pwc-archive/papers-with-abstracts huggingface dataset:
Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm
Authors: Simon Streicher, Johan du Preez
Date: 2021-09-30 00:00:00

Probabilistic graphical models (PGMs) are powerful tools for solving systems of complex relationships over a variety of probability distributions. However, while tree-structured PGMs always result in efficient and exact solutions, inference on graph (or loopy) structured PGMs is not guaranteed to discover the optimal solutions. It is in principle possible to convert loopy PGMs to an equivalent tree structure, but this is usually impractical for interesting problems due to exponential blow-up. To address this, we developed the purge-and-merge algorithm. This algorithm iteratively nudges a malleable graph structure towards a tree structure by selectively merging factors. The merging process is designed to avoid exponential blow-up by way of sparse structures from which redundancy is purged as the algorithm progresses. We set up tasks to test the algorithm on constraint-satisfaction puzzles such as Sudoku, Fill-a-pix, and Kakuro, and it outperformed other PGM-based approaches reported in the literature. While the tasks we set focussed on the binary logic of CSP, we believe the purge-and-merge algorithm could be extended to general PGM inference.
On the Trustworthiness of Tree Ensemble Explainability Methods
Authors: Angeline Yasodhara, Azin Asgarian, Diego Huang, Parinaz Sobhani
Date: 2021-09-30 00:00:00
Tasks: Feature Importance

The recent increase in the deployment of machine learning models in critical domains such as healthcare, criminal justice, and finance has highlighted the need for trustworthy methods that can explain these models to stakeholders. Feature importance methods (e.g. gain and SHAP) are among the most popular explainability methods used to address this need. For any explainability technique to be trustworthy and meaningful, it has to provide an explanation that is accurate and stable. Although the stability of local feature importance methods (explaining individual predictions) has been studied before, there is yet a knowledge gap about the stability of global features importance methods (explanations for the whole model). Additionally, there is no study that evaluates and compares the accuracy of global feature importance methods with respect to feature ordering. In this paper, we evaluate the accuracy and stability of global feature importance methods through comprehensive experiments done on simulations as well as four real-world datasets. We focus on tree-based ensemble methods as they are used widely in industry and measure the accuracy and stability of explanations under two scenarios: 1) when inputs are perturbed 2) when models are perturbed. Our findings provide a comparison of these methods under a variety of settings and shed light on the limitations of global feature importance methods by indicating their lack of accuracy with and without noisy inputs, as well as their lack of stability with respect to: 1) increase in input dimension or noise in the data; 2) perturbations in models initialized by different random seeds or hyperparameter settings.
Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series
Authors: Jingwei Li
Date: 2021-09-30 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Statistic modeling and data-driven learning are the two vital fields that attract many attentions. Statistic models intend to capture and interpret the relationships among variables, while data-based learning attempt to extract information directly from the data without pre-processing through complex models. Given the extensive studies in both fields, a subtle issue is how to properly integrate data based methods with existing knowledge or models. In this paper, based on the time series data, we propose two different directions to integrate the two, a decomposition-based method and a method exploiting the statistic extraction of data features. The first one decomposes the data into linear stable, nonlinear stable and unstable parts, where suitable statistical models are used for the linear stable and nonlinear stable parts while the appropriate machine learning tools are used for the unstable parts. The second one applies statistic models to extract statistics features of data and feed them as additional inputs into the machine learning platform for training. The most critical and challenging thing is how to determine and extract the valuable information from mathematical or statistical models to boost the performance of machine learning algorithms. We evaluate the proposal using time series data with varying degrees of stability. Performance results show that both methods can outperform existing schemes that use models and learning separately, and the improvements can be over 60%. Both our proposed methods are promising in bridging the gap between model-based and data-driven schemes and integrating the two to provide an overall higher learning performance.
Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions
Authors: Sourav Mukherjee, David Widmark, Vince DiMascio, Tim Oates
Date: 2021-09-30 00:00:00

Accurate specification of standard occupational classification (SOC) code is critical to the success of many U.S. work visa applications. Determination of correct SOC code relies on careful study of job requirements and comparison to definitions given by the U.S. Bureau of Labor Statistics, which is often a tedious activity. In this paper, we apply methods from natural language processing (NLP) to computationally determine SOC code based on job description. We implement and empirically evaluate a broad variety of predictive models with respect to quality of prediction and training time, and identify models best suited for this task.
ORBITS: Online Recovery of Missing Blocks in Multiple Time Series Streams
Authors: Mourad Khayati, Ines Arous, Zakhar Tymchenko, Philippe Cudré-Mauroux
Date: 2020-11-01 00:00:00
Proceeding: Proceedings of the VLDB Endowment (PVLDB) 2020 11
Tasks: Imputation, Multivariate Time Series Imputation, Time Series, Time Series Analysis, Time Series Streams

With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real-time monitoring but also compromise the quality of online data analyses. Effective streaming recovery (imputation) techniques either have a quadratic runtime complexity, which is infeasible for any moderately sized data or cannot recover more than one time series at a time.
In this paper, we introduce a new online recovery technique to recover multiple time series streams in linear time. Our recovery technique implements a novel incremental version of the Centroid Decomposition technique and reduces its complexity from quadratic to linear. Using this incremental technique, missing blocks are efficiently recovered in a continuous manner based on previous recoveries. We formally prove the correctness of our new incremental computation, which yields an accurate recovery. Our experimental results on real-world time series show that our recovery technique is, on average, 30% more accurate than the state of the art while being vastly more efficient.
FiLMing Multimodal Sarcasm Detection with Attention
Authors: Sundesh Gupta, Aditya Shah, Miten Shah, Laribok Syiemlieh, Chandresh Maurya
Date: 2021-08-09 00:00:00
Tasks: Opinion Mining, Sarcasm Detection, Sentiment Analysis

Sarcasm detection identifies natural language expressions whose intended meaning is different from what is implied by its surface meaning. It finds applications in many NLP tasks such as opinion mining, sentiment analysis, etc. Today, social media has given rise to an abundant amount of multimodal data where users express their opinions through text and images. Our paper aims to leverage multimodal data to improve the performance of the existing systems for sarcasm detection. So far, various approaches have been proposed that uses text and image modality and a fusion of both. We propose a novel architecture that uses the RoBERTa model with a co-attention layer on top to incorporate context incongruity between input text and image attributes. Further, we integrate feature-wise affine transformation by conditioning the input image through FiLMed ResNet blocks with the textual features using the GRU network to capture the multimodal information. The output from both the models and the CLS token from RoBERTa is concatenated and used for the final prediction. Our results demonstrate that our proposed model outperforms the existing state-of-the-art method by 6.14% F1 score on the public Twitter multimodal sarcasm detection dataset.
Interleaved One-shot Semi-Persistent Scheduling for BSM Transmissions in C-V2X Networks
Authors: Abdurrahman Fouda, Randall Berry, Ivan Vukovic
Date: 2021-09-30 00:00:00
Tasks: Scheduling

Cellular vehicle-to-everything (C-V2X) networks are regarded as one of the main pillars to enable efficient and sustainable Intelligent Transportation Systems (ITS) safety applications and services. Such services rely on the concept of exchanging periodic status updates (i.e., basic safety messages (BSMs)) between nearby vehicular users (VUEs). Hence, it is essential to ensure small inter-packet gaps (IPGs) between successive BSMs from nearby VUEs. Large IPGs, due to successive packet losses, can result in stale information at a VUE. In this paper, we study the tail behavior of the IPG and the information age (IA) distributions using C-V2X transmission mode 4 (a decentralized resource allocation method based on semi-persistent scheduling (SPS)). Specifically, we investigate improvements and trade-offs introduced by the SAE-specified concept of one-shot transmissions. We use a high-fidelity system-level simulator that closely follows the SPS process of C-V2X transmission mode 4 to evaluate the performance of the interleaved one-shot SPS transmissions. Our numerical results show that the tails of the IA and IPG complementary cumulative distribution functions (CCDFs) are significantly improved when one-shot transmissions are enabled in various simulation scenarios.
RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks
Authors: Lukas Brunke, SiQi Zhou, Angela P. Schoellig
Date: 2021-10-01 00:00:00
Tasks: Model Predictive Control

In this work we address the problem of performing a repetitive task when we have uncertain observations and dynamics. We formulate this problem as an iterative infinite horizon optimal control problem with output feedback. Previously, this problem was solved for linear time-invariant (LTI) system for the case when noisy full-state measurements are available using a robust iterative learning control framework, which we refer to as robust learning-based model predictive control (RL-MPC). However, this work does not apply to the case when only noisy observations of part of the state are available. This limits the applicability of current approaches in practice: First, in practical applications we typically do not have access to the full state. Second, uncertainties in the observations, when not accounted for, can lead to instability and constraint violations. To overcome these limitations, we propose a combination of RL-MPC with robust output feedback model predictive control, named robust learning-based output feedback model predictive control (RLO-MPC). We show recursive feasibility and stability, and prove theoretical guarantees on the performance over iterations. We validate the proposed approach with a numerical example in simulation and a quadrotor stabilization task in experiments.
SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection
Authors: Thi Ngoc Tho Nguyen, Karn N. Watcharasupat, Ngoc Khanh Nguyen, Douglas L. Jones, Woon-Seng Gan
Date: 2021-10-01 00:00:00
Tasks: Direction of Arrival Estimation, Event Detection, Sound Event Detection, Sound Event Localization and Detection

Sound event localization and detection (SELD) consists of two subtasks, which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes, direction-of-arrival estimation uses amplitude and/or phase differences between microphones to estimate source directions. As a result, it is often difficult to jointly optimize these two subtasks. We propose a novel feature called Spatial cue-Augmented Log-SpectrogrAm (SALSA) with exact time-frequency mapping between the signal power and the source directional cues, which is crucial for resolving overlapping sound sources. The SALSA feature consists of multichannel log-spectrograms stacked along with the normalized principal eigenvector of the spatial covariance matrix at each corresponding time-frequency bin. Depending on the microphone array format, the principal eigenvector can be normalized differently to extract amplitude and/or phase differences between the microphones. As a result, SALSA features are applicable for different microphone array formats such as first-order ambisonics (FOA) and multichannel microphone array (MIC). Experimental results on the TAU-NIGENS Spatial Sound Events 2021 dataset with directional interferences showed that SALSA features outperformed other state-of-the-art features. Specifically, the use of SALSA features in the FOA format increased the F1 score and localization recall by 6% each, compared to the multichannel log-mel spectrograms with intensity vectors. For the MIC format, using SALSA features increased F1 score and localization recall by 16% and 7%, respectively, compared to using multichannel log-mel spectrograms with generalized cross-correlation spectra.
Comparing Run Time Assurance Approaches for Safe Spacecraft Docking
Authors: Kyle Dunlap, Michael Hibbard, Mark Mote, Kerianne Hobbs
Date: 2021-10-01 00:00:00

Run Time Assurance (RTA) systems are online safety verification techniques that filter the output of a primary controller to assure safety. RTA approaches are used in safety-critical control to intervene when a performance-driven primary controller would cause the system to violate safety constraints. This paper presents four categories of RTA approaches based on their membership to explicit or implicit monitoring and switching or optimization interventions. To validate the feasibility of each approach and compare computation time, four RTAs are defined for a three-dimensional spacecraft docking example with safety constraints on velocity.
Finite Time Exact Quantized Average Consensus with Limited Resources and Transmission Stopping for Energy-Aware Networks
Authors: Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
Date: 2021-10-01 00:00:00
Tasks: Autonomous Vehicles

Composed of spatially distributed sensors and actuators that communicate through wireless networks, networked control systems are emerging as a fundamental infrastructure technology in 5G and IoT technologies, including diverse applications, such as autonomous vehicles, UAVs, and various sensing devices. In order to increase flexibility and reduce deployment and maintenance costs, many such applications consider battery-powered or energy-harvesting networks, which bring additional limitations on the energy consumption of the wireless network. Specifically, the operation of battery-powered or energy-harvesting wireless communication networks needs to guarantee (i) efficient communication between nodes and (ii) preservation of available energy. Motivated by these novel requirements, in this paper, we present and analyze a novel distributed average consensus algorithm, which (i) operates exclusively on quantized values (in order to guarantee efficient communication and data storage), and (ii) relies on event-driven updates (in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed algorithm and show that its execution, on any time-invariant and strongly connected digraph, will allow all nodes to reach, in finite time, a common consensus value that is equal to the exact average (represented as the ratio of two quantized values). Furthermore, we show that our algorithm allows each node to cease transmissions once the exact average of the initial quantized values has been reached (in order to preserve its battery energy). Then, we present upper bounds on (i) the number of transmissions and computations each node has to perform during the execution of the algorithm, and (ii) the memory and energy requirements of each node in order for the algorithm to be executed.
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems
Authors: S M Nahid Mahmud, Moad Abudia, Scott A Nivison, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2021-10-01 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The ability to learn and execute optimal control policies safely is critical to realization of complex autonomy, especially where task restarts are not available and/or the systems are safety-critical. Safety requirements are often expressed in terms of state and/or control constraints. Methods such as barrier transformation and control barrier functions have been successfully used, in conjunction with model-based reinforcement learning, for safe learning in systems under state constraints, to learn the optimal control policy. However, existing barrier-based safe learning methods rely on full state feedback. In this paper, an output-feedback safe model-based reinforcement learning technique is developed that utilizes a novel dynamic state estimator to implement simultaneous learning and control for a class of safety-critical systems with partially observable state.
A New Approach for Verification of Delay Coobservability of Discrete-Event Systems
Authors: Yunfeng Hou, Qingdu Li, Yunfeng Ji, Gang Wang, Ching-Yen Weng
Date: 2021-10-01 00:00:00
Tasks: Fault Diagnosis

In decentralized networked supervisory control of discrete-event systems (DESs), the local supervisors observe event occurrences subject to observation delays to make correct control decisions. Delay coobservability describes whether these local supervisors can make sufficient observations. In this paper, we provide an efficient way to verify delay coobservability. For each controllable event, we partition the specification language into a finite number of sets such that strings in different sets have different lengths. For each of the sets, we construct a verifier to check if delay coobservability holds for the controllable event. The computational complexity of the proposed approach is polynomial with respect to the number of states, the number of events, and the upper bounds on observation delays and only exponential with respect to the number of local supervisors. It has lower complexity order than the existing approaches. In addition, we investigate the relationship between the decentralized supervisory control of networked DESs and the decentralized fault diagnosis of networked DESs and show that delay $K$-codiagnosability is transformable to delay coobservability. Thus, techniques for the verification of delay coobservability can be leveraged to verify delay $K$-codiagnosability.
Neural Networks Compensation of Systems with Multi-segment Piecewise Linear Nonlinearities
Authors: Jun Oh Jang
Date: 2021-10-01 00:00:00

A neural networks (NN) compensator is designed for systems with multi-segment piecewise-linear nonlinearities. The compensator uses the back stepping technique with NN for inverting the multi-segment piecewise-linear nonlinearities in the feedforward path. This scheme provides a general procedure for determining the dynamic pre-inversion of an invertible dynamic system using NN. A tuning algorithm is presented for the NN compensator which yields a stable closed-loop system. In the case of nonlinear stability proofs, the tracking error is small. It is noted that PI controller without NN compensation requires much higher gain to achieve same performance. It is also difficult to ensure the stability of such highly nonlinear systems using only PI controllers. Using NN compensation, stability of the system is proven, and tracking errors can be arbitrarily kept small by increasing the gain. The NN weight errors are basically bounded in terms of input weight and hidden weight. Simulation results show the effectiveness of the piecewise linear NN compensator in the system. This scheme is applicable to xy table-like servo system and shows neural network stability proofs. In addition, the NN piecewise linear nonlinearity compensation can be further and applied to backlash, hysteresis, and another actuator nonlinear compensation.
In-Circuit Differential-Mode Impedance Extraction at the AC Input of a Motor Drive System
Authors: Arjuna Weerasinghe, Zhenyu Zhao, Fei Fan, Pengfei Tu, Kye Yak See
Date: 2021-10-01 00:00:00

The in-circuit differential-mode (DM) impedance at the AC input of a motor drive system (MDS) serves as a key parameter to evaluate and estimate the DM electromagnetic interference (EMI) noise caused by the switching of power semiconductor devices in the MDS. This paper discusses a single-probe setup (SPS) with frequency-domain measurement to extract the in-circuit DM impedance of an MDS under its different operating modes. The advantages of the SPS are its non-contact measurement and simple structure.
Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial
Authors: Ian R. Manchester, Max Revay, Ruigang Wang
Date: 2021-10-01 00:00:00
Tasks: BIG-bench Machine Learning

This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and robustness constraints. The main ideas are drawn from contraction analysis and robust control, but adapted to problems in which large-scale models can be learnt with behavioural guarantees. We illustrate the methods with applications in robust image recognition and system identification.