pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Sparse-grid Gauss-Hermite Filter
Date: 2018-03-25 00:00:00

In this paper, a new nonlinear filter based on sparse-grid quadrature method
has been proposed. The proposed filter is named as adaptive sparse-grid
Gauss-Hermite filter (ASGHF). Ordinary sparse-grid technique treats all the
dimensions equally, whereas the ASGHF assigns a fewer number of points along
the dimensions with lower nonlinearity. It uses adaptive tensor product to
construct multidimensional points until a predefined error tolerance level is
reached. The performance of the proposed filter is illustrated with two
nonlinear filtering problems. Simulation results demonstrate that the new
algorithm achieves a similar accuracy as compared to sparse-grid Gauss-Hermite
filter (SGHF) and Gauss-Hermite filter (GHF) with a considerable reduction in
computational load. Further, in the conventional GHF and SGHF, any increase in
the accuracy level may result in an unacceptably high increase in the
computational burden. However, in ASGHF, a little increase in estimation
accuracy is possible with a limited increase in computational burden by varying
the error tolerance level and the error weighting parameter. This enables the
online estimator to operate near full efficiency with a predefined
computational budget.
ALOHA-NOMA for Massive Machine-to-Machine IoT Communication
Date: 2018-03-25 00:00:00

This paper proposes a new medium access control (MAC) protocol for Internet
of Things (IoT) applications incorporating pure ALOHA with power domain
non-orthogonal multiple access (NOMA) in which the number of transmitters are
not known as a priori information and estimated with multi-hypothesis testing.
The proposed protocol referred to as ALOHA-NOMA is not only scalable, energy
efficient and matched to the low complexity requirements of IoT devices, but it
also significantly increases the throughput. Specifically, throughput is
increased to 1.27 with ALOHA-NOMA when 5 users can be separated via a SIC
(Successive Interference Cancellation) receiver in comparison to the classical
result of 0.18 in pure ALOHA. The results further show that there is a greater
than linear increase in throughput as the number of active IoT devices
increases.
Fusion of Correlated Decisions Using Regular Vine Copulas
Date: 2018-11-18 00:00:00

In this paper, we propose a regular vine copula based methodology for the
fusion of correlated decisions. Regular vine copula is an extremely flexible
and powerful graphical model to characterize complex dependence among multiple
modalities. It can express a multivariate copula by using a cascade of
bivariate copulas, the so-called pair copulas. Assuming that local detectors
are single threshold binary quantizers and taking complex dependence among
sensor decisions into account, we design an optimal fusion rule using a regular
vine copula under the Neyman-Pearson framework. In order to reduce the
computational complexity resulting from the complex dependence, we propose an
efficient and computationally light regular vine copula based optimal fusion
algorithm. Numerical experiments are conducted to demonstrate the effectiveness
of our approach.
Extinction debt repayment via timely habitat restoration
Date: 2018-03-25 00:00:00

Habitat destruction threatens the viability of many populations, but its full
consequences can take considerable time to unfold. Much of the discourse
surrounding extinction debts--the number of species that persist transiently
following habitat loss, despite being headed for extinction--frames ultimate
population crashes as the means of settling the debt. However, slow population
decline also opens an opportunity to repay the debt by restoring habitat. The
timing necessary for such habitat restoration to rescue a population from
extinction has not been well studied. Here we determine habitat restoration
deadlines for a spatially implicit Levins/Tilman population model modified by
an Allee effect. We find that conditions that hinder detection of an extinction
debt also provide forgiving restoration timeframes. Our results highlight the
importance of transient dynamics in restoration and suggest the beginnings of
an analytic theory to understand outcomes of temporary press perturbations in a
broad class of ecological systems.
The cooling-off effect of price limits in the Chinese stock markets
Date: 2018-03-26 00:00:00

In this paper, we investigate the cooling-off effect (opposite to the magnet
effect) from two aspects. Firstly, from the viewpoint of dynamics, we study the
existence of the cooling-off effect by following the dynamical evolution of
some financial variables over a period of time before the stock price hits its
limit. Secondly, from the probability perspective, we investigate, with the
logit model, the existence of the cooling-off effect through analyzing the
high-frequency data of all A-share common stocks traded on the Shanghai Stock
Exchange and the Shenzhen Stock Exchange from 2000 to 2011 and inspecting the
trading period from the opening phase prior to the moment that the stock price
hits its limits. A comparison is made of the properties between up-limit hits
and down-limit hits, and the possible difference will also be compared between
bullish and bearish market state by dividing the whole period into three
alternating bullish periods and three bearish periods. We find that the
cooling-off effect emerges for both up-limit hits and down-limit hits, and the
cooling-off effect of the down-limit hits is stronger than that of the up-limit
hits. The difference of the cooling-off effect between bullish period and
bearish period is quite modest. Moreover, we examine the sub-optimal orders
effect, and infer that the professional individual investors and institutional
investors play a positive role in the cooling-off effects. All these findings
indicate that the price limit trading rule exerts a positive effect on
maintaining the stability of the Chinese stock markets.
Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates
Date: 2018-03-26 00:00:00

We employ the thermal optimal path method to explore both the long-term and
short-term interaction patterns between the onshore CNY and offshore CNH
exchange rates (2012-2015). For the daily data, the CNY and CNH exchange rates
show a weak alternate lead-lag structure in most of the time periods. When CNY
and CNH display a large disparity, the lead-lag relationship is uncertain and
depends on the prevailing market factors. The minute-scale interaction pattern
between the CNY and CNH exchange rates change over time according to different
market situations. We find that US dollar appreciation is associated with a
lead-lag relationship running from offshore to onshore, while a (contrarian)
Renminbi appreciation is associated with a lead-lag relationship running from
onshore to offshore. These results are robust with respect to different
sub-sample analyses and variations of the key smoothing parameter of the TOP
method.
Cliquet option pricing with Meixner processes
Date: 2018-03-26 00:00:00

We investigate the pricing of cliquet options in a geometric Meixner model.
The considered option is of monthly sum cap style while the underlying stock
price model is driven by a pure-jump Meixner--L\'{e}vy process yielding Meixner
distributed log-returns. In this setting, we infer semi-analytic expressions
for the cliquet option price by using the probability distribution function of
the driving Meixner--L\'{e}vy process and by an application of Fourier
transform techniques. In an introductory section, we compile various facts on
the Meixner distribution and the related class of Meixner--L\'{e}vy processes.
We also propose a customized measure change preserving the Meixner distribution
of any Meixner process.
Panel Data Analysis with Heterogeneous Dynamics
Date: 2019-01-15 00:00:00
Tasks: valid

This paper proposes a model-free approach to analyze panel data with
heterogeneous dynamic structures across observational units. We first compute
the sample mean, autocovariances, and autocorrelations for each unit, and then
estimate the parameters of interest based on their empirical distributions. We
then investigate the asymptotic properties of our estimators using double
asymptotics and propose split-panel jackknife bias correction and inference
based on the cross-sectional bootstrap. We illustrate the usefulness of our
procedures by studying the deviation dynamics of the law of one price. Monte
Carlo simulations confirm that the proposed bias correction is effective and
yields valid inference in small samples.
Enabling Aloha-NOMA for Massive M2M Communication in IoT Networks
Date: 2018-03-26 00:00:00

The Internet of things (IoT), which is the network of physical devices
embedded with sensors, actuators, and connec- tivity, is being accelerated into
the mainstream by the emergence of 5G wireless networking. This paper presents
an uncoordinated non-orthogonal random access protocol, an enhancement to the
recently introduced Aloha-NOMA protocol, which provides high throughput, while
being matched to the low complexity requirements and the sporadic traffic
pattern of IoT devices. Under ideal conditions it has been shown that
Aloha-NOMA, using power-domain orthogonality, can significantly increase the
throughput using SIC (Successive Interference Cancellation) to enable correct
reception of multiple simultaneous transmitted signals. For this ideal
performance, the enhanced Aloha-NOMA receiver adaptively learns the number of
active devices (which is not known a priori) using a form of multi-hypothesis
testing. For small numbers of simultaneous transmissions, it is shown that
there can be substantial throughput gain of 6.9 dB relative to pure Aloha for
0.25 probability of transmission and up to 3 active transmitters.
Cluster analysis of stocks using price movements of high frequency data from National Stock Exchange
Date: 2018-03-26 00:00:00

This paper aims to develop new techniques to describe joint behavior of
stocks, beyond regression and correlation. For example, we want to identify the
clusters of the stocks that move together. Our work is based on applying Kernel
Principal Component Analysis(KPCA) and Functional Principal Component
Analysis(FPCA) to high frequency data from NSE. Since we dealt with high
frequency data with a tick size of 30 seconds, FPCA seems to be an ideal
choice. FPCA is a functional variant of PCA where each sample point is
considered to be a function in Hilbert space L^2. On the other hand, KPCA is an
extension of PCA using kernel methods. Results obtained from FPCA and Gaussian
Kernel PCA seems to be in synergy but with a lag. There were two prominent
clusters that showed up in our analysis, one corresponding to the banking
sector and another corresponding to the IT sector. The other smaller clusters
were seen from the automobile industry and the energy sector. IT sector was
seen interacting with these small clusters. The learning gained from these
interactions is substantial as one can use it significantly to develop trading
strategies for intraday traders.
SIG-DB: leveraging homomorphic encryption to Securely Interrogate privately held Genomic DataBases
Date: 2018-03-26 00:00:00

Genomic data are becoming increasingly valuable as we develop methods to
utilize the information at scale and gain a greater understanding of how
genetic information relates to biological function. Advances in synthetic
biology and the decreased cost of sequencing are increasing the amount of
privately held genomic data. As the quantity and value of private genomic data
grows, so does the incentive to acquire and protect such data, which creates a
need to store and process these data securely. We present an algorithm for the
Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm
enables databases of genomic sequences to be searched with an encrypted query
sequence without revealing the query sequence to the Database Owner or any of
the database sequences to the Querier. SIG-DB is the first application of its
kind to take advantage of locality-sensitive hashing and homomorphic encryption
to allow generalized sequence-to-sequence comparisons of genomic data.
Formation of autophagosomes coincides with relaxation of membrane curvature
Date: 2018-03-26 00:00:00

Autophagy is an intracellular degradation process that employs complex
membrane dynamics to isolate and break down cellular components. However, many
unanswered questions remain concerning remodeling of autophagic membranes.
Here, we focus on the advantages of theoretical modelling to study the
formation of autophagosomes and to understand the origin of autophagosomal
membranes. Starting from the well-defined geometry of final autophagosomes we
ask the question of how these organelles can be formed by combining various
preautophagosomal membranes such as vesicles, membrane tubules or sheets. We
analyse the geometric constraints of autophagosome formation by taking the area
of the precursor membranes and their internal volume into account. Our results
suggest that vesicle fusion contributes little to the formation of
autophagosomes. In the second part we quantify the curvature of the precursors
and report that the formation of autophagosomes is associated with a strong
relaxation of membrane curvature energy. This effect we find for a wide range
of membrane asymmetries. It is especially strong for small distances between
both autophagosomal membranes, as observed in vivo. We quantify the membrane
bending energies of all precursors by considering membrane asymmetries. We
propose that the generation and supply of pre-autophagosomal membranes is one
limiting step for autophagosome formation.
Modeling power angle spectrum and antenna pattern directions in multipath propagation environment
Date: 2018-03-26 00:00:00

Most propagation models do not consider the influence of antenna patterns on
the parameters and characteristics of received signals. This assumption is
equivalent to the use of isotropic or omnidirectional antennas in these models.
Empirical measurement results indicate that the radiation pattern, gain and
direction of directional antennas significantly influence on properties of the
received signal. This fact shows that consideration the directional antennas in
propagation models is very important especially in the context of emerging
telecommunication technologies such as beamforming or massive MIMO. The purpose
of this paper is to present the modeling method of power angular spectrum and
direction of antenna patterns in a multipath propagation environment.
Big Data Challenges in Genome Informatics
Authors: Ka-Chun Wong
Date: 2018-03-20 00:00:00

In recent years, we have witnessed a dramatic data explosion in genomics, thanks to the improvement in sequencing technologies and the drastically decreasing costs. We are entering the era of millions of available genomes. Notably, each genome can be composed of billions of nucleotides stored as plain text files in GigaBytes (GBs). It is undeniable that those genome data impose unprecedented data challenges for us. In this article, we briefly discuss the big data challenges associated with genomics in recent years.
Alignment-Free Sequence Analysis and Applications
Date: 2018-03-26 00:00:00

Genome and metagenome comparisons based on large amounts of next-generation
sequencing (NGS) data pose significant challenges for alignment-based
approaches due to the huge data size and the relatively short length of the
reads. Alignment-free approaches based on the counts of word patterns in NGS
data do not depend on the complete genome and are generally computationally
efficient. Thus, they contribute significantly to genome and metagenome
comparison. Recently, novel statistical approaches have been developed for the
comparison of both long and shotgun sequences. These approaches have been
applied to many problems including the comparison of gene regulatory regions,
genome sequences, metagenomes, binning contigs in metagenomic data,
identification of virus-host interactions, and detection of horizontal gene
transfers. We provide an updated review of these applications and other related
developments of word-count based approaches for alignment-free sequence
analysis.
Demystifying Core Ranking in Pinterest Image Search
Authors: Zhu Linhong
Date: 2018-03-26 00:00:00
Tasks: Image Retrieval

Pinterest Image Search Engine helps hundreds of millions of users discover
interesting content everyday. This motivates us to improve the image search
quality by evolving our ranking techniques. In this work, we share how we
practically design and deploy various ranking pipelines into Pinterest image
search ecosystem. Specifically, we focus on introducing our novel research and
study on three aspects: training data, user/image featurization and ranking
models. Extensive offline and online studies compared the performance of
different models and demonstrated the efficiency and effectiveness of our final
launched ranking models.
Epileptic Seizure Detection: A Deep Learning Approach
Date: 2018-03-27 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG), Seizure Detection, Time Series Analysis

Epilepsy is the second most common brain disorder after migraine. Automatic
detection of epileptic seizures can considerably improve the patients' quality
of life. Current Electroencephalogram (EEG)-based seizure detection systems
encounter many challenges in real-life situations. The EEGs are non-stationary
signals and seizure patterns vary across patients and recording sessions.
Moreover, EEG data are prone to numerous noise types that negatively affect the
detection accuracy of epileptic seizures. To address these challenges, we
introduce the use of a deep learning-based approach that automatically learns
the discriminative EEG features of epileptic seizures. Specifically, to reveal
the correlation between successive data samples, the time-series EEG data are
first segmented into a sequence of non-overlapping epochs. Second, Long
Short-Term Memory (LSTM) network is used to learn the high-level
representations of the normal and the seizure EEG patterns. Third, these
representations are fed into Softmax function for training and classification.
The results on a well-known benchmark clinical dataset demonstrate the
superiority of the proposed approach over the existing state-of-the-art
methods. Furthermore, our approach is shown to be robust in noisy and real-life
conditions. Compared to current methods that are quite sensitive to noise, the
proposed method maintains its high detection performance in the presence of
common EEG artifacts (muscle activities and eye-blinking) as well as white
noise.
A Web Scraping Methodology for Bypassing Twitter API Restrictions
Authors: Hernandez-Suarez A., Sanchez-Perez G., Toscano-Medina K., Martinez-Hernandez V., Sanchez V., Perez-Meana H.
Date: 2018-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis

Retrieving information from social networks is the first and primordial step
many data analysis fields such as Natural Language Processing, Sentiment
Analysis and Machine Learning. Important data science tasks relay on historical
data gathering for further predictive results. Most of the recent works use
Twitter API, a public platform for collecting public streams of information,
which allows querying chronological tweets for no more than three weeks old. In
this paper, we present a new methodology for collecting historical tweets
within any date range using web scraping techniques bypassing for Twitter API
restrictions.
On Fairness of Systemic Risk Measures
Date: 2019-04-24 00:00:00
Tasks: Fairness

In our previous paper, "A Unified Approach to Systemic Risk Measures via
Acceptance Set" (\textit{Mathematical Finance, 2018}), we have introduced a
general class of systemic risk measures that allow for random allocations to
individual banks before aggregation of their risks. In the present paper, we
prove the dual representation of a particular subclass of such systemic risk
measures and the existence and uniqueness of the optimal allocation related to
them. We also introduce an associated utility maximization problem which has
the same optimal solution as the systemic risk measure. In addition, the
optimizer in the dual formulation provides a \textit{risk allocation} which is
fair from the point of view of the individual financial institutions. The case
with exponential utilities which allows for explicit computation is treated in
details.
A Perfect Specialization Model for Gravity Equation in Bilateral Trade based on Production Structure
Date: 2018-03-27 00:00:00

Although initially originated as a totally empirical relationship to explain
the volume of trade between two partners, gravity equation has been the focus
of several theoretic models that try to explain it. Specialization models are
of great importance in providing a solid theoretic ground for gravity equation
in bilateral trade. Some research papers try to improve specialization models
by adding imperfect specialization to model, but we believe it is unnecessary
complication. We provide a perfect specialization model based on the phenomenon
we call tradability, which overcomes the problems with simpler initial. We
provide empirical evidence using estimates on panel data of bilateral trade of
40 countries over 10 years that support the theoretical model. The empirical
results have implied that tradability is the only reason for deviations of data
from basic perfect specialization models.
Automatic Minimisation of Masking in Multitrack Audio using Subgroups
Date: 2018-03-28 00:00:00

The iterative process of masking minimisation when mixing multitrack audio is
a challenging optimisation problem, in part due to the complexity and
non-linearity of auditory perception. In this article, we first propose a
multitrack masking metric inspired by the MPEG psychoacoustic model. We
investigate different audio processing techniques to manipulate the frequency
and dynamic characteristics of the signal in order to reduce masking based on
the proposed metric. We also investigate whether or not automatically mixing
using subgrouping is beneficial or not to perceived quality and clarity of a
mix. Evaluation results suggest that our proposed masking metric when used in
an automatic mixing framework can be used to reduce inter-channel auditory
masking as well as improve the perceived quality and perceived clarity of a
mix. Furthermore, our results suggest that using subgrouping in an automatic
mixing framework can be used to improve the perceived quality and perceived
clarity of a mix.
Student-Teacher Learning for BLSTM Mask-based Speech Enhancement
Date: 2018-03-27 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

Spectral mask estimation using bidirectional long short-term memory (BLSTM)
neural networks has been widely used in various speech enhancement
applications, and it has achieved great success when it is applied to
multichannel enhancement techniques with a mask-based beamformer. However, when
these masks are used for single channel speech enhancement they severely
distort the speech signal and make them unsuitable for speech recognition. This
paper proposes a student-teacher learning paradigm for single channel speech
enhancement. The beamformed signal from multichannel enhancement is given as
input to the teacher network to obtain soft masks. An additional cross-entropy
loss term with the soft mask target is combined with the original loss, so that
the student network with single-channel input is trained to mimic the soft mask
obtained with multichannel input through beamforming. Experiments with the
CHiME-4 challenge single channel track data shows improvement in ASR
performance.