pwc-archive/papers-with-abstracts huggingface dataset:
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables
Authors: Xiaoting Wang, Xiaozhe Wang, Hao Sheng, Xi Lin
Date: 2020-10-27 00:00:00
Tasks: Computational Efficiency

The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC). In this paper, a novel data-driven sparse polynomial chaos expansion (DDSPCE) method is proposed for estimating the probabilistic characteristics (e.g., mean, variance, probability distribution) of probabilistic TTC (PTTC). Specifically, the proposed method, requiring no pre-assumed probabilistic distributions of random inputs, exploits data sets directly in estimating the PTTC. Besides, a sparse scheme is integrated to improve the computational efficiency. Numerical studies on the modified IEEE 118-bus system demonstrate that the proposed DDSPCE method can achieve accurate estimation for the probabilistic characteristics of PTTC with a high efficiency. Moreover, numerical results reveal the great significance of incorporating discrete random inputs in PTTC and ATC assessment, which nevertheless was not given sufficient attention.
Modeling and control of malaria dynamics in fish farming regions
Authors: Felipe J. P. Antunes, M. Soledad Aronna, Cláudia T. Codeço
Date: 2020-10-27 00:00:00

In this work we propose a model that represents the relation between fish ponds, the mosquito population and the transmission of malaria. It has been observed that in the Amazonic region of Acre, in the North of Brazil, fish farming is correlated to the transmission of malaria when carried out in artificial ponds that become breeding sites. Evidence has been found indicating that cleaning the vegetation from the edges of the crop tanks helps to control the size of the mosquito population. We use our model to determine the effective contribution of fish farming practices on malaria transmission dynamics. The model consists of a nonlinear system of ordinary differential equations with jumps at the cleaning time, which act as impulsive controls. We study the asymptotic behaviour of the system in function of the intensity and periodicity of the cleaning, and the value of the parameters. In particular, we state sufficient conditions under which the mosquito population is eliminated or persists, and under which the malaria is eliminated or becomes endemic. We prove our conditions by applying results for cooperative systems with concave nonlinearities.
How additive manufacturing can boost the bioactivity of baked functional foods
Authors: Sara M Oliveira, Alice Gruppi, Marta V. Vieira, Gabriela M. Souza, António A. Vicente, José A. C. Teixeira, Pablo Fuciños, Giorgia Spigno, Lorenzo M. Pastrana
Date: 2020-10-27 00:00:00

The antioxidant activity of baked foods is of utmost interest when envisioning enhancing their health benefits. Incorporating functional ingredients is challenging since their bioactivity naturally declines during baking. In this study, 3D food printing and design of experiments are employed to clarify how the antioxidant activity of cookies enriched with encapsulated polyphenols can be maximized. A synergistic effect between encapsulation, time, temperature, number of layers, and infill of the printed cookies was observed on the moisture and antioxidant activity. Four-layer cookies with 30 % infill provided the highest bioactivity and phenolic content if baked for 10 min and at 180 {\deg}C. The bioacitivity and total phenolic content improved by 115 % and 173 %, respectively, comparing to free extract cookies. Moreover, the proper combination of the design and baking variables allowed to vary the bioactivity of cooked cookies (moisture 3-5 %) between 300 to 700 {\mu}molTR/gdry. The additive manufacture of foods with interconnected pores could accelerate baking and browning, or reduce thermal degradation. This represents a potential approach to enhance the functional and healthy properties of cookies or other thermal treated bioactive food products.
SSLIDE: Sound Source Localization for Indoors based on Deep Learning
Authors: Yifan Wu, Roshan Ayyalasomayajula, Michael J. Bianco, Dinesh Bharadia, Peter Gerstoft
Date: 2020-10-27 00:00:00
Tasks: Decoder, Deep Learning, Sound Source Localization

This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The spatial features of sound signals received by each microphone are extracted and represented as likelihood surfaces for the sound source locations in each point. Our DNN consists of an encoder network followed by two decoders. The encoder obtains a compressed representation of the input likelihoods. One decoder resolves the multipath caused by reverberation, and the other decoder estimates the source location. Experiments based on both the simulated and experimental data show that our method can not only outperform multiple signal classification (MUSIC), steered response power with phase transform (SRP-PHAT), sparse Bayesian learning (SBL), and a competing convolutional neural network (CNN) approach in the reverberant environment but also achieve a good generalization performance.
Evaluating the structure-coefficient theorem of evolutionary game theory
Authors: Alex McAvoy, John Wakeley
Date: 2020-10-27 00:00:00

In order to accommodate the empirical fact that population structures are rarely simple, modern studies of evolutionary dynamics allow for complicated and highly-heterogeneous spatial structures. As a result, one of the most difficult obstacles lies in making analytical deductions, either qualitative or quantitative, about the long-term outcomes of evolution. The "structure-coefficient" theorem is a well-known approach to this problem for mutation-selection processes under weak selection, but a general method of evaluating the terms it comprises is lacking. Here, we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable demographic measures. This method encompasses a large family of evolutionary update mechanisms and extends the theorem to allow for asymmetric contests to provide a better understanding of the mutation-selection balance under more realistic circumstances. We apply the method to study social goods produced and distributed among individuals in spatially-heterogeneous populations, where asymmetric interactions emerge naturally and the outcome of selection varies dramatically depending on the nature of the social good, the spatial topology, and frequency with which mutations arise.
Equivariant Filter (EqF)
Authors: Pieter van Goor, Tarek Hamel, Robert Mahony
Date: 2020-10-27 00:00:00

The kinematics of many systems encountered in robotics, mechatronics, and avionics are naturally posed on homogeneous spaces, that is, their state lies in a smooth manifold equipped with a transitive Lie group symmetry. This paper proposes a novel filter, the Equivariant Filter (EqF), by posing the observer state on the symmetry group, linearising global error dynamics derived from the equivariance of the system, and applying extended Kalman filter design principles. We show that equivariance of the system output can be exploited to reduce linearisation error and improve filter performance. Simulation experiments of an example application show that the EqF significantly outperforms the extended Kalman filter and that the reduced linearisation error leads to a clear improvement in performance.
Maximum Spectral Measures of Risk with given Risk Factor Marginal Distributions
Authors: Mario Ghossoub, Jesse Hall, David Saunders
Date: 2020-10-27 00:00:00

We consider the problem of determining an upper bound for the value of a spectral risk measure of a loss that is a general nonlinear function of two factors whose marginal distributions are known, but whose joint distribution is unknown. The factors may take values in complete separable metric spaces. We introduce the notion of Maximum Spectral Measure (MSP), as a worst-case spectral risk measure of the loss with respect to the dependence between the factors. The MSP admits a formulation as a solution to an optimization problem that has the same constraint set as the optimal transport problem, but with a more general objective function. We present results analogous to the Kantorovich duality, and we investigate the continuity properties of the optimal value function and optimal solution set with respect to perturbation of the marginal distributions. Additionally, we provide an asymptotic result characterizing the limiting distribution of the optimal value function when the factor distributions are simulated from finite sample spaces. The special case of Expected Shortfall and the resulting Maximum Expected Shortfall is also examined.
Adaptation Properties Allow Identification of Optimized Neural Codes
Authors: Luke Rast, Jan Drugowitsch
Date: 2020-10-28 00:00:00
Proceeding: NeurIPS 2020 12

The adaptation of neural codes to the statistics of their environment is well captured by efficient coding approaches. Here we solve an inverse problem: characterizing the objective and constraint functions that efficient codes appear to be optimal for, on the basis of how they adapt to different stimulus distributions. We formulate a general efficient coding problem, with flexible objective and constraint functions and minimal parametric assumptions. Solving special cases of this model, we provide solutions to broad classes of Fisher information-based efficient coding problems, generalizing a wide range of previous results. We show that different objective function types impose qualitatively different adaptation behaviors, while constraints enforce characteristic deviations from classic efficient coding signatures. Despite interaction between these effects, clear signatures emerge for both unconstrained optimization problems and information-maximizing objective functions. Asking for a fixed-point of the neural code adaptation, we find an objective-independent characterization of constraints on the neural code. We use this result to propose an experimental paradigm that can characterize both the objective and constraint functions that an observed code appears to be optimized for.
Microfluidics for single-cell study of antibiotic tolerance and persistence induced by nutrient limitation
Authors: Stefany Moreno-Gámez, Alma Dal Co, Simon van Vliet, Martin Ackermann
Date: 2020-10-28 00:00:00

Nutrient limitation is one of the most common triggers of antibiotic tolerance and persistence. Here, we present two microfluidic setups to study how spatial and temporal variation in nutrient availability lead to increased survival of bacteria to antibiotics. The first setup is designed to mimic the growth dynamics of bacteria in spatially structured populations (e.g. biofilms) and can be used to study how spatial gradients in nutrient availability, created by the collective metabolic activity of a population, increase antibiotic tolerance. The second setup captures the dynamics of feast-and-famine cycles that bacteria recurrently encounter in nature, and can be used to study how phenotypic heterogeneity in growth resumption after starvation increases survival of clonal bacterial populations. In both setups, the growth rates and metabolic activity of bacteria can be measured at the single-cell level. This is useful to build a mechanistic understanding of how spatiotemporal variation in nutrient availability triggers bacteria to enter phenotypic states that increase their tolerance to antibiotics.
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition
Authors: Shi-Yan Weng, Berlin Chen
Date: 2020-10-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, Masked Language Modeling, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

The attention-based encoder-decoder modeling paradigm has achieved promising results on a variety of speech processing tasks like automatic speech recognition (ASR), text-to-speech (TTS) and among others. This paradigm takes advantage of the generalization ability of neural networks to learn a direct mapping from an input sequence to an output sequence, without recourse to prior knowledge such as audio-text alignments or pronunciation lexicons. However, ASR models stemming from this paradigm are prone to overfitting, especially when the training data is limited. Inspired by SpecAugment and BERT-like masked language modeling, we propose in the paper a decoder masking based training approach for end-to-end (E2E) ASR models. During the training phase we randomly replace some portions of the decoder's historical text input with the symbol [mask], in order to encourage the decoder to robustly output a correct token even when parts of its decoding history are masked or corrupted. The proposed approach is instantiated with the top-of-the-line transformer-based E2E ASR model. Extensive experiments on the Librispeech960h and TedLium2 benchmark datasets demonstrate the superior performance of our approach in comparison to some existing strong E2E ASR systems.
Age of Information Analysis in Hyperledger Fabric Blockchain-enabled Monitoring Networks
Authors: Minsu Kim, Sungho Lee, Chanwon Park, Jemin Lee
Date: 2020-10-28 00:00:00

Age of information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems where timeliness is of importance. In this paper, we explore the data freshness in the Hyperledger Fabric Blockchain-enabled monitoring network (HeMN) by leveraging the AoI metric. In HeMN, status updates from sources are transmitted through an uplink and recorded in a Hyperledger Fabric (HLF) network. To provide a stochastic guarantee of data freshness, we derive a closed-form of AoI violation probability by considering the transmission latency and the consensus latency. Then, we validate our analytic results through the implemented HLF platform. We also investigate the effect of the target successful transmission probability (STP) on the AoI violation probability.
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting
Authors: Amir Weiss, Arie Yeredor
Date: 2020-10-28 00:00:00
Tasks: Computational Efficiency

Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "classical" array structures, such as uniform linear arrays, not as many are found in the context of the more "modern" sparse arrays. In this paper, we present a novel blind calibration method for $2$-level nested arrays. Specifically, and despite recent contradicting claims in the literature, we show that the Least-Squares (LS) approach can in fact be used for this purpose with such arrays. Moreover, the LS approach gives rise to optimally-weighted LS joint estimation of the sensors' gains and phases offsets, which leads to more accurate calibration, and in turn, to higher accuracy in subsequent estimation tasks (e.g., direction-of-arrival). Our method, which can be extended to $K$-level arrays ($K>2$), is superior to the current state of the art both in terms of accuracy and computational efficiency, as we demonstrate in simulation.
Enhanced Blind Calibration of Uniform Linear Arrays with One-Bit Quantization by Kullback-Leibler Divergence Covariance Fitting
Authors: Amir Weiss, Arie Yeredor
Date: 2020-10-28 00:00:00
Tasks: Quantization

One-bit quantization has recently become an attractive option for data acquisition in cutting edge applications, due to the increasing demand for low power and higher sampling rates. Subsequently, the rejuvenated one-bit array processing field is now receiving more attention, as "classical" array processing techniques are adapted / modified accordingly. However, array calibration, often an instrumental preliminary stage in array processing, has so far received little attention in its one-bit form. In this paper, we present a novel solution approach for the blind calibration problem, namely, without using known calibration signals. In order to extract information within the second-order statistics of the quantized measurements, we propose to estimate the unknown sensors' gains and phases offsets according to a Kullback-Leibler Divergence (KLD) covariance fitting criterion. We then provide a quasi-Newton solution algorithm, with a consistent initial estimate, and demonstrate the improved accuracy of our KLD-based estimates in simulations.
RIS-aided Joint Localization and Synchronization with a Single-Antenna MmWave Receiver
Authors: Alessio Fascista, Angelo Coluccia, Henk Wymeersch, Gonzalo Seco-Granados
Date: 2020-10-28 00:00:00
Tasks: Position

MmWave multiple-input single-output (MISO) systems using a single-antenna receiver are regarded as promising solution for the near future, before the full-fledged 5G MIMO will be widespread. However, for MISO systems synchronization cannot be performed jointly with user localization unless two-way transmissions are used. In this paper we show that thanks to the use of a reconfigurable intelligent surface (RIS), joint localization and synchronization is possible with only downlink MISO transmissions. The direct maximum likelihood (ML) estimator for the position and clock offset is derived. To obtain a good initialization for the ML optimization, a decoupled, relaxed estimator of position and delays is also devised, which does not require knowledge of the clock offset. Results show that the proposed approach attains the Cram\'er-Rao lower bound even for moderate values of the system parameters.
Modeling European regional FDI flows using a Bayesian spatial Poisson interaction model
Date: 2020-10-28 00:00:00

This paper presents an empirical study of spatial origin and destination
effects of European regional FDI dyads. Recent regional studies primarily focus
on locational determinants, but ignore bilateral origin- and intervening
factors, as well as associated spatial dependence. This paper fills this gap by
using observations on interregional FDI flows within a spatially augmented
Poisson interaction model. We explicitly distinguish FDI activities between
three different stages of the value chain. Our results provide important
insights on drivers of regional FDI activities, both from origin and
destination perspectives. We moreover show that spatial dependence plays a key
role in both dimensions.
Projecting the optimal control strategy on invasive plants combining effects of herbivores and native plants resistance
Date: 2020-10-22 00:00:00
Tasks: Management

Understanding how to limit biological invasion is critical, especially in the
context of accelerating anthropogenic ecological changes. Although biological
invasion success could be explained by the lack of natural enemies in new
regions, recent studies have revealed that resident herbivores often do have a
substantial effect on both native and invasive plants. Very few studies have
included consideration of native plant resistance while estimating methods of
controlling invasion; hence, it is unclear to what extent the interactive
effects of controlling approaches and native plants' resistance could slow down
or even inhibit biological invasion. We developed a spatial modeling framework,
using a paired logistic equation model, with considerations of the dispersal
processes, to capture the dynamics change of native and invasive plants under
various strategies of control. We found that when biocontrol agents could have
a strong effect on invasive plant, that could almost completely limit the
invasion, together with a high native plant resistance. However, a high
application frequency is needed make an efficient impact, whereas, a low
frequency treatment leads to nearly the same outcome as the no treatment case.
Lastly, we showed that evenly controlling a larger area with a weaker effect
still lead to a better outcome than focusing on small patches with a stronger
effect. Overall, this study has some management implications, such as how to
determine the optimal allocation strategy.
Exploiting Angular Multiplexing for Polarization-diversity in Off-axis Digital Holography
Authors: Sjoerd van der Heide, Rutger van Anrooij, Menno van den Hout, Nicolas K. Fontaine, Roland Ryf, Haoshuo Chen, Mikael Mazur, Jose Enrique Antonio-Lopez, Juan Carlos Alvarado-Zacarias, Ton Koonen, Rodrigo Amezcua-Correa, Chigo Okonkwo
Date: 2020-10-27 00:00:00
Tasks: Diversity

Digital holography measures the complex optical field and transfer matrix of a device, polarization-diversity is often achieved through spatial multiplexing. We introduce angular multiplexing, to increase flexibility in the optical setup. Comparatively, similar values for cross-talk and mode-dependent loss are measured for a photonic lantern.
Monetary-fiscal interactions under price level targeting
Date: 2020-10-28 00:00:00

The adoption of a "makeup" strategy is one of the proposals in the ongoing
review of the Fed's monetary policy framework. Another suggestion, to avoid the
zero lower bound, is a more active role for fiscal policy. We put together
these ideas to study monetary-fiscal interactions under price level targeting.
Under price level targeting and a fiscally-led regime, we find that following a
deflationary demand shock: (i) the central bank increases (rather than
decreases) the policy rate; (ii) the central bank, thus, avoids the zero lower
bound; (iii) price level targeting is generally welfare improving if compared
to inflation targeting.
Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion
Authors: Samuel Chevalier, Luca Schenato, Luca Daniel
Date: 2020-10-28 00:00:00

This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage profile in electrical power distribution networks. The algorithm is accordingly termed the Accelerated-PPF (APPF), since it can accelerate "any" sampling-based PPF solver. As the APPF runs, it concurrently generates a low-dimensional subspace of orthonormalized solution vectors. This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles. When constructing and updating the subspace, the power flow problem must still be solved on the full nonlinear system. In order to accelerate the computation of these solutions, a Neumann expansion of a modified power flow Jacobian is implemented. Applicable when load bus injections are small, this Neumann expansion allows for a considerable speed up of Jacobian system solves during the standard Newton iterations. APPF test results, from experiments run on the full IEEE 8500-node test feeder, are finally presented.
Frequency-Undersampled Short-Time Fourier Transform
Authors: Daichi Kitahara
Date: 2020-10-28 00:00:00

The short-time Fourier transform (STFT) usually computes the same number of frequency components as the frame length while overlapping adjacent time frames by more than half. As a result, the number of components of a spectrogram matrix becomes more than twice the signal length, and hence STFT is hardly used for signal compression. In addition, even if we modify the spectrogram into a desired one by spectrogram-based signal processing, it is re-changed during the inversion as long as it is outside the range of STFT. In this paper, to reduce the number of components of a spectrogram while maintaining the analytical ability, we propose the frequency-undersampled STFT (FUSTFT), which computes only half the frequency components. We also present the inversions with and without the periodic condition, including their different properties. In simple numerical examples of audio signals, we confirm the validity of FUSTFT and the inversions.