pwc-archive/papers-with-abstracts huggingface dataset:
A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function
Date: 2018-11-19 00:00:00

Heart failure is a major cause for premature death. Given heterogeneity of
the heart failure syndrome, identifying genetic determinants of cardiac
function and structure may provide greater insights into heart failure. Despite
progress in understanding the genetic basis of heart failure through genome
wide association studies, heritability of heart failure is not well understood.
Gaining further insights into mechanisms that contribute to heart failure
requires systematic approaches that go beyond single trait analysis. We
integrated Bayesian multi-trait approach and Bayesian networks for the analysis
of 10 correlated traits of cardiac structure and function measured for 3387
individuals with whole exome sequence data. While using single-trait based
approaches did not find any significant genetic variant, applying the
integrative Bayesian multi-trait approach, we identified 3 novel variants
located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac
traits such as left ventricular volume index, parasternal long axis
interventricular septum thickness, and mean left ventricular wall thickness.
Among these, the rare variant NC_000009.11:g.116346115C>A (rs144636307) in RGS3
showed pleiotropic effect on left ventricular mass index, left ventricular
volume index and Maximum left atrial anterior-posterior diameter while RGS3 can
inhibit TGF-beta signaling associated with left ventricle dilation and systolic
dysfunction.
Cryptoasset Factor Models
Authors: Zura Kakushadze
Date: 2018-11-19 00:00:00

We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In "cryptoassets" we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market data is available. Based on our empirical analysis, we identify the leading factor that appears to strongly contribute into daily cryptoasset returns. Our results suggest that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting.
Automated Pain Detection from Facial Expressions using FACS: A Review
Authors: Chen Zhanli, Ansari Rashid, Wilkie Diana
Date: 2018-11-13 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), valid

Facial pain expression is an important modality for assessing pain,
especially when the patient's verbal ability to communicate is impaired. The
facial muscle-based action units (AUs), which are defined by the Facial Action
Coding System (FACS), have been widely studied and are highly reliable as a
method for detecting facial expressions (FE) including valid detection of pain.
Unfortunately, FACS coding by humans is a very time-consuming task that makes
its clinical use prohibitive. Significant progress on automated facial
expression recognition (AFER) has led to its numerous successful applications
in FACS-based affective computing problems. However, only a handful of studies
have been reported on automated pain detection (APD), and its application in
clinical settings is still far from a reality. In this paper, we review the
progress in research that has contributed to automated pain detection, with
focus on 1) the framework-level similarity between spontaneous AFER and APD
problems; 2) the evolution of system design including the recent development of
deep learning methods; 3) the strategies and considerations in developing a
FACS-based pain detection framework from existing research; and 4) introduction
of the most relevant databases that are available for AFER and APD studies. We
attempt to present key considerations in extending a general AFER framework to
an APD framework in clinical settings. In addition, the performance metrics are
also highlighted in evaluating an AFER or an APD system.
Free-Space Optical Communications with Detector Arrays
Date: 2018-11-19 00:00:00

Detector arrays are commonly used for free-space optical communications in
deep space. Such detector arrays---by virtue of their size---help in the
collection of the optical signal even when there is some misalignment between
the transmitter and receiver systems. In this paper, we argue that for the
common Gaussian beam profile, a detector array receiver is more useful for
minimizing the probability of error than a single detector receiver of the same
dimensions. Furthermore, the improvement in the error probability is more
pronounced for low signal-to-noise ratio conditions, and the probability of
error decreases monotonically as a function of the number of detectors in the
array. However, communication with detector arrays results in a larger
computational complexity at the receiver. Additionally, such detector arrays
are also more advantageous for beam position tracking on the detector array in
order to minimize the pointing loss.
A Smart System for Selection of Optimal Product Images in E-Commerce
Authors: Chaudhuri Abon, Messina Paolo, Kokkula Samrat, Subramanian Aditya, Krishnan Abhinandan, Gandhi Shreyansh, Magnani Alessandro, Kandaswamy Venkatesh
Date: 2018-11-12 00:00:00
Tasks: BIG-bench Machine Learning, image-classification, Image Classification, Management

In e-commerce, content quality of the product catalog plays a key role in
delivering a satisfactory experience to the customers. In particular, visual
content such as product images influences customers' engagement and purchase
decisions. With the rapid growth of e-commerce and the advent of artificial
intelligence, traditional content management systems are giving way to
automated scalable systems. In this paper, we present a machine learning driven
visual content management system for extremely large e-commerce catalogs. For a
given product, the system aggregates images from various suppliers, understands
and analyzes them to produce a superior image set with optimal image count and
quality, and arranges them in an order tailored to the demands of the
customers. The system makes use of an array of technologies, ranging from deep
learning to traditional computer vision, at different stages of analysis. In
this paper, we outline how the system works and discuss the unique challenges
related to applying machine learning techniques to real-world data from
e-commerce domain. We emphasize how we tune state-of-the-art image
classification techniques to develop solutions custom made for a massive,
diverse, and constantly evolving product catalog. We also provide the details
of how we measure the system's impact on various customer engagement metrics.
Generating a Training Dataset for Land Cover Classification to Advance Global Development
Authors: Nachmany Yoni, Alemohammad Hamed
Date: 2018-11-14 00:00:00
Tasks: General Classification, Land Cover Classification, Semantic Segmentation, valid

Semantic segmentation of land cover classes is fundamental for agricultural
and economic development work, from sustainable forestry to urban planning, yet
existing training datasets have significant limitations. To generate an open
and comprehensive training library of high resolution Earth imagery and high
quality land cover classifications, public Sentinel-2 data at 10 m spatial
resolution was matched with accurate GlobeLand30 labels from 2010, which were
filtered by agreement with an intermediary Sentinel-2 classification at 20 m
produced during atmospheric correction. Scene-level classifications were
predicted by Random Forests trained on valid reflectance data and the filtered
labels, and achieved over 80% model accuracy for a variety of locations.
Further work is required to aggregate individual scene classifications for
annual labels and to test the approach in more locations, before crowdsourcing
human validation. The goal is to create a sustained community-wide effort to
generate image labels not only for land cover, but also very specific images
for major agriculture crops across the world and other thematic categories of
interest to the global development community.
Photorealistic Facial Synthesis in the Dimensional Affect Space
Authors: Kollias Dimitrios, Cheng Shiyang, Pantic Maja, Zafeiriou Stefanos
Date: 2018-11-11 00:00:00

This paper presents a novel approach for synthesizing facial affect, which is
based on our annotating 600,000 frames of the 4DFAB database in terms of
valence and arousal. The input of this approach is a pair of these emotional
state descriptors and a neutral 2D image of a person to whom the corresponding
affect will be synthesized. Given this target pair, a set of 3D facial meshes
is selected, which is used to build a blendshape model and generate the new
facial affect. To synthesize the affect on the 2D neutral image, 3DMM fitting
is performed and the reconstructed face is deformed to generate the target
facial expressions. Last, the new face is rendered into the original image.
Both qualitative and quantitative experimental studies illustrate the
generation of realistic images, when the neutral image is sampled from a
variety of well known databases, such as the Aff-Wild, AFEW, Multi-PIE,
AFEW-VA, BU-3DFE, Bosphorus.
Non-invasive thermal comfort perception based on subtleness magnification and deep learning for energy efficiency
Authors: Cheng Xiaogang, Yang Bin, Hedman Anders, Olofsson Thomas, Li Haibo, Van Gool Luc
Date: 2018-11-12 00:00:00

Human thermal comfort measurement plays a critical role in giving feedback
signals for building energy efficiency. A non-invasive measuring method based
on subtleness magnification and deep learning (NIDL) was designed to achieve a
comfortable, energy efficient built environment. The method relies on skin
feature data, e.g., subtle motion and texture variation, and a 315-layer deep
neural network for constructing the relationship between skin features and skin
temperature. A physiological experiment was conducted for collecting feature
data (1.44 million) and algorithm validation. The non-invasive measurement
algorithm based on a partly-personalized saturation temperature model (NIPST)
was used for algorithm performance comparisons. The results show that the mean
error and median error of the NIDL are 0.4834 Celsius and 0.3464 Celsius which
is equivalent to accuracy improvements of 16.28% and 4.28%, respectively.
Simulating single-coil MRI from the responses of multiple coils
Date: 2019-05-27 00:00:00
Tasks: Sensitivity

We convert the information-rich measurements of parallel and phased-array MRI
into noisier data that a corresponding single-coil scanner could have taken.
Specifically, we replace the responses from multiple receivers with a linear
combination that emulates the response from only a single, aggregate receiver,
replete with the low signal-to-noise ratio and phase problems of any single one
of the original receivers (combining several receivers is necessary, however,
since the original receivers usually have limited spatial sensitivity). This
enables experimentation in the simpler context of a single-coil scanner prior
to development of algorithms for the full complexity of multiple receiver
coils.
Arbitrage Opportunities in CDS Term Structure: Theory and Implications for OTC Derivatives
Authors: Raymond Brummelhuis, Zhongmin Luo
Date: 2018-11-20 00:00:00
Tasks: Management

Absence-of-Arbitrage (AoA) is the basic assumption underpinning derivatives pricing theory. As part of the OTC derivatives market, the CDS market not only provides a vehicle for participants to hedge and speculate on the default risks of corporate and sovereign entities, it also reveals important market-implied default-risk information concerning the counterparties with which financial institutions trade, and for which these financial institutions have to calculate various valuation adjustments (collectively referred to as XVA) as part of their pricing and risk management of OTC derivatives, to account for counterparty default risks. In this study, we derive No-arbitrage conditions for CDS term structures, first in a positive interest rate environment and then in an arbitrary one. Using an extensive CDS dataset which covers the 2007-09 financial crisis, we present a catalogue of 2,416 pairs of anomalous CDS contracts which violate the above conditions. Finally, we show in an example that such anomalies in the CDS term structure can lead to persistent arbitrage profits and to nonsensical default probabilities. The paper is a first systematic study on CDS-term-structure arbitrage providing model-free AoA conditions supported by ample empirical evidence.
Modeling aggressive market order placements with Hawkes factor models
Date: 2018-11-20 00:00:00
Tasks: Clustering

Price changes are induced by aggressive market orders in stock market. We
introduce a bivariate marked Hawkes process to model aggressive market order
arrivals at the microstructural level. The order arrival intensity is marked by
an exogenous part and two endogenous processes reflecting the self-excitation
and cross-excitation respectively. We calibrate the model for an SSE stock. We
find that the exponential kernel with a smooth cut-off (i.e. the subtraction of
two exponentials) produces much better calibration than the monotonous
exponential kernel (i.e. the sum of two exponentials). The exogenous baseline
intensity explains the $U$-shaped intraday pattern. Our empirical results show
that the endogenous submission clustering is mainly caused by self-excitation
rather than cross-excitation.
Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity
Date: 2018-11-20 00:00:00
Tasks: Bayesian Inference

In this study, Bayesian inference is developed for structural vector
autoregressive models in which the structural parameters are identified via
Markov-switching heteroskedasticity. In such a model, restrictions that are
just-identifying in the homoskedastic case, become over-identifying and can be
tested. A set of parametric restrictions is derived under which the structural
matrix is globally or partially identified and a Savage-Dickey density ratio is
used to assess the validity of the identification conditions. The latter is
facilitated by analytical derivations that make the computations fast and
numerical standard errors small. As an empirical example, monetary models are
compared using heteroskedasticity as an additional device for identification.
The empirical results support models with money in the interest rate reaction
function.
Attentive Neural Architecture Incorporating Song Features For Music Recommendation
Authors: Sachdeva Noveen, Gupta Kartik, Pudi Vikram
Date: 2018-11-20 00:00:00
Tasks: Future prediction, Music Recommendation, Recommendation Systems

Recommender Systems are an integral part of music sharing platforms. Often
the aim of these systems is to increase the time, the user spends on the
platform and hence having a high commercial value. The systems which aim at
increasing the average time a user spends on the platform often need to
recommend songs which the user might want to listen to next at each point in
time. This is different from recommendation systems which try to predict the
item which might be of interest to the user at some point in the user lifetime
but not necessarily in the very near future. Prediction of the next song the
user might like requires some kind of modeling of the user interests at the
given point of time. Attentive neural networks have been exploiting the
sequence in which the items were selected by the user to model the implicit
short-term interests of the user for the task of next item prediction, however
we feel that the features of the songs occurring in the sequence could also
convey some important information about the short-term user interest which only
the items cannot. In this direction, we propose a novel attentive neural
architecture which in addition to the sequence of items selected by the user,
uses the features of these items to better learn the user short-term
preferences and recommend the next song to the user.
An updated review of (sub-)optimal diversification models
Date: 2018-11-20 00:00:00

In the past decade many researchers have proposed new optimal portfolio
selection strategies to show that sophisticated diversification can outperform
the na\"ive 1/N strategy in out-of-sample benchmarks. Providing an updated
review of these models since DeMiguel et al. (2009b), I test sixteen strategies
across six empirical datasets to see if indeed progress has been made. However,
I find that none of the recently suggested strategies consistently outperforms
the 1/N or minimum-variance approach in terms of Sharpe ratio,
certainty-equivalent return or turnover. This suggests that simple
diversification rules are not in fact inefficient, and gains promised by
optimal portfolio choice remain unattainable out-of-sample due to large
estimation errors in expected returns. Therefore, further research effort
should be devoted to both improving estimation of expected returns, and
possibly exploring diversification rules that do not require the estimation of
expected returns directly, but also use other available information about the
stock characteristics.
Study of Multi-Step Knowledge-Aided Iterative Nested MUSIC for Direction Finding
Date: 2018-11-19 00:00:00

In this work, we propose a subspace-based algorithm for direction-of-arrival
(DOA) estimation applied to the signals impinging on a two-level nested array,
referred to as multi-step knowledge-aided iterative nested MUSIC method
(MS-KAI-Nested-MUSIC), which significantly improves the accuracy of the
original Nested-MUSIC. Differently from existing knowledge-aided methods
applied to uniform linear arrays (ULAs), which make use of available known DOAs
to improve the estimation of the covariance matrix of the input data, the
proposed Multi-Step KAI-Nested-MU employs knowledge of the structure of the
augmented sample covariance matrix, which is obtained by exploiting the
difference co-array structure covariance matrix, and its perturbation terms and
the gradual incorporation of prior knowledge, which is obtained on line. The
effectiveness of the proposed technique can be noticed by simulations focusing
on uncorrelated closely-spaced sources.
Economics of disagreement -- financial intuition for the Rényi divergence
Authors: Andrei N. Soklakov
Date: 2018-11-20 00:00:00

Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap which is often perceived as a lack of practical intuition regarding probabilistic and statistical concepts. Take for instance the R\'enyi divergence which is a well-known statistical quantity specifically designed as a measure of disagreement between probabilistic models. Despite its widespread use in science and engineering, the R\'enyi divergence remains a highly abstract axiomatically-motivated measure. Certainly, it offers no practical insight as to how disagreements can be resolved. Here we propose to address disagreements using the methods of financial economics. In particular, we show how a large class of disagreements can be transformed into investment opportunities. The expected financial performance of such investments quantifies the amount of disagreement in a tangible way. This provides intuition for statistical concepts such as the R\'enyi divergence which becomes connected to the financial performance of optimized investments. Investment optimization takes into account individual opinions as well as attitudes towards risk. The result is a market-like social mechanism by which funds flow naturally to support a more accurate view. Such social mechanisms can help us with difficult disagreements (e.g., financial arguments concerning the future climate). In terms of scientific validation, we used the findings of independent neurophysiological experiments as well as our own research on the equity premium.
Identifiability from a few species for a class of biochemical reaction networks
Date: 2019-04-25 00:00:00

Under mass-action kinetics, biochemical reaction networks give rise to
polynomial autonomous dynamical systems whose parameters are often difficult to
estimate. We deal in this paper with the problem of identifying the kinetic
parameters of a class of biochemical networks which are abundant, such as
multisite phosphorylation systems and phosphorylation cascades (for example,
MAPK cascades). For any system of this class we explicitly exhibit a single
species for each connected component of the associated digraph such that the
successive total derivatives of its concentration allow us to identify all the
parameters occurring in the component. The number of derivatives needed is
bounded essentially by the length of the corresponding connected component of
the digraph. Moreover, in the particular case of the cascades, we show that the
parameters can be identified from a bounded number of successive derivatives of
the last product of the last layer. This theoretical result induces also a
heuristic interpolation-based identifiability procedure to recover the values
of the rate constants from exact measurements.
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification
Date: 2018-11-20 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Transfer Learning

Recently, deep neural networks (DNNs) have been the subject of intense
research for the classification of radio frequency (RF) signals, such as
synthetic aperture radar (SAR) imagery or micro-Doppler signatures. However, a
fundamental challenge is the typically small amount of data available due to
the high costs and resources required for measurements. Small datasets limit
the depth of DNNs implementable, and limit performance. In this work, a novel
method for generating diversified radar micro-Doppler signatures using
Kinect-based motion capture simulations is proposed as a training database for
transfer learning with DNNs. In particular, it is shown that together with
residual learning, the proposed DivNet approach allows for the construction of
deeper neural networks and offers improved performance in comparison to
transfer learning from optical imagery. Furthermore, it is shown that
initializing the network using diversified synthetic micro-Doppler signatures
enables not only robust performance for previously unseen target profiles, but
also class generalization. Results are presented for 7-class and 11-class human
activity recognition scenarios using a 4-GHz continuous wave (CW)
software-defined radar.
An analysis of cryptocurrencies conditional cross correlations
Date: 2019-02-26 00:00:00
Tasks: Management

This letter explores the behavior of conditional correlations among main
cryptocurrencies, stock and bond indices, and gold, using a generalized DCC
class model. From a portfolio management point of view, asset correlation is a
key metric in order to construct efficient portfolios. We find that: (i)
correlations among cryptocurrencies are positive, albeit varying across time;
(ii) correlations with Monero are more stable across time; (iii) correlations
between cryptocurrencies and traditional financial assets are negligible.