pwc-archive/papers-with-abstracts huggingface dataset:
Integrated Supervised Adaptive Control for the More Electric Aircraft
Date: 2020-01-17 00:00:00

The innovative concept of Electric Aircraft is a challenging topic involving
different control objectives. For instance, it becomes possible to reduce the
size and the weight of the generator by using the battery as an auxiliary
generator in some operation phases. However, control strategies with different
objectives can be conflicting and they can produce undesirable effects, even
instability. For this reason an integrated design approach is needed, where
stability can be guaranteed in any configuration. In other words, the design of
the supervisory controller must be interlaced with that of low-level
controllers. Moreover, uncertainties and noisy signals require robust control
techniques and the use of adaptiveness in the control algorithm. In this paper,
an aeronautic application aiming at recharging batteries and to use the battery
to withstand generator overloads is addressed. Detailed and rigorous stability
proofs are given for any control configuration, including the switching phases
among different control objectives. Effectiveness of the proposed strategies is
shown by using a detailed simulator including switching electronic components.
Dynamic core-periphery structure of information sharing networks in the entorhinal cortex and the hippocampus
Date: 2020-01-17 00:00:00
Tasks: Functional Connectivity, Hippocampus

Neural computation is associated with the emergence, reconfiguration and
dissolution of cell assemblies in the context of varying oscillatory states.
Here, we describe the complex spatio-temporal dynamics of cell assemblies
through temporal network formalism. We use a sliding window approach to extract
sequences of networks of information sharing among single units in hippocampus
and enthorinal cortex during anesthesia and study how global and node-wise
functional connectivity properties evolve along time. First, we find that
information sharing networks display, at any time, a core-periphery structure
in which an integrated core of more tightly functionally interconnected units
link to more loosely connected network leaves. However the units participating
to the core or to the periphery substantially change across time-windows.
Second, we find that discrete network states can be defined on top of this
continuously ongoing liquid core-periphery reorganization. Switching between
network states results in a more abrupt modification of the units belonging to
the core and is only loosely linked to transitions between global oscillatory
states. Third, we characterize different styles of temporal connectivity that
cells can exhibit within each state of the sharing network. While inhibitory
cells tend to be central, we show that, otherwise, anatomical localization only
poorly influences the patterns of temporal connectivity of the different cells.
Cells can also change temporal connectivity style when the network changes
state. Altogether, these findings reveal that the sharing of information
mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell
assemblies have a rich spatiotemporal structure, which could not have been
identified by more conventional time- or state-averaged analyses of functional
connectivity.
ESO architectures in the trajectory tracking ADR controller for a mechanical system: a comparison
Date: 2020-01-20 00:00:00

Proper operation of the Active Disturbance Rejection (ADR) controller
requires a precise determination of the so-called total disturbance affecting
the considered dynamical system, usually estimated by the Extended State
Observer (ESO). The observation quality of total disturbance has a significant
impact on the control error values, making room for a potential improvement of
control system performance using different structures of ESO. In this article,
we provide a quantitative comparison between the Luenberger and Astolfi/Marconi
(AM) observers designed for three different extended state representations and
utilized in the trajectory tracking ADR controller designed for a mechanical
system. Included results were obtained in the simple simulation case, followed
by the experimental validation on the main axis of a telescope mount.
The sub-fractional CEV model
Authors: Axel A. Araneda, Nils Bertschinger
Date: 2020-01-17 00:00:00
Tasks: model

The sub-fractional Brownian motion (sfBm) is a stochastic process, characterized by non-stationarity in their increments and long-range dependency, considered as an intermediate step between the standard Brownian motion (Bm) and the fractional Brownian motion (fBm). The mixed process, a linear combination between a Bm and an independent sfBm, called mixed sub-fractional Brownian motion (msfBm), keeps the features of the sfBm adding the semi-martingale property for H>3/4, is a suitable candidate to use in price fluctuation modeling, in particular for option pricing. In this note, we arrive at the European Call price under the Constant Elasticity of Variance (CEV) model driven by a mixed sub-fractional Brownian motion. Empirical tests show the capacity of the proposed model to capture the temporal structure of option prices across different maturities.
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography
Date: 2020-01-17 00:00:00
Tasks: Denoising, Super-Resolution

The quality of the reconstructed photoacoustic image largely depends on the
amount of photoacoustic (PA) boundary data available, which in turn is
proportional to the number of detectors employed. In case of limited data
(owing to less number of detectors due to cost/instrumentation constraints),
the reconstructed PA images suffer from artifacts and are often noisy. In this
work, for the first time, a deep learning based model was developed to super
resolve and denoise the photoacoustic sinogram data. The proposed method was
compared with existing nearest neighbor interpolation and wavelet based
denoising techniques and was shown to outperform them both in numerical and
in-vivo cases. The improvement obtained in Root Mean Square Error (RMSE) and
Peak Signal to Noise Ratio (PSNR) for the reconstructed PA image using the
sinogram data that was super-resolved and denoised using proposed neural
network based method was as high as 41.70 % and 6.93 dB respectively compared
to utilizing limited sinogram data.
Trading on the Floor after Sweeping the Book
Authors: Vassilis Polimenis
Date: 2020-01-17 00:00:00

Informed traders need to trade fast in order to profit from their private information before it becomes public. Fast electronic markets provide such liquidity. Slow markets provide execution in an auction based trading floor. Hybrid markets combine both execution venues. In its main result, the paper shows that to compensate for their slow and risky executions, trading floors need to be at least twice as deep as the sweeping facility. Furthermore, when a stand-alone trading floor is enhanced with the addition of a sweeping facility, overall informed trading will decline because it is easier for informed traders to extract the full value of their private info.
Using Double Contractions to Derive the Structure of Slice-Wise Multiplications of Tensors with Applications to Semi-Blind MIMO OFDM
Date: 2020-01-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The slice-wise multiplication of two tensors is required in a variety of
tensor decompositions (including PARAFAC2 and PARATUCK2) and is encountered in
many applications, including the analysis of multidimensional biomedical data
(EEG, MEG, etc.) or multi-carrier MIMO systems. In this paper, we propose a new
tensor representation that is not based on a slice-wise (matrix) description,
but can be represented by a double contraction of two tensors. Such a double
contraction of two tensors can be efficiently calculated via generalized
unfoldings. It leads to new tensor models of the investigated system that do
not depend on the chosen unfolding and reveal the tensor structure of the data
model (such that all possible unfoldings can be seen at the same time). As an
example, we apply this new concept to the design of new receivers for
multi-carrier MIMO systems in wireless communications. In particular, we
consider MIMO OFDM systems with and without Khatri-Rao coding. The proposed
receivers exploit the channel correlation between adjacent subcarriers, require
the same amount of training symbols as traditional OFDM techniques, but have an
improved performance in terms of the symbol error rate. Furthermore, we show
that the spectral efficiency of the Khatri-Rao coded MIMO-OFDM can be increased
by introducing "random coding" such that the "coding matrix" also contains
useful information symbols. Considering this transmission technique, we derive
a tensor model and two types of receivers for randomly coded MIMO-OFDM systems
using the double contraction of two tensors.
A tail dependence-based MST and their topological indicators in modelling systemic risk in the European insurance sector
Date: 2020-03-10 00:00:00
Tasks: Time Series, Time Series Analysis

In the present work we analyse the dynamics of indirect connections between
insurance companies that result from market price channels. In our analysis we
assume that the stock quotations of insurance companies reflect market
sentiments which constitute a very important systemic risk factor.
Interlinkages between insurers and their dynamics have a direct impact on
systemic risk contagion in the insurance sector. We propose herein a new hybrid
approach to the analysis of interlinkages dynamics based on combining the
copula-DCC-GARCH model and Minimum Spanning Trees (MST). Using the
copula-DCC-GARCH model we determine the tail dependence coefficients. Then, for
each analysed period we construct MST based on these coefficients. The dynamics
is analysed by means of time series of selected topological indicators of the
MSTs in the years 2005-2019. Our empirical results show the usefulness of the
proposed approach to the analysis of systemic risk in the insurance sector. The
times series obtained from the proposed hybrid approach reflect the phenomena
occurring on the market. The analysed MST topological indicators can be
considered as systemic risk predictors.
GEFF: Graph Embedding for Functional Fingerprinting
Date: 2020-01-18 00:00:00
Tasks: Functional Connectivity, Graph Embedding

It has been well established that Functional Connectomes (FCs), as estimated
from functional MRI (fMRI) data, have an individual fingerprint that can be
used to identify an individual from a population (subject-identification).
Although identification rate is high when using resting-state FCs, other tasks
show moderate to low values. Furthermore, identification rate is
task-dependent, and is low when distinct cognitive states, as captured by
different fMRI tasks, are compared. Here we propose an embedding framework,
GEFF (Graph Embedding for Functional Fingerprinting), based on group-level
decomposition of FCs into eigenvectors. GEFF creates an eigenspace
representation of a group of subjects using one or more task FCs (Learning
Stage). In the Identification Stage, we compare new instances of FCs from the
Learning subjects within this eigenspace (validation dataset). The validation
dataset contains FCs either from the same tasks as the Learning dataset or from
the remaining tasks that were not included in Learning. Assessment of
validation FCs within the eigenspace results in significantly increased
subject-identification rates for all fMRI tasks tested and potentially
task-independent fingerprinting process. It is noteworthy that combining
resting-state with one fMRI task for GEFF Learning Stage covers most of the
cognitive space for subject identification. In addition to
subject-identification, GEFF was also used for identification of cognitive
states, i.e. to identify the task associated to a given FC, regardless of the
subject being already in the Learning dataset or not (subject-independent
task-identification). In addition, we also show that eigenvectors from the
Learning Stage can be characterized as task-dominant, subject dominant or
neither, providing a deeper insight into the extent of variance in functional
connectivity across individuals and cognitive states.
Load Shaping Based Privacy Protection in Smart Grids: An Overview
Date: 2020-01-18 00:00:00

Fine-grained energy usage data collected by Smart Meters (SM) is one of the
key components of the smart grid (SG). While collection of this data enhances
efficiency and flexibility of SG, it also poses a serious threat to the privacy
of consumers. Through techniques such as nonintrusive appliance load monitoring
(NALM), this data can be used to identify the appliances being used, and hence
disclose the private life of the consumer. Various methods have been proposed
in the literature to preserve the consumer privacy. This paper focuses on load
shaping (LS) methods, which alters the consumption data by means of household
amenities in order to ensure privacy. An overview of the privacy protection
techniques, as well as heuristics of the LS methods, privacy measures, and
household amenities used for privacy protection are presented in order to
thoroughly analyze the effectiveness and applicability of these methods to
smart grid systems. Finally, possible research directions related to privacy
protection in smart grids are discussed.
Ultrasound in Locomotion Research -- The Quest for Wider Views
Date: 2020-01-18 00:00:00

In a systematic review, we investigate current applications of ultrasound in
locomotion research. Shortcomings in the range of view of ultrasound systems
affect the direct validation of musculoskeletal simulations as inverse
approaches have to be applied. We present currently used methods to estimate
muscle and tendon length in human plantarflexors.
System-on-Chip Security Assertions
Date: 2020-01-18 00:00:00

Assertions are widely used for functional validation as well as coverage
analysis for both software and hardware designs. Assertions enable runtime
error detection as well as faster localization of errors. While there is a vast
literature on both software and hardware assertions for monitoring functional
scenarios, there is limited effort in utilizing assertions to monitor
System-on-Chip (SoC) security vulnerabilities. In this paper, we identify
common SoC security vulnerabilities by analyzing the design. To monitor these
vulnerabilities, we define several classes of assertions to enable runtime
checking of security vulnerabilities. Our experimental results demonstrate that
the security assertions generated by our proposed approach can detect all the
inserted vulnerabilities while the functional assertions generated by
state-of-the-art assertion generation techniques fail to detect most of them.
Computing the probability of gene trees concordant with the species tree in the multispecies coalescent
Date: 2020-01-18 00:00:00

The multispecies coalescent process models the genealogical relationships of
genes sampled from several species, enabling useful predictions about phenomena
such as the discordance between the gene tree and the species phylogeny due to
incomplete lineage sorting. Conversely, knowledge of large collections of gene
trees can inform us about several aspects of the species phylogeny, such as its
topology and ancestral population sizes. A fundamental open problem in this
context is how to efficiently compute the probability of a gene tree topology,
given the species phylogeny. Although a number of algorithms for this task have
been proposed, they either produce approximate results, or, when they are
exact, they do not scale to large data sets. In this paper, we present some
progress towards exact and efficient computation of the probability of a gene
tree topology. We provide a new algorithm that, given a species tree and the
number of genes sampled for each species, calculates the probability that the
gene tree topology will be concordant with the species tree. Moreover, we
provide an algorithm that computes the probability of any specific gene tree
topology concordant with the species tree. Both algorithms run in polynomial
time and have been implemented in Python. Experiments show that they are able
to analyse data sets where thousands of genes are sampled, in a matter of
minutes to hours.
Efficient and Robust Estimation of the Generalized LATE Model
Authors: Haitian Xie
Date: 2020-01-19 00:00:00

This paper studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. The DML estimator is semiparametric efficient and suitable for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
Noise control and utility: from regulatory network to spatial patterning
Authors: Qing Nie, Lingxia Qiao, Yuchi Qiu, Lei Zhang, Wei Zhao
Date: 2020-01-19 00:00:00

Stochasticity (or noise) at cellular and molecular levels has been observed extensively as a universal feature for living systems. However, how living systems deal with noise while performing desirable biological functions remains a major mystery. Regulatory network configurations, such as their topology and timescale, are shown to be critical in attenuating noise, and noise is also found to facilitate cell fate decision. Here we review major recent findings on noise attenuation through regulatory control, the benefit of noise via noise-induced cellular plasticity during developmental patterning, and summarize key principles underlying noise control.
Minimum Symbol-Error Probability Symbol-Level Precoding with Intelligent Reflecting Surface
Authors: Mingjie Shao, Qiang Li, Wing-Kin Ma
Date: 2020-01-19 00:00:00

Recently, the use of intelligent reflecting surface (IRS) has gained considerable attention in wireless communications. By intelligently adjusting the passive reflection angle, IRS is able to assist the base station (BS) to extend the coverage and improve spectral efficiency. This paper considers a joint symbol-level precoding (SLP) and IRS reflecting design to minimize the symbol-error probability (SEP) of the intended users in an IRS-aided multiuser MISO downlink. We formulate the SEP minimization problems to pursue uniformly good performance for all users for both QAM and PSK constellations. The resulting problem is non-convex and we resort to alternating minimization to obtain a stationary solution. Simulation results demonstrate that under the aid of IRS our proposed design indeed enhances the bit-error rate performance. In particular, the performance improvement is significant when the number of IRS elements is large.
A comment on paper of Kim et al. on mechanisms of hysteresis in human brain networks: comparing with theoretical m-adic model
Date: 2020-01-19 00:00:00

This comment is aimed to point out that the recent work due to Kim, et al. in
which the clinical and experiential assessment of a brain network model
suggests that asymmetry of synchronization suppression is the key mechanism of
hysteresis has coupling with our theoretical hysteresis model of
unconscious-conscious interconnection based on dynamics on m-adic trees.
Antibiotics, duration of infectiousness, and transmission of disease
Date: 2020-01-20 00:00:00

Humans, domestic animals, orchard crops, and ornamental plants are commonly
treated with antibiotics in response to bacterial infection. By curing
infectious individuals, antibiotic therapy might limit the spread of contagious
disease among hosts. But antibiotic suppression of within-host pathogen density
might also reduce the probability that the host is otherwise removed from
infectious status before recovery. When rates of both recovery and removal
(isolation or mortality) depend directly on within-host density, antibiotic
therapy can relax the removal rate and so increase between-host disease
transmission. In this paper a deterministic within-host dynamics drives the
infectious host's probability of infection transmission, as well as the host's
time-dependent probability of surviving to recovery. The model varies (1)
inoculum size, (2) the time elapsing between infection and initiation of
therapy, (2) antibiotic efficacy, and (3) the size/susceptibility of groups
encountered by an infectious host. Results identify conditions where antibiotic
treatment simultaneously increases host survival and increases the expected
number of new infections. That is, antibiotics might convert a rare, serious
bacterial disease into a common, but treatable infection.
Dietary Restriction of Amino Acids for Cancer Therapy
Authors: Jian-Sheng Kang
Date: 2020-01-20 00:00:00

Biosyntheses of proteins, nucleotides and fatty acids, are essential for the malignant proliferation and survival of cancer cells. Cumulating research findings show that amino acid restrictions are potential strategies for cancer interventions. Meanwhile, dietary strategies are popular among cancer patients. However, there is still lacking solid rationale to clarify what is the best strategy, why and how it is. Here, integrated analyses and comprehensive summaries for the abundances, signalling and functions of amino acids in proteomes, metabolism, immunity and food compositions, suggest that, intermittent fasting or intermittent dietary lysine restriction with normal maize as an intermittent staple food for days or weeks, might have the value and potential for cancer prevention or therapy. Moreover, dietary supplements were also discussed for cancer cachexia including dietary immunomodulatory.
A Framework of Robust Transmission Design for IRS-aided MISO Communications with Imperfect Cascaded Channels
Authors: Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Arumugam Nallanathan
Date: 2020-01-20 00:00:00

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the worst-case rate constraints under the bounded CSI error model and the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the worst-case rate constraints by using the S-procedure and the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.