pwc-archive/papers-with-abstracts huggingface dataset:
Identifying the Topology of Undirected Networks from Diffused Non-stationary Graph Signals
Date: 2019-01-30 00:00:00

We address the problem of inferring an undirected graph from nodal
observations, which are modeled as non-stationary graph signals generated by
local diffusion dynamics that depend on the structure of the unknown network.
Using the so-called graph-shift operator (GSO), which is a matrix
representation of the graph, we first identify the eigenvectors of the shift
matrix from realizations of the diffused signals, and then estimate the
eigenvalues by imposing desirable properties on the graph to be recovered.
Different from the stationary setting where the eigenvectors can be obtained
directly from the covariance matrix of the observations, here we need to
estimate first the unknown diffusion (graph) filter -- a polynomial in the GSO
that preserves the sought eigenbasis. To carry out this initial system
identification step, we exploit different sources of information on the
arbitrarily-correlated input signal driving the diffusion on the graph. We
first explore the simpler case where the observations, the input information,
and the unknown graph filter are linearly related. We then address the case
where the relation is given by a system of matrix quadratic equations, which
arises in pragmatic scenarios where only the second-order statistics of the
inputs are available. While such quadratic filter identification problem boils
down to a non-convex fourth-order polynomial minimization, we discuss
identifiability conditions, propose algorithms to approximate the solution and
analyze their performance. Numerical tests illustrate the effectiveness of the
proposed topology inference algorithms in recovering brain, social, financial
and urban transportation networks using synthetic and real-world signals.
Measuring the Complexity of Consciousness
Date: 2018-01-11 00:00:00

The quest for a scientific description of consciousness has given rise to new
theoretical and empirical paradigms for the investigation of phenomenological
contents as well as clinical disorders of consciousness. An outstanding
challenge in the field is to develop measures that uniquely quantify global
brain states tied to consciousness. In particular, information-theoretic
complexity measures such as integrated information have recently been proposed
as measures of conscious awareness. This suggests a new framework to
quantitatively classify states of consciousness. However, it has proven
increasingly difficult to apply these complexity measures to realistic brain
networks. In part, this is due to high computational costs incurred when
implementing these measures on realistically large network dimensions.
Nonetheless, complexity measures for quantifying states of consciousness are
important for assisting clinical diagnosis and therapy. This article is meant
to serve as a lookup table of measures of consciousness, with particular
emphasis on clinical applicability of these measures. We consider both,
principle-based complexity measures as well as empirical measures tested on
patients. We address challenges facing these measures with regard to realistic
brain networks, and where necessary, suggest possible resolutions.
How nonuniform contact profiles of T cell receptors modulate thymic selection outcomes
Date: 2018-01-11 00:00:00

T cell receptors (TCRs) bind foreign or self-peptides attached to major
histocompatibility complex (MHC) molecules, and the strength of this
interaction determines T cell activation. Optimizing the ability of T cells to
recognize a diversity of foreign peptides yet be tolerant of self-peptides is
crucial for the adaptive immune system to properly function. This is achieved
by selection of T cells in the thymus, where immature T cells expressing
unique, stochastically generated TCRs interact with a large number of
self-peptide-MHC; if a TCR does not bind strongly enough to any
self-peptide-MHC, or too strongly with at least one self-peptide-MHC, the T
cell dies. Past theoretical work cast thymic selection as an extreme value
problem, and characterized the statistical enrichment or depletion of amino
acids in the post-selection TCR repertoire, showing how T cells are selected to
be able to specifically recognize peptides derived from diverse pathogens, yet
have limited self-reactivity. Here, we investigate how the degree of enrichment
is modified by nonuniform contacts that a TCR makes with peptide-MHC.
Specifically, we were motivated by recent experiments showing that amino acids
at certain positions of a TCR sequence have large effects on thymic selection
outcomes, and crystal structure data that reveal a nonuniform contact profile
between a TCR and its peptide-MHC ligand. Using a representative TCR contact
profile as an illustration, we show via simulations that the degree of
enrichment now varies by position according to the contact profile, and,
importantly, it depends on the implementation of nonuniform contacts during
thymic selection. We explain these nontrivial results analytically. Our study
has implications for understanding the selection forces that shape the
functionality of the post-selection TCR repertoire.
Tractable models of self-sustaining autocatalytic networks
Date: 2018-03-27 00:00:00

Self-sustaining autocatalytic networks play a central role in living systems,
from metabolism at the origin of life, simple RNA networks, and the modern
cell, to ecology and cognition. A collectively autocatalytic network that can
be sustained from an ambient food set is also referred to more formally as a
`Reflexively Autocatalytic F-generated' (RAF) set. In this paper, we first
investigate a simplified setting for studying RAFs, which are nevertheless
relevant to real biochemistry and allows for a more exact mathematical analysis
based on graph-theoretic concepts. This, in turn, allows for the development of
efficient (polynomial-time) algorithms for questions that are computationally
NP-hard in the general RAF setting. We then show how this simplified setting
for RAF systems leads naturally to a more general notion of RAFs that are
`generative' (they can be built up from simpler RAFs) and for which efficient
algorithms carry over to this more general setting. Finally, we show how
classical RAF theory can be extended to deal with ensembles of catalysts as
well as the assignment of rates to reactions according to which catalysts (or
combinations of catalysts) are available.
Solving Dynamic Discrete Choice Models: Integrated or Expected Value Function?
Date: 2018-01-11 00:00:00
Tasks: Discrete Choice Models

Dynamic Discrete Choice Models (DDCMs) are important in the structural
estimation literature. Since the structural errors are practically always
continuous and unbounded in nature, researchers often use the expected value
function. The idea to solve for the expected value function made solution more
practical and estimation feasible. However, as we show in this paper, the
expected value function is impractical compared to an alternative: the
integrated (ex ante) value function. We provide brief descriptions of the
inefficacy of the former, and benchmarks on actual problems with varying
cardinality of the state space and number of decisions. Though the two
approaches solve the same problem in theory, the benchmarks support the claim
that the integrated value function is preferred in practice.
Comorbid CAD and Ventricular Hypertrophy Compromise The Perfusion of Myocardial Tissue at Subcritical Stenosis of Epicardial Coronaries
Date: 2018-01-11 00:00:00

BACKGROUND: Most studies of CAD revascularization have been based on and
reported according to angiographic criteria which don't consider the relation
between the resulting effective flow distal to the stenosis and the demand of a
hypertrophied myocardial tissue.
  MODEL: Mathematical model of the myocardial perfusion in comorbid CAD and
ventricular hypertrophy using Poiseuille's law. The analysis yields that the
curve, which represents the relation between the perfusion and the severity of
CAD depending on angiographic and/or angiophysiologic criteria, is shifted to
the right by the effect of myocardial tissue hypertrophy. The right shift of
said curve, which is directly proportional to the degree of ventricular
hypertrophy, indicates that the perfusion of the corresponding myocardial
tissue is compromised at angiographically and/or angiophysiologically
subsignificant stenosis of the supplying epicardial vessel.
  RESULTS: Patients with comorbid CAD and left ventricular hypertrophy are more
sensitive to CAD-related hemodynamic changes. They are more prone to develop
ischemic complications, than their peers with isolated CAD regarding the same
degree of coronary stenosis.
  CONCLUSION: Patients with comorbid CAD and ventricular hypertrophy suffer
from myocardial hypoperfusion at angiographically and/or angiophysiologically
subcritical epicardial stenosis. Accordingly; the comorbidity of both diseases
should be considered upon designing of the treatment regime.
Asymptotic Static Hedge via Symmetrization
Date: 2018-01-12 00:00:00

This paper is a continuation of Akahori-Barsotti-Imamura (2017) and where the
authors i) showed that a payment at a random time, which we call timing risk,
is decomposed into an integral of static positions of knock-in type barrier
options, ii) proposed an iteration of static hedge of a timing risk by
regarding the hedging error by a static hedge strategy of Bowie-Carr type with
respect to a barrier option as a timing risk, and iii) showed that the error
converges to zero by infinitely many times of iteration under a condition on
the integrability of a relevant function. Even though many diffusion models
including generic 1-dimensional ones satisfy the required condition, a
construction of the iterated static hedge that is applicable to any uniformly
elliptic diffusions is postponed to the present paper because of its
mathematical difficulty. We solve the problem in this paper by relying on the
symmetrization, a technique first introduced in Imamura-Ishigaki-Okumura (2014)
and generalized in Akahori-Imamura (2014), and also work on parametrix, a
classical technique from perturbation theory to construct a fundamental
solution of a partial differential equation. Due to a lack of continuity in the
diffusion coefficient, however, a careful study of the integrability of the
relevant functions is required. The long lines of proof itself could be a
contribution to the parametrix analysis.
Emergent memory in cell signaling: Persistent adaptive dynamics in cascades can arise from the diversity of relaxation time-scales
Date: 2018-01-12 00:00:00
Tasks: Diversity

The mitogen-activated protein kinase (MAPK) signaling cascade, an
evolutionarily conserved motif present in all eukaryotic cells, is involved in
coordinating critical cell-fate decisions, regulating protein synthesis, and
mediating learning and memory. While the steady-state behavior of the pathway
stimulated by a time-invariant signal is relatively well-understood, we show
using a computational model that it exhibits a rich repertoire of transient
adaptive responses to changes in stimuli. When the signal is switched on, the
response is characterized by long-lived modulations in frequency as well as
amplitude. On withdrawing the stimulus, the activity decays over timescales
much longer than that of phosphorylation-dephosphorylation processes,
exhibiting reverberations characterized by repeated spiking in the activated
MAPK concentration. The long-term persistence of such post-stimulus activity
suggests that the cascade retains memory of the signal for a significant
duration following its removal, even in the absence of any explicit feedback or
cross-talk with other pathways. We find that the molecular mechanism underlying
this behavior is related to the existence of distinct relaxation rates for the
different cascade components. This results in the imbalance of fluxes between
different layers of the cascade, with the repeated reuse of activated kinases
as enzymes when they are released from sequestration in complexes leading to
one or more spike events following the removal of the stimulus. The persistent
adaptive response reported here, indicative of a cellular "short-term" memory,
suggests that this ubiquitous signaling pathway plays an even more central role
in information processing by eukaryotic cells.
Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present
Date: 2018-01-12 00:00:00

In a continuous-time setting where a risk-averse agent controls the drift of
an output process driven by a Brownian motion, optimal contracts are linear in
the terminal output; this result is well-known in a setting with moral hazard
and -under stronger assumptions - adverse selection. We show that this result
continues to hold when in addition reservation utilities are type-dependent.
This type of problem occurs in the study of optimal compensation problems
involving competing principals.
Gene regulatory network inference: an introductory survey
Date: 2018-12-19 00:00:00
Tasks: Survey

Gene regulatory networks are powerful abstractions of biological systems.
Since the advent of high-throughput measurement technologies in biology in the
late 90s, reconstructing the structure of such networks has been a central
computational problem in systems biology. While the problem is certainly not
solved in its entirety, considerable progress has been made in the last two
decades, with mature tools now available. This chapter aims to provide an
introduction to the basic concepts underpinning network inference tools,
attempting a categorisation which highlights commonalities and relative
strengths. While the chapter is meant to be self-contained, the material
presented should provide a useful background to the later, more specialised
chapters of this book.
Regression Based Expected Shortfall Backtesting
Date: 2019-09-21 00:00:00
Tasks: regression

This paper introduces novel backtests for the risk measure Expected Shortfall
(ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a
regression framework for the ES stand-alone is infeasible, and thus, our tests
are based on a joint regression for the Value at Risk and the ES, which allows
for different test specifications. These ES backtests are the first which
solely backtest the ES in the sense that they only require ES forecasts as
input parameters. As the tests are potentially subject to model
misspecification, we provide asymptotic theory under misspecification for the
underlying joint regression. We find that employing a misspecification robust
covariance estimator substantially improves the tests' performance. We compare
our backtests to existing approaches and find that our tests outperform the
competitors throughout all considered simulations. In an empirical
illustration, we apply our backtests to ES forecasts for 200 stocks of the S&P
500 index.
On Partly Overloaded Spreading Sequences with Variable Spreading Factor
Date: 2018-01-12 00:00:00

Future wireless communications systems are expected to support multi-service
operation, i.e. especially multi-rate as well as multi-level quality of service
(QoS) requirements. This evolution is mainly driven by the success of the
Internet of Things (IoT) and the growing presence of machine type communication
(MTC). Whereas in the last years information in wireless communication systems
was mainly generated or at least requested by humans and was also processed by
humans, we can now see a paradigm shift since so-called machine type
communication is gaining growing importance. Along with these changes we also
encounter changes regarding the quality of service requirements, data rate
requirements, latency constraints, different duty cycles et cetera. The
challenge for new communication systems will therefore be to enable different
user types and their different requirements efficiently. In this paper, we
present partly overloaded spreading sequences, i.e. sequences which are
globally orthogonal and sequences which interfere with a subset of sequences
while being orthogonal to the globally orthogonal sequences. Additionally, we
are able to vary the spreading factor of these sequences, which allows us to
flexibly assign appropriate sequences to different service types or user types
respectively. We propose the use of these sequences for a CDMA channel access
method which is able to flexibly support different traffic types.
How pairwise coevolutionary models capture the collective residue variability in proteins
Date: 2018-01-12 00:00:00
Tasks: Protein Structure Prediction

Global coevolutionary models of homologous protein families, as constructed
by direct coupling analysis (DCA), have recently gained popularity in
particular due to their capacity to accurately predict residue-residue contacts
from sequence information alone, and thereby to facilitate tertiary and
quaternary protein structure prediction. More recently, they have also been
used to predict fitness effects of amino-acid substitutions in proteins, and to
predict evolutionary conserved protein-protein interactions. These models are
based on two currently unjustified hypotheses: (a) correlations in the
amino-acid usage of different positions are resulting collectively from
networks of direct couplings; and (b) pairwise couplings are sufficient to
capture the amino-acid variability. Here we propose a highly precise inference
scheme based on Boltzmann-machine learning, which allows us to systematically
address these hypotheses. We show how correlations are built up in a highly
collective way by a large number of coupling paths, which are based on the
protein's three-dimensional structure. We further find that pairwise
coevolutionary models capture the collective residue variability across
homologous proteins even for quantities which are not imposed by the inference
procedure, like three-residue correlations, the clustered structure of protein
families in sequence space or the sequence distances between homologs. These
findings strongly suggest that pairwise coevolutionary models are actually
sufficient to accurately capture the residue variability in homologous protein
families.
Coexistence of several currencies in presence of increasing returns to adoption
Date: 2018-01-12 00:00:00

We present a simplistic model of the competition between different
currencies. Each individual is free to choose the currency that minimizes his
transaction costs, which arise whenever his exchanging relations have chosen a
different currency. We show that competition between currencies does not
necessarily converge to the emergence of a single currency. For large systems,
we prove that two distinct communities using different currencies in the
initial state will remain forever in this fractionalized state.
Physical Principles of Retroviral Integration in the Human Genome
Authors: Davide Michieletto, Marina Lusic, Davide Marenduzzo, Enzo Orlandini
Date: 2018-01-12 00:00:00

Certain retroviruses, including HIV, insert their DNA in a non-random fraction of the host genome via poorly understood selection mechanisms. Here, we develop a biophysical model for retroviral integrations as stochastic and quasi-equilibrium topological reconnections between polymers. We discover that physical effects, such as DNA accessibility and elasticity, play important and universal roles in this process. Our simulations predict that integration is favoured within nucleosomal and flexible DNA, in line with experiments, and that these biases arise due to competing energy barriers associated with DNA deformations. By considering a long chromosomal region in human T-cells during interphase, we discover that at these larger scales integration sites are predominantly determined by chromatin accessibility. Finally, we propose and solve a reaction-diffusion problem that recapitulates the distribution of HIV hot-spots within T-cells. With few generic assumptions, our model can rationalise experimental observations and identifies previously unappreciated physical contributions to retroviral integration site selection.
Robust Sparse Fourier Transform Based on The Fourier Projection-Slice Theorem
Date: 2017-12-11 00:00:00

The state-of-the-art automotive radars employ multidimensional discrete
Fourier transforms (DFT) in order to estimate various target parameters. The
DFT is implemented using the fast Fourier transform (FFT), at sample and
computational complexity of $O(N)$ and $O(N \log N)$, respectively, where $N$
is the number of samples in the signal space. We have recently proposed a
sparse Fourier transform based on the Fourier projection-slice theorem
(FPS-SFT), which applies to multidimensional signals that are sparse in the
frequency domain. FPS-SFT achieves sample complexity of $O(K)$ and
computational complexity of $O(K \log K)$ for a multidimensional, $K$-sparse
signal. While FPS-SFT considers the ideal scenario, i.e., exactly sparse data
that contains on-grid frequencies, in this paper, by extending FPS-SFT into a
robust version (RFPS-SFT), we emphasize on addressing noisy signals that
contain off-grid frequencies; such signals arise from radar applications. This
is achieved by employing a windowing technique and a voting-based frequency
decoding procedure; the former reduces the frequency leakage of the off-grid
frequencies below the noise level to preserve the sparsity of the signal, while
the latter significantly lowers the frequency localization error stemming from
the noise. The performance of the proposed method is demonstrated both
theoretically and numerically.
Fast Methods for Solving the Cluster Containment Problem for Phylogenetic Networks
Date: 2018-01-14 00:00:00

Genetic and comparative genomic studies indicate that extant genomes are more
properly considered to be a fusion product of random mutations over generations
and genomic material transfers between individuals of different lineages. This
has motivated researchers to adopt phylogenetic networks and other general
models to study genome evolution. One important problem arising from
reconstruction and verification of phylogenetic networks is the cluster
containment problem, namely determining whether or not a cluster of taxa is
displayed in a phylogenetic network. In this work, a new upper bound for this
NP-complete problem is established through an efficient reduction to the SAT
problem. Two efficient (albeit exponential time) methods are also implemented.
It is developed on the basis of generalization of the so-called
reticulation-visible property of phylogenetic networks.
Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks
Date: 2018-01-14 00:00:00
Tasks: Decision Making, Mathematical Proofs

Finding actions that satisfy the constraints imposed by both external inputs
and internal representations is central to decision making. We demonstrate that
some important classes of constraint satisfaction problems (CSPs) can be solved
by networks composed of homogeneous cooperative-competitive modules that have
connectivity similar to motifs observed in the superficial layers of neocortex.
The winner-take-all modules are sparsely coupled by programming neurons that
embed the constraints onto the otherwise homogeneous modular computational
substrate. We show rules that embed any instance of the CSPs planar four-color
graph coloring, maximum independent set, and Sudoku on this substrate, and
provide mathematical proofs that guarantee these graph coloring problems will
convergence to a solution. The network is composed of non-saturating linear
threshold neurons. Their lack of right saturation allows the overall network to
explore the problem space driven through the unstable dynamics generated by
recurrent excitation. The direction of exploration is steered by the constraint
neurons. While many problems can be solved using only linear inhibitory
constraints, network performance on hard problems benefits significantly when
these negative constraints are implemented by non-linear multiplicative
inhibition. Overall, our results demonstrate the importance of instability
rather than stability in network computation, and also offer insight into the
computational role of dual inhibitory mechanisms in neural circuits.