pwc-archive/papers-with-abstracts huggingface dataset:
Random Search with Memory in Patchy Media: Exploration-Exploitation Tradeoff
Date: 2017-01-11 00:00:00

How to best exploit patchy resources? This long-standing question belongs to
the extensively studied class of explore/exploit problems that arise in a wide
range of situations, from animal foraging, to robotic exploration, and to human
decision processes. Despite its broad relevance, the issue of optimal
exploitation has previously only been tackled through two paradigmatic limiting
models---patch-use and random search---that do not account for the interplay
between searcher motion within patches and resource depletion. Here, we bridge
this gap by introducing a minimal patch exploitation model that incorporates
this coupling: the searcher depletes the resources along its random-walk
trajectory within a patch and travels to a new patch after it takes
$\mathcal{S}$ consecutive steps without finding resources. We compute the
distribution of the amount of resources $F_t$ consumed by time $t$ for this
non-Markovian random walker and show that exploring multiple patches is
beneficial. In one dimension, we analytically derive the optimal strategy to
maximize $F_t$. We show that this strategy is robust with respect to the
distribution of resources within patches and the criterion for leaving a given
patch. We also show that $F_t$ can be optimized in the ecologically-relevant
case of two-dimensional patchy environments.
The microstructural foundations of leverage effect and rough volatility
Date: 2016-09-16 00:00:00

We show that typical behaviors of market participants at the high frequency
scale generate leverage effect and rough volatility. To do so, we build a
simple microscopic model for the price of an asset based on Hawkes processes.
We encode in this model some of the main features of market microstructure in
the context of high frequency trading: high degree of endogeneity of market,
no-arbitrage property, buying/selling asymmetry and presence of metaorders. We
prove that when the first three of these stylized facts are considered within
the framework of our microscopic model, it behaves in the long run as a Heston
stochastic volatility model, where leverage effect is generated. Adding the
last property enables us to obtain a rough Heston model in the limit,
exhibiting both leverage effect and rough volatility. Hence we show that at
least part of the foundations of leverage effect and rough volatility can be
found in the microstructure of the asset.
From quantum mechanics to finance: Microfoundations for jumps, spikes and high volatility phases in diffusion price processes
Date: 2016-10-17 00:00:00

We present an agent behavior based microscopic model that induces jumps,
spikes and high volatility phases in the price process of a traded asset. We
transfer dynamics of thermally activated jumps of an unexcited/ excited two
state system discussed in the context of quantum mechanics to agent
socio-economic behavior and provide microfoundations. After we link the
endogenous agent behavior to price dynamics we establish the circumstances
under which the dynamics converge to an It\^o-diffusion price processes in the
large market limit.
Generalized residual vector quantization for large scale data
Authors: Liu Shicong, Shao Junru, Lu Hongtao
Date: 2016-09-17 00:00:00
Tasks: Information Retrieval, Quantization, Retrieval

Vector quantization is an essential tool for tasks involving large scale
data, for example, large scale similarity search, which is crucial for
content-based information retrieval and analysis. In this paper, we propose a
novel vector quantization framework that iteratively minimizes quantization
error. First, we provide a detailed review on a relevant vector quantization
method named \textit{residual vector quantization} (RVQ). Next, we propose
\textit{generalized residual vector quantization} (GRVQ) to further improve
over RVQ. Many vector quantization methods can be viewed as the special cases
of our proposed framework. We evaluate GRVQ on several large scale benchmark
datasets for large scale search, classification and object retrieval. We
compared GRVQ with existing methods in detail. Extensive experiments
demonstrate our GRVQ framework substantially outperforms existing methods in
term of quantization accuracy and computation efficiency.
Replica Analysis for the Duality of the Portfolio Optimization Problem
Date: 2016-09-18 00:00:00
Tasks: Portfolio Optimization

In the present paper, the primal-dual problem consisting of the investment
risk minimization problem and the expected return maximization problem in the
mean-variance model is discussed using replica analysis. As a natural extension
of the investment risk minimization problem under only a budget constraint that
we analyzed in a previous study, we herein consider a primal-dual problem in
which the investment risk minimization problem with budget and expected return
constraints is regarded as the primal problem, and the expected return
maximization problem with budget and investment risk constraints is regarded as
the dual problem. With respect to these optimal problems, we analyze a quenched
disordered system involving both of these optimization problems using the
approach developed in statistical mechanical informatics, and confirm that both
optimal portfolios can possess the primal-dual structure. Finally, the results
of numerical simulations are shown to validate the effectiveness of the
proposed method.
Statistical properties and dynamics of phenotype components in individual bacteria
Date: 2018-05-15 00:00:00

Cellular phenotype is characterized by different components such as cell
size, protein content and cell cycle time. These are global variables that are
the outcome of multiple internal microscopic processes. Accordingly, they
display some universal statistical properties and scaling relations, such as
distribution collapse and relation between moments. Cell size statistics and
its relation to growth and division has been mostly studied separately from
proteins and other cellular variables. Here we present experimental and
theoretical analyses of these phenotype components in a unified framework that
reveals their correlations and interactions inside the cell. We measure these
components simultaneously in single cells over dozens of generations, quantify
their correlations, and compare population to temporal statistics. We find that
cell size and highly expressed proteins have very similar dynamics over growth
and division cycles, which result in parallel statistical properties, both
universal and individual. In particular, while distribution shapes of
fluctuations along time are common to all cells and components, other
properties are variable and remain distinct in individual cells for a
surprisingly large number of generations. These include temporal averages of
cell size and protein content, and the structure of their auto-correlation
functions. We explore possible roles of the different components in controlling
cell growth and division. We find that in order to stabilize exponential
accumulation and division of all components across generations, coupled
dynamics among them is required. Finally, we incorporate effective coupling
within the cell cycle with a phenomenological mapping across consecutive
cycles, and show that this model reproduces the entire array of experimental
observations.
Finite-State Channel Models for Signal Transduction in Neural Systems
Date: 2016-09-18 00:00:00

Information theory provides powerful tools for understanding communication
systems. This analysis can be applied to intercellular signal transduction,
which is a means of chemical communication among cells and microbes. We discuss
how to apply information-theoretic analysis to ligand-receptor systems, which
form the signal carrier and receiver in intercellular signal transduction
channels. We also discuss the applications of these results to neuroscience.
Static vs adapted optimal execution strategies in two benchmark trading models
Date: 2016-09-18 00:00:00
Tasks: Vocal Bursts Valence Prediction

We consider the optimal solutions to the trade execution problem in the two
different classes of i) fully adapted or adaptive and ii) deterministic or
static strategies, comparing them. We do this in two different benchmark
models. The first model is a discrete time framework with an information flow
process, dealing with both permanent and temporary impact, minimizing the
expected cost of the trade. The second model is a continuous time framework
where the objective function is the sum of the expected cost and a value at
risk (or expected shortfall) type risk criterion. Optimal adapted solutions are
known in both frameworks from the original works of Bertsimas and Lo (1998) and
Gatheral and Schied (2011). In this paper we derive the optimal static
strategies for both benchmark models and we study quantitatively the
improvement in optimality when moving from static strategies to fully adapted
ones. We conclude that, in the benchmark models we study, the difference is not
relevant, except for extreme unrealistic cases for the model or impact
parameters. This indirectly confirms that in the similar framework of Almgren
and Chriss (2000) one is fine deriving a static optimal solution, as done by
those authors, as opposed to a fully adapted one, since the static solution
happens to be tractable and known in closed form.
Evolution as a coexistence mechanism: Does genetic architecture matter?
Date: 2017-06-12 00:00:00

Species sharing a prey or a predator species may go extinct due to
exploitative or apparent competition. We examine whether evolution of the
shared species acts as a coexistence mechanism and to what extent the answer
depends on the genetic architecture underlying trait evolution. In our models
of exploitative and apparent competition, the shared species evolves its
defense or prey use. Evolving species are either haploid or diploid. A single
locus pleiotropically determines prey nutritional quality and predator attack
rates. When pleiotropy is sufficiently antagonistic (e.g. nutritional prey are
harder to capture), eco-evolutionary assembly culminates in one of two stable
states supporting only two species. When pleiotropy is weakly antagonistic or
synergistic, assembly is intransitive: species-genotype pairs are cyclically
displaced by rare invasions of the missing genotypes or species. This
intransitivity allows for coexistence if, along its equilibria, the geometric
mean of recovery rates exceeds the geometric mean of loss rates of the rare
genotypes or species. By affecting these rates, synergistic pleiotropy can
mediate coexistence, while antagonistic pleiotropy does not. For diploid
populations experiencing weak antagonistic pleiotropy, superadditive allelic
contributions to fitness can mitigate coexistence via an eco-evolutionary
storage effect. Density-dependence and mutations also promote coexistence.
These results highlight how the efficacy of evolution as a coexistence
mechanism may depend on the underlying genetic architecture.
A Deterministic Mathematical Model for Bidirectional Excluded Flow with Langmuir Kinetics
Date: 2016-09-19 00:00:00
Tasks: Translation

In many important cellular processes, including mRNA translation, gene
transcription, phosphotransfer, and intracellular transport, biological
"particles" move along some kind of "tracks". The motion of these particles can
be modeled as a one-dimensional movement along an ordered sequence of sites.
The biological particles (e.g., ribosomes, RNAPs, phosphate groups, motor
proteins) have volume and cannot surpass one another. In some cases, there is a
preferred direction of movement along the track, but in general the movement
may be two-directional, and furthermore the particles may attach or detach from
various regions along the tracks (e.g. ribosomes may drop off the mRNA molecule
before reaching a stop codon). We derive a new deterministic mathematical model
for such transport phenomena that may be interpreted as the dynamic mean-field
approximation of an important model from mechanical statistics called the
asymmetric simple exclusion process (ASEP) with Langmuir kinetics. Using tools
from the theory of monotone dynamical systems and contraction theory we show
that the model admits a unique globally asymptotically stable equilibrium. This
means that the occupancy in all the sites along the lattice converges to a
steady-state value that depends on the parameters but not on the initial
conditions. We also show that the model entrains (or phase locks) to periodic
excitations in any of its forward, backward, attachment, or detachment rates.
We demonstrate an application of this phenomenological transport model for
analyzing the effect of ribosome drop off in mRNA translation. One may perhaps
expect that drop off from a jammed site may increase the total flow by reducing
congestion. Our results show that this is not true. Drop off has a substantial
effect on the flow, yet always leads to a reduction in the steady-state protein
production rate.
Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover
Date: 2016-09-19 00:00:00

Excitatory synaptic connections in the adult neocortex consist of multiple
synaptic contacts, almost exclusively formed on dendritic spines. Changes of
dendritic spine shape and volume, a correlate of synaptic strength, can be
tracked in vivo for weeks. Here, we present a combined model of spike-timing
dependent dendritic spine plasticity and turnover that explains the steady
state multi-contact configuration of synapses in adult neocortical networks. In
this model, many presynaptic neurons compete to make strong synaptic
connections onto postsynaptic neurons, while the synaptic contacts comprising
each connection cooperate via postsynaptic firing. We demonstrate that the
model is consistent with experimentally observed long-term dendritic spine
dynamics under steady-state and lesion induced conditions, and show that
cooperation of multiple synaptic contacts is crucial for stable, long-term
synaptic memories. In simulations of a simplified network of barrel cortex, our
plasticity rule reproduces whisker-trimming induced rewiring of
thalamo-cortical and recurrent synaptic connectivity on realistic time scales.
Self-tolerance and autoimmunity in a minimal model of the idiotypic network
Date: 2016-09-19 00:00:00

We consider self-tolerance and its failure -autoimmunity- in a minimal
mathematical model of the idiotypic network. A node in the network represents a
clone of B-lymphocytes and its antibodies of the same idiotype which is encoded
by a bitstring. The links between nodes represent possible interactions between
clones of almost complementary idiotype. A clone survives only if the number of
populated neighbored nodes is neither too small nor too large. The dynamics is
driven by the influx of lymphocytes with randomly generated idiotype from the
bone marrow. Previous work has revealed that the network evolves towards a
highly organized modular architecture, characterized by groups of nodes which
share statistical properties. The structural properties of the architecture can
be described analytically, the statistical properties determined from
simulations are confirmed by a modular mean-field theory. To model the presence
of self we permanently occupy one or several nodes. These nodes influence their
linked neighbors, the autoreactive clones, but are themselves not affected by
idiotypic interactions. The architecture is very similar to the case without
self, but organized such that the neighbors of self are only weakly occupied,
thus providing self-tolerance. This supports the perspective that self-reactive
clones, which regularly occur in healthy organisms, are controlled by
anti-idiotypic clones. We discuss how perturbations, like an infection with
foreign antigen, a change in the influx of new idiotypes, or the random removal
of idiotypes, may lead to autoreactivity and devise protocols which cause a
reconstitution of the self-tolerant state. The results could be helpful to
understand network and probabilistic aspects of autoimmune disorders.
The reproductive number of Zika in municipalities of Antioquia, Colombia: stratifying the potential transmission of an ongoing epidemic
Date: 2016-09-19 00:00:00
Tasks: Management

Introduction: Zika epidemic in America was declared a public health emergency
of international concern after the rapid spread in the region. Stratification
of the potential transmission of the disease is needed to address the efforts
surveillance and disease control. The goal of this research is to compare the
basic reproductive number of Zika in different municipalities, from an SIR
model with implicit vector dynamics, based on the daily case reporting data of
Antioquia, Colombia, the second most affected country after Brazil. Methods: An
simple SIR model with implicit vector dynamics was derived and used. The
approximate solution of the model in terms of individuals recovered in each
time unit, allowed to obtain estimate of the model parameters including the
basic reproduction number (Ro) and its 95% confidence intervals. These
parameters were estimated via fitting the solution of the model to the daily
reported cumulative cases data from the regional surveillance system. Results:
Ro was estimated for 20 municipalities, all located at less than 2200 meters
above sea level. From January to April 2016, between 17 and 347 cases were
reported from these municipalities. Of these, 15 municipalities had a high
potential for transmission (Ro>1) and 5 had less potential (Ro<1), although in
3 of these, transmission was later possible because its upper confidence
interval of Ro was greater than one. Conclusion: Surveillance and control of
Zika should be directed primarily to municipalities with Ro>1. Furthermore,
strategies that will strengthen the detection and management of cases in the
remaining municipalities should be beneficial to the whole Antioquia region.
On the role of $\rm CD8^+$ T cells in determining recovery time from influenza virus infection
Date: 2016-09-20 00:00:00

Myriad experiments have identified an important role for $\rm CD8^+$ T cell
response mechanisms in determining recovery from influenza A virus infection.
Animal models of influenza infection further implicate multiple elements of the
immune response in defining the dynamical characteristics of viral infection.
To date, influenza virus models, while capturing particular aspects of the
natural infection history, have been unable to reproduce the full gamut of
observed viral kinetic behaviour in a single coherent framework. Here, we
introduce a mathematical model of influenza viral dynamics incorporating all
major immune components (innate, humoral and cellular) and explore its
properties with a particular emphasis on the role of cellular immunity.
Calibrated against a range of murine data, our model is capable of
recapitulating observed viral kinetics from a multitude of experiments.
Importantly, the model predicts a robust exponential relationship between the
level of effector $\rm CD8^+$ T cells and recovery time, whereby recovery time
rapidly decreases to a fixed minimum recovery time with an increasing level of
effector $\rm CD8^+$ T cells. We find support for this relationship in recent
clinical data from influenza A(H7N9) hospitalised patients. The exponential
relationship implies that people with a lower level of naive $\rm CD8^+$ T
cells may receive significantly more benefit from induction of additional
effector $\rm CD8^+$ T cells arising from immunological memory, itself
established through either previous viral infection or T cell-based vaccines.
How to distinguish conformational selection and induced fit based on chemical relaxation rates
Date: 2016-09-20 00:00:00

Protein binding often involves conformational changes. Important questions
are whether a conformational change occurs prior to a binding event
('conformational selection') or after a binding event ('induced fit'), and how
conformational transition rates can be obtained from experiments. In this
article, we present general results for the chemical relaxation rates of
conformational-selection and induced-fit binding processes that hold for all
concentrations of proteins and ligands and, thus, go beyond the standard
pseudo-first-order approximation of large ligand concentration. These results
allow to distinguish conformational-selection from induced-fit processes - also
in cases in which such a distinction is not possible under pseudo-first-order
conditions - and to extract conformational transition rates of proteins from
chemical relaxation data.
Relation between Gene Content and Taxonomy in Chloroplasts
Date: 2016-09-20 00:00:00
Tasks: Relation, Specificity

The aim of this study is to investigate the relation that can be found
between the phylogeny of a large set of complete chloroplast genomes, and the
evolution of gene content inside these sequences. Core and pan genomes have
been computed on \textit{de novo} annotation of these 845 genomes, the former
being used for producing well-supported phylogenetic tree while the latter
provides information regarding the evolution of gene contents over time. It
details too the specificity of some branches of the tree, when specificity is
obtained on accessory genes. After having detailed the material and methods, we
emphasize some remarkable relation between well-known events of the chloroplast
history, like endosymbiosis, and the evolution of gene contents over the
phylogenetic tree.
Measuring Consciousness
Date: 2018-02-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

A measurement based formula for consciousness, C, as a function of time t, is
constructed. The formula depends on identifying a natural relevant
self-generated, time-dependent dynamical process inherent in any entity. For
human beings the relevant dynamical process identified is the ensemble of brain
waves, observed in EEG measurements, that are represented in the model by their
measured time dependent correlation functions. These correlation functions
define the accessible dynamical state of the brain at any moment of time. From
them a time dependent probability function, P(t), is extracted by using a
mathematical identity. According to information theory, -P(t) log P(t), is a
measure of the information contained in the brain waves. Consciousness, C, is
defined by this information theory formula: it is not localized, does not
depend on specific hardwire details of the brain, but reflects the information
content present in brain waves. Justifications, based on observational
evidence, are given for the formula and it is shown that C reflects the degree
of "awareness" that a person has at a given moment of time. The model explains
the observed time delay between when a brain wave is seen to initiate an action
and when there is awareness that the action has been initiated, in terms of the
way brain waves processes information. Some testable consequences of the model,
including the role dreaming sleep plays in long term memory storage, are
discussed. It is also shown that non living entities have C=0.