pwc-archive/papers-with-abstracts huggingface dataset:
Modified Profile Likelihood Inference and Interval Forecast of the Burst of Financial Bubbles
Date: 2016-02-26 00:00:00
Tasks: Management, Time Series Analysis

We present a detailed methodological study of the application of the modified
profile likelihood method for the calibration of nonlinear financial models
characterised by a large number of parameters. We apply the general approach to
the Log-Periodic Power Law Singularity (LPPLS) model of financial bubbles. This
model is particularly relevant because one of its parameters, the critical time
$t_c$ signalling the burst of the bubble, is arguably the target of choice for
dynamical risk management. However, previous calibrations of the LPPLS model
have shown that the estimation of $t_c$ is in general quite unstable. Here, we
provide a rigorous likelihood inference approach to determine $t_c$, which
takes into account the impact of the other nonlinear (so-called "nuisance")
parameters for the correct adjustment of the uncertainty on $t_c$. This
provides a rigorous interval estimation for the critical time, rather than a
point estimation in previous approaches. As a bonus, the interval estimations
can also be obtained for the nuisance parameters ($m,\omega$, damping), which
can be used to improve filtering of the calibration results. We show that the
use of the modified profile likelihood method dramatically reduces the number
of local extrema by constructing much simpler smoother log-likelihood
landscapes. The remaining distinct solutions can be interpreted as genuine
scenarios that unfold as the time of the analysis flows, which can be compared
directly via their likelihood ratio. Finally, we develop a multi-scale profile
likelihood analysis to visualize the structure of the financial data at
different scales (typically from 100 to 750 days). We test the methodology
successfully on synthetic price time series and on three well-known historical
financial bubbles.
Construction of Gene and Species Trees from Sequence Data incl. Orthologs, Paralogs, and Xenologs
Date: 2016-02-26 00:00:00

Phylogenetic reconstruction aims at finding plausible hypotheses of the
evolutionary history of genes or species based on genomic sequence information.
The distinction of orthologous genes (genes that having a common ancestry and
diverged after a speciation) is crucial and lies at the heart of many genomic
studies. However, existing methods that rely only on 1:1 orthologs to infer
species trees are strongly restricted to a small set of allowed genes that
provide information about the species tree. The use of larger gene sets that
consist in addition of non-orthologous genes (e.g. so-called paralogous or
xenologous genes) considerably increases the information about the evolutionary
history of the respective species. In this work, we introduce a novel method to
compute species phylogenies based on sequence data including orthologs,
paralogs or even xenologs.
A multilayer PPI network analysis of different life stages in C. elegans
Date: 2016-02-26 00:00:00
Tasks: Diversity

Molecular networks act as the backbone of cellular activities, providing an
{excellent} opportunity to understand the developmental changes in an organism.
While network data usually constitute only stationary network graphs,
constructing multilayer PPI network may provide clues to the particular
developmental role at each {stage of life} and may unravel the importance of
these developmental changes. The developmental biology model of {Caenorhabditis
elegans} {analyzed} here provides a ripe platform to understand the patterns of
evolution during life stages of an organism. In the present study, the widely
studied network properties exhibit overall similar statistics for all the PPI
layers. Further, the analysis of the degree-degree correlation and spectral
properties not only reveals crucial differences in each PPI layer but also
indicates the presence of the varying complexity among them. The PPI layer of
Nematode life stage exhibits various network properties different to rest of
the PPI layers, indicating the specific role of cellular diversity and
developmental transitions at this stage. The framework presented here provides
a direction to explore and understand developmental changes occurring in
different life stages of an organism.
Exact Weighted Minwise Hashing in Constant Time
Authors: Shrivastava Anshumali
Date: 2016-02-26 00:00:00

Weighted minwise hashing (WMH) is one of the fundamental subroutine, required
by many celebrated approximation algorithms, commonly adopted in industrial
practice for large scale-search and learning. The resource bottleneck of the
algorithms is the computation of multiple (typically a few hundreds to
thousands) independent hashes of the data. The fastest hashing algorithm is by
Ioffe \cite{Proc:Ioffe_ICDM10}, which requires one pass over the entire data
vector, $O(d)$ ($d$ is the number of non-zeros), for computing one hash.
However, the requirement of multiple hashes demands hundreds or thousands
passes over the data. This is very costly for modern massive dataset.
  In this work, we break this expensive barrier and show an expected constant
amortized time algorithm which computes $k$ independent and unbiased WMH in
time $O(k)$ instead of $O(dk)$ required by Ioffe's method. Moreover, our
proposal only needs a few bits (5 - 9 bits) of storage per hash value compared
to around $64$ bits required by the state-of-art-methodologies. Experimental
evaluations, on real datasets, show that for computing 500 WMH, our proposal
can be 60000x faster than the Ioffe's method without losing any accuracy. Our
method is also around 100x faster than approximate heuristics capitalizing on
the efficient "densified" one permutation hashing schemes
\cite{Proc:OneHashLSH_ICML14}. Given the simplicity of our approach and its
significant advantages, we hope that it will replace existing implementations
in practice.
No such thing as a risk-neutral market
Date: 2016-02-26 00:00:00

A very brief history of relative valuation in neoclassical finance since 1973
is presented, with attention to core currency issues for emerging economies.
Price formation is considered in the context of hierarchical causality, with
discussion focussed on identifying mathematical modelling challenges for robust
and transparent regulation of interactions.
Escaping the trap of 'blocking': a kinetic model linking economic development and political competition
Date: 2015-12-17 00:00:00
Tasks: Blocking

In this paper we present a kinetic model with stochastic game-type
interactions, analyzing the relationship between the level of political
competition in a society and the degree of economic liberalization. The above
issue regards the complex interactions between economy and institutional
policies intended to introduce technological innovations in a society, where
technological innovations are intended in a broad sense comprehending reforms
critical to production. A special focus is placed on the political replacement
effect described in a macroscopic model by Acemoglu and Robinson (AR-model,
henceforth), which can determine the phenomenon of innovation 'blocking',
possibly leading to economic backwardness. One of the goals of our modelization
is to obtain a mesoscopic dynamical model whose macroscopic outputs are
qualitatively comparable with stylized facts of the AR-model. A set of
numerical solutions is presented showing the non monotonous relationship
between economic liberization and political competition, which can be
considered as an emergent phenomenon of the complex socio-economic interaction
dynamic.
Progress towards quantitative design principles of multicellular systems
Date: 2016-02-26 00:00:00
Tasks: Diversity

Living systems, particularly multicellular systems, often seem hopelessly
complex. But recent studies have suggested that beneath this complexity, there
may be unifying quantitative principles that we are only now starting to
unravel. All cells interact with their environments and with other cells.
Communication among cells is a primary means for cells to interact with each
other. The complexity of these multicellular systems, due to the large numbers
of cells and the diversity of intracellular and intercellular interactions,
makes understanding multicellular systems a daunting task. To overcome this
challenge, we will likely need judicious simplifications and conceptual
frameworks that can reveal design principles that are shared among diverse
multicellular systems. Here we review some recent progress towards developing
such frameworks.
Extrinsic and intrinsic correlations in molecular information transmission
Date: 2016-02-26 00:00:00

Cells measure concentrations of external ligands by capturing ligand
molecules with cell surface receptors. The numbers of molecules captured by
different receptors co-vary because they depend on the same extrinsic ligand
fluctuations. However, these numbers also counter-vary due to the intrinsic
stochasticity of chemical processes because a single molecule randomly captured
by a receptor cannot be captured by another. Such structure of receptor
correlations is generally believed to lead to an increase in information about
the external signal compared to the case of independent receptors. We analyze a
solvable model of two molecular receptors and show that, contrary to this
widespread expectation, the correlations have a small and negative effect on
the information about the ligand concentration. Further, we show that
measurements that average over multiple receptors are almost as informative as
those that track the states of every individual one.
A Rank-Based Approach to Zipf's Law
Date: 2016-02-27 00:00:00

An Atlas model is a rank-based system of continuous semimartingales for which
the steady-state values of the processes follow a power law, or Pareto
distribution. For a power law, the log-log plot of these steady-state values
versus rank is a straight line. Zipf's law is a power law for which the slope
of this line is -1. In this note, rank-based conditions are found under which
an Atlas model will follow Zipf's law. An advantage of this rank-based approach
is that it provides information about the dynamics of systems that result in
Zipf's law.
Demographic stochasticity and resource autocorrelation control biological invasions in heterogeneous landscapes
Date: 2016-02-28 00:00:00

Classical models of biological invasions assess species spread in homogeneous
landscapes by assuming constant growth rates and random local movement.
Mounting evidence suggests, however, that demographic stochasticity,
environmental heterogeneity and non-random movement of individuals affect
considerably the spread dynamics. Here, we show that the dynamics of biological
invasions are controlled by the spatial heterogeneity of the resource
distribution. We show theoretically that increasing the landscape resource
autocorrelation length causes a reduction in the average speed of species
spread. Demographic stochasticity plays a key role in the slowdown, which is
streghtened when individuals can actively move towards resources. The reduction
in the front propagation speed is verified in laboratory microcosm experiments
with the flagellated protist Euglena gracilis by comparing spread in habitats
characterized by different resource heterogeneity. Our theoretical and
experimental findings highlight the need to account for the intrinsic
stochasticity of population dynamics to describe spread in spatially extended
landscapes, which are inevitably characterized by heterogeneous spatial
distributions of resources controlling vital rates. Our work identifies the
resource autocorrelation length as a key modulator and a simple measure of
landscape susceptibility to biological invasions, with implications for
predicting the characters of biological invasions within naturally
heterogeneous environmental corridors.
Brain State Control by Closed-Loop Environmental Feedback
Date: 2016-02-29 00:00:00

Brain state regulates sensory processing and motor control for adaptive
behavior. Internal mechanisms of brain state control are well studied, but the
role of external modulation from the environment is not well understood. Here,
we examined the role of closed-loop environmental (CLE) feedback, in comparison
to open-loop sensory input, on brain state and behavior in diverse vertebrate
systems. In fictively swimming zebrafish, CLE feedback for optomotor stability
controlled brain state by reducing coherent neuronal activity. The role of CLE
feedback in brain state was also shown in a model of rodent active whisking,
where brief interruptions in this feedback enhanced signal-to-noise ratio for
detecting touch. Finally, in monkey visual fixation, artificial CLE feedback
suppressed stimulus-specific neuronal activity and improved behavioral
performance. Our findings show that the environment mediates continuous
closed-loop feedback that controls neuronal gain, regulating brain state, and
that brain function is an emergent property of brain-environment interactions.
Folding of Protein L with implications for collapse in the denatured state ensemble
Date: 2016-02-29 00:00:00
Tasks: Protein Folding

A fundamental question in protein folding is whether the coil to globule
collapse transition occurs during the initial stages of folding (burst-phase)
or simultaneously with the protein folding transition. Single molecule
fluorescence resonance energy transfer (FRET) and small angle X-ray scattering
(SAXS) experiments disagree on whether Protein L collapse transition occurs
during the burst-phase of folding. We study Protein L folding using a
coarse-grained model and molecular dynamics simulations. The collapse
transition in Protein L is found to be concomitant with the folding transition.
In the burst-phase of folding, we find that FRET experiments overestimate
radius of gyration, $R_g$, of the protein due to the application of Gaussian
polymer chain end-to-end distribution to extract $R_g$ from the FRET
efficiency. FRET experiments estimate $\approx$ 6\AA \ decrease in $R_g$ when
the actual decrease is $\approx$ 3\AA \ on Guanidinium Chloride denaturant
dilution from 7.5M to 1M, and thereby suggesting pronounced compaction in the
protein dimensions in the burst-phase. The $\approx$ 3\AA \ decrease is close
to the statistical uncertainties of the $R_g$ data measured from SAXS
experiments, which suggest no compaction, leading to a disagreement with the
FRET experiments. The transition state ensemble (TSE) structures in Protein L
folding are globular and extensive in agreement with the $\Psi$-analysis
experiments. The results support the hypothesis that the TSE of single domain
proteins depend on protein topology, and are not stabilised by local
interactions alone.
X-ray experiment provides a way to reveal the distinction between discrete and continuous conformation of myosin head
Date: 2016-02-29 00:00:00
Tasks: Position

The corner stone of the classical model after Huxley and Simmons is
supposition that a myosin head can reside only in several discrete states and
irregularly jumps from one state to another. Until now, it has not been found a
way to experimentally verify this supposition although confirmation or
refutation of the existence of discrete states is crucial for the solution of
myosin motor problem. Here I show that a set of equal myosin heads arranged
equidistantly along an actin filament produce X-ray pattern which varies with
the type of conformation. If the lever arms of all myosin heads reside in one
and the same position (continuous conformation), all the heads have the same
form-factor and equally scatter electromagnetic wave. In this case, only the
geometric factor associated with a spatial ordering of the heads will determine
the X-ray pattern. The situation changes if the average lever arm position is
the same, but inherently every head can reside only in several diverse discrete
states, hopping irregularly from one to another. In this case, the form-factors
corresponding to distinct states are dissimilar, and the increments in phases
of X-rays scattered by different heads are different as well. Inasmuch as every
quantum of radiation interacts with the heads residing in different states,
this results in additional interference and some peaks in the X-ray pattern
should slacken or even extinct as compared with the pattern from the heads with
the continuous-type conformation. The formulas describing both cases are
compared in this article. In general, the distinction between X-ray patterns is
insignificant, but they could be appreciably different at some stages of
conformation process (respective lever arm position depends on the type of
discrete model). Consequently, one can with luck attempt to find out this
difference using a high-sensitive equipment.
Lyapunov Indices and the Poincar\'e Mapping in a Study of the Stability of the Krebs Cycle
Date: 2016-01-28 00:00:00

On the basis of a mathematical model, we continue the study of the metabolic
Krebs cycle (or the tricarboxilic acid cycle). For the first time, we consider
its consistency and stability, which depend on the dissipation of a
transmembrane potential formed by the respiratory chain in the plasmatic
membrane of a cell. The phase-parametric characteristic of the dynamics of the
ATP level depending on a given parameter is constructed. The scenario of
formation of multiple autoperiodic and chaotic modes is presented. Poincar\'{e}
sections and mappings are constructed. The stability of modes and the
fractality of the obtained bifurcations are studied. The full spectra of
Lyapunov indices, divergences, KS-entropies, horizons of predictability, and
Lyapunov dimensionalities of strange attractors are calculated. Some
conclusions about the structural-functional connections determining the
dependence of the cell respiration cyclicity on the synchronization of the
functioning of the tricarboxilic acid cycle and the electron transport chain
are presented.
Fairs for e-commerce: the benefits of aggregating buyers and sellers
Date: 2016-02-29 00:00:00

In recent years, many new and interesting models of successful online
business have been developed. Many of these are based on the competition
between users, such as online auctions, where the product price is not fixed
and tends to rise. Other models, including group-buying, are based on
cooperation between users, characterized by a dynamic price of the product that
tends to go down. There is not yet a business model in which both sellers and
buyers are grouped in order to negotiate on a specific product or service. The
present study investigates a new extension of the group-buying model, called
fair, which allows aggregation of demand and supply for price optimization, in
a cooperative manner. Additionally, our system also aggregates products and
destinations for shipping optimization. We introduced the following new
relevant input parameters in order to implement a double-side aggregation: (a)
price-quantity curves provided by the seller; (b) waiting time, that is, the
longer buyers wait, the greater discount they get; (c) payment time, which
determines if the buyer pays before, during or after receiving the product; (d)
the distance between the place where products are available and the place of
shipment, provided in advance by the buyer or dynamically suggested by the
system. To analyze the proposed model we implemented a system prototype and a
simulator that allow to study effects of changing some input parameters. We
analyzed the dynamic price model in fairs having one single seller and a
combination of selected sellers. The results are very encouraging and motivate
further investigation on this topic.
The Capacity of Private Information Retrieval
Authors: Sun Hua, Jafar Syed A.
Date: 2017-02-27 00:00:00
Tasks: Information Retrieval, Retrieval

In the private information retrieval (PIR) problem a user wishes to retrieve,
as efficiently as possible, one out of $K$ messages from $N$ non-communicating
databases (each holds all $K$ messages) while revealing nothing about the
identity of the desired message index to any individual database. The
information theoretic capacity of PIR is the maximum number of bits of desired
information that can be privately retrieved per bit of downloaded information.
For $K$ messages and $N$ databases, we show that the PIR capacity is
$(1+1/N+1/N^2+\cdots+1/N^{K-1})^{-1}$. A remarkable feature of the capacity
achieving scheme is that if we eliminate any subset of messages (by setting the
message symbols to zero), the resulting scheme also achieves the PIR capacity
for the remaining subset of messages.
Neural and perceptual signatures of efficient sensory coding
Date: 2016-02-29 00:00:00

The mammalian brain is a metabolically expensive device, and evolutionary
pressures have presumably driven it to make productive use of its resources.
For sensory areas, this concept has been expressed more formally as an
optimality principle: the brain maximizes the information that is encoded about
relevant sensory variables, given available resources. Here, we develop this
efficiency principle for encoding a sensory variable with a heterogeneous
population of noisy neurons, each responding to a particular range of values.
The accuracy with which the population represents any particular value depends
on the number of cells that respond to that value, their selectivity, and their
response levels. We derive the optimal solution for these parameters in closed
form, as a function of the probability of stimulus values encountered in the
environment. This optimal neural population also imposes limitations on the
ability of the organism to discriminate different values of the encoded
variable. As a result, we predict an explicit relationship between the
statistical properties of the environment, the allocation and selectivity of
neurons within populations, and perceptual discriminability. We test this
relationship for three visual and two auditory attributes, and find that it is
remarkably consistent with existing data.
On stabilizing the variance of dynamic functional brain connectivity time series
Date: 2016-04-13 00:00:00
Tasks: Time Series, Time Series Analysis

Assessment of dynamic functional brain connectivity (dFC) based on fMRI data
is an increasingly popular strategy to investigate temporal dynamics of the
brain's large-scale network architecture. Current practice when deriving
connectivity estimates over time is to use the Fisher transform which aims to
stabilize the variance of correlation values that fluctuate around varying true
correlation values. It is however unclear how well the stabilization of signal
variance performed by the Fisher transform works for each connectivity time
series, when the true correlation is assumed to be fluctuating. This is of
importance because many subsequent analyses either assume or perform better
when the time series have stable variance or adheres to an approximate Gaussian
distribution. In this paper, using simulations and analysis of resting-state
fMRI data, we analyze the effect of applying different variance stabilization
strategies on connectivity time-series. We here focus our investigation on the
Fisher transform, the Box Cox transform and an approach that combines both
transforms. Our results show that, if the intention of stabilizing the variance
is to use metrics on the time series where stable variance or a Gaussian
distribution is desired (e.g. clustering), the Fisher transform is not optimal
and may even skew connectivity time series away from being Gaussian. Further,
we show that the suboptimal performance of the Fisher transform can be
substantially improved by including an additional Box-Cox transformation after
the dFC time series has been Fisher transformed.