pwc-archive/papers-with-abstracts huggingface dataset:
A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case
Date: 2018-04-20 00:00:00
Tasks: Quantization

This paper considers least-square based estimation of the amplitude and
square amplitude of a quantized sine wave, done by considering random initial
record phase. Using amplitude- and frequency-domain modeling techniques, it is
shown that the estimator is inconsistent, biased and has a variance that may be
underestimated if the simple model of quantization is applied. The effects of
both sine wave offset values and additive Gaussian noise are taken into
account. General estimator properties are derived, without making simplifying
assumptions on the role of the quantization process, to allow assessment of
measurement uncertainty, when this least-square procedure is used.
Verifying Text Summaries of Relational Data Sets
Authors: Jo Saehan, Trummer Immanuel, Yu Weicheng, Liu Daniel, Wang Xuezhi, Yu Cong, Mehta Niyati
Date: 2018-08-30 00:00:00
Tasks: Articles, Fact Checking

We present a novel natural language query interface, the AggChecker, aimed at
text summaries of relational data sets. The tool focuses on natural language
claims that translate into an SQL query and a claimed query result. Similar in
spirit to a spell checker, the AggChecker marks up text passages that seem to
be inconsistent with the actual data. At the heart of the system is a
probabilistic model that reasons about the input document in a holistic
fashion. Based on claim keywords and the document structure, it maps each text
claim to a probability distribution over associated query translations. By
efficiently executing tens to hundreds of thousands of candidate translations
for a typical input document, the system maps text claims to correctness
probabilities. This process becomes practical via a specialized processing
backend, avoiding redundant work via query merging and result caching.
Verification is an interactive process in which users are shown tentative
results, enabling them to take corrective actions if necessary.
  Our system was tested on a set of 53 public articles containing 392 claims.
Our test cases include articles from major newspapers, summaries of survey
results, and Wikipedia articles. Our tool revealed erroneous claims in roughly
a third of test cases. A detailed user study shows that users using our tool
are in average six times faster at checking text summaries, compared to generic
SQL interfaces. In fully automated verification, our tool achieves
significantly higher recall and precision than baselines from the areas of
natural language query interfaces and fact-checking.
Domain Adversarial for Acoustic Emotion Recognition
Date: 2018-04-20 00:00:00
Tasks: Attribute, Emotion Recognition, Speech Emotion Recognition

The performance of speech emotion recognition is affected by the differences
in data distributions between train (source domain) and test (target domain)
sets used to build and evaluate the models. This is a common problem, as
multiple studies have shown that the performance of emotional classifiers drop
when they are exposed to data that does not match the distribution used to
build the emotion classifiers. The difference in data distributions becomes
very clear when the training and testing data come from different domains,
causing a large performance gap between validation and testing performance. Due
to the high cost of annotating new data and the abundance of unlabeled data, it
is crucial to extract as much useful information as possible from the available
unlabeled data. This study looks into the use of adversarial multitask training
to extract a common representation between train and test domains. The primary
task is to predict emotional attribute-based descriptors for arousal, valence,
or dominance. The secondary task is to learn a common representation where the
train and test domains cannot be distinguished. By using a gradient reversal
layer, the gradients coming from the domain classifier are used to bring the
source and target domain representations closer. We show that exploiting
unlabeled data consistently leads to better emotion recognition performance
across all emotional dimensions. We visualize the effect of adversarial
training on the feature representation across the proposed deep learning
architecture. The analysis shows that the data representations for the train
and test domains converge as the data is passed to deeper layers of the
network. We also evaluate the difference in performance when we use a shallow
neural network versus a \emph{deep neural network} (DNN) and the effect of the
number of shared layers used by the task and domain classifiers.
Practical Issues in the Synthesis of Ternary Sequences
Date: 2018-04-20 00:00:00

Several issues related to the practical synthesis of ternary sequences with
specified spectra are addressed in this paper. Specifically, sequences with
harmonic multiples of two and three suppressed are studied, given their
relevance when testing and characterizing nonlinear systems. In particular, the
effect of non-uniform Digital to Analog Converter (DAC) levels on the spectral
properties of the generated signal is analyzed. It is analytically shown that
the DAC non-uniform levels result in degraded harmonic suppression performance.
Moreover, a new approach is proposed for designing ternary sequences, which is
flexible and can be adapted to suit different requirements. The resulting
sequences, denoted as randomized constrained sequences, are characterized
theoretically by deriving an analytical expression of the power spectral
density. Furthermore, they are extensively compared with three synthesis
approaches proposed in the literature. The approach is validated by numerical
simulations and experimental results, showing the potential to achieve harmonic
suppression performance of approximately 100 dB.
Progress and open problems in evolutionary dynamics
Date: 2018-04-20 00:00:00
Tasks: Diversity

Evolution has fascinated quantitative and physical scientists for decades:
how can the random process of mutation, recombination, and duplication of
genetic information generate the diversity of life? What determines the rate of
evolution? Are there quantitative laws that govern and constrain evolution? Is
evolution repeatable or predictable? Historically, the study of evolution
involved classifying and comparing species, typically based on morphology. In
addition to phenotypes on the organismal and molecular scales, we now use
whole-genome sequencing to uncover not only the differences between species but
also to characterize genetic diversity within-species in unprecedented detail.
This diversity can be compared to predictions of quantitative models of
evolutionary dynamics. Here, we review key theoretical models of population
genetics and evolution along with examples of data from lab evolution
experiments, longitudinal sampling of viral populations, microbial communities
and the studies of immune repertoires. In all these systems, evolution is
shaped by often variable biological and physical environments. While these
variable environments can be modeled implicitly in cases such as host-pathogen
co-evolution, the dynamic environment, and emerging ecology often cannot be
ignored. Integrating dynamics on different scales, both in terms of observation
and theoretical models, remains a major challenge towards a better
understanding of evolution.
A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction
Authors: Wang Yanshan, Sohn Sunghwan, Liu Sijia, Shen Feichen, Wang Liwei, Atkinson Elizabeth J., Amin Shreyasee, Liu Hongfang
Date: 2018-04-20 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Word Embeddings

Objective: To automatically create large labeled training datasets and reduce
the efforts of feature engineering for training accurate machine learning
models for clinical information extraction. Materials and Methods: We propose a
distant supervision paradigm empowered by deep representation for extracting
information from clinical text. In this paradigm, the rule-based NLP algorithms
are utilized to generate weak labels and create large training datasets
automatically. Additionally, we use pre-trained word embeddings as deep
representation to eliminate the need of task-specific feature engineering for
machine learning. We evaluated the effectiveness of the proposed paradigm on
two clinical information extraction tasks: smoking status extraction and
proximal femur (hip) fracture extraction. We tested three prevalent machine
learning models, namely, Convolutional Neural Networks (CNN), Support Vector
Machine (SVM), and Random Forrest (RF). Results: The results indicate that CNN
is the best fit to the proposed distant supervision paradigm. It outperforms
the rule-based NLP algorithms given large datasets by capturing additional
extraction patterns. We also verified the advantage of word embedding feature
representation in the paradigm over term frequency-inverse document frequency
(tf-idf) and topic modeling representations. Discussion: In the clinical
domain, the limited amount of labeled data is always a bottleneck for applying
machine learning. Additionally, the performance of machine learning approaches
highly depends on task-specific feature engineering. The proposed paradigm
could alleviate those problems by leveraging rule-based NLP algorithms to
automatically assign weak labels and eliminating the need of task-specific
feature engineering using word embedding feature representation.
Secure OFDM System Design and Capacity Analysis under Disguised Jamming
Date: 2019-03-21 00:00:00

In this paper, we propose a securely precoded OFDM (SP-OFDM) system for
efficient and reliable transmission under disguised jamming, where the jammer
intentionally misleads the receiver by mimicking the characteristics of the
authorized signal, and causes complete communication failure. More
specifically, we bring off a dynamic constellation by introducing secure shared
randomness between the legitimate transmitter and receiver, and hence break the
symmetricity between the authorized signal and the disguised jamming. We
analyze the channel capacities of both the traditional OFDM and SP-OFDM under
hostile jamming using the arbitrarily varying channel (AVC) model. It is shown
that the deterministic coding capacity of the traditional OFDM is zero under
the worst disguised jamming. On the other hand, due to the secure randomness
shared between the authorized transmitter and receiver, SP-OFDM can achieve a
positive capacity under disguised jamming since the AVC channel corresponding
to SP-OFDM is not symmetrizable. A remarkable feature of the proposed SP-OFDM
scheme is that while achieving strong jamming resistance, it has roughly the
same high spectral efficiency as the traditional OFDM system. The robustness of
the proposed SP-OFDM scheme under disguised jamming is demonstrated through
both theoretic and numerical analyses.
Analytical Path-Integral Pricing of Moving-Barrier Options under non-Gaussian Distributions
Date: 2018-04-20 00:00:00

In this work we present an analytical model, based on the path-integral
formalism of Statistical Mechanics, for pricing options using first-passage
time problems involving both fixed and deterministically moving absorbing
barriers under possible non-gaussian distributions of the underlying object. We
adapt to our problem a model originally proposed to describe the formation of
galaxies in the universe of De Simone et al (2011), which uses cumulant
expansions in terms of the Gaussian distribution, and we generalize it to take
into acount drift and cumulants of orders higher than three. From the
probability density function, we obtain an analytical pricing model, not only
for vanilla options (thus removing the need of volatility smile inherent to the
Black-Scholes model), but also for fixed or deterministically moving barrier
options. Market prices of vanilla options are used to calibrate the model, and
barrier option pricing arising from the model is compared to the price resulted
from the relative entropy model.
Between perfectly critical and fully irregular: a reverberating model captures and predicts cortical spike propagation
Date: 2019-03-12 00:00:00

Knowledge about the collective dynamics of cortical spiking is very
informative about the underlying coding principles. However, even most basic
properties are not known with certainty, because their assessment is hampered
by spatial subsampling, i.e. the limitation that only a tiny fraction of all
neurons can be recorded simultaneously with millisecond precision. Building on
a novel, subsampling-invariant estimator, we fit and carefully validate a
minimal model for cortical spike propagation. The model interpolates between
two prominent states: asynchronous and critical. We find neither of them in
cortical spike recordings across various species, but instead identify a narrow
reverberating regime. This approach enables us to predict yet unknown
properties from very short recordings and for every circuit individually,
including responses to minimal perturbations, intrinsic network timescales, and
the strength of external input compared to recurrent activation - thereby
informing about the underlying coding principles for each circuit, area, state
and task.
Conditional heteroskedasticity in crypto-asset returns
Date: 2018-12-24 00:00:00
Tasks: Time Series, Time Series Analysis

This paper examines the time series properties of cryptocurrency assets, such
as Bitcoin, using established econometric inference techniques, namely models
of the GARCH family. The contribution of this study is twofold. I explore the
time series properties of cryptocurrencies, a new type of financial asset on
which there appears to be little or no literature. I suggest an improved
econometric specification to that which has been recently proposed in Chu et al
(2017), the first econometric study to examine the price dynamics of the most
popular cryptocurrencies. Questions regarding the reliability of their study
stem from the authors mis-diagnosing the distribution of GARCH innovations.
Checks are performed on whether innovations are Gaussian or GED by using
Kolmogorov type non-parametric tests and Khmaladze's martingale transformation.
Null of gaussianity is strongly rejected for all GARCH(p,q) models, with $p,q
\in \{1,\ldots,5 \}$, for all cryptocurrencies in sample. For tests of
normality, I make use of the Gauss-Kronrod quadrature. Parameters of GARCH
models are estimated with generalized error distribution innovations using
maximum likelihood. For calculating P-values, the parametric bootstrap method
is used. Arguing against Chu et al (2017), I show that there is a strong
empirical argument against modelling innovations under some common assumptions.
Empirical Equilibrium
Date: 2020-07-13 00:00:00

We study the foundations of empirical equilibrium, a refinement of Nash
equilibrium that is based on a non-parametric characterization of empirical
distributions of behavior in games (Velez and Brown,2020b arXiv:1907.12408).
The refinement can be alternatively defined as those Nash equilibria that do
not refute the regular QRE theory of Goeree, Holt, and Palfrey (2005). By
contrast, some empirical equilibria may refute monotone additive randomly
disturbed payoff models. As a by product, we show that empirical equilibrium
does not coincide with refinements based on approximation by monotone additive
randomly disturbed payoff models, and further our understanding of the
empirical content of these models.
Valuation of contingent convertible catastrophe bonds - the case for equity conversion
Date: 2018-04-21 00:00:00

Within the context of the banking-related literature on contingent
convertible bonds, we comprehensively formalise the design and features of a
relatively new type of insurance-linked security, called a contingent
convertible catastrophe bond (CocoCat). We begin with a discussion of its
design and compare its relative merits to catastrophe bonds and
catastrophe-equity puts. Subsequently, we derive analytical valuation formulae
for index-linked CocoCats under the assumption of independence between natural
catastrophe and financial markets risks. We model natural catastrophe losses by
a time-inhomogeneous compound Poisson process, with the interest-rate process
governed by the Longstaff model. By using an exponential change of measure on
the loss process, as well as a Girsanov-like transformation to synthetically
remove the correlation between the share and interest-rate processes, we obtain
these analytical formulae. Using selected parameter values in line with earlier
research, we empirically analyse our valuation formulae for index-linked
CocoCats. An analysis of the results reveals that the CocoCat prices are most
sensitive to changing interest-rates, conversion fractions and the threshold
levels defining the trigger times.
A Spherical Probability Distribution Model of the User-Induced Mobile Phone Orientation
Date: 2018-04-22 00:00:00

This paper presents a statistical modeling approach of the real-life
user-induced randomness due to mobile phone orientations for different phone
usage types. As well-known, the radiated performance of a wireless device
depends on its orientation and position relative to the user. Therefore,
realistic handset usage models will lead to more accurate Over-The-Air
characterization measurements for antennas and wireless devices in general. We
introduce a phone usage classification based on the network access modes, e.g.,
voice (circuit switched) or nonvoice (packet switched) services, and the use of
accessories such as wired or Bluetooth handsets, or a speaker-phone during the
network access session. The random phone orientation is then modelled by the
spherical von Mises-Fisher distribution for each of the identified phone usage
types. A finite mixture model based on the individual probability distribution
functions and heuristic weights is also presented. The models are based on data
collected from built-in accelerometer measurements. Our approach offers a
straightforward modeling of the user-induced random orientation for different
phone usage types. The models can be used in the design of better handsets and
antenna systems as well as for the design and optimization of wireless
networks.
Price Competition with Geometric Brownian motion in Exchange Rate Uncertainty
Date: 2018-04-22 00:00:00

We analyze an operational policy for a multinational manufacturer to hedge
against exchange rate uncertainties and competition. We consider a single
product and single period. Because of long-lead times, the capacity investment
must done before the selling season begins when the exchange rate between the
two countries is uncertain. we consider a duopoly competition in the foreign
country. We model the exchange rate as a random variable. We investigate the
impact of competition and exchange rate on optimal capacities and optimal
prices. We show how competition can impact the decision of the home
manufacturer to enter the foreign market.
Binding Pathway of Opiates to $\mu$ Opioid Receptors Revealed by Unsupervised Machine Learning
Date: 2018-04-23 00:00:00
Tasks: BIG-bench Machine Learning

Many important analgesics relieve pain by binding to the $\mu$-Opioid
Receptor ($\mu$OR), which makes the $\mu$OR among the most clinically relevant
proteins of the G Protein Coupled Receptor (GPCR) family. Despite previous
studies on the activation pathways of the GPCRs, the mechanism of opiate
binding and the selectivity of $\mu$OR are largely unknown. We performed
extensive molecular dynamics (MD) simulation and analysis to find the selective
allosteric binding sites of the $\mu$OR and the path opiates take to bind to
the orthosteric site. In this study, we predicted that the allosteric site is
responsible for the attraction and selection of opiates. Using Markov state
models and machine learning, we traced the pathway of opiates in binding to the
orthosteric site, the main binding pocket. Our results have important
implications in designing novel analgesics.
Econometric Modeling of Regional Electricity Spot Prices in the Australian Market
Date: 2018-04-23 00:00:00
Tasks: regression, Time Series, Time Series Analysis

Wholesale electricity markets are increasingly integrated via high voltage
interconnectors, and inter-regional trade in electricity is growing. To model
this, we consider a spatial equilibrium model of price formation, where
constraints on inter-regional flows result in three distinct equilibria in
prices. We use this to motivate an econometric model for the distribution of
observed electricity spot prices that captures many of their unique empirical
characteristics. The econometric model features supply and inter-regional trade
cost functions, which are estimated using Bayesian monotonic regression
smoothing methodology. A copula multivariate time series model is employed to
capture additional dependence -- both cross-sectional and serial-- in regional
prices. The marginal distributions are nonparametric, with means given by the
regression means. The model has the advantage of preserving the heavy
right-hand tail in the predictive densities of price. We fit the model to
half-hourly spot price data in the five interconnected regions of the
Australian national electricity market. The fitted model is then used to
measure how both supply and price shocks in one region are transmitted to the
distribution of prices in all regions in subsequent periods. Finally, to
validate our econometric model, we show that prices forecast using the proposed
model compare favorably with those from some benchmark alternatives.
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
Date: 2018-04-23 00:00:00
Tasks: Time Series, Time Series Analysis

Call centers' managers are interested in obtaining accurate point and
distributional forecasts of call arrivals in order to achieve an optimal
balance between service quality and operating costs. We present a strategy for
selecting forecast models of call arrivals which is based on three pillars: (i)
flexibility of the loss function; (ii) statistical evaluation of forecast
accuracy; (iii) economic evaluation of forecast performance using money
metrics. We implement fourteen time series models and seven forecast
combination schemes on three series of daily call arrivals. Although we focus
mainly on point forecasts, we also analyze density forecast evaluation. We show
that second moments modeling is important both for point and density
forecasting and that the simple Seasonal Random Walk model is always
outperformed by more general specifications. Our results suggest that call
center managers should invest in the use of forecast models which describe both
first and second moments of call arrivals.
Myotubularin MTM1 Involved in Centronuclear Myopathy and its Roles in Human and Yeast Cells
Date: 2018-04-23 00:00:00
Tasks: Specificity

Mutations in the MTM1 gene, encoding the phosphoinositide phosphatase
myotubularin, are responsible for the X-linked centronuclear myopathy (XLCNM)
or X-linked myotubular myopathy (XLMTM). The MTM1 gene was first identified in
1996 and its function as a PtdIns3P and PtdIns(,5)P2 phosphatase was discovered
in 2000. In recent years, very important progress has been made to set up good
models to study MTM1 and the XLCNM disease such as knockout or knockin mice,
the Labrador Retriever dog, the zebrafish and the yeast Saccharomyces
cerevisiae. These helped to better understand the cellular function of MTM1 and
of its four conserved domains: PH-GRAM (Pleckstrin
Homology-Glucosyltransferase, Rab-like GTPase Activator and Myotubularin), RID
(Rac1-Induced recruitment Domain), PTP/DSP (Protein Tyrosine
Phosphatase/Dual-Specificity Phosphatase) and SID (SET-protein Interaction
Domain). This review presents the cellular function of human myotubularin MTM1
and its yeast homolog yeast protein Ymr1, and the role of MTM1 in the
centronuclear myopathy (CNM) disease.