pwc-archive/papers-with-abstracts huggingface dataset:
Hardware-Software Codesign for Software Defined Radio: IEEE 802.11p receiver case study
Date: 2020-03-24 00:00:00

Software Defined Radio (SDR) platforms are useful tools to design new
wireless technologies or to improve specifications of existing ones. The IEEE
802.11p is the de-facto standard for Wireless Vehicular Ad-hoc NETworks
(VANETs). It has been implemented on GNU Radio SDR [1], which experiences
frames decoding/encoding latency. In this paper an FPGA based SDR is proposed
as a solution to accelerate frame decoding of IEEE 802.11p waveforms. We share
our experience in designing and validating an SDR on the top of real FPGA based
embedded hardware architecture. On the top of an FPGA Zynq platform, we port an
SDR GNU Radio following hardware/software (HW/SW) codesign approach. A real
time profiling of the SDR system shows that the OFDM equalizer with FFT
software function is the most time consuming task of the whole SDR receiver
functions (34.74%). We suggest hardware acceleration of the FFT processing
function using the free logic gates of the FPGA Zynq as a Hardware (HW)
accelerator. The HW OFDM Equalizer with FFT sub-function could accelerate the
SDR chain processing in comparison with a pure software implementation. Our
FPGA based SDR design is a key step toward proposing an embedded SDR. It would
be an alternative for not only the IEEE 802.11p standard but for any OFDM based
SDR.
Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize Principle
Authors: Andreas Brendel, Walter Kellermann
Date: 2020-03-20 00:00:00
Tasks: blind source separation

Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and eigenvalue decomposition. The presented update rules are shown to outperform competing state-of-the-art methods in terms of convergence speed at a comparable runtime due to the restriction to unitary demixing matrices. This is demonstrated by experiments with recorded real-world data.
Deep Generative Variational Autoencoding for Replay Spoof Detection in Automatic Speaker Verification
Date: 2020-03-21 00:00:00
Tasks: Speaker Verification

Automatic speaker verification (ASV) systems are highly vulnerable to
presentation attacks, also called spoofing attacks. Replay is among the
simplest attacks to mount - yet difficult to detect reliably. The
generalization failure of spoofing countermeasures (CMs) has driven the
community to study various alternative deep learning CMs. The majority of them
are supervised approaches that learn a human-spoof discriminator. In this
paper, we advocate a different, deep generative approach that leverages from
powerful unsupervised manifold learning in classification. The potential
benefits include the possibility to sample new data, and to obtain insights to
the latent features of genuine and spoofed speech. To this end, we propose to
use variational autoencoders (VAEs) as an alternative backend for replay attack
detection, via three alternative models that differ in their
class-conditioning. The first one, similar to the use of Gaussian mixture
models (GMMs) in spoof detection, is to train independently two VAEs - one for
each class. The second one is to train a single conditional model (C-VAE) by
injecting a one-hot class label vector to the encoder and decoder networks. Our
final proposal integrates an auxiliary classifier to guide the learning of the
latent space. Our experimental results using constant-Q cepstral coefficient
(CQCC) features on the ASVspoof 2017 and 2019 physical access subtask datasets
indicate that the C-VAE offers substantial improvement in comparison to
training two separate VAEs for each class. On the 2019 dataset, the C-VAE
outperforms the VAE and the baseline GMM by an absolute 9 - 10% in both equal
error rate (EER) and tandem detection cost function (t-DCF) metrics. Finally,
we propose VAE residuals - the absolute difference of the original input and
the reconstruction as features for spoofing detection.
Extended Prony Analysis on Power System Oscillation Under a Near-Resonance Condition
Date: 2020-03-21 00:00:00

Power system oscillations under a large disturbance often exhibit distorted
waveforms as captured by increasingly deployed phasor measurement units. One
cause is the occurrence of a near-resonance condition among several dominant
modes that are influenced by nonlinear transient dynamics of generators. This
paper proposes an Extended Prony Analysis method for measurement-based modal
analysis. Based on the normal form theory, it compares analyses on transient
and post-transient waveforms to distinguish a resonance mode caused by a
near-resonance condition from natural modes so that the method can give more
accurate modal properties than a traditional Prony Analysis method, especially
for large disturbances. The new method is first demonstrated in detail on
Kundur's two-area system and then tested on the IEEE 39-bus system to show its
performance under a near-resonance condition.
Reliability Analysis of Component Level Redundant Topologies for solid state Fault Current Limiter
Date: 2020-03-21 00:00:00

Experience shows that semiconductor switches in power electronics systems are
the most vulnerable components. One of the most common ways to solve this
reliability challenge is component-level redundant design. There are four
possible configurations for the redundant design in component-level. This paper
presents a comparative reliability analysis between different component-level
redundant designs for solid state fault current limiter (SSFCL). The aim of the
proposed analysis is to determine the more reliable component-level redundant
configuration. The mean time to failure (MTTF) is used as the reliability
parameter. Considering both fault types (open circuit and short circuit), the
MTTFs of different configurations are calculated. It is demonstrated that more
reliable configuration depends on the junction temperature of the semiconductor
switches in the steady state. That junction temperature is a function of i)
ambient temperature, ii) power loss of the semiconductor switch and iii)
thermal resistance of heat sink. Also, results sensitivity to each parameter is
investigated. The results show that in different conditions, various
configurations have higher reliability. The experimental results are presented
to clarify the theory and feasibility of the proposed approaches. At last,
levelized costs of different configurations are analyzed for a fair comparison.
Network structure of COVID-19 spread and the lacuna in India's testing strategy
Date: 2020-03-21 00:00:00

We characterize the network of COVID-19 spread in India and find that the
transmission rate is 0.43, with daily case growth driven by individuals who
contracted the virus abroad. We explore the question of whether this represents
exponentially decaying dynamics or is simply an artefact of India's testing
strategy. Testing has largely been limited to individuals travelling from
high-risk countries and their immediate contacts, meaning that the network
reflects positive identifications from a biased testing sample. Given generally
low levels of testing and an almost complete absence of testing for community
spread, there is significant risk that we may be missing out on the actual
nature of outbreak. India still has an apparently low current caseload, with
possibly a small window of time to act, and should therefore aggressively and
systematically expand random testing for community spread, including for
asymptomatic cases. This will help understand true transmission characteristics
and plan appropriately for the immediate future.
One model is not enough: heterogeneity in cryptocurrencies' multifractal profiles
Authors: Aurelio F. Bariviera
Date: 2020-03-21 00:00:00
Tasks: Time Series, Time Series Analysis

This paper studies of the multifractal dynamics in 84 cryptocurrencies. It fills an important gap in the literature, by studying this market using two alternative multi-scaling methodologies. We find compelling evidence that cryptocurrencies have different degree of long range dependence, and --more importantly -- follow different stochastic processes. Some of them follow models closer to monofractal fractional Gaussian noises, while others exhibit complex multifractal dynamics. Regarding the source of multifractality, our results are mixed. Time series shuffling produces a reduction in the level of multifractality, but not enough to offset it. We find an association of kurtosis with multifractality.
Where do we stand in cryptocurrencies economic research? A survey based on hybrid analysis
Authors: Aurelio F. Bariviera, Ignasi Merediz-Solà
Date: 2020-03-21 00:00:00

This survey develops a dual analysis, consisting, first, in a bibliometric examination and, second, in a close literature review of all the scientific production around cryptocurrencies conducted in economics so far. The aim of this paper is twofold. On the one hand, proposes a methodological hybrid approach to perform comprehensive literature reviews. On the other hand, we provide an updated state of the art in cryptocurrency economic literature. Our methodology emerges as relevant when the topic comprises a large number of papers, that make unrealistic to perform a detailed reading of all the papers. This dual perspective offers a full landscape of cryptocurrency economic research. Firstly, by means of the distant reading provided by machine learning bibliometric techniques, we are able to identify main topics, journals, key authors, and other macro aggregates. Secondly, based on the information provided by the previous stage, the traditional literature review provides a closer look at methodologies, data sources and other details of the papers. In this way, we offer a classification and analysis of the mounting research produced in a relative short time span.
Bayesian Recursive Estimation on the Rotation Group
Date: 2020-03-25 00:00:00

Tracking on the rotation group is a key component of many modern systems for
estimation of the motion of rigid bodies. To address this problem, here we
describe a Bayesian algorithm that relies on directional measurements for
tracking on the special orthogonal (rotation) group. Its novelty lies in the
use of maximum entropy distributions on these groups as models for the priors,
and justifiable approximation algorithms that permit recursive implementation
of such a model. We provide the solutions in a recursive closed form. In the
two-dimensional case the parameters of the prior and posterior distributions
can be computed exactly and the solution has low complexity. Adoption of this
approach eliminates the problem of angle wrapping. In higher dimensions the
exact solution cannot be computed, and it is necessary to make (very close)
approximations, which is done here. We demonstrate in simulations that, in
contrast with some other approaches, our algorithm produces very accurate and
statistically meaningful outputs.
NSM Converges to a k-NN Regressor Under Loose Lipschitz Estimates
Date: 2020-03-22 00:00:00

Although it is known that having accurate Lipschitz estimates is essential
for certain models to deliver good predictive performance, refining this
constant in practice can be a difficult task especially when the input
dimension is high. In this work, we shed light on the consequences of employing
loose Lipschitz bounds in the Nonlinear Set Membership (NSM) framework, showing
that the model converges to a nearest neighbor regressor (k-NN with k=1). This
convergence process is moreover not uniform, and is monotonic in the univariate
case. An intuitive geometrical interpretation of the result is then given and
its practical implications are discussed.
Reanimating a Dead Economy: Financial and Economic Analysis of a Zombie Outbreak
Authors: Zachary Feinstein
Date: 2020-03-22 00:00:00

In this paper, we study the financial and economic implications of a zombie epidemic on a major industrialized nation. We begin with a consideration of the epidemiological modeling of the zombie contagion. The emphasis of this work is on the computation of direct and indirect financial consequences of this contagion of the walking dead. A moderate zombie outbreak leaving 1 million people dead in a major industrialized nation could result in GDP losses of 23.44% over the subsequent year and a drop in financial market of 29.30%. We conclude by recommending policy actions necessary to prevent this potential economic collapse.
Non-backtracking walks reveal compartments in sparse chromatin interaction networks
Authors: K. Polovnikov, A. Gorsky, S. Nechaev, S. V. Razin, S. Ulianov
Date: 2020-03-22 00:00:00
Tasks: Community Detection, Stochastic Block Model

Chromatin communities stabilized by protein machinery play essential role in gene regulation and refine global polymeric folding of the chromatin fiber. However, treatment of these communities in the framework of the classical network theory (stochastic block model, SBM) does not take into account intrinsic linear connectivity of the chromatin loci. Here we propose the "polymer" block model, paving the way for community detection in polymer networks. On the basis of this new model we modify the non-backtracking flow operator and suggest the first protocol for annotation of compartmental domains in sparse single cell Hi-C matrices. In particular, we prove that our approach corresponds to the maximum entropy principle. The benchmark analyses demonstrates that the spectrum of the polymer non-backtracking operator resolves the true compartmental structure up to the theoretical detectability threshold, while all commonly used operators fail above it. We test various operators on real data and conclude that the sizes of the non-backtracking single cell domains are most close to the sizes of compartments from the population data. Moreover, the found domains clearly segregate in the gene density and correlate with the population compartmental mask, corroborating biological significance of our annotation of the chromatin compartmental domains in single cells Hi-C matrices.
LOOPER: Inferring computational algorithms enacted by neuronal population dynamics
Date: 2020-03-22 00:00:00

Recording simultaneous activity of hundreds of neurons is now possible.
Existing methods can model such population activity, but do not directly reveal
the computations used by the brain. We present a fully unsupervised method that
models neuronal activity and reveals the computational strategy. The method
constructs a topological model of neuronal dynamics consisting of
interconnected loops. Transitions between loops mark computationally-salient
decisions. We accurately model activation of 100s of neurons in the primate
cortex during a working memory task. Dynamics of a recurrent neural network
(RNN) trained on the same task are topologically identical suggesting that a
similar computational strategy is used. The RNN trained on a modified dataset,
however, reveals a different topology. This topology predicts specific novel
stimuli that consistently elicit incorrect responses with near perfect
accuracy. Thus, our methodology yields a quantitative model of neuronal
activity and reveals the computational strategy used to solve the task.
Topological Data Analysis of Collective and Individual Epithelial Cells using Persistent Homology of Loops
Authors: Dhananjay Bhaskar, William Y. Zhang, Ian Y. Wong
Date: 2020-03-22 00:00:00
Tasks: Topological Data Analysis

Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into complex multicellular patterns, which are challenging to classify without a priori information. Classically, different phases and phase transitions have been described based on local ordering, which may not capture structural features at larger length scales. Instead, topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology) and can compare different particle configurations based on the "cost" of reorganizing one configuration into another. Here, we demonstrate a topology-based machine learning approach for unsupervised profiling of individual and collective phases based on large-scale loops. We show that these topological loops (i.e. dimension 1 homology) are robust to variations in particle number and density, particularly in comparison to connected components (i.e. dimension 0 homology). We use TDA to map out phase diagrams for simulated particles with varying adhesion and propulsion, at constant population size as well as when proliferation is permitted. Next, we use this approach to profile our recent experiments on the clustering of epithelial cells in varying growth factor conditions, which are compared to our simulations. Finally, we characterize the robustness of this approach at varying length scales, with sparse sampling, and over time. Overall, we envision TDA will be broadly applicable as a model-agnostic approach to analyze active systems with varying population size, from cytoskeletal motors to motile cells to flocking or swarming animals.
Robust Adaptive Control Barrier Functions: An Adaptive & Data-Driven Approach to Safety (Extended Version)
Date: 2020-05-28 00:00:00
Tasks: Model Predictive Control

A new framework is developed for control of constrained nonlinear systems
with structured parametric uncertainties. Forward invariance of a safe set is
achieved through online parameter adaptation and data-driven model estimation.
The new adaptive data-driven safety paradigm is merged with a recent adaptive
control algorithm for systems nominally contracting in closed-loop. This
unification is more general than other safety controllers as closed-loop
contraction does not require the system be invertible or in a particular form.
Additionally, the approach is less expensive than nonlinear model predictive
control as it does not require a full desired trajectory, but rather only a
desired terminal state. The approach is illustrated on the pitch dynamics of an
aircraft with uncertain nonlinear aerodynamics.
Coprime Inner/Outer Factorization of SISO Time-Delay Systems and FIR Structure of Their Optimal H-Infinity Controllers
Date: 2020-03-23 00:00:00

The approach in Foias et al. (1996) is one of the well-developed methods to
design H-infinity controllers for general infinite dimensional systems. This
approach is applicable if the plant admits a special coprime inner/outer
factorization. We give the largest class of single-input-single-output (SISO)
time delay systems for which this factorization is possible and factorize the
admissible plants. Based on this factorization, we compute the optimal
H-infinity performance and eliminate unstable pole-zero cancellations in the
optimal H-infinity controller. We extend the results on the finite impulse
response (FIR) structure of optimal H-infinity controllers by showing that this
structure appears not only for plants with input/output (I/O) delays, but also
for general SISO time-delay plants.
3D unknown view tomography via rotation invariants
Date: 2020-03-23 00:00:00

In this paper, we study the problem of reconstructing a 3D point source model
from a set of 2D projections at unknown view angles. Our method obviates the
need to recover the projection angles by extracting a set of rotation-invariant
features from the noisy projection data. From the features, we reconstruct the
density map through a constrained nonconvex optimization. We show that the
features have geometric interpretations in the form of radial and pairwise
distances of the model. We further perform an ablation study to examine the
effect of various parameters on the quality of the estimated features from the
projection data. Our results showcase the potential of the proposed method in
reconstructing point source models in various noise regimes.
Market Efficient Portfolios in a Systemic Economy
Authors: Kerstin Awiszus, Agostino Capponi, Stefan Weber
Date: 2020-03-23 00:00:00

We study the ex-ante minimization of market inefficiency, defined in terms of minimum deviation of market prices from fundamental values, from a centralized planner's perspective. Prices are pressured from exogenous trading actions of leverage targeting banks, which rebalance their portfolios in response to asset shocks. We characterize market inefficiency in terms of two key drivers, the banks' systemic significance and the statistical moments of asset shocks, and develop an explicit expression for the matrix of asset holdings which minimizes such inefficiency. Our analysis shows that to reduce inefficiencies, portfolio holdings should deviate more from a full diversification strategy if there is little heterogeneity in banks' systemic significance.
Multi-Terminal DC Fault Identification for MMC-HVDC Systems based on Modal Analysis -- A Localized Protection Scheme
Date: 2020-06-22 00:00:00

We propose a localized protection scheme based on modal analysis in
multi-terminal modular multilevel converter (MMC) based high voltage DC (HVDC)
systems. The paper addresses the issues of localized protection scheme based DC
fault identification, such as differentiating between external and internal
faults, classification of type of fault contingency i.e., pole to pole
(\textit{PTP}) or pole to ground (\textit{PTG}) for high impedance faults
(HIFs) in the system. The scheme works on equivalent network of multi-terminal
MMC-HVDC system for a DC fault, using phase-modal transformation to analyse
line-mode and zero-mode voltage across the current limiting reactor (CLR) for
different possible contingencies in the presence of fault resistance. The
protection scheme is validated to be reliable for HIFs and in the presence of
White Gaussian Noise (WGN) in measurement. The scheme operation is validated to
be intact for varying fault location, fault resistances and system transients.
Constrained Controller and Observer Design by Inverse Optimality
Authors: Mario Zanon, Alberto Bemporad
Date: 2020-03-23 00:00:00
Tasks: Model Predictive Control

Model Predictive Control (MPC) is often tuned by trial and error. When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active. We formulate this problem as a controller matching similar to [1]-[3], which we extend to a more general framework. We prove that a positive-definite stage cost matrix yielding this matching property can be computed for all stabilizing linear controllers. Additionally, we prove that the constrained estimation problem can also be solved similarly, by matching a linear observer with a Moving Horizon Estimator (MHE). Finally, we discuss various aspects of the practical implementation of the proposed technique in some examples.
Connectivity Maintenance: Global and Optimized approach through Control Barrier Functions
Date: 2020-03-23 00:00:00

Connectivity maintenance is an essential aspect to consider while controlling
a multi-robot system. In general, a multi-robot system should be connected to
obtain a certain common objective. Connectivity must be kept regardless of the
control strategy or the objective of the multi-robot system. Two main methods
exist for connectivity maintenance: keep the initial connections (local
connectivity) or allow modifications to the initial connections, but always
keeping the overall system connected (global connectivity). In this paper we
present a method that allows, at the same time, to maintain global connectivity
and to implement the desired control strategy (e.g., consensus, formation
control, coverage), all in an optimized fashion. For this purpose, we defined
and implemented a Control Barrier Function that can incorporate constraints and
objectives. We provide a mathematical proof of the method, and we demonstrate
its versatility with simulations of different applications.