pwc-archive/papers-with-abstracts huggingface dataset:
Power Efficient Trajectory Optimization for the Cellular-Connected Aerial Vehicles
Date: 2019-06-22 00:00:00

Aerial vehicles have recently attracted significant attention in a variety of
commercial and civilian applications due to their high mobility, flexible
deployment and cost-effectiveness. To leverage these promising features, the
aerial users have to satisfy two critical requirements: First, they have to
maintain a reliable communication link to the ground base stations (GBSs)
throughout their flights, to support command and control data flows. Second,
the aerial vehicles have to minimize their propulsion power consumption to
remain functional until the end of their mission. In this paper, we study the
trajectory optimization problem for an aerial user flying over an area
including a set of GBSs. The objective of this problem is to find the
trajectory of the aerial user so that the total propulsion-related power
consumption of the aerial user is minimized while a cellular-connectivity
constraint is satisfied. This problem is a non-convex mixed integer non-linear
problem and hence, it is challenging to find the solution. To deal with, first,
the problem is relaxed and reformulated to a more mathematically tractable
form. Then, using successive convex approximation (SCA) technique, an iterative
algorithm is proposed to convert the problem into a sequence of convex problems
which can be solved efficiently.
Deep urban unaided precise Global Navigation Satellite System vehicle positioning
Authors: Todd E. Humphreys, Matthew J. Murrian, Lakshay Narula
Date: 2019-06-23 00:00:00

This paper presents the most thorough study to date of vehicular carrier-phase differential GNSS (CDGNSS) positioning performance in a deep urban setting unaided by complementary sensors. Using data captured during approximately 2 hours of driving in and around the dense urban center of Austin, TX, a CDGNSS system is demonstrated to achieve 17-cm-accurate 3D urban positioning (95% probability) with solution availability greater than 87%. The results are achieved without any aiding by inertial, electro-optical, or odometry sensors. Development and evaluation of the unaided GNSS-based precise positioning system is a key milestone toward the overall goal of combining precise GNSS, vision, radar, and inertial sensing for all-weather high-integrity high-absolute-accuracy positioning for automated and connected vehicles. The system described and evaluated herein is composed of a densely-spaced reference network, a software-defined GNSS receiver, and a real-time kinematic (RTK) positioning engine. A performance sensitivity analysis reveals that navigation data wipeoff for fully-modulated GNSS signals and a dense reference network are key to high-performance urban RTK positioning. A comparison with existing unaided systems for urban GNSS processing indicates that the proposed system has significantly greater availability or accuracy.
Harnessing behavioral diversity to understand circuits for cognition
Date: 2019-06-23 00:00:00
Tasks: Diversity

With the increasing acquisition of large-scale neural recordings comes the
challenge of inferring the computations they perform and understanding how
these give rise to behavior. Here, we review emerging conceptual and
technological advances that begin to address this challenge, garnering insights
from both biological and artificial neural networks. We argue that neural data
should be recorded during rich behavioral tasks, to model cognitive processes
and estimate latent behavioral variables. Careful quantification of animal
movements can also provide a more complete picture of how movements shape
neural dynamics and reflect changes in brain state, such as arousal or stress.
Artificial neural networks (ANNs) could serve as an important tool to connect
neural dynamics and rich behavioral data. ANNs have already begun to reveal how
particular behaviors can be optimally solved, generating hypotheses about how
observed neural activity might drive behavior and explaining diversity in
behavioral strategies.
Secure Multi-party Computation for Cloud-based Control
Date: 2019-06-23 00:00:00
Tasks: Privacy Preserving

In this chapter, we will explore the cloud-outsourced privacy-preserving
computation of a controller on encrypted measurements from a (possibly
distributed) system, taking into account the challenges introduced by the
dynamical nature of the data. The privacy notion used in this work is that of
cryptographic multi-party privacy, i.e., the computation of a functionality
should not reveal anything more than what can be inferred only from the inputs
and outputs of the functionality. The main theoretical concept used towards
this goal is Homomorphic Encryption, which allows the evaluation of sums and
products on encrypted data, and, when combined with other cryptographic
techniques, such as Secret Sharing, results in a powerful tool for solving a
wide range of secure multi-party problems. We will rigorously define these
concepts and discuss how multi-party privacy can be enforced in the
implementation of a Model Predictive Controller, which encompasses computing
stabilizing control actions by solving an optimization problem on encrypted
data.
Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems
Date: 2019-06-24 00:00:00
Tasks: Edge-computing, Form

Mobile edge computing (MEC) has recently emerged as a promising technology to
release the tension between computation-intensive applications and
resource-limited mobile terminals (MTs). In this paper, we study the
delay-optimal computation offloading in computation-constrained MEC systems. We
consider the computation task queue at the MEC server due to its constrained
computation capability. In this case, the task queue at the MT and that at the
MEC server are strongly coupled in a cascade manner, which creates complex
interdependencies and brings new technical challenges. We model the computation
offloading problem as an infinite horizon average cost Markov decision process
(MDP), and approximate it to a virtual continuous time system (VCTS) with
reflections. Different to most of the existing works, we develop the dynamic
instantaneous rate estimation for deriving the closed-form approximate priority
functions in different scenarios. Based on the approximate priority functions,
we propose a closed-form multi-level water-filling computation offloading
solution to characterize the influence of not only the local queue state
information (LQSI) but also the remote queue state information (RQSI). A
extension is provided from single MT single MEC server scenarios to multiple
MTs multiple MEC servers scenarios and several insights are derived. Finally,
the simulation results show that the proposed scheme outperforms the
conventional schemes.
Independent channels for miRNA biosynthesis ensure efficient static and dynamic control in the regulation of the early stages of myogenesis
Date: 2019-06-24 00:00:00

Motivated by recent experimental work, we define and study a deterministic
model of the complex miRNA-based regulatory circuit that putatively controls
the early stage of myogenesis in human. We aim in particular at a quantitative
understanding of (i) the roles played by the separate and independent miRNA
biosynthesis channels (one involving a miRNA-decoy system regulated by an
exogenous controller, the other given by transcription from a distinct genomic
locus) that appear to be crucial for the differentiation program, and of (ii)
how competition to bind miRNAs can efficiently control molecular levels in such
an interconnected architecture. We show that optimal static control via the
miRNA-decoy system constrains kinetic parameters in narrow ranges where the
channels are tightly cross-linked. On the other hand, the alternative locus for
miRNA transcription can ensure that the fast concentration shifts required by
the differentiation program are achieved, specifically via non-linear response
of the target to even modest surges in the miRNA transcription rate. While
static, competition-mediated regulation can be achieved by the miRNA-decoy
system alone, both channels are essential for the circuit's overall
functionality, suggesting that that this type of joint control may represent a
minimal optimal architecture in different contexts.
Inter-residue, inter-protein and inter-family coevolution: bridging the scales
Date: 2019-06-24 00:00:00

Interacting proteins coevolve at multiple but interconnected scales, from the
residue-residue over the protein-protein up to the family-family level. The
recent accumulation of enormous amounts of sequence data allows for the
development of novel, data-driven computational approaches. Notably, these
approaches can bridge scales within a single statistical framework. While being
currently applied mostly to isolated problems on single scales, their immense
potential for an evolutionary informed, structural systems biology is steadily
emerging.
Simultaneous Harvest-and-Transmit Ambient Backscatter Communications under Rayleigh Fading
Date: 2019-06-24 00:00:00
Tasks: Management

Ambient backscatter communications is an emerging paradigm and a key enabler
for pervasive connectivity of low-powered wireless devices. It is primarily
beneficial in the Internet of things (IoT) and the situations where computing
and connectivity capabilities expand to sensors and miniature devices that
exchange data on a low power budget. The premise of the ambient backscatter
communication is to build a network of devices capable of operating in a
battery-free manner by means of smart networking, radio frequency (RF) energy
harvesting and power management at the granularity of individual bits and
instructions. Due to this innovation in communication methods, it is essential
to investigate the performance of these devices under practical constraints. To
do so, this article formulates a model for wireless-powered ambient backscatter
devices and derives a closed-form expression of outage probability under
Rayleigh fading. Based on this expression, the article provides the
power-splitting factor that balances the tradeoff between energy harvesting and
achievable data rate. Our results also shed light on the complex interplay of a
power-splitting factor, amount of harvested energy, and the achievable data
rates.
Online Variational Bayesian Subspace Filtering with Applications
Date: 2019-06-24 00:00:00
Tasks: Imputation, Matrix Completion

Matrix completion and robust principal component analysis have been widely
used for the recovery of data suffering from missing entries or outliers. In
many real-world applications however, the data is also time-varying, and the
naive approach of per-snapshot recovery is both expensive and sub-optimal. This
paper develops generative Bayesian models that fit sequential multivariate
measurements arising from a low-dimensional time-varying subspace. A
variational Bayesian subspace filtering approach is proposed that learns the
underlying subspace and its state-transition matrix. Different from the
plethora of deterministic counterparts, the proposed approach utilizes
automatic relevance determination priors that obviate the need to tune key
parameters such as rank and noise power. We also propose a forward-backward
algorithm that allows the updates to be carried out at low complexity.
Extensive tests over traffic and electricity data demonstrate the superior
imputation, outlier rejection, and temporal prediction prowess of the proposed
algorithm over the state-of-the-art matrix/tensor completion algorithms.
S\'eparation de sources doublement non stationnaire
Date: 2019-06-24 00:00:00
Tasks: blind source separation

Blind source separation (BSS) techniques aims at joint estimation of source
signals and a mixing matrix from observations of mixtures. This paper addresses
a doubly nonstationary BSS problem, where the mixing matrix is time dependent
and sources are nonstationary, more precisely deformed stationary signals,
following the model of [1]. An algorithm for joint BSS and estimation of
stationarity-breaking deformations and spectra is introduced, that exploits
suitable approximations for the behavior of the wavelet transform of such
nonstationary signals. The performance of the approach is evaluated on
numerical simulations, and compared with other nonstationary BSS algorithms.
Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall
Date: 2019-06-21 00:00:00

A joint conditional autoregressive expectile and Expected Shortfall framework
is proposed. The framework is extended through incorporating a measurement
equation which models the contemporaneous dependence between the realized
measures and the latent conditional expectile. Nonlinear threshold
specification is further incorporated into the proposed framework. A Bayesian
Markov Chain Monte Carlo method is adapted for estimation, whose properties are
assessed and compared with maximum likelihood via a simulation study.
One-day-ahead VaR and ES forecasting studies, with seven market indices,
provide empirical support to the proposed models.
Self-repairing Classification Algorithms for Chemical Sensor Array
Date: 2019-06-24 00:00:00
Tasks: Classification

Chemical sensors are usually affected by drift, have low fabrication
reproducibility and can experience failure or breaking events over the long
term. Albeit improvements in fabrication processes are often slow and
inadequate for completely surmounting these issues, data analysis can be used
as of now to improve the available device performances. The present paper
illustrates an algorithm, called Self-Repairing (SR), developed for repairing
classification models after the occurrences of failures in sensor arrays. The
procedure considers replacing broken sensors with replicas and eventually
Self-Repairing algorithm trains these blank elements. Unlike the habitual
alternatives reported in literature, SR performs this operation without the
need of a whole new recalibration, references gas measurements or transfer
dataset and, at the same time, without interrupting the on-going procedure of
gas identification. Furthermore, Self-Repairing algorithm can utilize most of
the standard classifiers as core algorithm; in this paper SR has been applied
to k-NN, PLS-DA and LDA as examples. Models have been tested in a synthetic and
real scenario considering sensor arrays affected by drift and eventually by
failures. Real experiment has been performed with a set of metal oxide sensors
over an 18-months period. Finally, the algorithm has been compared with
standard version of chosen classifiers (k-NN, LDA and PLS-DA) showing superior
performances of Self-Repairing and increasing the tolerance versus consecutive
failures.
Cooperativity, Absolute Interaction, and Algebraic Optimization
Date: 2019-06-24 00:00:00

We consider a measure of cooperativity based on the minimal absolute
interaction required to generate an observed titration behavior. We describe
the corresponding algebraic optimization problem and show how it can be solved
using the nonlinear algebra tool \texttt{SCIP}. Moreover, we compute the
minimal absolute interactions for various binding polynomials that describe the
oxygen binding of various hemoglobins under different conditions. While
calculated minimal absolute interactions are consistent with the expected
outcome of the chemical modifications, it ranks the cooperativity of the
molecules differently than the maximal Hill slope.
Long Run Feedback in the Broker Call Money Market
Authors: Alex Garivaltis
Date: 2019-06-24 00:00:00

I unravel the basic long run dynamics of the broker call money market, which is the pile of cash that funds margin loans to retail clients (read: continuous time Kelly gamblers). Call money is assumed to supply itself perfectly inelastically, and to continuously reinvest all principal and interest. I show that the relative size of the money market (that is, relative to the Kelly bankroll) is a martingale that nonetheless converges in probability to zero. The margin loan interest rate is a submartingale that converges in mean square to the choke price $r_\infty:=\nu-\sigma^2/2$, where $\nu$ is the asymptotic compound growth rate of the stock market and $\sigma$ is its annual volatility. In this environment, the gambler no longer beats the market asymptotically a.s. by an exponential factor (as he would under perfectly elastic supply). Rather, he beats the market asymptotically with very high probability (think 98%) by a factor (say 1.87, or 87% more final wealth) whose mean cannot exceed what the leverage ratio was at the start of the model (say, $2:1$). Although the ratio of the gambler's wealth to that of an equivalent buy-and-hold investor is a submartingale (always expected to increase), his realized compound growth rate converges in mean square to $\nu$. This happens because the equilibrium leverage ratio converges to $1:1$ in lockstep with the gradual rise of margin loan interest rates.
Optimal Least-Squares Estimator and Precoder for Energy Beamforming over IQ-Impaired Channels
Date: 2019-06-24 00:00:00

Usage of low-cost hardware in large antenna arrays and low-power wireless
devices in Internet-of-Things (IoT) has led to the degradation of practical
beamforming gains due to the underlying hardware impairments like
in-phase-and-quadrature-phase imbalance (IQI). To address this timely concern,
we present a new nontrivial closed-form expression for the globally-optimal
least-squares estimator (LSE) for the IQI-influenced channel between a
multiantenna transmitter and single-antenna IoT device. Thereafter, to maximize
the realistic transmit beamforming gains, a novel precoder design is derived
that accounts for the underlying IQI for maximizing received power in both
single and multiuser settings. Lastly, the simulation results, demonstrating a
significant -8dB improvement in the mean-squared error of the proposed LSE over
existing benchmarks, show that the optimal precoder designing is more critical
than accurately estimating IQI-impaired channels. Also, the proposed
jointly-optimal LSE and beamformer outperforms the existing designs by
providing 24% enhancement in the mean signal power received under IQI.
A free energy principle for a particular physics
Date: 2019-06-24 00:00:00

This monograph attempts a theory of every 'thing' that can be distinguished
from other things in a statistical sense. The ensuing statistical
independencies, mediated by Markov blankets, speak to a recursive composition
of ensembles (of things) at increasingly higher spatiotemporal scales. This
decomposition provides a description of small things; e.g., quantum mechanics -
via the Schrodinger equation, ensembles of small things - via statistical
mechanics and related fluctuation theorems, through to big things - via
classical mechanics. These descriptions are complemented with a Bayesian
mechanics for autonomous or active things. Although this work provides a
formulation of every thing, its main contribution is to examine the
implications of Markov blankets for self-organisation to nonequilibrium
steady-state. In brief, we recover an information geometry and accompanying
free energy principle that allows one to interpret the internal states of
something as representing or making inferences about its external states. The
ensuing Bayesian mechanics is compatible with quantum, statistical and
classical mechanics and may offer a formal description of lifelike particles.
Deep Neural Network Based Resource Allocation for V2X Communications
Date: 2019-06-24 00:00:00

This paper focuses on optimal transmit power allocation to maximize the
overall system throughput in a vehicle-to-everything (V2X) communication
system. We propose two methods for solving the power allocation problem namely
the weighted minimum mean square error (WMMSE) algorithm and the deep
learning-based method. In the WMMSE algorithm, we solve the problem using block
coordinate descent (BCD) method. Then we adopt supervised learning technique
for the deep neural network (DNN) based approach considering the power
allocation from the WMMSE algorithm as the target output. We exploit an
efficient implementation of the mini-batch gradient descent algorithm for
training the DNN. Extensive simulation results demonstrate that the DNN
algorithm can provide very good approximation of the iterative WMMSE algorithm
reducing the computational overhead significantly.
Against the Norm: Modeling Daily Stock Returns with the Laplace Distribution
Date: 2019-06-25 00:00:00

Modeling stock returns is not a new task for mathematicians, investors, and
portfolio managers, but it remains a difficult objective due to the ebb and
flow of stock markets. One common solution is to approximate the distribution
of stock returns with a normal distribution. However, normal distributions
place infinitesimal probabilities on extreme outliers, but these outliers are
of particular importance in the practice of investing. In this paper, we
investigate the normality of the distribution of daily returns of major stock
market indices. We find that the normal distribution is not a good model for
stock returns, even over several years' worth of data. Moreover, we propose
using the Laplace distribution as a model for daily stock returns.
Millimeter-Wave Path Loss at 73 GHz in Indoor and Outdoor Airport Environments
Date: 2019-06-25 00:00:00

In this paper, two large-scale fading path loss models are presented based on
indoor and outdoor channel measurements at 73 GHz. The line-of-sight
millimeter-wave propagation measurement campaigns were uniquely conducted
within the indoor and outdoor environments at an airport setting, i.e., the
Boise Airport. The channel measurements were made with directional transmit and
receive antennas with a 24 dBi gain at different receive antenna heights. From
the measured data, we obtained the parameters of two path loss models, i.e.,
the close-in reference distance model (CIM) and the floating-intercept model
(FIM). Results show that the path loss exponents estimated from the CIM are
very close to that of the free-space path loss model, while the FIM provides a
better fit to the measurement data.
Probabilistic model predictive safety certification for learning-based control
Date: 2019-06-25 00:00:00
Tasks: Autonomous Driving, Bayesian Inference, Reinforcement Learning (RL)

Reinforcement learning (RL) methods have demonstrated their efficiency in
simulation environments. However, many applications for which RL offers great
potential, such as autonomous driving, are also safety critical and require a
certified closed-loop behavior in order to meet safety specifications in the
presence of physical constraints. This paper introduces a concept, called
probabilistic model predictive safety certification (PMPSC), which can be
combined with any RL algorithm and provides provable safety certificates in
terms of state and input chance constraints for potentially large-scale
systems. The certificate is realized through a stochastic tube that safely
connects the current system state with a terminal set of states, that is known
to be safe. A novel formulation in terms of a convex receding horizon problem
allows a recursively feasible real-time computation of such probabilistic
tubes, despite the presence of possibly unbounded disturbances. A design
procedure for PMPSC relying on bayesian inference and recent advances in
probabilistic set invariance is presented. Using a numerical car simulation,
the method and its design procedure are illustrated by enhancing a simple RL
algorithm with safety certificates.