pwc-archive/papers-with-abstracts huggingface dataset:
Spacecraft Relative Motion Planning Using Chained Chance-Constrained Admissible Sets
Date: 2019-10-17 00:00:00
Tasks: Motion Planning

With the increasing interest in proximity and docking operations, there is a
growing interest in spacecraft relative motion control. This paper extends a
previously proposed constrained relative motion approach based on chained
positively invariant sets to the case where the spacecraft dynamics are
controlled using output feedback on noisy measurements and are subject to
stochastic disturbances. It is shown that non-convex polyhedral exclusion zone
constraints can be handled. The methodology consists of a virtual net of static
equilibria nodes in the Clohessy-Wiltshire-Hill frame. Connectivity between
nodes is determined through the use of chance-constrained admissible sets,
guaranteeing that constraints are met with a specified probability.
Forecasting under Long Memory and Nonstationarity
Date: 2019-10-17 00:00:00
Tasks: Time Series, Time Series Analysis

Long memory in the sense of slowly decaying autocorrelations is a stylized
fact in many time series from economics and finance. The fractionally
integrated process is the workhorse model for the analysis of these time
series. Nevertheless, there is mixed evidence in the literature concerning its
usefulness for forecasting and how forecasting based on it should be
implemented.
  Employing pseudo-out-of-sample forecasting on inflation and realized
volatility time series and simulations we show that methods based on fractional
integration clearly are superior to alternative methods not accounting for long
memory, including autoregressions and exponential smoothing. Our proposal of
choosing a fixed fractional integration parameter of $d=0.5$ a priori yields
the best results overall, capturing long memory behavior, but overcoming the
deficiencies of methods using an estimated parameter.
  Regarding the implementation of forecasting methods based on fractional
integration, we use simulations to compare local and global semiparametric and
parametric estimators of the long memory parameter from the Whittle family and
provide asymptotic theory backed up by simulations to compare different mean
estimators. Both of these analyses lead to new results, which are also of
interest outside the realm of forecasting.
Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference
Authors: Ruoxuan Xiong, Markus Pelger
Date: 2019-10-18 00:00:00
Tasks: Causal Inference, counterfactual, Missing Values

This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time under general adoption patterns where the units can be affected by unobserved factors.
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT
Date: 2019-10-18 00:00:00
Tasks: Edge-computing, Scheduling

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is
a prominent concept, where a UAV equipped with a MEC server is deployed to
serve a number of terminal devices (TDs) of Internet of Things (IoT) in a
finite period. In this paper, each TD has a certain latency-critical
computation task in each time slot to complete. Three computation strategies
can be available to each TD. First, each TD can operate local computing by
itself. Second, each TD can partially offload task bits to the UAV for
computing. Third, each TD can choose to offload task bits to access point (AP)
via UAV relaying. We propose a new optimization problem formulation that aims
to minimize the total energy consumption including communication-related
energy, computation-related energy and UAV's flight energy by optimizing the
bits allocation, time slot scheduling and power allocation as well as UAV
trajectory design. As the formulated problem is non-convex and difficult to
find the optimal solution, we solve the problem by two parts, and obtain the
near optimal solution with within a dozen of iterations. Finally, numerical
results are given to validate the proposed algorithm, which is verified to be
efficient and superior to the other benchmark cases.
Enhancing Temporal Logic Falsification with Specification Transformation and Valued Booleans
Date: 2019-10-18 00:00:00
Tasks: Industrial Robots

Cyber-Physical Systems (CPSs) are systems with both physical and software
components, for example cars and industrial robots. Since these systems exhibit
both discrete and continuous dynamics, they are complex and it is thus
difficult to verify that they behave as expected. Falsification of temporal
logic properties is an approach to find counterexamples to CPSs by means of
simulation. In this paper, we propose two additions to enhance the capability
of falsification and make it more viable in a large-scale industrial setting.
The first addition is a framework for transforming specifications from a
signal-based model into Signal Temporal Logic. The second addition is the use
of Valued Booleans and an additive robust semantics in the falsification
process. We evaluate the performance of the additive robust semantics on a set
of benchmark models, and we can see that which semantics are preferable depend
both on the model and on the specification.
Data-Driven Model-Free Adaptive Predictive Control and its Stability Analysis
Date: 2020-11-17 00:00:00

In this paper, a novel full form dynamic linearization (FFDL) data-driven
model-free adaptive predictive control (MFAPC) method is proposed for a class
of discrete-time single-input single-output nonlinear systems. The novelty of
MFAPC is that preliminary physical model and the Lyapunov stability theory are
not required for the controller design and theoretical analysis. Instead, the
proposed MFAPC only uses a new dynamic linearization method called
pseudo-gradient (PG) vector, which is merely related to the input/output (I/O)
measurement data. The main contributions of this paper are: First, a novel
MFAPC with adjustable parameters is proposed; Second, we have proved the
bounded-input bounded-output stability, the monotonic convergence of the
tracking error, and the internal stability of the proposed method. Third, the
proposed MFAPC can be considered as an elegant extension of the current MFAC.
The simulations have been carried out to verify the effectiveness of the
proposed MFAPC.
Signal-Based Properties of Cyber-Physical Systems: Taxonomy and Logic-based Characterization
Date: 2019-10-18 00:00:00

The behavior of a cyber-physical system (CPS) is usually defined in terms of the input and output signals processed by sensors and actuators. Requirements specifications of CPSs are typically expressed using signal-based temporal properties. Expressing such requirements is challenging, because of (1) the many features that can be used to characterize a signal behavior; (2) the broad variation in expressiveness of the specification languages (i.e., temporal logics) used for defining signal-based temporal properties. Thus, system and software engineers need effective guidance on selecting appropriate signal behavior types and an adequate specification language, based on the type of requirements they have to define. In this paper, we present a taxonomy of the various types of signal-based properties and provide, for each type, a comprehensive and detailed description as well as a formalization in a temporal logic. Furthermore, we review the expressiveness of state-of-the-art signal-based temporal logics in terms of the property types identified in the taxonomy. Moreover, we report on the application of our taxonomy to classify the requirements specifications of an industrial case study in the aerospace domain, in order to assess the feasibility of using the property types included in our taxonomy and the completeness of the latter.
The concerted emergence of well-known spatial and temporal ecological patterns in an evolutionary food web model in space
Date: 2019-10-18 00:00:00
Tasks: Position

Ecological systems show a variety of characteristic patterns of biodiversity
in space and time. It is a challenge for theory to find models that can
reproduce and explain the observed patterns. Since the advent of island
biogeography these models revolve around speciation, dispersal, and extinction,
but they usually neglect trophic structure. Here, we propose and study a
spatially extended evolutionary food web model that allows us to study large
spatial systems with several trophic layers. Our computer simulations show that
the model gives rise simultaneously to several biodiversity patterns in space
and time, from species abundance distributions to the waxing and waning of
geographic ranges. We find that trophic position in the network plays a crucial
role when it comes to the time evolution of range sizes, because the trophic
context restricts the occurrence and survival of species especially on higher
trophic levels.
Utilizing Dependencies to Obtain Subsets of Reachable Sets
Date: 2020-11-16 00:00:00
Tasks: Fault Detection, Model Predictive Control

Reachability analysis, in general, is a fundamental method that supports
formally-correct synthesis, robust model predictive control, set-based
observers, fault detection, invariant computation, and conformance checking, to
name but a few. In many of these applications, one requires to compute a
reachable set starting within a previously computed reachable set. While it was
previously required to re-compute the entire reachable set, we demonstrate that
one can leverage the dependencies of states within the previously computed set.
As a result, we almost instantly obtain an over-approximative subset of a
previously computed reachable set by evaluating analytical maps. The advantages
of our novel method are demonstrated for falsification of systems, optimization
over reachable sets, and synthesizing safe maneuver automata. In all of these
applications, the computation time is reduced significantly.
Wideband Perfect Absorption Polarization Insensitive Reconfigurable Graphene Metasurface for THz Wireless Environment
Date: 2019-10-18 00:00:00

In this work, we investigated a simple structured graphene terahertz (THz)
metasurface (MSF) with perfect absorption, wideband, polarization insensitive,
oblique incidence insensitive and frequency reconfiguration. The graphene MSF
structure is composed of a two-dimensional periodic array of graphene
meta-atoms deposited on the silicon substrate terminated by a metal ground
plane. The performance of the proposed MSF is numerically analyzed. An
equivalent circuit model of the structure and its closed-form solution is
introduced. The graphene MSF thin structure at 2.5 THz provides 100% of
absorption with wide bandwidth, zero reflection and zero transmission at normal
incidence in both transverse electric (TE) and transverse magnetic (TM)
polarization. Under oblique incidence, the absorption is maintained at higher
than 95%. Moreover, the graphene MSF structure has the advantage of frequency
reconfiguration. The excellent absorption performance is maintained at all
reconfigurable frequencies upon reconfiguration. The results reveal the
effectiveness of the THz MSF with graphene meta-atoms, which can be promising
for THz wireless environment.
Translation position extracting in incoherent Fourier ptychography
Date: 2019-10-17 00:00:00
Tasks: Position, Super-Resolution, Translation

Incoherent Fourier ptychography (IFP) is a newly developed super-resolution
method, where accurate knowledge of translation positions is essential for
image reconstruction.To release this limitation, we propose a preprocessing
algorithm capable of extracting translation positions of the structure light
directly from raw images of IFP, termed translation position extracting (TPE).
TPE mainly involves two steps. First, the speckle parts mixed in the acquired
intensities, in which the illumination motion is encoded, are isolated by
intensity averaging and division. Then the cross-correlations of the speckle
dataset are computed to determine the shift positions. TPE-IFP improves the
previous IFP by removal of the requirement for prior knowledge of translation
positions. Its effectiveness is demonstrated by obtaining high-quality
super-resolution images in absence of location information in both simulations
and experiments. By further relaxing the practical conditions, the proposed TPE
may accelerate the applications of IFP. What is more, as a preprocessing
approach, TPE might also contribute to the estimation of pattern positions for
the similar speckle-based imaging.
Markov Decision Processes For Multi-Objective Satellite Task Planning
Date: 2019-10-18 00:00:00
Tasks: Management, Scheduling, Task Planning

This paper presents a semi-Markov decision process (SMDP) formulation of the
satellite task scheduling problem. This formulation can consider multiple
operational objectives simultaneously and plan transitions between distinct
functional modes. We consider the problem of scheduling image collections,
ground contacts, sun-pointed periods for battery recharging, and data recorder
management for an agile, resource-constrained Earth-observing spacecraft. By
considering multiple mission objectives simultaneously, the algorithm is able
to find optimized task schedule that satisfies all operational constraints in a
single planning step, thus reducing the operational complexity and number of
steps involved in mission planning. We present two solution approaches based on
forward search and Monte Carlo Tree search. We baseline against rule-based,
graph search, and mixed-integer-linear programming approaches. The SMDP
formulation is evaluated in both single-objective and multi-objective
scenarios. The SMDP solution is found to perform comparably with the baseline
methods at greatly increased speed in single-objective scenarios and greater
schedule reward in multi-objective.
A Pulse Width Modulation based Power-elastic and Robust Mixed-signal Perceptron Design
Date: 2019-10-17 00:00:00

Neural networks are exerting burgeoning influence in emerging artificial
intelligence applications at the micro-edge, such as sensing systems and image
processing. As many of these systems are typically self-powered, their circuits
are expected to be resilient and efficient in the presence of continuous power
variations caused by the harvesters.
  In this paper, we propose a novel mixed-signal (i.e. analogue/digital)
approach of designing a power-elastic perceptron using the principle of pulse
width modulation (PWM). Fundamental to the design are a number of parallel
inverters that transcode the input-weight pairs based on the principle of PWM
duty cycle. Since PWM-based inverters are typically agnostic to amplitude and
frequency variations, the perceptron shows a high degree of power elasticity
and robustness under these variations. We show extensive design analysis in
Cadence Analog Design Environment tool using a 3x3 perceptron circuit as a case
study to demonstrate the resilience in the presence of parameric variations.
Topological data analysis of zebrafish patterns
Date: 2019-10-18 00:00:00
Tasks: Interpretable Machine Learning, Topological Data Analysis

Self-organized pattern behavior is ubiquitous throughout nature, from fish
schooling to collective cell dynamics during organism development.
Qualitatively these patterns display impressive consistency, yet variability
inevitably exists within pattern-forming systems on both microscopic and
macroscopic scales. Quantifying variability and measuring pattern features can
inform the underlying agent interactions and allow for predictive analyses.
Nevertheless, current methods for analyzing patterns that arise from collective
behavior only capture macroscopic features, or rely on either manual inspection
or smoothing algorithms that lose the underlying agent-based nature of the
data. Here we introduce methods based on topological data analysis and
interpretable machine learning for quantifying both agent-level features and
global pattern attributes on a large scale. Because the zebrafish is a model
organism for skin pattern formation, we focus specifically on analyzing its
skin patterns as a means of illustrating our approach. Using a recent
agent-based model, we simulate thousands of wild-type and mutant zebrafish
patterns and apply our methodology to better understand pattern variability in
zebrafish. Our methodology is able to quantify the differential impact of
stochasticity in cell interactions on wild-type and mutant patterns, and we use
our methods to predict stripe and spot statistics as a function of varying
cellular communication. Our work provides a new approach to automatically
quantifying biological patterns and analyzing agent-based dynamics so that we
can now answer critical questions in pattern formation at a much larger scale.
Heterogeneously parameterized tube model predictive control for LPV systems
Date: 2019-10-18 00:00:00
Tasks: Model Predictive Control

This paper presents a heterogeneously parameterized tube-based model
predictive control (MPC) design applicable to linear parameter-varying (LPV)
systems. In a heterogeneous tube, the parameterizations of the tube cross
sections and the associated control laws are allowed to vary along the
prediction horizon. Two extreme cases that can be described in this framework
are scenario MPC (high complexity, larger domain of attraction) and homothetic
tube MPC with a simple time-invariant control parameterization (low complexity,
smaller domain of attraction). In the proposed framework, these extreme
parameterizations, as well as other parameterizations of intermediate
complexity, can be combined within a single tube. By allowing for more
flexibility in the parameterization design, one can influence the trade-off
between computational cost and the size of the domain of attraction. Sufficient
conditions on the parameterization structure are developed under which
recursive feasibility and closed-loop stability are guaranteed. A specific
parameterization that combines the principles of scenario and homothetic tube
MPC is proposed and it is shown to satisfy the required conditions. The
properties of the approach, including its capability of achieving improved
complexity/performance trade-offs, are demonstrated using two numerical
examples.
Mechanosensitive Self-Assembly of Myosin II Minifilaments
Date: 2019-10-18 00:00:00

Self-assembly and force generation are two central processes in biological
systems that usually are considered in separation. However, the signals that
activate non-muscle myosin II molecular motors simultaneously lead to
self-assembly into myosin II minifilaments as well as progression of the motor
heads through the crossbridge cycle. Here we investigate theoretically the
possible effects of coupling these two processes. Our assembly model, which
builds upon a consensus architecture of the minifilament, predicts a critical
aggregation concentration at which the assembly kinetics slows down
dramatically. The combined model predicts that increasing actin filament
concentration and force both lead to a decrease in the critical aggregation
concentration. We suggest that due to these effects, myosin II minifilaments in
a filamentous context might be in a critical state that reacts faster to
varying conditions than in solution. We finally compare our model to
experiments by simulating fluorescence recovery after photobleaching.
Healthy... Distress... Default
Authors: Zura Kakushadze
Date: 2019-10-18 00:00:00

We discuss a simple, exactly solvable model of stochastic stock dynamics that incorporates regime switching between healthy and distressed regimes. Using this model, which is analytically tractable, we discuss a way of extracting expected returns for stocks from realized CDS spreads, essentially, the CDS market sentiment about future stock returns. This alpha/signal could be useful in a cross-sectional (statistical arbitrage) context for equities trading.
Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Communications
Date: 2019-10-18 00:00:00

Millimeter wave (mmWave) communications are evolving as a promising
technology to meet the ever increasing data rate requirements. However, high
directivity and severe path loss make it vulnerable to blockages, which could
be frequent in indoor or urban environments. To address this issue, intelligent
reflecting surfaces (IRSs) are introduced to provide additional adjustable
reflected paths. Most prior works assume that elements of IRSs have an infinite
phase resolution, which is difficult to be realized in practical systems. In
this paper, IRSs with low-resolution phase shifters are considered. We aim to
maximize the receive signal power at the user by jointly optimizing discrete
phase shifts of IRSs and the transmit beamforming vector at the base station
for mmWave downlink systems. An analytical near-optimal solution is developed
by exploiting some important characteristics of mmWave channels. Our
theoretical analysis reveals that low-resolution phase shifters can still
achieve a receive signal power that increases quadratically with the number of
reflecting elements. Simulation results are provided to corroborate our
analysis and show the effectiveness of the proposed solution.
A multilevel analysis to systemic exposure: insights from local and system-wide information
Date: 2019-10-18 00:00:00

In the aftermath of the financial crisis, the growing literature on financial
networks has widely documented the predictive power of topological
characteristics (e.g. degree centrality measures) to explain the systemic
impact or systemic vulnerability of financial institutions. In this work, we
show that considering alternative topological measures based on local
sub-network environment improves our ability to identify systemic institutions.
To provide empirical evidence, we apply a two-step procedure. First, we recover
network communities (i.e. close-peer environment) on a spillover network of
financial institutions. Second, we regress alternative measures of
vulnerability on three levels of topological measures: the global level (i.e.
firm topological characteristics computed over the whole system), local level
(i.e. firm topological characteristics computed over the community) and
aggregated level by averaging individual characteristics over the community.
The sample includes $46$ financial institutions (banks, broker-dealers,
insurance and real-estate companies) listed in the Standard \& Poor's 500
index. Our results confirm the informational content of topological metrics
based on close-peer environment. Such information is different from the one
embeds in traditional system wide topological metrics and is proved to be
predictor of distress for financial institutions in time of crisis.