pwc-archive/papers-with-abstracts huggingface dataset:
Radiation Efficiency Aware High-Mobility Massive MIMO with Antenna Selection
Date: 2019-02-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High-mobility wireless communications have received a lot of attentions in
the past few years. In this paper, we consider angle-domain Doppler shifts
compensation scheme to reduce the channel time variation for the high-mobility
uplink from a high-speed terminal (HST) to a base station (BS). We propose to
minimize Doppler spread by antenna weighting under the constraint of
maintaining radiation efficiency. The sequential parametric convex
approximation (SPCA) algorithm is exploited to solve the above non-convex
problem. Moreover, in order to save the RF chains, we further exploit the idea
of antenna selection for high-mobility wireless communications. Simulations
verify the effectiveness of the proposed studies.
A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving
Date: 2019-02-21 00:00:00
Tasks: Management

For the last ten years, utilities have observed on-going transitions on
consumers' load curves. The previously flat load curves have more frequently
turned into duck-shape. This is jointly caused by the increasing household
loads as well as the popularity of rooftop solar photovoltaic. Such load curve
transition challenges the operational flexibility of the existing systems and
greatly increases the per-MWh energy costs. Peak shaving, in this context,
becomes a critical task to demand-side management. Owing to the development of
Battery Energy Storage Systems (BESS), numerous peak shaving strategies have
been developed and implemented. In this paper, by applying a stacked
autoencoder (SAE)-based residential peak load curve forecasting technology, we
further lift the peaking shaving capabilities of BESSs to a new level. The
proposed strategy takes into account the welfares of both generation-side and
demand-side and reaches an optimal balance. A comprehensive case study using
smart meter data demonstrates the effectiveness of the proposed method in peak
shaving application.
In Vivo Wireless Channel Modeling
Date: 2019-02-21 00:00:00

In this chapter, the state of the art of in vivo wireless channel
characterization has been presented. Various studies described in the
literature are dedicated to the in vivo communication channel, and they
consider different parameters in studying various anatomical regions.
Furthermore, the location-dependent characteristics of in vivo wireless
communication at 915 MHz are analyzed in detail via numerical and experimental
investigations. A complete model for the in vivo channel is not available and
remains an open research problem. However, considering the expected future
growth of implanted technologies and their potential use for the detection and
diagnosis of various health-related issues in the human body, the channel
modeling studies should be further extended to develop better and more
efficient communications systems for future in vivo systems.
An\'alise t\'ermica\-fluidodin\^amica de um transformador de energia a seco atrav\'es da din\^amica dos fluidos computacional
Date: 2019-02-19 00:00:00

The cooling system is a key factor in power transformer designs. The frequent
need for optimized projects further increases the importance of development
through of increasingly developed tools. This work aims to evaluate a
transformer design, in which the refrigeration is performed through of exhaust
fans and heat exchangers installed on the lower side of the transformer. The
model is elaborated applying a symmetry, reducing the model to be analyzed. In
order to evaluate the best configuration in terms of positioning, flow rate and
quantity of understand the main thermal and fluid dynamics characteristics of
the flow inside the transformer, the Computational Fluid Dynamics tool is used.
The methodology employed is to solve the numerically, through the ANSYS CFX
computational package, the flow and energy equations inside the transformer
tank.
Simplify Power Flow Calculation Using Terminal Circuit and PMU Measurements
Date: 2019-02-19 00:00:00

Power flow calculation methods have been developed in decades using power
injections and Newton-Raphson method. The nonlinear characteristics of the
power flow to the bus voltage require Jacobian matrix reformation and
refactorization in each iteration. Power network is composed by resistors,
reactors, and capacitors which is a linear circuit when investigating the node
voltages with the node current injections. To take the advantage of the
linearity, this paper proposed to use terminal circuit model and PMU voltage
phase angle measurements to simplify power flow calculation. When updating
current injections at the righthand side of power flow equations and using PMU
voltage phase angle measurements representing PV buses voltage phase angle, the
Jacobian matrix is constant during the iteration. The simplification reduces
the computational efforts and improves the computation efficiency. The proposed
method is tested on the IEEE 14-bus and IEEE 118-bus standard systems. The
results are validated by traditional power flow solution and the computation
efficiency is demonstrated.
Membrane morphologies induced by arc-shaped scaffolds are determined by arc angle and coverage
Authors: Francesco Bonazzi, Thomas R. Weikl
Date: 2019-02-22 00:00:00
Tasks: ARC

The intricate shapes of biological membranes such as tubules and membrane stacks are induced by proteins. In this article, we systematically investigate the membrane shapes induced by arc-shaped scaffolds such as proteins and protein complexes with coarse-grained modeling and simulations. We find that arc-shaped scaffolds induce membrane tubules at membrane coverages larger than a threshold of about 40%, irrespective of their arc angle. The membrane morphologies at intermediate coverages below this tubulation threshold, in contrast, strongly depend on the arc angle. Scaffolds with arc angles of about 60 degree akin to N-BAR domains do not change the membrane shape at coverages below the tubulation threshold, while scaffolds with arc angles larger than about 120 degree induce double-membrane stacks at intermediate coverages. The scaffolds stabilize the curved membrane edges that connect the membrane stacks, as suggested for complexes of reticulon proteins. Our results provide general insights on the determinants of membrane shaping by arc-shaped scaffolds.
Counterfactual Inference in Duration Models with Random Censoring
Date: 2019-02-22 00:00:00
Tasks: counterfactual, Counterfactual Inference

We propose a counterfactual Kaplan-Meier estimator that incorporates
exogenous covariates and unobserved heterogeneity of unrestricted
dimensionality in duration models with random censoring. Under some regularity
conditions, we establish the joint weak convergence of the proposed
counterfactual estimator and the unconditional Kaplan-Meier (1958) estimator.
Applying the functional delta method, we make inference on the cumulative
hazard policy effect, that is, the change of duration dependence in response to
a counterfactual policy. We also evaluate the finite sample performance of the
proposed counterfactual estimation method in a Monte Carlo study.
Radar and Communication Co-existence: an Overview
Date: 2019-02-22 00:00:00

Increased amounts of bandwidth are required to guarantee both
high-quality/high-rate wireless services (4G and 5G) and reliable sensing
capabilities such as automotive radar, air traffic control, earth geophysical
monitoring and security applications. Therefore, co-existence between radar and
communication systems using overlapping bandwidths has been a primary
investigation field in recent years. Various signal processing techniques such
as interference mitigation, pre-coding or spatial separation, and waveform
design allow both radar and communications to share the spectrum. This article
reviews recent work on co-existence between radar and communication systems,
including signal models, waveform design and signal processing techniques. Our
goal is to survey contributions in this area in order to provide a primary
starting point for new researchers interested in these problems.
ABM of osteoblast's mechanotransduction pathway: time patterns of critical events
Authors: Gianluca Ascolani, Timothy M. Skerry, Damien Lacroix, Enrico Dall'Ara, Aban Shuaib
Date: 2019-02-23 00:00:00

Background: Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix and intracellular actions. By controlling the balance between the intracellular and extracellular domains, the mechanotransduction process determines optimal functionality of the skeletal dynamics, and it is one of the possible causes of osteophatological diseases. Results: Mechanotransduction in a single osteoblast under external mechanical perturbations has been modelled in the agent based framework to reproduce the dynamics of the stochastic reaction diffusion process among molecules in the cytoplasm, nuclear and extracellular domains. The amount of molecules and fluctuations of each molecular class has been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the directing processes from the molecular signals in order to derive the distribution of recurrence of events. Conclusions: We observed large fluctuations enclose information hidden in the noise which is beyond the dynamic variations of molecular baselines. Studying the system under different parametric conditions and stimuli, the results have shown that the waiting time distributions of each molecule are a signature of the system's dynamics. The behaviours of the molecular waiting times change with the changing of parameters presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in the mechanotransduction pathway.
Robust Principal Component Analysis with Non-Sparse Errors
Date: 2019-11-13 00:00:00

We show that when a high-dimensional data matrix is the sum of a low-rank
matrix and a random error matrix with independent entries, the low-rank
component can be consistently estimated by solving a convex minimization
problem. We develop a new theoretical argument to establish consistency without
assuming sparsity or the existence of any moments of the error matrix, so that
fat-tailed continuous random errors such as Cauchy are allowed. The results are
illustrated by simulations.
Closed-End Formula for options linked to Target Volatility Strategies
Authors: Luca Di Persio, Luca Prezioso, Kai Wallbaum
Date: 2019-02-23 00:00:00
Tasks: Articles

Recent years have seen an emerging class of structured financial products based on options linked to dynamic asset allocation strategies. One of the most chosen approach is the so-called target volatility mechanism. It shifts between risky and riskless assets to control the volatility of the overall portfolio. Even if a series of articles have been already devoted to the analysis of options linked to the target volatility mechanism, this paper is the first, to the best of our knowledge, that tries to develop closed-end formulas for VolTarget options. In particular, we develop closed-end formulas for option prices and some key hedging parameters within a Black and Scholes setting, assuming the underlying follows a target volatility mechanism.
Time Distribution for Persistent Viral Infection
Date: 2019-07-10 00:00:00

We study the early stages of viral infection, and the distribution of times
to obtain a persistent infection. The virus population proliferates by entering
and reproducing inside a target cell until a sufficient number of new virus
particles are released via a burst, with a given burst size distribution, which
results in the death of the infected cell. Starting with a 2D model describing
the joint dynamics of the virus and infected cell populations, we analyze the
corresponding master equation using the probability generating function
formalism. Exploiting time-scale separation between the virus and infected cell
dynamics, the 2D model can be cast into an effective 1D model. To this end, we
solve the 1D model analytically for a particular choice of burst size
distribution. In the general case, we solve the model numerically by performing
extensive Monte-Carlo simulations, and demonstrate the equivalence between the
2D and 1D models by measuring the Kullback-Leibler divergence between the
corresponding distributions. Importantly, we find that the distribution of
infection times is highly skewed with a "fat" exponential right tail. This
indicates that there is non-negligible portion of individuals with an infection
time, significantly longer than the mean, which may have implications on when
HIV tests should be performed.
Working Paper: Improved Stock Price Forecasting Algorithm based on Feature-weighed Support Vector Regression by using Grey Correlation Degree
Date: 2019-02-24 00:00:00
Tasks: Decision Making, regression, Stock Prediction

With the widespread engineering applications ranging from artificial
intelligence and big data decision-making, originally a lot of tedious
financial data processing, processing and analysis have become more and more
convenient and effective. This paper aims to improve the accuracy of stock
price forecasting. It improves the support vector machine regression algorithm
by using grey correlation analysis (GCA) and improves the accuracy of stock
prediction. This article first divides the factors affecting the stock price
movement into behavioral factors and technical factors. The behavioral factors
mainly include weather indicators and emotional indicators. The technical
factors mainly include the daily closing data and the HS 300 Index, and then
measure relation through the method of grey correlation analysis. The
relationship between the stock price and its impact factors during the trading
day, and this relationship is transformed into the characteristic weight of
each impact factor. The weight of the impact factors of all trading days is
weighted by the feature weight, and finally the support vector regression (SVR)
is used. The forecast of the revised stock trading data was compared based on
the forecast results of technical indicators (MSE, MAE, SCC, and DS) and
unmodified transaction data, and it was found that the forecast results were
significantly improved.
Complementary Fractional Dimensional Order of Nyquist Sinc Sequences for Time Division Multiplexing
Date: 2019-02-24 00:00:00

High speed data transmission is enabled by time and wavelength division
multiplexing. Here is introduced fractional dimension order of Nyquist pulses
sequences for orthogonal time division multiplexing. Firstly, with a
representation of the Nyquist sinc sequence by a cosine Fourier series, in one
side it is introduced the complementary Nyquist sinc sequences as a better
option for data transmission. On the other side, a possibility of optical time
delay by an electrical phase shifter for optical time division multiplexing is
theoretically demonstrated. In continue, the fractional dimensional order of
signal is defined to open a new window for data transmission. Moving of
function in fractional dimension can be realized as a new freedom for a signal
processing. In other words, dimension itself is a dimension. In this regard,
dimensional transformation is introduced to give a mapping of the function in
dimensional domain or variations of function in fractional dimension. This
mapping gives more information about signal in different point of view like
Fourier transformation. Then, the complementary fractional dimensional order of
Nyquist sinc sequences is defined to reach higher data rate. It has not a
unique solution, however; the best set of solutions for data transmission must
be taken in to accounted. Furthermore, the trajectory of fractional dimension
can be found by a numerical iterative algorithm which will be explained in the
appendix.
Channel adversarial training for cross-channel text-independent speaker recognition
Date: 2019-02-25 00:00:00
Tasks: Domain Adaptation, Speaker Recognition, Text-Independent Speaker Recognition

The conventional speaker recognition frameworks (e.g., the i-vector and
CNN-based approach) have been successfully applied to various tasks when the
channel of the enrolment dataset is similar to that of the test dataset.
However, in real-world applications, mismatch always exists between these two
datasets, which may severely deteriorate the recognition performance.
Previously, a few channel compensation algorithms have been proposed, such as
Linear Discriminant Analysis (LDA) and Probabilistic LDA. However, these
methods always require the collections of different channels from a specific
speaker, which is unrealistic to be satisfied in real scenarios. Inspired by
domain adaptation, we propose a novel deep-learning based speaker recognition
framework to learn the channel-invariant and speaker-discriminative speech
representations via channel adversarial training. Specifically, we first employ
a gradient reversal layer to remove variations across different channels. Then,
the compressed information is projected into the same subspace by adversarial
training. Experiments on test datasets with 54,133 speakers demonstrate that
the proposed method is not only effective at alleviating the channel mismatch
problem, but also outperforms state-of-the-art speaker recognition methods.
Compared with the i-vector-based method and the CNN-based method, our proposed
method achieves significant relative improvement of 44.7% and 22.6%
respectively in terms of the Top1 recall.
Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks
Date: 2019-02-25 00:00:00
Tasks: BIG-bench Machine Learning, Virology

The Third-Generation in DNA sequencing has emerged in the last few years
using new technologies that allow the production of long-read sequences.
Applications of the Third-Generation sequencing enable real-time and on-site
data production, changing the research paradigms in environmental and medical
sampling in virology. To take full advantage of large-scale data generated from
long-read sequencing, an innovation in the downstream data analysis is
necessary. Here, we discuss futuristic methods using machine learning
approaches to analyze big genetic data. Machine learning combines pattern
recognition and computational learning to perform predictive and exploratory
data analysis. In particular, deep learning is a field of machine learning that
is used to solve complex problems through artificial neural networks. Unlike
other methods, features can be learned using neural networks entirely from data
without manual specifications. We discuss the future of 21st-century virology
by presenting futuristic approaches for virus studies using real-time data
production and on-site data analysis with the Third-Generation Sequencing and
machine learning methods. We first introduce the basic concepts in conventional
statistical models and methods in virology, building gradually into the
necessity of innovating the downstream data analysis to meet the advances in
sequencing technologies. We argue that artificial neural networks can innovate
the downstream data analysis, as they can learn from big datasets without model
assumptions or feature specifications, as opposed to the current data analysis
in bioinformatics. Furthermore, we discuss how futuristic methods using
artificial neural networks combined with long-read sequences can revolutionize
virus studies, using specific examples in supervised and unsupervised settings.
Identifiability Conditions for Multi-channel Blind Deconvolution with Short Filters
Date: 2019-02-26 00:00:00

This work considers the multi-channel blind deconvolution problem under the
assumption that the channels are short. First, we investigate the ill-posedness
issues inherent to blind deconvolution problems and sufficient and necessary
conditions on the channels that guarantee well-posedness are derived. Following
previous work on blind deconvolution, the problem is then reformulated as a
low-rank matrix recovery problem and solved by nuclear norm minimization.
Numerical experiments show the effectiveness of this algorithm under a certain
generative model for the input signal and the channels, both in the noiseless
and in the noisy case.
Joint Timing Offset and Channel Estimation for Multi-user UFMC Uplink
Date: 2019-02-25 00:00:00

Universal filtered multi-carrier (UFMC), which groups and filters subcarriers
before transmission, is a potential multi-carrier modulation technique
investigated for the emerging Machine-Type Communications (MTC). Considering
the relaxed timing synchronization requirement of UFMC, we design a novel joint
timing synchronization and channel estimation method for multi-user UFMC uplink
transmission. Aiming at reducing overhead for higher system performance, the
joint estimation problem is formulated using atomic norm minimization that
enhances the sparsity of timing offset in the continuous frequency domain.
Simulation results show that the proposed method can achieve considerable
performance gain, as compared with its counterparts.
Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study
Date: 2019-03-27 00:00:00
Tasks: Action Understanding, EEG, Electroencephalogram (EEG), Imitation Learning

Analogous to the mirror neuron system repeatedly described in monkeys as a
possible substrate for imitation learning and/or action understanding, a
neuronal execution/observation matching system (OEMS) is assumed in humans, but
little is known to what extent this system is activated in non-experimental,
real-life conditions. In the present case study, we investigated brain activity
of this system during natural, non-experimental motor behavior as it occurred
during playing of the board game "Malefiz". We compared spectral modulations of
the high-gamma band related to ipsilateral reaching movement execution and
observation of the same kind of movement using electrocorticography (ECoG) in
one participant. Spatially coincident activity during both conditions execution
and observation was recorded at electrode contacts over the premotor/primary
motor cortex. The topography and amplitude of the high-gamma modulations
related to both, movement observation and execution were clearly spatially
correlated over several fronto-parietal brain areas. Thus, our findings
indicate that a network of cortical areas contributes to the human OEMS, beyond
primary/premotor cortex including Brocas area and the temporo-parieto-occipital
junction area, in real-life conditions.
Climate Change and Agriculture: Subsistence Farmers' Response to Extreme Heat
Date: 2019-02-28 00:00:00

This paper examines how subsistence farmers respond to extreme heat. Using
micro-data from Peruvian households, we find that high temperatures reduce
agricultural productivity, increase area planted, and change crop mix. These
findings are consistent with farmers using input adjustments as a short-term
mechanism to attenuate the effect of extreme heat on output. This response
seems to complement other coping strategies, such as selling livestock, but
exacerbates the drop in yields, a standard measure of agricultural
productivity. Using our estimates, we show that accounting for land adjustments
is important to quantify damages associated with climate change.