pwc-archive/papers-with-abstracts huggingface dataset:
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
Authors: Jure Vogrinc, Samuel Livingstone, Giacomo Zanella
Date: 2022-01-04 00:00:00

We study the class of first-order locally-balanced Metropolis--Hastings algorithms introduced in Livingstone & Zanella (2021). To choose a specific algorithm within the class the user must select a balancing function $g:\mathbb{R} \to \mathbb{R}$ satisfying $g(t) = tg(1/t)$, and a noise distribution for the proposal increment. Popular choices within the class are the Metropolis-adjusted Langevin algorithm and the recently introduced Barker proposal. We first establish a universal limiting optimal acceptance rate of 57% and scaling of $n^{-1/3}$ as the dimension $n$ tends to infinity among all members of the class under mild smoothness assumptions on $g$ and when the target distribution for the algorithm is of the product form. In particular we obtain an explicit expression for the asymptotic efficiency of an arbitrary algorithm in the class, as measured by expected squared jumping distance. We then consider how to optimise this expression under various constraints. We derive an optimal choice of noise distribution for the Barker proposal, optimal choice of balancing function under a Gaussian noise distribution, and optimal choice of first-order locally-balanced algorithm among the entire class, which turns out to depend on the specific target distribution. Numerical simulations confirm our theoretical findings and in particular show that a bi-modal choice of noise distribution in the Barker proposal gives rise to a practical algorithm that is consistently more efficient than the original Gaussian version.
Open Access Dataset for Electromyography based Multi-code Biometric Authentication
Authors: Ashirbad Pradhan, Jiayuan He, Ning Jiang
Date: 2022-01-04 00:00:00
Tasks: Gesture Recognition

Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize multi-length codes or passwords (for example, by performing different gestures). However, current EMG-based biometric research has two critical limitations: 1) a small subject pool, compared to other more established biometric traits, and 2) limited to single-session or single-day data sets. In this study, forearm and wrist EMG data were collected from 43 participants over three different days with long separation while they performed static hand and wrist gestures. The multi-day biometric authentication resulted in a median EER of 0.017 for the forearm setup and 0.025 for the wrist setup, comparable to well-established biometric traits suggesting consistent performance over multiple days. The presented large-sample multi-day data set and findings could facilitate further research on EMG-based biometrics and other gesture recognition-based applications.
S-Walk: Accurate and Scalable Session-based Recommendationwith Random Walks
Authors: Minjin Choi, jinhong Kim, Joonsek Lee, Hyunjung Shim, Jongwuk Lee
Date: 2022-01-04 00:00:00
Tasks: Computational Efficiency, Recommendation Systems, Session-Based Recommendations

Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy and scalability, we propose a novel session-based recommendation with a random walk, namely S-Walk. Precisely, S-Walk effectively captures intra- and inter-session correlations by handling high-order relationships among items using random walks with restart (RWR). By adopting linear models with closed-form solutions for transition and teleportation matrices that constitute RWR, S-Walk is highly efficient and scalable. Extensive experiments demonstrate that S-Walk achieves comparable or state-of-the-art performance in various metrics on four benchmark datasets. Moreover, the model learned by S-Walk can be highly compressed without sacrificing accuracy, conducting two or more orders of magnitude faster inference than existing DNN-based models, making it suitable for large-scale commercial systems.
Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection
Authors: Yoshinao Ishii, Satoshi Koide, Keiichiro Hayakawa
Date: 2020-07-09 00:00:00
Proceeding: International Journal of Data Science and Analytics 2020 7
Tasks: Outlier Detection

Unsupervised outlier detection without the need for clean data has attracted great attention because it is suitable for real-world problems as a result of its low data collection costs. Reconstruction-based methods are popular approaches for unsupervised outlier detection. These methods decompose a data matrix into low-dimensional manifolds and an error matrix. Then, samples with a large error are detected as outliers. To achieve high outlier detection accuracy, when data are corrupted by large noise, the detection method should have the following two properties: (1) it should be able to decompose the data under the L0-norm constraint on the error matrix and (2) it should be able to reflect the nonlinear features of the data in the manifolds. Despite significant efforts, no method with both of these properties exists. To address this issue, we propose a novel reconstruction-based method: “L0-norm constrained autoencoders (L0-AE).” L0-AE uses autoencoders to learn low-dimensional manifolds that capture the nonlinear features of the data and uses a novel optimization algorithm that can decompose the data under the L0-norm constraints on the error matrix. This novel L0-AE algorithm provably guarantees the convergence of the optimization if the autoencoder is trained appropriately. The experimental results show that L0-AE is more robust, accurate and stable than other unsupervised outlier detection methods not only for artificial datasets with corrupted samples but also artificial datasets with well-known outlier distributions and real datasets. Additionally, the results show that the accuracy of L0-AE is moderately stable to changes in the parameter of the constrained term, and for real datasets, L0-AE achieves higher accuracy than the baseline non-robustified method for most parameter values.
An approach to interfacing the brain with quantum computers: practical steps and caveats
Authors: Eduardo Reck Miranda, Satvik Venkatesh, Jose D. Martın-Guerrero, Carlos Hernani-Morales, Lucas Lamata, Enrique Solano
Date: 2022-01-04 00:00:00

We report on the first proof-of-concept system demonstrating how one can control a qubit with mental activity. We developed a method to encode neural correlates of mental activity as instructions for a quantum computer. Brain signals are detected utilizing electrodes placed on the scalp of a person, who learns how to produce the required mental activity to issue instructions to rotate and measure a qubit. Currently, our proof-of-concept runs on a software simulation of a quantum computer. At the time of writing, available quantum computing hardware and brain activity sensing technology are not sufficiently developed for real-time control of quantum states with the brain. But we are one step closer to interfacing the brain with real quantum machines, as improvements in hardware technology at both fronts become available in time to come. The paper ends with a discussion on some of the challenging problems that need to be addressed before we can interface the brain with quantum hardware.
Mechanism Design with Informational Punishment
Authors: Benjamin Balzer, Johannes Schneider
Date: 2022-01-04 00:00:00

We introduce \emph{informational punishment} to the design of mechanisms that compete with an exogenous status quo mechanism: Players can send garbled public messages with some delay, and others cannot commit to ignoring them. Optimal informational punishment ensures that full participation is without loss, even if any single player can publicly enforce the status quo mechanism. Informational punishment permits using a standard revelation principle, is independent of the mechanism designer's objective, and operates exclusively off the equilibrium path. It is robust to refinements and applies in informed-principal settings. We provide conditions that make it robust to opportunistic signal designers.
Programmatic Reward Design by Example
Authors: Weichao Zhou, Wenchao Li
Date: 2021-12-14 00:00:00
Tasks: Reinforcement Learning (RL)

Reward design is a fundamental problem in reinforcement learning (RL). A misspecified or poorly designed reward can result in low sample efficiency and undesired behaviors. In this paper, we propose the idea of programmatic reward design, i.e. using programs to specify the reward functions in RL environments. Programs allow human engineers to express sub-goals and complex task scenarios in a structured and interpretable way. The challenge of programmatic reward design, however, is that while humans can provide the high-level structures, properly setting the low-level details, such as the right amount of reward for a specific sub-task, remains difficult. A major contribution of this paper is a probabilistic framework that can infer the best candidate programmatic reward function from expert demonstrations. Inspired by recent generative-adversarial approaches, our framework searches for the most likely programmatic reward function under which the optimally generated trajectories cannot be differentiated from the demonstrated trajectories. Experimental results show that programmatic reward functionslearned using this framework can significantly outperform those learned using existing reward learning algo-rithms, and enable RL agents to achieve state-of-the-artperformance on highly complex tasks.
Programming Knowledge Tracing: A Comprehensive Dataset and A New Model
Authors: Renyu Zhu, Dongxiang Zhang, Chengcheng Han, Ming Gao, Xuesong Lu, Weining Qian, Aoying Zhou
Date: 2021-12-11 00:00:00
Tasks: Clone Detection, Knowledge Tracing

In this paper, we study knowledge tracing in the domain of programming education and make two important contributions. First, we harvest and publish so far the most comprehensive dataset, namely BePKT, which covers various online behaviors in an OJ system, including programming text problems, knowledge annotations, user-submitted code and system-logged events. Second, we propose a new model PDKT to exploit the enriched context for accurate student behavior prediction. More specifically, we construct a bipartite graph for programming problem embedding, and design an improved pre-training model PLCodeBERT for code embedding, as well as a double-sequence RNN model with exponential decay attention for effective feature fusion. Experimental results on the new dataset BePKT show that our proposed model establishes state-of-the-art performance in programming knowledge tracing. In addition, we verify that our code embedding strategy based on PLCodeBERT is complementary to existing knowledge tracing models to further enhance their accuracy. As a side product, PLCodeBERT also results in better performance in other programming-related tasks such as code clone detection.
Exponentially Convergent Direct Adaptive Pole Placement Control of Plants with Unmatched Uncertainty under FE Condition
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2022-01-04 00:00:00

A new method of direct adaptive pole placement control (APPC) is developed for plants with unmatched uncertainty, which linearly depends on a state vector. It guarantees the exponential stability of a control system and exponential convergence of control law adjustable parameters to their true values when the regressor is finitely exciting. Considering the known classical APPC schemes and adaptive methods with exponential regulation, the advantages of the proposed one are that it does not require a priori information on a control input matrix and ensures the monotonic transient behavior of each adjustable parameter of the control law. The theoretical results are supported by the numerical experiments.
Semi-global Periodic Event-triggered Output Regulation for Nonlinear Multi-agent Systems
Authors: Shiqi Zheng, Peng Shi, Huiyan Zhang
Date: 2022-01-04 00:00:00

This study focuses on periodic event-triggered (PET) cooperative output regulation problem for a class of nonlinear multi-agent systems. The key feature of PET mechanism is that event-triggered conditions are required to be monitored only periodically. This approach is beneficial for Zeno behavior exclusion and saving of battery energy of onboard sensors. At first, new PET distributed observers are proposed to estimate the leader information. We show that the estimation error converges to zero exponentially with a known convergence rate under asynchronous PET communication. Second, a novel PET output feedback controller is designed for the underlying strict feedback nonlinear multi-agent systems. Based on a state transformation technique and a local PET state observer, the cooperative semi-global output regulation problem can be solved by the proposed new control design technique. Simulation results of multiple Lorenz systems illustrate that the developed control scheme is effective.
Deep Learning-based Predictive Control of Battery Management for Frequency Regulation
Authors: Yun Li, Yixiu Wang, Yifu Chen, Kaixun Hua, Jiayang Ren, Ghazaleh Mozafari, Qiugang Lu, Yankai Cao
Date: 2022-01-04 00:00:00
Tasks: Management, Model Predictive Control, Reinforcement Learning (RL)

This paper proposes a deep learning-based optimal battery management scheme for frequency regulation (FR) by integrating model predictive control (MPC), supervised learning (SL), reinforcement learning (RL), and high-fidelity battery models. By taking advantage of deep neural networks (DNNs), the derived DNN-approximated policy is computationally efficient in online implementation. The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation. The SL process speeds up the subsequent RL process by providing a good initialization. By utilizing RL algorithms, one prominent property of the proposed scheme is that it can learn from the data generated by simulating the FR policy on the high-fidelity battery simulator to adjust the DNN-approximated policy, which is originally based on low-fidelity battery model. A case study using real-world data of FR signals and prices is performed. Simulation results show that, compared to conventional MPC schemes, the proposed deep learning-based scheme can effectively achieve higher economic benefits of FR participation while maintaining lower online computational cost.
On real structured controllability/stabilizability/stability radius: Complexity and unified rank-relaxation based methods
Authors: Yuan Zhang, Yuanqing Xia, Yufeng Zhan
Date: 2022-01-04 00:00:00

This paper addresses the real structured controllability, stabilizability, and stability radii (RSCR, RSSZR, and RSSR, respectively) of linear systems, which involve determining the distance (in terms of matrix norms) between a (possibly large-scale) system and its nearest uncontrollable, unstabilizable, and unstable systems, respectively, with a prescribed affine structure. This paper makes two main contributions. First, by demonstrating that determining the feasibilities of RSCR and RSSZR is NP-hard when the perturbations have a general affine parameterization, we prove that computing these radii is NP-hard. Additionally, we prove the NP-hardness of a problem related to the RSSR. These hardness results are independent of the matrix norm used. Second, we develop unified rank-relaxation based algorithms for these problems, which can handle both the Frobenius norm and the $2$-norm based problems and share the same framework for the RSCR, RSSZR, and RSSR problems. These algorithms utilize the low-rank structure of the original problems and relax the corresponding rank constraints with a regularized truncated nuclear norm term. Moreover, a modified version of these algorithms can find local optima with performance specifications on the perturbations, under appropriate conditions. Finally, simulations suggest that the proposed methods, despite being in a simple framework, can find local optima as good as several existing methods.
Tie-line Security Regions in High Dimension for Renewable Accommodations
Authors: Wei Lin, Hua Jiang, Zhifang Yang
Date: 2022-01-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Tie-line power exchanges among regional power systems facilitate renewable accommodations. Power exchanges can be calculated via a tie-line security region that provides the feasible region of the coupling parameters among regional power systems. However, a tie-line security region is a high-dimension polytope due to multiple time periods and border buses inherently in power system operations, leading to the considerable computational burden. A fast calculation method for tie-line security regions in high dimension is studied in this paper. The high-dimension polytope across all the time periods is decomposed as a Cartesian production of lower-dimension polytopes at each time period by leveraging dispatch levels of generations. For each lower-dimension polytope, the computational burden brought by multiple border buses is alleviated by aggregating tie-line power. Also, minimum renewable curtailments are preserved by incorporating an additional dimension in the tie-line security region. For the coupling parameters located within our tie-line security region, a feasible decision of the regional power system exists. Finally, the tie-line security region is used to reduce renewable curtailments in an interconnected power system under a decentralized and non-iterative framework. The performance of the presented methods is corroborated in the IEEE 9-bus system, a 661-bus utility system and a five-region system.
Selective Inhibition and Recruitment of Linear-Threshold Thalamocortical Networks
Authors: Michael McCreesh, Jorge Cortés
Date: 2022-01-03 00:00:00

Neuroscientific evidence shows that for most brain networks all pathways between cortical regions either pass through the thalamus or a transthalamic parallel route exists for any direct corticocortical connection. This paper seeks to formally study the dynamical behavior of the resulting thalamocortical brain networks with a view to characterizing the inhibitory role played by the thalamus and its benefits. We employ a linear-threshold mesoscale model for individual brain subnetworks and study both hierarchical and star-connected thalamocortical networks. Using tools from singular perturbation theory and switched systems, we show that selective inhibition and recruitment can be achieved in such networks through a combination of feedback and feedforward control. Various simulations throughout the exposition illustrate the benefits resulting from the presence of the thalamus regarding failsafe mechanisms, required control magnitude, and network performance.
Neurons as hierarchies of quantum reference frames
Authors: Chris Fields, James F. Glazebrook, Michael Levin
Date: 2022-01-04 00:00:00

Conceptual and mathematical models of neurons have lagged behind empirical understanding for decades. Here we extend previous work in modeling biological systems with fully scale-independent quantum information-theoretic tools to develop a uniform, scalable representation of synapses, dendritic and axonal processes, neurons, and local networks of neurons. In this representation, hierarchies of quantum reference frames act as hierarchical active-inference systems. The resulting model enables specific predictions of correlations between synaptic activity, dendritic remodeling, and trophic reward. We summarize how the model may be generalized to nonneural cells and tissues in developmental and regenerative contexts.
What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature
Authors: Jonathan Roth, Pedro H. C. Sant'Anna, Alyssa Bilinski, John Poe
Date: 2022-01-04 00:00:00
Tasks: Econometrics

This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of ``canonical'' assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on $(i)$ multiple periods and variation in treatment timing, $(ii)$ potential violations of parallel trends, or $(iii)$ alternative frameworks for inference. Our discussion highlights the different ways that the DiD literature has advanced beyond the canonical model, and helps to clarify when each of the papers will be relevant for empirical work. We conclude by discussing some promising areas for future research.
A Double Robust Approach for Non-Monotone Missingness in Multi-Stage Data
Authors: Shenshen Yang
Date: 2022-01-04 00:00:00

Multivariate missingness with a non-monotone missing pattern is complicated to deal with in empirical studies. The traditional Missing at Random (MAR) assumption is difficult to justify in such cases. Previous studies have strengthened the MAR assumption, suggesting that the missing mechanism of any variable is random when conditioned on a uniform set of fully observed variables. However, empirical evidence indicates that this assumption may be violated for variables collected at different stages. This paper proposes a new MAR-type assumption that fits non-monotone missing scenarios involving multi-stage variables. Based on this assumption, we construct an Augmented Inverse Probability Weighted GMM (AIPW-GMM) estimator. This estimator features an asymmetric format for the augmentation term, guarantees double robustness, and achieves the closed-form semiparametric efficiency bound. We apply this method to cases of missingness in both endogenous regressor and outcome, using the Oregon Health Insurance Experiment as an example. We check the correlation between missing probabilities and partially observed variables to justify the assumption. Moreover, we find that excluding incomplete data results in a loss of efficiency and insignificant estimators. The proposed estimator reduces the standard error by more than 50% for the estimated effects of the Oregon Health Plan on the elderly.
The financial value of the within-government political network: Evidence from Chinese municipal corporate bonds
Authors: Jaehyuk Choi, Lei Lu, Heungju Park, Sungbin Sohn
Date: 2022-01-04 00:00:00

This paper examines the effect of the political network of Chinese municipal leaders on the pricing of municipal corporate bonds. Using municipal leaders' working experience to measure the political network, we find that this network reduces the bond issuance yield spreads by improving the credit ratings of the issuer, the local government financing vehicle. The relationship between political networks and issuance yield spreads is strengthened in areas where financial markets and legal systems are less developed.