pwc-archive/papers-with-abstracts huggingface dataset:
Mixed Quality of Service in Cell-Free Massive MIMO
Date: 2018-06-19 00:00:00

Cell-free massive multiple-input multiple-output (MIMO) is a potential key
technology for fifth generation wireless communication networks. A mixed
quality-of-service (QoS) problem is investigated in the uplink of a cell-free
massive MIMO system where the minimum rate of non-real time users is maximized
with per user power constraints whilst the rate of the real-time users (RTUs)
meet their target rates. First an approximated uplink user rate is derived
based on available channel statistics. Next, the original mixed QoS problem is
formulated in terms of receiver filter coefficients and user power allocations
which can iteratively be solved through two sub-problems, namely, receiver
filter coefficient design and power allocation, which are dealt with using a
generalized eigenvalue problem and geometric programming, respectively.
Numerical results show that with the proposed scheme, while the rates of RTUs
meet the QoS constraints, the $90\%$-likely throughput improves significantly,
compared to a simple benchmark scheme.
Explicit solutions to utility maximization problems in a regime-switching market model via Laplace transforms
Date: 2018-04-23 00:00:00

We study the problem of utility maximization from terminal wealth in which an
agent optimally builds her portfolio by investing in a bond and a risky asset.
The asset price dynamics follow a diffusion process with regime-switching
coefficients modeled by a continuous-time finite-state Markov chain. We
consider an investor with a Constant Relative Risk Aversion (CRRA) utility
function. We deduce the associated Hamilton-Jacobi-Bellman equation to
construct the solution and the optimal trading strategy and verify optimality
by showing that the value function is the unique constrained viscosity solution
of the HJB equation. By means of a Laplace transform method, we show how to
explicitly compute the value function and illustrate the method with the two-
and three-states cases. This method is interesting in its own right and can be
adapted in other applications involving hybrid systems and using other types of
transforms with basic properties similar to the Laplace transform.
An accumulator model for primes and targets with independent response activation rates: Basic equations for average response times
Date: 2018-04-24 00:00:00

In response priming tasks, speeded responses are performed toward target
stimuli preceded by prime stimuli. Responses are slower and error rates are
higher when prime and target are assigned to different responses, compared to
assignment to the same response, and those priming effects increase with
prime-target SOA. Here, we generalize Vorberg et al.'s (2003) accumulator model
of response priming, where response activation is first controlled exclusively
by the prime and then taken over by the actual target. Priming thus occurs by
motor conflict because a response-inconsistent prime can temporarily drive the
process towards the incorrect response. While the original model assumed prime
and target signals to be identical in strength, we allow different rates of
response activation (cf. Mattler & Palmer, 2012; Schubert et al., 2012). Our
model predicts that stronger primes mainly increase priming effects in response
times and error rates, whereas stronger targets mainly diminish response times
and priming effects.
On the Impact of Fixed Point Hardware for Optical Fiber Nonlinearity Compensation Algorithms
Date: 2018-04-20 00:00:00
Tasks: Quantization

Nonlinearity mitigation using digital signal processing has been shown to
increase the achievable data rates of optical fiber transmission links. One
especially effective technique is digital back propagation (DBP), an algorithm
capable of simultaneously compensating for linear and nonlinear channel
distortions. The most significant barrier to implementing this technique,
however, is its high computational complexity. In recent years, there have been
several proposed alternatives to DBP with reduced computational complexity,
although such techniques have not demonstrated performance benefits
commensurate with the complexity of implementation. In order to fully
characterize the computational requirements of DBP, there is a need to model
the algorithm behavior when constrained to the logic used in a digital coherent
receiver. Such a model allows for the analysis of any signal recovery algorithm
in terms of true hardware complexity which, crucially, includes the bit-depth
of the multiplication operation. With a limited bit depth, there is
quantization noise, introduced with each arithmetic operation, and it can no
longer be assumed that the conventional DBP algorithm will outperform its low
complexity alternatives. In this work, DBP and a single nonlinear step DBP
implementation, the \textit{Enhanced Split Step Fourier} method (ESSFM), were
compared with linear equalization using a generic software model of fixed point
hardware. The requirements of bit depth and fast Fourier transform (FFT) size
are discussed to examine the optimal operating regimes for these two schemes of
digital nonlinearity compensation. For a 1000 km transmission system, it was
found that (assuming an optimized FFT size), in terms of SNR, the ESSFM
algorithm outperformed the conventional DBP for all hardware resolutions up to
13 bits.
Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
Date: 2018-04-23 00:00:00
Tasks: Quantization

In this paper, we propose a Singular-Value-Decomposition-based
variable-resolution Analog to Digital Converter (ADC) bit allocation design for
a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver.
We derive the optimality condition for bit allocation under a power constraint.
This condition ensures optimal receiver performance in the Mean Squared Error
(MSE) sense. We derive the MSE expression and show that it approaches the
Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog
combiner, the digital combiner, and the bit allocation matrix. We attempt to
minimize the CRLB with respect to the bit allocation matrix by making suitable
assumptions regarding the structure of the combiners. In doing so, the bit
allocation design reduces to a set of simple inequalities consisting of ADC
bits, channel singular values and covariance of the quantization noise along
each RF path. This results in a simple and computationally efficient bit
allocation algorithm. Using simulations, we show that the MSE performance of
our proposed bit allocation is very close to that of the Full Search (FS) bit
allocation. We also show that the computational complexity of our proposed
method has an order of magnitude improvement compared to FS and Genetic
Algorithm based bit allocation of $\cite{Zakir1}$
Constrained optimal design of automotive radar arrays using the Weiss-Weinstein Bound
Date: 2018-04-23 00:00:00

We propose a design strategy for optimizing antenna positions in linear
arrays for far-field Direction of Arrival (DoA) estimation of narrow-band
sources in collocated MIMO radar. Our methodology allows to consider any
spatial constraints and number of antennas, using as optimization function the
Weiss- Weinstein bound formulated for an observation model with random target
phase and known SNR, over a pre-determined Field-of-View (FoV). Optimized
arrays are calculated for the typical case of a 77GHz MIMO radar of 3Tx and 4Rx
channels. Simulations demonstrate a performance improvement of the proposed
arrays compared to the corresponding uniform and minimum redundancy arrays for
a wide regime of SNR values.
Adaptive Beam Tracking with the Unscented Kalman Filter for Millimeter Wave Communication
Date: 2018-04-23 00:00:00

Millimeter wave (mmWave) communication links for 5G cellular technology
require high beamforming gain to overcome channel impairments and achieve high
throughput. While much work has focused on estimating mmWave channels and
designing beamforming schemes, the time dynamic nature of mmWave channels
quickly renders estimates stale and increases sounding overhead. We model the
underlying time dynamic state space of mmWave channels and design sounding
beamformers suitable for tracking in a Kalman filtering framework. Given an
initial channel estimate, filtering efficiently leads to refined estimates and
allows forward prediction for higher sustained beamforming gain during data
transmission. From tracked prior channel estimates, adaptively chosen optimal
and constrained suboptimal beams reduce sounding overhead while minimizing
estimation error.
A Theoretical Limit to Physicalism: A Non-Technical Explanation of the Gemini Theorem
Date: 2018-04-10 00:00:00

The Gemini theorem asserts that, given certain reasonable assumptions, no
physical system can be certainly aware of its own existence. The theorem can be
proved algorithmically, but the proof of this theorem is somewhat obscure, and
there exists very little literature on it. The purpose of this article is to
provide a brief non-technical summary of the theorem and its proof, with a view
to stimulating critical discussion of the proof and its implications. Since the
theorem implies that a violation of the conservation of energy will take place
within the brains of conscious human beings, it has obvious implications for
any physical theory.
A multi-level collaborative filtering method that improves recommendations
Authors: Polatidis Nikolaos, Georgiadis Christos K.
Date: 2018-04-24 00:00:00
Tasks: Collaborative Filtering, Decision Making, Recommendation Systems

Collaborative filtering is one of the most used approaches for providing
recommendations in various online environments. Even though collaborative
recommendation methods have been widely utilized due to their simplicity and
ease of use, accuracy is still an issue. In this paper we propose a multi-level
recommendation method with its main purpose being to assist users in decision
making by providing recommendations of better quality. The proposed method can
be applied in different online domains that use collaborative recommender
systems, thus improving the overall user experience. The efficiency of the
proposed method is shown by providing an extensive experimental evaluation
using five real datasets and with comparisons to alternatives.
Closed-form approximations in derivatives pricing: The Kristensen-Mele approach
Date: 2018-04-24 00:00:00
Tasks: Form

Kristensen and Mele (2011) developed a new approach to obtain closed-form
approximations to continuous-time derivatives pricing models. The approach uses
a power series expansion of the pricing bias between an intractable model and
some known auxiliary model. Since the resulting approximation formula has
closed-form it is straightforward to obtain approximations of greeks. In this
thesis I will introduce Kristensen and Mele's methods and apply it to a variety
of stochastic volatility models of European style options as well as a model
for commodity futures. The focus of this thesis is the effect of different
model choices and different model parameter values on the numerical stability
of Kristensen and Mele's approximation.
Compact finite difference method for pricing European and American options under jump-diffusion models
Date: 2018-04-23 00:00:00

In this article, a compact finite difference method is proposed for pricing
European and American options under jump-diffusion models. Partial
integro-differential equation and linear complementary problem governing
European and American options respectively are discretized using Crank-Nicolson
Leap-Frog scheme. In proposed compact finite difference method, the second
derivative is approximated by the value of unknowns and their first derivative
approximations which allow us to obtain a tri-diagonal system of linear
equations for the fully discrete problem. Further, consistency and stability
for the fully discrete problem are also proved. Since jump-diffusion models do
not have smooth initial conditions, the smoothing operators are employed to
ensure fourth-order convergence rate. Numerical illustrations for pricing
European and American options under Merton jump-diffusion model are presented
to validate the theoretical results.
Emerging Market Corporate Bonds as First-to-Default Baskets
Date: 2018-04-24 00:00:00

Emerging market hard-currency bonds are an asset class of growing importance,
and contain exposure to an EM sovereign and the underlying industry. The
authors investigate how to model this as a modification of the well-known
first-to-default (FtD) basket, using the structural model, and find the
approach feasible.
Optimal segregation of proteins: phase transitions and symmetry breaking
Date: 2018-04-24 00:00:00

Asymmetric segregation of key proteins at cell division -- be it a beneficial
or deleterious protein -- is ubiquitous in unicellular organisms and often
considered as an evolved trait to increase fitness in a stressed environment.
Here, we provide a general framework to describe the evolutionary origin of
this asymmetric segregation. We compute the population fitness as a function of
the protein segregation asymmetry $a$, and show that the value of $a$ which
optimizes the population growth manifests a phase transition between symmetric
and asymmetric partitioning phases. Surprisingly, the nature of phase
transition is different for the case of beneficial proteins as opposed to
proteins which decrease the single-cell growth rate. Our study elucidates the
optimization problem faced by evolution in the context of protein segregation,
and motivates further investigation of asymmetric protein segregation in
biological systems.
Economic inequality and Islamic Charity: An exploratory agent-based modeling approach
Date: 2018-04-24 00:00:00

Economic inequality is one of the pivotal issues for most of economic and
social policy makers across the world to insure the sustainable economic growth
and justice. In the mainstream school of economics, namely neoclassical
theories, economic issues are dealt with in a mechanistic manner. Such a
mainstream framework is majorly focused on investigating a socio-economic
system based on an axiomatic scheme where reductionism approach plays a vital
role. The major limitations of such theories include unbounded rationality of
economic agents, reducing the economic aggregates to a set of predictable
factors and lack of attention to adaptability and the evolutionary nature of
economic agents. In tackling deficiencies of conventional economic models, in
the past two decades, some new approaches have been recruited. One of those
novel approaches is the Complex adaptive systems (CAS) framework which has
shown a very promising performance in action. In contrast to mainstream school,
under this framework, the economic phenomena are studied in an organic manner
where the economic agents are supposed to be both boundedly rational and
adaptive. According to it, the economic aggregates emerge out of the ways
agents of a system decide and interact. As a powerful way of modeling CASs,
Agent-based models (ABMs) has found a growing application among academicians
and practitioners. ABMs show that how simple behavioral rules of agents and
local interactions among them at micro-scale can generate surprisingly complex
patterns at macro-scale. In this paper, ABMs have been used to show (1) how an
economic inequality emerges in a system and to explain (2) how sadaqah as an
Islamic charity rule can majorly help alleviating the inequality and how
resource allocation strategies taken by charity entities can accelerate this
alleviation.
Joint Channel Estimation and User Grouping for Massive MIMO Systems
Date: 2018-11-21 00:00:00

This paper addresses the problem of joint downlink channel estimation and
user grouping in massive multiple-input multiple-output (MIMO) systems, where
the motivation comes from the fact that the channel estimation performance can
be improved if we exploit additional common sparsity among nearby users. In the
literature, a commonly used group sparsity model assumes that users in each
group share a uniform sparsity pattern. In practice, however, this
oversimplified assumption usually fails to hold, even for physically close
users. Outliers deviated from the uniform sparsity pattern in each group may
significantly degrade the effectiveness of common sparsity, and hence bring
limited (or negative) gain for channel estimation. To better capture the group
sparse structure in practice, we provide a general model having two sparsity
components: commonly shared sparsity and individual sparsity, where the
additional individual sparsity accounts for any outliers. Then, we propose a
novel sparse Bayesian learning (SBL)-based framework to address the joint
channel estimation and user grouping problem under the general sparsity model.
The framework can fully exploit the common sparsity among nearby users and
exclude the harmful effect from outliers simultaneously. Simulation results
reveal substantial performance gains over the existing state-of-the-art
baselines.
Chocs technologiques, chocs des prix et fluctuations du ch\^omage en R\'epublique D\'emocratique du Congo
Date: 2018-04-23 00:00:00

The main purpose of this research is to analyze the effects of macroeconomic
shocks on unemployment fluctuations in the Democratic Republic of Congo (DRC).
Using the SVECM model on DRC data for the period 1960 to 2014, the conclusion
is that the high and persistent level of unemployment is mainly explained
permanently by technological and price shocks.
Critical analysis of human progress: Its negative and positive sides in the late-capitalism
Date: 2018-04-24 00:00:00

The concept of progress has characterized human society from millennia.
However, this concept is elusive and too often given for certain. The goal of
this paper is to suggest a general definition of human progress that satisfies,
whenever possible the conditions of independence, generality, epistemological
applicability and empirical correctness. This study proposes, within a
pragmatic approach, human progress as an inexhaustible process driven by an
ideal of maximum wellbeing of purposeful people which, on attainment of any of
its goals or objectives for increasing wellbeing, then seek another
consequential goal and objective, endlessly, which more closely approximates
its ideal fixed in new socioeconomic contexts over time and space. The human
progress, in the global, capitalistic, and post-humanistic Era, improves the
fundamental life-interests represented by health, wealth, expansion of
knowledge, technology and freedom directed to increase wellbeing throughout the
society. These factors support the acquisition by humanity of better and more
complex forms of life. However, this study shows the inconsistency of the
equation economic growth= progress because human progress also generates,
during its continuous process without limit, negative effects for human being,
environment and society.
An initial exploration of vicarious and in-scene calibration techniques for small unmanned aircraft systems
Date: 2018-04-25 00:00:00

The use of small unmanned aircraft systems (sUAS) for applications in the
field of precision agriculture has demonstrated the need to produce temporally
consistent imagery to allow for quantitative comparisons. In order for these
aerial images to be used to identify actual changes on the ground, conversion
of raw digital count to reflectance, or to an atmospherically normalized space,
needs to be carried out. This paper will describe an experiment that compares
the use of reflectance calibration panels, for use with the empirical line
method (ELM), against a newly proposed ratio of the target radiance and the
downwelling radiance, to predict the reflectance of known targets in the scene.
We propose that the use of an on-board downwelling light sensor (DLS) may
provide the sUAS remote sensing practitioner with an approach that does not
require the expensive and time consuming task of placing known reflectance
standards in the scene. Three calibration methods were tested in this study:
2-Point ELM, 1-Point ELM, and At-altitude Radiance Ratio (AARR). Our study
indicates that the traditional 2-Point ELM produces the lowest mean error in
band effective reflectance factor, 0.0165. The 1-Point ELM and AARR produce
mean errors of 0.0343 and 0.0287 respectively. A modeling of the proposed AARR
approach indicates that the technique has the potential to perform better than
the 2-Point ELM method, with a 0.0026 mean error in band effective reflectance
factor, indicating that this newly proposed technique may prove to be a viable
alternative with suitable on-board sensors.
Open(G)PIAS: An open source solution for the construction of a high-precision Acoustic-Startle-Response (ASR) setup for tinnitus screening and threshold estimation in rodents
Date: 2018-04-25 00:00:00

The acoustic startle reflex (ASR) that can be induced by a loud sound
stimulus can be used as a versatile tool to, e.g., estimate hearing thresholds
or identify subjective tinnitus percepts in rodents. These techniques are based
on the fact that the ASR amplitude can be suppressed by a pre-stimulus of
lower, non-startling intensity, an effect named pre-pulse inhibition (PPI). For
hearing threshold estimation, pure tone pre-stimuli of varying amplitudes are
presented before an intense noise burst serving as startle stimulus. The amount
of suppression of the ASR amplitude as a function of the pre-stimulus intensity
can be used as a behavioral correlate to determine the hearing ability. For
tinnitus assessment, the pure-tone pre-stimulus is replaced by a gap of silence
in a narrowband noise background, a paradigm termed GPIAS (gap-pre-pulse
inhibition of the acoustic startle response). A proper application of these
paradigms depend on a reliable measurement of the ASR amplitudes, an exact
stimulus presentation in terms of frequency and intensity. Here we introduce a
novel open source solution for the construction of a low-cost ASR setup for the
above mentioned purpose. The complete software for data acquisition and
stimulus presentation is written in Python 3.6 and is provided as an anaconda
package. Furthermore, we provide a construction plan for the sensory system
based on low-cost hardware components. Exemplary data show that the ratios
(1-PPI) of the pre and post trauma ASR amplitudes can be well described by a
lognormal distribution being in good accordance to previous studies with
already established setups. Hence, the open access solution described here will
help to further establish the ASR method in many laboratories and thus
facilitate and standardize research in animal models of tinnitus or hearing
loss.
Artificial Intelligence-Based Techniques for Emerging Robotics Communication: A Survey and Future Perspectives
Date: 2018-04-28 00:00:00

This paper reviews the current development of artificial intelligence (AI)
techniques for the application area of robot communication. The study of the
control and operation of multiple robots collaboratively toward a common goal
is fast growing. Communication among members of a robot team and even including
humans is becoming essential in many real-world applications. The survey
focuses on the AI techniques for robot communication to enhance the
communication capability of the multi-robot team, making more complex
activities, taking an appreciated decision, taking coordinated action, and
performing their tasks efficiently.