pwc-archive/papers-with-abstracts huggingface dataset:
Quantum Cognition: The possibility of processing with nuclear spins in the brain
Date: 2015-08-29 00:00:00

The possibility that quantum processing with nuclear spins might be operative
in the brain is proposed and then explored. Phosphorus is identified as the
unique biological element with a nuclear spin that can serve as a qubit for
such putative quantum processing - a neural qubit - while the phosphate ion is
the only possible qubit-transporter. We identify the "Posner molecule",
$\text{Ca}_9 (\text{PO}_4)_6$, as the unique molecule that can protect the
neural qubits on very long times and thereby serve as a (working)
quantum-memory. A central requirement for quantum-processing is quantum
entanglement. It is argued that the enzyme catalyzed chemical reaction which
breaks a pyrophosphate ion into two phosphate ions can quantum entangle pairs
of qubits. Posner molecules, formed by binding such phosphate pairs with
extracellular calcium ions, will inherit the nuclear spin entanglement. A
mechanism for transporting Posner molecules into presynaptic neurons during a
"kiss and run" exocytosis, which releases neurotransmitters into the synaptic
cleft, is proposed. Quantum measurements can occur when a pair of Posner
molecules chemically bind and subsequently melt, releasing a shower of
intra-cellular calcium ions that can trigger further neurotransmitter release
and enhance the probability of post-synaptic neuron firing. Multiple entangled
Posner molecules, triggering non-local quantum correlations of neuron firing
rates, would provide the key mechanism for neural quantum processing.
Implications, both in vitro and in vivo, are briefly mentioned.
The mysterious orphans of Mycoplasmataceae
Date: 2015-08-24 00:00:00

Background: The length of a protein sequence is largely determined by its
function, i.e. each functional group is associated with an optimal size.
However, comparative genomics revealed that proteins length may be affected by
additional factors. In 2002 it was shown that in bacterium Escherichia coli and
the archaeon Archaeoglobus fulgidus, protein sequences with no homologs are, on
average, shorter than those with homologs. Most experts now agree that the
length distributions are distinctly different between protein sequences with
and without homologs in bacterial and archaeal genomes. In this study, we
examine this postulate by a comprehensive analysis of all annotated prokaryotic
genomes and focusing on certain exceptions.
  Results: We compared lengths distributions of having homologs proteins (HHPs)
and non-having homologs proteins (orphans or ORFans) in all currently annotated
completely sequenced prokaryotic genomes. As expected, the HHPs and ORFans have
strikingly different length distributions in almost all genomes. As previously
established, the HHPs, indeed, are, on average, longer than the ORFans, and the
length distributions for the ORFans have a relatively narrow peak, in contrast
to the HHPs, whose lengths spread over a wider range of values. However, about
thirty genomes do not obey these rules. Practically all genomes of Mycoplasma
and Ureaplasma have atypical ORFans distributions, with the mean lengths of
ORFan larger than the mean lengths of HHPs. These genera constitute over 80% of
atypical genomes.
  Conclusions: We confirmed on a ubiquitous set of genomes the previous
observation that HHPs and ORFans have different gene length distributions. We
also showed that Mycoplasmataceae genomes have distinctive distributions of
ORFans lengths. We offer several possible biological explanations of this
phenomenon.
Financial Knudsen number: breakdown of continuous price dynamics and asymmetric buy and sell structures confirmed by high precision order book information
Date: 2015-08-25 00:00:00

We generalise the description of the dynamics of the order book of financial
markets in terms of a Brownian particle embedded in a fluid of incoming,
exiting and annihilating particles by presenting a model of the velocity on
each side (buy and sell) independently. The improved model builds on the
time-averaged number of particles in the inner layer and its change per unit
time, where the inner layer is revealed by the correlations between price
velocity and change in the number of particles (limit orders). This allows us
to introduce the Knudsen number of the financial Brownian particle motion and
its asymmetric version (on the buy and sell sides). Not being considered
previously, the asymmetric Knudsen numbers are crucial in finance in order to
detect asymmetric price changes. The Knudsen numbers allows us to characterise
the conditions for the market dynamics to be correctly described by a
continuous stochastic process. Not questioned until now for large liquid
markets such as the USD/JPY and EUR/USD exchange rates, we show that there are
regimes when the Knudsen numbers are so high that discrete particle effects
dominate, such as during market stresses and crashes. We document the presence
of imbalances of particles depletion rates on the buy and sell sides that are
associated with high Knudsen numbers and violent directional price changes.
This indicator can detect the direction of the price motion at the early stage
while the usual volatility risk measure is blind to the price direction.
Enhancement of Kv1.3 Potassium Conductance by Extremely Low Frequency Electromagnetic Field
Date: 2015-08-25 00:00:00

Theoretical and experimental evidences support the hypothesis that extremely
low-frequency electromagnetic fields can affect voltage-gated channels. Little
is known, however, about their effect on potassium channels. Kv1.3, a member of
the voltage-gated potassium channels family originally discovered in the brain,
is a key player in important biological processes including antigen-dependent
activation of T-cells during the immune response. We report that Kv1.3
expressed in CHO-K1 cells can be modulated in cell subpopulations by extremely
low frequency and relatively low intensity electromagnetic fields. In
particular, we observed that field exposure can cause an enhancement of Kv1.3
potassium conductance and that the effect lasts for several minutes after field
removal. The results contribute to put immune and nervous system responses to
extremely low-frequency electromagnetic fields into a new perspective, with
Kv1.3 playing a pivotal molecular role.
  Keywords: immunotherapy, immunomodulation, potassium channels, gating,
electromagnetic fields
Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer
Date: 2016-08-11 00:00:00
Tasks: Portfolio Optimization

We solve a multi-period portfolio optimization problem using D-Wave Systems'
quantum annealer. We derive a formulation of the problem, discuss several
possible integer encoding schemes, and present numerical examples that show
high success rates. The formulation incorporates transaction costs (including
permanent and temporary market impact), and, significantly, the solution does
not require the inversion of a covariance matrix. The discrete multi-period
portfolio optimization problem we solve is significantly harder than the
continuous variable problem. We present insight into how results may be
improved using suitable software enhancements, and why current quantum
annealing technology limits the size of problem that can be successfully solved
today. The formulation presented is specifically designed to be scalable, with
the expectation that as quantum annealing technology improves, larger problems
will be solvable using the same techniques.
Large-scale EM Analysis of the Drosophila Antennal Lobe with Automatically Computed Synapse Point Clouds
Date: 2015-08-25 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The promise of extracting connectomes and performing useful analysis on large
electron microscopy (EM) datasets has been an elusive dream for many years.
Tracing in even the smallest portions of neuropil requires copious human
annotation, the rate-limiting step for generating a connectome. While a
combination of improved imaging and automatic segmentation will lead to the
analysis of increasingly large volumes, machines still fail to reach the
quality of human tracers. Unfortunately, small errors in image segmentation can
lead to catastrophic distortions of the connectome.
  In this paper, to analyze very large datasets, we explore different
mechanisms that are less sensitive to errors in automation. Namely, we advocate
and deploy extensive synapse detection on the entire antennal lobe (AL)
neuropil in the brain of the fruit fly Drosophila, a region much larger than
any densely annotated to date. The resulting synapse point cloud produced is
invaluable for determining compartment boundaries in the AL and choosing
specific regions for subsequent analysis. We introduce our methodology in this
paper for region selection and show both manual and automatic synapse
annotation results. Finally, we note the correspondence between image datasets
obtained using the synaptic marker, antibody nc82, and our datasets enabling
registration between light and EM image modalities.
A computational spectral approach to interest rate models
Date: 2015-08-25 00:00:00
Tasks: Uncertainty Quantification

The Polynomial Chaos Expansion (PCE) technique recovers a finite second order
random variable exploiting suitable linear combinations of orthogonal
polynomials which are functions of a given stochas- tic quantity {\xi}, hence
acting as a kind of random basis. The PCE methodology has been developed as a
mathematically rigorous Uncertainty Quantification (UQ) method which aims at
providing reliable numerical estimates for some uncertain physical quantities
defining the dynamic of certain engineering models and their related
simulations. In the present paper we exploit the PCE approach to analyze some
equity and interest rate models considering, without loss of generality, the
one dimensional case. In particular we will take into account those models
which are based on the Geometric Brownian Motion (gBm), e.g. the Vasicek model,
the CIR model, etc. We also provide several numerical applications and results
which are discussed for a set of volatility values. The latter allows us to
test the PCE technique on a quite large set of different scenarios, hence
providing a rather complete and detailed investigation on PCE-approximation's
features and properties, such as the convergence of statistics, distribution
and quantiles. Moreover we give results concerning both an efficiency and an
accuracy study of our approach by comparing our outputs with the ones obtained
adopting the Monte Carlo approach in its standard form as well as in its
enhanced version.
Prediction of Cyberbullying Incidents on the Instagram Social Network
Authors: Hosseinmardi Homa, Mattson Sabrina Arredondo, Rafiq Rahat Ibn, Han Richard, Lv Qin, Mishr Shivakant
Date: 2015-08-25 00:00:00
Tasks: Prediction

Cyberbullying is a growing problem affecting more than half of all American
teens. The main goal of this paper is to investigate fundamentally new
approaches to understand and automatically detect and predict incidents of
cyberbullying in Instagram, a media-based mobile social network. In this work,
we have collected a sample data set consisting of Instagram images and their
associated comments. We then designed a labeling study and employed human
contributors at the crowd-sourced CrowdFlower website to label these media
sessions for cyberbullying. A detailed analysis of the labeled data is then
presented, including a study of relationships between cyberbullying and a host
of features such as cyberaggression, profanity, social graph features, temporal
commenting behavior, linguistic content, and image content. Using the labeled
data, we further design and evaluate the performance of classifiers to
automatically detect and pre- dict incidents of cyberbullying and
cyberaggression.
Hunting, food subsidies, and mesopredator release: the dynamics of crop-raiding baboons in a managed landscape
Date: 2015-08-25 00:00:00

The establishment of protected areas or parks has become an important tool
for wildlife conservation. However, frequent occurrences of human-wildlife
conflict at the edges of these parks can undermine their conservation goals.
Many African protected areas have experienced concurrent declines of apex
predators alongside increases in both baboon abundance and the density of
humans living near the park boundary. Baboons then take excursions outside of
the park to raid crops for food, conflicting with the human population. We
model the interactions of mesopredators (baboons), apex predators and shared
prey in the park to analyze how four components affect the proportion of time
that mesopredators choose to crop-raid: 1) the presence of apex predators; 2)
nutritional quality of the crops; 3) mesopredator "shyness" about leaving the
park; and 4) human hunting of mesopredators. We predict that the presence of
apex predators in the park is the most effective method for controlling
mesopredator abundance, and hence significantly reduces their impact on crops.
Human hunting of mesopredators is less effective as it only occurs during
crop-raiding excursions. Furthermore, making crops less attractive, for
instance by planting crops further from the park boundary or farming less
nutritional crops, can reduce the amount of time mesopredators crop-raid.
A white noise approach to insider trading
Date: 2015-08-26 00:00:00

We present a new approach to the optimal portfolio problem for an insider
with logarithmic utility. Our method is based on white noise theory, stochastic
forward integrals, Hida-Malliavin calculus and the Donsker delta function.
Robustness of movement models: can models bridge the gap between temporal scales of data sets and behavioural processes?
Date: 2015-08-26 00:00:00

Discrete-time random walks and their extensions are common tools for
analyzing animal movement data. In these analyses, resolution of temporal
discretization is a critical feature. Ideally, a model both mirrors the
relevant temporal scale of the biological process of interest and matches the
data sampling rate. Challenges arise when resolution of data is too coarse due
to technological constraints, or when we wish to extrapolate results or compare
results obtained from data with different resolutions. Drawing loosely on the
concept of robustness in statistics, we propose a rigorous mathematical
framework for studying movement models' robustness against changes in temporal
resolution. In this framework, we define varying levels of robustness as formal
model properties, focusing on random walk models with spatially-explicit
component. With the new framework, we can investigate whether models can
validly be applied to data across varying temporal resolutions and how we can
account for these different resolutions in statistical inference results. We
apply the new framework to movement-based resource selection models,
demonstrating both analytical and numerical calculations, as well as a Monte
Carlo simulation approach. While exact robustness is rare, the concept of
approximate robustness provides a promising new direction for analyzing
movement models.
Computational Study of Molecular Mechanisms of Caffeine Actions
Date: 2015-08-26 00:00:00

Caffeine (CAF) is one of the most widely and regularly consumed biologically
active substances. We use computer simulation approach to the study of CAF
activity by searching for its possible complexes with biopolymer fragments. The
principal CAF target at physiologically important concentrations refers to
adenosine receptors. It is a common opinion that CAF is a competitive
antagonist of adenosine. At the first step to molecular level elucidation of
CAF action, we have found a set of the minima of the interaction energy between
CAF and the fragments of human A1 adenosine receptor. Molecular mechanics is
the main method for the calculations of the energy of interactions between CAF
and the biopolymer fragments. We use the Monte Carlo simulation to follow
various mutual arrangements of CAF molecule near the receptor. It appears that
the deepest energy minima refer to hydrogen-bond formation of CAF with amino
acid residues involved in interactions with adenosine, its agonists and
antagonists. The results suggest that the formation of such CAF-receptor
complexes enforced by a close packing of CAF and the receptor fragments is the
reason of CAF actions on nervous system. CAF can block the atomic groups of the
adenosine repressors responsible for the interactions with adenosine, not
necessary by the formation of H bonds with them, but simply hide these groups
from the interactions with adenosine.
Geometry of Spatial Memory Replay
Date: 2016-03-20 00:00:00
Tasks: Hippocampus

Place cells in the rat hippocampus play a key role in creating the animal's
internal representation of the world. During active navigation, these cells
spike only in discrete locations, together encoding a map of the environment.
Electrophysiological recordings have shown that the animal can revisit this map
mentally, during both sleep and awake states, reactivating the place cells that
fired during its exploration in the same sequence they were originally
activated. Although consistency of place cell activity during active navigation
is arguably enforced by sensory and proprioceptive inputs, it remains unclear
how a consistent representation of space can be maintained during spontaneous
replay. We propose a model that can account for this phenomenon and suggests
that a spatially consistent replay requires a number of constraints on the
hippocampal network that affect its synaptic architecture and the statistics of
synaptic connection strengths.
There are no caterpillars in a wicked forest
Date: 2015-08-26 00:00:00

Species trees represent the historical divergences of populations or species,
while gene trees trace the ancestry of individual gene copies sampled within
those populations. In cases involving rapid speciation, gene trees with
topologies that differ from that of the species tree can be most probable under
the standard multispecies coalescent model, making species tree inference more
difficult. Such anomalous gene trees are not well understood except for some
small cases. In this work, we establish one constraint that applies to trees of
any size: gene trees with "caterpillar" topologies cannot be anomalous. The
proof of this involves a new combinatorial object, called a population history,
which keeps track of the number of coalescent events in each ancestral
population.
Estimating Reproducibility in Genome-Wide Association Studies
Date: 2015-08-27 00:00:00

Genome-wide association studies (GWAS) are widely used to discover genetic
variants associated with diseases. To control false positives, all findings
from GWAS need to be verified with additional evidences, even for associations
discovered from a high power study. Replication study is a common verification
method by using independent samples. An association is regarded as true
positive with a high confidence when it can be identified in both primary study
and replication study. Currently, there is no systematic study on the behavior
of positives in the replication study when the positive results of primary
study are considered as the prior information.
  In this paper, two probabilistic measures named Reproducibility Rate (RR) and
False Irreproducibility Rate (FIR) are proposed to quantitatively describe the
behavior of primary positive associations (i.e. positive associations
identified in the primary study) in the replication study. RR is a conditional
probability measuring how likely a primary positive association will also be
positive in the replication study. This can be used to guide the design of
replication study, and to check the consistency between the results of primary
study and those of replication study. FIR, on the contrary, measures how likely
a primary positive association may still be a true positive even when it is
negative in the replication study. This can be used to generate a list of
potentially true associations in the irreproducible findings for further
scrutiny. The estimation methods of these two measures are given. Simulation
results and real experiments show that our estimation methods have high
accuracy and good prediction performance.
The Neural Particle Filter
Date: 2016-11-30 00:00:00
Tasks: State Estimation

The robust estimation of dynamically changing features, such as the position
of prey, is one of the hallmarks of perception. On an abstract, algorithmic
level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing
signals based on the history of observations, provides a mathematical framework
for dynamic perception in real time. Since the general, nonlinear filtering
problem is analytically intractable, particle filters are considered among the
most powerful approaches to approximating the solution numerically. Yet, these
algorithms prevalently rely on importance weights, and thus it remains an
unresolved question how the brain could implement such an inference strategy
with a neuronal population. Here, we propose the Neural Particle Filter (NPF),
a weight-less particle filter that can be interpreted as the neuronal dynamics
of a recurrently connected neural network that receives feed-forward input from
sensory neurons and represents the posterior probability distribution in terms
of samples. Specifically, this algorithm bridges the gap between the
computational task of online state estimation and an implementation that allows
networks of neurons in the brain to perform nonlinear Bayesian filtering. The
model captures not only the properties of temporal and multisensory integration
according to Bayesian statistics, but also allows online learning with a
maximum likelihood approach. With an example from multisensory integration, we
demonstrate that the numerical performance of the model is adequate to account
for both filtering and identification problems. Due to the weightless approach,
our algorithm alleviates the 'curse of dimensionality' and thus outperforms
conventional, weighted particle filters in higher dimensions for a limited
number of particles.
How Evolution Learns to Generalise: Principles of under-fitting, over-fitting and induction in the evolution of developmental organisation
Date: 2015-08-27 00:00:00

One of the most intriguing questions in evolution is how organisms exhibit
suitable phenotypic variation to rapidly adapt in novel selective environments
which is crucial for evolvability. Recent work showed that when selective
environments vary in a systematic manner, it is possible that development can
constrain the phenotypic space in regions that are evolutionarily more
advantageous. Yet, the underlying mechanism that enables the spontaneous
emergence of such adaptive developmental constraints is poorly understood. How
can natural selection, given its myopic and conservative nature, favour
developmental organisations that facilitate adaptive evolution in future
previously unseen environments? Such capacity suggests a form of
\textit{foresight} facilitated by the ability of evolution to accumulate and
exploit information not only about the particular phenotypes selected in the
past, but regularities in the environment that are also relevant to future
environments. Here we argue that the ability of evolution to discover such
regularities is analogous to the ability of learning systems to generalise from
past experience. Conversely, the canalisation of evolved developmental
processes to past selective environments and failure of natural selection to
enhance evolvability in future selective environments is directly analogous to
the problem of over-fitting and failure to generalise in machine learning. We
show that this analogy arises from an underlying mechanistic equivalence by
showing that conditions corresponding to those that alleviate over-fitting in
machine learning enhance the evolution of generalised developmental
organisations under natural selection. This equivalence provides access to a
well-developed theoretical framework that enables us to characterise the
conditions where natural selection will find general rather than particular
solutions to environmental conditions.