pwc-archive/papers-with-abstracts huggingface dataset:
A Multi-faceted OCR Framework for Artificial Urdu News Ticker Text Recognition
Authors: Sami-Ur-Rehman, Burhan Ul Tayyab, Muhammad Ferjad Naeem, Adnan Ul-Hasan, Faisal Shafait
Date: 2018-04-24 00:00:00
Proceeding: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS) 2018 4
Tasks: Optical Character Recognition (OCR), Retrieval

Content based information search and retrieval has allowed for easier access to data. While Latin based scripts have gained attention and support from academia and industry, there is limited support for cursive script languages, like Urdu. In this paper, we present the first instance of Urdu news ticker detection and recognition and take a micron sized step towards the goal of super intelligence. The presented solution allows for automating the transcription, indexing and captioning of Urdu news video content. We present the first comprehensive data set, to our knowledge, for Urdu news ticker recognition, collected from 41 different news channels. The data set covers both high and low quality channels, distorted and blurred news tickers, making the data set an ideal test case for any automatic Urdu News Recognition system in future. We identify and address the key challenges in Urdu News Ticker text recognition …
COVID-WideNet—A capsule network for COVID-19 detection
Authors: Harsh Panwar
Date: 2022-03-22 00:00:00
Proceeding: Applied Soft Computing 2022 3
Tasks: COVID-19 Diagnosis, Diagnostic, Specificity

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly for using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for the diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in a fast and efficient diagnosing COVID-19 symptoms, and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity, respectively. This may also assist radiologists to detect COVID and its variant like delta.
Bootstrapping Structural Change Tests
Authors: Otilia Boldea, Adriana Cornea-Madeira, Alastair R. Hall
Date: 2018-11-09 00:00:00

This paper analyses the use of bootstrap methods to test for parameter change in linear models estimated via Two Stage Least Squares (2SLS). Two types of test are considered: one where the null hypothesis is of no change and the alternative hypothesis involves discrete change at k unknown break-points in the sample; and a second test where the null hypothesis is that there is discrete parameter change at l break-points in the sample against an alternative in which the parameters change at l + 1 break-points. In both cases, we consider inferences based on a sup-Wald-type statistic using either the wild recursive bootstrap or the wild fixed bootstrap. We establish the asymptotic validity of these bootstrap tests under a set of general conditions that allow the errors to exhibit conditional and/or unconditional heteroskedasticity, and report results from a simulation study that indicate the tests yield reliable inferences in the sample sizes often encountered in macroeconomics. The analysis covers the cases where the first-stage estimation of 2SLS involves a model whose parameters are either constant or themselves subject to discrete parameter change. If the errors exhibit unconditional heteroskedasticity and/or the reduced form is unstable then the bootstrap methods are particularly attractive because the limiting distributions of the test statistics are not pivotal.
Day-ahead Schedule Considering the Participation of Electric Vehicles in Primary Frequency Response
Authors: Amanda Fernandes, Miguel Carrión, Ruth Domínguez, Rodrigo Ramos, Ahda Pavani, Werbeston Oliveira
Date: 2022-07-19 00:00:00
Tasks: Scheduling

The insertion of renewable sources in power systems may cause a decrease in the system's equivalent inertia, which result in the instability of the power system. On the other hand, energy storage systems have proven to be an effective tool to increase the flexibility in the operation of energy systems, which may favor integrating renewable energy sources. In this way, plug-in electric vehicle (PEVs) batteries can be used as storages when such vehicles are parked and connected to the grid. In this work, the contribution of PEVs to primary frequency response is analyzed in systems dominated by renewable sources. A day-ahead scheduling model is developed considering PEVs groups can actively participate in electricity markets, supporting day-ahead reserve capacity and providing primary frequency response. The proposed model is implemented in the distribution system of the Federal University of Amap\'a.
Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios
Authors: Jingxuan Yang, Honglin He, Yi Zhang, Shuo Feng, Henry X. Liu
Date: 2022-07-19 00:00:00
Tasks: regression

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times.
Reconfigurable Plug-and-play Distributed Model Predictive Control for Reference Tracking
Authors: Ahmed Aboudonia, Andrea Martinelli, Nicolas Hoischen, John Lygeros
Date: 2022-07-19 00:00:00
Tasks: Model Predictive Control

A plug-and-play model predictive control (PnP MPC) scheme is proposed for varying-topology networks to track piecewise constant references. The proposed scheme allows subsystems to occasionally join and leave the network while preserving asymptotic stability and recursive feasibility and comprises two main phases. In the redesign phase, passivity-based control is used to ensure that asymptotic stability of the network is preserved. In the transition phase, reconfigurable terminal ingredients are used to ensure that the distributed MPC problem is initially feasible after the PnP operation. The efficacy of the proposed scheme is evaluated by applying it to a network of mass-spring-damper systems and comparing it to a benchmark scheme. It is found that the novel redesign phase results in faster PnP operations, whereas the novel transition phase increases flexibility by accepting more requests.
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking
Authors: Ahmed Aboudonia, Goran Banjac, Annika Eichler, John Lygeros
Date: 2022-07-19 00:00:00
Tasks: Distributed Optimization, Model Predictive Control

A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline. Unlike many standard existing schemes, this scheme yields large feasible regions without performing offline centralized computations. Although the resulting optimal control problem (OCP) is a semidefinite program (SDP), an SDP scalability method based on diagonal dominance is used to approximate the derived SDP by a second-order cone program. The OCPs of the proposed scheme and its approximation are amenable to distributed optimization. Both schemes are evaluated using a power network example and compared to a scheme where the terminal controller is reconfigured online as well. It is found that fixing the terminal controller results in better performance, noticeable reduction in computational cost and similar feasible region compared to the case in which this controller is reconfigured online.
Nonlinear Model Predictive Control Framework For Cooperative Three-Agent Target Defense Game
Authors: Amith Manoharan, P. B. Sujit
Date: 2022-07-19 00:00:00
Tasks: Model Predictive Control

This paper presents cooperative target defense guidance strategies using nonlinear model predictive control (NMPC) framework for a target-attacker-defender (TAD) game. The TAD game consists of an attacker and a cooperative target-defender pair. The attacker's objective is to capture the target, whereas the target-defender team acts together such that the defender can intercept the attacker and ensure target survival. We assume that the cooperative target-defender pair do not have perfect knowledge of the attacker states, and hence the states are estimated using an Extended Kalman Filter (EKF). The capture analysis based on the Apollonius circles is performed to identify the target survival regions. The efficacy of the NMPC-based solution is evaluated through extensive numerical simulations. The results show that the NMPC-based solution offers robustness to the different unknown attacker models and has better performance than CLOS and A-CLOS based strategies.
Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes
Authors: Saeid Sadeghi Vilni, Mohammad Moltafet, Markus Leinonen, Marian Codreanu
Date: 2022-07-19 00:00:00
Tasks: Q-Learning, Scheduling

We consider a multi-source hybrid automatic repeat request (HARQ) based system, where a transmitter sends status update packets of random arrival (i.e., uncontrollable sampling) and generate-atwill (i.e., controllable sampling) sources to a destination through an error-prone channel. We develop transmission scheduling policies to minimize the average number of transmissions subject to an average age of information (AoI) constraint. First, we consider known environment (i.e., known system statistics) and develop a near-optimal deterministic transmission policy and a low-complexity dynamic transmission (LC-DT) policy. The former policy is derived by casting the main problem into a constrained Markov decision process (CMDP) problem, which is then solved using the Lagrangian relaxation, relative value iteration algorithm, and bisection. The LC-DT policy is developed via the drift-plus-penalty (DPP) method by transforming the main problem into a sequence of per-slot problems. Finally, we consider unknown environment and devise a learning-based transmission policy by relaxing the CMDP problem into an MDP problem using the DPP method and then adopting the deep Q-learning algorithm. Numerical results show that the proposed policies achieve near-optimal performance and illustrate the benefits of HARQ in status updating.
Pareto Optimal Strategies for Event-Triggered Estimation
Authors: Anne Theurkauf, Nisar Ahmed, Morteza Lahijanian
Date: 2022-07-18 00:00:00

Although resource-limited networked autonomous systems must be able to efficiently and effectively accomplish tasks, better conservation of resources often results in worse task performance. We specifically address the problem of finding strategies for managing measurement communication costs between agents. A well understood technique for trading off communication costs with estimation accuracy is event triggering (ET), where measurements are only communicated when useful, e.g., when Kalman filter innovations exceed some threshold. In the absence of measurements, agents can use implicit information to achieve results almost as well as when explicit data is always communicated. However, there are no methods for setting this threshold with formal guarantees on task performance. We fill this gap by developing a novel belief space discretization technique to abstract a continuous space dynamics model for ET estimation to a discrete Markov decision process, which scalably accommodates threshold-sensitive ET estimator error covariances. We then apply an existing probabilistic trade-off analysis tool to find the set of all optimal trade-offs between resource consumption and task performance. From this set, an ET threshold selection strategy is extracted. Simulated results show our approach identifies non-trivial trade-offs between performance and energy savings, with only modest computational effort.
Proposal and Description of a Test System with Wind, Hydro and Fossil Fuel Power Plants for Static Analyses
Authors: Victor Neumann, Roman Kuiava, Rodrigo A. Ramos, Ahda Pavani
Date: 2022-07-18 00:00:00

This article presents and describes a 229 bus test system that includes wind, hydro and fossil fuel power plants. It represents the Northeast subsystem of the Brazilian Interconnected Power System (BIPS). The test system supplies a load of 4.17 GW, being 13% powered by wind farms, which is the current wind power penetration level of the BIPS. The data comprehends different load levels based on the typical load behavior and typical capacity factors of wind, hydro and fossil fuel plants, as well as the capacity of transmission and sub-transmission lines, transformers, and the adopted structure for the test system. The data is compiled considering models and operating scenarios of the BIPS, and allow performing studies of static voltage stability, sensitivity of voltage stability margin considering the wind farms, and multi-objective optimization considering market constraints. The results of the simulations with the test system indicate the consistency of their data structure and its applicability to different studies of electric power systems.
Lateral predictive coding revisited: Internal model, symmetry breaking, and response time
Authors: Zhen-Ye Huang, Xin-Yi Fan, Jianwen Zhou, Hai-Jun Zhou
Date: 2022-07-19 00:00:00

Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception. It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors. Previous studies on predictive coding emphasized top-down feedback interactions in hierarchical multi-layered networks but largely ignored lateral recurrent interactions. We perform analytical and numerical investigations in this work on the effects of single-layer lateral interactions. We consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written digits. We find that learning will generally break the interaction symmetry between peer neurons, and that high input correlation between two neurons does not necessarily bring strong direct interactions between them. The optimized network responds to familiar input signals much faster than to novel or random inputs, and it significantly reduces the correlations between the output states of pairs of neurons.
Asymptotic Properties of Endogeneity Corrections Using Nonlinear Transformations
Authors: Jörg Breitung, Alexander Mayer, Dominik Wied
Date: 2022-07-19 00:00:00
Tasks: regression

This paper considers a linear regression model with an endogenous regressor which arises from a nonlinear transformation of a latent variable. It is shown that the corresponding coefficient can be consistently estimated without external instruments by adding a rank-based transformation of the regressor to the model and performing standard OLS estimation. In contrast to other approaches, our nonparametric control function approach does not rely on a conformably specified copula. Furthermore, the approach allows for the presence of additional exogenous regressors which may be (linearly) correlated with the endogenous regressor(s). Consistency and asymptotic normality of the estimator are proved and the estimator is compared with copula based approaches by means of Monte Carlo simulations. An empirical application on wage data of the US current population survey demonstrates the usefulness of our method.
Bias correction and uniform inference for the quantile density function
Authors: Grigory Franguridi
Date: 2022-07-19 00:00:00

For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct the confidence bands that are asymptotically exact uniformly over the entire domain $[0,1]$. The proposed procedures rely on the pivotality of the studentized bias-corrected estimator and known anti-concentration properties of the Gaussian approximation for its supremum.
Symmetric reduced form voting
Authors: Xu Lang, Debasis Mishra
Date: 2022-07-19 00:00:00
Tasks: Form

We study a model of voting with two alternatives in a symmetric environment. We characterize the interim allocation probabilities that can be implemented by a symmetric voting rule. We show that every such interim allocation probabilities can be implemented as a convex combination of two families of deterministic voting rules: qualified majority and qualified anti-majority. We also provide analogous results by requiring implementation by a symmetric monotone (strategy-proof) voting rule and by a symmetric unanimous voting rule. We apply our results to show that an ex-ante Rawlsian rule is a convex combination of a pair of qualified majority rules.
A Game-theoretic Model of the Consumer Behavior Under Pay-What-You-Want Pricing Strategy
Authors: Vahid Ashrafimoghari, Jordan W. Suchow
Date: 2022-07-18 00:00:00
Tasks: Fairness

In a digital age where companies face rapid changes in technology, consumer trends, and business environments, there is a critical need for continual revision of the business model in response to disruptive innovation. A pillar of innovation in business practices is the adoption of novel pricing schemes, such as Pay-What-You-Want (PWYW). In this paper, we employed game theory and behavioral economics to model consumers' behavior in response to a PWYW pricing strategy where there is an information asymmetry between the consumer and supplier. In an effort to minimize the information asymmetry, we incorporated the supplier's cost and the consumer's reference prices as two parameters that might influence the consumer's payment decision. Our model shows that consumers' behavior varies depending on the available information. As a result, when an external reference point is provided, the consumer tends to pay higher amounts to follow the social herd or respect her self-image. However, the external reference price can also decrease her demand when, in the interest of fairness, she forgoes the purchase because the amount she is willing to pay is less that what she recognizes to be an unrecoverable cost to the supplier.
Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks
Authors: Chen Quan, Nandan Sriranga, Haodong Yang, Yunghsiang S. Han, Baocheng Geng, Pramod K. Varshney
Date: 2022-07-18 00:00:00

In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision. In this work, we investigate the effect of data falsification attacks on the performance of EEOT-based systems. We derive the probability of error for an EEOT-based system under attack and find an upper bound (UB) on the expected number of transmissions required to make the final decision. Moreover, we tighten this UB by solving an optimization problem via integer programming (IP). We also obtain the FC's optimal threshold which guarantees the optimal detection performance of the EEOT-based system. Numerical and simulation results indicate that it is possible to reduce transmissions while still ensuring the quality of the decision with an appropriately designed threshold.
Streamable Neural Fields
Authors: Junwoo Cho, Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park
Date: 2022-07-20 00:00:00

Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network parameters, the data transfer by sending and receiving the entire model parameters prevents this emerging technology from being used in many practical scenarios. We propose streamable neural fields, a single model that consists of executable sub-networks of various widths. The proposed architectural and training techniques enable a single network to be streamable over time and reconstruct different qualities and parts of signals. For example, a smaller sub-network produces smooth and low-frequency signals, while a larger sub-network can represent fine details. Experimental results have shown the effectiveness of our method in various domains, such as 2D images, videos, and 3D signed distance functions. Finally, we demonstrate that our proposed method improves training stability, by exploiting parameter sharing.
DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
Authors: Kailiang Zhong, Fengtong Xiao, Yan Ren, Yaorong Liang, Wenqing Yao, Xiaofeng Yang, Ling Cen
Date: 2022-07-19 00:00:00
Tasks: Causal Inference, Multi-Task Learning

Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference.