pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints
Date: 2019-11-28 00:00:00
Tasks: Decoder

Optimality principles have been useful in explaining many aspects of
biological systems. In the context of neural encoding in sensory areas,
optimality is naturally formulated in a Bayesian setting, as neural tuning
which minimizes mean decoding error. Many works optimize Fisher information,
which approximates the Minimum Mean Square Error (MMSE) of the optimal decoder
for long encoding time, but may be misleading for short encoding times. We
study MMSE-optimal neural encoding of a multivariate stimulus by uniform
populations of spiking neurons, under firing rate constraints for each neuron
as well as for the entire population. We show that the population-level
constraint is essential for the formulation of a well-posed problem having
finite optimal tuning widths, and optimal tuning aligns with the principal
components of the prior distribution. Numerical evaluation of the
two-dimensional case shows that encoding only the dimension with higher
variance is optimal for short encoding times. We also compare direct MMSE
optimization to optimization of several proxies to MMSE, namely Fisher
information, Maximum Likelihood estimation error, and the Bayesian Cram\'er-Rao
bound. We find that optimization of these measures yield qualitatively
misleading results regarding MMSE-optimal tuning and its dependence on encoding
time and energy constraints.
Moment Propagation of Discrete-Time Stochastic Polynomial Systems using Truncated Carleman Linearization
Date: 2019-11-28 00:00:00

We propose a method to compute an approximation of the moments of a discrete-time stochastic polynomial system. We use the Carleman linearization technique to transform this finite-dimensional polynomial system into an infinite-dimensional linear one. After taking expectation and truncating the induced deterministic dynamics, we obtain a finite-dimensional linear deterministic system, which we then use to iteratively compute approximations of the moments of the original polynomial system at different time steps. We provide upper bounds on the approximation error for each moment and show that, for large enough truncation limits, the proposed method precisely computes moments for sufficiently small degrees and numbers of time steps. We use our proposed method for safety analysis to compute bounds on the probability of the system state being outside a given safety region. Finally, we illustrate our results on two concrete examples, a stochastic logistic map and a vehicle dynamics under stochastic disturbance.
Testing for high frequency features in a noisy signal
Date: 2019-11-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Given nonstationary data, one generally wants to extract the trend from the
noise by smoothing or filtering. However, it is often important to delineate a
third intermediate category, that we call high frequency (HF) features: this is
the case in our motivating example, which consists in experimental measurements
of the time-dynamics of depolymerising protein fibrils average size. One may
intuitively visualise HF features as the presence of fast, possibly
nonstationary and transient oscillations, distinct from a slowly-varying trend
envelope. The aim of this article is to propose an empirical definition of HF
features and construct estimators and statistical tests for their presence
accordingly, when the data consists of a noisy nonstationary 1-dimensional
signal. We propose a parametric characterization in the Fourier domain of the
HF features by defining a maximal amplitude and distance to low frequencies of
significant energy. We introduce a data-driven procedure to estimate these
parameters, and compute a p-value proxy based on a statistical test for the
presence of HF features. The test is first conducted on simulated signals where
the ratio amplitude of the HF features to the level of the noise is controlled.
The test detects HF features even when the level of noise is five times larger
than the amplitude of the oscillations. In a second part, the test is conducted
on experimental data from Prion disease experiments and it confirms the
presence of HF features in these signals with significant confidence.
Modelling Load-Changing Attacks in Cyber-Physical Systems
Date: 2019-12-17 00:00:00
Tasks: Management

Cyber-Physical Systems (CPS) are present in many settings addressing a myriad
of purposes. Examples are Internet-of-Things (IoT) or sensing software embedded
in appliances or even specialised meters that measure and respond to
electricity demands in smart grids. Due to their pervasive nature, they are
usually chosen as recipients for larger scope cyber-security attacks. Those
promote system-wide disruptions and are directed towards one key aspect such as
confidentiality, integrity, availability or a combination of those
characteristics. Our paper focuses on a particular and distressing attack where
coordinated malware infected IoT units are maliciously employed to
synchronously turn on or off high-wattage appliances, affecting the grid's
primary control management. Our model could be extended to larger (smart)
grids, Active Buildings as well as similar infrastructures. Our approach models
Coordinated Load-Changing Attacks (CLCA) also referred as GridLock or BlackIoT,
against a theoretical power grid, containing various types of power plants. It
employs Continuous-Time Markov Chains where elements such as Power Plants and
Botnets are modelled under normal or attack situations to evaluate the effect
of CLCA in power reliant infrastructures. We showcase our modelling approach in
the scenario of a power supplier (e.g. power plant) being targeted by a botnet.
We demonstrate how our modelling approach can quantify the impact of a botnet
attack and be abstracted for any CPS system involving power load management in
a smart grid. Our results show that by prioritising the type of power-plants,
the impact of the attack may change: in particular, we find the most impacting
attack times and show how different strategies impact their success. We also
find the best power generator to use depending on the current demand and
strength of attack.
Data-Driven Spectrum Cartography via Deep Completion Autoencoders
Date: 2019-11-28 00:00:00
Tasks: Management, Spectrum Cartography

Spectrum maps, which provide RF spectrum metrics such as power spectral
density for every location in a geographic area, find numerous applications in
wireless communications such as interference control, spectrum management,
resource allocation, and network planning to name a few. Spectrum cartography
techniques construct these maps from a collection of measurements collected by
spatially distributed sensors. Due to the nature of the propagation of
electromagnetic waves, spectrum maps are complicated functions of the spatial
coordinates. For this reason, model-free approaches have been preferred.
However, all existing schemes rely on some interpolation algorithm unable to
learn from data. This work proposes a novel approach to spectrum cartography
where propagation phenomena are learned from data. The resulting algorithms can
therefore construct a spectrum map from a significantly smaller number of
measurements than existing schemes since the spatial structure of shadowing and
other phenomena is previously learned from maps in other environments. Besides
the aforementioned new paradigm, this is also the first work to perform
spectrum cartography with deep neural networks. To exploit the manifold
structure of spectrum maps, a deep network architecture is proposed based on
completion autoencoders.
On the Importance of Opponent Modeling in Auction Markets
Authors: Mahmoud Mahfouz, Angelos Filos, Cyrine Chtourou, Joshua Lockhart, Samuel Assefa, Manuela Veloso, Danilo Mandic, Tucker Balch
Date: 2019-11-28 00:00:00

The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions. Decision-makers would rather not ignore the impact of other participants on these dynamics and should employ tools and models that take this into account. To this end, we demonstrate the efficacy of applying opponent-modeling in a number of simulated market settings. While our simulations are simplified representations of actual market dynamics, they provide an idealized "playground" in which our techniques can be demonstrated and tested. We present this work with the aim that our techniques could be refined and, with some effort, scaled up to the full complexity of real-world market scenarios. We hope that the results presented encourage practitioners to adopt opponent-modeling methods and apply them online systems, in order to enable not only reactive but also proactive decisions to be made.
Stochastic Event Generation Through Markovian Jumps: Generalized Distribution and Minimal Representations
Date: 2019-11-28 00:00:00

Stochastic Event Timing is a fundamental issue in developing both analytic
and simulation models for stochastic systems. Generalized Erlang distributions
are quite useful for generating those random events in a quite general way by
inserting intermediary states with markovian jumps. One very important and
celebrated generalization of the Erlang distribution was made by D. R. Cox in
the middle 50's. This paper discuss further the Cox generalization and presents
an even more general topology, capable of representing any practical
distribution. As an application, we revisit the classical problem of the first
two moments matching, and derive minimal topologies in terms of number of
states, then the results are compared with those found in literature. At the
end of the paper, we show how the generalized structure can be use for timing
general stochastic discrete-event models for analytic and simulation purposes.
Mathematical modelling of the interaction between cancer cells and an oncolytic virus: insights into the effects of treatment protocols
Authors: Adrianne L. Jenner, Chae-Ok Yun, Peter S. Kim, Adelle C. F. Coster
Date: 2019-11-28 00:00:00

Oncolytic virotherapy is an experimental cancer treatment that uses genetically engineered viruses to target and kill cancer cells. One major limitation of this treatment is that virus particles are rapidly cleared by the immune system, preventing them from arriving at the tumour site. To improve virus survival and infectivity modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. While PEG modification appeared to improve plasma retention and initial infectivity it also increased the virus particle arrival time. We derive a mathematical model that describes the interaction between tumour cells and an oncolytic virus. We tune our model to represent the experimental data by Kim et al. (2011) and obtain optimised parameters. Our model provides a platform from which predictions may be made about the response of cancer growth to other treatment protocols beyond those in the experiments. Through model simulations we find that the treatment protocol affects the outcome dramatically. We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. The relative significance of the modification of the virus and the crucial role it plays in optimising treatment efficacy is explored.
Systematic external evaluation of published population pharmacokinetic models for tacrolimus in adult liver transplant recipients
Date: 2019-11-29 00:00:00
Tasks: Prediction

Background:Diverse tacrolimus population pharmacokinetic models in adult
liver transplant recipients have been established to describe the PK
characteristics of tacrolimus in the last two decades. However, their
extrapolated predictive performance remains unclear.Therefore,in this study,we
aimed to evaluate their external predictability and identify their potential
influencing factors. Methods:The external predictability of each selected popPK
model was evaluated using an independent dataset of 84 patients with 572 trough
concentrations prospectively collected from Huashan Hospital. Prediction and
simulation based diagnostics and Bayesian forecasting were conducted to
evaluate model predictability. Furthermore, the effect of model structure on
the predictive performance was investigated.Results:Sixteen published popPK
models were assessed. In prediction-based diagnostics,the prediction error
within 30% was below 50% in all the published models. The simulation based
normalised prediction distribution error test and visual predictive check
indicated large discrepancies between the observations and simulations in most
of the models. Bayesian forecasting showed improvement in model predictability
with two to three prior observations. Additionally, the predictive performance
of the nonlinear Michaelis Menten model was superior to that of linear
compartment models,indicating the underlying nonlinear kinetics of tacrolimus
in liver transplant recipients.Conclusions:The published models performed
inadequately in prediction and simulation based diagnostics. Bayesian
forecasting may improve the predictive performance of the models. Furthermore,
nonlinear kinetics of tacrolimus may be mainly caused by the properties of the
drug itself, and incorporating nonlinear kinetics may be considered to improve
model predictability.
Pricing and hedging short-maturity Asian options in local volatility models
Authors: Jaehyun Kim, Hyungbin Park, Jonghwa Park
Date: 2019-11-29 00:00:00

This paper discusses the short-maturity behavior of Asian option prices and hedging portfolios. We consider the risk-neutral valuation and the delta value of the Asian option having a H\"older continuous payoff function in a local volatility model. The main idea of this analysis is that the local volatility model can be approximated by a Gaussian process at short maturity $T.$ By combining this approximation argument with Malliavin calculus, we conclude that the short-maturity behaviors of Asian option prices and the delta values are approximately expressed as those of their European counterparts with volatility $$\sigma_{A}(T):=\sqrt{\frac{1}{T^3}\int_0^T\sigma^2(t,S_0)(T-t)^2\,dt}\,,$$ where $\sigma(\cdot,\cdot)$ is the local volatility function and $S_0$ is the initial value of the stock. In addition, we show that the convergence rate of the approximation is determined by the H\"older exponent of the payoff function. Finally, the short-maturity asymptotics of Asian call and put options are discussed from the viewpoint of the large deviation principle.
The Microstructure of Stochastic Volatility Models with Self-Exciting Jump Dynamics
Date: 2019-11-29 00:00:00

We provide a general probabilistic framework within which we establish
scaling limits for a class of continuous-time stochastic volatility models with
self-exciting jump dynamics. In the scaling limit, the joint dynamics of asset
returns and volatility is driven by independent Gaussian white noises and two
independent Poisson random measures that capture the arrival of exogenous
shocks and the arrival of self-excited shocks, respectively. Various
well-studied stochastic volatility models with and without self-exciting
price/volatility co-jumps are obtained as special cases under different scaling
regimes. We analyze the impact of external shocks on the market dynamics,
especially their impact on jump cascades and show in a mathematically rigorous
manner that many small external shocks may tigger endogenous jump cascades in
asset returns and stock price volatility.
Bistability in a SIRS model with general nonmonotone and saturated incidence rate
Date: 2019-11-29 00:00:00

In this paper, we consider a SIRS model with general nonmonotone and
saturated incidence rate and perform stability and bifurcation analysis. We
show that the system has saddle-node bifurcation and displays bistable
behavior. We obtain the critical thresholds that characterize the dynamical
behaviors of the model. We find with surprise that the system always admits a
disease free equilibrium E0 which is always asymptotically stable. Numerical
simulations are carried out to verify our results.
Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks
Date: 2019-11-29 00:00:00
Tasks: Management, Scheduling

In wireless caching networks, the design of the content delivery method must
consider random user requests, caching states, network topology, and
interference management. In this paper, we establish a general framework for
content delivery in wireless caching networks without stringent assumptions
that restrict the network structure, delivery link, and interference model.
Based on the framework, we propose a dynamic and distributed link scheduling
and power allocation scheme for content delivery that is assisted by
belief-propagation (BP) algorithms. Considering content-requesting users and
potential caching nodes, the scheme achieves three critical purposes of
wireless caching networks: 1) limiting the delay of user request satisfactions,
2) maintaining the power efficiency of caching nodes, and 3) managing
interference among users. In addition, we address the intrinsic problem of the
BP algorithm in our network model, proposing a matching algorithm for
one-to-one link scheduling. Simulation results show that the proposed scheme
provides almost the same delay performance as the optimal scheme found through
an exhaustive search at the expense of a little additional power consumption
and does not require a clustering method and orthogonal resources in a
large-scale D2D network.
Dynamic Optimal Choice When Rewards are Unbounded Below
Date: 2019-11-29 00:00:00

We propose a new approach to solving dynamic decision problems with rewards
that are unbounded below. The approach involves transforming the Bellman
equation in order to convert an unbounded problem into a bounded one. The major
advantage is that, when the conditions stated below are satisfied, the
transformed problem can be solved by iterating with a contraction mapping.
While the method is not universal, we show by example that many common decision
problems do satisfy our conditions.
Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario
Date: 2019-11-29 00:00:00

In URLLC, short packet transmission is adopted to reduce latency, such that
conventional Shannon's capacity formula is no longer applicable, and the
achievable data rate in finite blocklength becomes a complex expression with
respect to the decoding error probability and the blocklength. To provide URLLC
service in a factory automation scenario, we consider that the central
controller transmits different packets to a robot and an actuator, where the
actuator is located far from the controller, and the robot can move between the
controller and the actuator. In this scenario, we consider four fundamental
downlink transmission schemes, including orthogonal multiple access (OMA),
non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA
(C-NOMA) schemes. For all these transmission schemes, we aim for jointly
optimizing the blocklength and power allocation to minimize the decoding error
probability of the actuator subject to the reliability requirement of the
robot, the total energy constraints, as well as the latency constraints. We
further develop low-complexity algorithms to address the optimization problems
for each transmission scheme. { For the general case with more than two
devices, we also develop a low-complexity efficient algorithm for the OMA
scheme.} Our results show that the relay-assisted transmission significantly
outperforms the OMA scheme, while NOMA scheme performs well when the
blocklength is very limited. We further show that the relay-assisted
transmission has superior performance over the C-NOMA scheme due to larger
feasible region of the former scheme.
High Order Singular Value Decomposition for Plant Biodiversity Estimation
Date: 2019-11-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We propose a new method to estimate plant biodiversity with R{\'e}nyi and Rao
indexes through the so called High Order Singular Value Decomposition (HOSVD)
of tensors. Starting from NASA multispectral images we evaluate biodiversity
and we compare original biodiversity estimates with those realised via the
HOSVD compression methods for big data. Our strategy turns out to be extremely
powerful in terms of storage memory and precision of the outcome. The obtained
results are so promising that we can support the efficiency of our method in
the ecological framework.
SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors
Date: 2020-06-11 00:00:00
Tasks: Denoising, Image Reconstruction

Two features desired in a three-dimensional (3D) optical tomographic image
reconstruction algorithm are the ability to reduce imaging artifacts and to do
fast processing of large data volumes. Traditional iterative inversion
algorithms are impractical in this context due to their heavy computational and
memory requirements. We propose and experimentally validate a novel scalable
iterative mini-batch algorithm (SIMBA) for fast and high-quality optical
tomographic imaging. SIMBA enables high-quality imaging by combining two
complementary information sources: the physics of the imaging system
characterized by its forward model and the imaging prior characterized by a
denoising deep neural net. SIMBA easily scales to very large 3D tomographic
datasets by processing only a small subset of measurements at each iteration.
We establish the theoretical fixed-point convergence of SIMBA under
nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on
both simulated and experimentally collected intensity diffraction tomography
(IDT) datasets. Our results show that SIMBA can significantly reduce the
computational burden of 3D image formation without sacrificing the imaging
quality.
Drug dissemination strategy with an SEIR-based SUC model
Date: 2020-01-19 00:00:00

According to the features of drug addiction, this paper constructs an
SEIR-based SUC model to describe and predict the spread of drug addiction.
Predictions are that the number of drug addictions will continue to fluctuate
with reduced amplitude and eventually stabilize. To seek the fountainhead of
heroin, we identified the most likely origins of drugs in Philadelphia, PA,
Cuyahoga and Hamilton, OH, Jefferson, KY, Kanawha, WV, and Bedford, VA. Based
on the facts, advised concentration includes the spread of Oxycodone,
Hydrocodone, Heroin, and Buprenorphine. In other words, drug transmission in
the two states of Ohio and Pennsylvania require awareness. According to the
propagation curve predicted by our model, the transfer of KY state is still in
its early stage, while that of VA, WV is in the middle point, and OH, PA in its
latter ones. As a result of this, the number of drug addictions in KY, OH, and
VA is projected to increase in three years. For methodology, with the Principal
component analysis technique, 22 variables in socio-economic data related to
the continuous use of Opioid drugs was filtered, where the 'Relationship' Part
deserves a highlight.
  Based on them, by using the K-means algorithm, 464 counties were categorized
into three baskets. To combat the opioid crisis, a specific action will discuss
in the sensitivity analysis section. After modeling and analytics, innovation
is required to control addicts and advocate anti-drug news campaigns. This part
also verified the effectiveness of model when $d_1<0.2; r_1,r_2,r_3<0.3;
15<\beta_1,\beta_2,\beta_3<25$. In other words, if such boundary exceeded, the
number of drug addictions may rocket and peak in a short period.