pwc-archive/papers-with-abstracts huggingface dataset:
New hybrid control of a 2 DoF Robot Arm
Date: 2020-10-24 00:00:00
Tasks: Friction

Robot arms have been using in different systems, which the control of
designed in desired trajectory is the main task. Also, it is anticipated that
while in operation the developed 2DoF robot arm will be constantly encountered
with noises such as friction forces. A new integral sliding mode control
(NISMC) is therefore being introduced to suppress noise due to its robustness.
Then, New hybrid control system (NHISMC) is proposed, which constantly
calculates an error value and applies a correction value to the system. This
will enhance trajectory and minimize tracking error. In comparison with two
other controllers, such as traditional sliding mode control (SMC) and NISMC,
experimental results confirmed the efficacy of the proposed control method.
New compound fractional sliding mode control and super-twisting control of a MEMS gyroscope
Date: 2020-10-24 00:00:00

In this research we propose a new compound Fractional Order Sliding Mode
Controller (FOSMC) and SuperTwisting Controller (FOSMC+STC) to control of a
MEMS gyroscope. A new sliding mode surface has been defined to design the
proposed new sliding mode controller. The main advantages of a FOSMC is its
high tracking performance and robustness against external perturbation, but it
is susceptible to chattering. By augmenting a STC with a FOSMC, the chattering
phenomenon is eliminated, singularity problem is solved and systems robustness
has significatnetly improved. Simulation results validate the effectiveness of
the proposed control approach.
New compound control algorithm in sliding mode control to reduce the chattering phenomenon: experimental validation
Date: 2020-10-24 00:00:00

In this work, a new SMS is proposed to achieve high tracking and suitable
robustness. However, the chattering phenomenon should be regarded as the main
drawback of the SMC. Therefore, a new compound control algorithm is used for
reducing the chattering phenomenon. The applied compound control law constantly
evaluates the error and send the correct value to the system. This
significantly will reduce the chattering phenomenon. The performance of the
control methods validated by applying on a robot arm experimentally.
Y-Vector: Multiscale Waveform Encoder for Speaker Embedding
Authors: Ge Zhu, Fei Jiang, Zhiyao Duan
Date: 2020-10-24 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies as speech features. Recent studies attempted to extract speaker embeddings directly from raw waveforms and have shown competitive results. In this paper, we propose a novel multi-scale waveform encoder that uses three convolution branches with different time scales to compute speech features from the waveform. These features are then processed by squeeze-and-excitation blocks, a multi-level feature aggregator, and a time delayed neural network (TDNN) to compute speaker embedding. We show that the proposed embeddings outperform existing raw-waveform-based speaker embeddings on speaker verification by a large margin. A further analysis of the learned filters shows that the multi-scale encoder attends to different frequency bands at its different scales while resulting in a more flat overall frequency response than any of the single-scale counterparts.
Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation
Authors: Isao Yagi, Shunya Maruyama, Takanobu Mizuta
Date: 2020-10-25 00:00:00

A leveraged ETF is a fund aimed at achieving a rate of return several times greater than that of the underlying asset such as Nikkei 225 futures. Recently, it has been suggested that rebalancing trades of a leveraged ETF may destabilize the financial markets. An empirical study using an agent-based simulation indicated that a rebalancing trade strategy could affect the price formation of an underlying asset market. However, no leveraged ETF trading method for suppressing the increase in volatility as much as possible has yet been proposed. In this paper, we compare different strategies of trading for a proposed trading model and report the results of our investigation regarding how best to suppress an increase in market volatility. As a result, it was found that as the minimum number of orders in a rebalancing trade increases, the impact on the market price formation decreases.
Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity
Authors: Isao Yagi, Yuji Masuda, Takanobu Mizuta
Date: 2020-10-25 00:00:00

Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern, but no studies have systematically discussed their relationship with major market liquidity indicators, including volume, tightness, resiliency, and depth. In this study, we used agent-based simulations to compare the major liquidity indicators in an artificial market where an HFT participated was compared to one where no HFT participated. The results showed that all liquidity indicators in the market where an HFT participated improved more than those in the market where no HFT participated. Furthermore, as a result of investigating the correlations between the major liquidity indicators in our simulations and the extant empirical literature, we found that market liquidity can be measured not only by the major liquidity indicators but also by execution rate. Therefore, it is suggested that it could be appropriate to employ execution rate as a novel liquidity indicator in future studies.
Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation
Authors: Guoliang Cheng, Lele Liao, Hongsheng Chen, Jing Lu
Date: 2020-10-25 00:00:00
Tasks: Acoustic echo cancellation, blind source separation

The mismatch between the numerical and actual nonlinear models is a challenge to nonlinear acoustic echo cancellation (NAEC) when the nonlinear adaptive filter is utilized. To alleviate this problem, we combine a basis-generic expansion of the memoryless nonlinearity into semi-blind source separation (SBSS). By regarding all the basis functions of the far-end input signal as the known equivalent reference signals, an SBSS updating algorithm is derived following the constrained scaled natural gradient strategy. Unlike the commonly utilized adaptive algorithm, the proposed SBSS is based on the independence between the near-end signal and the reference signals, and is less sensitive to the mismatch of nonlinearity between the numerical and actual models. Experimental results show that the proposed method outperforms conventional methods in terms of echo return loss enhancement (ERLE) and near-end speech quality evaluated by perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI).
Crowdsourcing approach for subjective evaluation of echo impairment
Authors: Ross Cutler, Babak Naderi, Markus Loide, Sten Sootla, Ando Saabas
Date: 2020-10-25 00:00:00

The quality of acoustic echo cancellers (AECs) in real-time communication systems is typically evaluated using objective metrics like ERLE and PESQ, and less commonly with lab-based subjective tests like ITU-T Rec. P.831. We will show that these objective measures are not well correlated to subjective measures. We then introduce an open-source crowdsourcing approach for subjective evaluation of echo impairment which can be used to evaluate the performance of AECs. We provide a study that shows this tool is accurate and highly reproducible. This new tool has been recently used in the ICASSP 2021 AEC Challenge which made the challenge possible to do quickly and cost effectively.
Spiking Neural Network Based Low-Power Radioisotope Identification using FPGA
Authors: Xiaoyu Huang, Edward Jones, Siru Zhang, Shouyu Xie, Steve Furber, Yannis Goulermas, Edward Marsden, Ian Baistow, Srinjoy Mitra, Alister Hamilton
Date: 2020-10-25 00:00:00

this paper presents a detailed methodology of a Spiking Neural Network (SNN) based low-power design for radioisotope identification. A low power cost of 72 mW has been achieved on FPGA with the inference accuracy of 100% at 10 cm test distance and 97% at 25 cm. The design verification and chip validation methods are presented. It also discusses SNN simulation on SpiNNaker for rapid prototyping and various considerations specific to the application such as test distance, integration time, and SNN hyperparameter selections.
Subjective Evaluation of Noise Suppression Algorithms in Crowdsourcing
Authors: Babak Naderi, Ross Cutler
Date: 2020-10-25 00:00:00

The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec. P.835, in which participants rate background noise, speech signal, and overall quality separately. This paper introduces an open-source toolkit for conducting subjective quality evaluation of noise suppressed speech in crowdsourcing. We followed the ITU-T Rec. P.835, and P.808 and highly automate the process to prevent moderator's error. To assess the validity of our evaluation method, we compared the Mean Opinion Scores (MOS), calculate using ratings collected with our implementation, and the MOS values from a standard laboratory experiment conducted according to the ITU-T Rec P.835. Results show a high validity in all three scales namely background noise, speech signal and overall quality (average PCC = 0.961). Results of a round-robin test (N=5) showed that our implementation is also a highly reproducible evaluation method (PCC=0.99). Finally, we used our implementation in the INTERSPEECH 2021 Deep Noise Suppression Challenge as the primary evaluation metric, which demonstrates it is practical to use at scale. The results are analyzed to determine why the overall performance was the best in terms of background noise and speech quality.
Cognitive Preadaptation for Resilient Adaptive Control
Date: 2020-10-25 00:00:00

In this paper, we investigate a novel control architecture and algorithm for
incorporating preadaption functions. We propose a preadaptation mechanism that
can augment any adaptive control scheme and improve its resilience. Through
simulations of a flight control system we illustrate the effectiveness of the
preadaptation mechanism in improving the adaptation. We show that the
preadaptation mechanism we propose can reduce the peak of the response by as
much as $50\%$. The scenarios we present also show that the preadaptation
mechanism is effective across a wide range of scenarios suggesting that the
mechanism is reliable.
D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRI
Authors: Christopher A. Metzler, Gordon Wetzstein
Date: 2020-10-25 00:00:00
Tasks: Denoising, Image Reconstruction

Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However, in general the "effective noise", that is the difference between the true signal and the intermediate solution, within the iterations of P&P algorithms is neither Gaussian nor white. This fact makes existing denoising algorithms suboptimal. In this work, we propose a CNN architecture for removing colored Gaussian noise and combine it with the recently proposed VDAMP algorithm, whose effective noise follows a predictable colored Gaussian distribution. We apply the resulting denoising-based VDAMP (D-VDAMP) algorithm to variable density sampled compressive MRI where it substantially outperforms existing techniques.
Wideband Index Modulation with Circularly-Shifted Chirps
Authors: Safi Shams Muhtasimul Hoque, Chao-Yu Chen, Alphan Sahin
Date: 2020-10-25 00:00:00

In this study, we propose a wideband index modulation (IM) based on circularly-shifted chirps. To derive the proposed method, we first prove that a Golay complementary pair (GCP) can be constructed by linearly combining the Fourier series of chirps. We show that Fresnel integrals and/or Bessel functions, arising from sinusoidal and linear chirps, respectively, can lead to GCPs. We then exploit discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-s-OFDM) to obtain a low-complexity transmitter and receiver. We also discuss its generalization for achieving a trade-off between peak-to-mean envelope power ratio (PMEPR) and spectral efficiency (SE). Through comprehensive simulations, we compare the proposed scheme with DFT-s-OFDM with IM, orthogonal frequency division multiplexing (OFDM) with IM and complementary sequences (CSs) from Reed-Muller (RM) code. Our numerical results show that the proposed method limits the PMEPR while exploiting the frequency.
Endogenous Representation of Asset Returns
Authors: Zhipu Zhou, Alexander Shkolnik, Sang-Yun Oh
Date: 2020-10-25 00:00:00

Factor modeling of asset returns has been a dominant practice in investment science since the introduction of the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). The factors, which account for the systematic risk, are either specified or interpreted to be exogenous. They explain a significant portion of the risk in large portfolios. We propose a framework that asks how much of the risk, that we see in equity markets, may be explained by the asset returns themselves. To answer this question, we decompose the asset returns into an endogenous component and the remainder, and analyze the properties of the resulting risk decomposition. Statistical methods to estimate this decomposition from data are provided along with empirical tests. Our results point to the possibility that most of the risk in equity markets may be explained by a sparse network of interacting assets (or their issuing firms). This sparse network can give the appearance of a set exogenous factors where, in fact, there may be none. We illustrate our results with several case studies.
Cooperative Energy Management of HVAC via Transactive Energy
Authors: Qing Yang, Hao Wang
Date: 2020-10-26 00:00:00
Tasks: energy management, energy trading, Management

Heating, Ventilation, and Air Conditioning (HVAC) energy consumption accounts for a significant part of the total energy consumption of buildings and households. The ubiquitous adoption of distributed renewable energy and smart meters helps to decarbonize the HVAC energy consumption and improve energy efficiency. However, how to scale up HVAC energy management for a group of users while persevering users' privacy remains a big challenge. In this work, we utilize the concept of transactive energy to build a cooperative energy management system for independent HVAC units in a distributed manner. Specifically, we develop a distributed energy trading algorithm that consists of two layers based on the alternating direction method of multipliers method. The distributed energy trading algorithm achieves optimal trading performance and also preserves users' privacy. Furthermore, we evaluate the performance of the distributed trading algorithm by extensive simulations with real-world data. Simulation results show that the energy trading algorithm converges fast and the cooperative energy platform reduces the user's individual cost by up to 50% and lowers the overall cost of all users by 23%.
Solar PV Frequency Control in the U.S. EI and ERCOT Interconnections -- Case Studies and Recommendations
Authors: Shutang You
Date: 2020-10-21 00:00:00

This paper studied the solar PV frequency control in the U.S. Eastern Interconnection (EI) and Texas Interconnection (ERCOT) systems. The studied frequency control approaches include droop frequency control, inertia control, and droop-inertia-combined frequency control. The control effects of different frequency controls of PV in the EI and ERCOT are studied using actual high PV penetration interconnection grid models to provide suggestions to the future revision of future PV frequency control standards.
Distributed Node-Specific Block-Diagonal LCMV Beamforming in Wireless Acoustic Sensor Networks
Date: 2020-10-26 00:00:00

This paper derives the analytical solution of a novel distributed
node-specific block-diagonal linearly constrained minimum variance beamformer
from the centralized linearly constrained minimum variance (LCMV) beamformer
when considering that the noise covariance matrix is block-diagonal. To further
reduce the computational complexity of the proposed beamformer, the
ShermanMorrison-Woodbury formula is introduced to compute the inversion of
noise sample covariance matrix. By doing so, the exchanged signals can be
computed with lower dimensions between nodes, where the optimal LCMV beamformer
is still available at each node as if each node is to transmit its all raw
sensor signal observations. The proposed beamformer is fully distributable
without imposing restrictions on the underlying network topology or scaling
computational complexity, i.e., there is no increase in the per-node complexity
when new nodes are added to the networks. Compared with state-of-the-art
distributed node-specific algorithms that are often time-recursive, the
proposed beamformer exactly solves the LCMV beamformer optimally frame by
frame, which has much lower computational complexity and is more robust to
acoustic transfer function estimation error and voice activity detector error.
Numerous experimental results are presented to validate the effectiveness of
the proposed beamformer.
What can be learned from satisfaction assessments?
Authors: Naftali Cohen, Simran Lamba, Prashant Reddy
Date: 2020-10-26 00:00:00
Tasks: Active Learning, Survey

Companies survey their customers to measure their satisfaction levels with the company and its services. The received responses are crucial as they allow companies to assess their respective performances and find ways to make needed improvements. This study focuses on the non-systematic bias that arises when customers assign numerical values in ordinal surveys. Using real customer satisfaction survey data of a large retail bank, we show that the common practice of segmenting ordinal survey responses into uneven segments limit the value that can be extracted from the data. We then show that it is possible to assess the magnitude of the irreducible error under simple assumptions, even in real surveys, and place the achievable modeling goal in perspective. We finish the study by suggesting that a thoughtful survey design, which uses either a careful binning strategy or proper calibration, can reduce the compounding non-systematic error even in elaborated ordinal surveys. A possible application of the calibration method we propose is efficiently conducting targeted surveys using active learning.
Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition
Date: 2020-10-26 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition, Speech Synthesis, text-to-speech, Text to Speech

Neural text-to-speech (TTS) approaches generally require a huge number of
high quality speech data, which makes it difficult to obtain such a dataset
with extra emotion labels. In this paper, we propose a novel approach for
emotional TTS synthesis on a TTS dataset without emotion labels. Specifically,
our proposed method consists of a cross-domain speech emotion recognition (SER)
model and an emotional TTS model. Firstly, we train the cross-domain SER model
on both SER and TTS datasets. Then, we use emotion labels on the TTS dataset
predicted by the trained SER model to build an auxiliary SER task and jointly
train it with the TTS model. Experimental results show that our proposed method
can generate speech with the specified emotional expressiveness and nearly no
hindering on the speech quality.
Robust Optimization Approaches for Portfolio Selection: A Computational and Comparative Analysis
Authors: A. Georgantas
Date: 2020-10-26 00:00:00
Tasks: Management, Portfolio Optimization

The field of portfolio selection is an active research topic, which combines elements and methodologies from various fields, such as optimization, decision analysis, risk management, data science, forecasting, etc. The modeling and treatment of deep uncertainties for future asset returns is a major issue for the success of analytical portfolio selection models. Recently, robust optimization (RO) models have attracted a lot of interest in this area. RO provides a computationally tractable framework for portfolio optimization based on relatively general assumptions on the probability distributions of the uncertain risk parameters. Thus, RO extends the framework of traditional linear and non-linear models (e.g., the well-known mean-variance model), incorporating uncertainty through a formal and analytical approach into the modeling process. Robust counterparts of existing models can be considered as worst-case re-formulations as far as deviations of the uncertain parameters from their nominal values are concerned. Although several RO models have been proposed in the literature focusing on various risk measures and different types of uncertainty sets about asset returns, analytical empirical assessments of their performance have not been performed in a comprehensive manner. The objective of this study is to fill in this gap in the literature. More specifically, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005-2016.
Options Pricing for Two Stocks by Black Sholes Time Fractional Order NonLinear Partial Differential Equation
Authors: Kamran Zakaria, Saeed Hafeez
Date: 2020-10-26 00:00:00
Tasks: Form

The BS equations with fractional order two asset price models give a better prediction of options pricing in the monetary market. In this paper, the changed form of BS-condition with two asset price models dependent on the Liovelle-Caputo derivative for good predictions of options prices are utilized. The analytical solution is demonstrated in form of convergent infinite series and obtained by the properties of Samudu Transform.
Identifiability and interpretability of hybrid, gray-box models
Date: 2020-10-26 00:00:00

Model identifiability concerns the uniqueness of uncertain model parameters
to be estimated from available process data and is often thought of as a
prerequisite for physical interpretability of the parameters in a mechanistic
model. Nevertheless, model identifiability is rarely accomplished in practice
due to both stochastic and deterministic uncertainties: lack of sufficient
variability in the data, noisy measurements, erroneous model structure,
stochasticity and locality of the optimization algorithm, initial values, and
even the parameter of the true underlying process. For gray-box, hybrid models,
model identifiability is even less obtainable due to a higher number of
parameters compared to mechanistic models. We illustrate by means of an
industrial case study, modeling of a production choke valve in a petroleum
well, how physical interpretation of the physical model parameters may be
preserved for non-identifiable models by utilizing parameter regularization in
the estimation problem. Further, we design and train interpretable gray-box
choke models on historical process data from six wells on the petroleum asset
Edvard Grieg. Comparing the prediction performance of the gray-box model to a
mechanistic model for the median well shows a significant increase in
prediction accuracy.