pwc-archive/papers-with-abstracts huggingface dataset:
A regional model for estimating the aboveground carbon density of Borneo's tropical forests from airborne laser scanning
Date: 2017-05-25 00:00:00

Borneo contains some of the world's most biodiverse and carbon dense tropical
forest, but this 750,000-km2 island has lost 62% of its old-growth forests
within the last 40 years. Efforts to protect and restore the remaining forests
of Borneo hinge on recognising the ecosystem services they provide, including
their ability to store and sequester carbon. Airborne Laser Scanning (ALS) is a
remote sensing technology that allows forest structural properties to be
captured in great detail across vast geographic areas. In recent years ALS has
been integrated into state-wide assessment of forest carbon in Neotropical and
African regions, but not yet in Asia. For this to happen new regional models
need to be developed for estimating carbon stocks from ALS in tropical Asia, as
the forests of this region are structurally and compositionally distinct from
those found elsewhere in the tropics. By combining ALS imagery with data from
173 permanent forest plots spanning the lowland rain forests of Sabah, on the
island of Borneo, we develop a simple-yet-general model for estimating forest
carbon stocks using ALS-derived canopy height and canopy cover as input
metrics. An advanced feature of this new model is the propagation of
uncertainty in both ALS- and ground-based data, allowing uncertainty in
hectare-scale estimates of carbon stocks to be quantified robustly. We show
that the model effectively captures variation in aboveground carbons stocks
across extreme disturbance gradients spanning tall dipterocarp forests and
heavily logged regions, and clearly outperforms existing ALS-based models
calibrated for the tropics, as well as currently available satellite-derived
products. Our model provides a simple, generalised and effective approach for
mapping forest carbon stocks in Borneo, providing a key tool to support the
protection and restoration of its tropical forests.
Ribosome Flow Model with Extended Objects
Date: 2017-05-25 00:00:00
Tasks: model, Translation

We study a deterministic mechanistic model for the flow of ribosomes along
the mRNA molecule, called the ribosome flow model with extended objects
(RFMEO). This model encapsulates many realistic features of translation
including non-homogeneous transition rates along the mRNA, the fact that every
ribosome covers several codons, and the fact that ribosomes cannot overtake one
another.
  The RFMEO is a mean-field approximation of an important model from
statistical mechanics called the totally asymmetric simple exclusion process
with extended objects (TASEPEO). We demonstrate that the RFMEO describes
biophysical aspects of translation better than previous mean-field
approximations, and that its predictions correlate well with those of TASEPEO.
However, unlike TASEPEO, the RFMEO is amenable to rigorous analysis using tools
from systems and control theory. We show that the ribosome density profile
along the mRNA in the RFMEO converges to a unique steady-state density that
depends on the length of the mRNA, the transition rates along it, and the
number of codons covered by every ribosome, but not on the initial density of
ribosomes along the mRNA. In particular, the protein production rate also
converges to a unique steady-state. Furthermore, if the transition rates along
the mRNA are periodic with a common period T then the ribosome density along
the mRNA and the protein production rate converge to a unique periodic pattern
with period T, that is, the model entrains to periodic excitations in the
transition rates.
  We believe that the RFMEO could be useful for modeling, understanding, and
re-engineering translation as well as other important biological processes.
Numerical investigation of Differential Biological Models via RBF collocation Method with Genetic Strategy
Authors: Fardin Salehi, Soleiman Hashemi-Shahraki, Mohammad Kazem Fallah, Mohammad Hemami
Date: 2017-05-15 00:00:00

In this paper, we use Kansa method for solving the system of differential equations in the area of biology. One of the challenges in Kansa method is picking out an optimum value for Shape parameter in Radial Basis Function to achieve the best result of the method because there are not any available analytical approaches for obtaining optimum Shape parameter. For this reason, we design a genetic algorithm to detect a close optimum Shape parameter. The experimental results show that this strategy is efficient in the systems of differential models in biology such as HIV and Influenza. Furthermore, we prove that using Pseudo-Combination formula for crossover in genetic strategy leads to convergence in the nearly best selection of Shape parameter.
Noise Control for DNA Computing
Date: 2017-06-20 00:00:00

Synthetic biology is a growing interdisciplinary field, with far-reaching
applications, which aims to design biochemical systems that behave in a desired
manner. With the advancement of strand-displacement DNA computing, a large
class of abstract biochemical networks may be physically realized using DNA
molecules. Methods for systematic design of the abstract systems with
prescribed behaviors have been predominantly developed at the (less-detailed)
deterministic level. However, stochastic effects, neglected at the
deterministic level, are increasingly found to play an important role in
biochemistry. In such circumstances, methods for controlling the intrinsic
noise in the system are necessary for a successful network design at the
(more-detailed) stochastic level. To bridge the gap, the noise-control
algorithm for designing biochemical networks is developed in this paper. The
algorithm structurally modifies any given reaction network under mass-action
kinetics, in such a way that (i) controllable state-dependent noise is
introduced into the stochastic dynamics, while (ii) the deterministic dynamics
are preserved. The capabilities of the algorithm are demonstrated on a
production-decay reaction system, and on an exotic system displaying
bistability. For the production-decay system, it is shown that the algorithm
may be used to redesign the network to achieve noise-induced multistability.
For the exotic system, the algorithm is used to redesign the network to control
the stochastic switching, and achieve noise-induced oscillations.
Approximate and Stochastic Greedy Optimization
Authors: Ye Nan, Bartlett Peter
Date: 2017-05-25 00:00:00

We consider two greedy algorithms for minimizing a convex function in a
bounded convex set: an algorithm by Jones [1992] and the Frank-Wolfe (FW)
algorithm. We first consider approximate versions of these algorithms. For
smooth convex functions, we give sufficient conditions for convergence, a
unified analysis for the well-known convergence rate of O(1/k) together with a
result showing that this rate is the best obtainable from the proof technique,
and an equivalence result for the two algorithms. We also consider approximate
stochastic greedy algorithms for minimizing expectations. We show that
replacing the full gradient by a single stochastic gradient can fail even on
smooth convex functions. We give a convergent approximate stochastic Jones
algorithm and a convergent approximate stochastic FW algorithm for smooth
convex functions. In addition, we give a convergent approximate stochastic FW
algorithm for nonsmooth convex functions. Convergence rates for these
algorithms are given and proved.
Competent hosts and endemicity of multi-host diseases
Date: 2018-11-27 00:00:00

In this paper we propose a method to study a general vector-hosts
mathematical model in order to explain how the changes in biodiversity could
influence the dynamics of vector-borne diseases. We find that under the
assumption of frequency-dependent transmission, i.e. the assumption that the
number of contacts are diluted by the total population of hosts, the presence
of a competent host is a necessary condition for the existence of an endemic
state. In addition, we obtain that in the case of an endemic disease with a
unique competent and resilient host, an increase in its density amplifies the
disease.
Role of distal enhancers in shaping 3D-folding patterns and defining human-specific features of interphase chromatin architecture in embryonic stem cells
Date: 2017-05-25 00:00:00

Molecular and genetic definitions of human-specific changes to genomic
regulatory networks (GRNs) contributing to development of unique to human
phenotypes remain a highly significant challenge. Genome-wide proximity
placement analysis of diverse families of human-specific genomic regulatory
loci (HSGRL) identified topologically-associating domains (TADs) that are
significantly enriched for HSGRL and designated rapidly-evolving in humans TADs
(Genome Biol Evol. 2016 8; 2774-88). Here, the analysis of HSGRL, hESC-enriched
enhancers, super-enhancers (SEs), and specific sub-TAD structures termed
super-enhancer domains (SEDs) has been performed. Markedly distinct features of
the principal regulatory structures of interphase chromatin evolved in the hESC
genome compared to mouse: the SED quantity is 3-fold higher and the median SED
size is significantly larger. Concomitantly, the overall TAD quantity is
increased by 42% while the median TAD size is significantly decreased (p =
9.11E-37) in the hESC genome. Present analyses illustrate a putative global
role for HSGRL in shaping the human-specific features of the interphase
chromatin organization and functions, which are facilitated by accelerated
creation of new enhancers associated with targeted placement of HSGRL at
defined genomic coordinates. A trend toward the convergence of TAD and SED
architectures of interphase chromatin in the hESC genome may reflect changes of
3D-folding patterns of linear chromatin fibers designed to enhance both
regulatory complexity and functional precision of GRNs by creating
predominantly a single gene per regulatory domain structures.
A multi-stage representation of cell proliferation as a Markov process
Date: 2019-07-22 00:00:00

The stochastic simulation algorithm commonly known as Gillespie's algorithm
is now used ubiquitously in the modelling of biological processes in which
stochastic effects play an important role. In well-mixed scenarios at the
sub-cellular level it is often reasonable to assume that times between
successive reaction/interaction events are exponentially distributed and can be
appropriately modelled as a Markov process and hence simulated by the Gillespie
algorithm. However, Gillespie's algorithm is routinely applied to model
biological systems for which it was never intended. In particular, processes in
which cell proliferation is important should not be simulated naively using the
Gillespie algorithm since the history-dependent nature of the cell cycle breaks
the Markov process. The variance in experimentally measured cell cycle times is
far less than in an exponential cell cycle time distribution with the same
mean.
  Here we suggest a method of modelling the cell cycle that restores the
memoryless property to the system and is therefore consistent with simulation
via the Gillespie algorithm. By breaking the cell cycle into a number of
independent exponentially distributed stages we can restore the Markov property
at the same time as more accurately approximating the appropriate cell cycle
time distributions. The consequences of our revised mathematical model are
explored analytically. We demonstrate the importance of employing the correct
cell cycle time distribution by considering two models incorporating cellular
proliferation (one spatial and one non-spatial) and demonstrating that changing
the cell cycle time distribution makes quantitative and qualitative differences
to their outcomes. Our adaptation will allow modellers and experimentalists
alike to appropriately represent cellular proliferation, whilst still being
able to take advantage of the Gillespie algorithm.
Intrinsic dynamical fluctuations of PNS myelin
Date: 2017-10-26 00:00:00

The ultrastructure fluctuations and complex dynamics of the multi-layered
membrane structure of myelin are fundamental for understanding and control its
formation process and its degeneration and repair in neurological diseases such
as multiple sclerosis (MS). Myelin is considered a liquid-crystal but
information are confined to its average structure due to limitations of the
available standard techniques. To overcome this limitation in this work we have
used Scanning micro X-ray Diffraction (S{\mu}XRD) which is a unique
non-invasive probe of both k-space and real space allowing to visualize
disorder in myelin with high spatial resolution in real space. We have used
this method to examine the myelin sheath in the sciatic nerve of Xenopus
laevis. Our results open could open new venues for understanding formation and
degradation of myelin.
KlusTree: Clustering Answer Trees from Keyword Search on Graphs
Authors: Mohanty Madhulika, Ramanath Maya
Date: 2017-05-27 00:00:00
Tasks: Clustering, Natural Language Queries

Graph structured data on the web is now massive as well as diverse, ranging
from social networks, web graphs to knowledge-bases. Effectively querying this
graph structured data is non-trivial and has led to research in a variety of
directions -- structured queries, keyword and natural language queries,
automatic translation of these queries to structured queries, etc. We are
concerned with a class of queries called relationship queries, which are
usually expressed as a set of keywords (each keyword denoting a named entity).
The results returned are a set of ranked trees, each of which denotes
relationships among the various keywords. The result list could consist of
hundreds of answers. The problem of keyword search on graphs has been explored
for over a decade now, but an important aspect that is not as extensively
studied is that of user experience. We propose KlusTree, which presents
clustered results to the users instead of a list of all the results. In our
approach, the result trees are represented using language models and are
clustered using JS divergence as a distance measure. We compare KlusTree with
the well-known approaches based on isomorphism and tree-edit distance based
clustering. The user evaluations show that KlusTree outperforms the other two
in providing better clustering, thereby enriching user experience, revealing
interesting patterns and improving result interpretation by the user.
Mini-Flash Crashes, Model Risk, and Optimal Execution
Date: 2018-08-12 00:00:00
Tasks: model

Oft-cited causes of mini-flash crashes include human errors, endogenous
feedback loops, the nature of modern liquidity provision, fundamental value
shocks, and market fragmentation. We develop a mathematical model which
captures aspects of the first three explanations. Empirical features of recent
mini-flash crashes are present in our framework. For example, there are periods
when no such events will occur. If they do, even just before their onset,
market participants may not know with certainty that a disruption will unfold.
Our mini-flash crashes can materialize in both low and high trading volume
environments and may be accompanied by a partial synchronization in order
submission.
  Instead of adopting a classically-inspired equilibrium approach, we borrow
ideas from the optimal execution literature. Each of our agents begins with
beliefs about how his own trades impact prices and how prices would move in his
absence. They, along with other market participants, then submit orders which
are executed at a common venue. Naturally, this leads us to explicitly
distinguish between how prices actually evolve and our agents' opinions. In
particular, every agent's beliefs will be expressly incorrect.
Standardised Reputation Measurement
Date: 2017-05-28 00:00:00

Well-defined formal definitions for sentiment and opinion are extended to
incorporate the necessary elements to provide a formal quantitative definition
of reputation. This definition takes the form of a time-based index, in which
each element is a function of a collection of opinions mined during a given
time period. The resulting formal definition is validated against informal
notions of reputation. Practical aspects of data procurement to support such a
reputation index are discussed. The assumption that all mined opinions comprise
a complete set is questioned. A case is made that unexpressed positive
sentiment exists, and can be quantified.
Natural mortality of Trachurus novaezelandiae and their size selection by purse seines off south-eastern Australia
Date: 2017-05-29 00:00:00
Tasks: Survival Analysis

The natural mortality (M) and purse-seine catchability and selectivity were
estimated for Trachurus novaezelandiae, Richardson, 1843 (yellowtail scad)-a
small inshore pelagic species harvested off south-eastern Australia. Hazard
functions were applied to two decades of data describing catches (mostly stable
at a mean +- SE of 315 +- 14 t p.a.) and effort (declining from a maximum of
2289 to 642 boat days between 1999/00 and 2015/16) and inter-dispersed (over
nine years) annual estimates of size-at-age (0+ to 18 years) to enable survival
analysis. The data were best described by a model with eight parameters,
including catchability (estimated at < 0.1 x 10-7 boat day-1), M (0.22 year-1)
and variable age-specific selection up to 6 years with a 50% retention among
5-year olds (larger than the estimated age at maturation). The low catchability
implied minimal fishing mortality by the purse-seine fleet. Ongoing monitoring
and applied gear-based studies are required to validate purse-seine
catchability and selectivity, but the data nevertheless imply T. novaezelandiae
could incur substantial additional fishing effort and, in doing, so alleviate
pressure on other regional small pelagics.
Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations
Date: 2017-12-22 00:00:00
Tasks: compressed sensing

Atomic radii and charges are two major parameters used in implicit solvent
electrostatics and energy calculations. The optimization problem for charges
and radii is under-determined, leading to uncertainty in the values of these
parameters and in the results of solvation energy calculations using these
parameters. This paper presents a new method for quantifying this uncertainty
in implicit solvation calculations of small molecules using surrogate models
based on generalized polynomial chaos (gPC) expansions. There are relatively
few atom types used to specify radii parameters in implicit solvation
calculations; therefore, surrogate models for these low-dimensional spaces
could be constructed using least-squares fitting. However, there are many more
types of atomic charges; therefore, construction of surrogate models for the
charge parameter space requires compressed sensing combined with an iterative
rotation method to enhance problem sparsity. We demonstrate the application of
the method by presenting results for the uncertainties in small molecule
solvation energies based on these approaches. The method presented in this
paper is a promising approach for efficiently quantifying uncertainty in a wide
range of force field parameterization problems, including those beyond
continuum solvation calculations.The intent of this study is to provide a way
for developers of implicit solvent model parameter sets to understand the
sensitivity of their target properties (solvation energy) on underlying choices
for solute radius and charge parameters.
Graph coarse-graining reveals differences in the module-level structure of functional brain networks
Date: 2017-05-29 00:00:00

Network analysis is rapidly becoming a standard tool for studying functional
magnetic resonance imaging (fMRI) data. In this framework, different brain
areas are mapped to the nodes of a network, whose links depict functional
dependencies between the areas. The sizes of the areas that the nodes portray
vary between studies. Recently, it has been recommended that the original
volume elements, voxels, of the imaging experiment should be used as the
network nodes to avoid artefacts and biases. However, this results in a large
numbers of nodes and links, and the sheer amount of detail may obscure
important network features that are manifested on larger scales. One fruitful
approach to detecting such features is to partition networks into modules, i.e.
groups of nodes that are densely connected internally but have few connections
between them. However, attempting to understand how functional networks differ
by simply comparing their individual modular structures can be a daunting task,
and results may be hard to interpret. We show that instead of comparing
different partitions, it is beneficial to analyze differences in the
connectivity between and within the very same modules in networks obtained
under different conditions. We develop a network coarse-graining methodology
that provides easily interpretable results and allows assessing the statistical
significance of observed differences. The feasibility of the method is
demonstrated by analyzing fMRI data recorded from 13 healthy subjects during
rest and movie viewing. While independent partitioning of the networks
corresponding to the the two conditions yields few insights on their
differences, network coarse-graining allows us to pinpoint e.g. the increased
number of intra-module links within the visual cortex during movie viewing.
A scalable solution to the nearest neighbor search problem through local-search methods on neighbor graphs
Authors: Eric S. Tellez, Guillermo Ruiz, Edgar Chavez, Mario Graff
Date: 2017-05-29 00:00:00
Tasks: Clustering

Near neighbor search (NNS) is a powerful abstraction for data access; however, data indexing is troublesome even for approximate indexes. For intrinsically high-dimensional data, high-quality fast searches demand either indexes with impractically large memory usage or preprocessing time. In this paper, we introduce an algorithm to solve a nearest-neighbor query $q$ by minimizing a kernel function defined by the distance from $q$ to each object in the database. The minimization is performed using metaheuristics to solve the problem rapidly; even when some methods in the literature use this strategy behind the scenes, our approach is the first one using it explicitly. We also provide two approaches to select edges in the graph's construction stage that limit memory footprint and reduce the number of free parameters simultaneously. We carry out a thorough experimental comparison with state-of-the-art indexes through synthetic and real-world datasets; we found out that our contributions achieve competitive performances regarding speed, accuracy, and memory in almost any of our benchmarks.
Observability and Synchronization of Neuron Models
Date: 2017-05-29 00:00:00

Observability is the property that enables to distinguish two different
locations in $n$-dimensional state space from a reduced number of measured
variables, usually just one. In high-dimensional systems it is therefore
important to make sure that the variable recorded to perform the analysis
conveys good observability of the system dynamics. In the case of networks
composed of neuron models, the observability of the network depends
nontrivially on the observability of the node dynamics and on the topology of
the network. The aim of this paper is twofold. First, a study of observability
is conducted using four well-known neuron models by computing three different
observability coefficients. This not only clarifies observability properties of
the models but also shows the limitations of applicability of each type of
coefficients in the context of such models. Second, a multivariate singular
spectrum analysis (M-SSA) is performed to detect phase synchronization in
networks composed by neuron models. This tool, to the best of the authors'
knowledge has not been used in the context of networks of neuron models. It is
shown that it is possible to detect phase synchronization i)~without having to
measure all the state variables, but only one from each node, and ii)~without
having to estimate the phase.