pwc-archive/papers-with-abstracts huggingface dataset:
Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network
Authors: Muhammad Uzair Zahid, Serkan Kiranyaz, Turker Ince, Ozer Can Devecioglu, Muhammad E. H. Chowdhury, Amith Khandakar, Anas Tahir, Moncef Gabbouj
Date: 2020-12-29 00:00:00

Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Therefore, in this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1D segmentation map of R- peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-DB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB). Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Compared to all competing methods, the proposed approach can reduce the false-positives and false-negatives in Holter ECG signals by more than 54% and 82%, respectively. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision.
Constrained optimisation of preliminary spacecraft configurations under the design-for-demise paradigm
Authors: Mirko Trisolini, Hugh G. Lewis, Camilla Colombo
Date: 2020-12-27 00:00:00

In the past few years, the interest towards the implementation of design-for-demise measures has increased steadily. Most mid-sized satellites currently launched and already in orbit fail to comply with the casualty risk threshold of 0.0001. Therefore, satellites manufacturers and mission operators need to perform a disposal through a controlled re-entry, which has a higher cost and increased complexity. Through the design-for-demise paradigm, this additional cost and complexity can be removed as the spacecraft is directly compliant with the casualty risk regulations. However, building a spacecraft such that most of its parts will demise may lead to designs that are more vulnerable to space debris impacts, thus compromising the reliability of the mission. In fact, the requirements connected to the demisability and the survivability are in general competing. Given this competing nature, trade-off solutions can be found, which favour the implementation of design-for-demise measures while still maintaining the spacecraft resilient to space debris impacts. A multi-objective optimisation framework has been developed by the authors in previous works. The framework's objective is to find preliminary design solutions considering the competing nature of the demisability and the survivability of a spacecraft since the early stages of the mission design. In this way, a more integrated design can be achieved. The present work focuses on the improvement of the multi-objective optimisation framework by including constraints. The paper shows the application of the constrained optimisation to two relevant examples: the optimisation of a tank assembly and the optimisation of a typical satellite configuration.
Incremental learning with online SVMs on LiDAR sensory data
Authors: Le Dinh Van Khoa, ZhiYuan Chen
Date: 2020-12-30 00:00:00
Tasks: Incremental Learning

The pipelines transmission system is one of the growing aspects, which has existed for a long time in the energy industry. The cost of in-pipe exploration for maintaining service always draws lots of attention in this industry. Normally exploration methods (e.g. Magnetic flux leakage and eddy current) will establish the sensors stationary for each pipe milestone or carry sensors to travel inside the pipe. It makes the maintenance process very difficult due to the massive amount of sensors. One of the solutions is to implement machine learning techniques for the analysis of sensory data. Although SVMs can resolve this issue with kernel trick, the problem is that computing the kernel depends on the data size too. It is because the process can be exaggerated quickly if the number of support vectors becomes really large. Particularly LiDAR spins with an extremely rapid rate and the flow of input data might eventually lead to massive expansion. In our proposed approach, each sample is learned in an instant way and the supported kernel is computed simultaneously. In this research, incremental learning approach with online support vector machines (SVMs) is presented, which aims to deal with LiDAR sensory data only.
Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN
Authors: Guanxiong Shen, Junqing Zhang, Alan Marshall, Linning Peng, Xianbin Wang
Date: 2020-12-30 00:00:00

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Authors: Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang
Date: 2021-01-03 00:00:00
Tasks: Community Detection

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
Cy: Chaotic yolo for user intended image encryption and sharing in social media
Authors: Meysam Asgari-Chenaghlu, Mohammad-Reza Feizi-Derakhshi, Narjes Nikzad-Khasmakhi, Ali-Reza Feizi-Derakhshi, Majid Ramezani, Zoleikha Jahanbakhsh-Nagadeh, Taymaz Rahkar-Farshi, Elnaz Zafarani-Moattar, Mehrdad Ranjbar-Khadivi, Mohammad-Ali Balafar
Date: 2021-01-04 00:00:00
Tasks: object-detection, Object Detection

Social media is an inseparable part of our daily life where we post and share photos and media related to our life and in some cases we intend to share them between specific people. This intended and cherry picked sharing of media needs a better solution rather than simply picking users. Some social media platforms do not restrict other users from sharing timeline posts of others; meaning, one can simply forward a post from another person to a third one and no data preservation has been applied. In most cases we do not intend to secure the whole media and only important parts of it are intended to be secured. In this work we propose a novel method based on YoloV3 object detection and chaotic image encryption to overcome the issue of user intended data preservation in social media platforms. Our proposed method is capable of both automatic image encryption on full or user selected regions. Statistical and cryptographic analysis show superiority of our method compared to other state-of-the-art methods while it keeps the speed as high as possible for online and realtime use cases.
Multi-Candidate Word Segmentation using Bi-directional LSTM Neural Networks
Authors: Theerapat Lapjaturapit, Kobkrit Viriyayudhakom, Thanaruk Theeramunkong
Date: 2018-05-07 00:00:00
Tasks: Segmentation, Thai Word Segmentation

Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for segmentation. As a test-bed, the well-known bidirectional long short-term memory (BiLSTM) units are used with eleven contexts in a deep neural network. As performance indices, three measures; recall, precision and f-measure, are plotted with respect to various thresholds for both boundary level and word level evaluation. By a number of experiments, the result shows that the multi-candidate word segmentation can help us increase the recalls while maintaining the precisions.
In-situ crosslinked wet spun collagen triple helices with nanoscale-regulated ciprofloxacin release capability
Authors: M. Tarik Arafat, Giuseppe Tronci, David J. Wood, Stephen J. Russell
Date: 2021-01-05 00:00:00

The design of antibacterial-releasing coatings or wrapping materials with controlled drug release capability is a promising strategy to minimise risks of infection and medical device failure in vivo. Collagen fibres have been employed as medical device building block, although they still fail to display controlled release capability, competitive wet-state mechanical properties, and retained triple helix organisation. We investigated this challenge by pursuing a multiscale design approach integrating drug encapsulation, in-situ covalent crosslinking and fibre spinning. By selecting ciprofloxacin (Cip) as a typical antibacterial drug, wet spinning was selected as a triple helix-friendly route towards Cip-encapsulated collagen fibres; whilst in situ crosslinking of fibre-forming triple helices with 1,3 phenylenediacetic acid (Ph) was hypothesised to yield Ph-Cip {\pi}-{\pi} stacking aromatic interactions and enable controlled drug release. Higher tensile modulus and strength were measured in Ph crosslinked fibres compared to state-of-the-art carbodiimide crosslinked controls. Cip-encapsulated Ph-crosslinked fibres revealed decreased elongation at break and significantly-enhanced drug retention in vitro with respect to Cip-free variants and carbodiimide-crosslinked controls, respectively. This multiscale manufacturing strategy provides new insight aiming at wet spun collagen triple helices with nanoscale-regulated tensile properties and drug release capability.
3D Orientation Estimation with Multiple 5G mmWave Base Stations
Authors: Mohammad A. Nazari, Gonzalo Seco-Granados, Pontus Johannisson, Henk Wymeersch
Date: 2021-01-05 00:00:00

We consider the problem of estimating the 3D orientation of a user, using the downlink mmWave signals received from multiple base stations. We show that the received signals from several base stations, having known positions, can be used to estimate the unknown orientation of the user. We formulate the estimation problem as a maximum likelihood estimation problem in the the manifold of rotation matrices. In order to provide an initial estimate to solve the non-linear non-convex optimization problem, we resort to a least squares estimation problem that exploits the underlying geometry. Our numerical results show that the problem of orientation estimation can be solved when the signals from at least two base stations are received. We also provide the orientation lower error bound, showing a narrow gap between the performance of the proposed estimators and the bound.
A redox-responsive hyaluronic acid-based hydrogel for chronic wound management
Authors: Ziyu Gao, Ben Golland, Giuseppe Tronci, Paul D. Thornton
Date: 2021-01-05 00:00:00
Tasks: Decision Making, Diagnostic, Management

Polymer-based hydrogels have been widely applied for chronic wound therapeutics, due to their well-acclaimed wound exudate management capability. At the same time, there is still an unmet clinical need for simple wound diagnostic tools to assist clinical decision-making at the point of care and deliver on the vision of patient-personalised wound management. To explore this challenge, we present a one-step synthetic strategy to realise a redox-responsive, hyaluronic acid (HA)-based hydrogel that is sensitive to wound environment-related variations in glutathione (GSH) concentration. By selecting aminoethyl disulfide (AED) as a GSH-sensitive crosslinker and considering GSH concentration variations in active and non-self-healing wounds, we investigated the impact of GSH-induced AED cleavage on hydrogel dimensions, aiming to build GSH-size relationships for potential point-of-care wound diagnosis. The hydrogel was also found to be non-cytotoxic and aided L929 fibroblast growth and proliferation over seven days in vitro. Such a material offers a very low-cost tool for the visual detection of a target analyte that varies dependent on the status of the cells and tissues (wound detection) and may be further exploited as an implant for fibroblast growth and tissue regeneration (wound repair).
Kernel Estimation of Spot Volatility with Microstructure Noise Using Pre-Averaging
Authors: José E. Figueroa-López, Bei Wu
Date: 2020-04-04 00:00:00

We first revisit the problem of estimating the spot volatility of an It\^o semimartingale using a kernel estimator. We prove a Central Limit Theorem with optimal convergence rate for a general two-sided kernel. Next, we introduce a new pre-averaging/kernel estimator for spot volatility to handle the microstructure noise of ultra high-frequency observations. We prove a Central Limit Theorem for the estimation error with an optimal rate and study the optimal selection of the bandwidth and kernel functions. We show that the pre-averaging/kernel estimator's asymptotic variance is minimal for exponential kernels, hence, justifying the need of working with kernels of unbounded support as proposed in this work. We also develop a feasible implementation of the proposed estimators with optimal bandwidth. Monte Carlo experiments confirm the superior performance of the devised method.
Optimal Localization with Sequential Pseudorange Measurements for Moving Users in a Time Division Broadcast Positioning System
Authors: Sihao Zhao, Xiao-Ping Zhang, Xiaowei Cui, Mingquan Lu
Date: 2021-01-05 00:00:00
Tasks: Position

In a time division broadcast positioning system (TDBPS), a user device (UD) determines its position by obtaining sequential time-of-arrival (TOA) or pseudorange measurements from signals broadcast by multiple synchronized base stations (BSs). The existing localization method using sequential pseudorange measurements and a linear clock drift model for the TDPBS, namely LSPM-D, does not compensate the position displacement caused by the UD movement and will result in position error. In this paper, depending on the knowledge of the UD velocity, we develop a set of optimal localization methods for different cases. First, for known UD velocity, we develop the optimal localization method, namely LSPM-KVD, to compensate the movement-caused position error. We show that the LSPM-D is a special case of the LSPM-KVD when the UD is stationary with zero velocity. Second, for the case with unknown UD velocity, we develop a maximum likelihood (ML) method to jointly estimate the UD position and velocity, namely LSPM-UVD. Third, in the case that we have prior distribution information of the UD velocity, we present a maximum a posteriori (MAP) estimator for localization, namely LSPM-PVD. We derive the Cramer-Rao lower bound (CRLB) for all three estimators and analyze their localization error performance. We show that the position error of the LSPM-KVD increases as the assumed known velocity deviates from the true value. As expected, the LSPM-KVD has the smallest position error while the LSPM-PVD and the LSPM-UVD are more robust when the prior knowledge of the UD velocity is limited. Numerical results verify the theoretical analysis on the optimality and the positioning accuracy of the proposed methods.
Relativistic Control: Feedback Control of Relativistic Dynamics
Authors: Song Fang, Quanyan Zhu
Date: 2019-12-06 00:00:00

Strictly speaking, Newton's second law of motion is only an approximation of the so-called relativistic dynamics, i.e., Einstein's modification of the second law based on his theory of special relativity. Although the approximation is almost exact when the velocity of the dynamical system is far less than the speed of light, the difference will become larger and larger (and will eventually go to infinity) as the velocity approaches the speed of light. Correspondingly, feedback control of such dynamics should also take this modification into consideration (though it will render the system nonlinear), especially when the velocity is relatively large. Towards this end, we start this note by studying the state-space representation of the relativistic dynamics. We then investigate on how to employ the feedback linearization approach for such relativistic dynamics, based upon which an additional linear controller may then be designed. As such, the feedback linearization together with the linear controller compose the overall relativistic feedback control law. We also provide discussions on, e.g., controllability, state feedback and output feedback, as well as PID control, in the relativistic setting.
Poisson channel with binary Markov input and average sojourn time constraint
Authors: Mark Sinzger, Maximilian Gehri, Heinz Koeppl
Date: 2021-01-05 00:00:00

A minimal model for gene expression, consisting of a switchable promoter together with the resulting messenger RNA, is equivalent to a Poisson channel with a binary Markovian input process. Determining its capacity is an optimization problem with respect to two parameters: the average sojourn times of the promoter's active (ON) and inactive (OFF) state. An expression for the mutual information is found by solving the associated filtering problem analytically on the level of distributions. For fixed peak power, three bandwidth-like constraints are imposed by lower-bounding (i) the average sojourn times (ii) the autocorrelation time and (iii) the average time until a transition. OFF-favoring optima are found for all three constraints, as commonly encountered for the Poisson channel. In addition, constraint (i) exhibits a region that favors the ON state, and (iii) shows ON-favoring local optima.
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
Authors: Jinlei Zhang, Hongshu Che, Feng Chen, Wei Ma, Zhengbing He
Date: 2020-08-08 00:00:00
Tasks: Benchmarking, Management, Prediction

Short-term origin-destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-term traffic forecasting methods, the short-term OD flow prediction possesses three unique characteristics: (1) data availability: real-time OD flow is not available during the prediction; (2) data dimensionality: the dimension of the OD flow is much higher than the cardinality of transportation networks; (3) data sparsity: URT OD flow is spatiotemporally sparse. There is a great need to develop novel OD flow forecasting method that explicitly considers the unique characteristics of the URT system. To this end, a channel-wise attentive split-convolutional neural network (CAS-CNN) is proposed. The proposed model consists of many novel components such as the channel-wise attention mechanism and split CNN. In particular, an inflow/outflow-gated mechanism is innovatively introduced to address the data availability issue. We further originally propose a masked loss function to solve the data dimensionality and data sparsity issues. The model interpretability is also discussed in detail. The CAS-CNN model is tested on two large-scale real-world datasets from Beijing Subway, and it outperforms the rest of benchmarking methods. The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.
Gene network robustness as a multivariate character
Authors: Arnaud Le Rouzic
Date: 2021-01-05 00:00:00

Robustness to genetic or environmental disturbances is often considered as a key property of living systems. Yet, in spite of being discussed since the 1950s, how robustness emerges from the complexity of genetic architectures and how it evolves still remains unclear. In particular, whether or not robustness is independent to various sources of perturbations conditions the range of adaptive scenarios that can be considered. For instance, selection for robustness to heritable mutations is likely to be modest and indirect, and its evolution might result from indirect selection on a pleiotropically-related character (e.g., homeostasis). Here, I propose to treat various robustness measurements as quantitative characters, and study theoretically, by individual-based simulations, their propensity to evolve independently. Based on a simple evolutionary model of a gene regulatory network, I showed that five measurements of the robustness of gene expression to genetic or non-genetic disturbances were substantially correlated. Yet, robustness was mutationally variable in several dimensions, and robustness components could evolve differentially under direct selection pressure. Therefore, the fact that the sensitivity of gene expression to mutations and environmental factors rely on the same gene networks does not preclude distinct evolutionary histories of robustness components. This article has been peer-reviewed and recommended by Peer Community In Evolutionary Biology Posted: 30th March 2022 (https://doi.org/10.24072/pci.evolbiol.100138)