pwc-archive/papers-with-abstracts huggingface dataset:
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets
Date: 2020-03-31 00:00:00
Tasks: Time Series, Time Series Analysis

This study attempts to investigate into the structure and features of global
equity markets from a time-frequency perspective. An analysis grounded on this
framework allows one to capture information from a different dimension, as
opposed to the traditional time domain analyses, where multiscale structures of
financial markets are clearly extracted. In financial time series, multiscale
features manifest themselves due to presence of multiple time horizons. The
existence of multiple time horizons necessitates a careful investigation of
each time horizon separately as market structures are not homogenous across
different time horizons. The presence of multiple time horizons, with varying
levels of complexity, requires one to investigate financial time series from a
heterogeneous market perspective where market players are said to operate at
different investment horizons. This thesis extends the application of
time-frequency based wavelet techniques to: i) analyse the interdependence of
global equity markets from a heterogeneous investor perspective with a special
focus on the Indian stock market, ii) investigate the contagion effect, if any,
of financial crises on Indian stock market, and iii) to study fractality and
scaling properties of global equity markets and analyse the efficiency of
Indian stock markets using wavelet based long memory methods.
Active exploration in adaptive model predictive control
Date: 2020-03-31 00:00:00
Tasks: model, Model Predictive Control

A dual adaptive model predictive control (MPC) algorithm is presented for
linear, time-invariant systems subject to bounded disturbances and parametric
uncertainty in the state-space matrices. Online set-membership identification
is performed to reduce the uncertainty and thus control affects both the
informativity of identification and the system's performance. The main
contribution of the paper is to include this dual effect in the MPC
optimization problem using a predicted worst-case cost in the objective
function. This allows the controller to perform active exploration, that is,
the control input reduces the uncertainty in the regions of the parameter space
that have most influence on the performance. Additionally, the MPC algorithm
ensures robust constraint satisfaction of state and input constraints.
Advantages of the proposed algorithm are shown by comparing it to a passive
adaptive MPC algorithm from the literature.
DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer
Authors: Shilian Zheng, Shichuan Chen, Xiaoniu Yang
Date: 2020-03-31 00:00:00

A canonical wireless communication system consists of a transmitter and a receiver. The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process. We design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure, in which global pooling is used to improve the adaptability of the network to different input signal lengths. Multiple binary classifiers are used at the final classification layer to achieve multi-bit information stream recovery. We also exploit the DeepReceiver for unified blind reception of multiple modulation and coding schemes (MCSs) by including signal samples of corresponding MCSs in the training set. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under the influence of various factors such as noise, RF impairments, multipath fading, cochannel interference, dynamic environment, and unified reception of multiple MCSs.
Immuno-epidemiological model of two-stage epidemic growth
Date: 2020-03-31 00:00:00
Tasks: model, Vocal Bursts Valence Prediction

Epidemiological data on seasonal influenza show that the growth rate of the
number of infected individuals can increase passing from one exponential growth
rate to another one with a larger exponent. Such behavior is not described by
conventional epidemiological models. In this work an immuno-epidemiological
model is proposed in order to describe this two-stage growth. It takes into
account that the growth in the number of infected individuals increases the
initial viral load and provides a passage from the first stage of epidemic
where only people with weak immune response are infected to the second stage
where people with strong immune response are also infected. This scenario may
be viewed as an increase of the effective number of susceptible increasing the
effective growth rate of infected.
A novel Algorithm for Hydrostatic-mechanical Mobile Machines with a Dual-Clutch Transmission
Date: 2020-04-23 00:00:00

Mobile machines using a hydrostatic transmission is highly efficient under
lower working-speed condition but less capable at higher transport velocities.
To enhance overall efficiency, we have improved the powertrain design by
combining a hydrostatic transmission with a dual-clutch transmission (DCT).
Compared with other mechanical gearboxes, the DCT avoids the interruption of
torque transmission in the process of shifting without sacrificing more
transmission efficiency. However, there are some problems of unstable torque
transmission during the shifting process, and an excessive torque drop
occurring at the end of the gear shift, which result in a poor drive comfort.
To enhance the performance of the novel structural possibility of powertrain
design, we designed a novel control strategy for the motor torque and the
clutch torques during the shifting process. The controller's task is tracking
the target drive torque and completing the gear shift as quickly as possible
with acceptable smoothness requirements. In the process of the controller
design, a specific control algorithm is firstly designed for a 10 ton wheel
loader. The model-based simulation has validated the control effect. As a
result, the control strategy employing clutch and motor torque control to
achieve a smooth shifting process is feasible since drive torque is well
tracked, and highly dynamical actuators are not required. Furthermore, only two
calibration parameters are designed to adjust the control performance
systematically. The novel control strategy is generalized for other mobile
machines with different sizes.
Event-Triggered Quantized Average Consensus via Mass Summation
Date: 2020-03-27 00:00:00
Tasks: Quantization

We study the distributed average consensus problem in multi-agent systems
with directed communication links that are subject to quantized information
flow. The goal of distributed average consensus is for the nodes, each
associated with some initial value, to obtain the average (or some value close
to the average) of these initial values. In this paper, we present and analyze
novel distributed averaging algorithms which operate exclusively on quantized
values (specifically, the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and rely
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We characterize the
properties of the proposed distributed averaging protocols on quantized values
and show that their execution, on any time-invariant and strongly connected
digraph, will allow all agents to reach, in finite time, a common consensus
value represented as the ratio of two quantized values that is equal to the
exact average. We conclude with examples that illustrate the operation,
performance, and potential advantages of the proposed algorithms.
Verification of Nonblockingness in Bounded Petri Nets With Minimax Basis Reachability Graphs
Authors: Chao Gu, Ziyue Ma, Zhiwu Li, Alessandro Giua
Date: 2020-03-31 00:00:00
Tasks: Blocking

This paper proposes a semi-structural approach to verify the nonblockingness of a Petri net. We construct a structure, called minimax basis reachability graph (minimax-BRG): it provides an abstract description of the reachability set of a net while preserving all information needed to test if the net is blocking. We prove that a bounded deadlock-free Petri net is nonblocking if and only if its minimax-BRG is unobstructed, which can be verified by solving a set of integer constraints and then examining the minimax-BRG. For Petri nets that are not deadlock-free, one needs to determine the set of deadlock markings. This can be done with an approach based on the computation of maximal implicit firing sequences enabled by the markings in the minimax-BRG. The approach we developed does not require the construction of the reachability graph and has wide applicability.
Single-pixel coherent diffraction imaging
Date: 2020-03-30 00:00:00
Tasks: Retrieval

Complex-field imaging is indispensable for numerous applications at
wavelengths from X-ray to THz, with amplitude describing transmittance (or
reflectivity) and phase revealing intrinsic structure of the target object.
Coherent diffraction imaging (CDI) employs iterative phase retrieval algorithms
to process diffraction measurements and is the predominant non-interferometric
method to image complex fields. However, the working spectrum of CDI is quite
narrow, because the diffraction measurements on which it relies require dense
array detection with ultra-high dynamic range. Here we report a single-pixel
CDI technique that works for a wide waveband. A single-pixel detector instead
of an array sensor is employed in the far field for detection. It repeatedly
records the DC-only component of the diffracted wavefront scattered from an
object as it is illuminated by a sequence of binary modulation patterns. This
decreases the measurements' dynamic range by several orders of magnitude. We
employ an efficient single-pixel phase-retrieval algorithm to jointly recover
the object's 2D amplitude and phase maps from the 1D intensity-only
measurements. No a priori object information is needed in the recovery process.
We validate the technique's quantitative phase imaging nature using both
calibrated phase objects and biological samples, and demonstrate its wide
working spectrum with both 488-nm visible light and 980-nm near-infrared light.
Our approach paves the way for complex-field imaging in a wider waveband where
2D detector arrays are not available, with broad applications in life and
material sciences.
Parametric analysis of COVID-19 expansion in European countries in the period of February to June 2020
Authors: Martin Spousta
Date: 2020-03-31 00:00:00

The data on number of registered cases of COVID-19 disease in twenty European countries is analyzed by the least-squares fitting procedure with generic analytic functions. Three regimes of the expansion of the disease are identified and quantified -- early exponential expansion, damped exponential, and linear expansion. Differences among countries in the early expansion period are quantified. The velocity of the expansion in the exponential regime lies within one standard deviation from the average value for 11 countries. The number of infected individuals at the initial time is excessively high for Italy, 7 standard deviations from the average value. Method for predicting the expansion based on extrapolation in the parametric space is presented. One-week predictions based on extrapolations have average precision of 18% and 29% during the later period of the damped exponential expansion for the case of Italy and Czechia, respectively. The method based on extrapolations in the parametric space may provide an elementary method to quantify the impact of restrictive measures on the spreading of the disease.
The fractal time growth of COVID-19 pandemic: an accurate self-similar model, and urgent conclusions
Date: 2020-03-31 00:00:00
Tasks: Time Series, Time Series Analysis

Current available data of the worldwide impact of the COVID-19 pandemic has
been analyzed using dimensional analysis and self-similarity hypotheses. We
show that the time series of infected population and deaths of the most
impacted and unprepared countries exhibits an asymptotic power law behavior,
compatible with the propagation of a signal in a fractal network. We propose a
model which predicts an asymptotically self-similar expansion of deaths in time
before containment, and the final death toll under total containment measures,
as a function of the delay in taking those measures after the expansion is
observed. The physics of the model resembles the expansion of a flame in a
homogeneous domain with a fractal dimension 3.75. After containment measures
are taken, the natural fractal structure of the network is drastically altered
and a secondary evolution is observed. This evolution, akin to the homogeneous
combustion in a static isolated enclosure with a final quenching, has a
characteristic time of 20.1 days, according to available data of the pandemic
behavior in China. The proposed model is remarkably consistent with available
data, which supports the simplifying hypotheses made in the model. A universal
formulation for a quarantine as a function of that delay is also proposed.
Studying the Effect of Digital Stain Separation of Histopathology Images on Image Search Performance
Date: 2020-03-31 00:00:00
Tasks: Image Retrieval, Retrieval

Due to recent advances in technology, digitized histopathology images are now
widely available for both clinical and research purposes. Accordingly, research
into computerized image analysis algorithms for digital histopathology images
has been progressing rapidly. In this work, we focus on image retrieval for
digital histopathology images. Image retrieval algorithms can be used to find
similar images and can assist pathologists in making quick and accurate
diagnoses. Histopathology images are typically stained with dyes to highlight
features of the tissue, and as such, an image analysis algorithm for
histopathology should be able to process colour images and determine relevant
information from the stain colours present. In this study, we are interested in
the effect that stain separation into their individual stain components has on
image search performance. To this end, we implement a basic k-nearest
neighbours (kNN) search algorithm on histopathology images from two publicly
available data sets (IDC and BreakHis) which are: a) converted to greyscale, b)
digitally stain-separated and c) the original RGB colour images. The results of
this study show that using H\&E separated images yields search accuracies
within one or two percent of those obtained with original RGB images, and that
superior performance is observed using the H\&E images in most scenarios we
tested.
Learning-Based Multi-Channel Access in 5G and Beyond Networks with Fast Time-Varying Channels
Date: 2020-03-28 00:00:00
Tasks: Deep Reinforcement Learning

We propose a learning-based scheme to investigate the dynamic multi-channel
access (DMCA) problem in the fifth generation (5G) and beyond networks with
fast time-varying channels wherein the channel parameters are unknown. The
proposed learning-based scheme can maintain near-optimal performance for a long
time, even in the sharp changing channels. This scheme greatly reduces
processing delay, and effectively alleviates the error due to decision lag,
which is cased by the non-immediacy of the information acquisition and
processing. We first propose a psychology-based personalized quality of service
model after introducing the network model with unknown channel parameters and
the streaming model. Then, two access criteria are presented for the living
streaming model and the buffered streaming model. Their corresponding
optimization problems are also formulated. The optimization problems are solved
by learning-based DMCA scheme, which combines the recurrent neural network with
deep reinforcement learning. In the learning-based DMCA scheme, the agent
mainly invokes the proposed prediction-based deep deterministic policy gradient
algorithm as the learning algorithm. As a novel technical paradigm, our scheme
has strong universality, since it can be easily extended to solve other
problems in wireless communications. The real channel data-based simulation
results validate that the performance of the learning-based scheme approaches
that derived from the exhaustive search when making a decision at each
time-slot, and is superior to the exhaustive search method when making a
decision at every few time-slots.
COVID-19 Outbreak in Pakistan: Model-Driven Impact Analysis and Guidelines
Date: 2020-03-31 00:00:00

Motivated by the rapid spread of COVID-19 all across the globe, we have
performed simulations of a system dynamic epidemic spread model in different
possible situations. The simulation, not only captures the model dynamic of the
spread of the virus, but also, takes care of population and mobility data. The
model is calibrated based on epidemic data and events specifically of Pakistan,
which can easily be generalized. The simulation results are quite disturbing,
indicating that, even with stringent social distancing and testing strategies
and for a quite long time (even beyond one year), the spread would be
significant (in tens of thousands). The real alarm is when some of these
measures got leaked for a short time within this duration, which may result in
catastrophic situation when millions of people would be infected.
On the comparison of incompatibility of split systems across different numbers of taxa
Authors: Michael Hendriksen, Nils Kapust
Date: 2020-03-31 00:00:00

The concept of $k$-compatibility measures how many phylogenetic trees it would take to display all splits in a given set. A set of trees that display every single possible split is termed a \textit{universal tree set}. In this note, we find $A(n)$, the minimal size of a universal tree set for $n$ taxa. By normalising the $k$-compatibility using $A(n)$, one can then compare incompatibility of split systems across different taxa sizes. We demonstrate this application by comparing two SplitsTree networks of different sizes derived from archaeal genomes.
Propagation and mitigation of epidemics in a scale-free network
Date: 2020-03-31 00:00:00

The epidemic curve and the final extent of the COVID-19 pandemic are usually
predicted from the rate of early exponential raising using the SIR model. These
predictions implicitly assume a full social mixing, which is not plausible
generally. Here I am showing a counterexample to the these predictions, based
on random propagation of an epidemic in Barab\'asi--Albert scale-free network
models. The start of the epidemic suggests $R_0=2.6$, but unlike $\Omega\approx
70\%{}$ predicted by the SIR model, they reach a final extent of only
$\Omega\approx 4\%{}$ without external mitigation and $\Omega\approx
0.5$--$1.5\%{}$ with mitigation. Daily infection rate at the top is also 1--1.5
orders of magnitude less than in SIR models. Quarantining only the 1.5\%{} most
active superspreaders has similar effect on extent and top infection rate as
blind quarantining a random 50\%{} of the full community.
When Ramanujan meets time-frequency analysis in complicated time series analysis
Date: 2020-03-31 00:00:00
Tasks: Time Series, Time Series Analysis

To handle time series with complicated oscillatory structure, we propose a
novel time-frequency (TF) analysis tool that fuses the short time Fourier
transform (STFT) and periodic transform (PT). Since many time series oscillate
with time-varying frequency, amplitude and non-sinusoidal oscillatory pattern,
a direct application of PT or STFT might not be suitable. However, we show that
by combining them in a proper way, we obtain a powerful TF analysis tool. We
first combine the Ramanujan sums and $l_1$ penalization to implement the PT. We
call the algorithm Ramanujan PT (RPT). The RPT is of its own interest for other
applications, like analyzing short signal composed of components with integer
periods, but that is not the focus of this paper. Second, the RPT is applied to
modify the STFT and generate a novel TF representation of the complicated time
series that faithfully reflect the instantaneous frequency information of each
oscillatory components. We coin the proposed TF analysis the Ramanujan de-shape
(RDS) and vectorized RDS (vRDS). In addition to showing some preliminary
analysis results on complicated biomedical signals, we provide theoretical
analysis about RPT. Specifically, we show that the RPT is robust to three
commonly encountered noises, including envelop fluctuation, jitter and additive
noise.
VoltKey: Using Power Line Noise for Zero-Involvement Pairing and Authentication (Demo Abstract)
Date: 2020-03-31 00:00:00

We present VoltKey, a method that transparently generates secret keys for
colocated devices, leveraging spatiotemporally unique noise contexts observed
in commercial power line infrastructure. VoltKey extracts randomness from power
line noise and securely converts it into an authentication token. Nearby
devices which observe the same noise patterns on the powerline generate
identical keys. The unique noise pattern observed only by trusted devices
connected to a local power line prevents malicious devices without physical
access from obtaining unauthorized access to the network. VoltKey is
implemented inside of a standard USB power supply as a platform-agnostic
bolt-on addition to any IoT or mobile device or any wireless access point that
is connected to the power outlet.
Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Collaborative Fog Computing
Authors: Antoine Paris, Hamed Mirghasemi, Ivan Stupia, Luc Vandendorpe
Date: 2020-03-31 00:00:00
Tasks: CPU, Distributed Computing, Diversity

Motivated by applications such as on-device collaborative neural network inference, this work investigates edge-facilitated collaborative fog computing - in which edge-devices collaborate with each other and with the edge of the network to complete a processing task - to augment the computing capabilities of individual edge-devices while optimizing the collaboration for energy-efficiency. Collaborative computing is modeled using the Map-Reduce distributed computing framework, consisting in two rounds of computations separated by a communication phase. The computing load is optimally distributed among the edge-devices, taking into account their diversity in term of computing and communications capabilities. In addition, edge-devices local parameters such as CPU clock frequency and RF transmit power are also optimized for energy-efficiency. The corresponding optimization problem can be shown to be convex and optimality conditions can be obtained through Lagrange duality theory. A waterfilling-like interpretation for the size of the computing load assigned to each edge-device is given. Numerical experiments demonstrate the benefits of the proposed optimal collaborative-computing scheme over various other schemes in several respects. Most notably, the proposed scheme exhibits increased probability of successfully dealing with heavier computations and/or smaller latency along with energy-efficiency gains of up to two orders of magnitude. Both improvements come from the scheme ability to optimally leverage edge-devices diversity.