pwc-archive/papers-with-abstracts huggingface dataset:
Kernel Estimation for Panel Data with Heterogeneous Dynamics
Date: 2019-05-27 00:00:00
Tasks: Time Series, Time Series Analysis

This paper proposes nonparametric kernel-smoothing estimation for panel data
to examine the degree of heterogeneity across cross-sectional units. We first
estimate the sample mean, autocovariances, and autocorrelations for each unit
and then apply kernel smoothing to compute their density functions. The
dependence of the kernel estimator on bandwidth makes asymptotic bias of very
high order affect the required condition on the relative magnitudes of the
cross-sectional sample size (N) and the time-series length (T). In particular,
it makes the condition on N and T stronger and more complicated than those
typically observed in the long-panel literature without kernel smoothing. We
also consider a split-panel jackknife method to correct bias and construction
of confidence intervals. An empirical application and Monte Carlo simulations
illustrate our procedure in finite samples.
Fierce selection and interference in B-cell repertoire response to chronic HIV-1
Date: 2020-07-25 00:00:00

During chronic infection, HIV-1 engages in a rapid coevolutionary arms race
with the host's adaptive immune system. While it is clear that HIV exerts
strong selection on the adaptive immune system, the characteristics of the
somatic evolution that shape the immune response are still unknown. Traditional
population genetics methods fail to distinguish chronic immune response from
healthy repertoire evolution. Here, we infer the evolutionary modes of B-cell
repertoires and identify complex dynamics with a constant production of better
B-cell receptor mutants that compete, maintaining large clonal diversity and
potentially slowing down adaptation. A substantial fraction of mutations that
rise to high frequencies in pathogen engaging CDRs of B-cell receptors (BCRs)
are beneficial, in contrast to many such changes in structurally relevant
frameworks that are deleterious and circulate by hitchhiking. We identify a
pattern where BCRs in patients who experience larger viral expansions undergo
stronger selection with a rapid turnover of beneficial mutations due to clonal
interference in their CDR3 regions. Using population genetics modeling, we show
that the extinction of these beneficial mutations can be attributed to the rise
of competing beneficial alleles and clonal interference. The picture is of a
dynamic repertoire, where better clones may be outcompeted by new mutants
before they fix.
Correlating Cellular Features with Gene Expression using CCA
Date: 2018-02-24 00:00:00

To understand the biology of cancer, joint analysis of multiple data
modalities, including imaging and genomics, is crucial. The involved nature of
gene-microenvironment interactions necessitates the use of algorithms which
treat both data types equally. We propose the use of canonical correlation
analysis (CCA) and a sparse variant as a preliminary discovery tool for
identifying connections across modalities, specifically between gene expression
and features describing cell and nucleus shape, texture, and stain intensity in
histopathological images. Applied to 615 breast cancer samples from The Cancer
Genome Atlas, CCA revealed significant correlation of several image features
with expression of PAM50 genes, known to be linked to outcome, while Sparse CCA
revealed associations with enrichment of pathways implicated in cancer without
leveraging prior biological understanding. These findings affirm the utility of
CCA for joint phenotype-genotype analysis of cancer.
Identifying the occurrence or non occurrence of cognitive bias in situations resembling the Monty Hall problem
Date: 2018-02-25 00:00:00
Tasks: Decision Making

People reason heuristically in situations resembling inferential puzzles such
as Bertrand's box paradox and the Monty Hall problem. The practical
significance of that fact for economic decision making is uncertain because a
departure from sound reasoning may, but does not necessarily, result in a
"cognitively biased" outcome different from what sound reasoning would have
produced. Criteria are derived here, applicable to both experimental and
non-experimental situations, for heuristic reasoning in an inferential-puzzle
situations to result, or not to result, in cognitively bias. In some
situations, neither of these criteria is satisfied, and whether or not agents'
posterior probability assessments or choices are cognitively biased cannot be
determined.
The Dividend Discount Model with Multiple Growth Rates of Any Order for Stock Evaluation
Date: 2018-02-25 00:00:00

In this paper we provide a general solution for the dividend discount model
in order to compute the intrinsic value of a common stock that allows for
multiple stage growth rates of any predetermined number of periods. A
mathematical proof is provided for the suggested general solution. A numerical
application is also presented. The solution introduced in this paper is
expected to improve on the precision of stock valuation, which might be of
fundamental importance for investors as well as financial institutions.
Deep Neural Network for Learning to Rank Query-Text Pairs
Authors: Song Baoyang
Date: 2018-02-25 00:00:00
Tasks: Document Ranking, Information Retrieval, Learning-To-Rank, Retrieval

This paper considers the problem of document ranking in information retrieval
systems by Learning to Rank. We propose ConvRankNet combining a Siamese
Convolutional Neural Network encoder and the RankNet ranking model which could
be trained in an end-to-end fashion. We prove a general result justifying the
linear test-time complexity of pairwise Learning to Rank approach. Experiments
on the OHSUMED dataset show that ConvRankNet outperforms systematically
existing feature-based models.
RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker
Date: 2018-08-23 00:00:00
Tasks: Position

Adaptive algorithm based on multi-channel linear prediction is an effective
dereverberation method balancing well between the attenuation of the long-term
reverberation and the dereverberated speech quality. However, the abrupt change
of the speech source position, usually caused by the shift of the speakers,
forms an obstacle to the adaptive algorithm and makes it difficult to guarantee
both the fast convergence speed and the optimal steady-state behavior. In this
paper, the RLS-based adaptive multi-channel linear prediction method is
investigated and a time-varying forgetting factor based on the relative
weighted change of the adaptive filter coefficients is proposed to effectively
tracing the abrupt change of the target speaker position. The advantages of the
proposed scheme are demonstrated in the simulations and experiments.
Frequency domain TRINICON-based blind source separation method with multi-source activity detection for sparsely mixed signals
Date: 2018-02-25 00:00:00
Tasks: Action Detection, Activity Detection, blind source separation

The TRINICON ('Triple-N ICA for convolutive mixtures') framework is an
effective blind signal separation (BSS) method for separating sound sources
from convolutive mixtures. It makes full use of the non-whiteness,
non-stationarity and non-Gaussianity properties of the source signals and can
be implemented either in time domain or in frequency domain, avoiding the
notorious internal permutation problem. It usually has best performance when
the sources are continuously mixed. In this paper, the offline dual-channel
frequency domain TRINICON implementation for sparsely mixed signals is
investigated, and a multi-source activity detection is proposed to locate the
active period of each source, based on which the filter updating strategy is
regularized to improve the separation performance. The objective metric
provided by the BSSEVAL toolkit is utilized to evaluate the performance of the
proposed scheme.
Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery
Date: 2018-02-25 00:00:00
Tasks: 3D Reconstruction

In this paper we discuss the potential and challenges regarding SAR-optical
stereogrammetry for urban areas, using very-high-resolution (VHR) remote
sensing imagery. Since we do this mainly from a geometrical point of view, we
first analyze the height reconstruction accuracy to be expected for different
stereogrammetric configurations. Then, we propose a strategy for simultaneous
tie point matching and 3D reconstruction, which exploits an epipolar-like
search window constraint. To drive the matching and ensure some robustness, we
combine different established handcrafted similarity measures. For the
experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and
MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR
imagery is generally feasible with 3D positioning accuracies in the
meter-domain, although the matching of these strongly hetereogeneous
multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar
(SAR), optical images, remote sensing, data fusion, stereogrammetry
Exceeding the Nonlinear Shannon-Limit in Coherent Optical Communications by MIMO Machine Learning
Date: 2019-04-18 00:00:00

The nonlinear Shannon capacity limit has been identified as the fundamental
barrier to the maximum rate of transmitted information in optical
communications. In long-haul high-bandwidth optical networks, this limit is
mainly attributed to deterministic Kerr-induced fiber nonlinearities and from
the interaction of amplified spontaneous emission noise from cascaded optical
amplifiers with fiber nonlinearity: the stochastic parametric noise
amplification. Unlike earlier impractical approaches that compensate solely
deterministic nonlinearities, here we demonstrate a novel electronic-based deep
neural network with multiple-inputs and outputs (MIMO) that tackles the
interplay of deterministic and stochastic nonlinearity manifestation in
coherent optical signals. Our demonstration shows that MIMO deep learning can
compensate nonlinear inter-carrier crosstalk effects even in the presence of
frequency stochastic variations, which has hitherto been considered impossible.
Our solution significantly outperforms conventional machine learning and
gold-standard nonlinear equalizers without sacrificing computational
complexity, leading to record-breaking transmission performance for up to 40
Gbit/sec high-spectral-efficient optical signals.
Statistical properties of 3D cell geometry from 2D slices
Date: 2018-06-26 00:00:00

Although cell shape can reflect the mechanical and biochemical properties of
the cell and its environment, quantification of 3D cell shapes within 3D
tissues remains difficult, typically requiring digital reconstruction from a
stack of 2D images. We investigate a simple alternative technique to extract
information about the 3D shapes of cells in a tissue; this technique connects
the ensemble of 3D shapes in the tissue with the distribution of 2D shapes
observed in independent 2D slices. Using cell vertex model geometries, we find
that the distribution of 2D shapes allows clear determination of the mean value
of a 3D shape index. We analyze the errors that may arise in practice in the
estimation of the mean 3D shape index from 2D imagery and find that typically
only a few dozen cells in 2D imagery are required to reduce uncertainty below
2\%. This framework could be naturally extended to estimate additional 3D
geometric features and quantify their uncertainty in other materials.
Optimal contract for a fund manager, with capital injections and endogenous trading constraints
Date: 2018-02-26 00:00:00
Tasks: Management

In this paper, we construct a solution to the optimal contract problem for
delegated portfolio management of the fist-best (risk-sharing) type. The
novelty of our result is (i) in the robustness of the optimal contract with
respect to perturbations of the wealth process (interpreted as capital
injections), and (ii) in the more general form of principals objective
function, which is allowed to depend directly on the agents strategy, as
opposed to being a function of the generated wealth only. In particular, the
latter feature allows us to incorporate endogenous trading constraints in the
contract. We reduce the optimal contract problem to the following inverse
problem: for a given portfolio (defined in a feedback form, as a random field),
construct a stochastic utility whose optimal portfolio coincides with the given
one. We characterize the solution to this problem through a Stochastic Partial
Differential Equation (SPDE), prove its well-posedness, and compute the
solution explicitly in the Black-Scholes model.
Data-Driven Source Separation Based on Simplex Analysis
Date: 2018-02-26 00:00:00
Tasks: blind source separation

Blind source separation (BSS) is addressed, using a novel data-driven
approach, based on a well-established probabilistic model. The proposed method
is specifically designed for separation of multichannel audio mixtures. The
algorithm relies on spectral decomposition of the correlation matrix between
different time frames. The probabilistic model implies that the column space of
the correlation matrix is spanned by the probabilities of the various speakers
across time. The number of speakers is recovered by the eigenvalue decay, and
the eigenvectors form a simplex of the speakers' probabilities. Time frames
dominated by each of the speakers are identified exploiting convex geometry
tools on the recovered simplex. The mixing acoustic channels are estimated
utilizing the identified sets of frames, and a linear umixing is performed to
extract the individual speakers. The derived simplexes are visually
demonstrated for mixtures of 2, 3 and 4 speakers. We also conduct a
comprehensive experimental study, showing high separation capabilities in
various reverberation conditions.
Persistent homology analysis of ion aggregation and hydrogen-bonding network
Date: 2018-02-26 00:00:00

Despite the great advancement of experimental tools and theoretical models, a
quantitative characterization of the microscopic structures of ion aggregates
and its associated water hydrogen-bonding networks still remains a challenging
problem. In this paper, a newly-invented mathematical method called persistent
homology is introduced, for the first time, to quantitatively analyze the
intrinsic topological properties of ion aggregation systems and
hydrogen-bonding networks. Two most distinguishable properties of persistent
homology analysis of assembly systems are as follows. First, it does not
require a predefined bond length to construct the ion or hydrogen network.
Persistent homology results are determined by the morphological structure of
the data only. Second, it can directly measure the size of circles or holes in
ion aggregates and hydrogen-bonding networks. To validate our model, we
consider two well-studied systems, i.e., NaCl and KSCN solutions, generated
from molecular dynamics simulations. They are believed to represent two
morphological types of aggregation, i.e., local clusters and extended ion
network. It has been found that the two aggregation types have distinguishable
topological features and can be characterized by our topological model very
well. For hydrogen-bonding networks, KSCN systems demonstrate much more
dramatic variations in their local circle structures with the concentration
increase. A consistent increase of large-sized local circle structures is
observed and the sizes of these circles become more and more diverse. In
contrast, NaCl systems show no obvious increase of large-sized circles. Instead
a consistent decline of the average size of circle structures is observed and
the sizes of these circles become more and more uniformed with the
concentration increase.
Three-Dimensional Photoacoustic Tomography using Delay Multiply and Sum Beamforming Algorithm
Date: 2018-02-26 00:00:00
Tasks: Image Reconstruction

Photoacoustic imaging (PAI), is a promising medical imaging technique that
provides the high contrast of the optical imaging and the resolution of
ultrasound (US) imaging. Among all the methods, Three-dimensional (3D) PAI
provides a high resolution and accuracy. One of the most common algorithms for
3D PA image reconstruction is delay-and-sum (DAS). However, the quality of the
reconstructed image obtained from this algorithm is not satisfying, having high
level of sidelobes and a wide mainlobe. In this paper, delay-multiply-and-sum
(DMAS) algorithm is suggested to overcome these limitations in 3D PAI. It is
shown that DMAS algorithm is an appropriate reconstruction technique for 3D PAI
and the reconstructed images using this algorithm are improved in the terms of
the width of mainlobe and sidelobes, compared to DAS. Also, the quantitative
results show that DMAS improves signal-to-noise ratio (SNR) and
full-width-half-maximum (FWHM) for about 25 dB and 0.2 mm, respectively,
compared to DAS.
Model-Based Photoacoustic Image Reconstruction using Compressed Sensing and Smoothed L0 Norm
Date: 2018-02-26 00:00:00
Tasks: compressed sensing, Image Reconstruction

Photoacoustic imaging (PAI) is a novel medical imaging modality that uses the
advantages of the spatial resolution of ultrasound imaging and the high
contrast of pure optical imaging. Analytical algorithms are usually employed to
reconstruct the photoacoustic (PA) images as a result of their simple
implementation. However, they provide a low accurate image. Model-based (MB)
algorithms are used to improve the image quality and accuracy while a large
number of transducers and data acquisition are needed. In this paper, we have
combined the theory of compressed sensing (CS) with MB algorithms to reduce the
number of transducer. Smoothed version of L0-norm (SL0) was proposed as the
reconstruction method, and it was compared with simple iterative reconstruction
(IR) and basis pursuit. The results show that S$\ell_0$ provides a higher image
quality in comparison with other methods while a low number of transducers
were. Quantitative comparison demonstrates that, at the same condition, the SL0
leads to a peak-signal-to-noise ratio for about two times of the basis pursuit.
Effects of Important Parameters Variations on Computing Eigenspace-Based Minimum Variance Weights for Ultrasound Tissue Harmonic Imaging
Date: 2018-02-26 00:00:00

In recent years, the minimum variance (MV) beamforming has been widely
studied due to its high resolution and contrast in B-mode Ultrasound imaging
(USI). However, the performance of the MV beamformer is degraded at the
presence of noise, as a result of the inaccurate covariance matrix estimation
which leads to a low quality image. Second harmonic imaging (SHI) provides many
advantages over the conventional pulse-echo USI, such as enhanced axial and
lateral resolutions. However, the low signal-to-noise ratio (SNR) is a major
problem in SHI. In this paper, Eigenspace-based minimum variance (EIBMV)
beamformer has been employed for second harmonic USI. The Tissue Harmonic
Imaging (THI) is achieved by Pulse Inversion (PI) technique. Using the EIBMV
weights, instead of the MV ones, would lead to reduced sidelobes and improved
contrast, without compromising the high resolution of the MV beamformer (even
at the presence of a strong noise). In addition, we have investigated the
effects of variations of the important parameters in computing EIBMV weights,
i.e., K, L, and {\delta}, on the resolution and contrast obtained in SHI. The
results are evaluated using numerical data (using point target and cyst
phantoms), and the proper parameters of EIBMV are indicated for THI.
An Image Registration Based Technique for Noninvasive Vascular Elastography
Date: 2018-02-26 00:00:00
Tasks: Image Registration

Non-invasive vascular elastography is an emerging technique in vascular
tissue imaging. During the past decades, several techniques have been suggested
to estimate the tissue elasticity by measuring the displacement of the Carotid
vessel wall. Cross correlation-based methods are the most prevalent approaches
to measure the strain exerted in the wall vessel by the blood pressure. In the
case of a low pressure, the displacement is too small to be apparent in
ultrasound imaging, especially in the regions far from the center of the
vessel, causing a high error of displacement measurement. On the other hand,
increasing the compression leads to a relatively large displacement in the
regions near the center, which reduces the performance of the cross
correlation-based methods. In this study, a non-rigid image registration-based
technique is proposed to measure the tissue displacement for a relatively large
compression. The results show that the error of the displacement measurement
obtained by the proposed method is reduced by increasing the amount of
compression while the error of the cross correlationbased method rises for a
relatively large compression. We also used the synthetic aperture imaging
method, benefiting the directivity diagram, to improve the image quality,
especially in the superficial regions. The best relative root-mean-square error
(RMSE) of the proposed method and the adaptive cross correlation method were
4.5% and 6%, respectively. Consequently, the proposed algorithm outperforms the
conventional method and reduces the relative RMSE by 25%.
Revisiting Transmission Scheduling in RF Energy Harvesting Wireless Communications
Date: 2018-02-20 00:00:00
Tasks: Scheduling

The transmission scheduling is a critical problem in radio frequency (RF)
energy harvesting communications. Existing transmission strategies in an
RF-based energy harvesting system is mainly based on a classic model, in which
the data transmission is scheduled in a fixed feasible energy tunnel. In this
paper, we re-examine the classic energy harvesting model and show through the
theoretical analysis and experimental results that the bounds of feasible
energy tunnel are dynamic, which can be affected by the transmission scheduling
due to the impact of residual energy on the harvested one. To describe a
practical energy harvesting process more accurately, a new model is proposed by
adding a feedback loop that reflects the interplay between the energy harvest
and the data transmission. Furthermore, to improve network performance, we
revisit the design of an optimal transmission scheduling strategy based on the
new model. To handle the challenge of the endless feedback loop in the new
model, a recursive algorithm is developed. The simulation results reveal that
the new transmission scheduling strategy can balance the efficiency of energy
reception and energy utilization regardless of the length of energy packets,
achieving improved throughput performance for wireless communications.
Probability distribution function of crossover frequency of operational amplifiers
Authors: Todor M. Mishonov, Vladimir G. Marinov, Victor I. Danchev, Emil G. Petkov, Aleksander P. Petkov, Iglika M. Dimitrova, Vassil N. Gourev, Nikola S. Serafimov, Aleksander A. Stefanov, Albert M. Varonov
Date: 2018-02-13 00:00:00
Tasks: Relation

For the first time the cumulative distribution function of the crossover frequency of a contemporary operational amplifier ADA4898-2 is experimentally studied. Using a USB Lock-In amplifier, which allows automatic frequency sweep of the current response of a non-inverting amplifier with significant static amplification, we measure the crossover frequency of 200 samples of ADA4898-2 operational amplifiers. This new method gives a significant advantage in accuracy and speed of study of every operational amplifier. The theory we use is based on the universal relation between time dependent output and input voltages. This common relation for all operational amplifiers is applicable for frequencies much smaller than the crossover frequency and the frequencies of non-dominant poles. In other words, this approximation is adequate, when an operational amplifier is included in a circuit with significant amplification.