pwc-archive/papers-with-abstracts huggingface dataset:
A Sub-mm$^3$ Ultrasonic Free-floating Implant for Multi-mote Neural Recording
Date: 2019-07-17 00:00:00

A 0.8 mm$^3$ wireless, ultrasonically powered, free-floating neural recording
implant is presented. The device is comprised only of a 0.25 mm$^2$ recording
IC and a single piezoceramic resonator that is used for both power harvesting
and data transmission. Uplink data transmission is performed by analog
amplitude modulation of the ultrasound echo. Using a 1.78 MHz main carrier, >35
kbps/mote equivalent uplink data rate is achieved. A technique to linearize the
echo amplitude modulation is introduced, resulting in <1.2\% static
nonlinearity of the received signal over a $\pm$10 mV input range. The IC
dissipates 37.7 $\mu$W, while the neural recording front-end consumes 4 $\mu$W
and achieves a noise floor of 5.3 $\mu$V$_{rms}$ in a 5 kHz bandwidth. This
work improves sub-mm recording mote depth by >2.5x, resulting in the highest
measured depth/volume ratio by $\sim$3x. Orthogonal subcarrier modulation
enables simultaneous operation of multiple implants, using a single-element
ultrasound external transducer. Dual-mote simultaneous power up and data
transmission is demonstrated at a rate of 7 kS/s at the depth of 50 mm.
Compressive spectral imaging based on hexagonal blue noise coded apertures
Date: 2019-05-21 00:00:00

Coded aperture snapshot spectral imager (CASSI) is a computational imaging
system that acquires a three dimensional (3D) spectral data cube by single or a
few two dimensional (2D) measurements. Binary random coded apertures with
square pixels are primarily implemented in CASSI systems to modulate the
spectral images in spatial domain. The design and optimization of coded
apertures was shown to improve the imaging performance of these systems
significantly. This work proposes a different approach to code design. Instead
of traditional squared tiled coded elements, hexagonal tiled elements are used.
The dislocation between the binary hexagonal pixels on coded apertures and the
square pixels on detector introduces equivalent grey-scale spatial modulation
to increase the degrees of freedom in the sensing matrix, thus further
improving the spectral imaging performance. Then, this paper presents an
optimal structure under the criterion of satisfying the restricted isometry
property (RIP) with high probability, coined blue noise (BN) coded apertures.
In addition, this paper studies and verifies the proposed hexagonal blue noise
coded aperture method on a general CASSI system, where the resolution of the
coded aperture is equivalent to that of the detector. Based on the RIP
criterion, this paper theoretically proves the superiority of the hexagonal
blue noise coded aperture over the traditional random coded aperture with
square lattice.
One-bit LFMCW Radar: Spectrum Analysis and Target Detection
Authors: Benzhou Jin, Jiang Zhu, Qihui Wu, Yuhong Zhang, Zhiwei Xu
Date: 2019-05-23 00:00:00
Tasks: Dimensionality Reduction, Quantization

One-bit radar, performing signal sampling and quantization by a one-bit ADC, is a promising technology for many civilian applications due to its low-cost and low-power consumptions. In this paper, problems encountered by one-bit LFMCW radar are studied and a two-stage target detection method termed as the dimension-reduced generalized approximate message passing (DR-GAMP) approach is proposed. Firstly, the spectrum of one-bit quantized signals in a scenario with multiple targets is analyzed. It is indicated that high-order harmonics may result in false alarms (FAs) and cannot be neglected. Secondly, based on the spectrum analysis, the DR-GAMP approach is proposed to carry out target detection. Specifically, linear preprocessing methods and target predetection are firstly adopted to perform the dimension reduction, and then, the GAMP algorithm is utilized to suppress high-order harmonics and recover true targets. Finally, numerical simulations are conducted to evaluate the performance of one-bit LFMCW radar under typical parameters. It is shown that compared to the conventional radar applying linear processing methods, one-bit LFMCW radar has about $1.3$ dB performance gain when the input signal-to-noise ratios (SNRs) of targets are low. In the presence of a strong target, it has about $1.0$ dB performance loss.
Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian models
Authors: Ludovic Goudenège, Andrea Molent, Antonino Zanette
Date: 2019-05-22 00:00:00
Tasks: BIG-bench Machine Learning, GPR

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black-Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed formula for integration. Moreover, these two methods solve the backward dynamic programming problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite this model is only bidimensional, the whole history of the process impacts on the price, and handling all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.
Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation
Date: 2019-05-23 00:00:00
Tasks: energy management, Management

The home energy management problem has many different facets, including
economic viability, data uncertainty and quality of strategy employed. The
existing literature in this area focuses on individual aspects of this problem
without a detailed, holistic analysis of the results with regards to
practicality in implementation. In this paper, we fill this gap by performing a
comprehensive comparison of seven different energy management strategies, each
with different levels of practicality, sophistication and computational
requirements. We analyse the results in the context of these three
characteristics, and also critique the modelling assumptions made by each
strategy. Our analysis finds that using a more sophisticated energy management
strategy may not necessarily improve the performance and economic viability of
the PV-battery system due to the effects of modeling assumptions, such as the
treatment of uncertainties in the input data and battery degradation effects.
Diagnosis and Prediction of the 2015 Chinese Stock Market Bubble
Date: 2019-06-13 00:00:00

In this study, we perform a novel analysis of the 2015 financial bubble in
the Chinese stock market by calibrating the Log Periodic Power Law Singularity
(LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early
2014 to June 2015. The back tests of the 2015 Chinese stock market bubbles
indicates that the LPPLS model can readily detect the bubble behavior of the
faster-than-exponential increase corrected by the accelerating
logarithm-periodic oscillations in the 2015 Chinese Stock market. The existence
of log-periodicity is detected by applying the Lomb spectral analysis on the
detrended residuals. The Ornstein-Uhlenbeck property and the stationarity of
the LPPLS fitting residuals are confirmed by the two Unit-root tests
(Philips-Perron test and Dickery-Fuller test). According to our analysis, the
actual critical day t_c can be well predicted by the LPPLS model as far back as
two months before the actual bubble crash. Compared to the traditional
optimization method used in the LPPLS model, we find the covariance matrix
adaptation evolution strategy (CMA-ES) to have a significantly lower
computation cost, and thus recommend this as a better alternative algorithm for
LPPLS model fit. Furthermore, in the LPPLS fitting with expanding windows, the
gap (tc -t2) shows a significant decrease when the end day t2 approaches the
actual bubble crash time. The change rate of the gap (tc-t2) may be used as an
additional indicator besides the key indicator tc to improve the prediction of
bubble burst.
Detection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator
Date: 2019-06-13 00:00:00
Tasks: Causal Identification

We present an advance bubble detection methodology based on the Log Periodic
Power Law Singularity (LPPLS) confidence indicator for the early causal
identification of positive and negative bubbles in the Chinese stock market
using the daily data on the Shanghai Shenzhen CSI 300 stock market index from
January 2002 through April 2018. We account for the damping condition of LPPLS
model in the search space and implement the stricter filter conditions for the
qualification of the valid LPPLS fits by taking account of the maximum relative
error, performing the Lomb log-periodic test of the detrended residual, and
unit-root tests of the logarithmic residual based on both the Phillips-Perron
test and Dickey-Fuller test to improve the performance of LPPLS confidence
indicator. Our analysis shows that the LPPLS detection strategy diagnoses the
positive bubbles and negative bubbles corresponding to well-known historical
events, implying the detection strategy based on the LPPLS confidence indicator
has an outstanding performance to identify the bubbles in advance. We find that
the probability density distribution of the estimated beginning time of bubbles
appears to be skewed and the mass of the distribution is concentrated on the
area where the price starts to have an obvious super-exponentially growth. This
study is the first work in the literature that identifies the existence of
bubbles in the Chinese stock market using the daily data of CSI 300 index with
the advance bubble detection methodology of LPPLS confidence indicator. We have
shown that it is possible to detect the potential positive and negative bubbles
and crashes ahead of time, which in turn limits the bubble sizes and eventually
minimizes the damages from the bubble crash.
Real-time Prediction of Bitcoin Bubble Crashes
Date: 2019-06-13 00:00:00
Tasks: Diagnostic, Prediction, Time Series, Time Series Analysis

In the past decade, Bitcoin as an emerging asset class has gained widespread
public attention because of their extraordinary returns in phases of extreme
price growth and their unpredictable massive crashes. We apply the log-periodic
power law singularity (LPPLS) confidence indicator as a diagnostic tool for
identifying bubbles using the daily data on Bitcoin price in the past two
years. We find that the LPPLS confidence indicator based on the daily Bitcoin
price data fails to provide effective warnings for detecting the bubbles when
the Bitcoin price suffers from a large fluctuation in a short time, especially
for positive bubbles. In order to diagnose the existence of bubbles and
accurately predict the bubble crashes in the cryptocurrency market, this study
proposes an adaptive multilevel time series detection methodology based on the
LPPLS model and finer (than daily) timescale for the Bitcoin price data. We
adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the
adaptive multilevel time series detection methodology. The results show that
the LPPLS confidence indicator based on this new method is an outstanding
instrument to effectively detect the bubbles and accurately forecast the bubble
crashes, even if a bubble exists in a short time. In addition, we discover that
the short-term LPPLS confidence indicator highly sensitive to the extreme
fluctuations of Bitcoin price can provide some useful insights into the bubble
status on a shorter time scale - on a day to week scale, and the long-term
LPPLS confidence indicator has a stable performance in terms of effectively
monitoring the bubble status on a longer time scale - on a week to month scale.
The adaptive multilevel time series detection methodology can provide real-time
detection of bubbles and advanced forecast of crashes to warn of the imminent
risk.
High-Mobility Massive MIMO Communications:Doppler Compensation and Transmit Diversity
Date: 2019-05-24 00:00:00
Tasks: Diversity, Vocal Bursts Intensity Prediction

High-mobility wireless communications have been receiving a lot attentions.
In this paper, we consider the highmobility uplink transmission from a
high-speed terminal (HST) to a base station (BS). We propose two transmit
diversity schemes for high-mobility massive communications with angle domain
Doppler compensation. In the first scheme, we propose the idea of block-wise
angle domain Doppler compensation, such that the uplink equivalent channel
would exhibit the feature of nearly perfect independent block fading. The
signal space diversity encoding technique is then exploited to achieve transmit
diversity. In the second scheme, the multiple parallel beamformers with Doppler
compensation are exclusively occupied to transmit multiple orthogonal
space-time block coding (OSTBC) data streams. Both analysis and numerical
results verify that both the proposed schemes can achieve transmit diversity
and outperform the conventional scheme.
Transmit Antenna Selection and Beamformer Design for Secure Spatial Modulation with rough CSI of Eve
Date: 2019-05-24 00:00:00

The security of spatial modulation (SM) aided networks can always be improved
by reducing the desired link's power at the cost of degrading its bit error
ratio performance and assuming the power consumed to artificial noise (AN)
projection (ANP). We formulate the joint optimization problem of maximizing the
secrecy rate (Max-SR) over the transmit antenna selection and ANP in the
context of secure SM-aided networks, which is mathematically a non-linear mixed
integer programming problem. In order to solve this problem, we provide a pair
of solutions, namely joint and separate solutions. Specifically, an accurate
approximation of the SR is used for reducing the computational complexity, and
the optimal AN covariance matrix (ANCM) is found by convex optimization for any
given active antenna group (AAG). Then, given a large set of AAGs, simulated
annealing mechanism is invoked for optimizing the choice of AAG, where the
corresponding ANCM is recomputed by this optimization method as well when the
AAG changes. To further reduce the complexity of the above-mentioned joint
optimization, a low-complexity two-stage separate optimization method is also
proposed. Furthermore, when the number of transmit antennas tends to infinity,
the Max-SR problem becomes equivalent to that of maximizing the ratio of the
desired user's signal-to-interference-plus-noise ratio to the eavesdropper's.
Thus our original problem reduces to a fractional programming problem, hence a
significant computational complexity reduction can be achieved for the
optimization problem. Our simulation results show that the proposed algorithms
outperform the existing leakage-based null-space projection scheme in terms of
the SR performance attained, and drastically reduces the complexity at a slight
SR performance reduction.
Information Rate in Ultra-Wideband Optical Fiber Communication Systems Accounting for High-Order Dispersion
Date: 2019-09-24 00:00:00

The effect of Kerr-induced optical fiber nonlinearities in C-band (5 THz)
EDFA and C+L-band (12.5 THz) Raman-amplified optical communication systems has
been studied considering the impact of third-order fiber dispersion. The
performance of digital nonlinearity compensation with single channel and
250-GHz bandwidth in both EDFA and Raman amplified systems has been
investigated, respectively. The achievable information rates (AIRs) and optimum
code rates in each individual transmission channel have been evaluated for the
DP-64QAM, the DP-256QAM and the DP-1024QAM modulation formats, both with and
without the use of the probabilistic shaping technique. It is found that, for
all considered modulation formats, the signal-to-noise ratios, AIRs and code
rates exhibit significantly asymmetric behavior about the central channel due
to the presence of the third-order dispersion. This provides a new insight that
the forward error correction schemes have to be optimized asymmetrically, on a
per-channel basis, to maximize the overall throughput.
Detection of Thalassaemia Carriers by Automated Feature Extraction of Dried Blood Drops
Date: 2019-05-24 00:00:00

Thalassaemia, triggered by defects in the globin genes, is one of the most
common monogenic diseases. The beta-thalassaemia carrier state is clinically
asymptomatic, thus, making it onerous to diagnose. The current gold standard
technique is implausible to be used for onsite carrier detection as the method
necessitates expensive instruments, skilled manpower and time. In this study,
we have tried to classify the carriers from the healthy samples based on their
blood droplet drying patterns using image analysis based tools and subsequently
develop an in-house program for automated classification of the same. This
automatic, rapid, less laborious and cost-effective technique will
significantly increase the total number of carriers that are screened for
thalassaemia per year in the country, thus, reducing the burden in the state
run advanced health facilities.
Information parity in complex networks
Date: 2019-05-28 00:00:00

A growing interest in complex networks theory results in an ongoing demand
for new analytical tools. We propose a novel measure based on information
theory that provides a new perspective for a better understanding of networked
systems: Termed "information parity," it quantifies the consonance of influence
among nodes with respect to the whole network architecture. Considering the
statistics of geodesic distances, information parity detects how similar a pair
of nodes can influence and be influenced by the network. This allows us to
quantify the quality of information gathered by the nodes. To demonstrate the
method's potential, we evaluate a social network and human brain networks. Our
results indicate that emerging phenomena like an ideological orientation of
nodes in a social network is severely influenced by their information parities.
We also show that anatomical brain networks have a greater information parity
in inter-hemispheric correspondent regions placed near the sagittal plane.
Finally, functional networks have, on average, greater information parity for
inter-hemispheric correspondent regions in comparison to the whole network. We
find that a pair of regions with high information parity exhibits higher
correlation, suggesting that the functional correlations between cortical
regions can be partially explained by the symmetry of their overall influences
of the whole brain.
A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization
Date: 2019-05-24 00:00:00
Tasks: Deep Learning

In this paper, we present a deep learning based wireless transceiver. We
describe in detail the corresponding artificial neural network architecture,
the training process, and report on excessive over-the-air measurement results.
We employ the end-to-end training approach with an autoencoder model that
includes a channel model in the middle layers as previously proposed in the
literature. In contrast to other state-of-the-art results, our architecture
supports learning time synchronization without any manually designed signal
processing operations. Moreover, the neural transceiver has been tested over
the air with an implementation in software defined radio. Our experimental
results for the implemented single antenna system demonstrate a raw bit-rate of
0.5 million bits per second. This exceeds results from comparable systems
presented in the literature and suggests the feasibility of high throughput
deep learning transceivers.
Doubling Phase Shifters for Efficient Hybrid Precoder Design in Millimeter-Wave Communication Systems
Date: 2019-05-25 00:00:00

Hybrid precoding is a cost-effective approach to support directional
transmissions for millimeter-wave (mm-wave) communications, but its precoder
design is highly complicated. In this paper, we propose a new hybrid precoder
implementation, namely the double phase shifter (DPS) implementation, which
enables highly tractable hybrid precoder design. Efficient algorithms are then
developed for two popular hybrid precoder structures, i.e., the fully- and
partially-connected structures. For the fully-connected one, the RF-only
precoding and hybrid precoding problems are formulated as a least absolute
shrinkage and selection operator (LASSO) problem and a low-rank matrix
approximation problem, respectively. In this way, computationally efficient
algorithms are provided to approach the performance of the fully digital one
with a small number of radio frequency (RF) chains. On the other hand, the
hybrid precoder design in the partially-connected structure is identified as an
eigenvalue problem. To enhance the performance of this cost-effective
structure, dynamic mapping from RF chains to antennas is further proposed, for
which a greedy algorithm and a modified K-means algorithm are developed.
Simulation results demonstrate the performance gains of the proposed hybrid
precoding algorithms over existing ones. It shows that, with the proposed DPS
implementation, the fully-connected structure enjoys both satisfactory
performance and low design complexity while the partially-connected one serves
as an economic solution with low hardware complexity.
Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
Date: 2019-12-16 00:00:00

Time-varying parameter (TVP) models have the potential to be
over-parameterized, particularly when the number of variables in the model is
large. Global-local priors are increasingly used to induce shrinkage in such
models. But the estimates produced by these priors can still have appreciable
uncertainty. Sparsification has the potential to reduce this uncertainty and
improve forecasts. In this paper, we develop computationally simple methods
which both shrink and sparsify TVP models. In a simulated data exercise we show
the benefits of our shrink-then-sparsify approach in a variety of sparse and
dense TVP regressions. In a macroeconomic forecasting exercise, we find our
approach to substantially improve forecast performance relative to shrinkage
alone.
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
Date: 2019-05-26 00:00:00
Tasks: Position, regression

We applied several regression and deep learning methods to predict fluid
intelligence scores from T1-weighted MRI scans as part of the ABCD
Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel
intensities and probabilistic tissue-type labels derived from these as features
to train the models. The best predictive performance (lowest mean-squared
error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a
mean-squared error of 69.7204 on the validation set and 92.1298 on the test
set. This placed our group in the fifth position on the validation leader board
and first place on the final (test) leader board.