pwc-archive/papers-with-abstracts huggingface dataset:
Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures
Date: 2018-05-25 00:00:00

What transpires from recent research is that temperatures and radiative
forcing seem to be characterized by a linear trend with two changes in the rate
of growth. The first occurs in the early 60s and indicates a very large
increase in the rate of growth of both temperature and radiative forcing
series. This was termed as the "onset of sustained global warming". The second
is related to the more recent so-called hiatus period, which suggests that
temperatures and total radiative forcing have increased less rapidly since the
mid-90s compared to the larger rate of increase from 1960 to 1990. There are
two issues that remain unresolved. The first is whether the breaks in the slope
of the trend functions of temperatures and radiative forcing are common. This
is important because common breaks coupled with the basic science of climate
change would strongly suggest a causal effect from anthropogenic factors to
temperatures. The second issue relates to establishing formally via a proper
testing procedure that takes into account the noise in the series, whether
there was indeed a `hiatus period' for temperatures since the mid 90s. This is
important because such a test would counter the widely held view that the
hiatus is the product of natural internal variability. Our paper provides tests
related to both issues. The results show that the breaks in temperatures and
radiative forcing are common and that the hiatus is characterized by a
significant decrease in their rate of growth. The statistical results are of
independent interest and applicable more generally.
Are multi-factor Gaussian term structure models still useful? An empirical analysis on Italian BTPs
Date: 2018-05-25 00:00:00

In this paper, we empirically study models for pricing Italian sovereign
bonds under a reduced form framework, by assuming different dynamics for the
short-rate process. We analyze classical Cox-Ingersoll-Ross and Vasicek
multi-factor models, with a focus on optimization algorithms applied in the
calibration exercise. The Kalman filter algorithm together with a maximum
likelihood estimation method are considered to fit the Italian term-structure
over a 12-year horizon, including the global financial crisis and the euro area
sovereign debt crisis. Analytic formulas for the gradient vector and the
Hessian matrix of the likelihood function are provided.
Subdivisions of the posteromedial cortex in disorders of consciousness
Date: 2018-05-25 00:00:00

Evidence suggests that disruptions of the posteromedial cortex (PMC) and
posteromedial corticothalamic connectivity contribute to disorders of
consciousness (DOCs). While most previous studies treated the PMC as a whole,
this structure is functionally heterogeneous. The present study investigated
whether particular subdivisions of the PMC are specifically associated with
DOCs. Participants were DOC patients, 21 vegetative state/unresponsive
wakefulness syndrome (VS/UWS), 12 minimally conscious state (MCS), and 29
healthy controls. Individual PMC and thalamus were divided into distinct
subdivisions by their fiber tractograpy to each other and default mode regions,
and white matter integrity and brain activity between/within subdivisions were
assessed. The thalamus was represented mainly in the dorsal and posterior
portions of the PMC, and the white matter tracts connecting these subdivisions
to the thalamus had less integrity in VS/UWS patients than in MCS patients and
healthy controls, as well as in patients who did not recover after 12 months
than in patients who did. The structural substrates were validated by finding
impaired functional fluctuations within this PMC subdivision. This study is the
first to show that tracts from dorsal and posterior subdivisions of the PMC to
the thalamus contribute to DOCs.
Optimal stimulation protocol in a bistable synaptic consolidation model
Date: 2018-05-25 00:00:00

Consolidation of synaptic changes in response to neural activity is thought
to be fundamental for memory maintenance over a timescale of hours. In
experiments, synaptic consolidation can be induced by repeatedly stimulating
presynaptic neurons. However, the effectiveness of such protocols depends
crucially on the repetition frequency of the stimulations and the mechanisms
that cause this complex dependence are unknown. Here we propose a simple
mathematical model that allows us to systematically study the interaction
between the stimulation protocol and synaptic consolidation. We show the
existence of optimal stimulation protocols for our model and, similarly to LTP
experiments, the repetition frequency of the stimulation plays a crucial role
in achieving consolidation. Our results show that the complex dependence of LTP
on the stimulation frequency emerges naturally from a model which satisfies
only minimal bistability requirements.
Cryptocurrency Equilibria Through Game Theoretic Optimization
Date: 2019-04-13 00:00:00

Optimization methods are used to determine equilibria of investment in
cryptocurrencies. The basic assumptions involve existence of a core group (the
"wealthy") that fears the loss of substantial assets through government
seizure. Speculators constitute another group that tends to introduce
volatility and risk for the wealthy. The wealthy must divide their assets
between the home currency and the cryptocurrency, while the government decides
on the probability of seizing a fraction the assets of this group. Under the
assumption that each group exhibits risk aversion through a utility function,
we establish the existence and uniqueness of Nash equilibrium. Also examined is
the more realistic optimization problem in which the government policy cannot
be reversed, while the wealthy can adjust their allocation in reaction to the
government's designation of probability. The methodology leads to an
understanding the equilibrium market capitalization of cryptocurrencies.
A Time Decomposition and Coordination Strategy for Power System Multi-Interval Operation
Date: 2018-05-25 00:00:00
Tasks: Scheduling

This paper presents a time decomposition strategy to reduce the computational
complexity of power system multi-interval operation problems. We focus on the
economic dispatch problem. The considered scheduling horizon is decomposed into
multiple smaller sub-horizons. The first time interval of each sub-horizon is
modeled as the coupling interval between two consecutive sub-horizons. The
interdependencies between the sub-horizons are mathematically modeled using
ramp rates of generating units. A distributed coordination strategy, which is
based on auxiliary problem principle, is developed to coordinate the economic
dispatch solutions of the sub-horizons to find an optimal solution for the
whole operation horizon. We also propose an initializing technique to start the
iterative coordination algorithm from a good-enough point. This technique
enhances the convergence rate significantly. The proposed algorithm is deployed
to solve a week-ahead economic dispatch problem on the IEEE 118-bus system, and
promising results are obtained.
Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms
Date: 2019-03-28 00:00:00

Hyperspectral images provide much more information than conventional imaging
techniques, allowing a precise identification of the materials in the observed
scene, but because of the limited spatial resolution, the observations are
usually mixtures of the contributions of several materials. The spectral
unmixing problem aims at recovering the spectra of the pure materials of the
scene (endmembers), along with their proportions (abundances) in each pixel. In
order to deal with the intra-class variability of the materials and the induced
spectral variability of the endmembers, several spectra per material,
constituting endmember bundles, can be considered. However, the usual abundance
estimation techniques do not take advantage of the particular structure of
these bundles, organized into groups of spectra. In this paper, we propose to
use group sparsity by introducing mixed norms in the abundance estimation
optimization problem. In particular, we propose a new penalty which
simultaneously enforces group and within group sparsity, to the cost of being
nonconvex. All the proposed penalties are compatible with the abundance
sum-to-one constraint, which is not the case with traditional sparse
regression. We show on simulated and real datasets that well chosen penalties
can significantly improve the unmixing performance compared to the naive bundle
approach.
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
Authors: Johanna Senk, Espen Hagen, Sacha J. van Albada, Markus Diesmann
Date: 2018-05-25 00:00:00

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about 1 mm$^2$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials (LFPs) can be computed from the simulated spiking activity. We here extend a local network and LFP model to an area of 4x4 mm$^2$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of LFP signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around 50 Hz may explain the recent report of an apparent band-pass filter effect in the spatial reach of the LFP.
Quantifying Local Randomness in Human DNA and RNA Sequences Using Erdos Motifs
Date: 2018-09-29 00:00:00

In 1932, Paul Erdos asked whether a random walk constructed from a binary
sequence can achieve the lowest possible deviation (lowest discrepancy), for
the sequence itself and for all its subsequences formed by homogeneous
arithmetic progressions. Although avoiding low discrepancy is impossible for
infinite sequences, as recently proven by Terence Tao, attempts were made to
construct such sequences with finite lengths. We recognize that such
constructed sequences (we call these "Erdos sequences") exhibit certain
hallmarks of randomness at the local level: they show roughly equal frequencies
of subsequences, and at the same time exclude the trivial periodic patterns.
For the human DNA we examine the frequency of a set of Erdos motifs of
length-10 using three nucleotides-to-binary mappings. The particular length-10
Erdos sequence is derived by the length-11 Mathias sequence and is identical
with the first 10 digits of the Thue-Morse sequence, underscoring the fact that
both are deficient in periodicities. Our calculations indicate that: (1) the
purine (A and G)/pyridimine (C and T) based Erdos motifs are greatly
underrepresented in the human genome, (2) the strong(G and C)/weak(A and T)
based Erdos motifs are slightly overrepresented, (3) the densities of the two
are negatively correlated, (4) the Erdos motifs based on all three mappings
being combined are slightly underrepresented, and (5) the strong/weak based
Erdos motifs are greatly overrepresented in the human messenger RNA sequences.
Relative Transfer Function Estimation Exploiting Spatially Separated Microphones in a Diffuse Noise Field
Date: 2018-07-12 00:00:00
Tasks: Speech Enhancement

Many multi-microphone speech enhancement algorithms require the relative
transfer function (RTF) vector of the desired speech source, relating the
acoustic transfer functions of all array microphones to a reference microphone.
In this paper, we propose a computationally efficient method to estimate the
RTF vector in a diffuse noise field, which requires an additional microphone
that is spatially separated from the microphone array, such that the spatial
coherence between the noise components in the microphone array signals and the
additional microphone signal is low. Assuming this spatial coherence to be
zero, we show that an unbiased estimate of the RTF vector can be obtained.
Based on real-world recordings experimental results show that the proposed RTF
estimator outperforms state-of-the-art estimators using only the microphone
array signals in terms of estimation accuracy and noise reduction performance.
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels
Date: 2018-05-25 00:00:00
Tasks: Emotion Recognition, Multi-class Classification, Speech Emotion Recognition

This study introduces a method to design a curriculum for machine-learning to
maximize the efficiency during the training process of deep neural networks
(DNNs) for speech emotion recognition. Previous studies in other
machine-learning problems have shown the benefits of training a classifier
following a curriculum where samples are gradually presented in increasing
level of difficulty. For speech emotion recognition, the challenge is to
establish a natural order of difficulty in the training set to create the
curriculum. We address this problem by assuming that ambiguous samples for
humans are also ambiguous for computers. Speech samples are often annotated by
multiple evaluators to account for differences in emotion perception across
individuals. While some sentences with clear emotional content are consistently
annotated, sentences with more ambiguous emotional content present important
disagreement between individual evaluations. We propose to use the disagreement
between evaluators as a measure of difficulty for the classification task. We
propose metrics that quantify the inter-evaluation agreement to define the
curriculum for regression problems and binary and multi-class classification
problems. The experimental results consistently show that relying on a
curriculum based on agreement between human judgments leads to statistically
significant improvements over baselines trained without a curriculum.
Mathematical Analysis of Chemical Reaction Systems
Date: 2018-05-25 00:00:00

The use of mathematical methods for the analysis of chemical reaction systems
has a very long history, and involves many types of models: deterministic
versus stochastic, continuous versus discrete, and homogeneous versus spatially
distributed. Here we focus on mathematical models based on deterministic
mass-action kinetics. These models are systems of coupled nonlinear
differential equations on the positive orthant. We explain how mathematical
properties of the solutions of mass-action systems are strongly related to key
properties of the networks of chemical reactions that generate them, such as
specific versions of reversibility and feedback interactions.
UWB Channel Sounding and Modeling for UAV Air-to-Ground Propagation Channels
Date: 2018-05-25 00:00:00

Unmanned aerial vehicles (UAVs) are expected to be used extensively in the
near future in applications such as aerial surveillance, transportation, and
disaster assistance. The conditions under which UAVs operate are different from
those of conventional piloted aircrafts. This necessitates development of new
air-to-ground (AG) propagation channel models for UAVs. To our best knowledge,
there are limited studies in the literature on sounding and modeling of
ultrawideband (UWB) AG propagation channels. In this work, comprehensive UWB
measurements are conducted for various UAV communication scenarios using Time
Domain P410 UWB kits. Both time and frequency domain analysis of the measured
data are carried out. Based on the measured data, stochastic path loss and
multipath channel models are developed to characterize AG UWB propagation
channels.
A Nonlocal InSAR Filter for High-Resolution DEM Generation from TanDEM-X Interferograms
Date: 2018-05-26 00:00:00
Tasks: Denoising, Diversity

This paper presents a nonlocal InSAR filter with the goal of generating
digital elevation models of higher resolution and accuracy from bistatic
TanDEM-X strip map interferograms than with the processing chain used in
production. The currently employed boxcar multilooking filter naturally
decreases the resolution and has inherent limitations on what level of noise
reduction can be achieved. The proposed filter is specifically designed to
account for the inherent diversity of natural terrain by setting several
filtering parameters adaptively. In particular, it considers the local fringe
frequency and scene heterogeneity, ensuring proper denoising of interferograms
with considerable underlying topography as well as urban areas. A comparison
using synthetic and TanDEM-X bistatic strip map datasets with existing InSAR
filters shows the effectiveness of the proposed techniques, most of which could
readily be integrated into existing nonlocal filters. The resulting digital
elevation models outclass the ones produced with the existing global TanDEM-X
DEM processing chain by effectively increasing the resolution from 12m to 6m
and lowering the noise level by roughly a factor of two.
Sparse Antenna and Pulse Placement for Colocated MIMO Radar
Date: 2018-05-27 00:00:00
Tasks: Diversity

Multiple input multiple output (MIMO) radar is known for its superiority over
conventional radar due to its antenna and waveform diversity. Although higher
angular resolution, improved parameter identifiability, and better target
detection are achieved, the hardware costs (due to multiple transmitters and
multiple receivers) and high energy consumption (multiple pulses) limit the
usage of MIMO radars in large scale networks. On one hand, higher angle and
velocity estimation accuracy is required, but on the other hand, a lower number
of antennas/pulses is desirable. To achieve such a compromise, in this work,
the Cram'er-Rao lower bound (CRLB) for the angle and velocity estimator is
employed as a performance metric to design the antenna and pulse placement. It
is shown that the CRLB derived for two targets is a more appropriate criterion
in comparison with the single-target CRLB since the two-target CRLB takes into
account both the mainlobe width and sidelobe level of the ambiguity function.
In this paper, several algorithms for antenna and pulse selection based on
convex and submodular optimization are proposed. Numerical experiments are
provided to illustrate the developed theory.
On a stochastic model of epidemic spread with an application to competing infections
Date: 2018-05-27 00:00:00
Tasks: valid

A simple, but ``classical``, stochastic model for epidemic spread in a
finite, but large, population is studied. The progress of the epidemic can be
divided into three different phases that requires different tools to analyse.
Initially the process is approximated by a branching process. It is discussed
for how long time this approximation is valid. When a non-negligible proportion
of the population is already infected the process can be studied using
differential equations. In a final phase the spread will fade out. The results
are used to investigate what happens if two strains of infectious agents, with
different potential for spread, are simultaneously introduced in a totally
susceptible population. It is assumed that an infection causes immunity, and
that a person can only be infected by one strain. The two epidemics will
initially develop approximately as independent branching processes. However, if
both strains causes large epidemics they will, due to immunity, eventually
interact. We will mainly be interested in the final outcome of the spread,
i.e., how large proportion of the population is infected by the different
strains.
The Strategic Reference Gene: an organismal theory of inclusive fitness
Date: 2019-05-13 00:00:00

How to define and use the concept of inclusive fitness is a contentious topic
in evolutionary theory. Inclusive fitness can be used to calculate selection on
a focal gene, but it is also applied to whole organisms. Individuals are then
predicted to appear designed as if to maximise their inclusive fitness,
provided that certain conditions are met (formally when interactions between
individuals are 'additive'). Here we argue that applying the concept of
inclusive fitness to organisms is justified under far broader conditions than
previously shown, but only if it is appropriately defined. Specifically, we
propose that organisms should maximise the sum of their offspring (including
any accrued due to the behaviour/phenotype of relatives), plus any effects on
their relatives' offspring production, weighted by relatedness. In contrast,
most theoreticians have argued that a focal individual's inclusive fitness
should exclude any offspring accrued due to the behaviour of relatives. Our
approach is based on the notion that long-term evolution follows the genome's
'majority interest' of building coherent bodies that are efficient 'vehicles'
for gene propagation. A gene favoured by selection that reduces the propagation
of unlinked genes at other loci (e.g. meiotic segregation distorters that lower
sperm production) is eventually neutralised by counter-selection throughout the
rest of the genome. Most phenotypes will therefore appear as if designed to
maximise the propagation of any given gene in a focal individual and its
relatives.
Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks
Date: 2018-05-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

While there has been substantial amount of work in speaker diarization
recently, there are few efforts in jointly employing lexical and acoustic
information for speaker segmentation. Towards that, we investigate a speaker
diarization system using a sequence-to-sequence neural network trained on both
lexical and acoustic features. We also propose a loss function that allows for
selecting not only the speaker change points but also the best speaker at any
time by allowing for different speaker groupings. We incorporate Mel Frequency
Cepstral Coefficients (MFCC) as an acoustic feature alongside lexical
information that are obtained from conversations from the Fisher dataset. Thus,
we show that acoustics provide complementary information to the lexical
modality. The experimental results show that sequence-to-sequence system
trained on both word sequences and MFCC can improve on speaker diarization
result compared to the system that only relies on lexical modality or the
baseline MFCC-based system. In addition, we test the performance of our
proposed method with Automatic Speech Recognition (ASR) transcripts. While the
performance on ASR transcripts drops, the Diarization Error Rate (DER) of our
proposed method still outperforms the traditional method based on Bayesian
Information Criterion (BIC).