pwc-archive/papers-with-abstracts huggingface dataset:
GEAR: On Optimal Decision Making with Auxiliary Data
Authors: Hengrui Cai, Rui Song, Wenbin Lu
Date: 2021-04-21 00:00:00
Tasks: Decision Making

Personalized optimal decision making, finding the optimal decision rule (ODR) based on individual characteristics, has attracted increasing attention recently in many fields, such as education, economics, and medicine. Current ODR methods usually require the primary outcome of interest in samples for assessing treatment effects, namely the experimental sample. However, in many studies, treatments may have a long-term effect, and as such the primary outcome of interest cannot be observed in the experimental sample due to the limited duration of experiments, which makes the estimation of ODR impossible. This paper is inspired to address this challenge by making use of an auxiliary sample to facilitate the estimation of ODR in the experimental sample. We propose an auGmented inverse propensity weighted Experimental and Auxiliary sample-based decision Rule (GEAR) by maximizing the augmented inverse propensity weighted value estimator over a class of decision rules using the experimental sample, with the primary outcome being imputed based on the auxiliary sample. The asymptotic properties of the proposed GEAR estimators and their associated value estimators are established. Simulation studies are conducted to demonstrate its empirical validity with a real AIDS application.
Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome
Authors: Hengrui Cai, Wenbin Lu, Rui Song
Date: 2021-04-21 00:00:00
Tasks: Decision Making

We consider the optimal decision-making problem in a primary sample of interest with multiple auxiliary sources available. The outcome of interest is limited in the sense that it is only observed in the primary sample. In reality, such multiple data sources may belong to heterogeneous studies and thus cannot be combined directly. This paper proposes a new framework to handle heterogeneous samples and address the limited outcome simultaneously through a novel calibrated optimal decision-making method, by leveraging the common intermediate outcomes in multiple data sources. Specifically, our method allows the baseline covariates across different samples to have either homogeneous or heterogeneous distributions. Under the equal conditional means of intermediate outcomes in different samples given baseline covariates and the treatment information, we show that the proposed estimator of the conditional mean outcome is asymptotically normal and more efficient than using the primary sample solely. Extensive experiments on simulated datasets demonstrate empirical validity and improved efficiency using our approach, followed by a real application to electronic health records.
Automatic Double Machine Learning for Continuous Treatment Effects
Authors: Sylvia Klosin
Date: 2021-04-21 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we introduce and prove asymptotic normality for a new nonparametric estimator of continuous treatment effects. Specifically, we estimate the average dose-response function - the expected value of an outcome of interest at a particular level of the treatment level. We utilize tools from both the double debiased machine learning (DML) and the automatic double machine learning (ADML) literatures to construct our estimator. Our estimator utilizes a novel debiasing method that leads to nice theoretical stability and balancing properties. In simulations our estimator performs well compared to current methods.
Deep Music Retrieval for Fine-Grained Videos by Exploiting Cross-Modal-Encoded Voice-Overs
Authors: Tingtian Li, Zixun Sun, Haoruo Zhang, Jin Li, Ziming Wu, Hui Zhan, Yipeng Yu, Hengcan Shi
Date: 2021-04-21 00:00:00
Tasks: Pseudo Label, Retrieval, Triplet

Recently, the witness of the rapidly growing popularity of short videos on different Internet platforms has intensified the need for a background music (BGM) retrieval system. However, existing video-music retrieval methods only based on the visual modality cannot show promising performance regarding videos with fine-grained virtual contents. In this paper, we also investigate the widely added voice-overs in short videos and propose a novel framework to retrieve BGM for fine-grained short videos. In our framework, we use the self-attention (SA) and the cross-modal attention (CMA) modules to explore the intra- and the inter-relationships of different modalities respectively. For balancing the modalities, we dynamically assign different weights to the modal features via a fusion gate. For paring the query and the BGM embeddings, we introduce a triplet pseudo-label loss to constrain the semantics of the modal embeddings. As there are no existing virtual-content video-BGM retrieval datasets, we build and release two virtual-content video datasets HoK400 and CFM400. Experimental results show that our method achieves superior performance and outperforms other state-of-the-art methods with large margins.
Gravitational Collapse and Neutrino Emission of Population III Massive Stars
Authors: Ken'ichiro Nakazato, Kohsuke Sumiyoshi, Shoichi Yamada
Date: 2005-09-29 00:00:00

Pop III stars are the first stars in the universe. They do not contain metals and their formation and evolution may be different from that of stars of later generations. In fact, according to the theory of star formation, Pop III stars might have very massive components ($\sim 100 - 10000M_\odot$). In this paper, we compute the spherically symmetric gravitational collapse of these Pop III massive stars. We solve the general relativistic hydrodynamics and neutrino transfer equations simultaneously, treating neutrino reactions in detail. Unlike supermassive stars ($\gtrsim 10^5 M_\odot$), the stars of concern in this paper become opaque to neutrinos. The collapse is simulated until after an apparent horizon is formed. We confirm that the neutrino transfer plays a crucial role in the dynamics of gravitational collapse, and find also that the $\beta$-equilibration leads to a somewhat unfamiliar evolution of electron fraction. Contrary to the naive expectation, the neutrino spectrum does not become harder for more massive stars. This is mainly because the neutrino cooling is more efficient and the outer core is more massive as the stellar mass increases. Here the outer core is the outer part of the iron core falling supersonically. We also evaluate the flux of relic neutrino from Pop III massive stars. As expected, the detection of these neutrinos is difficult for the currently operating detectors. However, if ever observed, the spectrum will enable us to obtain the information on the formation history of Pop III stars. We investigate 18 models covering the mass range of $300 - 10^4 M_\odot$, making this study the most detailed numerical exploration of spherical gravitational collapse of Pop III massive stars. This will also serve as an important foundation for multi-dimensional investigations.
COVID-19 sentiment analysis via deep learning during the rise of novel cases
Authors: Rohitash Chandra, Aswin Krishna
Date: 2021-04-05 00:00:00
Tasks: Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification

Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where the rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the first major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.
Extracting resilience metrics from distribution utility data using outage and restore process statistics
Authors: Nichelle'Le K. Carrington, Ian Dobson, Zhaoyu Wang
Date: 2020-11-02 00:00:00

Resilience curves track the accumulation and restoration of outages during an event on an electric distribution grid. We show that a resilience curve generated from utility data can always be decomposed into an outage process and a restore process and that these processes generally overlap in time. We use many events in real utility data to characterize the statistics of these processes, and derive formulas based on these statistics for resilience metrics such as restore duration, customer hours not served, and outage and restore rates. The formulas express the mean value of these metrics as a function of the number of outages in the event. We also give a formula for the variability of restore duration, which allows us to predict a maximum restore duration with 95% confidence. Overall, we give a simple and general way to decompose resilience curves into outage and restore processes and then show how to use these processes to extract resilience metrics from standard distribution system data.
Stochastic Model Predictive Control for Linear Systems with Unbounded Additive Uncertainties
Authors: Fei Li, Huiping Li, Yuyao He
Date: 2021-04-21 00:00:00
Tasks: Model Predictive Control

This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to unbounded additive uncertainties. The new methods are developed by formulating the chance constraints into deterministic form, which are treated in analogy with robust constraints, by using the probabilistic reachable set. Firstly, the probabilistically resolvable time-varying tube-based stochastic model predictive control algorithm is designed by employing the time-varying probabilistic reachable sets as the tubes. Secondly, by utilizing the probabilistic positively invariant set, the probabilistically resolvable constant tube-based stochastic model predictive control algorithm is developed by employing the constantly tightened constraints in the entire prediction horizons. In addition, to enhance the feasibility of the algorithms, the soft constraints are imposed to the state initializations. The algorithm feasibility and closed-loop stability results are provided. The efficacy of the approaches are demonstrated by means of numerical simulations.
Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems
Authors: Lai Wei, Ryan Mccloy, Jie Bao
Date: 2021-04-21 00:00:00
Tasks: Chemical Process, Diversity

Flexible manufacturing has been the trend in the area of the modern chemical process nowadays. One of the essential characteristics of flexible manufacturing is to track time-varying target trajectories (e.g. diversity and quantity of products). A possible tool to achieve time-varying targets is contraction theory. However, the contraction theory was developed for continuous time systems and there lacks analysis and synthesis tools for discrete-time systems. This article develops a systematic approach to discrete-time contraction analysis and control synthesis using Discrete-time Control Contraction Metrics (DCCM) which can be implemented using Sum of Square (SOS) programming. The proposed approach is demonstrated by illustrative example.
Feedback Stabilization and Output Tracking for Discrete-Time Lipschitz Nonlinear Systems via Iterative Convex Approximations
Authors: Sebastian A. Nugroho, Suyash C. Vishnoi, Ahmad F. Taha, Christian G. Claudel
Date: 2021-04-21 00:00:00

The stabilization of unstable nonlinear systems and tracking control are challenging engineering problems due to the encompassed nonlinearities in dynamic systems and their scale. In the past decades, numerous observer-based control designs for dynamic systems in which the nonlinearity belongs to Lipschitz functions have been proposed. However, most of them only focus on output feedback and consequently, state feedback design remains less developed. To that end, this paper is dedicated to the problem of full-state feedback controller design for discrete-time Lipschitz nonlinear systems. In addition, we present a simple iterative method for improving the convergence of the closed-loop performance. It is later demonstrated that our approach can be conveniently extended and utilized for output tracking.
Distributed Cooperative Driving in Multi-Intersection Road Networks
Authors: Huaxin Pei, Yi Zhang, Qinghua Tao, Shuo Feng, Li Li
Date: 2021-04-21 00:00:00

Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years. However, cooperative driving in multi-intersection road networks remains to be further investigated, because many algorithms for isolated intersection cannot be directly adopted for road networks. In this paper, we propose a distributed strategy to appropriately decompose the problem into small-scale sub-problems that address vehicle cooperation within limited temporal-spatial areas and meanwhile assure appropriate coordination between adjacent areas by specially designed information exchange. Simulation results demonstrate the efficiency-complexity balanced advantage of the proposed strategy under various traffic demand settings.
Analyzing the Effect of Persistent Asset Switches on a Class of Hybrid-Inspired Optimization Algorithms
Authors: Matina Baradaran, Justin H. Le, Andrew R. Teel
Date: 2021-04-21 00:00:00

Convex optimization challenges are currently pervasive in many science and engineering domains. In many applications of convex optimization, such as those involving multi-agent systems and resource allocation, the objective function can persistently switch during the execution of an optimization algorithm. Motivated by such applications, we analyze the effect of persistently switching objectives in continuous-time optimization algorithms. In particular, we take advantage of existing robust stability results for switched systems with distinct equilibria and extend these results to systems described by differential inclusions, making the results applicable to recent optimization algorithms that employ differential inclusions for improving efficiency and/or robustness. Within the framework of hybrid systems theory, we provide an accurate characterization, in terms of Omega-limit sets, of the set to which the optimization dynamics converge. Finally, by considering the switching signal to be constrained in its average dwell time, we establish semi-global practical asymptotic stability of these sets with respect to the dwell-time parameter.
Variance Reduction of Quadcopter Trajectory Tracking in Turbulent Wind
Authors: Asma Tabassum, Rohit K. S. S. Vuppala, He Bai, Kursat Kara
Date: 2021-04-20 00:00:00

We consider a quadcopter operating in a turbulent windy environment. The turbulent environment may be imposed on a quadcopter by structures, landscapes, terrains and most importantly by the unique physical phenomena in the lower atmosphere. Turbulence can negatively impact quadcopter's performance and operations. Modeling turbulence as a stochastic random input, we investigate control designs that can reduce the turbulence effects on the quadcopter's motion. In particular, we design a minimum cost variance (MCV) controller aiming to minimize the cost in terms of its weighted sum of mean and variance. We linearize the quadcopter dynamics and examine the MCV controller derived from a set of coupled algebraic Riccati equations (CARE) with full-state feedback. Our preliminary simulation results show reduction in variance and in mean trajectory tracking error compared to a traditional linear quadratic regulator (LQR).
Optimal Curing Strategy for Competing Epidemics Spreading over Complex Networks
Authors: Juntao Chen, Yunhan Huang, Rui Zhang, Quanyan Zhu
Date: 2020-11-29 00:00:00

Optimal curing strategy of suppressing competing epidemics spreading over complex networks is a critical issue. In this paper, we first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed curing strategy globally optimizes the trade-off between the curing cost and the severity of epidemics in the network. In addition, we provide structural results on the predictability of epidemic spreading by showing the existence and uniqueness of the solution. We also demonstrate the robustness of curing strategy by showing the continuity of epidemic severity with respect to the applied curing effort. A gradient descent algorithm based on a fixed-point iterative scheme is proposed to find the optimal curing strategy. Depending on the system parameters, the curing strategy can lead to switching between equilibria of the epidemic network as the control cost varies. Finally, we use case studies to corroborate and illustrate the obtained theoretical results.
Identification of Peer Effects with Miss-specified Peer Groups: Missing Data and Group Uncertainty
Authors: Christiern Rose, Lizi Yu
Date: 2021-04-21 00:00:00

We consider identification of peer effects under peer group miss-specification. Two leading cases are missing data and peer group uncertainty. Missing data can take the form of some individuals being entirely absent from the data. The researcher need not have any information on missing individuals and need not even know that they are missing. We show that peer effects are nevertheless identifiable under mild restrictions on the probabilities of observing individuals, and propose a GMM estimator to estimate the peer effects. In practice this means that the researcher need only have access to an individual level sample with group identifiers. Group uncertainty arises when the relevant peer group for the outcome under study is unknown. We show that peer effects are nevertheless identifiable if the candidate groups are nested within one another and propose a non-linear least squares estimator. We conduct a Monte-Carlo experiment to demonstrate our identification results and the performance of the proposed estimators, and apply our method to study peer effects in the career decisions of junior lawyers.
A Rational Inattention Theory of Echo Chamber
Authors: Lin Hu, Anqi Li, Xu Tan
Date: 2021-04-21 00:00:00

We develop a rational inattention theory of echo chambers, where players allocate limited attention across biased primary sources and other players to gather information about an uncertain state. The resulting Poisson attention network transmits information from the primary source to a player either directly or indirectly through other players. Rational inattention creates heterogeneous information demands among players who are biased toward different decisions. In an echo-chamber equilibrium, each player focuses on his own-biased source and like-minded friends, who attend to the same primary source as his and can serve as secondary sources if the attention channel from the primary source to him is disrupted. We establish conditions for the emergence of echo-chamber equilibria, characterize the attention networks within echo chambers, and offer insights for designing and regulating information platforms.
Nonlinear Pricing with Misspecified and Arbitrary Perception of the Marginal Price
Authors: Diego Alejandro Murillo Taborda
Date: 2021-04-20 00:00:00

In the context of nonlinear prices, the empirical evidence suggests that the consumers have cognitive biases represented in a limited understanding of nonlinear price structures, and they respond to some alternative perceptions of the marginal prices. In particular, consumers usually make choices based more on the average than the marginal prices, which can result in a suboptimal behavior. Taking the misspecification in the marginal price as exogenous, this document analyzes how is the optimal quadratic price scheme for a monopolist and the optimal quadratic price scheme that maximizes welfare when there is a continuum of representative consumers with an arbitrary perception of the marginal price. Under simple preferences and costs functional forms and very straightforward hypotheses, the results suggest that the bias in the marginal price doesn't affect the maximum welfare attainable with quadratic price schemes, and it has a negligible effect over the efficiency cost caused by the monopolist, so the misspecification in the marginal price is not relevant for welfare increasing policies. However, almost always the misspecification in the marginal price is beneficial for the monopolist. An interesting result is that under these functional forms, more misspecification in the marginal price is beneficial for both the consumers and the monopolist if the level of bias is low, so not always is socially optima to have educated consumers about the real marginal price. Finally, the document shows that the bias in the marginal price has a negative effect on reduction of aggregate consumption using two-tier increasing tariffs, which are commonly used for reduction of aggregate consumption.
Policy with stochastic hysteresis
Authors: Georgii Riabov, Aleh Tsyvinski
Date: 2021-04-20 00:00:00
Tasks: Form

The paper develops a general methodology for analyzing policies with path-dependency (hysteresis) in stochastic models with forward looking optimizing agents. Our main application is a macro-climate model with a path-dependent climate externality. We derive in closed form the dynamics of the optimal Pigouvian tax, that is, its drift and diffusion coefficients. The dynamics of the present marginal damages is given by the recently developed functional It\^o formula. The dynamics of the conditional expectation process of the future marginal damages is given by a new total derivative formula that we prove. The total derivative formula represents the evolution of the conditional expectation process as a sum of the expected dynamics of hysteresis with respect to time, a form of a time derivative, and the expected dynamics of hysteresis with the shocks to the trajectory of the stochastic process, a form of a stochastic derivative. We then generalize the results. First, we propose a general class of hysteresis functionals that permits significant tractability. Second, we characterize in closed form the dynamics of the stochastic hysteresis elasticity that represents the change in the whole optimal policy process with an introduction of small hysteresis effects. Third, we determine the optimal policy process.