pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Sizing of Stand-alone Solar PV Systems via Automated Formal Synthesis
Date: 2019-09-28 00:00:00
Tasks: Program Synthesis

There exist various methods and tools to size solar photovoltaic systems;
however, these tools rely on simulations, which do not cover all aspects of the
design space during the search for optimal solution. In prior studies in
optimal sizing, the focus was always on criteria or objectives. Here, we
present a new sound and automated approach to obtain optimal sizing using an
unprecedented program synthesis. Our variant of counterexample guided inductive
synthesis (CEGIS) approach has two phases linking the technical and cost
analysis: first we synthesize a feasible candidate based on power reliability,
but that may not achieve the lowest cost; second, the candidate is then
verified iteratively with a lower bound cost via symbolic model checking. If
the verification step does not fail, the lower bound is adjusted; and if it
fails, a counterexample provides the optimal solution. Experimental results
using seven case studies and commercial equipment data show that our synthesis
method can produce within an acceptable run-time the optimal system sizing. We
also present a comparative with a specialized simulation tool over real
photovoltaic systems to show the effectiveness of our approach, which can
provide a more detailed and accurate solution than that simulation tool.
Structures, Functions, and Mechanisms of Filament Forming Enzymes: A Renaissance of Enzyme Filamentation
Date: 2019-09-28 00:00:00

Filament formation by non-cytoskeletal enzymes has been known for decades,
yet only relatively recently has its wide-spread role in enzyme regulation and
biology come to be appreciated. This comprehensive review summarizes what is
known for each enzyme confirmed to form filamentous structures in vitro, and
for the many that are known only to form large self-assemblies within cells.
For some enzymes, studies describing both the in vitro filamentous structures
and cellular self-assembly formation are also known and described. Special
attention is paid to the detailed structures of each type of enzyme filament,
as well as the roles the structures play in enzyme regulation and in biology.
Where it is known or hypothesized, the advantages conferred by enzyme
filamentation are reviewed. Finally, the similarities, differences, and
comparison to the SgrAI system are also highlighted.
Robust delay-dependent LPV output-feedback blood pressure control with real-time Bayesian estimation
Date: 2019-09-28 00:00:00

Mean arterial blood pressure (MAP) dynamics estimation and its automated
regulation could benefit the clinical and emergency resuscitation of critical
patients. In order to address the variability and complexity of the MAP
response of a patient to vasoactive drug infusion, a parameter-varying model
with a varying time delay is considered to describe the MAP dynamics in
response to drugs. The estimation of the varying parameters and the delay is
performed via a Bayesian-based multiple-model square root cubature Kalman
filtering approach. The estimation results validate the effectiveness of the
proposed random-walk dynamics identification method using collected animal
experiment data. Following the estimation algorithm, an automated drug delivery
scheme to regulate the MAP response of the patient is carried out via
time-delay linear parameter-varying (LPV) control techniques. In this regard,
an LPV gain-scheduled output-feedback controller is designed to meet the MAP
response requirements of tracking a desired reference MAP target and guarantee
robustness against norm-bounded uncertainties and disturbances. In this
context, parameter-dependent Lyapunov-Krasovskii functionals are used to derive
sufficient conditions for the robust stabilization of a general LPV system with
an arbitrarily varying time delay and the results are provided in a convex
linear matrix inequality (LMI) constraint framework. Finally, to evaluate the
performance of the proposed MAP regulation approach, closed-loop simulations
are conducted and the results confirm the effectiveness of the proposed control
method against various simulated clinical scenarios.
Modelling the health impact of food taxes and subsidies with price elasticities: the case for additional scaling of food consumption using the total food expenditure elasticity
Date: 2019-09-29 00:00:00

Background Food taxes and subsidies are one intervention to address poor
diets. Price elasticity (PE) matrices are commonly used to model the change in
food purchasing. Usually a PE matrix is generated in one setting then applied
to another setting with differing starting consumption and prices of foods.
This violates econometric assumptions resulting in likely misestimation of
total food consumption. We illustrate rescaling all consumption after applying
a PE matrix using a total food expenditure elasticity (TFEe, the expenditure
elasticity for all food combined given the policy induced change in the total
price of food). We use case studies of NZ$2 per 100g saturated fat (SAFA) tax,
NZ$0.4 per 100g sugar tax, and a 20% fruit and vegetable (F&V) subsidy. Methods
We estimated changes in food purchasing using a NZ PE matrix applied
conventionally, then with TFEe adjustment. Impacts were quantified for total
food expenditure and health adjusted life years (HALYs) for the total NZ
population alive in 2011 over the rest of their lifetime using a multistate
lifetable model. Results Two NZ studies gave TFEes of 0.68 and 0.83, with
international estimates ranging from 0.46 to 0.90. Without TFEe adjustment,
total food expenditure decreased with the tax policies and increased with the
F&V subsidy, implausible directions of shift given economic theory. After TFEe
adjustment, HALY gains reduced by a third to a half for the two taxes and
reversed from an apparent health loss to a health gain for the F&V subsidy.
With TFEe adjustment, HALY gains (in 1000s) were 1,805 (95% uncertainty
interval 1,337 to 2,340) for the SAFA tax, 1,671 (1,220 to 2,269) for the sugar
tax, and 953 (453 to 1,308) for the F&V subsidy. Conclusions If PE matrices are
applied in settings beyond where they were derived, additional scaling is
likely required. We suggest that the TFEe is a useful scalar.
Differentially Private Controller Synthesis With Metric Temporal Logic Specifications
Date: 2019-09-29 00:00:00
Tasks: Cloud Computing

Privacy is an important concern in various multiagent systems in which data
collected from the agents are sensitive. We propose a differentially private
controller synthesis approach for multi-agent systems subject to high-level
specifications expressed in metric temporal logic (MTL). We consider a setting
where each agent sends data to a cloud (computing station) through a set of
local hubs and the cloud is responsible for computing the control inputs of the
agents. Specifically, each agent adds privacy noise (e.g., Gaussian noise)
point-wise in time to its own outputs before sharing them with a local hub.
Each local hub runs a Kalman filter to estimate the state of the corresponding
agent and periodically sends such state estimates to the cloud. The cloud
computes the optimal inputs for each agent subject to an MTL specification.
While guaranteeing differential privacy of each agent, the controller is also
synthesized to ensure a probabilistic guarantee for satisfying the MTL
specification.We provide an implementation of the proposed method on a
simulation case study with two Baxter-On-Wheels robots as the agents.
Undiscounted Bandit Games
Date: 2020-08-25 00:00:00

We analyze undiscounted continuous-time games of strategic experimentation
with two-armed bandits. The risky arm generates payoffs according to a L\'{e}vy
process with an unknown average payoff per unit of time which nature draws from
an arbitrary finite set. Observing all actions and realized payoffs, plus a
free background signal, players use Markov strategies with the common posterior
belief about the unknown parameter as the state variable. We show that the
unique symmetric Markov perfect equilibrium can be computed in a simple closed
form involving only the payoff of the safe arm, the expected current payoff of
the risky arm, and the expected full-information payoff, given the current
belief. In particular, the equilibrium does not depend on the precise
specification of the payoff-generating processes.
Not so Particular about Calibration: Smile Problem Resolved
Date: 2019-09-29 00:00:00

We present a novel Monte Carlo based LSV calibration algorithm that applies
to all stochastic volatility models, including the non-Markovian rough
volatility family. Our framework overcomes the limitations of the particle
method proposed by Guyon and Henry-Labord\`ere (2012) and theoretically
guarantees a variance reduction without additional computational complexity.
Specifically, we obtain a closed-form and exact calibration method that allows
us to remove the dependency on both the kernel function and bandwidth
parameter. This makes the algorithm more robust and less prone to errors or
instabilities in a production environment. We test the efficiency of our
algorithm on various hybrid (rough) local stochastic volatility models.
Novel Balanced Single/Dual-band Bandpass Filters Based on Circular Patch Resonator
Date: 2019-09-30 00:00:00

A new approach for designing the single-band and dual-band balanced bandpass
filters (BPFs) based on the circular patch resonator is proposed in this work.
By modifying the output balanced ports of the balanced BPFs to inhibit or
transport TM31 mode of the circular patch resonator under the differential-mode
(DM) excitation, a single-band or dual-band balanced BPF can realized. As the
odd modes could be restrained under the common-mode (CM) excitation, the
balanced BPFs could achieve high CM suppression in the DM passbands. Meanwhile,
several perturbing ways are introduced to improve the performance of the
balanced BPFs. Specifically, perturbation vias are introduced to disturb the
electric field distribution to improve the CM suppression in the DM passbands,
and slots are cut on the circular patch resonators to perturb the current
distribution to modify the second DM passband of the dual-band balanced BPF.
Furthermore, the proposed single-band and dual-band balanced BPFs are
fabricated and characterized. Good agreement can be achieved between the
simulated and measured results of the fabricated balanced BPFs, which validates
the feasibility of the proposed design method.
Performance Analysis of SSHC Rectifiers used in Ultrasonic Wireless Power Transfer
Authors: Sijun Du
Date: 2019-09-30 00:00:00

One of the key design considerations for biomedical implants is to minimize the system volume while achieving high performance. An attractive approach to power biomedical implants is to use wireless power transfer (WPT) with ultrasound. To receive the ultrasonic energy, a piezoelectric transducer is implemented, which is designed to have the resonance frequency at the frequency of the ultrasonic wave. To extract the received energy from the transducer, an energy rectification circuit is typically required. The commonly used rectifier is called a synchronized switch harvesting on inductor (SSHI) rectifier, which employs an inductor to increase the energy efficiency. However, the employment of inductor limits the system miniaturization since it usually needs to be around 1's mH. The synchronized switch harvesting on capacitors (SSHC) rectifier was recently proposed to be used on vibration energy harvesting and this architecture achieves inductor-less fully integrated design. In a vibration energy harvesting system, the piezoelectric transducer usually has large inherent capacitance and low resonant frequency. But the piezoelectric transducer used in ultrasonic WPT typically has very small capacitance and much higher resonant frequency. In this paper, the performance of a SSHC rectifier used in ultrasonic WPT is analyzed. The analysis covers system size and performance. Based on the analysis, this paper presents an important design consideration on designing a SSHC rectifier to be used in ultrasonic WPT, which is the allowed maximal ON resistance in the capacitor-to-capacitor charging loop in function of the transducer capacitance, transducer resonant frequency and the number of SSHC stages.
Adaptive MPC under Time Varying Uncertainty: Robust and Stochastic
Authors: Monimoy Bujarbaruah, Xiaojing Zhang, Marko Tanaskovic, Francesco Borrelli
Date: 2019-09-30 00:00:00
Tasks: Model Predictive Control

This paper deals with the problem of formulating an adaptive Model Predictive Control strategy for constrained uncertain systems. We consider a linear system, in presence of bounded time varying additive uncertainty. The uncertainty is decoupled as the sum of a process noise with known bounds, and a time varying offset that we wish to identify. The time varying offset uncertainty is assumed unknown point-wise in time. Its domain, called the Feasible Parameter Set, and its maximum rate of change are known to the control designer. As new data becomes available, we refine the Feasible Parameter Set with a Set Membership Method based approach, using the known bounds on process noise. We consider two separate cases of robust and probabilistic constraints on system states, with hard constraints on actuator inputs. In both cases, we robustly satisfy the imposed constraints for all possible values of the offset uncertainty in the Feasible Parameter Set. By imposing adequate terminal conditions, we prove recursive feasibility and stability of the proposed algorithms. The efficacy of the proposed robust and stochastic Adaptive MPC algorithms is illustrated with detailed numerical examples.
Trajectory Planning of Weakly Supervised Aircraft
Date: 2019-09-30 00:00:00
Tasks: Management, Trajectory Planning

In this paper, we propose a novel framework of air traffic management (ATM).
The framework is in particular characterized by the trajectory planning of
weakly supervised aircraft; the air traffic control (ATC) does not completely
determine the trajectory of each aircraft unlike conventional planning methods,
but determines allowable safe sets of trajectories. ATC requests pilots to
select their own trajectories from the sets, and the pilots determine ones by
pursuing their own aims. For example, the selection can be based on pilot
preferences and airline strategies. This two stage ATM system contributes to
simultaneously achieve the both objectives of the ATC and pilots such as fuel
cost minimization under safety management. The effectiveness of the proposed
ATM system is demonstrated in a simulation using actual air traffic data.
Symbolic proof of bistability in reaction networks
Authors: Angélica Torres, Elisenda Feliu
Date: 2019-09-30 00:00:00

Deciding whether and where a system of parametrized ordinary differential equations displays bistability, that is, has at least two asymptotically stable steady states for some choice of parameters, is a hard problem. For systems modeling biochemical reaction networks, we introduce a procedure to determine, exclusively via symbolic computations, the stability of the steady states for unspecified parameter values. In particular, our approach fully determines the stability type of all steady states of a broad class of networks. To this end, we combine the Hurwitz criterion, reduction of the steady state equations to one univariate equation, and structural reductions of the reaction network. Using our method, we prove that bistability occurs in open regions in parameter space for many relevant motifs in cell signaling.
Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems
Authors: Dipankar Maity, Panagiotis Tsiotras
Date: 2019-09-30 00:00:00
Tasks: Quantization, Scheduling

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 joint optimal controller synthesis and quantizer scheduling for a partially observed Quantized-Feedback Linear-Quadratic-Gaussian (QF-LQG) system, where the measurements are quantized before being sent to the controller. The system is presented with several choices of quantizers, along with the cost of using each quantizer. The objective is to jointly select the quantizers and synthesize the controller to strike an optimal balance between control performance and quantization cost. When the innovation signal is quantized instead of the measurement, the problem is decoupled into two optimization problems: one for optimal controller synthesis, and the other for optimal quantizer selection. The optimal controller is found by solving a Riccati equation and the optimal quantizer selection policy is found by solving a linear program (LP)- both of which can be solved offline.
Partial Uncertainty and Applications to Risk-Averse Valuation
Authors: Anastasis Kratsios
Date: 2019-09-30 00:00:00

This paper introduces an intermediary between conditional expectation and conditional sublinear expectation, called R-conditioning. The R-conditioning of a random-vector in $L^2$ is defined as the best $L^2$-estimate, given a $\sigma$-subalgebra and a degree of model uncertainty. When the random vector represents the payoff of derivative security in a complete financial market, its R-conditioning with respect to the risk-neutral measure is interpreted as its risk-averse value. The optimization problem defining the optimization R-conditioning is shown to be well-posed. We show that the R-conditioning operators can be used to approximate a large class of sublinear expectations to arbitrary precision. We then introduce a novel numerical algorithm for computing the R-conditioning. This algorithm is shown to be strongly convergent. Implementations are used to compare the risk-averse value of a Vanilla option to its traditional risk-neutral value, within the Black-Scholes-Merton framework. Concrete connections to robust finance, sensitivity analysis, and high-dimensional estimation are all treated in this paper.
ProtRank: Bypassing the imputation of missing values in differential expression analysis of proteomic data
Date: 2019-09-30 00:00:00
Tasks: Imputation, Missing Values

Data from discovery proteomic and phosphoproteomic experiments typically
include missing values that correspond to proteins that have not been
identified in the analyzed sample. Replacing the missing values with random
numbers, a process known as "imputation", avoids apparent infinite fold-change
values. However, the procedure comes at a cost: Imputing a large number of
missing values has the potential to significantly impact the results of the
subsequent differential expression analysis. We propose a method that
identifies differentially expressed proteins by ranking their observed changes
with respect to the changes observed for other proteins. Missing values are
taken into account by this method directly, without the need to impute them. We
illustrate the performance of the new method on two distinct datasets and show
that it is robust to missing values and, at the same time, provides results
that are otherwise similar to those obtained with edgeR which is a state-of-art
differential expression analysis method. The new method for the differential
expression analysis of proteomic data is available as an easy to use Python
package.
CrowdEstimator: Approximating Crowd Sizes with Multi-modal Data for Internet-of-Things Services
Date: 2019-09-30 00:00:00

Crowd mobility has been paid attention for the Internet-of-things (IoT)
applications. This paper addresses the crowd estimation problem and builds an
IoT service to share the crowd estimation results across different systems. The
crowd estimation problem is to approximate the crowd size in a targeted area
using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi
probe request packets ("Wi-Fi probes" for short) broadcasted by mobile devices
to solve this problem. However, using only Wi-Fi probes to estimate the crowd
size may result in inaccurate results due to various environmental
uncertainties which may lead to crowd overestimation or underestimation.
Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals
is time-varying and invisible. This paper introduces auxiliary sensors,
stereoscopic cameras, to collect the near ground-truth at a specified
calibration choke point. Two calibration algorithms are proposed to solve the
crowd estimation problem. The key idea is to calibrate the Wi-Fi-only crowd
estimation based on the correlations between the two types of data modalities.
Then, to share the calibrated results across systems required by different
stakeholders, our system is integrated with the FIWARE-based IoT platform. To
verify the proposed system, we have launched an indoor pilot study in the
Wellington Railway Station and an outdoor pilot study in the Christchurch
Re:START Mall in New Zealand. The large-scale pilot studies show that
stereoscopic cameras can reach minimum accuracy of 85\% and high precision
detection for providing the near ground-truth. The proposed calibration
algorithms reduce estimation errors by 43.68% on average compared to the
Wi-Fi-only approach.
Computational prediction of replication sites in DNA sequences using complex number representation
Date: 2019-09-27 00:00:00

Computational prediction of origin of replication (ORI) has been of great
interest in bioinformatics and several methods including GC-skew,
auto-correlation etc. have been explored in the past. In this paper, we have
extended the auto-correlation method to predict ORI location with much higher
resolution for prokaryotes and eukaryotes, which can be very helpful in
experimental validation of the computational predictions. The proposed complex
correlation method (iCorr) converts the genome sequence into a sequence of
complex numbers by mapping the nucleotides to {+1,-1,+i,-i} instead of {+1,-1}
used in the auto-correlation method (here, i is square root of -1). Thus, the
iCorr method exploits the complete spatial information about the positions of
all the four nucleotides unlike the earlier auto-correlation method which uses
the positional information of only one nucleotide. Also, the earlier
auto-correlation method required visual inspection of the obtained graphs to
identify the location of origin of replication. The proposed iCorr method does
away with this need and is able to identify the origin location simply by
picking the peak in the iCorr graph.