pwc-archive/papers-with-abstracts huggingface dataset:
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation
Authors: Minghui Zhang, Xin Yu, Hanxiao Zhang, Hao Zheng, Weihao Yu, Hong Pan, Xiangran Cai, Yun Gu
Date: 2021-09-07 00:00:00
Tasks: Transfer Learning

3D Convolutional Neural Networks (CNNs) have been widely adopted for airway segmentation. The performance of 3D CNNs is greatly influenced by the dataset while the public airway datasets are mainly clean CT scans with coarse annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19 CT scans). In this work, we proposed a new dual-stream network to address the variability between the clean domain and noisy domain, which utilizes the clean CT scans and a small amount of labeled noisy CT scans for airway segmentation. We designed two different encoders to extract the transferable clean features and the unique noisy features separately, followed by two independent decoders. Further on, the transferable features are refined by the channel-wise feature recalibration and Signed Distance Map (SDM) regression. The feature recalibration module emphasizes critical features and the SDM pays more attention to the bronchi, which is beneficial to extracting the transferable topological features robust to the coarse labels. Extensive experimental results demonstrated the obvious improvement brought by our proposed method. Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans.
Analysis of MRI Biomarkers for Brain Cancer Survival Prediction
Authors: Subhashis Banerjee, Sushmita Mitra, Lawrence O. Hall
Date: 2021-09-03 00:00:00
Tasks: feature selection, Survival Prediction

Prediction of Overall Survival (OS) of brain cancer patients from multi-modal MRI is a challenging field of research. Most of the existing literature on survival prediction is based on Radiomic features, which does not consider either non-biological factors or the functional neurological status of the patient(s). Besides, the selection of an appropriate cut-off for survival and the presence of censored data create further problems. Application of deep learning models for OS prediction is also limited due to the lack of large annotated publicly available datasets. In this scenario we analyse the potential of two novel neuroimaging feature families, extracted from brain parcellation atlases and spatial habitats, along with classical radiomic and geometric features; to study their combined predictive power for analysing overall survival. A cross validation strategy with grid search is proposed to simultaneously select and evaluate the most predictive feature subset based on its predictive power. A Cox Proportional Hazard (CoxPH) model is employed for univariate feature selection, followed by the prediction of patient-specific survival functions by three multivariate parsimonious models viz. Coxnet, Random survival forests (RSF) and Survival SVM (SSVM). The brain cancer MRI data used for this research was taken from two open-access collections TCGA-GBM and TCGA-LGG available from The Cancer Imaging Archive (TCIA). Corresponding survival data for each patient was downloaded from The Cancer Genome Atlas (TCGA). A high cross validation $C-index$ score of $0.82\pm.10$ was achieved using RSF with the best $24$ selected features. Age was found to be the most important biological predictor. There were $9$, $6$, $6$ and $2$ features selected from the parcellation, habitat, radiomic and region-based feature groups respectively.
The Convergence of Blockchain, IoT and 6G: Potential, Opportunities, Challenges and Research Roadmap
Authors: Abu Jahid, Mohammed H. AlSharif, Trevor J. Hall
Date: 2021-09-07 00:00:00

The world is undergoing a profound transformation with the advent of intelligent information era. 6G networks envisioned being the game changer in next generation wireless communication systems that will address the challenges of limited information speed escalated with the augmentation of billions of data applications encountered by the current fifth generation (5G) networks. Some key radical technologies in 6G together with existing 5G candidate schemes will guarantee the expected quality of experience (QoE) to attain ubiquitous wireless connectivity for the Internet of Everything (IoE) ranging from the telecom industry to digital smart industries. Blockchain technology (BCT) has gained significant attention due to undertake the decentralization, transparency, spectrum resource scarcity, inherent privacy and security, poor interoperability, confidentiality, and emerging smart applications domains including Industrial IoT and Industry 4.0. The mismatch between the requirements of many data intensive disruptive IoT applications and 5G network capabilities steered the demand of decentralized BCT based 6G architecture. Inspired by these facts, this paper studies an extensive survey to draw a new direction of blockchain integration into 6G mobile networks, IoT technologies, and smart industries focusing the potential merits and challenges in terms of infrastructure sharing, computational loads, latency, bandwidth overhead, business model, sustainability goals, and edge intelligence. We highlighted the convergence of IoT in blockchain to enable intelligent distribution in future industrial IoT and the technical model of 6G networks to realize the successful deployment of BCT schemes. This paper pointed out the current intriguing challenges, canvassed the mitigation techniques, and plausible future research opportunities that may benefit the pursuit of this vision.
IEEE BigData 2021 Cup: Soft Sensing at Scale
Authors: Sergei Petrov, Chao Zhang, Jaswanth Yella, Yu Huang, Xiaoye Qian, Sthitie Bom
Date: 2021-09-07 00:00:00

IEEE BigData 2021 Cup: Soft Sensing at Scale is a data mining competition organized by Seagate Technology, in association with the IEEE BigData 2021 conference. The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques. In this paper we go into the details of the challenge and describe the data set provided to participants. We define the metrics of interest, baseline models, and describe approaches we found meaningful which may be a good starting point for further analysis. We discuss the results obtained with our approaches and give insights on what potential challenges participants may run into. Students, researchers, and anyone interested in working on a major industrial problem are welcome to participate in the challenge!
Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems
Authors: Mahmoud Zaher, Özlem Tuğfe Demir, Emil Björnson, Marina Petrova
Date: 2021-09-07 00:00:00
Tasks: Fairness

This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. The downlink performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, under two optimization objectives for power allocation: sum spectral efficiency (SE) maximization and proportional fairness. We present iterative centralized algorithms for solving these problems. Aiming at a less computationally complex and also distributed scalable solution, we train a deep neural network (DNN) to approximate the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation, based on large-scale fading (LSF) parameters, as the pre-processed input to the DNN. We train the DNN to refine the heuristic scheme, thereby providing higher SE, using only local information at each AP. Another distributed DNN that exploits side information assumed to be available at the central processing unit is designed for improved performance. Further, we develop a clustered DNN model where the LSF parameters of a small number of APs, forming a cluster within a relatively large network, are used to jointly approximate the power coefficients of the cluster.
Photonics-assisted wideband RF self-interference cancellation with digital domain amplitude and delay pre-matching
Authors: Taixia Shi, Moxuan Han, Yang Chen
Date: 2021-09-07 00:00:00

A photonics-based digital and analog self-interference cancellation approach for in-band full-duplex communication systems and frequency-modulated continuous-wave radar systems is reported. One dual-drive Mach-Zehnder modulator is used to implement the analog self-interference cancellation by pre-adjusting the delay and amplitude of the reference signal applied to the dual-drive Mach-Zehnder modulator in the digital domain. The amplitude is determined via the received signal power, while the delay is searched by the cross-correlation and bisection methods. Furthermore, recursive least squared or normalized least mean square algorithms are used to suppress the residual self-interference in the digital domain. Quadrature phase-shift keying modulated signals and linearly frequency-modulated signals are used to experimentally verify the proposed method. The analog cancellation depth is around 20 dB, and the total cancellation depth is more than 36 dB for the 2-Gbaud quadrature phase-shift keying modulated signals. For the linearly frequency-modulated signals, the analog and total cancellation depths are around 19 dB and 34 dB, respectively.
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables
Authors: Bharath Srinivas Prabakaran, Asima Akhtar, Semeen Rehman, Osman Hasan, Muhammad Shafique
Date: 2021-09-07 00:00:00
Tasks: Model Compression

In this work, we propose the BioNetExplorer framework to systematically generate and explore multiple DNN architectures for bio-signal processing in wearables. Our framework adapts key neural architecture parameters to search for an embedded DNN with a low hardware overhead, which can be deployed in wearable edge devices to analyse the bio-signal data and to extract the relevant information, such as arrhythmia and seizure. Our framework also enables hardware-aware DNN architecture search using genetic algorithms by imposing user requirements and hardware constraints (storage, FLOPs, etc.) during the exploration stage, thereby limiting the number of networks explored. Moreover, BioNetExplorer can also be used to search for DNNs based on the user-required output classes; for instance, a user might require a specific output class due to genetic predisposition or a pre-existing heart condition. The use of genetic algorithms reduces the exploration time, on average, by 9x, compared to exhaustive exploration. We are successful in identifying Pareto-optimal designs, which can reduce the storage overhead of the DNN by ~30MB for a quality loss of less than 0.5%. To enable low-cost embedded DNNs, BioNetExplorer also employs different model compression techniques to further reduce the storage overhead of the network by up to 53x for a quality loss of <0.2%.
Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things
Authors: Ruhul Amin Khalil, Nasir Saeed, Mohammad Inayatullah Babar, Tariqullah Jan, Sadia Din
Date: 2021-09-07 00:00:00

Localization of sensor nodes in the Internet of Underwater Things (IoUT) is of considerable significance due to its various applications, such as navigation, data tagging, and detection of underwater objects. Therefore, in this paper, we propose a hybrid Bayesian multidimensional scaling (BMDS) based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical, magnetic induction, and acoustic technologies. These technologies are already used for communication in the underwater environment; however, lacking localization solutions. Optical and magnetic induction communication achieves higher data rates for short communication. On the contrary, acoustic waves provide a low data rate for long-range underwater communication. The proposed method collectively uses optical, magnetic induction, and acoustic communication-based ranging to estimate the underwater sensor nodes' final locations. Moreover, we also analyze the proposed scheme by deriving the hybrid Cramer Rao lower bound (HCRLB). Simulation results provide a complete comparative analysis of the proposed method with the literature.
Near-Field Millimeter-Wave Imaging via Arrays in the Shape of Polyline
Authors: Shuoguang Wang, Shiyong Li, Guoqiang Zhao, Houjun Sun
Date: 2021-09-07 00:00:00
Tasks: Computational Efficiency

This paper proposes a polyline shaped array based system scheme, associated with mechanical scanning along the perpendicular direction of the array, for near-field millimeter-wave (MMW) imaging. Each section of the polyline is a chord of a circle with equal length. The polyline array, which can be realized as a monostatic array or a multistatic one, is capable of providing more observation angles than the linear or planar arrays. Further, we present the related three-dimensional (3-D) imaging algorithms based on a hybrid processing in the time domain and the spatial frequency domain. The nonuniform fast Fourier transform (NUFFT) is utilized to improve the computational efficiency. Simulations and experimental results are provided to demonstrate the efficacy of the proposed method in comparison with the back-projection (BP) algorithm.
A Recursive Delay Estimation Algorithm for Linear Multivariable Systems with Time-varying Delays
Authors: Iman Shafikhani, Hazhar Sufi Karimi, Mohammad Mohammadian, Amin Ramezani, hamid reza momeni
Date: 2021-09-06 00:00:00
Tasks: State Estimation

Time delay estimation plays a critical role in control, stabilization and state estimation of many practical system with time delay. In this paper, we propose a method to estimate delay for discrete time linear multiple-input multiple-output systems with time-varying input delays. This method is purposefully given for situations where only a limited amount of information is available for the system. Although, this approach is primarily developed in a deterministic framework, it can also be applied to noisy data under special circumstances. In addition, switched linear autoregressive models with exogenous inputs are introduced as possible applications of the presented algorithm provided that the switching frequencies are small. Finally, effectiveness of the algorithm is illustrated by two numerical examples.
Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection
Authors: Runyu Mao, Mackenzie Tweardy, Stephan W. Wegerich, Craig J. Goergen, George R. Wodicka, Fengqing Zhu
Date: 2021-09-06 00:00:00
Tasks: Denoising, Photoplethysmography (PPG)

Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is very sensitive to motion which can corrupt the signal's quality. Existing Motion Artifact (MA) reduction techniques have been developed and evaluated using either synthetic noisy signals or signals collected during high-intensity activities - both of which are difficult to generalize for real-life scenarios. Therefore, it is valuable to collect realistic PPG signals while performing Activities of Daily Living (ADL) to develop practical signal denoising and analysis methods. In this work, we propose an automatic pseudo clean PPG generation process for reliable PPG signal selection. For each noisy PPG segment, the corresponding pseudo clean PPG reduces the MAs and contains rich temporal details depicting cardiac features. Our experimental results show that 71% of the pseudo clean PPG collected from ADL can be considered as high quality segment where the derived MAE of heart rate and respiration rate are 1.46 BPM and 3.93 BrPM, respectively. Therefore, our proposed method can determine the reliability of the raw noisy PPG by considering quality of the corresponding pseudo clean PPG signal.
A Dynamic Population Model of Strategic Interaction and Migration under Epidemic Risk
Authors: Ezzat Elokda, Saverio Bolognani, Ashish R. Hota
Date: 2021-09-07 00:00:00

In this paper, we show how a dynamic population game can model the strategic interaction and migration decisions made by a large population of agents in response to epidemic prevalence. Specifically, we consider a modified susceptible-asymptomatic-infected-recovered (SAIR) epidemic model over multiple zones. Agents choose whether to activate (i.e., interact with others), how many other agents to interact with, and which zone to move to in a time-scale which is comparable with the epidemic evolution. We define and analyze the notion of equilibrium in this game, and investigate the transient behavior of the epidemic spread in a range of numerical case studies, providing insights on the effects of the agents' degree of future awareness, strategic migration decisions, as well as different levels of lockdown and other interventions. One of our key findings is that the strategic behavior of agents plays an important role in the progression of the epidemic and can be exploited in order to design suitable epidemic control measures.
Power Management of Microgrid Integrated with Electric Vehicles in Residential Parking Station
Authors: Hojun Jin, Sarvar Hussain Nengroo, Sangkeum Lee, Dongsoo Har
Date: 2021-09-07 00:00:00
Tasks: Management

Lately, increasing number of electric vehicles (EVs) in residential parking station has become an important issue, because excessive number of EVs can destabilize the power system during peak hours with high charging power requested. When the power system of the residential parking station takes the structure of microgrid (MG), power provision for the EVs requires efficient power management scheme. To minimize the maintenance cost of the MG and maintain the grid stability, the MG needs to balance the charging/discharging power of EVs in the parking station. To achieve these goals, this paper proposes a charging/discharging algorithm suitable for the power management of the MG configured with EVs. Multi-objective optimization is taken to MG to minimize the maintenance cost and the grid dependency while maximizing the use of photovoltaic (PV) power and the utilization of EVs as energy storage systems (ESSs). In our approach, to increase the usefulness of discharging power of EVs, the base load and the PV power production are considered together for power management to mitigate imbalances incurred between them. As a result, proposed approach demonstrates superior power management performance as compared to other comparable ones.
Flexibility Evaluation of Domestic Electric Water Heater Aggregates
Authors: F. Conte, B. Gabriele, S. Massucco, F. Silvestro, D. Cirio, L. Croci
Date: 2021-03-01 00:00:00

In this paper, a method to evaluate the flexibility of aggregates of domestic electric water heaters is proposed and applied to the Italian case. Flexibility is defined as the capability of the aggregate to vary its power demand for a given time interval. The evaluation method consists of a Monte Carlo analysis, that uses the thermal model of electric water heaters and a proper elaboration of the external inputs, such as ambient and cold water temperatures, and hot water demand. The case of large aggregates defined along the Italian territory has been studied showing the dependence of flexibility on seasons and on time.
Large-Scale System Identification Using a Randomized SVD
Authors: Han Wang, James Anderson
Date: 2021-09-06 00:00:00
Tasks: parameter estimation

Learning a dynamical system from input/output data is a fundamental task in the control design pipeline. In the partially observed setting there are two components to identification: parameter estimation to learn the Markov parameters, and system realization to obtain a state space model. In both sub-problems it is implicitly assumed that standard numerical algorithms such as the singular value decomposition (SVD) can be easily and reliably computed. When trying to fit a high-dimensional model to data, for example in the cyber-physical system setting, even computing an SVD is intractable. In this work we show that an approximate matrix factorization obtained using randomized methods can replace the standard SVD in the realization algorithm while maintaining the non-asymptotic (in data-set size) performance and robustness guarantees of classical methods. Numerical examples illustrate that for large system models, this is the only method capable of producing a model.
Encoding and ordering X-cactuses
Authors: Andrew Francis, Katharina T. Huber, Vincent Moulton, Taoyang Wu
Date: 2021-09-07 00:00:00

Phylogenetic networks are a generalization of evolutionary or phylogenetic trees that are commonly used to represent the evolution of species which cross with one another. A special type of phylogenetic network is an {\em $X$-cactus}, which is essentially a cactus graph in which all vertices with degree less than three are labelled by at least one element from a set $X$ of species. In this paper, we present a way to {\em encode} $X$-cactuses in terms of certain collections of partitions of $X$ that naturally arise from $X$-cactuses. Using this encoding, we also introduce a partial order on the set of $X$-cactuses (up to isomorphism), and derive some structural properties of the resulting partially ordered set. This includes an analysis of some properties of its least upper and greatest lower bounds. Our results not only extend some fundamental properties of phylogenetic trees to $X$-cactuses, but also provides a new approach to solving topical problems in phylogenetic network theory such as deriving consensus networks.
Analysis of Regularized Learning in Banach Spaces for Linear-functional Data
Authors: Qi Ye
Date: 2021-09-07 00:00:00

This article delves into the study of the theory of regularized learning in Banach spaces for linear-functional data. It encompasses discussions on representer theorems, pseudo-approximation theorems, and convergence theorems. Regularized learning is designed to minimize regularized empirical risks over a Banach space. The empirical risks are calculated by utilizing training data and multi-loss functions. The input training data are composed of linear functionals in a predual space of the Banach space to capture discrete local information from multimodal data and multiscale models. Through the regularized learning, approximations of the exact solution to an unidentified or uncertain original problem are globally achieved. In the convergence theorems, the convergence of the approximate solutions to the exact solution is established through the utilization of the weak* topology of the Banach space. The theorems of regularized learning are utilized in the interpretation of classical machine learning, such as support vector machines and artificial neural networks.
Backpropagation and fuzzy algorithm Modelling to Resolve Blood Supply Chain Issues in the Covid-19 Pandemic
Authors: Aan Erlansari, Rusdi Effendi, Funny Farady C, Andang Wijanarko, Boko Susilo, Reza Hardiansyah
Date: 2021-09-06 00:00:00

Bloodstock shortages and its uncertain demand has become a major problem for all countries worldwide. Therefore, this study aims to provide solution to the issues of blood distribution during the Covid-19 Pandemic at Bengkulu, Indonesia. The Backpropagation algorithm was used to improve the possibility of discovering available and potential donors. Furthermore, the distances, age, and length of donation were measured to obtain the right person to donate blood when it needed. The Backpropagation uses three input layers to classify eligible donors, namely age, body, weight, and bias. In addition, the system through its query automatically counts the variables via the Fuzzy Tahani and simultaneously access the vast database.