pwc-archive/papers-with-abstracts huggingface dataset:
Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking
Authors: Brian P. Hanley
Date: 2020-10-21 00:00:00

Four radical ideas are presented. First, that the rationale for cancellation of principal can be modified in modern banking. Second, that non-cancellation of loan principal upon payment may cure an old problem of maintenance of positive equity in the non-governmental sector. Third and fourth, that crediting this money to local/state government, and crediting to at-risk loans that create new utility value, creates an additional virtuous monetary circuit that ties finances of government directly to commercial activity. Taking these steps can cure a problem I have identified with modern monetary theory, which is that breaking the monetary circuit of taxation in the minds of politicians will free them from centuries of restraint, optimizing their opportunities for implementing tyranny. It maintains and strengthens the current circuit, creating a new, more direct monetary circuit that in some respects combats inequality.
Molecular crowding in single eukaryotic cells: using cell environment biosensing and single-molecule optical microscopy to probe dependence on extracellular ionic strength, local glucose conditions, and sensor copy number
Authors: Jack W Shepherd, Sarah Lecinski, Jasmine Wragg, Sviatlana Shashkova, Chris MacDonald, Mark C Leake
Date: 2020-10-21 00:00:00

The physical and chemical environment inside cells is of fundamental importance to all life but has traditionally been difficult to determine on a subcellular basis. Here we combine cutting-edge genomically integrated FRET biosensing to readout localized molecular crowding in single live yeast cells. Confocal microscopy allows us to build subcellular crowding heatmaps using ratiometric FRET, while whole-cell analysis demonstrates crowding is reduced when yeast is grown in elevated glucose concentrations. Simulations indicate that the cell membrane is largely inaccessible to these sensors and that cytosolic crowding is broadly uniform across each cell over a timescale of seconds. Millisecond single-molecule optical microscopy was used to track molecules and obtain brightness estimates that enabled calculation of crowding sensor copy numbers. The quantification of diffusing molecule trajectories paves the way for correlating subcellular processes and the physicochemical environment of cells under stress.
Multi-robot Implicit Control of Herds
Authors: Eduardo Sebastián, Eduardo Montijano
Date: 2020-10-21 00:00:00

This paper presents a novel control strategy to herd a group of non-cooperative evaders by means of a team of robotic herders. In herding problems, the motion of the evaders is typically determined by strong nonlinear reactive dynamics, escaping from the herders. Many applications demand the herding of numerous and/or heterogeneous entities, making the development of flexible control solutions challenging. In this context, our main contribution is a control approach that finds suitable herding actions even when the nonlinearities in the evaders' dynamics yield to implicit equations. We resort to numerical analysis theory to characterise the existence conditions of such actions and propose two design methods to compute them, one transforming the continuous time implicit system into an expanded explicit system, and the other applying a numerical method to find the action in discrete time. Simulations and real experiments validate the proposal in different scenarios.
Multi-task Metric Learning for Text-independent Speaker Verification
Authors: Yafeng Chen, Wu Guo, Jingjing Shi, Jiajun Qi, Tan Liu
Date: 2020-10-21 00:00:00
Tasks: Metric Learning, Speaker Verification, Text-Independent Speaker Verification

In this work, we introduce metric learning (ML) to enhance the deep embedding learning for text-independent speaker verification (SV). Specifically, the deep speaker embedding network is trained with conventional cross entropy loss and auxiliary pair-based ML loss function. For the auxiliary ML task, training samples of a mini-batch are first arranged into pairs, then positive and negative pairs are selected and weighted through their own and relative similarities, and finally the auxiliary ML loss is calculated by the similarity of the selected pairs. To evaluate the proposed method, we conduct experiments on the Speaker in the Wild (SITW) dataset. The results demonstrate the effectiveness of the proposed method.
The UPC Speaker Verification System Submitted to VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20)
Date: 2020-10-27 00:00:00
Tasks: Binary Classification, Speaker Recognition, Speaker Verification, Triplet

This report describes the submission from Technical University of Catalonia
(UPC) to the VoxCeleb Speaker Recognition Challenge (VoxSRC-20) at Interspeech
2020. The final submission is a combination of three systems. System-1 is an
autoencoder based approach which tries to reconstruct similar i-vectors,
whereas System-2 and -3 are Convolutional Neural Network (CNN) based siamese
architectures. The siamese networks have two and three branches, respectively,
where each branch is a CNN encoder. The double-branch siamese performs binary
classification using cross entropy loss during training. Whereas, our
triple-branch siamese is trained to learn speaker embeddings using triplet
loss. We provide results of our systems on VoxCeleb-1 test, VoxSRC-20
validation and test sets.
A Test for Kronecker Product Structure Covariance Matrix
Authors: Patrik Guggenberger, Frank Kleibergen, Sophocles Mavroeidis
Date: 2020-10-21 00:00:00

We propose a test for a covariance matrix to have Kronecker Product Structure (KPS). KPS implies a reduced rank restriction on a certain transformation of the covariance matrix and the new procedure is an adaptation of the Kleibergen and Paap (2006) reduced rank test. To derive the limiting distribution of the Wald type test statistic proves challenging partly because of the singularity of the covariance matrix estimator that appears in the weighting matrix. We show that the test statistic has a chi square limiting null distribution with degrees of freedom equal to the number of restrictions tested. Local asymptotic power results are derived. Monte Carlo simulations reveal good size and power properties of the test. Re-examining fifteen highly cited papers conducting instrumental variable regressions, we find that KPS is not rejected in 56 out of 118 specifications at the 5% nominal size.
A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild
Date: 2020-10-21 00:00:00

This paper examines the up-to-dateness of installed firmware versions of IoT
devices accessible via public internet. It analyzes datasets of 1.06m devices
collected from the IoT search engine Censys and maps the results against the
latest firmware version each manufacturer offers. By applying the SEMMA data
mining process, a fully scalable and adaptive approach is developed. This
approach relies on three data artifacts: raw data from Censys, a mapping table
with firmware versions and a keyword search list. The preliminary results
confirm the heterogeneity of connected IoT devices. They show that
manufacturer, device type and country influence the up-to-dateness of firmware.
The results suggest users as a "weak link" as they do not update the firmware
of their devices in a timely manner. However, the heterogeneity leads to
results not showing a high reliability, yet.
Minimum Mean-Squared-Error Autocorrelation Processing in Coprime Arrays
Authors: Dimitris G. Chachlakis, Tongdi Zhou, Fauzia Ahmad, Panos P. Markopoulos
Date: 2020-10-21 00:00:00

Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased number of sources. To that end, the receiver estimates the autocorrelation matrix of a larger virtual uniform linear array (coarray), by applying selection or averaging to the physical array's autocorrelation estimates, followed by spatial-smoothing. Both selection and averaging have been designed under no optimality criterion and attain arbitrary (suboptimal) Mean-Squared-Error (MSE) estimation performance. In this work, we design a novel coprime array receiver that estimates the coarray autocorrelations with Minimum-MSE (MMSE), for any probability distribution of the source DoAs. Our extensive numerical evaluation illustrates that the proposed MMSE approach returns superior autocorrelation estimates which, in turn, enable higher DoA estimation performance compared to standard counterparts.
Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis
Authors: Aleksandr Kharin
Date: 2020-10-21 00:00:00
Tasks: Model Selection

Estimation of the number of signals in the presence of noise is an important problem in several areas of statistical signal processing. There are a number of modern works on the design of an optimal solution to this problem in terms of some criteria. Each criterion generates a model order selection (MOS) algorithm. However, the minimum error probability criterion has not received significant attention, despite its importance. In this paper, we propose a new approach to the design of MOS algorithms partially based on the minimum error probability criterion. Also, we pay a lot of attention to the performance and consistency analysis of the MOS algorithms. In this study, an abridged error probability is used as a universal performance measure of the MOS algorithms. We propose a theoretical framework that makes it possible to provide a parametric consistency analysis and to obtain closed-form expressions for the abridged error probabilities of a wide range of MOS algorithms. As an example, we applied this framework to the consistency and performance analysis of several MOS algorithms. Using the obtained results, we provide a parametric optimization of the presented MOS algorithms. Finally, we examine a quasilikelihood approach to the design and analysis of the MOS algorithms. The proposed theoretical framework is used to obtain the abridged error probabilities as functions of the unknown signal parameter. These functions, in turn, allow us to find the scope of the quasilikelihood approach.
Adaptive Frequency-Regulation Demand Response Using Substation Solar Irradiance Measurement in High-PV Power Systems
Authors: Shutang You
Date: 2020-10-21 00:00:00

This letter proposes a distributed and adaptive demand response approach for primary frequency regulation in high-PV power systems. Using solar irradiance measurement at substations, the proposed approach allows accurate estimation of real-time system inertia of high-PV systems, thus facilitating the estimation of power imbalance after a contingency. The test results from the U.S. Electric Reliability Council of Texas (ERCOT) high-PV penetration models validated its effectiveness.
Large-Scale High PV Power Grid Dynamic Model Development -- A Case Study on the U.S. Eastern Interconnection
Authors: Shutang You
Date: 2020-10-21 00:00:00

Power systems are undergoing a transformation toward a low-carbon non-synchronous generation portfolio. A major concern for system planners and operators is the system dynamics in the high renewable penetration future. Because of the scale of the system and numerous components involved, it is extremely difficult to develop high PV dynamic models based upon actual power system models. The main contribution of this paper is providing an example of developing high PV penetration models based on the validated dynamic model of an actual large-scale power grid - the U.S. Eastern Interconnection system. The displacement of conventional generators by PV is realized by optimization. Combining the PV distribution optimization and the validated dynamic model information, this approach avoids the uncertainties brought about by transmission planning. As the existing dynamic models can be validated by measurements, this approach improves the credibility of the high PV models in representing future power grids. This generic approach can be applied to develop high PV dynamic models for other actual large-scale systems.
Compressive Sensing Based Situational Awareness and Sensor Placement for DC Microgrids with Relatively Fixed Operation Patterns
Authors: Shutang You, Yilu Liu
Date: 2020-10-21 00:00:00
Tasks: Compressive Sensing, State Estimation

This paper proposes a DC microgrid state estimation and sensor placement method based on compressive sensing. Formulations of various types of measurements and components are developed under the proposed framework. A measurement placing strategy to minimize the coherence of the measurement matrix and thus increase estimation accuracy is presented. Simulation results show that the proposed state estimation and sensor placing approach can effectively reduce the number of sensors to achieve a certain level of estimation accuracy.
Quantifying Uncertainties in Estimates of Income and Wealth Inequality
Date: 2020-10-21 00:00:00
Tasks: Survey

I measure the uncertainty affecting estimates of economic inequality in the
US and investigate how accounting for properly estimated standard errors can
affect the results of empirical and structural macroeconomic studies. In my
analysis, I rely upon two data sets: the Survey of Consumer Finances (SCF),
which is a triennial survey of household financial condition, and the
Individual Tax Model Public Use File (PUF), an annual sample of individual
income tax returns. While focusing on the six income and wealth shares of the
top 10 to the top 0.01 percent between 1988 and 2018, my results suggest that
ignoring uncertainties in estimated wealth and income shares can lead to
erroneous conclusions about the current state of the economy and, therefore,
lead to inaccurate predictions and ineffective policy recommendations. My
analysis suggests that for the six top-decile income shares under
consideration, the PUF estimates are considerably better than those constructed
using the SCF; for wealth shares of the top 10 to the top 0.5 percent, the SCF
estimates appear to be more reliable than the PUF estimates; finally, for the
two most granular wealth shares, the top 0.1 and 0.01 percent, both data sets
present non-trivial challenges that cannot be readily addressed.
Improving Audio Anomalies Recognition Using Temporal Convolutional Attention Network
Date: 2020-10-21 00:00:00

Anomalous audio in speech recordings is often caused by speaker voice
distortion, external noise, or even electric interferences. These obstacles
have become a serious problem in some fields, such as recording high-quality
music and speech processing. In this paper, a novel approach using a temporal
convolutional attention network (TCAN) is proposed to process this problem. The
use of temporal conventional network (TCN) can capture long range patterns
using a hierarchy of temporal convolutional filters. To enhance the ability to
tackle audio anomalies in different acoustic conditions, an attention mechanism
is used in TCN, where a self-attention block is added after each temporal
convolutional layer. This aims to highlight the target related features and
mitigate the interferences from irrelevant information. To evaluate the
performance of the proposed model, audio recordings are collected from the
TIMIT dataset, and are then changed by adding five different types of audio
distortions: gaussian noise, magnitude drift, random dropout, reduction of
temporal resolution, and time warping. Distortions are mixed at different
signal-to-noise ratios (SNRs) (5dB, 10dB, 15dB, 20dB, 25dB, 30dB). The
experimental results show that the use of proposed model can yield good
classification performances and outperforms some strong baseline methods, such
as the LSTM and TCN based models, by about 3$\sim$ 10\% relatively.
Frequency Response Study on the ERCOT under High Photovoltaic (PV) Penetration Conditions
Authors: Xuemeng Zhang, Shutang You, Yong liu, Yilu Liu
Date: 2020-10-21 00:00:00

Solar photovoltaic (PV) generation is growing rapidly around the world. However, PV generation, based on inverter, is fundamentally different from conventional synchronous generators. It is of vital importance to understand the impacts of increased penetration of PV generation on power system dynamic performance. This paper investigates frequency response of the Electric Reliability Council of Texas (ERCOT) with high PV penetration in the future year. In this work, a realistic baseline dynamic model is validated using synchrophasor measurements. Then, dynamic simulation is performed to evaluate the impacts of high PV generation on frequency response.
Comprehensive Performance Analysis of Objective Quality Metrics for Digital Holography
Authors: Ayyoub Ahar, Tobias Birnbaum, Maksymilian Chlipala, Weronika Zaperty, Saeed Mahmoudpour, Tomasz Kozacki, Malgorzata Kujawinska, Peter Schelkens
Date: 2020-10-21 00:00:00

Objective quality assessment of digital holograms has proven to be a challenging task. While prediction of perceptual quality of the recorded 3D content from the holographic wavefield is an open problem; perceptual quality assessment from content after rendering, requires a time-consuming rendering step and a multitude of possible viewports. In this research, we use 96 Fourier holograms of the recently released HoloDB database to evaluate the performance of well-known and state-of-the-art image quality metrics on digital holograms. We compare the reference holograms with their distorted versions: (i) before rendering on the real and imaginary parts of the quantized complex-wavefield, (ii) after converting Fourier to Fresnel holograms, (iii) after rendering, on the quantized amplitude of the reconstructed data, and (iv) after subsequently removing speckle noise using a Wiener filter. For every experimental track, the quality metric predictions are compared to the Mean Opinion Scores (MOS) gathered on a 2D screen, light field display and a holographic display. Additionally, a statistical analysis of the results and a discussion on the performance of the metrics are presented. The tests demonstrate that while for each test track a few quality metrics present a highly correlated performance compared to the multiple sets of available MOS, none of them demonstrates a consistently high-performance across all four test-tracks.
Photovoltaic (PV) Virtual Inertia and Fast Frequency Regulation in High PV Power Grids
Authors: Shutang You
Date: 2020-10-21 00:00:00

This paper studies the frequency response using PV. Multiple control strategies are considered and simulated in the high PV ERCOT model, including inertia control, synthetic governor control, and AGC control. The impact of different parameters in PV inertia control and their correlation and impact on frequency response are analyzed. The simulation results show that PV farm has potential to provide multiple types of grid service to support system frequency. This paper also proposed a distributed fast frequency control approach that can better leverage the PV headroom reserve to improve the system frequency nadir after contingencies.
The role of liquid-liquid phase separation in regulating enzyme activity
Authors: Brian G. O'Flynn, Tanja Mittag
Date: 2020-10-21 00:00:00

Liquid-liquid phase separation is now recognized as a common mechanism for regulating enzyme activity in cells. Insights from studies in cells are complemented by in vitro studies aimed at developing better understanding of mechanisms underlying such control. These mechanisms are often based on the influence of LLPS on the physicochemical properties of the enzyme's environment. Biochemical mechanisms underlying such regulation include the potential for concentrating reactants together, tuning reaction rates, and controlling competing metabolic pathways. LLPS is thus a powerful tool with extensive utilities for the cell, e.g. for consolidating cell survival under stress or rerouting metabolic pathways in response to the energy state of the cell. By examining the evidence of how LLPS affects enzyme catalysis, we can begin to understand emerging concepts and expand our understanding of enzyme catalysis in living cells.
Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees
Authors: Sam Spencer, Lav R. Varshney
Date: 2020-10-21 00:00:00

Previous work has shown that for contagion processes on extended star networks (trees with exactly one node of degree > 2), there is a simple, closed-form expression for a highly accurate approximation to the maximum likelihood infection source. Here, we generalize that result to a class of hypertrees which, although somewhat structurally analogous, provides a much richer representation space. In particular, this approach can be used to estimate patient zero sources, even when the infection has been propagated via large group gatherings rather than person-to-person spread, and when it is spreading through interrelated social bubbles with varying degrees of overlap. In contact tracing contexts, this estimator may be used to identify the source of a local outbreak, which can then be used for forward tracing or for further backward tracing (by similar or other means) to an upstream source.
Statistical Properties of a Modified Welch Method That Uses Sample Percentiles
Authors: Felix Schwock, Shima Abadi
Date: 2020-10-22 00:00:00

We present and analyze an alternative, more robust approach to the Welch's overlapped segment averaging (WOSA) spectral estimator. Our method computes sample percentiles instead of averaging over multiple periodograms to estimate power spectral densities (PSDs). Bias and variance of the proposed estimator are derived for varying sample sizes and arbitrary percentiles. We have found excellent agreement between our expressions and data sampled from a white Gaussian noise process.
Momentum Contrast Speaker Representation Learning
Date: 2020-10-22 00:00:00
Tasks: Contrastive Learning, Metric Learning, Representation Learning, Speaker Recognition, Speaker Verification

Unsupervised representation learning has shown remarkable achievement by
reducing the performance gap with supervised feature learning, especially in
the image domain. In this study, to extend the technique of unsupervised
learning to the speech domain, we propose the Momentum Contrast for VoxCeleb
(MoCoVox) as a form of learning mechanism. We pre-trained the MoCoVox on the
VoxCeleb1 by implementing instance discrimination. Applying MoCoVox for speaker
verification revealed that it outperforms the state-of-the-art metric
learning-based approach by a large margin. We also empirically demonstrate the
features of contrastive learning in the speech domain by analyzing the
distribution of learned representations. Furthermore, we explored which pretext
task is adequate for speaker verification. We expect that learning speaker
representation without human supervision helps to address the open-set speaker
recognition.