pwc-archive/papers-with-abstracts huggingface dataset:
Hypergraph based Subnetwork Extraction using Fusion of Task and Rest Functional Connectivity
Date: 2018-01-15 00:00:00
Tasks: Functional Connectivity, Relation

Functional subnetwork extraction is commonly used to explore the brain's
modular structure. However, reliable subnetwork extraction from functional
magnetic resonance imaging (fMRI) data remains challenging due to the
pronounced noise in neuroimaging data. In this paper, we proposed a high order
relation informed approach based on hypergraph to combine the information from
multi-task data and resting state data to improve subnetwork extraction. Our
assumption is that task data can be beneficial for the subnetwork extraction
process, since the repeatedly activated nodes involved in diverse tasks might
be the canonical network components which comprise pre-existing repertoires of
resting state subnetworks. Our proposed high order relation informed subnetwork
extraction based on a strength information embedded hypergraph, (1) facilitates
the multisource integration for subnetwork extraction, (2) utilizes information
on relationships and changes between the nodes across different tasks, and (3)
enables the study on higher order relations among brain network nodes. On real
data, we demonstrated that fusing task activation, task-induced connectivity
and resting state functional connectivity based on hypergraphs improves
subnetwork extraction compared to employing a single source from either rest or
task data in terms of subnetwork modularity measure, inter-subject
reproducibility, along with more biologically meaningful subnetwork
assignments.
Genetic robustness of let-7 miRNA sequence-structure pairs
Date: 2018-01-11 00:00:00

Genetic robustness, the preservation of evolved phenotypes against genotypic
mutations, is one of the central concepts in evolution. In recent years a large
body of work has focused on the origins, mechanisms, and consequences of
robustness in a wide range of biological systems. In particular, research on
ncRNAs studied the ability of sequences to maintain folded structures against
single-point mutations. In these studies, the structure is merely a reference.
However, recent work revealed evidence that structure itself contributes to the
genetic robustness of ncRNAs. We follow this line of thought and consider
sequence-structure pairs as the unit of evolution and introduce the spectrum of
inverse folding rates (IFR-spectrum) as a measurement of genetic robustness.
Our analysis of the miRNA let-7 family captures key features of
structure-modulated evolution and facilitates the study of robustness against
multiple-point mutations.
Predictive Systems Toxicology
Date: 2018-01-15 00:00:00

In this review we address to what extent computational techniques can augment
our ability to predict toxicity. The first section provides a brief history of
empirical observations on toxicity dating back to the dawn of Sumerian
civilization. Interestingly, the concept of dose emerged very early on, leading
up to the modern emphasis on kinetic properties, which in turn encodes the
insight that toxicity is not solely a property of a compound but instead
depends on the interaction with the host organism. The next logical step is the
current conception of evaluating drugs from a personalized medicine
point-of-view. We review recent work on integrating what could be referred to
as classical pharmacokinetic analysis with emerging systems biology approaches
incorporating multiple omics data. These systems approaches employ advanced
statistical analytical data processing complemented with machine learning
techniques and use both pharmacokinetic and omics data. We find that such
integrated approaches not only provide improved predictions of toxicity but
also enable mechanistic interpretations of the molecular mechanisms
underpinning toxicity and drug resistance. We conclude the chapter by
discussing some of the main challenges, such as how to balance the inherent
tension between the predictive capacity of models, which in practice amounts to
constraining the number of features in the models versus allowing for rich
mechanistic interpretability, i.e. equipping models with numerous molecular
features. This challenge also requires patient-specific predictions on
toxicity, which in turn requires proper stratification of patients as regards
how they respond, with or without adverse toxic effects. In summary, the
transformation of the ancient concept of dose is currently successfully
operationalized using rich integrative data encoded in patient-specific models.
Seismic signal sparse time-frequency analysis by Lp-quasinorm constraint
Date: 2018-04-16 00:00:00

Time-frequency analysis has been applied successfully in many fields.
However, the traditional methods, like short time Fourier transform and Cohen
distribution, suffer from the low resolution or the interference of the cross
terms. To solve these issues, we put forward a new sparse time-frequency
analysis model by using the Lp-quasinorm constraint, which is capable of
fitting the sparsity prior knowledge in the frequency domain. In the proposed
model, we regard the short time truncated data as the observation of sparse
representation and design a dictionary matrix, which builds up the relationship
between the short time measurement and the sparse spectrum. Based on the
relationship and the Lp-quasinorm feasible domain, the proposed model is
established. The alternating direction method of multipliers (ADMM) is adopted
to solve the proposed model. Experiments are then conducted on several
theoretical signals and applied to the seismic signal spectrum decomposition,
indicating that the proposed method is able to obtain a higher time-frequency
distribution than state-of-the-art time-frequency methods. Thus, the proposed
method is of great importance to reservoir exploration.
Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression
Date: 2018-01-16 00:00:00
Tasks: global-optimization

Neurostimulation is a promising therapy for abating epileptic seizures.
However, it is extremely difficult to identify optimal stimulation patterns
experimentally. In this study human recordings are used to develop a functional
24 neuron network statistical model of hippocampal connectivity and dynamics.
Spontaneous seizure-like activity is induced in-silico in this reconstructed
neuronal network. The network is then used as a testbed to design and validate
a wide range of neurostimulation patterns. Commonly used periodic trains were
not able to permanently abate seizures at any frequency. A simulated annealing
global optimization algorithm was then used to identify an optimal stimulation
pattern which successfully abated 92% of seizures. Finally, in a fully
responsive, or "closed-loop" neurostimulation paradigm, the optimal stimulation
successfully prevented the network from entering the seizure state. We propose
that the framework presented here for algorithmically identifying
patient-specific neurostimulation patterns can greatly increase the efficacy of
neurostimulation devices for seizures.
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules
Date: 2018-01-16 00:00:00

Most elementary behaviors such as moving the arm to grasp an object or
walking into the next room to explore a museum evolve on the time scale of
seconds; in contrast, neuronal action potentials occur on the time scale of a
few milliseconds. Learning rules of the brain must therefore bridge the gap
between these two different time scales.
  Modern theories of synaptic plasticity have postulated that the co-activation
of pre- and postsynaptic neurons sets a flag at the synapse, called an
eligibility trace, that leads to a weight change only if an additional factor
is present while the flag is set. This third factor, signaling reward,
punishment, surprise, or novelty, could be implemented by the phasic activity
of neuromodulators or specific neuronal inputs signaling special events. While
the theoretical framework has been developed over the last decades,
experimental evidence in support of eligibility traces on the time scale of
seconds has been collected only during the last few years.
  Here we review, in the context of three-factor rules of synaptic plasticity,
four key experiments that support the role of synaptic eligibility traces in
combination with a third factor as a biological implementation of neoHebbian
three-factor learning rules.
Censored Quantile Instrumental Variable Estimation with Stata
Date: 2019-09-24 00:00:00

Many applications involve a censored dependent variable and an endogenous
independent variable. Chernozhukov et al. (2015) introduced a censored quantile
instrumental variable estimator (CQIV) for use in those applications, which has
been applied by Kowalski (2016), among others. In this article, we introduce a
Stata command, cqiv, that simplifes application of the CQIV estimator in Stata.
We summarize the CQIV estimator and algorithm, we describe the use of the cqiv
command, and we provide empirical examples.
Unmanned Aerial Vehicle-Aided Communications: Joint Transmit Power and Trajectory Optimization
Date: 2018-01-14 00:00:00

This letter investigates the transmit power and trajectory optimization
problem for unmanned aerial vehicle (UAV)-aided networks. Different from
majority of the existing studies with fixed communication infrastructure, a
dynamic scenario is considered where a flying UAV provides wireless services
for multiple ground nodes simultaneously. To fully exploit the controllable
channel variations provided by the UAV's mobility, the UAV's transmit power and
trajectory are jointly optimized to maximize the minimum average throughput
within a given time length. For the formulated non-convex optimization with
power budget and trajectory constraints, this letter presents an efficient
joint transmit power and trajectory optimization algorithm. Simulation results
validate the effectiveness of the proposed algorithm and reveal that the
optimized transmit power shows a water-filling characteristic in spatial
domain.
Shooting High or Low: Do Countries Benefit from Entering Unrelated Activities?
Date: 2018-03-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Countries tend to diversify their exports by entering products that are
related to their current exports. Yet this average behavior is not
representative of every diversification path. In this paper, we introduce a
method to identify periods when countries enter unrelated products. We analyze
the economic diversification paths of 93 countries between 1965 and 2014 and
find that countries enter unrelated products in only about 7.2% of all
observations. We find that countries enter more unrelated products when they
are at an intermediate level of economic development, and when they have higher
levels of human capital. Finally, we ask whether countries entering more
unrelated products grow faster than those entering only related products. The
data shows that countries that enter more unrelated activities experience a
small but significant increase in future economic growth, compared to countries
with a similar level of income, human capital, capital stock per worker, and
economic complexity.
Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks with Stochastic Energy Harvesting
Date: 2018-01-16 00:00:00
Tasks: Combinatorial Optimization

We address the two fundamental problems of spatial field reconstruction and
sensor selection in het- erogeneous sensor networks. We consider the case where
two types of sensors are deployed: the first consists of expensive, high
quality sensors; and the second, of cheap low quality sensors, which are
activated only if the intensity of the spatial field exceeds a pre-defined
activation threshold (eg. wind sensors). In addition, these sensors are powered
by means of energy harvesting and their time varying energy status impacts on
the accuracy of the measurement that may be obtained. We account for this
phenomenon by encoding the energy harvesting process into the second moment
properties of the additive noise, resulting in a spatial heteroscedastic
process. We then address the following two important problems: (i) how to
efficiently perform spatial field reconstruction based on measurements obtained
simultaneously from both networks; and (ii) how to perform query based sensor
set selection with predictive MSE performance guarantee. We first show that the
resulting predictive posterior distribution, which is key in fusing such
disparate observations, involves solving intractable integrals. To overcome
this problem, we solve the first problem by developing a low complexity
algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next,
building on the S-BLUE, we address the second problem, and develop an efficient
algorithm for query based sensor set selection with performance guarantee. Our
algorithm is based on the Cross Entropy method which solves the combinatorial
optimization problem in an efficient manner. We present a comprehensive study
of the performance gain that can be obtained by augmenting the high-quality
sensors with low-quality sensors using both synthetic and real insurance storm
surge database known as the Extreme Wind Storms Catalogue.
Non Intrusive Load Monitoring in Chaotic Switching Networks
Date: 2018-01-12 00:00:00
Tasks: Non-Intrusive Load Monitoring, regression, Time Series, Time Series Analysis

In this work, a non intrusive load disaggregation scheme is proposed. By
using a kernel based nonlinear regression strategy, the switching dynamic of an
electric network, simulated as a set of RLC circuits with chaotic switching, is
approximated using a time series of the total power consumption. The results
suggest that the employed methodology can be useful in the design of efficient
load disaggregation schemes.
UAV Offloading: Spectrum Trading Contract Design for UAV Assisted 5G Networks
Date: 2018-01-28 00:00:00
Tasks: Scheduling

Unmanned Aerial Vehicle (UAV) has been recognized as a promising way to
assist future wireless communications due to its high flexibility of deployment
and scheduling. In this paper, we focus on temporarily deployed UAVs that
provide downlink data offloading in some regions under a macro base station
(MBS). Since the manager of the MBS and the operators of the UAVs could be of
different interest groups, we formulate the corresponding spectrum trading
problem by means of contract theory, where the manager of the MBS has to design
an optimal contract to maximize its own revenue. Such contract comprises a set
of bandwidth options and corresponding prices, and each UAV operator only
chooses the most profitable one from all the options in the whole contract. We
analytically derive the optimal pricing strategy based on fixed bandwidth
assignment, and then propose a dynamic programming algorithm to calculate the
optimal bandwidth assignment in polynomial time. By simulations, we compare the
outcome of the MBS optimal contract with that of a social optimal one, and find
that a selfish MBS manager sells less bandwidth to the UAV operators.
Uplink Interference Mitigation Techniques for Coexistence of 5G mmWave Users with Incumbents at 70 and 80 GHz
Date: 2018-11-22 00:00:00

The millimeter wave spectra at 71-76GHz (70GHz) and 81-86GHz (80GHz) have the
potential to endow fifth-generation new radio (5G-NR) with mobile connectivity
at gigabit rates. However, a pressing issue is the presence of incumbent
systems in these bands, which are primarily point-to-point fixed stations
(FSs). In this paper, we first identify the key properties of incumbents by
parsing databases of existing stations in major cities to devise several
modeling guidelines and characterize their deployment geometry and antenna
specifications. Second, we develop a detailed uplink interference framework to
compute the aggregate interference from outdoor 5G-NR users into FSs. We then
present several case studies in dense populated areas, using actual incumbent
databases and building layouts. Our simulation results demonstrate promising 5G
coexistence at 70GHz and 80GHz as the majority of FSs experience interference
well below the noise floor thanks to the propagation losses in these bands and
the deployment geometry of the incumbent and 5G systems. For the few FSs that
may incur higher interference, we propose several passive interference
mitigation techniques such as angular-based exclusion zones and spatial power
control. Simulation results show that the techniques can effectively protect
FSs, without tangible degradation of the 5G coverage.
Asking Biological Questions of Physical Systems: the Device Approach to Emergent Properties
Authors: Bob Eisenberg
Date: 2018-01-16 00:00:00

Life occurs in concentrated `Ringer Solutions' derived from seawater that Lesser Blum studied for most of his life. As we worked together, Lesser and I realized that the questions asked of those solutions were quite different in biology from those in the physical chemistry he knew. Biology is inherited. Information is passed by handfuls of atoms in the genetic code. A few atoms in the proteins built from the code change macroscopic function. Indeed, a few atoms often control biological function in the same sense that a gas pedal controls the speed of a car. Biological questions then are most productive when they are asked in the context of evolution. What function does a system perform? How is the system built to perform that function? What forces are used to perform that function? How are the modules that perform functions connected to make the machinery of life. Physiologists have shown that much of life is a nested hierarchy of devices, one on top of another, linking atomic ions in concentrated solutions to current flow through proteins, current flow to voltage signals, voltage signals to changes in current flow, all connected to make a regenerative system that allows electrical action potentials to move meters, under the control of a few atoms. The hierarchy of devices allows macroscopic properties to emerge from atomic scale interactions. The structures of biology create these devices. The concentration and electrical fields of biology power these devices, more than anything else. The resulting organisms reproduce. Evolution selects the organisms that reproduce more and thereby selects the devices that allow macroscopic control to emerge from the atomic structures of genes and proteins and their motions.
Numerical analysis on quadratic hedging strategies for normal inverse Gaussian models
Date: 2018-01-17 00:00:00

The authors aim to develop numerical schemes of the two representative
quadratic hedging strategies: locally risk minimizing and mean-variance hedging
strategies, for models whose asset price process is given by the exponential of
a normal inverse Gaussian process, using the results of Arai et al. \cite{AIS},
and Arai and Imai. Here normal inverse Gaussian process is a framework of
L\'evy processes frequently appeared in financial literature. In addition, some
numerical results are also introduced.
A subordinated CIR intensity model with application to Wrong-Way risk CVA
Date: 2018-01-17 00:00:00

Credit Valuation Adjustment (CVA) pricing models need to be both flexible and
tractable. The survival probability has to be known in closed form (for
calibration purposes), the model should be able to fit any valid Credit Default
Swap (CDS) curve, should lead to large volatilities (in line with CDS options)
and finally should be able to feature significant Wrong-Way Risk (WWR) impact.
The Cox-Ingersoll-Ross model (CIR) combined with independent positive jumps and
deterministic shift (JCIR++) is a very good candidate : the variance (and thus
covariance with exposure, i.e. WWR) can be increased with the jumps, whereas
the calibration constraint is achieved via the shift. In practice however,
there is a strong limit on the model parameters that can be chosen, and thus on
the resulting WWR impact. This is because only non-negative shifts are allowed
for consistency reasons, whereas the upwards jumps of the JCIR++ need to be
compensated by a downward shift. To limit this problem, we consider the
two-side jump model recently introduced by Mendoza-Arriaga \& Linetsky, built
by time-changing CIR intensities. In a multivariate setup like CVA,
time-changing the intensity partly kills the potential correlation with the
exposure process and destroys WWR impact. Moreover, it can introduce a forward
looking effect that can lead to arbitrage opportunities. In this paper, we use
the time-changed CIR process in a way that the above issues are avoided. We
show that the resulting process allows to introduce a large WWR effect compared
to the JCIR++ model. The computation cost of the resulting Monte Carlo
framework is reduced by using an adaptive control variate procedure.
Eliminating the effect of rating bias on reputation systems
Authors: Wu Leilei, Ren Zhuoming, Ren Xiao-Long, Zhang Jianlin, Lü Linyuan
Date: 2018-01-17 00:00:00

The ongoing rapid development of the e-commercial and interest-base websites
make it more pressing to evaluate objects' accurate quality before
recommendation by employing an effective reputation system. The objects'
quality are often calculated based on their historical information, such as
selected records or rating scores, to help visitors to make decisions before
watching, reading or buying. Usually high quality products obtain a higher
average ratings than low quality products regardless of rating biases or
errors. However many empirical cases demonstrate that consumers may be misled
by rating scores added by unreliable users or deliberate tampering. In this
case, users' reputation, i.e., the ability to rating trustily and precisely,
make a big difference during the evaluating process. Thus, one of the main
challenges in designing reputation systems is eliminating the effects of users'
rating bias on the evaluation results. To give an objective evaluation of each
user's reputation and uncover an object's intrinsic quality, we propose an
iterative balance (IB) method to correct users' rating biases. Experiments on
two online video-provided Web sites, namely MovieLens and Netflix datasets,
show that the IB method is a highly self-consistent and robust algorithm and it
can accurately quantify movies' actual quality and users' stability of rating.
Compared with existing methods, the IB method has higher ability to find the
"dark horses", i.e., not so popular yet good movies, in the Academy Awards.