pwc-archive/papers-with-abstracts huggingface dataset:
Towards Assume-Guarantee Profiles for Autonomous Vehicles
Date: 2019-09-12 00:00:00
Tasks: Autonomous Vehicles, Self-Driving Cars

Rules or specifications for autonomous vehicles are currently formulated on a
case-by-case basis, and put together in a rather ad-hoc fashion. As a step
towards eliminating this practice, we propose a systematic procedure for
generating a set of supervisory specifications for self-driving cars that are
1) associated with a distributed assume-guarantee structure and 2)
characterizable by the notion of consistency and completeness. Besides helping
autonomous vehicles make better decisions on the road, the assume-guarantee
contract structure also helps address the notion of blame when undesirable
events occur. We give several game-theoretic examples to demonstrate
applicability of our framework.
Direct and Indirect Effects based on Changes-in-Changes
Date: 2019-10-22 00:00:00

We propose a novel approach for causal mediation analysis based on
changes-in-changes assumptions restricting unobserved heterogeneity over time.
This allows disentangling the causal effect of a binary treatment on a
continuous outcome into an indirect effect operating through a binary
intermediate variable (called mediator) and a direct effect running via other
causal mechanisms. We identify average and quantile direct and indirect effects
for various subgroups under the condition that the outcome is monotonic in the
unobserved heterogeneity and that the distribution of the latter does not
change over time conditional on the treatment and the mediator. We also provide
a simulation study and an empirical application to the Jobs II programme.
Continuous Time Random Walk with correlated waiting times. The crucial role of inter-trade times in volatility clustering
Date: 2020-04-11 00:00:00

In many physical, social or economical phenomena we observe changes of a
studied quantity only in discrete, irregularly distributed points in time. The
stochastic process used by physicists to describe this kind of variables is the
Continuous Time Random Walk (CTRW). Despite the popularity of this type of
stochastic processes and strong empirical motivation, models with a long-term
memory within the sequence of time intervals between observations are missing.
Here, we fill this gap by introducing a new family of CTRWs. The memory is
introduced to the model by the assumption that many consecutive time intervals
can be the same. Surprisingly, in this process we can observe a slowly decaying
nonlinear autocorrelation function without a fat-tailed distribution of time
intervals. Our model applied to high-frequency stock market data can
successfully describe the slope of decay of nonlinear autocorrelation function
of stock market returns. The model achieves this result with no dependence
between consecutive price changes. It proves the crucial role of inter-event
times in the volatility clustering phenomenon observed in all stock markets.
Matching Estimators with Few Treated and Many Control Observations
Authors: Bruno Ferman
Date: 2019-09-11 00:00:00
Tasks: valid

We analyze the properties of matching estimators when there are few treated, but many control observations. We show that, under standard assumptions, the nearest neighbor matching estimator for the average treatment effect on the treated is asymptotically unbiased in this framework. However, when the number of treated observations is fixed, the estimator is not consistent, and it is generally not asymptotically normal. Since standard inference methods are inadequate, we propose alternative inference methods, based on the theory of randomization tests under approximate symmetry, that are asymptotically valid in this framework. We show that these tests are valid under relatively strong assumptions when the number of treated observations is fixed, and under weaker assumptions when the number of treated observations increases, but at a lower rate relative to the number of control observations.
A Barrier Function Approach to Finite-Time Stochastic System Verification and Control
Date: 2019-09-10 00:00:00

This paper studies the problem of enforcing safety of a stochastic dynamical
system over a finite-time horizon. We use stochastic control barrier functions
as a means to quantify the probability that a system exits a given safe region
of the state space in finite time. A barrier certificate condition that bounds
the expected value of the barrier function over the time horizon is recast as a
sum-of-squares optimization problem for efficient numerical computation. Unlike
prior works, the proposed certificate condition includes a state-dependent
upper bound on the evolution of the expectation. We present formulations for
both continuous-time and discrete-time systems. Moreover, for systems for which
the drift dynamics are affine-in-control, we propose a method for synthesizing
polynomial state feedback controllers that achieve a specified probability of
safety. Several case studies are presented which benchmark and illustrate the
performance of our verification and control method in the continuous-time and
discrete-time domains.
Vector Space and Matrix Methods in Signal and System Theory
Date: 2019-09-11 00:00:00
Tasks: Abstract Algebra

The tools, ideas, and insights from linear algebra, abstract algebra, and
functional analysis can be extremely useful to signal processing and system
theory in various areas of engineering, science, and social science including
approximation, optimization, parameter identification, big data, etc. Indeed,
many important ideas can be developed from the simple operator equation
  A x = b
  by considering it in a variety of ways. If x and if b are vectors from the
same or, perhaps, different vector spaces and A is an operator, there are three
interesting questions that can be asked which provide a setting for a broad
study.
On the performance of some new Multiuser FSO-MIMO Communication Systems
Date: 2019-08-25 00:00:00

The practical implementation of maximum likelihood detection is limited by
its high complexity as well as requiring perfect channel state information.
Although conventional blind detection techniques reduce complexity, they
degrade performance and require blind channel state information. In this paper
(for the first time), a deep learning based blind detection and a joint blind
detection-constellation shaping structure are presented (to solve this
problem). This paper (deeply) goes through the problem and discusses several
(practical) scenarios, including single user, multiuser with resource (channel)
allocation, and multiuser without resource allocation (multiuser interference).
In order to show the universality of the proposed systems, wide atmospheric
turbulence regimes, from weak to strong are considered, and single input single
output, as well as multi-input multi-output structures are considered. Results
indicate that without channel estimation, a deep learning based (blind)
detector (despite its very few complexity, and despite conventional systems
require it), could have a very favorable performance at all around. So, it is
expected that practical implementations of the proposed structures greatly
reduce cost, and processing latency, while maintaining performance close enough
to the outstanding conventional systems.
A Survey on Machine Learning for Optical Communication [Machine Learning View]
Date: 2019-08-21 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning (ML) for Optical Communication (OC) is certainly a hot topic
emerged recently and will continue to raise interest at least for the next few
years. The rate of research development in this area is growing very rapidly.
Novelty of this research direction resides mainly in the peculiarity of the
application field, rather than in the methodological approaches, which are (at
least up to now) state-of-the-art ML algorithms. Reviewing the literature shows
that many of the ML algorithms have not yet been used in this area, and many of
the OC applications are not considered yet, which reflects the fact that the
research topic is pristine. Accordingly, tutorial investigations are quiet
necessary in this filed to help researchers be aware about the last
progressions and cavities of this field. Although several tutorials have been
released recently, they considered this topic from OC view, and neglected ML
view. However, it is required to have an investigations about the ML algorithms
used in this subject. Accordingly, for the first time, this paper reviews ML
for OC literature from ML viewpoint. This view could be really helpful because
only OC experts work on ML for OC, and they are not ML experts, so it could
really help them to have a comprehensive view on the ML subjects implantable in
OC. It has worth to mention that compared with other works, this survey reviews
much more investigations; therefore, it has more generality, and gives the
reader to have a comprehensive overview on this topic.
Correlation-based Initialization Algorithm for Tensor-based HSI Compression Methods
Date: 2019-08-27 00:00:00
Tasks: Tensor Decomposition

Tensor decomposition (TD) is widely used in hyperspectral image (HSI)
compression. The initialization of factor matrix in tensor decomposition can
determine the HSI compression performance. It is worth noting that HSI is
highly correlated in bands. However, this phenomenon is ignored by the previous
TD method. Aiming at improving the HSI compression performance, we propose a
method called correlation-based TD initialization algorithm. As HSI is well
approximated by means of a reference band. In accordance with the SVD result of
the reference band, the initialized factor matrices of TD are produced. We
compare our methods with random and SVD-based initialization methods. The
experimental results reveal that our correlation-based TD initialization method
is capable of significantly reducing the computational cost of TD while keeping
the initialization quality and compression performance.
Functional Principal Component Analysis as a Versatile Technique to Understand and Predict the Electric Consumption Patterns
Date: 2020-02-26 00:00:00

Understanding and predicting the electric consumption patterns in the short-,
mid- and long-term, at the distribution and transmission level, is a
fundamental asset for smart grids infrastructure planning, dynamic network
reconfiguration, dynamic energy pricing and savings, and thus energy
efficiency. This work introduces the Functional Principal Component Analysis
(FPCA) as a versatile method to both investigate and predict, at different
level of spatial aggregation, the consumption patterns. The method was applied
to a unique and sensitive dataset that includes electric consumption and
contractual information of Milan metropolitan area. The decomposition of the
load patterns into principal functions was found to be a powerful method to
identify the physical and behavioral causes underlying the daily consumptions,
given knowledge of exogenous variables such as calendar and meteorological
data. The effectiveness of long-term predictions based on principal functions
was proved on Milan's metropolitan area data and assessed on a
publicly-available dataset.
Robust Utility Maximization with Drift and Volatility Uncertainty
Date: 2019-09-11 00:00:00

We give explicit solutions for utility maximization of terminal wealth
problem $u(X_T)$ in the presence of Knightian uncertainty in continuous time
$[0,T]$ in a complete market. We assume there is uncertainty on both drift and
volatility of the underlying stocks, which induce nonequivalent measures on
canonical space of continuous paths $\O$. We take that the uncertainty set
resides in compact sets that are time dependent. In this framework, we solve
the robust optimization problem with logarithmic, power and exponential utility
functions, explicitly.
Multitask Classification and Segmentation for Cancer Diagnosis in Mammography
Date: 2019-09-11 00:00:00
Tasks: Classification, Multi-Task Learning

Annotation cost is a bottleneck for collecting massive data in mammography,
especially for training deep neural networks. In this paper, we study the use
of heterogeneous levels of annotation granularity to improve predictive
performances. More precisely, we introduce a multi-task learning scheme for
training convolutional neural network (ConvNets), which combines segmentation
and classification, using image-level and pixel-level annotations. In this way,
different objectives can be used to regularize training by sharing intermediate
deep representations. Successful experiments are carried out on the Digital
Database of Screening Mammography (DDSM) to validate the relevance of the
proposed approach.
Design of a High Step-up DC-DC Power Converter with Voltage Multiplier Cells and Reduced Losses on Semiconductors for Photovoltaic Systems
Date: 2019-09-12 00:00:00

A high step up dc dc converter based on an isolated dc dc converter with
voltage multiplier cells for photovoltaic systems is essentially introduced in
this paper. The proposed converter can provide a high step up voltage gain. The
switch voltage stress and losses on semiconductors are significantly reduced
through this work. Furthermore, the proposed converter can reliably offer and
provide continuous input current which can be basically used for integrating
photovoltaic systems to convert 30 V to 480 V dc bus. The ripple on the input
current is minimized due to the isolated converter, and the proposed converter
is fed by a single input voltage. The operation modes and the characteristics
of the aforementioned converter are thoroughly analyzed. The components
selection, simulation results and experiment results are mainly verified by
using MATALB Simulink. Consequently, a 360 W hardware prototype is implemented
to validate the design and the theory.
Recovering Preferences from Finite Data
Authors: Christopher P. Chambers, Federico Echenique, Nicolas Lambert
Date: 2019-09-12 00:00:00
Tasks: valid

We study preferences estimated from finite choice experiments and provide sufficient conditions for convergence to a unique underlying "true" preference. Our conditions are weak, and therefore valid in a wide range of economic environments. We develop applications to expected utility theory, choice over consumption bundles, menu choice and intertemporal consumption. Our framework unifies the revealed preference tradition with models that allow for errors.
Covariance steering in zero-sum linear-quadratic two-player differential games
Date: 2019-09-12 00:00:00
Tasks: Vocal Bursts Valence Prediction

We formulate a new class of two-person zero-sum differential games, in a
stochastic setting, where a specification on a target terminal state
distribution is imposed on the players. We address such added specification by
introducing incentives to the game that guides the players to steer the join
distribution accordingly. In the present paper, we only address linear
quadratic games with Gaussian target distribution. The solution is
characterized by a coupled Riccati equations system, resembling that in the
standard linear quadratic differential games. Indeed, once the incentive
function is calculated, our problem reduces to a standard one. Tthe framework
developed in this paper extends previous results in covariance control, a fast
growing research area. On the numerical side, problems herein are reformulated
as convex-concave minimax problems for which efficient and reliable algorithms
are available.
Estimation and Applications of Quantile Regression for Binary Longitudinal Data
Date: 2019-09-12 00:00:00
Tasks: Computational Efficiency, quantile regression, regression

This paper develops a framework for quantile regression in binary
longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is
designed to fit the model and its computational efficiency is demonstrated in a
simulation study. The proposed approach is flexible in that it can account for
common and individual-specific parameters, as well as multivariate
heterogeneity associated with several covariates. The methodology is applied to
study female labor force participation and home ownership in the United States.
The results offer new insights at the various quantiles, which are of interest
to policymakers and researchers alike.
The consequence of substrates of large-scale rigidity on actin network tension in adherent cells
Authors: Ian Manifacier, Kevin Beussman, Sangyoon Han, Nathan Sniadecki, Imad About, Jean-Louis Milan
Date: 2019-09-12 00:00:00

There is compelling evidence that substrate stiffness affects cell adhesion as well as cytoskeleton organization and contractile activity. This work was designed to study the cytoskeletal contractile activity of cells plated on microposts of different stiffness using a numerical model simulating the intracellular tension of individual cells. We allowed cells to adhere onto micropost substrates of various rigidities and used experimental traction force data to infer cell contractility using a numerical model. The model discriminates between the influence of substrate stiffness on cell tension and shows that higher substrate stiffness leads to an increase in intracellular tension. The strength of this model is its ability to calculate the mechanical state of each cell in accordance to its individual cytoskeletal structure. This is achieved by regenerating a numerical cytoskeleton based
Computational model combined with in vitro experiments to analyse mechanotransduction during mesenchymal stem cell adhesion
Authors: Jean-Louis Milan, Sandrine Lavenus, Paul Pilet, Guy Louarn, Sylvie Wendling, Dominique Heymann, Pierre Layrolle, Patrick Chabrand
Date: 2019-09-12 00:00:00

The shape that stem cells reach at the end of adhesion process influences their differentiation. Rearrangement of cytoskeleton and modification of intracellular tension may activate mechanotransduction pathways controlling cell commitment. In the present study, the mechanical signals involved in cell adhesion were computed in in vitro stem cells of different shapes using a single cell model, the so-called Cytoskeleton Divided Medium (CDM) model. In the CDM model, the filamentous cytoskeleton and nucleoskeleton networks were represented as a mechanical system of multiple tensile and compressive interactions between the nodes of a divided medium. The results showed that intracellular tonus, focal adhesion forces as well as nuclear deformation increased with cell spreading. The cell model was also implemented to simulate the adhesion process of a cell that spreads on protein-coated substrate by emitting filopodia and creating new distant focal adhesion points. As a result, the cell model predicted cytoskeleton reorganisation and reinforcement during cell spreading. The present model quantitatively computed the evolution of certain elements of mechanotransduction and may be a powerful tool for understanding cell mechanobiology and designing biomaterials with specific surface properties to control cell adhesion and differentiation.
Efficient and Robust Equilibrium Strategies of Utilities in Day-ahead Market with Load Uncertainty
Authors: Tianyu Zhao, Hanling Yi, Minghua Chen, Chenye Wu, Yunjian Xu
Date: 2019-09-12 00:00:00

We consider the scenario where $N$ utilities strategically bid for electricity in the day-ahead market and balance the mismatch between the committed supply and actual demand in the real-time market, with uncertainty in demand and local renewable generation in consideration. We model the interactions among utilities as a non-cooperative game, in which each utility aims at minimizing its per-unit electricity cost. We investigate utilities' optimal bidding strategies and show that all utilities bidding according to (net load) prediction is a unique pure strategy Nash Equilibrium with two salient properties. First, it incurs no loss of efficiency; hence, competition among utilities does not increase the social cost. Second, it is robust and (0, $N-1$) fault immune. That is, fault behaviors of irrational utilities only help to reduce other rational utilities' costs. The expected market supply-demand mismatch is minimized simultaneously, which improves the planning and supply-and-demand matching efficiency of the electricity supply chain. We prove the results hold under the settings of correlated prediction errors and a general class of real-time spot pricing models, which capture the relationship between the spot price, the day-ahead clearing price, and the market-level mismatch. Simulations based on real-world traces corroborate our theoretical findings. Our study adds new insights to market mechanism design. In particular, we derive a set of fairly general sufficient conditions for the market operator to design real-time pricing schemes so that the interactions among utilities admit the desired equilibrium.
Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models
Date: 2020-02-25 00:00:00
Tasks: Time Series, Time Series Analysis

The growing share of renewable energy makes the optimization of power flows
in power system models computationally more complicated, due to the widely
distributed weather-dependent electricity generation. This article evaluates
two methods to reduce the temporal complexity of a power transmission grid
model with storage expansion planning. The goal of the reduction techniques is
to accelerate the computation of the linear optimal power flow of the grid
model. This is achieved by choosing a small number of representative time
periods to represent one whole year. To select representative time periods, a
hierarchical clustering is used to aggregate either adjacent hours
chronologically or independently distributed coupling days into clusters of
time series. The aggregation efficiency is evaluated by means of the error of
the objective value and the computational time reduction. Further, both the
influence of the network size and the efficiency of parallel computation in the
optimization process are analysed. As a test case, the transmission grid of the
northernmost German federal state of Schleswig-Holstein with a scenario
corresponding to the year 2035 is considered. The considered scenario is
characterized by a high share of installed renewables.
The Adaptive Tobit Kalman Filter: Tracking Position with Censored Measurements in the IoT
Date: 2019-09-12 00:00:00
Tasks: Position

In the Internet of Things (IoT) paradigm, distributed sensors and actuators
can observe and act on their environment, communicating wirelessly. In this
context, filtering the observations and tracking the network and environment
state over time is extremely important, and the Kalman Filter (KF) is one of
the most common tools for this. Several of these applications deal with
censored data, either because of sensor saturation or limited detection
regions: when censoring happens, all measurements below a certain threshold are
clipped to the threshold value. The recently proposed Tobit Kalman Filter (TKF)
is an adjusted version of the KF that can deal with censored measurements.
However, like the traditional KF, it needs full knowledge of the process and
measurement noise covariances to work, which are not always available in
practice. In this work, we relax this assumption and propose the Adaptive Tobit
Kalman Filter (ATKF), which can dynamically estimate the process and
measurement noise along with the hidden state of the system from the censored
measurements. We apply our solution to navigation and positioning IoT
scenarios, obtaining a negligible performance loss with regard to the TKF, even
with no a priori knowledge of the noise statistics.