pwc-archive/papers-with-abstracts huggingface dataset:
Correlation between COVID-19 morbidity and mortality rates in Japan and local population density, temperature and absolute humidity
Date: 2020-07-30 00:00:00

This study analyzed the morbidity and mortality rates of the COVID-19
pandemic in different prefectures of Japan. Under the constraint that daily
maximum confirmed deaths and daily maximum cases should exceed 4 and 10,
respectively, 14 prefectures were included, and cofactors affecting the
morbidity and mortality rates were evaluated. In particular, the number of
confirmed deaths was assessed excluding the cases of nosocomial infections and
nursing home patients. A mild correlation was observed between morbidity rate
and population density (R2=0.394). In addition, the percentage of the elderly
per population was also found to be non-negligible. Among weather parameters,
the maximum temperature and absolute humidity averaged over the duration were
found to be in modest correlation with the morbidity and mortality rates,
excluding the cases of nosocomial infections. The lower morbidity and mortality
are observed for higher temperature and absolute humidity. Multivariate
analysis considering these factors showed that determination coefficients for
the spread, decay, and combined stages were 0.708, 0.785, and 0.615,
respectively. These findings could be useful for intervention planning during
future pandemics, including a potential second COVID-19 outbreak.
Energy Efficiency and Hover Time Optimization in UAV-based HetNets
Authors: S. T. Muntaha, S. A. Hassan, H. Jung, M. S. Hossain
Date: 2020-07-28 00:00:00

In this paper, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations.
Insider Trading with Temporary Price Impact
Authors: Weston Barger, Ryan Donnelly
Date: 2020-07-28 00:00:00

We model an informed agent with information about the future value of an asset trying to maximize profits when subjected to a transaction cost as well as a market maker tasked with setting fair transaction prices. In a single auction model, equilibrium is characterized by the unique root of a particular polynomial. Analysis of this polynomial with small levels of risk-aversion and transaction costs reveal a dimensionless parameter which captures several orders of asymptotic accuracy of the equilibrium behaviour. In a continuous time analogue of the single auction model, incorporation of a transaction costs allows the informed agent's optimal trading strategy to be obtained in feedback form. Linear equilibrium is characterized by the unique solution to a system of two ordinary differential equations, of which one is forward in time and one is backward. When transaction costs are in effect, the price set by the market maker in equilibrium is not fully revealing of the informed agent's private signal, leaving an information gap at the end of the trading interval. When considering vanishing transaction costs, the equilibrium trading strategy and pricing rules converge to their frictionless counterparts.
MMSE Channel Estimation for Two-Port Demodulation Reference Signals in New Radio
Authors: Dejin Kong, Xiang-Gen Xia, Pei Liu, Qibiao Zhu
Date: 2020-07-28 00:00:00

Two-port demodulation reference signals (DMRS) have been employed in new radio (NR) recently. In this paper, we firstly propose a minimum mean square error (MMSE) scheme with full priori knowledge (F-MMSE) to achieve the channel estimation of two-port DMRS in NR. When the two ports are assigned to different users, the full priori knowledge of two ports is not easy to be obtained for one user. Then, we present a MMSE scheme with partial priori knowledge (P-MMSE). Finally, numerical results show that the proposed schemes achieve satisfactory channel estimation performance. Moreover, for both mean square error and bit error ratio metrics, the proposed schemes can achieve better performance compared with the classical discrete Fourier transform based channel estimation. Particularly, P-MMSE scheme delivers almost the same performance compared with F-MMSE scheme by a small amount of prior knowledge.
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction
Date: 2020-07-28 00:00:00
Tasks: Clustering, Dimensionality Reduction

Signals emitted by LiDAR sensors would often be negatively influenced during
transmission by rain, fog, dust, atmospheric particles, scattering of light and
other influencing factors, causing noises in point cloud images. To address
this problem, this paper develops a new noise reduction method to filter LiDAR
point clouds, i.e. an adaptive clustering method based on principal component
analysis (PCA). Different from the traditional filtering methods that directly
process three-dimension (3D) point cloud data, the proposed method uses
dimension reduction to generate two-dimension (2D) data by extracting the first
principal component and the second principal component of the original data
with little information attrition. In the 2D space spanned by two principal
components, the generated 2D data are clustered for noise reduction before
being restored into 3D. Through dimension reduction and the clustering of the
generated 2D data, this method derives low computational complexity,
effectively removing noises while retaining details of environmental features.
Compared with traditional filtering algorithms, the proposed method has higher
precision and recall. Experimental results show a F-score as high as 0.92 with
complexity reduced by 50% compared with traditional density-based clustering
method.
Hierarchical Control of Multi-Agent Systems using Online Reinforcement Learning
Date: 2020-07-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), State Space Models

We propose a new reinforcement learning based approach to designing
hierarchical linear quadratic regulator (LQR) controllers for heterogeneous
linear multi-agent systems with unknown state-space models and separated
control objectives. The separation arises from grouping the agents into
multiple non-overlapping groups, and defining the control goal as two distinct
objectives. The first objective aims to minimize a group-wise
block-decentralized LQR function that models group-level mission. The second
objective, on the other hand, tries to minimize an LQR function between the
average states (centroids) of the groups. Exploiting this separation, we
redefine the weighting matrices of the LQR functions in a way that they allow
us to decouple their respective algebraic Riccati equations. Thereafter, we
develop a reinforcement learning strategy that uses online measurements of the
agent states and the average states to learn the respective controllers based
on the approximate Riccati equations. Since the first controller is
block-decentralized and, therefore, can be learned in parallel, while the
second controller is reduced-dimensional due to averaging, the overall design
enjoys a significantly reduced learning time compared to centralized
reinforcement learning.
A Probabilistic Approach to Driver Assistance for Delay Reduction at Congested Highway Lane Drops
Date: 2020-07-27 00:00:00

This paper proposes an onboard advance warning system based on a
probabilistic prediction model that advises vehicles on when to change lanes
for an upcoming lane drop. The prediction model estimates the probability of
reaching a goal state on the road using one or multiple lane changes. This
estimate is based on several traffic-related parameters such as the
distribution of inter-vehicle headway distances, as well as driver-related
parameters like lane change duration. For an upcoming lane drop, the advance
warning system uses the model and vehicle conditions at the moment to
continuously estimate the probability of successfully changing lanes under
those conditions before reaching the lane end, and advises the driver or
autonomous vehicle to change lanes when that probability dips below a certain
threshold. In a case study, the proposed system was used on a segment of the
I-81 interstate highway with two lane drops - transitioning from four lanes to
two lanes - to advise vehicles on avoiding the lane drops. The results show
that the proposed system can reduce average delay up to 50% and maximum delay
up to 33%, depending on traffic flow and the ratio of vehicles equipped with
the advance warning system.
Intelligent reflecting surface enhanced wideband MIMO-OFDM communications: From practical model to reflection optimization
Authors: Hongyu Li, Wenhao Cai, Yang Liu, Ming Li, Qian Liu, Qingqing Wu
Date: 2020-07-26 00:00:00

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.
Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning
Authors: Jingzhi Hu, Hongliang Zhang, Lingyang Song, Robert Schober, H. Vincent Poor
Date: 2020-07-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Due to the advantages of flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) have great potential for sensing applications in the next generation of cellular networks, which will give rise to a cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the UAVs execute sensing tasks through cooperative sensing and transmission to minimize the age of information (AoI). However, the cooperative sensing and transmission is tightly coupled with the UAVs' trajectories, which makes the trajectory design challenging. To tackle this challenge, we propose a distributed sense-and-send protocol, where the UAVs determine the trajectories by selecting from a discrete set of tasks and a continuous set of locations for sensing and transmission. Based on this protocol, we formulate the trajectory design problem for AoI minimization and propose a compound-action actor-critic (CA2C) algorithm to solve it based on deep reinforcement learning. The CA2C algorithm can learn the optimal policies for actions involving both continuous and discrete variables and is suited for the trajectory design. {Our simulation results show that the CA2C algorithm outperforms four baseline algorithms}. Also, we show that by dividing the tasks, cooperative UAVs can achieve a lower AoI compared to non-cooperative UAVs.
A decomposition formula for fractional Heston jump diffusion models
Authors: Marc Lagunas-Merino, Salvador Ortiz-Latorre
Date: 2020-07-28 00:00:00

We present an option pricing formula for European options in a stochastic volatility model. In particular, the volatility process is defined using a fractional integral of a diffusion process and both the stock price and the volatility processes have jumps in order to capture the market effect known as leverage effect. We show how to compute a martingale representation for the volatility process. Finally, using It\^o calculus for processes with discontinuous trajectories, we develop a first order approximation formula for option prices. There are two main advantages in the usage of such approximating formulas to traditional pricing methods. First, to improve computational effciency, and second, to have a deeper understanding of the option price changes in terms of changes in the model parameters.
On the use of GNSS for Automatic Detection of Attenuating Environments
Authors: Stefan Ruehrup
Date: 2020-07-28 00:00:00

When different radio applications share the same spectrum, the separation by attenuating material is a way to mitigate potential interference. The indoor restriction for WLAN devices in 5150-5350 MHz is an example for a regulatory measure that aims at having WLAN devices operating in an environment that provides sufficient attenuation to enable sharing with other services. In this paper we investigate whether an attenuating environment can be automatically detected without user interaction. Instead of detecting an indoor location, we are directly looking for a detection of an attenuating environment. The basic idea is that signals from global navigation satellite services (GNSS) can be received practically everywhere on earth where there is a view to the sky. Where these signals are attenuated, the receiving device is assumed to be in an attenuating environment. In order to characterize such environment, we evaluate the detectable GNSS satellites and their carrier-to-noise density. Example measurements show that GNSS raw data can help to distinguish between low-attenuating locations and higher-attenuating locations. These measurements were conducted with GNSS receiver in an off-the-shelf Android tablet in order to show the feasibility of the approach.
Structure-based inhibitor screening of natural products against NSP15 of SARS- CoV-2 revealed Thymopentin and Oleuropein as potent inhibitors
Authors: Ramachandran Vijayan, Samudrala Gourinath
Date: 2020-07-28 00:00:00

Coronaviruses are enveloped, non-segmented positive-sense RNA viruses that have the largest genome among RNA viruses. The genome contains a large replicase ORF encodes nonstructural proteins (NSPs), structural and accessory genes. NSP15 is a nidoviral RNA uridylate-specific endoribonuclease (NendoU) has C-terminal catalytic domain. The endoribonuclease activity of NSP15 interferes with the innate immune response of the host. Here, we screened Selleckchem Natural product database of compounds against the NSP15, Thymopentin and Oleuropein showed highest binding energies. The binding of these molecules was further validated by Molecular dynamic simulation and found very stable complexes. These drugs might serve as effective counter molecules in the reduction of virulence of this virus. Future validation of both these inhibitors are worth consideration for patients being treated for COVID -19.
Optimal Tree Topology for a Submarine Cable Network With Constrained Internodal Latency
Authors: Tianjiao Wang, Zengfu Wang, Bill Moran, Moshe Zukerman
Date: 2020-07-28 00:00:00

This paper provides an optimized cable path planning solution for a tree-topology network in an irregular 2D manifold in a 3D Euclidean space, with an application to the planning of submarine cable networks. Our solution method is based on total cost minimization, where the individual cable costs are assumed to be linear to the length of the corresponding submarine cables subject to latency constraints between pairs of nodes. These latency constraints limit the cable length and number of hops between any pair of nodes. Our method combines the Fast Marching Method (FMM) and a new Integer Linear Programming (ILP) formulation for Minimum Spanning Tree (MST) where there are constraints between pairs of nodes. We note that this problem of MST with constraints is NP-complete. Nevertheless, we demonstrate that ILP running time is adequate for the great majority of existing cable systems. For cable systems for which ILP is not able to find the optimal solution within an acceptable time, we propose an alternative heuristic algorithm based on Prim's algorithm. In addition, we apply our FMM/ILP-based algorithm to a real-world cable path planning example and demonstrate that it can effectively find an MST with latency constraints between pairs of nodes.
A calibration-free method for biosensing in cell manufacturing
Date: 2020-07-27 00:00:00
Tasks: Attribute

Chimeric antigen receptor T cell therapy has demonstrated innovative
therapeutic effectiveness in fighting cancers; however, it is extremely
expensive due to the intrinsic patient-to-patient variability in cell
manufacturing. We propose in this work a novel calibration-free statistical
framework to effectively recover critical quality attributes under the
patient-to-patient variability. Specifically, we model this variability via a
patient-specific calibration parameter, and use readings from multiple
biosensors to construct a patient-invariance statistic, thereby alleviating the
effect of the calibration parameter. A carefully formulated optimization
problem and an algorithmic framework are presented to find the best
patient-invariance statistic and the model parameters. Using the
patient-invariance statistic, we can recover the critical quality attribute of
interest, free from the calibration parameter. We demonstrate improvements of
the proposed calibration-free method in different simulation experiments. In
the cell manufacturing case study, our method not only effectively recovers
viable cell concentration for monitoring, but also reveals insights for the
cell manufacturing process.
Giant solitary fibrous tumor of the pleura
Authors: Javier Alfonso Pinedo-Onofre, Euridice Robles-Perez, Erika Sagrario Peña-Mirabal, Jose Amado Hernandez-Carrillo, Jose Luis Tellez-Becerra
Date: 2020-07-28 00:00:00
Tasks: Descriptive, Diagnostic, Management, Prognosis

Background: Solitary fibrous tumor is the second primary malignancy of the pleura and can reach up to 39cm in diameter; to be referred to as giant it must occupy at least 40% of the affected hemithorax. Although this tumor usually shows a benign behavior malignancy criteria have been described. The aim of the study was to assess the initial evaluation diagnostic procedures surgical management treatment outcome and prognosis. Methods: We performed a descriptive observational longitudinal and retrospective study from 2002 to 2006 on patients who underwent surgery with a diagnosis of giant solitary fibrous tumor of the pleura. Results: Six patients were included; 83.3% were females. Mean age was 48 years. All patients were symptomatic mainly dyspnea cough and chest pain; 66.7% were left-sided. Preoperative angiography and embolization were performed in 83.3% cases with successful surgical resection. The predominant blood supply was derived from the internal mammalian artery. Intraoperative complication rate was 17%. A vascular pedicle was found in 66.7%. The largest lesion was 40cm in diameter and weighed 4500g. Only one case showed high mitotic activity. Mean follow-up to date is 14 months. Conclusions: Symptomatology found was consistent with previous reports but in higher percentages. Accurate diagnosis is critical because surgical resection involves a potential cure; long-term follow-up is mandatory. Preoperative embolization is recommended due to tumor size.
Analysis of the Global Banking Network by Random Matrix Theory
Authors: Ali Namaki, Jamshid Ardalankia, Reza Raei, Leila Hedayatifar, Ali Hosseiny, Emmanuel Haven, G. Reza Jafari
Date: 2020-07-28 00:00:00

Since 2008, the network analysis of financial systems is one of the most important subjects in economics. In this paper, we have used the complexity approach and Random Matrix Theory (RMT) for analyzing the global banking network. By applying this method on a cross border lending network, it is shown that the network has been denser and the connectivity between peripheral nodes and the central section has risen. Also, by considering the collective behavior of the system and comparing it with the shuffled one, we can see that this network obtains a specific structure. By using the inverse participation ratio concept, we can see that after 2000, the participation of different modes to the network has increased and tends to the market mode of the system. Although no important change in the total market share of trading occurs, through the passage of time, the contribution of some countries in the network structure has increased. The technique proposed in the paper can be useful for analyzing different types of interaction networks between countries.
A Research Journey of Full-Duplex at University of California from Self-Interference Cancellation to Wireless Network Security
Authors: Yingbo Hua
Date: 2020-07-29 00:00:00

This article provides an overview of research on full-duplex at the University of California, Riverside, in the past decade. This research was initially focused on self-interference (SI) cancellation, then moved to applications of full-duplex to improve network spectral efficiency, and in recent years advanced to discover full-duplex's potentials for wireless network security. The research on SI cancellation has resulted in both hardware-based SI cancellation results and some advanced theoretical architectures which show promises but are yet to be tested via advanced hardware implementations. The applications of full-duplex for optimized spectral efficiency in ad hoc, cognitive and cellular networks have shown how to optimize power allocation among full-duplex nodes, in their antenna beamspace and over multiple subcarriers. Full-duplex has also been found to be highly beneficial for improving secrecy capacity between legitimate users against eavesdropping. Among the new capabilities that full-duplex provides for network security is an effective anti-eavesdropping channel estimation scheme which is not possible for nodes without full-duplex.