pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning based Spectral CT Imaging
Authors: Weiwen Wu, Dianlin Hu, Chuang Niu, Lieza Vanden Broeke, Anthony P. H. Butler, Peng Cao, James Atlas, Alexander Chernoglazov, Varut Vardhanabhuti, Ge Wang
Date: 2020-08-28 00:00:00
Tasks: compressed sensing, Computed Tomography (CT), Deblurring, Deep Learning, Image Deblurring, Image Reconstruction

Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific projection has a low signal-noise-ratio (SNR) than the current-integrating counterpart, which makes image reconstruction a unique challenge. Traditional wisdom is to use prior knowledge based iterative methods. However, this kind of methods demands a great computational cost. Inspired by deep learning, here we first develop a deep learning based reconstruction method; i.e., U-net with L_p^p-norm, Total variation, Residual learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the Various Multi-scale Feature Fusion and Multichannel Filtering Enhancement with a denser connection encoding architecture for residual learning and feature fusion. To address the image deblurring problem associated with the $L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the $L_p^p$-loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods. Finally, the anisotropically weighted total variation is employed to characterize the sparsity in the spatial-spectral domain to regularize the proposed network. In particular, we validate our ULTRA networks on three large-scale spectral CT datasets, and obtain excellent results relative to the competing algorithms. In conclusion, our quantitative and qualitative results in numerical simulation and preclinical experiments demonstrate that our proposed approach is accurate, efficient and robust for high-quality spectral CT image reconstruction.
An adaptive random experiment design method for engineering experiment
Authors: Zhou Qiao, Duan Xiaochang, Tang Wei
Date: 2020-08-27 00:00:00
Tasks: Experimental Design, Quantization

This paper proposes an adaptive random experiment design (ARED) algorithm that can be applied to optimize the multiple factors and levels experiments. The algorithm takes real-time model error as the adaptive condition, and outputs a model that conforms to the error quantization standard based on the automatic process. According to the actual experimental scenario, the similar number of test cases were selected between the ARED method and the comparative experimental design method under the bimodal Gaussian function, the bimodal surface function and the peaks function, respectively. simultaneously, the support vector machine (SVM) algorithm is used to construct the model for the selected test cases, and the verification surface (or curve) is predicted. The qualitative and quantitative analysis is carried out at two-slice of applicability and precision. The results show that the ARED method can be applied to the experiment of multi-factor, and has better precision and applicability than the comparative experimental methods.
Reviewing climate change and agricultural market competitiveness
Date: 2020-08-31 00:00:00

The paper is a collection of knowledge regarding the phenomenon of climate
change, competitiveness, and literature linking the two phenomena to
agricultural market competitiveness. The objective is to investigate the peer
reviewed and grey literature on the subject to explore the link between climate
change and agricultural market competitiveness and also explore an appropriate
technique to validate the presumed relationship empirically. The paper
concludes by identifying implications for developing an agricultural
competitiveness index while incorporating the climate change impacts, to
enhance the potential of agricultural markets for optimizing the agricultural
sectors competitiveness.
Data-driven Outer-Loop Control Using Deep Reinforcement Learning for Trajectory Tracking
Date: 2020-08-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Reference tracking systems involve a plant that is stabilized by a local
feedback controller and a command center that indicates the reference set-point
the plant should follow. Typically, these systems are subject to limitations
such as disturbances, systems delays, constraints, uncertainties,
underperforming controllers, and unmodeled parameters that do not allow them to
achieve the desired performance. In situations where it is not possible to
redesign the inner-loop system, it is usual to incorporate an outer-loop
control that instructs the system to follow a modified reference path such that
the resultant path is close to the ideal one. Typically, strategies to design
the outer-loop control need to know a model of the system, which can be an
unfeasible task. In this paper, we propose a framework based on deep
reinforcement learning that can learn a policy to generate a modified reference
that improves the system's performance in a non-invasive and model-free
fashion. To illustrate the effectiveness of our approach, we present two
challenging cases in engineering: a flight control with a pilot model that
includes human reaction delays, and a mean-field control problem for a massive
number of space-heating devices. The proposed strategy successfully designs a
reference signal that works even in situations that were not seen during the
learning process.
Contingent Convertible Bonds in Financial Networks
Authors: Giovanni Calice, Carlo Sala, Daniele Tantari
Date: 2020-08-31 00:00:00

We study the role of contingent convertible bonds (CoCos) in a complex network of interconnected banks. By studying the system's phase transitions, we reveal that the structure of the interbank network is of fundamental importance for the effectiveness of CoCos as a financial stability enhancing mechanism. Our results show that, under some network structures, the presence of CoCos can increase (and not reduce) financial fragility, because of the occurring of unneeded triggers and consequential suboptimal conversions that damage CoCos investors. We also demonstrate that, in the presence of a moderate financial shock, lightly interconnected financial networks are more robust than highly interconnected networks. This makes them a potentially optimal choice for both CoCos issuers and buyers.
Identification of Semiparametric Panel Multinomial Choice Models with Infinite-Dimensional Fixed Effects
Authors: Wayne Yuan Gao, Ming Li
Date: 2020-08-31 00:00:00

This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity. Our identification strategy exploits multivariate monotonicity in parametric indexes, and uses the logical contraposition of an intertemporal inequality on choice probabilities to obtain identifying restrictions. We provide a consistent estimation procedure, and demonstrate the practical advantages of our method with Monte Carlo simulations and an empirical illustration on popcorn sales with the Nielsen data.
Energy-efficient Wireless Charging and Computation Offloading In MEC Systems
Authors: Rafia Malik, Mai Vu
Date: 2020-08-31 00:00:00
Tasks: Edge-computing

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) supporting multiple users requesting data computation and wireless charging. We propose an integrated solution with computation offloading to satisfy the largest proportion of requested wireless charging while keeping the energy consumption at the minimum subject to the MEC-AP transmit power and latency constraints. We propose a novel algorithm to perform data partitioning, time allocation, transmit power control and design the optimal energy beamforming for wireless charging. Our resource allocation scheme offers an energy minimizing solution compared to other schemes while also delivering higher amount of transferred charge to the users.
When Costly Punishment Becomes Evolutionarily Beneficial
Date: 2020-08-31 00:00:00
Tasks: Diversity

Cooperation in evolutionary biology means paying a cost, c, to enjoy
benefits, b. A defector is one who does not pay any cost but enjoys the
benefits of cooperators. Human societies, especially, have evolved a strategy
to discourage defection, punishment. Costly punishment is a type of punishment
where an agent in a biological network or some cooperative scheme pays a cost
to ensure another agent incurs some cost. A previous study shows how parameters
like diversity in neighbors across games, density of connectivity, and costly
punishment influence the evolution of cooperation in non-regular networks. In
this study, evolution in regular networks due to the influence of costly
punishment is also considered, specifically spatial lattices. This study
compares observations between non-regular and regular networks as these
parameters change and brings a clearer understanding of interactions that occur
in these networks. The models, results and analysis bring a new understanding
to game theory and punishment. The results show that costly punishment never
arises as a sole evolutionary strategy. However, in evolutionary games where
costly punishers could evolve more favorable strategies, the initial presence
of costly punishers would bring about high average payoffs in all types of
regular networks and heterogeneous networks. In regular networks, every node
has the same degree, k. Although punishment is conventionally thought to be
anti-progressive, in the presence of diversity in neighbors, it magnifies the
payoff of a group for all heterogeneous networks. In regular networks however,
diversity in neighbors is not required for costly punishment to boost average
payoff. This suggests an answer to the question on why costly punishment has
been favored by natural selection, which is particularly obvious in human
populations.
Exactly Optimal Bayesian Quickest Change Detection for Hidden Markov Models
Authors: Jason J. Ford, Jasmin James, Timothy L. Molloy
Date: 2020-08-31 00:00:00
Tasks: Change Detection

This paper considers the quickest detection problem for hidden Markov models (HMMs) in a Bayesian setting. We construct an augmented HMM representation of the problem that allows the application of a dynamic programming approach to prove that Shiryaev's rule is an (exact) optimal solution. This augmented representation highlights the problem's fundamental information structure and suggests possible relaxations to more exotic change event priors not appearing in the literature. Finally, this augmented representation also allows us to present an efficient computational method for implementing the optimal solution.
Head-to-nerve analysis of electromechanical impairments of diffuse axonal injury
Authors: Ilaria Cinelli, Michel Destrade, Peter McHugh, Antonia Trotta, Michael Gilchrist, Maeve Duffy
Date: 2020-08-31 00:00:00

The aim was to investigate mechanical and functional failure of diffuse axonal injury (DAI) in nerve bundles following frontal head impacts, by finite element simulations. Anatomical changes following traumatic brain injury are simulated at the macroscale by using a 3D head model. Frontal head impacts at speeds of 2.5-7.5 m/s induce mild-to-moderate DAI in the white matter of the brain. Investigation of the changes in induced electromechanical responses at the cellular level is carried out in two scaled nerve bundle models, one with myelinated nerve fibres, the other with unmyelinated nerve fibres. DAI occurrence is simulated by using a real-time fully coupled electromechanical framework, which combines a modulated threshold for spiking activation and independent alteration of the electrical properties for each three-layer fibre in the nerve bundle models. The magnitudes of simulated strains in the white matter of the brain model are used to determine the displacement boundary conditions in elongation simulations using the 3D nerve bundle models. At high impact speed, mechanical failure occurs at lower strain values in large unmyelinated bundles than in myelinated bundles or small unmyelinated bundles; signal propagation continues in large myelinated bundles during and after loading, although there is a large shift in baseline voltage during loading; a linear relationship is observed between the generated plastic strain in the nerve bundle models and the impact speed and nominal strains of the head model. The myelin layer protects the fibre from mechanical damage, preserving its functionalities.
An algorithm for dividing quaternions
Authors: Aleksandr Cariow, Galina Cariowa
Date: 2020-08-30 00:00:00

In this work, a rationalized algorithm for calculating the quotient of two quaternions is presented which reduces the number of underlying real multiplications. Hardware for fast multiplication is much more expensive than hardware for fast addition. Therefore, reducing the number of multiplications in VLSI processor design is usually a desirable task. The performing of a quaternion division using the naive method takes 16 multiplications, 15 additions, 4 squarings and 4 divisions of real numbers while the proposed algorithm can compute the same result in only 8 multiplications (or multipliers in hardware implementation case), 31 additions, 4 squaring and 4 division of real numbers.
String Stability of Connected Vehicle Platoons under Lossy V2V Communication
Authors: Vamsi Vegamoor, Sivakumar Rathinam, Swaroop Darbha
Date: 2020-08-28 00:00:00

Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput. In order to avoid a pile-up in such platoons, it is important to ensure platoon (string) stability, which is the focus of this work. As per conventional definition of string stability, the power (2-norm) of the spacing error signals should not amplify downstream in a platoon. But in practice, it is the infinity-norm of the spacing error signal that dictates whether a collision occurs. We address this discrepancy in the first part of our work, where we reconsider string stability from a safety perspective and develop an upper limit on the maximum spacing error in a homogeneous platoon as a function of the acceleration maneuver of the lead vehicle. In the second part of this paper, we extend our previous results by providing the minimum achievable time headway for platoons with two-predecessor lookup schemes experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed time headway selection algorithms.
Effects of Correlated Noise on the Performance of Persistence Based Dynamic State Detection Methods
Authors: Joshua Tempelman, Audun Myers, Jeffrey Scruggs, Firas Khasawneh
Date: 2020-08-31 00:00:00
Tasks: Diagnostic, Time Series, Time Series Analysis

The ability to characterize the state of dynamic systems has been a pertinent task in the time series analysis community. Traditional measures such as Lyapunov exponents are often times difficult to recover from noisy data, especially if the dimensionality of the system is not known. More recent binary and network based testing methods have delivered promising results for unknown deterministic systems, however noise injected into a periodic signal leads to false positives. Recently, we showed the advantage of using persistent homology as a tool for achieving dynamic state detection for systems with no known model and showed its robustness to white Gaussian noise. In this work, we explore the robustness of the persistence based methods to the influence of colored noise and show that colored noise processes of the form $1/f^{\alpha}$ lead to false positive diagnostic at lower signal to noise ratios for $\alpha<0$.
Frequency Domain Stability Method for Reset Systems
Authors: Ali Ahmadi Dastjerdi, Alessandro Astolfi, S. H. HosseinNia
Date: 2020-08-28 00:00:00

Today, linear PID controllers cannot satisfy requirements of high-precision industry due to the development of technology. According to the literature, reset controllers can overcome this important barrier. However, similar to other non-linear controllers, stability analysis for these controllers is complex and needs parametric models of the systems. Consequently, the applicability of these controllers may diminish in industry. The well-known Hb method is one of the solutions of this significant problem. Nevertheless, assessing the Hb condition in the frequency-domain is complex, particularly for high dimensional plants. Furthermore, it cannot assess UBIBS stability of reset control systems in the case of reseting to non-zero values. In this paper, the aforementioned problems have been solved for the first and second order reset elements and a frequency-domain approach for assessing stability of reset control systems is developed. Finally, some practical examples are presented to demonstrate the effectiveness of the proposed approach to use the frequency response measurement directly to assess the stability.
The LoRa Modulation Over Rapidly-Varying Channels: Are the Higher Spreading Factors Necessarily More Robust?
Authors: Harishwar Reddy Bapathu, Siddhartha S. Borkotoky
Date: 2020-09-02 00:00:00

The chirp spread spectrum (CSS) modulation scheme is employed by the physical layer of the Long Range (LoRa) communication technology. In this paper, we examine the performance of CSS over time-varying channels whose gain may change during the reception of a LoRa frame. This is in contrast to the usually employed model in the literature, which assumes the channel gain to be constant throughout a frame. Specifically, we investigate the effects of exponentially correlated Rayleigh fading on the frame-error rate of a CSS receiver in which the channel gain is estimated at the beginning of each frame. Our primary observation is that over rapidly-varying channels, the robustness benefits of the larger spreading factors tend to disappear as the payload size grows. This observation, which is contrary to the common perception that higher spreading factors necessarily provide greater immunity against noise, highlights the need to consider channel characteristics and payload sizes in allocating the spreading factor for reliable and energy-efficient LoRa communications.
Automated Market Makers for Decentralized Finance (DeFi)
Authors: Yongge Wang
Date: 2020-09-03 00:00:00
Tasks: scoring rule

This paper compares mathematical models for automated market makers including logarithmic market scoring rule (LMSR), liquidity sensitive LMSR (LS-LMSR), constant product/mean/sum, and others. It is shown that though LMSR may not be a good model for Decentralized Finance (DeFi) applications, LS-LMSR has several advantages over constant product/mean based automated market makers. However, LS-LMSR requires complicated computation (i.e., logarithm and exponentiation) and the cost function curve is concave. In certain DeFi applications, it is preferred to have computationally efficient cost functions with convex curves to conform with the principle of supply and demand. This paper proposes and analyzes constant circle/ellipse based cost functions for automated market makers. The proposed cost functions are computationally efficient (only requires multiplication and square root calculation) and have several advantages over widely deployed constant product cost functions. For example, the proposed market makers are more robust against front-runner (slippage) attacks.
Heterogeneity and Superspreading Effect on Herd Immunity
Date: 2020-10-12 00:00:00

We model and calculate the fraction of infected population necessary for herd
immunity to occur, taking into account the heterogeneity in infectiousness and
susceptibility, as well as the correlation between the two parameters. We show
that these cause the reproduction number to decrease with progression, and
consequently have a drastic effect on the estimate of the necessary percentage
of the population that has to contract the disease for herd immunity to be
reached. We discuss the implications to COVID-19 and other pandemics.
Network Thermodynamical Modelling of Bioelectrical Systems: A Bond Graph Approach
Authors: Peter J. Gawthrop, Michael Pan
Date: 2020-09-04 00:00:00

Interactions between biomolecules, electrons and protons are essential to many fundamental processes sustaining life. It is therefore of interest to build mathematical models of these bioelectrical processes not only to enhance understanding but also to enable computer models to complement in vitro and in vivo experiments.Such models can never be entirely accurate; it is nevertheless important that the models are compatible with physical principles. Network Thermodynamics, as implemented with bond graphs, provide one approach to creating physically compatible mathematical models of bioelectrical systems. This is illustrated using simple models of ion channels, redox reactions, proton pumps and electrogenic membrane transporters thus demonstrating that the approach can be used to build mathematical and computer models of a wide range of bioelectrical systems.
Analysis of the Results of Metadynamics Simulations by metadynminer and metadynminer3d
Authors: Dalibor Trapl, Vojtěch Spiwok
Date: 2020-08-22 00:00:00
Tasks: Drug Discovery

The molecular simulations solve the equation of motion of molecular systems, making 3D shapes of molecules four-dimensional by adding the time coordinate. These methods have a great potential in drug discovery because they can realistically model the structures of protein molecules targeted by drugs as well as the process of binding of a potential drug to its molecular target. However, routine application of biomolecular simulations is hampered by the very high computational costs of this method. Several methods have been developed to address this problem. One of them, metadynamics, disfavors states of the simulated system that have been already visited and thus forces the system to explore new and new states. Here we present the package metadynminer and metadynminer3d to analyze and visualize results from metadynamics, in particular those produced by a popular metadynamics package Plumed.
A Effective Carrier Phase Recovery Method in Tigth Time-Packing Fast than Nyquist Optical Communication System
Authors: Peng Sun, Xiaoguang Zhang, Dongwei Pan, Lixia Xi, Wenbo Zhang, Xianfeng Tang
Date: 2020-08-24 00:00:00

We propose a new scheme that combines polybinary transformaton and corrected-BPS to compensate noise for PDM-FTN-QPSK when its accelerated factor is 0.5,which has 3.3 dB OSNR gain when phase noise is 800 kHz.