pwc-archive/papers-with-abstracts huggingface dataset:
"Class-Type" Identification-Based Internal Models in Multivariable Nonlinear Output Regulation
Date: 2020-04-21 00:00:00
Tasks: Vocal Bursts Type Prediction

The paper deals with the problem of output regulation in a "non-equilibrium"
context for a special class of multivariable nonlinear systems stabilizable by
high-gain feedback. A post-processing internal model design suitable for the
multivariable nature of the system, which might have more inputs than
regulation errors, is proposed. Uncertainties in the system and exosystem are
dealt with by assuming that the ideal steady state input belongs to a certain
"class of signals" by which an appropriate model set for the internal model can
be derived. The adaptation mechanism for the internal model is then cast as an
identification problem and a least square solution is specifically developed.
In line with recent developments in the field, the vision that emerges from the
paper is that approximate, possibly asymptotic, regulation is the appropriate
way of approaching the problem in a multivariable and uncertain context. New
insights about the use of identification tools in the design of adaptive
internal models are also presented.
Task-dependent modulation of the visual sensory thalamus assists visual-speech recognition
Date: 2018-05-24 00:00:00
Tasks: Face Identification, speech-recognition, Speech Recognition, Visual Speech Recognition

The cerebral cortex modulates early sensory processing via feed-back
connections to sensory pathway nuclei. The functions of this top-down
modulation for human behavior are poorly understood. Here, we show that
top-down modulation of the visual sensory thalamus (the lateral geniculate
body, LGN) is involved in visual-speech recognition. In two independent
functional magnetic resonance imaging (fMRI) studies, LGN response increased
when participants processed fast-varying features of articulatory movements
required for visual-speech recognition, as compared to temporally more stable
features required for face identification with the same stimulus material. The
LGN response during the visual-speech task correlated positively with the
visual-speech recognition scores across participants. In addition, the
task-dependent modulation was present for speech movements and did not occur
for control conditions involving non-speech biological movements. In
face-to-face communication, visual speech recognition is used to enhance or
even enable understanding what is said. Speech recognition is commonly
explained in frameworks focusing on cerebral cortex areas. Our findings suggest
that task-dependent modulation at subcortical sensory stages has an important
role for communication: Together with similar findings in the auditory modality
the findings imply that task-dependent modulation of the sensory thalami is a
general mechanism to optimize speech recognition.
An Adaptive Optimal Mapping Selection Algorithm for PNC using Variable QAM Modulation
Date: 2018-05-15 00:00:00

Fifth generation (5G) wireless networks will need to serve much higher user
densities than existing 4G networks, and will therefore require an enhanced
radio access network (RAN) infrastructure. Physical layer network coding (PNC)
has been shown to enable such high densities with much lower backhaul load than
approaches such as Cloud-RAN and coordinated multipoint (CoMP). In this letter,
we present an engineering applicable PNC scheme which allows different
cooperating users to use different modulation schemes, according to the
relative strength of their channels to a given access point. This is in
contrast with compute-and-forward and previous PNC schemes which are designed
for two-way relay channel. A two-stage search algorithm to identify the optimum
PNC mappings for given channel state information and modulation is proposed in
this letter. Numerical results show that the proposed scheme achieves low bit
error rate with reduced backhaul load.
Analysis and Design of Commutation-Based Circulator-Receivers for Integrated Full-Duplex Wireless
Date: 2018-05-15 00:00:00

Previously, we presented a non-magnetic, nonreciprocal N-path-filter-based
circulator-receiver (circ.-RX) architecture for full-duplex (FD) wireless which
merges a commutation-based linear periodically-time-varying (LPTV) non-magnetic
circulator with a down-converting mixer and directly provides the baseband (BB)
receiver signals at its output, while suppressing the noise contribution of one
set of the commutating switches. The architecture also incorporates an on-chip
balance network to enhance the transmitter (TX)-receiver (RX) isolation. In
this paper, we present a detailed analysis of the architecture, including a
noise analysis and an analysis of the effect of the balance network. The
analyses are verified by simulation and measurement results of a 65 nm CMOS 750
MHz circulator-receiver prototype. The circulator-receiver can handle up to +8
dBm of TX power, with 8 dB noise figure (NF) and 40 dB average isolation over
20 MHz RF bandwidth (BW). In conjunction with digital self-interference (SI)
and its third-order intermodulation (IM3) cancellation, the FD circ.-RX
demonstrates 80 dB overall SI suppression for up to +8 dBm TX average output
power. The claims are also verified through an FD demonstration where a -50 dBm
weak desired received signal is recovered while transmitting a 0 dBm
average-power OFDM-like TX signal.
Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis
Date: 2018-12-19 00:00:00
Tasks: parameter estimation, Specificity

Supervision of the physical layer of optical networks is an extremely
relevant subject. To detect fiber faults, single-ended solutions such as the
Optical Time Domain Reflectometer (OTDR) allow for precise measurements of
fault profiles. Combining the OTDR with a signal processing approach for
high-dimensional sparse parameter estimation allows for automated and reliable
results in reduced time. In this work, a measurement system composed of a
Photon-Counting OTDR data acquisition unit and a processing unit based on a
Linearized Bregman Iterations algorithm for automatic fault finding is
proposed. An in-depth comparative study of the proposed algorithm's
fault-finding prowess in the presence of noise is presented. Characteristics
such as sensitivity, specificity, processing time, and complexity, are analysed
in simulated environments. Real-life measurements that are conducted using the
Photon-Counting OTDR subsystem for data acquisition and the Linearized
Bregman-based processing unit for automated data analysis demonstrated accurate
results. It is concluded that the proposed measurement system is particularly
well suited to the task of fault finding. The natural characteristic of the
algorithm fosters embedding the solution in digital hardware, allowing for
reduced costs and processing time.
Pre-Hospital Management of Acute Myocardial Infarction Using Tele-Electrocardiography System
Date: 2018-05-15 00:00:00
Tasks: Diagnostic, Management

A comprehensive survey revealed that many patients of Acute Myocardial
Infarction reach to the hospital so late to deliver an effective treatment.
This leads to poor treatment outcome which increases the mortality rates.
Multiple reasons such as lack of diagnostic facilities in the rural health care
centers and absence of cardiologists in Emergency Rooms inhibit the timely
treatment. In this study, we aimed to develop an effective system to
pre-hospital management of Acute Myocardial Infarction. The effectiveness of
early thrombolysis in Myocardial Infarction is well established, particularly
with regard to its positive effect on reducing the mortality rates. Our
proposed system by using tele-electrocardiography technology enables
cardiologist to analyze the patient's signal far away from a medical center.
This system transmits the collected ECG signal as well some vital signs to the
medical center by using internet network. In order to review and analyze the
recorded data, an ECG Viewer software is added to this system. The automatic
measurement algorithm and some additional tools embedded in the ECG Viewer
provide a convenient platform for ECG signal analysis by the cardiologist in
the medical center. The designed system has been tested as a prototype in
Tehran Emergency Service Center. The evaluation of the proposed system shows
acceptable results in recording, transmission, and accurate analysis of ECG
signals to early diagnosis of Acute Myocardial Infarction.
Overcome Competitive Exclusion in Ecosystems
Date: 2019-05-15 00:00:00

Explaining biodiversity in nature is a fundamental problem in ecology. An
outstanding challenge is embodied in the so-called Competitive Exclusion
Principle: two species competing for one limiting resource cannot coexist at
constant population densities, or more generally, the number of consumer
species in steady coexistence cannot exceed that of resources. The fact that
competitive exclusion is rarely observed in natural ecosystems has not been
fully understood. Here we show that by forming chasing triplets among the
consumers and resources in the consumption process, the Competitive Exclusion
Principle can be naturally violated. The modeling framework developed here is
broadly applicable and can be used to explain the biodiversity of many
consumer-resource ecosystems and hence deepens our understanding of
biodiversity in nature.
Understanding bias in the introduction of variation as an evolutionary cause
Date: 2018-05-15 00:00:00

Our understanding of evolution is shaped strongly by how we conceive of its
fundamental causes. In the original Modern Synthesis, evolution was defined as
a process of shifting the frequencies of available alleles at many loci
affecting a trait under selection. Events of mutation that introduce novelty
were not considered evolutionary causes, but proximate causes acting at the
wrong level. Today it is clear that long-term evolutionary dynamics depend on
the dynamics of mutational introduction. Yet, the implications of this
dependency remain unfamiliar, and have not yet penetrated into high-level
debates over evolutionary theory. Modeling the influence of biases in the
introduction process reveals behavior previously unimagined, as well as
behavior previously considered impossible. Quantitative biases in the
introduction of variation can impose biases on the outcome of evolution without
requiring high mutation rates or neutral evolution. Mutation-biased adaptation,
a possibility not previously imagined, has been observed among diverse taxa.
Directional trends are possible under a sustained bias. Biases that are
developmental in origin may have an effect analogous to mutational biases.
Structuralist arguments invoking the relative accessibility of forms in
state-space can be understood as references to the role of biases in the
introduction of variation. That is, the characteristic concerns of molecular
evolution, evo-devo and structuralism can be interpreted to implicate a kind of
causation absent from the original Modern Synthesis.
Can Insider Trading Be Committed Without Trading?
Date: 2018-05-16 00:00:00

Before a person can be prosecuted and convicted for insider trading, he must
first execute the overt act of trading. If no sale of security is consummated,
no crime is also consummated. However, through a complex and insidious
combination of various financial instruments, one can capture the same amount
of gains from insider trading without undertaking an actual trade. Since the
crime of insider trading involves buying or selling a security, a more
sophisticated insider can circumvent the language of the Securities Regulation
Code by replicating the economic equivalent of a sale without consummating a
sale as defined by law.
FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning
Authors: Shah Pararth, Fiser Marek, Faust Aleksandra, Kew J. Chase, Hakkani-Tur Dilek
Date: 2018-05-16 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning (RL), Robot Navigation

Understanding and following directions provided by humans can enable robots
to navigate effectively in unknown situations. We present FollowNet, an
end-to-end differentiable neural architecture for learning multi-modal
navigation policies. FollowNet maps natural language instructions as well as
visual and depth inputs to locomotion primitives. FollowNet processes
instructions using an attention mechanism conditioned on its visual and depth
input to focus on the relevant parts of the command while performing the
navigation task. Deep reinforcement learning (RL) a sparse reward learns
simultaneously the state representation, the attention function, and control
policies. We evaluate our agent on a dataset of complex natural language
directions that guide the agent through a rich and realistic dataset of
simulated homes. We show that the FollowNet agent learns to execute previously
unseen instructions described with a similar vocabulary, and successfully
navigates along paths not encountered during training. The agent shows 30%
improvement over a baseline model without the attention mechanism, with 52%
success rate at novel instructions.
Volatility swaps valuation under stochastic volatility with jumps and stochastic intensity
Date: 2018-05-18 00:00:00

In this paper, a pricing formula for volatility swaps is delivered when the
underlying asset follows the stochastic volatility model with jumps and
stochastic intensity. By using Feynman-Kac theorem, a partial integral
differential equation is obtained to derive the joint moment generating
function of the previous model.
  Moreover, discrete and continuous sampled volatility swap pricing formulas
are given by employing transform techniques and the relationship between two
pricing formulas is discussed. Finally, some numerical simulations are reported
to support the results presented in this paper.
Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies
Date: 2018-05-16 00:00:00
Tasks: Decision Making

This paper introduces a method for linking technological improvement rates
(i.e. Moore's Law) and technology adoption curves (i.e. S-Curves). There has
been considerable research surrounding Moore's Law and the generalized versions
applied to the time dependence of performance for other technologies. The prior
work has culminated with methodology for quantitative estimation of
technological improvement rates for nearly any technology. This paper examines
the implications of such regular time dependence for performance upon the
timing of key events in the technological adoption process. We propose a simple
crossover point in performance which is based upon the technological
improvement rates and current level differences for target and replacement
technologies. The timing for the cross-over is hypothesized as corresponding to
the first 'knee'? in the technology adoption "S-curve" and signals when the
market for a given technology will start to be rewarding for innovators. This
is also when potential entrants are likely to intensely experiment with
product-market fit and when the competition to achieve a dominant design
begins. This conceptual framework is then back-tested by examining two
technological changes brought about by the internet, namely music and video
transmission. The uncertainty analysis around the cases highlight opportunities
for organizations to reduce future technological uncertainty. Overall, the
results from the case studies support the reliability and utility of the
conceptual framework in strategic business decision-making with the caveat that
while technical uncertainty is reduced, it is not eliminated.
B.SAR - Blind SAR Data Focusing
Date: 2018-05-16 00:00:00

Synthetic Aperture RADAR is a radar imaging technique in which the relative
motion of the sensor is used to synthesize a very long antenna and obtain high
spatial resolution. The increasing interest of the scientific community to
simplify SAR sensors and develop automatic system to quickly obtain a
sufficiently good precision image is fostered by the will of developing
low-cost/light-weight SAR systems to be carried by drones. Standard SAR raw
data processing techniques assume uniform motion of the satellite and a fixed
antenna beam pointing sideway orthogonally to the motion path, assumed
rectilinear. In the same hypothesis, a novel blind data focusing technique is
presented, able to obtain good quality images of the inspected area without the
use of ancillary data information. Despite SAR data processing is a well
established imaging technology that has become fundamental in several fields
and applications, in this paper a novel approach has been used to exploit
coherent illumination, demonstrating the possibility of extracting a large part
of the ancillary data information from the raw data itself, to be used in the
focusing procedure. Preliminary results are presented for ERS raw data
focusing. The proposed Matlab software is distributed under the Noncommercial,
Share Alike 4.0, International Creative Common license by the authors.
Which portfolio is better? A discussion of several possible comparison criteria
Authors: Henryk Gzyl, Alfredo Rios
Date: 2018-05-16 00:00:00

During the last few years, there has been an interest in comparing simple or heuristic procedures for portfolio selection, such as the naive, equal weights, portfolio choice, against more "sophisticated" portfolio choices, and in explaining why, in some cases, the heuristic choice seems to outperform the sophisticated choice. We believe that some of these results may be due to the comparison criterion used. It is the purpose of this note to analyze some ways of comparing the performance of portfolios. We begin by analyzing each criterion proposed on the market line, in which there is only one random return. Several possible comparisons between optimal portfolios and the naive portfolio are possible and easy to establish. Afterwards, we study the case in which there is no risk free asset. In this way, we believe some basic theoretical questions regarding why some portfolios may seem to outperform others can be clarified.
SmartTable: A Spreadsheet Program with Intelligent Assistance
Authors: Zhang Shuo, Zada Vugar Abdul, Balog Krisztian
Date: 2018-05-16 00:00:00

We introduce SmartTable, an online spreadsheet application that is equipped
with intelligent assistance capabilities. With a focus on relational tables,
describing entities along with their attributes, we offer assistance in two
flavors: (i) for populating the table with additional entities (rows) and (ii)
for extending it with additional entity attributes (columns). We provide
details of our implementation, which is also released as open source. The
application is available at http://smarttable.cc.
Brain Age from the Electroencephalogram of Sleep
Date: 2018-05-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Interpretable Machine Learning

The human electroencephalogram (EEG) of sleep undergoes profound changes with
age. These changes can be conceptualized as "brain age", which can be compared
to an age norm to reflect the deviation from normal aging process. Here, we
develop an interpretable machine learning model to predict brain age based on
two large sleep EEG datasets: the Massachusetts General Hospital sleep lab
dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health
Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean
absolute deviation of 8.1 years between brain age and chronological age in the
healthy participants in the MGH dataset. As validation, we analyze a subset of
SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years
difference in brain age. Participants with neurological and psychiatric
diseases, as well as diabetes and hypertension medications show an older brain
age compared to chronological age. The findings raise the prospect of using
sleep EEG as a biomarker for healthy brain aging.
Evolution of macromolecular structure: a 'double tale' of biological accretion
Authors: Derek Caetano-Anollés, Kelsey Caetano-Anollés, Gustavo Caetano-Anollés
Date: 2018-04-30 00:00:00

The evolution of structure in biology is driven by accretion and change. Accretion brings together disparate parts to form bigger wholes. Change provides opportunities for growth and innovation. Here we review patterns and processes that are responsible for a 'double tale' of evolutionary accretion at various levels of complexity, from proteins and nucleic acids to high-rise building structures in cities. Parts are at first weakly linked and associate variously. As they diversify, they compete with each other and are selected for performance. The emerging interactions constrain their structure and associations. This causes parts to self-organize into modules with tight linkage. In a second phase, variants of the modules evolve and become new parts for a new generative cycle of higher-level organization. Evolutionary genomics and network biology support the 'double tale' of structural module creation and validate an evolutionary principle of maximum abundance that drives the gain and loss of modules.
Graph-Based Resource Allocation with Conflict Avoidance for V2V Broadcast Communications
Date: 2018-05-16 00:00:00
Tasks: Graph Matching

In this paper we present a graph-based resource allocation scheme for
sidelink broadcast vehicle-to-vehicle (V2V) communications. Harnessing
available information on the geographical position of vehicles and spectrum
resources utilization, eNodeBs are capable of allotting the same set of
sidelink resources to several different vehicles in order for them to broadcast
their signals. Hence, vehicles sharing the same resources would ideally be in
different communications clusters for the interference level-generated due to
resource repurposing-to be maintained under control. Within a communications
cluster, it is crucial that vehicles transmit in orthogonal time resources to
prevent conflicts as vehicles-with half-duplex radio interfaces--cannot
transmit and receive simultaneously. In this research, we have envisaged a
solution based on a bipartite graph, where vehicles and spectrum resources are
represented by vertices whereas the edges represent the achievable rate in each
resource based on the signal-to-interference-plus-noise ratio (SINR) that
vehicles perceive. The aforementioned constraint on time orthogonality of
allocated resources can be approached by aggregating conflicting vertices into
macro-vertices which, in addition, narrows the search space yielding a solution
with computational complexity equivalent to the conventional graph matching
problem. We show mathematically and through simulations that the proposed
approach yields an optimal solution. In addition, we provide simulations
showing that the proposed method outperforms other competing approaches,
specially in scenarios with high vehicular density.
Identification of the source of an interferer by comparison with known carriers using a single satellite
Date: 2018-05-17 00:00:00

We describe a method for identifying the source of a satellite interferer
using a single satellite. The technique relies on the fact that the strength of
a carrier signal measured at the downlink station varies with time due to a
number of factors, and we use a quantum-inspired algorithm to compute a
'signature' for a signal, which captures part of the pattern of variation that
is characteristic of the uplink antenna. We define a distance measure to
numerically quantify the degree of similarity between two signatures, and by
computing the distances between the signature for an interfering carrier and
the signatures of the known carriers being relayed by the same satellite at the
same time, we can identify the antenna that the interferer originated from, if
a known carrier is being relayed from it. As a proof of concept we evaluate the
performance of the technique using a simple statistical model applied to
measured carrier data.