pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Controller Synthesis and Dynamic Quantizer Switching for Linear-Quadratic-Gaussian Systems
Date: 2020-01-31 00:00:00
Tasks: Quantization

In networked control systems, often the sensory signals are quantized before
being transmitted to the controller. Consequently, performance is affected by
the coarseness of this quantization process. Modern communication technologies
allow users to obtain resolution-varying quantized measurements based on the
prices paid. In this paper, we consider optimal controller synthesis of a
Quantized-Feedback Linear-Quadratic-Gaussian (QF-LQG) system where the
measurements are to be quantized before being transmitted to the controller.
The system is presented with several choices of quantizers, along with the cost
of operating each quantizer. The objective is to jointly select the quantizers
and the controller that would maintain an optimal balance between control
performance and quantization cost. Under certain assumptions, this problem can
be decoupled into two optimization problems: one for optimal controller
synthesis and the other for optimal quantizer selection. We show that,
similarly to the classical LQG problem, the optimal controller synthesis
subproblem is characterized by Riccati equations. On the other hand, the
optimal quantizer selection policy is found by solving a certain
Markov-Decision-Process (MDP).
Data-driven biological network alignment that uses topological, sequence, and functional information
Authors: Shawn Gu, Tijana Milenkovic
Date: 2020-01-31 00:00:00
Tasks: Network Embedding

Many proteins remain functionally unannotated. Sequence alignment (SA) uncovers missing annotations by transferring functional knowledge between species' sequence-conserved regions. Because SA is imperfect, network alignment (NA) complements SA by transferring functional knowledge between conserved biological network, rather than just sequence, regions of different species. Existing NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions' functional relatedness. However, we recently found that functionally unrelated proteins are almost as topologically similar as functionally related proteins. So, we redefined NA as a data-driven framework, TARA, which learns from network and protein functional data what kind of topological relatedness (rather than similarity) between proteins corresponds to the proteins' functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, its alignments yielded higher protein functional prediction accuracy than alignments of existing NA methods, even those that used both topological and sequence information. Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods.
Cooperative Energy Scheduling for Microgrids under Peak Demand Energy Plans
Date: 2020-01-31 00:00:00
Tasks: Fairness, Scheduling

A cooperative energy scheduling method is proposed that allows joint energy
optimization for a group of microgrids to achieve cost savings that the
microgrids could not achieve individually. The discussed microgrids may be
commercial entities in a distribution network under utility electricity rate
plans comprising both Time of Use (ToU) and peak demand charge. Defining a
stable operation as a situation where all microgrids would be willing to
participate, it is shown that under such rate plans and in particular due to
the peak demand charge, a cost distribution that is seemingly fair does not
necessarily result in a stable cooperation. These results are derived in this
paper using concepts from cooperative games. It is therefore sought to devise a
stable cost distribution algorithm that, while maximizing some measure of
fairness among the participating microgrids, ensures they all benefit from
their participation. A simple case study is presented that demonstrates
fairness and stability aspects of the cooperation.
A large deviation principle linking lineage statistics to fitness in microbial populations
Date: 2020-01-31 00:00:00

In exponentially proliferating populations of microbes, the population
typically doubles at a rate less than the average doubling time of a
single-cell due to variability at the single-cell level. It is known that the
distribution of generation times obtained from a single lineage is, in general,
insufficient to determine a population's growth rate. Is there an explicit
relationship between observables obtained from a single lineage and the
population growth rate? We show that a population's growth rate can be
represented in terms of averages over isolated lineages. This lineage
representation is related to a large deviation principle that is a generic
feature of exponentially proliferating populations. Due to the large deviation
structure of growing populations, the number of lineages needed to obtain an
accurate estimate of the growth rate depends exponentially on the duration of
the lineages, leading to a non-monotonic convergence of the estimate, which we
verify in both synthetic and experimental data sets.
Resilient Finite Time Consensus: A Discontinuous Systems Perspective
Date: 2020-03-19 00:00:00

Many algorithms have been proposed in prior literature to guarantee resilient
multi-agent consensus in the presence of adversarial attacks or faults. The
majority of prior work present excellent results that focus on discrete-time or
discretized continuous-time systems. Fewer authors have explored applying
similar resilient techniques to continuous-time systems without discretization.
These prior works typically consider asymptotic convergence and make
assumptions such as continuity of adversarial signals, the existence of a dwell
time between switching instances for the system dynamics, or the existence of
trusted agents that do not misbehave. In this paper, we expand the study of
resilient continuous-time systems by removing many of these assumptions and
using discontinuous systems theory to provide conditions for normally-behaving
agents with nonlinear dynamics to achieve consensus in finite time despite the
presence of adversarial agents.
Event triggering control for dynamical systems with designable minimum inter-event time
Date: 2020-01-31 00:00:00

This paper presents a class of event-triggering rules for dynamical control
systems with guaranteed positive minimum inter-event time (MIET). We first
propose an event-based function design with guaranteed control performance
under a clock-like variable for general nonlinear systems, and later specify
them to general linear systems. Compared to the existing static and dynamic
triggering mechanisms, the proposed triggering rules hold the robust global
event-separation property, and can be easily implemented on practical digital
platform. Namely, it is shown that the minimum inter-event time can be flexibly
adapted to the various hardware limitations. Finally, several numerical
simulations are given to illustrate the theoretical results.
Analysis and optimal control of a malaria mathematical model under resistance and population movement
Date: 2020-01-31 00:00:00

In this work, two mathematical models for malaria under resistance are
presented. More precisely, the first model shows the interaction between humans
and mosquitoes inside a patch under infection of malaria when the human
population is resistant to antimalarial drug and mosquitoes population is
resistant to insecticides. For the second model, human-mosquitoes population
movements in two patches is analyzed under the same malaria transmission
dynamic established in one patch. For a single patch, existence and stability
conditions for the equilibrium solutions in terms of the local basic
reproductive number are developed. These results reveal the existence of a
forward bifurcation and the global stability of disease-free equilibrium. In
the case of two patches, a theoretical and numerical framework on sensitivity
analysis of parameters is presented. After that, the use of antimalarial drugs
and insecticides are incorporated as control strategies and an optimal control
problem is formulated. Numerical experiments are carried out in both models to
show the feasibility of our theoretical results.
Estimating Welfare Effects in a Nonparametric Choice Model: The Case of School Vouchers
Authors: Vishal Kamat, Samuel Norris
Date: 2020-01-31 00:00:00
Tasks: counterfactual

We develop new robust discrete choice tools to learn about the average willingness to pay for a price subsidy and its effects on demand given exogenous, discrete variation in prices. Our starting point is a nonparametric, nonseparable model of choice. We exploit the insight that our welfare parameters in this model can be expressed as functions of demand for the different alternatives. However, while the variation in the data reveals the value of demand at the observed prices, the parameters generally depend on its values beyond these prices. We show how to sharply characterize what we can learn when demand is specified to be entirely nonparametric or to be parameterized in a flexible manner, both of which imply that the parameters are not necessarily point identified. We use our tools to analyze the welfare effects of price subsidies provided by school vouchers in the DC Opportunity Scholarship Program. We find that the provision of the status quo voucher and a wide range of counterfactual vouchers of different amounts can have positive and potentially large benefits net of costs. The positive effect can be explained by the popularity of low-tuition schools in the program; removing them from the program can result in a negative net benefit. We also find that various standard logit specifications, in comparison, limit attention to demand functions with low demand for the voucher, which do not capture the large magnitudes of benefits credibly consistent with the data.
From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics
Authors: Susanna Manrubia, José A. Cuesta, Jacobo Aguirre, Sebastian E. Ahnert, Lee Altenberg, Alejandro V. Cano, Pablo Catalán, Ramon Diaz-Uriarte, Santiago F. Elena, Juan Antonio García-Martín, Paulien Hogeweg, Bhavin S. Khatri, Joachim Krug, Ard A. Louis, Nora S. Martin, Joshua L. Payne, Matthew J. Tarnowski, Marcel Weiß
Date: 2020-02-02 00:00:00

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves into a critical and constructive attitude in our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications
Authors: Niloofar Mehrnia, Sinem Coleri
Date: 2020-02-03 00:00:00

A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events occurring due to significant fading. In this paper, we propose a novel methodology based on extreme value theory (EVT) to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. This methodology includes techniques for fitting the lower tail distribution of the received power to the generalized Pareto distribution (GPD), determining the optimum threshold over which the tail statistics are derived, ascertaining the optimum stopping condition on the number of samples required to estimate the tail statistics by using GPD, and finally, assessing the validity of the derived Pareto model. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate that the proposed methodology provides the best fit to the collected data, significantly outperforming the conventional extrapolation-based methods. Moreover, the usage of the EVT in the proposed method decreases the required number of samples for estimating the tail statistics by about $7 \times 10^5$.
Minimum Length Scheduling for Full Duplex Time-Critical Wireless Powered Communication Networks
Authors: Muhammad Shahid Iqbal, Yalcin Sadi, Sinem Coleri
Date: 2020-02-03 00:00:00
Tasks: Scheduling

Radio frequency (RF) energy harvesting is key in attaining perpetual lifetime for time-critical wireless powered communication networks due to full control on energy transfer, far field region, small and low-cost circuitry. In this paper, we propose a novel minimum length scheduling problem to determine the optimal power control, time allocation and transmission schedule subject to data, energy causality and maximum transmit power constraints in a full-duplex wireless powered communication network. We first formulate the problem as a mixed integer non-linear programming problem and conjecture that the problem is NP-hard. As a solution strategy, we demonstrate that the power control and time allocation, and scheduling problems can be solved separately in the optimal solution. For the power control and time allocation problem, we derive the optimal solution by using Karush-Kuhn-Tucker conditions. For the scheduling, we introduce a penalty function allowing reformulation as a sum penalty minimization problem. Upon derivation of the optimality conditions based on the characteristics of the penalty function, we propose two polynomial-time heuristic algorithms and a reduced-complexity exact algorithm employing smart pruning techniques. Via extensive simulations, we illustrate that the proposed heuristic schemes outperform the previously proposed schemes for predetermined transmission order of users and achieve close-to-optimal solutions.
Fairness-Aware Hybrid Precoding for mmWave NOMA Unicast/Multicast Transmissions in Industrial IoT
Date: 2020-02-27 00:00:00
Tasks: Fairness

This paper investigates dual-layer non-orthogonally superimposed
transmissions for industrial internet of things (IoT) millimeter-wave
communications. Essentially, the overlayer is a ubiquitous multicast signal
devised to serve all the devices in coverage with a common message, i.e.,
critical control packet. The underlayer is a composite signal that consists of
private unicast messages. Due to safety implications, it is critical that all
devices can decode the multicast information. To ensure this requirement, we
jointly optimize the hybrid precoder, analog combiners, power allocation, and
fairness. Specifically, we incorporate a power splitting constraint between the
two overlaid signals and enforce supplementary per-device constraints to
guarantee multicast fairness. Performance is evaluated in terms of the spectral
efficiency, multicast fairness, and bit error rate, thus corroborating the
feasibility of our proposed scheme.
Executable Biochemical Space for Specification and Analysis of Biochemical Systems
Date: 2020-01-21 00:00:00

We present the second generation of a rule-based language called Biochemical
Space Language (BCSL) that combines the advantages of different approaches and
thus makes an effort to overcome several problems with existing solutions. The
key aspect of the language is the level of abstraction it uses, which allows
scalable and compact hierarchical specification of biochemical entities. This
abstraction enables unique analysis techniques to reason about properties of
models written in the language on the semantic and syntactic level.
Can one hear the shape of a target zone?
Authors: Jean-Louis Arcand, Max-Olivier Hongler, Shekhar Hari Kumar, Daniele Rinaldo
Date: 2020-02-03 00:00:00

We develop an exchange rate target zone model with finite exit time and non-Gaussian tails. We show how the tails are a consequence of time-varying investor risk aversion, which generates mean-preserving spreads in the fundamental distribution. We solve explicitly for stationary and non-stationary exchange rate paths, and show how both depend continuously on the distance to the exit time and the target zone bands. This enables us to show how central bank intervention is endogenous to both the distance of the fundamental to the band and the underlying risk. We discuss how the feasibility of the target zone is shaped by the set horizon and the degree of underlying risk, and we determine a minimum time at which the required parity can be reached. We prove that increases in risk after a certain threshold can yield endogenous regime shifts where the ``honeymoon effects'' vanish and the target zone cannot be feasibly maintained. None of these results can be obtained by means of the standard Gaussian or affine models. Numerical simulations allow us to recover all the exchange rate densities established in the target zone literature. The generality of our framework has important policy implications for modern target zone arrangements.
Audio-Visual Calibration with Polynomial Regression for 2-D Projection Using SVD-PHAT
Date: 2020-02-12 00:00:00
Tasks: regression, Sound Source Localization

This paper proposes a straightforward 2-D method to spatially calibrate the
visual field of a camera with the auditory field of an array microphone by
generating and overlaying an acoustic image over an optical image. Using a
low-cost microphone array and an off-the-shelf camera, we show that polynomial
regression can deal efficiently with non-linear camera distortion, and that a
recently proposed sound source localization method for real-time processing,
SVD-PHAT, can be adapted for this task.
Making Sense of Computational Psychiatry
Date: 2020-04-14 00:00:00

In psychiatry, we often speak of constructing "models." Here we try to make
sense of what such a claim might mean, starting with the most fundamental
question: "What is (and isn't) a model?". We then discuss, in a concrete
measurable sense, what it means for a model to be useful. In so doing, we first
identify the added value that a computational model can provide, in the context
of accuracy and power. We then present the limitations of standard statistical
methods and provide suggestions for how we can expand the explanatory power of
our analyses by reconceptualizing statistical models as dynamical systems.
Finally, we address the problem of model building, suggesting ways in which
computational psychiatry can escape the potential for cognitive biases imposed
by classical hypothesis-driven research, exploiting deep systems-level
information contained within neuroimaging data to advance our understanding of
psychiatric neuroscience.
Mobility Management for Cellular-Connected UAVs: A Learning-Based Approach
Date: 2020-03-10 00:00:00
Tasks: Management, Reinforcement Learning (RL)

The pervasiveness of the wireless cellular network can be a key enabler for
the deployment of autonomous unmanned aerial vehicles (UAVs) in beyond visual
line of sight scenarios without human control. However, traditional cellular
networks are optimized for ground user equipment (GUE) such as smartphones
which makes providing connectivity to flying UAVs very challenging. Moreover,
ensuring better connectivity to a moving cellular-connected UAV is notoriously
difficult due to the complex air-to-ground path loss model. In this paper, a
novel mechanism is proposed to ensure robust wireless connectivity and mobility
support for cellular-connected UAVs by tuning the downtilt (DT) angles of all
the GBSs. By leveraging tools from reinforcement learning (RL), DT angles are
dynamically adjusted by using a model-free RL algorithm. The goal is to provide
efficient mobility support in the sky by maximizing the received signal quality
at the UAV while also maintaining good throughput performance of the ground
users. Simulation results show that the proposed RL-based mobility management
(MM) technique can reduce the number of handovers while maintaining the
performance goals, compared to the baseline MM scheme in which the network
always keeps the DT angle fixed.
Rack Force Estimation for Driving on Uneven Road Surfaces
Date: 2020-05-12 00:00:00

The force transmitted from the front tires and tie rods to the steering rack
of a vehicle, called the rack force, significantly influences the torque
experienced by a driver at the steering wheel. As a result, estimates of rack
force are used in a wide variety of advanced driver assist systems. Existing
methods for producing rack force estimates are either susceptible to steering
system disturbances or are only applicable for driving on roads with low
frequency profile variations such as road slopes. In this paper we present a
model that can produce disturbance-free rack force estimates for driving on
roads with high frequency profile variations, such as road cleats and potholes,
in addition to roads with low frequency profile variations. We validate the
estimation accuracy of our model by presenting results from two driving
experiments that were performed on test tracks with known low and high
frequency road profile variations. We further demonstrate the merits of our
model relative to the existing models by comparing the various estimates to
rack force measurements obtained using a sensor mounted in the test vehicle.
Analysis of Blocking in mmWave Cellular Systems: Application to Relay Positioning
Authors: Cristian García Ruiz, Antonio Pascual-Iserte, Olga Muñoz
Date: 2020-02-05 00:00:00
Tasks: Blocking

Within the framework of 5G, blockage effects occurring in the mmWave band are critical. Previous works describe the effects of blockages in isolated and multiple links for simple blocking objects, modeled with mathematical tools such as stochastic geometry and random shape theory. Our study uses these tools to characterize a scenario with $N$ links, including the possible correlation among them in terms of blocking for several models of blocking objects. We include numerical evaluations highlighting that assuming independence among the links' blocking elements is a too-brief simplification and does not accurately describe the real scenario. This paper also applies the formulation developed for the case of $N$ links to optimize the relay positioning in mmWave cells for coverage enhancement, that is, to minimize the communication failure probability. We also show that both link budget and blockages affect the optimum positioning of the relays as they are both essential for successful transmission.