pwc-archive/papers-with-abstracts huggingface dataset:
$\mathcal{H}_2/\mathcal{H}_{-}$ Distributed Fault Detection and Isolation for Heterogeneous Multi-Agent Systems
Authors: Thiem V. Pham, Quynh T. T. Nguyen
Date: 2021-06-05 00:00:00
Tasks: Fault Detection

The paper deals with the problem of distributed fault detection and isolation (FDI) for a group of heterogeneous multi-agent systems. The developed formation for the FDI is taken into account as a distributed observer design methodology, where the interaction between the agent and its neighbors is described as a vector of distributed relative output measurements. Based on two performance indexes $\mathcal{H}_2$ and $\mathcal{H}_{-}$, sufficient conditions are given to ensure the residual signals robust to the disturbances and sensitive with respect to the fault signals. In addition, we show that by using our proposed approach, each agent is able to estimate both its own states and states of its nearest neighbors in the presence of disturbances and faults. Finally, numerical simulations are provided to demonstrate the effectiveness of the theoretically analyzed results.
Full-Dimensional Rate Enhancement for UAV-Enabled Communications via Intelligent Omni-Surface
Authors: Yifan Liu, Bin Duo, Qingqing Wu, Xiaojun Yuan, Yonghui Li
Date: 2021-06-05 00:00:00

This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS). Different from the state-of-the-art reconfigurable intelligent surface (RIS) that only reflects incident signals, the IOS can simultaneously reflect and transmit the signals, thereby providing full-dimensional rate enhancement. To tackle such a problem, we formulate it by jointly optimizing the IOS's phase shift and the UAV trajectory. Although it is difficult to solve it optimally due to its non-convexity, we propose an efficient iterative algorithm to obtain a high-quality suboptimal solution. Simulation results show that the IOS-assisted UAV communications can achieve more significant improvement in achievable rates than other benchmark schemes.
MPC-based Realtime Power System Control with DNN-based Prediction/Sensitivity-Estimation
Authors: Ramij Raja Hossain, Ratnesh Kumar
Date: 2021-06-05 00:00:00
Tasks: Model Predictive Control, Sensitivity

This paper presents a model predictive control (MPC)-based online real-time adaptive control scheme for emergency voltage control in power systems. Despite tremendous success in various applications, real-time implementation of MPC for control in power systems has not been successful due to its online computational burden for large-sized systems that takes more time than available between the two control decisions. This long-standing problem is addressed here by developing a novel MPC-based adaptive control framework which (i) adapts the nominal offline computed control, by successive control corrections, at each control decision point using the latest measurements, (ii) utilizes data-driven approach for prediction of voltage trajectory and its sensitivity with respect to control using trained deep neural networks (DNNs). In addition, a realistic coordination scheme among control inputs of static var compensators (SVC), load-shedding (LS), and load tap-changers (LTC) is presented with a goal of maintaining bus voltages within a predefined permissible range, where the delayed effect of LTC action is also incorporated in a novel way. The performance of the proposed scheme is validated for IEEE 9-bus as well as 39-bus systems, with $\pm 20\%$ variations in nominal loading conditions. We also show that the proposed new scheme speeds up the online computation by a factor of 20 bringing it down to under one-tenth the control interval, making the MPC-based power system control practically feasible.
Constrained Ellipse Fitting for Efficient Parameter Mapping with Phase-cycled bSSFP MRI
Authors: Kübra Keskin, Uğur Yılmaz, Tolga Çukur
Date: 2021-06-06 00:00:00
Tasks: Anatomy, parameter estimation, Sensitivity

Balanced steady-state free precession (bSSFP) imaging enables high scan efficiency in MRI, but differs from conventional sequences in terms of elevated sensitivity to main field inhomogeneity and nonstandard T2/T1-weighted tissue contrast. To address these limitations, multiple bSSFP images of the same anatomy are commonly acquired with a set of different RF phase-cycling increments. Joint processing of phase-cycled acquisitions serves to mitigate sensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions were also leveraged to estimate relaxation parameters based on explicit signal models. While effective, these model-based methods often involve a large number of acquisitions (N~10-16), degrading scan efficiency. Here, we propose a new constrained ellipse fitting method (CELF) for parameter estimation with improved efficiency and accuracy in phase-cycled bSSFP MRI. CELF is based on the elliptical signal model framework for complex bSSFP signals; and it introduces geometrical constraints on ellipse properties to improve estimation efficiency, and dictionary-based identification to improve estimation accuracy. CELF generates maps of T1, T2, off-resonance and on-resonant bSSFP signal by employing a separate B1 map to mitigate sensitivity to flip angle variations. Our results indicate that CELF can produce accurate off-resonance and banding-free bSSFP maps with as few as N=4 acquisitions, while estimation accuracy for relaxation parameters is notably limited by biases from microstructural sensitivity of bSSFP imaging.
On the "mementum" of Meme Stocks
Authors: Michele Costola, Matteo Iacopini, Carlo R. M. A. Santagiustina
Date: 2021-06-07 00:00:00

The meme stock phenomenon is yet to be explored. In this note, we provide evidence that these stocks display common stylized facts on the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock "mementum" which exhibits a different characterization with respect to other stocks with high volumes of activity (persistent and not) on social media. Understanding these properties helps the investors and market authorities in their decision.
Linear Rescaling to Accurately Interpret Logarithms
Authors: Nick Huntington-Klein
Date: 2021-06-06 00:00:00
Tasks: regression

The standard approximation of a natural logarithm in statistical analysis interprets a linear change of \(p\) in \(\ln(X)\) as a \((1+p)\) proportional change in \(X\), which is only accurate for small values of \(p\). I suggest base-\((1+p)\) logarithms, where \(p\) is chosen ahead of time. A one-unit change in \(\log_{1+p}(X)\) is exactly equivalent to a \((1+p)\) proportional change in \(X\). This avoids an approximation applied too broadly, makes exact interpretation easier and less error-prone, improves approximation quality when approximations are used, makes the change of interest a one-log-unit change like other regression variables, and reduces error from the use of \(\log(1+X)\).
Prevention Is Better Than Cure: Experimental Evidence From Milk Fever Incidence in Dairy Animals of Haryana, India
Authors: A. G. Adeeth Cariappa, B. S. Chandel, Gopal Sankhala, Veena Mani, Sendhil R, Anil Kumar Dixit, B. S. Meena
Date: 2021-06-07 00:00:00
Tasks: valid

Calcium deficiency in high yielding bovines during calving causes milk fever which leads to economic losses of around INR 1000 crores (USD 137 million) per annum in Haryana, India. With increasing milk production, the risk of milk fever is continuously rising. In the context, we aim to address the most fundamental research question: What is the effect of a preventive health product (anionic mineral mixture (AMM)) on milk fever incidence, milk productivity and farmers income? In an effort to contribute to the scanty economic literature on effect of preventive measures on nutritional deficiency disorders in dairy animals, specifically, on AMM effects in India, this study uses a randomized controlled design to estimate internally valid estimates. Using data from 200 dairy farms, results indicate that milk fever incidence decreases from 21 per cent at baseline to 2 per cent in treated animals at follow-up. Further, AMM leads to a 12 per cent and 38 per cent increase in milk yield and farmers net income, respectively. Profits earned due to the prevention of milk fever [INR 16000 (USD 218.7)] overweighs the losses from milk fever [INR 4000 (USD 54.7)]; thus, prevention using AMM is better than cure.
The Intellectual Property Protection System of the Foreign Investment Law: Basic Structure, Motivation and Game Logic
Authors: Luo Ying
Date: 2021-06-07 00:00:00

The intellectual property protection system constructed by China's Foreign Investment Law has opened a new phase of rule of law protection of intellectual property rights for foreign-invested enterprises, which is an important institutional support indispensable for optimizing the business environment under the rule of law.The development of the regime was influenced by the major concerns of investors' home countries, the "innovation-driven development" strategy, and the trend towards a high level of stringent protection of international intellectual property and investment rules.In addition, there is a latent game of interests between multiple subjects, which can be analyzed by constructing two standard formal game models according to legal game theory.The first game model aims to compare and analyze the gains and losses of China and India's IPR protection system for foreign-invested enterprises to attract foreign investment.The second game model is designed to analyze the benefits of China and foreign investors under their respective possible behaviors before and after the inclusion of IPR protection provisions in the Foreign Investment Law, with the optimal solution being a "moderately cautious" strategy for foreign investors and a "strict enforcement" strategy for China.
The Rohingyas of Rakhine State: Social Evolution and History in the Light of Ethnic Nationalism
Authors: Sarwar J. Minar, Abdul Halim
Date: 2021-06-05 00:00:00

Recent event of ousting Rohingyas from Rakhine State by the Tatmadaw provoked worldwide public-and-academic interest in history and social evolution of the Rohingyas, and this is to what the article is devoted. As the existing literature presents a debate over Who are the Rohingyas?, and How legitimate is their claim over Rakhine State?, the paper reinvestigates the issues using a qualitative research method. Compiling a detailed history, the paper finds that Rohingya community developed through historically complicated processes marked by invasions and counter-invasions. The paper argues many people entered Bengal from Arakan before British brought people into Rakhine state. The Rohingyas believe Rakhine State is their ancestral homeland and they developed a sense of Ethnic Nationalism. Their right over Rakhine State is as significant as other groups. The paper concludes that the UN must pursue solution to the crisis and the government should accept the Rohingyas as it did the land or territory.
Optimal Taxation of Assets
Authors: Nicolaus Tideman, Thomas Mecherikunnel
Date: 2021-06-05 00:00:00

The optimal taxation of assets requires attention to two concerns: 1) the elasticity of the supply of assets and 2) the impact of taxing assets on distributional objectives. The most efficient way to attend to these two concerns is to tax assets of different types separately, rather than having one tax on all assets. When assets are created by specialized effort rather than by saving, as with innovations, discoveries of mineral deposits and development of unregulated natural monopolies, it is interesting to consider a regime in which the government awards a prize for the creation of the asset and then collects the remaining value of the asset in taxes. Analytically, the prize is like a wage after taxes. In this perspective, prizes are awarded based on a variation on optimal taxation theory, while assets of different types are taxed in divergent ways, depending on their characteristics. Some categories of assets are abolished.
Mathematical Vocoder Algorithm : Modified Spectral Inversion for Efficient Neural Speech Synthesis
Authors: Hyun Gon Ryu, Jeong-Hoon Kim, Simon See
Date: 2021-06-06 00:00:00
Tasks: CPU, GPU, Speech Synthesis

In this work, we propose a new mathematical vocoder algorithm(modified spectral inversion) that generates a waveform from acoustic features without phase estimation. The main benefit of using our proposed method is that it excludes the training stage of the neural vocoder from the end-to-end speech synthesis model. Our implementation can synthesize high fidelity speech at approximately 20 Mhz on CPU and 59.6MHz on GPU. This is 909 and 2,702 times faster compared to real-time. Since the proposed methodology is not a data-driven method, it is applicable to unseen voices and multiple languages without any additional work. The proposed method is expected to adapt for researching on neural network models capable of synthesizing speech at the studio recording level.
Improving Channel Decorrelation for Multi-Channel Target Speech Extraction
Authors: Jiangyu Han, Wei Rao, Yannan Wang, Yanhua Long
Date: 2021-06-06 00:00:00
Tasks: Speech Extraction

Target speech extraction has attracted widespread attention. When microphone arrays are available, the additional spatial information can be helpful in extracting the target speech. We have recently proposed a channel decorrelation (CD) mechanism to extract the inter-channel differential information to enhance the reference channel encoder representation. Although the proposed mechanism has shown promising results for extracting the target speech from mixtures, the extraction performance is still limited by the nature of the original decorrelation theory. In this paper, we propose two methods to broaden the horizon of the original channel decorrelation, by replacing the original softmax-based inter-channel similarity between encoder representations, using an unrolled probability and a normalized cosine-based similarity at the dimensional-level. Moreover, new combination strategies of the CD-based spatial information and target speaker adaptation of parallel encoder outputs are also investigated. Experiments on the reverberant WSJ0 2-mix show that the improved CD can result in more discriminative differential information and the new adaptation strategy is also very effective to improve the target speech extraction.
RTNeural: Fast Neural Inferencing for Real-Time Systems
Authors: Jatin Chowdhury
Date: 2021-06-06 00:00:00

RTNeural is a neural inferencing library written in C++. RTNeural is designed to be used in systems with hard real-time constraints, with additional emphasis on speed, flexibility, size, and convenience. The motivation and design of the library are described, as well as real-world use-cases, and performance comparisons with other neural inferencing libraries.
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement
Authors: Sefik Emre Eskimez, Xiaofei Wang, Min Tang, Hemin Yang, Zirun Zhu, Zhuo Chen, Huaming Wang, Takuya Yoshioka
Date: 2021-06-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

With the surge of online meetings, it has become more critical than ever to provide high-quality speech audio and live captioning under various noise conditions. However, most monaural speech enhancement (SE) models introduce processing artifacts and thus degrade the performance of downstream tasks, including automatic speech recognition (ASR). This paper proposes a multi-task training framework to make the SE models unharmful to ASR. Because most ASR training samples do not have corresponding clean signal references, we alternately perform two model update steps called SE-step and ASR-step. The SE-step uses clean and noisy signal pairs and a signal-based loss function. The ASR-step applies a pre-trained ASR model to training signals enhanced with the SE model. A cross-entropy loss between the ASR output and reference transcriptions is calculated to update the SE model parameters. Experimental results with realistic large-scale settings using ASR models trained on 75,000-hour data show that the proposed framework improves the word error rate for the SE output by 11.82% with little compromise in the SE quality. Performance analysis is also carried out by changing the ASR model, the data used for the ASR-step, and the schedule of the two update steps.
Reinforce-Aligner: Reinforcement Alignment Search for Robust End-to-End Text-to-Speech
Authors: Hyunseung Chung, Sang-Hoon Lee, Seong-Whan Lee
Date: 2021-06-05 00:00:00
Tasks: text-to-speech, Text to Speech

Text-to-speech (TTS) synthesis is the process of producing synthesized speech from text or phoneme input. Traditional TTS models contain multiple processing steps and require external aligners, which provide attention alignments of phoneme-to-frame sequences. As the complexity increases and efficiency decreases with every additional step, there is expanding demand in modern synthesis pipelines for end-to-end TTS with efficient internal aligners. In this work, we propose an end-to-end text-to-waveform network with a novel reinforcement learning based duration search method. Our proposed generator is feed-forward and the aligner trains the agent to make optimal duration predictions by receiving active feedback from actions taken to maximize cumulative reward. We demonstrate accurate alignments of phoneme-to-frame sequence generated from trained agents enhance fidelity and naturalness of synthesized audio. Experimental results also show the superiority of our proposed model compared to other state-of-the-art TTS models with internal and external aligners.
Minimum Norm Method for Linear and Planar Sparse Arrays
Authors: Tyler M. Trosclair, Kaushallya Adhikari
Date: 2021-06-07 00:00:00
Tasks: Direction of Arrival Estimation

Coprime and nested arrays are sparse arrays with enhanced degrees of freedom, which can be exploited in direction of arrival estimation using algorithms such as product processing, min processing, and MUSIC. This paper applies the minimum norm method for direction of arrival estimation. Comparison of the root mean squared errors and probabilities of resolution of the minimum norm method with MUSIC for a given linear coprime or nested array demonstrates the superiority of the minimum norm method. Specifically, minimum norm method exhibits lower mean squared error, narrower peaks at the locations of the true sources, and a lower noise floor in the spatial spectral estimate. This work also formulates two different minimum norm methods for planar sparse arrays: direct and linear. Comparison of the linear minimum norm method with the linear MUSIC for planar arrays also demonstrates higher accuracy of the minimum norm method.
Product Processing for Tapered Sparse Arrays
Authors: Daniel Sartori, Kaushallya Adhikari
Date: 2021-06-07 00:00:00

The product processor output has recently been introduced as a spatial power spectral density estimate, unifying product arrays such as coprime arrays, nested arrays, and standard uniform line arrays. The expected value and covariance function of this estimate for a white Gaussian process was derived in previous work over these various array configurations. However, this prior work used a uniform taper in all cases. In this paper, we show that when product arrays are windowed with non-uniform tapers, the expected value of the product processor output is the convolution of the true spatial power spectral density with the spatial Fourier transform of the difference coarray. This expected value makes a Fourier transform pair with a spatial autocorrelation estimate obtained by windowing the true autocorrelation function. We also derive the covariance function of the product processor output with non-uniform tapers, and compare these derived statistics for the aforementioned array geometries. Also, in prior work, the moments were provided only for linear arrays; this paper extends the estimation results to multidimensional arrays.
Absolute Eigenvalues-Based Covariance Matrix Estimation for a Sparse Array
Authors: Kaushallya Adhikari
Date: 2021-06-07 00:00:00

The ensemble covariance matrix of a wide sense stationary signal spatially sampled by a full linear array is positive semi-definite and Toeplitz. However, the direct augmented covariance matrix of an augmentable sparse array is Toeplitz but not positive semi-definite, resulting in negative eigenvalues that pose inherent challenges in its applications, including model order estimation and source localization. The positive eigenvalues-based covariance matrix for augmentable sparse arrays is robust but the matrix is unobtainable when all noise eigenvalues of the direct augmented matrix are negative, which is a possible case. To address this problem, we propose a robust covariance matrix for augmentable sparse arrays that leverages both positive and negative noise eigenvalues. The proposed covariance matrix estimate can be used in conjunction with subspace based algorithms and adaptive beamformers to yield accurate signal direction estimates.
Configuring an Intelligent Reflecting Surface for Wireless Communications
Authors: Emil Björnson
Date: 2021-06-07 00:00:00

The IEEE Signal Processing Society is proud to announce the eighth edition of the Signal Processing Cup: an exciting challenge to control a wireless propagation environment using an intelligent reflecting surface. An intelligent reflecting surface is a two-dimensional array of metamaterial whose interaction with electromagnetic waves can be controlled, e.g., by tuning the impedance variations over the surface. These surfaces might be used in the sixth generation (6G) mobile technology to direct wireless signals from a transmitter towards a receiver, to raise the communication performance. The goal of the challenge is to characterize the behavior of an intelligent reflecting surface based on received signals from an over-the-air signaling phase and develop a control algorithm to configure the surface to aid wireless communications.
Dynamic Portfolio Cuts: A Spectral Approach to Graph-Theoretic Diversification
Authors: Alvaro Arroyo, Bruno Scalzo, Ljubisa Stankovic, Danilo P. Mandic
Date: 2021-06-07 00:00:00

Stock market returns are typically analyzed using standard regression, yet they reside on irregular domains which is a natural scenario for graph signal processing. To this end, we consider a market graph as an intuitive way to represent the relationships between financial assets. Traditional methods for estimating asset-return covariance operate under the assumption of statistical time-invariance, and are thus unable to appropriately infer the underlying true structure of the market graph. This work introduces a class of graph spectral estimators which cater for the nonstationarity inherent to asset price movements, and serve as a basis to represent the time-varying interactions between assets through a dynamic spectral market graph. Such an account of the time-varying nature of the asset-return covariance allows us to introduce the notion of dynamic spectral portfolio cuts, whereby the graph is partitioned into time-evolving clusters, allowing for online and robust asset allocation. The advantages of the proposed framework over traditional methods are demonstrated through numerical case studies using real-world price data.