pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Smoothing for the Extended Object Random Matrix Model
Date: 2019-01-11 00:00:00
Tasks: Object, Object Tracking, Position

The random matrix model is popular in extended object tracking, due to its
relative simplicity and versatility. In this model, the extended object state
consists of a kinematic vector for the position and motion parameters
(velocity, etc), and an extent matrix. Two versions of the model can be found
in literature, one where the state density is modelled by a conditional
density, and one where the state density is modelled by a factorized density.
In this paper, we present closed form Bayesian smoothing expression for both
the conditional and the factorised model. In a simulation study, we compare the
performance of different versions of the smoother.
Development of a system to profile foot temperatures of the plantar and the periphery
Date: 2019-01-17 00:00:00
Tasks: Diagnostic

Foot temperature profiling is of utmost importance is mitigating the adverse
effects due to foot complications especially due to diabetes. Contactless
temperature monitoring methods could be used effectively in large scale for
patient screening. Near-infrared thermography has proven to be convenient and
accurate for temperature profiling. The objective of this study is to develop a
diagnostic device using the said imaging technology to detect as well as
progress monitoring of foot complications. The device we have developed is
capable of scanning the foot plantar and the periphery and it is also
accompanied by a semi-supervised thermal image analysis algorithm which is
convenient to the clinician. Preliminary clinical testing conducted using 6
diabetic subjects out of which 2 had ulcers in either foot and 9 non-diabetic
subjects 2 of which had wounds on the plantar. The system was able to detect
the ulcerated areas and wounds with the algorithm developed specifically for
thermal image analysis.
On a family of stochastic SVIR influenza epidemic models and maximum likelihood estimation
Date: 2019-01-15 00:00:00

This study presents a family of stochastic models for the dynamics of
influenza in a closed human population. We consider treatment for the disease
in the form of vaccination, and incorporate the periods of effectiveness of the
vaccine and infectiousness for the individuals in the population. Our model is
a SVIR model, with trinomial transition probabilities, where all individuals
who recover from the disease acquire permanent natural immunity against the
strain of the disease. Special SVIR models in the family are presented, based
on the structure of the probability of getting infection and vaccination at any
instant. The methods of maximum likelihood, and expectation maximization are
derived for the parameters of the chain. Moreover, estimators for some
epidemiological assessment parameters, such as the basic reproduction number
are computed. Numerical simulation examples are presented for the model.
Slow decay of impact in equity markets: insights from the ANcerno database
Date: 2019-01-22 00:00:00

We present an empirical study of price reversion after the executed
metaorders. We use a data set with more than 8 million metaorders executed by
institutional investors in the US equity market. We show that relaxation takes
place as soon as the metaorder ends:{while at the end of the same day it is on
average $\approx 2/3$ of the peak impact, the decay continues the next days,
following a power-law function at short time scales, and converges to a
non-zero asymptotic value at long time scales (${\sim 50}$ days) equal to
$\approx 1/2$ of the impact at the end of the first day.} Due to a significant,
multiday correlation of the sign of executed metaorders, a careful
deconvolution of the \emph{observed} impact must be performed to extract the
estimate of the impact decay of isolated metaorders.
Frequency Control of Decoupled Synchronous Machine Using Koopman Operator Based Model Predictive
Date: 2019-01-14 00:00:00

Conventional generators have been retired or replaced by renewable energy
because of the utility long-standing goals. However, instead of decommissioning
the entire plant, the rotating mass can be utilized as a storage unit to
mitigate the frequency issues due to these changes in the grid. The goal is to
design a control utilizing the retired machine interfaced with the grid through
a back to back converter referred to as decoupled synchronous machine system
(DSMS) to damp frequency oscillations. However, in a practical setting, it is
often not possible for a utility to obtain access to the detailed state
equations of such devices from the vendor making the addition of another layer
of control a challenging problem. Therefore, a purely data driven approach to
nonlinear control design using Koopman operator based framework is proposed for
this application. The effectiveness of the proposed system is demonstrated in
the Kundur two-area system.
A Dual Alternating Direction Method of Multipliers for Image Decomposition and Restoration
Date: 2019-01-24 00:00:00

In this paper, we develop a dual alternating direction method of multipliers
(ADMM) for an image decomposition model. In this model, an image is divided
into two meaningful components, i.e., a cartoon part and a texture part. The
optimization algorithm that we develop not only gives the cartoon part and the
texture part of an image but also gives the restored image (cartoon part +
texture part). We also present the global convergence and the local linear
convergence rate for the algorithm under some mild conditions. Numerical
experiments demonstrate the efficiency and robustness of the dual ADMM (dADMM).
Furthermore, we can obtain relatively higher signalto-noise ratio (SNR)
comparing to other algorithms. It shows that the choice of the algorithm is
also important even for the same model.
lassopack: Model selection and prediction with regularized regression in Stata
Date: 2019-01-16 00:00:00
Tasks: Model Selection, regression, Time Series, Time Series Analysis

This article introduces lassopack, a suite of programs for regularized
regression in Stata. lassopack implements lasso, square-root lasso, elastic
net, ridge regression, adaptive lasso and post-estimation OLS. The methods are
suitable for the high-dimensional setting where the number of predictors $p$
may be large and possibly greater than the number of observations, $n$. We
offer three different approaches for selecting the penalization (`tuning')
parameters: information criteria (implemented in lasso2), $K$-fold
cross-validation and $h$-step ahead rolling cross-validation for cross-section,
panel and time-series data (cvlasso), and theory-driven (`rigorous')
penalization for the lasso and square-root lasso for cross-section and panel
data (rlasso). We discuss the theoretical framework and practical
considerations for each approach. We also present Monte Carlo results to
compare the performance of the penalization approaches.
Joint Blocklength and Location Optimization for URLLC-enabled UAV Relay Systems
Date: 2019-01-16 00:00:00

This letter considers the unmanned aerial vehicle (UAV)-enabled relay system
to deliver command information under ultra-reliable and low-latency
communication (URLLC) requirements. We aim to jointly optimize the blocklength
allocation and the UAV's location to minimize the decoding error probability
subject to the latency requirement. The achievable data rate under finite
blocklength regime is adopted. A novel perturbation-based iterative algorithm
is proposed to solve this problem. Simulation results show that the proposed
algorithm can achieve the same performance as the exhaustive search method, and
significantly outperforms the existing algorithms.
Detection of delayed target response in SAR
Date: 2019-01-15 00:00:00

Delayed target response in synthetic aperture radar (SAR) imaging can be
obscured by the range-delay ambiguity and speckle. To analyze the range-delay
ambiguity, one extends the standard SAR formulation and allows both the target
reflectivity and the image to depend not only on the coordinates, but also on
the response delay. However, this still leaves the speckle unaccounted for. Yet
speckle is commonly found in SAR images of extended targets, and a statistical
approach is usually employed to describe it. We have developed a simple model
of a delayed scatterer by modifying the random function that describes a
homogeneous extended scatterer. Our model allows us to obtain a relation
between the deterministic parameters of the target model and statistical
moments of the SAR image. We assume a regular shape of the antenna trajectory,
and our model targets are localized in at least one space-time coordinate; this
permits analytical formulation for statistical moments of the image. The
problem of reconstruction of coordinate-delay reflectivity function is reduced
to that of discrimination between instantaneous and delayed scatterers; for the
latter problem, the maximum likelihood based image processing procedure has
been developed. We perform Monte-Carlo simulation and evaluate performance of
the classification procedure for a simple dependence of scatterer reflectivity
on the delay time.
Optimizing IoT Energy Efficiency on Edge (EEE): a Cross-layer Design in a Cognitive Mesh Network
Date: 2020-01-10 00:00:00

Battery-powered wireless IoT devices are now widely seen in many critical
applications. Given the limited battery capacity and inaccessibility to
external power recharge, optimizing energy efficiency (EE) plays a vital role
in prolonging the lifetime of these IoT devices. However, a sheer amount of
existing works only focus on the EE design at the infrastructure level such as
base stations (BSs) but with little attention to the EE design at the device
level. In this paper, we propose a novel idea that aims to shift energy
consumption to a grid-powered cognitive radio mesh network thus preserving
energy of battery-powered devices. Under this line of thinking, we cast the
design into a cross-layer optimization problem with an objective to maximize
devices' energy efficiency. To solve this problem, we propose a parametric
transformation technique to convert the original problem into a more tractable
one. A baseline scheme is used to demonstrate the advantage of our design. We
also carry out extensive simulations to exhibit the optimality of our proposed
algorithms and the network performance under various settings.
Comparative Analysis of Content-based Personalized Microblog Recommendations [Experiments and Analysis]
Authors: Taniskidou Efi Karra, Papadakis George, Giannakopoulos George, Koubarakis Manolis
Date: 2019-01-16 00:00:00
Tasks: Management

Microblogging platforms constitute a popular means of real-time communication
and information sharing. They involve such a large volume of user-generated
content that their users suffer from an information deluge. To address it,
numerous recommendation methods have been proposed to organize the posts a user
receives according to her interests. The content-based methods typically build
a text-based model for every individual user to capture her tastes and then
rank the posts in her timeline according to their similarity with that model.
Even though content-based methods have attracted lots of interest in the data
management community, there is no comprehensive evaluation of the main factors
that affect their performance. These are: (i) the representation model that
converts an unstructured text into a structured representation that elucidates
its characteristics, (ii) the source of the microblog posts that compose the
user models, and (iii) the type of user's posting activity. To cover this gap,
we systematically examine the performance of 9 state-of-the-art representation
models in combination with 13 representation sources and 3 user types over a
large, real dataset from Twitter comprising 60 users. We also consider a wide
range of 223 plausible configurations for the representation models in order to
assess their robustness with respect to their internal parameters. To
facilitate the interpretation of our experimental results, we introduce a novel
taxonomy of representation models. Our analysis provides novel insights into
the performance and functionality of the main factors determining the
performance of content-based recommendation in microblogs.
Shared Causal Paths underlying Alzheimer's dementia and Type 2 Diabetes
Date: 2019-01-16 00:00:00
Tasks: Causal Inference, Vocal Bursts Type Prediction

Background: Although Alzheimer's disease (AD) is a central nervous system
disease and type 2 diabetes mellitus (T2DM) is a metabolic disorder, an
increasing number of genetic epidemiological studies show clear link between AD
and T2DM. The current approach to uncovering the shared pathways between AD and
T2DM involves association analysis; however, such analyses lack power to
discover the mechanisms of the diseases. Methods: We develop novel statistical
methods to shift the current paradigm of genetic analysis from association
analysis to deep causal inference for uncovering the shared mechanisms between
AD and T2DM, and develop pipelines to infer multilevel omics causal networks
which lead to shifting the current paradigm of genetic analysis from genetic
analysis alone to integrated causal genomic, epigenomic, transcriptional and
phenotypic data analysis. To discover common causal paths from genetic variants
to AD and T2DM, we also develop algorithms that can automatically search the
causal paths from genetic variants to diseases and Results: The proposed
methods and algorithms are applied to ROSMAP dataset with 432 individuals who
simultaneously had genotype, RNA-seq, DNA methylation and some phenotypes. We
construct multi-omics causal networks and identify 13 shared causal genes, 16
shared causal pathways between AD and T2DM, and 754 gene expression and 101
gene methylation nodes that were connected to both AD and T2DM in multi-omics
causal networks. Conclusions: The results of application of the proposed
pipelines for identifying causal paths to real data analysis of AD and T2DM
provided strong evidence to support the link between AD and T2DM and unraveled
causal mechanism to explain this link.
Hybrid Modelling in Oncology: Sucesses, Challenges and Hopes
Date: 2019-01-17 00:00:00
Tasks: Data Integration, Drug Discovery

In this review we make the statement that hybrid models in oncology are
required as a mean for enhanced data integration. In the context of systems
oncology, experimental and clinical data need to be at the heart of the models
developments from conception to validation to ensure a relevant use of the
models in the clinical context. The main applications pursued are to improve
diagnosis and to optimize therapies.We first present the Successes achieved
thanks to hybrid modelling approaches to advance knowledge, treatments or drug
discovery. Then we present the Challenges than need to be addressed to allow
for a better integration of the model parts and of the data into the models.
And Finally, the Hopes with a focus towards making personalised medicine a
reality. Mathematics Subject Classification. 35Q92, 68U20, 68T05, 92-08, 92B05.
Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach
Authors: Jérôme Lelong
Date: 2019-01-17 00:00:00
Tasks: regression

In this work, we propose a new policy iteration algorithm for pricing Bermudan options when the payoff process cannot be written as a function of a lifted Markov process. Our approach is based on a modification of the well-known Longstaff Schwartz algorithm, in which we basically replace the standard least square regression by a Wiener chaos expansion. Not only does it allow us to deal with a non Markovian setting, but it also breaks the bottleneck induced by the least square regression as the coefficients of the chaos expansion are given by scalar products on the L^2 space and can therefore be approximated by independent Monte Carlo computations. This key feature enables us to provide an embarrassingly parallel algorithm.
Improper Signaling for SISO Two-user Interference Channels with Additive Asymmetric Hardware Distortion
Date: 2019-09-11 00:00:00

Hardware non-idealities are among the main performance restrictions for
upcoming wireless communication systems. Asymmetric hardware distortions (HWD)
happen when the impairments of the I/Q branches are correlated or imbalanced,
which in turn generate improper additive interference at the receiver side.
When the interference is improper, as well as in other interference-limited
scenarios, improper Gaussian signaling (IGS) has been shown to provide rate
and/or power efficiency benefits. In this paper, we investigate the rate
benefits of IGS in a two-user interference channel (IC) with additive
asymmetric HWD when interference is treated as noise. We propose two iterative
algorithms to optimize the parameters of the improper transmit signals. We
first rewrite the rate region as an
pseudosignal-to-interference-plus-noise-ratio (PSINR) region and employ
majorization minimization and fractional programming to find a suboptimal
solution for the achievable user rates. Then, we propose a simplified algorithm
based on a separate optimization of the powers and complementary variances of
the users, which exhibits lower computational complexity. We show that IGS can
improve the performance of the two-user IC with additive HWD. Our proposed
algorithms outperform proper Gaussian signaling and competing IGS algorithms in
the literature that do not consider asymmetric HWD.
Detecting Sound-Absorbing Materials in a Room from a Single Impulse Response using a CRNN
Date: 2019-10-27 00:00:00

The materials of surfaces in a room play an important room in shaping the
auditory experience within them. Different materials absorb energy at different
levels. The level of absorption also varies across frequencies. This paper
investigates how cues from a measured impulse response in the room can be
exploited by machines to detect the materials present. With this motivation,
this paper proposes a method for estimating the probability of presence of 10
material categories, based on their frequency-dependent absorption
characteristics. The method is based on a CNN-RNN, trained as a multi-task
classifier. The network is trained using a priori knowledge about the
absorption characteristics of materials from the literature. In the experiments
shown, the network is tested on over 5,00 impulse responses and 167 materials.
The F1 score of the detections was 98%, with an even precision and recall. The
method finds direct applications in architectural acoustics and in creating
more parsimonious models for acoustic reflections.
A Learning Framework for An Accurate Prediction of Rainfall Rates
Date: 2019-01-17 00:00:00
Tasks: regression

The present work is aimed to examine the potential of advanced machine
learning strategies to predict the monthly rainfall (precipitation) for the
Indus Basin, using climatological variables such as air temperature,
geo-potential height, relative humidity and elevation. In this work, the focus
is on thirteen geographical locations, called index points, within the basin.
Arguably, not all of the hydrological components are relevant to the
precipitation rate, and therefore, need to be filtered out, leading to a
lower-dimensional feature space. Towards this goal, we adopted the gradient
boosting method to extract the most contributive features for precipitation
rate prediction. Five state-of-the-art machine learning methods have then been
trained where pearson correlation coefficient and mean absolute error have been
reported as the prediction performance criteria. The Random Forest regression
model outperformed the other regression models achieving the maximum pearson
correlation coefficient and minimum mean absolute error for most of the index
points. Our results suggest the relative humidity (for pressure levels of 300
mb and 150 mb, respectively), the u-direction wind (for pressure level of 700
mb), air temperature (for pressure levels of 150 mb and 10 mb, respectively) as
the top five influencing features for accurate forecasting the precipitation
rate.
A Manifold Regularized Multi-Task Learning Model for IQ Prediction from Multiple fMRI Paradigms
Date: 2019-01-17 00:00:00
Tasks: Functional Connectivity, Multi-Task Learning

Multi-modal brain functional connectivity (FC) data have shown great
potential for providing insights into individual variations in behavioral and
cognitive traits. The joint learning of multi-modal imaging data can utilize
the intrinsic association, and thus can boost the learning performance.
Although several multi-task based learning models have already been proposed by
viewing the feature learning on each modality as one task, most of them ignore
the geometric structure information inherent in the modalities, which may play
an important role in extracting discriminative features. In this paper, we
propose a new manifold regularized multi-task learning model by simultaneously
considering between-subject and between-modality relationships. Besides
employing a group-sparsity regularizer to jointly select a few common features
across multiple tasks (modalities), we design a novel manifold regularizer to
preserve the structure information both within and between modalities in our
model. This will make our model more adaptive for realistic data analysis. Our
model is then validated on the Philadelphia Neurodevelopmental Cohort dataset,
where we regard our modalities as functional MRI (fMRI) data collected under
two paradigms. Specifically, we conduct experimental studies on fMRI based FC
network data in two task conditions for intelligence quotient (IQ) prediction.
The results demonstrate that our proposed model can not only achieve improved
prediction performance, but also yield a set of IQ-relevant biomarkers.
A New Approach for Steady-State Analysis of a Hybrid ac-dc Microgrid
Date: 2019-01-17 00:00:00

The dc grid became more popular, by emerging the distributed generations
(DGs). Despite this popularity, the dc grid is not yet widely used because the
majority of loads in a power system are ac, which means the ac grid is still
the dominant grid in the power system. Therefore, the concept of a hybrid ac-dc
microgrid was emerged because of this contradiction. Hybrid ac-dc microgrid was
introduced in order to exploit the benefits of both ac and dc microgrids.
However, the combination of both ac and dc microgrids will add more complexity
to the network. Because in all studies for hybrid ac-dc microgrid, such as
steady-state analysis or dynamic study, two sets of equations should be
considered and solved either separately or simultaneously, the solutions that
were presented before. These solutions increase the time of simulation and
operation. In this paper, a novel procedure for steady-state analysis of a
general hybrid ac-dc microgrid is proposed. In this technique, the dc
microgrids will be transferred to the ac side by proposing two lemmas and then
the whole grid will be analyzed as one ac network. It will be proved that not
only the new ac grid has the same power flow result with the initial topology
of the ac-dc microgrid, but also the simulation time of the proposed method is
less than the other existing methods.
Single cell data explosion: Deep learning to the rescue
Authors: A. K. M. Azad, Fatemeh Vafaee
Date: 2019-01-18 00:00:00
Tasks: Deep Learning

The plethora of single-cell multi-omics data is getting treatment with deep learning, a revolutionary method in artificial intelligence, which has been increasingly expanding its reign over the bioscience frontiers.