pwc-archive/papers-with-abstracts huggingface dataset:
The effects of heterogeneity on stochastic cycles in epidemics
Date: 2017-05-12 00:00:00

Models of biological processes are often subject to different sources of
noise. Developing an understanding of the combined effects of different types
of uncertainty is an open challenge. In this paper, we study a variant of the
susceptible-infective-recovered model of epidemic spread, which combines both
agent-to-agent heterogeneity and intrinsic noise. We focus on epidemic cycles,
driven by the stochasticity of infection and recovery events, and study in
detail how heterogeneity in susceptibilities and propensities to pass on the
disease affects these quasi-cycles. While the system can only be described by a
large hierarchical set of equations in the transient regime, we derive a
reduced closed set of equations for population-level quantities in the
stationary regime. We analytically obtain the spectra of quasi-cycles in the
linear-noise approximation. We find that the characteristic frequency of these
cycles is typically determined by population averages of susceptibilities and
infectivities, but that their amplitude depends on higher-order moments of the
heterogeneity. We also investigate the synchronisation properties and phase lag
between different groups of susceptible and infected individuals.
Murphy Diagrams: Forecast Evaluation of Expected Shortfall
Date: 2017-05-12 00:00:00

Motivated by the Basel 3 regulations, recent studies have considered joint
forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring
functions can be used to evaluate forecast performance in this context.
However, little intuitive or empirical guidance is currently available, which
renders the choice of scoring function awkward in practice. We therefore
develop graphical checks (Murphy diagrams) of whether one forecast method
dominates another under a relevant class of scoring functions, and propose an
associated hypothesis test. We illustrate these tools with simulation examples
and an empirical analysis of S&P 500 and DAX returns.
Concussion classification via deep learning using whole-brain white matter fiber strains
Date: 2018-01-15 00:00:00
Tasks: Deep Learning, Injury Prediction

Developing an accurate and reliable injury predictor is central to the
biomechanical studies of traumatic brain injury. State-of-the-art efforts
continue to rely on empirical, scalar metrics based on kinematics or
model-estimated tissue responses explicitly pre-defined in a specific brain
region of interest. They could suffer from loss of information. A single
training dataset has also been used to evaluate performance but without
cross-validation. In this study, we developed a deep learning approach for
concussion classification using implicit features of the entire voxel-wise
white matter fiber strains. Using reconstructed American National Football
League (NFL) injury cases, leave-one-out cross-validation was employed to
objectively compare injury prediction performances against two baseline machine
learning classifiers (support vector machine (SVM) and random forest (RF)) and
four scalar metrics via univariate logistic regression (Brain Injury Criterion
(BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the
corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep
learning and machine learning classifiers consistently outperformed all scalar
injury metrics across all performance categories in cross-validation (e.g.,
average accuracy of 0.844 vs. 0.746, and average area under the receiver
operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing
dataset). Nevertheless, deep learning achieved the best cross-validation
accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for
SVM and RF, respectively). These findings demonstrate the superior performances
of deep learning in concussion prediction, and suggest its promise for future
applications in biomechanical investigations of traumatic brain injury.
Parametric Imaging of FDG-PET Data Using Physiology and Iterative Regularization: Application to the Hepatic and Renal Systems
Date: 2017-05-11 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The present paper proposes a novel computational method for parametric
imaging of nuclear medicine data. The mathematical procedure is general enough
to work for compartmental models of diverse complexity and is effective in the
determination of the parametric maps of all kinetic parameters governing tracer
flow. We consider applications to [18F]-fluorodeoxyglucose Positron Emission
Tomography (FDG-PET) data and analyze the two-compartment catenary model
describing the standard FDG metabolization by an homogeneous tissue, e.g. the
liver, and the three-compartment non-catenary model representing the renal
physiology. The proposed imaging method starts from the reconstructed FDG-PET
images of tracer concentration and preliminarily applies image processing
algorithms for noise reduction and image segmentation processes for selecting
the region enclosing the organ of physiologic interest. The optimization scheme
solves pixelwise the non-linear inverse problem of determining the kinetic
parameters from dynamic concentration data through a Gauss-Newton iterative
algorithm with a penalty term accounting for the ill-posedness of the problem.
We tested our imaging approach on FDG-PET data of murine models obtained by
means of a dedicated microPET system, and we analyzed different PET slices
containing axial sections of the liver and axial sections of the kidneys. The
reconstructed parametric images proved to be reliable and qualitatively
effective in the description of the local FDG metabolism with respect to the
different physiologies.
How sustainable are different levels of consciousness?
Date: 2017-05-12 00:00:00

The human brain processes a wide variety of inputs and does so either
consciously or subconsciously. According to the Global Workspace theory,
conscious processing involves broadcasting of information to several regions of
the brain and subconscious processing involves more localized processing. This
theoretical paper aims to expand on some of the aspects of the Global Workspace
theory: how the properties of incoming information result in it being processed
subconsciously or consciously; why processing can be either be sustained or
short-lived; how the Global Workspace theory may apply both to real-time
sensory input as well as to internally retained information. This paper
proposes that: familiar input which does not elicit intense emotions becomes
processed subconsciously and such processing can be continuous and sustained;
input that elicits relatively intense emotions is subjected to highly
sustainable conscious processing; input can also undergo meta-conscious
processing. Such processing is not very sustainable but can exert control over
other cognitive processes. This paper also discusses possible benefits of
regulating cognitive processes this way.
Calibration and Filtering of Exponential L\'evy Option Pricing Models
Date: 2017-05-13 00:00:00
Tasks: Bayesian Inference, parameter estimation, Time Series, Time Series Analysis

The accuracy of least squares calibration using option premiums and particle
filtering of price data to find model parameters is determined. Derivative
models using exponential L\'evy processes are calibrated using regularized
weighted least squares with respect to the minimal entropy martingale measure.
Sequential importance resampling is used for the Bayesian inference problem of
time series parameter estimation with proposal distribution determined using
extended Kalman filter. The algorithms converge to their respective global
optima using a highly parallelizable statistical optimization approach using a
grid of initial positions. Each of these methods should produce the same
parameters. We investigate this assertion.
Multi-task Deep Neural Networks in Automated Protein Function Prediction
Date: 2017-05-28 00:00:00
Tasks: Drug Discovery, Protein Function Prediction

In recent years, deep learning algorithms have outperformed the state-of-the
art methods in several areas thanks to the efficient methods for training and
for preventing overfitting, advancement in computer hardware, the availability
of vast amount data. The high performance of multi-task deep neural networks in
drug discovery has attracted the attention to deep learning algorithms in
bioinformatics area. Here, we proposed a hierarchical multi-task deep neural
network architecture based on Gene Ontology (GO) terms as a solution to protein
function prediction problem and investigated various aspects of the proposed
architecture by performing several experiments. First, we showed that there is
a positive correlation between performance of the system and the size of
training datasets. Second, we investigated whether the level of GO terms on GO
hierarchy related to their performance. We showed that there is no relation
between the depth of GO terms on GO hierarchy and their performance. In
addition, we included all annotations to the training of a set of GO terms to
investigate whether including noisy data to the training datasets change the
performance of the system. The results showed that including less reliable
annotations in training of deep neural networks increased the performance of
the low performed GO terms, significantly. We evaluated the performance of the
system using hierarchical evaluation method. Mathews correlation coefficient
was calculated as 0.75, 0.49 and 0.63 for molecular function, biological
process and cellular component categories, respectively. We showed that deep
learning algorithms have a great potential in protein function prediction area.
We plan to further improve the DEEPred by including other types of annotations
from various biological data sources. We plan to construct DEEPred as an open
access online tool.
Benchmark for Complex Answer Retrieval
Authors: Nanni Federico, Mitra Bhaskar, Magnusson Matt, Dietz Laura
Date: 2017-05-13 00:00:00
Tasks: Information Retrieval, Retrieval

Retrieving paragraphs to populate a Wikipedia article is a challenging task.
The new TREC Complex Answer Retrieval (TREC CAR) track introduces a
comprehensive dataset that targets this retrieval scenario. We present early
results from a variety of approaches -- from standard information retrieval
methods (e.g., tf-idf) to complex systems that using query expansion using
knowledge bases and deep neural networks. The goal is to offer future
participants of this track an overview of some promising approaches to tackle
this problem.
Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks
Authors: Rao Jinfeng, Ture Ferhan, He Hua, Jojic Oliver, Lin Jimmy
Date: 2017-05-13 00:00:00
Tasks: speech-recognition, Speech Recognition

We tackle the novel problem of navigational voice queries posed against an
entertainment system, where viewers interact with a voice-enabled remote
controller to specify the program to watch. This is a difficult problem for
several reasons: such queries are short, even shorter than comparable voice
queries in other domains, which offers fewer opportunities for deciphering user
intent. Furthermore, ambiguity is exacerbated by underlying speech recognition
errors. We address these challenges by integrating word- and character-level
representations of the queries and by modeling voice search sessions to capture
the contextual dependencies in query sequences. Both are accomplished with a
probabilistic framework in which recurrent and feedforward neural network
modules are organized in a hierarchical manner. From a raw dataset of 32M voice
queries from 2.5M viewers on the Comcast Xfinity X1 entertainment system, we
extracted data to train and test our models. We demonstrate the benefits of our
hybrid representation and context-aware model, which significantly outperforms
models without context as well as the current deployed product.
Complexity in cancer stem cells and tumor evolution: towards precision medicine
Date: 2017-05-14 00:00:00

In this review, we discuss recent advances on the plasticity of cancer stem
cells and highlight their relevance to understand the metastatic process and to
guide therapeutic interventions. Recent results suggest that the strict
hierarchical structure of cancer cell populations advocated by the cancer stem
cell model must be reconsidered since the depletion of cancer stem cells leads
the other tumor cells to switch back into the cancer stem cell phenotype. This
plasticity has important implications for metastasis since migrating cells do
not need to be cancer stem cells in order to seed a metastasis. We also discuss
the important role of the immune system and the microenvironment in modulating
phenotypic switching and suggest possible avenues to exploit our understanding
of this process to develop an effective strategy for precision medicine.
Cross-modal codification of images with auditory stimuli: a language for the visually impaired
Date: 2017-05-15 00:00:00

In this study we describe a methodology to realize visual images cognition in
the broader sense, by a cross-modal stimulation through the auditory channel.
An original algorithm of conversion from bi-dimensional images to sounds has
been established and tested on several subjects. Our results show that subjects
where able to discriminate with a precision of 95\% different sounds
corresponding to different test geometric shapes. Moreover, after brief
learning sessions on simple images, subjects where able to recognize among a
group of 16 complex and never-trained images a single target by hearing its
acoustical counterpart. Rate of recognition was found to depend on image
characteristics, in 90% of the cases, subjects did better than choosing at
random. This study contribute to the understanding of cross-modal perception
and help for the realization of systems that use acoustical signals to help
visually impaired persons to recognize objects and improve navigation
Cholesterol impairment contributes to neuroserpin aggregation
Date: 2017-05-15 00:00:00

Intraneural accumulation of misfolded proteins is a common feature of several
neurodegenerative pathologies including Alzheimer's and Parkinson's diseases,
and Familial Encephalopathy with Neuroserpin Inclusion Bodies (FENIB). FENIB is
a rare disease due to a point mutation in neuroserpin which accelerates protein
aggregation in the endoplasmic reticulum (ER). Here we show that cholesterol
depletion induced either by prolonged exposure to statins or by inhibiting the
sterol regulatory binding-element protein (SREBP) pathway also enhances
aggregation of neuroserpin proteins. These findings can be explained
considering a computational model of protein aggregation under non-equilibrium
conditions, where a decrease in the rate of protein clearance improves
aggregation. Decreasing cholesterol in cell membranes affects their biophysical
properties, including their ability to form the vesicles needed for protein
clearance, as we illustrate by a simple mathematical model. Taken together,
these results suggest that cholesterol reduction induces neuroserpin
aggregation, even in absence of specific neuroserpin mutations. The new
mechanism we uncover could be relevant also for other neurodegenerative
diseases associated with protein aggregation.
Determinants of gait stability while walking on a treadmill: a machine learning approach
Date: 2017-10-26 00:00:00
Tasks: BIG-bench Machine Learning

Dynamic balance in human locomotion can be assessed through the local dynamic
stability (LDS) method. Whereas gait LDS has been used successfully in many
settings and applications, little is known about its sensitivity to individual
characteristics of healthy adults. Therefore, we reanalyzed a large dataset of
accelerometric data measured for 100 healthy adults from 20 to 70 years of age
performing 10 min. treadmill walking. We sought to assess the extent to which
the variations of age, body mass and height, sex, and preferred walking speed
(PWS) could influence gait LDS. The random forest (RF) and multiple adaptive
regression splines (MARS) algorithms were selected for their good bias-variance
tradeoff and their capabilities to handle nonlinear associations. First,
through variable importance measure (VIM), we used RF to evaluate which
individual characteristics had the highest influence on gait LDS. Second, we
used MARS to detect potential interactions among individual characteristics
that may influence LDS. The VIM and MARS results indicated that PWS and age
correlated with LDS, whereas no associations were found for sex, body height,
and body mass. Further, the MARS model detected an age by PWS interaction: on
one hand, at high PWS, gait stability is constant across age while, on the
other hand, at low PWS, gait instability increases substantially with age. We
conclude that it is advisable to consider the participants' age as well as
their PWS to avoid potential biases in evaluating dynamic balance through LDS.
Plankton: the paradox and the power law
Date: 2017-05-15 00:00:00

Two basic features of assemblages of unicellular plankton: (1) their high
biodiversity and (2) the power-law structure of their abundance, can be
explained by an allometric scaling of cell growth and mortality with respect to
cell size. To show this, we describe a numerical study of a size-structured,
multispecies, population-dynamic model; the model has a single resource,
supporting an arbitrary number of phytoplankton and zooplankton species. If the
number of plankton species is large enough, the death rate of prey and cell
growth rate of predators have approximate allometric scalings with cell size.
Together, these scalings give rise to an equilibrium distribution of abundance
near the power law, on which many species can coexist. Scalings of this kind
cannot be achieved if the number of species is small. This suggests that the
conjunction of species-richness and power-law structures in plankton
communities is more than a coincidence. Although the exact allometric scalings
used here should not be expected in practice, exclusion of species should be
relatively slow if they lie close to the power law. Thus the forces needed to
achieve coexistence could be effective, even if they are relatively weak.
DeepRT: deep learning for peptide retention time prediction in proteomics
Date: 2017-05-15 00:00:00
Tasks: Deep Learning, Dimensionality Reduction

Accurate predictions of peptide retention times (RT) in liquid chromatography
have many applications in mass spectrometry-based proteomics. Herein, we
present DeepRT, a deep learning based software for peptide retention time
prediction. DeepRT automatically learns features directly from the peptide
sequences using the deep convolutional Neural Network (CNN) and Recurrent
Neural Network (RNN) model, which eliminates the need to use hand-crafted
features or rules. After the feature learning, principal component analysis
(PCA) was used for dimensionality reduction, then three conventional machine
learning methods were utilized to perform modeling. Two published datasets were
used to evaluate the performance of DeepRT and we demonstrate that DeepRT
greatly outperforms previous state-of-the-art approaches ELUDE and GPTime.
Muscle Fatigue Analysis Using OpenSim
Date: 2017-05-16 00:00:00

In this research, attempts are made to conduct concrete muscle fatigue
analysis of arbitrary motions on OpenSim, a digital human modeling platform. A
plug-in is written on the base of a muscle fatigue model, which makes it
possible to calculate the decline of force-output capability of each muscle
along time. The plug-in is tested on a three-dimensional, 29 degree-of-freedom
human model. Motion data is obtained by motion capturing during an arbitrary
running at a speed of 3.96 m/s. Ten muscles are selected for concrete analysis.
As a result, the force-output capability of these muscles reduced to 60%-70%
after 10 minutes' running, on a general basis. Erector spinae, which loses
39.2% of its maximal capability, is found to be more fatigue-exposed than the
others. The influence of subject attributes (fatigability) is evaluated and
discussed.
Optimized brute-force algorithms for the bifurcation analysis of a spin-glass-like neural network model
Date: 2017-05-16 00:00:00

Bifurcation theory is a powerful tool for studying how the dynamics of a
neural network model depends on its underlying neurophysiological parameters.
However, bifurcation theory has been developed mostly for smooth dynamical
systems and for continuous-time non-smooth models, which prevents us from
understanding the changes of dynamics in some widely used classes of artificial
neural network models. This article is an attempt to fill this gap, through the
introduction of algorithms that perform a semi-analytical bifurcation analysis
of a spin-glass-like neural network model with binary firing rates and
discrete-time evolution. Our approach is based on a numerical brute-force
search of the stationary and oscillatory solutions of the spin-glass model,
from which we derive analytical expressions of its bifurcation structure by
means of the state-to-state transition probability matrix. The algorithms
determine how the network parameters affect the degree of multistability, the
emergence and the period of the neural oscillations, and the formation of
symmetry-breaking in the neural populations. While this technique can be
applied to networks with arbitrary (generally asymmetric) connectivity
matrices, in particular we introduce a highly efficient algorithm for the
bifurcation analysis of sparse networks. We also provide some examples of the
obtained bifurcation diagrams and a Python implementation of the algorithms.
Shorting in Speculative Markets
Date: 2019-07-29 00:00:00

We propose a continuous-time model of trading with heterogeneous beliefs.
Risk-neutral agents face quadratic costs-of-carry on positions and thus their
marginal valuations decrease with the size of their position, as it would be
the case for risk-averse agents. In the equilibrium models of heterogeneous
beliefs that followed Harrison-Kreps, investors are risk-neutral, short-selling
is prohibited and agents face constant marginal costs of carrying positions.
The resulting resale option guarantees that the price exceeds the price of the
asset when speculation is ruled out; the difference is identified as a bubble.
In our model increasing marginal costs entail that the price depends on asset
supply. Second, agents also value an option to delay, and this may cause the
market to equilibrate below the buy-and-hold price. Third, we introduce the
possibility of short-selling. A Hamilton-Jacobi-Bellman equation of a novel
form quantifies precisely the influence of the costs-of-carry on the price. An
unexpected decrease in shorting costs may lead to the collapse of a bubble;
this links the financial innovations that facilitated shorting of MBSs to the
subsequent collapse of prices.
Analytic techniques for option pricing under a hyperexponential L\'{e}vy model
Date: 2017-05-16 00:00:00

We develop series expansions in powers of $q^{-1}$ and $q^{-1/2}$ of
solutions of the equation $\psi(z) = q$, where $\psi(z)$ is the Laplace
exponent of a hyperexponential L\'{e}vy process. As a direct consequence we
derive analytic expressions for the prices of European call and put options and
their Greeks (Theta, Delta, and Gamma) and a full asymptotic expansion of the
short-time Black-Scholes at-the-money implied volatility. Further we
demonstrate how the speed of numerical algorithms for pricing exotic options,
which are based on the Laplace transform, may be increased.