pwc-archive/papers-with-abstracts huggingface dataset:
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework
Authors: Yizhou Peng, Jicheng Zhang, Haobo Zhang, HaiHua Xu, Hao Huang, Eng Siong Chng
Date: 2020-10-22 00:00:00
Tasks: speech-recognition, Speech Recognition, Transfer Learning

Human can recognize speech, as well as the peculiar accent of the speech simultaneously. However, present state-of-the-art ASR system can rarely do that. In this paper, we propose a multilingual approach to recognizing English speech, and related accent that speaker conveys using DNN-HMM framework. Specifically, we assume different accents of English as different languages. We then merge them together and train a multilingual ASR system. During decoding, we conduct two experiments. One is a monolingual ASR-based decoding, with the accent information embedded at phone level, realizing word-based accent recognition (AR), and the other is a multilingual ASR-based decoding, realizing an approximated utterance-based AR. Experimental results on an 8-accent English speech recognition show both methods can yield WERs close to the conventional ASR systems that completely ignore the accent, as well as desired AR accuracy. Besides, we conduct extensive analysis for the proposed method, such as transfer learning without-domain data exploitation, cross-accent recognition confusion, as well as characteristics of accented-word.
The NTU-AISG Text-to-speech System for Blizzard Challenge 2020
Date: 2020-10-22 00:00:00
Tasks: text-to-speech, Text to Speech

We report our NTU-AISG Text-to-speech (TTS) entry systems for the Blizzard
Challenge 2020 in this paper. There are two TTS tasks in this year's challenge,
one is a Mandarin TTS task, the other is a Shanghai dialect TTS task. We have
participated both. One of the main challenges is to build TTS systems with
low-resource constraints, particularly for the case of Shanghai dialect, of
which about three hours data are available to participants. To overcome the
constraint, we adopt an average-speaker modeling method. That is, we first
employ external Mandarin data to train both End-to-end acoustic model and
WaveNet vocoder, then we use Shanghai dialect to tune the acoustic model and
WaveNet vocoder respectively. Apart from this, we have no Shanghai dialect
lexicon despite syllable transcripts are provided for the training data. Since
we are not sure if similar syllable transcripts are provided for the evaluation
data during the training stage, we use Mandarin lexicon for Shanghai dialect
instead. With the letter, as decomposed from the corresponding Mandarin
syllable, as input, though the naturalness and original speaker similarity of
the synthesized speech are good, subjective evaluation results indicate the
intelligibility of the synthesized speech is deeply undermined for the Shanghai
dialect TTS system.
Conditional Systemic Risk Measures
Authors: Alessandro Doldi, Marco Frittelli
Date: 2020-10-22 00:00:00

We investigate to which extent the relevant features of (static) Systemic Risk Measures can be extended to a conditional setting. After providing a general dual representation result, we analyze in greater detail Conditional Shortfall Systemic Risk Measures. In the particular case of exponential preferences, we provide explicit formulas that also allow us to show a time consistency property. Finally, we provide an interpretation of the allocations associated to Conditional Shortfall Systemic Risk Measures as suitably defined equilibria. Conceptually, the generalization from static to conditional Systemic Risk Measures can be achieved in a natural way, even though the proofs become more technical than in the unconditional framework.
Fault diagnosis for linear heterodirectional hyperbolic ODE-PDE systems using backstepping-based trajectory planning
Authors: Ferdinand Fischer, Joachim Deutscher
Date: 2020-10-22 00:00:00
Tasks: Fault Detection, Fault Diagnosis, Trajectory Planning

This paper is concerned with the fault diagnosis problem for general linear heterodirectional hyperbolic ODE-PDE systems. A systematic solution is presented for additive time-varying actuator, process and sensor faults in the presence of disturbances. The faults and disturbances are represented by the solutions of finite-dimensional signal models, which allow to take a large class of signals into account. For disturbances, that are only bounded, a threshold for secured fault diagnosis is derived. By applying integral transformations to the system an algebraic fault detection equation to detect faults in finite time is obtained. The corresponding integral kernels result from the realization of a finite-time transition between a non-equilibrium initial state and a vanishing final state of a hyperbolic ODE-PDE system. For this new challenging problem, a systematic trajectory planning approach is presented. In particular, this problem is facilitated by mapping the kernel equations into backstepping coordinates and tracing the solution of the transition problem back to a simple trajectory planning. The fault diagnosis for a $4\times 4$ heterodirectional hyperbolic system coupled with a second order ODE demonstrates the results of the paper.
Survival probability and size of lineages in antibody affinity maturation
Authors: Marco Molari, Rémi Monasson, Simona Cocco
Date: 2020-10-22 00:00:00

Affinity Maturation (AM) is the process through which the immune system is able to develop potent antibodies against new pathogens it encounters, and is at the base of the efficacy of vaccines. At its core AM is analogous to a Darwinian evolutionary process, where B-cells mutate and are selected on the base of their affinity for an Antigen (Ag), and Ag availability tunes the selective pressure. In cases when this selective pressure is high the number of B-cells might quickly decrease and the population might risk extinction in what is known as a population bottleneck. Here we study the probability for a B-cell lineage to survive this bottleneck scenario as a function of the progenitor affinity for the Ag. Using recursive relations and probability generating functions we derive expressions for the average extinction time and progeny size for lineages that go extinct. We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and initial population size. Our study suggests the population bottleneck phenomenology might represent a limit case in the space of biologically plausible maturation scenarios, whose characterization could help guide the process of vaccine development.
Extraction of long k-mers using spaced seeds
Authors: Miika Leinonen, Leena Salmela
Date: 2020-10-22 00:00:00

The extraction of k-mers from sequencing reads is an important task in many bioinformatics applications, such as all DNA sequence analysis methods based on de Bruijn graphs. These methods tend to be more accurate when the used k-mers are unique in the analyzed DNA, and thus the use of longer k-mers is preferred. When the read lengths of short read sequencing technologies increase, the error rate will become the determining factor for the largest possible value of k. Here we propose LoMeX which uses spaced seeds to extract long k-mers accurately even in the presence of sequencing errors. Our experiments show that LoMeX can extract long k-mers from current Illumina reads with a higher recall than a standard k-mer counting tool. Furthermore, our experiments on simulated data show that when the read length further increases, the performance of standard k-mer counters declines, whereas LoMeX still extracts long k-mers successfully.
Super-resolution of periodic signals from short sequences of samples
Authors: Marek W. Rupniewski
Date: 2020-10-22 00:00:00
Tasks: Super-Resolution

Reconstruction of undersampled periodic signals of unknown period is an important signal processing operation. It is especially difficult operation when the sequences of samples are short and no information on the inter-sequence time distances is given. For such a case, there exist some algorithms that allow for approximation of the sampled signal. However, these algorithms require either bandlimitedness of the signal, or noiseless samples. In this paper, we propose a novel algorithm which does not require the signal to be bandlimited and it can cope with additive noise in the samples. The algorithm is illustrated and validated with real data.
Symbolic Self-triggered Control of Continuous-time Non-deterministic Systems without Stability Assumptions for 2-LTL Specifications
Date: 2020-10-22 00:00:00

We propose a symbolic self-triggered controller synthesis procedure for
non-deterministic continuous-time nonlinear systems without stability
assumptions. The goal is to compute a controller that satisfies two objectives.
The first objective is represented as a specification in a fragment of LTL,
which we call 2-LTL. The second one is an energy objective, in the sense that
control inputs are issued only when necessary, which saves energy. To this end,
we first quantise the state and input spaces, and then translate the controller
synthesis problem to the computation of a winning strategy in a mean-payoff
parity game. We illustrate the feasibility of our method on the example of a
navigating nonholonomic robot.
A novel convolutional neural network model to remove muscle artifacts from EEG
Authors: Haoming Zhang, Chen Wei, Mingqi Zhao, Haiyan Wu, Quanying Liu
Date: 2020-10-22 00:00:00
Tasks: Denoising, EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts. In recent years, deep learning models have been used for denoising of electroencephalography (EEG) data and provided comparable performance with that of traditional techniques. However, the performance of the existing networks in electromyograph (EMG) artifact removal was limited and suffered from the over-fitting problem. Here we introduce a novel convolutional neural network (CNN) with gradually ascending feature dimensions and downsampling in time series for removing muscle artifacts in EEG data. Compared with other types of convolutional networks, this model largely eliminates the over-fitting and significantly outperforms four benchmark networks in EEGdenoiseNet. Our study suggested that the deep network architecture might help avoid overfitting and better remove EMG artifacts in EEG.
Iterative Decomposition of Joint Chance Constraints in OPF
Date: 2020-10-22 00:00:00

In chance-constrained OPF models, joint chance constraints (JCCs) offer a
stronger guarantee on security compared to single chance constraints (SCCs).
Using Boole's inequality or its improved versions to decompose JCCs into SCCs
is popular, yet the conservativeness introduced is still significant. In this
letter, a non-parametric iterative framework is proposed to achieve the
decomposition of JCCs with negligible conservativeness. An adaptive risk
allocation strategy is also proposed and embedded in the framework. Results on
an IEEE test case show that the conservativeness using the framework is nearly
eliminated, thereby reducing the generation cost considerably.
Using graph theory to compute Laplace operators arising in a model for blood flow in capillary network
Authors: David Terman, Yousef Hannawi
Date: 2020-10-21 00:00:00

Maintaining cerebral blood flow is critical for adequate neuronal function. Previous computational models of brain capillary networks have predicted that heterogeneous cerebral capillary flow patterns result in lower brain tissue partial oxygen pressures. It has been suggested that this may lead to number of diseases such as Alzheimer's disease, acute ischemic stroke, traumatic brain injury and ischemic heart disease. We have previously developed a computational model that was used to describe in detail the effect of flow heterogeneities on tissue oxygen levels. The main result in that paper was that, for a general class of capillary networks, perturbations of segment diameters or conductances always lead to decreased oxygen levels. This result was verified using both numerical simulations and mathematical analysis. However, the analysis depended on a novel conjecture concerning the Laplace operator of functions related to the segment flow rates and how they depend on the conductances. The goal of this paper is to give a mathematically rigorous proof of the conjecture for a general class of networks. The proof depends on determining the number of trees and forests in certain graphs arising from the capillary network.
Pushing The Limit of Type I Codebook For FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach
Authors: Kai Li, Ying Li, Lei Cheng, Qingjiang Shi, Zhi-Quan Luo
Date: 2020-10-22 00:00:00
Tasks: Vocal Bursts Type Prediction

There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems. In particular, two types of codebooks (namely Type I and Type II codebooks) are introduced with different resolution and overhead requirements. Although the Type I codebook based scheme requires lower feedback overhead, its channel state information (CSI) reconstruction and beamforming performance are not as good as those from the Type II codebook based scheme. However, since the Type I codebook based scheme has been widely used in 4G systems for many years, replacing it by the Type II codebook based scheme overnight is too costly to be an option. Therefore, in this paper, using Type I codebook, we leverage advances in cutting plane method to optimize the CSI reconstruction at the base station (BS), in order to close the gap between these two codebook based beamforming schemes. Numerical results based on channel samples from QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) are presented to show the excellent performance of the proposed algorithm in terms of beamforming vector acquisition.
Perceptual Loss based Speech Denoising with an ensemble of Audio Pattern Recognition and Self-Supervised Models
Date: 2020-10-22 00:00:00
Tasks: Denoising, Emotion Classification, Multi-Task Learning, Speech Denoising

Deep learning based speech denoising still suffers from the challenge of
improving perceptual quality of enhanced signals. We introduce a generalized
framework called Perceptual Ensemble Regularization Loss (PERL) built on the
idea of perceptual losses. Perceptual loss discourages distortion to certain
speech properties and we analyze it using six large-scale pre-trained models:
speaker classification, acoustic model, speaker embedding, emotion
classification, and two self-supervised speech encoders (PASE+, wav2vec 2.0).
We first build a strong baseline (w/o PERL) using Conformer Transformer
Networks on the popular enhancement benchmark called VCTK-DEMAND. Using
auxiliary models one at a time, we find acoustic event and self-supervised
model PASE+ to be most effective. Our best model (PERL-AE) only uses acoustic
event model (utilizing AudioSet) to outperform state-of-the-art methods on
major perceptual metrics. To explore if denoising can leverage full framework,
we use all networks but find that our seven-loss formulation suffers from the
challenges of Multi-Task Learning. Finally, we report a critical observation
that state-of-the-art Multi-Task weight learning methods cannot outperform hand
tuning, perhaps due to challenges of domain mismatch and weak complementarity
of losses.
Extracellular Matrix regulates the morphodynamics of 3D migrating cancer cells
Authors: Christopher Z. Eddy, Helena Raposo, Ryan Wong, Bo Sun
Date: 2020-10-22 00:00:00
Tasks: Navigate

Cell shape is linked to cell function. The significance of cell morphodynamics, namely the temporal fluctuation of cell shape, is much less understood. Here we study the morphodynamics of MDA-MB-231 cells in type I collagen extracellular matrix (ECM). We systematically vary ECM physical properties by tuning collagen concentrations, alignment, and gelation temperatures. We find that morphodynamics of 3D migrating cells are externally controlled by ECM mechanics and internally modulated by Rho-signaling. We employ machine learning to classify cell shape into four different morphological phenotypes, each corresponding to a distinct migration mode. As a result, we map cell morphodynamics at mesoscale into the temporal evolution of morphological phenotypes. We characterize the mesoscale dynamics including occurrence probability, dwell time and transition matrix at varying ECM conditions, which demonstrate the complex phenotype landscape and optimal pathways for phenotype transitions. In light of the mesoscale dynamics, we show that 3D cancer cell motility is a hidden Markov process whereby the step size distributions of cell migration are coupled with simultaneous cell morphodynamics. We also show that morphological phenotype transitions facilitate cancer cells to navigate non-uniform ECM such as traversing the interface between matrices of two distinct microstructures. In conclusion, we demonstrate that 3D migrating cancer cells exhibit rich morphodynamics that is regulated by ECM mechanics, Rho-signaling, and is closely related with cell motility. Our results pave the way to the functional understanding and mechanical programming of cell morphodynamics for normal and malignant cells.
A Calculation Model for Estimating Effect of COVID-19 Contact-Confirming Application (COCOA) on Decreasing Infectors
Authors: Yuto Omae, Jun Toyotani, Kazuyuki Hara, Yasuhiro Gon, Hirotaka Takahashi
Date: 2020-10-17 00:00:00

As of 2020, COVID-19 is spreading in the world. In Japan, the Ministry of Health, Labor and Welfare developed COVID-19 Contact-Confirming Application (COCOA). The researches to examine the effect of COCOA are still not sufficient. We develop a mathematical model to examine the effect of COCOA and show examined result.
The basis of easy controllability in Boolean networks
Authors: Enrico Borriello, Bryan C. Daniels
Date: 2020-10-22 00:00:00

Effective control of biological systems can often be achieved through the control of a surprisingly small number of distinct variables. We bring clarity to such results using the formalism of Boolean dynamical networks, analyzing the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics. Analyzing 49 existing biological network models, we find strong numerical evidence that the average number of nodes that must be forced scales logarithmically with the number of original attractors. This suggests that biological networks may be typically easy to control even when the number of interacting components is large. We provide a theoretical explanation of the scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes. We further identify characteristics of dynamics that can invalidate this scaling, and speculate about how this relates more broadly to non-biological systems.
Is Machine Learning Able to Detect and Classify Failure in Piezoresistive Bone Cement Based on Electrical Signals?
Authors: Hamid Ghaednia, Crystal E. Owens, Lily E. Keiderling, Kartik M. Varadarajan, A. John Hart, Joseph H. Schwab, Tyler T. Tallman
Date: 2020-10-23 00:00:00
Tasks: BIG-bench Machine Learning

At an estimated cost of $8 billion annually in the United States, revision surgeries to total joint replacements represent a substantial financial burden to the health care system. Fixation failures, such as implant loosening, wear, and mechanical instability of the poly(methyl methacrylate) (PMMA) cement, which bonds the implant to the bone, are the main causes of long-term implant failure. Early and accurate diagnosis of cement failure is critical for developing novel therapeutic strategies and reducing the high risk of a misjudged revision. Unfortunately, prevailing imaging modalities, notably plain radiographs, struggle to detect the precursors of implant failure and are often interpreted incorrectly. Our prior work has shown that the modification of PMMA bone cement with low concentrations of conductive fillers makes it piezoresistive and therefore self-sensing such that when combined with a conductivity imaging modality, such as electrical impedance tomography (EIT), it is possible to monitor load transfer across the PMMA using cost-effective, physiologically benign, and real-time electrical measurements. Herein, we expand upon these results by integrating machine learning techniques with EIT. We survey different machine learning algorithms and principal component analysis for application to this problem, including neural networks on voltage readings of an EIT phantom for tracking position of a sample, specifying defect location, and classifying defect types. Our results show advantage of neural network with more than 91.9 %, 95.5 %, and 98 % accuracy in interpreting EIT signals for location tracking, specifying defect location, and defect classification respectively. These preliminary results show that the combination of smart materials, EIT, and machine learning may be a powerful tool for diagnosing the origin and evolution of failure in joint replacement.
Estimation of Groundwater Storage Variations in Indus River Basin using GRACE Data
Authors: Yahya Sattar, Zubair Khalid
Date: 2020-10-23 00:00:00
Tasks: Management

The depletion and variations of groundwater storage~(GWS) are of critical importance for sustainable groundwater management. In this work, we use Gravity Recovery and Climate Experiment (GRACE) to estimate variations in the terrestrial water storage~(TWS) and use it in conjunction with the Global Land Data Assimilation System~(GLDAS) data to extract GWS variations over time for Indus river basin~(IRB). We present a data processing framework that processes and combines these data-sets to provide an estimate of GWS changes. We also present the design of a band-limited optimally concentrated window function for spatial localization of the data in the region of interest. We construct the so-called optimal window for the IRB region and use it in our processing framework to analyze the GWS variations from 2005 to 2015. Our analysis reveals the expected seasonal variations in GWS and signifies groundwater depletion on average over the time period. Our proposed processing framework can be used to analyze spatio-temporal variations in TWS and GWS for any region of interest.
Forecasting With Factor-Augmented Quantile Autoregressions: A Model Averaging Approach
Date: 2020-10-23 00:00:00
Tasks: quantile regression

This paper considers forecasts of the growth and inflation distributions of
the United Kingdom with factor-augmented quantile autoregressions under a model
averaging framework. We investigate model combinations across models using
weights that minimise the Akaike Information Criterion (AIC), the Bayesian
Information Criterion (BIC), the Quantile Regression Information Criterion
(QRIC) as well as the leave-one-out cross validation criterion. The unobserved
factors are estimated by principal components of a large panel with N
predictors over T periods under a recursive estimation scheme. We apply the
aforementioned methods to the UK GDP growth and CPI inflation rate. We find
that, on average, for GDP growth, in terms of coverage and final prediction
error, the equal weights or the weights obtained by the AIC and BIC perform
equally well but are outperformed by the QRIC and the Jackknife approach on the
majority of the quantiles of interest. In contrast, the naive QAR(1) model of
inflation outperforms all model averaging methodologies.