pwc-archive/papers-with-abstracts huggingface dataset:
Where did you tweet from? Inferring the origin locations of tweets based on contextual information
Authors: Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read
Date: 2022-11-18 00:00:00
Tasks: Natural Language Understanding

Public conversations on Twitter comprise many pertinent topics including disasters, protests, politics, propaganda, sports, climate change, epidemics/pandemic outbreaks, etc., that can have both regional and global aspects. Spatial discourse analysis rely on geographical data. However, today less than 1% of tweets are geotagged; in both cases--point location or bounding place information. A major issue with tweets is that Twitter users can be at location A and exchange conversations specific to location B, which we call the Location A/B problem. The problem is considered solved if location entities can be classified as either origin locations (Location As) or non-origin locations (Location Bs). In this work, we propose a simple yet effective framework--the True Origin Model--to address the problem that uses machine-level natural language understanding to identify tweets that conceivably contain their origin location information. The model achieves promising accuracy at country (80%), state (67%), city (58%), county (56%) and district (64%) levels with support from a Location Extraction Model as basic as the CoNLL-2003-based RoBERTa. We employ a tweet contexualizer (locBERT) which is one of the core components of the proposed model, to investigate multiple tweets' distributions for understanding Twitter users' tweeting behavior in terms of mentioning origin and non-origin locations. We also highlight a major concern with the currently regarded gold standard test set (ground truth) methodology, introduce a new data set, and identify further research avenues for advancing the area.
Direct data-driven LPV control of nonlinear systems: An experimental result
Authors: Chris Verhoek, Hossam S. Abbas, Roland Tóth
Date: 2022-11-30 00:00:00
Tasks: Form, Scheduling

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.
Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
Authors: Chris Verhoek, Roland Tóth, Hossam S. Abbas
Date: 2022-11-30 00:00:00
Tasks: Scheduling

The framework of linear parameter-varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work, we derive novel methods that allow to synthesize LPV state-feedback controllers directly from only a single sequence of data and guarantee stability and performance of the closed-loop system. We show that if the measured open-loop data from the system satisfies a persistency of excitation condition, then the full open-loop and closed-loop input-scheduling-state behavior can be represented using only the data. With this representation we formulate data-driven analysis and synthesis problems, where the latter yields controllers that guarantee stability and performance in terms of infinite horizon quadratic cost, generalized $\mathcal{H}_2$-norm and $\ell_2$-gain of the closed-loop system. The controllers are synthesized by solving a semi-definite program. Additionally, we provide a synthesis method to handle noisy measurement data. Competitive performance of the proposed data-driven synthesis methods is demonstrated w.r.t. model-based synthesis in multiple simulation studies, including a nonlinear unbalanced disc system.
On Data-Driven Stochastic Output-Feedback Predictive Control
Authors: Guanru Pan, Ruchuan Ou, Timm Faulwasser
Date: 2022-11-30 00:00:00
Tasks: LEMMA

The fundamental lemma by Jan C. Willems and co-authors enables the representation of all input-output trajectories of a linear time-invariant system by measured input-output data. This result has proven to be pivotal for data-driven control. Building on a stochastic variant of the fundamental lemma, this paper presents a data-driven output-feedback predictive control scheme for stochastic Linear Time-Invariant (LTI) systems. The considered LTI systems are subject to non-Gaussian disturbances about which only information about their first two moments is known. Leveraging polynomial chaos expansions, the proposed scheme is centered around a data-driven stochastic Optimal Control Problem (OCP). Through tailored online design of initial conditions, we provide sufficient conditions for the recursive feasibility of the proposed output-feedback scheme based on a data-driven design of the terminal ingredients of the OCP. Furthermore, we provide a robustness analysis of the closed-loop performance. A numerical example illustrates the efficacy of the proposed scheme.
On the use of energy tanks for robotic systems
Authors: Federico Califano, Ramy Rashad, Cristian Secchi, Stefano Stramigioli
Date: 2022-11-30 00:00:00

In this document we describe and discuss energy tanks, a control algorithm which has gained popularity inside the robotics and control community over the last years. This article has the threefold scope of i) introducing to the reader the topic in a simple yet precise way, starting with a throughout description of the energy-aware framework, where energy tanks find their genesis; ii) summarising the range of applications of energy tanks, including an original reflection about different formulations of those; iii) discussing limits and future challenges involving energy tanks and energy-aware control in general.
Optimal Allocation of Virtual Inertia and Droop Control for Renewable Energy in Stochastic Look-Ahead Power Dispatch
Authors: Yukang Shen, Wenchuan Wu, Shumin Sun, Bin Wang
Date: 2022-11-30 00:00:00
Tasks: Scheduling

To stabilize the frequency of the renewable energy sources (RESs) dominated power system, frequency supports are required by RESs through virtual inertia emulation or droop control in the newly published grid codes. Since the long-term RES prediction involves significant errors, we need online configure the frequency control parameters of RESs in a rolling manner to improve the operation economics under the premise of stabilizing system frequency. To address this concern, this paper proposes a frequency constrained stochastic look-ahead power dispatch (FCS-LAPD) model to formulate the frequency control parameters of RESs and Energy Storage Systems (ESSs) as scheduling variables, which can optimally allocate the virtual inertia and droop coefficient of RESs and ESSs. In this FCS-LAPD model, the uncertainties of RESs are characterized using Gaussian Mixture Model (GMM). The required reserves are determined by frequency control parameters, and the reserve cost coefficients are adjusted properly to allocate the reserves according to the predicted power generation. Due to the nonlinearity of the frequency nadir constraint, a convex hull approximation method is proposed to linearize it with guaranteed feasibility. The proposed FCS-LAPD is ultimately cast as an instance of quadratic programming and can be efficiently solved. Case studies on modified IEEE 24-bus system and a provincial power system in China are conducted to show the effectiveness of the proposed model.
Quadratic Programming for Continuous Control of Safety-Critical Multi-Agent Systems Under Uncertainty
Authors: Si Wu, Tengfei Liu, Magnus Egerstedt, Zhong-Ping Jiang
Date: 2022-11-30 00:00:00
Tasks: Collision Avoidance, continuous-control, Continuous Control

This paper studies the control problem for safety-critical multi-agent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position-velocity relation, and the actuation system describes the dynamic response of the actual velocity to the velocity reference signal. The notion of input-to-output stability (IOS) is employed to characterize the essential velocity-tracking capability of the actuation system. The uncertain actuation dynamics may cause infeasibility or discontinuous solutions of QP algorithms for collision avoidance. Also, the interaction between the controlled integrator and the uncertain actuation dynamics may lead to significant robustness issues. By using nonlinear control methods and numerical optimization methods, this paper first contributes a new feasible-set reshaping technique and a refined QP algorithm for feasibility, robustness, and local Lipschitz continuity. Then, we present a nonlinear small-gain analysis to handle the inherent interaction for guaranteed safety of the closed-loop multi-agent system. The proposed methods are illustrated by numerical simulations and a physical experiment.
Robust Learning of Nonlinear Dynamical Systems with Safety and Stability Properties
Authors: Iman Salehi, Ghananeel Rotithor, Ashwin P. Dani
Date: 2022-11-30 00:00:00
Tasks: LEMMA

The paper presents a robust parameter learning methodology for identification of nonlinear dynamical system from data while satisfying safety and stability constraints in the context of learning from demonstration (LfD) methods. Extreme Learning Machines (ELM) is used to approximate the system model, whose parameters are learned subject to the safety and stability constraints obtained using zeroing barrier and Lyapunov-based stability analysis in the presence of model uncertainties and external disturbances. A constrained Quadratic Program (QP) is developed, which accounts for the ELM function reconstruction error, to estimate the ELM parameters. Furthermore, a robustness lemma is presented, which proves that the learned system model guarantees safety and stability in the presence of disturbances. The method is tested in simulations. Trajectory reconstruction accuracy of the method is compared against state-of-the-art LfD methods using swept error area (SEA) metric. Robustness of the learned model is tested by conducting Monte Carlo tests. The proposed method is implemented on a Baxter robot for a pick-and-place task where the robot is constrained to an ellipsoidal safety region.
An Integrated Ride-Matching Model for Shared Mobility on Demand Services
Authors: Kerem Tuncel, Haris N. Koutsopoulos, Zhenliang Ma
Date: 2022-11-30 00:00:00
Tasks: Autonomous Vehicles

Shared mobility on demand (MoD) services are receiving increased attention as many high volume ride-hailing companies are offering shared services (e.g. UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous vehicles (AVs) promises further operational opportunities to benefit from these developments as AVs enable a centrally operated and fully connected fleet. There are two fundamental tasks for a shared MoD service: ride-matching and vehicle rebalancing. Traditionally, these two functions are performed sequentially and independently. In this paper, we propose and formulate an integrated ride-matching problem which aims to integrate ride-matching and rebalancing into a single formulation. The integrated problem benefits from interactions between these two tasks. We also propose a methodology to solve the integrated shared ride-matching problem by using supply level information based on a grid representation of the city network. We demonstrate the effectiveness of the proposed methodology through a comparative case study using a benchmark sequential approach and an open source data set. Our results show that the integrated model is able to serve at least the same amount of passengers with significant gains in terms of level of service and sustainability metrics.
Collaborative learning model predictive control for repetitive tasks
Authors: Paula Chanfreut, José María Maestre, Eduardo F. Camacho, Francesco Borrelli
Date: 2022-11-29 00:00:00
Tasks: model, Model Predictive Control

This paper presents a cloud-based learning model predictive controller that integrates three interacting components: a set of agents, which must learn to perform a finite set of tasks with the minimum possible local cost; a coordinator, which assigns the tasks to the agents; and the cloud, which stores data to facilitate the agents' learning. The tasks consist in traveling repeatedly between a set of target states while satisfying input and state constraints. In turn, the state constraints may change in time for each of the possible tasks. To deal with it, different modes of operation, which establish different restrictions, are defined. The agents' inputs are found by solving local model predictive control (MPC) problems where the terminal set and cost are defined from previous trajectories. The data collected by each agent is uploaded to the cloud and made accessible to all their peers. Likewise, similarity between tasks is exploited to accelerate the learning process. The applicability of the proposed approach is illustrated by simulation results.
DSO-DERA Coordination for the Wholesale Market Participation of Distributed Energy Resources
Authors: Cong Chen, Subhonmesh Bose, Lang Tong
Date: 2022-11-29 00:00:00

We design a coordination mechanism between a distribution system operator (DSO) and distributed energy resource aggregators (DERAs) participating directly in the wholesale electricity market. Aimed at ensuring system reliability while providing open access to DERAs, the coordination mechanism includes a forward auction that allocates access limits to aggregators based on aggregators' bids that represent their benefits of aggregation. The proposed coordination mechanism results in decoupled DSO and DERAs operations that satisfy the network constraints, independent of the stochasticity of the renewables, wholesale real-time locational marginal prices, and individual DERA's aggregation policy. Optimal bidding strategies by competitive aggregators are also derived. The efficiency of the coordination mechanism and the locational price distribution at buses of a radial distribution grid are demonstrated for a 141-bus radial network.
Interval Valued Fuzzy Modeling and Indirect Adaptive Control of Quadrotor
Authors: Moufid Bouhentala, Mouna Ghanai, Kheireddine Chafaa
Date: 2022-11-29 00:00:00

In this paper, a combination of fuzzy clustering estimation and sliding mode control is used to control a quadrotor system, whose mathematical model is complex and has unknown elements, including structure, parameters, and so on. In addition, they may be affected by external environmental disturbances. At first, the nonlinear unknown part of the system is estimated by a fuzzy model, A new method is presented for constructing a Takagi-Sugeno (TS) interval-valued fuzzy model (IVFM) based on inputoutput data of the identified system. Following the construction of the fuzzy model that estimates the unknown part of the quadrotor system, a control and on-line adjusting of the fuzzy modeled part of dynamics is used. In this step, the system model will be estimated in adaptive form so that the dynamic equations can be used in sliding mode control. Finally, the proposed technique is applied, and the simulation results are presented to show the effectiveness of this approach in controlling the quadrotor with unknown nonlinear dynamics.
A Tutorial on Neural Networks and Gradient-free Training
Authors: Turibius Rozario, Arjun Trivedi, Ankit Goel
Date: 2022-11-26 00:00:00

This paper presents a compact, matrix-based representation of neural networks in a self-contained tutorial fashion. Specifically, we develop neural networks as a composition of several vector-valued functions. Although neural networks are well-understood pictorially in terms of interconnected neurons, neural networks are mathematical nonlinear functions constructed by composing several vector-valued functions. Using basic results from linear algebra, we represent a neural network as an alternating sequence of linear maps and scalar nonlinear functions, also known as activation functions. The training of neural networks requires the minimization of a cost function, which in turn requires the computation of a gradient. Using basic multivariable calculus results, the cost gradient is also shown to be a function composed of a sequence of linear maps and nonlinear functions. In addition to the analytical gradient computation, we consider two gradient-free training methods and compare the three training methods in terms of convergence rate and prediction accuracy.
Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models
Authors: Boyuan Zhang
Date: 2022-11-30 00:00:00

The assumption of group heterogeneity has become popular in panel data models. We develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to a same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence. The whole framework is built on the nonparametric Bayesian method, which implicitly specifies a distribution over the group partitions, and so the posterior analysis takes the uncertainty of the latent group structure into account. Monte Carlo experiments reveal that adding prior knowledge yields more accurate estimates of coefficient and scores predictive gains over alternative estimators. We apply our method to two empirical applications. In a first application to forecasting U.S. CPI inflation, we illustrate that prior knowledge of groups improves density forecasts when the data is not entirely informative. A second application revisits the relationship between a country's income and its democratic transition; we identify heterogeneous income effects on democracy with five distinct groups over ninety countries.
Trust and Time Preference: Measuring a Causal Effect in a Random-Assignment Experiment
Authors: Linas Nasvytis
Date: 2022-11-30 00:00:00
Tasks: Bayesian Inference, Experimental Design

Large amounts of evidence suggest that trust levels in a country are an important determinant of its macroeconomic growth. In this paper, we investigate one channel through which trust might support economic performance: through the levels of patience, also known as time preference in the economics literature. Following Gabaix and Laibson (2017), we first argue that time preference can be modelled as optimal Bayesian inference based on noisy signals about the future, so that it is affected by the perceived certainty of future outcomes. Drawing on neuroscience literature, we argue that the mechanism linking trust and patience could be facilitated by the neurotransmitter oxytocin. On the one hand, it is a neural correlate of trusting behavior. On the other, it has an impact on the brain's encoding of prediction error, and could therefore increase the perceived certainty of a neural representation of a future event. The relationship between trust and time preference is tested experimentally using the Trust Game. While the paper does not find a significant effect of trust on time preference or the levels of certainty, it proposes an experimental design that can successfully manipulate people's short-term levels of trust for experimental purposes.
Crowdfunding as Entrepreneurial Investment: The Role of Local Knowledge Spillover
Authors: Filippo Marchesani, Francesca Masciarelli
Date: 2022-11-30 00:00:00

This paper explores the role of local knowledge spillover and human capital as a driver of crowdfunding investment. The role of territory has already been studied in terms of campaign success, but the impact of territory on the use of financial sources like equity crowdfunding is not yet known. Using a sample of 435 equity crowdfunding campaigns in 20 Italian regions during a 4-year period (from 2016 to 2019), this paper evaluates the impact of human capital flow on the adoption of crowdfunding campaigns. Our results show that inbound knowledge in the region, measured in terms of ability to attract national and international students, has a significant effect on the adoption of crowdfunding campaigns in the region itself.
Security Issuance, Institutional Investors and Quid Pro Quo
Authors: Gaurab Aryal, Zhaohui Chen, Yuchi Yao, Chris Yung
Date: 2022-11-30 00:00:00

Securities issuance through intermediaries is subject to agency problems and informational frictions. We examine these effects using SPAC data. We identify ``premium'' investors whose participation is linked to lower liquidation risk, higher returns, and lower redemption rates, consistent with both informational rents and agency frictions. In contrast, ``non-premium'' investors engage in non-agency quid pro quo relationships. Specifically, they receive high returns from an intermediary (quid) in exchange for a tacit agreement to participate in weaker future deals (quo). These relationships serve as insurance for issuers and intermediaries, enabling more issuers to access markets.
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks
Authors: Shankhanil Mitra, Saiyam Jogani, Rajiv Soundararajan
Date: 2022-11-30 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

Designing learning-based no-reference (NR) video quality assessment (VQA) algorithms for camera-captured videos is cumbersome due to the requirement of a large number of human annotations of quality. In this work, we propose a semi-supervised learning (SSL) framework exploiting many unlabelled and very limited amounts of labelled authentically distorted videos. Our main contributions are two-fold. Leveraging the benefits of consistency regularization and pseudo-labelling, our SSL model generates pairwise pseudo-ranks for the unlabelled videos using a student-teacher model on strongweak augmented videos. We design the strong-weak augmentations to be quality invariant to use the unlabelled videos effectively in SSL. The generated pseudo-ranks are used along with the limited labels to train our SSL model. Our primary focus in SSL for NR VQA is to learn the mapping from video feature representations to the quality scores. We compare various feature extraction methods and show that our SSL framework can lead to improved performance on these features. In addition to the existing features, we present a spatial and temporal feature extraction method based on predicting spatial and temporal entropic differences. We show that these features help achieve a robust performance when trained with limited data providing a better baseline to apply SSL. Extensive experiments on three popular VQA datasets demonstrate that a combination of our novel SSL approach and features achieves an impressive performance in terms of correlation with human perception, even though the number of human-annotated videos may be limited.