pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic thermodynamic limit on E. coli adaptation by Information geometric approach
Date: 2019-07-13 00:00:00

Biological systems process information under noisy environment. Sensory
adaptation model of E. coli is suitable for investigation because of its
simplicity. To understand the adaptation processing quantitatively, stochastic
thermodynamic approach has been attempted. Information processing can be
assumed as state transition of a system that consists of signal transduction
molecules using thermodynamic approach, and efficiency can be measured as
thermodynamic cost. Recently, using information geometry and stochastic
thermodynamics, a relationship between speed of the transition and the
thermodynamic cost has been investigated for a chemical reaction model. Here,
we introduce this approach to sensory adaptation model of E. coli, and examined
a relationship between adaptation speed and the thermodynamic cost, and
efficiency of the adaptation speed. For increasing external noise level in
stimulation, the efficiency decreased, but the efficiency was highly robust to
external stimulation strength. Moreover, we demonstrated that there is the best
noise to achieve the adaptation in the aspect of thermodynamic efficiency. Our
quantification method provides a framework to understand the adaptation speed
and the thermodynamic cost for various biological systems.
Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models
Date: 2018-05-28 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

This article introduces two absolutely continuous global-local shrinkage
priors to enable stochastic variable selection in the context of
high-dimensional matrix exponential spatial specifications. Existing approaches
as a means to dealing with overparameterization problems in spatial
autoregressive specifications typically rely on computationally demanding
Bayesian model-averaging techniques. The proposed shrinkage priors can be
implemented using Markov chain Monte Carlo methods in a flexible and efficient
way. A simulation study is conducted to evaluate the performance of each of the
shrinkage priors. Results suggest that they perform particularly well in
high-dimensional environments, especially when the number of parameters to
estimate exceeds the number of observations. For an empirical illustration we
use pan-European regional economic growth data.
Learning Temporal Structures of Random Patterns
Authors: Sun Yanlong, Wang Hongbin
Date: 2018-05-28 00:00:00
Tasks: Event Segmentation, Sensitivity

A cornerstone of human statistical learning is the ability to extract
temporal regularities / patterns from random sequences. Here we present a
method of computing pattern time statistics with generating functions for
first-order Markov trials and independent Bernoulli trials. We show that the
pattern time statistics cover a wide range of measurements commonly used in
existing studies of both human and machine learning of stochastic processes,
including probability of alternation, temporal correlation between pattern
events, and related variance / risk measures. Moreover, we show that recurrent
processing and event segmentation by pattern overlap may provide a coherent
explanation for the sensitivity of the human brain to the rich statistics and
the latent structures in the learning environment.
Tilting Approximate Models
Authors: Andreas Tryphonides
Date: 2018-05-28 00:00:00
Tasks: counterfactual

Model approximations are common practice when estimating structural or quasi-structural models. The paper considers the econometric properties of estimators that utilize projections to reimpose information about the exact model in the form of conditional moments. The resulting estimator efficiently combines the information provided by the approximate law of motion and the moment conditions. The paper develops the corresponding asymptotic theory and provides simulation evidence that tilting substantially reduces the mean squared error for parameter estimates. It applies the methodology to pricing long-run risks in aggregate consumption in the US, whereas the model is solved using the Campbell and Shiller (1988) approximation. Tilting improves empirical fit and results suggest that approximation error is a source of upward bias in estimates of risk aversion and downward bias in the elasticity of intertemporal substitution.
Using spike train distances to identify the most discriminative neuronal subpopulation
Date: 2018-09-14 00:00:00

Background: Spike trains of multiple neurons can be analyzed following the
summed population (SP) or the labeled line (LL) hypothesis. Responses to
external stimuli are generated by a neuronal population as a whole or the
individual neurons have encoding capacities of their own. The SPIKE-distance
estimated either for a single, pooled spike train over a population or for each
neuron separately can serve to quantify these responses.
  New Method: For the SP case we compare three algorithms that search for the
most discriminative subpopulation over all stimulus pairs. For the LL case we
introduce a new algorithm that combines neurons that individually separate
different pairs of stimuli best.
  Results: The best approach for SP is a brute force search over all possible
subpopulations. However, it is only feasible for small populations. For more
realistic settings, simulated annealing clearly outperforms gradient algorithms
with only a limited increase in computational load. Our novel LL approach can
handle very involved coding scenarios despite its computational ease.
  Comparison with Existing Methods: Spike train distances have been extended to
the analysis of neural populations interpolating between SP and LL coding. This
includes parametrizing the importance of distinguishing spikes being fired in
different neurons. Yet, these approaches only consider the population as a
whole. The explicit focus on subpopulations render our algorithms
complimentary.
  Conclusions: The spectrum of encoding possibilities in neural populations is
broad. The SP and LL cases are two extremes for which our algorithms provide
correct identification results.
A Macroscopic Portfolio Model: From Rational Agents to Bounded Rationality
Date: 2018-10-26 00:00:00
Tasks: Model Predictive Control, Portfolio Optimization

We introduce a microscopic model of interacting financial agents, where each
agent is characterized by two portfolios; money invested in bonds and money
invested in stocks. Furthermore, each agent is faced with an optimization
problem in order to determine the optimal asset allocation. The stock price
evolution is driven by the aggregated investment decision of all agents. In
fact, we are faced with a differential game since all agents aim to invest
optimal. Mathematically such a problem is ill posed and we introduce the
concept of Nash equilibrium solutions to ensure the existence of a solution.
Especially, we denote an agent who solves this Nash equilibrium exactly a
rational agent. As next step we use model predictive control to approximate the
control problem. This enables us to derive a precise mathematical
characterization of the degree of rationality of a financial agent. This is a
novel concept in portfolio optimization and can be regarded as a general
approach. In a second step we consider the case of a fully myopic agent, where
we can solve the optimal investment decision of investors analytically. We
select the running cost to be the expected missed revenue of an agent and we
assume quadratic transaction costs. More precisely the expected revenues are
determined by a combination of a fundamentalist or chartist strategy. Then we
derive the mean field limit of the microscopic model in order to obtain a
macroscopic portfolio model. The novelty in comparison to existent
macroeconomic models in literature is that our model is derived from
microeconomic dynamics. The resulting portfolio model is a three dimensional
ODE system which enables us to derive analytical results. Simulations reveal,
that our model is able to replicate the most prominent features of financial
markets, namely booms and crashes.
Caenorhabditis elegans and the network control framework - FAQs
Date: 2018-05-28 00:00:00

Control is essential to the functioning of any neural system. Indeed, under
healthy conditions the brain must be able to continuously maintain a tight
functional control between the system's inputs and outputs. One may therefore
hypothesise that the brain's wiring is predetermined by the need to maintain
control across multiple scales, maintaining the stability of key internal
variables, and producing behaviour in response to environmental cues. Recent
advances in network control have offered a powerful mathematical framework to
explore the structure-function relationship in complex biological, social, and
technological networks, and are beginning to yield important and precise
insights for neuronal systems. The network control paradigm promises a
predictive, quantitative framework to unite the distinct datasets necessary to
fully describe a nervous system, and provide mechanistic explanations for the
observed structure and function relationships. Here, we provide a thorough
review of the network control framework as applied to C. elegans, in the style
of a FAQ. We present the theoretical, computational, and experimental aspects
of network control, and discuss its current capabilities and limitations,
together with the next likely advances and improvements. We further present the
Python code to enable exploration of control principles in a manner specific to
this prototypical organism.
Modeling the residential electricity consumption within a restructured power market
Date: 2018-06-12 00:00:00

The United States' power market is featured by the lack of judicial power at
the federal level. The market thus provides a unique testing environment for
the market organization structure. At the same time, the econometric modeling
and forecasting of electricity market consumption become more challenging.
Import and export, which generally follow simple rules in European countries,
can be a result of direct market behaviors. This paper seeks to build a general
model for power consumption and using the model to test several hypotheses.
A State Space Technique for Wildlife Position Estimation Using Non-Simultaneous Signal Strength Measurements
Date: 2018-05-28 00:00:00
Tasks: Position

A novel state-space technique is presented to estimate the location and
airborne movements of VHF tagged wildlife individuals with fixed VHF arrays.
The approach combines a movement model (Ornstein- Uhlenbeck random process in
the transverse (horizontal) plane and a Cox- Ingersoll-Ross process in the
vertical direction) to ensure biologically-consistent trajectories in
three-dimensions, and an observation model to account for the effect of range,
altitude and bearing angle on the received signal strength. The observation
model of received signals accounts for low-end saturation from receiver noise,
high-end saturation from receiver non-linearities as well as a wireless
multipath phenomena, which modulates the received signal according to range,
the altitude and radiation characteristics of the Yagi array. A pattern
function for the Yagi array is synthesized that facilitates linearization of
the received signals and subsequent application of Kalman filtering.
  We first validate the model using a simulated trajectory and then estimate
the space-time trajectory of a migrating VHF-tagged shorebird, which was
tracked with a regional automated radio telemetry network. The algorithm
accurately predicted the average movement trajectory given the system
parameters and the initial conditions (average error $<$ 1 km). The modeled
shorebird track represents a first estimate in three-dimensional (3D) of a
radio tagged bird using a fixed telemetry array, and was qualitatively
reasonable, but exhibited some sensitivity in the vertical plane and to initial
conditions.
Wireless Localization for mmWave Networks in Urban Environments
Date: 2018-05-29 00:00:00

Millimeter wave (mmWave) technology is expected to be a major component of 5G
wireless networks. Ultra-wide bandwidths of mmWave signals and the possibility
of utilizing large number of antennas at the transmitter and the receiver allow
accurate identification of multipath components in temporal and angular
domains, making mmWave systems advantageous for localization applications. In
this paper, we analyze the performance of a two-step mmWave localization
approach that can utilize time-of-arrival, angle-of-arrival, and
angle-of-departure from multiple nodes in an urban environment with both
line-of-sight (LOS) and non-LOS (NLOS) links. Networks with/without
radio-environmental mapping (REM) are considered, where a network with REM is
able to localize nearby scatterers. Estimation of a UE location is challenging
due to large numbers of local optima in the likelihood function. To address
this problem, a gradient-assisted particle filter (GAPF) estimator is proposed
to accurately estimate a user equipment (UE) location as well as the locations
of nearby scatterers. Monte Carlo simulations show that the GAPF estimator
performance matches the Cramer-Rao bound (CRB). The estimator is also used to
create an REM. It is seen that significant localization gains can be achieved
by increasing beam directionality or by utilizing REM.
Offloading of Users in NOMA-HetNet Using Repulsive Point Process
Date: 2018-05-29 00:00:00

Ever increasing number of cellular users and their high data requirements,
necessitates need for improvement in the present heterogeneous cellular
networks (HetNet). Carrier sensing prevents base stations within a certain
range of the transmitter from transmitting and hence aids in reducing the
interference. Non-orthogonal multiple access (NOMA) has proven its superiority
for the 5th generation (5G) networks. This work proposes a mathematical model
for an improved HetNet with macro base station (MBS) and femto base station
(FBS) tier. The FBS tier is equipped to support NOMA and carrier sensing for
its transmissions. Offloading is performed for load balancing in HetNet where
the macro users (MU) from congested MBS tier are offloaded to the FBS tier. The
FBS tier pairs the offloaded MU (OMU) with an appropriate pairing user (PU) to
perform NOMA. The performance of the OMU is studied under different channel
conditions with respect to the available PU at the FBS and some useful
observations are drawn. A decrease in outage probability by $74.04\%$ for cell
center user (CCU) and $48.65\%$ for cell edge user (CEU) is observed for low
density FBS. The outage probability decreases by $99.60\%$, for both the CCU
and CEU, for high density FBS using the proposed carrier sensing in NOMA. The
results are validated using simulations.
Discrete Linear Canonical Transform Based on Hyperdifferential Operators
Date: 2019-02-13 00:00:00

Linear canonical transforms (LCTs) are of importance in many areas of science
and engineering with many applications. Therefore a satisfactory discrete
implementation is of considerable interest. Although there are methods that
link the samples of the input signal to the samples of the linear canonical
transformed output signal, no widely-accepted definition of the discrete LCT
has been established. We introduce a new approach to defining the discrete
linear canonical transform (DLCT) by employing operator theory. Operators are
abstract entities that can have both continuous and discrete concrete
manifestations. Generating the continuous and discrete manifestations of LCTs
from the same abstract operator framework allows us to define the continuous
and discrete transforms in a structurally analogous manner. By utilizing
hyperdifferential operators, we obtain a DLCT matrix which is totally
compatible with the theory of the discrete Fourier transform (DFT) and its dual
and circulant structure, which makes further analytical manipulations and
progress possible. The proposed DLCT is to the continuous LCT, what the DFT is
to the continuous Fourier transform (FT). The DLCT of the signal is obtained
simply by multiplying the vector holding the samples of the input signal by the
DLCT matrix.
A Count of Palm Trees from Satellite Image
Date: 2018-05-27 00:00:00

In this research the number of palm trees was calculated from the satellite
image programmatically, taking advantage of the accuracy of the spatial
resolution of satellite image, the abilities of software recognition, and
characteristics of the palm tree, which give it a systematic top view can be
distinguished from the satellite image and the manner of cultivation and
vertical growth and stability form for long periods of time. While other trees
are irregular in shape mostly because of their twisted branches. Palm trees
consist of a long stem, a large head, and a large flare that is almost circular
and consists of large tufts. The palms have large self-shadows other than
ordinary leaves. The large shadows and the circular shape of the upper view
give it a special feature that we could use to design a program that
distinguishes the shape of the palm without all the trees. Then it counts the
number of palms in any field shown in the satellite image. This method is
useful in counting the number of palm trees for commercial, agricultural or
environmental purposes. It is also can be applied to high-resolution satellite
imagery such as QuickBird because the resolution of the images is 0.6 meters.
Less accurate images such as the 10-meter SPOT do not show the interior shadows
of the top view of the palm enough, nor the accurate satellites (5 meters),
while the interior shadows appear in high-resolution images only (0.6 meters)
or below. It can also be applied to aerial images of less capacity because they
are more accurate of course. Satellite images can be obtained free from Google
Earth explorer, which can be downloaded free from the Google website. It
connects the user to a global database of high-resolution images for all
regions of the world.
Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG
Date: 2018-09-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

This study explores the responses to ketamine in patients with
treatment-resistant depression (TRD) using a wearable forehead
electroencephalography (EEG) device. We recruited fifty-five outpatients with
TRD who were randomised into three approximately equal-sized groups (A: 0.5
mg/kg ketamine; B: 0.2 mg/kg ketamine; and C: normal saline) under double-blind
conditions. The ketamine responses were measured by EEG signals and Hamilton
Depression Rating Scale (HDRS) scores. At baseline, responders showed a
significantly weaker EEG theta power than did non- responders (p < 0.05).
Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and
theta cordance at post-treatment than at baseline (p < 0.05). Furthermore, our
baseline EEG predictor classified responders and non-responders with 81.3 +-
9.5% accuracy, 82.1 +- 8.6% sensitivity and 91.9 +- 7.4% specificity. In
conclusion, the rapid antidepressant effects of mixed doses of ketamine are
associated with prefrontal EEG power, asymmetry and cordance at baseline and
early post-treatment changes. The prefrontal EEG patterns at baseline may
account for recognising ketamine effects in advance. Our randomised, double-
blind, placebo-controlled study provides information regarding clinical impacts
on the potential targets underlying baseline identification and early changes
from the effects of ketamine in patients with TRD.
Estimation and Inference for Policy Relevant Treatment Effects
Date: 2020-07-16 00:00:00
Tasks: counterfactual

The policy relevant treatment effect (PRTE) measures the average effect of
switching from a status-quo policy to a counterfactual policy. Estimation of
the PRTE involves estimation of multiple preliminary parameters, including
propensity scores, conditional expectation functions of the outcome and
covariates given the propensity score, and marginal treatment effects. These
preliminary estimators can affect the asymptotic distribution of the PRTE
estimator in complicated and intractable manners. In this light, we propose an
orthogonal score for double debiased estimation of the PRTE, whereby the
asymptotic distribution of the PRTE estimator is obtained without any influence
of preliminary parameter estimators as far as they satisfy mild requirements of
convergence rates. To our knowledge, this paper is the first to develop limit
distribution theories for inference about the PRTE.
Decision Making of Maximizers and Satisficers Based on Collaborative Explanations
Authors: Coba Ludovik, Zanker Markus, Rook Laurens, Symeonidis Panagiotis
Date: 2018-05-29 00:00:00
Tasks: Decision Making

Rating-based summary statistics are ubiquitous in e-commerce, and often are
crucial components in personalized recommendation mechanisms. Largely left
unexplored, however, is the issue to what extent the descriptives of rating
distributions influence the decision making of online consumers. We conducted a
conjoint experiment to explore how different summarizations of rating
distributions (i.e., in the form of the number of ratings, mean, variance,
skewness or the origin of the ratings) impact users' decision making. Results
from over 200 participants indicate that users are primarily guided by the mean
and the number of ratings and to a lesser degree by the variance, and the
origin of a rating. We also looked into the maximizing behavioral tendencies of
our participants, and found that in particular participants scoring high on the
Decision Difficulty subscale displayed other sensitivities regarding the way in
which rating distributions were summarized than others.
Impact of group management and transfer on individual sociality in Highland cattle (Bos taurus)
Date: 2018-10-13 00:00:00
Tasks: Management

The sociality of cattle facilitates the maintenance of herd cohesion and
synchronisation, making these species the ideal choice for domestication as
livestock for humans. However, livestock populations are not self-regulated,
and farmers transfer individuals across different groups throughout their lives
for reasons such as genetic mixing, reproduction and pastureland management.
Individuals consequently have to adapt to different group compositions during
their lives rather than choose their own herd mates, as they would do in the
wild. These changes may lead to social instability and stress, entailing
potentially negative effects on animal welfare. In this study, we assess how
the transfer of Highland cattle (Bos taurus) impacts individual and group
social network measures. Four groups with nine different compositions and 18
individual transfers were studied to evaluate 1) the effect of group
composition on individual social centralities and 2) the effect of group
composition changes on these centralities. As shown in previous works, dyadic
associations are stronger between individuals with similar age and dominance
rank. This study reveals that the relative stability of dyadic spatial
relationships between changes in group composition or enclosure is due to the
identities of transferred individuals more than the quantity of individuals
that are transferred. Older cattle had higher network centralities than other
individuals. The centrality of individuals was also affected by their sex and
the number of familiar individuals in the group. This study reveals the
necessity of understanding the social structure of a group to predict social
instability following the transfer of individuals between groups. The
developing of guidelines for the modification of group composition could
improve livestock management and reduce stress for the animals concerned.