pwc-archive/papers-with-abstracts huggingface dataset:
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions
Authors: Juan Carrillo, Mark Crowley
Date: 2020-09-22 00:00:00
Tasks: image-classification, Image Classification, Spatial Interpolation

During the winter season, real-time monitoring of road surface conditions is critical for the safety of drivers and road maintenance operations. Previous research has evaluated the potential of image classification methods for detecting road snow coverage by processing images from roadside cameras installed in RWIS (Road Weather Information System) stations. However, there are a limited number of RWIS stations across Ontario, Canada; therefore, the network has reduced spatial coverage. In this study, we suggest improving performance on this task through the integration of images and weather data collected from the RWIS stations with images from other MTO (Ministry of Transportation of Ontario) roadside cameras and weather data from Environment Canada stations. We use spatial statistics to quantify the benefits of integrating the three datasets across Southern Ontario, showing evidence of a six-fold increase in the number of available roadside cameras and therefore improving the spatial coverage in the most populous ecoregions in Ontario. Additionally, we evaluate three spatial interpolation methods for inferring weather variables in locations without weather measurement instruments and identify the one that offers the best tradeoff between accuracy and ease of implementation.
Vertical Power Flow Forecast with LSTMs Using Regular Training Update Strategies
Authors: Katharina Brauns, Christoph Scholz, Andre Baier, Dominik Jost
Date: 2020-09-22 00:00:00
Tasks: Time Series Analysis

The strong growth of renewable energy sources and the high volatility in power generation of these sources, as well as the increasing amount of volatile energy consumption is leading to major challenges in the electrical grid. In order to ensure safety and reliability in the electricity grid, the power flow in the grid needs to be observed to prevent overloading. Furthermore, the energy supply and consumption need to be continuously balanced to ensure the security of energy supply. Therefore a high quality of power flow forecasts for the next few hours within the grid are needed. In this paper we investigate forecasts of the vertical power flow at transformer between the medium and high voltage grid. Forecasting the vertical power flow is challenging due to constantly changing characteristics of the power flow at the transformer. This is mainly a result of dynamic grid topologies, changes in the installed assets, maintenance of the transformer itself as well as the volatile generation. In this paper we present a novel approach to deal with these challenges. For the multi step time series forecasts a Long-Short Term Memory (LSTM) is used. In our presented approach an update process where the model is retrained regularly is investigated and compared to baseline models. The model is retrained as soon as a sufficient amount of new measurements are available. This retraining should capture changes in the characteristic of the transformer that the model has not yet seen in the past and therefore cannot be predicted by the model. For the regular update process we investigate different strategies where especially the number of used epochs are considered, but also different learning rates are used. We show that our new approach significantly outperforms the investigated baseline approaches.
Transient Classification in low SNR Gravitational Wave data using Deep Learning
Authors: Rahul Nigam, Amit Mishra, Pranath Reddy
Date: 2020-09-20 00:00:00
Tasks: Astronomy, Deep Learning, General Classification, Time Series, Time Series Analysis, Time Series Classification, Time Series Forecasting

The recent advances in Gravitational-wave astronomy have greatly accelerated the study of Multimessenger astrophysics. There is a need for the development of fast and efficient algorithms to detect non-astrophysical transients and noises due to the rate and scale at which the data is being provided by LIGO and other gravitational wave observatories. These transients and noises can interfere with the study of gravitational waves and binary mergers and induce false positives. Here, we propose the use of deep learning algorithms to detect and classify these transient signals. Traditional statistical methods are not well designed for dealing with temporal signals but supervised deep learning techniques such as RNN-LSTM and deep CNN have proven to be effective for solving problems such as time-series forecasting and time-series classification. We also use unsupervised models such as Total variation, Principal Component Analysis, Support Vector Machine, Wavelet decomposition or Random Forests for feature extraction and noise reduction and then study the results obtained by RNN-LSTM and deep CNN for classifying the transients in low-SNR signals. We compare the results obtained by the combination of various unsupervised models and supervised models. This method can be extended to real-time detection of transients and merger signals using deep-learning optimized GPU's for early prediction and study of various astronomical events. We will also explore and compare other machine learning models such as MLP, Stacked Autoencoder, Random forests, extreme learning machine, Support Vector machine and logistic regression classifier.
Delay Characterization of Mobile Edge Computing for 6G Time-Sensitive Services
Authors: Jianyu Cao, Wei Feng, Ning Ge, Jianhua Lu
Date: 2020-09-17 00:00:00
Tasks: Edge-computing

Time-sensitive services (TSSs) have been widely envisioned for future sixth generation (6G) wireless communication networks. Due to its inherent low-latency advantage, mobile edge computing (MEC) will be an indispensable enabler for TSSs. The random characteristics of the delay experienced by users are key metrics reflecting the quality of service (QoS) of TSSs. Most existing studies on MEC have focused on the average delay. Only a few research efforts have been devoted to other random delay characteristics, such as the delay bound violation probability and the probability distribution of the delay, by decoupling the transmission and computation processes of MEC. However, if these two processes could not be decoupled, the coupling will bring new challenges to analyzing the random delay characteristics. In this paper, an MEC system with a limited computation buffer at the edge server is considered. In this system, the transmission process and computation process form a feedback loop and could not be decoupled. We formulate a discrete-time two-stage tandem queueing system. Then, by using the matrix-geometric method, we obtain the estimation methods for the random delay characteristics, including the probability distribution of the delay, the delay bound violation probability, the average delay and the delay standard deviation. The estimation methods are verified by simulations. The random delay characteristics are analyzed by numerical experiments, which unveil the coupling relationship between the transmission process and computation process for MEC. These results will largely facilitate elaborate allocation of communication and computation resources to improve the QoS of TSSs.
COVID-19: Forecasting mortality given mobility trend data and non-pharmaceutical interventions
Date: 2020-09-29 00:00:00

We develop a novel hybrid epidemiological model and a specific methodology
for its calibration to distinguish and assess the impact of mobility
restrictions (given by Apple's mobility trends data) from other complementary
non-pharmaceutical interventions (NPIs) used to control the spread of COVID-19.
Using the calibrated model, we estimate that mobility restrictions contribute
to 47 % (US States) and 47 % (worldwide) of the overall suppression of the
disease transmission rate using data up to 13/08/2020. The forecast capacity of
our model was evaluated doing four-weeks ahead predictions. Using data up to
30/06/20 for calibration, the mean absolute percentage error (MAPE) of the
prediction of cumulative deceased individuals was 5.0 % for the United States
(51 states) and 6.7 % worldwide (49 countries). This MAPE was reduced to 3.5%
for the US and 3.8% worldwide using data up to 13/08/2020. We find that the
MAPE was higher for the total confirmed cases at 11.5% worldwide and 10.2% for
the US States using data up to 13/08/2020. Our calibrated model achieves an
average R-Squared value for cumulative confirmed and deceased cases of 0.992
using data up to 30/06/20 and 0.98 using data up to 13/08/20.
Intelligent Risk Alarm for Asthma Patients using Artificial Neural Networks
Authors: Rawabi A. Aroud, Anas H. Blasi, Mohammed A. Alsuwaiket
Date: 2020-09-24 00:00:00

Asthma is a chronic disease of the airways of the lungs. It results in inflammation and narrowing of the respiratory passages, which prevents air flow into the airways and leads to frequent bouts of shortness of breath with wheezing accompanied by coughing and phlegm after exposure to inhalation of substances that provoke allergic reactions or irritation of the respiratory system. Data mining in healthcare system is very important in diagnosing and understanding data, so data mining aims to solve basic problems in diagnosing diseases due to the complexity of diagnosing asthma. Predicting chemicals in the atmosphere is very important and one of the most difficult problems since the last century. In this paper, the impact of chemicals on asthma patient will be presented and discussed. Sensor system called MQ5 will be used to examine the smoke and nitrogen content in the atmosphere. MQ5 will be inserted in a wristwatch that checks the smoke and nitrogen content in the patients place, the system shall issue a warning alarm if this gas affects the person with asthma. It will be based on the Artificial Neural Networks (ANN) algorithm that has been built using data that containing a set of chemicals such as carbon monoxide, NMHC (GT) acid gas, C6H6 (GT) Gasoline, NOx (GT) Nitrogen Oxide, and NO2 (GT) Nitrogen Dioxide. The temperature and humidity will be also used as they can negatively affect asthma patient. Finally, the rating model was evaluated and achieved 99.58% classification accuracy.
Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles
Authors: Karl Schwenk, Stefan Meisenbacher, Benjamin Briegel, Tim Harr, Veit Hagenmeyer, Ralf Mikut
Date: 2020-09-23 00:00:00

Smart charging of Electric Vehicles (EVs) reduces operating costs, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enables efficient power grid support. Today, however, uncoordinated charging, e.g. governed by users' habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g. battery aging costs, and give suggestions for further research. Using typical on-board sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging requires precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs' operating costs by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g. energy arbitrage, depends on battery aging costs and the electricity price spread.
A modified age-structured SIR model for COVID-19 type viruses
Date: 2020-09-23 00:00:00
Tasks: Vocal Bursts Type Prediction

We present a modified age-structured SIR model based on known patterns of
social contact and distancing measures within Washington, USA. We find that
population age-distribution has a significant effect on disease spread and
mortality rate, and contribute to the efficacy of age-specific contact and
treatment measures. We consider the effect of relaxing restrictions across less
vulnerable age-brackets, comparing results across selected groups of varying
population parameters. Moreover, we analyze the mitigating effects of
vaccinations and examine the effectiveness of age-targeted distributions.
Lastly, we explore how our model can applied to other states to reflect
social-distancing policy based on different parameters and metrics.
Microtubule-based actin transport and localization in a spherical cell
Authors: Marco Saltini, Bela M. Mulder
Date: 2020-09-25 00:00:00

The interaction between actin filaments and microtubules is crucial for many eukaryotic cellular processes, such as, among others, cell polarization, cell motility and cellular wound healing. The importance of this interaction has long been recognised, yet very little is understood about both the underlying mechanisms and the consequences for the spatial (re)organization of the cellular cytoskeleton. At the same time, understanding the causes and the consequences of the interaction between different biomolecular components are key questions for \emph{in vitro} research involving reconstituted biomolecular systems, especially in the light of current interest in creating minimal synthetic cells. In this light, recent \emph{in vitro} experiments have shown that the actin-microtubule interaction mediated by the cytolinker TipAct, which binds to actin lattice and microtubule tip, causes the directed transport of actin filaments. We develop an analytical theory of dynamically unstable microtubules, nucleated from the center of a spherical cell, in interaction with actin filaments. We show that, depending on the balance between the diffusion of unbound actin filaments and propensity to bind microtubules, actin is either concentrated in the center of the cell, where the density of microtubules is highest, or becomes localized to the cell cortex.
Master regulators of evolution and the microbiome in higher dimensions
Authors: Holger Eble, Michael Joswig, Lisa Lamberti, Will Ludington
Date: 2020-09-25 00:00:00

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a new high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g. the rbs locus and pykF) and species (e.g. Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide mathematical intuition and justification for exploration of biological networks in higher dimensions.
A consolidated view of loss functions for supervised deep learning-based speech enhancement
Date: 2020-09-25 00:00:00
Tasks: Speech Enhancement

Deep learning-based speech enhancement for real-time applications recently
made large advancements. Due to the lack of a tractable perceptual optimization
target, many myths around training losses emerged, whereas the contribution to
success of the loss functions in many cases has not been investigated isolated
from other factors such as network architecture, features, or training
procedures. In this work, we investigate a wide variety of loss spectral
functions for a recurrent neural network architecture suitable to operate in
online frame-by-frame processing. We relate magnitude-only with phase-aware
losses, ratios, correlation metrics, and compressed metrics. Our results reveal
that combining magnitude-only with phase-aware objectives always leads to
improvements, even when the phase is not enhanced. Furthermore, using
compressed spectral values also yields a significant improvement. On the other
hand, phase-sensitive improvement is best achieved by linear domain losses such
as mean absolute error.
Novel Visible Light Communication Assisted Perspective-Four-Line Algorithm for Indoor Localization
Authors: Lin Bai
Date: 2020-09-25 00:00:00
Tasks: Indoor Localization, Position

In this paper, we propose a novel visible light communication (VLC) assisted Perspective-fourLine algorithm (V-P4L) for practical indoor localization. The basic idea of V-P4L is to joint VLC and computer vision to achieve high accuracy regardless of LED height differences. In particular, we first exploit the space-domain information to estimate the orientation and coordinate of a single rectangular LED luminaire in the camera coordinate system based on the plane and solid geometry. Then, based on the time-domain information transmitted by VLC and the estimated luminaire information, V-P4L can estimate the position and pose of the camera by the single-view geometry theory and the linear least square (LLS) method. To further mitigate the effect of height differences among LEDs on localization accuracy, we then propose a correction algorithm of V-P4L based on the LLS method and a simple optimization method. Due to the combination of time- and space-domain information, V-P4L only requires a single luminaire for localization without limitation on the correspondences between the features and their projections. Simulation results show that for V-P4L the position error is always less than 15 cm and the orientation error is always less than 3{\deg} using popular indoor luminaires.
Fused Low-Earth-Orbit GNSS
Authors: Peter A. Iannucci, Todd E. Humphreys
Date: 2020-09-25 00:00:00

Traditional Global Navigation Satellite System (GNSS) immunity to interference may be approaching a practical performance ceiling. Greater gains are possible outside traditional GNSS orbits and spectrum. GNSS from low Earth orbit (LEO) has long been viewed as promising but expensive, requiring large constellations for rapid navigation solutions. The recent emergence of commercial broadband LEO mega-constellations invites study on dual-purposing these for both communications -- their primary mission -- and a secondary positioning, navigation, and timing (PNT) service. Operating at shorter wavelengths than traditional GNSS, these constellations would permit highly directive, relatively compact receiver antennas. PNT-specific on-orbit resources would not be required: the transmitters, antennas, clocks, and spectrum of the hosting broadband network would suffice for PNT. Non-cooperative use of LEO signals for PNT is an option, but cooperation with the constellation operator ("fusion" with its communications mission) eases the burden of tracking a dense, low-altitude constellation from the ground and enables a receiver to produce single-epoch stand-alone PNT solutions. This paper proposes such a cooperative concept, termed fused LEO GNSS. Viability hinges on opportunity cost, or the burden a secondary PNT mission imposes on the communications constellation operator. This is assessed in terms of time-space-bandwidth product and energy budget. It is shown that a near-instantaneous-fix PNT service over $\pm60${\deg} latitude (covering 99.8% of the world's population) with positioning performance superior to traditional GNSS pseudoranging would cost less than 1.6% of downlink capacity for the largest of the new constellations, SpaceX's Starlink. This allocation is comparable to adding one user consuming 5.7 Mbps of broadband service to each cell.
Network geometry and market instability
Authors: Areejit Samal, Hirdesh K. Pharasi, Sarath Jyotsna Ramaia, Harish Kannan, Emil Saucan, Jürgen Jost, Anirban Chakraborti
Date: 2020-09-25 00:00:00

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier-Ricci curvature and Forman-Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or `business-as-usual' periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.
Practical Option Valuations of Futures Contracts with Negative Underlying Prices
Authors: Anatoliy Swishchuk, Ana Roldan-Contreras, Elham Soufiani, Guillermo Martinez, Mohsen Seifi, Nishant Agrawal, Yao Yao
Date: 2020-09-25 00:00:00

Here we propose two alternatives to Black 76 to value European option future contracts in which the underlying market prices can be negative or mean reverting. The two proposed models are Ornstein-Uhlenbeck (OU) and continuous time GARCH (generalized autoregressive conditionally heteroscedastic). We then analyse the values and compare them with Black 76, the most commonly used model, when the underlying market prices are positive
An Optimal Computing Budget Allocation Tree Policy for Monte Carlo Tree Search
Date: 2020-09-25 00:00:00

We analyze a tree search problem with an underlying Markov decision process,
in which the goal is to identify the best action at the root that achieves the
highest cumulative reward. We present a new tree policy that optimally
allocates a limited computing budget to maximize a lower bound on the
probability of correctly selecting the best action at each node. Compared to
widely used Upper Confidence Bound (UCB) tree policies, the new tree policy
presents a more balanced approach to manage the exploration and exploitation
trade-off when the sampling budget is limited. Furthermore, UCB assumes that
the support of reward distribution is known, whereas our algorithm relaxes this
assumption. Numerical experiments demonstrate the efficiency of our algorithm
in selecting the best action at the root.