pwc-archive/papers-with-abstracts huggingface dataset:
Controller Synthesis for Multi-Agent Systems With Intermittent Communication: A Metric Temporal Logic Approach
Date: 2019-09-22 00:00:00
Tasks: Position

This paper develops a controller synthesis approach for a multi-agent system
(MAS) with intermittent communication. We adopt a leader-follower scheme, where
a mobile leader with absolute position sensors switches among a set of
followers without absolute position sensors to provide each follower with
intermittent state information.We model the MAS as a switched system. The
followers are to asymptotically reach a predetermined consensus state. To
guarantee the stability of the switched system and the consensus of the
followers, we derive maximum and minimal dwell-time conditions to constrain the
intervals between consecutive time instants at which the leader should provide
state information to the same follower. Furthermore, the leader needs to
satisfy practical constraints such as charging its battery and staying in
specific regions of interest. Both the maximum and minimum dwell-time
conditions and these practical constraints can be expressed by metric temporal
logic (MTL) specifications. We iteratively compute the optimal control inputs
such that the leader satisfies the MTL specifications, while guaranteeing
stability and consensus of the followers. We implement the proposed method on a
case study with three mobile robots as the followers and one quadrotor as the
leader.
DHX36-mediated G-quadruplex unfolding is ATP-independent?
Date: 2019-09-22 00:00:00

Chen et al. solved the crystal structure of bovine DHX36 bound to a DNA with
a G-quadruplex (G4) and a single-stranded DNA segment. They believed that the
mechanism they proposed may represent a general model for describing how a
G4-unfolding helicase recognizes and unfolds G4 DNA. Their conclusion is
interesting, however, we noticed that their linear DNA substrate (DNAMyc) that
harbors a Myc-promoter-derived G4-forming sequence was directly used without
pre-folding. This raises the question whether the structure they obtained
really reflects DHX36-mediated G4 recognition and unfolding, or just only
represents a DHX36-binding-induced quasi-folded G4 structure. By a combination
of polymerase extension, DMS footprinting, stopped-flow, and smFRET assays, we
obtained clear evidences that do not support their ATP-independent one-base
translocation structural model. We further revealed that the oscillation of
FRET signal they observed should correspond to a repetitive G4 binding, but not
unfolding, by DHX36.
Mind the Last Spike -- Firing Rate Models for Mesoscopic Populations of Spiking Neurons
Date: 2019-09-22 00:00:00

The dominant modeling framework for understanding cortical computations are
heuristic firing rate models. Despite their success, these models fall short to
capture spike synchronization effects, to link to biophysical parameters and to
describe finite-size fluctuations. In this opinion article, we propose that the
refractory density method (RDM), also known as age-structured population
dynamics or quasi-renewal theory, yields a powerful theoretical framework to
build rate-based models for mesoscopic neural populations from realistic neuron
dynamics at the microscopic level. We review recent advances achieved by the
RDM to obtain efficient population density equations for networks of
generalized integrate-and-fire (GIF) neurons -- a class of neuron models that
has been successfully fitted to various cell types. The theory not only
predicts the nonstationary dynamics of large populations of neurons but also
permits an extension to finite-size populations and a systematic reduction to
low-dimensional rate dynamics. The new types of rate models will allow a
re-examination of models of cortical computations under biological constraints.
Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities
Date: 2020-02-11 00:00:00
Tasks: Epidemiology

Identifying important nodes for disease spreading is a central topic in
network epidemiology. We investigate how well the position of a node,
characterized by standard network measures, can predict its epidemiological
importance in any graph of a given number of nodes. This is in contrast to
other studies that deal with the easier prediction problem of ranking nodes by
their epidemic importance in given graphs. As a benchmark for epidemic
importance, we calculate the exact expected outbreak size given a node as the
source. We study exhaustively all graphs of a given size, so do not restrict
ourselves to certain generative models for graphs, nor to graph data sets. Due
to the large number of possible nonisomorphic graphs of a fixed size, we are
limited to 10-node graphs. We find that combinations of two or more
centralities are predictive ($R^2$ scores of 0.91 or higher) even for the most
difficult parameter values of the epidemic simulation. Typically, these
successful combinations include one normalized spectral centralities (such as
PageRank or Katz centrality) and one measure that is sensitive to the number of
edges in the graph.
Using Machine Learning to Predict Realized Variance
Date: 2019-09-22 00:00:00
Tasks: BIG-bench Machine Learning, regression, Time Series, Time Series Analysis

In this paper we formulate a regression problem to predict realized
volatility by using option price data and enhance VIX-styled volatility
indices' predictability and liquidity. We test algorithms including regularized
regression and machine learning methods such as Feedforward Neural Networks
(FNN) on S&P 500 Index and its option data. By conducting a time series
validation we find that both Ridge regression and FNN can improve volatility
indexing with higher prediction performance and fewer options required. The
best approach found is to predict the difference between the realized
volatility and the VIX-styled index's prediction rather than to predict the
realized volatility directly, representing a successful combination of human
learning and machine learning. We also discuss suitability of different
regression algorithms for volatility indexing and applications of our findings.
Habitat fragmentation: the possibility of a patch disrupting its neighbor
Date: 2019-09-22 00:00:00

This paper starts from the Fisher-Kolmogorov-Petrovskii-Piskunov equation to
model diffusive populations. The main result, according to this model, is that
two connected patches in a system do not always contribute to each other.
Specifically, inserting a large fragment next to a small one is always positive
for life inside the small patch, while inserting a very small patch next to a
large one can be negative for life inside the large fragment. This result,
obtained to homogeneously fragmented regions, is possible from the general case
expression for the minimum sizes in a system of two patches. This expression by
itself is an interesting result, because it allows the study of other
characteristics not included in the present work.
Inference for Linear Conditional Moment Inequalities
Authors: Isaiah Andrews, Jonathan Roth, Ariel Pakes
Date: 2019-09-22 00:00:00
Tasks: valid

We show that moment inequalities in a wide variety of economic applications have a particular linear conditional structure. We use this structure to construct uniformly valid confidence sets that remain computationally tractable even in settings with nuisance parameters. We first introduce least favorable critical values which deliver non-conservative tests if all moments are binding. Next, we introduce a novel conditional inference approach which ensures a strong form of insensitivity to slack moments. Our recommended approach is a hybrid technique which combines desirable aspects of the least favorable and conditional methods. The hybrid approach performs well in simulations calibrated to Wollmann (2018), with favorable power and computational time comparisons relative to existing alternatives.
Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs
Authors: Vivek Khatana, Govind Saraswat, Sourav Patel, Murti V. Salapaka
Date: 2019-09-22 00:00:00
Tasks: Distributed Optimization

In this article, we propose a new approach, optimize then agree for minimizing a sum $ f = \sum_{i=1}^n f_i(x)$ of convex objective functions over a directed graph. The optimize then agree approach decouples the optimization step and the consensus step in a distributed optimization framework. The key motivation for optimize then agree is to guarantee that the disagreement between the estimates of the agents during every iteration of the distributed optimization algorithm remains under any apriori specified tolerance; existing algorithms do not provide such a guarantee which is required in many practical scenarios. In this method, each agent during each iteration maintains an estimate of the optimal solution and, utilizes its locally available gradient information along with a finite-time approximate consensus protocol to move towards the optimal solution (hence the name Gradient-Consensus algorithm). We establish that the proposed algorithm has a global R-linear rate of convergence if the aggregate function $f$ is strongly convex and Lipschitz differentiable. We also show that under the relaxed assumption of $f_i$'s being convex and Lipschitz differentiable, the objective function error residual decreases at a Q-linear rate (in terms of the number of gradient computation steps) until it reaches a small value, which can be managed using the tolerance value specified on the finite-time approximate consensus protocol; no existing method in the literature has such strong convergence guarantees when $f_i$ are not necessarily strongly convex functions. The communication overhead for the improved guarantees on meeting constraints and better convergence of our algorithm is $O(k\log k)$ iterates in comparison to $O(k)$ of the traditional algorithms. Further, we numerically evaluate the performance of the proposed algorithm by solving a distributed logistic regression problem.
Flying batteries: In-flight battery switching to increase multirotor flight time
Date: 2020-03-26 00:00:00

We present a novel approach to increase the flight time of a multirotor via
mid-air docking and in-flight battery switching. A main quadcopter flying using
a primary battery has a docking platform attached to it. A 'flying battery' - a
small quadcopter carrying a secondary battery - is equipped with docking legs
that can mate with the main quadcopter's platform. Connectors between the legs
and the platform establish electrical contact on docking, and enable power
transfer from the secondary battery to the main quadcopter. A custom-designed
circuit allows arbitrary switching between the primary battery and secondary
battery. We demonstrate the concept in a flight experiment involving repeated
docking, battery switching, and undocking. The experiment increases the flight
time of the main quadcopter by a factor of 4.7x compared to solo flight, and
2.2x a theoretical limit for that given multirotor. Importantly, this increase
in flight time is not associated with a large increase in overall vehicle mass
or size, leaving the main quadcopter in fundamentally the same safety class.
Analysis and Comparison of the LDPC and Reed Solomon Encodings in Mitigating the Impact of Clipping Noise in OFDM-Based VLC
Date: 2019-09-22 00:00:00

The linear error-correcting codes are known to be well suited for battling
and correcting the burst errors caused by noise in the wireless data
transmission system. However, different types of codes offer different decoding
and burst-error-correcting capabilities. This paper compares the
Low-Density-Parity Check (LDPC) and Reed Solomon (RS) encoding schemes in
battling and correcting the burst error caused by the clipping distortion
occurred due to the dynamic range constraints in an Orthogonal Frequency
Division Multiplexing (OFDM) based Visible Light Communication (VLC). The
unipolar conversion applied to the output of the multiplexer in this system
results in a clipping noise which distorts the data symbols on the subcarriers
in OFDM block. Considering that such distortion impacts the data symbol on each
subcarrier differently, RS and LDP are used to encode the data block before
being modulated for mapping the OFDM block. In order to control the extreme
value of the output of the multiplexer, the transmitter applies puncturing to
the generated codeword before mapping OFDM subcarriers, leaving the
corresponding subcarriers of the punctured symbols empty. This will lead to the
reduction of clipping events in the optical front-end and will mitigate the
impact of nonlinear distortion on the modulated symbols for the occupied
subcarriers. The redundancy in the codeword generated by the encoder is used
not only to control the clipping probability by shortening the number of active
subcarriers but also for the reconstruction of the original codeword and
correction of the errors caused by channel noise. This work investigates the
ability of LDCP and RS encoders in battling the effects of clipping noise in
the frequency domain and compares their performances in improving the bit error
ratio (BER) performance of an OFDM-based VLC.
Sum Rate Capacity of MIMO HetNet Systems in the Presence of Channel Estimation Error
Date: 2019-09-22 00:00:00

In this paper, the impact of channel estimation error (CEE) on the sum-rate
capacity of multiple-input-multiple-output (MIMO) heterogeneous networks
(HetNets) is investigated. It is assumed that the receiver is a linear minimum
mean-square (LMMS) receiver. The architecture is based on the deployment of
macro base stations with large antenna arrays and a secondary tier of small
cell base stations having fewer number of antenna arrays. The key contribution
of the paper is to highlight the noticeable impact of CEE on the sum-rate
capacity of macro-cells. Furthermore, it is shown that CEE has only marginal
impact on the sum-rate capacity of small cells. Simulations for the sum rate of
macro users versus that of the small cell users for time-division duplex (TDD)
are performed and the results are compared with the case when channel
estimation error is present.
Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System With One-Bit Quantization Via Approximate Message Passing
Date: 2019-09-23 00:00:00
Tasks: compressed sensing, Quantization

We develop a direction of arrival (DoA) and channel estimation algorithm for
the one-bit quantized millimeter-wave (mmWave) massive multiple-input
multiple-output (MIMO) system. By formulating the estimation problem as a noisy
one-bit compressed sensing problem, we propose a computationally efficient
gridless solution based on the expectation-maximization generalized approximate
message passing (EMGAMP) approach. The proposed algorithm does not need the
prior knowledge about the number of DoAs and outperforms the existing methods
in distinguishing extremely close DoAs for the case of one-bit quantization.
Both the DoAs and the channel coefficients are estimated for the case of
one-bit quantization. The simulation results show that the proposed algorithm
has effective estimation performances when the DoAs are very close to each
other.
Specification Testing in Nonparametric Instrumental Quantile Regression
Date: 2019-09-23 00:00:00
Tasks: Econometrics, quantile regression, regression

There are many environments in econometrics which require nonseparable
modeling of a structural disturbance. In a nonseparable model with endogenous
regressors, key conditions are validity of instrumental variables and
monotonicity of the model in a scalar unobservable variable. Under these
conditions the nonseparable model is equivalent to an instrumental quantile
regression model. A failure of the key conditions, however, makes instrumental
quantile regression potentially inconsistent. This paper develops a methodology
for testing the hypothesis whether the instrumental quantile regression model
is correctly specified. Our test statistic is asymptotically normally
distributed under correct specification and consistent against any alternative
model. In addition, test statistics to justify the model simplification are
established. Finite sample properties are examined in a Monte Carlo study and
an empirical illustration is provided.
Deep Reinforcement Learning for Smart Home Energy Management
Date: 2019-12-19 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

In this paper, we investigate an energy cost minimization problem for a smart
home in the absence of a building thermal dynamics model with the consideration
of a comfortable temperature range. Due to the existence of model uncertainty,
parameter uncertainty (e.g., renewable generation output, non-shiftable power
demand, outdoor temperature, and electricity price) and temporally-coupled
operational constraints, it is very challenging to determine the optimal energy
management strategy for scheduling Heating, Ventilation, and Air Conditioning
(HVAC) systems and energy storage systems in the smart home. To address the
challenge, we first formulate the above problem as a Markov decision process,
and then propose an energy management strategy based on Deep Deterministic
Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does
not require the prior knowledge of uncertain parameters and building thermal
dynamics model. Simulation results based on real-world traces demonstrate the
effectiveness and robustness of the proposed strategy.
Consistent and Efficient Pricing of SPX and VIX Options under Multiscale Stochastic Volatility
Date: 2019-09-23 00:00:00

This study provides a consistent and efficient pricing method for both
Standard & Poor's 500 Index (SPX) options and the Chicago Board Options
Exchange's Volatility Index (VIX) options under a multiscale stochastic
volatility model. To capture the multiscale volatility of the financial market,
our model adds a fast scale factor to the well-known Heston volatility and we
derive approximate analytic pricing formulas for the options under the model.
The analytic tractability can greatly improve the efficiency of calibration
compared to fitting procedures with the finite difference method or Monte Carlo
simulation. Our experiment using options data from 2016 to 2018 shows that the
model reduces the errors on the training sets of the SPX and VIX options by
9.9% and 13.2%, respectively, and decreases the errors on the test sets of the
SPX and VIX options by 13.0\% and 16.5\%, respectively, compared to the
single-scale model of Heston. The error reduction is possible because the
additional factor reflects short-term impacts on the market, which is difficult
to achieve with only one factor. It highlights the necessity of modeling
multiscale volatility.
A deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics
Date: 2019-09-24 00:00:00
Tasks: Asset Management, Management, Sensor Fusion, Variational Inference

Multi-sensor systems are proliferating the asset management industry and by
proxy, the structural health management community. Asset managers are beginning
to require a prognostics and health management system to predict and assess
maintenance decisions. These systems handle big machinery data and multi-sensor
fusion and integrate remaining useful life prognostic capabilities. We
introduce a deep adversarial learning approach to damage prognostics. A
non-Markovian variational inference-based model incorporating an adversarial
training algorithm framework was developed. The proposed framework was applied
to a public multi-sensor data set of turbofan engines to demonstrate its
ability to predict remaining useful life. We find that using the deep
adversarial based approach results in higher performing remaining useful life
predictions.
Dynamics of symmetric SSVI smiles and implied volatility bubbles
Date: 2019-09-23 00:00:00

We develop a dynamic version of the SSVI parameterisation for the total implied variance, ensuring that European vanilla option prices are martingales, hence preventing the occurrence of arbitrage, both static and dynamic. Insisting on the constraint that the total implied variance needs to be null at the maturity of the option, we show that no model--in our setting--allows for such behaviour. This naturally gives rise to the concept of implied volatility bubbles, whereby trading in an arbitrage-free way is only possible during part of the life of the contract, but not all the way until expiry.
Time-limited pseudo-optimal H$_2$-model order reduction
Date: 2020-06-24 00:00:00

A model order reduction algorithm is presented that generates a reduced-order
model of the original high-order model, which ensures high-fidelity within the
desired time interval. The reduced model satisfies a subset of the first-order
optimality conditions for time-limited H$_2$-model reduction problem. The
algorithm uses a computationally efficient Krylov subspace-based framework to
generate the reduced model, and it is applicable to large-scale systems. The
reduced-order model is parameterized to enforce a subset of the first-order
optimality conditions in an iteration-free way. We also propose an adaptive
framework of the algorithm, which ensures a monotonic decay in error
irrespective of the choice of interpolation points and tangential directions.
The efficacy of the algorithm is validated on benchmark model reduction
problems.
EV-based Smart E-mobility System -- Part I: Concept
Date: 2019-09-23 00:00:00

The first of this two-paper series proposes and elaborates a concept of
electric vehicle (EV)-based e-mobility system. To this end, models designed to
reap the benefits of EVs' flexibility in the literature almost exclusively
consider charging stations as active players exploiting the EVs' flexibility.
Such stations are seen as static loads able to provide flexibility only when
EVs are connected to them. However, this standpoint suffers from two major
issues. First, the charging stations need to anticipate some important
parameters of the incoming EVs, e.g. time of arrival and departure,
state-of-energy of the EV's battery at arrival and the required state-of-energy
at its departure. Second, it observes the EVs only when they are connected to
these charging stations, thus overlooking the arbitrage and charging
opportunities when the EVs are connected to other charging stations. This paper
proposes a new viewpoint, where EVs are observed as dynamic movable storage
systems which can provide flexibility at any charging station. The paper
defines both systems, the existing one, where the flexibility is viewed from
the standpoint of charging stations, and the proposed one, where the
flexibility is viewed from the EVs' standpoint.
Compressed Gradient Methods with Hessian-Aided Error Compensation
Authors: Sarit Khirirat, Sindri Magnússon, Mikael Johansson
Date: 2019-09-23 00:00:00

The emergence of big data has caused a dramatic shift in the operating regime for optimization algorithms. The performance bottleneck, which used to be computations, is now often communications. Several gradient compression techniques have been proposed to reduce the communication load at the price of a loss in solution accuracy. Recently, it has been shown how compression errors can be compensated for in the optimization algorithm to improve the solution accuracy. Even though convergence guarantees for error-compensated algorithms have been established, there is very limited theoretical support for quantifying the observed improvements in solution accuracy. In this paper, we show that Hessian-aided error compensation, unlike other existing schemes, avoids the accumulation of compression errors on quadratic problems. We also present strong convergence guarantees of Hessian-based error compensation for stochastic gradient descent. Our numerical experiments highlight the benefits of Hessian-based error compensation, and demonstrate that similar convergence improvements are attained when only a diagonal Hessian approximation is used.