pwc-archive/papers-with-abstracts huggingface dataset:
Characterization of blood pressure and heart rate oscillations of POTS patients via uniform phase empirical mode decomposition
Date: 2020-01-24 00:00:00
Tasks: Diagnostic

Objective: Postural Orthostatic Tachycardia Syndrome (POTS) is associated
with the onset of tachycardia upon postural change. The current diagnosis
involves the measurement of heart rate (HR) and blood pressure (BP) during
head-up tilt (HUT) or active standing test. A positive diagnosis is made if HR
changes with more than 30 bpm (40 bpm in patients aged 12-19 years), ignoring
all of the BP and most of the HR signals. This study examines 0.1 Hz
oscillations in systolic arterial blood pressure (SBP) and HR signals providing
additional metrics characterizing the dynamics of the baroreflex. Methods: We
analyze data from 28 control subjects and 28 POTS patients who underwent HUT.
We extract beat-to-beat HR and SBP during a 10 min interval including 5 minutes
of baseline and 5 minutes of HUT. We employ Uniform Phase Empirical Mode
Decomposition (UPEMD) to extract 0.1 Hz stationary modes from both signals and
use random forest machine learning and k-means clustering to analyze the
outcomes. Results show that the amplitude of the 0.1 Hz oscillations is higher
in POTS patients and that the phase response between the two signals is shorter
(p < 0.005). Conclusion: POTS is associated with an increase in the amplitude
of SBP and HR 0.1 Hz oscillation and a shortening of the phase between the two
signals. Significance: The 0.1 Hz phase response and oscillation amplitude
metrics provide new markers that can improve POTS diagnostic augmenting the
existing diagnosis protocol only analyzing the change in heart rate.
Quality of Transmission Estimation for Network Planning: how to handle single-channel nonlinear effects?
Date: 2019-10-23 00:00:00

We propose an analytical method to evaluate the equivalent SPM component of
the NLI generated by each fiber span, to enabling a fully disaggregated
evaluation of the GSNR degradation, as required in network planning.
Liver Segmentation from Multimodal Images using HED-Mask R-CNN
Date: 2019-10-23 00:00:00
Tasks: Computed Tomography (CT), Edge Detection, Image Enhancement, Liver Segmentation, Segmentation

Precise segmentation of the liver is critical for computer-aided diagnosis
such as pre-evaluation of the liver for living donor-based transplantation
surgery. This task is challenging due to the weak boundaries of organs,
countless anatomical variations, and the complexity of the background. Computed
tomography (CT) scanning and magnetic resonance imaging (MRI) images have
different parameters and settings. Thus, images acquired from different
modalities differ from one another making liver segmentation challenging task.
We propose an efficient liver segmentation with the combination of
holistically-nested edge detection (HED) and the Mask-region-convolutional
neural network (R-CNN) to address these challenges. The proposed HED-Mask R-CNN
approach is based on effective identification of edge maps from multimodal
images. The proposed system firstly applies a preprocessing step of image
enhancement to get the 'primal sketches' of the abdomen. Then the HED network
is applied to enhanced CT and MRI modality images to get a better edge map.
Finally, the Mask R-CNN is used to segment the liver from edge map images. We
used a dataset of 20 CT patients and 9 MR patients from the CHAOS challenge.
The system is trained on CT and MRI images separately and then converted to 2D
slices. We significantly improved the segmentation accuracy of CT and MRI
images on a database with a Dice value of 0.94 for CT, 0.89 for T2-weighted MRI
and 0.91 for T1-weighted MRI.
Mobile Edge Computing in Unmanned Aerial Vehicle Networks
Date: 2019-10-12 00:00:00
Tasks: Edge-computing

Unmanned aerial vehicle (UAV)-enabled communication networks are promising in
the fifth and beyond wireless communication systems. In this paper, we shed
light on three UAV-enabled mobile edge computing (MEC) architectures. Those
architectures have been receiving ever increasing research attention for
improving computation performance and decreasing execution latency by
integrating UAV into MEC networks. We present a comprehensive survey for the
state-of-the-art research in this domain. Important implementation issues are
clarified. Moreover, in order to provide an enlightening guidance for future
research directions, key challenges and open issues are discussed.
Inference of Binary Regime Models with Jump Discontinuities
Authors: Milan Kumar Das, Anindya Goswami, Sharan Rajani
Date: 2019-10-23 00:00:00
Tasks: Algorithmic Trading, Time Series, Time Series Analysis

Identifying the instances of jumps in a discrete-time-series sample of a jump diffusion model is a challenging task. We have developed a novel statistical technique for jump detection and volatility estimation in a return time series data using a threshold method. The consistency of the volatility estimator has been obtained. Since we have derived the threshold and the volatility estimator simultaneously by solving an implicit equation, we have obtained unprecedented accuracy across a wide range of parameter values. Using this method, the increments attributed to jumps have been removed from a large collection of historical data of Indian sectorial indices. Subsequently, we have tested the presence of regime-switching dynamics in the volatility coefficient using a new discriminating statistic. The statistic has been shown to be sensitive to the transition kernel of the regime-switching model. We perform the testing using the Bootstrap method and find a clear indication of presence of multiple regimes of volatility in the data. A link to all Python codes is given in the conclusion. The methodology is suitable for analyzing high-frequency data and may be applied for algorithmic trading.
Nonparametric identification of an interdependent value model with buyer covariates from first-price auction bids
Date: 2019-10-23 00:00:00

This paper introduces a version of the interdependent value model of Milgrom
and Weber (1982), where the signals are given by an index gathering signal
shifters observed by the econometrician and private ones specific to each
bidders. The model primitives are shown to be nonparametrically identified from
first-price auction bids under a testable mild rank condition. Identification
holds for all possible signal values. This allows to consider a wide range of
counterfactuals where this is important, as expected revenue in second-price
auction. An estimation procedure is briefly discussed.
Pricing Economic Dispatch with AC Power Flow via Local Multipliers and Conic Relaxation
Authors: Mariola Ndrio, Anna Winnicki, Subhonmesh Bose
Date: 2019-10-23 00:00:00

We analyze pricing mechanisms in electricity markets with AC power flow equations that define a nonconvex feasible set for the economic dispatch problem. Specifically, we consider two possible pricing schemes. The first among these prices are derived from Lagrange multipliers that satisfy Karush-Kuhn-Tucker conditions for local optimality of the nonconvex market clearing problem. The second is derived from optimal dual multipliers of the convex semidefinite programming (SDP) based relaxation of the market clearing problem. Relationships between these prices, their revenue adequacy and market equilibrium properties are derived and compared. The SDP prices are shown to equal distribution locational marginal prices derived with second-order conic relaxations of power flow equations over radial distribution networks. We illustrate our theoretical findings through numerical experiments.
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
Authors: Niko Hauzenberger, Florian Huber, Gary Koop, Luca Onorante
Date: 2019-10-23 00:00:00
Tasks: Bayesian Inference, regression

In this paper, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, a hierarchical mixture model on the TVPs is introduced. The resulting model closely mimics a random coefficients specification which groups the TVPs into several regimes. These flexible mixtures allow for TVPs that feature a small, moderate or large number of structural breaks. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the KT regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our models to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.
A Safety Constrained Control Framework for UAVs in GPS Denied Environment
Date: 2020-04-12 00:00:00
Tasks: State Estimation

Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied
environments, which can lead to potentially dangerous situations. To avoid
intolerable sensor drifts in the presence of GPS spoofing attacks, we propose a
safety constrained control framework that adapts the UAV at a path re-planning
level to support resilient state estimation against GPS spoofing attacks. The
attack detector is used to detect GPS spoofing attacks based on the resilient
state estimation and provides a switching criterion between the robust control
mode and emergency control mode. To quantify the safety margin, we introduce
the escape time which is defined as a safe time under which the state
estimation error remains within a tolerable error with designated confidence.
An attacker location tracker (ALT) is developed to track the location of the
attacker and estimate the output power of the spoofing device by the unscented
Kalman filter (UKF) with sliding window outputs. Using the estimates from ALT,
an escape controller (ESC) is designed based on the constrained model
predictive controller (MPC) such that the UAV escapes from the effective range
of the spoofing device within the escape time.
Keeping Them Honest: a Trustless Multi-Agent Algorithm to Validate Transactions Cleared on Blockchain using Physical Sensors
Date: 2019-10-24 00:00:00

In recent years, many Blockchain based frameworks for transacting commodities
on a congestible network have been proposed. In particular, as the number of
controllable grid connected assets increases, there is a need for a
decentralized, coupled economic and control mechanism to dynamically balance
the entire electric grid. Blockchain based Transactive Energy (TE) systems have
gained significant momentum as an approach to sustain the reliability and
security of the power grid in order to support the flexibility of electricity
demand. What is lacking in these designs, however, is a mechanism that
physically verifies all the energy transactions, to keep the various inherently
selfish players honest. In this paper, we introduce a secure peer-to-peer
network mechanism for the physical validation of economic transactions cleared
over a distributed ledger. The framework is $\textit{secure}$ in the sense that
selfish and malicious agents that are trying to inject false data into the
network are prevented from adversely affecting the optimal functionality of the
verification process by detecting and isolating them from the communication
network. Preliminary simulations focusing on TE show the workings of this
framework.
Optimal Future Sub-Transmission Volt-Var Planning Tool to Enable High PV Penetration
Date: 2019-10-24 00:00:00
Tasks: All

This paper proposes a reactive power planning tool for sub-transmission
systems to mitigate voltage violations and fluctuations caused by high
photovoltaic (PV) penetration and intermittency with a minimum investment cost.
The tool considers all existing volt-ampere reactive (var) assets in both
sub-transmission and distribution systems to reduce the need of new equipment.
The planning tool coordinates with an operational volt-var optimization tool to
determine all scenarios with voltage violations and verify the planning
results. The planning result of each scenario is the solution of a proposed
optimal power-flow framework with efficient techniques to handle a high number
of discrete variables. The final planning decision is obtained from the
planning results of all selected violated scenarios by using two different
approaches - direct combination of all single-step solutions and final
investment decision based only on the scenarios that are representative for the
power-flow voltage violations at most time steps. The final planning decision
is verified using a realistic large-scale sub-transmission system and 5-minute
PV and load data. The results show a significant voltage violation reduction
with a less investment cost for additional var equipment compared to
conventional approaches.
Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition
Date: 2019-10-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Separation

We investigate the effect of speaker localization on the performance of
speech recognition systems in a multispeaker, multichannel environment. Given
the speaker location information, speech separation is performed in three
stages. In the first stage, a simple delay-and-sum (DS) beamformer is used to
enhance the signal impinging from the speaker location which is then used to
estimate a time-frequency mask corresponding to the localized speaker using a
neural network. This mask is used to compute the second order statistics and to
derive an adaptive beamformer in the third stage. We generated a multichannel,
multispeaker, reverberated, noisy dataset inspired from the well studied
WSJ0-2mix and study the performance of the proposed pipeline in terms of the
word error rate (WER). An average WER of $29.4$% was achieved using the ground
truth localization information and $42.4$% using the localization information
estimated via GCC-PHAT. The signal-to-interference ratio (SIR) between the
speakers has a higher impact on the ASR performance, to the extent of reducing
the WER by $59$% relative for a SIR increase of $15$ dB. By contrast,
increasing the spatial distance to $50^\circ$ or more improves the WER by $23$%
relative only
Automatic Arm Motion Recognition Based on Radar Micro-Doppler Signature Envelopes
Authors: Zhengxin Zeng, Moeness Amin, Tao Shan
Date: 2019-10-23 00:00:00

In considering human-machine interface (HMI) for smart environment, a simple but effective method is proposed for automatic arm motion recognition with a Doppler radar sensor. Arms, in lieu of hands, have stronger radar cross-section and can be recognized from relatively longer distances. An energy-based thresholding algorithm is applied to the spectrograms to extract the micro-Doppler (MD) signature envelopes. The positive and negative frequency envelopes are concatenated to form a feature vector. The nearest neighbor (NN) classifier with Manhattan distance (L1) is then used to recognize the arm motions. It is shown that this simple method yields classification accuracy above 97 percent for six classes of arm motions. Despite its simplicity, the proposed method is superior to those of handcrafted featurebased classifications and low-dimension representation techniques based on principal component analysis (PCA), and is comparable to convolutional neural network (CNN).
CD44 alternative splicing is a sensor of intragenic DNA methylation in tumors
Date: 2019-10-24 00:00:00

DNA methylation (meDNA) is a suspected modulator of alternative splicing,
while splicing in turn is involved in tumour formations nearly as frequently as
DNA mutations. Yet, the impact of meDNA on tumorigenesis via its effect on
splicing has not been thoroughly explored. Here, we find that HCT116 colon
carcinoma cells inactivated for the DNA methylases DNMT1 and DNMT3b undergo a
partial epithelial to mesenchymal transition (EMT) associated with alternative
splicing of the CD44 transmembrane receptor. The skipping of CD44 variant exons
is in part explained by altered expression or splicing of splicing and
chromatin factors. A direct effect of meDNA on alternative splicing was
sustained by transient depletion of DNMT1 and the methyl-binding genes MBD1,
MBD2, and MBD3. Yet, local changes in intragenic meDNA also altered recruitment
of MBD1 protein and of the chromatin factor HP1$\gamma$ known to alter
transcriptional pausing and alternative splicing decisions. We further tested
if meDNA level has sufficiently strong direct impact on the outcome of
alternative splicing to have a predictive value in the MCF10A model for breast
cancer progression and in patients with acute lymphoblastic leukemia (B ALL).
We found that a small number of differentially spliced genes mostly involved in
splicing and signal transduction is systematically correlated with local meDNA.
Altogether, our observations suggest that, although DNA methylation has
multiple avenues to alternative splicing, its indirect effect may be also
mediated through alternative splicing isoforms of these sensors of meDNA.
Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study
Date: 2019-10-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Electrophysiological signals (electroencephalography, EEG, and
magnetoencephalography , MEG), as many natural processes, exhibit
scale-invariance properties resulting in a power-law (1/f) spectrum.
Interestingly, EEG and MEG differ in their slopes, which could be explained by
several mechanisms, including non-resistive properties of tissues. Our goal in
the present study is to estimate the impact of space/frequency structure of
source signals as a putative mechanism to explain spectral scaling properties
of neuroimaging signals. We performed simulations based on the summed
contribution of cortical patches with different sizes (ranging from 0.4 to
104.2 cm 2). Small patches were attributed signals of high frequencies, whereas
large patches were associated with signals of low frequencies, on a logarithmic
scale. The tested parameters included i) the space/frequency structure (range
of patch sizes and frequencies) and ii) the amplitude factor c parametrizing
the spatial scale ratios. We found that the space/frequency structure may cause
differences between EEG and MEG scale-free spectra that are compatible with
real data findings reported in previous studies. We also found that below a
certain spatial scale, there were no more differences between EEG and MEG,
suggesting a limit for the resolution of both methods. Our work provides an
explanation of experimental findings. This does not rule out other mechanisms
for differences between EEG and MEG, but suggests an important role of
spatio-temporal structure of neural dynamics. This can help the analysis and
interpretation of power-law measures in EEG and MEG, and we believe our results
can also impact computational modeling of brain dynamics, where different local
connectivity structures could be used at different frequencies.
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification
Date: 2020-04-28 00:00:00
Tasks: Dictionary Learning, EEG, Electroencephalogram (EEG), Seizure Detection, Sensitivity, Sparse Representation-based Classification, Specificity

Identifying seizure activities in non-stationary electroencephalography (EEG)
is a challenging task, since it is time-consuming, burdensome, and dependent on
expensive human resources and subject to error and bias. A computerized seizure
identification scheme can eradicate the above problems, assist clinicians and
benefit epilepsy research. So far, several attempts were made to develop
automatic systems to help neurophysiologists accurately identify epileptic
seizures. In this research, a fully automated system is presented to
automatically detect the various states of the epileptic seizure. The proposed
method is based on sparse representation-based classification (SRC) theory and
the proposed dictionary learning using electroencephalogram (EEG) signals.
Furthermore, the proposed method does not require additional preprocessing and
extraction of features which is common in the existing methods. The proposed
method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9
scenarios. It is also robust to the measurement noise of level as much as 0 dB.
Compared to state-of-the-art algorithms and other common methods, the proposed
method outperformed them in terms of sensitivity, specificity and accuracy.
Moreover, it includes the most comprehensive scenarios for epileptic seizure
detection, including different combinations of 2 to 5 class scenarios. The
proposed automatic identification of epileptic seizures method can reduce the
burden on medical professionals in analyzing large data through visual
inspection as well as in deprived societies suffering from a shortage of
functional magnetic resonance imaging (fMRI) equipment and specialized
physician.
Block based refitting in $\ell_{12}$ sparse regularisation
Authors: Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
Date: 2019-10-22 00:00:00
Tasks: Image Restoration

In many linear regression problems, including ill-posed inverse problems in image restoration, the data exhibit some sparse structures that can be used to regularize the inversion. To this end, a classical path is to use $\ell_{12}$ block based regularization. While efficient at retrieving the inherent sparsity patterns of the data -- the support -- the estimated solutions are known to suffer from a systematical bias. We propose a general framework for removing this artifact by refitting the solution towards the data while preserving key features of its structure such as the support. This is done through the use of refitting block penalties that only act on the support of the estimated solution. Based on an analysis of related works in the literature, we introduce a new penalty that is well suited for refitting purposes. We also present a new algorithm to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good behavior of the proposed block penalty for refitting solutions of Total Variation and Total GeneralizedVariation models.
Improved functional MRI activation mapping in white matter through diffusion-adapted spatial filtering
Date: 2019-10-24 00:00:00
Tasks: Denoising, Diffusion  MRI, Specificity

Brain activation mapping using functional MRI (fMRI) based on blood
oxygenation level-dependent (BOLD) contrast has been conventionally focused on
probing gray matter, the BOLD contrast in white matter having been generally
disregarded. Recent results have provided evidence of the functional
significance of the white matter BOLD signal, showing at the same time that its
correlation structure is highly anisotropic, and related to the diffusion
tensor in shape and orientation. This evidence suggests that conventional
isotropic Gaussian filters are inadequate for denoising white matter fMRI data,
since they are incapable of adapting to the complex anisotropic domain of white
matter axonal connections. In this paper we explore a graph-based description
of the white matter developed from diffusion MRI data, which is capable of
encoding the anisotropy of the domain. Based on this representation we design
localized spatial filters that adapt to white matter structure by leveraging
graph signal processing principles. The performance of the proposed filtering
technique is evaluated on semi-synthetic data, where it shows potential for
greater sensitivity and specificity in white matter activation mapping,
compared to isotropic filtering.