pwc-archive/papers-with-abstracts huggingface dataset:
Resource Allocation for mmWave-NOMA Communication through Multiple Access Points Considering Human Blockages
Authors: Foad Barghikar, Foroogh S. Tabataba, Mehdi Naderi Soorki
Date: 2020-05-27 00:00:00
Tasks: Scheduling

In this paper, a new framework for optimizing the resource allocation in a millimeter-wave-non-orthogonal multiple access (mmWave-NOMA) communication for crowded venues is proposed. MmWave communications suffer from severe blockage caused by obstacles such as the human body, especially in a dense region. Thus, a detailed method for modeling the blockage events in the in-venue scenarios is introduced. Also, several mmWave access points are considered in different locations. To maximize the network sum rate, the resource allocation problem is formulated as a mixed integer non-linear programming, which is NP-hard in general. Hence, a three-stage low-complex solution is proposed to solve the problem. At first, a user scheduling algorithm, i.e., modified worst connection swapping (MWCS), is proposed. Secondly, the antenna allocation problem is solved using the simulated annealing algorithm. Afterward, to maximize the network sum rate and guarantee the quality of service constraints, a non-convex power allocation optimization problem is solved by adopting the difference of convex programming approach. The simulation results show that, under the blockage effect, the proposed mmWave-NOMA scheme performs on average 23% better than the conventional mmWave-orthogonal multiple access scheme. Moreover, the performance of proposed solution is 11.4% lower than the optimal value while reducing complexity by 96%.
A connection between bacterial chemotactic network and optimal filtering
Authors: Kento Nakamura, Tetsuya J. Kobayashi
Date: 2020-05-27 00:00:00

The chemotactic network of Escherichia coli has been studied extensively both biophysically and information-theoretically. Nevertheless, the connection between these two aspects is still elusive. In this work, we report such a connection by showing that a standard biochemical model of the chemotactic network is mathematically equivalent to an information-theoretically optimal filtering dynamics. Moreover, we demonstrate that an experimentally observed nonlinear response relation can be reproduced from the optimal dynamics. These results suggest that the biochemical network of E. coli chemotaxis is designed to optimally extract gradient information in a noisy condition.
Notes on the SWIFT method based on Shannon Wavelets for Option Pricing
Authors: Fabien Le Floc'h
Date: 2020-05-27 00:00:00

This note shows that the cosine expansion based on the Vieta formula is equivalent to a discretization of the Parseval identity. We then evaluate the use of simple direct algorithms to compute the Shannon coefficients for the payoff. Finally, we explore the efficiency of a Filon quadrature instead of the Vieta formula for the coefficients related to the probability density function.
Antidepressant-like Effects of Neuropeptide SF (NPSF)
Date: 2020-05-27 00:00:00

Neuropeptide SF (NPSF) is a member of RFamide neuropeptides that play diverse
roles in central nervous system. Little is know about the effects of NPSF on
brain functions. Antidepressant-like effect of NPSF was studied in modified
mice FST. NPSF showed the antidepressant-like effects by decreasing the
immobility time and increasing the climbing and swimming time. Furthermore, the
involvement of the adrenergic, serotonergic, cholinergic or dopaminergic
receptors in the antidepressant-like effect of NPSF was studied in modified
mice FST. Mice were pretreated with a non-selective {\alpha}-adrenergic
receptor antagonist phenoxybenzamine, a \b{eta}-adrenergic receptor antagonist,
propranolol, a non-selective 5-HT2 serotonergic receptor antagonist,
cyproheptadine, nonselective muscarinic acetylcholine receptor antagonist,
atropine, or D2, D3, D4 dopamine receptor antagonist, haloperidol. The present
results confirmed that the antidepressant-like effect of NPSF is mediated, at
least in part, by an interaction of the {\alpha}-adrenergic, 5-HT2
serotonergic, muscarinic acetylcholine receptors and D2, D3, D4 dopamine
receptor in a modified mouse FST.
Simulation of the COVID-19 pandemic on the social network of Slovenia: estimating the intrinsic forecast uncertainty
Date: 2020-05-27 00:00:00

In the article a virus transmission model is constructed on a simplified
social network. The social network consists of more than 2 million nodes, each
representing an inhabitant of Slovenia. The nodes are organised and
interconnected according to the real household and elderly-care center
distribution, while their connections outside these clusters are semi-randomly
distributed and fully-linked. The virus spread model is coupled to the disease
progression model. The ensemble approach with the perturbed transmission and
disease parameters is used to quantify the ensemble spread, a proxy for the
forecast uncertainty. The presented ongoing forecasts of COVID-19 epidemic in
Slovenia are compared with the collected Slovenian data. Results show that
infection is currently twice more likely to transmit within households/elderly
care centers than outside them. We use an ensemble of simulations (N = 1000) to
inversely obtain posterior distributions of model parameters and to estimate
the COVID-19 forecast uncertainty. We found that in the uncontrolled epidemic,
the intrinsic uncertainty mostly originates from the uncertainty of the virus
biology, i.e. its reproductive number. In the controlled epidemic with low
ratio of infected population, the randomness of the social network becomes the
major source of forecast uncertainty, particularly for the short-range
forecasts. Social-network-based models are thus essential for improving
epidemics forecasting.
Analysis of Human Exposure to Electromagnetic Fields in 5G Uplink and Downlink
Authors: Seungmo Kim
Date: 2020-05-27 00:00:00

Concern has been widely acknowledged about human health---e.g., heating of the eyes and skin--from exposure to electromagnetic fields (EMF) produced by wireless transmitters. Mobile telecommunications rely on an extensive network of base stations (BSs) and handheld devices that transmit signals via EMF. There is chance of aggravation due to two important changes that will be seen in future cellular networks. First, the number of BSs will remarkably grow with the proliferation of small-cell networks, which will expose humans to EMF more often. Second, highly concentrated EMF beams will be generated by employing larger antenna arrays to overcome faster EMF energy attenuation in higher-frequency bands such as millimeter wave (mmW) spectrum, which will increase damage if the main beam points to the human body. However, the two changes can be exploited as leverages for (i) wider selection of alternative BSs and (ii) more precise beamforming to a desired user equipment (UE) with less EMF leakage to other directions, respectively. Harnessing the two changes, we have been investigating the human health impacts of 5G wireless systems. This extended abstract summarizes our findings thus far.
Efficient adaptive designs for clinical trials of interventions for COVID-19
Date: 2020-05-25 00:00:00

The COVID-19 pandemic has led to an unprecedented response in terms of
clinical research activity. An important part of this research has been focused
on randomized controlled clinical trials to evaluate potential therapies for
COVID-19. The results from this research need to be obtained as rapidly as
possible. This presents a number of challenges associated with considerable
uncertainty over the natural history of the disease and the number and
characteristics of patients affected, and the emergence of new potential
therapies. These challenges make adaptive designs for clinical trials a
particularly attractive option. Such designs allow a trial to be modified on
the basis of interim analysis data or stopped as soon as sufficiently strong
evidence has been observed to answer the research question, without
compromising the trial's scientific validity or integrity. In this paper we
describe some of the adaptive design approaches that are available and discuss
particular issues and challenges associated with their use in the pandemic
setting. Our discussion is illustrated by details of four ongoing COVID-19
trials that have used adaptive designs.
Optimal control of nonlinear systems with unsymmetrical input constraints and its applications to the UAV circumnavigation problem
Authors: Yangguang Yu, Xiangke Wang, Zhiyong Sun, Lincheng Shen
Date: 2020-05-27 00:00:00

In this paper, a novel design scheme is introduced to solve the optimal control problem for nonlinear systems with unsymmetrical and state-dependent input constraints. By introducing an initial stabilizing control policy as the baseline of the constructed optimal control policy, we remove the assumption in the current study for the adaptive optimal control, that is, the internal dynamics should hold zero when the state of the system is in the origin. Particularly, nonlinear control systems with partially-unknown dynamics are investigated and the procedure to acquire the corresponding optimal control policy is presented. The stability for the closed-loop dynamics and the optimality of the obtained control policy are both proved. Besides, we apply the proposed control design framework to solve the optimal circumnavigation problem based on the accumulative Fisher information for a fixed-wing unmanned aerial vehicle (UAV). The control performance of our algorithm is compared with that of the existing circumnavigation control policy in a numerical simulation.
A Precoding Aided Space Domain Index Modulation Scheme for Downlink Multiuser MIMO Systems
Authors: Nuno Souto, Américo Correia
Date: 2020-05-27 00:00:00

In this correspondence, we propose a space domain index modulation (IM) scheme for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems. Instead of the most common approach where spatial bits select active receiver antennas, in the presented scheme the spatial information is mapped onto the transmitter side. This allows IM to better exploit large dimensional antenna settings which are typically easier to deploy at the base station. In order to mitigate inter-user interference and allow single user detection, a precoder is adopted at the BS. Furthermore two alternative enhanced signal construction methods are proposed for minimizing the transmitted power or enable an implementation with a reduced number of RF chains. Simulation results for different scenarios show that the proposed approach can be an attractive alternative to conventional precoded MU-MIMO.
Immunomodulatory role of black tea in the mitigation of cancer induced by inorganic arsenic
Authors: Ravi Kiran, Swati Tyagi, Syed Abbas, Madhumita Roy, A. Taraphder
Date: 2020-05-27 00:00:00

We present a model analysis of the tumor and normal cell growth under the influence of a carcinogenic agent, an immunomdulator (IM) and variable influx of immune cells including relevant interactions. The tumor growth is facilitated by carcinogens such as inorganic arsenic while the IM considered here is black tea (Camellia sinesnsis). The model with variable influx of immune cells is observed to have considerable advantage over the constant influx model, and while the tumor cell population is greatly mitigated, normal cell population remains above healthy levels. The evolution of normal and tumor cells are computed from the proposed model and their local stabilities are investigated analytically. Numerical simulations are performed to study the long term dynamics and an estimation of the effects of various factors is made. This helps in developing a balanced strategy for tumor mitigation without the use of chemotherapeutic drugs that usually have strong side-effects.
Genetic algorithm with cross validation-based epidemic model and application to early diffusion of COVID-19 in Algeria
Authors: Mohamed Taha Rouabah, Abdellah Tounsi, Nacer Eddine Belaloui
Date: 2020-05-26 00:00:00

A dynamical epidemic model optimized using genetic algorithm and cross validation method to overcome the overfitting problem is proposed. The cross validation procedure is applied so that available data are split into a training subset used to fit the algorithm's parameters, and a smaller subset used for validation. This process is tested on the countries of Italy, Spain, Germany and South Korea before being applied to Algeria. Interestingly, our study reveals an inverse relationship between the size of the training sample and the number of generations required in the genetic algorithm. Moreover, the enhanced compartmental model presented in this work is proven to be a reliable tool to estimate key epidemic parameters and non-measurable asymptomatic infected portion of the susceptible population in order to establish realistic nowcast and forecast of epidemic's evolution. The model is employed to study the COVID-19 outbreak dynamics in Algeria between February 25th and May 24th, 2020. The basic reproduction number and effective reproduction number on May 24th, after three months of the outbreak, are estimated to be 3.78 (95% CI 3.033-4.53) and 0.651 (95% CI 0.539-0.761) respectively. Disease incidence, CFR and IFR are also calculated. Numerical programs developed for the purpose of this study are made publicly accessible for reproduction and further use.
Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids
Authors: Akhil Soman, Amee Trivedi, David Irwin, Beka Kosanovic, Benjamin McDaniel, Prashant Shenoy
Date: 2020-05-25 00:00:00
Tasks: Load Forecasting

Battery-based energy storage has emerged as an enabling technology for a variety of grid energy optimizations, such as peak shaving and cost arbitrage. A key component of battery-driven peak shaving optimizations is peak forecasting, which predicts the hours of the day that see the greatest demand. While there has been significant prior work on load forecasting, we argue that the problem of predicting periods where the demand peaks for individual consumers or micro-grids is more challenging than forecasting load at a grid scale. We propose a new model for peak forecasting, based on deep learning, that predicts the k hours of each day with the highest and lowest demand. We evaluate our approach using a two year trace from a real micro-grid of 156 buildings and show that it outperforms the state of the art load forecasting techniques adapted for peak predictions by 11-32%. When used for battery-based peak shaving, our model yields annual savings of $496,320 for a 4 MWhr battery for this micro-grid.
Adequacy of 5.9 GHz Band for Safety-Critical Operations with DSRC
Authors: Seungmo Kim, Min Jae Suh
Date: 2020-05-10 00:00:00

The recent reform of the 5.9 GHz by the United States Federal Communications Commission (FCC) provoked the need for lightening the load of a vehicle-to-everything (V2X) communications network based on Dedicated Short Range Communications (DSRC). We have collected mathematical analyses and numerical results supporting our proposal of lightening a DSRC network by prioritizing the transmission based on the risk of accident to which a vehicle is exposed.
Robot-assisted Backscatter Localization for IoT Applications
Authors: Shengkai Zhang, Wei Wang, Sheyang Tang, Shi Jin, Tao Jiang
Date: 2020-05-21 00:00:00
Tasks: Indoor Localization

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.
Monitoring fetal electroencephalogram intrapartum: a systematic literature review
Authors: Aude Castel, Yael Frank, John Feltner, Floyd Karp, Catherine Albright, Martin G. Frasch
Date: 2020-05-27 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), Systematic Literature Review

Background: Studies about the feasibility of monitoring fetal electroencephalogram (fEEG) during labor began in the early 1940s. By the 1970s, clear diagnostic and prognostic benefits from intrapartum fEEG monitoring were reported, but until today, this monitoring technology has remained a curiosity. Objectives: Our goal was to review the studies reporting the use of fEEG including the insights from interpreting fEEG patterns in response to uterine contractions during labor. We also used the most relevant information gathered from clinical studies to provide recommendations for enrollment in the unique environment of a labor and delivery unit. Data sources: PubMed. Eligibility criteria: The search strategy was: ("fetus"[MeSH Terms] OR "fetus"[All Fields] OR "fetal"[All Fields]) AND ("electroencephalography"[MeSH Terms] OR "electroencephalography"[All Fields] OR "eeg"[All Fields]) AND (Clinical Trial[ptyp] AND "humans"[MeSH Terms]). Because the landscape of fEEG research has been international, we included studies in English, French, German, and Russian. Results: From 256 screened studies, 40 studies were ultimately included in the qualitative analysis. We summarize and report features of fEEG which clearly show its potential to act as a direct biomarker of fetal brain health during delivery, ancillary to fetal heart rate monitoring. However, clinical prospective studies are needed to further establish the utility of fEEG monitoring intrapartum. We identified clinical study designs likely to succeed in bringing this intrapartum monitoring modality to the bedside. Limitations: Despite 80 years of studies in clinical cohorts and animal models, the field of research on intrapartum fEEG is still nascent and shows great promise to augment the currently practiced electronic fetal monitoring.
Dynamic Coupling and Market Instability
Authors: Christopher D. Clack, Elias Court, Dmitrijs Zaparanuks
Date: 2020-05-27 00:00:00

We examine dynamic coupling and feedback effects between High Frequency Traders (HFTs) and how they can destabilize markets. We develop a general framework for modelling dynamic interaction based on recurrence relations, and use this to show how unexpected latency and feedback can trigger oscillatory instability between HFT market makers with inventory constraints. Our analysis suggests that the modelled instability is an unintentional emergent behaviour of the market that does not depend on the complexity of HFT strategies - even apparently stable strategies are vulnerable. Feedback instability can lead to substantial movements in market prices such as price spikes and crashes.
Modeling cell crawling strategies with a bistable model: From amoeboid to fan-shaped cell motion
Authors: E. Moreno, S. Flemming, F. Font, M. Holschneider, C. Beta, S. Alonso
Date: 2020-05-27 00:00:00

Eukaryotic cell motility involves a complex network of interactions between biochemical components and mechanical processes. The cell employs this network to polarize and induce shape changes that give rise to membrane protrusions and retractions, ultimately leading to locomotion of the entire cell body. The combination of a nonlinear reaction-diffusion model of cell polarization, noisy bistable kinetics, and a dynamic phase field for the cell shape permits us to capture the key features of this complex system to investigate several motility scenarios, including amoeboid and fan-shaped forms as well as intermediate states with distinct displacement mechanisms. We compare the numerical simulations of our model to live cell imaging experiments of motile {\it Dictyostelium discoideum} cells under different developmental conditions. The dominant parameters of the mathematical model that determine the different motility regimes are identified and discussed.
Learning Secured Modulation With Deep Adversarial Neural Networks
Authors: Hesham Mohammed, Dola Saha
Date: 2020-05-27 00:00:00

Growing interest in utilizing the wireless spectrum by heterogeneous devices compels us to rethink the physical layer security to protect the transmitted waveform from an eavesdropper. We propose an end-to-end symmetric key neural encryption and decryption algorithm with a modulation technique, which remains undeciphered by an eavesdropper, equipped with the same neural network and trained on the same dataset as the intended users. We solve encryption and modulation as a joint problem for which we map the bits to complex analog signals, without adhering to any particular encryption algorithm or modulation technique. We train to cooperatively learn encryption and decryption algorithms between our trusted pair of neural networks, while eavesdropper's model is trained adversarially on the same data to minimize the error. We introduce a discrete activation layer with a defined gradient to combat noise in a lossy channel. Our results show that a trusted pair of users can exchange data bits in both clean and noisy channels, where a trained adversary cannot decipher the data.
On BT-limited Signals
Authors: Xiang-Gen Xia
Date: 2020-05-28 00:00:00

In this paper, we introduce and characterize a subspace of bandlimited signals. The subspace consists of all $\Omega$ bandlimited signals such that the non-zero parts of their Fourier transforms are pieces of some $T$ bandlimited signals. The signals in the subspace are called BT-limited signals and the subspace is named as BT-limited signal space. For BT-limited signals, a signal extrapolation with an analytic error estimate exists outside the interval [-T, T] of given signal values with errors. Some new properties about and applying BT-limited signals are also presented.