pwc-archive/papers-with-abstracts huggingface dataset:
Online regularization of complex-valued neural networks for structure optimization in wireless-communication channel prediction
Date: 2019-09-17 00:00:00
Tasks: Prediction

This paper proposes online-learning complex-valued neural networks (CVNNs) to
predict future channel states in fast-fading multipath mobile communications.
CVNN is suitable for dealing with a fading communication channel as a single
complex-valued entity. This framework makes it possible to realize accurate
channel prediction by utilizing its high generalization ability in the complex
domain. However, actual communication environments are marked by rapid and
irregular changes, thus causing fluctuation of communication channel states.
Hence, an empirically selected stationary network gives only limited prediction
accuracy. In this paper, we introduce regularization in the update of the CVNN
weights to develop online dynamics that can self-optimize its effective network
size by responding to such channel-state changes. It realizes online adaptive,
highly accurate and robust channel prediction with dynamical adjustment of the
network size. We demonstrate its online adaptability in simulations and real
wireless-propagation experiments.
A Complexity-Efficient High Resolution Propagation Parameter Estimation Algorithm for Ultra-Wideband Large-Scale Uniform Circular Array
Date: 2019-02-12 00:00:00
Tasks: parameter estimation

Millimeter wave (mm-wave) communication with large-scale antenna array
configuration is seen as the key enabler of the next generation communication
systems. Accurate knowledge of the mm-wave propagation channels is fundamental
and essential. In this contribution, a novel complexity-efficient high
resolution parameter estimation (HRPE) algorithm is proposed for the mm-wave
channel with large-scale uniform circular array (UCA) applied. The proposed
algorithm is able to obtain the high-resolution estimation results of the
spherical channel propagation parameters. The prior channel information in the
delay domain, i.e., the delay trajectories of individual propagation paths
observed across the array elements, is exploited, by combining the
high-resolution estimation principle and the phase mode excitation technique.
Fast initializations, effective interference cancellations and reduced
searching spaces achieved by the proposed schemes significantly decrease the
algorithm complexity. Furthermore, the channel spatial non-stationarity in path
gain across the array elements is considered for the first time in the
literature for propagation parameter estimation, which is beneficial to obtain
more realistic results as well as to decrease the complexity. A mm-wave
measurement campaign at the frequency band of 28-30 GHz using a large-scale UCA
is exploited to demonstrate and validate the proposed HRPE algorithm.
Uplink Sum-Rate and Power Scaling Laws for Multi-User Massive MIMO-FBMC Systems
Date: 2019-06-12 00:00:00

This paper analyses the performance of filter bank multicarrier (FBMC)
signaling in conjunction with offset quadrature amplitude modulation (OQAM) in
multi-user (MU) massive multiple-input multiple-output (MIMO) systems.
Initially, closed form expressions are derived for tight lower bounds
corresponding to the achievable uplink sum-rates for FBMC-based single-cell MU
massive MIMO systems relying on maximum ratio combining (MRC), zero forcing
(ZF) and minimum mean square error (MMSE) receiver processing with/without
perfect channel state information (CSI) at the base station (BS). This is
achieved by exploiting the statistical properties of the intrinsic interference
that is characteristic of FBMC systems. Analytical results are also developed
for power scaling in the uplink of MU massive MIMO-FBMC systems. The above
analysis of the achievable sum-rates and corresponding power scaling laws is
subsequently extended to multi-cell scenarios considering both perfect as well
as imperfect CSI, and the effect of pilot contamination. The
delay-spread-induced performance erosion imposed on the linear processing aided
BS receiver is numerically quantified by simulations. Numerical results are
presented to demonstrate the close match between our analysis and simulations,
and to illustrate and compare the performance of FBMC and traditional
orthogonal frequency division multiplexing (OFDM)-based MU massive MIMO
systems.
Optimizing Ranking Models in an Online Setting
Authors: Oosterhuis Harrie, de Rijke Maarten
Date: 2019-01-29 00:00:00
Tasks: Learning-To-Rank

Online Learning to Rank (OLTR) methods optimize ranking models by directly
interacting with users, which allows them to be very efficient and responsive.
All OLTR methods introduced during the past decade have extended on the
original OLTR method: Dueling Bandit Gradient Descent (DBGD). Recently, a
fundamentally different approach was introduced with the Pairwise
Differentiable Gradient Descent (PDGD) algorithm. To date the only comparisons
of the two approaches are limited to simulations with cascading click models
and low levels of noise. The main outcome so far is that PDGD converges at
higher levels of performance and learns considerably faster than DBGD-based
methods. However, the PDGD algorithm assumes cascading user behavior,
potentially giving it an unfair advantage. Furthermore, the robustness of both
methods to high levels of noise has not been investigated. Therefore, it is
unclear whether the reported advantages of PDGD over DBGD generalize to
different experimental conditions. In this paper, we investigate whether the
previous conclusions about the PDGD and DBGD comparison generalize from ideal
to worst-case circumstances. We do so in two ways. First, we compare the
theoretical properties of PDGD and DBGD, by taking a critical look at
previously proven properties in the context of ranking. Second, we estimate an
upper and lower bound on the performance of methods by simulating both ideal
user behavior and extremely difficult behavior, i.e., almost-random
non-cascading user models. Our findings show that the theoretical bounds of
DBGD do not apply to any common ranking model and, furthermore, that the
performance of DBGD is substantially worse than PDGD in both ideal and
worst-case circumstances. These results reproduce previously published findings
about the relative performance of PDGD vs. DBGD and generalize them to
extremely noisy and non-cascading circumstances.
Near-field Ultra-wideband mmWave Channel Characterization Using Successive Cancellation Beamspace UCA Algorithm
Date: 2019-01-29 00:00:00

Of the wide palette of 5G features, ultra-wide bandwidth and large-scale
antenna configuration are regarded as the essential enabling technology
components at millimeter wave (mmWave) communication. Accurate knowledge of
delay and angle information of multipath components is essential for many
applications in mmWave systems. There is a strong need for a low
computation-cost channel estimation algorithm for such systems, where typically
adopted far-field and narrowband assumptions might be violated. In this work, a
generic yet novel beamspace uniform circular array (UCA) beamforming algorithm
with successive cancellation scheme is proposed to jointly detect the impinging
angle and delay of the multipath components. The proposed algorithm is
computationally cheap and it works for ultra-wideband (UWB) systems in the
near-field conditions. Both numerical simulations and experimental validation
results are provided to demonstrate the effectiveness and robustness of the
proposed algorithm, compared to the state-of-art works.
Voltage Profile Improvement of Distribution Grid by Using a New Control Approach on Injected Reactive Power of Plug-in Electric Vehicle Parking Lots to Grid
Date: 2019-01-29 00:00:00

Establishing electrical parking lots will become more important by increasing
the use of electric vehicles (EVs). These parking lots not only can be seen as
high electrical power consumption loads which can cause the voltage drop at
feeders but also they can be used as electrical power plants that can help the
main power grid during the load peak hour or any congestion. Therefore, finding
an optimum place for the building of these lots, in which the deviation of the
voltage at the feeder, power loss in the grid, and cost be minimized, is
essential. In this paper, a new method of vehicle-to-grid reactive power
support (V2GQ) has been used to add the model of plug-in electric vehicle (PEV)
as one part of the objective function to find the optimum place of parking
lots. The non-sorting genetic algorithm II (NSGA2) is used here as an
optimization algorithm to find the optimum voltage profile based on the
location of parking lots. For validation of the purposed method, a 33-bus
standard distribution network has been studied.
MIMO Applications for Multibeam Satellites
Date: 2019-01-29 00:00:00

High throughput satellites employing multibeam antennas and full frequency
reuse for broadband satellite services are considered in this paper. Such
architectures offer, for example, a cost-effective solution to optimize data
delivery and extend the coverage areas in future 5G networks. We propose the
application of the multiple-input-multiple-output (MIMO) technology in both the
feeder link and the multiuser downlink. Spatial multiplexing of different data
streams is performed in a common feeder beam. In the user links, MIMO with
multiple beams is exploited to simultaneously serve different users in the same
frequency channel. Under particular design constraints, effective spatial
separation of the multiple user signals is possible. To mitigate the
interstream interference in the MIMO feeder link as well as the multiuser
downlink, precoding of the transmit signals is applied. Simulation results
illustrate the performance gains in terms of sum throughput.
Stochastic modelling reveals mechanisms of metabolic heterogeneity
Date: 2019-01-29 00:00:00

Phenotypic variation is a hallmark of cellular physiology. Metabolic
heterogeneity, in particular, underpins single-cell phenomena such as microbial
drug tolerance and growth variability. Much research has focussed on
transcriptomic and proteomic heterogeneity, yet it remains unclear if such
variation permeates to the metabolic state of a cell. Here we propose a
stochastic model to show that complex forms of metabolic heterogeneity emerge
from fluctuations in enzyme expression and catalysis. The analysis predicts
clonal populations to split into two or more metabolically distinct
subpopulations. We reveal mechanisms not seen in deterministic models, in which
enzymes with unimodal expression distributions lead to metabolites with a
bimodal or multimodal distribution across the population. Based on published
data, the results suggest that metabolite heterogeneity may be more pervasive
than previously thought. Our work casts light on links between gene expression
and metabolism, and provides a theory to probe the sources of metabolite
heterogeneity.
Persuasion-based Robust Sensor Design Against Attackers with Unknown Control Objectives
Date: 2020-09-11 00:00:00

In this paper, we introduce a robust sensor design framework to provide
"persuasion-based" defense in stochastic control systems against an unknown
type attacker with a control objective exclusive to its type. For effective
control, such an attacker's actions depend on its belief on the underlying
state of the system. We design a robust "linear-plus-noise" signaling strategy
to encode sensor outputs in order to shape the attacker's belief in a strategic
way and correspondingly to persuade the attacker to take actions that lead to
minimum damage with respect to the system's objective. The specific model we
adopt is a Gauss-Markov process driven by a controller with a (partially)
"unknown" malicious/benign control objective. We seek to defend against the
worst possible distribution over control objectives in a robust way under the
solution concept of Stackelberg equilibrium, where the sensor is the leader. We
show that a necessary and sufficient condition on the covariance matrix of the
posterior belief is a certain linear matrix inequality and we provide a
closed-form solution for the associated signaling strategy. This enables us to
formulate an equivalent tractable problem, indeed a semi-definite program, to
compute the robust sensor design strategies "globally" even though the original
optimization problem is non-convex and highly nonlinear. We also extend this
result to scenarios where the sensor makes noisy or partial measurements.
Finally, we analyze the ensuing performance numerically for various scenarios.
Using Score Distributions to Compare Statistical Significance Tests for Information Retrieval Evaluation
Authors: Parapar Javier, Losada David E., Presedo-Quindimil Manuel A., Barreiro Alvaro
Date: 2019-01-30 00:00:00
Tasks: Information Retrieval, Retrieval

Statistical significance tests can provide evidence that the observed
difference in performance between two methods is not due to chance. In
Information Retrieval, some studies have examined the validity and suitability
of such tests for comparing search systems. We argue here that current methods
for assessing the reliability of statistical tests suffer from some
methodological weaknesses, and we propose a novel way to study significance
tests for retrieval evaluation. Using Score Distributions, we model the output
of multiple search systems, produce simulated search results from such models,
and compare them using various significance tests. A key strength of this
approach is that we assess statistical tests under perfect knowledge about the
truth or falseness of the null hypothesis. This new method for studying the
power of significance tests in Information Retrieval evaluation is formal and
innovative. Following this type of analysis, we found that both the sign test
and Wilcoxon signed test have more power than the permutation test and the
t-test. The sign test and Wilcoxon signed test also have a good behavior in
terms of type I errors. The bootstrap test shows few type I errors, but it has
less power than the other methods tested.
A new PIR-based method for real-time tracking
Date: 2019-12-23 00:00:00

Pyroelectric infrared (PIR) sensors are considered to be promising devices
for device-free localization due to its advantages of low cost, less intrusive,
and the immunity from multi-path fading. However, most of the existing
PIR-based localization systems only utilize the binary information of PIR
sensors and therefore require a large number of PIR sensors and a careful
deployment. A few works directly map the raw data of PIR sensors to one's
location using machine learning approaches. However, these approaches require
to collect abundant training data and suffer from environmental change. In this
paper, we propose a PIR-based device-free localization approach based on the
raw data of PIR sensors. The key of this approach is to extract a new type of
location information called as the azimuth change. The extraction of the
azimuth change relies on the physical properties of PIR sensors. Therefore, no
abundant training data are needed and the system is robust to environmental
change. Through experiments, we demonstrated that a device-free localization
system incorporating the information of azimuth change outperforms the
state-of-the-art approaches in terms of higher location accuracy. In addition,
the information of the azimuth change can be easily integrated with other
PIR-based localization systems to improve their localization accuracy.
Minimal Investment Risk with Cost and Return Constraints: A Replica Analysis
Date: 2019-01-30 00:00:00
Tasks: Portfolio Optimization

Previous studies into the budget constraint of portfolio optimization
problems based on statistical mechanical informatics have not considered that
the purchase cost per unit of each asset is distinct. Moreover, the fact that
the optimal investment allocation differs depending on the size of investable
funds has also been neglected. In this paper, we approach the problem of
investment risk minimization using replica analysis. This problem imposes cost
and return constraints. We also derive the macroscopic theory indicated by the
optimal solution and confirm the validity of our proposed method through
numerical experiments.
Pressure Actuated Cellular Structures
Date: 2020-08-22 00:00:00

This postdoctoral thesis starts by reviewing the historic development of
airplane structures and high lift devices from an engineering point of view.
However, the main purpose of this document is the development of a novel
concept for shape changing, gapless high lift devices that is inspired by the
nastic movement of plants. A particular focus is put on the efficient
simulation and optimization of compliant pressure actuated cellular structures.
CoreNEURON : An Optimized Compute Engine for the NEURON Simulator
Date: 2019-01-30 00:00:00
Tasks: GPU

The NEURON simulator has been developed over the past three decades and is
widely used by neuroscientists to model the electrical activity of neuronal
networks. Large network simulation projects using NEURON have supercomputer
allocations that individually measure in the millions of core hours.
Supercomputer centers are transitioning to next generation architectures and
the work accomplished per core hour for these simulations could be improved by
an order of magnitude if NEURON was able to better utilize those new hardware
capabilities. In order to adapt NEURON to evolving computer architectures, the
compute engine of the NEURON simulator has been extracted and has been
optimized as a library called CoreNEURON. This paper presents the design,
implementation and optimizations of CoreNEURON. We describe how CoreNEURON can
be used as a library with NEURON and then compare performance of different
network models on multiple architectures including IBM BlueGene/Q, Intel
Skylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existing
NEURON network models with 4-7x less memory usage and 2-7x less execution time
while maintaining binary result compatibility with NEURON.
Equilibrium Asset Pricing with Transaction Costs
Authors: Martin Herdegen, Johannes Muhle-Karbe, Dylan Possamaï
Date: 2019-01-30 00:00:00

We study risk-sharing economies where heterogenous agents trade subject to quadratic transaction costs. The corresponding equilibrium asset prices and trading strategies are characterised by a system of nonlinear, fully-coupled forward-backward stochastic differential equations. We show that a unique solution generally exists provided that the agents' preferences are sufficiently similar. In a benchmark specification with linear state dynamics, the illiquidity discounts and liquidity premia observed empirically correspond to a positive relationship between transaction costs and volatility.
3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries
Date: 2019-12-17 00:00:00
Tasks: Physical Simulations

Recent advances in electron microscopy have enabled the imaging of single
cells in 3D at nanometer length scale resolutions. An uncharted frontier for in
silico biology is the ability to simulate cellular processes using these
observed geometries. Enabling such simulations requires watertight meshing of
electron micrograph images into 3D volume meshes, which can then form the basis
of computer simulations of such processes using numerical techniques such as
the Finite Element Method. In this paper, we describe the use of our recently
rewritten mesh processing software, GAMer 2, to bridge the gap between poorly
conditioned meshes generated from segmented micrographs and boundary marked
tetrahedral meshes which are compatible with simulation. We demonstrate the
application of a workflow using GAMer 2 to a series of electron micrographs of
neuronal dendrite morphology explored at three different length scales and show
that the resulting meshes are suitable for finite element simulations. This
work is an important step towards making physical simulations of biological
processes in realistic geometries routine. Innovations in algorithms to
reconstruct and simulate cellular length scale phenomena based on emerging
structural data will enable realistic physical models and advance discovery at
the interface of geometry and cellular processes. We posit that a new frontier
at the intersection of computational technologies and single cell biology is
now open.
Top performing stocks recommendation strategy for portfolio
Date: 2019-08-10 00:00:00
Tasks: regression

Stock return forecasting is of utmost importance in the business world. This
has been the favourite topic of research for many academicians since decades.
Recently, regularization techniques have reported to tremendously increase the
forecast accuracy of the simple regression model. Still, this model cannot
incorporate the effect of things like a major natural disaster, large foreign
influence, etc. in its prediction. Such things affect the whole stock market
and are very unpredictable. Thus, it is more important to recommend top stocks
rather than predicting exact stock returns. The present paper modifies the
regression task to output value for each stock which is more suitable for
ranking the stocks by expected returns. Two large datasets consisting of
altogether 1205 companies listed at Indian exchanges were used for
experimentation. Five different metrics were used for evaluating the different
models. Results were also analysed subjectively through plots. The results
showed the superiority of the proposed techniques.
Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations
Date: 2019-10-17 00:00:00
Tasks: regression

Modeling counterparty risk is computationally challenging because it requires
the simultaneous evaluation of all the trades with each counterparty under both
market and credit risk. We present a multi-Gaussian process regression
approach, which is well suited for OTC derivative portfolio valuation involved
in CVA computation. Our approach avoids nested simulation or simulation and
regression of cash flows by learning a Gaussian metamodel for the
mark-to-market cube of a derivative portfolio. We model the joint posterior of
the derivatives as a Gaussian process over function space, with the spatial
covariance structure imposed on the risk factors. Monte-Carlo simulation is
then used to simulate the dynamics of the risk factors. The uncertainty in
portfolio valuation arising from the Gaussian process approximation is
quantified numerically. Numerical experiments demonstrate the accuracy and
convergence properties of our approach for CVA computations, including a
counterparty portfolio of interest rate swaps.
On the Sum of Fisher-Snedecor F Variates and its Application to Maximal-Ratio Combining
Date: 2019-01-30 00:00:00

Capitalizing on the recently proposed Fisher-Snedecor F composite fading
model, in this letter, we investigate the sum of independent but not
identically distributed (i.n.i.d.) Fisher-Snedecor F variates. First, a novel
closed-form expression is derived for the moment generating function of the
instantaneous signal-to-noise ratio. Based on this, the corresponding
probability density function and cumulative distribution function of the sum of
i.n.i.d. Fisher- Snedecor F variates are derived, which are subsequently
employed in the analysis of multiple branch maximal-ratio combining (MRC).
Specifically, we investigate the impact of multipath and shadowed fading on the
outage probability and outage capacity of MRC based receivers. In addition, we
derive exact closed-form expressions for the average bit error rate of coherent
binary modulation schemes followed by an asymptotic analysis which provides
further insights into the effect of the system parameters on the overall
performance. Importantly, it is shown that the effect of multipath fading on
the system performance is more pronounced than that of shadowing.