pwc-archive/papers-with-abstracts huggingface dataset:
Impact of Motor Stator Winding Faults on Common-Mode Current
Authors: Fei Fan, Zhenyu Zhao, Pengfei Tu, Huamin Jie, Kye Yak See
Date: 2021-10-06 00:00:00

This paper investigates the influence of different motor stator failures on the common-mode (CM) current. The stator winding failures will affect the motor's CM impedance, and also increase the unbalance of the differential-mode (DM) noise path. The experimental results show that the former will increase the CM current induced by the CM noise source, while the latter will cause the DM current induced by the DM noise source to be converted into the CM current.
An Online Scheduling Algorithm for a Community Energy Storage System
Authors: Nathaniel Tucker, Mahnoosh Alizadeh
Date: 2021-10-05 00:00:00
Tasks: Scheduling

In this paper, we consider a community energy storage (CES) system that is shared by various electricity consumers who want to charge and discharge the CES throughout a given time span. We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users. Moreover, we consider the case where requests to charge/discharge the CES arrive in an online fashion and the CES manager must immediately allocate charging power and energy capacity to fulfill the request or reject the request altogether. The objective of the CES manager is to maximize the total value gained by all of the users of the CES while accounting for the operational constraints of the CES. We discuss an algorithm titled \textsc{CommunityEnergyScheduling} that acts as a pricing mechanism based on online primal-dual optimization as a solution to the CES manager's problem. The online algorithm estimates the dual variables (prices) in real-time to allow for requests to be allocated or rejected immediately as they arrive. Furthermore, the proposed method promotes charging and discharging cancellations to reduce the CES's usage at popular times and is able to handle the inherent stochastic nature of the requests to charge/discharge stemming from randomness in users' net load patterns and weather uncertainties. Additionally, we are able to show that the algorithm is able to handle any adversarially chosen request sequence and will always yield total welfare within a factor of 1/a of the offline optimal welfare.
Distributed Privacy-Preserving Electric Vehicle Charging Control Based on Secret Sharing
Authors: Xiang Huo, Mingxi Liu
Date: 2021-10-05 00:00:00
Tasks: Distributed Optimization, Management, Privacy Preserving

Cooperative electric vehicle (EV) charging control has emerged as a key component in grid-edge resource (GER) management. However, customers' privacy remains a major barrier to large-scale implementation of EV charging control. In this paper, we develop a distributed privacy-preserving EV charging control protocol based on secret sharing (SS) that 1) achieves scalability over EV population size; 2) enjoys higher computation efficiency compared to homomorphic encryption (HE) based methods; and 3) secures the privacy of the participating EVs against honest-but-curious adversaries and external eavesdroppers. The cooperative EV charging control is formulated to achieve overnight valley-filling and framed into the projected gradient algorithm (PGA) structure as a distributed optimization problem. SS is integrated into PGA to achieve secure updates of both primal and dual variables. Theoretical security analyses and simulations in a residential area are conducted to prove the privacy preservation guarantee as well as the efficacy and efficiency of the proposed privacy preservation method. Broadly, the proposed method can be readily extended to various GER control applications.
Lotka-Volterra versus May-Leonard formulations of the spatial stochastic Rock-Paper-Scissors model: the missing link
Authors: P. P. Avelino, B. F. de Oliveira, R. S. Trintin
Date: 2021-10-06 00:00:00

The Rock-Paper-Scissors (RPS) model successfully reproduces some of the main features of simple cyclic predator-prey systems with interspecific competition observed in nature. Still, lattice-based simulations of the spatial stochastic RPS model are known to give rise to significantly different results, depending on whether the three state Lotka-Volterra or the four state May-Leonard formulation is employed. This is true independently of the values of the model parameters and of the use of either a von Neumann or a Moore neighborhood. With the objective of reducing the impact of the use of a discrete lattice, in this paper we introduce a simple modification to the standard spatial stochastic RPS model in which the range of the search of the nearest neighbor may be extended up to a maximum euclidean radius $R$. We show that, with this adjustment, the Lotka-Volterra and May-Leonard formulations can be designed to produce similar results, both in terms of dynamical properties and spatial features, by means of an appropriate parameter choice. In particular, we show that this modified spatial stochastic RPS model naturally leads to the emergence of spiral patterns in both its three and four state formulations.
New insights into price drivers of crude oil futures markets: Evidence from quantile ARDL approach
Authors: Hao-Lin Shao, Ying-Hui Shao, Yan-Hong Yang
Date: 2021-10-06 00:00:00

This paper investigates the cointegration between possible determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of WTI and newly-launched Shanghai crude oil futures (SC) via the Autoregressive Distributed Lag (ARDL) model and Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results confirm that economic policy uncertainty, stock markets, interest rates and coronavirus panic are important drivers of WTI futures prices. Our findings also suggest that the US and China's stock markets play vital roles in movements of SC futures prices. Meanwhile, CSI300 stock index has a significant positive short-run impact on SC futures prices while S\&P500 prices possess a positive nexus with SC futures prices both in long-run and short-run. Overall, these empirical evidences provide practical implications for investors and policymakers.
Interpreting Unconditional Quantile Regression with Conditional Independence
Authors: David M. Kaplan
Date: 2020-10-07 00:00:00
Tasks: counterfactual, quantile regression, regression

This note provides additional interpretation for the counterfactual outcome distribution and corresponding unconditional quantile "effects" defined and estimated by Firpo, Fortin, and Lemieux (2009) and Chernozhukov, Fern\'andez-Val, and Melly (2013). With conditional independence of the policy variable of interest, these methods estimate the policy effect for certain types of policies, but not others. In particular, they estimate the effect of a policy change that itself satisfies conditional independence.
Gambits: Theory and Evidence
Authors: Shiva Maharaj, Nicholas Polson, Christian Turk
Date: 2021-10-05 00:00:00
Tasks: Decision Making, Sequential Decision Making

Gambits are central to human decision-making. Our goal is to provide a theory of Gambits. A Gambit is a combination of psychological and technical factors designed to disrupt predictable play. Chess provides an environment to study gambits and behavioral game theory. Our theory is based on the Bellman optimality path for sequential decision-making. This allows us to calculate the $Q$-values of a Gambit where material (usually a pawn) is sacrificed for dynamic play. On the empirical side, we study the effectiveness of a number of popular chess Gambits. This is a natural setting as chess Gambits require a sequential assessment of a set of moves (a.k.a. policy) after the Gambit has been accepted. Our analysis uses Stockfish 14.1 to calculate the optimal Bellman $Q$ values, which fundamentally measures if a position is winning or losing. To test whether Bellman's equation holds in play, we estimate the transition probabilities to the next board state via a database of expert human play. This then allows us to test whether the \emph{Gambiteer} is following the optimal path in his decision-making. Our methodology is applied to the popular Stafford and reverse Stafford (a.k.a. Boden-Kieretsky-Morphy) Gambit and other common ones including the Smith-Morra, Goring, Danish and Halloween Gambits. We build on research in human decision-making by proving an irrational skewness preference within agents in chess. We conclude with directions for future research.
Unsupervised Audio-Caption Aligning Learns Correspondences between Individual Sound Events and Textual Phrases
Authors: Huang Xie, Okko Räsänen, Konstantinos Drossos, Tuomas Virtanen
Date: 2021-10-06 00:00:00
Tasks: Event Detection, Retrieval, Sound Event Detection

We investigate unsupervised learning of correspondences between sound events and textual phrases through aligning audio clips with textual captions describing the content of a whole audio clip. We align originally unaligned and unannotated audio clips and their captions by scoring the similarities between audio frames and words, as encoded by modality-specific encoders and using a ranking-loss criterion to optimize the model. After training, we obtain clip-caption similarity by averaging frame-word similarities and estimate event-phrase correspondences by calculating frame-phrase similarities. We evaluate the method with two cross-modal tasks: audio-caption retrieval, and phrase-based sound event detection (SED). Experimental results show that the proposed method can globally associate audio clips with captions as well as locally learn correspondences between individual sound events and textual phrases in an unsupervised manner.
Prosody-TTS: An end-to-end speech synthesis system with prosody control
Authors: Giridhar Pamisetty, K. Sri Rama Murty
Date: 2021-10-06 00:00:00
Tasks: Rhythm, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

End-to-end text-to-speech synthesis systems achieved immense success in recent times, with improved naturalness and intelligibility. However, the end-to-end models, which primarily depend on the attention-based alignment, do not offer an explicit provision to modify/incorporate the desired prosody while synthesizing the signal. Moreover, the state-of-the-art end-to-end systems use autoregressive models for synthesis, making the prediction sequential. Hence, the inference time and the computational complexity are quite high. This paper proposes Prosody-TTS, an end-to-end speech synthesis model that combines the advantages of statistical parametric models and end-to-end neural network models. It also has a provision to modify or incorporate the desired prosody by controlling the fundamental frequency (f0) and the phone duration. Generating speech samples with appropriate prosody and rhythm helps in improving the naturalness of the synthesized speech. We explicitly model the duration of the phoneme and the f0 to have control over them during the synthesis. The model is trained in an end-to-end fashion to directly generate the speech waveform from the input text, which in turn depends on the auxiliary subtasks of predicting the phoneme duration, f0, and mel spectrogram. Experiments on the Telugu language data of the IndicTTS database show that the proposed Prosody-TTS model achieves state-of-the-art performance with a mean opinion score of 4.08, with a very low inference time.
Generalization Ability of MOS Prediction Networks
Authors: Erica Cooper, Wen-Chin Huang, Tomoki Toda, Junichi Yamagishi
Date: 2021-10-06 00:00:00
Tasks: Prediction

Automatic methods to predict listener opinions of synthesized speech remain elusive since listeners, systems being evaluated, characteristics of the speech, and even the instructions given and the rating scale all vary from test to test. While automatic predictors for metrics such as mean opinion score (MOS) can achieve high prediction accuracy on samples from the same test, they typically fail to generalize well to new listening test contexts. In this paper, using a variety of networks for MOS prediction including MOSNet and self-supervised speech models such as wav2vec2, we investigate their performance on data from different listening tests in both zero-shot and fine-tuned settings. We find that wav2vec2 models fine-tuned for MOS prediction have good generalization capability to out-of-domain data even for the most challenging case of utterance-level predictions in the zero-shot setting, and that fine-tuning to in-domain data can improve predictions. We also observe that unseen systems are especially challenging for MOS prediction models.
A Survey on Recent Deep Learning-driven Singing Voice Synthesis Systems
Authors: Yin-Ping Cho, Fu-Rong Yang, Yung-Chuan Chang, Ching-Ting Cheng, Xiao-Han Wang, Yi-Wen Liu
Date: 2021-10-06 00:00:00
Tasks: Deep Learning, Singing Voice Synthesis

Singing voice synthesis (SVS) is a task that aims to generate audio signals according to musical scores and lyrics. With its multifaceted nature concerning music and language, producing singing voices indistinguishable from that of human singers has always remained an unfulfilled pursuit. Nonetheless, the advancements of deep learning techniques have brought about a substantial leap in the quality and naturalness of synthesized singing voice. This paper aims to review some of the state-of-the-art deep learning-driven SVS systems. We intend to summarize their deployed model architectures and identify the strengths and limitations for each of the introduced systems. Thereby, we picture the recent advancement trajectory of this field and conclude the challenges left to be resolved both in commercial applications and academic research.
MediumVC: Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features
Authors: Yewei Gu, Zhenyu Zhang, Xiaowei Yi, Xianfeng Zhao
Date: 2021-10-06 00:00:00
Tasks: Voice Conversion

To realize any-to-any (A2A) voice conversion (VC), most methods are to perform symmetric self-supervised reconstruction tasks (Xi to Xi), which usually results in inefficient performances due to inadequate feature decoupling, especially for unseen speakers. We propose a two-stage reconstruction task (Xi to Yi to Xi) using synthetic specific-speaker speeches as intermedium features, where A2A VC is divided into two stages: any-to-one (A2O) and one-to-Any (O2A). In the A2O stage, we propose a new A2O method: SingleVC, by employing a noval data augment strategy(pitch-shifted and duration-remained, PSDR) to accomplish Xi to Yi. In the O2A stage, MediumVC is proposed based on pre-trained SingleVC to conduct Yi to Xi. Through such asymmetrical reconstruction tasks (Xi to Yi in SingleVC and Yi to Xi in MediumVC), the models are to capture robust disentangled features purposefully. Experiments indicate MediumVC can enhance the similarity of converted speeches while maintaining a high degree of naturalness.
Sub-band coding of hexagonal images
Authors: Md Mamunur Rashid, Usman R. Alim
Date: 2021-10-06 00:00:00

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid.
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge
Authors: Syed Talha Bukhari, Hassan Mohy-ud-Din
Date: 2021-10-06 00:00:00
Tasks: Brain Tumor Segmentation, Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in medical image segmentation tasks. A common feature in most top-performing CNNs is an encoder-decoder architecture inspired by the U-Net. For multi-region brain tumor segmentation, 3D U-Net architecture and its variants provide the most competitive segmentation performances. In this work, we propose an interesting extension of the standard 3D U-Net architecture, specialized for brain tumor segmentation. The proposed network, called E1D3 U-Net, is a one-encoder, three-decoder fully-convolutional neural network architecture where each decoder segments one of the hierarchical regions of interest: whole tumor, tumor core, and enhancing core. On the BraTS 2018 validation (unseen) dataset, E1D3 U-Net demonstrates single-prediction performance comparable with most state-of-the-art networks in brain tumor segmentation, with reasonable computational requirements and without ensembling. As a submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge, we also evaluate our proposal on the BraTS 2021 dataset. E1D3 U-Net showcases the flexibility in the standard 3D U-Net architecture which we exploit for the task of brain tumor segmentation.
Hyperspectral Neutron CT with Material Decomposition
Authors: Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman
Date: 2021-10-06 00:00:00

Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. In combination with uniquely characteristic isotopic neutron cross-section spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample. By preforming ERNI measurements over several rotational views, an isotope decomposed 3D computed tomography is possible. We demonstrate a method involving a robust and automated background estimation based on a linear programming formulation. The extremely high noise due to low count measurements is overcome using a sparse coding approach. It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application.
High-throughput lensless whole slide imaging via continuous height-varying modulation of tilted sensor
Authors: Shaowei Jiang, Chengfei Guo, Patrick Hu, Derek Hu, Pengming Song, Tianbo Wang, Zichao Bian, Zibang Zhang, Guoan Zheng
Date: 2021-09-28 00:00:00
Tasks: Retrieval, Super-Resolution

We report a new lensless microscopy configuration by integrating the concepts of transverse translational ptychography and defocus multi-height phase retrieval. In this approach, we place a tilted image sensor under the specimen for linearly-increasing phase modulation along one lateral direction. Similar to the operation of ptychography, we laterally translate the specimen and acquire the diffraction images for reconstruction. Since the axial distance between the specimen and the sensor varies at different lateral positions, laterally translating the specimen effectively introduces defocus multi-height measurements while eliminating axial scanning. Lateral translation further introduces sub-pixel shift for pixel super-resolution imaging and naturally expands the field of view for rapid whole slide imaging. We show that the equivalent height variation can be precisely estimated from the lateral shift of the specimen, thereby addressing the challenge of precise axial positioning in conventional multi-height phase retrieval. Using a sensor with a 1.67-micron pixel size, our low-cost and field-portable prototype can resolve 690-nm linewidth on the resolution target. We show that a whole slide image of a blood smear with a 120-mm^2 field of view can be acquired in 18 seconds. We also demonstrate accurate automatic white blood cell counting from the recovered image. The reported approach may provide a turnkey solution for addressing point-of-care- and telemedicine-related challenges.
Quantifying and Computing Covariance Uncertainty
Authors: Filip Elvander, Johan Karlsson, Toon van Waterschoot
Date: 2021-10-06 00:00:00

In this work, we consider the problem of bounding the values of a covariance function corresponding to a continuous-time stationary stochastic process or signal. Specifically, for two signals whose covariance functions agree on a finite discrete set of time-lags, we consider the maximal possible discrepancy of the covariance functions for real-valued time-lags outside this discrete grid. Computing this uncertainty corresponds to solving an infinite dimensional non-convex problem. However, we herein prove that the maximal objective value may be bounded from above by a finite dimensional convex optimization problem, allowing for efficient computation by standard methods. Furthermore, we empirically observe that for the case of signals whose spectra are supported on an interval, this upper bound is sharp, i.e., provides an exact quantification of the covariance uncertainty.
Designing Binary Sequence Set with Optimized Correlation Properties via ADMM Approach
Authors: Jiangtao Wang, Yongchao Wang
Date: 2021-10-06 00:00:00

In this paper, we design low correlation binary sequences favorable in wireless communication and radar applications. First, we formulate the designing problem as a nonconvex combination optimization problem with flexible correlation interval; second, by relaxing constraints and introducing auxiliary variables, the original minimization problem is equivalent to a consensus continuous optimization problem; third, to achieve its good approximate solution efficiently, we propose the distributed executable algorithms based on alternating direction method of multipliers (ADMM); fourth, we prove that the proposed ADMM algorithms can converge to some stationary point of the approximate problem. Moreover, the computational complexity analysis is considered. Simulation results demonstrate that the proposed ADMM approaches outperform state-of-the-art ones in either computational cost or selection of correlation interval of the designed binary sequences.
Fast sensor placement by enlarging principle submatrix for large-scale linear inverse problems
Authors: Fen Wang, Gene Cheung, Taihao Li, Ying Du, Yu-Ping Ruan
Date: 2021-10-06 00:00:00

Sensor placement for linear inverse problems is the selection of locations to assign sensors so that the entire physical signal can be well recovered from partial observations. In this paper, we propose a fast sampling algorithm to place sensors. Specifically, assuming that the field signal $\mathbf{f}$ is represented by a linear model $\mathbf{f}=\pmb{\phi}\mathbf{g}$, it can be estimated from partial noisy samples via an unbiased least-squares (LS) method, whose expected mean square error (MSE) depends on chosen samples. First, we formulate an approximate MSE problem, and then prove it is equivalent to a problem related to a principle submatrix of $\pmb{\phi}\pmb{\phi}^\top$ indexed by sample set. To solve the formulated problem, we devise a fast greedy algorithm with simple matrix-vector multiplications, leveraging a matrix inverse formula. To further reduce complexity, we reuse results in the previous greedy step for warm start, so that candidates can be evaluated via lightweight vector-vector multiplications. Extensive experiments show that our proposed sensor placement method achieved the lowest sensor sampling time and the best performance compared to state-of-the-art schemes.