pwc-archive/papers-with-abstracts huggingface dataset:
Meta-analysis of Gene Expression in Neurodegenerative Diseases Reveals Patterns in GABA Synthesis and Heat Stress Pathways
Date: 2019-09-16 00:00:00

Neurodegenerative diseases are characterized as the progressive loss of
neural cells, e.g. neurons, glial cells. Ageing, monogenic variations, viral
infections, and many other factors are determined and speculated as causes for
them. While many individual genes, such as APP for Alzheimer disease and HTT
for Huntington disease, and biological pathways are studied for
neurodegenerative diseases, system-wide pathogenesis studies are limited. In
this study, we carried out a meta-analysis of RNA-Seq studies for three
neurodegenerative diseases, namely Alzheimer's disease, Parkinson's disease and
Amyotrophic Lateral Sclerosis (ALS) to minimize the batch effect derived
differences and identify the similarly altered factors among studies. Our main
assumption is that these three diseases share some pathological pathway
pattern. For this purpose, we downloaded publicly available Alzheimer's disease
(84 patients + 33 controls = 117 individuals), Parkinson's disease (28 patients
+ 43 controls = 71 individuals) and ALS (2 studies: 46 patients + 25 control =
71 individuals) RNA-Seq data from Sequence Read Archive (SRA) database. The
significantly differentially expressed genes common to these studies were first
identified and analyzed for the patterns in their pathways and variations. Our
meta-analysis revealed the shared nature of differential gene expression and
mutation load of the cellular heat stress response and GABA synthesis in
neurodegenerative diseases. The downregulated GABA synthesis-related genes
(e.g. GAD1 and GAD2) and the upregulated cellular heat stress response-related
genes (e.g. DNAJB6 and HSP90AA1), in addition to their expression patterns,
contain unique variations in samples from patients with neurodegenerative
diseases. The significance of genes and pathways we identified in this study
corroborated by the recent literature on neurodegenerative diseases.
From topological analyses to functional modeling: the case of hippocampus
Date: 2019-09-17 00:00:00
Tasks: Hippocampus

Topological data analyses are rapidly turning into key tools for quantifying
large volumes of neurobiological data, e.g., for organizing the spiking outputs
of large neuronal ensembles and thus gaining insights into the information
produced by various networks. Below we discuss a case in which several
convergent topological analyses not only provide a description of the data
structure, but also produce insights into how these data may be processed in
the hippocampus---a brain part that plays a key role in learning and memory.
The resulting functional model provides a unifying framework for integrating
spiking information at different timescales and understanding the course of
spatial learning at different levels of spatiotemporal granularity. In
particular, the model allows quantifying contributions of various physiological
phenomena---brain waves, synaptic strengths, synaptic architectures, etc., into
spatial cognition.
Coherence Statistics of Structured Random Ensembles and Support Detection Bounds for OMP
Date: 2019-09-17 00:00:00

A structured random matrix ensemble that maintains constant modulus entries
and unit-norm columns, often called a random phase-rotated (RPR) matrix, is
considered in this paper. We analyze the coherence statistics of RPR
measurement matrices and apply them to acquire probabilistic performance
guarantees of orthogonal matching pursuit (OMP) for support detection (SD). It
is revealed via numerical simulations that the SD performance guarantee
provides a tight characterization, especially when the signal is sparse.
Novel Rigorous Multimode Equivalent Network Formulation for Boxed Planar Circuits with Arbitrarily Shaped Metallizations
Date: 2019-09-17 00:00:00

The Multimode Equivalent Network (MEN) formulation has been traditionally
employed for the efficient and accurate analysis of waveguide devices. In this
paper, we extend the use of the MEN to the analysis of zero thickness,
multilayer planar circuits in a metallic enclosure. The formulation is
developed for arbitrary shape metallic areas and includes both internal and
external ports in the transverse plane. The BI-RME method is applied to analyze
the arbitrary shaped metallizations and to compute the coupling integrals
needed to solve the corresponding integral equations. On this basis, typical
shielded microstrip circuits of complex geometries, are analyzed in the common
frame of the MEN technique. To validate the theoretical formulation, several
shielded filtering microstrip structures are analyzed, showing good agreement
with respect to both other commercial tools and experimental measurements.
Stock market microstructure inference via multi-agent reinforcement learning
Authors: J. Lussange, I. Lazarevich, S. Bourgeois-Gironde, S. Palminteri, B. Gutkin
Date: 2019-09-17 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed. In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via model-free reinforcement learning. We calibrate the model to real market data from the London Stock Exchange over the years $2007$ to $2018$, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals. Agent learning thus enables model emulation of the microstructure with greater realism.
A Simulation Approach to Multi-station Solar Irradiance Data Considering Temporal Correlations
Date: 2019-09-12 00:00:00

Solar energy is one of important renewable energy sources and simulation of
solar irradiance can be used as input for simulation of photovoltaic (PV)
generation. This paper proposes a simulation algorithm of multi-station solar
irradiance data considering temporal correlations. First of all, we group all
the days of the observed data to k clusters for each station based on their
daily features of solar irradiance and the daily states constitute Markov chain
of days. Then, we reduce state permutations of different stations before
getting Markov Transition Probability Matrix (MTPM). In terms of the observed
data and MTPM, the simulation approach is proposed. Finally, we test our
approach by applying to solar irradiance data of three stations and show that
the properties of simulated data match those of the observed data.
MathDL: Mathematical deep learning for D3R Grand Challenge 4
Date: 2019-09-17 00:00:00
Tasks: Deep Learning, Pose Prediction, Prediction

We present the performances of our mathematical deep learning (MathDL) models
for D3R Grand Challenge 4 (GC4). This challenge involves pose prediction,
affinity ranking, and free energy estimation for beta secretase 1 (BACE) as
well as affinity ranking and free energy estimation for Cathepsin S (CatS). We
have developed advanced mathematics, namely differential geometry, algebraic
graph, and/or algebraic topology, to accurately and efficiently encode high
dimensional physical/chemical interactions into scalable low-dimensional
rotational and translational invariant representations. These representations
are integrated with deep learning models, such as generative adversarial
networks (GAN) and convolutional neural networks (CNN) for pose prediction and
energy evaluation, respectively. Overall, our MathDL models achieved the top
place in pose prediction for BACE ligands in Stage 1a. Moreover, our
submissions obtained the highest Spearman correlation coefficient on the
affinity ranking of 460 CatS compounds, and the smallest centered root mean
square error on the free energy set of 39 CatS molecules. It is worthy to
mention that our method for docking pose predictions has significantly improved
from our previous ones.
The uncertain surgery time in integrated sequencing, planning and scheduling problem of the surgical ward
Date: 2019-12-22 00:00:00
Tasks: Management, Scheduling

The surgical department and adequate access to health care are critical
problems. The operating room plays a fundamental role in the performance of a
hospital. The real problem faces several issues to collect data and optimize
scheduling and planning. In this paper, the planning and integral scheduling
problems were taken into consideration including surgeon work times, patients'
surgery priority, and preparation time of the operation room after each
surgery. Based on this, two mathematical models have been developed. The first
model is a mixed-integer programming model that minimizes the total patient
waiting time. This model was designed for three sections: Public Health Unit
(PHU), Operating Rooms, and Post Anesthesia Care Unit (PACU). This model
includes scheduling, allocating surgery personnel, and sequencing the groups of
patients in each section. The decision-makers, for patients, demonstrate the
experts would determine some priorities. The service times for the patients at
every stage are affected by the proficiency of surgery personnel. In the second
model, a robust optimization approach has been used due to overcoming the
uncertain surgery times. The proposed robust model indicated that the
fluctuations of solutions in the future are less than the deterministic model
at an acceptable level. The computational results reveal some good managerial
insights for hospital administrators. The managers could use this model to
improve system management, quality of services to patients, and patient
satisfaction.
To Learn or Not to Learn: Deep Learning Assisted Wireless Modem Design
Date: 2019-09-17 00:00:00
Tasks: BIG-bench Machine Learning

Deep learning is driving a radical paradigm shift in wireless communications,
all the way from the application layer down to the physical layer. Despite
this, there is an ongoing debate as to what additional values artificial
intelligence (or machine learning) could bring to us, particularly on the
physical layer design; and what penalties there may have? These questions
motivate a fundamental rethinking of the wireless modem design in the
artificial intelligence era. Through several physical-layer case studies, we
argue for a significant role that machine learning could play, for instance in
parallel error-control coding and decoding, channel equalization, interference
cancellation, as well as multiuser and multiantenna detection. In addition, we
will also discuss the fundamental bottlenecks of machine learning as well as
their potential solutions in this paper.
Consistent Initialization of the Laplace Transform
Date: 2019-09-16 00:00:00

Consistent initialization of the Laplace transform has been a fundamental and
long-standing issue. The consistency of the L- approach has been questioned,
yet it is a popular approach since the L+ approach requires a priori
computation of the 0+ initial conditions, which becomes a diligent task from
the available methods. Also, the L+ Laplace transform of the impulse becomes
zero, thus involving an inconsistency. In contrast to these direct approaches,
some studies propose convoluted methods to address the issue. Here, a direct,
facile, and first-principles novel approach for the L+ transform is proposed.
It computes the consistent 0+ initial conditions and solution based on
singular-nonsingular decomposition of the system model. The inconsistency
associated with the L+ Laplace transform of discontinuous functions and impulse
is not involved in the nonsingular part. The emergence of discontinuity is
easily elucidated from the singular part, and the solution yields the same 0+
initial values as the initial conditions used. Further, physical
first-principles can be used in the midst of the computation to validate the
results to ensure error-free execution.
Shared Control Between Pilots and Autopilots: Illustration of a Cyber-Physical Human System
Date: 2020-04-17 00:00:00
Tasks: Decision Making

Although increased automation has made it easier to control aircraft,
ensuring a safe interaction between the pilots and the autopilots is still a
challenging problem, especially in the presence of severe anomalies. Current
approach consists of autopilot solutions that disengage themselves when they
become ineffective. This may cause reengagement of the pilot at the worst
possible time, which can result in undesired consequences. In this paper, a
series of research studies that propose pilot-autopilot interaction schemes
based on the Capacity for Maneuver (CfM) concept, are covered. CfM refers to
the remaining capacity of the actuators that can be used for bringing the
aircraft to safety. It is demonstrated that CfM-based pilot-autopilot
interaction schemes, or Shared Control Architectures (SCA), can be promising
alternatives to the existing schemes. Two SCAs are tested in the experiments.
In SCA1, the pilot takes over the control from the autopilot using the
monitored CfM information. In SCA2, the pilot takes on the role of a supervisor
helping the autopilot whenever a need arises, based on the CfM information.
Whenever the aircraft experiences a severe anomaly, the pilot assesses the
situation based on his/her CfM monitoring and intervenes by providing two
control system parameter estimates to the autopilot. This increases the
effectiveness of the autopilot. Using human-in-the-loop simulations, it is
shown that CfM based interactions provides smaller tracking errors and larger
overall CfM. The subjects including a commercial airline pilot and several
university students are trained using a systematic procedure. Creation of new
cyber physical & human systems is inevitable along with deeper engagement with
the social science community so as to get better insight into human decision
making. The results reported here should be viewed as a first of several steps
in this research direction.
The value of knowing the market price of risk
Authors: Katia Colaneri, Stefano Herzel, Marco Nicolosi
Date: 2019-09-17 00:00:00

This paper presents an optimal allocation problem in a financial market with one risk-free and one risky asset, when the market is driven by a stochastic market price of risk. We solve the problem in continuous time, for an investor with a Constant Relative Risk Aversion (CRRA) utility, under two scenarios: when the market price of risk is observable (the {\em full information case}), and when it is not (the {\em partial information case}). The corresponding market models are complete in the partial information case and incomplete in the other case, hence the two scenarios exhibit rather different features. We study how the access to more accurate information on the market price of risk affects the optimal strategies and we determine the maximal price that the investor would be willing to pay to get such information. In particular, we examine two cases of additional information, when an exact observation of the market price of risk is available either at time $0$ only (the {\em initial information case}), or during the whole investment period (the {\em dynamic information case}).
Adaptive Leader-Following Consensus for Multiple Euler-Lagrange Systems with an Uncertain Leader System
Date: 2019-09-17 00:00:00

In this paper, we study the leader-following consensus problem of multiple
Euler-Lagrange systems subject to an uncertain leader system. We first
establish an adaptive distributed observer for a neutrally stable linear leader
system whose system matrix is not known exactly. Under standard assumptions,
this adaptive distributed observer can estimate and pass the leader's state to
each follower through the communication network of the system without knowing
the leader's system matrix exactly. Under the additional assumption that the
leader's state is persistently exciting, this adaptive distributed observer can
also asymptotically learn the parameters of the leader's system matrix. On the
basis of this adaptive distributed observer, we further synthesize an adaptive
distributed control law to solve our problem via the certainty equivalence
principle. Our result allows the leader-following consensus problem of multiple
Euler-Lagrange systems to be solved even if none of the followers knows the
system matrix of the leader system exactly.
Distributional conformal prediction
Authors: Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
Date: 2019-09-17 00:00:00
Tasks: Conformal Prediction, counterfactual, Prediction, Prediction Intervals, regression, Time Series, Time Series Analysis, valid

We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression. Our approach can be applied to important prediction problems including cross-sectional prediction, k-step-ahead forecasts, synthetic controls and counterfactual prediction, and individual treatment effects prediction. Our method exploits the probability integral transform and relies on permuting estimated ranks. Unlike regression residuals, ranks are independent of the predictors, allowing us to construct conditionally valid prediction intervals under heteroskedasticity. We establish approximate conditional validity under consistent estimation and provide approximate unconditional validity under model misspecification, overfitting, and with time series data. We also propose a simple "shape" adjustment of our baseline method that yields optimal prediction intervals.
Blockchain of Signature Material Combining Cryptographic Hash Function and DNA Steganography
Date: 2019-09-17 00:00:00

An ideal signature material and method, which can be used to prove the
authenticity of a physical item and against forgery, should be immune to the
fast developments in digital and engineering technologies. Herein, the design
of signature material combining cryptographic hash function and DNA
steganography is proposed. The encrypting materials are used to construct a
series of time-stamped records (blockchain) associated with published hash
values, while each DNA-encrypted block is associated with a set of DNA keys.
The decrypted DNA information, as digital keys, can be validated through a hash
function to compare with the published hash values. The blocks can also be
cross-referenced among different related signatures. While both digital
cryptography and DNA steganography can have large key size, automated brutal
force search is far more labor intensive and expensive for DNA steganography
with wet lab experiments, as compared to its digital counterpart. Moreover, the
time-stamped blockchain structure permits the incorporation of new
cryptographic functions and DNA steganographies over time, thus can evolve over
time without losing the continuous history line.
Adaptive and Dynamically Constrained Process Noise Estimation for Orbit Determination
Authors: Nathan Stacey, Simone D'Amico
Date: 2019-09-17 00:00:00
Tasks: Autonomous Navigation, Noise Estimation

This paper introduces two new algorithms to accurately estimate the process noise covariance of a discrete-time Kalman filter online for robust orbit determination in the presence of dynamics model uncertainties. Common orbit determination process noise techniques, such as state noise compensation and dynamic model compensation, require offline tuning and a priori knowledge of the dynamical environment. Alternatively, the process noise covariance can be estimated through adaptive filtering. However, many adaptive filtering techniques are not applicable to onboard orbit determination due to computational cost or the assumption of a linear time-invariant system. Furthermore, existing adaptive filtering techniques do not constrain the process noise covariance according to the underlying continuous-time dynamical model, and there has been limited work on adaptive filtering with colored process noise. To overcome these limitations, a novel approach is developed which optimally fuses state noise compensation and dynamic model compensation with covariance matching adaptive filtering. This yields two adaptive and dynamically constrained process noise covariance estimation techniques. Unlike many adaptive filtering approaches, the new techniques accurately extrapolate over measurement outages and do not rely on ad hoc methods to ensure the process noise covariance is positive semi-definite. The benefits of the proposed algorithms are demonstrated through two case studies: an illustrative linear system and the autonomous navigation of two spacecraft orbiting an asteroid.
On reconstruction algorithms for signals sparse in Hermite and Fourier domains
Date: 2019-09-17 00:00:00
Tasks: Compressive Sensing, Denoising

This thesis consists of original contributions in the area of digital signal
processing. The reconstruction of signals sparse (highly concentrated) in
various transform domains is the primary problem analyzed in the thesis. The
considered domains include Fourier, discrete Hermite, one-dimensional and
two-dimensional discrete cosine transform, as well as various time-frequency
representations. Sparse signals are reconstructed using sparsity measures,
being, in fact, the measures of signal concentration in the considered domains.
The thesis analyzes the compressive sensing reconstruction algorithms and
introduces new approaches to the problem at hand. The missing samples influence
on analyzed transform domains is studied in detail, establishing the relations
with the general compressive sensing theory. This study provides new insights
on phenomena arising due to the reduced number of signal samples. The
theoretical contributions involve new exact mathematical expressions which
describe performance and outcomes of reconstruction algorithms, also including
the study of the influence of additive noise, sparsity level and the number of
available measurements on the reconstruction performance, exact expressions for
reconstruction errors and error probabilities. Parameter optimization of the
discrete Hermite transform is also studied, as well as the additive noise
influence on Hermite coefficients, resulting in new parameter optimization and
denoising algorithms. Additionally, an algorithm for the decomposition of
multivariate multicomponent signals is introduced, as well as an instantaneous
frequency estimation algorithm based on the Wigner distribution. Extensive
numerical examples and experiments with real and synthetic data validate the
presented theory and shed a new light on practical applications of the results.