pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Filter Design for Consensus on Random Directed Graphs
Date: 2018-02-27 00:00:00

Optimal design of consensus acceleration graph filters relates closely to the
eigenvalues of the consensus iteration matrix. This task is complicated by
random networks with uncertain iteration matrix eigenvalues. Filter design
methods based on the spectral asymptotics of consensus iteration matrices for
large-scale, random undirected networks have been previously developed both for
constant and for time-varying network topologies. This work builds upon these
results by extending analysis to large-scale, constant, random directed
networks. The proposed approach uses theorems by Girko that analytically
produce deterministic approximations of the empirical spectral distribution for
suitable non-Hermitian random matrices. The approximate empirical spectral
distribution defines filtering regions in the proposed filter optimization
problem, which must be modified to accommodate complex-valued eigenvalues.
Presented numerical simulations demonstrate good results. Additionally,
limitations of the proposed method are discussed.
Spectral Statistics of Directed Networks with Random Link Model Transpose-Asymmetry
Date: 2018-02-27 00:00:00

Stochastic network influences complicate graph filter design by producing
uncertainty in network iteration matrix eigenvalues, the points at which the
graph filter response is defined. While joint statistics for the eigenvalues
typically elude analysis, predictable spectral asymptotics can emerge for large
scale networks. Previously published works successfully analyze large-scale
networks described by undirected graphs and directed graphs with
transpose-symmetric distributions, focusing on consensus acceleration filter
design for time-invariant networks as an application. This work expands upon
these results by enabling analysis of certain large-scale directed networks
described by transpose-asymmetric distributions. Specifically, efficiently
computable spectral density approximations are possible for
transpose-asymmetric percolation network models with node-transitive symmetry
group and normal mean matrix. Numerical simulations support the derived
approximations and application to consensus filters.
Signal propagation in sensing and reciprocating cellular systems with spatial and structural heterogeneity
Date: 2018-02-25 00:00:00

Sensing and reciprocating cellular systems (SARs) are important for the
operation of many biological systems. Production in interferon (IFN) SARs is
achieved through activation of the Jak-Stat pathway, and downstream
upregulation of IFN regulatory factor (IRF)-3 and IFN transcription, but the
role that high and low affinity IFNs play in this process remains unclear. We
present a comparative between a minimal spatio-temporal partial differential
equation (PDE) model and a novel spatio-structural-temporal (SST) model for the
consideration of receptor, binding, and metabolic aspects of SAR behaviour.
Using the SST framework, we simulate single- and multi-cluster paradigms of IFN
communication. Simulations reveal a cyclic process between the binding of IFN
to the receptor, and the consequent increase in metabolism, decreasing the
propensity for binding due to the internal feed-back mechanism. One observes
the effect of heterogeneity between cellular clusters, allowing them to
individualise and increase local production, and within clusters, where we
observe `sub popular quiescence'; a process whereby intra-cluster
subpopulations reduce their binding and metabolism such that other such
subpopulations may augment their production. Finally, we observe the ability
for low affinity IFN to communicate a long range signal, where high affinity
cannot, and the breakdown of this relationship through the introduction of cell
motility. Biological systems may utilise cell motility where environments are
unrestrictive and may use fixed system, with low affinity communication, where
a localised response is desirable.
Deciphering general characteristics of residues constituting allosteric communication paths
Date: 2018-02-27 00:00:00

Considering all the PDB annotated allosteric proteins (from ASD - AlloSteric
Database) belonging to four different classes (kinases, nuclear receptors,
peptidases and transcription factors), this work has attempted to decipher
certain consistent patterns present in the residues constituting the allosteric
communication sub-system (ACSS). The thermal fluctuations of hydrophobic
residues in ACSSs were found to be significantly higher than those present in
the non-ACSS part of the same proteins, while polar residues showed the
opposite trend.
  The basic residues and hydroxyl residues were found to be slightly more
predominant than the acidic residues and amide residues in ACSSs, hydrophobic
residues were found extremely frequently in kinase ACSSs. Despite having
different sequences and different lengths of ACSS, they were found to be
structurally quite similar to each other - suggesting a preferred structural
template for communication. ACSS structures recorded low RMSD and high Akaike
Information Criterion(AIC) scores among themselves. While the ACSS networks for
all the groups of allosteric proteins showed low degree centrality and
closeness centrality, the betweenness centrality magnitudes revealed nonuniform
behavior. Though cliques and communities could be identified within the ACSS,
maximal-common-subgraph considering all the ACSS could not be generated,
primarily due to the diversity in the dataset. Barring one particular case, the
entire ACSS for any class of allosteric proteins did not demonstrate "small
world" behavior, though the sub-graphs of the ACSSs, in certain cases, were
found to form small-world networks.
Irregularity-Aware Graph Fourier Transforms
Date: 2018-12-19 00:00:00

In this paper, we present a novel generalization of the graph Fourier
transform (GFT). Our approach is based on separately considering the
definitions of signal energy and signal variation, leading to several possible
orthonormal GFTs. Our approach includes traditional definitions of the GFT as
special cases, while also leading to new GFT designs that are better at taking
into account the irregular nature of the graph. As an illustration, in the
context of sensor networks we use the Voronoi cell area of vertices in our GFT
definition, showing that it leads to a more sensible definition of graph signal
energy even when sampling is highly irregular.
Translucent windows: How uncertainty in competitive interactions impacts detection of community pattern
Date: 2018-02-28 00:00:00
Tasks: Clustering

Trait variation and similarity among coexisting species can provide a window
into the mechanisms that maintain their coexistence. Recent theoretical
explorations suggest that competitive interactions will lead to groups, or
clusters, of species with similar traits. However, theoretical predictions
typically assume complete knowledge of the map between competition and measured
traits. These assumptions limit the plausible application of these patterns for
inferring competitive interactions in nature. Here we relax these restrictions
and find that the clustering pattern is robust to contributions of unknown or
unobserved niche axes. However, it may not be visible unless measured traits
are close proxies for niche strategies. We conclude that patterns along single
niche axes may reveal properties of interspecific competition in nature, but
detecting these patterns requires natural history expertise firmly tying traits
to niches.
Risk-neutral valuation under differential funding costs, defaults and collateralization
Date: 2018-02-28 00:00:00

We develop a unified valuation theory that incorporates credit risk
(defaults), collateralization and funding costs, by expanding the replication
approach to a generality that has not yet been studied previously and reaching
valuation when replication is not assumed. This unifying theoretical framework
clarifies the relationship between the two valuation approaches: the adjusted
cash flows approach pioneered for example by Brigo, Pallavicini and co-authors
([12, 13, 34]) and the classic replication approach illustrated for example by
Bielecki and Rutkowski and co-authors ([3, 8]). In particular, results of this
work cover most previous papers where the authors studied specific replication
models.
RACORN-K: Risk-Aversion Pattern Matching-based Portfolio Selection
Date: 2018-02-28 00:00:00
Tasks: Management

Portfolio selection is the central task for assets management, but it turns
out to be very challenging. Methods based on pattern matching, particularly the
CORN-K algorithm, have achieved promising performance on several stock markets.
A key shortage of the existing pattern matching methods, however, is that the
risk is largely ignored when optimizing portfolios, which may lead to
unreliable profits, particularly in volatile markets. We present a
risk-aversion CORN-K algorithm, RACORN-K, that penalizes risk when searching
for optimal portfolios. Experiments on four datasets (DJIA, MSCI, SP500(N),
HSI) demonstrate that the new algorithm can deliver notable and reliable
improvements in terms of return, Sharp ratio and maximum drawdown, especially
on volatile markets.
Massive MIMO relaying with linear precoding in correlated channels under limited feedback
Date: 2018-02-28 00:00:00
Tasks: User Simulation

In this paper we study on a massive MIMO relay system with linear precoding
under the conditions of imperfect channel state information at the transmitter
(CSIT) and per-user channel transmit correlation. In our system the
source-relay channels are massive multiple-input multiple-output (MIMO) ones
and the relay-destination channels are massive multiple-input single-output
(MISO) ones. Large random matrix theory (RMT) is used to derive a deterministic
equivalent of the signal-to-interference-plus-noise ratio (SINR) at each user
in massive MIMO amplify-forward and decode-forward (M-MIMO-ADF) relaying with
regularized zero-forcing (RZF) precoding, as the number of transmit antennas
and users M,K approaches to infinity and M>>K. In this paper we obtain a
closed-form expression for the deterministic equivalent of
h^H_kW(hat)_lh(hat)_k, and we give two theorems and a corollary to derive the
deterministic equivalent of the SINR at each user. Simulation results show that
the deterministic equivalent of the SINR at each user in M-MIMO-ADF relaying
and the results of Theorem 1, Theorem 2, Proposition 1 and Corollary 1 are
accurate.
Shifting Maximum Eigenvalue Detection in Low SNR Environment
Date: 2018-03-27 00:00:00

Maximum eigenvalue detection (MED) is an important application of random
matrix theory in spectrum sensing and signal detection. However, in small
signal-to-noise ratio environment, the maximum eigenvalue of the representative
signal is at the edge of Marchenko-Pastur (M-P) law bulk and meets the
Tracy-Widom distribution. Since the distribution of Tracy-Widom has no
closed-form expression, it brings great difficulty in processing. In this
paper, we propose a shifting maximum eigenvalue (SMED) algorithm, which shifts
the maximum eigenvalue out of the M-P law bulk by combining an auxiliary signal
associated with the signal to be detected. According to the random matrix
theory, the shifted maximum eigenvalue is consistent with Gaussian
distribution. The proposed SMED not only simplifies the detection algorithm,
but also greatly improve the detection performance. In this paper, the
performance of SMED, MED and trace (FMD) algorithm is analyzed and the
theoretical performance comparisons are obtained. The algorithm and theoretical
results are verified by the simulations in different signal environments.
An Analysis on Caching Placement for Millimeter/Micro Wave Hybrid Networks
Date: 2018-03-26 00:00:00

In this paper, we consider a hybrid millimeter wave (mmWave) and micro wave
($\mu$Wave) network from the perspective of \emph{wireless caching} and study
the optimal probabilistic content/file caching placement at desirable base
stations (BSs) using a stochastic geometric framework. Considering the average
success probability (ASP) of file delivery as the performance metric, we derive
expressions for the association probability of the typical user to the mmWave
and $\mu$Wave networks. Accordingly, we provide an upper bound for the ASP of
file delivery and formulate the content caching placement scheme as an
optimization problem with respect to caching probabilities, that jointly
optimizes the ASP of file delivery considering both content placement and
delivery phases. In particular, we consider the caching placement strategy
under both noise-limited and interference-limited environments. We numerically
evaluate the performance of the proposed caching schemes under essential
factors, such as blockages in the mmWave network, cluster radius, BS density,
and path loss and compare it with uniform caching placement, caching $M$ most
popular contents, and random caching placement. Numerical results demonstrate
the superiority of the proposed caching scheme over others, albeit certain
trade-offs.
Multiscale relevance and informative encoding in neuronal spike trains
Date: 2019-12-20 00:00:00
Tasks: POS

Neuronal responses to complex stimuli and tasks can encompass a wide range of
time scales. Understanding these responses requires measures that characterize
how the information on these response patterns are represented across multiple
temporal resolutions. In this paper we propose a metric -- which we call
multiscale relevance (MSR) -- to capture the dynamical variability of the
activity of single neurons across different time scales. The MSR is a
non-parametric, fully featureless indicator in that it uses only the time
stamps of the firing activity without resorting to any a priori covariate or
invoking any specific structure in the tuning curve for neural activity. When
applied to neural data from the mEC and from the ADn and PoS regions of
freely-behaving rodents, we found that neurons having low MSR tend to have low
mutual information and low firing sparsity across the correlates that are
believed to be encoded by the region of the brain where the recordings were
made. In addition, neurons with high MSR contain significant information on
spatial navigation and allow to decode spatial position or head direction as
efficiently as those neurons whose firing activity has high mutual information
with the covariate to be decoded and significantly better than the set of
neurons with high local variations in their interspike intervals. Given these
results, we propose that the MSR can be used as a measure to rank and select
neurons for their information content without the need to appeal to any a
priori covariate.
Optimal encoding in stochastic latent-variable Models
Date: 2020-06-24 00:00:00

In this work we explore encoding strategies learned by statistical models of
sensory coding in noisy spiking networks. Early stages of sensory communication
in neural systems can be viewed as encoding channels in the
information-theoretic sense. However, neural populations face constraints not
commonly considered in communications theory. Using restricted Boltzmann
machines as a model of sensory encoding, we find that networks with sufficient
capacity learn to balance precision and noise-robustness in order to adaptively
communicate stimuli with varying information content. Mirroring variability
suppression observed in sensory systems, informative stimuli are encoded with
high precision, at the cost of more variable responses to frequent, hence less
informative stimuli. Curiously, we also find that statistical criticality in
the neural population code emerges at model sizes where the input statistics
are well captured. These phenomena have well-defined thermodynamic
interpretations, and we discuss their connection to prevailing theories of
coding and statistical criticality in neural populations.
Variable domain N-linked glycosylation and negative surface charge are key features of monoclonal ACPA: implications for B-cell selection
Date: 2018-02-28 00:00:00

Autoreactive B cells have a central role in the pathogenesis of rheumatoid
arthritis (RA), and recent findings have proposed that anti-citrullinated
protein autoantibodies (ACPA) may be directly pathogenic. Herein, we
demonstrate the frequency of variable-region glycosylation in single-cell
cloned mAbs. A total of 14 ACPA mAbs were evaluated for predicted N-linked
glycosylation motifs in silico and compared to 452 highly-mutated mAbs from RA
patients and controls. Variable region N-linked motifs (N-X-S/T) were
strikingly prevalent within ACPA (100%) compared to somatically hypermutated
(SHM) RA bone marrow plasma cells (21%), and synovial plasma cells from
seropositive (39%) and seronegative RA (7%). When normalized for SHM, ACPA
still had significantly higher frequency of N-linked motifs compared to all
studied mAbs including highly-mutated HIV broadly-neutralizing and
malaria-associated mAbs. The Fab glycans of ACPA-mAbs were highly sialylated,
contributed to altered charge, but did not influence antigen binding. The
analysis revealed evidence of unusual B-cell selection pressure and
SHM-mediated decreased in surface charge and isoelectric point in ACPA. It is
still unknown how these distinct features of anti-citrulline immunity may have
an impact on pathogenesis. However, it is evident that they offer selective
advantages for ACPA+ B cells, possibly also through non-antigen driven
mechanisms.
On the Low-Complexity, Hardware-Friendly Tridiagonal Matrix Inversion for Correlated Massive MIMO Systems
Date: 2018-02-21 00:00:00

In massive MIMO (M-MIMO) systems, one of the key challenges in the
implementation is the large-scale matrix inversion operation, as widely used in
channel estimation, equalization, detection, and decoding procedures.
Traditionally, to handle this complexity issue, several low-complexity matrix
inversion approximation methods have been proposed, including the classic
Cholesky decomposition and the Neumann series expansion (NSE). However, the
conventional approaches failed to exploit neither the special structure of
channel matrices nor the critical issues in the hardware implementation, which
results in poorer throughput performance and longer processing delay. In this
paper, by targeting at the correlated M-MIMO systems, we propose a modified NSE
based on tridiagonal matrix inversion approximation (TMA) to accommodate the
complexity as well as the performance issue in the conventional hardware
implementation, and analyze the corresponding approximation errors. Meanwhile,
we investigate the VLSI implementation for the proposed detection algorithm
based on a Xilinx Virtex-7 XC7VX690T FPGA platform. It is shown that for
correlated massive MIMO systems, it can achieve near-MMSE performance and $630$
Mb/s throughput. Compared with other benchmark systems, the proposed pipelined
TMA detector can get high throughput-to-hardware ratio. Finally, we also
propose a fast iteration structure for further research.
Time Varying Channel Tracking with Spatial and Temporal BEM for Massive MIMO Systems
Date: 2018-02-27 00:00:00

In this paper, we propose a channel tracking method for massive multi-input
and multi-output systems under both time-varying and spatial-varying
circumstance. Exploiting the characteristics of massive antenna array, a
spatial-temporal basis expansion model is designed to reduce the effective
dimensions of up-link and down-link channel, which decomposes channel state
information into the time-varying spatial information and gain information. We
firstly model the users movements as a one-order unknown Markov process, which
is blindly learned by the expectation and maximization (EM) approach. Then, the
up-link time varying spatial information can be blindly tracked by Taylor
series expansion of the steering vector, while the rest up-link channel gain
information can be trained by only a few pilot symbols. Due to angle
reciprocity (spatial reciprocity), the spatial information of the down-link
channel can be immediately obtained from the up-link counterpart, which greatly
reduces the complexity of down-link channel tracking. Various numerical results
are provided to demonstrate the effectiveness of the proposed method.
Partial Identification of Expectations with Interval Data
Date: 2018-02-28 00:00:00

A conditional expectation function (CEF) can at best be partially identified
when the conditioning variable is interval censored. When the number of bins is
small, existing methods often yield minimally informative bounds. We propose
three innovations that make meaningful inference possible in interval data
contexts. First, we prove novel nonparametric bounds for contexts where the
distribution of the censored variable is known. Second, we show that a class of
measures that describe the conditional mean across a fixed interval of the
conditioning space can often be bounded tightly even when the CEF itself
cannot. Third, we show that a constraint on CEF curvature can either tighten
bounds or can substitute for the monotonicity assumption often made in interval
data applications. We derive analytical bounds that use the first two
innovations, and develop a numerical method to calculate bounds under the
third. We show the performance of the method in simulations and then present
two applications. First, we resolve a known problem in the estimation of
mortality as a function of education: because individuals with high school or
less are a smaller and thus more negatively selected group over time, estimates
of their mortality change are likely to be biased. Our method makes it possible
to hold education rank bins constant over time, revealing that current
estimates of rising mortality for less educated women are biased upward in some
cases by a factor of three. Second, we apply the method to the estimation of
intergenerational mobility, where researchers frequently use coarsely measured
education data in the many contexts where matched parent-child income data are
unavailable. Conventional measures like the rank-rank correlation may be
uninformative once interval censoring is taken into account; CEF interval-based
measures of mobility are bounded tightly.
A Spectral Method for Stable Bispectrum Inversion with Application to Multireference Alignment
Date: 2018-02-28 00:00:00

We focus on an alignment-free method to estimate the underlying signal from a
large number of noisy randomly shifted observations. Specifically, we estimate
the mean, power spectrum, and bispectrum of the signal from the observations.
Since bispectrum contains the phase information of the signal, reliable
algorithms for bispectrum inversion is useful in many applications. We propose
a new algorithm using spectral decomposition of the normalized bispectrum
matrix for this task. For clean signals, we show that the eigenvectors of the
normalized bispectrum matrix correspond to the true phases of the signal and
its shifted copies. In addition, the spectral method is robust to noise. It can
be used as a stable and efficient initialization technique for local non-convex
optimization for bispectrum inversion.
Dimensional Analysis in Economics: A Study of the Neoclassical Economic Growth Model
Date: 2018-02-28 00:00:00

The fundamental purpose of the present research article is to introduce the
basic principles of Dimensional Analysis in the context of the neoclassical
economic theory, in order to apply such principles to the fundamental relations
that underlay most models of economic growth. In particular, basic instruments
from Dimensional Analysis are used to evaluate the analytical consistency of
the Neoclassical economic growth model. The analysis shows that an adjustment
to the model is required in such a way that the principle of dimensional
homogeneity is satisfied.