pwc-archive/papers-with-abstracts huggingface dataset:
Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices
Authors: Jozef Baruník, Matěj Nevrla
Date: 2018-06-15 00:00:00

This paper investigates how two important sources of risk -- market tail risk and extreme market volatility risk -- are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral beta representation of risk based on the decomposition of covariance between indicator functions that capture fluctuations over various frequencies. We study the asymptotic behavior of the proposed estimators of such risk. Empirically, we find that tail risk is a short-term phenomenon, whereas extreme volatility risk is priced by investors in the long term when pricing a cross-section of individual stocks. In addition, we study popular industry, size and value, profit, investment or book-to-market portfolios, as well as portfolios constructed from various asset classes, portfolios sorted on cash flow duration and other strategies. These results reveal that tail-dependent and horizon-specific risks are priced heterogeneously across datasets and are important sources of risk for investors.
High Performance Computing in Medical Image Analysis HuSSaR
Date: 2018-06-16 00:00:00
Tasks: CPU, GPU, Medical Image Analysis, Vocal Bursts Intensity Prediction

In our former works we have made serious efforts to improve the performance
of medical image analysis methods with using ensemble-based systems. In this
paper, we present a novel hardware-based solution for the efficient adoption of
our complex, fusion-based approaches for real-time applications. Even though
most of the image processing problems and the increasing amount of data have
high-performance computing(HPC) demand, there is still a lack of corresponding
dedicated HPC solutions for several medical tasks. To widen this bottleneck we
have developed a Hybrid Small Size high performance computing Resource
(abbreviated by HuSSaR) which efficiently alloys CPU and GPU technologies,
mobile and has an own cooling system to support easy mobility and wide
applicability. Besides a proper technical description, we include several
practical examples from the clinical data processing domain in this work. For
more details see also:
https://arato.inf.unideb.hu/kovacs.laszlo/research_hybridmicrohpc.html
Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence
Date: 2018-06-16 00:00:00
Tasks: Sensor Fusion

Tracking people in a video sequence is one of the fields of interest in
computer vision. It has broad applications in motion capture and surveillance.
However, due to the complexity of human dynamic structure, detecting and
tracking are not straightforward. Consequently, different detection and
tracking techniques with different applications and performance have been
developed. To minimize the noise between the prediction and measurement during
tracking, Kalman filter has been used as a filtering technique. At the same
time, in most cases, detection and tracking results from a single sensor is not
enough to detect and track a person. To avoid this problem, using a
multi-sensor fusion technique is indispensable. In this paper, a comparative
survey of detection, tracking and multi-sensor fusion methods are presented.
Linear stability analysis of morphodynamics during tissue regeneration in plants
Date: 2018-06-21 00:00:00

One of the key characteristics of multicellular organisms is the ability to
establish and maintain shapes, or morphologies, under a variety of physical and
chemical perturbations. A quantitative description of the underlying
morphological dynamics is a critical step to fully understand the
self-organising properties of multicellular systems. Although many powerful
mathematical tools have been developed to analyse stochastic dynamics, rarely
these are applied to experimental developmental biology. Here, we take root tip
regeneration in the plant model system Arabidopsis thaliana as an example of
robust morphogenesis in living tissue and present a novel approach to quantify
and model the relaxation of the system to its unperturbed morphology. By
generating and analysing time-lapse series of regenerating root tips captured
with confocal microscopy, we are able to extract and model the dynamics of key
morphological traits at cellular resolution. We present a linear stability
analysis of its Markovian dynamics, with the stationary state representing the
intact root in the space of morphological traits. We find that the resulting
eigenvalues can be classified into two groups, suggesting the co-existence of
two distinct temporal scales during the process of regeneration. We discuss the
possible biological implications of our specific results and suggest future
experiments to further probe the self-organising properties of living tissue.
Rapid micro fluorescence in situ hybridization in tissue sections
Date: 2018-06-17 00:00:00
Tasks: Diagnostic

This paper describes a micro fluorescence in situ hybridization
({\mu}FISH)-based rapid detection of cytogenetic biomarkers on formalin-fixed
paraffin embedded (FFPE) tissue sections. We demonstrated this method in the
context of detecting human epidermal growth factor 2 (HER2) in breast tissue
sections. This method uses a non-contact microfluidic scanning probe (MFP),
which localizes FISH probes at the micrometer length-scale to selected cells of
the tissue section. The scanning ability of the MFP allows for a versatile
implementation of FISH on tissue sections. We demonstrated the use of
oligonucleotide FISH probes in ethylene carbonate-based buffer enabling rapid
hybridization within < 1 min for chromosome enumeration and 10-15 min for
assessment of the HER2 status in FFPE sections. We further demonstrated
recycling of FISH probes for multiple sequential tests using a defined volume
of probes by forming hierarchical hydrodynamic flow confinements. This
microscale method is compatible with the standard FISH protocols and with the
Instant Quality (IQ) FISH assay, reduces the FISH probe consumption ~100-fold
and the hybridization time 4-fold, resulting in an assay turnaround time of < 3
h. We believe rapid {\mu}FISH has the potential of being used in pathology
workflows as a standalone method or in combination with other molecular methods
for diagnostic and prognostic analysis of FFPE sections.
Discrete structure of the brain rhythms
Date: 2018-06-17 00:00:00

Neuronal activity in the brain generates synchronous oscillations of the
Local Field Potential (LFP). The traditional analyses of the LFPs are based on
decomposing the signal into simpler components, such as sinusoidal harmonics.
However, a common drawback of such methods is that the decomposition primitives
are usually presumed from the onset, which may bias our understanding of the
signal's structure. Here, we introduce an alternative approach that allows an
impartial, high resolution, hands-off decomposition of the brain waves into a
small number of discrete, frequency-modulated oscillatory processes, which we
call oscillons. In particular, we demonstrate that mouse hippocampal LFP
contain a single oscillon that occupies the $\theta$-frequency band and a
couple of $\gamma$-oscillons that correspond, respectively, to slow and fast
$\gamma$-waves. Since the oscillons were identified empirically, they may
represent the actual, physical structure of synchronous oscillations in
neuronal ensembles, whereas Fourier-defined "brain waves" are nothing but
poorly resolved oscillons.
Designing diagnostic platforms for analysis of disease patterns and probing disease emergence
Authors: Ali Rohani
Date: 2018-06-17 00:00:00
Tasks: Diagnostic

The emerging era of personalized medicine relies on medical decisions, practices, and products being tailored to the individual patient. Point-of-care systems, at the heart of this model, play two important roles. First, they are required for identifying subjects for optimal therapies based on their genetic make-up and epigenetic profile. Second, they will be used for assessing the progression of such therapies. Central to this vision is designing systems that, with minimal user-intervention, can transduce complex signals from biosystems in complement with clinical information to inform medical decision within point-of-care settings. To reach our ultimate goal of developing point-of-care systems and realizing personalized medicine, we are taking a multistep systems-level approach towards understanding cellular processes and biomolecular profiles, to quantify disease states and external interventions.
Sigmoid function based intensity transformation for parameter initialization in MRI-PET Registration Tool for Preclinical Studies
Date: 2018-06-18 00:00:00

Images from Positron Emission Tomography (PET) deliver functional data such
as perfusion and metabolism. On the other hand, images from Magnetic Resonance
Imaging (MRI) provide information describing anatomical structures. Fusing the
complementary information from the two modalities is helpful in oncology. In
this project, we implemented a complete tool allowing semi-automatic MRI-PET
registration for small animal imaging in the preclinical studies. A two-stage
hierarchical registration approach is proposed. First, a global affine
registration is applied. For robust and fast registration, principal component
analysis (PCA) is used to compute the initial parameters for the global affine
registration. Since, only the low intensities in the PET volume reveal the
anatomic information on the MRI scan, we proposed a non-uniform intensity
transformation to the PET volume to enhance the contrast of the low intensity.
This helps to improve the computation of the centroid and principal axis by
increasing the contribution of the low intensities. Then, the globally
registered image is given as input to the second stage which is a local
deformable registration (B-spline registration). Mutual information is used as
metric function for the optimization. A multi-resolution approach is used in
both stages. The registration algorithm is supported by graphical user
interface (GUI) and visualization methods so that the user can interact easily
with the process. The performance of the registration algorithm is validated by
two medical experts on seven different datasets on abdominal and brain areas
including noisy and difficult image volumes.
Modeling Musical Taste Evolution with Recurrent Neural Networks
Authors: Quadrana Massimo, Reznakova Marta, Ye Tao, Schmidt Erik, Vahabi Hossein
Date: 2018-06-18 00:00:00

Finding the music of the moment can often be a challenging problem, even for
well-versed music listeners. Musical tastes are constantly in flux, and the
problem of developing computational models for musical taste dynamics presents
a rich and nebulous problem space. A variety of factors all play some role in
determining preferences (e.g., popularity, musicological, social, geographical,
generational), and these factors vary across different listeners and contexts.
In this paper, we leverage a massive dataset on internet radio station creation
from a large music streaming company in order to develop computational models
of listener taste evolution. We delve deep into the complexities of this
domain, identifying some of the unique challenges that it presents, and develop
a model utilizing recurrent neural networks. We apply our model to the problem
of next station prediction and show that it not only outperforms several
baselines, but excels at long tail music personalization, particularly by
learning the long-term dependency structure of listener music preference
evolution.
Dense People Counting Using IR-UWB Radar with a Hybrid Feature Extraction Method
Date: 2018-06-18 00:00:00

People counting is one of the hottest issues in sensing applications. The
impulse radio ultra-wideband (IR-UWB) radar has been extensively applied to
count people, providing a device-free solution without illumination and privacy
concerns. However, performance of current solutions is limited in congested
environments due to the superposition and obstruction of signals. In this
letter, a hybrid feature extraction method based on curvelet transform and
distance bin is proposed. 2-D radar matrix features are extracted in multiple
scales and multiple angles by applying the curvelet transform. Furthermore, the
distance bin is introduced by dividing each row of the matrix into several bins
along the propagating distance to select features. The radar signal dataset in
three dense scenarios is constructed, including people randomly walking in the
constrained area with densities of 3 and 4 persons per square meter, and
queueing with an average distance of 10 centimeters. The number of people is up
to 20 in the dataset. Four classifiers including decision tree, AdaBoost,
random forest and neural network are compared to validate the hybrid features,
and random forest performs the highest accuracies of all above 97% in three
dense scenarios. Moreover, to ensure the reliability of the hybrid features,
three other features including cluster features, activity features and CNN
features are compared. The experimental results reveal that the proposed hybrid
feature extraction method exhibits stable performance with significantly
superior effectiveness.
Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks
Date: 2018-06-18 00:00:00

Correlation networks were used to detect characteristics which, although
fixed over time, have an important influence on the evolution of prices over
time. Potentially important features were identified using the websites and
whitepapers of cryptocurrencies with the largest userbases. These were assessed
using two datasets to enhance robustness: one with fourteen cryptocurrencies
beginning from 9 November 2017, and a subset with nine cryptocurrencies
starting 9 September 2016, both ending 6 March 2018. Separately analysing the
subset of cryptocurrencies raised the number of data points from 115 to 537,
and improved robustness to changes in relationships over time. Excluding USD
Tether, the results showed a positive association between different
cryptocurrencies that was statistically significant. Robust, strong positive
associations were observed for six cryptocurrencies where one was a fork of the
other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the
existence of a group of cryptocurrencies particularly associated with Cardano,
and a separate group correlated with Ethereum. The data was not consistent with
a token's functionality or creation mechanism being the dominant determinants
of the evolution of prices over time but did suggest that factors other than
speculation contributed to the price.
Adaptive transmission for radar arrays using Weiss-Weinstein bounds
Date: 2018-08-14 00:00:00

We present an algorithm for adaptive selection of pulse repetition frequency
or antenna activations for Doppler and DoA estimation. The adaptation is
performed sequentially using a Bayesian filter, responsible for updating the
belief on parameters, and a controller, responsible for selecting transmission
variables for the next measurement by optimizing a prediction of the estimation
error. This selection optimizes the Weiss-Weinstein bound for a
multi-dimensional frequency estimation model based on array measurements of a
narrow-band far-field source. A particle filter implements the update of the
posterior distribution after each new measurement is taken, and this posterior
is further approximated by a Gaussian or a uniform distribution for which
computationally fast expressions of the Weiss-Weinstein bound are analytically
derived. We characterize the controller's optimal choices in terms of SNR and
variance of the current belief, discussing their properties in terms of the
ambiguity function and comparing them with optimal choices of other
Weiss-Weinstein bound constructions in the literature. The resulting algorithms
are analyzed in simulations where we showcase a practically feasible real-time
evaluation based on look-up tables or small neural networks trained off-line.
The Origin and the Resolution of Nonuniqueness in Linear Rational Expectations
Date: 2019-04-29 00:00:00

The nonuniqueness of rational expectations is explained: in the stochastic,
discrete-time, linear, constant-coefficients case, the associated free
parameters are coefficients that determine the public's most immediate
reactions to shocks. The requirement of model-consistency may leave these
parameters completely free, yet when their values are appropriately specified,
a unique solution is determined. In a broad class of models, the requirement of
least-square forecast errors determines the parameter values, and therefore
defines a unique solution. This approach is independent of dynamical stability,
and generally does not suppress model dynamics.
  Application to a standard New Keynesian example shows that the traditional
solution suppresses precisely those dynamics that arise from rational
expectations. The uncovering of those dynamics reveals their incompatibility
with the new I-S equation and the expectational Phillips curve.
Theory of chemical evolution of molecule compositions in the universe, in the Miller-Urey experiment and the mass distribution of interstellar and intergalactic molecules
Date: 2019-11-30 00:00:00

Chemical evolution is essential in understanding the origins of life. We
present a theory for the evolution of molecule masses and show that small
molecules grow by random diffusion and large molecules by a preferential
attachment process leading eventually to life's molecules. It reproduces
correctly the distribution of molecules found via mass spectroscopy for the
Murchison meteorite and estimates the start of chemical evolution back to 12.8
billion years following the birth of stars and supernovae. From the Frontier
mass between the random and preferential attachment dynamics the birth time of
molecule families can be estimated. Amino acids emerge about 165 million years
after chemical elements emerge in stars. Using the scaling of reaction rates
with the distance of the molecules in space we recover correctly the few days
emergence time of amino acids in the Miller-Urey experiment. The distribution
of interstellar and extragalactic molecules are both consistent with the
evolutionary mass distribution, and their age is estimated to 108 and 65
million years after the start of evolution. From the model, we can determine
the number of different molecule compositions at the time of the emergence of
Earth to be 1.6 million and the number of molecule compositions in interstellar
space to a mere 719 species.
A Weighted Superposition of Functional Contours Model for Modelling Contextual Prominence of Elementary Prosodic Contours
Date: 2018-06-18 00:00:00

The way speech prosody encodes linguistic, paralinguistic and non-linguistic
information via multiparametric representations of the speech signals is still
an open issue. The Superposition of Functional Contours (SFC) model proposes to
decompose prosody into elementary multiparametric functional contours through
the iterative training of neural network contour generators using
analysis-by-synthesis. Each generator is responsible for computing
multiparametric contours that encode one given linguistic, paralinguistic and
non-linguistic information on a variable scope of rhythmic units. The
contributions of all generators' outputs are then overlapped and added to
produce the prosody of the utterance. We propose an extension of the contour
generators that allows them to model the prominence of the elementary contours
based on contextual information. WSFC jointly learns the patterns of the
elementary multiparametric functional contours and their weights dependent on
the contours' contexts. The experimental results show that the proposed
weighted SFC (WSFC) model can successfully capture contour prominence and thus
improve SFC modelling performance. The WSFC is also shown to be effective at
modelling the impact of attitudes on the prominence of functional contours
cuing syntactic relations in French, and that of emphasis on the prominence of
tone contours in Chinese.
Long-time large deviations for the multi-asset Wishart stochastic volatility model and option pricing
Date: 2018-06-18 00:00:00

We prove a large deviations principle for the class of multidimensional
affine stochastic volatility models considered in (Gourieroux, C. and Sufana,
R., J. Bus. Econ. Stat., 28(3), 2010), where the volatility matrix is modelled
by a Wishart process. This class extends the very popular Heston model to the
multivariate setting, thus allowing to model the joint behaviour of a basket of
stocks or several interest rates. We then use the large deviation principle to
obtain an asymptotic approximation for the implied volatility of basket options
and to develop an asymptotically optimal importance sampling algorithm, to
reduce the number of simulations when using Monte-Carlo methods to price
derivatives.
Distributional Advantage Actor-Critic
Authors: Li Shangda, Bing Selina, Yang Steven
Date: 2018-06-10 00:00:00
Tasks: Q-Learning, quantile regression

In traditional reinforcement learning, an agent maximizes the reward
collected during its interaction with the environment by approximating the
optimal policy through the estimation of value functions. Typically, given a
state s and action a, the corresponding value is the expected discounted sum of
rewards. The optimal action is then chosen to be the action a with the largest
value estimated by value function. However, recent developments have shown both
theoretical and experimental evidence of superior performance when value
function is replaced with value distribution in context of deep Q learning [1].
In this paper, we develop a new algorithm that combines advantage actor-critic
with value distribution estimated by quantile regression. We evaluated this new
algorithm, termed Distributional Advantage Actor-Critic (DA2C or QR-A2C) on a
variety of tasks, and observed it to achieve at least as good as baseline
algorithms, and outperforming baseline in some tasks with smaller variance and
increased stability.
Emergence of Metachronal Waves in Active Microtubule Arrays
Authors: Stephen E Martin, Matthew E Brunner, Joshua M Deutsch
Date: 2018-06-19 00:00:00

The physical mechanism behind the spontaneous formation of metachronal waves in microtubule arrays in a low Reynolds number fluid has been of interest for the past several years, yet is still not well understood. We present a model implementing the hydrodynamic coupling hypothesis from first principles, and use this model to simulate kinesin-driven microtubule arrays and observe their emergent behavior. The results of simulations are compared to known experimental observations by Sanchez et al. By varying parameters, we determine regimes in which the metachronal wave phenomenon emerges, and categorize other types of possible microtubule motion outside these regimes.