pwc-archive/papers-with-abstracts huggingface dataset:
Robust Inference on Infinite and Growing Dimensional Time Series Regression
Authors: Abhimanyu Gupta, Myung Hwan Seo
Date: 2019-11-20 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Time Series Regression

We develop a class of tests for time series models such as multiple regression with growing dimension, infinite-order autoregression and nonparametric sieve regression. Examples include the Chow test and general linear restriction tests of growing rank $p$. Employing such increasing $p$ asymptotics, we introduce a new scale correction to conventional test statistics which accounts for a high-order long-run variance (HLV) that emerges as $ p $ grows with sample size. We also propose a bias correction via a null-imposed bootstrap to alleviate finite sample bias without sacrificing power unduly. A simulation study shows the importance of robustifying testing procedures against the HLV even when $ p $ is moderate. The tests are illustrated with an application to the oil regressions in Hamilton (2003).
Performance Monitoring for Live Systems with Soft FEC and Multilevel Modulation
Date: 2020-02-18 00:00:00

Performance monitoring is an essential function for margin measurements in
live systems. Historically, system budgets have been described by the Q-factor
converted from the bit error rate (BER) under binary modulation and direct
detection. The introduction of hard-decision forward error correction (FEC) did
not change this. In recent years technologies have changed significantly to
comprise coherent detection, multilevel modulation and soft FEC. In such
advanced systems, different metrics such as (nomalized) generalized mutual
information (GMI/NGMI) and asymmetric information (ASI) are regarded as being
more reliable. On the other hand, Q budgets are still useful because pre-FEC
BER monitoring is established in industry for live system monitoring.
  The pre-FEC BER is easily estimated from available information of the number
of flipped bits in the FEC decoding, which does not require knowledge of the
transmitted bits that are unknown in live systems. Therefore, the use of
metrics like GMI/NGMI/ASI for performance monitoring has not been possible in
live systems. However, in this work we propose a blind soft-performance
estimation method. Based on a histogram of log-likelihood-values without the
knowledge of the transmitted bits, we show how the ASI can be estimated.
  We examined the proposed method experimentally for 16 and 64-ary quadrature
amplitude modulation (QAM) and probabilistically shaped 16, 64, and 256-QAM in
recirculating loop experiments. We see a relative error of 3.6%, which
corresponds to around 0.5 dB signal-to-noise ratio difference for binary
modulation, in the regime where the ASI is larger than the assumed FEC
threshold. For this proposed method, the digital signal processing circuitry
requires only a minimal additional function of storing the L-value histograms
before the soft-decision FEC decoder.
Color-wise Attention Network for Low-light Image Enhancement
Date: 2020-05-16 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement

Absence of nearby light sources while capturing an image will degrade the
visibility and quality of the captured image, making computer vision tasks
difficult. In this paper, a color-wise attention network (CWAN) is proposed for
low-light image enhancement based on convolutional neural networks. Motivated
by the human visual system when looking at dark images, CWAN learns an
end-to-end mapping between low-light and enhanced images while searching for
any useful color cues in the low-light image to aid in the color enhancement
process. Once these regions are identified, CWAN attention will be mainly
focused to synthesize these local regions, as well as the global image. Both
quantitative and qualitative experiments on challenging datasets demonstrate
the advantages of our method in comparison with state-of-the-art methods.
Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control
Date: 2020-03-18 00:00:00

This paper presents a novel learning framework to construct Koopman
eigenfunctions for unknown, nonlinear dynamics using data gathered from
experiments. The learning framework can extract spectral information from the
full nonlinear dynamics by learning the eigenvalues and eigenfunctions of the
associated Koopman operator. We then exploit the learned Koopman eigenfunctions
to learn a lifted linear state-space model. To the best of our knowledge, our
method is the first to utilize Koopman eigenfunctions as lifting functions for
EDMD-based methods. We demonstrate the performance of the framework in state
prediction and closed loop trajectory tracking of a simulated cart pole system.
Our method is able to significantly improve the controller performance while
relying on linear control methods to do nonlinear control.
Statistical Inference on Partially Linear Panel Model under Unobserved Linearity
Date: 2019-11-20 00:00:00

A new statistical procedure, based on a modified spline basis, is proposed to
identify the linear components in the panel data model with fixed effects.
Under some mild assumptions, the proposed procedure is shown to consistently
estimate the underlying regression function, correctly select the linear
components, and effectively conduct the statistical inference. When compared to
existing methods for detection of linearity in the panel model, our approach is
demonstrated to be theoretically justified as well as practically convenient.
We provide a computational algorithm that implements the proposed procedure
along with a path-based solution method for linearity detection, which avoids
the burden of selecting the tuning parameter for the penalty term. Monte Carlo
simulations are conducted to examine the finite sample performance of our
proposed procedure with detailed findings that confirm our theoretical results
in the paper. Applications to Aggregate Production and Environmental Kuznets
Curve data also illustrate the necessity for detecting linearity in the
partially linear panel model.
5G framework concepts for the next generation networks
Date: 2019-06-20 00:00:00
Tasks: Diversity

Mobile communication technologies have been evolving for many years with each
generation transforming the way we experience new services. As the Smartphone
market has significantly expanded in recent years and expected to grow more in
years to come, the network evolution must continue to keep up the pace with
users demand even beyond the common usage connectivity. The envisioned market
space for the next generation technology is driven by requirements to enhance
mobile broadband connectivity, reach a massive range of machine type
communication (MTC), and target services with ultra-reliable and low latency
(URLLC) communications. To deliver these requirements, 5G must be designed with
scalability and diversity across many components from spectrum, core network,
radio access and devices
Model-based optimal AML consolidation treatment
Authors: Felix Jost, Enrico Schalk, Daniela Weber, Hartmut Doehner, Thomas Fischer, Sebastian Sager
Date: 2019-11-20 00:00:00
Tasks: Decision Making, model

Neutropenia is an adverse event commonly arising during intensive chemotherapy of acute myeloid leukemia (AML). It is often associated with infectious complications. Mathematical modeling, simulation, and optimization of the treatment process would be a valuable tool to support clinical decision making, potentially resulting in less severe side effects and deeper remissions. However, until now, there has been no validated mathematical model available to simulate the effect of chemotherapy treatment on white blood cell (WBC) counts and leukemic cells simultaneously. We developed a population pharmacokinetic/pharmacodynamic (PK/PD) model combining a myelosuppression model considering endogenous granulocyte-colony stimulating factor (G-CSF), a PK model for cytarabine (Ara-C), a subcutaneous absorption model for exogenous G-CSF, and a two-compartment model for leukemic blasts. This model was fitted to data of 44 AML patients during consolidation therapy with a novel Ara-C plus G-CSF schedule from a phase II controlled clinical trial. Additionally, we were able to optimize treatment schedules with respect to disease progression, WBC nadirs, and the amount of Ara-C and G-CSF. The developed PK/PD model provided good prediction accuracies and an interpretation of the interaction between WBCs, G-CSF, and blasts. For 14 patients (those with available bone marrow blast counts), we achieved a median 4.2-fold higher WBC count at nadir, which is the most critical time during consolidation therapy. The simulation results showed that relative bone marrow blast counts remained below the clinically important threshold of 5%, with a median of 60% reduction in Ara-C.
Can artificial neural networks supplant the polygene risk score for risk prediction of complex disorders given very large sample sizes?
Date: 2019-11-20 00:00:00

Genome-wide association studies (GWAS) provide a means of examining the
common genetic variation underlying a range of traits and disorders. In
addition, it is hoped that GWAS may provide a means of differentiating affected
from unaffected individuals. This has potential applications in the area of
risk prediction. Current attempts to address this problem focus on using the
polygene risk score (PRS) to predict case-control status on the basis of GWAS
data. However this approach has so far had limited success for complex traits
such as schizophrenia (SZ). This is essentially a classification problem.
Artificial neural networks (ANNs) have been shown in recent years to be highly
effective in such applications. Here we apply an ANN to the problem of
distinguishing SZ patients from unaffected controls. We compare the
effectiveness of the ANN with the PRS in classifying individuals by
case-control status based only on genetic data from a GWAS. We use the
schizophrenia dataset from the Psychiatric Genomics Consortium (PGC) for this
study. Our analysis indicates that the ANN is more sensitive to sample size
than the PRS. As larger and larger sample sizes become available, we suggest
that ANNs are a promising alternative to the PRS for classification and risk
prediction for complex genetic disorders.
A Scrambled Method of Moments
Date: 2019-11-20 00:00:00
Tasks: Time Series, Time Series Analysis

Quasi-Monte Carlo (qMC) methods are a powerful alternative to classical
Monte-Carlo (MC) integration. Under certain conditions, they can approximate
the desired integral at a faster rate than the usual Central Limit Theorem,
resulting in more accurate estimates. This paper explores these methods in a
simulation-based estimation setting with an emphasis on the scramble of Owen
(1995). For cross-sections and short-panels, the resulting Scrambled Method of
Moments simply replaces the random number generator with the scramble
(available in most softwares) to reduce simulation noise. Scrambled Indirect
Inference estimation is also considered. For time series, qMC may not apply
directly because of a curse of dimensionality on the time dimension. A simple
algorithm and a class of moments which circumvent this issue are described.
Asymptotic results are given for each algorithm. Monte-Carlo examples
illustrate these results in finite samples, including an income process with
"lots of heterogeneity."
Manipulable outcomes within the class of scoring voting rules
Date: 2020-09-25 00:00:00

Coalitional manipulation in voting is considered to be any scenario in which
a group of voters decide to misrepresent their vote in order to secure an
outcome they all prefer to the first outcome of the election when they vote
honestly. The present paper is devoted to study coalitional manipulability
within the class of scoring voting rules. For any such rule and any number of
alternatives, we introduce a new approach allowing to characterize all the
outcomes that can be manipulable by a coalition of voters. This gives us the
possibility to find the probability of manipulable outcomes for some
well-studied scoring voting rules in case of small number of alternatives and
large electorates under a well-known assumption on individual preference
profiles.
Bounded Temporal Fairness for FIFO Financial Markets
Authors: Vasilios Mavroudis
Date: 2019-11-20 00:00:00
Tasks: Fairness

Financial exchange operators cater to the needs of their users while simultaneously ensuring compliance with the financial regulations. In this work, we focus on the operators' commitment for fair treatment of all competing participants. We first discuss unbounded temporal fairness and then investigate its implementation and infrastructure requirements for exchanges. We find that these requirements can be fully met only under ideal conditions and argue that unbounded fairness in FIFO markets is unrealistic. To further support this claim, we analyse several real-world incidents and show that subtle implementation inefficiencies and technical optimizations suffice to give unfair advantages to a minority of the participants. We finally introduce, {\epsilon}-fairness, a bounded definition of temporal fairness and discuss how it can be combined with non-continuous market designs to provide equal participant treatment with minimum divergence from the existing market operation.
Robust Learning Model Predictive Control for Linear Systems Performing Iterative Tasks
Authors: Ugo Rosolia, Xiaojing Zhang, Francesco Borrelli
Date: 2019-11-21 00:00:00
Tasks: Model Predictive Control

A robust Learning Model Predictive Controller (LMPC) for uncertain systems performing iterative tasks is presented. At each iteration of the control task the closed-loop state, input and cost are stored and used in the controller design. This paper first illustrates how to construct robust invariant sets and safe control policies exploiting historical data. Then, we propose an iterative LMPC design procedure, where data generated by a robust controller at iteration $j$ are used to design a robust LMPC at the next $j+1$ iteration. We show that this procedure allows us to iteratively enlarge the domain of the control policy and it guarantees recursive constraints satisfaction, input to state stability and performance bounds for the certainty equivalent closed-loop system. The use of an adaptive prediction horizon is the key element of the proposed design. The effectiveness of the proposed control scheme is illustrated on a linear system subject to bounded additive disturbance.
Distributed Iterative CT Reconstruction using Multi-Agent Consensus Equilibrium
Date: 2019-11-21 00:00:00
Tasks: CT Reconstruction, Image Reconstruction

Model-Based Image Reconstruction (MBIR) methods significantly enhance the
quality of computed tomographic (CT) reconstructions relative to analytical
techniques, but are limited by high computational cost. In this paper, we
propose a multi-agent consensus equilibrium (MACE) algorithm for distributing
both the computation and memory of MBIR reconstruction across a large number of
parallel nodes. In MACE, each node stores only a sparse subset of views and a
small portion of the system matrix, and each parallel node performs a local
sparse-view reconstruction, which based on repeated feedback from other nodes,
converges to the global optimum. Our distributed approach can also incorporate
advanced denoisers as priors to enhance reconstruction quality. In this case,
we obtain a parallel solution to the serial framework of Plug-n-play (PnP)
priors, which we call MACE-PnP. In order to make MACE practical, we introduce a
partial update method that eliminates nested iterations and prove that it
converges to the same global solution. Finally, we validate our approach on a
distributed memory system with real CT data. We also demonstrate an
implementation of our approach on a massive supercomputer that can perform
large-scale reconstruction in real-time.
Hybrid quantile estimation for asymmetric power GARCH models
Date: 2019-11-21 00:00:00
Tasks: quantile regression, regression

Asymmetric power GARCH models have been widely used to study the higher order
moments of financial returns, while their quantile estimation has been rarely
investigated. This paper introduces a simple monotonic transformation on its
conditional quantile function to make the quantile regression tractable. The
asymptotic normality of the resulting quantile estimators is established under
either stationarity or non-stationarity. Moreover, based on the estimation
procedure, new tests for strict stationarity and asymmetry are also
constructed. This is the first try of the quantile estimation for
non-stationary ARCH-type models in the literature. The usefulness of the
proposed methodology is illustrated by simulation results and real data
analysis.
Binding and segregation of proteins in membrane adhesion: Theory, modelling, and simulations
Authors: Thomas R. Weikl, Jinglei Hu, Batuhan Kav, Bartosz Rozycki
Date: 2019-11-21 00:00:00

The adhesion of biomembranes is mediated by the binding of membrane-anchored receptor and ligand proteins. The proteins can only bind if the separation between apposing membranes is sufficiently close to the length of the protein complexes, which leads to an interplay between protein binding and membrane shape. In this article, we review current models of biomembrane adhesion and novel insights obtained from the models. Theory and simulations with elastic-membrane and coarse-grained molecular models of biomembrane adhesion indicate that the binding of proteins in membrane adhesion strongly depends on nanoscale shape fluctuations of the apposing membranes, which results in binding cooperativity. A length mismatch between protein complexes leads to repulsive interactions that are caused by membrane bending and act as a driving force for the length-based segregation of proteins during membrane adhesion.
Event-triggered Add-on Safety for Connected and Automated Vehicles Using Road-side Network Infrastructure
Date: 2019-11-21 00:00:00
Tasks: Friction

This paper proposes an event-triggered add-on safety mechanism to adjust the
control parameters for timely braking in a networked vehicular system while
maintaining maneuverability. Passenger vehicle maneuverability is significantly
affected by the combined-slip friction effect, in which larger longitudinal
tire slips result in considerable drop in lateral tire forces. This is of
higher importance when unexpected dangerous situations occur on the road and
immediate actions, such as braking, need to be taken to avoid collision. Harsh
braking can lead to high-slip and loss of maneuverability, hence, timely
braking is essential to reduce high-slip scenarios. In addition to the vehicles
own active safety systems, the proposed event-triggered add-on safety is
activated upon being informed about dangers by the road-side infrastructure.
The aim is to incorporate the add-on safety feature to adjust the automatic
control parameters for smooth and timely braking such that a collision is
avoided while vehicle's maneuverability is maintained. We study two different
wireless technologies for communication between the infrastructure and the
vehicles, the Long-Term Evolution (LTE) and the fifth generation (5G) schemes.
The framework is validated through high-fidelity software simulations and the
advantages of including the add-on feature to augment the safety margins for
each communication technology is evaluated.
Convolutional Neural Network-based Optical Camera Communication System for Internet of Vehicles
Date: 2019-11-20 00:00:00
Tasks: vehicle detection

The evolution of internet of vehicles (IoV) and the growing use of mobile
devices with the development of the Internet of Things, demand has grown for
alternative wireless communication technologies. As a promising alternative,
optical-camera communication (OCC) has emerged that uses light-emitting diode
(LED) and camera as transmitter and receiver respectively. Since LEDs and
cameras are exploring in traffic lights, vehicles, and public lightings, OCC
has the potential to handle the transport systems intelligently. Though some
technologies have been proposed or developed, these are not mature enough to
uphold the huge requirements of IoV. However, most of the OCC applications are
limited to single vehicle and there has been limited focus on the use of
multiple vehicles detection (spatial) or fast processing (temporal) systems.
Also, there has no system to challenge with the bad weather condition. In this
research, a vision camera and high-speed camera has been proposed to provide
multiple vehicle detection and fast data processing. Here, a convolutional
neural network (CNN) based vehicular OCC system has been introduced to
guarantee communication to maintain communication at the adverse condition and
near-infrared is proposed in addition to visible lights to provide long range
and secure communication. To support IoV communication, software-defined
networking (SDN) has been proposed. Finally, the results represent the required
conditions for vehicular OCC system analysis and improved performance
demonstration using the proposed intelligent system.
Optimal Sampling & Reconstruction: Theory and Applications
Date: 2019-11-21 00:00:00
Tasks: Image Reconstruction

The optimization of MRI data sampling and image reconstruction methods has
been a priority for the MRI community since the very early days of the field.
Designing an "optimal" method requires the definition of an optimality metric
(i.e., a quantitative evaluation of the "goodness" of different competing
approaches that allows an objective comparison between them). However, a key
challenge is that there are many different possible ways of quantitatively
evaluating the "goodness" of a data sampling scheme or a reconstruction result,
and there are no acquisition or reconstruction methods that are known to be
universally optimal with respect to all of these possible metrics
simultaneously. Thus, optimization of MRI methods requires a subjective choice
about what aspects of quality matter most in the context of a given MRI
experiment, and subsequently the subjective choice of an optimality metric that
hopefully does a reasonable job of quantifying those aspects of quality. Once
these choices are made, the optimization problem becomes well-defined, and it
remains to choose an algorithm that can identify data sampling or image
reconstruction methods that are optimal with respect to the chosen metric. All
of these choices are generally nontrivial.
  In this presentation, we will discuss optimal sampling and reconstruction
designs from multiple different perspectives, including ideas from information
and estimation theory and various practical perspectives.