pwc-archive/papers-with-abstracts huggingface dataset:
Population Pharmacokinetic Study of Tacrolimus in Pediatric Patients with Primary Nephrotic Syndrome: A Comparison of Linear and Nonlinear Michaelis Menten Pharmacokinetic Model
Date: 2020-02-26 00:00:00

Background Little is known about the population pharmacokinetics (PPK) of
tacrolimus (TAC) in pediatric primary nephrotic syndrome (PNS). This study
aimed to compare the predictive performance between nonlinear and linear PK
models and investigate the significant factors of TAC PK characteristics in
pediatric PNS. Methods Data were obtained from 71 pediatric patients with PNS,
along with 525 TAC trough concentrations at steady state. The demographic,
medical, and treatment details were collected. Genetic polymorphisms were
analyzed. The PPK models were developed using nonlinear mixed effects model
software. Two modeling strategies, linear compartmental and nonlinear Michaelis
Menten (MM) models, were evaluated and compared. Results Body weight, age,
daily dose of TAC, co-therapy drugs (including azole antifungal agents and
diltiazem), and CYP3A5*3 genotype were important factors in the final linear
model (onecompartment model), whereas only body weight, codrugs, and CYP3A5*3
genotype were the important factors in the nonlinear MM model. Apparent
clearance and volume of distribution in the final linear model were 7.13 L/h
and 142 L, respectively. The maximal dose rate (Vmax) of the nonlinear MM model
was 1.92 mg/day and the average concentration at steady state at half-Vmax (Km)
was 1.98 ng/mL. The nonlinear model described the data better than the linear
model. Dosing regimens were proposed based on the nonlinear PK model.Conclusion
Our findings demonstrate that the nonlinear MM model showed better predictive
performance than the linear compartmental model, providing reliable support for
optimizing TAC dosing and adjustment in children with PNS.
Graph signal interpolation with Positive Definite Graph Basis Functions
Date: 2019-12-10 00:00:00

For the interpolation of graph signals with generalized shifts of a graph
basis function (GBF), we introduce the concept of positive definite functions
on graphs. This concept merges kernel-based interpolation with spectral theory
on graphs and can be regarded as a graph analog of radial basis function
interpolation in euclidean spaces or spherical basis functions. We provide
several descriptions of positive definite functions on graphs, the most
relevant one is a Bochner-type characterization in terms of positive Fourier
coefficients. These descriptions allow us to design GBF's and to study GBF
interpolation in more detail: we are able to characterize the native spaces of
the interpolants, we provide explicit estimates for the interpolation error and
obtain bounds for the numerical stability. As a final application, we show how
GBF interpolation can be used to get quadrature formulas on graphs.
Channel Estimation, Interference Cancellation, and Symbol Detection for Communications on Overlapping Channels
Authors: Minh Tri Nguyen, Long Bao Le
Date: 2019-12-04 00:00:00

In this paper, we propose the joint interference cancellation, fast fading channel estimation, and data symbol detection for a general interference setting where the interfering source and the interfered receiver are unsynchronized and occupy overlapping channels of different bandwidths. The interference must be canceled before the channel estimation and data symbol detection of the desired communication are performed. To this end, we have to estimate the Effective Interference Coefficients (EICs) and then the desired fast fading channel coefficients. We construct a two-phase framework where the EICs and desired channel coefficients are estimated using the joint maximum likelihood-maximum a posteriori probability (JML-MAP) criteria in the first phase; and the MAP based data symbol detection is performed in the second phase. Based on this two-phase framework, we also propose an iterative algorithm for interference cancellation, channel estimation and data detection. We analyze the channel estimation error, residual interference, symbol error rate (SER) achieved by the proposed framework. We then discuss how to optimize the pilot density to achieve the maximum throughput. Via numerical studies, we show that our design can effectively mitigate the interference for a wide range of SNR values, our proposed channel estimation and symbol detection design can achieve better performances compared to the existing method. Moreover, we demonstrate the improved performance of the iterative algorithm with respect to the non-iterative counterpart.
Sensors Design for Large-Scale Boolean Networks via Pinning Observability
Authors: Shiyong Zhu, Jianquan Lu, Jie Zhong, Yang Liu, Jinde Cao
Date: 2019-12-05 00:00:00

In this paper, a set of sensors is constructed via the pinning observability approach with the help of observability criteria given in [1] and [2], in order to make the given Boolean network (BN) be observable. Given the assumption that system states can be accessible, an efficient pinning control scheme is developed to generate an observable BN by adjusting the network structure rather than just to check system observability. Accordingly, the sensors are constructed, of which the form is consistent with that of state feedback controllers in the designed pinning control. Since this pinning control approach only utilizes node-to-node message communication instead of global state space information, the time complexity is dramatically reduced from $O(2^{2n})$ to $O(n^2+n2^d)$, where where $n$ and $d$ are respectively the node number of the considered BN and the largest in-degree of vertices in its network structure. Finally, we design the sensors for the reduced D. melanogaster segmentation polarity gene network and the T-cell receptor kinetics, respectively.
Revisiting the Epps effect using volume time averaging: An exercise in R
Authors: Patrick Chang, Roger Bukuru, Tim Gebbie
Date: 2019-12-05 00:00:00

We revisit and demonstrate the Epps effect using two well-known non-parametric covariance estimators; the Malliavin and Mancino (MM), and Hayashi and Yoshida (HY) estimators. We show the existence of the Epps effect in the top 10 stocks from the Johannesburg Stock Exchange (JSE) by various methods of aggregating Trade and Quote (TAQ) data. Concretely, we compare calendar time sampling with two volume time sampling methods: asset intrinsic volume time averaging, and volume time averaging synchronised in volume time across assets relative to the least and most liquid asset clocks. We reaffirm the argument made in much of the literature that the MM estimator is more representative of trade time reality because it does not over-estimate short-term correlations in an asynchronous event driven world. We confirm well known market phenomenology with the aim of providing some standardised R based simulation tools.
Separating vascular and neuronal effects of age on fMRI BOLD signals
Date: 2020-05-19 00:00:00
Tasks: Functional Connectivity

Accurate identification of brain function is necessary to understand the
neurobiology of cognitive ageing, and thereby promote well-being across the
lifespan. A common tool used to investigate neurocognitive ageing is functional
magnetic resonance imaging (fMRI). However, although fMRI data are often
interpreted in terms of neuronal activity, the blood-oxygen-level-dependent
(BOLD) signal measured by fMRI includes contributions of both vascular and
neuronal factors, which change differentially with age. While some studies
investigate vascular ageing factors, the results of these studies are not well
known within the field of neurocognitive ageing and therefore vascular
confounds in neurocognitive fMRI studies are common. In contrast to over 10,000
BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct
for vascular effects. However, neurovascular ageing is not only a confound in
fMRI, but an important feature in its own right, to be assessed alongside
measures of neuronal ageing. We review current approaches to dissociate
neuronal and vascular components of BOLD-fMRI of regional activity and
functional connectivity. We highlight emerging evidence that vascular
mechanisms in the brain do not simply control blood flow to support the
metabolic needs of neurons, but form complex neurovascular interactions that
influence neuronal function in health and disease.
Write and Read Channel Models for 1S1R Crossbar Resistive Memory with High Line Resistance
Date: 2020-10-12 00:00:00

Crossbar resistive memory with 1 Selector 1 Resistor (1S1R) structure is
attractive for low-cost and high-density nonvolatile memory applications. As
technology scales down to the single-nm regime, the increasing resistivity of
wordline/bitline becomes a limiting factor to device reliability. This paper
presents write/read communication channels while considering the line
resistance and device variabilities by statistically relating the degraded
write/read margins and the channel parameters. Binary asymmetric channel (BAC)
models are proposed for the write/read operations. Simulations based on these
models suggest that the bit-error rate of devices are highly non-uniform across
the memory array. These models provide quantitative tools for evaluating the
trade-offs between memory reliability and design parameters, such as array
size, technology nodes, and aspect ratio, and also for designing
coding-theoretic solutions that would be most effective for crossbar memory.
BitMEX Funding Correlation with Bitcoin Exchange Rate
Date: 2019-11-26 00:00:00

This paper examines the relationship between Inverse Perpetual Swap
contracts, a Bitcoin derivative akin to futures and the margin funding interest
rates levied on BitMEX. This paper proves the Heteroskedastic nature of funding
rates and goes onto establish a causal relationship between the funding rates
and the Bitcoin inverse Perpetual swap contracts based on Granger causality.
The paper further dwells into developing a predictive model for funding rates
using best-fitted GARCH models. Implications of the results are presented, and
funding rates as a predictive tool for gauging the market trend is discussed.
Reversible Data Hiding in Encrypted Images Using MSBs Integration and Histogram Modification
Authors: Ammar Mohammadi
Date: 2019-12-06 00:00:00

This paper presents a reversible data hiding in encrypted image that employs based notions of the RDH in plain-image schemes including histogram modification and prediction-error computation. In the proposed method, original image may be encrypted by desire encryption algorithm. Most significant bit (MSB) of encrypted pixels are integrated to vacate room for embedding data bits. Integrated ones will be more resistant against failure of reconstruction if they are modified for embedding data bits. At the recipient, we employ chess-board predictor for lossless reconstruction of the original image by the aim of prediction-error analysis. Comparing to existent RDHEI algorithms, not only we propose a separable method to extract data bits, but also content-owner may attain a perfect reconstruction of the original image without having data hider key. Experimental results confirm that the proposed algorithm outperforms state of the art ones.
Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting
Authors: Huimei Han, Jun Zhao, Zehui Xiong, Dusit Niyato, Wenchao Zhai, Marco Di Renzo, Quoc-Viet Pham, Weidang Lu
Date: 2019-12-07 00:00:00

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless com-munication systems, enhances the spectral and energy efficiency by intelligently adjusting the propaga-tion conditions between a base station (BS) and mobile equipments (MEs). An RIS consists of manylow-cost passive reflecting elements to improve the quality of the received signal. In this paper, westudy the problem of power control at the BS for the RIS aided physical-layer broadcasting. Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposedoptimization methods, we derive lower bounds to quantify the average transmit power at the BS as afunction of the number of MEs, the number of RIS elements, and the number of antennas at the BS.The simulation results demonstrated that the average transmit power at the BS is close to the lowerbound in an RIS aided system, and is significantly lower than the average transmit power in conventionalschemes without the RIS.
Data-Driven Model Reduction for Multilinear Control Systems via Tensor Trains
Date: 2020-01-24 00:00:00
Tasks: tensor algebra

In this paper, we explore the role of tensor algebra in balanced truncation
(BT) based model reduction/identification for high-dimensional
multilinear/linear time invariant systems. In particular, we employ tensor
train decomposition (TTD), which provides a good compromise between numerical
stability and level of compression, and has an associated algebra that
facilitates computations. Using TTD, we propose a new BT approach which we
refer to as higher-order balanced truncation, and consider different
data-driven variations including higher-order empirical gramians, higher-order
balanced proper orthogonal decomposition and a higher-order eigensystem
realization algorithm. We perform computational and memory complexity analysis
for these different flavors of TTD based BT methods, and compare with the
corresponding standard BT methods in order to develop insights into where the
proposed framework may be beneficial. We provide numerical results on simulated
and experimental datasets showing the efficacy of the proposed framework.
Simplified stochastic calculus with applications in Economics and Finance
Authors: Aleš Černý, Johannes Ruf
Date: 2019-12-08 00:00:00

The paper introduces a simple way of recording and manipulating general stochastic processes without explicit reference to a probability measure. In the new calculus, operations traditionally presented in a measure-specific way are instead captured by tracing the behaviour of jumps (also when no jumps are physically present). The calculus is fail-safe in that, under minimal assumptions, all informal calculations yield mathematically well-defined stochastic processes. The calculus is also intuitive as it allows the user to pretend all jumps are of compound Poisson type. The new calculus is very effective when it comes to computing drifts and expected values that possibly involve a change of measure. Such drift calculations yield, for example, partial integro-differential equations, Hamilton-Jacobi-Bellman equations, Feynman-Kac formulae, or exponential moments needed in numerous applications. We provide several illustrations of the new technique, among them a novel result on the Margrabe option to exchange one defaultable asset for another.
Deep Prototypical Networks Based Domain Adaptation for Fault Diagnosis
Date: 2019-12-11 00:00:00
Tasks: Domain Adaptation, Fault Diagnosis

Due to the existence of dataset shifts, the distributions of data acquired
from different working conditions show significant differences in real-world
industrial applications, which leads to performance degradation of traditional
machine learning methods. This work provides a framework that combines
supervised domain adaptation with prototype learning for fault diagnosis. The
main idea of domain adaptation is to apply the Siamese architecture to learn a
latent space where the mapped features are inter-class separable and
intra-class similar for both source and target domains. Moreover, the
prototypical layer utilizes the features from Siamese architecture to learn
prototype representations of each class. This supervised method is attractive
because it needs very few labeled target samples. Moreover, it can be further
extended to address the problem when the classes from the source and target
domains are not completely overlapping. The model must generalize to unseen
classes in the source dataset, given only a few examples of each new target
class. Experimental results, on the Case Western Reserve University bearing
dataset, show the effectiveness of the proposed framework. With increasing
target samples in training, the model quickly converges with high
classification accuracy.
Control Under Action-Dependent Markov Packet Drops: An Event-Triggered Approach
Date: 2019-12-28 00:00:00

In this paper, we consider the problem of second moment stabilization of a
scalar linear plant with process noise. We assume that the sensor must
communicate with the controller over an unreliable channel, whose state evolves
according to a Markov chain, with the transition matrix on a timestep depending
on whether there is a transmission or not on that timestep. Under such a
setting, we propose an event-triggered transmission policy which meets the
objective of exponential convergence of the second moment of the plant state to
an ultimate bound. Furthermore, we provide upper bounds on the transmission
fraction of the proposed policy. The guarantees on performance and transmission
fraction are verified using simulations.
Distributed Feedback Control of Multi-Channel Linear Systems
Authors: Fengjiao Liu, Lili Wang, Daniel Fullmer, A. Stephen Morse
Date: 2019-12-09 00:00:00

In this paper it is established that any jointly controllable, jointly observable, multi-channel, discrete or continuous time linear system with a strongly connected neighbor (communication) graph can be exponentially stabilized with any pre-specified convergence rate using a time-invariant distributed linear control. As an illustration of how this finding can be used to deal with certain distributed tracking problems, a solution is given to a distributed set-point control problem for a continuous-time, multi-channel linear system in which each and every agent with access to the system is able to independently adjust its scalar-valued, controlled output to any desired set-point value. To better understand the constraints on controller design, the distributed control problem is recast as a classical decentralized control problem. Armed with the tools of decentralized control, including the notion of a "fixed spectrum", it is possible to show quite surprisingly that if the only information each agent is allowed to share with its neighbors is its measured output, then distributed stabilization in some cases is impossible. Using well-known decentralized control concepts, lower bounds are derived on the dimensions of the shared sub-states of local controllers which, if satisfied, guarantee that there will be no fixed closed-loop system eigenvalues to contend with. The decentralized control perspective also enables one to assert that without imposing a partitioning constraint, the closed-loop spectrum of any jointly controllable, jointly observable multi-channel linear system with a strongly connected neighbor graph, can be freely assigned with distributed feedback control. It is also explained why in the face of finite delays, exponential stabilization at any prescribed convergence rate can still be achieved with distributed control, at least for discrete-time multi-channel linear systems.
Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices
Authors: Somayeh Kokabisaghi, Mohammadesmaeil Ezazi, Reza Tehrani, Nourmohammad Yaghoubi
Date: 2019-12-09 00:00:00

In this paper, we model the impact of oil price volatility on Tehranstock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward neural net-works. The period of study is from 2008 to 2018 that is split in two periods during international energy sanction and post-sanction. The results show that Feed-forward neural networks perform well in predicting stock market and industry, which means oil price volatility has a significant impact on stock and industry market indices. During post-sanction and global financial crisis, the model performs better in predicting industry index. Additionally, oil price-stock market index prediction performs better in the period of international sanctions. Herein, these results are, up to some extent, important for financial market analysts and policy makers to understand which factors and when influence the financial market, especially in an oil-dependent country such asIran with uncertainty in the international politics. Keywords: Feed-forward neural networks,Industry index,International energy sanction,Oil price volatility,Tehran stock index
Modeling competitive interactions and plant-soil feedback in vegetation dynamics
Date: 2019-12-12 00:00:00

Plant-soil feedback is recognized as a causal mechanism for the emergence of
vegetation patterns of the same species especially when water is not a limiting
resource (e.g. humid environments). Nevertheless, in the field, plants rarely
grow in monoculture but compete with other plant species. In these cases,
plant-soil feedback was shown to play a key role in plant-species coexistence.
Using a mathematical model consisting of four PDEs, we investigate mechanisms
of inter- and intra-specific plant-soil feedback on the coexistence of two
competing plant species. In particular, the model takes into account both
negative and positive feedback influencing the growth of the same and the other
plant species. Both the coexistence of the plant species and the dominance of a
particular plant species is examined with respect to all model parameters
together with the emergence of spatial vegetation patterns.
Exact solution of stochastic gene expression models with bursting, cell cycle and replication dynamics
Authors: Casper H. L. Beentjes, Ruben Perez-Carrasco, Ramon Grima
Date: 2019-12-09 00:00:00

The bulk of stochastic gene expression models in the literature do not have an explicit description of the age of a cell within a generation and hence they cannot capture events such as cell division and DNA replication. Instead, many models incorporate cell cycle implicitly by assuming that dilution due to cell division can be described by an effective decay reaction with first-order kinetics. If it is further assumed that protein production occurs in bursts then the stationary protein distribution is a negative binomial. Here we seek to understand how accurate these implicit models are when compared with more detailed models of stochastic gene expression. We derive the exact stationary solution of the chemical master equation describing bursty protein dynamics, binomial partitioning at mitosis, age-dependent transcription dynamics including replication, and random interdivision times sampled from Erlang or more general distributions; the solution is different for single lineage and population snapshot settings. We show that protein distributions are well approximated by the solution of implicit models (a negative binomial) when the mean number of mRNAs produced per cycle is low and the cell cycle length variability is large. When these conditions are not met, the distributions are either almost bimodal or else display very flat regions near the mode and cannot be described by implicit models. We also show that for genes with low transcription rates, the size of protein noise has a strong dependence on the replication time, it is almost independent of cell cycle variability for lineage measurements and increases with cell cycle variability for population snapshot measurements. In contrast for large transcription rates, the size of protein noise is independent of replication time and increases with cell cycle variability for both lineage and population measurements.