pwc-archive/papers-with-abstracts huggingface dataset:
Energy-Efficient Buffer-Aided Relaying Systems with Opportunistic Spectrum Access
Date: 2020-02-27 00:00:00

In this paper, an energy-efficient cross-layer design framework is proposed
for cooperative relaying networks, which takes into account the influence of
spectrum utilization probability. Specifically, random arrival traffic is
considered and an adaptive modulation and coding (AMC) scheme is adopted in the
cooperative transmission system to improve the system performance. The average
packet dropping rate of the relay-buffer is studied at first. With the packet
dropping rate and stationary distribution of the system state, the closed-form
expression of the delay is derived. Then the energy efficiency for
relay-assisted transmission is investigated, which takes into account the
queueing process of the relay and the source. In this context, an energy
efficiency optimization problem is formulated to determine the optimum strategy
of power and time allocation for the relay-assisted cooperative system.
Finally, the energy efficient switching strategy between the relay assisted
transmission and the direct transmission is obtained, where packet
transmissions have different delay requirements. In addition, energy efficient
transmission policy with AMC is obtained. Numerical results demonstrate the
effectiveness of the proposed design improving the energy efficiency.
Properties of a new $R$-estimator of shape matrices
Authors: Stefano Fortunati, Alexandre Renaux, Frédéric Pascal
Date: 2020-02-27 00:00:00

This paper aims at presenting a simulative analysis of the main properties of a new $R$-estimator of shape matrices in Complex Elliptically Symmetric (CES) distributed observations. First proposed by Hallin, Oja and Paindaveine for the real-valued case and then extended to the complex field in our recent work, this $R$-estimator has the remarkable property to be, at the same time, \textit{distributionally robust} and \textit{semiparametric efficient}. Here, the efficiency of different possible configurations of this $R$-estimator are investigated by comparing the resulting Mean Square Error (MSE) with the Constrained Semiparametric Cram\'{e}r-Rao Bound (CSCRB). Moreover, its robustness to outliers is assessed and compared with the one of the celebrated Tyler's estimator.
Ultrasound Detection Arrays Via Coded Hadamard Apertures
Authors: Evgeny Hahamovich, Amir Rosenthal
Date: 2020-02-27 00:00:00

In the medical fields, ultrasound detection is often performed with piezoelectric arrays that enable one to simultaneously map the acoustic fields at several positions. In this work, we develop a novel method for transforming a single-element ultrasound detector into an effective detection array by spatially filtering the incoming acoustic fields using a binary acoustic mask coded with cyclic Hadamard patterns. By scanning the mask in front of the detector, we obtain a multiplexed measurement dataset from which a map of the acoustic field is analytically constructed. We experimentally demonstrate our method by transforming a single-element ultrasound detector into 1D arrays with up to 59 elements.
Open Set Modulation Recognition Based on Dual-Channel LSTM Model
Date: 2020-02-27 00:00:00
Tasks: speech-recognition, Speech Recognition

Deep neural networks have achieved great success in computer vision, speech
recognition and many other areas. The potential of recurrent neural networks
especially the Long Short-Term Memory (LSTM) for open set communication signal
modulation recognition is investigated in this letter. Time-domain sampled
signals are first converted to two normalized matrices which will be fed into a
four layer Dual-Channel LSTM network tailored for open set modulation
recognition. With two cascaded Dual-Channel LSTM layers, the designed network
can automatically learn sequence-correlated features from the raw data. With
center loss and weibull distribution, proposed algorithm can recognize partial
open set modulations. Experiments on the public RadioML dataset indicates that
different analog and digital modulations can be effectively classified by the
proposed model, while partial open set modulations can be recognized.
Quantitative analysis on the dataset shows that the proposed method can achieve
an average accuracy of 90.2% at varying SNR ranging from 0dB to 18dB in
classifying the considered 11 classes, while accuracy of open set experiment
dramatically improved by 14.2%.
Millisecond insect tracking system
Date: 2020-02-27 00:00:00
Tasks: Navigate

Animals such as insects have provided a rich source of inspiration for
designing robots. For example, animals navigate to goals via efficient
coordination of individual motor actions, and demonstrate natural solutions to
problems also faced by engineers. Recording individual body part positions
during large scale movement would therefore be useful. Such multi-scale
observations, however, are challenging. With video, for example, there is
typically a trade-off between the volume over which an animal can be recorded
and spatial resolution within the volume. Even with high pixel-count cameras,
motion blur can be a challenge when using available light. Here we present a
new approach for tracking animals, such as insects, with an optical system that
bypasses this tradeoff by actively pointing a telephoto video camera at the
animal. This system is based around high-speed pan-tilt mirrors which steer an
optical path shared by a quadrant photodiode and a high-resolution, high-speed
telephoto video recording system. The mirror is directed to lock on to the
image of a 25-milligram retroreflector worn by the animal. This system allows
high-magnification videography with reduced motion blur over a large tracking
volume. With our prototype, we obtained millisecond order closed-loop latency
and recorded videos of flying insects in a tracking volume extending to an
axial distance of 3 meters and horizontally and vertically by 40 degrees. The
system offers increased capabilities compared to other video recording
solutions and may be useful for the study of animal behavior and the design of
bio-inspired robots.
Dwell-Time Based Stability Analysis and L2 Control of LPV Systems with Piecewise Constant Parameters and Delay
Date: 2020-03-10 00:00:00

Dwell-time based stability conditions for a class of LPV systems with
piecewise constant parameters under time-varying delay are derived using
clock-dependent Lyapunov-Krasovskii functional. Sufficient synthesis conditions
for clock-dependent gain-scheduled state-feedback controllers ensuring
L2-performance are also provided. Several numerical and practical examples, to
illustrate the efficacy of the results, are given.
Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges
Authors: Paz Grimberg, Tobias Lauinger, Damon McCoy
Date: 2020-02-27 00:00:00
Tasks: Algorithmic Trading

Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$ flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow Jones Industrial Average lose $9\%$ of its value within minutes, to automated order "spoofing" algorithms. In this paper, we build a set of methodologies to characterize and empirically measure different algorithmic trading strategies in Binance, a large centralized cryptocurrency exchange, using a complete data set of historical trades. We find that a sub-strategy of triangular arbitrage is widespread, where bots convert between two coins through an intermediary coin, and obtain a favorable exchange rate compared to the direct one. We measure the profitability of this strategy, characterize its risks, and outline two strategies that algorithmic trading bots use to mitigate their losses. We find that this strategy yields an exchange ratio that is $0.144\%$, or $14.4$ basis points (bps) better than the direct exchange ratio. $2.71\%$ of all trades on Binance are attributable to this strategy.
Feasibility of Heart Sound Analysis in Individuals Supported with Left Ventricular Assist Devices
Date: 2020-02-27 00:00:00
Tasks: Diagnostic

Left ventricular assist devices (LVADs) are surgically implanted mechanical
pumps that improve survival rates for individuals with advanced heart failure.
While life-saving, LVAD therapy is also associated with high morbidity, which
can be partially attributed to the difficulties in identifying an LVAD
complication before an adverse event occurs. Methods that are currently used to
monitor for complications in LVAD-supported individuals require frequent
clinical assessments at specialized LVAD centers. Remote analysis of digitally
recorded precordial sounds has the potential to provide an inexpensive
point-of-care diagnostic tool to assess both device function and the degree of
cardiac support in LVAD recipients, facilitating real-time, remote monitoring
for early detection of complications. To our knowledge, prior studies of
precordial sounds in LVAD-supported individuals have analyzed LVAD noise rather
than intrinsic heart sounds, due to a focus on detecting pump complications,
and perhaps the obscuring of heart sounds by LVAD noise. In this letter, we
describe an adaptive filtering method to remove sounds generated by the LVAD,
making it possible to automatically isolate and analyze underlying heart
sounds. We present preliminary results describing acoustic signatures of heart
sounds extracted from in vivo data obtained from LVAD-supported individuals.
These findings are significant as they provide proof-of-concept evidence for
further exploration of heart sound analysis in LVAD-supported individuals to
identify cardiac abnormalities and changes in LVAD support.
An epidemic model highlighting humane social awareness and vector-host lifespan ratio variation
Date: 2020-02-27 00:00:00

Many vector-borne disease epidemic models neglect the fact that in modern
human civilization, social awareness as well as self-defence system are
overwhelming against advanced propagation of the disease. News are becoming
more effortlessly accessible through social media and mobile apps, while
apparatuses for disease prevention are inclined to be more abundant and
affordable. Here we study a simple host-vector model in which media-triggered
social awareness and seasonality in vector breeding are taken into account.
There appears a certain threshold indicating the alarming outbreak; the number
of infective human individuals above which shall actuate the self-defence
system for the susceptible subpopulation. A model where the infection rate
revolves in the likelihood of poverty, reluctancy, tiresomeness, perceiving the
disease as being easily curable, absence of medical access, and overwhelming
hungrier vectors is proposed. Further discoveries are made from undertaking
disparate time scales between human and vector population dynamics. The
resulting slow-fast system discloses notable dynamics in which solution
trajectories confine to the slow manifold and critical manifold, before finally
ending up at equilibria. How coinciding the slow manifold with the critical
manifold enhances periodic forcing is also studied. The finding on hysteresis
loops gives insights of how defining alarming outbreak critically perturbs the
basic reproductive number, which later helps keep the incidence cycle on small
magnitudes.
Optimal self-induced stochastic resonance in multiplex neural networks: electrical versus chemical synapses
Date: 2020-06-13 00:00:00

Electrical and chemical synapses shape the dynamics of neural networks and
their functional roles in information processing have been a longstanding
question in neurobiology. In this paper, we investigate the role of synapses on
the optimization of the phenomenon of self-induced stochastic resonance in a
delayed multiplex neural network by using analytical and numerical methods. We
consider a two-layer multiplex network, in which at the intra-layer level
neurons are coupled either by electrical synapses or by inhibitory chemical
synapses. For each isolated layer, computations indicate that weaker electrical
and chemical synaptic couplings are better optimizers of self-induced
stochastic resonance. In addition, regardless of the synaptic strengths,
shorter electrical synaptic delays are found to be better optimizers of the
phenomenon than shorter chemical synaptic delays, while longer chemical
synaptic delays are better optimizers than longer electrical synaptic delays --
in both cases, the poorer optimizers are in fact worst. It is found that
electrical, inhibitory, or excitatory chemical multiplexing of the two layers
having only electrical synapses at the intra-layer levels can each optimize the
phenomenon. And only excitatory chemical multiplexing of the two layers having
only inhibitory chemical synapses at the intra-layer levels can optimize the
phenomenon. These results may guide experiments aimed at establishing or
confirming the mechanism of self-induced stochastic resonance in networks of
artificial neural circuits, as well as in real biological neural networks.
Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump
Date: 2020-02-28 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, regression

Drug-induced liver injury (DILI) is the most common cause of acute liver
failure and a frequent reason for withdrawal of candidate drugs during
preclinical and clinical testing. An important type of DILI is cholestatic
liver injury, caused by buildup of bile salts within hepatocytes; it is
frequently associated with inhibition of bile salt transporters, such as the
bile salt export pump (BSEP). Reliable in silico models to predict BSEP
inhibition directly from chemical structures would significantly reduce costs
during drug discovery and could help avoid injury to patients. Unfortunately,
models published to date have been insufficiently accurate to encourage wide
adoption. We report our development of classification and regression models for
BSEP inhibition with substantially improved performance over previously
published models. Our model development leveraged the ATOM Modeling PipeLine
(AMPL) developed by the ATOM Consortium, which enabled us to train and evaluate
thousands of candidate models. In the course of model development, we assessed
a variety of schemes for chemical featurization, dataset partitioning and class
labeling, and identified those producing models that generalized best to novel
chemical entities. Our best performing classification model was a neural
network with ROC AUC = 0.88 on our internal test dataset and 0.89 on an
independent external compound set. Our best regression model, the first ever
reported for predicting BSEP IC50s, yielded a test set $R^2 = 0.56$ and mean
absolute error 0.37, corresponding to a mean 2.3-fold error in predicted IC50s,
comparable to experimental variation. These models will thus be useful as
inputs to mechanistic predictions of DILI and as part of computational
pipelines for drug discovery.
Security Measures for Grids against Rank-1 Undetectable Time-Synchronization Attacks
Date: 2020-09-15 00:00:00
Tasks: State Estimation

Time-synchronization attacks on phasor measurement units (PMU) pose a real
threat to smart grids; it was shown that they are feasible in practice and that
they can have a non-negligible negative impact on the state estimation, without
triggering the bad-data detection mechanisms. Previous works identified
vulnerability conditions when targeted PMUs measure a single phasor. Yet, PMUs
are capable of measuring several quantities. We present novel vulnerability
conditions in the general case where PMUs measure any number of phasors and can
share the same time reference. One is a sufficient condition that does not
depend on the measurement values. We propose a security requirement that
prevents it and provide a greedy offline algorithm that enforces it. If this
security requirement is satisfied, there is still a possibility that the grid
can be attacked, although we conjecture that it is very unlikely. We identify
two sufficient and necessary vulnerability conditions which depend on the
measurement values. For each, we provide a metric that shows the distance
between the observed and vulnerability conditions. We recommend their
monitoring for security. Numerical results, on the IEEE-39 bus benchmark with
real load profiles, show that the measurements of a grid satisfying our
security requirement are far from vulnerable.
IoT Connectivity Technologies and Applications: A Survey
Date: 2020-02-28 00:00:00
Tasks: Compressive Sensing, Survey

The Internet of Things (IoT) is rapidly becoming an integral part of our life
and also multiple industries. We expect to see the number of IoT connected
devices explosively grows and will reach hundreds of billions during the next
few years. To support such a massive connectivity, various wireless
technologies are investigated. In this survey, we provide a broad view of the
existing wireless IoT connectivity technologies and discuss several new
emerging technologies and solutions that can be effectively used to enable
massive connectivity for IoT. In particular, we categorize the existing
wireless IoT connectivity technologies based on coverage range and review
diverse types of connectivity technologies with different specifications. We
also point out key technical challenges of the existing connectivity
technologies for enabling massive IoT connectivity. To address the challenges,
we further review and discuss some examples of promising technologies such as
compressive sensing (CS) random access, non-orthogonal multiple access (NOMA),
and massive multiple input multiple output (mMIMO) based random access that
could be employed in future standards for supporting IoT connectivity. Finally,
a classification of IoT applications is considered in terms of various service
requirements. For each group of classified applications, we outline its
suitable IoT connectivity options.
Analyzing large frequency disruptions in power systems using large deviations theory
Date: 2020-05-24 00:00:00

We propose a method for determining the most likely cause, in terms of
conventional generator outages and renewable fluctuations, of power system
frequency reaching a predetermined level that is deemed unacceptable to the
system operator. Our parsimonious model of system frequency incorporates
primary and secondary control mechanisms, and supposes that conventional
outages occur according to a Poisson process and renewable fluctuations follow
a diffusion process. We utilize a large deviations theory based approach that
outputs the most likely cause of a large excursion of frequency from its
desired level. These results yield the insight that current levels of renewable
power generation do not significantly increase system vulnerability in terms of
frequency deviations relative to conventional failures. However, for a large
range of model parameters it is possible that such vulnerabilities may arise as
renewable penetration increases.
Finding Optimal Total Active Reflection Coefficient and Realized Gain for Multi-port Lossy Antennas
Authors: Miloslav Capek, Lukas Jelinek, Michal Masek
Date: 2020-02-24 00:00:00

A numerically effective description of the total active reflection coefficient and realized gain are studied for multi-port antennas. Material losses are fully considered. The description is based on operators represented in an entire-domain port-mode basis, i.e., on matrices with favorably small dimensions. Optimal performance is investigated and conditions on optimal excitation and matching are derived. The solution to the combinatorial problem of optimal ports' placement and optimal feeding synthesis is also accomplished. Four examples of various complexity are numerically studied, demonstrating the advantages of the proposed method. The final formulas can easily be implemented in existing electromagnetic simulators using integral equation solver.
Privacy-Preserving Distributed Clustering for Electrical Load Profiling
Date: 2020-02-26 00:00:00
Tasks: Clustering, Privacy Preserving

Electrical load profiling supports retailers and distribution network
operators in having a better understanding of the consumption behavior of
consumers. However, traditional clustering methods for load profiling are
centralized and require access to all the smart meter data, thus causing
privacy issues for consumers and retailers. To tackle this issue, we propose a
privacy-preserving distributed clustering framework for load profiling by
developing a privacy-preserving accelerated average consensus (PP-AAC)
algorithm with proven convergence. Using the proposed framework, we modify
several commonly used clustering methods, including k-means, fuzzy C-means, and
Gaussian mixture model, to provide privacy-preserving distributed clustering
methods. In this way, load profiling can be performed only by local
calculations and information sharing between neighboring data owners without
sacrificing privacy. Meanwhile, compared to traditional centralized clustering
methods, the computational time consumed by each data owner is significantly
reduced. The privacy and complexity of the proposed privacy-preserving
distributed clustering framework are analyzed. The correctness, efficiency,
effectiveness, and privacy-preserving feature of the proposed framework and the
proposed PP-AAC algorithm are verified using a real-world Irish residential
dataset.
Dependence of chlorophyll content in leaves from light regime, electromagnetic fields and plant species
Authors: Alexander Kholmanskiy, Nataliya Zaytseva
Date: 2020-02-27 00:00:00

The regularity of the distribution of chlorophylls content in a series of 30 cultivated plants and 75 steppe grasses was studied. The increased content of chlorophyll and magnesium in vegetables and grains compared with greens and steppe grasses is associated with more complex genetics of metabolism, which has stages of flowering and fruiting. The chlorophyll content increases with the use of LED phytoirradiators with an emission band coinciding with the first absorption band of chlorophyll. Industrial electromagnetic fields can affect the biosynthesis of pigments in deciduous trees, but cultivated herbaceous plants are not sensitive to them.
Sympatric speciation based on pure assortative mating
Date: 2020-02-28 00:00:00

Although geographic isolation has been shown to play a key role in promoting
reproductive isolation, it is now believed that speciation can also happen in
sympatry and with considerable gene flow. Here we present a model of sympatric
speciation based on assortative mating that does not require a genetic
threshold for reproduction, i.e., that does not directly associate genetic
differences between individuals with reproductive incompatibilities. In the
model individuals mate with the most similar partner in their pool of potential
mates, irrespective of how dissimilar it might be. We show that assortativity
alone can lead to the formation of clusters of genetically similar individuals.
The absence of a minimal genetic similarity for mating implies the constant
generation of hybrids and brings up the old problem of species definition.
Here, we define species based on clustering of genetically similar individuals
but allowing genetic flow among different species. We show that the results
obtained with the present model are in good agreement with empirical data, in
which different species can still reproduce and generate hybrids.