pwc-archive/papers-with-abstracts huggingface dataset:
On the robustness of model-based algorithms for photoacoustic tomography: comparison between time and frequency domains
Authors: L. Hirsch, M. G. Gonzalez, L. Rey Vega
Date: 2021-09-28 00:00:00
Tasks: Image Reconstruction

For photoacoustic image reconstruction, certain parameters such as sensor positions and speed of sound have a major impact in the reconstruction process and must be carefully determined before data acquisition. Uncertainties in these parameters can lead to errors produced by a modeling mismatch, hindering the reconstruction process and severely affecting the resulting image quality. Therefore, in this work we study how modeling errors arising from uncertainty in sensor locations affect the images obtained by matrix model-based reconstruction algorithms based on time domain and frequency domain models of the photoacoustic problem. The effects on the reconstruction performance with respect to the uncertainty in the knowledge of the sensors location is compared and analyzed both in a qualitative and quantitative fashion for both time and frequency models. Ultimately, our study shows that the frequency domain approach is more sensitive to this kind of modeling errors. These conclusions are supported by numerical experiments and a theoretical sensitivity analysis of the mathematical operator for the direct problem.
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography
Authors: Michael T. McCann, Marc L. Klasky, Jennifer L. Schei, Saiprasad Ravishankar
Date: 2020-12-11 00:00:00
Tasks: Computed Tomography (CT)

We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a training set. X-ray CT is widely used in medical and industrial applications. X-ray scatter, if not accounted for during reconstruction, creates a loss of contrast in CT reconstructions and introduces severe artifacts including cupping, shading, and streaks. Even when these qualitative artifacts are not apparent, scatter can pose a major obstacle in obtaining quantitatively accurate reconstructions. Our approach to estimating scatter is, first, to generate a training set of 2D radiographs with and without scatter using particle transport simulation software. To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it. We compared local and global (fit on full data sets) versions of several X-ray scatter models, including two from the recent literature, as well as a recent deep learning-based scatter model, in the context of descattering and quantitative density reconstruction of simulated, spherically symmetrical, single-material objects comprising shells of various densities. Our results show that, when applied locally, even simple models provide state-of-the-art descattering, reducing the error in density reconstruction due to scatter by more than half.
Enhancing Channel Shortening Based Physical Layer Security Using Coordinated Multipoint
Authors: Muhammad Sohaib J. Solaija, Hanadi Salman, Huseyin Arslan
Date: 2021-09-29 00:00:00
Tasks: Management

Wireless networks have become imperative in all areas of human life. As such, one of the most critical concerns in next-generation networks is ensuring the security and privacy of user data/communication. Cryptography has been conventionally used to tackle this, but it may not be scalable (in terms of key exchange and management) with the increasingly heterogeneous network deployments. Physical layer security (PLS) provides a promising alternative, but struggles when an attacker boasts a better wireless channel as compared to the legitimate user. This work leverages the coordinated multipoint concept and its distributed transmission points, in conjunction with channel shortening, to address this problem. Results show significant degradation of the bit-error-rate experienced at the eavesdropper as compared to state-of-the-art channel shortening-based PLS methods.
Joint Estimation of Multiple RF Impairments Using Deep Multi-Task Learning
Authors: Mehmet Ali Aygul, Ebubekir Memisoglu, Huseyin Arslan
Date: 2021-09-29 00:00:00
Tasks: Multi-Task Learning

Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments.
BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain
Authors: Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
Date: 2021-09-29 00:00:00
Tasks: Edge-computing

The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low quality of services (QoS), data privacy and system security vulnerabilities. In this paper, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data sharing scheme which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contract-based authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.
Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning
Authors: Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
Date: 2021-09-29 00:00:00
Tasks: channel selection, Deep Reinforcement Learning, Edge-computing

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.
Postural Orthostatic Tachycardia Syndrome explained using abaroreflex response model
Authors: Justen Geddes, Johnny T Ottesen, Jesper Mehlsen, Mette S. Olufsen
Date: 2021-09-27 00:00:00

Recent studies have shown that Postural Orthostatic Tachycardia Syndrome (POTS) patients have abnormal low frequency $\approx 0.1$ Hz blood pressure and heart rate dynamics. These dynamics are attributed to the baroreflex and can give insight into the mechanistic causes which are the basis for proposed subgroups of POTS. In this study we develop a baroreflex model replicating the low-frequency dynamics observed in POTS patient data as well as represent subgroups of POTS in our model. We utilize signal processing to quantify the effects that model parameters have on low-frequency oscillations. Results show that key physiological parameters that represent the hypothesized causes are central to our model's ability to reproduce observed dynamics from patient data.
Swapping in lattice-based cell migration models
Authors: Shahzeb Raja Noureen, Jennifer P. Owen, Richard L. Mort, Christian A. Yates
Date: 2021-09-28 00:00:00

Cell migration is frequently modelled using on-lattice agent-based models (ABMs) that employ the excluded volume interaction. However, cells are also capable of exhibiting more complex cell-cell interactions, such as adhesion, repulsion, pulling, pushing and swapping. Although the first four of these have already been incorporated into mathematical models for cell migration, swapping has not been well studied in this context. In this paper, we develop an ABM for cell movement in which an active agent can 'swap' its position with another agent in its neighbourhood with a given swapping probability. We consider a two-species system for which we derive the corresponding macroscopic model and compare it with the average behaviour of the ABM. We see good agreement between the ABM and the macroscopic density. We also analyse the movement of agents at an individual level in the single-species as well as two-species scenarios to quantify the effects of swapping on an agent's motility.
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit
Authors: Jacob Neumann, Yen Ting Lin, Abhishek Mallela, Ely F. Miller, Joshua Colvin, Abell T. Duprat1, Ye Chen, William S. Hlavacek, Richard G. Posner
Date: 2021-09-29 00:00:00
Tasks: Bayesian Inference, Management, Numerical Integration, Scheduling

Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g., numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters.
Bayesian hypothesis testing and hierarchical modelling of ivermectin effectiveness in treating Covid-19
Authors: Martin Neil, Norman Fenton
Date: 2021-08-18 00:00:00

Ivermectin is an antiparasitic drug that some have claimed is an effective treatment for reducing Covid-19 deaths. To test this claim, two recent peer reviewed papers both conducted a meta-analysis on a similar set of randomized controlled trials data, applying the same classical statistical approach. Although the statistical results were similar, one of the papers (Bryant et al, 2021) concluded that ivermectin was effective for reducing Covid-19 deaths, while the other (Roman et al, 2021) concluded that there was insufficient quality of evidence to support the conclusion Ivermectin was effective. This paper applies a Bayesian approach, to a subset of the same trial data, to test several causal hypotheses linking Covid-19 severity and ivermectin to mortality and produce an alternative analysis to the classical approach. Applying diverse alternative analysis methods which reach the same conclusions should increase overall confidence in the result. We show that there is strong evidence to support a causal link between ivermectin, Covid-19 severity and mortality, and: i) for severe Covid-19 there is a 90.7% probability the risk ratio favours ivermectin; ii) for mild/moderate Covid-19 there is an 84.1% probability the risk ratio favours ivermectin. To address concerns expressed about the veracity of some of the studies we evaluate the sensitivity of the conclusions to any single study by removing one study at a time. In the worst case, where (Elgazzar 2020) is removed, the results remain robust, for both severe and mild to moderate Covid-19. The paper also highlights advantages of using Bayesian methods over classical statistical methods for meta-analysis. All studies included in the analysis were prior to data on the delta variant.
Coulomb-like Model for International Trade Flow and Derivation of Distribution Function for Trade Flow Strength
Authors: Mikrajuddin Abdullah
Date: 2021-07-28 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Dielectric Constant

To describe international trade flows, we propose the coulomb force formulation, in which the magnitude of the charge represents gross domestic product (GDP) and the distance between countries is the bilateral distance, the product of spatial distance and "dielectric constant," rather than the spatial distance as used in the gravitation model, allowing it to be time dependent. The "dielectric constant" is influenced by factors such as warfare, transportation disruptions, trade agreements, social, geography, politics, culture, and others. The GDP and distance power parameters were estimated using data from high-GDP countries' export-import transactions. We also developed a trade strength distribution equation that fits World Bank data reasonably well over a decade.
Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach
Authors: Ali Al-Ameer, Khaled Alshehri
Date: 2021-09-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We propose a convex formulation for a trading system with the Conditional Value-at-Risk as a risk-adjusted performance measure under the notion of Direct Reinforcement Learning. Due to convexity, the proposed approach can uncover a lucrative trading policy in a "pure" online manner where it can interactively learn and update the policy without multi-epoch training and validation. We assess our proposed algorithm on a real financial market where it trades one of the largest US trust funds, SPDR, for three years. Numerical experiments demonstrate the algorithm's robustness in detecting central market-regime switching. Moreover, the results show the algorithm's effectiveness in extracting profitable policy while meeting an investor's risk preference under a conservative frictional market with a transaction cost of 0.15% per trade.
An Improved BAT Algorithm for Solving Job Scheduling Problems in Hotels and Restaurants
Authors: Tarik A. Rashid, Chra I. Shekho Toghramchi, Heja Sindi, Abeer Alsadoon, Nebojsa Bacanin, Shahla U. Umar, A. S. Shamsaldin, Mokhtar Mohammadi
Date: 2021-07-25 00:00:00
Tasks: Scheduling

One popular example of metaheuristic algorithms from the swarm intelligence family is the Bat algorithm (BA). The algorithm was first presented in 2010 by Yang and quickly demonstrated its efficiency in comparison with other common algorithms. The BA is based on echolocation in bats. The BA uses automatic zooming to strike a balance between exploration and exploitation by imitating the deviations of the bat's pulse emission rate and loudness as it searches for prey. The BA maintains solution diversity using the frequency-tuning technique. In this way, the BA can quickly and efficiently switch from exploration to exploitation. Therefore, it becomes an efficient optimizer for any application when a quick solution is needed. In this paper, an improvement on the original BA has been made to speed up convergence and make the method more practical for large applications. To conduct a comprehensive comparative analysis between the original BA, the modified BA proposed in this paper, and other state-of-the-art bio-inspired metaheuristics, the performance of both approaches is evaluated on a standard set of 23 (unimodal, multimodal, and fixed-dimension multimodal) benchmark functions. Afterward, the modified BA was applied to solve a real-world job scheduling problem in hotels and restaurants. Based on the achieved performance metrics, the proposed MBA establishes better global search ability and convergence than the original BA and other approaches.
Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph
Authors: InjySarhan, MarcoSpruit
Date: 2021-12-05 00:00:00
Proceeding: Knowledge-Based Systems 2021 12
Tasks: Deep Attention, Knowledge Graphs, NER, Open Information Extraction, Retrieval, Word Embeddings

Instant analysis of cybersecurity reports is a fundamental challenge for security experts as an immeasurable amount of cyber information is generated on a daily basis, which necessitates automated information extraction tools to facilitate querying and retrieval of data. Hence, we present Open-CyKG: an Open Cyber Threat Intelligence (CTI) Knowledge Graph (KG) framework that is constructed using an attention-based neural Open Information Extraction (OIE) model to extract valuable cyber threat information from unstructured Advanced Persistent Threat (APT) reports. More specifically, we first identify relevant entities by developing a neural cybersecurity Named Entity Recognizer (NER) that aids in labeling relation triples generated by the OIE model. Afterward, the extracted structured data is canonicalized to build the KG by employing fusion techniques using word embeddings. As a result, security professionals can execute queries to retrieve valuable information from the Open-CyKG framework. Experimental results demonstrate that our proposed components that build up Open-CyKG outperform state-of-the-art models.
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction
Authors: Tarek Berghout, Leila-Hayet Mouss, Toufik Bentrcia, Mohamed Benbouzid
Date: 2021-09-29 00:00:00
Proceeding: IEEE transaction on energy conversion 2021 9
Tasks: Interpretability Techniques for Deep Learning, Management, Prognosis, Transfer Learning

Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e. bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validations are performed using the PRONOSTIA bearing degradation datasets.
Sim2Real for Soft Robotic Fish via Differentiable Simulation
Authors: John Z. Zhang, Yu Zhang, Pingchuan Ma, Elvis Nava, Tao Du, Philip Arm, Wojciech Matusik, Robert K. Katzschmann
Date: 2021-09-30 00:00:00
Tasks: MORPH

Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots. We address this gap with our differentiable simulation tool by learning the material parameters of our soft robotic fish. On the example of a soft robotic fish, we demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters from quasi-static data via differentiable simulation and apply it to the prediction of dynamic performance. Our method identifies physically plausible Young's moduli for various soft silicone elastomers and stiff acetal copolymers used in creation of our three different robotic fish tail designs. We show that our method is compatible with varying internal geometry of the actuators, such as the number of hollow cavities. Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware to millimeter-accuracy and within 3 percent error normalized to actuator length. We provide a differentiable and robust estimate of the thrust force using a neural network thrust predictor; this estimate allows for accurate modeling of our experimental setup measuring bollard pull. This work presents a prototypical hardware and simulation problem solved using our differentiable framework; the framework can be applied to higher dimensional parameter inference, learning control policies, and computational design due to its differentiable character.