pwc-archive/papers-with-abstracts huggingface dataset:
A generative angular model of protein structure evolution
Date: 2020-09-21 00:00:00
Tasks: model, valid

Recently described stochastic models of protein evolution have demonstrated
that the inclusion of structural information in addition to amino acid
sequences leads to a more reliable estimation of evolutionary parameters. We
present a generative, evolutionary model of protein structure and sequence that
is valid on a local length scale. The model concerns the local dependencies
between sequence and structure evolution in a pair of homologous proteins. The
evolutionary trajectory between the two structures in the protein pair is
treated as a random walk in dihedral angle space, which is modelled using a
novel angular diffusion process on the two-dimensional torus. Coupling sequence
and structure evolution in our model allows for modelling both "smooth"
conformational changes and "catastrophic" conformational jumps, conditioned on
the amino acid changes. The model has interpretable parameters and is
comparatively more realistic than previous stochastic models, providing new
insights into the relationship between sequence and structure evolution. For
example, using the trained model we were able to identify an apparent
sequence-structure evolutionary motif present in a large number of homologous
protein pairs. The generative nature of our model enables us to evaluate its
validity and its ability to simulate aspects of protein evolution conditioned
on an amino acid sequence, a related amino acid sequence, a related structure
or any combination thereof.
Bounds on stationary moments in stochastic chemical kinetics
Date: 2016-12-30 00:00:00

In the stochastic formulation of chemical kinetics, the stationary moments of
the population count of species can be described via a set of linear equations.
However, except for some specific cases such as systems with linear reaction
propensities, the moment equations are underdetermined as a lower order moment
might depend upon a higher order moment. Here, we propose a method to find
lower, and upper bounds on stationary moments of molecular counts in a chemical
reaction system. The method exploits the fact that statistical moments of any
positive-valued random variable must satisfy some constraints. Such constraints
can be expressed as nonlinear inequalities on moments in terms of their lower
order moments, and solving them in conjugation with the stationary moment
equations results in bounds on the moments. Using two examples of biochemical
systems, we illustrate that not only one obtains upper and lower bounds on a
given stationary moment, but these bounds also improve as one uses more moment
equations and utilizes the inequalities for the corresponding higher order
moments. Our results provide avenues for development of moment approximations
that provide explicit bounds on moment dynamics for systems whose dynamics are
otherwise intractable.
The Industry Supply Function and the Long-Run Competitive Equilibrium with Heterogeneous Firms
Date: 2019-09-23 00:00:00

In developing the theory of long-run competitive equilibrium (LRCE), Marshall
(1890) used the notion of a representative firm. The identity of this firm,
however, remained unclear. Subsequent theory either focused on the case where
all firms are identical or else incorporated heterogeneity but disregarded the
notion of a representative firm. Using Hopenhayn's (1992) model of competitive
industry dynamics, we extend the theory of LRCE to account for heterogeneous
firms and show that the long-run supply function can indeed be characterized as
the solution to the minimization of a representative average cost function.
Investor Experiences and Financial Market Dynamics
Date: 2019-02-27 00:00:00

How do macro-financial shocks affect investor behavior and market dynamics?
Recent evidence on experience effects suggests a long-lasting influence of
personally experienced outcomes on investor beliefs and investment, but also
significant differences across older and younger generations. We formalize
experience-based learning in an OLG model, where different cross-cohort
experiences generate persistent heterogeneity in beliefs, portfolio choices,
and trade. The model allows us to characterize a novel link between investor
demographics and the dependence of prices on past dividends, while also
generating known features of asset prices, such as excess volatility and return
predictability. The model produces new implications for the cross-section of
asset holdings, trade volume, and investors' heterogenous responses to recent
financial crises, which we show to be in line with the data.
FolksoDrivenCloud: an annotation and process application for social collaborative networking
Authors: Mas Massimiliano Dal
Date: 2016-12-30 00:00:00
Tasks: Cloud Computing, Distributed Computing

In this paper we present the FolksoDriven Cloud (FDC) built on Cloud and on
Semantic technologies. Cloud computing has emerged in these recent years as the
new paradigm for the provision of on-demand distributed computing resources.
Semantic Web can be used for relationship between different data and
descriptions of services to annotate provenance of repositories on ontologies.
The FDC service is composed of a back-end which submits and monitors the
documents, and a user front-end which allows users to schedule on-demand
operations and to watch the progress of running processes. The impact of the
proposed method is illustrated on a user since its inception.
Complimentary Phylogenetic Signals for Morphological Characters and Quantitative 3D Shape Data within genus Homo
Date: 2016-12-30 00:00:00
Tasks: Articles

Estimating the phylogeny of the genus Homo is entering a new phase of vastly
improved data and methodology. There is increasing evidence of 6 to 10
competing species/lineages at any point in the last half million years, making
the elucidation of the relationships of individual specimens particularly
important. Recent estimates of the phylogeny of key specimens include Waddell
(2013, 2014, 2015, 2016), and Mounier et al. (2016). These are made with quite
different data (3D skull shapes and discrete morphological characters,
respectively) and methods of analysis (unweighted least squares fitting of
distances, OLS+, and reweighted maximum parsimony, respectively). Initial
inspection of the trees in these articles might leave the impression of a great
deal of disagreement and confused results. Here it is shown this need not be
the case, and that these two types of data and analysis may be indicating a
very similar tree, one that is in good agreement also with subjective current
wisdom/expert opinions on particular parts of the phylogeny. The precise
location of the African LH18 specimen arises as key to a better understanding
of the likely form of the last common ancestors of H. sapiens and Neanderthals.
A diverse approach seems to bring forth much more agreement of trees than
otherwise perceived, and argues against being dogmatic about methods of
phylogenetic analysis particularly when working with difficult problems.
A computational investigation of the relationships between single-neuron and network dynamics in the cerebral cortex
Date: 2016-12-31 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Functions of brain areas in complex animals are believed to rely on the
dynamics of networks of neurons rather than on single neurons. On the other
hand, the network dynamics reflect and arise from the integration and
coordination of the activity of populations of single neurons. Understanding
how single-neurons and neural-circuits dynamics complement each other to
produce brain functions is thus of paramount importance. LFPs and EEGs are good
indicators of the dynamics of mesoscopic and macroscopic populations of
neurons, while microscopic-level activities can be documented by measuring the
membrane potential, the synaptic currents or the spiking activity of individual
neurons. In this thesis we develop mathematical modelling and mathematical
analysis tools that can help the interpretation of joint measures of neural
activity at microscopic and mesoscopic or macroscopic scales. In particular, we
develop network models of recurrent cortical circuits that can clarify the
impact of several aspects of single-neuron (i.e., microscopic-level) dynamics
on the activity of the whole neural population (as measured by LFP). We then
develop statistical tools to characterize the relationship between the action
potential firing of single neurons and mass signals. We apply these latter
analysis techniques to joint recordings of the firing activity of individual
cell-type identified neurons and mesoscopic (i.e., LFP) and macroscopic (i.e.,
EEG) signals in the mouse neocortex. We identified several general aspects of
the relationship between cell-specific neural firing and mass circuit activity,
providing for example general and robust mathematical rules which infer
single-neuron firing activity from mass measures such as the LFP and the EEG.
Loss of brain inter-frequency hubs in Alzheimer's disease
Date: 2017-03-10 00:00:00
Tasks: Diagnostic

Alzheimer's disease (AD) causes alterations of brain network structure and
function. The latter consists of connectivity changes between oscillatory
processes at different frequency channels. We proposed a multi-layer network
approach to analyze multiple-frequency brain networks inferred from
magnetoencephalographic recordings during resting-states in AD subjects and
age-matched controls. Main results showed that brain networks tend to
facilitate information propagation across different frequencies, as measured by
the multi-participation coefficient (MPC). However, regional connectivity in AD
subjects was abnormally distributed across frequency bands as compared to
controls, causing significant decreases of MPC. This effect was mainly
localized in association areas and in the cingulate cortex, which acted, in the
healthy group, as a true inter-frequency hub. MPC values significantly
correlated with memory impairment of AD subjects, as measured by the total
recall score. Most predictive regions belonged to components of the
default-mode network that are typically affected by atrophy, metabolism
disruption and amyloid-beta deposition. We evaluated the diagnostic power of
the MPC and we showed that it led to increased classification accuracy (78.39%)
and sensitivity (91.11%). These findings shed new light on the brain functional
alterations underlying AD and provide analytical tools for identifying
multi-frequency neural mechanisms of brain diseases.
Multinomial method for option pricing under Variance Gamma
Date: 2018-02-14 00:00:00

This paper presents a multinomial method for option pricing when the
underlying asset follows an exponential Variance Gamma process. The continuous
time Variance Gamma process is approximated by a discrete time Markov chain
with the same firsts four cumulants. This approach is particularly convenient
for pricing American and Bermudan options, which can be exercised at any time
up to expiration date. Numerical computations of European and American options
are presented, and compared with results obtained with finite differences
methods and with the Black Scholes model.
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals
Date: 2016-12-31 00:00:00
Tasks: Compressive Sensing, Dimensionality Reduction, Time Series Analysis

Local climate conditions play a major role in the development of the mosquito
population responsible for transmitting Dengue Fever. Since the {\em Aedes
Aegypti} mosquito is also a primary vector for the recent Zika and Chikungunya
epidemics across the Americas, a detailed monitoring of periods with favorable
climate conditions for mosquito profusion may improve the timing of
vector-control efforts and other urgent public health strategies. We apply
dimensionality reduction techniques and machine-learning algorithms to climate
time series data and analyze their connection to the occurrence of Dengue
outbreaks for seven major cities in Brazil. Specifically, we have identified
two key variables and a period during the annual cycle that are highly
predictive of epidemic outbreaks. The key variables are the frequency of
precipitation and temperature during an approximately two month window of the
winter season preceding the outbreak. Thus simple climate signatures may be
influencing Dengue outbreaks even months before their occurrence. Some of the
more challenging datasets required usage of compressive-sensing procedures to
estimate missing entries for temperature and precipitation records. Our results
indicate that each Brazilian capital considered has a unique frequency of
precipitation and temperature signature in the winter preceding a Dengue
outbreak. Such climate contributions on vector populations are key factors in
dengue dynamics which could lead to more accurate prediction models and early
warning systems. Finally, we show that critical temperature and precipitation
signatures may vary significantly from city to city, suggesting that the
interplay between climate variables and dengue outbreaks is more complex than
generally appreciated.
Patent Retrieval: A Literature Review
Authors: Shalaby Walid, Zadrozny Wlodek
Date: 2018-12-20 00:00:00
Tasks: Domain Adaptation, Information Retrieval, Retrieval

With the ever increasing number of filed patent applications every year, the
need for effective and efficient systems for managing such tremendous amounts
of data becomes inevitably important. Patent Retrieval (PR) is considered the
pillar of almost all patent analysis tasks. PR is a subfield of Information
Retrieval (IR) which is concerned with developing techniques and methods that
effectively and efficiently retrieve relevant patent documents in response to a
given search request. In this paper we present a comprehensive review on PR
methods and approaches. It is clear that, recent successes and maturity in IR
applications such as Web search cannot be transferred directly to PR without
deliberate domain adaptation and customization. Furthermore, state-of-the-art
performance in automatic PR is still around average in terms of recall. These
observations motivate the need for interactive search tools which provide
cognitive assistance to patent professionals with minimal effort. These tools
must also be developed in hand with patent professionals considering their
practices and expectations. We additionally touch on related tasks to PR such
as patent valuation, litigation, licensing, and highlight potential
opportunities and open directions for computational scientists in these
domains.
Allee dynamics: Growth, extinction and range expansion
Date: 2017-06-12 00:00:00

In population biology, the Allee dynamics refer to negative growth rates
below a critical population density. In this Letter, we study a
reaction-diffusion (RD) model of population growth and dispersion in one
dimension, which incorporates the Allee effect in both the growth and mortality
rates. In the absence of diffusion, the bifurcation diagram displays regions of
both finite population density and zero population density, i.e., extinction.
The early signatures of the transition to extinction at a bifurcation point are
computed in the presence of additive noise. For the full RD model, the
existence of travelling wave solutions of the population density is
demonstrated. The parameter regimes in which the travelling wave advances
(range expansion) and retreats are identified. In the weak Allee regime, the
transition from the pushed to the pulled wave is shown as a function of the
mortality rate constant. The results obtained are in agreement with the recent
experimental observations on budding yeast populations.
Bet-hedging against demographic fluctuations
Date: 2017-08-16 00:00:00

Biological organisms have to cope with stochastic variations in both the
external environment and the internal population dynamics. Theoretical studies
and laboratory experiments suggest that population diversification could be an
effective bet-hedging strategy for adaptation to varying environments. Here we
show that bet-hedging can also be effective against demographic fluctuations
that pose a trade-off between growth and survival for populations even in a
constant environment. A species can maximize its overall abundance in the long
term by diversifying into coexisting subpopulations of both "fast-growing" and
"better-surviving" individuals. Our model generalizes statistical physics
models of birth-death processes to incorporate dispersal, during which new
populations are founded, and can further incorporate variations of local
environments. In this way we unify different bet-hedging strategies against
demographic and environmental variations as a general means of adaptation to
both types of uncertainties in population growth.
iCorr : Complex correlation method to detect origin of replication in prokaryotic and eukaryotic genomes
Date: 2017-02-19 00:00:00

Computational prediction of origin of replication (ORI) has been of great
interest in bioinformatics and several methods including GC Skew, Z curve,
auto-correlation etc. have been explored in the past. In this paper, we have
extended the auto-correlation method to predict ORI location with much higher
resolution for prokaryotes. The proposed complex correlation method (iCorr)
converts the genome sequence into a sequence of complex numbers by mapping the
nucleotides to {+1,-1,+i,-i} instead of {+1,-1} used in the auto-correlation
method (here, 'i' is square root of -1). Thus, the iCorr method uses
information about the positions of all the four nucleotides unlike the earlier
auto-correlation method which uses the positional information of only one
nucleotide. Also, this earlier method required visual inspection of the
obtained graphs to identify the location of origin of replication. The proposed
iCorr method does away with this need and is able to identify the origin
location simply by picking the peak in the iCorr graph. The iCorr method also
works for a much smaller segment size compared to the earlier auto-correlation
method, which can be very helpful in experimental validation of the
computational predictions. We have also developed a variant of the iCorr method
to predict ORI location in eukaryotes and have tested it with the
experimentally known origin locations of S. cerevisiae with an average accuracy
of 71.76%.
Encoding Sensory and Motor Patterns as Time-Invariant Trajectories in Recurrent Neural Networks
Date: 2017-08-13 00:00:00

Much of the information the brain processes and stores is temporal in nature
- a spoken word or a handwritten signature, for example, is defined by how it
unfolds in time. However, it remains unclear how neural circuits encode complex
time-varying patterns. We show that by tuning the weights of a recurrent neural
network (RNN), it can recognize and then transcribe spoken digits. The model
elucidates how neural dynamics in cortical networks may resolve three
fundamental challenges: first, encode multiple time-varying sensory and motor
patterns as stable neural trajectories; second, generalize across relevant
spatial features; third, identify the same stimuli played at different speeds -
we show that this temporal invariance emerges because the recurrent dynamics
generate neural trajectories with appropriately modulated angular velocities.
Together our results generate testable predictions as to how recurrent networks
may use different mechanisms to generalize across the relevant spatial and
temporal features of complex time-varying stimuli.
Optimal Mean-Reverting Spread Trading: Nonlinear Integral Equation Approach
Date: 2017-01-11 00:00:00
Tasks: Position

We study several optimal stopping problems that arise from trading a
mean-reverting price spread over a finite horizon. Modeling the spread by the
Ornstein-Uhlenbeck process, we analyze three different trading strategies: (i)
the long-short strategy; (ii) the short-long strategy, and (iii) the chooser
strategy, i.e. the trader can enter into the spread by taking either long or
short position. In each of these cases, we solve an optimal double stopping
problem to determine the optimal timing for starting and subsequently closing
the position. We utilize the local time-space calculus of Peskir (2005a) and
derive the nonlinear integral equations of Volterra-type that uniquely char-
acterize the boundaries associated with the optimal timing decisions in all
three problems. These integral equations are used to numerically compute the
optimal boundaries.
The Diverse Club: The Integrative Core of Complex Networks
Date: 2017-06-27 00:00:00

A complex system can be represented and analyzed as a network, where nodes
represent the units of the network and edges represent connections between
those units. For example, a brain network represents neurons as nodes and axons
between neurons as edges. In many networks, some nodes have a
disproportionately high number of edges. These nodes also have many edges
between each other, and are referred to as the rich club. In many different
networks, the nodes of this club are assumed to support global network
integration. However, another set of nodes potentially exhibits a connectivity
structure that is more advantageous to global network integration. Here, in a
myriad of different biological and man-made networks, we discover the diverse
club--a set of nodes that have edges diversely distributed across the network.
The diverse club exhibits, to a greater extent than the rich club, properties
consistent with an integrative network function--these nodes are more highly
interconnected and their edges are more critical for efficient global
integration. Moreover, we present a generative evolutionary network model that
produces networks with a diverse club but not a rich club, thus demonstrating
that these two clubs potentially evolved via distinct selection pressures.
Given the variety of different networks that we analyzed--the c. elegans, the
macaque brain, the human brain, the United States power grid, and global air
traffic--the diverse club appears to be ubiquitous in complex networks. These
results warrant the distinction and analysis of two critical clubs of nodes in
all complex systems.
Efficient asymptotic variance reduction when estimating volatility in high frequency data
Date: 2018-06-27 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper shows how to carry out efficient asymptotic variance reduction
when estimating volatility in the presence of stochastic volatility and
microstructure noise with the realized kernels (RK) from [Barndorff-Nielsen et
al., 2008] and the quasi-maximum likelihood estimator (QMLE) studied in [Xiu,
2010]. To obtain such a reduction, we chop the data into B blocks, compute the
RK (or QMLE) on each block, and aggregate the block estimates. The ratio of
asymptotic variance over the bound of asymptotic efficiency converges as B
increases to the ratio in the parametric version of the problem, i.e. 1.0025 in
the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of
stochastic sampling times and jump in the price process is examined carefully.
The finite sample performance of both estimators is investigated in
simulations, while empirical work illustrates the gain in practice.
Optimal liquidation in a Level-I limit order book for large tick stocks
Date: 2017-11-14 00:00:00

We propose a framework to study the optimal liquidation strategy in a limit
order book for large-tick stocks, with spread equal to one tick. All order book
events (market orders, limit orders and cancellations) occur according to
independent Poisson processes, with parameters depending on price move
directions. Our goal is to maximise the expected terminal wealth of an agent
who needs to liquidate her positions within a fixed time horizon. Assuming that
the agent trades (through sell limit order or/and sell market order) only when
the price moves, we model her liquidation procedure as a semi-Markov decision
process, and compute the semi-Markov kernel using Laplace method in the
language of queueing theory. The optimal liquidation policy is then solved by
dynamic programming, and illustrated numerically.