pwc-archive/papers-with-abstracts huggingface dataset:
IntLIM: Integration using Linear Models of metabolomics and gene expression data
Date: 2018-02-28 00:00:00

Integration of transcriptomic and metabolomic data improves functional
interpretation of disease-related metabolomic phenotypes, and facilitates
discovery of putative metabolite biomarkers and gene targets. For this reason,
these data are increasingly collected in large cohorts, driving a need for the
development of novel methods for their integration. Of note,
clinical/translational studies typically provide snapshot gene and metabolite
profiles and, oftentimes, most metabolites are not identified. Thus, in these
types of studies, pathway/network approaches that take into account the
complexity of gene-metabolite relationships may neither be applicable nor
readily uncover novel relationships. With this in mind, we propose a simple
linear modeling approach to capture phenotype-specific gene-metabolite
associations, with the assumption that co-regulation patterns reflect
functionally related genes and metabolites. The proposed linear model,
metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether
gene-metabolite relationships differ by phenotype, by testing whether the
relationship in one phenotype is significantly different from the relationship
in another phenotype (via an interaction gene:phenotype p-value). Interaction
p-values for all possible gene-metabolite pairs are computed and significant
pairs are clustered by the directionality of associations. We implemented our
approach as an R package, IntLIM, which includes a user-friendly Shiny app. We
applied IntLIM to two published datasets, collected in NCI-60 cell lines and in
human breast tumor and non-tumor tissue. We demonstrate that IntLIM captures
relevant tumor-specific gene-metabolite associations involved in cancer-related
pathways. and also uncover novel relationships that could be tested
experimentally. The IntLIM R package is publicly available in GitHub
(https://github.com/mathelab/IntLIM).
Correlated random walks of human embryonic stem cell in-vitro
Date: 2018-02-27 00:00:00

We perform a detailed analysis of the migratory motion of human embryonic
stem cells in two-dimensions, both when isolated and in close proximity to
another cell, recorded with time-lapse microscopic imaging. We show that
isolated cells tend to perform an unusual locally anisotropic walk, moving
backwards and forwards along a preferred local direction correlated over a
timescale of around 50 minutes and aligned with the axis of the cell
elongation. Increasing elongation of the cell shape is associated with
increased instantaneous migration speed. We also show that two cells in close
proximity tend to move in the same direction, with the average separation of 70
um or less and the correlation length of around 25 um, a typical cell diameter.
These results can be used as a basis for the mathematical modelling of the
formation of clonal hESC colonies.
Synthetic Control Methods and Big Data
Date: 2018-02-28 00:00:00
Tasks: counterfactual, Time Series, Time Series Analysis

Many macroeconomic policy questions may be assessed in a case study
framework, where the time series of a treated unit is compared to a
counterfactual constructed from a large pool of control units. I provide a
general framework for this setting, tailored to predict the counterfactual by
minimizing a tradeoff between underfitting (bias) and overfitting (variance).
The framework nests recently proposed structural and reduced form machine
learning approaches as special cases. Furthermore, difference-in-differences
with matching and the original synthetic control are restrictive cases of the
framework, in general not minimizing the bias-variance objective. Using
simulation studies I find that machine learning methods outperform traditional
methods when the number of potential controls is large or the treated unit is
substantially different from the controls. Equipped with a toolbox of
approaches, I revisit a study on the effect of economic liberalisation on
economic growth. I find effects for several countries where no effect was found
in the original study. Furthermore, I inspect how a systematically important
bank respond to increasing capital requirements by using a large pool of banks
to estimate the counterfactual. Finally, I assess the effect of a changing
product price on product sales using a novel scanner dataset.
Cooperation dynamics of generalized reciprocity in state-based social dilemmas
Date: 2019-02-28 00:00:00

We introduce a framework for studying social dilemmas in networked societies
where individuals follow a simple state-based behavioral mechanism based on
generalized reciprocity, which is rooted in the principle "help anyone if
helped by someone". Within this general framework, which applies to a wide
range of social dilemmas including, among others, public goods, donation and
snowdrift games, we study the cooperation dynamics on a variety of complex
network examples. By interpreting the studied model through the lenses of
nonlinear dynamical systems, we show that cooperation through generalized
reciprocity always emerges as the unique attractor in which the overall level
of cooperation is maximized, while simultaneously exploitation of the
participating individuals is prevented. The analysis elucidates the role of the
network structure, here captured by a local centrality measure which uniquely
quantifies the propensity of the network structure to cooperation, by dictating
the degree of cooperation displayed both at microscopic and macroscopic level.
We demonstrate the applicability of the analysis on a practical example by
considering an interaction structure that couples a donation process with a
public goods game.
Eigen-Inference Precoding for Coarsely Quantized Massive MU-MIMO System with Imperfect CSI
Date: 2019-04-20 00:00:00

This work considers the precoding problem in massive multiuser multiple-input
multiple-output (MU-MIMO) systems equipped with low-resolution
digital-to-analog converters (DACs). In previous literature on this topic, it
is commonly assumed that the channel state information (CSI) is perfectly
known. However, in practical applications the CSI is inevitably contaminated by
noise. In this paper, we propose, for the first time, an eigen-inference (EI)
precoding scheme to improve the error performance of the coarsely quantized
massive MU-MIMO systems under imperfect CSI, which is mathematically modeled by
a sum of two rectangular random matrices (RRMs). Instead of performing analysis
based on the RRM, using Girko's Hermitization trick, the proposed method
leverages the block random matrix theory by augmenting the RRM into a block
symmetric channel matrix (BSCA). Specially, we derive the empirical
distribution of the eigenvalues of the BSCA and establish the limiting spectra
distribution connection between the true BSCA and its noisy observation. Then,
based on these theoretical results, we propose an EI-based moments matching
method for CSI-related noise level estimation and a rotation invariant
estimation method for CSI reconstruction. Based on the cleaned CSI, the
quantized precoding problem is tackled via the Bussgang theorem and the
Lagrangian multiplier method. The prosed methods are lastly verified by
numerical simulations and the results demonstrate the effectiveness of the
proposed precoder.
An Event-based Diffusion LMS Strategy
Date: 2018-03-11 00:00:00

We consider a wireless sensor network consists of cooperative nodes, each of
them keep adapting to streaming data to perform a least-mean-squares
estimation, and also maintain information exchange among neighboring nodes in
order to improve performance. For the sake of reducing communication overhead,
prolonging batter life while preserving the benefits of diffusion cooperation,
we propose an energy-efficient diffusion strategy that adopts an event-based
communication mechanism, which allow nodes to cooperate with neighbors only
when necessary. We also study the performance of the proposed algorithm, and
show that its network mean error and MSD are bounded in steady state. Numerical
results demonstrate that the proposed method can effectively reduce the network
energy consumption without sacrificing steady-state network MSD performance
significantly.
Speech Enhancement in Adverse Environments Based on Non-stationary Noise-driven Spectral Subtraction and SNR-dependent Phase Compensation
Date: 2018-02-19 00:00:00
Tasks: Noise Estimation, Speech Enhancement

A two-step enhancement method based on spectral subtraction and phase
spectrum compensation is presented in this paper for noisy speeches in adverse
environments involving non-stationary noise and medium to low levels of SNR.
The magnitude of the noisy speech spectrum is modified in the first step of the
proposed method by a spectral subtraction approach, where a new noise
estimation method based on the low frequency information of the noisy speech is
introduced. We argue that this method of noise estimation is capable of
estimating the non-stationary noise accurately. The phase spectrum of the noisy
speech is modified in the second step consisting of phase spectrum
compensation, where an SNR-dependent approach is incorporated to determine the
amount of compensation to be imposed on the phase spectrum. A modified complex
spectrum is obtained by aggregating the magnitude from the spectral subtraction
step and modified phase spectrum from the phase compensation step, which is
found to be a better representation of enhanced speech spectrum. Speech files
available in the NOIZEUS database are used to carry extensive simulations for
evaluation of the proposed method.
An explicit staggered-grid method for numerical simulation of large-scale natural gas pipeline networks
Date: 2018-02-28 00:00:00

We present an explicit second order staggered finite difference (FD)
discretization scheme for forward simulation of natural gas transport in
pipeline networks. By construction, this discretization approach guarantees
that the conservation of mass condition is satisfied exactly. The mathematical
model is formulated in terms of density, pressure, and mass flux variables, and
as a result permits the use of a general equation of state to define the
relation between the gas density and pressure for a given temperature. In a
single pipe, the model represents the dynamics of the density by propagation of
a non-linear wave according to a variable wave speed. We derive compatibility
conditions for linking domain boundary values to enable efficient, explicit
simulation of gas flows propagating through a network with pressure changes
created by gas compressors. We compare Kiuchi's implicit method and an explicit
operator splitting method with our staggered grid method, and perform numerical
experiments to validate the convergence order of the new method. In addition,
we perform several computations to investigate the influence of non-ideal
equation of state models and temperature effects into pipeline simulations with
boundary conditions over various time and space scales.
C-3PO: Click-sequence-aware DeeP Neural Network (DNN)-based Pop-uPs RecOmmendation
Authors: Huang TonTon Hsien-De, Kao Hung-Yu
Date: 2018-12-20 00:00:00
Tasks: Collaborative Filtering

With the emergence of mobile and wearable devices, push notification becomes
a powerful tool to connect and maintain the relationship with App users, but
sending inappropriate or too many messages at the wrong time may result in the
App being removed by the users. In order to maintain the retention rate and the
delivery rate of advertisement, we adopt Deep Neural Network (DNN) to develop a
pop-up recommendation system "Click sequence-aware deeP neural network
(DNN)-based Pop-uPs recOmmendation (C-3PO)" enabled by collaborative
filtering-based hybrid user behavioral analysis. We further verified the system
with real data collected from the product Security Master, Clean Master and CM
Browser, supported by Leopard Mobile Inc. (Cheetah Mobile Taiwan Agency). In
this way, we can know precisely about users' preference and frequency to click
on the push notification/pop-ups, decrease the troublesome to users
efficiently, and meanwhile increase the click through rate of push
notifications/pop-ups.
Energy Disaggregation for SMEs using Recurrence Quantification Analysis
Date: 2018-02-28 00:00:00

Energy disaggregation determines the energy consumption of individual
appliances from the total demand signal, which is recorded using a single
monitoring device. There are varied approaches to this problem, which are
applied to different settings. Here, we focus on small and medium enterprises
(SMEs) and explore useful applications for energy disaggregation from the
perspective of SMEs. More precisely, we use recurrence quantification analysis
(RQA) of the aggregate and the individual device signals to create a
two-dimensional map, which is an outlined region in a reduced information space
that corresponds to 'normal' energy demand. Then, this map is used to monitor
and control future energy consumption within the example business so to improve
their energy efficiency practices. In particular, our proposed method is shown
to detect when an appliance may be faulty and if an unexpected, additional
device is in use.
Segmented Successive Cancellation List Polar Decoding with Tailored CRC
Date: 2018-03-16 00:00:00
Tasks: Decoder

As the first error correction codes provably achieving the symmetric capacity
of binary-input discrete memory-less channels (B-DMCs), polar codes have been
recently chosen by 3GPP for eMBB control channel. Among existing algorithms,
CRC-aided successive cancellation list (CA-SCL) decoding is favorable due to
its good performance, where CRC is placed at the end of the decoding and helps
to eliminate the invalid candidates before final selection. However, the good
performance is obtained with a complexity increase that is linear in list size
$L$. In this paper, the tailored CRC-aided SCL (TCA-SCL) decoding is proposed
to balance performance and complexity. Analysis on how to choose the proper CRC
for a given segment is proposed with the help of \emph{virtual transform} and
\emph{virtual length}. For further performance improvement, hybrid automatic
repeat request (HARQ) scheme is incorporated. Numerical results have shown
that, with the similar complexity as the state-of-the-art, the proposed TCA-SCL
and HARQ-TCA-SCL schemes achieve $0.1$ dB and $0.25$ dB performance gain at
frame error rate $\textrm{FER}=10^{-2}$, respectively. Finally, an efficient
TCA-SCL decoder is implemented with FPGA demonstrating its advantages over
CA-SCL decoder.
Optimal investment-consumption problem: post-retirement with minimum guarantee
Authors: Hassan Dadashi
Date: 2018-02-22 00:00:00

We study the optimal investment-consumption problem for a member of defined contribution plan during the decumulation phase. For a fixed annuitization time, to achieve higher final annuity, we consider a variable consumption rate. Moreover, to have a minimum guarantee for the final annuity, a safety level for the wealth process is considered. To solve the stochastic optimal control problem via dynamic programming, we obtain a Hamilton-Jacobi-Bellman (HJB) equation on a bounded domain. The existence and uniqueness of classical solutions are proved through the dual transformation. We apply the finite difference method to find numerical approximations of the solution of the HJB equation. Finally, the simulation results for the optimal investment-consumption strategies, optimal wealth process and the final annuity for different admissible ranges of consumption are given. Furthermore, by taking into account the market present value of the cash flows before and after the annuitization, we compare the outcomes of different scenarios.
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection
Authors: Zhan Yusen, Da Qing, Xiao Fei, Zeng An-xiang, Yu Yang
Date: 2018-03-14 00:00:00
Tasks: Combinatorial Optimization, Decision Making, feature selection, Sequential Decision Making

In industrial large-scale search systems, such as Taobao.com search for
commodities, the quality of the ranking result is getting continually improved
by introducing more factors from complex procedures, e.g., deep neural networks
for extracting image factors. Meanwhile, the increasing of the factors demands
more computation resource and raises the system response latency. It has been
observed that a search instance usually requires only a small set of effective
factors, instead of all factors. Therefore, removing ineffective factors
significantly improves the system efficiency. This paper studies the
\emph{Contextual Factor Selection} (CFS), which selects only a subset of
effective factors for every search instance, for a well balance between the
search quality and the response latency. We inject CFS into the search engine
ranking score to accelerate the engine, considering both ranking effectiveness
and efficiency. The learning of the CFS model involves a combinatorial
optimization, which is transformed as a sequential decision-making problem.
Solving the problem by reinforcement learning, we propose the RankCFS, which
has been assessed in an off-line environment as well as a real-world on-line
environment (Taobao.com). The empirical results show that, the proposed CFS
approach outperforms several existing supervised/unsupervised methods for
feature selection in the off-line environment, and also achieves significant
real-world performance improvement, in term of service latency, in daily test
as well as Singles' Day Shopping Festival in $2017$.
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA
Date: 2018-06-26 00:00:00
Tasks: Dimensionality Reduction, Tensor Decomposition

The widespread use of multisensor technology and the emergence of big data
sets have brought the necessity to develop more versatile tools to represent
higher-order data with multiple aspects and high dimensionality. Data in the
form of multidimensional arrays, also referred to as tensors, arises in a
variety of applications including chemometrics, hyperspectral imaging, high
resolution videos, neuroimaging, biometrics, and social network analysis. Early
multiway data analysis approaches reformatted such tensor data as large vectors
or matrices and then resorted to dimensionality reduction methods developed for
classical two-way analysis such as PCA. However, one cannot discover hidden
components within multiway data using conventional PCA. To this end, tensor
decomposition methods which are flexible in the choice of the constraints and
that extract more general latent components have been proposed. In this paper,
we review the major tensor decomposition methods with a focus on problems
targeted by classical PCA. In particular, we present tensor methods that aim to
solve three important challenges typically addressed by PCA: dimensionality
reduction, i.e. low-rank tensor approximation, supervised learning, i.e.
learning linear subspaces for feature extraction, and robust low-rank tensor
recovery. We also provide experimental results to compare different tensor
models for both dimensionality reduction and supervised learning applications.
Performance of a Relay-Assisted Hybrid FSO/RF Communication System
Date: 2018-11-20 00:00:00

This paper investigates performance of a relay assisted hybrid Free Space
Optical / Radio Frequency (FSO/RF) communication system. The proposed structure
is particularly recommended anywhere that direct RF communication between
mobile users and base station is not possible due to atmospheric conditions. In
this system, a multiuser RF link connects mobile users to relay and a FSO link
connects relay to the base station. It is the first time that effect of number
of users within the cell, on the performance of such structure, is
investigated. Also it is the first time that performance of a dual hop hybrid
FSO / RF system is investigated in Negative Exponential atmospheric turbulence.
Considering wide range of atmospheric turbulence regimes, from moderate to
saturated, for the first time, closed-form expressions are derived for Bit
Error Rate (BER) and outage probability (P_out) of the proposed structure.
MATLAB simulations verified accuracy of the derived expressions. Considering
fixed and adaptive gain amplify and forward protocols at relay, it is shown
that adaptive gain relay is less sensitive to the number of users within the
cell. It is also shown that fixed gain relay, despite its low complexity, has
better performance; because its gain is adjusted such that the performance be
favorable even at the worst case scenario.
Permutation Tests for Equality of Distributions of Functional Data
Authors: Federico A. Bugni, Joel L. Horowitz
Date: 2018-03-02 00:00:00

Economic data are often generated by stochastic processes that take place in continuous time, though observations may occur only at discrete times. For example, electricity and gas consumption take place in continuous time. Data generated by a continuous time stochastic process are called functional data. This paper is concerned with comparing two or more stochastic processes that generate functional data. The data may be produced by a randomized experiment in which there are multiple treatments. The paper presents a method for testing the hypothesis that the same stochastic process generates all the functional data. The test described here applies to both functional data and multiple treatments. It is implemented as a combination of two permutation tests. This ensures that in finite samples, the true and nominal probabilities that each test rejects a correct null hypothesis are equal. The paper presents upper and lower bounds on the asymptotic power of the test under alternative hypotheses. The results of Monte Carlo experiments and an application to an experiment on billing and pricing of natural gas illustrate the usefulness of the test.
Inferring correlations associated to causal interactions in brain signals using autoregressive models
Date: 2019-11-20 00:00:00

The specific connectivity of a neuronal network is reflected in the dynamics
of the signals recorded on its nodes. The analysis of how the activity in one
node predicts the behaviour of another gives the directionality in their
relationship. However, each node is composed of many different elements which
define the properties of the links. For instance, excitatory and inhibitory
neuronal subtypes determine the functionality of the connection. Classic
indexes such as the Granger causality (GC) quantifies these interactions, but
they do not infer into the mechanism behind them. Here, we introduce an
extension of the well-known GC that analyses the correlation associated to the
specific influence that a transmitter node has over the receiver. This way, the
G-causal link has a positive or negative effect if the predicted activity
follows directly or inversely, respectively, the dynamics of the sender. The
method is validated in a neuronal population model, testing the paradigm that
excitatory and inhibitory neurons have a differential effect in the
connectivity. Our approach correctly infers the positive or negative coupling
produced by different types of neurons. Our results suggest that the proposed
approach provides additional information on the characterization of G-causal
connections, which is potentially relevant when it comes to understanding
interactions in the brain circuits.
New copulas based on general partitions-of-unity (part III) - the continuous case (extended version)
Date: 2019-05-22 00:00:00
Tasks: Management, Unity

In this paper we discuss a natural extension of infinite discrete
partition-of-unity copulas which were recently introduced in the literature to
continuous partition of copulas with possible applications in risk management
and other fields. We present a general simple algorithm to generate such
copulas on the basis of the empirical copula from high-dimensional data sets.
In particular, our constructions also allow for an implementation of positive
tail dependence which sometimes is a desirable property of copula modelling, in
particular for internal models under Solvency II.