pwc-archive/papers-with-abstracts huggingface dataset:
A Radio Frequency Non-reciprocal Network Based on Switched Acoustic Delay Lines
Date: 2018-03-28 00:00:00

This work demonstrates the first non-reciprocal network based on switched
low-loss acoustic delay lines. The 4-port circulator is built upon a recently
reported frequency-independent, programmable, non-reciprocal framework based on
switched delay lines. The design space for such a system, including the origins
of the insertion loss and harmonic responses, is theoretically investigated,
illustrating that the key to better performance and low-cost modulation signal
synthesis lies in a large delay. To implement a large delay, we resort to
in-house fabricated low-loss, wide-band lithium niobate (LiNbO3) SH0 mode
acoustic delay lines employing single-phase unidirectional transducers (SPUDT).
The 4-port circulator, consisting of two switch modules and one delay line
module, has been modularly designed, assembled, and tested. The design process
employs time-domain full circuit simulation and the results match well with
measurements. A 18.8 dB non-reciprocal contrast between insertion loss (IL =
6.6 dB) and isolation (25.4 dB) has been achieved over a fractional bandwidth
of 8.8% at a center frequency 155 MHz, using a record low switching frequency
of 877.19 kHz. The circulator also shows 25.9 dB suppression for the
intra-modulated tone and 30 dBm for IIP3. Upon further development, such a
system can potentially lead to future wide-band, low-loss chip-scale
nonreciprocal RF systems with unprecedented programmability.
Online monitoring of local taxi travel momentum and congestion effects using projections of taxi GPS-based vector fields
Date: 2018-06-30 00:00:00
Tasks: Data Visualization

Ubiquitous taxi trajectory data has made it possible to apply it to different
types of travel analysis. Of interest is the need to allow someone to monitor
travel momentum and associated congestion in any location in space in real
time. However, despite an abundant literature in taxi data visualization and
its applicability to travel analysis, no easy method exists. To measure taxi
travel momentum at a location, current methods require filtering taxi
trajectories that stop at a location at a particular time range, which is
computationally expensive. We propose an alternative, computationally cheaper
way based on pre-processing vector fields from the trajectories. Algorithms are
formalized for generating vector kernel density to estimate a travel-model-free
vector field-based representation of travel momentum in an urban space. The
algorithms are shared online as an open source GIS 3D extension called
VectorKD. Using 17 million daily taxi GPS points within Beijing over a four-day
period, we demonstrate how to generate in real time a series of projections
from a continuously updated vector field of taxi travel momentum to query a
point of interest anywhere in a city, such as the CBD or the airport. This
method allows a policy-maker to automatically identify temporal net influxes of
travel demand to a location. The proposed methodology is shown to be over
twenty times faster than a conventional selection query of trajectories. We
also demonstrate, using taxi data entering the Beijing Capital International
Airport and the CBD, how we can quantify in nearly real time the occurrence and
magnitude of inbound or outbound queueing and congestion periods due to taxis
cruising or waiting for passengers, all without having to fit any mathematical
queueing model to the data.
Deeply Supervised Semantic Model for Click-Through Rate Prediction in Sponsored Search
Authors: Gligorijevic Jelena, Gligorijevic Djordje, Stojkovic Ivan, Bai Xiao, Goyal Amit, Obradovic Zoran
Date: 2018-03-28 00:00:00
Tasks: Click-Through Rate Prediction

In sponsored search it is critical to match ads that are relevant to a query
and to accurately predict their likelihood of being clicked. Commercial search
engines typically use machine learning models for both query-ad relevance
matching and click-through-rate (CTR) prediction. However, matching models are
based on the similarity between a query and an ad, ignoring the fact that a
retrieved ad may not attract clicks, while click models rely on click history,
being of limited use for new queries and ads. We propose a deeply supervised
architecture that jointly learns the semantic embeddings of a query and an ad
as well as their corresponding CTR.We also propose a novel cohort negative
sampling technique for learning implicit negative signals. We trained the
proposed architecture using one billion query-ad pairs from a major commercial
web search engine. This architecture improves the best-performing baseline deep
neural architectures by 2\% of AUC for CTR prediction and by statistically
significant 0.5\% of NDCG for query-ad matching.
The comparison of Higuchi fractal dimension and Sample Entropy analysis of sEMG: effects of muscle contraction intensity and TMS
Date: 2018-03-28 00:00:00

The aim of the study was to examine how the complexity of surface
electromyogram (sEMG) signal, estimated by Higuchi fractal dimension (HFD) and
Sample Entropy (SampEn), change depending on muscle contraction intensity and
external perturbation of the corticospinal activity during muscle contraction
induced by single-pulse Transcranial Magnetic Stimulation (spTMS). HFD and
SampEn were computed from sEMG signal recorded at three various levels of
voluntary contraction before and after spTMS. After spTMS, both HFD and SampEn
decreased at medium compared to the mild contraction. SampEn increased, while
HFD did not change significantly at strong compared to medium contraction.
spTMS significantly decreased both parameters at all contraction levels. When
same parameters were computed from the mathematically generated sine-wave
calibration curves, the results show that SampEn has better accuracy at lower
(0-40 Hz) and HFD at higher (60-120 Hz) frequencies. Changes in the sEMG
complexity associated with increased muscle contraction intensity cannot be
accurately depicted by a single complexity measure. Examination of sEMG should
entail both SampEn and HFD as they provide complementary information about
different frequency components of sEMG. Further studies are needed to explain
the implication of changes in nonlinear parameters and their relation to
underlying sEMG physiological processes.
Controversy in Evolutionary Theory: A multilevel view of the issues
Date: 2018-03-30 00:00:00

A conflict exists between field biologists and physiologists ("functional
biologists" or "evolutionary ecologists") on the one hand and those working in
molecular evolution ("evolutionary biologists" or "population geneticists") on
the other concerns the relative importance of natural selection and genetic
drift. This paper is concerned with this issue in the case of vertebrates such
as birds, fishes, mammals, and specifically humans, and views the issue in that
context from a multilevel perspective. It proposes that the resolution is that
adaptive selection outcomes occurring at the organism level chain down to
determine outcomes at the genome level. The multiple realizability of higher
level processes at lower levels then causes the adaptive nature of such
processes at the organism level to be largely hidden at the genomic level. The
discussion is further related to the "negative view" of selection, the Evo-Devo
and Extended Evolutionary Synthesis views, and the levels of selection debate,
where processes at the population level can also chain down in a similar way
Learn to segment single cells with deep distance estimator and deep cell detector
Date: 2019-04-23 00:00:00
Tasks: Cell Segmentation, Classification, Segmentation

Single cell segmentation is critical and challenging in live cell imaging
data analysis. Traditional image processing methods and tools require
time-consuming and labor-intensive efforts of manually fine-tuning parameters.
Slight variations of image setting may lead to poor segmentation results.
Recent development of deep convolutional neural networks(CNN) provides a
potentially efficient, general and robust method for segmentation. Most
existing CNN-based methods treat segmentation as a pixel-wise classification
problem. However, three unique problems of cell images adversely affect
segmentation accuracy: lack of established training dataset, few pixels on cell
boundaries, and ubiquitous blurry features. The problem becomes especially
severe with densely packed cells, where a pixel-wise classification method
tends to identify two neighboring cells with blurry shared boundary as one
cell, leading to poor cell count accuracy and affecting subsequent analysis.
Here we developed a different learning strategy that combines strengths of CNN
and watershed algorithm. The method first trains a CNN to learn Euclidean
distance transform of binary masks corresponding to the input images. Then
another CNN is trained to detect individual cells in the Euclidean distance
transform. In the third step, the watershed algorithm takes the outputs from
the previous steps as inputs and performs the segmentation. We tested the
combined method and various forms of the pixel-wise classification algorithm on
segmenting fluorescence and transmitted light images. The new method achieves
similar pixel accuracy but significant higher cell count accuracy than
pixel-wise classification methods do, and the advantage is most obvious when
applying on noisy images of densely packed cells.
Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN
Date: 2019-08-14 00:00:00
Tasks: Decision Making

Blind enumeration of the number of transmit antennas and blind identification
of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO
signal identification for both military and commercial applications.
Conventional approaches treat them as two independent problems, namely the
source number enumeration and the presence detection of space-time redundancy,
respectively. In this paper, we develop a joint blind identification algorithm
to determine the number of transmit antennas and MIMO scheme simultaneously. By
restructuring the received signals, we derive three subspace-rank features
based on the signal subspace-rank to determine the number of transmit antennas
and identify space-time redundancy. Then, a Gerschgorin radii-based method and
a feed-forward neural network are employed to calculate these three features,
and a minimal weighted norm-1 distance metric is utilized for decision making.
In particular, our approach can identify additional MIMO schemes, which most
previous works have not considered, and is compatible with both single-carrier
and orthogonal frequency division multiplexing (OFDM) systems. Simulation
results verify the viability of our proposed approach for single-carrier and
OFDM systems and demonstrate its favorable identification performance for a
short observation period with acceptable complexity.
Stochastic Hybrid Models of Gene Regulatory Networks - A PDE Approach
Date: 2018-09-20 00:00:00

A widely used approach to describe the dynamics of gene regulatory networks
is based on the chemical master equation, which considers probability
distributions over all possible combinations of molecular counts. The analysis
of such models is extremely challenging due to their large discrete state
space. We therefore propose a hybrid approximation approach based on a system
of partial differential equations, where we assume a continuous-deterministic
evolution for the protein counts. We discuss efficient analysis methods for
both modeling approaches and compare their performance. We show that the hybrid
approach yields accurate results for sufficiently large molecule counts, while
reducing the computational effort from one ordinary differential equation for
each state to one partial differential equation for each mode of the system.
Furthermore, we give an analytical steady-state solution of the hybrid model
for the case of a self-regulatory gene.
Attentive Statistics Pooling for Deep Speaker Embedding
Date: 2019-02-25 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

This paper proposes attentive statistics pooling for deep speaker embedding
in text-independent speaker verification. In conventional speaker embedding,
frame-level features are averaged over all the frames of a single utterance to
form an utterance-level feature. Our method utilizes an attention mechanism to
give different weights to different frames and generates not only weighted
means but also weighted standard deviations. In this way, it can capture
long-term variations in speaker characteristics more effectively. An evaluation
on the NIST SRE 2012 and the VoxCeleb data sets shows that it reduces equal
error rates (EERs) from the conventional method by 7.5% and 8.1%, respectively.
Efficient vaccination strategies for epidemic control using network information
Date: 2018-03-29 00:00:00

Background: Network-based interventions are most powerful against epidemics
when the full network structure is known. However, resource constraints often
require decisions based on partial network data. We investigated how the
effectiveness of network-based vaccination schemes varied based on the accuracy
of network data available. Methods: We simulated propagating an SIR process on
static empirical social networks from 75 rural Indian villages. First, we used
regression analysis to predict the percentage of individuals ever infected
(cumulative incidence) based on village-level networks. Second, we simulated
vaccinating 10% of each village at baseline, selecting vaccinees using 1 of 5
network-based approaches: at random (Random); random contacts of random
individuals (Nomination); random high-degree individuals (High Degree); highest
degree (Highest Degree); or most central (Central). The first three approaches
require only sample data; the latter two require full network data. We also
simulated imposing a limit on how many contacts an individual can nominate
(Fixed Choice Design, FCD), which reduces data collection burden but generates
only partially observed networks. Results: We found the mean and standard
deviation of the degree distribution to strongly predict cumulative incidence.
In simulations, Nomination reduced cumulative incidence by one-sixth compared
to Random vaccination; full network methods reduced infection by two-thirds.
High Degree had intermediate effectiveness. Somewhat surprisingly, FCD
truncating individuals' degrees at three was as effective as using complete
networks. Conclusions: Using even partial network information to prioritize
vaccines at either the village or individual level substantially improved
epidemic outcomes. Such approaches may be feasible and effective in outbreak
settings, and full ascertainment of network structure may not be required.
Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS
Date: 2018-03-30 00:00:00
Tasks: Form

Group zero-attracting LMS and its reweighted form have been proposed for
addressing system identification problems with structural group sparsity in the
parameters to estimate. Both algorithms however suffer from a trade-off between
sparsity degree and estimation bias and, in addition, between convergence speed
and steady-state performance like most adaptive filtering algorithms. It is
therefore necessary to properly set their step size and regularization
parameter. Based on a model of their transient behavior, we introduce a
variable-parameter variant of both algorithms to address this issue. By
minimizing their mean-square deviation at each time instant, we obtain
closed-form expressions of the optimal step size and regularization parameter.
Simulation results illustrate the effectiveness of the proposed algorithms.
Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis
Date: 2018-03-29 00:00:00
Tasks: Time Series, Time Series Analysis

The Hilbert transform (HT) and associated Gabor analytic signal (GAS)
representation are well-known and widely used mathematical formulations for
modeling and analysis of signals in various applications. In this study, like
the HT, to obtain quadrature component of a signal, we propose the novel
discrete Fourier cosine quadrature transforms (FCQTs) and discrete Fourier sine
quadrature transforms (FSQTs), designated as Fourier quadrature transforms
(FQTs). Using these FQTs, we propose sixteen Fourier-Singh analytic signal
(FSAS) representations with following properties: (1) real part of eight FSAS
representations is the original signal and imaginary part is the FCQT of the
real part, (2) imaginary part of eight FSAS representations is the original
signal and real part is the FSQT of the real part, (3) like the GAS, Fourier
spectrum of the all FSAS representations has only positive frequencies, however
unlike the GAS, the real and imaginary parts of the proposed FSAS
representations are not orthogonal to each other. The Fourier decomposition
method (FDM) is an adaptive data analysis approach to decompose a signal into a
set of small number of Fourier intrinsic band functions which are AM-FM
components. This study also proposes a new formulation of the FDM using the
discrete cosine transform (DCT) with the GAS and FSAS representations, and
demonstrate its efficacy for improved time-frequency-energy representation and
analysis of nonlinear and non-stationary time series.
An empirical approach to the relationship between emotion and music production quality
Date: 2018-03-29 00:00:00

In music production, the role of the mix engineer is to take recorded music
and convey the expressed emotions as professionally sounding as possible. We
investigated the relationship between music production quality and musically
induced and perceived emotions. A listening test was performed where 10
critical listeners and 10 non-critical listeners evaluated 10 songs. There were
two mixes of each song, the low quality mix and the high quality mix. Each
participant's subjective experience was measured directly through questionnaire
and indirectly by examining peripheral physiological changes, change in facial
expressions and the number of head nods and shakes they made as they listened
to each mix. We showed that music production quality had more of an emotional
impact on critical listeners. Also, critical listeners had significantly
different emotional responses to non-critical listeners for the high quality
mixes and to a lesser extent the low quality mixes. The findings suggest that
having a high level of skill in mix engineering only seems to matter in an
emotional context to a subset of music listeners.
Bi-Demographic Changes and Current Account using SVAR Modeling
Date: 2019-03-28 00:00:00

The paper aims to explore the impacts of bi-demographic structure on the
current account and growth. Using a SVAR modeling, we track the dynamic impacts
between these underlying variables. New insights have been developed about the
dynamic interrelation between population growth, current account and economic
growth. The long-run net impact on economic growth of the domestic working
population growth and demand labor for emigrants is positive, due to the
predominant contribution of skilled emigrant workers. Besides, the positive
long-run contribution of emigrant workers to the current account growth largely
compensates the negative contribution from the native population, because of
the predominance of skilled compared to unskilled workforce. We find that a
positive shock in demand labor for emigrant workers leads to an increasing
effect on native active age ratio. Thus, the emigrants appear to be more
complements than substitutes for native workers.
Mortality in a heterogeneous population - Lee-Carter's methodology
Date: 2018-03-29 00:00:00
Tasks: Management

The EU Solvency II directive recommends insurance companies to pay more
attention to the risk management methods. The sense of risk management is the
ability to quantify risk and apply methods that reduce uncertainty. In life
insurance, the risk is a consequence of the random variable describing the life
expectancy. The article will present a proposal for stochastic mortality
modeling based on the Lee and Carter methodology. The maximum likelihood method
is often used to estimate parameters in mortality models. This method assumes
that the population is homogeneous and the number of deaths has the Poisson
distribution. The aim of this article is to change assumptions about the
distribution of the number of deaths. The results indicate that the model can
get a better match to historical data, when the number of deaths has a negative
binomial distribution.
A comprehensive review of Chronic Kidney Disease of Unknown Etiology
Date: 2018-03-28 00:00:00
Tasks: Epidemiology

This review was written to provide a comprehensive summary of the suggested
etiologies of Chronic Kidney Disease of Unknown Etiology (CKDu) in Sri Lanka.
In this review, Chronic Kidney Disease (CKD) is explained in detail and its
known etiologies are discussed. CKDu is defined and its epidemiology is
discussed, with the compilation of statistic from over 15 research papers
through the years 2000 to present.
The inseparability of sampling and time and its influence on attempts to unify the molecular and fossil records
Date: 2018-03-29 00:00:00

The two major approaches to studying macroevolution in deep time are the
fossil record and reconstructed relationships among extant taxa from molecular
data. Results based on one approach sometimes conflict with those based on the
other, with inconsistencies often attributed to inherent flaws of one (or the
other) data source. What is unquestionable is that both the molecular and
fossil records are limited reflections of the same evolutionary history, and
any contradiction between them represents a failure of our existing models to
explain the patterns we observe. Fortunately, the different limitations of each
record provide an opportunity to test or calibrate the other, and new
methodological developments leverage both records simultaneously. However, we
must reckon with the distinct relationships between sampling and time in the
fossil record and molecular phylogenies. These differences impact our
recognition of baselines, and the analytical incorporation of age estimate
uncertainty. These differences in perspective also influence how different
practitioners view the past and evolutionary time itself, bearing important
implications for the generality of methodological advancements, and differences
in the philosophical approach to macroevolutionary theory across fields.
Simulation Methods for Stochastic Storage Problems: A Statistical Learning Perspective
Date: 2018-03-30 00:00:00
Tasks: Experimental Design, regression

We consider solution of stochastic storage problems through regression Monte
Carlo (RMC) methods. Taking a statistical learning perspective, we develop the
dynamic emulation algorithm (DEA) that unifies the different existing
approaches in a single modular template. We then investigate the two central
aspects of regression architecture and experimental design that constitute DEA.
For the regression piece, we discuss various non-parametric approaches, in
particular introducing the use of Gaussian process regression in the context of
stochastic storage. For simulation design, we compare the performance of
traditional design (grid discretization), against space-filling, and several
adaptive alternatives. The overall DEA template is illustrated with multiple
examples drawing from natural gas storage valuation and optimal control of
back-up generator in a microgrid.