pwc-archive/papers-with-abstracts huggingface dataset:
fMRI Based Cerebral Instantaneous Parameters for Automatic Alzheimer's, Mild Cognitive Impairment and Healthy Subject Classification
Date: 2019-04-16 00:00:00
Tasks: feature selection

Automatic identification and categorization of Alzheimer's patients and the
ability to distinguish between different levels of this disease can be very
helpful to the research community in this field, since other non-automatic
approaches are very time-consuming and are highly dependent on experts'
experience. Herein, we propose the utility of cerebral instantaneous phase and
envelope information in order to discriminate between Alzheimer's patients, MCI
subjects and healthy normal individuals from functional magnetic resonance
imaging (fMRI) data. To this end, after performing the region-of-interest (ROI)
analysis on fMRI data, different features covering power, entropy and coherency
aspects of data are derived from instantaneous phase and envelope sequences of
ROI signals. Various sets of features are calculated and fed to a sequential
forward floating feature selection (SFFFS) to choose the most discriminative
and informative sets of features. A Student's t-test has been used to select
the most relevant features from chosen sets. Finally, a K-NN classifier is used
to distinguish between classes in a three-class categorization problem. The
reported performance in overall accuracy using fMRI data of 111 combined
subjects, is 80.1% with 80.0% Sensitivity to both Alzheimer's and Normal
categories distinction and is comparable to the state-of-the-art approaches
recently proposed in this regard. The significance of obtained results was
statistically confirmed by evaluating through standard classification
performance indicators. The obtained results illustrate that introduced
analytic phase and envelope feature indexes derived from the ROI signals are
significantly discriminative in distinguishing between Alzheimer's patients and
Normal healthy subject.
Optimal mechanism for the sale of a durable good
Authors: Laura Doval, Vasiliki Skreta
Date: 2019-04-16 00:00:00

A buyer wishes to purchase a durable good from a seller who in each period chooses a mechanism under limited commitment. The buyer's valuation is binary and fully persistent. We show that posted prices implement all equilibrium outcomes of an infinite-horizon, mechanism selection game. Despite being able to choose mechanisms, the seller can do no better and no worse than if he chose prices in each period, so that he is subject to Coase's conjecture. Our analysis marries insights from information and mechanism design with those from the literature on durable goods. We do so by relying on the revelation principle in Doval and Skreta (2020).
Clearing price distributions in call auctions
Authors: M. Derksen, B. Kleijn, R. de Vilder
Date: 2019-04-16 00:00:00

We propose a model for price formation in financial markets based on clearing of a standard call auction with random orders, and verify its validity for prediction of the daily closing price distribution statistically. The model considers random buy and sell orders, placed following demand- and supply-side valuation distributions; an equilibrium equation then leads to a distribution for clearing price and transacted volume. Bid and ask volumes are left as free parameters, permitting possibly heavy-tailed or very skewed order flow conditions. In highly liquid auctions, the clearing price distribution converges to an asymptotically normal central limit, with mean and variance in terms of supply/demand-valuation distributions and order flow imbalance. By means of simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting a distinction between high- and low-volume auction price variance. To verify the validity of the model statistically, we predict a year's worth of daily closing price distributions for 5 constituents of the Eurostoxx 50 index; Kolmogorov-Smirnov statistics and QQ-plots demonstrate with ample statistical significance that the model predicts closing price distributions accurately, and compares favourably with alternative methods of prediction.
The Impact of UCMAS Training Program on Potentiating Cognitive Capacity among 9-12 Year-Old Primary Schoolers in Shiraz
Date: 2019-04-16 00:00:00

The Universal Concept of Mental Arithmetic System (UCMAS) is a modern
representation of an ancient art of mental arithmetic. Although such a training
is hypothesized to result in potential effects on mental capacity and cognitive
performance in experienced trainees, this has not sufficiently been
systematically studied. The present study was an attempt to compare the
objective testing score of Less-trained (less than 6 months of training) and
experienced (over 36 months of training) UCMAS-trained young adolescents in
Shiraz. Thirty healthy participants aged 9-12 were recruited from UCMAS
training centers in Shiraz. The two study arms (comprising 15 less-trained and
15 experienced children) were ensured to be sex- and age-matched. The Cambridge
Brain Science-Cognitive Platform (CBS-CP) was employed as a media-rich computer
testing battery for cognitive assessments. Moreover, cerebral blood levels of
the participants was recorded through Hemoencephalography upon taking CBS-CP
tasks (ANI task of each participant).The experienced UCMAS-trained participants
were found to outperform in the Spatial Span (p=0.004), Digit Span (p=0.014)
and Monkey Ladder (P=0.022) tests in which Short-Term Memory and
Visuo-Constructive skills play important roles. Nevertheless, our findings
revealed no statistically significant difference between-group differences in
test scores which measured other cognitive domains. Adding to the existing body
of evidence on the effect of UCMAS training on potentiating cognitive capacity,
our findings suggest that experienced UCMAS trainees tend to outperform in some
key cognitive domains including Short-Term Memory and Visuo-Constructive
Capacity. Further experimental neuroscience studies using quantitative
electroencephalography and functional magnetic- or optic-neuroimaging would
shed further light to neurodynamics of such a differentiation.
Consumer Privacy and Serial Monopoly
Date: 2019-04-16 00:00:00

We examine the implications of consumer privacy when preferences today depend
upon past consumption choices, and consumers shop from different sellers in
each period. Although consumers are ex ante identical, their initial
consumption choices cannot be deterministic. Thus ex post heterogeneity in
preferences arises endogenously. Consumer privacy improves social welfare,
consumer surplus and the profits of the second-period seller, while reducing
the profits of the first period seller, relative to the situation where
consumption choices are observed by the later seller.
Quantifying cancer epithelial-mesenchymal plasticity and its association with stemness and immune response
Authors: Dongya Jia, Xuefei Li, Federico Bocci, Shubham Tripathi, Youyuan Deng, Mohit Kumar Jolly, Jose N. Onuchic, Herbert Levine
Date: 2019-04-16 00:00:00

Cancer cells can acquire a spectrum of stable hybrid epithelial/mesenchymal (E/M) states during epithelial-mesenchymal transition (EMT). Cells in these hybrid E/M phenotypes often combine epithelial and mesenchymal features and tend to migrate collectively commonly as small clusters. Such collectively migrating cancer cells play a pivotal role in seeding metastases and their presence in cancer patients indicates an adverse prognostic factor. Moreover, cancer cells in hybrid E/M phenotypes tend to be more associated with stemness which endows them with tumor-initiation ability and therapy resistance. Most recently, cells undergoing EMT have been shown to promote immune suppression for better survival. A systematic understanding of the emergence of hybrid E/M phenotypes and the connection of EMT with stemness and immune suppression would contribute to more effective therapeutic strategies. In this review, we first discuss recent efforts combining theoretical and experimental approaches to elucidate mechanisms underlying EMT multi-stability (i.e. the existence of multiple stable phenotypes during EMT) and the properties of hybrid E/M phenotypes. Following we discuss non-cell-autonomous regulation of EMT by cell cooperation and extracellular matrix. Afterwards, we discuss various metrics that can be used to quantify EMT spectrum. We further describe possible mechanisms underlying the formation of clusters of circulating tumor cells. Last but not least, we summarize recent systems biology analysis of the role of EMT in the acquisition of stemness and immune suppression.
Energy Saving Strategy Based on Profiling
Date: 2019-04-12 00:00:00

Constraints imposed by power consumption and the related costs are one of the
key roadblocks to the design and development of next generation exascale
systems. To mitigate these issues, strategies that reduce the power consumption
of the processor are the need of the hour. Techniques such as Dynamic Voltage
and Frequency Scaling (DVFS) exist which reduce the power consumption of a
processor at runtime but they should be used in such a manner so that their
overhead does not hamper application performance. In this paper, we propose an
energy saving strategy which operates on timeslice basis to apply DVFS under a
user defined performance constraint. Results show energy savings up to 7% when
NAS benchmarks are tested on a laptop platform
Base spacing distribution analysis for human genome
Authors: Andrzej Z. Górski, Monika Piwowar
Date: 2019-04-16 00:00:00

The distribution of bases spacing in human genome was investigated. An analysis of the frequency of occurrence in the human genome of different sequence lengths flanked by one type of nucleotide was carried out showing that the distribution has no self-similar (fractal) structure. The results nevertheless revealed several characteristic features: (i) the distribution for short-range spacing is quite similar to the purely stochastic sequences; (ii) the distribution for long-range spacing essentially deviates from the random sequence distribution, showing strong long-range correlations; (iii) the differences between (A, T) and (C, G) bases are quite significant; (iv) the spacing distribution displays tiny oscillations.
Optimal loss-carry-forward taxation for L\'{e}vy risk processes stopped at general draw-down time
Date: 2019-04-17 00:00:00

Motivated by Kyprianou and Zhou (2009), Wang and Hu (2012), Avram et al.
(2017), Li et al. (2017) and Wang and Zhou (2018), we consider in this paper
the problem of maximizing the expected accumulated discounted tax payments of
an insurance company, whose reserve process (before taxes are deducted) evolves
as a spectrally negative L\'{e}vy process with the usual exclusion of negative
subordinator or deterministic drift. Tax payments are collected according to
the very general loss-carry-forward tax system introduced in Kyprianou and Zhou
(2009). To achieve a balance between taxation optimization and solvency, we
consider an interesting modified objective function by considering the expected
accumulated discounted tax payments of the company until the general draw-down
time, instead of until the classical ruin time. The optimal tax return function
together with the optimal tax strategy is derived, and some numerical examples
are also provided.
Molecular Diversity and Network Complexity in Growing Protocells
Authors: Atsushi Kamimura, Kunihiko Kaneko
Date: 2019-04-17 00:00:00
Tasks: Diversity

A great variety of molecular components is encapsulated in cells. Each of these components is replicated for cell reproduction. To address an essential role of the huge diversity of cellular components, we study a model of protocells that convert resources into catalysts with the aid of a catalytic reaction network. As the resources are limited, it is shown that diversity in intracellular components is increased to allow the use of diverse resources for cellular growth. Scaling relation is demonstrated between resource abundances and molecular diversity. We then study how the molecule species diversify and complex catalytic reaction networks develop through the evolutionary course. It is shown that molecule species first appear, at some generations, as parasitic ones that do not contribute to replication of other molecules. Later, the species turn to be host species that support the replication of other species. With this successive increase of host species, a complex joint network evolves. The present study sheds new light on the origin of molecular diversity and complex reaction network at the primitive stage of a cell.
A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour
Date: 2020-01-20 00:00:00
Tasks: GPU, Prediction, Time Series, Time Series Analysis

We propose a heterogeneous simultaneous graphical dynamic linear model
(H-SGDLM), which extends the standard SGDLM framework to incorporate a
heterogeneous autoregressive realised volatility (HAR-RV) model. This novel
approach creates a GPU-scalable multivariate volatility estimator, which
decomposes multiple time series into economically-meaningful variables to
explain the endogenous and exogenous factors driving the underlying
variability. This unique decomposition goes beyond the classic one step ahead
prediction; indeed, we investigate inferences up to one month into the future
using stocks, FX futures and ETF futures, demonstrating its superior
performance according to accuracy of large moves, longer-term prediction and
consistency over time.
Mathematical modeling of variability in intracellular signaling
Date: 2019-04-17 00:00:00
Tasks: Decision Making

Cellular signaling is essential in information processing and decision
making. Therefore, a variety of experimental approaches have been developed to
study signaling on bulk and single-cell level. Single-cell measurements of
signaling molecules demonstrated a substantial cell-to-cell variability,
raising questions about its causes and mechanisms and about how cell
populations cope with or exploit cellular heterogeneity. To gain insights from
single-cell signaling data, analysis and modeling approaches have been
introduced. This review discusses these modeling approaches, with a focus on
recent advances in the development and calibration of mechanistic models.
Additionally, it outlines current and future challenges.
A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel
Date: 2020-01-18 00:00:00

In multinomial response models, idiosyncratic variations in the indirect
utility are generally modeled using Gumbel or normal distributions. This study
makes a strong case to substitute these thin-tailed distributions with a
t-distribution. First, we demonstrate that a model with a t-distributed error
kernel better estimates and predicts preferences, especially in
class-imbalanced datasets. Our proposed specification also implicitly accounts
for decision-uncertainty behavior, i.e. the degree of certainty that
decision-makers hold in their choices relative to the variation in the indirect
utility of any alternative. Second, after applying a t-distributed error kernel
in a multinomial response model for the first time, we extend this
specification to a generalized continuous-multinomial (GCM) model and derive
its full-information maximum likelihood estimator. The likelihood involves an
open-form expression of the cumulative density function of the multivariate
t-distribution, which we propose to compute using a combination of the
composite marginal likelihood method and the separation-of-variables approach.
Third, we establish finite sample properties of the GCM model with a
t-distributed error kernel (GCM-t) and highlight its superiority over the GCM
model with a normally-distributed error kernel (GCM-N) in a Monte Carlo study.
Finally, we compare GCM-t and GCM-N in an empirical setting related to
preferences for electric vehicles (EVs). We observe that accounting for
decision-uncertainty behavior in GCM-t results in lower elasticity estimates
and a higher willingness to pay for improving the EV attributes than those of
the GCM-N model. These differences are relevant in making policies to expedite
the adoption of EVs.
All-optical frequency processor for networking applications
Date: 2019-04-17 00:00:00
Tasks: All

We propose an electro-optic approach for transparent optical networking, in
which frequency channels are actively transformed into any desired mapping in a
wavelength-multiplexed environment. Based on electro-optic phase modulators and
Fourier-transform pulse shapers, our all-optical frequency processor (AFP) is
examined numerically for the specific operations of frequency channel hopping
and broadcasting, and found capable of implementing these transformations with
favorable component requirements. Extending our analysis via a
mutual-information--based metric for system optimization, we show how to
optimize transformation performance under limited resources in a classical
context, contrasting the results with those found using metrics motivated by
quantum information, such as fidelity and success probability. Given its
compatibility with on-chip implementation, as well as elimination of
optical-to-electrical conversion in frequency channel switching, the AFP looks
to offer valuable potential in silicon photonic network design.
Sharp Bounds for the Marginal Treatment Effect with Sample Selection
Date: 2019-04-17 00:00:00

I analyze treatment effects in situations when agents endogenously select
into the treatment group and into the observed sample. As a theoretical
contribution, I propose pointwise sharp bounds for the marginal treatment
effect (MTE) of interest within the always-observed subpopulation under
monotonicity assumptions. Moreover, I impose an extra mean dominance assumption
to tighten the previous bounds. I further discuss how to identify those bounds
when the support of the propensity score is either continuous or discrete.
Using these results, I estimate bounds for the MTE of the Job Corps Training
Program on hourly wages for the always-employed subpopulation and find that it
is decreasing in the likelihood of attending the program within the
Non-Hispanic group. For example, the Average Treatment Effect on the Treated is
between \$.33 and \$.99 while the Average Treatment Effect on the Untreated is
between \$.71 and \$3.00.
Asymptotic Behavior of Bayesian Learners with Misspecified Models
Date: 2019-10-23 00:00:00

We consider an agent who represents uncertainty about the environment via a
possibly misspecified model. Each period, the agent takes an action, observes a
consequence, and uses Bayes' rule to update her belief about the environment.
This framework has become increasingly popular in economics to study behavior
driven by incorrect or biased beliefs. Current literature has characterized
asymptotic behavior under fairly specific assumptions. By first showing that
the key element to predict the agent's behavior is the frequency of her past
actions, we are able to characterize asymptotic behavior in general settings in
terms of the solutions of a generalization of a differential equation that
describes the evolution of the frequency of actions. We then present a series
of implications that can be readily applied to economic applications, thus
providing off-the-shelf tools that can be used to characterize behavior under
misspecified learning.
Ridge regularization for Mean Squared Error Reduction in Regression with Weak Instruments
Date: 2019-04-18 00:00:00
Tasks: regression

In this paper, I show that classic two-stage least squares (2SLS) estimates
are highly unstable with weak instruments. I propose a ridge estimator (ridge
IV) and show that it is asymptotically normal even with weak instruments,
whereas 2SLS is severely distorted and un-bounded. I motivate the ridge IV
estimator as a convex optimization problem with a GMM objective function and an
L2 penalty. I show that ridge IV leads to sizable mean squared error reductions
theoretically and validate these results in a simulation study inspired by data
designs of papers published in the American Economic Review.
Multi-stage Antenna Selection for Adaptive Beamforming in MIMO Arrays
Date: 2019-04-18 00:00:00
Tasks: Dimensionality Reduction

Increasing the number of transmit and receive elements in
multiple-input-multiple-output (MIMO) antenna arrays imposes a substantial
increase in hardware and computational costs. We mitigate this problem by
employing a reconfigurable MIMO array where large transmit and receive arrays
are multiplexed in a smaller set of k baseband signals. We consider four stages
for the MIMO array configuration and propose four different selection
strategies to offer dimensionality reduction in post-processing and achieve
hardware cost reduction in digital signal processing (DSP) and radio-frequency
(RF) stages. We define the problem as a determinant maximization and develop a
unified formulation to decouple the joint problem and select antennas/elements
in various stages in one integrated problem. We then analyze the performance of
the proposed selection approaches and prove that, in terms of the output SINR,
a joint transmit-receive selection method performs best followed by
matched-filter, hybrid and factored selection methods. The theoretical results
are validated numerically, demonstrating that all methods allow an excellent
trade-off between performance and cost.
Multiresolution time-of-arrival estimation from multiband radio channel measurements
Date: 2019-04-18 00:00:00

Achieving high resolution time-of-arrival (TOA) estimation in multipath
propagation scenarios from bandlimited observations of communication signals is
challenging because the multipath channel impulse response (CIR) is not
bandlimited. Modeling the CIR as a sparse sequence of Diracs, TOA estimation
becomes a problem of parametric spectral inference from observed bandlimited
signals. To increase resolution without arriving at unrealistic sampling rates,
we consider multiband sampling approach, and propose a practical multibranch
receiver for the acquisition. The resulting data model exhibits multiple shift
invariance structures, and we propose a corresponding multiresolution TOA
estimation algorithm based on the ESPRIT algorithm. The performance of the
algorithm is compared against the derived Cram\'er Rao Lower Bound, using
simulations with standardized ultra-wideband (UWB) channel models. We show that
the proposed approach provides high-resolution estimates while reducing
spectral occupancy and sampling costs compared to traditional UWB approaches.
Stochastic mechanism for improving selectivity of olfactory projection neurons
Date: 2019-04-18 00:00:00

A mechanism is proposed for increasing selectivity of olfactory bulb
projection neurons as compared to the olfactory receptor neurons, which could
operate under low odor concentration, when the lateral inhibition mechanism
becomes inefficient. The mechanism proposed is based on the threshold-type
reaction to stimuli a projection neuron receives from the receptor neurons, the
stochastic nature of those stimuli and electrical leakage in the projection
neurons. The mechanism operates at the level of individual projection neuron
and does not require involvement of other bulbar neurons.
  Keywords: olfactory receptor neuron; projection neuron; selectivity;
stochastic process; theory
Regulator-based risk statistics for portfolios
Authors: Xiaochuan Deng, Fei Sun
Date: 2019-04-16 00:00:00

Risk statistic is a critical factor not only for risk analysis but also for financial application. However, the traditional risk statistics may fail to describe the characteristics of regulator-based risk. In this paper, we consider the regulator-based risk statistics for portfolios. By further developing the properties related to regulator-based risk statistics, we are able to derive dual representation for such risk.