pwc-archive/papers-with-abstracts huggingface dataset:
Unmanned Aerial Vehicle and Optimal Relay for Extending Coverage in Post-Disaster Scenarios
Authors: Abdu Saif, Kaharudin Dimyati, Kamarul Ariffin Noordin, Nor Shahida Mohd Shah, Qazwan Abdullah, Mahathir Mohamad, Mahmod Abd Hakim Mohamad, Ahmed M. Al-Saman
Date: 2021-04-13 00:00:00

The malfunction or interruption of wireless coverage services has been shown to increase the mortality rate during natural disasters. Wireless coverage by an unmanned aerial vehicle (UAV) provides network coverage to ground user devices during and post-disaster events. The relay hops receive wireless coverage and can be forwarded to user devices that are out of coverage allowing reliable connectivity for large-scale user devices. This work evaluates the optimal relay hops performance to improve wireless coverage services and establish connectivity in post-disaster scenarios. The results demonstrate the UAV line of sights understanding to select an optimal relay for improving wireless coverage services. The path loss probability and system capacity were all affected by the user device distance and relay densities. The optimal relay hop distance and the UAV positions static are also investigated to improve coverage likelihood which could be especially useful for UAV deployment design. It is found that the dense relays node in UAV systems enhances the capacity coverage area and energy efficiency by decentralized connectivity through a multihop device to device wireless network.
Empirical Low-Altitude Air-to-Ground Spatial Channel Characterization for Cellular Networks Connectivity
Authors: Xuesong Cai, Tomasz Izydorczyk, José Rodríguez-Piñeiro, István Z. Kovács, Jeroen Wigard, Fernando M. L. Tavares, Preben E. Mogensen
Date: 2020-10-02 00:00:00
Tasks: parameter estimation

Cellular-connected unmanned aerial vehicles (UAVs) have recently attracted a surge of interest in both academia and industry. Understanding the air-to-ground (A2G) propagation channels is essential to enable reliable and/or high-throughput communications for UAVs and protect the ground user equipments (UEs). In this contribution, a recently conducted measurement campaign for the A2G channels is introduced. A uniform circular array (UCA) with 16 antenna elements was employed to collect the downlink signals of two different Long Term Evolution (LTE) networks, at the heights of 0-40m in three different, namely rural, urban and industrial scenarios. The channel impulse responses (CIRs) have been extracted from the received data, and the spatial/angular parameters of the multipath components in individual channels were estimated according to a high-resolution-parameter estimation (HRPE) principle. Based on the HRPE results, clusters of multipath components were further identified. Finally, comprehensive spatial channel characteristics were investigated in the composite and cluster levels at different heights in the three scenarios.
Port Parameter Extraction Based Self Consistent Coupled EM-Circuit FEM Solvers
Authors: O. H. Ramachandran, Scott O'Connor, Zane D. Crawford, Leo C. Kempel, B. Shanker
Date: 2021-04-12 00:00:00

Self consistent solution to electromagnetic (EM)-circuit systems is of significant interest for a number of applications. This has resulted in exhaustive research on means to couple them. In time domain, this typically involves a tight integration (or coupling) with field and non-linear circuit solvers. This is in stark contrast to coupled analysis of linear/weakly non-linear circuits and EM systems in frequency domain. Here, one typically extracts equivalent port parameters that are then fed into the circuit solver. Such an approach has several advantages; (a) the number of ports is typically smaller than the number of degrees of freedom, resulting in cost savings; (b) is circuit agnostic; (c) can be integrated with a variety of device models. Port extraction is tantamount to obtaining impulse response of the linear EM system. In time domain, the deconvolution required to effect this is unstable. Recently, a novel approach was developed for time domain integral equations to overcome this bottleneck. We extend this approach to time domain finite element method, and demonstrate its utility via a number of examples; significantly, we demonstrate that self consistent solutions obtained using either a fully coupled or port extraction is identical to the desired precision for non-linear circuit systems. This is shown within a nodal network. We also demonstrate integration of port extracted data directly with drift diffusion equation to model device physics.
Lifetime Optimization of Dense Wireless Sensor Networks Using Continuous Ring-sector Model
Authors: Arouna Ndam Njoya, Christopher Thron, Marah Nana Awa, Ado Adamou Abba Ari, Abdelhak Mourad Gueroui
Date: 2021-04-12 00:00:00

Wireless sensor networks (WSNs) are becoming increasingly utilized in applications that require remote collection of data on environmental conditions. In particular dense WSNs are emerging as an important sensing platforms for the Internet of Things (IoT). WSNs are able to generate huge volumes of raw data, which require network structuring and efficient collaboration between nodes to ensure efficient transmission. In order to reduce the amount of data carried in the network, data aggregation is used in WSNs to define a policy of data fusion and compression. In this paper, we investigate a model for data aggregation in a dense {WSN} with a single sink. The model divides a circular coverage region centered at the sink into patches which are intersections of sectors of concentric rings, and data in each patch is aggregated at a single node before transmission. Nodes only communicate with other nodes in the same sector. Based on these assumptions, we formulate a linear programming problem to maximize system lifetime by minimizing the maximum proportionate energy consumption over all nodes. Under a wide variety of conditions, the optimal solution employs two transmissions mechanisms: direct transmission, in which nodes send information directly to the sink; and stepwise transmission, in which nodes transmit information to adjacent nodes. An exact formula is given for the proportionate energy consumption rate of the network. Asymptotic forms of this exact solution are also derived, and are verified to agree with the linear programming solution. We investigate three strategies for improving system lifetime: nonuniform energy and information density; iterated compression; and modifications of rings. We conclude that iterated compression has the biggest effect in increasing system lifetime.
SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction
Authors: Xiangwen Deng, Junlin Zhu, Shangming Yang
Date: 2021-04-09 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition, Ensemble Learning

Emotion recognition based on EEG (electroencephalography) has been widely used in human-computer interaction, distance education and health care. However, the conventional methods ignore the adjacent and symmetrical characteristics of EEG signals, which also contain salient information related to emotion. In this paper, a spatial folding ensemble network (SFE-Net) is presented for EEG feature extraction and emotion recognition. Firstly, for the undetected area between EEG electrodes, an improved Bicubic-EEG interpolation algorithm is developed for EEG channels information completion, which allows us to extract a wider range of adjacent space features. Then, motivated by the spatial symmetric mechanism of human brain, we fold the input EEG channels data with five different symmetrical strategies, which enable the proposed network to extract the information of space features of EEG signals more effectively. Finally, a 3DCNN-based spatial, temporal extraction, and a multi-voting strategy of ensemble learning are integrated to model a new neural network. With this network, the spatial features of different symmetric folding signals can be extracted simultaneously, which greatly improves the robustness and accuracy of emotion recognition. The experimental results on DEAP and SEED datasets show that the proposed algorithm has comparable performance in terms of recognition accuracy.
10,000 km Straight-line Transmission using a Real-time Software-defined GPU-Based Receiver
Authors: Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yohinari Awaji, Hideaki Furukawa, Chigo Okonkwo
Date: 2021-04-08 00:00:00
Tasks: GPU, Video Prediction

Real-time operation of a software-defined, GPU-based optical receiver is demonstrated over a 100-span straight-line optical link. Performance of minimum-phase Kramers-Kronig 4-, 8-, 16-, 32-, and 64-QAM signals are evaluated at various distances.
Competition, Trait Variance Dynamics, and the Evolution of a Species' Range
Authors: Farshad Shirani, Judith R. Miller
Date: 2021-04-13 00:00:00

Geographic ranges of communities of species evolve in response to environmental, ecological, and evolutionary forces. Understanding the effects of these forces on species' range dynamics is a major goal of spatial ecology. Previous mathematical models have jointly captured the dynamic changes in species' population distributions and the selective evolution of fitness-related phenotypic traits in the presence of an environmental gradient. These models inevitably include some unrealistic assumptions, and biologically reasonable ranges of values for their parameters are not easy to specify. As a result, simulations of the seminal models of this type can lead to markedly different conclusions about the behavior of such populations, including the possibility of maladaptation setting stable range boundaries. Here, we harmonize such results by developing and simulating a continuum model of range evolution in a community of species that interact competitively while diffusing over an environmental gradient. Our model extends existing models by incorporating both competition and freely changing intraspecific trait variance. Simulations of this model predict a spatial profile of species' trait variance that is consistent with experimental measurements available in the literature. Moreover, they reaffirm interspecific competition as an effective factor in limiting species' ranges, even when trait variance is not artificially constrained. These theoretical results can inform the design of, as yet rare, empirical studies to clarify the evolutionary causes of range stabilization.
Combinatorics of polymer models of early metabolism
Authors: Oliver Weller-Davies, Mike Steel, Jotun Hein
Date: 2021-04-12 00:00:00

Polymer models are a widely used tool to study the prebiotic formation of metabolism at the origins of life. Counts of the number of reactions in these models are often crucial in probabilistic arguments concerning the emergence of autocatalytic networks. In the first part of this paper, we provide the first exact description of the number of reactions under widely applied model assumptions. Conclusions from earlier studies rely on either approximations or asymptotic counting, and we show that the exact counts lead to similar, though not always identical, asymptotic results. In the second part of the paper, we investigate a novel model assumption whereby polymers are invariant under spatial rotation. We outline the biochemical relevance of this condition and again give exact enumerative and asymptotic formulae for the number of reactions.
Mixed-horizon optimal feedback control as a model of human movement
Authors: Justinas Česonis, David W. Franklin
Date: 2021-04-13 00:00:00

Computational optimal feedback control (OFC) models in the sensorimotor control literature span a vast range of different implementations. Among the popular algorithms, finite-horizon, receding-horizon or infinite-horizon linear-quadratic regulators (LQR) have been broadly used to model human reaching movements. While these different implementations have their unique merits, all three have limitations in simulating the temporal evolution of visuomotor feedback responses. Here we propose a novel approach - a mixed-horizon OFC - by combining the strengths of the traditional finite-horizon and the infinite-horizon controllers to address their individual limitations. Specifically, we use the infinite-horizon OFC to generate durations of the movements, which are then fed into the finite-horizon controller to generate control gains. We then demonstrate the stability of our model by performing extensive sensitivity analysis of both re-optimisation and different cost functions. Finally, we use our model to provide a fresh look to previously published studies by reinforcing the previous results, providing alternative explanations to previous studies, or generating new predictive results for prior experiments.
Optimization of Sequential Microwave-Ultrasound-Assisted Extraction for Maximum Recovery of Quercetin and Total Flavonoids from Red Onion (Allium cepa L.) Skin Wastes
Authors: Zeinab Jabbari Velisdeh, Ghasem D. Najafpour, Maedeh Mohammadi, Fatemeh Poureini
Date: 2021-04-13 00:00:00

This study aimed to optimize the extraction conditions in order to maximize the recovery yields of quercetin and total flavonoids from red onion skin wastes using sequential microwave-ultrasound-assisted extraction. Five effective factors on quercetin extraction yield were investigated using response surface methodology. The method was successfully performed under optimal conditions of 60 s microwave irradiation followed by 15 min sonication at 70 {\deg}C, 70% ethanol with solvent to solid ratio of 30 mL/g. Based on the optimization results, ultrasound temperature was found to be a highly significant and influential factor for the recovery. The maximum recovery yields of quercetin and total flavonoids from red onion skin were estimated to be 10.32 and 12.52%, respectively. The predicted values for quercetin (10.05%) and total flavonoids (12.72%) were very close to the experimental results. The recovery yields obtained from different extraction methods were as follows: ultrasound-microwave-assisted extraction (7.66% quercetin and 10.18% total flavonoids), ultrasound-assisted extraction (5.36% quercetin and 8.34% total flavonoids), and microwave-assisted extraction (5.03% quercetin and 7.91 % total flavonoids). The validity of the projected model was examined by the obtained experimental data; in which, the validated model was suitable for the recovery of the valuable products from onion skin wastes for further scale-up in the food processes.
Mesoporous Silica Nanoparticles Decorated with Polycationic Dendrimers for Infection Treatment
Authors: Blanca Gonzalez, Montserrat Colilla, Jaime Diez, Daniel Pedraza, Marta Guembe, Isabel Izquierdo-Barba, Maria Vallet-Regi
Date: 2021-04-13 00:00:00

This work aims to provide an effective and novel solution for the treatment of infection by using nanovehicles loaded with antibiotics capable of penetrating the bacterial wall, thus increasing the antimicrobial effectiveness. These nanosystems, named "nanoantibiotics", are composed of mesoporous silica nanoparticles (MSNs), which act as nanocarriers of an antimicrobial agent (levofloxacin, LEVO) localized inside the mesopores. To provide the nanosystem of bacterial membrane interaction capability, a polycationic dendrimer, concretely the poly(propyleneimine) dendrimer of third generation (G3), was covalently grafted to the external surface of the LEVO-loaded MSNs. After physicochemical characterization of this nanoantibiotic, the release kinetics of LEVO and the antimicrobial efficacy of each released dosage were evaluated. Besides, internalization studies of the MSNs functionalized with the G3 dendrimer were carried out, showing a high penetrability throughout Gram-negative bacterial membranes. This work evidences that the synergistic combination of polycationic dendrimers as bacterial membrane permeabilization agents with LEVO-loaded MSNs triggers an efficient antimicrobial effect on Gram-negative bacterial biofilm. These positive results open up very promising expectations for their potential application in new infection therapies.
Analysis of optimal portfolio on finite and small time horizons for a stochastic volatility market model
Authors: Minglian Lin, Indranil SenGupta
Date: 2021-04-13 00:00:00
Tasks: Portfolio Optimization

In this paper, we consider the portfolio optimization problem in a financial market under a general utility function. Empirical results suggest that if a significant market fluctuation occurs, invested wealth tends to have a notable change from its current value. We consider an incomplete stochastic volatility market model, that is driven by both a Brownian motion and a jump process. At first, we obtain a closed-form formula for an approximation to the optimal portfolio in a small-time horizon. This is obtained by finding the associated Hamilton-Jacobi-Bellman integro-differential equation and then approximating the value function by constructing appropriate super-solution and sub-solution. It is shown that the true value function can be obtained by sandwiching the constructed super-solution and sub-solution. We also prove the accuracy of the approximation formulas. Finally, we provide a procedure for generating a close-to-optimal portfolio for a finite time horizon.
Application of maximal monotone operator method for solving Hamilton-Jacobi-Bellman equation arising from optimal portfolio selection problem
Authors: Daniel Sevcovic, Cyril Izuchukwu Udeani
Date: 2021-04-13 00:00:00
Tasks: Portfolio Optimization

In this paper, we investigate a fully nonlinear evolutionary Hamilton-Jacobi-Bellman (HJB) parabolic equation utilizing the monotone operator technique. We consider the HJB equation arising from portfolio optimization selection, where the goal is to maximize the conditional expected value of the terminal utility of the portfolio. The fully nonlinear HJB equation is transformed into a quasilinear parabolic equation using the so-called Riccati transformation method. The transformed parabolic equation can be viewed as the porous media type of equation with source term. Under some assumptions, we obtain that the diffusion function to the quasilinear parabolic equation is globally Lipschitz continuous, which is a crucial requirement for solving the Cauchy problem. We employ Banach's fixed point theorem to obtain the existence and uniqueness of a solution to the general form of the transformed parabolic equation in a suitable Sobolev space in an abstract setting. Some financial applications of the proposed result are presented in one-dimensional space.
Time is Money: The Equilibrium Trading Horizon and Optimal Arrival Price
Authors: Kevin Patrick Darby
Date: 2021-04-12 00:00:00

Executing even moderately large derivatives orders can be expensive and risky; it's hard to balance the uncertainty of working an order over time versus paying a liquidity premium for immediate execution. Here, we introduce the Time Is Money model, which calculates the Equilibrium Trading Horizon over which to execute an order within the adversarial forces of variance risk and liquidity premium. We construct a hypothetical at-the-money option within Arithmetic Brownian Motion and invert the Bachelier model to compute an inflection point between implied variance and liquidity cost as governed by a central limit order book, each in real time as they evolve. As a result, we demonstrate a novel, continuous-time Arrival Price framework. Further, we argue that traders should be indifferent to choosing between variance risk and liquidity cost, unless they have a predetermined bias or an exogenous position with a convex payoff. We, therefore, introduce half-life factor asymptotics to the model based on a convexity factor and compare results to existing models. We also describe a specialization of the model for trading a basket of correlated instruments, as exemplified by a futures calendar spread. Finally, we establish groundwork for microstructure optimizations as well as explore short term drift and conditional expected slippage within the Equilibrium Horizon framework.
A Bayesian analysis of gain-loss asymmetry
Authors: Andrea Giuseppe Di Iura, Giulia Terenzi
Date: 2021-04-13 00:00:00
Tasks: Sensitivity, Time Series, Time Series Analysis

We perform a quantitative analysis of the gain/loss asymmetry for financial time series by using a Bayesian approach. In particular, we focus on some selected indices and analyze the statistical significance of the asymmetry amount through a Bayesian generalization of the t-Test, which relaxes the normality assumption on the underlying distribution. We propose two different models for data distribution, we study the convergence of our method and we provide several graphical representations of our numerical results. Finally, we perform a sensitivity analysis with respect to model parameters in order to study the reliability and robustness of our results.
Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model
Authors: Jaydip Sen, Abhishek Dutta, Sidra Mehtab
Date: 2021-04-06 00:00:00

Designing robust systems for precise prediction of future prices of stocks has always been considered a very challenging research problem. Even more challenging is to build a system for constructing an optimum portfolio of stocks based on the forecasted future stock prices. We present a deep learning-based regression model built on a long-and-short-term memory network (LSTM) network that automatically scraps the web and extracts historical stock prices based on a stock's ticker name for a specified pair of start and end dates, and forecasts the future stock prices. We deploy the model on 75 significant stocks chosen from 15 critical sectors of the Indian stock market. For each of the stocks, the model is evaluated for its forecast accuracy. Moreover, the predicted values of the stock prices are used as the basis for investment decisions, and the returns on the investments are computed. Extensive results are presented on the performance of the model. The analysis of the results demonstrates the efficacy and effectiveness of the system and enables us to compare the profitability of the sectors from the point of view of the investors in the stock market.
A Novel Visualization System of Using Augmented Reality in Knee Replacement Surgery: Enhanced Bidirectional Maximum Correntropy Algorithm
Authors: Nitish Maharjan, Abeer Alsadoon, P. W. C. Prasad, Salma Abdullah, Tarik A. Rashid
Date: 2021-03-13 00:00:00
Tasks: Anatomy, Image Registration

Background and aim: Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion, and maximize the overlapping parts. Methodology: markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction. Results: This study has improved video precision to 0.57 mm~0.61 mm alignment error. Furthermore, with the use of bidirectional points, for example, forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improve the processing time as well. The processing time of video frames was improved to 7.4~11.74 fps. Conclusions: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favorable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc.