pwc-archive/papers-with-abstracts huggingface dataset:
Full Attitude Intelligent Controller Design of a Heliquad under Complete Failure of an Actuator
Authors: Eeshan Kulkarni, Suresh Sundaram
Date: 2020-11-15 00:00:00
Tasks: Position

In this paper, we design a reliable Heliquad and develop an intelligent controller to handle one actuators complete failure. Heliquad is a multi-copter similar to Quadcopter, with four actuators diagonally symmetric from the center. Each actuator has two control inputs; the first input changes the propeller blades collective pitch (also called variable pitch), and the other input changes the rotation speed. For reliable operation and high torque characteristic requirement for yaw control, a cambered airfoil is used to design propeller blades. A neural network-based control allocation is designed to provide complete control authority even under a complete loss of one actuator. Nonlinear quaternion based outer loop position control, with proportional-derivative inner loop for attitude control and neural network-based control allocation is used in controller design. The proposed controller and Heliquad designs performance is evaluated using a software-in-loop simulation to track the position reference command under failure. The results clearly indicate that the Heliquad with an intelligent controller provides necessary tracking performance even under a complete loss of one actuator.
Automatic dysarthric speech detection exploiting pairwise distance-based convolutional neural networks
Authors: P. Janbakhshi, I. Kodrasi, H. Bourlard
Date: 2020-11-15 00:00:00
Tasks: Management

Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we propose a novel automatic dysarthric speech detection approach based on analyses of pairwise distance matrices using convolutional neural networks (CNNs). We represent utterances through articulatory posteriors and consider pairs of phonetically-balanced representations, with one representation from a healthy speaker (i.e., the reference representation) and the other representation from the test speaker (i.e., test representation). Given such pairs of reference and test representations, features are first extracted using a feature extraction front-end, a frame-level distance matrix is computed, and the obtained distance matrix is considered as an image by a CNN-based binary classifier. The feature extraction, distance matrix computation, and CNN-based classifier are jointly optimized in an end-to-end framework. Experimental results on two databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed approach yields a high dysarthric speech detection performance, outperforming other CNN-based baseline approaches.
Multi-task single channel speech enhancement using speech presence probability as a secondary task training target
Authors: L. Wang, J. Zhu, I. Kodrasi
Date: 2020-11-15 00:00:00
Tasks: Multi-Task Learning, Speech Enhancement

To cope with reverberation and noise in single channel acoustic scenarios, typical supervised deep neural network~(DNN)-based techniques learn a mapping from reverberant and noisy input features to a user-defined target. Commonly used targets are the desired signal magnitude, a time-frequency mask such as the Wiener gain, or the interference power spectral density and signal-to-interference ratio that can be used to compute a time-frequency mask. In this paper, we propose to incorporate multi-task learning in such DNN-based enhancement techniques by using speech presence probability (SPP) estimation as a secondary task assisting the target estimation in the main task. The advantage of multi-task learning lies in sharing domain-specific information between the two tasks (i.e., target and SPP estimation) and learning more generalizable and robust representations. To simultaneously learn both tasks, we propose to use the adaptive weighting method of losses derived from the homoscedastic uncertainty of tasks. Simulation results show that the dereverberation and noise reduction performance of a single-task DNN trained to directly estimate the Wiener gain is higher than the performance of single-task DNNs trained to estimate the desired signal magnitude, the interference power spectral density, or the signal-to-interference ratio. Incorporating the proposed multi-task learning scheme to jointly estimate the Wiener gain and the SPP increases the dereverberation and noise reduction further.
Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks
Authors: Quang Nhat Le, Van-Dinh Nguyen, Nam-Phong Nguyen, Symeon Chatzinotas, Octavia A. Dobre, Ruiqin Zhao
Date: 2020-11-15 00:00:00
Tasks: Clustering, Fairness

The superior spectral efficiency (SE) and user fairness feature of non-orthogonal multiple access (NOMA) systems are achieved by exploiting user clustering (UC) more efficiently. However, a random UC certainly results in a suboptimal solution while an exhaustive search method comes at the cost of high complexity, especially for systems of medium-to-large size. To address this problem, we develop two efficient unsupervised machine learning (ML) based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system. Using full-pilot zero-forcing at access points, we derive the sum SE in closed-form expression taking into account the impact of intra-cluster pilot contamination, inter-cluster interference, and imperfect successive interference cancellation. To comprehensively assess the system performance, we formulate the sum SE optimization problem, and then develop a simple yet efficient iterative algorithm for its solution. In addition, the performance of collocated massive MIMO-NOMA (COmMIMO-NOMA) system is also characterized. Numerical results are provided to show the superior performance of the proposed UC algorithms compared to other baseline schemes. The effectiveness of applying NOMA in CFmMIMO and COmMIMO systems is also validated.
SOBMOR: Structured Optimization-Based Model Order Reduction
Authors: Paul Schwerdtner, Matthias Voigt
Date: 2020-11-15 00:00:00
Tasks: model

Model order reduction (MOR) methods that are designed to preserve structural features of a given full order model (FOM) often suffer from a lower accuracy when compared to their non-structure-preserving counterparts. In this paper, we present a framework for structure-preserving MOR, which allows to compute structured reduced order models (ROMs) with a much higher accuracy. The framework is based on parameter optimization, i.e., the elements of the system matrices of the ROM are iteratively varied to minimize an objective functional that measures the difference between the FOM and the ROM. The structural constraints can be encoded in the parametrization of the ROM. The method only depends on frequency response data and can thus be applied to a wide range of dynamical systems. We illustrate the effectiveness of our method on a port-Hamiltonian and on a symmetric second-order system in a comparison with other structure-preserving MOR algorithms.
Determining the atomic charge of calcium ion requires the information of its coordination geometry in an EF-hand motif
Authors: Pengzhi Zhang, Jaebeom Han, Piotr Cieplak, Margaret. S. Cheung
Date: 2020-11-15 00:00:00

It is challenging to parameterize the force field for calcium ions (Ca2+) in calcium-binding proteins because of their unique coordination chemistry that involves the surrounding atoms required for stability. In this work, we observed wide variation in Ca2+ binding loop conformations of the Ca2+-binding protein calmodulin (CaM), which adopts the most populated ternary structures determined from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on all twelve amino acids in the loop that coordinate Ca2+ in aqueous solution. Ca2+ charges were derived by fitting to the electrostatic potential (ESP) in the context of a classical or polarizable force field (PFF). We discovered that the atomic radius of Ca2+ in conventional force fields is too large for the QM calculation to capture the variation in the coordination geometry of Ca2+ in its ionic form, leading to unphysical charges. Specifically, we found that the fitted atomic charges of Ca2+ in the context of PFF depend on the coordinating geometry of electronegative atoms from the amino acids in the loop. Although nearby water molecules do not influence the atomic charge of Ca2+, they are crucial for compensating for the coordination of Ca2+ due to the conformational flexibility in the EF-hand loop. Our method advances the development of force fields for metal ions and protein binding sites in dynamic environments.
Adaptive Step Size Incremental Conductance Based Maximum Power Point Tracking (MPPT)
Date: 2020-11-15 00:00:00
Tasks: Point Tracking

Extracting maximum power available from photovoltaic arrays requires the
system operating at the maximum power point (MPP). Therefore, finding the MPP
is necessary for efficient operation of PV arrays. The MPP changes with
multiple environmental factors, mainly temperature and irradiance.
Traditionally, the incremental conductance technique with fixed step size was
used to find the MPP, which suffers from a trade-off between speed of
convergence and accuracy. In this work, we propose an incremental conductance
maximum power point tracking (MPPT) algorithm with a variable step size, which
adaptively changes the step size after each iteration based on how far away the
current operating point is from a new MPP. This mitigates the aforementioned
trade-off drastically by achieving faster convergence speed without the loss of
accuracy. A series of simulations involving variations in temperature and
irradiance were performed using MATLAB, and the speed of convergence and
accuracy were compared with the traditional IC technique.
Price formation and optimal trading in intraday electricity markets with a major player
Authors: Olivier Féron, Peter Tankov, Laura Tinsi
Date: 2020-11-15 00:00:00

We study price formation in intraday electricity markets in the presence of intermittent renewable generation. We consider the setting where a major producer may interact strategically with a large number of small producers. Using stochastic control theory we identify the optimal strategies of agents with market impact and exhibit the Nash equilibrium in closed form in the asymptotic framework of mean field games with a major player. This is a companion paper to [F\'eron, Tankov, and Tinsi, Price formation and optimal trading in intraday electricity markets, arXiv:2009.04786, 2020], where a similar model is developed in the setting of identical agents.
Inferring COPD Severity from Tidal Breathing
Authors: Kofi Odame, Graham Atkins, Maria Nyamukuru, Katherine Fearon
Date: 2020-11-16 00:00:00

Objective: To develop an algorithm that can infer the severity level of a COPD patient's airflow limitation from tidal breathing data that is collected by a wearable device. Methods: Data was collected from 25 single visit adult volunteers with a confirmed or suspected diagnosis of chronic obstructive pulmonary disease (COPD). The ground truth airflow limitation severity of each subject was determined by applying the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging criteria to the subject's spirometry results. Spirometry was performed in a pulmonary function test laboratory under the supervision of trained clinical staff. Separately, the subjects' respiratory signal was measured during quiet breathing, and a classification model was built to infer the subjects' level of airflow limitation from the measured respiratory signal. The classification model was evaluated against the ground truth using leave-one-out testing. Results: Severity of airway obstruction was classified as either mild/moderate or severe/very severe with an accuracy of 96.4%. Conclusion: Tidal breathing parameters that are measured with a wearable device can be used to distinguish between different levels of airflow limitation in COPD patients.
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech
Authors: Jianwei Yu, Shi-Xiong Zhang, Bo Wu, Shansong Liu, Shoukang Hu, Mengzhe Geng, Xunying Liu, Helen Meng, Dong Yu
Date: 2020-11-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Separation

Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades. However, recognition of overlapped speech remains a highly challenging task to date. To this end, multi-channel microphone array data are widely used in current ASR systems. Motivated by the invariance of visual modality to acoustic signal corruption and the additional cues they provide to separate the target speaker from the interfering sound sources, this paper presents an audio-visual multi-channel based recognition system for overlapped speech. It benefits from a tight integration between a speech separation front-end and recognition back-end, both of which incorporate additional video input. A series of audio-visual multi-channel speech separation front-end components based on TF masking, Filter&Sum and mask-based MVDR neural channel integration approaches are developed. To reduce the error cost mismatch between the separation and recognition components, the entire system is jointly fine-tuned using a multi-task criterion interpolation of the scale-invariant signal to noise ratio (Si-SNR) with either the connectionist temporal classification (CTC), or lattice-free maximum mutual information (LF-MMI) loss function. Experiments suggest that: the proposed audio-visual multi-channel recognition system outperforms the baseline audio-only multi-channel ASR system by up to 8.04% (31.68% relative) and 22.86% (58.51% relative) absolute WER reduction on overlapped speech constructed using either simulation or replaying of the LRS2 dataset respectively. Consistent performance improvements are also obtained using the proposed audio-visual multi-channel recognition system when using occluded video input with the face region randomly covered up to 60%.
Modelling and control of Mendelian and maternal inheritance for biological control of dengue vectors
Authors: Pastor Pérez Estigarribia, Pierre-Alexandre Bliman, Christian Schaerer
Date: 2020-11-16 00:00:00

Mosquitoes are vectors of viral diseases with epidemic potential in many regions of the world, and in absence of vaccines or therapies, their control is the main alternative. Chemical control through insecticides has been one of the conventional strategies, but induces insecticide resistance, which may affect other insects and cause ecological damage. Biological control, through the release of mosquitoes infected by the maternally inherited bacterium Wolbachia, which inhibits their vector competence, has been proposed as an alternative. The effects of both techniques may be intermingled in practice: prior insecticide spraying may debilitate wild population, so facilitating subsequent invasion by the bacterium; but the latter may also be hindered by the release of susceptible mosquitoes in an environment where the wild population became resistant, as a result of preexisting undesired exposition to insecticide. To tackle such situations, we propose here a unifying model allowing to account for the cross effects of both control techniques, and based on the latter, design release strategies able to infect a wild population. The latter are feedback laws, whose stabilizing properties are studied.
Block-Online Guided Source Separation
Authors: Shota Horiguchi, Yusuke Fujita, Kenji Nagamatsu
Date: 2020-11-16 00:00:00
Tasks: Speech Separation

We propose a block-online algorithm of guided source separation (GSS). GSS is a speech separation method that uses diarization information to update parameters of the generative model of observation signals. Previous studies have shown that GSS performs well in multi-talker scenarios. However, it requires a large amount of calculation time, which is an obstacle to the deployment of online applications. It is also a problem that the offline GSS is an utterance-wise algorithm so that it produces latency according to the length of the utterance. With the proposed algorithm, block-wise input samples and corresponding time annotations are concatenated with those in the preceding context and used to update the parameters. Using the context enables the algorithm to estimate time-frequency masks accurately only from one iteration of optimization for each block, and its latency does not depend on the utterance length but predetermined block length. It also reduces calculation cost by updating only the parameters of active speakers in each block and its context. Evaluation on the CHiME-6 corpus and a meeting corpus showed that the proposed algorithm achieved almost the same performance as the conventional offline GSS algorithm but with 32x faster calculation, which is sufficient for real-time applications.
Double blind vs. open review: an evolutionary game logit-simulating the behavior of authors and reviewers
Authors: Mantas Radzvilas, Francesco De Pretis, William Peden, Daniele Tortoli, Barbara Osimani
Date: 2020-11-16 00:00:00

Despite the tremendous successes of science in providing knowledge and technologies, the Replication Crisis has highlighted that scientific institutions have much room for improvement. Peer-review is one target of criticism and suggested reforms. However, despite numerous controversies peer review systems, plus the obvious complexity of the incentives affecting the decisions of authors and reviewers, there is very little systematic and strategic analysis of peer-review systems. In this paper, we begin to address this feature of the peer-review literature by applying the tools of game theory. We use simulations to develop an evolutionary model based around a game played by authors and reviewers, before exploring some of its tendencies. In particular, we examine the relative impact of double-blind peer-review and open review on incentivising reviewer effort under a variety of parameters. We also compare (a) the impact of one review system versus another with (b) other alterations, such as higher costs of reviewing. We find that is no reliable difference between peer-review systems in our model. Furthermore, under some conditions, higher payoffs for good reviewing can lead to less (rather than more) author effort under open review. Finally, compared to the other parameters that we vary, it is the exogenous utility of author effort that makes an important and reliable difference in our model, which raises the possibility that peer-review might not be an important target for institutional reforms.
Implicit Incentives for Fund Managers with Partial Information
Authors: Flavio Angelini, Katia Colaneri, Stefano Herzel, Marco Nicolosi
Date: 2020-11-16 00:00:00

We study the optimal asset allocation problem for a fund manager whose compensation depends on the performance of her portfolio with respect to a benchmark. The objective of the manager is to maximise the expected utility of her final wealth. The manager observes the prices but not the values of the market price of risk that drives the expected returns. The estimates of the market price of risk get more precise as more observations are available. We formulate the problem as an optimization under partial information. The particular structure of the incentives makes the objective function not concave. We solve the problem via the martingale method and, with a concavification procedure, we obtain the optimal wealth and the investment strategy. A numerical example shows the effect of learning on the optimal strategy.
Forecasting Probability of Default for Consumer Loan Management with Gaussian Mixture Models
Date: 2020-11-16 00:00:00
Tasks: Binary Classification, Decision Making, Management

Credit scoring is an essential tool used by global financial institutions and
credit lenders for financial decision making. In this paper, we introduce a new
method based on Gaussian Mixture Model (GMM) to forecast the probability of
default for individual loan applicants. Clustering similar customers with each
other, our model associates a probability of being healthy to each group. In
addition, our GMM-based model probabilistically associates individual samples
to clusters, and then estimates the probability of default for each individual
based on how it relates to GMM clusters. We provide applications for risk
managers and decision makers in banks and non-bank financial institutions to
maximize profit and mitigate the expected loss by giving loans to those who
have a probability of default below a decision threshold. Our model has a
number of advantages. First, it gives a probabilistic view of credit standing
for each individual applicant instead of a binary classification and therefore
provides more information for financial decision makers. Second, the expected
loss on the train set calculated by our GMM-based default probabilities is very
close to the actual loss, and third, our approach is computationally efficient.
Species exclusion and coexistence in a noisy voter model with a competition-colonization tradeoff
Date: 2020-11-16 00:00:00

We introduce an asymmetric noisy voter model to study the joint effect of immigration and a competition-dispersal tradeoff in the dynamics of two species competing for space in regular lattices. Individuals of one species can invade a nearest-neighbor site in the lattice, while individuals of the other species are able to invade sites at any distance but are less competitive locally, i.e., they establish with a probability $g \le 1$. The model also accounts for immigration, modeled as an external noise that may spontaneously replace an individual at a lattice site by another individual of the other species. This combination of mechanisms gives rise to a rich variety of outcomes for species competition, including exclusion of either species, mono-stable coexistence of both species at different population proportions, and bi-stable coexistence with proportions of populations that depend on the initial condition. Remarkably, in the bi-stable phase, the system undergoes a discontinuous transition as the intensity of immigration overcomes a threshold, leading to a half loop dynamics associated to a cusp catastrophe, which causes the irreversible loss of the species with the shortest dispersal range.