pwc-archive/papers-with-abstracts huggingface dataset:
Transcription Factor-DNA Binding Via Machine Learning Ensembles
Date: 2018-05-10 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

We present ensemble methods in a machine learning (ML) framework combining
predictions from five known motif/binding site exploration algorithms. For a
given TF the ensemble starts with position weight matrices (PWM's) for the
motif, collected from the component algorithms. Using dimension reduction, we
identify significant PWM-based subspaces for analysis. Within each subspace a
machine classifier is built for identifying the TF's gene (promoter) targets
(Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool.
Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string)
feature PWM-based subspaces that stand out in identifying gene targets. We
approach Problem 3 (binding sites) with a novel machine learning approach that
uses promoter string features and ML importance scores in a classification
algorithm locating binding sites across the genome. For target gene
identification this method improves performance (measured by the F1 score) by
about 10 percentage points over the (a) motif scanning method and (b) the
coexpression-based association method. Top motif outperformed 5 component
algorithms as well as two other common algorithms (BEST and DEME). For
identifying individual binding sites on a benchmark cross species database
(Tompa et al., 2005) we match the best performer without much human
intervention. It also improved the performance on mammalian TFs.
  The ensemble can integrate orthogonal information from different weak
learners (potentially using entirely different types of features) into a
machine learner that can perform consistently better for more TFs. The TF gene
target identification component (problem 1 above) is useful in constructing a
transcriptional regulatory network from known TF-target associations. The
ensemble is easily extendable to include more tools as well as future PWM-based
information.
Continuously Tunable Dual-mode Bandstop Filter
Date: 2018-05-10 00:00:00

A varactor-based tunable bandstop filter has been proposed in this article.
The proposed filter is based on a dualmode circuit developed by introducing
inductive and capacitive couplings into a notch filter. The frequency
tunability is achieved by using varactor diodes instead of the lumped
capacitors in the circuit. Next, the equivalent circuit model has been
implemented in planar microstrip technology using thin inductive traces and
varactor diodes. The fabricated filter prototype shows a continuous center
frequency tuning range of 0.66 - 0.99 GHz with a compact size of 0.12lg*0:16lg,
where lg is the guided wavelength at the middle frequency of the tuning range.
MIMO radar waveform design with practical constraints: A low-complexity approach
Date: 2018-05-10 00:00:00
Tasks: Radar waveform design

In this letter, we consider the multiple-input multiple-output (MIMO) radar
waveform design in the presence of signal-dependent clutters and additive white
Gaussian noise. By imposing the constant modulus constraint (CMC) and waveform
similarity constraint (SC), the signal-to-interference-plus-noise (SINR)
maximization problem is non-convex and NP-hard in general, which can be
transformed into a sequence of convex quadratically constrained quadratic
programming (QCQP) subproblems. Aiming at solving each subproblem efficiently,
we propose a low-complexity method termed Accelerated Gradient Projection
(AGP). In contrast to the conventional IPM based method, our proposed algorithm
achieves the same performance in terms of the receive SINR and the beampattern,
while notably reduces computational complexity.
Impact of axonal delay on structure development in a Linsker type network
Date: 2018-05-10 00:00:00

In his seminal, three paper series, Linsker provided a mechanism for how
random activity in the visual pathway could give rise to many of the features
observed experimentally in the early stages of visual processing. Owing to the
complexity of multilayer models, an implicit assumption in Linsker's and
subsequent papers has been that propagation delay is homogeneous and,
consequently, plays little functional role in neural behaviour. In this paper,
we relax this assumption to examine the impact of axonal distance dependent
propagation delay on neural learning. We show that propagation delay induces
lowpass filtering by dispersing the arrival times of spikes from presynaptic
neurons, providing a natural correlation cancellation mechanism for distal
connections. The cutoff frequency decreases as the radial propagation delay
within a layer, relative to propagation delay between the layers, increases,
introducing an upper limit on temporal resolution. Given that the postsynaptic
potential also acts as a lowpass filter, we show that the effective time
constant of each should enable the processing of similar scales of temporal
information. This result has implications for the visual system, in which
receptive field size and, thus, radial propagation delay, increases with
eccentricity. Furthermore, the network response is frequency dependent since
higher frequencies require increased input amplitude to compensate for
attenuation. This concords with frequency dependent contrast sensitivity in the
visual system, which changes with eccentricity and receptive field size.
Finally, we determine the eigenfunctions of both Linsker's network, and the
network with propagation delay. We show that the addition of propagation delay
stabilizes the leading eigenfunction to changes in homeostatic parameters, and
hence stabilizes the resulting receptive field structure.
WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval
Authors: Cohen Daniel, Yang Liu, Croft W. Bruce
Date: 2018-05-10 00:00:00
Tasks: Information Retrieval, Open-Domain Question Answering, Passage Retrieval, Question Answering, Retrieval

With the rise in mobile and voice search, answer passage retrieval acts as a
critical component of an effective information retrieval system for open domain
question answering. Currently, there are no comparable collections that address
non-factoid question answering within larger documents while simultaneously
providing enough examples sufficient to train a deep neural network. In this
paper, we introduce a new Wikipedia based collection specific for non-factoid
answer passage retrieval containing thousands of questions with annotated
answers and show benchmark results on a variety of state of the art neural
architectures and retrieval models. The experimental results demonstrate the
unique challenges presented by answer passage retrieval within topically
relevant documents for future research.
Structural Breaks in Time Series
Date: 2018-05-10 00:00:00
Tasks: Time Series, Time Series Analysis

This chapter covers methodological issues related to estimation, testing and
computation for models involving structural changes. Our aim is to review
developments as they relate to econometric applications based on linear models.
Substantial advances have been made to cover models at a level of generality
that allow a host of interesting practical applications. These include models
with general stationary regressors and errors that can exhibit temporal
dependence and heteroskedasticity, models with trending variables and possible
unit roots and cointegrated models, among others. Advances have been made
pertaining to computational aspects of constructing estimates, their limit
distributions, tests for structural changes, and methods to determine the
number of changes present. A variety of topics are covered. The first part
summarizes and updates developments described in an earlier review, Perron
(2006), with the exposition following heavily that of Perron (2008). Additions
are included for recent developments: testing for common breaks, models with
endogenous regressors (emphasizing that simply using least-squares is
preferable over instrumental variables methods), quantile regressions, methods
based on Lasso, panel data models, testing for changes in forecast accuracy,
factors models and methods of inference based on a continuous records
asymptotic framework. Our focus is on the so-called off-line methods whereby
one wants to retrospectively test for breaks in a given sample of data and form
confidence intervals about the break dates. The aim is to provide the readers
with an overview of methods that are of direct usefulness in practice as
opposed to issues that are mostly of theoretical interest.
Sparse System Identification in Pairs of FIR and TM Bases
Date: 2018-05-10 00:00:00

This paper considers the reconstruction of a sparse coefficient vector
{\theta} for a rational transfer function, under a pair of FIR and
Takenaka-Malmquist (TM) bases and from a limited number of linear
frequency-domain measurements. We propose to concatenate a limited number of
FIR and TM basis functions in the representation of the transfer function, and
prove the uniqueness of the sparse representation defined in the infinite
dimensional function space with pairs of FIR and TM bases. The sufficient
condition is given for replacing the l_0 optimal solution by the l_1 optimal
solution using FIR and TM bases with random samples on the upper unit circle,
as the foundation of reconstruction. The simulations verify that l_1
minimization can reconstruct the coefficient vector {\theta} with high
probability. It is shown that the concatenated FIR and TM bases give a much
sparser representation, with much lower reconstruction order than using only
FIR basis functions and less dependency on the knowledge of the true system
poles than using only TM basis functions.
Improving Value-at-Risk prediction under model uncertainty
Authors: Shige Peng, Shuzhen Yang, Jianfeng Yao
Date: 2018-05-10 00:00:00
Tasks: Management, model, Prediction

Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management. Hybrid methods combining AR-GARCH filtering with skewed-$t$ residuals and the extreme value theory-based approach are particularly recommended. This study introduces yet another VaR predictor, G-VaR, which follows a novel methodology. Inspired by the recent mathematical theory of sublinear expectation, G-VaR is built upon the concept of model uncertainty, which in the present case signifies that the inherent volatility of financial returns cannot be characterized by a single distribution but rather by infinitely many statistical distributions. By considering the worst scenario among these potential distributions, the G-VaR predictor is precisely identified. Extensive experiments on both the NASDAQ Composite Index and S\&P500 Index demonstrate the excellent performance of the G-VaR predictor, which is superior to most existing benchmark VaR predictors.
TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease
Date: 2018-08-30 00:00:00
Tasks: Disease Prediction, Prediction

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE)
Challenge compares the performance of algorithms at predicting future evolution
of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants
train their models and algorithms on historical data from the Alzheimer's
Disease Neuroimaging Initiative (ADNI) study or any other datasets to which
they have access. Participants are then required to make monthly forecasts over
a period of 5 years from January 2018, of three key outcomes for ADNI-3
rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale
Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These
individual forecasts are later compared with the corresponding future
measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The
first submission phase of TADPOLE was open for prize-eligible submissions
between 15 June and 15 November 2017. The submission system remains open via
the website: https://tadpole.grand-challenge.org, although since 15 November
2017 submissions are not eligible for the first round of prizes. This paper
describes the design of the TADPOLE Challenge.
Active User Detection of Uplink Grant-Free SCMA in Frequency Selective Channel
Date: 2018-05-10 00:00:00
Tasks: Compressive Sensing

Massive machine type communication (mMTC) is one of the three fifth
generation mobile networking (5G) key usage scenarios, which is characterized
by a very large number of connected devices typically transmitting a relatively
low volume of non-delay sensitive data. To support the mMTC communication, an
uplink (UL) grant-free sparse code multiple access (SCMA) system has been
proposed. In this system, the knowledge of user equipments' (UEs') status
should be obtained before decoding the data by a message passing algorithm
(MPA). An existing solution is to use the compressive sensing (CS) theory to
detect active UEs under the assumed condition of flat fading channel. But the
assumed condition is not suitable for the frequency selective channel and will
decrease the accuracy of active UEs detection. This paper proposes a new simple
module named refined active UE detector (RAUD), which is based on frequency
selective channel gain analyzing. By making full use of the channel gain and
analyzing the difference between characteristic values of the two status of
UEs, RAUD module can enhance the active UEs detection accuracy. Meanwhile, the
addition of the proposed module has a negligible effect on the complexity of UL
grant-free SCMA receiver.
Total, asymmetric and frequency connectedness between oil and forex markets
Date: 2019-02-15 00:00:00

We analyze total, asymmetric and frequency connectedness between oil and
forex markets using high-frequency, intra-day data over the period 2007 --
2017. By employing variance decompositions and their spectral representation in
combination with realized semivariances to account for asymmetric and frequency
connectedness, we obtain interesting results. We show that divergence in
monetary policy regimes affects forex volatility spillovers but that adding oil
to a forex portfolio decreases the total connectedness of the mixed portfolio.
Asymmetries in connectedness are relatively small. While negative shocks
dominate forex volatility connectedness, positive shocks prevail when oil and
forex markets are assessed jointly. Frequency connectedness is largely driven
by uncertainty shocks and to a lesser extent by liquidity shocks, which impact
long-term connectedness the most and lead to its dramatic increase during
periods of distress.
A mixture autoregressive model based on Student's $t$-distribution
Date: 2018-05-10 00:00:00

A new mixture autoregressive model based on Student's $t$-distribution is
proposed. A key feature of our model is that the conditional $t$-distributions
of the component models are based on autoregressions that have multivariate
$t$-distributions as their (low-dimensional) stationary distributions. That
autoregressions with such stationary distributions exist is not immediate. Our
formulation implies that the conditional mean of each component model is a
linear function of past observations and the conditional variance is also time
varying. Compared to previous mixture autoregressive models our model may
therefore be useful in applications where the data exhibits rather strong
conditional heteroskedasticity. Our formulation also has the theoretical
advantage that conditions for stationarity and ergodicity are always met and
these properties are much more straightforward to establish than is common in
nonlinear autoregressive models. An empirical example employing a realized
kernel series based on S&P 500 high-frequency data shows that the proposed
model performs well in volatility forecasting.
Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models
Date: 2018-05-10 00:00:00

We study the identification and estimation of structural parameters in
dynamic panel data logit models where decisions are forward-looking and the
joint distribution of unobserved heterogeneity and observable state variables
is nonparametric, i.e., fixed-effects model. We consider models with two
endogenous state variables: the lagged decision variable, and the time duration
in the last choice. This class of models includes as particular cases important
economic applications such as models of market entry-exit, occupational choice,
machine replacement, inventory and investment decisions, or dynamic demand of
differentiated products. The identification of structural parameters requires a
sufficient statistic that controls for unobserved heterogeneity not only in
current utility but also in the continuation value of the forward-looking
decision problem. We obtain the minimal sufficient statistic and prove
identification of some structural parameters using a conditional likelihood
approach. We apply this estimator to a machine replacement model.
The Capacity of Private Information Retrieval from Uncoded Storage Constrained Databases
Authors: Attia Mohamed Adel, Kumar Deepak, Tandon Ravi
Date: 2018-10-23 00:00:00
Tasks: Information Retrieval, Retrieval

Private information retrieval (PIR) allows a user to retrieve a desired
message from a set of databases without revealing the identity of the desired
message. The replicated databases scenario was considered by Sun and Jafar,
2016, where $N$ databases can store the same $K$ messages completely. A PIR
scheme was developed to achieve the optimal download cost given by $\left(1+
\frac{1}{N}+ \frac{1}{N^{2}}+ \cdots + \frac{1}{N^{K-1}}\right)$. In this work,
we consider the problem of PIR from storage constrained databases. Each
database has a storage capacity of $\mu KL$ bits, where $L$ is the size of each
message in bits, and $\mu \in [1/N, 1]$ is the normalized storage. On one
extreme, $\mu=1$ is the replicated databases case. On the other hand, when
$\mu= 1/N$, then in order to retrieve a message privately, the user has to
download all the messages from the databases achieving a download cost of
$1/K$. We aim to characterize the optimal download cost versus storage
trade-off for any storage capacity in the range $\mu \in [1/N, 1]$. For any
$(N,K)$, we show that the optimal trade-off between storage, $\mu$, and the
download cost, $D(\mu)$, is given by the lower convex hull of the $N$ pairs
$\left(\mu= \frac{t}{N},D(\mu) = \left(1+ \frac{1}{t}+ \frac{1}{t^{2}}+ \cdots
+ \frac{1}{t^{K-1}}\right)\right)$ for $t=1,2,\ldots, N$. To prove this result,
we first present the storage constrained PIR scheme for any $(N,K)$. We next
obtain a general lower bound on the download cost for PIR, which is valid for
the following storage scenarios: replicated or storage constrained, coded or
uncoded, and fixed or optimized. We then specialize this bound using the
uncoded storage assumption to obtain lower bounds matching the achievable
download cost of the storage constrained PIR scheme for any value of the
available storage.
Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis
Date: 2018-05-10 00:00:00
Tasks: Causal Discovery, Causal Inference

The mainstream of research in genetics, epigenetics and imaging data analysis
focuses on statistical association or exploring statistical dependence between
variables. Despite their significant progresses in genetic research,
understanding the etiology and mechanism of complex phenotypes remains elusive.
Using association analysis as a major analytical platform for the complex data
analysis is a key issue that hampers the theoretic development of genomic
science and its application in practice. Causal inference is an essential
component for the discovery of mechanical relationships among complex
phenotypes. Many researchers suggest making the transition from association to
causation. Despite its fundamental role in science, engineering and
biomedicine, the traditional methods for causal inference require at least
three variables. However, quantitative genetic analysis such as QTL, eQTL,
mQTL, and genomic-imaging data analysis requires exploring the causal
relationships between two variables. This paper will focus on bivariate causal
discovery. We will introduce independence of cause and mechanism (ICM) as a
basic principle for causal inference, algorithmic information theory and
additive noise model (ANM) as major tools for bivariate causal discovery.
Large-scale simulations will be performed to evaluate the feasibility of the
ANM for bivariate causal discovery. To further evaluate their performance for
causal inference, the ANM will be applied to the construction of gene
regulatory networks. Also, the ANM will be applied to trait-imaging data
analysis to illustrate three scenarios: presence of both causation and
association, presence of association while absence of causation, and presence
of causation, while lack of association between two variables.
Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective
Authors: Laura Liu
Date: 2018-05-10 00:00:00
Tasks: Density Estimation, Time Series, Time Series Analysis

This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients as well as cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, model this distribution nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors, and then estimate this distribution by combining information from the whole panel. Theoretically, I prove that in cross-sectional homoskedastic cases, both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Methodologically, I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. Monte Carlo simulations and an empirical application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches.
Efficiency in Micro-Behaviors and FL Bias
Date: 2018-05-11 00:00:00

In this paper, we propose a model which simulates odds distributions of
pari-mutuel betting system under two hypotheses on the behavior of bettors: 1.
The amount of bets increases very rapidly as the deadline for betting comes
near. 2. Each bettor bets on a horse which gives the largest expectation value
of the benefit. The results can be interpreted as such efficient behaviors do
not serve to extinguish the FL bias but even produce stronger FL bias.
A Robust Circle-criterion Observer-based Estimator for Discrete-time Nonlinear Systems in the Presence of Sensor Attacks and Measurement Noise
Date: 2018-09-19 00:00:00
Tasks: State Estimation

We address the problem of robust state estimation of a class of discrete-time
nonlinear systems with positive-slope nonlinearities when the sensors are
corrupted by (potentially unbounded) attack signals and bounded measurement
noise. We propose an observer-based estimator, using a bank of circle-criterion
observers, which provides a robust estimate of the system state in spite of
sensor attacks and measurement noise. We first consider the attack-free case
where there is measurement noise and we provide a design method for a robust
circle-criterion observer. Then, we consider the case when a sufficiently small
subset of sensors are subject to attacks and all sensors are affected by
measurement noise. We use our robust circle-criterion observer as the main
ingredient in building an estimator that provides robust state estimation in
this case. Finally, we propose an algorithm for isolating attacked sensors in
the case of bounded measurement noise. We test this algorithm through
simulations.