pwc-archive/papers-with-abstracts huggingface dataset:
An EEG-based Stereoscopic Research to Reveal the Brain's Response to What Happens Before and After Watching 2D and 3D Movies
Date: 2019-03-13 00:00:00
Tasks: Benchmarking, EEG, Electroencephalogram (EEG)

Despite knowing the reality of three-dimensional (3D) technology in the form
of eye fatigue, this technology continues to be retained by people (especially
the young community). To check what happens before and after watching a 2D and
3D movie and how this condition influences the human brain's power spectrum
density (PSD), a five-member test group was arranged. In this study,
electroencephalogram (EEG) was used as a neuroimaging method. EEG recordings of
five individuals were taken both before and after watching 2D and 3D movies.
After 2D/3D EEG recording, this record was divided into three stages for
analysis. These stages consisted of Relax, Watching, and Rest. This
benchmarking analysis included I) before and after watching the 2D movie (R2b
and R2a), II) before and after watching the 3D movie (R3b and R3a), and III)
after watching the 2D/3D movie (R2a and R3a). In the Relax and Rest stages, the
2D/3D EEG power differences in all channels of brain regions for the five EEG
bands, including delta ({\delta}), theta ({\theta}), alpha ({\alpha}), beta
(\b{eta}), and gamma ({\gamma}), were analyzed and compared.
An FPGA-based centralized visible light beacon network
Date: 2019-03-14 00:00:00
Tasks: Indoor Localization

Indoor localization systems based on Visible Light Communication (VLC) have
shown promising advantages compared with systems based on other wireless
technologies. In these systems, many VLC light-emitting diode (LED) anchors are
employed in an indoor space in which location identification messages are sent
to user devices in small packets. In normal beacon network models,
micro-controller (MCU) or low-end system-on-chip (SoC) are often the
coordinators which configure messages for one or many VLC-LED bulbs. In this
paper, we discuss about processing overload and implementation cost of the two
typical models of VLC beacon network in scenarios of a hundred of VLC-LED
anchors are taken into account. Finally, an FPGA-based centralized VLC
transmitter and its aided Nios II-based system has been introduced to enhance
the performance of the VLC beacon network. Besides, due to the centralized
processing, our system model is considered to be more cost-efficient than the
dedicated-processor-based models
Learning Automata Based Q-learning for Content Placement in Cooperative Caching
Date: 2019-03-30 00:00:00
Tasks: Q-Learning

An optimization problem of content placement in cooperative caching is
formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile
users. Firstly, a supervised feed-forward back-propagation connectionist model
based neural network (SFBC-NN) is invoked for user mobility and content
popularity prediction. More particularly, practical data collected from
GPS-tracker app on smartphones is tackled to test the accuracy of mobility
prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for
cooperative caching is proposed, in which learning automata (LA) is invoked for
Q-learning to obtain an optimal action selection in a random and stationary
environment. It is proven that the LA-based action selection scheme is capable
of enabling every state to select the optimal action with arbitrarily high
probability if Q-learning is able to converge to the optimal Q value
eventually. To characterize the performance of the proposed algorithms, the sum
MOS of users is applied to define the reward function. Extensive simulations
reveal that: 1) The prediction error of SFBC-NN lessen with the increase of
iterations and nodes; 2) the proposed LAQL achieves significant performance
improvement against traditional Q-learning; 3) the cooperative caching scheme
is capable of outperforming non-cooperative caching and random caching of 3%
and 4%.
Model-Driven Deep Learning Method for Jammer Suppression in Massive Connectivity Systems
Date: 2019-03-14 00:00:00

We present a method for separating collided signals from multiple users in
the presence of strong and wideband interference/jamming signal. More
specifically, we consider a massive connectivity setup where few, out of a
large number of users, equipped with spreading codes, synchronously transmit
symbols. The received signal is a noisy mixture of symbols transmitted through
users' flat fading channels, impaired by fast frequency hopping jamming signal
of relatively large power. In the absence of any conventional technique
suitable for the considered setup, we propose a "model-driven" deep learning
method, based on convolution neural network, to suppress jamming signal from
the received signal, and detect active users together with their transmitted
symbols. A numerical study of the proposed method confirms its effectiveness in
scenarios where classical techniques fail. As such, in a two user scenario with
wideband jamming signal of power $20$ dB above the power any active user, the
proposed algorithm achieves error rates $10^{-2}$ for a wide range of AWGN
variances.
Power Control and Frequency Band Selection Policies for Underlay MIMO Cognitive Radio
Date: 2019-03-15 00:00:00

We study power control and frequency band selection policies for multi-band
underlay MIMO cognitive radio with the objective of maximizing the rate of a
secondary user (SU) link while limiting the interference leakage towards
primary users (PUs) below a threshold. The goal of the SU in each policy is to
select one frequency band in each time slot and determine the transmit power.
To limit the interference towards PU in time-varying channels, we propose fixed
and dynamic transmit power control schemes which depend on PU traffic and the
temporal correlation of channels between the SU and the PU. We study the
performance of frequency band selection policies that use fixed or dynamic
power control. We show that dynamic frequency band selection policies, e.g.,
policies based on multi-armed bandit framework, wherein SU selects a different
frequency band in each slot, result in higher interference towards PU as
compared to the fixed band policy wherein SU stays on one band. We also provide
an expression for the gap between the rate achieved by SU under a clairvoyant
policy and the fixed band policy. It is observed that this gap reduces with
increased temporal correlation and with increased number of SU antennas.
QoS Aware Power Allocation and User Selection in Massive MIMO Underlay Cognitive Radio Networks
Date: 2019-03-15 00:00:00

We address the problem of power allocation and secondary user (SU) selection
in the downlink from a secondary base station (SBS) equipped with a large
number of antennas in an underlay cognitive radio network. A new optimization
framework is proposed in order to select the maximum number of SUs and compute
power allocations in order to satisfy instantaneous rate or QoS requirements of
SUs. The optimization framework also aims to restrict the interference to
primary users (PUs) below a predefined threshold using available imperfect CSI
at the SBS. In order to obtain a feasible solution for power allocation and
user selection, we propose a low-complexity algorithm called
DeleteSU-with-Maximum-Power-allocation (DMP). Theoretical analysis is provided
to compute the interference to PUs and the number of SUs exceeding the required
rate. The analysis and simulations show that the proposed DMP algorithm
outperforms the stateof-the art selection algorithm in terms of serving more
users with minimum rate constraints, and it approaches the optimal solution if
the number of antennas is an order of magnitude greater than the number of
users.
Exploiting product molecule number to consider reaction rate fluctuation in elementary reactions
Date: 2019-03-18 00:00:00

In many chemical reactions, reaction rate fluctuation is inevitable. Reaction
rates are different whenever chemical reaction occurs due to their dependence
on the number of reaction events or the product number. As such, understanding
the impact of rate fluctuation on product number counting statistics is of the
utmost importance when developing a quantitative explanation of chemical
reactions. In this work, we present a master equation that describes reaction
rates as a function of product number and time. Our equal reveals the
relationship between the reaction rate and product number fluctuation. Product
number counting statistics uncovers a stochastic property of the product
number; product number directly manipulates the reaction rate. Specifically, we
find that product number shows super-Poisson characteristics when the product
number increases, inducing an increase in the reaction rate. While, on the
other hand, when the product number shows sub-Poisson characteristics with an
increase in the product number, this is induced by a decrease in the reaction
rate. Furthermore, our analysis exploits reaction rate fluctuation, enabling
the quantification of the deviation of an elementary reaction process from a
renewal process.
Joint Antenna Array Mode Selection and User Assignment for Full-Duplex MU-MISO Systems
Date: 2019-03-15 00:00:00

This paper considers a full-duplex (FD) multiuser multiple-input
single-output system where a base station simultaneously serves both uplink
(UL) and downlink (DL) users on the same time-frequency resource. The crucial
barriers in implementing FD systems reside in the residual self-interference
and co-channel interference. To accelerate the use of FD radio in future
wireless networks, we aim at managing the network interference more effectively
by jointly designing the selection of half-array antenna modes (in the transmit
or receive mode) at the BS with time phases and user assignments. The first
problem of interest is to maximize the overall sum rate subject to
quality-of-service requirements, which is formulated as a highly non-concave
utility function followed by non-convex constraints. To address the design
problem, we propose an iterative low-complexity algorithm by developing new
inner approximations, and its convergence to a stationary point is guaranteed.
To provide more insights into the solution of the proposed design, a general
max-min rate optimization is further considered to maximize the minimum
per-user rate while satisfying a given ratio between UL and DL rates.
Furthermore, a robust algorithm is devised to verify that the proposed scheme
works well under channel uncertainty. Simulation results demonstrate that the
proposed algorithms exhibit fast convergence and substantially outperform
existing schemes.
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition
Date: 2019-04-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Clustering, Distant Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

The use of spatial information with multiple microphones can improve
far-field automatic speech recognition (ASR) accuracy. However, conventional
microphone array techniques degrade speech enhancement performance when there
is an array geometry mismatch between design and test conditions. Moreover,
such speech enhancement techniques do not always yield ASR accuracy improvement
due to the difference between speech enhancement and ASR optimization
objectives. In this work, we propose to unify an acoustic model framework by
optimizing spatial filtering and long short-term memory (LSTM) layers from
multi-channel (MC) input. Our acoustic model subsumes beamformers with multiple
types of array geometry. In contrast to deep clustering methods that treat a
neural network as a black box tool, the network encoding the spatial filters
can process streaming audio data in real time without the accumulation of
target signal statistics. We demonstrate the effectiveness of such MC neural
networks through ASR experiments on the real-world far-field data. We show that
our two-channel acoustic model can on average reduce word error rates (WERs)
by~13.4 and~12.7% compared to a single channel ASR system with the log-mel
filter bank energy (LFBE) feature under the matched and mismatched microphone
placement conditions, respectively. Our result also shows that our two-channel
network achieves a relative WER reduction of over~7.0% compared to conventional
beamforming with seven microphones overall.
A new SWATH ion library for mouse adult hippocampal neural stem cells
Date: 2019-03-15 00:00:00

Over the last years, the SWATH data-independent acquisition protocol
(Sequential Window acquisition of All THeoretical mass spectra) has become a
cornerstone for the worldwide proteomics community. In this approach, a
high-resolution quadrupole-ToF mass spectrometer acquires thousands of MS/MS
data by selecting not just a single precursor at a time, but by allowing a
broad m/z range to be fragmented. This acquisition window is then sequentially
moved from the lowest to the highest mass selection range. This technique
enables the acquisition of thousands of high-resolution MS/MS spectra per
minute in a standard LC-MS run. In the subsequent data analysis phase, the
corresponding dataset is searched in a triple quadrupole-like mode, thus not
considering the whole MS/MS scan spectrum, but by searching for several
precursor to fragment transitions that identify and quantify the corresponding
peptide. This search is made possible with the use of an ion library,
previously acquired in a classical data dependent, full-spectrum mode. The
SWATH protocol, combining the protein identification power of high-resolution
MS/MS spectra with the robustness and accuracy in analyte quantification of
triple-quad targeted workflows, has become very popular in proteomics research.
The major drawback lies in the ion library itself, which is normally demanding
and time-consuming to build. Conversely, through the realignment of
chromatographic retention times, an ion library of a given proteome can
relatively easily be tailored upon any proteomics experiment done on the same
proteome. We are thus hereby sharing with the worldwide proteomics community
our newly acquired ion library of mouse adult hippocampal neural stem cells.
Given the growing effort in neuroscience research involving proteomics
experiments, we believe that this data might be of great help for the
neuroscience community.
EMG wrist-hand motion recognition system for real-time Embedded platform
Date: 2019-03-15 00:00:00
Tasks: domain classification, Electromyography (EMG)

Electromyography (EMG) signal analysis is a popular method for controlling
prosthetic and gesture control equipment. For portable systems, such as
prosthetic limbs, real-time low-power operation on embedded processors is
critical, but to date, there has been no record of how existing EMG analysis
approaches support such deployments. This paper presents a novel approach to
time-domain classification of multi-channel EMG signals harnessed from
randomly-placed sensors according to the wrist-hand movements which caused
their occurrence. It shows how, by employing a very small set of time-domain
features, Kernel Fisher discriminant feature projection and Radial Bias
Function neural network classifiers, nine wrist-hand movements can be detected
with accuracy exceeding 99% - surpassing the state-of-the-art on record. It
also shows how, when deployed on ARM Cortex-A53, the processing time is not
only sufficient to enable real-time processing but is also a factor 50 shorter
than the leading time-frequency techniques on record.
Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators
Date: 2019-03-15 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we propose linear operator theoretic framework involving
Koopman operator for the data-driven identification of power system dynamics.
We explicitly account for noise in the time series measurement data and propose
robust approach for data-driven approximation of Koopman operator for the
identification of nonlinear power system dynamics. The identified model is used
for the prediction of state trajectories in the power system. The application
of the framework is illustrated using an IEEE nine bus test system.
Minimizing Age of Information in Cognitive Radio-based IoT Systems: Underlay or Overlay?
Date: 2019-08-20 00:00:00
Tasks: Natural Questions

We consider a cognitive radio-based Internet-of-Things (CR-IoT) network
consisting of one primary IoT (PIoT) system and one secondary IoT (SIoT)
system. The IoT devices of both the PIoT and the SIoT respectively monitor one
physical process and send randomly generated status updates to their associated
access points (APs). The timeliness of the status updates is important as the
systems are interested in the latest condition (e.g., temperature, speed and
position) of the IoT device. In this context, two natural questions arise: (1)
How to characterize the timeliness of the status updates in CR-IoT systems? (2)
Which scheme, overlay or underlay, is better in terms of the timeliness of the
status updates. To answer these two questions, we adopt a new performance
metric, named the age of information (AoI). We analyze the average peak AoI of
the PIoT and the SIoT for overlay and underlay schemes, respectively. Simple
asymptotic expressions of the average peak AoI are also derived when the PIoT
operates at high signal-to-noise ratio (SNR). Based on the asymptotic
expressions, we characterize a critical generation rate of the PIoT system,
which can determine the superiority of overlay and underlay schemes in terms of
the average peak AoI of the SIoT. Numerical results validate the theoretical
analysis and uncover that the overlay and underlay schemes can outperform each
other in terms of the average peak AoI of the SIoT for different system setups.
Non-intrusive speech quality assessment using neural networks
Date: 2019-03-16 00:00:00
Tasks: Audio Quality Assessment

Estimating the perceived quality of an audio signal is critical for many
multimedia and audio processing systems. Providers strive to offer optimal and
reliable services in order to increase the user quality of experience (QoE). In
this work, we present an investigation of the applicability of neural networks
for non-intrusive audio quality assessment. We propose three neural
network-based approaches for mean opinion score (MOS) estimation. We compare
our results to three instrumental measures: the perceptual evaluation of speech
quality (PESQ), the ITU-T Recommendation P.563, and the speech-to-reverberation
energy ratio. Our evaluation uses a speech dataset contaminated with
convolutive and additive noise, labeled using a crowd-based QoE evaluation,
evaluated with Pearson correlation with MOS labels, and mean-squared-error of
the estimated MOS. Our proposed approaches outperform the aforementioned
instrumental measures, with a fully connected deep neural network using
Mel-frequency features providing the best correlation (0.87) and the lowest
mean squared error (0.15)
Semimartingale theory of monotone mean--variance portfolio allocation
Authors: Aleš Černý
Date: 2019-03-16 00:00:00

We study dynamic optimal portfolio allocation for monotone mean--variance preferences in a general semimartingale model. Armed with new results in this area we revisit the work of Cui, Li, Wang and Zhu (2012, MAFI) and fully characterize the circumstances under which one can set aside a non-negative cash flow while simultaneously improving the mean--variance efficiency of the left-over wealth. The paper analyzes, for the first time, the monotone hull of the Sharpe ratio and highlights its relevance to the problem at hand.
Market Making under a Weakly Consistent Limit Order Book Model
Authors: Baron Law, Frederi Viens
Date: 2019-03-18 00:00:00
Tasks: Point Processes

We develop a new market-making model, from the ground up, which is tailored towards high-frequency trading under a limit order book (LOB), based on the well-known classification of order types in market microstructure. Our flexible framework allows arbitrary order volume, price jump, and bid-ask spread distributions as well as the use of market orders. It also honors the consistency of price movements upon arrivals of different order types. For example, it is apparent that prices should never go down on buy market orders. In addition, it respects the price-time priority of LOB. In contrast to the approach of regular control on diffusion as in the classical Avellaneda and Stoikov [1] market-making framework, we exploit the techniques of optimal switching and impulse control on marked point processes, which have proven to be very effective in modeling the order-book features. The Hamilton-Jacobi-Bellman quasi-variational inequality (HJBQVI) associated with the control problem can be solved numerically via finite-difference method. We illustrate our optimal trading strategy with a full numerical analysis, calibrated to the order-book statistics of a popular Exchanged-Traded Fund (ETF). Our simulation shows that the profit of market-making can be severely overstated under LOBs with inconsistent price movements.
Vegetation pattern formation in a sinuous free-scale landscape
Date: 2019-03-18 00:00:00

The original Hardenberg's model of biomass patterns in arid and semi-arid
regions is revisited to extend it to more general non flat regions. It is
proposed a technique to study these more generalized (non-flat) regions using
both a conservation criterion and a explicit spatial dependent function $\nu
(x)$. In this paper a study of dynamical stability around system's fixed points
made. Under the idea of predictability via air images a fitted relationship
among dynamical variables at stable fixed points is stablished. Also, is
presented a discrete version of the model, in the form of Cellular Automata
techniques, that allows to neglect the spatial scale and reproduces realistic
stable spatial patterns.
Deep Learning Based Pilot Design for Multi-user Distributed Massive MIMO Systems
Date: 2019-03-18 00:00:00

This letter proposes a deep learning based pilot design scheme to minimize
the sum mean square error (MSE) of channel estimation for multi-user
distributed massive multiple-input multiple-output (MIMO) systems. The pilot
signal of each user is expressed as a weighted superposition of orthonormal
pilot sequence basis, where the power assigned to each pilot sequence is the
corresponding weight. A multi-layer fully connected deep neural network (DNN)
is designed to optimize the power allocated to each pilot sequence to minimize
the sum MSE, which takes the channel large-scale fading coefficients as input
and outputs the pilot power allocation vector. The loss function of the DNN is
defined as the sum MSE, and we leverage the unsupervised learning strategy to
train the DNN. Simulation results show that the proposed scheme achieves better
sum MSE performance than other methods with low complexity.