pwc-archive/papers-with-abstracts huggingface dataset:
Morphological Reconstruction Improves Microvessel Mapping in Super-Resolution Ultrasound
Date: 2020-10-14 00:00:00
Tasks: Super-Resolution, Translation

Generation of super-resolution (SR) ultrasound (US) images, created from the
successive local-ization of individual microbubbles in the circulation, has
enabled the visualization of microvascular structure and flow at a level of
detail that was not possible previously. Despite rapid progress, tradeoffs
between spatial and temporal resolution may challenge the translation of this
promising technology to the clinic. To temper these trade-offs, we propose a
method based on morphological image reconstriction. This method can extract
from ultrafast contrast-enhanced ultrasound (CEUS) images hundreds of
microbubble peaks per image (312-by-180 pixels) with intensity values varying
by an order of magnitude. Specifically, it offers a fourfold increase in the
number of peaks detected per frame, requires on the order of 100 ms for
processing, and is robust to additive electronic noise (down to 3.6 dB CNR in
CEUS images). By integrating this method to a SR framework we demonstrate a
6-fold improvement in spatial resolution, as compared to CEUS, in imaging
chicken embryo microvessels. This method that is computationally efficient and,
thus, scalable to large data sets, may augment the abilities of SR-US in
imaging microvascular structure and function.
Improving Spiking Sparse Recovery via Non-Convex Penalties
Authors: Xiang Zhang, Lei Yu, Gang Zheng
Date: 2020-09-19 00:00:00
Tasks: Computational Efficiency

Compared with digital methods, sparse recovery based on spiking neural networks has great advantages like high computational efficiency and low power-consumption. However, current spiking algorithms cannot guarantee more accurate estimates since they are usually designed to solve the classical optimization with convex penalties, especially the $\ell_{1}$-norm. In fact, convex penalties are observed to underestimate the true solution in practice, while non-convex ones can avoid the underestimation. Inspired by this, we propose an adaptive version of spiking sparse recovery algorithm to solve the non-convex regularized optimization, and provide an analysis on its global asymptotic convergence. Through experiments, the accuracy is greatly improved under different adaptive ways.
On the implementation of the Universal Basic Income as a response to technological unemployment
Authors: Le Dong Hai Nguyen
Date: 2020-09-19 00:00:00

The effects of automation on our economy and society are more palpable than ever, with nearly half of jobs at risk of being fully executed by machines over the next decade or two. Policymakers and scholars alike have championed the Universal Basic Income (UBI) as a catch-all solution to this problem. This paper examines the shortcomings of UBI in addressing the automation-led large-scale displacement of labor by analyzing empirical data from previous UBI-comparable experiments and presenting theoretical projections that highlight disappointing impacts of UBI in the improvement of relevant living standards and employability metrics among pensioners. Finally, a recommendation shall be made for the retainment of existing means-tested welfare programs while bolstering funding and R&D for more up-to-date worker training schemes as a more effective solution to technological unemployment.
Detailed Dynamic Model of Antagonistic PAM System and its Experimental Validation: Sensor-less Angle and Torque Control with UKF
Authors: Takaya Shin, Takumi Ibayashi, Kiminao Kogiso
Date: 2020-09-19 00:00:00
Tasks: Friction

This study proposes a detailed nonlinear mathematical model of an antagonistic pneumatic artificial muscle (PAM) actuator system for estimating the joint angle and torque using an unscented Kalman filter (UKF). The proposed model is described in a hybrid state-space representation. It includes the contraction force of the PAM, joint dynamics, fluid dynamics of compressed air, mass flows of a valve, and friction models. A part of the friction models is modified to obtain a novel form of Coulomb friction depending on the inner pressure of the PAM. For model validation, offline and online UKF estimations and sensor-less tracking control of the joint angle and torque are conducted to evaluate the estimation accuracy and tracking control performance. The estimation accuracy is less than 7.91 %, and the steady-state tracking control performance is more than 94.75 %. These results confirm that the proposed model is detailed and could be used as the state estimator of an antagonistic PAM system.
Frequency selectivity of neural circuits with heterogeneous transmission delays
Date: 2020-09-19 00:00:00

Neurons are connected to other neurons by axons and dendrites that conduct
signals with finite velocities, resulting in delays between the firing of a
neuron and the arrival of the resultant impulse at other neurons. Since delays
greatly complicate the analytical treatment and interpretation of models, they
are usually neglected or taken to be uniform, leading to a lack in the
comprehension of the effects of delays in neural systems. This paper shows that
heterogeneous transmission delays make small groups of neurons respond
selectively to inputs with differing frequency spectra. By studying a single
integrate-and-fire neuron receiving correlated time-shifted inputs, it is shown
how the frequency response is linked to both the strengths and delay times of
the afferent connections. The results show that incorporating delays alters the
functioning of neural networks, and changes the effect that neural connections
and synaptic strengths have.
Planning low-carbon distributed power systems: Evaluating the role of energy storage
Authors: Jiachen Mao, Mehdi Jafari, Audun Botterud
Date: 2020-09-20 00:00:00

This paper introduces a mathematical formulation of energy storage systems into a generation capacity expansion framework to evaluate the role of energy storage in the decarbonization of distributed power systems. The modeling framework accounts for dynamic charging/discharging efficiencies and maximum cycling powers as well as cycle and calendar degradation of a Li-ion battery system. Results from a small-scale distributed power system indicate that incorporating the dynamic efficiencies and cycling powers of batteries in the generation planning problem does not significantly change the optimal generation portfolio, while adding substantial computational burden. In contrast, accounting for battery degradation leads to substantially different generation expansion outcomes, especially in deep decarbonization scenarios with larger energy storage capacities. Under the assumptions used in this study, it is found that battery energy storage is economically viable for 2020 only under strict carbon emission constraints. In contrast, given the projected technology advances and corresponding cost reductions, battery energy storage exhibits an attractive option to enable deep decarbonization in 2050.
Endogenous Stochastic Arbitrage Bubbles and the Black--Scholes model
Authors: Mauricio Contreras G
Date: 2020-09-20 00:00:00

This paper develops a model that incorporates the presence of stochastic arbitrage explicitly in the Black--Scholes equation. Here, the arbitrage is generated by a stochastic bubble, which generalizes the deterministic arbitrage model obtained in the literature. It is considered to be a generic stochastic dynamic for the arbitrage bubble, and a generalized Black--Scholes equation is then derived. The resulting equation is similar to that of the stochastic volatility models, but there are no undetermined parameters as the market price of risk. The proposed theory has asymptotic behaviors that are associated with the weak and strong arbitrage bubble limits. For the case where the arbitrage bubble's volatility is zero (deterministic bubble), the weak limit corresponds to the usual Black-Scholes model. The strong limit case also give a Black--Scholes model, but the underlying asset's mean value replaces the interest rate. When the bubble is stochastic, the theory also has weak and strong asymptotic limits that give rise to option price dynamics that are similar to the Black--Scholes model. Explicit formulas are derived for Gaussian and lognormal stochastic bubbles. Consequently, the Black--Scholes model can be considered to be a "low energy" limit of a more general stochastic model.
Semi-analytic pricing of double barrier options with time-dependent barriers and rebates at hit
Authors: Andrey Itkin, Dmitry Muravey
Date: 2020-09-20 00:00:00

We continue a series of papers devoted to construction of semi-analytic solutions for barrier options. These options are written on underlying following some simple one-factor diffusion model, but all the parameters of the model as well as the barriers are time-dependent. We managed to show that these solutions are systematically more efficient for pricing and calibration than, eg., the corresponding finite-difference solvers. In this paper we extend this technique to pricing double barrier options and present two approaches to solving it: the General Integral transform method and the Heat Potential method. Our results confirm that for double barrier options these semi-analytic techniques are also more efficient than the traditional numerical methods used to solve this type of problems.
Learning-Based Massive Beamforming
Authors: Siyuan Lu, Shengjie Zhao, Qingjiang Shi
Date: 2020-09-20 00:00:00

Developing resource allocation algorithms with strong real-time and high efficiency has been an imperative topic in wireless networks. Conventional optimization-based iterative resource allocation algorithms often suffer from slow convergence, especially for massive multiple-input-multiple-output (MIMO) beamforming problems. This paper studies learning-based efficient massive beamforming methods for multi-user MIMO networks. The considered massive beamforming problem is challenging in two aspects. First, the beamforming matrix to be learned is quite high-dimensional in case with a massive number of antennas. Second, the objective is often time-varying and the solution space is not fixed due to some communication requirements. All these challenges make learning representation for massive beamforming an extremely difficult task. In this paper, by exploiting the structure of the most popular WMMSE beamforming solution, we propose convolutional massive beamforming neural networks (CMBNN) using both supervised and unsupervised learning schemes with particular design of network structure and input/output. Numerical results demonstrate the efficacy of the proposed CMBNN in terms of running time and system throughput.
Physical Layer Security Over Mixture Gamma Distributed Fading Channels With Discrete Inputs: A Unified and General Analytical Framework
Authors: Chongjun Ouyang, Sheng Wu, Chunxiao Jiang, Julian Cheng, Hongwen Yang
Date: 2020-09-20 00:00:00

Physical layer security is investigated over mixture Gamma (MG) distributed fading channels with discrete inputs. By the Gaussian quadrature rules, closed-form expressions are derived to characterize the average secrecy rate (ASR) and secrecy outage probability (SOP), whose accuracy is validated by numerical simulations. To show more properties of the finite-alphabet signaling, we perform an asymptotic analysis on the secrecy metrics in the large limit of the average signal-to-noise ratio (SNR) of the main channel. Leveraging the Mellin transform, we find that the ASR and SOP converge to some constants as the average SNR increases and we derive novel expressions to characterize the rates of convergence. This work establishes a unified and general analytical framework for the secrecy performance achieved by discrete inputs.
How Market Ecology Explains Market Malfunction
Authors: Maarten P. Scholl, Anisoara Calinescu, J. Doyne Farmer
Date: 2020-09-20 00:00:00

Standard approaches to the theory of financial markets are based on equilibrium and efficiency. Here we develop an alternative based on concepts and methods developed by biologists, in which the wealth invested in a financial strategy is like the abundance of a species. We study a toy model of a market consisting of value investors, trend followers and noise traders. We show that the average returns of strategies are strongly density dependent, i.e. they depend on the wealth invested in each strategy at any given time. In the absence of noise the market would slowly evolve toward an efficient equilibrium, but the statistical uncertainty in profitability (which is adjusted to match real markets) makes this noisy and uncertain. Even in the long term, the market spends extended periods of time away from perfect efficiency. We show how core concepts from ecology, such as the community matrix and food webs, give insight into market behavior. The wealth dynamics of the market ecology explain how market inefficiencies spontaneously occur and gives insight into the origins of excess price volatility and deviations of prices from fundamental values.
What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?
Authors: Arthur S. de Sena, Dick Carrillo, Fang Fang, Pedro H. J. Nardelli, Daniel B. da Costa, Ugo S. Dias, Zhiguo Ding, Constantinos B. Papadias, Walid Saad
Date: 2020-09-20 00:00:00
Tasks: Fairness

Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks. A massive MIMO-NOMA system can deliver remarkable spectral improvements and low communication latency. Nevertheless, the uncontrollable stochastic behavior of the wireless channels can still degrade its performance. In this context, intelligent reflecting surface (IRS) has arisen as a promising technology for smartly overcoming the harmful effects of the wireless environment. The disruptive IRS concept of controlling the propagation channels via software can provide attractive performance gains to the communication networks, including higher data rates, improved user fairness, and, possibly, higher energy efficiency. In this article, in contrast to the existing literature, we demonstrate the main roles of IRSs in MIMO-NOMA systems. Specifically, we identify and perform a comprehensive discussion of the main performance gains that can be achieved in IRS-assisted massive MIMO-NOMA (IRS-NOMA) networks. We outline exciting futuristic use case scenarios for IRS-NOMA and expose the main related challenges and future research directions. Furthermore, throughout the article, we support our in-depth discussions with representative numerical results.
3D Aerial Highway: The Key Enabler of the Retail Industry Transformation
Authors: Nesrine Cherif, Wael Jaafar, Halim Yanikomeroglu, Abbas Yongacoglu
Date: 2020-09-20 00:00:00

The retail industry is already facing an inevitable transformation worldwide, and with the current pandemic situation, it is even accelerating. Indeed, consumer habits are shifting from brick-and-mortar stores to online shopping. The bottleneck in the end-to-end online shopping experience remains the efficient and quick delivery of goods to consumers. In this context, unmanned aerial vehicle (UAV) technology is seen as a potential solution to address cargo delivery issues. Hence, the number of cargo-UAVs is expected to skyrocket in the next few decades and the airspace to become densely crowded. To successfully deploy UAVs for mass cargo delivery, seamless and reliable cellular connectivity for highly mobile UAVs is required. There is an urgent need for organized and connected routes in the sky. Like highways for cargo trucks, 3D routes in the airspace should be designed for cargo-UAVs to fulfill their operations safely and efficiently. We refer to these routes as 3D aerial highway. In this paper, we thoroughly investigate the feasibility of the aerial highways paradigm. First, we discuss the motivations and concerns of the aerial highway paradigm. Then, we present our vision of the 3D aerial highway framework. Finally, we present related connectivity issues and their potential solutions.
Long Range Communication on Batteryless Devices
Authors: Simeon Babatunde, Nirnay Jain, Vishwas Powar
Date: 2020-09-20 00:00:00

Bulk of the existing Wireless Sensor Network (WSN) nodes are usually battery powered, stationary and mostly designed for short distance communication, with little to no consideration for constrained devices that operate solely on harvested energy. On many occasions, batteries and beefy super-capacitors are used to power these WSN, but these systems are prone to service-life degradation and current-leakages. Most of the systems implementing super capacitors do not account for leakages after exceeding the charge cycle threshold. Frequent battery maintenance and replacement at scale is non-trivial, labor-intensive and challenging task, especially on sensing nodes deployed in extreme harsh environments with limited human intervention. In this paper, we present the technique for achieving Kilometer range communication on batteryless constraint devices by harnessing the capabilities of LoRa technology.
Tight Bounds for Uncertain Time-Correlated Errors with Gauss-Markov Structure
Authors: Omar Garcia Crespillo, Mathieu Joerger, Steve Langel
Date: 2020-09-20 00:00:00

Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. This can be challenging for linear dynamic systems if the process noise or measurement errors have uncertain time correlation. In many systems (e.g., in satellite-based or inertial navigation systems), there are sources of time-correlated sensor errors that can be well modeled using Gauss-Markov processes (GMP). However, uncertainty in the GMP parameters, particularly in the correlation time constant, can cause misleading error estimation. In this paper, we develop new time-correlated models that ensure tight upper bounds on the estimation error variance, assuming that the actual error is a stationary GMP with a time constant that is only known to reside within an interval. We first use frequency-domain analysis to derive a stationary GMP model both in continuous and discrete time domain, which outperforms models previously described in the literature. Then, we achieve an even tighter estimation error bound using a non-stationary GMP model, for which we determine the minimum initial variance that guarantees bounding conditions. In both cases, the model can easily be implemented in a linear estimator like a Kalman filter.
Joint ptycho-tomography with deep generative priors
Authors: Selin Aslan, Zhengchun Liu, Viktor Nikitin, Tekin Bicer, Sven Leyffer, Doga Gursoy
Date: 2020-09-20 00:00:00
Tasks: Denoising, Retrieval

Joint ptycho-tomography is a powerful computational imaging framework to recover the refractive properties of a 3D object while relaxing the requirements for probe overlap that is common in conventional phase retrieval. We use an augmented Lagrangian scheme for formulating the constrained optimization problem and employ an alternating direction method of multipliers (ADMM) for the joint solution. ADMM allows the problem to be split into smaller and computationally more efficient subproblems: ptychographic phase retrieval, tomographic reconstruction, and regularization of the solution. We extend our ADMM framework with plug-and-play (PnP) denoisers by replacing the regularization subproblem with a general denoising operator based on machine learning. While the PnP framework enables integrating such learned priors as denoising operators, tuning of the denoiser prior remains challenging. To overcome this challenge, we propose a denoiser parameter to control the effect of the denoiser and to accelerate the solution. In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.
Underlaid FD D2D Communications in Massive MIMO Systems via Joint Beamforming and Power Allocation
Authors: Hung V. Vu, Tho Le-Ngoc
Date: 2020-09-20 00:00:00

This paper studies the benefits of incorporating underlaid full-duplex (FD) device-to-device (D2D) communications into massive multiple-input-multiple-output (MIMO) downlink systems. Due to the nature of cellular downlink and FD D2D transmission, the performances of cellular and D2D services are severely impaired due to high interference caused by base-stations (BSs) and D2D transceivers. As a consequence, integrating a large number of D2D links into exiting cellular networks might degrade the system performances. To overcome this challenge, utilizing the large uniform linear array (ULA) equipped at BSs, we propose a joint beamforming and power allocation design for average sum-rate maximization while considering the effects of interference to both cellular and D2D transmission. The problem formulation leads to a nonconvex vector-variable optimization problem, where we develop an efficient solution using a fractional programming (FP) based approach. Numerical results show that, at sufficiently high self-interference cancellation (SIC) levels and numbers of active D2D links, the FD D2D transmission provides a significant sum-rate improvement as compared to the half-duplex (HD) counterpart and pure cellular systems in absence of D2D.
Volterra mortality model: Actuarial valuation and risk management with long-range dependence
Authors: Ling Wang, Mei Choi Chiu, Hoi Ying Wong
Date: 2020-09-21 00:00:00
Tasks: Management

While abundant empirical studies support the long-range dependence (LRD) of mortality rates, the corresponding impact on mortality securities are largely unknown due to the lack of appropriate tractable models for valuation and risk management purposes. We propose a novel class of Volterra mortality models that incorporate LRD into the actuarial valuation, retain tractability, and are consistent with the existing continuous-time affine mortality models. We derive the survival probability in closed-form solution by taking into account of the historical health records. The flexibility and tractability of the models make them useful in valuing mortality-related products such as death benefits, annuities, longevity bonds, and many others, as well as offering optimal mean-variance mortality hedging rules. Numerical studies are conducted to examine the effect of incorporating LRD into mortality rates on various insurance products and hedging efficiency.
Coarse-Grained Nucleic Acid-Protein Model for Hybrid Nanotechnology
Authors: Jonah Procyk, Erik Poppleton, Petr Šulc
Date: 2020-09-21 00:00:00

The emerging field of hybrid DNA - protein nanotechnology brings with it the potential for many novel materials which combine the addressability of DNA nanotechnology with versatility of protein interactions. However, the design and computational study of these hybrid structures is difficult due to the system sizes involved. To aid in the design and in silico analysis process, we introduce here a coarse-grained DNA/RNA-protein model that extends the oxDNA/oxRNA models of DNA/RNA with a coarse-grained model of proteins based on an anisotropic network model representation. Fully equipped with analysis scripts and visualization, our model aims to facilitate hybrid nanomaterial design towards eventual experimental realization, as well as enabling study of biological complexes. We further demonstrate its usage by simulating DNA-protein nanocage, DNA wrapped around histones, and a nascent RNA in polymerase.