pwc-archive/papers-with-abstracts huggingface dataset:
Distributed $H_\infty$ Estimation Resilient to Biasing Attacks
Date: 2019-06-17 00:00:00

We consider the distributed $H_\infty$ estimation problem with an additional
requirement of resilience to biasing attacks. An attack scenario is considered
where an adversary misappropriates some of the observer nodes and injects
biasing signals into observer dynamics. The paper proposes a procedure for the
derivation of a distributed observer which endows each node with an attack
detector which also functions as an attack compensating feedback controller for
the main observer. Connecting these controlled observers into a network results
in a distributed observer whose nodes produce unbiased robust estimates of the
plant. We show that the gains for each controlled observer in the network can
be computed in a decentralized fashion, thus reducing vulnerability of the
network.
When Risks and Uncertainties Collide: Mathematical Finance for Arbitrage Markets in a Quantum Mechanical View
Authors: Simone Farinelli, Hideyuki Takada
Date: 2019-06-15 00:00:00

Geometric arbitrage theory reformulates a generic asset model possibly allowing for arbitrage by packaging all asset and their forward dynamics into a stochastic principal fibre bundle, with a connection whose parallel transport encodes discounting and portfolio rebalancing, and whose curvature measures, in this geometric language, the instantaneous arbitrage capability generated by the market itself. The asset and market portfolio dynamics have a quantum mechanical description, which is constructed by quantizing the deterministic version of the stochastic Lagrangian system describing a market allowing for arbitrage. Results, obtained by solving the Schroedinger equation, coincide with those obtained by solving the stochastic Euler Lagrange equations derived by a variational principle and providing therefore consistency.
A Dynamic Game Approach to Strategic Design of Secure and Resilient Infrastructure Network
Date: 2019-06-17 00:00:00

Infrastructure networks are vulnerable to both cyber and physical attacks.
Building a secure and resilient networked system is essential for providing
reliable and dependable services. To this end, we establish a two-player
three-stage game framework to capture the dynamics in the infrastructure
protection and recovery phases. Specifically, the goal of the infrastructure
network designer is to keep the network connected before and after the attack,
while the adversary aims to disconnect the network by compromising a set of
links. With costs for creating and removing links, the two players aim to
maximize their utilities while minimizing the costs. In this paper, we use the
concept of subgame perfect equilibrium (SPE) to characterize the optimal
strategies of the network defender and attacker. We derive the SPE explicitly
in terms of system parameters. We further investigate the resilience planning
of the defender and the strategic timing of attack of the adversary. Finally,
we use case studies of UAV-enabled communication networks for disaster recovery
to corroborate the obtained analytical results.
Brain Maturation Study during Adolescence Using Graph Laplacian Learning Based Fourier Transform
Date: 2019-06-17 00:00:00

Objective: Longitudinal neuroimaging studies have demonstrated that
adolescence is the crucial developmental epoch of continued brain growth and
change. A large number of researchers dedicate to uncovering the mechanisms
about brain maturity during adolescence. Motivated by both achievement in graph
signal processing and recent evidence that some brain areas act as hubs
connecting functionally specialized systems, we proposed an approach to detect
these regions from spectral analysis perspective. In particular, as human brain
undergoes substantial development throughout adolescence, we addressed the
challenge by evaluating the functional network difference among age groups from
functional magnetic resonance imaging (fMRI) observations. Methods: We treated
these observations as graph signals defined on the parcellated functional brain
regions and applied graph Laplacian learning based Fourier Transform (GLFT) to
transform the original graph signals into frequency domain. Eigen-analysis was
conducted afterwards to study the behavior of the corresponding brain regions,
which enables the characterization of brain maturation. Result: We first
evaluated our method on the synthetic data and further applied the method to
resting and task state fMRI imaging data from Philadelphia Neurodevelopmental
Cohort (PNC) dataset, comprised of normally developing adolescents from 8 to
22. The model provided a highest accuracy of 95.69% in distinguishing different
adolescence stages. Conclusion: We detected 13 hubs from resting state fMRI and
16 hubs from task state fMRI that are highly related to brain maturation
process. Significance: The proposed GLFT method is powerful in extracting the
brain connectivity patterns and identifying hub regions with a high prediction
power
Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks
Date: 2019-06-14 00:00:00
Tasks: Meta-Learning

Wireless sensor networks (WSN) acts as the backbone of Internet of Things
(IoT) technology. In WSN, field sensing and fusion are the most commonly seen
problems, which involve collecting and processing of a huge volume of spatial
samples in an unknown field to reconstruct the field or extract its features.
One of the major concerns is how to reduce the communication overhead and data
redundancy with prescribed fusion accuracy. In this paper, an integrated
communication and computation framework based on meta-learning is proposed to
enable adaptive field sensing and reconstruction. It consists of a
stochastic-gradient-descent (SGD) based base-learner used for the field model
prediction aiming to minimize the average prediction error, and a reinforcement
meta-learner aiming to optimize the sensing decision by simultaneously
rewarding the error reduction with samples obtained so far and penalizing the
corresponding communication cost. An adaptive sensing algorithm based on the
above two-layer meta-learning framework is presented. It actively determines
the next most informative sensing location, and thus considerably reduces the
spatial samples and yields superior performance and robustness compared with
conventional schemes. The convergence behavior of the proposed algorithm is
also comprehensively analyzed and simulated. The results reveal that the
proposed field sensing algorithm significantly improves the convergence rate.
On combining features for single-channel robust speech recognition in reverberant environments
Date: 2019-06-17 00:00:00
Tasks: Robust Speech Recognition, speech-recognition, Speech Recognition

This paper addresses the combination of complementary parallel speech
recognition systems to reduce the error rate of speech recognition systems
operating in real highly-reverberant environments. First, the testing
environment consists of recordings of speech in a calibrated real room with
reverberation times from 0.47 to 1.77 seconds and speaker-to-microphone
distances of 0.16 to 2.56 meters. We combined systems both at the level of the
DNN outputs and at the level of the final ASR outputs. Second, recognition
experiments with the reverb challenge are also reported. The results presented
here show that the combination of features can lead to WER improvements between
7% and 18% with speech recorded in real reverberant environments. Also, the
combination at DNN-output level is much more effective than at the
system-output level. However, cascading both schemes can still lead to smaller
reductions in WER.
Deep Xi as a Front-End for Robust Automatic Speech Recognition
Date: 2020-01-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Learning, Speech Enhancement, speech-recognition, Speech Recognition

Current front-ends for robust automatic speech recognition(ASR) include
masking- and mapping-based deep learning approaches to speech enhancement. A
recently proposed deep learning approach toa prioriSNR estimation, called
DeepXi, was able to produce enhanced speech at a higher quality and
intelligibility than current masking- and mapping-based approaches. Motivated
by this, we investigate Deep Xi as a front-end for robust ASR. Deep Xi is
evaluated using real-world non-stationary and coloured noise sources at
multiple SNR levels. Our experimental investigation shows that DeepXi as a
front-end is able to produce a lower word error rate than recent masking- and
mapping-based deep learning front-ends. The results presented in this work show
that Deep Xi is a viable front-end, and is able to significantly increase the
robustness of an ASR system. Availability: Deep Xi is available
at:https://github.com/anicolson/DeepXi
Bayesian fusion and multimodal DCM for EEG and fMRI
Date: 2019-06-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This paper asks whether integrating multimodal EEG and fMRI data offers a
better characterisation of functional brain architectures than either modality
alone. This evaluation rests upon a dynamic causal model that generates both
EEG and fMRI data from the same neuronal dynamics. We introduce the use of
Bayesian fusion to provide informative (empirical) neuronal priors - derived
from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI
data. To illustrate this procedure, we generated synthetic EEG and fMRI
timeseries for a mismatch negativity (or auditory oddball) paradigm, using
biologically plausible model parameters (i.e., posterior expectations from a
DCM of empirical, open access, EEG data). Using model inversion, we found that
Bayesian fusion provided a substantial improvement in marginal likelihood or
model evidence, indicating a more efficient estimation of model parameters, in
relation to inverting fMRI data alone. We quantified the benefits of multimodal
fusion with the information gain pertaining to neuronal and haemodynamic
parameters - as measured by the Kullback-Leibler divergence between their prior
and posterior densities. Remarkably, this analysis suggested that EEG data can
improve estimates of haemodynamic parameters; thereby furnishing
proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve
conditional dependencies between neuronal and haemodynamic estimators. These
results suggest that Bayesian fusion may offer a useful approach that exploits
the complementary temporal (EEG) and spatial (fMRI) precision of different data
modalities. We envisage the procedure could be applied to any multimodal
dataset that can be explained by a DCM with a common neuronal parameterisation.
Scenario Forecasting of Residential Load Profiles
Date: 2019-06-18 00:00:00
Tasks: Load Forecasting

Load forecasting is an integral part of power system operations and planning.
Due to the increasing penetration of rooftop PV, electric vehicles and demand
response applications, forecasting the load of individual and a small group of
households is becoming increasingly important. Forecasting the load accurately,
however, is considerable more difficult when only a few households are
included. A way to mitigate this challenge is to provide a set of scenarios
instead of one point forecast, so an operator or utility can consider a range
of behaviors. This paper proposes a novel scenario forecasting approach for
residential load using flow-based conditional generative models. Compared to
existing scenario forecasting methods, our approach can generate scenarios that
are not only able to infer possible future realizations of residential load
from the observed historical data but also realistic enough to cover a wide
range of behaviors. Particularly, the flow-based models utilize a flow of
reversible transformations to maximize the value of conditional density
function of future load given the past observations. In order to better capture
the complex temporal dependency of the forecasted future load on the condition,
we extend the structure design for the reversible transformations in flow-based
conditional generative models by strengthening the coupling between the output
and the conditional input in the transformations. The simulation results show
the flow-based designs outperform existing methods in scenario forecasting for
residential load by both providing more accurate and more diverse scenarios.
Multi-lineage evolution in viral populations driven by host immune systems
Date: 2019-06-18 00:00:00

Viruses evolve in the background of host immune systems that exert selective
pressure and drive viral evolutionary trajectories. This interaction leads to
different evolutionary patterns in antigenic space. Examples observed in nature
include the effectively one-dimensional escape characteristic of influenza A
and the prolonged coexistence of lineages in influenza B. Here we use an
evolutionary model for viruses in the presence of immune host systems with
finite memory to delineate parameter regimes of these patterns in a in
two-dimensional antigenic space. We find that for small effective mutation
rates and mutation jump ranges, a single lineage is the only stable solution.
Large effective mutation rates combined with large mutational jumps in
antigenic space lead to multiple stably co-existing lineages over prolonged
evolutionary periods. These results combined with observations from data
constrain the parameter regimes for the adaptation of viruses, including
influenza.
Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis
Date: 2019-10-31 00:00:00
Tasks: Time Series, Time Series Analysis

Multifractal detrended cross-correlation methodology is described and applied
to Foreign exchange (Forex) market time series. Fluctuations of high frequency
exchange rates of eight major world currencies over 2010-2018 period are used
to study cross-correlations. The study is motivated by fundamental questions in
complex systems' response to significant environmental changes and by potential
applications in investment strategies, including detecting triangular arbitrage
opportunities. Dominant multiscale cross-correlations between the exchange
rates are found to typically occur at smaller fluctuation levels. However
hierarchical organization of ties expressed in terms of dendrograms, with a
novel application of the multiscale cross-correlation coefficient, are more
pronounced at large fluctuations. The cross-correlations are quantified to be
stronger on average between those exchange rate pairs that are bound within
triangular relations. Some pairs from outside triangular relations are however
identified to be exceptionally strongly correlated as compared to the average
strength of triangular correlations.This in particular applies to those
exchange rates that involve Australian and New Zealand dollars and reflects
their economic relations. Significant events with impact on the Forex are shown
to induce triangular arbitrage opportunities which at the same time reduce
cross--correlations on the smallest time scales and act destructively on the
multiscale organization of correlations. In 2010--2018 such instances took
place in connection with the Swiss National Bank intervention and the weakening
of British pound sterling accompanying the initiation of Brexit procedure. The
methodology could be applicable to temporal and multiscale pattern detection in
any time series.
Measuring Time with Minimal Clocks
Authors: Andrei D. Robu, Christoph Salge, Chrystopher L. Nehaniv, Daniel Polani
Date: 2019-06-17 00:00:00

Being able to measure time, whether directly or indirectly, is a significant advantage for an organism. It allows for the timely reaction to regular or predicted events, reducing the pressure for fast processing of sensory input. Thus, clocks are ubiquitous in biology. In the present paper, we consider minimal abstract pure clocks in different configurations and investigate their characteristic dynamics. We are especially interested in optimally time-resolving clocks. Among these, we find fundamentally diametral clock characteristics, such as oscillatory behavior for purely local time measurement or decay-based clocks measuring time periods of a scale global to the problem. We include also sets of independent clocks ("clock bags"), sequential cascades of clocks and composite clocks with controlled dependency. Clock cascades show a "condensation effect" and the composite clock shows various regimes of markedly different dynamics.
Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials
Authors: Tasio Gonzalez-Raya, Enrique Solano, Mikel Sanz
Date: 2019-06-16 00:00:00
Tasks: Quantization, Quantum Machine Learning

The Hodgkin-Huxley model describes the conduction of the nervous impulse through the axon, whose membrane's electric response can be described employing multiple connected electric circuits containing capacitors, voltage sources, and conductances. These conductances depend on previous depolarizing membrane voltages, which can be identified with a memory resistive element called memristor. Inspired by the recent quantization of the memristor, a simplified Hodgkin-Huxley model including a single ion channel has been studied in the quantum regime. Here, we study the quantization of the complete Hodgkin-Huxley model, accounting for all three ion channels, and introduce a quantum source, together with an output waveguide as the connection to a subsequent neuron. Our system consists of two memristors and one resistor, describing potassium, sodium, and chloride ion channel conductances, respectively, and a capacitor to account for the axon's membrane capacitance. We study the behavior of both ion channel conductivities and the circuit voltage, and we compare the results with those of the single channel, for a given quantum state of the source. It is remarkable that, in opposition to the single-channel model, we are able to reproduce the voltage spike in an adiabatic regime. Arguing that the circuit voltage is a quantum variable, we find a purely quantum-mechanical contribution in the system voltage's second moment. This work represents a complete study of the Hodgkin-Huxley model in the quantum regime, establishing a recipe for constructing quantum neuron networks with quantum state inputs. This paves the way for advances in hardware-based neuromorphic quantum computing, as well as quantum machine learning, which might be more efficient resource-wise.
Regulation of Migration of Chemotactic Tumor Cells by the Spatial Distribution of the Collagen Fibers' Orientation
Authors: Youness Azimzade, Abbas Ali Saberi, Muhammad Sahimi
Date: 2019-06-18 00:00:00

Collagen fibers, an important component of the extracellular matrix (ECM), can both inhibit and promote cellular migration. {\it In-vitro} studies have revealed that the fibers' orientations are crucial to cellular invasion, while {\it in-vivo} investigations have led to the development of tumor-associated collagen signatures (TACS) as an important prognostic factor. Studying biophysical regulation of cell invasion and the effect of the fibers' oritentation not only deepens our understanding of the phenomenon, but also helps classifying the TACSs precisely, which is currently lacking. We present a stochastic model for random/chemotactic migration of cells in fibrous ECM, and study the role of the various factors in it. The model provides a framework, for the first time to our knowledge, for quantitative classification of the TACSs, and reproduces quantitatively recent experimental data for cell motility. It also indicates that the spatial distribution of the fibers' orientations and extended correlations between them, hitherto ignored, as well as dynamics of cellular motion all contribute to regulation of the cells' invasion length, which represents a measure of metastatic risk. Although the fibers' orientations trivially affect randomly moving cells, their effect on chemotactic cells is completely nontrivial and unexplored, which we study in this paper.
On the optimal parameters of a PSO-based algorithm for simulation of Endurance Time Excitation Functions
Date: 2019-10-30 00:00:00

This paper presents a particle swarm optimizer for production of endurance
time excitation functions. These excitations are intensifying acceleration time
histories that are used as input motions in endurance time method. The accuracy
of the endurance time methods heavily depends on the accuracy of endurance time
excitations. Unconstrained nonlinear optimization is employed to simulate these
excitations. Particle swarm optimization method as an evolutionary algorithm is
examined in this paper to achieve a more accurate endurance time excitation
function, where optimal parameters of the particle swarm optimization are first
determined using a parametric study on the involved variables. The proposed
method is verified and compared with the trust-region reflective method as a
classical optimization method and imperialist competitive algorithm as a
recently developed evolutionary method. Results show that the proposed method
leads to more accurate endurance time excitations.
Cortical computations via metastable activity
Date: 2019-06-18 00:00:00

Metastable brain dynamics are characterized by abrupt, jump-like modulations
so that the neural activity in single trials appears to unfold as a sequence of
discrete, quasi-stationary states. Evidence that cortical neural activity
unfolds as a sequence of metastable states is accumulating at fast pace.
Metastable activity occurs both in response to an external stimulus and during
ongoing, self-generated activity. These spontaneous metastable states are
increasingly found to subserve internal representations that are not locked to
external triggers, including states of deliberations, attention and
expectation. Moreover, decoding stimuli or decisions via metastable states can
be carried out trial-by-trial. Focusing on metastability will allow us to shift
our perspective on neural coding from traditional concepts based on
trial-averaging to models based on dynamic ensemble representations. Recent
theoretical work has started to characterize the mechanistic origin and
potential roles of metastable representations. In this article we review recent
findings on metastable activity, how it may arise in biologically realistic
models, and its potential role for representing internal states as well as
relevant task variables.
Signatures of crypto-currency market decoupling from the Forex
Date: 2019-07-18 00:00:00

Based on the high-frequency recordings from Kraken, a cryptocurrency exchange
and professional trading platform that aims to bring Bitcoin and other
cryptocurrencies into the mainstream, the multiscale cross-correlations
involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are
studied over the period between July 1, 2016 and December 31, 2018. It is shown
that the multiscaling characteristics of the exchange rate fluctuations related
to the cryptocurrency market approach those of the Forex. This, in particular,
applies to the BTC/ETH exchange rate, whose Hurst exponent by the end of 2018
started approaching the value of 0.5, which is characteristic of the mature
world markets. Furthermore, the BTC/ETH direct exchange rate has already
developed multifractality, which manifests itself via broad singularity
spectra. A particularly significant result is that the measures applied for
detecting cross-correlations between the dynamics of the BTC/ETH and EUR/USD
exchange rates do not show any noticeable relationships. This may be taken as
an indication that the cryptocurrency market has begun decoupling itself from
the Forex.