pwc-archive/papers-with-abstracts huggingface dataset:
The ineffectiveness of coherent risk measures
Authors: John Armstrong, Damiano Brigo
Date: 2019-02-26 00:00:00

We show that coherent risk measures are ineffective in curbing the behaviour of investors with limited liability or excessive tail-risk seeking behaviour if the market admits statistical arbitrage opportunities which we term $\rho$-arbitrage for a risk measure $\rho$. We show how to determine analytically whether such $\rho$-arbitrage portfolios exist in complete markets and in the Markowitz model. We also consider realistic numerical examples of incomplete markets and determine whether expected shortfall constraints are ineffective in these markets. We find that the answer depends heavily upon the probability model selected by the risk manager but that it is certainly possible for expected shortfall constraints to be ineffective in realistic markets. Since value at risk constraints are weaker than expected shortfall constraints, our results can be applied to value at risk. By contrast, we show that reasonable expected utility constraints are effective in any arbitrage-free market.
Self-respecting worker in the precarious gig economy: A dynamic principal-agent model
Authors: Zsolt Bihary, Péter Csóka, Péter Kerényi, Alexander Szimayer
Date: 2019-02-26 00:00:00

We introduce a dynamic principal-agent model to understand the nature of contracts between an employer and an independent gig worker. We model the worker's self-respect with an endogenous backward-looking participation constraint; he accepts a job offer if and only if its utility is at least as large as his reference value, which is based on the average of previously realized wages. If the dynamically changing reference value capturing the worker's demand is too high, then no contract is struck until the reference value hits a threshold. Below the threshold, contracts are offered and accepted, and the worker's wage demand follows a stochastic process. We apply our model to perfectly competitive and monopsonistic labor market structures and investigate first-best and second-best solutions. We show that a far-sighted employer with market power may sacrifice instantaneous profit to regulate the agent's demand. Moreover, the far-sighted employer implements increasing and path-dependent effort levels. Our model captures the worker's bargaining power by a vulnerability parameter that measures the rate at which his wage demand decreases when unemployed. With a low vulnerability parameter, the worker can afford to go unemployed and need not take a job at all costs. Conversely, a worker with high vulnerability can be exploited by the employer, and in this case our model also exhibits self-exploitation.
An optimization problem on the performance of FSO communication system
Date: 2019-01-18 00:00:00

Performance of Free Space Optical (FSO) communication system is affected by
atmospheric turbulences and pointing errors. These effects can easily be
mitigated by adapting natural system parameters such as wavelength. In this
paper, considering effects of pointing error and atmospheric turbulence, two
optimization models are presented on FSO communication system. In Model 1, the
normalized transmitter power is objective function, Bit Error Rate (BER) is
equality subjective. In Model 2 normalized transmitter power is equality
subjective, the BER is objective function. In both of them the normalized
wavelength is variable parameter. These models were previously investigated
using numerical methods; in the sense that they were solved asymptotically.
From this point of view, this paper regenerated these models and solved them by
a completely different analytical method, and derived a new exact solution.
Comparing exact and asymptotic methods shows some interesting results; the
asymptotic method achieves BER=10^(-9), but the presented new exact method
achieves BER=10^(-13), which is a significant difference. It means that even at
the worst case scenario such as pointing error, it is possible to have a very
good performance by only the transmitting wavelength a bit. Proposed models are
practical and obtained results show that this system is cost and power
effective.
Fair Estimation of Capital Risk Allocation
Authors: Tomasz R. Bielecki, Igor Cialenco, Marcin Pitera, Thorsten Schmidt
Date: 2019-02-26 00:00:00
Tasks: Fairness

In this paper we develop a novel methodology for estimation of risk capital allocation. The methodology is rooted in the theory of risk measures. We work within a general, but tractable class of law-invariant coherent risk measures, with a particular focus on expected shortfall. We introduce the concept of fair capital allocations and provide explicit formulae for fair capital allocations in case when the constituents of the risky portfolio are jointly normally distributed. The main focus of the paper is on the problem of approximating fair portfolio allocations in the case of not fully known law of the portfolio constituents. We define and study the concepts of fair allocation estimators and asymptotically fair allocation estimators. A substantial part of our study is devoted to the problem of estimating fair risk allocations for expected shortfall. We study this problem under normality as well as in a nonparametric setup. We derive several estimators, and prove their fairness and/or asymptotic fairness. Last, but not least, we propose two backtesting methodologies that are oriented at assessing the performance of the allocation estimation procedure. The paper closes with a substantial numerical study of the subject.
Fast Approximation of Frequent $k$-mers and Applications to Metagenomics
Date: 2019-02-26 00:00:00
Tasks: Learning Theory

Estimating the abundances of all $k$-mers in a set of biological sequences is
a fundamental and challenging problem with many applications in biological
analysis. While several methods have been designed for the exact or approximate
solution of this problem, they all require to process the entire dataset, that
can be extremely expensive for high-throughput sequencing datasets. While in
some applications it is crucial to estimate all $k$-mers and their abundances,
in other situations reporting only frequent $k$-mers, that appear with
relatively high frequency in a dataset, may suffice. This is the case, for
example, in the computation of $k$-mers' abundance-based distances among
datasets of reads, commonly used in metagenomic analyses. In this work, we
develop, analyze, and test, a sampling-based approach, called SAKEIMA, to
approximate the frequent $k$-mers and their frequencies in a high-throughput
sequencing dataset while providing rigorous guarantees on the quality of the
approximation. SAKEIMA employs an advanced sampling scheme and we show how the
characterization of the VC dimension, a core concept from statistical learning
theory, of a properly defined set of functions leads to practical bounds on the
sample size required for a rigorous approximation. Our experimental evaluation
shows that SAKEIMA allows to rigorously approximate frequent $k$-mers by
processing only a fraction of a dataset and that the frequencies estimated by
SAKEIMA lead to accurate estimates of $k$-mer based distances between
high-throughput sequencing datasets. Overall, SAKEIMA is an efficient and
rigorous tool to estimate $k$-mers abundances providing significant speed-ups
in the analysis of large sequencing datasets.
Self-Organization in Spontaneous Movements of Neonates generates Self-specifying Sensory Experiences
Date: 2019-02-26 00:00:00
Tasks: Time Series, Time Series Analysis

Movement experience and the coordination of perception and action are the
basis of developing body awareness, emotion, motivation and cognition and the
sense of self. The four limbs play a key role in the developing sense of body
ownership, agency and peripersonal space. Neonatal limb movements were
investigated respective processes of self-organization and developing body
awareness. Application of symbolic dynamics to kinematic data of radial
distance trajectories of the hands and feet determined 16 coordination patterns
according to the distance of the effectors to the body center. From time series
analysis of occurrence and recurrence of these configurations of 25 movement
episodes of 14 infants both characteristics of self-organization as well as
features assumed to provide neonates with experiences involved in the
development of body awareness were found. With increasing age a shift from
configurations with proximal to distal positions suggests a role of the
proximal-distal dimension in movement development. We conclude that
self-organization in spontaneous movements provides neonates with perceptual
body- and self-specifying stimuli involved in developing body awareness and
postulate the involvement of emotional and cognitive processes.
Quantum Capacitance-Limited MoS2 Biosensors Enable Remote Label-Free Enzyme Measurements
Date: 2018-12-21 00:00:00
Tasks: Diagnostic, Sensitivity

We have demonstrated atomically thin, quantum capacitance-limited,
field-effect transistors (FETs) that enable the detection of pH changes with
~75-fold higher sensitivity (4.4 V/pH) over the Nernst value of 59 mV/pH at
room temperature when used as a biosensor. The transistors, which are
fabricated from a monolayer of MoS2 with a room temperature ionic liquid (RTIL)
in place of a conventional oxide gate dielectric, exhibit very low intrinsic
noise resulting in a pH limit of detection (LOD) of 92x10^-6 at 10 Hz. This
high device performance, which is a function of the structure of our device, is
achieved by remotely connecting the gate to a pH sensing element allowing the
FETs to be reused. Because pH measurements are fundamentally important in
biotechnology, the low limit of detection demonstrated here will benefit
numerous applications ranging from pharmaceutical manufacturing to clinical
diagnostics. As an example, we experimentally quantified the function of the
kinase Cdk5, an enzyme implicated in Alzheimer's disease, at concentrations
that are 5-fold lower than physiological values, and with sufficient
time-resolution to allow the estimation of both steady-state and kinetic
parameters in a single experiment. The high sensitivity, low LOD and fast
turnaround time of the measurements will allow the development of early
diagnostic tools and novel therapeutics to detect and treat neurological
conditions years before currently possible.
Accurate Target Localization by using Artificial Pinnae of brown long-eared bat
Date: 2019-02-27 00:00:00

Echolocating bats locate the targets by echolocation. Many theoretical
frameworks have been suggested the abilities of bats are related to the shapes
of bats ears, but few artificial bat-like ears have been made to mimic the
abilities, the difficulty of which lies in the determination of the elevation
angle of the target. In this study, we present a device with artificial bat
pinnae modeling by the ears of brown long-eared bat (Plecotus auritus) which
can accurately estimate the elevation angle of the aerial target by virtue of
active sonar. An artificial neural-network with the labeled data obtained from
echoes as the trained and tested data is used and optimized by a tenfold
cross-validation technique. A decision method we named sliding window averaging
algorithm is designed for getting the estimation results of elevation. At last,
a right-angle pinnae construction is designed for determining direction of the
target. The results show a higher accuracy for the direction determination of
the single target. The results also demonstrate that for the Plecotus auritus
bat, not only the binaural shapes, but the binaural relative orientations also
play important roles in the target localization.
A physical mechanism of heterogeneity in stem cell, cancer and cancer stem cell
Date: 2019-02-27 00:00:00

Heterogeneity is ubiquitous in stem cells (SC), cancer cells (CS), and cancer
stem cells (CSC). SC and CSC heterogeneity is manifested as diverse
sub-populations with self-renewing and unique regeneration capacity. Moreover,
the CSC progeny possesses multiple plasticity and cancerous characteristics.
Many studies have demonstrated that cancer heterogeneity is one of the greatest
obstacle for therapy. This leads to the incomplete anti-cancer therapies and
transitory efficacy. Furthermore, numerous micro-metastasis leads to the wide
spread of the tumor cells across the body which is the beginning of metastasis.
The epigenetic processes (DNA methylation or histone remodification etc.) can
provide a source for certain heterogeneity. In this study, we develop a
mathematical model to quantify the heterogeneity of SC, CSC and cancer taking
both genetic and epigenetic effects into consideration. We uncovered the roles
and physical mechanisms of heterogeneity from the three aspects (SC, CSC and
cancer). In the adiabatic regime (relatively fast regulatory binding and
effective coupling among genes), seven native states (SC, CSC, Cancer,
Premalignant, Normal, Lesion and Hyperplasia) emerge. In non-adiabatic regime
(relatively slow regulatory binding and effective weak coupling among genes),
multiple meta-stable SC, CS, CSC and differentiated states emerged which can
explain the origin of heterogeneity. In other words, the slow regulatory
binding mimicking the epigenetics can give rise to heterogeneity. Elucidating
the origin of heterogeneity and dynamical interrelationship between
intra-tumoral cells has clear clinical significance in helping to understand
the cellular basis of treatment response, therapeutic resistance, and tumor
relapse.
Performance Analysis in DF Based Cooperative SWIPT Networks with Direct Link
Date: 2019-02-27 00:00:00

This letter proposes a dynamic power splitting scheme (DPSS) for
decode-and-forward (DF) based cooperative simultaneous wireless information and
power transfer (SWIPT) networks with direct link. The relay node adopts an
optimal dynamic power splitting factor determined by instantaneous channel
state information (CSI) to harvest energy and process information. The
expressions for the optimal dynamic power splitting factor, outage probability
and ergodic capacity of the proposed network are derived. Numerical results
show that the proposed scheme is better than or the same as the existing PS
schemes in terms of outage probability, while it achieves higher ergodic
capacity compared to the existing PS schemes.
Cortical recruitment and functional dynamics in postural control adaptation and habituation during vibratory proprioceptive stimulation
Date: 2019-02-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Maintaining upright posture is a complex task governed by the integration of
afferent sensorimotor and visual information with compensatory neuromuscular
reactions. The objective of this work was to characterize the visual dependency
and functional dynamics of cortical activation during postural control.
Proprioceptic vibratory stimulation of calf muscles at 85 Hz was performed to
evoke postural perturbation in open-eye (OE) and closed-eye (CE) experimental
trials, with pseudorandom binary stimulation phases divided into four segments
of 16 stimuli. 64-channel EEG was recorded; with perturbation epochs defined
using bipolar electrodes placed proximal to each vibrator. Finally, functional
connectivity assessment was explored via network segregation and integration
analyses. Spectra variation showed waveform and vision-dependent activation
within cortical regions specific to both postural adaptation and habituation.
Generalized spectral variation yielded significant shifts from low to high
frequencies in CE adaptation trials, with overall activity suppressed in
habituation; OE trials showed the opposite phenomenon, with both adaptation and
habituation yielding increases in spectral power. Finally, our analysis of
functional dynamics reveals novel cortical networks implicated in postural
control using EEG source-space brain networks. In particular, our reported
significant increase in local theta connectivity may signify the planning of
corrective steps and/or the analysis of falling consequences, while alpha band
network integration results reflect an inhibition of error detection within the
cingulate cortex, likely due to habituation. Our findings suggest that specific
cortical waveforms are dependent upon the availability of visual feedback, and
we present the first evidence that local and global brain networks undergo
characteristic modification during postural control.
Q-Gaussian diffusion in stock markets
Date: 2019-02-11 00:00:00

We analyze the Standard & Poor's 500 stock market index from the last 22
years. The probability density function of price returns exhibits two
well-distinguished regimes with self-similar structure: the first one displays
strong super-diffusion together with short-time correlations, and the second
one corresponds to weak super-diffusion with weak time correlations. Both
regimes are well-described by q-Gaussian distributions. The porous media
equation is used to derive the governing equation for these regimes, and the
Black-Scholes diffusion coefficient is explicitly obtained from the governing
equation.
Current knowledge and future research opportunities for modeling annual crop mixtures. A review
Date: 2019-02-27 00:00:00
Tasks: Management

Growing mixtures of annual arable crop species or genotypes is a promising
way to improve crop production without increasing agricultural inputs. To
design optimal crop mixtures, choices of species, genotypes, sowing proportion,
plant arrangement, and sowing date need to be made but field experiments alone
are not sufficient to explore such a large range of factors. Crop modeling
allows to study, understand and ultimately design cropping systems and is an
established method for sole crops. Recently, modeling started to be applied to
annual crop mixtures as well. Here, we review to what extent crop simulation
models and individual-based models are suitable to capture and predict the
specificities of annual crop mixtures. We argued that: 1) The crop mixture
spatio-temporal heterogeneity (influencing the occurrence of ecological
processes) determines the choice of the modeling approach (plant or crop
centered). 2) Only few crop models (adapted from sole crop models) and
individual-based models currently exist to simulate annual crop mixtures. 3)
Crop models are mainly used to address issues related to crop mixtures
management and to the integration of crop mixtures into larger scales such as
the rotation, whereas individual-based models are mainly used to identify plant
traits involved in crop mixture performance and to quantify the relative
contribution of the different ecological processes (niche complementarity,
facilitation, competition, plasticity) to crop mixture functioning. This review
highlights that modeling of annual crop mixtures is in its infancy and gives to
model users some important keys to choose the model based on the questions they
want to answer, with awareness of the strengths and weaknesses of each of the
modeling approaches.
Principles of efficient chemotactic pursuit
Date: 2019-02-27 00:00:00

In chemotaxis, cells are modulating their migration patterns in response to
concentration gradients of a guiding substance. Immune cells are believed to
use such chemotactic sensing for remotely detecting and homing in on pathogens.
Considering that an immune cells may encounter a multitude of targets with
vastly different migration properties, ranging from immobile to highly mobile,
it is not clear which strategies of chemotactic pursuit are simultaneously
efficient and versatile. We takle this problem theoretically and define a
tunable response function that maps temporal or spatial concentration gradients
to migration behavior. The seven free parameters of this response function are
optimized numerically with the objective of maximizing search efficiency
against a wide spectrum of target cell properties. Finally, we reverse-engineer
the best-performing parameter sets to uncover the principles of efficient
chemotactic pursuit under different biologically realistic boundary conditions.
Remarkably, the numerical optimization rediscovers chemotactic strategies that
are well-known in biological systems, such as the gradient-dependent swimming
and tumbling modes of E.coli. Some of our results may also be useful for the
design of chemotaxis experiments and for the development of algorithms that
automatically detect and quantify goal oriented behavior in measured immune
cell trajectories.
A tutorial on group effective connectivity analysis, part 1: first level analysis with DCM for fMRI
Date: 2019-02-27 00:00:00

Dynamic Causal Modelling (DCM) is the predominant method for inferring
effective connectivity from neuroimaging data. In the 15 years since its
introduction, the neural models and statistical routines in DCM have developed
in parallel, driven by the needs of researchers in cognitive and clinical
neuroscience. In this tutorial, we step through an exemplar fMRI analysis in
detail, reviewing the current implementation of DCM and demonstrating recent
developments in group-level connectivity analysis. In the first part of the
tutorial (current paper), we focus on issues specific to DCM for fMRI,
unpacking the relevant theory and highlighting practical considerations. In
particular, we clarify the assumptions (i.e., priors) used in DCM for fMRI and
how to interpret the model parameters. This tutorial is accompanied by all the
necessary data and instructions to reproduce the analyses using the SPM
software. In the second part (in a companion paper), we move from subject-level
to group-level modelling using the Parametric Empirical Bayes framework, and
illustrate how to test for commonalities and differences in effective
connectivity across subjects, based on imaging data from any modality.
A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB
Date: 2019-02-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG), parameter estimation, State Space Models

This tutorial provides a worked example of using Dynamic Causal Modelling
(DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject
variability in neural circuitry (effective connectivity). This involves
specifying a hierarchical model with two or more levels. At the first level,
state space models (DCMs) are used to infer the effective connectivity that
best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG).
Subject-specific connectivity parameters are then taken to the group level,
where they are modelled using a General Linear Model (GLM) that partitions
between-subject variability into designed effects and additive random effects.
The ensuing (Bayesian) hierarchical model conveys both the estimated connection
strengths and their uncertainty (i.e., posterior covariance) from the subject
to the group level; enabling hypotheses to be tested about the commonalities
and differences across subjects. This approach can also finesse parameter
estimation at the subject level, by using the group-level parameters as
empirical priors. We walk through this approach in detail, using data from a
published fMRI experiment that characterised individual differences in
hemispheric lateralization in a semantic processing task. The preliminary
subject specific DCM analysis is covered in detail in a companion paper. This
tutorial is accompanied by the example dataset and step-by-step instructions to
reproduce the analyses.