pwc-archive/papers-with-abstracts huggingface dataset:
Edge fires drive the shape and stability of tropical forests
Date: 2018-02-13 00:00:00
Tasks: Relation

In tropical regions, fires propagate readily in grasslands but typically
consume only edges of forest patches. Thus forest patches grow due to tree
propagation and shrink by fires in surrounding grasslands. The interplay
between these competing edge effects is unknown, but critical in determining
the shape and stability of individual forest patches, as well the
landscape-level spatial distribution and stability of forests. We analyze
high-resolution remote-sensing data from protected areas of the Brazilian
Cerrado and find that forest shapes obey a robust perimeter-area scaling
relation across climatic zones. We explain this scaling by introducing a
heterogeneous fire propagation model of tropical forest-grassland ecotones.
Deviations from this perimeter-area relation determine the stability of
individual forest patches. At a larger scale, our model predicts that the
relative rates of tree growth due to propagative expansion and long-distance
seed dispersal determine whether collapse of regional-scale tree cover is
continuous or discontinuous as fire frequency changes.
Multilevel nested simulation for efficient risk estimation
Date: 2019-02-14 00:00:00

We investigate the problem of computing a nested expectation of the form
$\mathbb{P}[\mathbb{E}[X|Y]
\!\geq\!0]\!=\!\mathbb{E}[\textrm{H}(\mathbb{E}[X|Y])]$ where $\textrm{H}$ is
the Heaviside function. This nested expectation appears, for example, when
estimating the probability of a large loss from a financial portfolio. We
present a method that combines the idea of using Multilevel Monte Carlo (MLMC)
for nested expectations with the idea of adaptively selecting the number of
samples in the approximation of the inner expectation, as proposed by (Broadie
et al., 2011). We propose and analyse an algorithm that adaptively selects the
number of inner samples on each MLMC level and prove that the resulting MLMC
method with adaptive sampling has an $\mathcal{O}\left(
\varepsilon^{-2}|\log\varepsilon|^2 \right)$ complexity to achieve a root
mean-squared error $\varepsilon$. The theoretical analysis is verified by
numerical experiments on a simple model problem. We also present a stochastic
root-finding algorithm that, combined with our adaptive methods, can be used to
compute other risk measures such as Value-at-Risk (VaR) and Conditional
Value-at-Risk (CVaR), with the latter being achieved with
$\mathcal{O}\left(\varepsilon^{-2}\right)$ complexity.
Understanding Book Popularity on Goodreads
Authors: Maity Suman Kalyan, Kumar Ayush, Mullick Ankan, Choudhary Vishnu, Mukherjee Animesh
Date: 2018-02-14 00:00:00

Goodreads has launched the Readers Choice Awards since 2009 where users are
able to nominate/vote books of their choice, released in the given year. In
this work, we question if the number of votes that a book would receive (aka
the popularity of the book) can be predicted based on the characteristics of
various entities on Goodreads. We are successful in predicting the popularity
of the books with high prediction accuracy (correlation coefficient ~0.61) and
low RMSE (~1.25). User engagement and author's prestige are found to be crucial
factors for book popularity.
Cognitive UAV Communication via Joint Trajectory and Power Control
Date: 2018-02-14 00:00:00

This paper investigates a new spectrum sharing scenario between unmanned
aerial vehicle (UAV) and terrestrial wireless communication systems. We
consider that a cognitive/secondary UAV transmitter communicates with a ground
secondary receiver (SR), in the presence of a number of primary terrestrial
communication links that operate over the same frequency band. We exploit the
UAV's controllable mobility via trajectory design, to improve the cognitive UAV
communication performance while controlling the co-channel interference at each
of the primary receivers (PRs). In particular, we maximize the average
achievable rate from the UAV to the SR over a finite mission/communication
period by jointly optimizing the UAV trajectory and transmit power allocation,
subject to constraints on the UAV's maximum speed, initial/final locations, and
average transmit power, as well as a set of interference temperature (IT)
constraints imposed at each of the PRs for protecting their communications.
However, the joint trajectory and power optimization problem is non-convex and
thus difficult to be solved optimally. To tackle this problem, we propose an
efficient algorithm that ensures to obtain a locally optimal solution by
applying the techniques of alternating optimization and successive convex
approximation (SCA). Numerical results show that our proposed joint UAV
trajectory and power control scheme significantly enhances the achievable rate
of the cognitive UAV communication system, as compared to benchmark schemes.
A mathematical study of CD8+ T cell responses calibrated with human data
Date: 2018-02-14 00:00:00

Complete understanding of the mechanisms regulating the proliferation and
differentiation that takes place during human immune CD8+ T cell responses is
still lacking. Human clinical data is usually limited to blood cell counts, yet
the initiation of these responses occurs in the draining lymph nodes;
antigen-specific effector and memory CD8+ T cells generated in the lymph nodes
migrate to those tissues where they are required. We use approximate Bayesian
computation with deterministic mathematical models of CD8+ T cell populations
(naive, central memory, effector memory and effector) and yellow fever virus
vaccine data to infer the dynamics of these CD8+ T cell populations in three
spatial compartments: draining lymph nodes, circulation and skin. We have made
use of the literature to obtain rates of division and death for human CD8+ T
cell population subsets and thymic export rates. Under the decreasing potential
hypothesis for differentiation during an immune response, we find that, as the
number of T cell clonotypes driven to an immune response increases, there is a
reduction in the number of divisions required to differentiate from a naive to
an effector CD8+ T cell, supporting the "division of labour" hypothesis
observed in murine studies. We have also considered the reverse differentiation
scenario, the increasing potential hypothesis. The decreasing potential model
is better supported by the yellow fever virus vaccine data.
Random Switching for High Performance DC-DC Power Converters
Date: 2018-02-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Random Pulse Width Modulation (RPWM) has been successfully applied in power
electronics for nearly 30 years. The effects of the various possible RPWM
strategies on the Power Spectral Density have been thoroughly studied. Despite
the effectiveness of RPWM in spreading harmonic content, an appeal is
consistently made to maintain the textbook Pulse Width Modulation scheme 'on
average'. Random Switching (RS) does away with this notion and
probabilistically operates the switch. In addition to fulfilling several
optimality conditions, including being the only viable switching strategy at
the theoretical limit of performance and having lower switching losses than any
other RPWM; RS allows for design of the DC behaviour separately from that of
the PSD. The pulse amplitude probability affects the DC and total PSD. The
first and second moment of the pulse length probability distribution affects
the shape of the envelope of the noise of the PSD. The minimum pulse length
acts like a selective harmonic filter. The PSD can therefore be shaped without
external filtering by changing these probabilities. Gaussian and Huffman pulse
length probabilities are shown to be good choices depending on whether
real-time PSD control or spectrum usage are the design goal. In addition, it is
shown that C\'uk's state space averaging model applies to RS, with $D \to p$,
hence no new tools are needed to understand the low frequency behavior or
control performance. A benefit of closed loop random switching is that no
filtering of the controlled variable is required. Randomly responding in a
biased manner dependent on the error is hence shown to be useful. There are
several good reasons to consider RS for high performance applications.
Selecting Microarchitecture Configuration of Processors for Internet of Things
Date: 2018-02-19 00:00:00
Tasks: Management

The Internet of Things (IoT) makes use of ubiquitous internet connectivity to
form a network of everyday physical objects for purposes of automation, remote
data sensing and centralized management/control. IoT objects need to be
embedded with processing capabilities to fulfill these services. The design of
processing units for IoT objects is constrained by various stringent
requirements, such as performance, power, thermal dissipation etc. In order to
meet these diverse requirements, a multitude of processor design parameters
need to be tuned accordingly. In this paper, we propose a temporally efficient
design space exploration methodology which determines power and performance
optimized microarchitecture configurations. We also discuss the possible
combinations of these microarchitecture configurations to form an effective
two-tiered heterogeneous processor for IoT applications. We evaluate our design
space exploration methodology using a cycle-accurate simulator (ESESC) and a
standard set of PARSEC and SPLASH2 benchmarks. The results show that our
methodology determines microarchitecture configurations which are within
2.23%-3.69% of the configurations obtained from fully exhaustive exploration
while only exploring 3%-5% of the design space. Our methodology achieves on
average 24.16x speedup in design space exploration as compared to fully
exhaustive exploration in finding power and performance optimized
microarchitecture configurations for processors.
Enhancement of Noisy Speech with Low Speech Distortion Based on Probabilistic Geometric Spectral Subtraction
Date: 2018-02-13 00:00:00
Tasks: Noise Estimation, Speech Enhancement

A speech enhancement method based on probabilistic geometric approach to
spectral subtraction (PGA) performed on short time magnitude spectrum is
presented in this paper. A confidence parameter of noise estimation is
introduced in the gain function of the proposed method to prevent subtraction
of the overestimated and underestimated noise, which not only removes the noise
efficiently but also prevents the speech distortion. The noise compensated
magnitude spectrum is then recombined with the unchanged phase spectrum to
produce a modified complex spectrum prior to synthesize an enhanced frame.
Extensive simulations are carried out using the speech files available in the
NOIZEUS database in order to evaluate the performance of the proposed method.
Close Miking Empirical Practice Verification: A Source Separation Approach
Date: 2018-02-13 00:00:00

Close miking represents a widely employed practice of placing a microphone
very near to the sound source in order to capture more direct sound and
minimize any pickup of ambient sound, including other, concurrently active
sources. It is used by the audio engineering community for decades for audio
recording, based on a number of empirical rules that were evolved during the
recording practice itself. But can this empirical knowledge and close miking
practice be systematically verified? In this work we aim to address this
question based on an analytic methodology that employs techniques and metrics
originating from the sound source separation evaluation field. In particular,
we apply a quantitative analysis of the source separation capabilities of the
close miking technique. The analysis is applied on a recording dataset obtained
at multiple positions of a typical musical hall, multiple distances between the
microphone and the sound source multiple microphone types and multiple level
differences between the sound source and the ambient acoustic component. For
all the above cases we compute the Source to Interference Ratio (SIR) metric.
The results obtained clearly demonstrate an optimum close-miking performance
that matches the current empirical knowledge of professional audio recording.
Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis
Date: 2018-02-14 00:00:00

Interbank markets are often characterised in terms of a core-periphery
network structure, with a highly interconnected core of banks holding the
market together, and a periphery of banks connected mostly to the core but not
internally. This paradigm has recently been challenged for short time scales,
where interbank markets seem better characterised by a bipartite structure with
more core-periphery connections than inside the core. Using a novel
core-periphery detection method on the eMID interbank market, we enrich this
picture by showing that the network is actually characterised by multiple
core-periphery pairs. Moreover, a transition from core-periphery to bipartite
structures occurs by shortening the temporal scale of data aggregation. We
further show how the global financial crisis transformed the market, in terms
of composition, multiplicity and internal organisation of core-periphery pairs.
By unveiling such a fine-grained organisation and transformation of the
interbank market, our method can find important applications in the
understanding of how distress can propagate over financial networks.
Hamiltonian dynamics and distributed chaos in DNA
Authors: A. Bershadskii
Date: 2018-02-14 00:00:00

It is shown that distributed chaos, generated by Hamiltonian DNA dynamics with spontaneously broken time translational symmetry, imprints itself on the DNA sequence of Arabidopsis thaliana (a model plant for genetic sequencing and mapping) and of the NRXN1 and BRCA2 human genes (as an example). The base-stacking interactions in the DNA duplex and degenerate codon groups have been discussed in this context.
Molecular Computing for Markov Chains
Date: 2018-02-14 00:00:00

In this paper, it is presented a methodology for implementing arbitrarily
constructed time-homogenous Markov chains with biochemical systems. Not only
discrete but also continuous-time Markov chains are allowed to be computed. By
employing chemical reaction networks (CRNs) as a programmable language,
molecular concentrations serve to denote both input and output values. One
reaction network is elaborately designed for each chain. The evolution of
species' concentrations over time well matches the transient solutions of the
target continuous-time Markov chain, while equilibrium concentrations can
indicate the steady state probabilities. Additionally, second-order Markov
chains are considered for implementation, with bimolecular reactions rather
that unary ones. An original scheme is put forward to compile unimolecular
systems to DNA strand displacement reactions for the sake of future physical
implementations. Deterministic, stochastic and DNA simulations are provided to
enhance correctness, validity and feasibility.
Sign Epistasis and the Geometry of Interactions
Date: 2018-02-14 00:00:00

Approaches to gene interactions based on sign epistasis have been highly
influential in recent time. Sign epistasis is useful for relating local and
global properties of fitness landscapes, as well as for analyzing evolutionary
trajectories and constraints. The geometric theory of gene interactions, on the
other hand, provides complete information on interactions in terms of minimal
dependence relations. We propose a new framework that combines aspects of both
approaches. In particular, we provide efficient tools for identifying sign
epistasis and related order perturbations in large genetic systems, with
applications to the malaria-causing parasite Plasmodium vivax. We found that
order perturbations beyond sign epistasis are prevalent in the drug-free
environment, which agrees well with the observation that reversed evolution
back to the ancestral type is difficult. As a theoretical application, we
investigate how rank orders of genotypes with respect to fitness relates to
additivity.
Stock Market Visualization
Date: 2018-02-11 00:00:00

We provide complete source code for a front-end GUI and its back-end
counterpart for a stock market visualization tool. It is built based on the
"functional visualization" concept we discuss, whereby functionality is not
sacrificed for fancy graphics. The GUI, among other things, displays a
color-coded signal (computed by the back-end code) based on how "out-of-whack"
each stock is trading compared with its peers ("mean-reversion"), and the most
sizable changes in the signal ("momentum"). The GUI also allows to efficiently
filter/tier stocks by various parameters (e.g., sector, exchange, signal,
liquidity, market cap) and functionally display them. The tool can be run as a
web-based or local application.
Communication via FRET in Nanonetworks of Mobile Proteins
Date: 2018-02-14 00:00:00

A practical, biologically motivated case of protein complexes (immunoglobulin
G and FcRII receptors) moving on the surface of mastcells, that are common
parts of an immunological system, is investigated. Proteins are considered as
nanomachines creating a nanonetwork. Accurate molecular models of the proteins
and the fluorophores which act as their nanoantennas are used to simulate the
communication between the nanomachines when they are close to each other. The
theory of diffusion-based Brownian motion is applied to model movements of the
proteins. It is assumed that fluorophore molecules send and receive signals
using the Forster Resonance Energy Transfer. The probability of the efficient
signal transfer and the respective bit error rate are calculated and discussed.
Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors
Date: 2018-02-14 00:00:00

In unit root testing, a piecewise locally stationary process is adopted to
accommodate nonstationary errors that can have both smooth and abrupt changes
in second- or higher-order properties. Under this framework, the limiting null
distributions of the conventional unit root test statistics are derived and
shown to contain a number of unknown parameters. To circumvent the difficulty
of direct consistent estimation, we propose to use the dependent wild bootstrap
to approximate the non-pivotal limiting null distributions and provide a
rigorous theoretical justification for bootstrap consistency. The proposed
method is compared through finite sample simulations with the recolored wild
bootstrap procedure, which was developed for errors that follow a
heteroscedastic linear process. Further, a combination of autoregressive sieve
recoloring with the dependent wild bootstrap is shown to perform well. The
validity of the dependent wild bootstrap in a nonstationary setting is
demonstrated for the first time, showing the possibility of extensions to other
inference problems associated with locally stationary processes.
Threshold response and bistability in gene regulation by small noncoding RNA
Date: 2018-02-15 00:00:00

In this paper, we study through mathematical modelling the combined effect of
transcriptional and translational regulation by proteins and small noncoding
RNAs (sRNA) in a genetic feedback motif that has an important role in the
survival of E.coli under stress associated with oxygen and energy availability.
We show that subtle changes in this motif can bring in drastically different
effects on the gene expression. In particular, we show that a threshold
response in the gene expression changes to a bistable response as the
regulation on sRNA synthesis or degradation is altered. These results are
obtained under deterministic conditions. Next, we study how the gene expression
is altered by additive and multiplicative noise which might arise due to
probabilistic occurrences of different biochemical events. Using the
Fokker-Planck formulation, we obtain steady state probability distributions for
sRNA concentration for the network motifs displaying bistability. The
probability distributions are found to be bimodal with two peaks at low and
high concentrations of sRNAs. We further study the variations in the
probability distributions under different values of noise strength and
correlations. The results presented here might be of interest for designing
synthetic network for artificial control.
Implementation of Massive MIMO Uplink Receiver on RaPro Prototyping Platform
Date: 2018-02-15 00:00:00

The updated physical layer standard of the fifth generation wireless
communication suggests the necessity of a rapid prototyping platform. To this
end, we develop RaPro, a multi-core general purpose processor-based massive
multiple-input-multiple-output (MIMO) prototyping platform. To enhance RaPro,
high performance detection and beamforming are needed, whereas both of them
request for accurate channel state information (CSI). In this paper, linear
minimum mean square error (LMMSE)-based channel estimator is adopted and
encapsulated inside RaPro to gain more accurate CSI. Considering the high
comlexity and unknown of channel statistics, we design low-complexity LMMSE
channel estimator to alleviate the rising complexity along with increasing
antenna number and set more computational resource aside for massive MIMO
uplink detection and downlink beamforming. Simulation results indicate the high
mean square error performance and robustness of designed low-complexity method.
Indoor and corridor scenario tests show prominent improvement in bit error rate
performance. Time cost analysis proves the practical use and real-time
transmission ability of the implemented uplink receiver on RaPro.
Black Hole Metric: Overcoming the PageRank Normalization Problem
Authors: Buzzanca Marco, Carchiolo Vincenza, Longheu Alessandro, Malgeri Michele, Mangioni Giuseppe
Date: 2018-02-15 00:00:00
Tasks: ARC

In network science, there is often the need to sort the graph nodes. While
the sorting strategy may be different, in general sorting is performed by
exploiting the network structure. In particular, the metric PageRank has been
used in the past decade in different ways to produce a ranking based on how
many neighbors point to a specific node. PageRank is simple, easy to compute
and effective in many applications, however it comes with a price: as PageRank
is an application of the random walker, the arc weights need to be normalized.
This normalization, while necessary, introduces a series of unwanted
side-effects. In this paper, we propose a generalization of PageRank named
Black Hole Metric which mitigates the problem. We devise a scenario in which
the side-effects are particularily impactful on the ranking, test the new
metric in both real and synthetic networks, and show the results.
Using mathematical modeling to ask meaningful biological questions through combination of bifurcation analysis and population heterogeneity
Date: 2018-02-15 00:00:00

Classical approaches to analyzing dynamical systems, including bifurcation
analysis, can provide invaluable insights into underlying structure of a
mathematical model, and the spectrum of all possible dynamical behaviors.
However, these models frequently fail to take into account population
heterogeneity, which, while critically important to understanding and
predicting the behavior of any evolving system, is a common simplification that
is made in analysis of many mathematical models of ecological systems. Attempts
to include population heterogeneity frequently result in expanding system
dimensionality, effectively preventing qualitative analysis. Reduction Theorem,
or Hidden keystone variable (HKV) method, allows incorporating population
heterogeneity while still permitting the use of previously existing classical
bifurcation analysis. A combination of these methods allows visualization of
evolutionary trajectories and making meaningful predictions about dynamics over
time of evolving populations. Here, we discuss three examples of combination of
these methods to augment understanding of evolving ecological systems. We
demonstrate what new meaningful questions can be asked through this approach,
and propose that the large existing literature of fully analyzed models can
reveal new and meaningful dynamical behaviors with the application of the
HKV-method, if the right questions are asked.