pwc-archive/papers-with-abstracts huggingface dataset:
Discrepancies of measured SAR between traditional and fast measuring systems
Date: 2020-02-28 00:00:00

Human exposure to mobile devices is traditionally measured by a system in
which the human body (or head) is modelled by a phantom and the energy absorbed
from the device is estimated based on the electric fields measured with a
single probe. Such a system suffers from low efficiency due to repeated
volumetric scanning within the phantom needed to capture the absorbed energy
throughout the volume. To speed up the measurement, fast SAR (specific
absorption rate) measuring systems have been developed. However, discrepancies
of measured results are observed between traditional and fast measuring
systems. In this paper, the discrepancies in terms of post-processing
procedures after the measurement of electric field (or its amplitude) are
investigated. Here, the concerned fast measuring system estimates SAR based on
the reconstructed field of the region of interest while the amplitude and phase
of electric field are measured on a single plane with a probe array. The
numerical results presented indicate that the fast SAR measuring system has the
potential to yield more accurate estimations than the traditional system, but
no conclusion can be made on which kind of system is superior without knowledge
of the field-reconstruction algorithms and the emitting source.
An Audio-Based Fault Diagnosis Method for Quadrotors Using Convolutional Neural Network and Transfer Learning
Authors: Wansong Liu, Zhu Chen, Minghui Zheng
Date: 2020-03-02 00:00:00
Tasks: Diagnostic, Fault Diagnosis, Transfer Learning

Quadrotor unmanned aerial vehicles (UAVs) have been developed and applied into several types of workplaces, such as warehouses, which usually involve human workers. The co-existence of human and UAVs brings new challenges to UAVs: potential failure of UAVs may cause risk and danger to surrounding human. Effective and efficient detection of such failure may provide early warning to the surrounding human workers and reduce such risk to human beings as much as possible. One of the commonest reasons that cause the failure of the UAV's flight is the physical damage to the propellers. This paper presents a method to detect the propellers' damage only based on the audio noise caused by the UAV's flight. The diagnostic model is developed based on convolutional neural network (CNN) and transfer learning techniques. The audio data is collected from the UAVs in real time, transformed into the time-frequency spectrogram, and used to train the CNN-based diagnostic model. The developed model is able to detect the abnormal features of the spectrogram and thus the physical damage of the propellers. To reduce the data dependence on the UAV's dynamic models and enable the utilization of the training data from UAVs with different dynamic models, the CNN-based diagnostic model is further augmented by transfer learning. As such, the refinement of the well-trained diagnostic model ground on other UAVs only requires a small amount of UAV's training data. Experimental tests are conducted to validate the diagnostic model with an accuracy of higher than 90%.
Optimizing the Total Production and Maintenance Cost of an Integrated Multi-Product Process and Maintenance Planning (IPPMP) Model
Authors: Mohammad Arani, Mousaalreza Dastmard, Zhila Dehdari Ebrahimi, Mohsen Momenitabar, Xian Liu
Date: 2020-03-02 00:00:00

Today, a competitive manufacturing environment imposes further production cost reduction on modern companies. Seeking proper recommendations in production and maintenance planning are the two essential cornerstones of effective production organizations. In the current research, we have considered the problem of integrated multi-product process and maintenance planning on a capacitated machine that is susceptible to random breakdown. Maintenance processes comprise general perfect repair (non-cyclical) as preventive maintenance (PM) in the early stages and minimal repair as corrective maintenance for the occurrence of machine breakdown. Furthermore, a rational presumption is reflected in the problem statement in which the time and cost of PM are pertinent to the interval between the prior perfect repair and current PM. The purpose served by this paper is to minimize the cost of production accompanying PM, and the expected corrective repair, consequently, a mixed-integer linear programming (MILP) model has been constructed to pave the way. The model investigated under two circumstances of the machine age effect and its absence. The outcome depicted that the presence of the machine age effect led to an accurate and lessen total cost calculation.
Conflict externalization and the quest for peace: theory and case evidence from Colombia
Date: 2020-08-25 00:00:00

I study the relationship between the likelihood of a violent domestic
conflict and the risk that such a conflict "externalizes" (i.e. spreads to
another country by creating an international dispute). I consider a situation
in which a domestic conflict between a government and a rebel group has the
potential to externalize. I show that the risk of externalization increases the
likelihood of a peaceful outcome, but only if the government is sufficiently
powerful relative to the rebels, the risk of externalization is sufficiently
high, and the foreign actor who can intervene in the domestic conflict is
sufficiently uninterested in material costs and benefits. I show how this model
helps to understand the recent and successful peace process between the
Colombian government and the country's most powerful rebel group, the
Revolutionary Armed Forces of Colombia (FARC).
A novel mathematical model of heterogeneous cell proliferation
Authors: Sean T. Vittadello, Scott W. McCue, Gency Gunasingh, Nikolas K. Haass, Matthew J. Simpson
Date: 2020-03-06 00:00:00

We present a novel mathematical model of heterogeneous cell proliferation where the total population consists of a subpopulation of slow-proliferating cells and a subpopulation of fast-proliferating cells. The model incorporates two cellular processes, asymmetric cell division and induced switching between proliferative states, which are important determinants for the heterogeneity of a cell population. As motivation for our model we provide experimental data that illustrate the induced-switching process. Our model consists of a system of two coupled delay differential equations with distributed time delays and the cell densities as functions of time. The distributed delays are bounded and allow for the choice of delay kernel. We analyse the model and prove the non-negativity and boundedness of solutions, the existence and uniqueness of solutions, and the local stability characteristics of the equilibrium points. We find that the parameters for induced switching are bifurcation parameters and therefore determine the long-term behaviour of the model. Numerical simulations illustrate and support the theoretical findings, and demonstrate the primary importance of transient dynamics for understanding the evolution of many experimental cell populations.
Implementability of Honest Multi-Agent Sequential Decision-Making with Dynamic Population
Date: 2020-05-19 00:00:00
Tasks: Decision Making, Sequential Decision Making

We study the design of decision-making mechanism for resource allocations
over a multi-agent system in a dynamic environment. Agents' privately observed
preference over resources evolves over time and the population is dynamic due
to the adoption of stopping rules. The proposed model designs the rules of
encounter for agents participating in the dynamic mechanism by specifying an
allocation rule and three payment rules to elicit agents' coupled decision
makings of honest preference reporting and optimal stopping over multiple
periods. The mechanism provides a special posted-price payment rule that
depends only on each agent's realized stopping time to directly influence the
population dynamics. This letter focuses on the theoretical implementability of
the rules in perfect Bayesian Nash equilibrium and characterizes necessary and
sufficient conditions to guarantee agents' honest equilibrium behaviors over
periods. We provide the design principles to construct the payments in terms of
the allocation rules and identify the restrictions of the designer's ability to
influence the population dynamics. The established conditions make the
designer's problem of finding multiple rules to determine an optimal allocation
rule.
Olive oil by-product as functional ingredient in bakery products
Authors: Mattia Di Nunzio, Gianfranco Picone, Federica Pasini, Elena Chiarello, Maria Fiorenza Caboni, Francesco Capozzi, Andrea Gianotti, Alessandra Bordoni
Date: 2020-02-28 00:00:00

By-products represent a major disposal problem for the food industry, but they are also promising sources of bioactive compounds. Olive pomace, one of the main by-products of olive oil production, is a potential low-cost, phenol-rich ingredient for the formulation of functional food. In this study, bakery products enriched with defatted olive pomace powder and their conventional counterparts were chemical characterized and in vitro digested. The bioaccessible fractions were supplemented to cultured human intestinal cells exposed to an inflammatory stimulus, and the anti-inflammatory effect and metabolome modification were evaluated. Although in all bakery products the enrichment with olive pomace significantly increased the total phenolic content, this increase was paralleled by an enhanced anti-inflammatory activity only in conventionally fermented bread. Therefore, while confirming olive oil by-products as functional ingredients for bakery food enrichment, our data highlight that changes in chemical composition cannot predict changes in functionality. Functionality should be first evaluated in biological in vitro systems, and then confirmed in human intervention studies.
A Best-Response Algorithm with Voluntary Communication and Mobility Protocols for Mobile Autonomous Teams Solving the Target Assignment Problem
Authors: Sarper Aydin, Ceyhun Eksin
Date: 2020-03-06 00:00:00

We consider a team of mobile autonomous robots with the aim to cover a given set of targets. Each robot aims to select a target to cover and physically reach it by the final time in coordination with other robots given the locations of targets. Robots are unaware of which targets other robots intend to cover. Each robot can control its mobility and who to send information to. We assume communication happens over a wireless channel that is subject to fading and failures. Given the setup, we propose a decentralized algorithm based on decentralized fictitious play in which robots reason about the selections and locations of other robots to decide which target to select, whether to communicate or not, who to communicate with, and where to move. Specifically, the communication actions of the robots are learning-aware, and their mobility actions are sensitive to the success probability of communication. We show that the decentralized algorithm guarantees that robots will cover their targets in finite time. Numerical simulations and experiments using a team of mobile robots confirm the target coverage in finite time and show that mobility control for communication and learning-aware voluntary communication protocols reduce the number of communication attempts in comparison to a benchmark distributed algorithm that relies on communication after every decision epoch.
Effects of neuronal variability on phase synchronization of neural networks
Date: 2020-02-27 00:00:00

An important idea in neural information processing is the
communication-through-coherence hypothesis, according to which communication
between two brain regions is effective only if they are phase-locked. Also of
importance is neuronal variability, a phenomenon in which a single neuron's
inter-firing times may be highly variable. In this work, we aim to connect
these two ideas by studying the effects of that variability on the capability
of neurons to reach phase synchronization. We simulate a network of
modified-Hodgkin-Huxley-bursting neurons possessing a small-world topology.
First, variability is shown to be correlated with the average degree of phase
synchronization of the network. Next, restricting to spatial variability -
which measures the deviation of firing times between all neurons in the network
- we show that it is positively correlated to a behavior we call promiscuity,
which is the tendency of neurons to to have their relative phases change with
time. This relation is observed in all cases we tested, regardless of the
degree of synchronization or the strength of the inter-neuronal coupling: high
variability implies high promiscuity (low duration of phase-locking), even if
the network as a whole is synchronized and the coupling is strong. We argue
that spatial variability actually generates promiscuity. Therefore, we conclude
that variability has a strong influence on both the degree and the manner in
which neurons phase synchronize, which is another reason for its relevance in
neural communication.
Minimizing the number of optimizations for efficient community dynamic flux balance analysis
Date: 2020-07-28 00:00:00

Dynamic flux balance analysis uses a quasi-steady state assumption to
calculate an organism's metabolic activity at each time-step of a dynamic
simulation, using the well-known technique of flux balance analysis. For
microbial communities, this calculation is especially costly and involves
solving a linear constrained optimization problem for each member of the
community at each time step. However, this is unnecessary and inefficient, as
prior solutions can be used to inform future time steps. Here, we show that a
basis for the space of internal fluxes can be chosen for each microbe in a
community and this basis can be used to simulate forward by solving a
relatively inexpensive system of linear equations at most time steps. We can
use this solution as long as the resulting metabolic activity remains within
the optimization problem's constraints (i.e. the solution to the linear system
of equations remains a feasible to the linear program). As the solution becomes
infeasible, it first becomes a feasible but degenerate solution to the
optimization problem, and we can solve a different but related optimization
problem to choose an appropriate basis to continue forward simulation. We
demonstrate the efficiency and robustness of our method by comparing with
currently used methods on a four species community, and show that our method
requires at least $91\%$ fewer optimizations to be solved. For reproducibility,
we prototyped the method using Python. Source code is available at
\verb|https://github.com/jdbrunner/surfin_fba|.
Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework
Authors: Shuo Feng, Yiheng Feng, Haowei Sun, Yi Zhang, Henry X. Liu
Date: 2020-03-08 00:00:00
Tasks: Bayesian Optimization

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this paper, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a new-designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study was performed and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.
Complexity Comparison between Two Optimal-Ordered SIC MIMO Detectors Based on Matlab Simulations
Authors: Yanpeng Wu, Hufei Zhu
Date: 2020-03-08 00:00:00

This paper firstly introduces our shared Matlab source code that simulates the two optimal-ordered SIC detectors proposed in [1] and [2]. Based on our shared Matlab code, we compare the computational complexities between the two detectors in [1] and [2] by theoretical complexity calculations and numerical experiments. We carry out theoretical complexity calculations to obtain the worst-case complexities for the two detectors in [1] and [2]. Then from the theoretical worst-case complexities, we make the conclusion that the detector proposed in [2] requires 9N 2 more floating-point operations (flops) than the detector proposed in [1], where N is the number of transmit antennas. Our numerical experiments also show that the detector in [2] requires more worst-case and average complexities than the detector in [1].
Nonlinear least-squares spline fitting with variable knots
Date: 2020-03-12 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we present a nonlinear least-squares fitting algorithm using
B-splines with free knots. Since its performance strongly depends on the
initial estimation of the free parameters (i.e. the knots), we also propose a
fast and efficient knot-prediction algorithm that utilizes numerical properties
of first-order B-splines. Using $\ell_p\;(p=1,2,\infty)$ norm solutions, we
also provide three different strategies for properly selecting the free knots.
Our initial predictions are then iteratively refined by means of a
gradient-based variable projection optimization. Our method is general in
nature and can be used to estimate the optimal number of knots in cases in
which no a-priori information is available. To evaluate the performance of our
method, we approximated a one-dimensional discrete time series and conducted an
extensive comparative study using both synthetic and real-world data. We chose
the problem of electrocardiogram (ECG) signal compression as a real-world case
study. Our experiments on the well-known PhysioNet MIT-BIH Arrhythmia database
show that the proposed method outperforms other knot-prediction techniques in
terms of accuracy while requiring much lower computational complexity.
How much is your Strangle worth? On the relative value of the $δ-$Symmetric Strangle under the Black-Scholes model
Authors: Ben Boukai
Date: 2020-03-09 00:00:00

Trading option strangles is a highly popular strategy often used by market participants to mitigate volatility risks in their portfolios. In this paper we propose a measure of the relative value of a delta-Symmetric Strangle and compute it under the standard Black-Scholes option pricing model. This new measure accounts for the price of the strangle, relative to the Present Value of the spread between the two strikes, all expressed, after a natural re-parameterization, in terms of delta and a volatility parameter. We show that under the standard BS option pricing model, this measure of relative value is bounded by a simple function of delta only and is independent of the time to expiry, the price of the underlying security or the prevailing volatility used in the pricing model. We demonstrate how this bound can be used as a quick {\it benchmark} to assess, regardless the market volatility, the duration of the contract or the price of the underlying security, the market (relative) value of the $\delta-$strangle in comparison to its BS (relative) price. In fact, the explicit and simple expression for this measure and bound allows us to also study in detail the strangle's exit strategy and the corresponding {\it optimal} choice for a value of delta.
Optical detection of Aflatoxins B in grained almonds using fluorescence spectroscopy and machine learning algorithms
Date: 2020-02-20 00:00:00
Tasks: Binary Classification

Aflatoxins are fungal metabolites extensively produced by many different
fungal species that may contaminate a wide range of agricultural food products.
They have been studied extensively because of being associated with various
chronic and acute diseases especially immunosuppression and cancer and their
presence in food is strictly monitored and regulated worldwide. Aflatoxin
detection and measurement relies mainly on chemical methods usually based on
chromatography approaches, and recently developed immunochemical based assays
that have advantages but also limitations, since these are expensive and
destructive techniques. Nondestructive, optical approaches are recently being
developed to assess presence of contamination in a cost and time effective way,
maintaining acceptable accuracy and reproducibility. In this paper are
presented the results obtained with a simple portable device for nondestructive
detection of aflatoxins in almonds. The presented approach is based on the
analysis of fluorescence spectra of slurried almonds under 375 nm wavelength
excitation. Experiments were conducted with almonds contaminated in the range
of 2.7-320.2 ng/g total aflatoxins B (AFB1 + AFB2) as determined by HPLC/FLD.
After applying pre-processing steps, spectral analysis was carried out by a
binary classification model based on SVM algorithm. A majority vote procedure
was then performed on the classification results. In this way we could achieve,
as best result, a classification accuracy of 94% (and false negative rate 5%)
with a threshold set at 6.4 ng/g. These results illustrate the feasibility of
such an approach in the great challenge of aflatoxin detection for food and
feed safety.
Sparse and Cosparse Audio Dequantization Using Convex Optimization
Authors: Pavel Záviška, Pavel Rajmic
Date: 2020-03-05 00:00:00
Tasks: Audio Dequantization

The paper shows the potential of sparsity-based methods in restoring quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform. The paper shows that the analysis-based model performs comparably to the synthesis-model, but the Gabor transform produces better results than the originally used cosine transform. Last but not least, we provide codes and data in a reproducible way.
Model for Thermal Comfort and Energy Saving Based on Individual Sensation Estimation
Date: 2020-03-10 00:00:00

In office spaces, the ratio of energy consumption of air conditioning and
lighting for maintaining the environment comfort is about 70%. On the other
hand, many people claim being dissatisfied with the temperature of the air
conditioning. Therefore, there is concern about work efficiency reduction
caused by the current air conditioning control. In this research, we propose an
automatic control system that improves both energy-saving and thermal comfort
of all indoor users by quantifying individual differences in thermal comfort
from vital information, on the basis of which the optimal settings of both air
conditioning and wearable systems that can directly heat and cool individuals
are determined. Various environments were simulated with different room sizes,
numbers of users in a room, and heating/cooling conditions. The simulation
results demonstrated the efficiency of the proposed system for both energy
saving and comfort maximization.