pwc-archive/papers-with-abstracts huggingface dataset:
Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising
Date: 2018-04-16 00:00:00
Tasks: Denoising, Image Denoising, Sensitivity

Thresholding of Curvelet Coefficients, for image denoising, drains out subtle
signal component in noise subspace. This produces ringing artifacts near edges
and granular effect in the denoised image. We found the noise sensitivity of
Curvelet phases (in contrast to their magnitude) reduces with higher noise
level. Thus, we preserved the phase of the coefficients below threshold at
coarser scale and estimated their magnitude by Joint Bilateral Filtering (JBF)
technique from the thresholded and noisy coefficients. In the finest scale, we
apply Bilateral Filter (BF) to keep edge information. Further, the Guided Image
Filter (GIF) is applied on the reconstructed image to localize the edges and to
preserve the small image details and textures. The lower noise sensitivity of
Curvelet phase at higher noise strength accelerate the performance of proposed
method over several state-of-theart techniques and provides comparable outcome
at lower noise levels.
CL Scholar: The ACL Anthology Knowledge Graph Miner
Authors: Singh Mayank, Dogga Pradeep, Patro Sohan, Barnwal Dhiraj, Dutt Ritam, Haldar Rajarshi, Goyal Pawan, Mukherjee Animesh
Date: 2018-04-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, graph construction, Natural Language Queries

We present CL Scholar, the ACL Anthology knowledge graph miner to facilitate
high-quality search and exploration of current research progress in the
computational linguistics community. In contrast to previous works,
periodically crawling, indexing and processing of new incoming articles is
completely automated in the current system. CL Scholar utilizes both textual
and network information for knowledge graph construction. As an additional
novel initiative, CL Scholar supports more than 1200 scholarly natural language
queries along with standard keyword-based search on constructed knowledge
graph. It answers binary, statistical and list based natural language queries.
The current system is deployed at http://cnerg.iitkgp.ac.in/aclakg. We also
provide REST API support along with bulk download facility. Our code and data
are available at https://github.com/CLScholar.
Performance Enhancement of Downlink NOMA by Combination with GSSK
Date: 2018-04-16 00:00:00

In non-orthogonal multiple access (NOMA), cell-edge users experience
significantly low spectral density because only some part of the total transmit
power is allocated. This leads to low spectral efficiency for the paired users
in NOMA. To overcome this problem, we propose an integration of NOMA and
generalized space shift keying (GSSK), called NOMA-GSSK, to improve the
spectral efficiency by exploiting the spatial domain. Spectral and energy
efficiency, bit error rate (BER), and computational complexity of the proposed
system were analyzed and compared to those of multiple-input multiple-output
NOMA (MIMO-NOMA). It is shown that NOMA-GSSK outperforms MIMO-NOMA.
Cochlear detection of double-slip motion in cello bowing
Date: 2018-04-16 00:00:00

A double-slip motion of a cello sound is investigated experimentally with a
bowing machine and analyzed using a Finite-Difference Time Domain (FDTD)
cochlear model. A double-slip sound is investigated. Here the sawtooth motion
of normal bowing is basically present, but within each period the bow hair
tears off the strings once more within the period, resulting in a blurred
sound. This additional intermediate slip appears around the middle of each
period and drifts temporally around while the sound progresses. When the
double-slip is perfectly in the middle of one period the sound is that of a
regular sawtooth motion. If not, two periodicities are present around double
the fundamental periodicity, making the sound arbitrary. Analyzing the sound
with a Wavelet-transform, the expected double-peak of two periodicities around
the second partial cannot be found. Analyzing the tone with a cochlear FDTD
model including the transfer of mechanical energy into spikes, the doubling and
even more complex behaviour is perfectly represented in the Interspike Interval
(ISI) of two adjacent spikes. This cochlear spike representation fits perfectly
to an amplitude peak detection algorithm, tracking the precise time point of
the double-slip within the fundamental period. Therefore the ear is able to
detect the double-slip motion right at the transition from the basilar membrane
motion into electrical spikes.
Universal Features in Phonological Neighbor Networks
Date: 2018-04-16 00:00:00

Human speech perception involves transforming a countinous acoustic signal
into discrete linguistically meaningful units, such as phonemes, while
simultaneously causing a listener to activate words that are similar to the
spoken utterance and to each other. The Neighborhood Activation Model
(NAM~\cite{Luce:1986,Luce:1998}) posits that phonological neighbors (two forms
[words] that differ by one phoneme) compete significantly for recognition as a
spoken word is heard. This definition of phonological similarity can be
extended to an entire corpus of forms to produce a phonological neighbor
network~\cite{Vitevitch:2008} (PNN). We study PNNs for five languages: English,
Spanish, French, Dutch, and German. Consistent with previous work, we find that
the PNNs share a consistent set of topological features. Using an approach that
generates random lexicons with increasing levels of phonological realism, we
show that even random forms with minimal relationship to any real language,
combined with only the empirical distribution of language-specific phonological
form lengths, are sufficient to produce the topological properties observed in
the real language PNNs. The resulting pseudo-PNNs are insensitive to the level
of lingusitic realism in the random lexicons but quite sensitive to the shape
of the form length distribution. We therefore conclude that "universal"
features seen across multiple languages are really string universals, not
language universals, and arise primarily due to limitations in the kinds of
networks generated by the one-step neighbor definition. Taken together, our
results indicate that caution is warranted when linking the dynamics of human
spoken word recognition to the topological properties of PNNs, and that the
investigation of alternative similarity metrics for phonological forms should
be a priority.
Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects
Date: 2020-09-22 00:00:00

To estimate the dynamic effects of an absorbing treatment, researchers often
use two-way fixed effects regressions that include leads and lags of the
treatment. We show that in settings with variation in treatment timing across
units, the coefficient on a given lead or lag can be contaminated by effects
from other periods, and apparent pretrends can arise solely from treatment
effects heterogeneity. We propose an alternative estimator that is free of
contamination, and illustrate the relative shortcomings of two-way fixed
effects regressions with leads and lags through an empirical application.
Homeostatic plasticity and emergence of functional networks in a whole-brain model at criticality
Date: 2018-09-27 00:00:00

Understanding the relationship between large-scale structural and functional
brain networks remains a crucial issue in modern neuroscience. Recently, there
has been growing interest in investigating the role of homeostatic plasticity
mechanisms, across different spatiotemporal scales, in regulating network
activity and brain functioning against a wide range of environmental conditions
and brain states (e.g., during learning, development, ageing, neurological
diseases). In the present study, we investigate how the inclusion of
homeostatic plasticity in a stochastic whole-brain model, implemented as a
normalization of the incoming node's excitatory input, affects the macroscopic
activity during rest and the formation of functional networks. Importantly, we
address the structure-function relationship both at the group and
individual-based levels. In this work, we show that normalization of the node's
excitatory input improves the correspondence between simulated neural patterns
of the model and various brain functional data. Indeed, we find that the best
match is achieved when the model control parameter is in its critical value and
that normalization minimizes both the variability of the critical points and
neuronal activity patterns among subjects. Therefore, our results suggest that
the inclusion of homeostatic principles lead to more realistic brain activity
consistent with the hallmarks of criticality. Our theoretical framework open
new perspectives in personalized brain modeling with potential applications to
investigate the deviation from criticality due to structural lesions (e.g.
stroke) or brain disorders.
Bitcoin market route to maturity? Evidence from return fluctuations, temporal correlations and multiscaling effects
Date: 2018-07-19 00:00:00

Based on 1-minute price changes recorded since year 2012, the fluctuation
properties of the rapidly-emerging Bitcoin (BTC) market are assessed over
chosen sub-periods, in terms of return distributions, volatility
autocorrelation, Hurst exponents and multiscaling effects. The findings are
compared to the stylized facts of mature world markets. While early trading was
affected by system-specific irregularities, it is found that over the months
preceding Apr 2018 all these statistical indicators approach the features
hallmarking maturity. This can be taken as an indication that the Bitcoin
market, and possibly other cryptocurrencies, carry concrete potential of
imminently becoming a regular market, alternative to the foreign exchange
(Forex). Since high-frequency price data are available since the beginning of
trading, the Bitcoin offers a unique window into the statistical
characteristics of a market maturation trajectory.
Enhancement of Throat Microphone Recordings Using Gaussian Mixture Model Probabilistic Estimator
Date: 2018-04-13 00:00:00

The throat microphone is a body-attached transducer that is worn against the
neck. It captures the signals that are transmitted through the vocal folds,
along with the buzz tone of the larynx. Due to its skin contact, it is more
robust to the environmental noise compared to the acoustic microphone that
picks up the vibrations through air pressure, and hence the all interventions.
The throat speech is partly intelligible, but gives unnatural and croaky sound.
This thesis tries to recover missing frequency bands of the throat speech and
investigates envelope and excitation mapping problem with joint analysis of
throat- and acoustic-microphone recordings. A new phone-dependent GMM-based
spectral envelope mapping scheme, which performs the minimum mean square error
(MMSE) estimation of the acoustic-microphone spectral envelope, has been
proposed. In the source-filter decomposition framework, we observed that the
spectral envelope difference of the excitation signals of throat- and
acoustic-microphone recordings is an important source of the degradation in the
throat-microphone voice quality. Thus, we also model spectral envelope
difference of the excitation signals as a spectral tilt vector, and propose a
new phone-dependent GMM-based spectral tilt mapping scheme to enhance throat
excitation signal. Experimental evaluations are performed to compare the
proposed mapping scheme using both objective and subjective evaluations.
Objective evaluations are performed with the log-spectral distortion (LSD) and
the wide-band perceptual evaluation of speech quality (PESQ) metrics.
Subjective evaluations are performed with A/B pair comparison listening test.
Both objective and subjective evaluations yield that the proposed
phone-dependent mapping consistently improves performances over the
state-of-the-art GMM estimators.
Are Abstracts Enough for Hypothesis Generation?
Authors: Sybrandt Justin, Carrabba Angelo, Herzog Alexander, Safro Ilya
Date: 2018-10-20 00:00:00

The potential for automatic hypothesis generation (HG) systems to improve
research productivity keeps pace with the growing set of publicly available
scientific information. But as data becomes easier to acquire, we must
understand the effect different textual data sources have on our resulting
hypotheses. Are abstracts enough for HG, or does it need full-text papers? How
many papers does an HG system need to make valuable predictions? How sensitive
is a general-purpose HG system to hyperparameter values or input quality? What
effect does corpus size and document length have on HG results? To answer these
questions we train multiple versions of knowledge network-based HG system,
Moliere, on varying corpora in order to compare challenges and trade offs in
terms of result quality and computational requirements. Moliere generalizes
main principles of similar knowledge network-based HG systems and reinforces
them with topic modeling components. The corpora include the abstract and
full-text versions of PubMed Central, as well as iterative halves of MEDLINE,
which allows us to compare the effect document length and count has on the
results. We find that, quantitatively, corpora with a higher median document
length result in marginally higher quality results, yet require substantially
longer to process. However, qualitatively, full-length papers introduce a
significant number of intruder terms to the resulting topics, which decreases
human interpretability. Additionally, we find that the effect of document
length is greater than that of document count, even if both sets contain only
paper abstracts. Reproducibility: Our code and data are available at
github.com/jsybran/moliere, and bit.ly/2GxghpM respectively.
Universal statistics of incubation periods and other detection times via diffusion models
Date: 2018-04-16 00:00:00

We suggest an explanation of typical incubation times statistical features
based on the universal behavior of exit times for diffusion models. We give a
mathematically rigorous proof of the characteristic right skewness of the
incubation time distribution for very general one-dimensional diffusion models.
Imposing natural simple conditions on the drift coefficient, we also study
these diffusion models under the assumption of noise smallness and show that
the limiting exit time distributions in the limit of vanishing noise are
Gaussian and Gumbel. Thus they match the existing data as well as the other
existing models do. The character of our models, however, allows us to argue
that the features of the exit time distributions that we describe are universal
and manifest themselves in various other situations where the times involved
can be described as detection or halting times, for example, response times
studied in psychology.
Appropriate kernels for Divisive Normalization explained by Wilson-Cowan equations
Date: 2018-04-16 00:00:00

The interaction between wavelet-like sensors in Divisive Normalization is
classically described through Gaussian kernels that decay with spatial
distance, angular distance and frequency distance. However, simultaneous
explanation of (a) distortion perception in natural image databases and (b)
contrast perception of artificial stimuli requires very specific modifications
in classical Divisive Normalization. First, the wavelet response has to be
high-pass filtered before the Gaussian interaction is applied. Then, distinct
weights per subband are also required after the Gaussian interaction. In
summary, the classical Gaussian kernel has to be left- and right-multiplied by
two extra diagonal matrices.
  In this paper we provide a lower-level justification for this specific
empirical modification required in the Gaussian kernel of Divisive
Normalization. Here we assume that the psychophysical behavior described by
Divisive Normalization comes from neural interactions following the
Wilson-Cowan equations. In particular, we identify the Divisive Normalization
response with the stationary regime of a Wilson-Cowan model. From this
identification we derive an expression for the Divisive Normalization kernel in
terms of the interaction kernel of the Wilson-Cowan equations. It turns out
that the Wilson-Cowan kernel is left- and-right multiplied by diagonal matrices
with high-pass structure. In conclusion, symmetric Gaussian inhibitory
relations between wavelet-like sensors wired in the lower-level Wilson-Cowan
model lead to the appropriate non-symmetric kernel that has to be empirically
included in Divisive Normalization to explain a wider range of phenomena.
Design of a Planar Eleven Antenna for Optimal MIMO Performance as a Wideband Micro Base-station Antenna
Date: 2018-04-16 00:00:00

A new low-profile planar Eleven antenna is designed for optimal MIMO
performance as a wideband MIMO antenna for micro base-stations in future
wireless communication systems. The design objective has been to optimize both
the reflection coefficient at the input port of the antenna and the 1-bitstream
and 2-bitstream MIMO efficiency of the antenna at the same time, in both the
Rich Isotropic MultiPath (RIMP) and Random Line-of-Sight (Random-LOS)
environments. The planar Eleven antenna can be operated in 2-, 4-, and 8-port
modes with slight modifications. The optimization is performed using genetic
algorithms. The effects of polarization deficiencies and antenna total embedded
efficiency on the MIMO performance of the antenna are further studied. A
prototype of the antenna has been fabricated and the design has been verified
by measurements against the simulations.
Quantifying the Economic Case for Electric Semi-Trucks
Date: 2018-04-16 00:00:00
Tasks: Autonomous Vehicles

There has been considerable interest in the electrification of freight
transport, particularly heavy-duty trucks to downscale the greenhouse-gas (GHG)
emissions from the transportation sector. However, the economic competitiveness
of electric semi-trucks is uncertain as there are substantial additional
initial costs associated with the large battery packs required. In this work,
we analyze the trade-off between the initial investment and the operating cost
for realistic usage scenarios to compare a fleet of electric semi-trucks with a
range of 500 miles with a fleet of diesel trucks. For the baseline case with
30% of fleet requiring battery pack replacements and a price differential of
US\$50,000, we find a payback period of about 3 years. Based on sensitivity
analysis, we find that the fraction of the fleet that requires battery pack
replacements is a major factor. For the case with 100% replacement fraction,
the payback period could be as high as 5-6 years. We identify the price of
electricity as the second most important variable, where a price of
US$0.14/kWh, the payback period could go up to 5 years. Electric semi-trucks
are expected to lead to savings due to reduced repairs and magnitude of these
savings could play a crucial role in the payback period as well. With increased
penetration of autonomous vehicles, the annual mileage of semi-trucks could
substantially increase and this heavily sways in favor of electric semi-trucks,
bringing down the payback period to around 2 years at an annual mileage of
120,000 miles. There is an undeniable economic case for electric semi-trucks
and developing battery packs with longer cycle life and higher specific energy
would make this case even stronger.
Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve
Date: 2018-04-17 00:00:00

Objective: To establish the performance of several drive and measurement
patterns in EIT imaging of neural activity in peripheral nerve, which involves
large impedance change in the nerve's anisotropic length axis. Approach: Eight
drive and measurement electrode patterns are compared using a finite element
(FE) four cylindrical shell model of a peripheral nerve and a 32 channel
dual-ring nerve cuff. The central layer of the FE model contains impedance
changes representative of neural activity of -0.3 in the length axis and -8.8 x
10-4 in the radial axis. Four of the electrode patterns generate longitudinal
drive current, which runs perpendicular to the anisotropic axis. Main results:
Transverse current patterns produce higher resolution than longitudinal
patterns but are also more susceptible to noise and errors, and exhibit poorer
sensitivity to impedance changes in central sample locations. Three of the four
longitudinal current patterns considered can reconstruct fascicle level
impedance changes with up to 0.2 mV noise and error, which corresponds to
between -5.5 and +0.18 dB of the normalised signal standard deviation. Reducing
the spacing between the two electrode rings in all longitudinal current
patterns reduced the signal to error ratio across all depth locations of the
sample. Significance: Electrode patterns which target the large impedance
change in the anisotropic length axis can provide improved robustness against
noise and errors, which is a critical step towards real time EIT imaging of
neural activity in peripheral nerve.
Reduced Order Fractional Fourier Transform A New Variant to Fractional Signal Processing Definition and Properties
Date: 2018-04-17 00:00:00

In this paper, a new variant to fractional signal processing is proposed
known as the Reduced Order Fractional Fourier Transform. Various properties
satisfied by its transformation kernel is derived. The properties associated
with the proposed Reduced Order Fractional Fourier Transform like shift,
modulation, time-frequency shift property are also derived and it is shown
mathematically that when the rotation angle of Reduced Order Fractional Fourier
Transform approaches 90 degrees, the proposed Reduced Order Fractional Fourier
Transform reduces to the conventional Fourier transform. Also, the Reduced
Order Fractional Fourier Transform of various kinds of signals is also derived
and it is shown that the obtained analytical expressions of different Reduced
Order Fractional Fourier Transform are a reduced form of the conventional
fractional Fourier transform. It is also shown that proposed definition of
Fractional Fourier Transform is easier to be handled analytically. Finally, the
convolution theorem associated with the proposed Reduced Order Fractional
Fourier Transform is derived with its various properties like shift
convolution, modulation convolution, and time-frequency shift modulation
properties. It has been shown that with this proposed new definition of
fractional Fourier transform, the convolution theorem has been reduced to
multiplication in the fractional frequency domain.