pwc-archive/papers-with-abstracts huggingface dataset:
Model-Free Practical Cooperative Control for Diffusively Coupled Systems
Authors: Miel Sharf, Anne Koch, Daniel Zelazo, Frank Allgöwer
Date: 2019-06-12 00:00:00

In this paper, we develop a data-based controller design framework for diffusively coupled systems with guaranteed convergence to an $\epsilon$-neighborhood of the desired formation. The controller is comprised of a fixed controller with an adjustable gain on each edge. Via passivity theory and network optimization we not only prove that there exists a gain attaining the desired formation control goal, but we present a data-based method to find an upper bound on this gain. Furthermore, by allowing for additional experiments, the conservatism of the upper bound can be reduced via iterative sampling schemes. The introduced scheme is based on the assumption of passive systems, which we relax by discussing different methods for estimating the systems' passivity shortage, as well as applying transformations passivizing them. Finally, we illustrate the developed model-free cooperative control scheme with a case study.
Nonparametric Identification and Estimation with Independent, Discrete Instruments
Date: 2019-06-12 00:00:00
Tasks: quantile regression, regression

In a nonparametric instrumental regression model, we strengthen the
conventional moment independence assumption towards full statistical
independence between instrument and error term. This allows us to prove
identification results and develop estimators for a structural function of
interest when the instrument is discrete, and in particular binary. When the
regressor of interest is also discrete with more mass points than the
instrument, we state straightforward conditions under which the structural
function is partially identified, and give modified assumptions which imply
point identification. These stronger assumptions are shown to hold outside of a
small set of conditional moments of the error term. Estimators for the
identified set are given when the structural function is either partially or
point identified. When the regressor is continuously distributed, we prove that
if the instrument induces a sufficiently rich variation in the joint
distribution of the regressor and error term then point identification of the
structural function is still possible. This approach is relatively tractable,
and under some standard conditions we demonstrate that our point identifying
assumption holds on a topologically generic set of density functions for the
joint distribution of regressor, error, and instrument. Our method also applies
to a well-known nonparametric quantile regression framework, and we are able to
state analogous point identification results in that context.
Growing green: the role of path dependency and structural jumps in the green economy expansion
Date: 2020-04-25 00:00:00

Existing research argues that countries increase their production basket by
adding products which require similar capabilities to those they already
produce, a process referred to as path dependency. Green economic growth is a
global movement that seeks to achieve economic expansion while at the same time
mitigating environmental risks. We postulate that countries engaging in green
economic growth are motivated to invest strategically to develop new
capabilities that will help them transition to a green economy. As a result,
they could potentially increase their production baskets not only by a path
dependent process but also by the non path dependent process we term, high
investment structural jumps. The main objective of this research is to
determine whether countries increase their green production basket mainly by a
process of path dependency, or alternatively, by a process of structural jumps.
We analyze data from 65 countries and over a period from years 2007 to 2017. We
focus on China as our main case study. The results of this research show that
countries not only increase their green production baskets based on their
available capabilities, following path dependency, but also expand to products
that path dependency does not predict by investing in innovating and developing
new environmental related technologies.
Simulated Epidemics in 3D Protein Structures to Detect Functional Properties
Date: 2019-12-12 00:00:00

The outcome of an epidemic is closely related to the network of interactions
between the individuals. Likewise, protein functions depend on the 3D
arrangement of their residues and on the underlying energetic interaction
network. Borrowing ideas from the theoretical framework that has been developed
to address the spreading of real diseases, we study the diffusion of a
fictitious epidemic inside the protein non-bonded interaction network. Our
approach allowed to probe the overall stability and the capability to propagate
information in the complex 3D-structures and proved to be very efficient in
addressing different problems, from the assessment of thermal stability to the
identification of allosteric sites.
From asymptotic properties of general point processes to the ranking of financial agents
Date: 2019-06-12 00:00:00
Tasks: Point Processes

We propose a general non-linear order book model that is built from the
individual behaviours of the agents. Our framework encompasses Markovian and
Hawkes based models. Under mild assumptions, we prove original results on the
ergodicity and diffusivity of such system. Then we provide closed form formulas
for various quantities of interest: stationary distribution of the best bid and
ask quantities, spread, liquidity fluctuations and price volatility. These
formulas are expressed in terms of individual order flows of market
participants. Our approach enables us to establish a ranking methodology for
the market makers with respect to the quality of their trading.
Coordinated Path Following Control of Fixed-wing Unmanned Aerial Vehicles
Authors: Hao Chen, Yirui Cong, Xiangke Wang, Xin Xu, Lincheng Shen
Date: 2019-06-13 00:00:00
Tasks: ARC

In this paper, we investigate the problem of coordinated path following for fixed-wing UAVs with speed constraints in 2D plane. The objective is to steer a fleet of UAVs along the path(s) while achieving the desired sequenced inter-UAV arc distance. In contrast to the previous coordinated path following studies, we are able through our proposed hybrid control law to deal with the forward speed and the angular speed constraints of fixed-wing UAVs. More specifically, the hybrid control law makes all the UAVs work at two different levels: those UAVs whose path following errors are within an invariant set (i.e., the designed coordination set) work at the coordination level; and the other UAVs work at the single-agent level. At the coordination level, we prove that even with speed constraints, the proposed control law can make sure the path following errors reduce to zero, while the desired arc distances converge to the desired value. At the single-agent level, the convergence analysis for the path following error entering the coordination set is provided. We develop a hardware-in-the-loop simulation testbed of the multi-UAV system by using actual autopilots and the X-Plane simulator. The effectiveness of the proposed approach is corroborated with both MATLAB and the testbed.
Absolute negative mobility in evolution
Date: 2020-01-17 00:00:00

We investigate a population-genetic model with a temporally-fluctuating
sawtooth fitness landscape. We numerically show that a counter-intuitive
behavior occurs where the rate of evolution of the system decreases as
selection pressure increases from zero. This phenomenon is understood by
analogy with absolute negative mobility in particle flow. A phenomenological
explanation about the direction of evolution is also provided.
Time scales in stock markets
Date: 2019-06-13 00:00:00
Tasks: Time Series, Time Series Analysis

Different investment strategies are adopted in short-term and long-term
depending on the time scales, even though time scales are adhoc in nature.
Empirical mode decomposition based Hurst exponent analysis and variance
technique have been applied to identify the time scales for short-term and
long-term investment from the decomposed intrinsic mode functions(IMF). Hurst
exponent ($H$) is around 0.5 for the IMFs with time scales from few days to 3
months, and $H\geq0.75$ for the IMFs with the time scales $\geq5$ months. Short
term time series [$X_{ST}(t)$] with time scales from few days to 3 months and
$H~0.5$ and long term time series [$X_{LT}(t)$] with time scales $\geq5$ and
$H\geq0.75$, which represent the dynamics of the market, are constructed from
the IMFs. The $X_{ST}(t)$ and $X_{LT}(t)$ show that the market is random in
short-term and correlated in long term. The study also show that the
$X_{LT}(t)$ is correlated with fundamentals of the company. The analysis will
be useful for investors to design the investment and trading strategy.
Information capacity of a network of spiking neurons
Date: 2019-11-24 00:00:00

We study a model of spiking neurons, with recurrent connections that result
from learning a set of spatio-temporal patterns with a spike-timing dependent
plasticity rule and a global inhibition. We investigate the ability of the
network to store and selectively replay multiple patterns of spikes, with a
combination of spatial population and phase-of-spike code. Each neuron in a
pattern is characterized by a binary variable determining if the neuron is
active in the pattern, and a phase-lag variable representing the spike-timing
order among the active units. After the learning stage, we study the dynamics
of the network induced by a brief cue stimulation, and verify that the network
is able to selectively replay the pattern correctly and persistently. We
calculate the information capacity of the network, defined as the maximum
number of patterns that can be encoded in the network times the number of bits
carried by each pattern, normalized by the number of synapses, and find that it
can reach a value $\alpha_\text{max}\simeq 0.27$, similar to the one of
sequence processing neural networks, and almost double of the capacity of the
static Hopfield model. We study the dependence of the capacity on the global
inhibition, connection strength (or neuron threshold) and fraction of neurons
participating to the patterns. The results show that a dual population and
temporal coding can be optimal for the capacity of an associative memory.
Dynamic 3-D measurement based on fringe-to-fringe transformation using deep learning
Date: 2019-12-30 00:00:00
Tasks: Retrieval

Fringe projection profilometry (FPP) has become increasingly important in
dynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase of
the measured object before shape profiling. However, traditional phase
retrieval techniques often require a large number of fringes, which may
generate motion-induced error for dynamic objects. In this paper, a novel phase
retrieval technique based on deep learning is proposed, which uses an
end-to-end deep convolution neural network to transform a single or two fringes
into the phase retrieval required fringes. When the object's surface is located
in a restricted depth, the presented network only requires a single fringe as
the input, which otherwise requires two fringes in an unrestricted depth. The
proposed phase retrieval technique is first theoretically analyzed, and then
numerically and experimentally verified on its applicability for dynamic 3-D
measurement.
Anomalous diffusion for neuronal growth on surfaces with controlled geometries
Authors: Ilya Yurchenko, Joao Marcos Vensi Basso, Vladyslav Serhiiovych Syrotenko, Cristian Staii
Date: 2019-06-11 00:00:00

Geometrical cues are known to play a very important role in neuronal growth and the formation of neuronal networks. Here, we present a detailed analysis of axonal growth and dynamics for neuronal cells cultured on patterned polydimethylsiloxane surfaces. We use fluorescence microscopy to image neurons, quantify their dynamics, and demonstrate that the substrate geometrical patterns cause strong directional alignment of axons. We quantify axonal growth and report a general stochastic approach that quantitatively describes the motion of growth cones. The growth cone dynamics is described by Langevin and Fokker-Planck equations with both deterministic and stochastic contributions. We show that the deterministic terms contain both the angular and speed dependence of axonal growth, and that these two contributions can be separated. Growth alignment is determined by surface geometry, and it is quantified by the deterministic part of the Langevin equation. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: speed and angular distributions, correlation functions, diffusion coefficients, characteristics speeds and damping coefficients. We demonstrate that axonal dynamics displays a cross-over from Brownian motion (Ornstein-Uhlenbeck process) at earlier times to anomalous dynamics (superdiffusion) at later times. The superdiffusive regime is characterized by non-Gaussian speed distributions and power law dependence of the axonal mean square length and the velocity correlation functions. These results demonstrate the importance of geometrical cues in guiding axonal growth, and could lead to new methods for bioengineering novel substrates for controlling neuronal growth and regeneration.
Low Photon Budget Phase Retrieval with Perceptual Loss Trained Deep Neural Networks
Date: 2019-06-12 00:00:00
Tasks: Retrieval

Deep neural networks (DNNs) are efficient solvers for ill-posed problems and
have been shown to outperform classical optimization techniques in several
computational imaging problems. DNNs are trained by solving an optimization
problem implies the choice of an appropriate loss function, i.e., the function
to optimize. In a recent paper [A. Goy \textit{et al.}, Phys. Rev. Lett.
121(24), 243902 (2018)], we showed that DNNs trained with the negative Pearson
correlation coefficient as the loss function are particularly fit for
photon-starved phase retrieval problems, which are fundamentally affected by
strong Poison noise. In this paper we demonstrate that the use of a perceptual
loss function significantly improves the reconstructions.
Modeling and Verifying Cyber-Physical Systems with Hybrid Active Objects
Date: 2019-06-13 00:00:00
Tasks: Translation

Formal modeling of cyber-physical systems (CPS) is hard, because they pose
the double challenge of combined discrete-continuous dynamics and concurrent
behavior. Existing formal specification and verification languages for CPS are
designed on top of their underlying proof search technology. They lack
high-level structuring elements. In addition, they are not efficiently
executable. This makes formal CPS models hard to understand and to validate,
hence impairs their usability. Instead, we suggest to model CPS in an Active
Objects (AO) language designed for concise, intuitive modeling of concurrent
systems. To this end, we extend the AO language ABS and its runtime environment
with Hybrid Active Objects (HAO). CPS models and requirements formalized in HAO
must follow certain communication patterns that permit automatic translation
into differential dynamic logic, a sequential hybrid program logic.
Verification is achieved by discharging the resulting formulas with the theorem
prover KeYmaera X. We demonstrate the practicality of our approach with case
studies.
Knock Intensity Distribution and a Stochastic Control Framework for Knock Control
Date: 2019-06-13 00:00:00

One of the main factors limiting the efficiency of spark-ignited engines is
the occurrence of engine knock. In high temperature and high pressure
in-cylinder conditions, the fuel-air mixture auto-ignites creating pressure
shock waves in the cylinder. Knock can significantly damage the engine and
hinder its performance; as such, conservative knock control strategies are
generally implemented that avoid such operating conditions at the cost of lower
thermal efficiencies. Significant improvements in the performance of
conventional knock controllers are possible if the properties of the knock
process are better characterized and exploited in knock controller designs. One
of the methods undertaken to better characterize knocking instances is to
employ a probabilistic approach, in which the likelihood of knock is derived
from the statistical distribution of knock intensity. In this paper, it is
shown that knock intensity values at a fixed operating point for single fuel
and dual fuel engines are accurately described using a mixed lognormal
distribution. The fitting accuracy is compared against those for a randomly
generated mixed-lognormally distributed data set, and shown to exceed a 95%
accuracy threshold for almost all of the operating points tested. Additionally,
this paper discusses a stochastic knock control approach that leverages the
mixed lognormal distribution to adjust spark timing based on knock intensity
measurements. This more informed knock control strategy would allow for
improvements in engine performance and fuel efficiency by minimizing knock
occurrences.
Modeling and Control of Combustion Phasing in Dual-Fuel Compression Ignition Engines
Date: 2019-06-13 00:00:00

Dual fuel engines can achieve high efficiencies and low emissions but also
can encounter high cylinder-to-cylinder variations on multi-cylinder engines.
In order to avoid these variations, they require a more complex method for
combustion phasing control such as model-based control. Since the combustion
process in these engines is complex, typical models of the system are complex
as well and there is a need for simpler, computationally efficient,
control-oriented models of the dual fuel combustion process. In this paper, a
mean-value combustion phasing model is designed and calibrated and two control
strategies are proposed. Combustion phasing is predicted using a knock integral
model, burn duration model and a Wiebe function and this model is used in both
an adaptive closed loop controller and an open loop controller. These two
control methodologies are tested and compared in simulations. Both control
strategies are able to reach steady state in 5 cycles after a transient and
have steady state errors in CA50 that are less than 0.1 crank angle degree
(CAD) with the adaptive control strategy and less than 1.5 CAD with the
model-based feedforward control method.
Global analysis of a predator-prey model with variable predator search rate
Date: 2019-06-13 00:00:00

We consider a modified Rosenzweig-MacArthur predator-prey model, based on the
premise that the search rate of predators is dependent on the prey density,
rather than constant. A complete analysis of the global behavior of the model
is presented, and shows that the model exhibits a dichotomy similar to the
classical Rosenzweig-MacArthur model: either the coexistence steady state is
globally stable; or it is unstable, and then a unique, globally stable limit
cycle exists. We discuss the similarities, but also important differences
between our model and the Rosenweig-MacArthur model. The main differences are
that: 1. The paradox of enrichment which always occurs in the
Rosenzweig-MacArthur model, does not always occur here, and 2. Even when the
paradox of enrichment occurs, predators can adapt by lowering their search
rate, and effectively stabilize the system.
An agent-based model for designing a financial market that works well
Date: 2019-06-14 00:00:00

Designing a financial market that works well is very important for developing
and maintaining an advanced economy, but is not easy because changing detailed
rules, even ones that seem trivial, sometimes causes unexpected large impacts
and side effects. A computer simulation using an agent-based model can directly
treat and clearly explain such complex systems where micro processes and macro
phenomena interact. Many effective agent-based models investigating human
behavior have already been developed. Recently, an artificial market model,
which is an agent-based model for a financial market, has started to contribute
to discussions on rules and regulations of actual financial markets. I
introduce an artificial market model to design financial markets that work well
and describe a previous study investigating tick size reduction. I hope that
more artificial market models will contribute to designing financial markets
that work well to further develop and maintain advanced economies.
Periodic Neural Codes and Sound Localization in Barn Owls
Authors: Lindsey S. Brown, Carina Curto
Date: 2019-06-14 00:00:00

Inspired by the sound localization system of the barn owl, we define a new class of neural codes, called periodic codes, and study their basic properties. Periodic codes are binary codes with a special patterned form that reflects the periodicity of the stimulus. Because these codes can be used by the owl to localize sounds within a convex set of angles, we investigate whether they are examples of convex codes, which have previously been studied for hippocampal place cells. We find that periodic codes are typically not convex, but can be completed to convex codes in the presence of noise. We introduce the convex closure and Hamming distance completion as ways of adding codewords to make a code convex, and describe the convex closure of a periodic code. We also find that the probability of the convex closure arising stochastically is greater for sparser codes. Finally, we provide an algebraic method using the neural ideal to detect if a code is periodic. We find that properties of periodic codes help to explain several aspects of the behavior observed in the sound localization system of the barn owl, including common errors in localizing pure tones.
Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization
Date: 2019-06-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Beamforming in multiple input multiple output (MIMO) systems is one of the
key technologies for modern wireless communication. Creating appropriate
sector-specific broadcast beams are essential for enhancing the coverage of
cellular network and for improving the broadcast operation for control signals.
However, in order to maximize the coverage, patterns for broadcast beams need
to be adapted based on the users' distribution and movement over time. In this
work, we present self-tuning sectorization: a deep reinforcement learning
framework to optimize MIMO broadcast beams autonomously and dynamically based
on user' distribution in the network. Taking directly UE measurement results as
input, deep reinforcement learning agent can track and predict the UE
distribution pattern and come up with the best broadcast beams for each cell.
Extensive simulation results show that the introduced framework can achieve the
optimal coverage, and converge to the oracle solution for both single sector
and multiple sectors environment, and for both periodic and Markov mobility
patterns.