pwc-archive/papers-with-abstracts huggingface dataset:
Active inference on discrete state-spaces: a synthesis
Date: 2020-03-28 00:00:00
Tasks: Decision Making, State Space Models

Active inference is a normative principle underwriting perception, action,
planning, decision-making and learning in biological or artificial agents. From
its inception, its associated process theory has grown to incorporate complex
generative models, enabling simulation of a wide range of complex behaviours.
Due to successive developments in active inference, it is often difficult to
see how its underlying principle relates to process theories and practical
implementation. In this paper, we try to bridge this gap by providing a
complete mathematical synthesis of active inference on discrete state-space
models. This technical summary provides an overview of the theory, derives
neuronal dynamics from first principles and relates this dynamics to biological
processes. Furthermore, this paper provides a fundamental building block needed
to understand active inference for mixed generative models; allowing continuous
sensations to inform discrete representations. This paper may be used as
follows: to guide research towards outstanding challenges, a practical guide on
how to implement active inference to simulate experimental behaviour, or a
pointer towards various in-silico neurophysiological responses that may be used
to make empirical predictions.
High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images
Date: 2020-01-20 00:00:00

Modern optical coherence tomography (OCT) devices provide volumetric images
with micrometer-scale spatial resolution and a temporal resolution beyond video
rate. In this work, we analyze an OCT-based prototypical tracking system which
processes 831 volumes per second, estimates translational motion, and
automatically adjusts the field-of-view, which has a size of few millimeters,
to follow a sample even along larger distances. The adjustment is realized by
two galvo mirrors and a motorized reference arm, such that no mechanical
movement of the scanning setup is necessary. Without requiring a marker or any
other knowledge about the sample, we demonstrate that reliable tracking of
velocities up to 25 mm/s is possible with mean tracking errors in the order of
0.25 mm. Further, we report successful tracking of lateral velocities up to 70
mm/s with errors below 0.3 mm.
Robust Precoder for Mitigating Inter-Symbol and Inter-Carrier Interferences in Coherent Optical FBMC/OQAM
Date: 2020-01-21 00:00:00

In this paper, a new precoder for a coherent optical filter bank multicarrier
with offset quadrature amplitude modulation (FBMC/OQAM) system is proposed. The
precoder is designed based on an iterative polynomial eigenvalue decomposition
(PEVD) algorithm to jointly mitigate the inter-symbol interference (ISI) and
inter-carrier interference (ICI). The PEVD algorithm is used to decompose a
polynomial channel matrix into polynomial eigenvectors and eigenvalues
matrices, then a precoder is designed based on the decomposed matrices. The
precoder acts as a filter applied to each subcarrier and to its adjacent
subcarriers. In addition, a new adaptive optimum energy algorithm is proposed
to truncate the insignificant precoder filter coefficients produced by the PEVD
algorithm and consequently reduce the computational complexity. A mathematical
model and algorithm for implementing the precoder are also presented.
Robustness of the proposed precoder has been analyzed and compared to existing
precoder in optical channel with different dispersion effects. Numerical
results demonstrate that the new precoder significantly outperforms the
existing precoder in terms of error vector magnitude (EVM), bit error rate
(BER), Frobenius norm of the error matrix, and computational complexity.
$\text{H}_{\infty}$ Tracking Control via Variable Gain Gradient Descent-Based Integral Reinforcement Learning for Unknown Continuous Time Nonlinear System
Date: 2020-01-21 00:00:00

Optimal tracking of continuous time nonlinear systems has been extensively
studied in literature. However, in several applications, absence of knowledge
about system dynamics poses a severe challenge to solving the optimal tracking
problem. This has found growing attention among researchers recently, and
integral reinforcement learning (IRL)-based method augmented with actor neural
network (NN) have been deployed to this end. However, very few studies have
been directed to model-free $H_{\infty}$ optimal tracking control that helps in
attenuating the effect of disturbances on the system performance without any
prior knowledge about system dynamics. To this end a recursive least
square-based parameter update was recently proposed. However, gradient
descent-based parameter update scheme is more sensitive to real-time variation
in plant dynamics. And experience replay (ER) technique has been shown to
improve the convergence of NN weights by utilizing past observations
iteratively. Motivated by these, this paper presents a novel parameter update
law based on variable gain gradient descent and experience replay technique for
tuning the weights of critic, actor and disturbance NNs.
Simultaneous input and state set-valued \mathcal{H}_{\infty}-observers for linear parameter-varying systems
Date: 2020-01-21 00:00:00

A fixed-order set-valued observer is presented for linear parameter-varying
systems with bounded-norm noise and under completely unknown attack signals,
which simultaneously finds bounded sets of states and unknown inputs that
include the true state and inputs. The proposed observer can be designed using
semidefinite programming with LMI constraints and is optimal in the minimum
\mathcal{H}_{\infty}-norm sense. We show that the strong detectability of each
constituent linear time-invariant system is a necessary condition for the
existence of such an observer, as well as the boundedness of set-valued
estimates. Furthermore, sufficient conditions are provided for the upper bounds
of the estimation errors to converge to steady state values and finally, the
results of such a set-valued observer are exhibited through an illustrative
example.
Q-ary Multi-Mode OFDM with Index Modulation
Date: 2020-03-18 00:00:00

In this paper, we propose a novel orthogonal frequency division multiplexing
with index modulation (OFDM-IM) scheme, which we call Q-ary multi-mode OFDM-IM
(Q-MM-OFDM-IM). In the proposed scheme, Q disjoint M-ary constellations are
used repeatedly on each subcarrier, and a maximum-distance separable code is
applied to the indices of these constellations to achieve the highest number of
index symbols. A low-complexity subcarrier-wise detection is shown possible for
the proposed scheme. Spectral efficiency (SE) and the error rate performance of
the proposed scheme are further analyzed. It is shown that the proposed scheme
exhibits a very flexible structure that is capable of encompassing conventional
OFDM as a special case. It is also shown that the proposed scheme is capable of
considerably outperforming the other OFDM-IM schemes and conventional OFDM in
terms of error and SE performance while preserving a low-complexity structure.
Deciphering the regulatory genome of $\textit{Escherichia coli}$, one hundred promoters at a time
Authors: William T. Ireland, Suzannah M. Beeler, Emanuel Flores-Bautista, Nathan M. Belliveau, Michael J. Sweredoski, Annie Moradian, Justin B. Kinney, Rob Phillips
Date: 2020-01-21 00:00:00

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium ${\it Escherichia coli}$, for $\approx$ 65$\%$ of the promoters we remain completely ignorant of their regulation. Until we have cracked this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method (Reg-Seq) linking a massively-parallel reporter assay and mass spectrometry to produce a base pair resolution dissection of more than 100 promoters in ${\it E. coli}$ in 12 different growth conditions. First, we show that our method recapitulates regulatory information from known sequences. Then, we examine the regulatory architectures for more than 80 promoters in the ${\it E. coli}$ genome which previously had no known regulation. In many cases, we also identify which transcription factors mediate their regulation. The method introduced here clears a path for fully characterizing the regulatory genome of model organisms, with the potential of moving on to an array of other microbes of ecological and medical relevance.
Grocery Store Flexibility Management Using Model Predictive Control With Neural Networks
Date: 2020-01-21 00:00:00
Tasks: Management, Model Predictive Control, Production Forecasting

As more and more energy is produced from renewable energy sources (RES), the
challenge for balancing production and consumption is being shifted to
consumers instead of the power grid. This requires new and intelligent ways of
flexibility management at individual building and district levels. To this end,
this paper presents a model based optimal control (MPC) algorithm embedded with
deep neural network for day-ahead consumption and production forecasting. The
algorithm is used to optimize a medium-sized grocery store energy consumption
located in Finland. System was tested in a simulation tool utilising real-life
power measurements from the grocery store. We report a $8.4\%$ reduction in
daily peak loads with flexibility provided by a $20$ kWh battery. On the other
hand, a significant benefit was not seen in trying to optimize with respect to
the energy spot price. We conclude that our approach is able to significantly
reduce peak loads in a grocery store without additional operational costs.
Deep Learning Enabled Real-Time Photoacoustic Tomography System via Single Data Acquisition Channel
Authors: Hengrong Lan, Daohuai Jiang, Feng Gao, Fei Gao
Date: 2020-01-21 00:00:00

Photoacoustic computed tomography (PACT) combines the optical contrast of optical imaging and the penetrability of sonography. In this work, we develop a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel. To reduce the channel number of DAQ, we superimpose 30 nearby channels' signals together in the analog domain, and shrinking to 4 channels of data (120/30=4). Furthermore, a four-to-one delay-line module is designed to combine these four channels' data into one channel before entering the single-channel DAQ, followed by decoupling the signals after data acquisition. To reconstruct the image from four superimposed 30-channels'PA signals, we train a dedicated deep learning model to reconstruct the final PA image. In this paper, we present the preliminary results of phantom and in-vivo experiments, which manifests its robust real-time imaging performance. The significance of this novel PACT system is that it dramatically reduces the cost of multi-channel DAQ module (from 120 channels to 1 channel), paving the way to a portable, low-cost and real-time PACT system.
IRS-Assisted Millimeter Wave Communications: Joint Power Allocation and Beamforming Design
Date: 2020-01-21 00:00:00

Intelligent reflecting surface (IRS) technology offers more feasible propagation paths for millimeter-wave (mmWave) communication systems to overcome blockage than existing technologies. In this paper, we consider a downlink wireless system with the IRS and formulate a joint power allocation and beamforming design problem to maximize the weighted sum-rate, which is a multi-variable optimization problem. To solve the problem, we propose a novel alternating manifold optimization based beamforming algorithm. Simulation results show that our proposed optimization algorithm outperforms existing algorithms significantly in improving the weighted sum-rate of the wireless communication system.
Task-Priority Control of Redundant Robotic Systems using Control Lyapunov and Control Barrier Function based Quadratic Programs
Authors: Erlend A. Basso, Kristin Y. Pettersen
Date: 2020-01-21 00:00:00

This paper presents a novel task-priority control framework for redundant robotic systems based on a hierarchy of control Lyapunov function (CLF) and control barrier function (CBF) based quadratic programs (QPs). The proposed method guarantees strict priority among different groups of tasks such as safety-related, operational and optimization tasks. Moreover, a soft priority measure in the form of penalty parameters can be employed to prioritize tasks at the same priority level. As opposed to kinematic control schemes, the proposed framework is a holistic approach to control of redundant robotic systems, which solves the redundancy resolution, dynamic control and control allocation problems simultaneously. Numerical simulations of a hyper-redundant articulated intervention autonomous underwater vehicle (AIAUV) is presented to validate the proposed framework.
Robust Symbol Detection in Overloaded NOMA Systems
Authors: Hiroki Iimori, Giuseppe Thadeu Freitas de Abreu, Hara Takanori, Koji Ishibashi, Razvan-Andrei Stoica, David Gonzalez G., Andreas Andrae, Osvaldo Gonsa
Date: 2020-01-21 00:00:00
Tasks: Compressive Sensing

We present a framework for the design of low-complexity and high-performance receivers for multidimensional overloaded NOMA systems. The framework is built upon a novel compressive sensing (CS) regularized maximum likelihood formulation of the discrete-input detection problem, in which the L0-norm is introduced to enforce adherence of the solution to the prescribed discrete symbol constellation. Unlike much of preceding literature, the method is not relaxed into the L1-norm, but rather approximated with a continuous and asymptotically exact expression without resorting to parallel interference cancellation. The objective function of the resulting formulation is thus a sum of concave-over-convex ratios, which is then tightly convexized via the quadratic transform, such that its solution can be obtained via the iteration of a simple closed-form expression that closely resembles that of the classic zero-forcing (ZF) receiver. By further transforming the aforementioned problem into a quadratically constrained quadratic program with one convex constraint (QCQP-1), the optimal regularization parameter to be used at each step of the iterative algorithm is then shown to be the largest generalized eigenvalue of a pair of matrices which are given in closed-form. The method so obtained, referred to as the IDLS, is then extended to address several factors of practical relevance, such as noisy conditions, imperfect CSI, and hardware impairments, thus yielding the Robust IDLS algorithm. Simulation results show that the proposed art significantly outperforms both classic receivers, such as the LMMSE, and recent CS-based alternatives, such as the SOAV and the SCSR detectors.
On Wind Speed Sensor Configurations and Altitude Control in Airborne Wind Energy Systems
Date: 2020-01-21 00:00:00

Real-time altitude control of airborne wind energy (AWE) systems can improve
performance by allowing turbines to track favorable wind speeds across a range
of operating altitudes. The current work explores the performance implications
of deploying an AWE system with sensor configurations that provide different
amounts of data to characterize wind speed profiles. We examine various control
objectives that balance trade-offs between exploration and exploitation, and
use a persistence model to generate a probabilistic wind speed forecast to
inform control decisions. We assess system performance by comparing power
production against baselines such as omniscient control and stationary flight.
We show that with few sensors, control strategies that reward exploration are
favored. We also show that with comprehensive sensing, the implications of
choosing a sub-optimal control strategy decrease. This work informs and
motivates the need for future research exploring online learning algorithms to
characterize vertical wind speed profiles.
Optimal Dispatch of Electrified Autonomous Mobility on Demand Vehicles during Power Outages
Date: 2020-01-21 00:00:00

The era of fully autonomous, electrified taxi fleets is rapidly approaching,
and with it the opportunity to innovate myriad on-demand services that extend
beyond the realm of human mobility. This project envisions a future where
autonomous plug-in electric vehicle (PEV) fleets can be dispatched as both a
taxi service and a source of on-demand power serving customers during power
outages. We develop a PDE-based scheme to manage the optimal dispatch of an
autonomous fleet to serve passengers and electric power demand during outages
as an additional stream of revenue. We use real world power outage and taxi
data from San Francisco for our case study, modeling the optimal dispatch of
several fleet sizes over the course of one day; we examine both moderate and
extreme outage scenarios. In the moderate scenario, the revenue earned serving
power demand is negligible compared with revenue earned serving passenger
trips. In the extreme scenario, supplying power accounts for between $1 and $2
million, amounting to between 32\% and 40\% more revenue than is earned serving
mobility only, depending on fleet size. While the overall value of providing
on-demand power depends on the frequency and severity of power outages, our
results show that serving power demand during large-scale outages can provide a
substantial value stream, comparable to the value to be earned providing grid
services.
On the Confidence in Bit-Alias Measurement of Physical Unclonable Functions
Date: 2020-01-21 00:00:00

Physical Unclonable Functions (PUFs) are modern solutions for cheap and
secure key storage. The security level strongly depends on a PUF's
unpredictability, which is impaired if certain bits of the PUF response tend
towards the same value on all devices. The expectation for the probability of 1
at some position in the response, the Bit-Alias, is a state-of-the-art metric
in this regard. However, the confidence interval of the Bit-Alias is never
considered, which can lead to an overestimation of a PUF's unpredictability.
Moreover, no tool is available to verify if the Bit-Alias is within given
limits. This work adapts a method for the calculation of confidence intervals
to Bit-Alias. It further proposes a statistical hypothesis test to verify if a
PUF design meets given specifications on Bit-Alias or bit-wise entropy.
Application to several published PUF designs demonstrates the methods'
capabilities. The results prove the need for a high number of samples when the
unpredictability of PUFs is tested. The proposed methods are publicly available
and should improve the design and evaluation of PUFs in the future.
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems
Date: 2020-01-21 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we propose a data-driven approach for uncertainty propagation
and reachability analysis in a dynamical system. The proposed approach relies
on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F)
and Koopman operators. The uncertainty can be characterized in terms of the
moments of a probability density function. We demonstrate how the P-F and
Koopman operators are used for propagating the moments. Time-series data is
used for the finite-dimensional approximation of the linear operators, thereby
enabling data-driven approach for moment propagation. Simulation results are
presented to demonstrate the effectiveness of the proposed method.
A Re-Examination of the Evidence used by Hooge et al (2018) "Is human classification by experienced untrained observers a gold standard in fixation detection?"
Date: 2020-05-13 00:00:00
Tasks: Open-Ended Question Answering

Hooge et al. asked the question: "Is human classification by experienced
untrained observers a gold standard in fixation detection?" They conclude the
answer is no. If they had entitled their paper: "Is human classification by
experienced untrained observers a gold standard in fixation detection when data
quality is very poor, data are error-filled, data presentation was not optimal,
and the analysis was seriously flawed?", I would have no case to make. In the
present report, I will present evidence to support my view that this latter
title is justified. The low quality data assessment is based on using a
relatively imprecise eye-tracker, the absence of head restraint for any
subjects, and the use of infants as the majority of subjects (60 of 70
subjects). Allowing subjects with more than 50% missing data (as much as 95%)
is also evidence of low quality data. The error-filled assessment is based on
evidence that a number of the "fixations" classified by "experts" have obvious
saccades within them, and that, apparently, a number of fixations were
classified on the basis of no signal at all. The evidence for non-optimal data
presentation stems from the fact that, in a number of cases, perfectly good
data was not presented to the coders. The flaws in the analysis are evidenced
by the fact that entire stretches of missing data were considered classified,
and that the measurement of saccade amplitude was based on many cases in which
there was no saccade at all. Without general evidence to the contrary, it is
correct to assume that some human classifiers under some conditions may meet
the criteria for a gold standard, and classifiers under other conditions may
not. This conditionality is not recognized by Hooge et al. A fair assessment
would conclude that whether or not humans can be considered a gold standard is
still very much an open question.
Tomographic and entropic analysis of modulated signals
Authors: A. S. Mastiukova, M. A. Gavreev, E. O. Kiktenko, A. K. Fedorov
Date: 2020-01-20 00:00:00

We study an application of the quantum tomography framework for the time-frequency analysis of modulated signals. In particular, we calculate optical tomographic representations and Wigner-Ville distributions for signals with amplitude and frequency modulations. We also consider time-frequency entropic relations for modulated signals, which are naturally associated with the Fourier analysis. A numerical toolbox for calculating optical time-frequency tomograms based on pseudo Wigner-Ville distributions for modulated signals is provided.
The role of extensional rheology in the oral phase of swallowing: an in vitro study
Authors: Marco Marconati, Marco Ramaioli
Date: 2020-01-21 00:00:00
Tasks: Management

Swallowing disorders deteriorate significantly the quality of life and can be lifethreatening. Texture modification using shear thinning food thickeners have proven effective in the management of dysphagia. Some studies have recently considered the positive role of cohesiveness, but there is still an insufficient understanding of the effect of the rheological properties of the liquid bolus on the dynamics of bolus transport, particularly when elasticity and extensional properties are combined with a shear thinning behaviour. This study combines steady shear, SAOS and capillary breakage extensional rheometry with an in vitro method to characterize the oral transport of elastic liquids. Bolus velocity and bolus length were measured from in vitro experiments using image analysis and related to the shear and extensional properties. A theory describing the bolus dynamics shows that the elastic and extensional properties do not influence significantly the oral transit dynamics. Conversely, in vitro results suggest that the extensional properties can affect the transition from the oral to the pharyngeal phase of swallowing, where thin, viscoelastic liquids lead to a fast transit, lower oral post-swallow residues and more compact bolus with a smoother surface, which may suggest a lower risk of fragmentation. This mechanistic explanation suggests that the benefit of the extensional properties of thin viscoelastic liquids in the management of dysphagia should be further evaluated in clinical trials.