pwc-archive/papers-with-abstracts huggingface dataset:
PGR: A Graph Repository of Protein 3D-Structures
Date: 2016-01-25 00:00:00
Tasks: Graph Mining

Graph theory and graph mining constitute rich fields of computational
techniques to study the structures, topologies and properties of graphs. These
techniques constitute a good asset in bioinformatics if there exist efficient
methods for transforming biological data into graphs. In this paper, we present
Protein Graph Repository (PGR), a novel database of protein 3D-structures
transformed into graphs allowing the use of the large repertoire of graph
theory techniques in protein mining. This repository contains graph
representations of all currently known protein 3D-structures described in the
Protein Data Bank (PDB). PGR also provides an efficient online converter of
protein 3D-structures into graphs, biological and graph-based description,
pre-computed protein graph attributes and statistics, visualization of each
protein graph, as well as graph-based protein similarity search tool. Such
repository presents an enrichment of existing online databases that will help
bridging the gap between graph mining and protein structure analysis. PGR data
and features are unique and not included in any other protein database. The
repository is available at http://wjdi.bioinfo.uqam.ca/.
Cross-Frequency Coupling of Neuronal Oscillations During Cognition
Date: 2016-03-31 00:00:00
Tasks: Decision Making, Hippocampus

How the brain co-ordinates the actions of distant regions in an efficient
manner is an open problem. Many believe that cross-frequency coupling between
the amplitude of high frequency local field potential oscillations in one
region and the phase of lower frequency signals in another may form a possible
mechanism. This work provides a preliminary study from both an experimental and
theoretical viewpoint, concentrating on possible coupling between the
hippocampus and pre-frontal cortex in rats during tasks involving working
memory, spatial navigation and decision making processes. Attempts to search
for such coupling events are made using newly developed MATLAB scripts. This
leads to the discovery of increased envelope-to-signal correlation (ESC)
between the 1-10 Hz hippocampus theta and 30-40 Hz pre-fontal cortex gamma
bands when a choice turn is approached during a T-maze task. From a theoretical
perspective, a standard connectionist modelling approach is extended to allow
for the formation of oscillations. Although detrimental to overall average task
performance, this did lead to a reduced increase in performance variation as
noise was increased, when compared to a standard non-oscillating model.
Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items
Authors: He Ruining, Lin Chunbin, McAuley Julian
Date: 2016-03-31 00:00:00

To build a fashion recommendation system, we need to help users retrieve
fashionable items that are visually similar to a particular query, for reasons
ranging from searching alternatives (i.e., substitutes), to generating stylish
outfits that are visually consistent, among other applications. In domains like
clothing and accessories, such considerations are particularly paramount as the
visual appearance of items is a critical feature that guides users' decisions.
However, existing systems like Amazon and eBay still rely mainly on keyword
search and recommending loosely consistent items (e.g. based on co-purchasing
or browsing data), without an interface that makes use of visual information to
serve the above needs. In this paper, we attempt to fill this gap by designing
and implementing an image-based query system, called Fashionista, which
provides a graphical interface to help users efficiently explore those items
that are not only visually similar to a given query, but which are also
fashionable, as determined by visually-aware recommendation approaches.
Methodologically, Fashionista learns a low-dimensional visual space as well as
the evolution of fashion trends from large corpora of binary feedback data such
as purchase histories of Women's Clothing & Accessories from Amazon, which we
use for this demonstration.
Automated point-neuron simplification of data-driven microcircuit models
Date: 2017-03-30 00:00:00

A method is presented for the reduction of morphologically detailed
microcircuit models to a point-neuron representation without human
intervention. The simplification occurs in a modular workflow, in the
neighborhood of a user specified network activity state for the reference
model, the "operating point". First, synapses are moved to the soma, correcting
for dendritic filtering by low-pass filtering the delivered synaptic current.
Filter parameters are computed numerically and independently for inhibitory and
excitatory input using a Green's function approach. Next, point-neuron models
for each neuron in the microcircuit are fit to their respective morphologically
detailed counterparts. Here, generalized integrate-and-fire point neuron models
are used, leveraging a recently published fitting toolbox. The fits are
constrained by currents and voltages computed in the morphologically detailed
partner neurons with soma corrected synapses at three depolarizations about the
user specified operating point. The result is a simplified circuit which is
well constrained by the reference circuit, and can be continuously updated as
the latter iteratively integrates new data. The modularity of the approach
makes it applicable also for other point-neuron and synapse models. The
approach is demonstrated on a recently reported reconstruction of a neocortical
microcircuit around an in vivo-like working point. The resulting simplified
network model is benchmarked to the reference morphologically detailed
microcircuit model for a range of simulated network protocols. The simplified
network is found to be slightly more sub-critical than the reference, with
otherwise good agreement for both quantitative and qualitative validations.
Option pricing under fast-varying long-memory stochastic volatility
Date: 2018-04-16 00:00:00

Recent empirical studies suggest that the volatility of an underlying price
process may have correlations that decay slowly under certain market
conditions. In this paper, the volatility is modeled as a stationary process
with long-range correlation properties in order to capture such a situation,
and we consider European option pricing. This means that the volatility process
is neither a Markov process nor a martingale. However, by exploiting the fact
that the price process is still a semimartingale and accordingly using the
martingale method, we can obtain an analytical expression for the option price
in the regime where the volatility process is fast mean-reverting. The
volatility process is modeled as a smooth and bounded function of a fractional
Ornstein-Uhlenbeck process. We give the expression for the implied volatility,
which has a fractional term structure.
The Mittag-Leffler Fitting of the Phillips Curve
Date: 2019-09-30 00:00:00
Tasks: Relation

In this paper, a mathematical model based on the one-parameter Mittag-Leffler
function is proposed to be used for the first time to describe the relation
between unemployment rate and inflation rate, also known as the Phillips curve.
The Phillips curve is in the literature often represented by an
exponential-like shape. On the other hand, Phillips in his fundamental paper
used a power function in the model definition. Considering that the ordinary as
well as generalised Mittag-Leffler function behaves between a purely
exponential function and a power function it is natural to implement it in the
definition of the model used to describe the relation between the data
representing the Phillips curve. For the modelling purposes the data of two
different European economies, France and Switzerland, were used and an
"out-of-sample" forecast was done to compare the performance of the
Mittag-Leffler model to the performance of the power-type and exponential-type
model. The results demonstrate that the ability of the Mittag-Leffler function
to fit data that manifest signs of stretched exponentials, oscillations or even
damped oscillations can be of use when describing economic relations and
phenomenons, such as the Phillips curve.
Getting rich quick with the Axiom of Choice
Date: 2017-03-21 00:00:00

This paper proposes new get-rich-quick schemes that involve trading in a
financial security with a non-degenerate price path. For simplicity the
interest rate is assumed zero. If the price path is assumed continuous, the
trader can become infinitely rich immediately after it becomes non-constant (if
it ever does). If it is assumed positive, he can become infinitely rich
immediately after reaching a point in time such that the variation of the log
price is infinite in any right neighbourhood of that point (whereas reaching a
point in time such that the variation of the log price is infinite in any left
neighbourhood of that point is not sufficient). The practical value of these
schemes is tempered by their use of the Axiom of Choice.
Commodity Dynamics: A Sparse Multi-class Approach
Date: 2016-10-12 00:00:00

The correct understanding of commodity price dynamics can bring relevant
improvements in terms of policy formulation both for developing and developed
countries. Agricultural, metal and energy commodity prices might depend on each
other: although we expect few important effects among the total number of
possible ones, some price effects among different commodities might still be
substantial. Moreover, the increasing integration of the world economy suggests
that these effects should be comparable for different markets. This paper
introduces a sparse estimator of the Multi-class Vector AutoRegressive model to
detect common price effects between a large number of commodities, for
different markets or investment portfolios. In a first application, we consider
agricultural, metal and energy commodities for three different markets. We show
a large prevalence of effects involving metal commodities in the Chinese and
Indian markets, and the existence of asymmetric price effects. In a second
application, we analyze commodity prices for five different investment
portfolios, and highlight the existence of important effects from energy to
agricultural commodities. The relevance of biofuels is hereby confirmed.
Overall, we find stronger similarities in commodity price effects among
portfolios than among markets.
DNA to DNA transcription might exist in eukaryotic cells
Date: 2018-01-29 00:00:00

Till now, in biological sciences, the term, transcription, mainly refers to
DNA to RNA transcription. But our recently published experimental findings
obtained from Plasmodium falciparum strongly suggest the existence of DNA to
DNA transcription in the genome of eukaryotic cells, which could shed some
light on the functions of certain noncoding DNA in the human and other
eukaryotic genomes.
GateKeeper: A New Hardware Architecture for Accelerating Pre-Alignment in DNA Short Read Mapping
Authors: Mohammed Alser, Hasan Hassan, Hongyi Xin, Oğuz Ergin, Onur Mutlu, Can Alkan
Date: 2016-04-06 00:00:00

Motivation: High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -- called short reads -- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and "candidate" locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (1) it is implemented using quadratic-time dynamic programming algorithms, and (2) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment operations. Results: We propose GateKeeper, a new hardware accelerator that functions as a pre-alignment step that quickly filters out most incorrect candidate locations. GateKeeper is the first design to accelerate pre-alignment using Field-Programmable Gate Arrays (FPGAs), which can perform pre-alignment much faster than software. GateKeeper can be integrated with any mapper that performs sequence alignment for verification. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing up to 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment techniques, Adjacency Filter and Shifted Hamming Distance (SHD), respectively. The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10. Availability: https://github.com/BilkentCompGen/GateKeeper
The statistical significance of multivariate Hawkes processes fitted to limit order book data
Date: 2016-04-15 00:00:00
Tasks: Point Processes

Hawkes processes have seen a number of applications in finance, due to their
ability to capture event clustering behaviour typically observed in financial
systems. Given a calibrated Hawkes process, of concern is the statistical fit
to empirical data, particularly for the accurate quantification of self- and
mutual-excitation effects. We investigate the application of a multivariate
Hawkes process with a sum-of-exponentials kernel and piecewise-linear
exogeneity factors, fitted to liquidity demand and replenishment events
extracted from limit order book data. We consider one-, two- and
three-exponential kernels, applying various tests to ascertain goodness-of-fit
and stationarity of residuals, as well as stability of the calibration
procedure. In line with prior research, it is found that performance across all
tests improves as the number of exponentials is increased, with a
sum-of-three-exponentials yielding the best fit to the given set of coupled
point processes.
The Affine Wealth Model: An agent-based model of asset exchange that allows for negative-wealth agents and its empirical validation
Date: 2018-02-15 00:00:00
Tasks: Diagnostic, Time Series, Time Series Analysis

We present a stochastic, agent-based, binary-transaction Asset-Exchange Model
(AEM) for wealth distribution that allows for agents with negative wealth. This
model retains certain features of prior AEMs such as redistribution and
wealth-attained advantage, but it also allows for shifts as well as scalings of
the agent density function. We derive the Fokker-Planck equation describing its
time evolution and we describe its numerical solution, including a methodology
for solving the inverse problem of finding the model parameters that best match
empirical data. Using this methodology, we compare the steady-state solutions
of the Fokker-Planck equation with data from the United States Survey of
Consumer Finances over a time period of 27 years. In doing so, we demonstrate
agreement with empirical data of an average error less than 0.16\% over this
time period. We present the model parameters for the US wealth distribution
data as a function of time under the assumption that the distribution responds
to their variation adiabatically. We argue that the time series of model
parameters thus obtained provides a valuable new diagnostic tool for analyzing
wealth inequality.
Multiple Comparative Metagenomics using Multiset k-mer Counting
Date: 2016-09-23 00:00:00

Background. Large scale metagenomic projects aim to extract biodiversity
knowledge between different environmental conditions. Current methods for
comparing microbial communities face important limitations. Those based on
taxonomical or functional assignation rely on a small subset of the sequences
that can be associated to known organisms. On the other hand, de novo methods,
that compare the whole sets of sequences, either do not scale up on ambitious
metagenomic projects or do not provide precise and exhaustive results.
  Methods. These limitations motivated the development of a new de novo
metagenomic comparative method, called Simka. This method computes a large
collection of standard ecological distances by replacing species counts by
k-mer counts. Simka scales-up today's metagenomic projects thanks to a new
parallel k-mer counting strategy on multiple datasets.
  Results. Experiments on public Human Microbiome Project datasets demonstrate
that Simka captures the essential underlying biological structure. Simka was
able to compute in a few hours both qualitative and quantitative ecological
distances on hundreds of metagenomic samples (690 samples, 32 billions of
reads). We also demonstrate that analyzing metagenomes at the k-mer level is
highly correlated with extremely precise de novo comparison techniques which
rely on all-versus-all sequences alignment strategy or which are based on
taxonomic profiling.
Finding Mutated Subnetworks Associated with Survival in Cancer
Date: 2016-09-10 00:00:00

Next-generation sequencing technologies allow the measurement of somatic
mutations in a large number of patients from the same cancer type. One of the
main goals in analyzing these mutations is the identification of mutations
associated with clinical parameters, such as survival time. This goal is
hindered by the genetic heterogeneity of mutations in cancer, due to the fact
that genes and mutations act in the context of pathways. To identify mutations
associated with survival time it is therefore crucial to study mutations in the
context of interaction networks.
  In this work we study the problem of identifying subnetworks of a large
gene-gene interaction network that have mutations associated with survival. We
formally define the associated computational problem by using a score for
subnetworks based on the test statistic of the log-rank test, a widely used
statistical test for comparing the survival of two populations. We show that
the computational problem is NP-hard and we propose a novel algorithm, called
Network of Mutations Associated with Survival (NoMAS), to solve it. NoMAS is
based on the color-coding technique, that has been previously used in other
applications to find the highest scoring subnetwork with high probability when
the subnetwork score is additive. In our case the score is not additive;
nonetheless, we prove that under a reasonable model for mutations in cancer
NoMAS does identify the optimal solution with high probability. We test NoMAS
on simulated and cancer data, comparing it to approaches based on single gene
tests and to various greedy approaches. We show that our method does indeed
find the optimal solution and performs better than the other approaches.
Moreover, on two cancer datasets our method identifies subnetworks with
significant association to survival when none of the genes has significant
association with survival when considered in isolation.
Decomposing the parameter space of biological networks via a numerical discriminant approach
Date: 2016-04-10 00:00:00

Many systems in biology, physics and engineering can be described by systems
of ordinary differential equation containing many parameters. When studying the
dynamic behavior of these large, nonlinear systems, it is useful to identify
and characterize the steady-state solutions as the model parameters vary, a
technically challenging problem in a high-dimensional parameter landscape.
Rather than simply determining the number and stability of steady-states at
distinct points in parameter space, we decompose the parameter space into
finitely many regions, the steady-state solutions being consistent within each
distinct region. From a computational algebraic viewpoint, the boundary of
these regions is contained in the discriminant locus. We develop global and
local numerical algorithms for constructing the discriminant locus and
classifying the parameter landscape. We showcase our numerical approaches by
applying them to molecular and cell-network models.
Low-density locality-sensitive hashing boosts metagenomic binning
Date: 2016-04-10 00:00:00

Metagenomic binning is an essential task in analyzing metagenomic sequence
datasets. To analyze structure or function of microbial communities from
environmental samples, metagenomic sequence fragments are assigned to their
taxonomic origins. Although sequence alignment algorithms can readily be used
and usually provide high-resolution alignments and accurate binning results,
the computational cost of such alignment-based methods becomes prohibitive as
metagenomic datasets continue to grow. Alternative compositional-based methods,
which exploit sequence composition by profiling local short k-mers in
fragments, are often faster but less accurate than alignment-based methods.
Inspired by the success of linear error correcting codes in noisy channel
communication, we introduce Opal, a fast and accurate novel compositional-based
binning method. It incorporates ideas from Gallager's low-density parity-check
code to design a family of compact and discriminative locality-sensitive
hashing functions that encode long-range compositional dependencies in long
fragments. By incorporating the Gallager LSH functions as features in a simple
linear SVM, Opal provides fast, accurate and robust binning for datasets
consisting of a large number of species, even with mutations and sequencing
errors. Opal not only performs up to two orders of magnitude faster than BWA,
an alignment-based binning method, but also achieves improved binning accuracy
and robustness to sequencing errors. Opal also outperforms models built on
traditional k-mer profiles in terms of robustness and accuracy. Finally, we
demonstrate that we can effectively use Opal in the "coarse search" stage of a
compressive genomics pipeline to identify a much smaller candidate set of
taxonomic origins for a subsequent alignment-based method to analyze, thus
providing metagenomic binning with high scalability, high accuracy and high
resolution.
Bacterial cartels at steady supply
Date: 2016-04-10 00:00:00

Metagenomics has revealed hundreds of bacterial species in almost all
microbiota. In a few well-studied cases, bacterial communities have been
observed to coordinate their metabolic fluxes. In principle, bacteria can
divide tasks to reap the benefits of specialization, as in human economies.
However, the benefits and stability of an economy of bacterial specialists are
far from obvious. Here, we physically model the population dynamics of bacteria
that compete for steadily supplied resources. Importantly, we explicitly model
the metabolic fluxes yielding cellular biomass production under the constraint
of a limited enzyme budget. In our framework, we find that population dynamics
generally leads to the coexistence of different metabolic types, which satisfy
an extended competitive exclusion principle (even allowing for adaptive
mutation). We establish that these consortia act as cartels, whereby population
dynamics pins down resource concentrations at values for which no other
strategy can invade. Finally, we propose that at steady supply, cartels of
competing strategies automatically yield maximum biomass, thereby achieving a
collective optimum.