pwc-archive/papers-with-abstracts huggingface dataset:
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of Mars
Authors: Riccardo La Grassa, Ignazio Gallo, Cristina Re, Gabriele Cremonese, Nicola Landro, Claudio Pernechele, Emanuele Simioni, Mattia Gatti
Date: 2022-08-15 00:00:00
Proceeding: Remote Sensing 2022 8
Tasks: Depth Estimation, Generative Adversarial Network, Monocular Depth Estimation, Stereo Matching, Super-Resolution

In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting the digital terrain model (DTM) and thus obtaining a 3D spatial mapping, which is necessary for a better analysis of planetary surfaces. However, the entire reconstruction process performed with the stereo-matching algorithm can be time consuming and can generate many artifacts. Coupled with the lack of adequate stereo coverage, it can pose a significant obstacle to 3D planetary mapping. Recently, many deep learning architectures have been proposed for monocular depth estimation, which aspires to predict the third dimension given a single 2D image, with considerable advantages thanks to the simplification of the reconstruction problem, leading to a significant increase in interest in deep models for the generation of super-resolution images and DTM estimation. In this paper, we combine these last two concepts into a single end-to-end model and introduce a new generative adversarial network solution that estimates the DTM at 4× resolution from a single monocular image, called SRDiNet (super-resolution depth image network). Furthermore, we introduce a sub-network able to apply a refinement using interpolated input images to better enhance the fine details of the final product, and we demonstrate the effectiveness of its benefits through three different versions of the proposal: SRDiNet with GAN approach, SRDiNet without adversarial network, and SRDiNet without the refinement learned network plus GAN approach. The results of Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) are reported, applying the best model along all Oxia Planum tiles and releasing a 3D product enhanced by 4×.
Remaining useful life estimation of engineered systems using vanilla LSTM neural networks
Authors: Yuting Wu, Mei Yuan, Shaopeng Dong, Li Lin, Yingqi Liu b
Date: 2018-01-31 00:00:00
Proceeding: Neurocomputing 2018 1
Tasks: Management

Long Short-Term Memory (LSTM) networks are a significant branch of Recurrent Neural Networks (RNN), capable of learning long-term dependencies. In recent years, vanilla LSTM (a variation of original LSTM above) has become the state-of-the-art model for a variety of machine learning problems, especially Natural Language Processing (NLP). However, in industry, this powerful Deep Neural Network (DNN) has not aroused wide concern. In research focusing on Prognostics and Health Management (PHM) technology for complex engineered systems, Remaining Useful Life (RUL) estimation is one of the most challenging problems, which can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize economic losses of the systems. Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises. In addition, to promote cognition ability about model degradation processes, a dynamic differential technology was proposed to extract inter-frame information. The whole proposition is illustrated and discussed by performing tests on a case of the health monitoring of aircraft turbofan engines which have four different issues. Performances of vanilla LSTM are benchmarked with standard RNN and Gated Recurrent Unit (GRU) LSTM. Results show the significance of performance improvement achieved by vanilla LSTM.
Power of Explanations: Towards automatic debiasing in hate speech detection
Authors: Yi Cai, Arthur Zimek, Gerhard Wunder, Eirini Ntoutsi
Date: 2022-09-07 00:00:00
Tasks: Fairness, Hate Speech Detection

Hate speech detection is a common downstream application of natural language processing (NLP) in the real world. In spite of the increasing accuracy, current data-driven approaches could easily learn biases from the imbalanced data distributions originating from humans. The deployment of biased models could further enhance the existing social biases. But unlike handling tabular data, defining and mitigating biases in text classifiers, which deal with unstructured data, are more challenging. A popular solution for improving machine learning fairness in NLP is to conduct the debiasing process with a list of potentially discriminated words given by human annotators. In addition to suffering from the risks of overlooking the biased terms, exhaustively identifying bias with human annotators are unsustainable since discrimination is variable among different datasets and may evolve over time. To this end, we propose an automatic misuse detector (MiD) relying on an explanation method for detecting potential bias. And built upon that, an end-to-end debiasing framework with the proposed staged correction is designed for text classifiers without any external resources required.
Estimation of circular statistics in the presence of measurement bias
Authors: Abdallah Alsammani, William C. Stacey, Stephen V. Gliske
Date: 2022-09-21 00:00:00

Background and objective. Circular statistics and Rayleigh tests are important tools for analyzing the occurrence of cyclic events. However, current methods fail in the presence of measurement bias, such as incomplete or otherwise non-uniform sampling. Consider, for example, studying 24-cyclicity but having data not recorded uniformly over the full 24-hour cycle. The objective of this paper is to present a method to estimate circular statistics and their statistical significance even in this circumstance. Methods. We present our objective as a special case of a more general problem: estimating probability distributions in the context of imperfect measurements, a highly studied problem in high energy physics. Our solution combines 1) existing approaches that estimate the measurement process via numeric simulation and 2) innovative use of linear parametrizations of the underlying distributions. We compute the estimation error for several toy examples as well as a real-world example: analyzing the 24-hour cyclicity of an electrographic biomarker of epileptic tissue controlled for state of vigilance. Results. Our method shows low estimation error. In a real-world example, we observed the corrected moments had a root mean square residual less than 0.007. We additionally found that, even with unfolding, Rayleigh test statistics still often underestimate the p-values (and thus overestimate statistical significance) in the presence of non-uniform sampling. Numerical estimation of statistical significance, as described herein, is thus preferable. Conclusions. The presented methods provide a robust solution to addressing incomplete or otherwise non-uniform sampling. The general method presented is also applicable to a wider set of analyses involving estimation of the true probability distribution adjusted for imperfect measurement processes.
A Comparison of Features for Replay Attack Detection
Authors: Zhifeng Xiea, Weibin Zhangb, Zhuxin Chen and Xiangmin Xu
Date: 2019-12-12 00:00:00
Proceeding: IOP Conf. Series: Journal of Physics: Conf. Series 1229 2019 12
Tasks: Speaker Verification

Speaker verification (ASV) systems are still vulnerable to different kinds of spoofing attacks, especially replay attack due to high-quality playback devices. Many countermeasures have been developed recently. Most of the efforts focus on the search for more salient features and many new features have been proposed. Five kinds of features, namely Mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), inverted Mel-frequency cepstral coefficients (IMFCCs), constant Q cepstral coefficients (CQCCs) and bottleneck features were compared on the public ASVspoof 2017 and BTAS 2016 datasets in this paper. Our experimental results show that MFCCs and bottleneck features yield comparable results. Both of them significantly outperform others (including the recently proposed CQCCs). However, the number of filters and cepstral bins are essential to the success of MFCCs.
Rank-preserving Multidimensional Mechanisms: an equivalence between identical-object and heterogeneous-object models
Authors: Sushil Bikhchandani, Debasis Mishra
Date: 2022-09-21 00:00:00
Tasks: Object

We show that the mechanism-design problem for a monopolist selling multiple, heterogeneous objects to a buyer with ex ante symmetric and additive values is equivalent to the mechanism-design problem for a monopolist selling identical objects to a buyer with decreasing marginal values. We derive three new results for the identical-objects model: (i) a new condition for revenue monotonicity of stochastic mechanisms, (ii) a sufficient condition on priors, such that prices in optimal deterministic mechanism are not increasing, and (iii) a simplification of incentive constraints for deterministic mechanisms. We use the equivalence to establish corresponding results in the heterogeneous-objects model.
NFTs: The Game is Afoot
Authors: Bernhard K Meister, Henry CW Price
Date: 2022-09-20 00:00:00

On the blockchain, NFT games have risen in popularity, spawning new types of digital assets. We present a simplified version of well-known NFT games, followed by a discussion of issues influencing the structure and stability of generic games. Where applicable, ideas from quantitative finance are incorporated, suggesting various design constraints. Following that, we explain three distinct methods for extracting value from NFT games. The first is to utilise NFT tokens as collateral outside of the game's walled garden; the second is to construct mutual beneficial games based on the participants' risk tolerance, and the third is to use Siegel's paradox in the case of multiple numeraires.
An Initial study on Birdsong Re-synthesis Using Neural Vocoders
Authors: Rhythm Bhatia, Tomi H. Kinnunen
Date: 2022-09-21 00:00:00
Tasks: Resynthesis, Speech Synthesis

Modern speech synthesis uses neural vocoders to model raw waveform samples directly. This increased versatility has expanded the scope of vocoders from speech to other domains, such as music. We address another interesting domain of bio-acoustics. We provide initial comparative analysis-resynthesis experiments of birdsong using traditional (WORLD) and two neural (WaveNet autoencoder, parallel WaveGAN) vocoders. Our subjective results indicate no difference in the three vocoders in terms of species discrimination (ABX test). Nonetheless, the WORLD vocoder samples were rated higher in terms of retaining bird-like qualities (MOS test). All vocoders faced issues with pitch and voicing. Our results indicate some of the challenges in processing low-quality wildlife audio data.
Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN
Authors: Yin-Ping Cho, Yu Tsao, Hsin-Min Wang, Yi-Wen Liu
Date: 2022-09-21 00:00:00
Tasks: Denoising, Generative Adversarial Network, Singing Voice Synthesis

Singing voice synthesis (SVS) is the computer production of a human-like singing voice from given musical scores. To accomplish end-to-end SVS effectively and efficiently, this work adopts the acoustic model-neural vocoder architecture established for high-quality speech and singing voice synthesis. Specifically, this work aims to pursue a higher level of expressiveness in synthesized voices by combining the diffusion denoising probabilistic model (DDPM) and \emph{Wasserstein} generative adversarial network (WGAN) to construct the backbone of the acoustic model. On top of the proposed acoustic model, a HiFi-GAN neural vocoder is adopted with integrated fine-tuning to ensure optimal synthesis quality for the resulting end-to-end SVS system. This end-to-end system was evaluated with the multi-singer Mpop600 Mandarin singing voice dataset. In the experiments, the proposed system exhibits improvements over previous landmark counterparts in terms of musical expressiveness and high-frequency acoustic details. Moreover, the adversarial acoustic model converged stably without the need to enforce reconstruction objectives, indicating the convergence stability of the proposed DDPM and WGAN combined architecture over alternative GAN-based SVS systems.
Language-based Audio Retrieval Task in DCASE 2022 Challenge
Authors: Huang Xie, Samuel Lipping, Tuomas Virtanen
Date: 2022-09-20 00:00:00
Tasks: Audio captioning, Retrieval

Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task 6, which aims at developing computational systems to model relationships between audio signals and free-form textual descriptions. Compared with audio captioning (Subtask 6A), which is about generating audio captions for audio signals, language-based audio retrieval (Subtask 6B) focuses on ranking audio signals according to their relevance to natural language textual captions. In DCASE 2022 Challenge, the provided baseline system for Subtask 6B was significantly outperformed, with top performance being 0.276 in mAP@10. This paper presents the outcome of Subtask 6B in terms of submitted systems' performance and analysis.
How Many Grid-Forming Converters Do We Need? A Perspective From Small Signal Stability and Power Grid Strength
Authors: Huanhai Xin, Chenxi Liu, Xia Chen, Yuxuan Wang, Eduardo Prieto-Araujo, Linbin Huang
Date: 2022-09-21 00:00:00

Grid-forming (GFM) control has been considered a promising solution for accommodating large-scale power electronics converters into modern power grids thanks to its grid-friendly dynamics, in particular, voltage source behavior on the AC side. The voltage source behavior of GFM converters can provide voltage support for the power grid, and therefore enhance the power grid (voltage) strength. However, grid-following (GFL) converters can also perform constant AC voltage magnitude control by properly regulating their reactive current, which may also behave like a voltage source. Currently, it still remains unclear what are the essential differences between the voltage source behaviors of GFL and GFM converters, and which type of voltage source behavior can enhance the power grid strength. In this paper, we will demonstrate that only GFM converters can provide effective voltage source behavior and enhance the power grid strength in terms of small signal dynamics. Based on our analysis, we further study the problem of how to configure GFM converters in the grid and how many GFM converters we will need. We investigate how the capacity ratio between GFM and GFL converters affects the equivalent power grid strength and thus the small signal stability of the system. We give guidelines on how to choose this ratio to achieve a desired stability margin. We validate our analysis using high-fidelity simulations.
Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
Authors: Tiancheng Li
Date: 2022-09-21 00:00:00
Tasks: Point Processes

As a fundamental information fusion approach, the arithmetic average (AA) fusion has recently been investigated for various random finite set (RFS) filter fusion in the context of multi-sensor multi-target tracking. It is not a straightforward extension of the ordinary density-AA fusion to the RFS distribution but has to preserve the form of the fusing multi-target density. In this work, we first propose a statistical concept, probability hypothesis density (PHD) consistency, and explain how it can be achieved by the PHD-AA fusion and lead to more accurate and robust detection and localization of the present targets. This forms a both theoretically sound and technically meaningful reason for performing inter-filter PHD AA-fusion/consensus, while preserving the form of the fusing RFS filter. Then, we derive and analyze the proper AA fusion formulations for most existing unlabeled/labeled RFS filters basing on the (labeled) PHD-AA/consistency. These derivations are theoretically unified, exact, need no approximation and greatly enable heterogenous unlabeled and labeled RFS density fusion which is separately demonstrated in two consequent companion papers.
Insurance Contract for High Renewable Energy Integration
Authors: Dongwei Zhao, Hao Wang, Jianwei Huang, Xiaojun Lin
Date: 2022-09-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty.
Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes
Authors: Kim P. Wabersich, Melanie N. Zeilinger
Date: 2020-06-05 00:00:00
Tasks: Model Predictive Control

This paper investigates the combination of model predictive control (MPC) concepts and posterior sampling techniques and proposes a simple constraint tightening technique to introduce cautiousness during explorative learning episodes. The provided theoretical analysis in terms of cumulative regret focuses on previously stated sufficient conditions of the resulting `Cautious Bayesian MPC' algorithm and shows Lipschitz continuity of the future reward function in the case of linear MPC problems. In the case of nonlinear MPC problems, it is shown that commonly required assumptions for nonlinear MPC optimization techniques provide sufficient criteria for model-based RL using posterior sampling. Furthermore, it is shown that the proposed constraint tightening implies a bound on the expected number of unsafe learning episodes in the linear and nonlinear case using a soft-constrained MPC formulation. The efficiency of the method is illustrated using numerical examples.
Towards improving the estimation performance of a given nonlinear observer: a multi-observer approach
Authors: E. Petri, R. Postoyan, D. Astolfi, D. Nešić, V. Andrieu
Date: 2022-09-21 00:00:00

Various methods are nowadays available to design observers for broad classes of systems. Nevertheless, the question of the tuning of the observer to achieve satisfactory estimation performance remains largely open. This paper presents a general supervisory design framework for online tuning of the observer gains with the aim of achieving various trade-offs between robustness and speed of convergence. We assume that a robust nominal observer has been designed for a general nonlinear system and the goal is to improve its performance. We present for this purpose a novel hybrid multi-observer, which consists of the nominal one and a bank of additional observer-like systems, that are collectively referred to as modes and that differ from the nominal observer only in their output injection gains. We then evaluate on-line the estimation cost of each mode of the multi-observer and, based on these costs, we select one of them at each time instant. Two different strategies are proposed. In the first one, initial conditions of the modes are reset each time the algorithm switches between different modes. In the second one, the initial conditions are not reset. We prove a convergence property for the hybrid estimation scheme and we illustrate the efficiency of the approach in improving the performance of a given nominal high-gain observer on a numerical example.
Stochastic MPC with Realization-Adaptive Constraint Tightening
Authors: Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli
Date: 2022-09-21 00:00:00

This paper presents a stochastic model predictive controller (SMPC) for linear time-invariant systems in the presence of additive disturbances. The distribution of the disturbance is unknown and is assumed to have a bounded support. A sample-based strategy is used to compute sets of disturbance sequences necessary for robustifying the state chance constraints. These sets are constructed offline using samples of the disturbance extracted from its support. For online MPC implementation, we propose a novel reformulation strategy of the chance constraints, where the constraint tightening is computed by adjusting the offline computed sets based on the previously realized disturbances along the trajectory. The proposed MPC is recursive feasible and can lower conservatism over existing SMPC approaches at the cost of higher offline computational time. Numerical simulations demonstrate the effectiveness of the proposed approach.
Robust Dynamic State Estimation of Multi-Machine Power Networks with Solar Farms and Dynamics Loads
Authors: Muhammad Nadeem, Ahmad F. Taha
Date: 2022-09-20 00:00:00
Tasks: State Estimation

Conventional state estimation routines of electrical grids are mainly reliant on dynamic models of fossil fuel-based resources. These models commonly contain differential equations describing synchronous generator models and algebraic equations modeling power flow/balance equations. Fuel-free power systems that are driven by inertia-less renewable energy resources will hence require new models and upgraded estimation routines. To that end, in this paper we propose a robust estimator for an interconnected model of power networks comprised of a comprehensive ninth order synchronous generator model, advanced power electronics-based models for photovoltaic (PV) power plants, constant power loads, constant impedance loads, and motor loads. The presented state estimator design is based on Lyapunov stability criteria for nonlinear differential algebraic equation (DAE) models and is posed as a convex semi-definite optimization problem. Thorough simulations studies have been carried out on IEEE-39 bus test system to showcase the robustness of the proposed estimator against unknown uncertainty from load demand and solar irradiance.