:00

Estimation of the wind speed plays an important role in many issues such as route determination of ships, efficient use of wind roses, and correct planning of agricultural activities. In this study, wind velocity estimation is calculated using artificial neural networks (ANN) and adaptive artificial neural fuzzy inference system (ANFIS) methods. The data required for estimation was obtained from the float named E1M3A, which is a float inside the POSEIDON float system. The proposed ANN is a Nonlinear Auto Regressive with External Input (NARX) type of artificial neural network with 3 layers, 50 neurons, 6 inputs and 1 output. The ANFIS system introduced is a fuzzy inference system with 6 inputs, 1 output, and 3 membership functions (MF) per input. The proposed systems were trained to make wind speed estimates after 3 hours and the data obtained were obtained and the successes of the systems were revealed by comparing the obtained values with real measurements. Mean Squarred Error (MSE) and the regression between the predictions and expected values (R) were used to evaluate the success of the estimation values obtained from the systems. According to estimation results, ANN achieved 2.19 MSE and 0.897 R values in training, 2.88 MSE and 0.866 R values in validation, and 2.93 MSE and 0.857 R values in testing. ANFIS method has obtained 0.31634 MSE and 0.99 R values
Analytical solution of linearized equations of the Morris-Lecar neuron model at large constant stimulation
Authors: A. V. Paraskevov, T. S. Zemskova
Date: 2019-12-09 00:00:00

The classical biophysical Morris-Lecar model of neuronal excitability predicts that upon stimulation of the neuron with a sufficiently large constant depolarizing current there exists a finite interval of the current values where periodic spike generation occurs. Above the upper boundary of this interval, there is four-stage damping of the spike amplitude: 1) minor primary damping, which reflects a typical transient to stationary dynamic state, 2) plateau of nearly undamped periodic oscillations, 3) strong damping, and 4) reaching a constant asymptotic value of the neuron potential. We have shown that in the vicinity of the asymptote the Morris-Lecar equations can be reduced to the standard equation for exponentially damped harmonic oscillations. Importantly, all coefficients of this equation can be explicitly expressed through parameters of the original Morris-Lecar model, enabling direct comparison of the numerical and analytical solutions for the neuron potential dynamics at later stages of the spike amplitude damping.
CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography
Authors: Yunfan Jiang, Jingjing Si, Rui Zhang, Godwin Enemali, Bin Zhou, Hugh McCann, Chang Liu
Date: 2020-10-08 00:00:00
Tasks: Image Reconstruction

Chemical Species Tomography (CST) has been widely used for in situ imaging of critical parameters, e.g. species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms the method is limited due to the inherently ill-posed and rank-deficient tomographic data inversion, and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel CST-based convolutional neural Network (CSTNet) for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature. CSTNet introduces a shared feature extractor that incorporates the CST measurement and sensor layout into the learning network. In addition, a dual-branch architecture is proposed for image reconstruction with crosstalk decoders that automatically learn the naturally correlated distributions of species concentration and temperature. The proposed CSTNet is validated both with simulated datasets, and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches, in terms of robustness to measurement noise and millisecond-level computing time. This is the first time, to the best of our knowledge, that a deep learning-based algorithm for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with severely limited number of laser beams.
Local Projections vs. VARs: Lessons From Thousands of DGPs
Authors: Dake Li, Mikkel Plagborg-Møller, Christian K. Wolf
Date: 2021-04-01 00:00:00

We conduct a simulation study of Local Projection (LP) and Vector Autoregression (VAR) estimators of structural impulse responses across thousands of data generating processes, designed to mimic the properties of the universe of U.S. macroeconomic data. Our analysis considers various identification schemes and several variants of LP and VAR estimators, employing bias correction, shrinkage, or model averaging. A clear bias-variance trade-off emerges: LP estimators have lower bias than VAR estimators, but they also have substantially higher variance at intermediate and long horizons. Bias-corrected LP is the preferred method if and only if the researcher overwhelmingly prioritizes bias. For researchers who also care about precision, VAR methods are the most attractive -- Bayesian VARs at short and long horizons, and least-squares VARs at intermediate and long horizons.
Normalizations and misspecification in skill formation models
Authors: Joachim Freyberger
Date: 2021-04-01 00:00:00

An important class of structural models studies the determinants of skill formation and the optimal timing of interventions. In this paper, I provide new identification results for these models and investigate the effects of seemingly innocuous scale and location restrictions on parameters of interest. To do so, I first characterize the identified set of all parameters without these additional restrictions and show that important policy-relevant parameters are point identified under weaker assumptions than commonly used in the literature. The implications of imposing standard scale and location restrictions depend on how the model is specified, but they generally impact the interpretation of parameters and can affect counterfactuals. Importantly, with the popular CES production function, commonly used scale restrictions are overidentifying and lead to biased estimators. Consequently, simply changing the units of measurements of observed variables might yield ineffective investment strategies and misleading policy recommendations. I show how existing estimators can easily be adapted to solve these issues. As a byproduct, this paper also presents a general and formal definition of when restrictions are truly normalizations.
Forecasting open-high-low-close data contained in candlestick chart
Authors: Huiwen Wang, Wenyang Huang, Shanshan Wang
Date: 2021-03-31 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Forecasting the (open-high-low-close)OHLC data contained in candlestick chart is of great practical importance, as exemplified by applications in the field of finance. Typically, the existence of the inherent constraints in OHLC data poses great challenge to its prediction, e.g., forecasting models may yield unrealistic values if these constraints are ignored. To address it, a novel transformation approach is proposed to relax these constraints along with its explicit inverse transformation, which ensures the forecasting models obtain meaningful openhigh-low-close values. A flexible and efficient framework for forecasting the OHLC data is also provided. As an example, the detailed procedure of modelling the OHLC data via the vector auto-regression (VAR) model and vector error correction (VEC) model is given. The new approach has high practical utility on account of its flexibility, simple implementation and straightforward interpretation. Extensive simulation studies are performed to assess the effectiveness and stability of the proposed approach. Three financial data sets of the Kweichow Moutai, CSI 100 index and 50 ETF of Chinese stock market are employed to document the empirical effect of the proposed methodology.
On the Perception of Plagiarism in Academia: Context and Intent
Authors: Aaron Gregory, Joshua Leeman
Date: 2021-04-01 00:00:00
Tasks: Ethics

Plagiarism is the representation of another author's language, thoughts, ideas, or expressions as one's own original work. In educational contexts, there are differing definitions of plagiarism depending on the institution. Prominent scholars of plagiarism include Rebecca Moore Howard, Susan Blum, Tracey Bretag, and Sarah Elaine Eaton, among others. Plagiarism is considered a violation of academic integrity and a breach of journalistic ethics. It is subject to sanctions such as penalties, suspension, expulsion from school or work, substantial fines and even incarceration. Recently, cases of "extreme plagiarism" have been identified in academia. The modern concept of plagiarism as immoral and originality as an ideal emerged in Europe in the 18th century, particularly with the Romantic movement. Generally, plagiarism is not in itself a crime, but like counterfeiting fraud can be punished in a court for prejudices caused by copyright infringement, violation of moral rights, or torts. In academia and industry, it is a serious ethical offense. Plagiarism and copyright infringement overlap to a considerable extent, but they are not equivalent concepts, and many types of plagiarism do not constitute copyright infringement, which is defined by copyright law and may be adjudicated by courts. Plagiarism might not be the same in all countries. Some countries, such as India and Poland, consider plagiarism to be a crime, and there have been cases of people being imprisoned for plagiarizing. In other instances plagiarism might be the complete opposite of "academic dishonesty," in fact some countries find the act of plagiarizing a professional's work flattering. Students who move to the United States and other Western countries from countries where plagiarism is not frowned upon often find the transition difficult.
Productivity development in the construction industry and human capital: a literature review
Authors: Matthias Bahr, Leif Laszig
Date: 2021-03-31 00:00:00
Tasks: Management

Tis paper is a literature review focusing on human capital, skills of employees, demographic change, management, training and their impact on productivity growth. Intrafirm behaviour has been recognized as a potentially important driver for productivity. Results from surveys show that management practices have become more structured, in the sense of involving more data collection and analysis. Furthermore, a strong positive correlation between the measured management quality and firm performance can be observed. Studies suggest that there is a positive association between management score and productivity growth. The lack or low level of employees' skills and qualifications might be in different ways a possible explanation for the observed slowdown of productivity growth. The main reason for the decline in skilled labor is the demographic change. Construction sectors are increasingly affected by demographic developments. Labour reserves in construction are largely exhausted. Shortage of qualified workforce is impacting project cost, schedules and quality.
A Pricing Mechanism to Jointly Mitigate Market Power and Environmental Externalities in Electricity Markets
Authors: Lamia Varawala, Mohammad Reza Hesamzadeh, György Dán, Derek Bunn, Juan Rosellón
Date: 2021-04-01 00:00:00

The electricity industry has been one of the first to face technological changes motivated by sustainability concerns. Whilst efficiency aspects of market design have tended to focus upon market power concerns, the new policy challenges emphasise sustainability. We argue that market designs need to develop remedies for market conduct integrated with regard to environmental externalities. Accordingly, we develop an incentive-based market clearing mechanism using a power network representation with a distinctive feature of incomplete information regarding generation costs. The shortcomings of price caps to mitigate market power, in this context, are overcome with the proposed mechanism.
Limit Theorems for Default Contagion and Systemic Risk
Authors: Hamed Amini, Zhongyuan Cao, Agnes Sulem
Date: 2021-04-01 00:00:00

We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network, subject to an exogenous macroeconomic shock. We show that, under some regularity assumptions, the default cascade model could be transferred to a death process problem represented by balls-and-bins model. We also reduce the dimension of the problem by classifying banks according to different types, in an appropriate type space. These types may be calibrated to real-world data by using machine learning techniques. We then state various limit theorems regarding the final size of default cascade over different types. In particular, under suitable assumptions on the degree and threshold distributions, we show that the final size of default cascade has asymptotically Gaussian fluctuations. We next state limit theorems for different system-wide wealth aggregation functions and show how the systemic risk measure, in a given stress test scenario, could be related to the structure and heterogeneity of financial networks. We finally show how these results could be used by a social planner to optimally target interventions during a financial crisis, with a budget constraint and under partial information of the financial network.
Expressive Text-to-Speech using Style Tag
Authors: Minchan Kim, Sung Jun Cheon, Byoung Jin Choi, Jong Jin Kim, Nam Soo Kim
Date: 2021-04-01 00:00:00
Tasks: Language Modeling, Language Modelling, TAG, text-to-speech, Text to Speech

As recent text-to-speech (TTS) systems have been rapidly improved in speech quality and generation speed, many researchers now focus on a more challenging issue: expressive TTS. To control speaking styles, existing expressive TTS models use categorical style index or reference speech as style input. In this work, we propose StyleTagging-TTS (ST-TTS), a novel expressive TTS model that utilizes a style tag written in natural language. Using a style-tagged TTS dataset and a pre-trained language model, we modeled the relationship between linguistic embedding and speaking style domain, which enables our model to work even with style tags unseen during training. As style tag is written in natural language, it can control speaking style in a more intuitive, interpretable, and scalable way compared with style index or reference speech. In addition, in terms of model architecture, we propose an efficient non-autoregressive (NAR) TTS architecture with single-stage training. The experimental result shows that ST-TTS outperforms the existing expressive TTS model, Tacotron2-GST in speech quality and expressiveness.
Bidirectional Multiscale Feature Aggregation for Speaker Verification
Authors: Jiajun Qi, Wu Guo, Bin Gu
Date: 2021-04-01 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

In this paper, we propose a novel bidirectional multiscale feature aggregation (BMFA) network with attentional fusion modules for text-independent speaker verification. The feature maps from different stages of the backbone network are iteratively combined and refined in both a bottom-up and top-down manner. Furthermore, instead of simple concatenation or element-wise addition of feature maps from different stages, an attentional fusion module is designed to compute the fusion weights. Experiments are conducted on the NIST SRE16 and VoxCeleb1 datasets. The experimental results demonstrate the effectiveness of the bidirectional aggregation strategy and show that the proposed attentional fusion module can further improve the performance.
Dispersion from Diffuse Reflectors and its Effect of Terahertz Wireless Communication Performance
Authors: Russ Messenger, Karl Strecker, Sabit Ekin, John O'Hara
Date: 2021-04-01 00:00:00

This work investigates the temporal dispersion of a wireless terahertz communication signal caused by reflection from a rough (diffuse) surface, and its subsequent impact on symbol error rate versus data rate. Broadband measurements of diffuse reflectors using terahertz time-domain spectroscopy were used to establish and validate a scattering model that uses stochastic methods to describe the effects of surface roughness on the phase and amplitude of a reflected terahertz signal, expressed as a communication channel transfer function. The modeled channel was used to simulate a quadrature phase shift keying (QPSK)- modulated wireless communication link to determine the relationships between symbol error rate and data rate as a function of surface roughness. The simulations reveal that surface roughness from wall texturing results in group delay dispersion that limits achievable data rate with low errors. A distinct dispersion limit in surface roughness is discovered beyond which unacceptable numbers of symbol errors begin to accrue for a given data rate.
Fundamental Performance Limits on Terahertz Wireless Links Imposed by Group Velocity Dispersion
Authors: Karl Strecker, Sabit Ekin, John OHara
Date: 2021-04-01 00:00:00

A theoretical framework and numerical simulations quantifying the impact of atmospheric group velocity dispersion on wireless terahertz communication link error rate were developed based upon experimental work. We present, for the first time, predictions of symbol error rate as a function of link distance, signal bandwidth, signal-to-noise ratio, and atmospheric conditions, revealing that long-distance, broadband terahertz communication systems may be limited by inter-symbol interference stemming from group velocity dispersion, rather than attenuation. In such dispersion limited links, increasing signal strength does not improve the symbol error rate and, consequently, theoretical predictions of symbol error rate based only on signal-to-noise ratio are invalid for the broadband case. This work establishes a new and necessary foundation for link budget analysis in future long-distance terahertz communication systems that accounts for the non-negligible effects of both attenuation and dispersion.
Communication-efficient Coordinated RSS-based Distributed Passive Localization via Drone Cluster
Authors: Xin Cheng, Weiping Shi, Wenlong Cai, Weiqiang Zhu, Tong Shen, Feng Shu, Jiangzhou Wang
Date: 2021-04-01 00:00:00

Recently, passive unmanned aerial vehicle (UAV) localization has become popular due to mobility and convenience. In this paper, we consider a scenario of using distributed drone cluster to estimate the position of a passive emitter via received signal strength (RSS). First, a distributed majorizeminimization (DMM) RSS-based localization method is proposed. To accelerate its convergence, a tight upper bound of the objective function from the primary one is derived. Furthermore, to reduce communication overhead, a distributed estimation scheme using the Fisher information matrix (DEF) is presented, with only requiring one-round communication between edge UAVs and center UAV. Additionally, a local search solution is used as the initial value of DEF. Simulation results show that the proposed DMM performs better than the existing distributed Gauss-Newton method (DGN) in terms of root of mean square error (RMSE) under a limited low communication overhead constraint. Moreover, the proposed DEF performs much better than MM in terms of RMSE, but has a higher computational complexity than the latter.