pwc-archive/papers-with-abstracts huggingface dataset:
Reinforcement Learning-Enabled Reliable Wireless Sensor Networks in Dynamic Underground Environments
Date: 2019-10-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Wireless underground sensor networks play an important role in underground
sensing such as climate-smart agriculture and underground infrastructure
monitoring. Existing works consider a static underground environment, which is
not practical since the dielectric parameters of soil change frequently due to
precipitation and harsh weather. This challenge cannot be ignored in real
implementation due to the drastic change of wireless underground channel. In
this paper, we study the effect of dynamic underground environment on wireless
communications for sensor networks. We use the real data collected by in-situ
sensors to train a Hidden Markov Model. Then, by using reinforcement learning,
we derive the optimal transmission policies for underground sensors to
efficiently use their energy and reduce the number of dropped and
unsuccessfully transmitted packets. Through simulations using real data, we
find that the developed algorithm can reduce the packet loss and transmit the
sensed data in a timely manner.
An Analytical Probabilistic Expression for Modeling Sum of Spatial-dependent Wind Power Output
Date: 2019-08-16 00:00:00

Applying probability-related knowledge to accurately explore and exploit the
inherent uncertainty of wind power output is one of the key issues that need to
be solved urgently in the development of smart grid. This letter develops an
analytical probabilistic expression for modeling sum of spatial-dependent wind
farm power output through introducing unit impulse function, copulas, and
Gaussian mixture model. A comparative Monte Carlo sampling study is given to
illustrate the validity of the proposed model.
Testing the Drift-Diffusion Model
Date: 2019-08-16 00:00:00
Tasks: model

The drift diffusion model (DDM) is a model of sequential sampling with
diffusion (Brownian) signals, where the decision maker accumulates evidence
until the process hits a stopping boundary, and then stops and chooses the
alternative that corresponds to that boundary. This model has been widely used
in psychology, neuroeconomics, and neuroscience to explain the observed
patterns of choice and response times in a range of binary choice decision
problems. This paper provides a statistical test for DDM's with general
boundaries. We first prove a characterization theorem: we find a condition on
choice probabilities that is satisfied if and only if the choice probabilities
are generated by some DDM. Moreover, we show that the drift and the boundary
are uniquely identified. We then use our condition to nonparametrically
estimate the drift and the boundary and construct a test statistic.
Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks
Authors: Qikun Xiang, Ido Nevat, Gareth W. Peters
Date: 2019-08-16 00:00:00
Tasks: Computational Efficiency

Spatial regression of random fields based on potentially biased sensing information is proposed in this paper. One major concern in such applications is that since it is not known a-priori what the accuracy of the collected data from each sensor is, the performance can be negatively affected if the collected information is not fused appropriately. For example, the data collector may measure the phenomenon inappropriately, or alternatively, the sensors could be out of calibration, thus introducing random gain and bias to the measurement process. Such readings would be systematically distorted, leading to incorrect estimation of the spatial field. To combat this detrimental effect, we develop a robust version of the spatial field model based on a mixture of Gaussian process experts. We then develop two different approaches for Bayesian spatial field reconstruction: the first algorithm is the Spatial Best Linear Unbiased Estimator (S-BLUE), in which one considers the quadratic loss function and restricts the estimator to the linear family of transformations; the second algorithm is based on empirical Bayes, which utilises a two-stage estimation procedure to produce accurate predictive inference in the presence of "misbehaving" sensors. In addition, we develop the distributed version of these two approaches to drastically improve the computational efficiency in large-scale settings. We present extensive simulation results using both synthetic datasets and semi-synthetic datasets with real temperature measurements and simulated distortions to draw useful conclusions regarding the performance of each of the algorithms.
Secure State Estimation and Control for Cyber Security of AC Microgrids
Date: 2019-08-16 00:00:00
Tasks: State Estimation

A timely, accurate, and secure dynamic state estimation is needed for
reliable monitoring and efficient control of microgrids. The synchrophasor
technology enables us to obtain synchronized measurements in real-time and to
develop dynamic state estimators for real-time monitoring and control of
microgrids. In this study, we consider an AC microgrid comprising several
synchronous generators and inverter-interface power supplies, and focus on
securely estimating the dynamic states of the microgrid from a set of corrupted
data. We propose a dynamic state estimator which enables the microgrid operator
to reconstruct the dynamic states of the microgrid from a set of corrupted
data. Finally, we consider an AC microgrid with the same topology as the IEEE
33-bus distribution system, and numerically show that the proposed secure
estimation algorithm can accurately reconstruct the attack signals.
Linear Stochastic Dividend Model
Authors: Sander Willems
Date: 2019-08-16 00:00:00
Tasks: model

In this paper we propose a new model for pricing stock and dividend derivatives. We jointly specify dynamics for the stock price and the dividend rate such that the stock price is positive and the dividend rate non-negative. In its simplest form, the model features a dividend rate that is mean-reverting around a constant fraction of the stock price. The advantage of directly specifying dynamics for the dividend rate, as opposed to the more common approach of modeling the dividend yield, is that it is easier to keep the distribution of cumulative dividends tractable. The model is non-affine but does belong to the more general class of polynomial processes, which allows us to compute all conditional moments of the stock price and the cumulative dividends explicitly. In particular, we have closed-form expressions for the prices of stock and dividend futures. Prices of stock and dividend options are accurately approximated using a moment matching technique based on the principle of maximal entropy.
Evolution of cooperation in networks with well-connected cooperators
Authors: Josefine Bohr Brask, Jonatan Bohr Brask
Date: 2019-08-16 00:00:00

Cooperative behavior constitutes a key aspect of human society and non-human animal systems, but explaining how cooperation evolves represents a major scientific challenge. It is now well established that social network structure plays a central role for the viability of cooperation. However, not much is known about the importance of the positions of cooperators in the networks for the evolution of cooperation. Here, we investigate how the spread of cooperation is affected by correlations between cooperativeness and individual social connectedness (such that cooperators occupy well-connected network positions). Using simulation models, we find that these correlations enhance cooperation in standard scale-free networks but not in standard Poisson networks. In contrast, when degree assortativity is increased such that individuals cluster with others of similar social connectedness, we find that Poisson networks can maintain high levels of cooperation, which can even exceed those of scale-free networks. We show that this is due to dynamics where bridge areas between social clusters act as barriers to the spread of defection. We also find that this positive effect on cooperation is sensitive to the presence of Trojan horses (defectors placed within cooperator clusters), which allow defection to invade. The results provide new knowledge about the conditions under which cooperation may evolve, and are also relevant to consider in regard to the design of cooperation studies.
Multiple-type Transmission Multiple-type Reception Framework on Molecular Communication
Date: 2019-08-16 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we propose a new Multiple-Input Multiple-Output (MIMO)
Molecular Communication (MC) system where multiple types of molecules are
utilized for transmission and reception of information. We call the proposed
framework as Multiple-type Transmission and Multiple-type Reception (MTMR). We
also obtain the bit error rate (BER) of the system and an optimization problem
is formulated to minimize BER by optimizing the drug dosage for designing drug
release mechanism. As numerical analysis shows, the BER of MIMO-MTMR in MC is
minimized to $\text{3.7}\times\text{10}^{\text{-3}}$ by considering the budget
of molecules as 10000. Furthermore, MIMO-MTMR outperforms Single-type
Transmission Single-type Reception MIMO from the BER performance point of view
approximately 54% for time slot 10s.
Optimal scheduling strategy for networked estimation with energy harvesting
Date: 2019-08-16 00:00:00
Tasks: Decision Making, Scheduling

Joint optimization of scheduling and estimation policies is considered for a
system with two sensors and two non-collocated estimators. Each sensor produces
an independent and identically distributed sequence of random variables, and
each estimator forms estimates of the corresponding sequence with respect to
the mean-squared error sense. The data generated by the sensors is transmitted
to the corresponding estimators, over a bandwidth-constrained wireless network
that can support a single packet per time slot. The access to the limited
communication resources is determined by a scheduler who decides which sensor
measurement to transmit based on both observations. The scheduler has an
energy-harvesting battery of limited capacity, which couples the
decision-making problem in time. Despite the overall lack of convexity of the
team decision problem, it is shown that this system admits globally optimal
scheduling and estimation strategies under the assumption that the
distributions of the random variables at the sensors are symmetric and
unimodal. Additionally, the optimal scheduling policy has a structure
characterized by a threshold function that depends on the time index and energy
level. A recursive algorithm for threshold computation is provided.
A model of discrete choice based on reinforcement learning under short-term memory
Date: 2019-08-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

A family of models of individual discrete choice are constructed by means of
statistical averaging of choices made by a subject in a reinforcement learning
process, where the subject has short, k-term memory span. The choice
probabilities in these models combine in a non-trivial, non-linear way the
initial learning bias and the experience gained through learning. The
properties of such models are discussed and, in particular, it is shown that
probabilities deviate from Luce's Choice Axiom, even if the initial bias
adheres to it. Moreover, we shown that the latter property is recovered as the
memory span becomes large.
  Two applications in utility theory are considered. In the first, we use the
discrete choice model to generate binary preference relation on simple
lotteries. We show that the preferences violate transitivity and independence
axioms of expected utility theory. Furthermore, we establish the dependence of
the preferences on frames, with risk aversion for gains, and risk seeking for
losses. Based on these findings we propose next a parametric model of choice
based on the probability maximization principle, as a model for deviations from
expected utility principle. To illustrate the approach we apply it to the
classical problem of demand for insurance.
SleepGuardian: An RF-based Healthcare System Guarding Your Sleep from Afar
Date: 2019-08-12 00:00:00
Tasks: Cloud Computing, Edge-computing

The ever accelerating process of urbanization urges more and more population
into the swelling cities. While city residents are enjoying an entertaining
life supported by advanced informatics techniques like 5G and cloud computing,
the same technologies have also gradually deprived their sleep, which is
crucial for their wellness. Therefore, sleep monitoring has drawn significant
attention from both research and industry communities. In this article, we
first review the sleep monitoring issue and point out three essential
properties of an ideal sleep healthcare system, i.e., realtime guarding,
fine-grained logging, and cost-effectiveness. Based on the analysis, we present
SleepGuardian, a Radio Frequence (RF) based sleep healthcare system leveraging
signal processing, edge computing and machine learning.SleepGuardian offers an
offline sleep logging service and an online abnormality warning service. The
offline service provides a fine-grained sleep log like timing and regularity of
bed time, onset of sleep and night time awakenings. The online service keeps
guarding the subject for any abnormal behaviors during sleep like intensive
body twitches and a sudden seizure attack. Once an abnormality happens,it will
automatically warn the designated contacts like a nearby emergency room or a
closeby relative.We prototype SleepGuardian with low-cost WiFi devices and
evaluate it in real scenarios. Experimental results demonstrate that
SleepGuardian is very effective.
Optimizing Differential Identifiability Improves Connectome Predictive Modeling of Cognitive Deficits in Alzheimer's Disease
Date: 2019-12-13 00:00:00
Tasks: Functional Connectivity

Functional connectivity, as estimated using resting state fMRI, has shown
potential in bridging the gap between pathophysiology and cognition. However,
clinical use of functional connectivity biomarkers is impeded by unreliable
estimates of individual functional connectomes and lack of generalizability of
models predicting cognitive outcomes from connectivity. To address these
issues, we combine the frameworks of connectome predictive modeling and
differential identifiability. Using the combined framework, we show that
enhancing the individual fingerprint of resting state functional connectomes
leads to robust identification of functional networks associated to cognitive
outcomes and also improves prediction of cognitive outcomes from functional
connectomes. Using a comprehensive spectrum of cognitive outcomes associated to
Alzheimer's disease, we identify and characterize functional networks
associated to specific cognitive deficits exhibited in Alzheimer's disease.
This combined framework is an important step in making individual level
predictions of cognition from resting state functional connectomes and in
understanding the relationship between cognition and connectivity.
Sparse Representation Based Efficient Radiation Symmetry Analysis Method for Cylindrical Model of Inertial Confinement Fusion
Date: 2019-08-17 00:00:00

Radiation symmetry evaluation is critical to the laser driven Inertial
Confinement Fusion (ICF), which is usually done by solving a view-factor
equation model. The model is nonlinear, and the number of equations can be very
large when the size of discrete mesh element is very small to achieve a
prescribed accuracy, which may lead to an intensive equation solving process.
In this paper, an efficient radiation symmetry analysis approach based on
sparse representation is presented, in which, 1) the Spherical harmonics,
annular Zernike polynomials and Legendre-Fourier polynomials are employed to
sparsely represent the radiation flux on the capsule and cylindrical cavity,
and the nonlinear energy equilibrium equations are transformed into the
equations with sparse coefficients, which means there are many redundant
equations, 2) only a few equations are selected to recover such sparse
coefficients with Latin hypercube sampling, 3) a Conjugate Gradient Subspace
Thresholding Pursuit (CGSTP) algorithm is then given to rapidly obtain such
sparse coefficients equation with as few iterations as possible. Finally, the
proposed method is validated with two experiment targets for Shenguang II and
Shenguang III laser facility in China. The results show that only one tenth of
computation time is required to solve one tenth of equations to achieve the
radiation flux with comparable accuracy. Further more, the solution is much
more efficient as the size of discrete mesh element decreases, in which, only
1.2\% computation time is required to obtain the accurate result.
Measuring international uncertainty using global vector autoregressions with drifting parameters
Date: 2019-12-17 00:00:00

This paper investigates the time-varying impacts of international
macroeconomic uncertainty shocks. We use a global vector autoregressive
specification with drifting coefficients and factor stochastic volatility in
the errors to model six economies jointly. The measure of uncertainty is
constructed endogenously by estimating a scalar driving the innovation
variances of the latent factors, which is also included in the mean of the
process. To achieve regularization, we use Bayesian techniques for estimation,
and introduce a set of hierarchical global-local priors. The adopted priors
center the model on a constant parameter specification with homoscedastic
errors, but allow for time-variation if suggested by likelihood information.
Moreover, we assume coefficients across economies to be similar, but provide
sufficient flexibility via the hierarchical prior for country-specific
idiosyncrasies. The results point towards pronounced real and financial effects
of uncertainty shocks in all countries, with differences across economies and
over time.
Entropic Dynamics of Stocks and European Options
Authors: Mohammad Abedi, Daniel Bartolomeo
Date: 2019-08-18 00:00:00

We develop an entropic framework to model the dynamics of stocks and European Options. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. The objective of the paper is to lay down an alternative framework for modeling dynamics. An important information about the dynamics of a stock's price is scale invariance. By imposing the scale invariant symmetry, we arrive at choosing the logarithm of the stock's price as the proper variable to model. The dynamics of stock log price is derived using two pieces of information, the continuity of motion and the directionality constraint. The resulting model is the same as the Geometric Brownian Motion, GBM, of the stock price which is manifestly scale invariant. Furthermore, we come up with the dynamics of probability density function, which is a Fokker--Planck equation. Next, we extend the model to value the European Options on a stock. Derivative securities ought to be prices such that there is no arbitrage. To ensure the no-arbitrage pricing, we derive the risk-neutral measure by incorporating the risk-neutral information. Consequently, the Black--Scholes model and the Black--Scholes-Merton differential equation are derived.
Entropic Dynamics of Exchange Rates and Options
Authors: Mohammad Abedi, Daniel Bartolomeo
Date: 2019-08-18 00:00:00

An Entropic Dynamics of exchange rates is laid down to model the dynamics of foreign exchange rates, FX, and European Options on FX. The main objective is to represent an alternative framework to model dynamics. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. Entropic Dynamics is an application of entropic inference, which is equipped with the entropic notion of time to model dynamics. The scale invariance is a symmetry of the dynamics of exchange rates, which is manifested in our formalism. To make the formalism manifestly invariant under this symmetry, we arrive at choosing the logarithm of the exchange rate as the proper variable to model. By taking into account the relevant information about the exchange rates, we derive the Geometric Brownian Motion, GBM, of the exchange rate, which is manifestly invariant under the scale transformation. Securities should be valued such that there is no arbitrage opportunity. To this end, we derive a risk-neutral measure to value European Options on FX. The resulting model is the celebrated Garman-Kohlhagen model.
Drug Release Management for Dynamic TDMA-Based Molecular Communication
Date: 2019-08-30 00:00:00
Tasks: Management

In this paper, we design a drug release mechanism for dynamic time division
multiple access (TDMA)-based molecular communication via diffusion (MCvD). In
the proposed scheme, the communication frame is divided into several time slots
over each of which a transmitter nanomachine is scheduled to convey its
information by releasing the molecules into the medium. To optimize the number
of released molecules and the time duration of each time slot (symbol
duration), we formulate a multi-objective optimization problem whose objective
functions are the bit error rate (BER) of each transmitter nanomachine. Based
on the number of released molecules and symbol durations, we consider four
cases, namely: "static-time static-number of molecules" (STSN), "static-time
dynamic-number of molecules" (STDN), "dynamic-time static-number of molecules"
(DTSN), and "dynamic-time dynamic-number of molecules" (DTDN). We consider
three types of medium in which the molecules are propagated, namely: "mild
diffusive environment" (MDE), "moderate diffusive environment" (MODE), and
"severe diffusive environment" (SDE). For the channel model, we consider a
3-dimensional (3D) diffusive environment, such as blood, with drift in three
directions. Simulation results show that the STSN approach is the least complex
one with BER around $\text{10}^{\text{-2}}$, but, the DTDN is the most complex
scenario with the BER around $\text{10}^{\text{-8}}$.
Beamforming Cancellation Design for Millimeter-Wave Full-Duplex
Authors: Ian P. Roberts, Sriram Vishwanath
Date: 2019-08-18 00:00:00

In recent years, there has been extensive research on millimeter-wave (mmWave) communication and on in-band full-duplex (FD) communication, but work on the combination of the two is relatively lacking. FD mmWave systems could offer increased spectral efficiency and decreased latency while also suggesting the redesign of existing mmWave applications. While FD technology has been well-explored for sub-6 GHz systems, the developed methods do not translate well to mmWave. This turns us to a method called beamforming cancellation (BFC), where the highly directional mmWave beams are steered to mitigate self-interference (SI) and enable simultaneous transmission and reception in-band. In this paper, we present BFC designs for two fully-connected hybrid beamforming scenarios, both of which sufficiently suppress the SI such that the sum spectral efficiency approaches that of a SI-free FD system. A simulation and its results are then used to verify our designs.
State Synchronization for Homogeneous Networks of Non-introspective Agents in Presence of Input Saturation -A Scale-free Protocol Design
Date: 2019-08-18 00:00:00

This paper studies global and semi-global regulated state synchronization of
homogeneous networks of non-introspective agents in presence of input
saturation based on additional information exchange where the reference
trajectory is given by a so-called exosystem which is assumed to be globally
reachable. Our protocol design methodology does not need any knowledge of the
directed network topology and the spectrum of the associated Laplacian matrix.
Moreover, the proposed protocol is scalable and achieves synchronization for
any arbitrary number of agents.