pwc-archive/papers-with-abstracts huggingface dataset:
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation
Date: 2020-08-21 00:00:00
Tasks: Computational Efficiency

Quasi-static ultrasound elastography (USE) is an imaging modality that
consists of determining a measure of deformation (i.e.strain) of soft tissue in
response to an applied mechanical force. The strain is generally determined by
estimating the displacement between successive ultrasound frames acquired
before and after applying manual compression. The computational efficiency and
accuracy of the displacement prediction, also known as time-delay estimation,
are key challenges for real-time USE applications. In this paper, we present a
novel deep-learning method for efficient time-delay estimation between
ultrasound radio-frequency (RF) data. The proposed method consists of a
convolutional neural network (CNN) that predicts a displacement field between a
pair of pre- and post-compression ultrasound RF frames. The network is trained
in an unsupervised way, by optimizing a similarity metric be-tween the
reference and compressed image. We also introduce a new regularization term
that preserves displacement continuity by directly optimizing the strain
smoothness. We validated the performance of our method by using both ultrasound
simulation and in vivo data on healthy volunteers. We also compared the
performance of our method with a state-of-the-art method called OVERWIND [17].
Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our
method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and
0.31, respectively. Our results suggest that our approach can effectively
predict accurate strain images. The unsupervised aspect of our approach
represents a great potential for the use of deep learning application for the
analysis of clinical ultrasound data.
What do we mean by the dimensionality of behavior?
Date: 2020-08-21 00:00:00

There is growing effort in the "physics of behavior" that aims at complete
quantitative characterization of animal movements under more complex,
naturalistic conditions. One reaction to the resulting explosion of data is the
search for low dimensional structure. Here I try to define more clearly what we
mean by the dimensionality of behavior, where observable behavior may consist
either of continuous trajectories or sequences of discrete states. This
discussion also serves to isolate situations in which the dimensionality of
behavior is effectively infinite. I conclude with some more general
perspectives about the importance of quantitative phenomenology.
The unintended consequences of inconsistent pandemic control policies
Date: 2020-10-28 00:00:00

Controlling the spread of COVID-19 - even after a licensed vaccine is
available - requires the effective use of non-pharmaceutical interventions:
physical distancing, limits on group sizes, mask wearing, etc. To date, such
interventions have neither been uniformly nor systematically implemented in
most countries. For example, even when under strict stay-at-home orders,
numerous jurisdictions granted exceptions and/or were in close proximity to
locations with entirely different regulations in place. Here, we investigate
the impact of such geographic inconsistencies in epidemic control policies by
coupling search and mobility data to a simple mathematical model of SARS-COV2
transmission. Our results show that while stay-at-home orders decrease contacts
in most areas of the US, some specific activities and venues often see an
increase in attendance. Indeed, over the month of March 2020, between 10 and
30% of churches in the US saw increases in attendance; even as the total number
of visits to churches declined nationally. This heterogeneity, where certain
venues see substantial increases in attendance while others close, suggests
that closure can cause individuals to find an open venue, even if that requires
longer-distance travel. And, indeed, the average distance travelled to churches
in the US rose by 13% over the same period. Strikingly, our model reveals that
across a broad range of model parameters, partial measures can often be worse
than none at all where individuals not complying with policies by traveling to
neighboring areas can create epidemics when the outbreak would otherwise have
been controlled. Taken together, our data analysis and modelling results
highlight the potential unintended consequences of inconsistent epidemic
control policies and stress the importance of balancing the societal needs of a
population with the risk of an outbreak growing into a large epidemic.
Low-complexity Architecture for AR(1) Inference
Authors: A. Borges Jr., R. J. Cintra, D. F. G. Coelho, V. S. Dimitrov
Date: 2020-08-21 00:00:00

In this Letter, we propose a low-complexity estimator for the correlation coefficient based on the signed $\operatorname{AR}(1)$ process. The introduced approximation is suitable for implementation in low-power hardware architectures. Monte Carlo simulations reveal that the proposed estimator performs comparably to the competing methods in literature with maximum error in order of $10^{-2}$. However, the hardware implementation of the introduced method presents considerable advantages in several relevant metrics, offering more than 95% reduction in dynamic power and doubling the maximum operating frequency when compared to the reference method.
Eco-Routing Navigation System for Electric Vehicles
Date: 2020-08-21 00:00:00

The goal of this project is to determine the feasibility of calculating a
travel route for an electric vehicle (EV) that will require the least amount of
energy for the trip, and thus extending the range of the EV. To achieve this
goal, the research team set up several objectives, including 1) to demonstrate
the accuracy of measurements from the data acquisition system; 2) to
demonstrate the validity of the proposed algorithm in estimating energy
consumption of the test EV (2013 NISSAN LEAF) in real-time; and 3) to
demonstrate the benefit from the prototype system in terms energy savings for
the test EV. The results from the project show that the proposed system can
fulfill the aforementioned objectives and provide up to 51% of energy
reduction, compared with the conventional navigation strategies (i.e., shortest
travel distance and least travel duration).
Experimental Black-box System identification and control of a Torus Cassegrain Telescope
Date: 2020-08-21 00:00:00
Tasks: Position

The Astronomical Observatory of the Sergio Arboleda University (Bogota,
Colombia) has as main instrument a Torus Classic Cassegrain telescope. In order
to improve its precision and accuracy in the tracking of celestial objects, the
Torus telescope requires to be properly automated. The motors, drivers and
sensors of the telescope are modelled as a black-box systems and experimentally
identified. Using the identified model several control laws are developed such
as Proportional, Integral and Derivative (PID) control and State-feedback
control for the velocity and position tracking and disturbance-rejection tasks.
On PMU Data Integrity under GPS Spoofing Attacks: A Sparse Error Correction Framework
Authors: Shashini De Silva, Jinsub Kim, Eduardo Cotilla-Sanchez, Travis Hagan
Date: 2020-08-21 00:00:00

Consider the problem of mitigating the impact on data integrity of phasor measurement units (PMUs) given a GPS spoofing attack. We present a sparse error correction framework to treat PMU measurements that are potentially corrupted due to a GPS spoofing attack. We exploit the sparse nature of a GPS spoofing attack, which is that only a small fraction of PMUs are affected by the attack. We first present attack identifiability conditions (in terms of network topology, PMU locations, and the number of spoofed PMUs) under which data manipulation by the spoofing attack is identifiable. The identifiability conditions have important implications on how the locations of PMUs affect their resilience to GPS spoofing attacks. To effectively correct spoofed PMU data, we present a sparse error correction approach wherein computation tasks are decomposed into smaller zones to ensure scalability. We present experimental results obtained from numerical simulations with the IEEE RTS-96 and IEEE 300 test networks to demonstrate the effectiveness of the proposed approach.
Spectral independent component analysis with noise modeling for M/EEG source separation
Authors: Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Date: 2020-08-21 00:00:00
Tasks: Denoising, Dimensionality Reduction, EEG, Electroencephalogram (EEG), Time Series Analysis

Background: Independent Component Analysis (ICA) is a widespread tool for exploration and denoising of electroencephalography (EEG) or magnetoencephalography (MEG) signals. In its most common formulation, ICA assumes that the signal matrix is a noiseless linear mixture of independent sources that are assumed non-Gaussian. A limitation is that it enforces to estimate as many sources as sensors or to rely on a detrimental PCA step. Methods: We present the Spectral Matching ICA (SMICA) model. Signals are modelled as a linear mixing of independent sources corrupted by additive noise, where sources and the noise are stationary Gaussian time series. Thanks to the Gaussian assumption, the negative log-likelihood has a simple expression as a sum of divergences between the empirical spectral covariance matrices of the signals and those predicted by the model. The model parameters can then be estimated by the expectation-maximization (EM) algorithm. Results: Experiments on phantom MEG datasets show that SMICA can recover dipole locations more precisely than usual ICA algorithms or Maxwell filtering when the dipole amplitude is low. Experiments on EEG datasets show that SMICA identifies a source subspace which contains sources that have less pairwise mutual information, and are better explained by the projection of a single dipole on the scalp. Comparison with existing methods: Noiseless ICA models lead to degenerate likelihood when there are fewer sources than sensors, while SMICA succeeds without resorting to prior dimension reduction. Conclusions: SMICA is a promising alternative to other noiseless ICA models based on non-Gaussian assumptions.
Cryopreservation of sugarcane embryogenic callus using a simplified freezing process
Authors: M. E. Martinez-Montero, M. T. Gonzalez-Arnao, C. Borroto-Nordelo, C. Puentes-Diaz, F. Engelmann
Date: 2020-08-21 00:00:00

A simplified freezing process was developed and successfully applied to embryogenic calluses of three sugarcane commercial hybrids (Saccharum sp. cv. CP 5243, C 91-301 and C 1051-73). To obtain optimal survival, the calluses were pretreated with a cryoprotective solution containing 10% DMSO and 0.3 to 0.75M sucrose. For freezing, the samples were immersed in an alcohol bath placed in a -40 degrees C freezer, thus allowing a freezing rate comprised between 0.4 and 0.6 degrees C/min. Samples were held at that temperature for 2 h before immersion in liquid nitrogen. The highest survival rates of cryopreserved calluses ranged between 20 and 94% depending on the variety, and fully developed plantlets could be obtained from regenerating calluses. Embryogenic calluses of one variety were stored for 14 months in liquid nitrogen without any effect on their survival rate and plantlet production.
Self-Tuning Control based on Modified Equivalent-Dynamic-Linearization Model
Authors: Feilong Zhang
Date: 2020-08-22 00:00:00

The current model-free adaptive control (MFAC) method is designed on the basis of the equivalent-dynamic-linearization model (EDLM) with neglect of the time delay and disturbance in practical. By comparisons with the current works about MFAC, i) a class of self-tuning controller is proposed based a new EDLM modified by the introduction of time delay and disturbance so as to reflect the real system more objectively. Thereafter, we classify the proposed controller into four cases to enable easier applications; ii) the controller design and stability analysis of system are achievable by analyzing the function of the closed-loop poles. In addition, the issue of how to choose the parameter \lambda in current MFAC by quantity is firstly finished by the analysis of zeros-poles placement and static error of system, whereas this can hardly be realized by the previous contraction mapping method; iii) the study on the proposed method is focused on linear model for easily mastering its working behavior; At last, two examples are used to demonstrate the effectiveness of the proposed method and to point out the deficiencies in the current MFAC theory.
Constrained Trading Networks
Date: 2020-08-22 00:00:00
Tasks: ARC

Trades based on bilateral (indivisible) contracts can be represented by a
network. Vertices correspond to agents while arcs represent the non-price
elements of a bilateral contract. Given prices for each arc, agents choose the
incident arcs that maximize their utility. We enlarge the model to allow for
polymatroidal constraints on the set of contracts that may be traded which can
be interpreted as modeling limited one for-one substitution. We show that for
two-sided markets there exists a competitive equilibrium however for
multi-sided markets this may not be possible.
Study of Intelligent Reflective Surface Assisted Communications with One-bit Phase Adjustments
Authors: Tianxiong Wang, Gaojie Chen, Justin P. Coon, Mihai-Alin Badiu
Date: 2020-08-22 00:00:00
Tasks: Diversity

We analyse the performance of a communication link assisted by an intelligent reflective surface (IRS) positioned in the far field of both the source and the destination. A direct link between the transmitting and receiving devices is assumed to exist. Perfect and imperfect phase adjustments at the IRS are considered. For the perfect phase configuration, we derive an approximate expression for the outage probability in closed form. For the imperfect phase configuration, we assume that each element of the IRS has a one-bit phase shifter (0{\deg}, 180{\deg}) and an expression for the outage probability is obtained in the form of an integral. Our formulation admits an exact asymptotic (high SNR) analysis, from which we obtain the diversity orders for systems with and without phase errors. We show these are N + 1 and (N + 3)/2, respectively. Numerical results confirm the theoretical analysis and verify that the reported results are more accurate than methods based on the central limit theorem (CLT).
Solution space of optimal heat pump schedules
Date: 2020-08-22 00:00:00

We study the space of optimal schedules for a heat pump with thermal energy
storage used in heating a residential building. We model the heating system as
a Mixed Integer Linear Program with the objective to minimise the cost of
heating. We generate a large number of realistic daily heat demands and
calculate the optimal schedule for the heat pump. In addition to cost savings
stemming from optimal running, we find that the space of optimal schedules is
large in practice, even for the simplest model of the heating system we use,
and that the optimal schedules are difficult to reproduce with statistical
models. These findings strengthen the case for the use of mathematical
optimisation in real-life applications.
Large Intelligent Surfaces with Channel Estimation Overhead: Achievable Rate and Optimal Configuration
Authors: Neel Kanth Kundu, Matthew R. McKay
Date: 2020-08-22 00:00:00

Large intelligent surfaces (LIS) present a promising new technology for enhancing the performance of wireless communication systems. Realizing the gains of LIS requires accurate channel knowledge, and in practice the channel estimation overhead can be large due to the passive nature of LIS. Here, we study the achievable rate of a LIS-assisted single-input single-output communication system, accounting for the pilot overhead of a least-squares channel estimator. We demonstrate that there exists an optimal $K^{*}$, which maximizes achievable rate by balancing the power gains offered by LIS and the channel estimation overhead. We present analytical approximations for $K^{*}$, based on maximizing an analytical upper bound on average achievable rate that we derive, and study the dependencies of $K^*$ on statistical channel and system parameters.
Level crossing statistics in a biologically motivated model of a long dynamic protrusion: passage times, random and extreme excursions
Authors: Swayamshree Patra, Debashish Chowdhury
Date: 2020-08-22 00:00:00

Long cell protrusions, which are effectively one-dimensional, are highly dynamic subcellular structures. Length of many such protrusions keep fluctuating about the mean value even in the the steady state. We develop here a stochastic model motivated by length fluctuations of a type of appendage of an eukaryotic cell called flagellum (also called cilium). Exploiting the techniques developed for the calculation of level-crossing statistics of random excursions of stochastic process, we have derived analytical expressions of passage times for hitting various thresholds, sojourn times of random excursions beyond the threshold and the extreme lengths attained during the lifetime of these model flagella. We identify different parameter regimes of this model flagellum that mimic those of the wildtype and mutants of a well known flagellated cell. By analysing our model in these different parameter regimes, we demonstrate how mutation can alter the level-crossing statistics even when the steady state length remains unaffected by the same mutation. Comparison of the theoretically predicted level crossing statistics, in addition to mean and variance of the length, in the steady state with the corresponding experimental data can be used in near future as stringent tests for the validity of the models of flagellar length control. The experimental data required for this purpose, though never reported till now, can be collected, in principle, using a method developed very recently for flagellar length fluctuations.
A Discrete-Time Matching Filtering Differentiator
Date: 2020-08-22 00:00:00

This paper presents a time discretization of the robust exact filtering
differentiator, a sliding mode differentiator coupled to filter, which provides
a suitable approximation to the derivatives of some noisy signals. This
proposal takes advantage of the homogeneity of the differentiator, allowing the
use of similar techniques to those of the linear systems. As in the original
case, the convergence robust exact filtering differentiator depends on the
bound of a higher-order derivative; nevertheless, this new realization can be
implemented with or without the knowledge of such constant. It is demonstrated
that the system's trajectories converge to a neighborhood of the origin with a
free-noise input. Finally, comparisons between the behavior of the
differentiator with different design parameters are presented.
Bayesian mechanics of perceptual inference and motor control in the brain
Authors: Chang Sub Kim
Date: 2020-08-22 00:00:00

The free energy principle (FEP) in the neurosciences stipulates that all viable agents induce and minimize informational free energy in the brain to fit their environmental niche. In this study, we continue our effort to make the FEP a more physically principled formalism by implementing free energy minimization based on the principle of least action. We build a Bayesian mechanics (BM) by casting the formulation reported in the earlier publication (Kim 2018) to considering active inference beyond passive perception. The BM is a neural implementation of variational Bayes under the FEP in continuous time. The resulting BM is provided as an effective Hamilton's equation of motion and subject to the control signal arising from the brain's prediction errors at the proprioceptive level. To demonstrate the utility of our approach, we adopt a simple agent-based model and present a concrete numerical illustration of the brain performing recognition dynamics by integrating BM in neural phase space. Furthermore, we recapitulate the major theoretical architectures in the FEP by comparing our approach with the common state-space formulations.
Low-Complexity Joint Power Allocation and Trajectory Design for UAV-Enabled Secure Communications with Power Splitting
Authors: Kaidi Xu, Ming-Min Zhao, Yunlong Cai, Lajos Hanzo
Date: 2020-08-23 00:00:00

An unmanned aerial vehicle (UAV)-aided secure communication system is conceived and investigated, where the UAV transmits legitimate information to a ground user in the presence of an eavesdropper (Eve). To guarantee the security, the UAV employs a power splitting approach, where its transmit power can be divided into two parts for transmitting confidential messages and artificial noise (AN), respectively. We aim to maximize the average secrecy rate by jointly optimizing the UAV's trajectory, the transmit power levels and the corresponding power splitting ratios allocated to different time slots during the whole flight time, subject to both the maximum UAV speed constraint, the total mobility energy constraint, the total transmit power constraint, and other related constraints. To efficiently tackle this non-convex optimization problem, we propose an iterative algorithm by blending the benefits of the block coordinate descent (BCD) method, the concave-convex procedure (CCCP) and the alternating direction method of multipliers (ADMM). Specially, we show that the proposed algorithm exhibits very low computational complexity and each of its updating steps can be formulated in a nearly closed form. Our simulation results validate the efficiency of the proposed algorithm.
Time-Varying and Nonlinearly Scaled Consensus of Multiagent Systems: A Generic Attracting Law Approach
Date: 2020-08-23 00:00:00

This paper presents the design and analysis of the finite/fixed-time scaled
consensus for multiagent systems. A study on a generic attracting law, the
certain classes of nonlinear systems that admit attractors with
finite/fixed-time convergence, is at first given for the consensus purpose. The
estimates for the lower and upper bounds on the settling time functions are
provided through the two-phase analysis. The given estimates are initial state
dependent, but the durations are finite, without regarding the values that the
initial states take. According to the generic attracting law, distributed
protocols are proposed for multiagent systems with undirected and
detail-balanced directed graphs, respectively, where the scaled strategies,
including time-varying and nonlinear scales, are adopted. It is shown that the
finite/fixed-time consensus for the multiagent system undertaken can still be
achieved, even though both time-varying and nonlinear scales are taken among
agents. Numerical simulation of two illustrative examples are given to verify
effectiveness of the proposed finite-duration consensus protocols.