pwc-archive/papers-with-abstracts huggingface dataset:
GOTaxon: Representing the evolution of biological functions in the Gene Ontology
Date: 2018-02-16 00:00:00
Tasks: Logical Reasoning

The Gene Ontology aims to define the universe of functions known for gene
products, at the molecular, cellular and organism levels. While the ontology is
designed to cover all aspects of biology in a "species independent manner", the
fact remains that many if not most biological functions are restricted in their
taxonomic range. This is simply because functions evolve, i.e. like other
biological characteristics they are gained and lost over evolutionary time.
Here we introduce a general method of representing the evolutionary gain and
loss of biological functions within the Gene Ontology. We then apply a variety
of techniques, including manual curation, logical reasoning over the ontology
structure, and previously published "taxon constraints" to assign evolutionary
gain and loss events to the majority of terms in the GO. These gain and loss
events now almost triple the number of terms with taxon constraints, and
currently cover a total of 76% of GO terms, including 40% of molecular function
terms, 78% of cellular component terms, and 89% of biological process terms.
  Database URL: GOTaxon is freely available at
https://github.com/haimingt/GOTaxonConstraint
Reduced integration and improved segregation of functional brain networks in Alzheimer's disease
Date: 2018-02-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Emerging evidence shows that cognitive deficits in Alzheimer disease (AD) are
associated with disruptions in brain functional connectivity. Thus, the
identification of alterations in AD functional networks has become a topic of
increasing interest. However, to what extent AD induces disruption of the
balance of local and global information processing in the human brain remains
elusive. The main objective of this study is to explore the dynamic topological
changes of AD networks in terms of brain network segregation and integration.
We used electroencephalography (EEG) data recorded from 20 participants (10 AD
patients and 10 healthy controls) during resting state. Functional brain
networks were reconstructed using EEG source connectivity computed in different
frequency bands. Graph theoretical analyses were performed assess differences
between both groups. Results revealed that AD networks, compared to networks of
age matched healthy controls, are characterized by lower global information
processing (integration) and higher local information processing (segregation).
Results showed also significant correlation between the alterations in the AD
patients functional brain networks and their cognitive scores. These findings
may contribute to the development of EEG network-based test that could
strengthen results obtained from currently used neurophysiological tests in
neurodegenerative diseases.
Reconstruction of the evolutionary history of gene gains and losses since the last universal common ancestor
Date: 2018-02-16 00:00:00

Gene gains and losses have shaped the gene repertoire of species since the
universal last common ancestor to species today. Genes in extant species were
gained at different historical times via de novo creation of new genes,
duplication of existing genes or transfer from genes of another species (HGT),
and get lost gradually. With the increasing number of sequenced genomes, some
comparative analyses have been done to quantify the evolutionary history of
gene gains and losses in restricted lineages like vertebrates, insects, fungi,
plants and so on. Here, we have constructed and analyzed over 10,000 gene
family trees to reconstruct the gene content of ancestral genomes at an
unprecedented scale, covering hundreds of genomes across all domains of life.
This is the most comprehensive genome-wide analysis of all events in gene
evolutionary histories. We find that our results are largely consistent with
earlier, less complete comparative studies on specific lineages such as the
vertebrates, but find significant differences especially in recent evolutionary
histories. We find that the rate of gene gain varies widely among branches of
the species tree, and find that some periods of rapid gene duplication are
associated with great extinctions in geological history.
Flawed Waveform Design of Augusto Aubry, Antonio DeMaio et al
Date: 2018-02-26 00:00:00

arXiv admin note: This submission has been withdrawn by arXiv administrators
due to unprofessional personal attack.
Algorithmic improvements for the CIECAM02 and CAM16 color appearance models
Authors: Nico Schlömer
Date: 2018-02-16 00:00:00

This note is concerned with the CIECAM02 color appearance model and its successor, the CAM16 color appearance model. Several algorithmic flaws are pointed out and remedies are suggested. The resulting color model is algebraically equivalent to CIECAM02/CAM16, but shorter, more efficient, and works correctly for all edge cases.
Simple Bounds for Utility Maximization with Small Transaction Costs
Authors: Bruno Bouchard, Johannes Muhle-Karbe
Date: 2018-02-16 00:00:00

Using elementary arguments, we show how to derive $\mathbf{L}_p$-error bounds for the approximation of frictionless wealth process in markets with proportional transaction costs. For utilities with bounded risk aversion, these estimates yield lower bounds for the frictional value function, which pave the way for its asymptotic analysis using stability results for viscosity solutions. Using tools from Malliavin calculus, we also derive simple sufficient conditions for the regularity of frictionless optimal trading strategies, the second main ingredient for the asymptotic analysis of small transaction costs.
Multi-user Beam-Alignment for Millimeter-Wave Networks
Date: 2018-02-16 00:00:00
Tasks: Scheduling

Millimeter-wave communications is the most promising technology for
next-generation cellular wireless systems, thanks to the large bandwidth
available compared to sub-6 GHz networks. Nevertheless, communication at these
frequencies requires narrow beams via massive MIMO and beamforming to overcome
the strong signal attenuation, and thus precise beam-alignment between
transmitter and receiver is needed. The resulting signaling overhead may become
a severe impairment, especially in mobile networks with high users density.
Therefore, it is imperative to optimize the beam-alignment protocol to minimize
the signaling overhead. In this paper, the design of energy efficient joint
beam-alignment protocols for two users is addressed, with the goal to minimize
the power consumption during data transmission, subject to rate constraints for
both users, under analog beamforming constraints. It is proved that a bisection
search algorithm is optimal. Additionally, the optimal scheduling strategy of
the two users in the data communication phase is optimized based on the outcome
of beam-alignment, according to a time division multiplexing scheme. The
numerical results show significant decrease in the power consumption for the
proposed joint beam-alignment scheme compared to exhaustive search and a
single-user beam-alignment scheme taking place separately for each user.
Inferring Information Flow in Spike-train Data Sets using a Trial-Shuffle Method
Date: 2018-10-25 00:00:00
Tasks: Functional Connectivity

Understanding information processing in the brain requires the ability to
determine the functional connectivity between the different regions of the
brain. We present a method using transfer entropy to extract this flow of
information between brain regions from spike-train data commonly obtained in
neurological experiments. Transfer entropy is a statistical measure based in
information theory that attempts to quantify the information flow from one
process to another, and has been applied to find connectivity in simulated
spike-train data. Due to statistical error in the estimator, inferring
functional connectivity requires a method for determining significance in the
transfer entropy values. We discuss the issues with numerical estimation of
transfer entropy and resulting challenges in determining significance before
presenting the trial-shuffle method as a viable option. The trial-shuffle
method, for spike-train data that is split into multiple trials, determines
significant transfer entropy values independently for each individual pair of
neurons by comparing to a created baseline distribution using a rigorous
statistical test. This is in contrast to either globally comparing all neuron
transfer entropy values or comparing pairwise values to a single baseline
value.
  In establishing the viability of this method by comparison to several
alternative approaches in the literature, we find evidence that preserving the
inter-spike-interval timing is important.
  We then use the trial-shuffle method to investigate information flow within a
model network as we vary model parameters. This includes investigating the
global flow of information within a connectivity network divided into two
well-connected subnetworks, going beyond local transfer of information between
pairs of neurons.
First-order bifurcation detection for dynamic complex networks
Date: 2018-02-17 00:00:00
Tasks: Dimensionality Reduction, Intrusion Detection

In this paper, we explore how network centrality and network entropy can be
used to identify a bifurcation network event. A bifurcation often occurs when a
network undergoes a qualitative change in its structure as a response to
internal changes or external signals. In this paper, we show that network
centrality allows us to capture important topological properties of dynamic
networks. By extracting multiple centrality features from a network for
dimensionality reduction, we are able to track the network dynamics underlying
an intrinsic low-dimensional manifold. Moreover, we employ von Neumann graph
entropy (VNGE) to measure the information divergence between networks over
time. In particular, we propose an asymptotically consistent estimator of VNGE
so that the cubic complexity of VNGE is reduced to quadratic complexity that
scales more gracefully with network size. Finally, the effectiveness of our
approaches is demonstrated through a real-life application of cyber intrusion
detection.
Protein motion in the nucleus: from anomalous diffusion to weak interactions
Date: 2018-02-17 00:00:00

Understanding how transcription factors (TFs) regulate mammalian gene
expression in space and time is a central topic in biology. To activate a gene,
a TF has first to diffuse in the available space of the nucleus until it
reaches a target DNA sequence or protein (target site). This eventually results
in the recruitment of the whole transcriptional machinery.
  All these processes take place in the mammalian nucleoplasm, a highly
organized and dynamic environment, in which some complexes transiently assemble
and break apart, whereas others appear more stable. This diversity of dynamic
behaviors arises from the number of biomolecules that make up the nucleoplasm
and their pairwise interactions. Indeed, interactions energies that span
several orders of magnitude, from covalent bounds to transient and dynamic
interactions can shape nuclear landscapes. Thus, the nuclear environment
determines how frequently and how fast a TF contacts its target site, and
indirectly gene expression. How exactly transient interactions are involved in
the regulation of TF diffusion is unclear, but are reflected by live cell
imaging techniques such as fluorescence correlation spectroscopy, fluorescence
recovery after photobleaching or single-particle tracking. Overall, the
macroscopic result of these microscopic interactions is almost always anomalous
diffusion, a phenomenon widely studied and modeled.
  Here, we review the connections between the anomalous diffusion of a TF and
the microscopic organization of the nucleus, including recently described
topologically associated domains and dynamic phase-separated compartments. We
propose that anomalous diffusion found in single particle tracking (SPT) data
result from weak and transient interactions with dynamic nuclear substructures,
and that SPT data analysis would benefit form a better description of such
structures.
TabVec: Table Vectors for Classification of Web Tables
Authors: Ghasemi-Gol Majid, Szekely Pedro
Date: 2018-02-17 00:00:00
Tasks: Classification, General Classification

There are hundreds of millions of tables in Web pages that contain useful
information for many applications. Leveraging data within these tables is
difficult because of the wide variety of structures, formats and data encoded
in these tables. TabVec is an unsupervised method to embed tables into a vector
space to support classification of tables into categories (entity, relational,
matrix, list, and non-data) with minimal user intervention. TabVec deploys
syntax and semantics of table cells, and embeds the structure of tables in a
table vector space. This enables superior classification of tables even in the
absence of domain annotations. Our evaluations in four real world domains show
that TabVec improves classification accuracy by more than 20% compared to three
state of the art systems, and that those systems require significant in domain
training to achieve good results.
Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality
Date: 2018-02-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

In this paper, electroencephalography (EEG) measurements are used to infer
change in cortical functional connectivity in response to change in audio
stimulus. Experiments are conducted wherein the EEG activity of human subjects
is recorded as they listen to audio sequences whose quality varies with time. A
causal information theoretic framework is then proposed to measure the
information flow between EEG sensors appropriately grouped into different
regions of interest (ROI) over the cortex. A new causal bidirectional
information (CBI) measure is defined as an improvement over standard directed
information measures for the purposes of identifying connectivity between ROIs
in a generalized cortical network setting. CBI can be intuitively interpreted
as a causal bidirectional modification of directed information, and inherently
calculates the divergence of the observed data from a multiple access channel
with feedback. Further, we determine the analytical relationship between the
different causal measures and compare how well they are able to distinguish
between the perceived audio quality. The connectivity results inferred indicate
a significant change in the rate of information flow between ROIs as the
subjects listen to different audio qualities, with CBI being the best in
discriminating between the perceived audio quality, compared to using standard
directed information measures.
How local in time is the no-arbitrage property under capital gains taxes ?
Date: 2018-09-26 00:00:00

In frictionless financial markets, no-arbitrage is a local property in time.
This means that a discrete time model is arbitrage-free if and only if there
does not exist a one-period-arbitrage. With capital gains taxes, this
equivalence fails. For a model with a linear tax and one non-shortable risky
stock, we introduce the concept of robust local no-arbitrage (RLNA) as the
weakest local condition which guarantees dynamic no-arbitrage. Under a sharp
dichotomy condition, we prove (RLNA). Since no-one-period-arbitrage is
necessary for no-arbitrage, the latter is sandwiched between two local
conditions, which allows us to estimate its non-locality.
  Furthermore, we construct a stock price process such that two long positions
in the same stock hedge each other. This puzzling phenomenon that cannot occur
in arbitrage-free frictionless markets (or markets with proportional
transaction costs) is used to show that no-arbitrage alone does not imply the
existence of an equivalent separating measure if the probability space is
infinite.
  Finally, we show that the model with a linear tax on capital gains can be
written as a model with proportional transaction costs by introducing several
fictitious securities.
Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data
Date: 2018-02-18 00:00:00

B cells develop high affinity receptors during the course of affinity
maturation, a cyclic process of mutation and selection. At the end of affinity
maturation, a number of cells sharing the same ancestor (i.e. in the same
"clonal family") are released from the germinal center, their amino acid
frequency profile reflects the allowed and disallowed substitutions at each
position. These clonal-family-specific frequency profiles, called "substitution
profiles", are useful for studying the course of affinity maturation as well as
for antibody engineering purposes. However, most often only a single sequence
is recovered from each clonal family in a sequencing experiment, making it
impossible to construct a clonal-family-specific substitution profile. Given
the public release of many high-quality large B cell receptor datasets, one may
ask whether it is possible to use such data in a prediction model for
clonal-family-specific substitution profiles. In this paper, we present the
method "Substitution Profiles Using Related Families" (SPURF), a penalized
tensor regression framework that integrates information from a rich assemblage
of datasets to predict the clonal-family-specific substitution profile for any
single input sequence. Using this framework, we show that substitution profiles
from similar clonal families can be leveraged together with simulated
substitution profiles and germline gene sequence information to improve
prediction. We fit this model on a large public dataset and validate the
robustness of our approach on an external dataset. Furthermore, we provide a
command-line tool in an open-source software package
(https://github.com/krdav/SPURF) implementing these ideas and providing easy
prediction using our pre-fit models.
Optimal allocation of attentional resource to multiple items with unequal relevance
Date: 2018-02-18 00:00:00

In natural perception, different items (objects) in a scene are rarely
equally relevant to the observer. The brain improves performance by directing
attention to the most relevant items, for example the ones most likely to be
probed. For a general set of probing probabilities, it is not known how
attentional resources should be allocated to maximize performance. Here, we
investigate the optimal strategy for allocating a fixed resource budget E among
N items when on each trial, only one item gets probed. We develop an efficient
algorithm that, for any concave utility function, reduces the N-dimensional
problem to a set of N one-dimensional problems that the brain could plausibly
solve. We find that the intuitive strategy of allocating resource in proportion
to the probing probabilities is in general not optimal. In particular, in some
tasks, if resource is low, the optimal strategy involves allocating zero
resource to items with a nonzero probability of being probed. Our work opens
the door to normatively guided studies of attentional allocation.
Modeling communication processes in the human connectome through cooperative learning
Date: 2018-02-19 00:00:00
Tasks: Functional Connectivity

Communication processes within the human brain at different cognitive states
are neither well understood nor completely characterized. We assess
communication processes in the human connectome using ant colony-inspired
cooperative learning algorithm, starting from a source with no a priori
information about the network topology, and cooperatively searching for the
target through a pheromone-inspired model. This framework relies on two
parameters, namely pheromone perception and edge perception, to define the
cognizance and subsequent behaviour of the ants on the network and, overall,
the communication processes happening between source and target nodes.
Simulations obtained through different configurations allow the identification
of path-ensembles that are involved in the communication between node pairs.
These path-ensembles may contain different number of paths depending on the
perception parameters and the node pair. In order to assess the different
communication regimes displayed on the simulations and their associations with
functional connectivity, we introduce two network measurements, effective
path-length and arrival rate. These communication features are tested as
individual as well as combined predictors of functional connectivity during
different tasks. Finally, different communication regimes are found in
different specialized functional networks. Overall, this framework may be used
as a test-bed for different communication regimes on top of an underlaying
topology.
Constrained Least Squares, SDP, and QCQP Perspectives on Joint Biconvex Radar Receiver and Waveform design
Date: 2018-02-19 00:00:00

Joint radar receive filter and waveform design is non-convex, but is
individually convex for a fixed receiver filter while optimizing the waveform,
and vice versa. Such classes of problems are fre- quently encountered in
optimization, and are referred to biconvex programs. Alternating minimization
(AM) is perhaps the most popu- lar, effective, and simplest algorithm that can
deal with bi-convexity. In this paper we consider new perspectives on this
problem via older, well established problems in the optimization literature. It
is shown here specifically that the radar waveform optimization may be cast as
constrained least squares, semi-definite programs (SDP), and quadratically
constrained quadratic programs (QCQP). The bi-convex constraint introduces sets
which vary for each iteration in the alternat- ing minimization. We prove
convergence of alternating minimization for biconvex problems with biconvex
constraints by showing the equivalence of this to a biconvex problem with
constrained Cartesian product convex sets but for convex hulls of small
diameter.
Pricing Options with Exponential Levy Neural Network
Date: 2018-09-15 00:00:00

In this paper, we propose the exponential Levy neural network (ELNN) for
option pricing, which is a new non-parametric exponential Levy model using
artificial neural networks (ANN). The ELNN fully integrates the ANNs with the
exponential Levy model, a conventional pricing model. So, the ELNN can improve
ANN-based models to avoid several essential issues such as unacceptable
outcomes and inconsistent pricing of over-the-counter products. Moreover, the
ELNN is the first applicable non-parametric exponential Levy model by virtue of
outstanding researches on optimization in the field of ANN. The existing
non-parametric models are too vulnerable to be employed in practice. The
empirical tests with S\&P 500 option prices show that the ELNN outperforms two
parametric models, the Merton and Kou models, in terms of fitting performance
and stability of estimates.
An implementation of the maximum-caliber principle by replica-averaged time-resolved restrained simulations
Date: 2018-04-24 00:00:00

Inferential methods can be used to integrate experimental informations and
molecular simulations. The maximum entropy principle provides a framework for
using equilibrium experimental data and it has been shown that replica-averaged
simulations, restrained using a static potential, are a practical and powerful
implementation of such principle. Here we show that replica-averaged
simulations restrained using a time-dependent potential are equivalent to the
principle of maximum caliber, the dynamic version of the principle of maximum
entropy, and thus may allow to integrate time-resolved data in molecular
dynamics simulations. We provide an analytical proof of the equivalence as well
as a computational validation making use of simple models and synthetic data.
Some limitations and possible solutions are also discussed.