pwc-archive/papers-with-abstracts huggingface dataset:
Deep phenotyping of cardiac function in heart transplant patients using cardiovascular systems models
Date: 2019-12-10 00:00:00
Tasks: Sensitivity

Heart transplant patients are followed with periodic right heart
catheterizations (RHCs) to identify post-transplant complications and guide
treatment. Post-transplant positive outcomes are associated with a steady
reduction of right ventricular and pulmonary arterial pressures, toward normal
levels of right-side pressure (about 20mmHg) measured by RHC. This study shows
more information about patient progression is obtained by combining standard
RHC measures with mechanistic computational cardiovascular systems models. This
study shows: to understand how cardiovascular system models can be used to
represent a patient's cardiovascular state, and to use these models to track
post-transplant recovery and outcome. To obtain reliable parameter estimates
comparable within and across datasets, we use sensitivity analysis, parameter
subset selection, and optimization to determine patient specific mechanistic
parameter that can be reliably extracted from the RHC data. Patient-specific
models are identified for ten patients from their first post-transplant RHC and
longitudinal analysis is done for five patients. Results of sensitivity
analysis and subset selection show we can reliably estimate seven
non-measurable quantities including ventricular diastolic relaxation, systemic
resistance, pulmonary venous elastance, pulmonary resistance, pulmonary
arterial elastance, pulmonary valve resistance and systemic arterial elastance.
Changes in parameters and predicted cardiovascular function post-transplant are
used to evaluate cardiovascular state during recovery in five patients. Of
these five patients, only one patient showed inconsistent trends during
recovery in ventricular pressure-volume relationships and power output. At a
four-year recovery time point this patient exhibited biventricular failure
along with graft dysfunction while the remaining four exhibited no
cardiovascular complications.
Optimal local estimates of visual motion in a natural environment
Date: 2018-12-31 00:00:00
Tasks: Motion Estimation

Many organisms, from flies to humans, use visual signals to estimate their
motion through the world. To explore the motion estimation problem, we have
constructed a camera/gyroscope system that allows us to sample, at high
temporal resolution, the joint distribution of input images and rotational
motions during a long walk in the woods. From these data we construct the
optimal estimator of velocity based on spatial and temporal derivatives of
image intensity in small patches of the visual world. Over the bulk of the
naturally occurring dynamic range, the optimal estimator exhibits the same
systematic errors seen in neural and behavioral responses, including the
confounding of velocity and contrast. These results suggest that apparent
errors of sensory processing may reflect an optimal response to the physical
signals in the environment.
Estimating information in time-varying signals
Date: 2018-12-31 00:00:00
Tasks: Time Series, Time Series Analysis

Across diverse biological systems -- ranging from neural networks to
intracellular signaling and genetic regulatory networks -- the information
about changes in the environment is frequently encoded in the full temporal
dynamics of the network nodes. A pressing data-analysis challenge has thus been
to efficiently estimate the amount of information that these dynamics convey
from experimental data. Here we develop and evaluate decoding-based estimation
methods to lower bound the mutual information about a finite set of inputs,
encoded in single-cell high-dimensional time series data. For biological
reaction networks governed by the chemical Master equation, we derive
model-based information approximations and analytical upper bounds, against
which we benchmark our proposed model-free decoding estimators. In contrast to
the frequently-used k-nearest-neighbor estimator, decoding-based estimators
robustly extract a large fraction of the available information from
high-dimensional trajectories with a realistic number of data samples. We apply
these estimators to previously published data on Erk and Ca signaling in
mammalian cells and to yeast stress-response, and find that substantial amount
of information about environmental state can be encoded by non-trivial response
statistics even in stationary signals. We argue that these single-cell,
decoding-based information estimates, rather than the commonly-used tests for
significant differences between selected population response statistics,
provide a proper and unbiased measure for the performance of biological
signaling networks.
Fractal representation of the power daily demand based on topological properties of Julia sets
Date: 2018-12-24 00:00:00

In a power system, the load demand considers two components such as the real
power (P) because of resistive elements, and the reactive power (Q) because
inductive or capacitive elements. This paper presents a graphical
representation of the electric power demand based on the topological properties
of the Julia Sets, with the purpose of observing the different graphic patterns
and relationship with the hourly load consumptions. An algorithm that iterates
complex numbers related to power is used to represent each fractal diagram of
the load demand. The results show some representative patterns related to each
value of the power consumption and similar behaviour in the fractal diagrams,
which allows to understand consumption behaviours from the different hours of
the day. This study allows to make a relation among the different consumptions
of the day to create relationships that lead to the prediction of different
behaviour patterns of the curves.
Microglial memory of early life stress and inflammation: susceptibility to neurodegeneration in adulthood
Authors: Paula Desplats, Ashley M. Gutierrez, Marta C. Antonelli, Martin G. Frasch
Date: 2019-01-01 00:00:00

We review evidence supporting the role of early life programming in the susceptibility for adult neurodegenerative diseases while highlighting questions and proposing avenues for future research to advance our understanding of this fundamental process. The key elements of this phenomenon are chronic stress, neuroinflammation triggering microglial polarization, microglial memory and their connection to neurodegeneration. We review the mediating mechanisms which may function as early biomarkers of increased susceptibility for neurodegeneration. Can we devise novel early life-modifying interventions to steer developmental trajectories to their optimum?
Microscopic 3D measurement of shiny surfaces based on a multi-frequency phase-shifting scheme
Date: 2018-12-31 00:00:00
Tasks: Stereo Matching, Surface Reconstruction

Microscopic fringe projection profilometry is a powerful 3D measurement
technique with a theoretical measurement accuracy better than one micron
provided that the measured targets can be imaged with good fringe visibility.
However, practically, the 3D shape of the measured surface can hardly be fully
reconstructed due to the defocus of the dense fringes and complex surface
reflexivity characteristics, which lead to low fringe quality and intensity
saturation. To address this problem, we propose to calculate phases of these
highlighted areas from a subset of the fringe sequence which is not subjected
to the intensity saturation. By using the proposed multi-frequency
phase-shifting scheme, the integrity of the 3D surface reconstruction can be
significantly improved. The ultimate phase maps obtained from unsaturated
intensities are used to achieve high-accuracy 3D recovering of shiny surfaces
based on a phase stereo matching method. Experimental results on different
metal surfaces show that our approach is able to retrieve the complete
morphology of shiny surfaces with high accuracy and fidelity.
Information Extraction from Scientific Literature for Method Recommendation
Authors: Luan Yi
Date: 2018-12-14 00:00:00

As a research community grows, more and more papers are published each year.
As a result there is increasing demand for improved methods for finding
relevant papers, automatically understanding the key ideas and recommending
potential methods for a target problem. Despite advances in search engines, it
is still hard to identify new technologies according to a researcher's need.
Due to the large variety of domains and extremely limited annotated resources,
there has been relatively little work on leveraging natural language processing
in scientific recommendation. In this proposal, we aim at making scientific
recommendations by extracting scientific terms from a large collection of
scientific papers and organizing the terms into a knowledge graph. In
preliminary work, we trained a scientific term extractor using a small amount
of annotated data and obtained state-of-the-art performance by leveraging large
amount of unannotated papers through applying multiple semi-supervised
approaches. We propose to construct a knowledge graph in a way that can make
minimal use of hand annotated data, using only the extracted terms,
unsupervised relational signals such as co-occurrence, and structural external
resources such as Wikipedia. Latent relations between scientific terms can be
learned from the graph. Recommendations will be made through graph inference
for both observed and unobserved relational pairs.
Delay and Power Tradeoff with Consideration of Caching Capabilities in Dense Wireless Networks
Date: 2019-03-19 00:00:00

Enabling caching capabilities in dense small cell networks (DSCNs) has a
direct impact on file delivery delay and power consumption. Most existing work
studied these two performance metrics separately in cache-enabled DSCNs.
However, file delivery delay and power consumption are coupled with each other
and cannot be minimized simultaneously. In this paper, we investigate the
optimal tradoff between these two performance metrics. Firstly, we formulate
the joint file delivery delay and power consumption optimization (JDPO) problem
where power control, user association and file placement are jointly
considered. Then we convert it to a form that can be handled by Generalized
Benders Decomposition (GDB). with GDB, we decompose the converted JDPO problem
into two smaller problems, i.e., primal problem related to power control and
master problem related to user association and file placement. An iterative
algorithm is proposed and proved to be $\epsilon$-optimal, in which the primal
problem and master problem are solved iteratively to approach the optimal
solution. To further reduce the complexity of the master problem, an
accelerated algorithm based on semi-definite relaxation is proposed. Finally,
the simulation results demonstrate that the proposed algorithm can approach the
optimal tradeoff between file delivery delay and power consumption.
Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for TurbineGovernor and Excitation System
Date: 2018-12-27 00:00:00
Tasks: Model Selection, parameter estimation, valid

In this paper a new method is introduced in order to modify identification
process of a gas power plant using a metaheuristic algorithm named Cuckoo
Search (CS). Simulations play a significant role in dynamic analyses of power
plants. This paper points out to a practical approach in model selection and
parameter estimation of gas power plants. The identification and validation
process concentrates on two subsystems: governor-turbine and exciter. Standard
models GGOV1 and STB6 are preferred for the dynamical structures of
governor-turbine and exciter respectively. Considering definite standard
structure, main parameters of dynamical model are pre estimated via system
identification methods based on field data. Then obtained parameters are tuned
carefully using an iterative Cuckoo algorithm. Models must be validated by
results derived via a trial and error series of simulation in comparison to
measured test data. The procedure gradually yields in a valid model with
precise estimated parameters. Simulation results show accuracy of identified
models. Besides, a whiteness analysis has been performed in order to show the
authenticity of the proposed method in another way. Despite various detailed
models, practical attempts of model selection, identification, and validation
in a real gas unit could rarely be found among literature. In this paper,
Chabahar power plant in Iran, with total install capacity of 320 MW, is chosen
as a benchmark for model validation.
Computational structured illumination for high-content fluorescent and phase microscopy
Date: 2019-03-27 00:00:00
Tasks: Super-Resolution, Vocal Bursts Intensity Prediction

High-content biological microscopy targets high-resolution imaging across
large fields-of-view (FOVs). Recent works have demonstrated that computational
imaging can provide efficient solutions for high-content microscopy. Here, we
use speckle structured illumination microscopy (SIM) as a robust and
cost-effective solution for high-content fluorescence microscopy with
simultaneous high-content quantitative phase (QP). This multi-modal
compatibility is essential for studies requiring cross-correlative biological
analysis. Our method uses laterally-translated Scotch tape to generate
high-resolution speckle illumination patterns across a large FOV. Custom
optimization algorithms then jointly reconstruct the sample's super-resolution
fluorescent (incoherent) and QP (coherent) distributions, while digitally
correcting for system imperfections such as unknown speckle illumination
patterns, system aberrations and pattern translations. Beyond previous linear
SIM works, we achieve resolution gains of 4x the objective's
diffraction-limited native resolution, resulting in 700 nm fluorescence and 1.2
um QP resolution, across a FOV of 2x2.7 mm^2, giving a space-bandwidth product
(SBP) of 60 megapixels.
Selection and the Distribution of Female Hourly Wages in the U.S
Authors: Iván Fernández-Val, Franco Peracchi, Aico van Vuuren, Francis Vella
Date: 2018-12-21 00:00:00
Tasks: Selection bias

We analyze the role of selection bias in generating the changes in the observed distribution of female hourly wages in the United States using CPS data for the years 1975 to 2020. We account for the selection bias from the employment decision by modeling the distribution of the number of working hours and estimating a nonseparable model of wages. We decompose changes in the wage distribution into composition, structural and selection effects. Composition effects have increased wages at all quantiles while the impact of the structural effects varies by time period and quantile. Changes in the role of selection only appear at the lower quantiles of the wage distribution. The evidence suggests that there is positive selection in the 1970s which diminishes until the later 1990s. This reduces wages at lower quantiles and increases wage inequality. Post 2000 there appears to be an increase in positive sorting which reduces the selection effects on wage inequality.
Optimal execution with dynamic risk adjustment
Date: 2019-07-13 00:00:00

This paper considers the problem of optimal liquidation of a position in a
risky security in a financial market, where price evolution are risky and
trades have an impact on price as well as uncertainty in the filling orders.
The problem is formulated as a continuous time stochastic optimal control
problem aiming at maximizing a generalized risk-adjusted profit and loss
function. The expression of the risk adjustment is derived from the general
theory of dynamic risk measures and is selected in the class of $g$-conditional
risk measures. The resulting theoretical framework is nonclassical since the
target function depends on backward components. We show that, under a quadratic
specification of the driver of a backward stochastic differential equation, it
is possible to find a closed form solution and an explicit expression of the
optimal liquidation policies. In this way it is immediate to quantify the
impact of risk-adjustment on the profit and loss and on the expression of the
optimal liquidation policies.
Resource-Scalable CNN Synthesis for IoT Applications
Authors: Motamedi Mohammad, Portillo Felix, Saffarpour Mahya, Fong Daniel, Ghiasi Soheil
Date: 2018-12-16 00:00:00

State-of-the-art image recognition systems use sophisticated Convolutional
Neural Networks (CNNs) that are designed and trained to identify numerous
object classes. Such networks are fairly resource intensive to compute,
prohibiting their deployment on resource-constrained embedded platforms. On one
hand, the ability to classify an exhaustive list of categories is excessive for
the demands of most IoT applications. On the other hand, designing a new
custom-designed CNN for each new IoT application is impractical, due to the
inherent difficulty in developing competitive models and time-to-market
pressure. To address this problem, we investigate the question of: "Can one
utilize an existing optimized CNN model to automatically build a competitive
CNN for an IoT application whose objects of interest are a fraction of
categories that the original CNN was designed to classify, such that the
resource requirement is proportionally scaled down?" We use the term resource
scalability to refer to this concept, and develop a methodology for automated
synthesis of resource scalable CNNs from an existing optimized baseline CNN.
The synthesized CNN has sufficient learning capacity for handling the given IoT
application requirements, and yields competitive accuracy. The proposed
approach is fast, and unlike the presently common practice of CNN design, does
not require iterative rounds of training trial and error.
Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence
Authors: da Silva Leonardo Enzo Brito, Elnabarawy Islam, Wunsch Donald C. II
Date: 2018-11-28 00:00:00
Tasks: Clustering, Quantization

This paper presents a novel adaptive resonance theory (ART)-based modular
architecture for unsupervised learning, namely the distributed dual vigilance
fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local
fuzzy ART modules. It is equipped with the distinctive features of distributed
higher-order activation and match functions, using dual vigilance parameters
responsible for cluster similarity and data quantization. Together, these allow
DDVFA to perform unsupervised modularization, create multi-prototype clustering
representations, retrieve arbitrarily-shaped clusters, and control its
compactness. Another important contribution is the reduction of
order-dependence, an issue that affects any agglomerative clustering method.
This paper demonstrates two approaches for mitigating order-dependence:
preprocessing using visual assessment of cluster tendency (VAT) or
postprocessing using a novel Merge ART module. The former is suitable for batch
processing, whereas the latter can be used in online learning. Experimental
results in the online learning mode carried out on 30 benchmark data sets show
that DDVFA cascaded with Merge ART statistically outperformed the best other
ART-based systems when samples were randomly presented. Conversely, they were
found to be statistically equivalent in the offline mode when samples were
pre-processed using VAT. Remarkably, performance comparisons to non-ART-based
clustering algorithms show that DDVFA (which learns incrementally) was also
statistically equivalent to the non-incremental (offline) methods of DBSCAN,
single linkage hierarchical agglomerative clustering (HAC), and k-means, while
retaining the appealing properties of ART. Links to the source code and data
are provided. Considering the algorithm's simplicity, online learning
capability, and performance, it is an ideal choice for many agglomerative
clustering applications.
Noise performance of the complex monopulse ratio
Authors: Marco Lanucara
Date: 2019-01-04 00:00:00

The paper provides a characterization of the complex monopulse ratio in terms of autocorrelation and power spectral density of its fluctuations during satellite tracking, taking into account the presence of additive noise on sum and difference channels. The considered spectral structure and statistical distribution of the incoming signal is of interest for satellite missions. In particular it is assumed that the signal available at the monopulse processor after frequency down conversion contains a Gaussian term produced by low pass filtering of a constant envelope modulation, plus a monochromatic component representative of a possible residual carrier. The results can be used for optimizing the design of a monopulse tracking system.
Privacy-Preserving Probabilistic Forecasting for Temporal-spatial Correlated Wind Farms
Date: 2018-12-17 00:00:00
Tasks: Privacy Preserving

Adopting Secure scalar product and Secure sum techniques, we propose a
privacy-preserving method to build the joint and conditional probability
distribution functions of multiple wind farms' output considering the
temporal-spatial correlation. The proposed method can protect the raw data of
wind farms (WFs) from disclosure, and are mathematically equivalent to the
centralized method which needs to gather the raw data of all WFs.
BOTDA Fiber Sensor System Based on FPGA Accelerated Support Vector Regression
Date: 2018-12-29 00:00:00
Tasks: CPU, regression, Structural Health Monitoring

Brillouin optical time domain analyzer (BOTDA) fiber sensors have shown
strong capability in static long haul distributed temperature/strain sensing.
However, in applications such as structural health monitoring and leakage
detection, real-time measurement is quite necessary. The measurement time of
temperature/strain in a BOTDA system includes data acquisition time and
post-processing time. In this work, we propose to use hardware accelerated
support vector regression (SVR) for the post-processing of the collected BOTDA
data. Ideal Lorentzian curves under different temperatures with different
linewidths are used to train the SVR model to determine the linear SVR decision
function. The performance of SVR is evaluated under different signal-to-noise
ratios (SNRs) experimentally. After the model coefficients are determined,
algorithm-specific hardware accelerators based on field programmable gate
arrays (FPGAs) are used to realize SVR decision function. During the
implementation, hardware optimization techniques based on loop dependence
analysis and batch processing are proposed to reduce the execution latency. Our
FPGA implementations can achieve up to 42x speedup compared with software
implementation on an i7-5960x computer. The post-processing time for 96,100
BGSs along 38.44-km FUT is only 0.46 seconds with FPGA board ZCU104, making the
post-processing time no longer a limiting factor for dynamic sensing. Moreover,
the energy efficiency of our FPGA implementation can reach up to 226.1x higher
than software implementation based on CPU.
Nonparametric Instrumental Variables Estimation Under Misspecification
Authors: Ben Deaner
Date: 2019-01-04 00:00:00
Tasks: Sensitivity, valid

Nonparametric Instrumental Variables (NPIV) analysis is based on a conditional moment restriction. We show that if this moment condition is even slightly misspecified, say because instruments are not quite valid, then NPIV estimates can be subject to substantial asymptotic error and the identified set under a relaxed moment condition may be large. Imposing strong a priori smoothness restrictions mitigates the problem but induces bias if the restrictions are too strong. In order to manage this trade-off we develop a methods for empirical sensitivity analysis and apply them to the consumer demand data previously analyzed in Blundell (2007) and Horowitz (2011).