pwc-archive/papers-with-abstracts huggingface dataset:
Optics-free imaging of complex, non-sparse QR-codes with Deep Neural Networks
Date: 2020-02-25 00:00:00

We demonstrate optics-free imaging of complex QR-codes using a bare image
sensor and a trained artificial neural network (ANN). The ANN is trained to
interpret the raw sensor data for human visualization. The image sensor is
placed at a specified gap from the QR code. We studied the robustness of our
approach by experimentally testing the output of the ANNs with system
perturbations of this gap, and the translational and rotational alignments of
the QR code to the image sensor. Our demonstration opens us the possibility of
using completely optics-free cameras for application-specific imaging of
complex, non-sparse objects.
SHIFT: A Highly Realistic Financial Market Simulation Platform
Authors: Thiago W. Alves, Ionut Florescu, George Calhoun, Dragos Bozdog
Date: 2020-02-25 00:00:00

This paper presents a new financial market simulator that may be used as a tool in both industry and academia for research in market microstructure. It allows multiple automated traders and/or researchers to simultaneously connect to an exchange-like environment, where they are able to asynchronously trade several financial assets at the same time. In its current iteration, this order-driven market implements the basic rules of U.S. equity markets, supporting both market and limit orders, and executing them in a first-in-first-out fashion. We overview the system architecture and we present possible use cases. We demonstrate how a set of automated agents is capable of producing a price process with characteristics similar to the statistics of real price from financial markets. Finally, we detail a market stress scenario and we draw, what we believe to be, interesting conclusions about crash events.
Firing rate of the leaky integrate-and-fire neuron with stochastic conductance-based synaptic inputs with short decay times
Date: 2020-02-25 00:00:00

We compute the firing rate of a leaky integrate-and-fire (LIF) neuron with
stochastic conductance-based inputs in the limit when synaptic decay times are
much shorter than the membrane time constant. A comparison of our analytical
results to numeric simulations is presented for a range of
biophysically-realistic parameters.
Automatic Segmentation, Feature Extraction and Comparison of Healthy and Stroke Cerebral Vasculature
Date: 2020-02-25 00:00:00
Tasks: Diagnostic

Accurate segmentation of cerebral vasculature and a quantitative assessment
of cerebrovascular morphology is critical to various diagnostic and therapeutic
purposes and is pertinent to studying brain health and disease. However, this
is still a challenging task due to the complexity of the vascular imaging data.
We propose an automated method for cerebral vascular segmentation without the
need of any manual intervention as well as a method to skeletonize the binary
volume to extract vascular geometric features which can characterize vessel
structure. We combine a probabilistic vessel-enhancing filtering with an
active-contour technique to segment magnetic resonance and computed tomography
angiograms (MRA and CTA) and subsequently extract the vessel centerlines and
diameters to calculate the geometrical properties of the vasculature. Our
method was validated using a 3D phantom of the Circle-of-Willis region with 84%
mean Dice Similarity and 85% mean Pearson Correlation with minimal modified
Hausdorff distance error. We applied this method to a dataset of healthy
subjects and stroke patients and present a quantitative comparison between
them. We found significant differences in the geometric features including
total length (2.88 +/- 0.38 m for healthy and 2.20 +/- 0.67 m for stroke),
volume (40.18 +/- 25.55 ml for healthy and 34.43 +/- 21.83 ml for stroke),
tortuosity (3.24 +/- 0.88 rad/cm for healthy and 5.80 +/- 0.92 rad/cm for
stroke) and fractality (box dimension 1.36 +/- 0.28 for healthy vs. 1.69 +/-
0.20 for stroke). This technique can be applied on any imaging modality and can
be used in the future to automatically obtain the 3D segmented vasculature for
diagnosis and treatment planning of Stroke and other cerebrovascular diseases
(CVD) in the clinic and also to study the morphological changes caused by
various CVD.
Zero-inertia Systems: Sufficient Conditions for Phasor Modeling
Date: 2020-02-25 00:00:00

Time-domain simulations are a critical tool for power system operators.
Depending on the instability mechanism under consideration and the system
characteristics, such as the time constants of controllers, either phasor or
Electro-Magnetic Transient (EMT) models should be employed. On the one hand,
EMT models provide a detailed-modeling of the system dynamics, thus increase
the reliability of stability analysis; on the other end, using these models
increase the computational times of simulations, slowing down the security
assessment process. To decrease computational time, system operators could
resort to phasor-mode simulations for a (hopefully large) subset of
disturbances. This paper investigates the appropriateness of
phasor-approximation models on simulating events related to power supply and
balance stability in zero-inertia systems. First, the stability boundaries,
which each model is able to identify, are analyzed; then sufficient conditions
for control parameters are derived, which allow using phasor-approximation
models to monitor power sharing among grid-forming converter-based resources.
Time-domain simulations are performed in PowerFactory DigSilent to verify the
results.
Hours Worked and the U.S. Distribution of Real Annual Earnings 1976-2019
Authors: Iván Fernández-Val, Franco Peracchi, Aico van Vuuren, Francis Vella
Date: 2020-02-25 00:00:00
Tasks: Sensitivity, Survey

We examine the impact of annual hours worked on annual earnings by decomposing changes in the real annual earnings distribution into composition, structural and hours effects. We do so via a nonseparable simultaneous model of hours, wages and earnings. Using the Current Population Survey for the survey years 1976--2019, we find that changes in the female distribution of annual hours of work are important in explaining movements in inequality in female annual earnings. This captures the substantial changes in their employment behavior over this period. Movements in the male hours distribution only affect the lower part of their earnings distribution and reflect the sensitivity of these workers' annual hours of work to cyclical factors.
Graph Vertex Sampling with Arbitrary Graph Signal Hilbert Spaces
Date: 2020-02-26 00:00:00

Graph vertex sampling set selection aims at selecting a set of ver-tices of a
graph such that the space of graph signals that can be reconstructed exactly
from those samples alone is maximal. In this context, we propose to extend
sampling set selection based on spectral proxies to arbitrary Hilbert spaces of
graph signals. Enabling arbitrary inner product of graph signals allows then to
better account for vertex importance on the graph for a sampling adapted to the
application. We first state how the change of inner product impacts sampling
set selection and reconstruction, and then apply it in the context of geometric
graphs to highlight how choosing an alternative inner product matrix can help
sampling set selection and reconstruction.
Comparing copy-number profiles under multi-copy amplifications and deletions
Authors: Garance Cordonnier, Manuel Lafond
Date: 2020-02-26 00:00:00

During cancer progression, malignant cells accumulate somatic mutations that can lead to genetic aberrations. In particular, evolutionary events akin to segmental duplications or deletions can alter the copy-number profile (CNP) of a set of genes in a genome. Our aim is to compute the evolutionary distance between two cells for which only CNPs are known. This asks for the minimum number of segmental amplifications and deletions to turn one CNP into another. This was recently formalized into a model where each event is assumed to alter a copy-number by $1$ or $-1$, even though these events can affect large portions of a chromosome. We propose a general cost framework where an event can modify the copy-number of a gene by larger amounts. We show that any cost scheme that allows segmental deletions of arbitrary length makes computing the distance strongly NP-hard. We then devise a factor $2$ approximation algorithm for the problem when copy-numbers are non-zero and provide an implementation called \textsf{cnp2cnp}. We evaluate our approach experimentally by reconstructing simulated cancer phylogenies from the pairwise distances inferred by \textsf{cnp2cnp} and compare it against two other alternatives, namely the \textsf{MEDICC} distance and the Euclidean distance. The experimental results show that our distance yields more accurate phylogenies on average than these alternatives if the given CNPs are error-free, but that the \textsf{MEDICC} distance is slightly more robust against error in the data. In all cases, our experiments show that either our approach or the \textsf{MEDICC} approach should preferred over the Euclidean distance.
BUT System for the Second DIHARD Speech Diarization Challenge
Date: 2020-02-26 00:00:00
Tasks: Clustering

This paper describes the winning systems developed by the BUT team for the
four tracks of the Second DIHARD Speech Diarization Challenge. For tracks 1 and
2 the systems were mainly based on performing agglomerative hierarchical
clustering (AHC) of x-vectors, followed by another x-vector clustering based on
Bayes hidden Markov model and variational Bayes inference. We provide a
comparison of the improvement given by each step and share the implementation
of the core of the system. For tracks 3 and 4 with recordings from the Fifth
CHiME Challenge, we explored different approaches for doing multi-channel
diarization and our best performance was obtained when applying AHC on the
fusion of per channel probabilistic linear discriminant analysis scores.
Further Geometric and Lyapunov Characterizations of Incrementally Stable Systems on Finsler Manifolds
Authors: Dongjun Wu, Guangren Duan
Date: 2020-02-26 00:00:00

In this paper, we report several new geometric and Lyapunov characterizations of incrementally stable systems on Finsler and Riemannian manifolds. A new and intrinsic proof of an important theorem in contraction analysis is given via the complete lift of the system. Based on this, two Lyapunov characterizations of incrementally stable systems are derived, namely, converse contraction theorems, and revelation of the connection between incremental stability and stability of an equilibrium point, in which the second result recovers and extends the classical Krasovskii's method.
Segmentation of carotid vessel wall using U-Net and segmentation average network
Date: 2020-02-26 00:00:00
Tasks: Segmentation

Segmentation of carotid vessel wall is required in vessel wall volume (VWV)
and local vessel-wall-plus-plaque thickness (VWT) quantification of the carotid
artery. Manual segmentation of the vessel wall is time-consuming and prone to
interobserver variability. In this paper, we proposed a convolution neural
network to segment the common carotid artery (CCA) from 3D carotid ultrasound
images. The proposed CNN involves three U-Nets that segmented the 3D ultrasound
(3DUS) images in the axial, lateral and frontal orientations. The segmentation
maps generated by three U-Nets were consolidated by a novel segmentation
average network (SAN) we proposed in this paper. The experimental results show
that the proposed CNN improved the Dice similarity coefficient (DSC) for vessel
wall segmentation from 64.8% to 67.5%, the sensitivity from 63.8% to 70.5%, and
the area under receiver operator characteristic curve (AUC) from 0.89 to 0.94.
Indicator & crowding Distance-Based Evolutionary Algorithm for Combined Heat and Power Economic Emission Dispatch
Date: 2020-02-21 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Heat and power have become the most indispensable resources. However, the
traditional ways of generating power and heat are inefficient and cause high
pollution; a CHP (Combined Heat and Power) unit can solve these problems well.
In recent years, more attention has been paid to energy conservation and
environmental protection, and Combined Heat and Power Economic Emission
Dispatch (CHPEED) has become an important multi-objective optimization problem.
In this paper, an Indicator & crowding Distance-based Evolutionary Algorithm
(IDBEA) is put forward for handling this non-convex and non-linear problem.
With consideration of the valve-point effects and power transmission loss,
IDBEA is tested on three standard test systems with different types, including
four units, five units and seven units. In the experiment, IDBEA is compared
with several evolutionary algorithms, the simulation results demonstrate that
IDBEA has strong stability and superiority, while the solutions show better
convergence and diversity than several typical algorithms.
Bioconjugated oligonucleotides: recent developments and thera-eutic applications
Date: 2020-02-26 00:00:00
Tasks: Specificity

Oligonucleotide-based agents have the potential to treat or cure almost any
disease, and are one of the key therapeutic drug classes of the future.
Bioconjugated oligonucleotides, a subset of this class, are emerging from basic
research and being successfully translated to the clinic. In this review, we
first briefly describe two approaches for inhibiting specific genes using
oligonucleotides -- antisense DNA (ASO) and RNA interference (RNAi) -- followed
by a discussion on delivery to cells. We then summarize and analyze recent
developments in bioconjugated oligonucleotides including those possessing
GalNAc, cell penetrating peptides, $\alpha$-tocopherol, aptamers, antibodies,
cholesterol, squalene, fatty acids, or nucleolipids. These novel conjugates
provide a means to enhance tissue targeting, cell internalization, endosomal
escape, target binding specificity, resistance to nucleases, and more. We next
describe those bioconjugated oligonucleotides approved for patient use or in
clinical trials. Finally, we summarize the state of the field, describe current
limitations, and discuss future prospects. Biocon-jugation chemistry is at the
centerpiece of this therapeutic oligonucleotide revolution, and significant
opportunities exist for development of new modification chemistries, for
mechanistic studies at the chemical-biology interface, and for translating such
agents to the clinic.
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling
Date: 2020-02-26 00:00:00
Tasks: Denoising, Image Reconstruction

Plug-and-play priors (PnP) is an image reconstruction framework that uses an
image denoiser as an imaging prior. Unlike traditional regularized inversion,
PnP does not require the prior to be expressible in the form of a
regularization function. This flexibility enables PnP algorithms to exploit the
most effective image denoisers, leading to their state-of-the-art performance
in various imaging tasks. In this paper, we propose a new denoiser scaling
technique to explicitly control the amount of PnP regularization.
Traditionally, the performance of PnP algorithms is controlled via intrinsic
parameters of the denoiser related to the noise variance. However, many
powerful denoisers, such as the ones based on convolutional neural networks
(CNNs), do not have tunable parameters that would allow controlling their
influence within PnP. To address this issue, we introduce a scaling parameter
that adjusts the magnitude of the denoiser input and output. We theoretical
justify the denoiser scaling from the perspectives of proximal optimization,
statistical estimation, and consensus equilibrium. Finally, we provide
numerical experiments demonstrating the ability of denoiser scaling to
systematically improve the performance of PnP for denoising CNN priors that do
not have explicitly tunable parameters.
Calculations of System Adequacy Considering Heat Transition Pathways
Date: 2020-05-24 00:00:00

The decarbonisation of heat in developed economies represents a significant
challenge, with increased penetration of electrical heating technologies
potentially leading to unprecedented increases in peak electricity demand. This
work considers a method to evaluate the impact of rapid electrification of heat
by utilising historic gas demand data. The work is intended to provide a
data-driven complement to popular generative heat demand models, with a
particular aim of informing regulators and actors in capacity markets as to how
policy changes could impact on medium-term system adequacy metrics (up to five
years ahead). Results from a GB case study show that the representation of heat
demand using scaled gas demand profiles increases the rate at which 1-in-20
system peaks grow by 60\%, when compared to the use of scaled electricity
demand profiles. Low end-use system efficiency, in terms of aggregate
coefficient of performance and demand side response capabilities, are shown to
potentially lead to a doubling of electrical demand-temperature sensitivity
following five years of heat demand growth.
Interference and Rate Analysis of Multinumerology NOMA
Date: 2020-02-26 00:00:00

5G communication systems and beyond are envisioned to support an extremely
diverse set of use cases with different performance requirements. These
different requirements necessitate the use of different numerologies for
increased flexibility. Non-orthogonal multiple access (NOMA) can potentially
attain this flexibility by superimposing user signals while offering improved
spectral efficiency (SE). However, users with different numerologies have
different symbol durations. When combined with NOMA, this changes the nature of
the interference the users impose on each other. This paper investigates a
multinumerology NOMA (MN-NOMA) scheme using successive interference
cancellation (SIC) as an enabler for coexistence of users with with different
numerologies. Analytical expressions for the inter-numerology interference
(INI) experienced by each user at the receiver are derived, where mean-squared
error (MSE) is the metric used to quantify INI. Using the MSE expressions, we
analytically derive achievable rates for each user in the MN-NOMA system. These
expressions are then evaluated and used to compare the SE performance of
MN-NOMA with that of its single-numerology counterpart. The proposed scheme can
achieve the desired flexibility in supporting diverse use cases in future
wireless networks. The scheme also gains the SE benefits of NOMA compared to
both multinumerology and single numerology orthogonal multiple access (OMA)
schemes.
Pupil Lovalization And Tracking For Video-Based Iris Biometrics
Date: 2020-02-16 00:00:00
Tasks: Pupil Detection, Robust classification

In this paper, we are interested in iris biometric applications. More
precisely, our contribution consists in designing both a pupil detection and a
tracking procedure from video sequences acquired by low-cost webcams. The
novelty of our approach relies on the fact that it is operational even with a
minimal user cooperation and, under bad illuminations and acquisition
conditions. A robust classification algorithm is designed to detect the pupil.
Moreover, a pupil tracker based on the extended Kalman filter is applied in
order to reduce the processing time. Experimental results are performed in
order to evaluate the performances of the proposed detection and tracking
system
Performance-based Post-earthquake Decision-making for Instrumented Buildings
Date: 2020-02-24 00:00:00
Tasks: Decision Making, Structural Health Monitoring

This paper presents a framework for decision-making regarding post-earthquake
assessment of instrumented buildings in a manner consistent with
performance-based design criteria. This framework is achieved by simultaneously
combining and advancing existing knowledge from seismic structural health
monitoring and performance-based earthquake engineering paradigms and consists
of 1) optimal sensor placement, 2) dynamic response reconstruction, 3) damage
estimation, and 4) performance-assessment and decision-making. In particular,
the main objective is to reconstruct inter-story drifts with a probabilistic
measure of exceeding performance-based acceptance limits and determining the
post-earthquake re-occupancy classification of the instrumented building of
interest. Since the proposed framework is probabilistic, the outcome can be
used to obtain the probability of losses based on the defined decision
variables and be integrated into a risk-based decision-making process by city
officials, building owners, and emergency managers. The framework is
illustrated using data from the Van Nuys hotel testbed, a seven-story
reinforced concrete building instrumented by the California Strong Motion
Instrumentation Program (Station 24386).
Generating Waveform Families using Multi-Tone Sinusoidal Frequency Modulation
Date: 2020-03-16 00:00:00

This paper presents a method for generating a family of waveforms with low
in-band Auto/Cross-Correlation Function (ACF/CCF) properties using the
Multi-Tone Sinusoidal Frequency Modulated (MTSFM) waveform model. The MTSFM
waveform's modulation function is represented using a Fourier series expansion.
The Fourier coefficients are utilized as a set of discrete parameters that can
be modified to optimize the waveform family's properties. The waveforms'
ACF/CCF properties are optimized utilizing a multi-objective optimization
problem. Each objective function is weighted to place emphasis on either low
ACF or CCF sidelobes. The resulting optimized MTSFM waveforms each possess a
thumbtack-like Ambiguity Function in addition to the specifically designed
ACF/CCF properties. Most importantly, the resulting MTSFM waveform families
possess both ideally low Peak-to-Average Power Ratios (PAPR) and high Spectral
Efficiency (SE) making them well suited for transmission on practical radar
transmitters.
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow
Date: 2020-02-26 00:00:00
Tasks: Retrieval

Imaging 3D nano-structures at very high resolution is crucial in a variety of
scientific fields. However, due to fundamental limitations of light propagation
we can only measure the object indirectly via 2D intensity measurements of the
3D specimen through highly nonlinear projection mappings where a variety of
information (including phase) is lost. Reconstruction therefore involves
inverting highly non-linear and seemingly non-invertible mappings. In this
paper, we introduce a novel technique where the 3D object is directly
reconstructed from an accurate non-linear propagation model. Furthermore, we
characterize the ambiguities of this model and leverage a priori knowledge to
mitigate their effect and also significantly reduce the required number of
measurements and hence the acquisition time. We demonstrate the performance of
our algorithm via numerical experiments aimed at nano-scale reconstruction of
3D integrated circuits. Moreover, we provide rigorous theoretical guarantees
for convergence to stationarity.