pwc-archive/papers-with-abstracts huggingface dataset:
Effect of Pixelation on the Parameter Estimation of Single Molecule Trajectories
Authors: Milad R. Vahid, Bernard Hanzon, Raimund J. Ober
Date: 2019-12-16 00:00:00
Tasks: parameter estimation

The advent of single molecule microscopy has revolutionized biological investigations by providing a powerful tool for the study of intercellular and intracellular trafficking processes of protein molecules which was not available before through conventional microscopy. In practice, pixelated detectors are used to acquire the images of fluorescently labeled objects moving in cellular environments. Then, the acquired fluorescence microscopy images contain the numbers of the photons detected in each pixel, during an exposure time interval. Moreover, instead of having the exact locations of detection of the photons, we only know the pixel areas in which the photons impact the detector. These challenges make the analysis of single molecule trajectories, from pixelated images, a complex problem. Here, we investigate the effect of pixelation on the parameter estimation of single molecule trajectories. In particular, we develop a stochastic framework to calculate the maximum likelihood estimates of the parameters of a stochastic differential equation that describes the motion of the molecule in living cells. We also calculate the Fisher information matrix for this parameter estimation problem. The analytical results are complicated through the fact that the observation process in a microscope prohibits the use of standard Kalman filter type approaches. The analytical framework presented here is illustrated with examples of low photon count scenarios for which we rely on Monte Carlo methods to compute the associated probability distributions.
Hybrid Transmission Distribution Co-simulation: Frequency Regulation using Battery Energy Storage
Date: 2019-12-16 00:00:00

Battery energy storage systems (BESS) are proving to be an effective solution
in providing frequency regulation services to the bulk grid. However, there are
several concerns for the transmission/distribution system operators (TSO/DSO)
with the frequent dispatching of the distribution-connected fast-responding
storage systems. Unfortunately, the existing decoupled models for transmission
and distribution (T&D) simulations are unable to capture the complex
interactions between the two systems especially concerning frequency regulation
problems due to rapidly varying distribution-connected distributed energy
resources (DERs). In this paper, an iteratively coupled T&D hybrid
co-simulation framework is developed to facilitate the planning studies for the
system operators to help integrate distribution-connected BESS in providing
frequency regulation services in response to highly variable DERs such as
photovoltaic generation (PVs). Specifically, the proposed framework helps
evaluate: (1) the effects of distribution-connected DERs/PVs on the response of
the system's automatic generation control (AGC) response, and (2) highlights
the use of BESS in providing frequency regulation services using integrated T&D
model. The proposed framework is demonstrated using the IEEE 9-bus transmission
system model (operating in dynamics mode) coupled with multiple EPRI Ckt-24
distribution system models (operating in quasi-static mode). It is shown that
the proposed co-simulation framework helps better visualize the system AGC
response and frequency regulation especially in the presence of high-levels of
DER generation variability requiring frequent dispatch of BESS.
Development of Smartphone Application for Evaluation of Passenger Comfort
Date: 2020-04-29 00:00:00

Nowadays, smartphones are not utilized for communications only. Smartphones
are equipped with a lot of sensors that can be utilized for different purposes.
For example, inertial sensors have been used extensively in recent years for
measuring and monitoring performance in many different applications. Basically,
data from the sensors are utilized for estimation of smartphone orientation.
There is a lot of applications which can utilize these data. This paper deals
with an algorithm developed for inertial sensors data utilization for vehicle
passenger comfort assessment.
Conditions for Hierarchical Supervisory Control under Partial Observation
Authors: Jan Komenda, Tomáš Masopust
Date: 2019-12-16 00:00:00

The fundamental problem in hierarchical supervisory control under partial observation is to find conditions preserving observability between the original (low-level) and the abstracted (high-level) plants. Two conditions for observable specifications were identified in the literature -- observation consistency (OC) and local observation consistency (LOC). However, the decidability of OC and LOC were left open. We show that both OC and LOC are decidable for regular systems. We further show that these conditions do not guarantee that supremal (normal or relatively observable) sublanguages computed on the low level and on the high level always coincide. To solve the issue, we suggest a new condition -- modified observation consistency -- and show that under this condition, the supremal normal sublanguages are preserved between the levels, while the supremal relatively observable high-level sublanguage is at least as good as the supremal relatively observable low-level sublanguage, i.e., the high-level solution may be even better than the low-level solution.
Distributed control of DC grids: integrating prosumers motives
Authors: Michele Cucuzzella, Thijs Bouman, Krishna Chaitanya Kosaraju, Geertje Schuitema, N. H. Lemmen, Steph Johnson-Zawadzki, Carlo Fischione, Linda Steg, Jacquelien M. A. Scherpen
Date: 2019-12-16 00:00:00

In this paper, a novel distributed control strategy addressing a (feasible) psycho-social-physical welfare problem in islanded Direct Current (DC) smart grids is proposed. Firstly, we formulate a (convex) optimization problem that allows prosumers to share current with each other, taking into account the technical and physical aspects and constraints of the grid (e.g., stability, safety), as well as psycho-social factors (i.e., prosumers' personal values). Secondly, we design a controller whose (unforced) dynamics represent the continuous time primal-dual dynamics of the considered optimization problem. Thirdly, a passive interconnection between the physical grid and the controller is presented. Global asymptotic convergence of the closed-loop system to the desired steady-state is proved and simulations based on collected data on psycho-social aspects illustrate and confirm the theoretical results.
Estimation of Auction Models with Shape Restrictions
Date: 2019-12-16 00:00:00
Tasks: counterfactual, Density Estimation

We introduce several new estimation methods that leverage shape constraints
in auction models to estimate various objects of interest, including the
distribution of a bidder's valuations, the bidder's ex ante expected surplus,
and the seller's counterfactual revenue. The basic approach applies broadly in
that (unlike most of the literature) it works for a wide range of auction
formats and allows for asymmetric bidders. Though our approach is not
restrictive, we focus our analysis on first--price, sealed--bid auctions with
independent private valuations. We highlight two nonparametric estimation
strategies, one based on a least squares criterion and the other on a maximum
likelihood criterion. We also provide the first direct estimator of the
strategy function. We establish several theoretical properties of our methods
to guide empirical analysis and inference. In addition to providing the
asymptotic distributions of our estimators, we identify ways in which
methodological choices should be tailored to the objects of their interest. For
objects like the bidders' ex ante surplus and the seller's counterfactual
expected revenue with an additional symmetric bidder, we show that our
input--parameter--free estimators achieve the semiparametric efficiency bound.
For objects like the bidders' inverse strategy function, we provide an easily
implementable boundary--corrected kernel smoothing and transformation method in
order to ensure the squared error is integrable over the entire support of the
valuations. An extensive simulation study illustrates our analytical results
and demonstrates the respective advantages of our least--squares and maximum
likelihood estimators in finite samples. Compared to estimation strategies
based on kernel density estimation, the simulations indicate that the smoothed
versions of our estimators enjoy a large degree of robustness to the choice of
an input parameter.
Radiomic features of multi-parametric MRI present stable associations with analogous histological features in brain cancer patients
Date: 2019-12-16 00:00:00

MR-derived radiomic features have demonstrated substantial predictive utility
in modeling different prognostic factors of glioblastomas and other brain
cancers. However, the biological relationship underpinning these predictive
models has been largely unstudied, with the generalizability of these models
also called into question. Here, we examine the localized relationship between
MR-derived radiomic features and histology-derived histomic features using a
dataset of 16 brain cancer patients. Tile-based radiomics features were
collected on T1W, post-contrast T1W, FLAIR, and DWI-derived ADC images acquired
prior to patient death, with analogous histomic features collected for autopsy
samples co-registered to the MRI. Features were collected for each original
image, as well as a 3D wavelet decomposition of each image, resulting in 837
features per MR image and histology image. Correlative analyses were used to
assess the degree of association between radiomic-histomic pairs for each MRI.
The influence of several confounds were also assessed using linear mixed effect
models for the normalized radiomic-histomic distance, testing for main effects
of scanners from different vendors and acquisition field strength. Results as a
whole were largely heterogenous, but several features demonstrated substantial
associations with their histomic analogs, particularly those derived from the
FLAIR and post-contrast T1W images. These most-associated features typically
presented as stable across confounding factors as well. These data suggest that
a subset of radiomic features are able to consistently capture texture
information about the underlying tissue histology.
Estimating a Behavioral New Keynesian Model
Date: 2019-12-16 00:00:00
Tasks: model

This paper analyzes identification issues of a behavorial New Keynesian model
and estimates it using likelihood-based and limited-information methods with
identification-robust confidence sets. The model presents some of the same
difficulties that exist in simple benchmark DSGE models, but the analytical
solution is able to indicate in what conditions the cognitive discounting
parameter (attention to the future) can be identified and the robust estimation
methods is able to confirm its importance for explaining the proposed
behavioral model.
Neural Mechanisms of Human Decision-Making
Date: 2019-12-16 00:00:00
Tasks: Decision Making

We present a computational and theoretical model of the neural mechanisms
underlying human decision-making. We propose a detailed model of the
interaction between brain regions, under a proposer-predictor-actor-critic
framework. Task-relevant areas of cortex propose a candidate plan using fast,
model-free, parallel constraint-satisfaction computations. Other areas of
cortex and medial temporal lobe can then predict likely outcomes of that plan
in this situation. This step is optional. This prediction-(or model-) based
computation produces better accuracy and generalization, at the expense of
speed. Next, linked regions of basal ganglia act to accept or reject the
proposed plan based on its reward history in similar contexts. Finally the
reward-prediction system acts as a critic to determine the value of the outcome
relative to expectations, and produce dopamine as a training signal for cortex
and basal ganglia. This model gains many constraints from the hypothesis that
the mechanisms of complex human decision-making are closely analogous to those
that have been empirically studied in detail for animal action-selection. We
argue that by operating sequentially and hierarchically, these same mechanisms
are responsible for the most complex human plans and decisions. Finally, we use
the computational model to generate novel hypotheses on causes of human risky
decision-making, and compare this to other theories of human decision-making.
Targeting glutamate metabolism in melanoma
Authors: Simar Singh, Raj Shah, Suzie Chen, Fabian V. Filipp
Date: 2019-12-16 00:00:00

The glutamate metabotropic receptor 1 (GRM1) drives oncogenesis when aberrantly activated in melanoma and several other cancers. Metabolomics reveals that patient-derived xenografts with GRM1-positive melanoma tumors exhibit elevated plasma glutamate levels associated with metastatic melanoma in vivo. Stable isotope tracing and GCMS analysis determined that cells expressing GRM1 fuel a substantial fraction of glutamate from glycolytic carbon. Stimulation of GRM1 by glutamate leads to activation of mitogenic signaling pathways, which in turn increases the production of glutamate, fueling autocrine feedback. Implementing a rational drug-targeting strategy, we critically evaluate metabolic bottlenecks in vitro and in vivo. Combined inhibition of glutamate secretion and biosynthesis is an effective rational drug targeting strategy suppressing tumor growth and restricting tumor bioavailability of glutamate.
Inverse scattering for reflection intensity phase microscopy
Date: 2019-12-16 00:00:00

Reflection phase imaging provides label-free, high-resolution
characterization of biological samples, typically using interferometric-based
techniques. Here, we investigate reflection phase microscopy from
intensity-only measurements under diverse illumination. We evaluate the forward
and inverse scattering model based on the first Born approximation for imaging
scattering objects above a glass slide. Under this design, the measured field
combines linear forward-scattering and height-dependent nonlinear
back-scattering from the object that complicates object phase recovery. Using
only the forward-scattering, we derive a linear inverse scattering model and
evaluate this model's validity range in simulation and experiment using a
standard reflection microscope modified with a programmable light source. Our
method provides enhanced contrast of thin, weakly scattering samples that
complement transmission techniques. This model provides a promising development
for creating simplified intensity-based reflection quantitative phase imaging
systems easily adoptable for biological research.
User manual and tutorial for ISIM1s: a tiny MATLAB package for single stage invariant manifold-guided impulsive stabilization of delay equations
Date: 2019-12-18 00:00:00

ISIM1s consists of a few MATLAB functions and a script that can be used to
derive stabilizing impulsive controllers for delay differential equations. This
document serves as both a manual and tutorial on the functionality of the
ISIM1s package. Brief background on the theoretically guaranteed stabilization
scenario are provided before the primary MATLAB script is explained. The
tutorial demonstrates how the package can be used to derive stabilizing
impulsive controllers for delay differential equations of various complexity
scales. Emphasis is placed on the role of various tuning parameters.
Process simulation and optimization of agro-systems by DNDC model
Authors: Jinyue Cui
Date: 2019-12-13 00:00:00

Many people are still facing hunger and the global food shortages is still an urgent problem. Meanwhile, global warming is still severe. Therefore, we propose a simulation-based optimization approach for improving crop yield and reducing the greenhouse gas emissions (GHG) of agriculture system. We simulated and verified the crop yield and carbon/nitrogen cycle with Denitrification-Decomposition (DNDC) model. A set of empirical equations of DNDC model were selected and implemented in gPROMS for obtaining the optimal solution of fertilizer usage. A case study shows that the optimized framework improves crop yield by 18%, when 72.42kg N/ha urea was used. Meanwhile the GHG emission of the system was reduced by 10%. The results show the necessity of optimal planning and usage of fertilizer in agriculture system.
An RSSI-based Wireless Sensor Node Localisation using Trilateration and Multilateration Methods for Outdoor Environment
Date: 2019-12-17 00:00:00
Tasks: Object Tracking

Localisation can be defined as estimating or finding a position of the node.
There are two techniques in localisation, which are range-based and range-free
techniques. This paper focusses on the Received Signal Strength Indicator
(RSSI) localisation method, which is categorised in a range-based technique
along with the time of arrival, time difference of arrival and angle of
arrival. Therefore, this study aims to compare the trilateration and
multilateration method for RSSI-based technique for localising the transmitted
(Tx) node. The wireless sensor module in the work used LOng-RAnge radio (LoRa)
with 868MHz frequency. Nowadays, wireless networks have been a key technology
for smart environments, monitoring, and object tracking due to low power
consumption with long-range connectivity. The number of received (Rx) nodes are
three and four for trilateration and multilateration methods, respectively. The
transmitted node is placed at 32 different coordinates within the 10x10 meter
outdoor area. The results show that error localisation obtained for General
Error Localisation (GER) for multilateration and trilateration is 1.83m and
2.30m, respectively. An additional, the maximum and minimum error for
multilateration and trilateration from 1.00 to 5.28m and 0.5 to 3.61m. The
study concludes that the multilateration method more accurate than
trilateration. Therefore, with the increasing number of Rx node, the accuracy
of localisation of the Tx node increases.
Leakage Localisation Using Cross Correlation for Wireless Three-Axis Vibration Sensors
Date: 2019-12-17 00:00:00

The objective of this paper is to investigate the application of the
cross-correlation technique in localizing the leakage in Acrylonitrile
Butadiene Styrene (ABS) pipeline using wireless three-axis vibration sensors.
Most of the existing leak localization techniques that utilized the
cross-correlation method is using acoustic and pressure sensors. This study
explored the impact of using three-axis vibration sensor ADXL335 to localize
the leakages in ABS pipeline. The study focused on the small leak size of 1 mm
and the effect of sensor distance and different water pressure is studied. The
vibration data used in the cross-correlation data were filtered against the
noise interference from the water pipeline. The source of noise interference
from the water pipeline are motor, water pump, and valve. The computed leak
localization accuracy against the actual leak distance from the three-axis was
analyzed. The leakage localization formula is improvised by adding buffer time
to have an accurate result. From the experiment, it was discovered that
three-axis vibration sensor can be used to localize the leakages in plastic
water pipeline and it is suitable for deployment in small to medium water
distribution network or within a building.
Signal Design for AF Relay Systems using Superposition Coding and Finite-Alphabet Inputs
Date: 2019-12-17 00:00:00

This paper focuses on the signal design in a Gaussian amplify-and-forward
(AF) relay system with superposition coding (SC) being applied at the relay,
which allows the source and relay to transmit their own information within two
time slots. Practical quadrature amplitude modulation (QAM) constellations are
adopted at the source and relay. To improve the system error performance, we
optimize the weight coefficients adopted at the source and relay to maximize
the minimum Euclidean distance of the received composite constellation, subject
to their individual average power constraints. The formulated optimization
problem is shown to be a mixed continuous-discrete one that is non-trivial to
resolve in general. By resorting to the punched Farey sequence, we manage to
obtain the optimal solution to the formulated problem by first partitioning the
entire feasible region into a finite number of sub-intervals and then taking
the maximum over all the possible sub-intervals. Simulation results are
provided to demonstrate the superior performance of our proposed design based
on SC over that using conventional time division multiple access (TDMA).
Nonlinear Equalization for TDMR Channels Using Neural Networks
Date: 2019-12-17 00:00:00

This paper presents new structure and adaptation criterion for equalization
of two-dimensional magnetic recording channels, as opposed to typical linear
equalizer with minimum mean square error (MMSE) as adaptation criterion. To
compensate for the nonlinear channel noise, we propose a neural network based
nonlinear equalizer and show it outperforms linear equalizer under the same
criterion. To achieve minimum bit error rate (BER) at the detector output, we
propose to adapt the equalizer with cross entropy between the true probability
of the bit and detector's estimate of it. We show minimizing the cross entropy
enables maximum likelihood adaptation, and results in lower detector BER than
the MSE criterion. Several variations of nonlinear equalizer structures with
cross entropy criterion are investigated. Compared to linear MMSE equalizer,
the proposed scheme can provide up to 22.76% detector BER reduction with only
6x increase in complexity.
Accessibility Percolation on Cartesian Power Graphs
Authors: Benjamin Schmiegelt, Joachim Krug
Date: 2019-12-17 00:00:00

A fitness landscape is a mapping from a space of discrete genotypes to the real numbers. A path in a fitness landscape is a sequence of genotypes connected by single mutational steps. Such a path is said to be accessible if the fitness values of the genotypes encountered along the path increase monotonically. We study accessible paths on random fitness landscapes of the House-of-Cards type, on which fitness values are independent, identically and continuously distributed random variables. The genotype space is taken to be a Cartesian power graph $\mathcal{A}^L$, where $L$ is the number of genetic loci and the allele graph $\mathcal{A}$ encodes the possible allelic states and mutational transitions on one locus. The probability of existence of accessible paths between two genotypes at a distance linear in $L$ displays a transition from 0 to a positive value at a threshold $\beta_c$ for the fitness difference between the initial and final genotype. We derive a lower bound on $\beta_c$ for general $\mathcal{A}$ and show that this bound is tight for a large class of allele graphs. Our results generalize previous results for accessibility percolation on the biallelic hypercube, and compare favorably to published numerical results for multiallelic Hamming graphs.