pwc-archive/papers-with-abstracts huggingface dataset:
Exponential Capacity in an Autoencoder Neural Network with a Hidden Layer
Date: 2017-05-21 00:00:00

A fundamental aspect of limitations in learning any computation in neural
architectures is characterizing their optimal capacities.
  An important, widely-used neural architecture is known as autoencoders where
the network reconstructs the input at the output layer via a representation at
a hidden layer.
  Even though capacities of several neural architectures have been addressed
using statistical physics methods, the capacity of autoencoder neural networks
is not well-explored.
  Here, we analytically show that an autoencoder network of binary neurons with
a hidden layer can achieve a capacity that grows exponentially with network
size.
  The network has fixed random weights encoding a set of dense input patterns
into a dense, expanded (or \emph{overcomplete}) hidden layer representation. A
set of learnable weights decodes the input patters at the output layer. We
perform a mean-field approximation of the model to reduce the model to a
perceptron problem with an input-output dependency. Carrying out Gardner's
\emph{replica} calculation, we show that as the expansion ratio, defined as the
number of hidden units over the number of input units, increases, the
autoencoding capacity grows exponentially even when the sparseness or the
coding level of the hidden layer representation is changed. The
replica-symmetric solution is locally stable and is in good agreement with
simulation results obtained using a local learning rule. In addition, the
degree of symmetry between the encoding and decoding weights monotonically
increases with the expansion ratio.
Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs
Date: 2017-05-22 00:00:00

Cortical microcircuits are very complex networks, but they are composed of a
relatively small number of stereotypical motifs. Hence one strategy for
throwing light on the computational function of cortical microcircuits is to
analyze emergent computational properties of these stereotypical microcircuit
motifs. We are addressing here the question how spike-timing dependent
plasticity (STDP) shapes the computational properties of one motif that has
frequently been studied experimentally: interconnected populations of pyramidal
cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental
studies suggest that these inhibitory neurons exert some form of divisive
inhibition on the pyramidal cells. We show that this data-based form of
feedback inhibition, which is softer than that of winner-take-all models that
are commonly considered in theoretical analyses, contributes to the emergence
of an important computational function through STDP: The capability to
disentangle superimposed firing patterns in upstream networks, and to represent
their information content through a sparse assembly code.
Studies of global and local entanglements of individual protein chains using the concept of knotoids
Date: 2017-05-22 00:00:00

We study here global and local entanglements of open protein chains by
implementing the concept of knotoids. Knotoids have been introduced in 2012 by
Vladimir Turaev as a generalization of knots in 3-dimensional space. More
precisely, knotoids are diagrams representing projections of open curves in 3D
space, in contrast to knot diagrams which represent projections of closed
curves in 3D space. The intrinsic difference with classical knot theory is that
the generalization provided by knotoids admits non-trivial topological
entanglement of the open curves provided that their geometry is frozen as it is
the case for crystallized proteins. Consequently, our approach doesn't require
the closure of chains into loops which implies that the geometry of analysed
chains does not need to be changed by closure in order to characterize their
topology. Our study revealed that the knotoid approach detects protein regions
that were classified earlier as knotted and also new, topologically interesting
regions that we classify as pre-knotted.
Aqueous solution interactions with sex hormone-binding globulin and estradiol: A theoretical investigation
Date: 2017-05-22 00:00:00

Sex hormone-binding globulin (SHBG) is a binding protein that regulates
availability of steroids hormones in the plasma. Although best known as steroid
carrier, studies have associated SHBG in modulating behavioral aspects related
to sexual receptivity. Among steroids, estradiol (17\b{eta}-estradiol,
oestradiol or E2) is well recognized as the most active endogenous female
hormone, exerting important roles in reproductive and nonreproductive
functions. Thus, in this study we aimed to employ molecular dynamics (MD) and
docking techniques for quantifying the interaction energy between a complex
aqueous solution, composed by different salts, SHBG and E2. Due to glucose
concentration resembles those observed in diabetic levels, special emphasis was
devoted to uncover the main consequences of this carbohydrate on the SHBG and
E2 molecules. We also examined possible energetic changes due to solution on
the binding energy of SHBG-E2 complex. In this framework, our calculations
uncovered a remarkable interaction energy between glucose and SHBG surface.
Surprisingly, we also observed solute components movement toward SHBG yielding
clusters surrounding the protein. This finding, corroborated by the higher
energy and shorter distance found between glucose and SHBG, suggests a scenario
in favor of a detainment state. In addition, in spite of protein superficial
area increment it does not exerted modification on binding site area nor over
binding energy SHBG-E2 complex. Finally, our calculations also highlighted an
interaction between E2 and glucose when the hormone was immersed in the
solution. In summary, our findings contribute for a better comprehension of
both SHBG and E2 interplay with aqueous solution components.
Synaptic Noise Facilitates the Emergence of Self-Organized Criticality in the Caenorhabditis elegans Neuronal Network
Date: 2017-05-22 00:00:00

Avalanches with power-law distributed size parameters have been observed in
neuronal networks. This observation might be a manifestation of the
self-organized criticality (SOC). Yet, the physiological mechanicsm of this
behavior is currently unknown. Describing synaptic noise as transmission
failures mainly originating from the probabilistic nature of neurotransmitter
release, this study investigates the potential of this noise as a mechanism for
driving the functional architecture of the neuronal networks towards SOC. To
this end, a simple finite state neuron model, with activity dependent and
synapse specific failure probabilities, was built based on the known anatomical
connectivity data of the nematode Ceanorhabditis elegans. Beginning from random
values, it was observed that synaptic noise levels picked out a set of synapses
and consequently an active subnetwork which generates power-law distributed
neuronal avalanches. The findings of this study brings up the possibility that
synaptic failures might be a component of physiological processes underlying
SOC in neuronal networks.
Using Macroeconomic Forecasts to Improve Mean Reverting Trading Strategies
Date: 2017-05-22 00:00:00

A large class of trading strategies focus on opportunities offered by the
yield curve. In particular, a set of yield curve trading strategies are based
on the view that the yield curve mean-reverts. Based on these strategies'
positive performance, a multiple pairs trading strategy on major currency pairs
was implemented. To improve the algorithm's performance, machine learning
forecasts of a series of pertinent macroeconomic variables were factored in, by
optimizing the weights of the trading signals. This resulted in a clear
improvement in the APR over the evaluation period, demonstrating that
macroeconomic indicators, not only technical indicators, should be considered
in trading strategies.
The brain as an efficient and robust adaptive learner
Date: 2017-05-22 00:00:00

Understanding how the brain learns to compute functions reliably, efficiently
and robustly with noisy spiking activity is a fundamental challenge in
neuroscience. Most sensory and motor tasks can be described as dynamical
systems and could presumably be learned by adjusting connection weights in a
recurrent biological neural network. However, this is greatly complicated by
the credit assignment problem for learning in recurrent network, e.g. the
contribution of each connection to the global output error cannot be determined
based only on locally accessible quantities to the synapse. Combining tools
from adaptive control theory and efficient coding theories, we propose that
neural circuits can indeed learn complex dynamic tasks with local synaptic
plasticity rules as long as they associate two experimentally established
neural mechanisms. First, they should receive top-down feedbacks driving both
their activity and their synaptic plasticity. Second, inhibitory interneurons
should maintain a tight balance between excitation and inhibition in the
circuit. The resulting networks could learn arbitrary dynamical systems and
produce irregular spike trains as variable as those observed experimentally.
Yet, this variability in single neurons may hide an extremely efficient and
robust computation at the population level.
Social Integration in Two-Sided Matching Markets
Date: 2018-07-23 00:00:00
Tasks: Vocal Bursts Valence Prediction

When several two-sided matching markets merge into one, it is inevitable that
some agents will become worse off if the matching mechanism used is stable. I
formalize this observation by defining the property of integration
monotonicity, which requires that every agent becomes better off after any
number of matching markets merge. Integration monotonicity is also incompatible
with the weaker efficiency property of Pareto optimality.
  Nevertheless, I obtain two possibility results. First, stable matching
mechanisms never hurt more than one-half of the society after the integration
of several matching markets occurs. Second, in random matching markets there
are positive expected gains from integration for both sides of the market,
which I quantify.
Origins of Life: A Problem for Physics
Date: 2017-05-23 00:00:00

The origins of life stands among the great open scientific questions of our
time. While a number of proposals exist for possible starting points in the
pathway from non-living to living matter, these have so far not achieved states
of complexity that are anywhere near that of even the simplest living systems.
A key challenge is identifying the properties of living matter that might
distinguish living and non-living physical systems such that we might build new
life in the lab. This review is geared towards covering major viewpoints on the
origin of life for those new to the origin of life field, with a forward look
towards considering what it might take for a physical theory that universally
explains the phenomenon of life to arise from the seemingly disconnected array
of ideas proposed thus far. The hope is that a theory akin to our other
theories in fundamental physics might one day emerge to explain the phenomenon
of life, and in turn finally permit solving its origins.
Quantum probability updating from zero prior (by-passing Cromwell's rule)
Date: 2017-05-23 00:00:00
Tasks: Decision Making

Cromwell's rule (also known as the zero priors paradox) refers to the
constraint of classical probability theory that if one assigns a prior
probability of 0 or 1 to a hypothesis, then the posterior has to be 0 or 1 as
well (this is a straightforward implication of how Bayes's rule works).
Relatedly, hypotheses with a very low prior cannot be updated to have a very
high posterior without a tremendous amount of new evidence to support them (or
to make other possibilities highly improbable). Cromwell's rule appears at odds
with our intuition of how humans update probabilities. In this work, we report
two simple decision making experiments, which seem to be inconsistent with
Cromwell's rule. Quantum probability theory, the rules for how to assign
probabilities from the mathematical formalism of quantum mechanics, provides an
alternative framework for probabilistic inference. An advantage of quantum
probability theory is that it is not subject to Cromwell's rule and it can
accommodate changes from zero or very small priors to significant posteriors.
We outline a model of decision making, based on quantum theory, which can
accommodate the changes from priors to posteriors, observed in our experiments.
Reference String Extraction Using Line-Based Conditional Random Fields
Authors: Körner Martin
Date: 2017-05-23 00:00:00

The extraction of individual reference strings from the reference section of
scientific publications is an important step in the citation extraction
pipeline. Current approaches divide this task into two steps by first detecting
the reference section areas and then grouping the text lines in such areas into
reference strings. We propose a classification model that considers every line
in a publication as a potential part of a reference string. By applying
line-based conditional random fields rather than constructing the graphical
model based on the individual words, dependencies and patterns that are typical
in reference sections provide strong features while the overall complexity of
the model is reduced.
A simple unified explanation of several genetic issues on today's human population and on archaic humans
Authors: Per H. Enflo, Gustavo A. Muñoz-Fernández, Juan B. Seoane-Sepúlveda
Date: 2017-05-23 00:00:00

We will give a simple, unified, possible explanation of several debated genetic issues on today's humans, Neandertals and Denisovans. In particular it is shown by means of a simple mathematical model why there is little genetic variation in todays's human population or in Western Neandertal population, why all mtDNA and y-chromosomes in today's humans seem to have African origin with no trace of Neandertal nor Denosovan mtDNA or y-chromosomes, why a big part of the European gene pool is young (from Neolitic time), and why today's East Asians have mode Neandertal genes than today's Europeans.
Herding boosts too-connected-to-fail risk in stock market of China
Date: 2018-04-25 00:00:00
Tasks: Diversity

The crowd panic and its contagion play non-negligible roles at the time of
the stock crash, especially for China where inexperienced investors dominate
the market. However, existing models rarely consider investors in networking
stocks and accordingly miss the exact knowledge of how panic contagion leads to
abrupt crash. In this paper, by networking stocks of sharing common mutual
funds, a new methodology of investigating the market crash is presented. It is
surprisingly revealed that the herding, which origins in the mimic of seeking
for high diversity across investment strategies to lower individual risk, will
produce too-connected-to-fail stocks and reluctantly boosts the systemic risk
of the entire market. Though too-connected stocks might be relatively stable
during the crisis, they are so influential that a small downward fluctuation
will cascade to trigger severe drops of massive successor stocks, implying that
their falls might be unexpectedly amplified by the collective panic and result
in the market crash. Our findings suggest that the whole picture of portfolio
strategy has to be carefully supervised to reshape the stock network.
A dynamical modeling to study the adaptive immune system and the influence of antibodies in the immune memory
Date: 2017-05-19 00:00:00

Immunological systems have been an abundant inspiration to contemporary
computer scientists. Problem solving strategies, stemming from known immune
system phenomena, have been successfully applied to challenging problems of
modern computing (MONROY, SAAB, GODINEZ, 2004). Simulation systems and
mathematical modeling are also beginning use to answer more complex
immunological questions as immune memory process and duration of vaccines,
where the regulation mechanisms are not still known sufficiently (LundegaarD,
Lund, Kesmir, Brunak, Nielsen, 2007).In this article we studied in machina a
approach to simulate the process of antigenic mutation and its implications for
the process of memory. Our results have suggested that the durability of the
immune memory is affected by the process of antigenic mutation and by
populations of soluble antibodies in the blood. The results also strongly
suggest that the decrease of the production of antibodies favors the global
maintenance of immune memory.
Transit and lifespan in neutrophil production: implications for drug intervention
Date: 2017-05-23 00:00:00

We compare and contrast the transit compartment ordinary differential
equation modelling approach with distributed and discrete delay differential
equation models. We focus on Quartino's extension to the Friberg transit
compartment model of myelosuppression, widely relied upon in the pharmaceutical
sciences to predict the neutrophil response after chemotherapy, and on a QSP
delay differential equation model of granulopoiesis. We extend the Quartino
model by considering a general number of transit compartments and introduce an
extra parameter which allows us to decouple the maturation time from the
production rate of cells, and review the well established linear chain
technique from the delay differential equation (DDE) literature which can be
used to reformulate transit compartment models with constant transit rates as
distributed delay DDEs. We perform a state-dependent time rescaling of the
Quartino model in order to apply the linear chain technique and rewrite the
Quartino model as a distributed delay DDE, which yields a discrete delay DDE
model in a certain parameter limit. We then perform stability and bifurcation
analyses on the models to situate such studies in a mathematical pharmacology
context.
  We show that both the original Friberg and the Quartino extension model
incorrectly define the mean maturation time, essentially treating the
proliferative pool as an additional maturation compartment, which can have far
reaching consequences on the development of future models of myelosuppression
in PK/PD.
Optimal Dividends in the Dual Risk Model under a Stochastic Interest Rate
Date: 2017-05-23 00:00:00

Optimal dividend strategy in dual risk model is well studied in the
literatures. But to the best of our knowledge, all the previous works assumes
deterministic interest rate. In this paper, we study the optimal dividends
strategy in dual risk model, under a stochastic interest rate, assuming the
discounting factor follows a geometric Brownian motion or exponential L\'evy
process. We will show that closed form solutions can be obtained.
A Quantum-like Model of Selection Behavior
Date: 2017-04-15 00:00:00
Tasks: model

In this paper, we introduce a new model of selection behavior under risk that
describes an essential cognitive process for comparing values of objects and
making a selection decision. This model is constructed by the quantum-like
approach that employs the state representation specific to quantum theory,
which has the mathematical framework beyond the classical probability theory.
We show that our quantum approach can clearly explain the famous examples of
anomalies for the expected utility theory, the Ellsberg paradox, the Machina
paradox and the disparity between WTA and WTP. Further, we point out that our
model mathematically specifies the characteristics of the probability weighting
function and the value function, which are basic concepts in the prospect
theory.
Quantum theory of time perception: phases,clocks and quantum algebra
Date: 2017-06-15 00:00:00

Experience of time is one of the primordial human experiences which is deeply
tied to human consciousness. But despite this intimate relation of time with
human conscious experience, time has proved to be very elusive. Particularly in
physics, though there is already some understanding of time, there are still so
many paradoxes that plague this understanding. In this paper we take rather a
different route to question of time. We first attempt to come up with a
theoretical understanding of time perception. Quite interestingly we find that
quantum theory provides an algebraic formulation within which we can understand
some essential aspects of time perception by human mind. We then ask whether a
similar formalism can furnish the understanding of time as well and find
connections of our formulation of time with similar works by other researchers.
Our underlying approach to question of time has been inspired by R. W. Hamilton
who considers algebra as science of pure time. Hence our work has an extensive
algebraic flavor. Our work also incorporates another approach based on
Kauffman's iterant algebra which relates time to underlying recursions and
oscillations. We believe that our work will initiate more investigations in
this direction.
Classifications of Innovations Survey and Future Directions
Date: 2017-04-14 00:00:00
Tasks: Classification, Management, Survey

The purpose of this paper is to focus on similarity and/or heterogeneity of
taxonomies of innovation present in the economic fields to show as the economic
literature uses different names to indicate the same type of technical change
and innovation, and the same name for different types of innovation. This
ambiguity of classification makes it impossible to compare the various studies;
moreover the numerous typologies existing in the economics of innovation,
technometrics, economics of technical change, management of technology, etc.,
have hindered the development of knowledge in these fields. The research
presents also new directions on the classification of innovation that try to
overcome these problems.
The free energy principle for action and perception: A mathematical review
Date: 2017-05-24 00:00:00
Tasks: Learning Theory

The 'free energy principle' (FEP) has been suggested to provide a unified
theory of the brain, integrating data and theory relating to action,
perception, and learning. The theory and implementation of the FEP combines
insights from Helmholtzian 'perception as inference', machine learning theory,
and statistical thermodynamics. Here, we provide a detailed mathematical
evaluation of a suggested biologically plausible implementation of the FEP that
has been widely used to develop the theory. Our objectives are (i) to describe
within a single article the mathematical structure of this implementation of
the FEP; (ii) provide a simple but complete agent-based model utilising the
FEP; (iii) disclose the assumption structure of this implementation of the FEP
to help elucidate its significance for the brain sciences.
Geometry of symmetric group-based models
Date: 2017-08-17 00:00:00

Phylogenetic models have polynomial parametrization maps. For symmetric
group-based models, Matsen studied the polynomial inequalities that
characterize the joint probabilities in the image of these parametrizations. We
employ this description for maximum likelihood estimation via numerical
algebraic geometry. In particular, we explore an example where the maximum
likelihood estimate does not exist, which would be difficult to discover
without using algebraic methods. We also study the embedding problem for
symmetric group-based models, i.e. we identify which mutation matrices are
matrix exponentials of rate matrices that are invariant under a group action.