pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Feedforward Neural Network Control with an Optimized Hidden Node Distribution
Authors: Qiong Liu, Dongyu Li, Shuzhi Sam Ge, Zhong Ouyang
Date: 2020-05-23 00:00:00
Tasks: Learning Theory

Composite adaptive radial basis function neural network (RBFNN) control with a lattice distribution of hidden nodes has three inherent demerits: 1) the approximation domain of adaptive RBFNNs is difficult to be determined a priori; 2) only a partial persistence of excitation (PE) condition can be guaranteed; and 3) in general, the required number of hidden nodes of RBFNNs is enormous. This paper proposes an adaptive feedforward RBFNN controller with an optimized distribution of hidden nodes to suitably address the above demerits. The distribution of the hidden nodes calculated by a K-means algorithm is optimally distributed along the desired state trajectory. The adaptive RBFNN satisfies the PE condition for the periodic reference trajectory. The weights of all hidden nodes will converge to the optimal values. This proposed method considerably reduces the number of hidden nodes, while achieving a better approximation ability. The proposed control scheme shares a similar rationality to that of the classical PID control in two special cases, which can thus be seen as an enhanced PID scheme with a better approximation ability. For the controller implemented by digital devices,the proposed method, for a manipulator with unknown dynamics, potentially achieves better control performance than model-based schemes with accurate dynamics.Simulation results demonstrate the effectiveness of the proposed scheme. This result provides a deeper insight into the coordination of the adaptive neural network control and the deterministic learning theory.
Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks
Date: 2020-05-23 00:00:00

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.
Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks
Date: 2020-08-20 00:00:00
Proceeding: Interspeech 2020 5
Tasks: Speech Enhancement

Recently, deep neural networks (DNNs) have been successfully used for speech
enhancement, and DNN-based speech enhancement is becoming an attractive
research area. While time-frequency masking based on the short-time Fourier
transform (STFT) has been widely used for DNN-based speech enhancement over the
last years, time domain methods such as the time-domain audio separation
network (TasNet) have also been proposed. The most suitable method depends on
the scale of the dataset and the type of task. In this paper, we explore the
best speech enhancement algorithm on two different datasets. We propose a
STFT-based method and a loss function using problem-agnostic speech encoder
(PASE) features to improve subjective quality for the smaller dataset. Our
proposed methods are effective on the Voice Bank + DEMAND dataset and compare
favorably to other state-of-the-art methods. We also implement a low-latency
version of TasNet, which we submitted to the DNS Challenge and made public by
open-sourcing it. Our model achieves excellent performance on the DNS Challenge
dataset.
Efficient Integration of Multi-channel Information for Speaker-independent Speech Separation
Date: 2020-08-11 00:00:00
Tasks: Deep Clustering, Open-Ended Question Answering, Speech Separation, Transfer Learning

Although deep-learning-based methods have markedly improved the performance
of speech separation over the past few years, it remains an open question how
to integrate multi-channel signals for speech separation. We propose two
methods, namely, early-fusion and late-fusion methods, to integrate
multi-channel information based on the time-domain audio separation network,
which has been proven effective in single-channel speech separation. We also
propose channel-sequential-transfer learning, which is a transfer learning
framework that applies the parameters trained for a lower-channel network as
the initial values of a higher-channel network. For fair comparison, we
evaluated our proposed methods using a spatialized version of the wsj0-2mix
dataset, which is open-sourced. It was found that our proposed methods can
outperform multi-channel deep clustering and improve the performance
proportionally to the number of microphones. It was also proven that the
performance of the late-fusion method is consistently higher than that of the
single-channel method regardless of the angle difference between speakers.
Proofreading through spatial gradients
Authors: Vahe Galstyan, Kabir Husain, Fangzhou Xiao, Arvind Murugan, Rob Phillips
Date: 2020-05-23 00:00:00
Tasks: Specificity

Key enzymatic processes in biology use the nonequilibrium error correction mechanism called kinetic proofreading to enhance their specificity. Kinetic proofreading typically requires several dedicated structural features in the enzyme, such as a nucleotide hydrolysis site and multiple enzyme-substrate conformations that delay product formation. Such requirements limit the applicability and the adaptability of traditional proofreading schemes. Here, we explore an alternative conceptual mechanism of error correction that achieves delays between substrate binding and subsequent product formation by having these events occur at distinct physical locations. The time taken by the enzyme-substrate complex to diffuse from one location to another is leveraged to discard wrong substrates. This mechanism does not require dedicated structural elements on the enzyme, making it easier to overlook in experiments but also making proofreading tunable on the fly. We discuss how tuning the length scales of enzyme or substrate concentration gradients changes the fidelity, speed and energy dissipation, and quantify the performance limitations imposed by realistic diffusion and reaction rates in the cell. Our work broadens the applicability of kinetic proofreading, and sets the stage for the study of spatial gradients as a possible route to specificity.
Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics
Authors: Khuram Naveed, Naveed Ur Rehman
Date: 2020-05-23 00:00:00
Tasks: Denoising

A multivariate signal denoising method is proposed which employs a novel multivariate goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete wavelet transform (DWT). In the proposed multivariate GoF test, we first utilize squared Mahalanobis distance (MD) measure to transform input multivariate data residing in M-dimensional space $\mathcal{R}^M$ to a single-dimensional space of positive real numbers $\mathcal{R}_+$, i.e., $\mathcal{R}^M \rightarrow \mathcal{R}_+$, where $M > 1$. Owing to the properties of the MD measure, the transformed data in $\mathcal{R}_+$ follows a distinct distribution. That enables us to apply the GoF test using statistic based on empirical distribution function (EDF) on the resulting data in order to define a test for multivariate normality. We further propose to apply the above test locally on multiple input data scales obtained from discrete wavelet transform, resulting in a multivariate signal denoising framework. Within the proposed method, the reference cumulative distribution function (CDF) is defined as a quadratic transformation of multivariate Gaussian random process. Consequently, the proposed method checks whether a set of DWT coefficients belong to multivariate reference distribution or not; the coefficients belonging to the reference distribution are discarded. The effectiveness of our proposed method is demonstrated by performing extensive simulations on both synthetic and real world datasets.
Leader-follower synchronization and ISS analysis for a network of boundary-controlled wave PDEs
Date: 2020-05-24 00:00:00

A network of agents, modeled by a class of wave PDEs, is under investigation.
One agent in the network plays the role of a leader, and all the remaining
"follower" agents are required to asymptotically track the state of the leader.
Only boundary sensing of the agent's state is assumed, and each agent is
controlled through the boundary by Neumann-type actuation. A linear interaction
protocol is proposed and analyzed by means of a Lyapunov-based approach. A
simple set of tuning rules, guaranteeing the exponential achievement of
synchronization, is obtained. In addition, an exponential ISS relation,
characterizing the effects on the tracking accuracy of boundary and in-domain
disturbances, is derived for the closed loop system.
Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS
Authors: Shaokang Hu, Zhiqiang Wei, Yuanxin Cai, Derrick Wing Kwan Ng, Jinhong Yuan
Date: 2020-05-24 00:00:00

This paper investigates multiuser multi-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can harvest power from the received signals. We study the joint design of the beamformer at an access point (AP) and the phase shifts and the power harvesting schedule at an IRS for maximizing the system sum-rate. The design is formulated as a non-convex optimization problem taking into account the capability of IRS elements to harvest wireless power for realizing self-sustainability. Subsequently, we propose a computationally-efficient alternating algorithm to obtain a suboptimal solution to the design problem. Our simulation results unveil that: 1) there is a non-trivial trade-off between the system sum-rate and self-sustainability in IRS-assisted systems; 2) the performance gain achieved by the proposed scheme is improved with an increasing number of IRS elements; 3) an IRS equipped with small bit-resolution discrete phase shifters is sufficient to achieve a considerable system sum-rate of an ideal case with continuous phase shifts.
Evaluation of Banking Sectors Development in Bangladesh in light of Financial Reform
Authors: Nusrat Jahan, K. M. Golam Muhiuddin
Date: 2020-05-24 00:00:00

Historically, the performance of the banking sector has been weak, characterized by weak asset quality, inadequate provisioning, and negative capitalization of state-owned banks. To overcome these problems, the initial phase of banking reform (1980-1990) focused on the promotion of private ownership and denationalization of nationalized commercial banks (SCBs). During the second phase of reform, Financial Sector Reform Project (FSRP) of World Bank was launched in 1990 with the focus on gradual deregulations of the interest rate structure, providing market-oriented incentives for priority sector lending and improvement in the debt recovery environment. Moreover, a large number of private commercial banks were granted licenses during the second phase of reforms. Bangladesh Bank adopted Basel-I norms in 1996 and Basel-II during 2010. Moreover, the Central Bank Strengthening Project initiated in 2003 focused on effective regulatory and supervisory system, particularly strengthening the legal framework of banking sector. This study evaluates how successfully the banking sector of Bangladesh has evolved over the past decades in light of financial reform measures undertaken to strengthen this sector.
Deep Learning-based Modulation Detection for NOMA Systems
Authors: Wenwu Xie, Jian Xiao, Jinxia Yang, Xin Peng, Chao Yu, Peng Zhu
Date: 2020-05-24 00:00:00
Tasks: Deep Learning, Denoising

Since the signal with strong power should be demodulated first for successive interference cancellation (SIC) demodulation in non-orthogonal multiple access (NOMA) systems, the base station (BS) should inform the near user terminal (UT), which has allocated higher power, of modulation mode of the far user terminal. To avoid unnecessary signaling overhead in this process, a blind detection algorithm of NOMA signal modulation mode is designed in this paper. Taking the joint constellation density diagrams of NOMA signal as the detection features, deep residual network is built for classification, so as to detect the modulation mode of NOMA signal. In view of the fact that the joint constellation diagrams are easily polluted by high intensity noise and lose their real distribution pattern, the wavelet denoising method is adopted to improve the quality of constellations. The simulation results represent that the proposed algorithm can achieve satisfactory detection accuracy in NOMA systems. In addition, the factors affecting the recognition performance are also verified and analyzed.
MIMO Speech Compression and Enhancement Based on Convolutional Denoising Autoencoder
Authors: You-Jin Li, Syu-Siang Wang, Yu Tsao, Borching Su
Date: 2020-05-24 00:00:00
Tasks: Denoising

For speech-related applications in IoT environments, identifying effective methods to handle interference noises and compress the amount of data in transmissions is essential to achieve high-quality services. In this study, we propose a novel multi-input multi-output speech compression and enhancement (MIMO-SCE) system based on a convolutional denoising autoencoder (CDAE) model to simultaneously improve speech quality and reduce the dimensions of transmission data. Compared with conventional single-channel and multi-input single-output systems, MIMO systems can be employed in applications that handle multiple acoustic signals need to be handled. We investigated two CDAE models, a fully convolutional network (FCN) and a Sinc FCN, as the core models in MIMO systems. The experimental results confirm that the proposed MIMO-SCE framework effectively improves speech quality and intelligibility while reducing the amount of recording data by a factor of 7 for transmission.
Good speciation and endogenous business cycles in a constraint satisfaction macroeconomic model
Authors: Dhruv Sharma, Jean-Philippe Bouchaud, Marco Tarzia, Francesco Zamponi
Date: 2020-05-24 00:00:00

We introduce a prototype agent-based model of the macroeconomy, with budgetary constraints at its core. The model is related to a class of constraint satisfaction problems (CSPs), which has been thoroughly investigated in computer science. The CSP paradigm allows us to propose an alternative price-setting mechanism: given agents' preferences and budgets, what set of prices satisfies the maximum number of agents? Such an approach permits the coupling of production and output within the economy to the allowed level of debt in a simplified framework. Within our model, we identify three different regimes upon varying the amount of debt that each agent can accumulate before defaulting. In presence of a very loose constraint on debt, endogenous crises leading to waves of synchronized bankruptcies are present. In the opposite regime of very tight debt constraining, the bankruptcy rate is extremely high and the economy remains structure-less. In an intermediate regime, the economy is stable with very low bankruptcy rate and no aggregate-level crises. This third regime displays a rich phenomenology:the system spontaneously and dynamically self-organizes in a set of cheap and expensive goods (i.e. some kind of "speciation"), with switches triggered by random fluctuations and feedback loops. Our analysis confirms the central role that debt levels play in the stability of the economy. More generally, our model shows that constraints at the individual scale can generate highly complex patterns at the aggregate level.
Analytical Channel Model and Link Design Optimization for Ground-to-HAP Free-Space Optical Communication Networks
Authors: Hossein Safi, Akbar Dargahi, Julian Cheng, Majid Safari
Date: 2020-05-24 00:00:00

Integrating high altitude platforms (HAPs) and free-space optical (FSO) communications is a promising solution to establish high data rate aerial links for the next-generation wireless networks. However, practical limitations such as pointing errors and angle-of-arrival (AOA) fluctuations of the optical beam due to the orientation deviations of hovering HAPs make it challenging to implement HAP-based FSO links. For a ground-to-HAP FSO link, tractable, closed-form statistical channel models are derived in this paper to simplify the optimal design of such systems. The proposed models include the combined effects of atmospheric turbulence regimes (i.e., log-normal and gamma-gamma), pointing error induced geometrical loss, pointing jitter variance caused by beam wander, detector aperture size, beam-width, and AOA fluctuations of the received optical beam. The analytical expressions are corroborated by performing Monte-Carlo simulations. Furthermore, closed-form expressions for the outage probability of the considered link under different turbulence regimes are derived. Detailed analysis is carried out to optimize the transmitted laser beam and the field-of-view of the receiver for minimizing outage probability under different channel conditions. The obtained analytical results can be applied to finding the optimal parameter values and designing ground-to-HAP FSO links without resorting to time-consuming simulations.
Modeling the effects of information-dependent vaccination behavior on meningitis transmission
Date: 2020-05-24 00:00:00

We propose a mathematical model to investigate the effects of
information-dependent vaccination behavior on meningitis transmission. The
information is represented by means of information index as early proposed in
(d'Onofrio et al., Theor. pop. biol., 2007). We perform a qualitative analysis
based on stability theory, focusing to the global stability of the disease free
equilibrium (DFE) and the related transcritical bifurcation taking place at the
threshold for the DFE. Finally, we assess the role of epidemiological and
information parameters in the model dynamics through numerical simulations. Our
simulations suggests that the impact of the human behavior critically depend on
the average information delay. For example, it can induce recurrent epidemics,
provided that transfer rate from the carrier to the infectious state is over a
threshold. Otherwise, the endemic equilibrium is (at least) locally stable.
Low-dose CT Enhancement Network with a Perceptual Loss Function in the Spatial Frequency and Image Domains
Date: 2020-05-26 00:00:00
Proceeding: MIDL 2019 7
Tasks: Diagnostic

We propose a dual-domain cascade of U-nets (i.e. a "W-net") operating in both
the spatial frequency and image domains to enhance low-dose CT (LDCT) images
without the need for proprietary x-ray projection data. The central slice
theorem motivated the use of the spatial frequency domain in place of the raw
sinogram. Data were obtained from the AAPM Low-dose Grand Challenge. A
combination of Fourier space (F) and/or image domain (I) U-nets and W-nets were
trained with a multi-scale structural similarity and mean absolute error loss
function to denoise filtered back projected (FBP) LDCT images while maintaining
perceptual features important for diagnostic accuracy. Deep learning
enhancements were superior to FBP LDCT images in quantitative and qualitative
performance with the dual-domain W-nets outperforming single-domain U-net
cascades. Our results suggest that spatial frequency learning in conjunction
with image-domain processing can produce superior LDCT enhancement than
image-domain-only networks.
Mechanics of the cellular actin cortex: from signalling to shape change
Authors: Manasi Kelkar, Pierre Bohec, Guillaume Charras
Date: 2020-05-24 00:00:00

The actin cortex is a thin layer of actin, myosin, and actin binding proteins that underlies the membrane of most animal cells. It is highly dynamic and can undergo remodelling on time-scales of tens of seconds thanks to protein turnover and myosin-mediated contractions. The cortex enables cells to resist external mechanical stresses, controls cell shape, and allows cells to exert forces on their neighbours. Thus, its mechanical properties are key to its physiological function. Here, we give an overview of how cortex composition, structure, and dynamics control cortex mechanics and cell shape. We use mitosis as an example to illustrate how global and local regulation of cortex mechanics give rise to a complex series of cell shape changes.
Optimal Measurement Configuration in Computational Diffractive Imaging
Date: 2020-05-25 00:00:00

Diffractive lenses have recently been applied to the domain of multispectral
imaging in the X-ray and UV regimes where they can achieve very high resolution
as compared to reflective and refractive optics. Conventionally, spectral
components are reconstructed by taking measurements at the focal planes.
However, the reconstruction quality can be improved by optimizing the
measurement configuration. In this work, we adapt a sequential backward
selection algorithm to search for a configuration which minimizes expected
reconstruction error. By approximating the forward system as a circular
convolution and making assumptions on the source and noise, we greatly reduce
the complexity of the algorithm. Numerical results show that the configuration
found by the algorithm significantly improves the reconstruction performance
compared to a standard configuration.
Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmuller Map in Retinotopic Mapping
Authors: Yanshuai Tu, Duyan Ta, Zhong-Lin Lu, Yalin Wang
Date: 2020-05-25 00:00:00

The mapping between the visual input on the retina to the cortical surface, i.e., retinotopic mapping, is an important topic in vision science and neuroscience. Human retinotopic mapping can be revealed by analyzing cortex functional magnetic resonance imaging (fMRI) signals when the subject is under specific visual stimuli. Conventional methods process, smooth, and analyze the retinotopic mapping based on the parametrization of the (partial) cortical surface. However, the retinotopic maps generated by this approach frequently contradict neuropsychology results. To address this problem, we propose an integrated approach that parameterizes the cortical surface, such that the parametric coordinates linearly relates the visual coordinate. The proposed method helps the smoothing of noisy retinotopic maps and obtains neurophysiological insights in human vision systems. One key element of the approach is the Error-Tolerant Teichmuller Map, which uniforms the angle distortion and maximizes the alignments to self-contradicting landmarks. We validated our overall approach with synthetic and real retinotopic mapping datasets. The experimental results show the proposed approach is superior in accuracy and compatibility. Although we focus on retinotopic mapping, the proposed framework is general and can be applied to process other human sensory maps.