pwc-archive/papers-with-abstracts huggingface dataset:
Is Style All You Need? Dependencies Between Emotion and GST-based Speaker Recognition
Authors: Morgan Sandler, Arun Ross
Date: 2022-11-15 00:00:00
Tasks: All, Emotion Classification, Speaker Recognition, Triplet

In this work, we study the hypothesis that speaker identity embeddings extracted from speech samples may be used for detection and classification of emotion. In particular, we show that emotions can be effectively identified by learning speaker identities by use of a 1-D Triplet Convolutional Neural Network (CNN) & Global Style Token (GST) scheme (e.g., DeepTalk Network) and reusing the trained speaker recognition model weights to generate features in the emotion classification domain. The automatic speaker recognition (ASR) network is trained with VoxCeleb1, VoxCeleb2, and Librispeech datasets with a triplet training loss function using speaker identity labels. Using an Support Vector Machine (SVM) classifier, we map speaker identity embeddings into discrete emotion categories from the CREMA-D, IEMOCAP, and MSP-Podcast datasets. On the task of speech emotion detection, we obtain 80.8% ACC with acted emotion samples from CREMA-D, 81.2% ACC with semi-natural emotion samples in IEMOCAP, and 66.9% ACC with natural emotion samples in MSP-Podcast. We also propose a novel two-stage hierarchical classifier (HC) approach which demonstrates +2% ACC improvement on CREMA-D emotion samples. Through this work, we seek to convey the importance of holistically modeling intra-user variation within audio samples
Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems
Authors: Fabio Santos
Date: 2022-11-02 00:00:00

Selecting an appropriate task is challenging for contributors to Open Source Software (OSS), mainly for those who are contributing for the first time. Therefore, researchers and OSS projects have proposed various strategies to aid newcomers, including labeling tasks. We investigate the automatic labeling of open issues strategy to help the contributors to pick a task to contribute. We label the issues with API-domains--categories of APIs parsed from the source code used to solve the issues. We plan to add social network analysis metrics from the issues conversations as new predictors. By identifying the skills, we claim the contributor candidates should pick a task more suitable. We analyzed interview transcripts and the survey's open-ended questions to comprehend the strategies used to assist in onboarding contributors and used to pick up an issue. We applied quantitative studies to analyze the relevance of the labels in an experiment and compare the strategies' relative importance. We also mined issue data from OSS repositories to predict the API-domain labels with comparable precision, recall, and F-measure with the state-of-art. We plan to use a skill ontology to assist the matching process between contributors and tasks. By analyzing the confidence level of the matching instances in ontologies describing contributors' skills and tasks, we might recommend issues for contribution. So far, the results showed that organizing the issues--which includes assigning labels is seen as an essential strategy for diverse roles in OSS communities. The API-domain labels are relevant for experienced practitioners. The predictions have an average precision of 75.5%. Labeling the issues indicates the skills involved in an issue. The labels represent possible skills in the source code related to an issue. By investigating this research topic, we expect to assist the new contributors in finding a task.
An environmental disturbance observer framework for autonomous surface vessels
Authors: Daniel Menges, Adil Rasheed
Date: 2022-11-15 00:00:00

This paper proposes a robust disturbance observer framework for maritime autonomous surface vessels considering model and measurement uncertainties. The core contribution lies in a nonlinear disturbance observer, reconstructing the forces on a vessel impacted by the environment. For this purpose, mappings are found leading to synchronized global exponentially stable error dynamics. With the stability theory of Lyapunov, it is proven that the error converges exponentially into a ball, even if the disturbances are highly dynamic. Since measurements are affected by noise and physical models can be erroneous, an unscented Kalman filter (UKF) is used to generate more reliable state estimations. In addition, a noise estimator is introduced, which approximates the noise strength. Depending on the severity of the measurement noise, the observed disturbances are filtered through a cascaded structure consisting of a weighted moving average (WMA) filter, a UKF, and the proposed disturbance observer. To investigate the capability of this observer framework, the environmental disturbances are simulated dynamically under consideration of different model and measurement uncertainties. It can be seen that the observer framework can approximate dynamical forces on a vessel impacted by the environment despite using a low measurement sampling rate, an erroneous model, and noisy measurements.
Implementing prescribed-time convergent control: sampling and robustness
Authors: Hernan Haimovich, Rodrigo Aldana-Lopez, Richard Seeber, David Gomez-Gutierrez
Date: 2022-11-15 00:00:00

According to recent results, convergence in a prespecified or prescribed finite time can be achieved under extreme model uncertainty if control is applied continuously over time. This paper shows that this extreme amount of uncertainty cannot be tolerated under sampling, not even if sampling could become infinitely frequent as the deadline is approached, unless the sampling strategy were designed according to the growth of the control action. Robustness under model uncertainty is analyzed and the amount of uncertainty that can be tolerated under sampling is quantified in order to formulate the least restrictive prescribed-time control problem that is practically implementable. Some solutions to this problem are given for a scalar system. Moreover, either under a-priori knowledge of bounds for initial conditions, or if the strategy can be selected after the first measurement becomes available, it is shown that the real, practically achievable objectives can also be reached with linear time-invariant control and uniform sampling. These derivations serve to yield insight into the real advantages that implementation of prescribed-time controllers may have.
Shared Network Effects in Time- versus Event-Triggered Consensus of a Single-Integrator Multi-Agent System
Authors: David Meister, Frank Dürr, Frank Allgöwer
Date: 2022-11-15 00:00:00

Event-triggered control has the potential to provide a similar performance level as time-triggered (periodic) control while triggering events less frequently. It therefore appears intuitive that it is also a viable approach for distributed systems to save scarce shared network resources used for inter-agent communication. While this motivation is commonly used also for multi-agent systems, a theoretical analysis of the impact of network effects on the performance of event- and time-triggered control for such distributed systems is currently missing. With this paper, we contrast event- and time-triggered control performance for a single-integrator consensus problem under consideration of a shared communication medium. We therefore incorporate transmission delays and packet loss in our analysis and compare the triggering scheme performance under two simple medium access control protocols. We find that network effects can degrade the performance of event-triggered control beyond the performance level of time-triggered control for the same average triggering rate if the network is used intensively. Moreover, the performance advantage of event-triggered control shrinks with an increasing number of agents and is even lost for sufficiently large networks in the considered setup.
Generalised Regret Optimal Controller Synthesis for Constrained Systems
Authors: Alexandre Didier, Melanie N. Zeilinger
Date: 2022-11-15 00:00:00

This paper presents a synthesis method for the generalised dynamic regret problem, comparing the performance of a strictly causal controller to the optimal non-causal controller under a weighted disturbance. This framework encompasses both the dynamic regret problem, considering the difference of the incurred costs, as well as the competitive ratio, which considers their ratio, and which have both been proposed as inherently adaptive alternatives to classical control methods. Furthermore, we extend the synthesis to the case of pointwise-in-time bounds on the disturbance and show that the optimal solution is no worse than the bounded energy optimal solution and is lower bounded by a constant factor, which is only dependent on the disturbance weight. The proposed optimisation-based synthesis allows considering systems subject to state and input constraints. Finally, we provide a numerical example which compares the synthesised controller performance to $\mathcal{H}_2$- and $\mathcal{H}_\infty$-controllers.
Embedded Model Control of Networked Control Systems: an Experimental Case-study -- Stability analysis and further results
Authors: Luca Nanu, Carlos Perez Montenegro, Luigi Colangelo, Carlo Novara
Date: 2022-11-15 00:00:00

In Networked Control Systems (NCS), the absence of physical communication links in the loop leads to relevant issues, such as measurement delays and asynchronous execution of the control commands. These issues may lead to unwanted control behaviours. This ArXiv paper is intended to give additional results to the work presented in "Embedded Model Control of Networked Control Systems: an Experimental Case-study". The last one presents an original approach, based on the Embedded Model Control, to deal with experimental scenarios characterized by asynchronous control timing. The effectiveness of the proposed approach is demonstrated with a differential-drive robot, first with high-fidelity simulations and finally with several experimental tests. Specifically, the present work aims to study the stability analysis of the EMC experimental setup and to give further experimental results, to complement those presented in the main paper, "Embedded Model Control of Networked Control Systems: an Experimental Case-study".
A Matlab and CasADi-based Implementation of RICE Dynamic Game
Authors: Yijun Chen, Guodong Shi
Date: 2022-11-14 00:00:00

The most widely used integrated assessment model for studying the economics of climate change is the dynamic/regional integrated model of climate and economy (DICE/RICE). In this document, we first represent the RICE-2011 model as a dynamic game, termed the RICE game. Then, both cooperative and non-cooperative solutions to the RICE game are considered. Next, a description of how to use the repository RICE-GAME on GitHub is provided. The repository RICE-GAME is a Matlab and CasADi-based implementation of the RICE game and its cooperative and non-cooperative solutions.
LQ Optimal Control for Power Tracking Operation of Wind Turbines
Authors: Aaron Grapentin, Arnold Sterle, Jörg Raisch, Christian A. Hans
Date: 2022-11-14 00:00:00
Tasks: Pitch control

In this paper, an approach for active power control of individual wind turbines is presented. State-of-the-art controllers typically employ separate control loops for torque and pitch control. In contrast, we use a multivariable control approach. In detail, active power control is achieved by using reference trajectories for generator speed, generator torque, and pitch angle such that a desired power demand is met if weather conditions allow. Then, a linear quadratic (LQ) optimal controller is used for reference tracking. In an OpenFAST simulation environment, the controller is compared to a state-of-the-art approach. The simulations show a similar active power tracking performance, while the LQ optimal controller results in lower mechanical wear. Moreover, the presented approach exhibits good reference tracking and by improving the reference trajectory generation further performance increases can be expected.
Estimating and detecting random processes on the unit circle
Authors: Changrong Liu, S. Suvorova, R. J. Evans, B. Moran, A. Melatos
Date: 2022-11-15 00:00:00
Tasks: Astronomy

The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical communications and, recently, continuous gravitational wave astronomy. In this paper we describe a Markov Chain Monte Carlo based procedure to compute a specific detection posterior density. We demonstrate via simulation that our approach results in an up to $25$ percent higher detection rate than Hidden Markov Model based solutions, which are generally considered to be the leading techniques for these problems.
Performance Limit of Fiber-Longitudinal Power Profile Estimation Methods
Authors: Takeo Sasai, Etsushi Yamazaki, Yoshiaki Kisaka
Date: 2022-11-15 00:00:00

This paper presents analytical results on longitudinal power profile estimation (PPE) methods, which visualize signal power evolution in optical fibers at a coherent receiver. The PPE can be formulated as an inverse problem of the nonlinear Schr\"odinger equation, where the nonlinear coefficient (and thus signal power) is reconstructed from boundary conditions, i.e., transmitted and received signals. Two types of PPE methods are reviewed and analyzed, including correlation-based methods (CMs) and minimum-mean-square-error-based methods (MMSEs). The analytical expressions for their output power profiles and spatial resolution are provided, and thus the theoretical performance limits of the two PPE methods and their differences are clarified. The derived equations indicate that the estimated power profiles of CMs can be understood as the convolution of a true power profile and a smoothing function. Consequently, the spatial resolution and measurement accuracy of CMs are limited, even under noiseless and distortionless conditions. Closed-form formulas for the spatial resolution of CMs are shown to be inversely proportional to the product of a chromatic dispersion coefficient and the square of signal bandwidth. With MMSEs, such a convolution effect is canceled out and the estimated power profiles approach a true power profile under a fine spatial step size.
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics
Authors: Esha Saha, Lam Si Tung Ho, Giang Tran
Date: 2022-11-11 00:00:00

Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for modelling and predicting infectious disease epidemics are compartmental models. They stratify the population into compartments according to health status and model the dynamics of these compartments using dynamical systems. However, these predefined systems may not capture the true dynamics of the epidemic due to the complexity of the disease transmission and human interactions. In order to overcome this drawback, we propose Sparsity and Delay Embedding based Forecasting (SPADE4) for predicting epidemics. SPADE4 predicts the future trajectory of an observable variable without the knowledge of the other variables or the underlying system. We use random features model with sparse regression to handle the data scarcity issue and employ Takens' delay embedding theorem to capture the nature of the underlying system from the observed variable. We show that our approach outperforms compartmental models when applied to both simulated and real data.
Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives
Authors: Zequn Jin, Lihua Lin, Zhengyu Zhang
Date: 2022-11-15 00:00:00
Tasks: counterfactual

This paper proposes a new class of heterogeneous causal quantities, named \textit{outcome conditioned} average structural derivatives (OASD) in a general nonseparable model. OASD is the average partial effect of a marginal change in a continuous treatment on the individuals located at different parts of the outcome distribution, irrespective of individuals' characteristics. OASD combines both features of ATE and QTE: it is interpreted as straightforwardly as ATE while at the same time more granular than ATE by breaking the entire population up according to the rank of the outcome distribution. One contribution of this paper is that we establish some close relationships between the \textit{outcome conditioned average partial effects} and a class of parameters measuring the effect of counterfactually changing the distribution of a single covariate on the unconditional outcome quantiles. By exploiting such relationship, we can obtain root-$n$ consistent estimator and calculate the semi-parametric efficiency bound for these counterfactual effect parameters. We illustrate this point by two examples: equivalence between OASD and the unconditional partial quantile effect (Firpo et al. (2009)), and equivalence between the marginal partial distribution policy effect (Rothe (2012)) and a corresponding outcome conditioned parameter. Because identification of OASD is attained under a conditional exogeneity assumption, by controlling for a rich information about covariates, a researcher may ideally use high-dimensional controls in data. We propose for OASD a novel automatic debiased machine learning estimator, and present asymptotic statistical guarantees for it. We prove our estimator is root-$n$ consistent, asymptotically normal, and semiparametrically efficient. We also prove the validity of the bootstrap procedure for uniform inference on the OASD process.
Graph Neural Networks for Causal Inference Under Network Confounding
Authors: Michael P. Leung, Pantelis Loupos
Date: 2022-11-15 00:00:00
Tasks: Causal Inference, Selection bias

This paper studies causal inference with observational data from a single large network. We consider a nonparametric model with interference in potential outcomes and selection into treatment. Both stages may be the outcomes of simultaneous equation models, which allow for endogenous peer effects. This results in high-dimensional network confounding where the network and covariates of all units constitute sources of selection bias. In contrast, the existing literature assumes that confounding can be summarized by a known, low-dimensional function of these objects. We propose to use graph neural networks (GNNs) to adjust for network confounding. When interference decays with network distance, we argue that the model has low-dimensional structure that makes estimation feasible and justifies the use of shallow GNN architectures.
Relevance of financial development and fiscal stability in dealing with disasters in Emerging Economies
Authors: Valeria Terrones, Richard S. J. Tol
Date: 2022-11-15 00:00:00

Previous studies show that natural disasters decelerate economic growth, and more so in countries with lower financial development. We confirm these results with more recent data. We are the first to show that fiscal stability reduces the negative economic impact of natural disasters in poorer countries, and that catastrophe bonds have the same effect in richer countries.
Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation
Authors: Lei Zhou
Date: 2022-11-15 00:00:00
Tasks: Data Augmentation, Medical Image Segmentation, Segmentation

Automated polyp segmentation technology plays an important role in diagnosing intestinal diseases, such as tumors and precancerous lesions. Previous works have typically trained convolution-based U-Net or Transformer-based neural network architectures with labeled data. However, the available public polyp segmentation datasets are too small to train the network sufficiently, suppressing each network's potential performance. To alleviate this issue, we propose a universal data augmentation technology to synthesize more data from the existing datasets. Specifically, we paste the polyp area into the same image's background in a spatial-exclusive manner to obtain a combinatorial number of new images. Extensive experiments on various networks and datasets show that the proposed method enhances the data efficiency and achieves consistent improvements over baselines. Finally, we hit a new state of the art in this task. We will release the code soon.
Time-Domain Classification of the Brain Reward System: Analysis of Natural and Drug Reward Driven Local Field Potential Signals in Hippocampus and Nucleus Accumbens
Authors: AmirAli Kalbasi, Shole Jamali, Mahdi Aliyari Shoorehdeli, Alireza Behzadnia, Abbas Haghparast
Date: 2022-11-15 00:00:00
Tasks: domain classification, Hippocampus

Addiction is a major public health concern characterized by compulsive reward-seeking behavior. The excitatory glutamatergic signals from the hippocampus (HIP) to the Nucleus accumbens (NAc) mediate learned behavior in addiction. Limited comparative studies have investigated the neural pathways activated by natural and unnatural reward sources. This study has evaluated neural activities in HIP and NAc associated with food (natural) and morphine (drug) reward sources using local field potential (LFP). We developed novel approaches to classify LFP signals into the source of reward and recorded regions by considering the time-domain feature of these signals. Proposed methods included a validation step of the LFP signals using autocorrelation, Lyapunov exponent and Hurst exponent to assess the meaningful stability of these signals (lack of chaos). By utilizing the probability density function (PDF) of LFP signals and applying Kullback-Leibler divergence (KLD), data were classified to the source of the reward. Also, HIP and NAc regions were visually separated and classified using the symmetrized dot pattern technique, which can be applied in real-time to ensure the deep brain region of interest is being targeted accurately during LFP recording. We believe our method provides a computationally light and fast, real-time signal analysis approach with real-world implementation.
On Energy Efficiency and Fairness Maximization in RIS-Assisted MU-MISO mmWave Communications
Authors: Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan
Date: 2022-11-15 00:00:00
Tasks: Fairness

Reconfigurable intelligent surfaces (RISs) are considered to be a promising solution to overcome the blockage issue in the millimeter-wave (mmWave) band. Energy efficiency is an important performance metric in mmWave systems with a large number of antennas. However, due to the severe path loss in mmWave systems, resource allocation algorithms tend to allocate most of the resources for the benefit of the users with higher channel gains. In this paper, we propose a lexicographic-based approach to find the optimal power allocation, RIS phase shift matrix, and analog precoders that maximize both energy efficiency and user fairness. We solve the corresponding multi-objective optimization problem in two stages. In the first stage, we maximize the energy efficiency, and in the second stage we maximize the fairness subject to a minimum energy efficiency constraint. We propose an alternating optimization procedure to solve the optimization problem in each stage. The optimal power allocation is found using Dinkelbach's method in the first stage and using convex optimization techniques in the second stage, the RIS phase shift matrix is found using a gradient ascent algorithm, and the analog precoder is determined using beam alignment. Numerical results show that the proposed algorithm can achieve an excellent trade-off between the energy efficiency and fairness by boosting the minimum weighted rate with a minor and controllable reduction in the energy efficiency.