pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Dynamic State-Input Estimation for Power Networks of Microgrids and Active Distribution Systems with Unknown Inputs
Authors: Bang L. H. Nguyen, Tuyen V. Vu, Joseph M. Guerrero, Mischael Steurer, Karl Schoder, Tuan Ngo
Date: 2021-08-03 00:00:00
Tasks: State Estimation

This paper proposes a joint input and state dynamic estimation scheme for power networks in microgrids and active distribution systems with unknown inputs. The conventional dynamic state estimation of power networks in the transmission system relies on the forecasting methods to obtain the state-transition model of state variables. However, under highly dynamic conditions in the operation of microgrids and active distribution networks, this approach may become ineffective as the forecasting accuracy is not guaranteed. To overcome such drawbacks, this paper employs the power networks model derived from the physical equations of branch currents. Specifically, the power network model is a linear state-space model, in which the state vector consists of branch currents, and the input vector consists of bus voltages. To estimate both state and input variables, we propose linear Kalman-based dynamic filtering algorithms in batch-mode regression form, considering the cross-correlation between states and inputs. For the scalability of the proposed scheme, the distributed implementation is also presented. Complementarily, the predicted state and input vectors are leveraged for bad data detection. Results carried out on a 13-bus microgrid system in real-time Opal-RT platform demonstrate the effectiveness of the proposed method in comparison with the traditional weighted least square and tracking state estimation methods.
Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms
Authors: Zhenhao Tang, Shikui Wang, Shengxian Cao, Yang Li, Tao Shen
Date: 2021-08-03 00:00:00
Tasks: feature selection, FLUE, Prediction, Time Series, Time Series Analysis

Aiming at the problem that delay time is difficult to determine and prediction accuracy is low in building prediction model of SCR system, a dynamic modeling scheme based on a hybrid of multiple data-driven algorithms was proposed. First, processed abnormal values and normalized the data. To improve the relevance of the input data, used MIC to estimate delay time and reconstructed production data. Then used combined feature selection method to determine input variables. To further mine data information, VMD was used to decompose input time series. Finally, established NOx emission prediction model combining ELM and EC model. Experimental results based on actual historical operating data show that the MAPE of predicted results is 2.61%. Model sensitivity analysis shows that besides the amount of ammonia injection, the inlet oxygen concentration and the flue gas temperature have a significant impact on NOx emission, which should be considered in SCR process control and optimization.
Wide-Area Damping Control for Interarea Oscillations in Power Grids Based on PMU Measurements
Authors: Ilias Zenelis, Xiaozhe Wang
Date: 2021-08-02 00:00:00

In this paper, a phasor measurement unit (PMU)-based wide-area damping control method is proposed to damp the interarea oscillations that threaten the modern power system stability and security. Utilizing the synchronized PMU data, the proposed almost model-free approach can achieve an effective damping for the selected modes using a minimum number of synchronous generators. Simulations are performed to show the validity of the proposed wide-area damping control scheme.
Stochastic gene transcription with non-competitive transcription regulatory architecture
Authors: Amit Kumar Das
Date: 2021-08-03 00:00:00

The transcription factors, such as activators and repressors, can interact with the promoter of gene either in a competitive or non-competitive way. In this paper, we construct a stochastic model with non-competitive transcriptional regulatory architecture and develop an analytical theory that re-establishes the experimental results with an improved data fitting. The analytical expressions in the theory allow us to study the nature of the system corresponding to any of its parameters, and hence enable us to find out the factors that govern the regulation of gene expression for that architecture. We notice that, along with transcriptional reinitiation and repressors, there are other parameters that can control the noisiness of this network. We also observe that, the Fano factor (at mRNA level) varies from sub-Poissonian regime to superPoissonian regime. In addition to the aforementioned properties, we observe some anomalous characteristics of the Fano factor (at mRNA level) and that of the variance of protein at lower activator concentrations in presence of repressor molecules. This model is useful to understand the architecture of interactions which may buffer the stochasticity inherent to gene transcription.
Nonperturbative renormalization for the neural network-QFT correspondence
Authors: Harold Erbin, Vincent Lahoche, Dine Ousmane Samary
Date: 2021-08-03 00:00:00

In a recent work arXiv:2008.08601, Halverson, Maiti and Stoner proposed a description of neural networks in terms of a Wilsonian effective field theory. The infinite-width limit is mapped to a free field theory, while finite $N$ corrections are taken into account by interactions (non-Gaussian terms in the action). In this paper, we study two related aspects of this correspondence. First, we comment on the concepts of locality and power-counting in this context. Indeed, these usual space-time notions may not hold for neural networks (since inputs can be arbitrary), however, the renormalization group provides natural notions of locality and scaling. Moreover, we comment on several subtleties, for example, that data components may not have a permutation symmetry: in that case, we argue that random tensor field theories could provide a natural generalization. Second, we improve the perturbative Wilsonian renormalization from arXiv:2008.08601 by providing an analysis in terms of the nonperturbative renormalization group using the Wetterich-Morris equation. An important difference with usual nonperturbative RG analysis is that only the effective (IR) 2-point function is known, which requires setting the problem with care. Our aim is to provide a useful formalism to investigate neural networks behavior beyond the large-width limit (i.e.~far from Gaussian limit) in a nonperturbative fashion. A major result of our analysis is that changing the standard deviation of the neural network weight distribution can be interpreted as a renormalization flow in the space of networks. We focus on translations invariant kernels and provide preliminary numerical results.
One-step, Wash-free, Nanoparticle Clustering-based Magnetic Particle Spectroscopy (MPS) Bioassay Method for Detection of SARS-CoV-2 Spike and Nucleocapsid Proteins in Liquid Phase
Authors: Kai Wu, Vinit Kumar Chugh, Venkatramana D. Krishna, Arturo di Girolamo, Yongqiang Andrew Wang, Renata Saha, Shuang Liang, Maxim C-J Cheeran, Jian-Ping Wang
Date: 2021-08-01 00:00:00
Tasks: Diagnostic

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an increasing quest for more accessible, easy-to-use, rapid, inexpensive, and high accuracy diagnostic tools. Traditional disease diagnostic methods such as qRT-PCR (quantitative reverse transcription-PCR) and ELISA (enzyme-linked immunosorbent assay) require multiple steps, trained technicians, and long turnaround time that may worsen the disease surveillance and pandemic control. In sight of this situation, a rapid, one-step, easy-to-use, and high accuracy diagnostic platform will be valuable for future epidemic control especially for regions with scarce medical resources. Herein, we report a magnetic particle spectroscopy (MPS) platform for detection of SARS-CoV-2 biomarkers: spike and nucleocapsid proteins.
Stochasticity of infectious outbreaks and consequences for optimal interventions
Authors: Roberto Morán-Tovar, Henning Gruell, Florian Klein, Michael Lässig
Date: 2021-08-02 00:00:00

Global strategies to contain a pandemic, such as social distancing and protective measures, are designed to reduce the overall transmission rate between individuals. Despite such measures, essential institutions, including hospitals, schools, and food producing plants, remain focal points of local outbreaks. Here we develop a model for the stochastic outbreak dynamics in such local communities. We derive analytical expressions for the probability of containment of the outbreak, which is complementary to the probability of seeding a deterministically growing epidemic. This probability depends on the statistics of the intra-community contact network and the initial conditions, in particular, on the contact degree of patient zero. Based on this model, we suggest surveillance protocols by which individuals are tested proportionally to their degree in the contact network. We characterize the efficacy of contact-based protocols as a function of the epidemiological and the contact network parameters, and show numerically that such protocols outperform random testing.
The macroeconomic cost of climate volatility
Authors: Piergiorgio Alessandri, Haroon Mumtaz
Date: 2021-07-21 00:00:00

We study the impact of climate volatility on economic growth exploiting data on 133 countries between 1960 and 2019. We show that the conditional (ex ante) volatility of annual temperatures increased steadily over time, rendering climate conditions less predictable across countries, with important implications for growth. Controlling for concomitant changes in temperatures, a +1 degree C increase in temperature volatility causes on average a 0.3 percent decline in GDP growth and a 0.7 percent increase in the volatility of GDP. Unlike changes in average temperatures, changes in temperature volatility affect both rich and poor countries.
Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display
Authors: Xinju Wu, Yun Zhang, Chunling Fan, Junhui Hou, Sam Kwong
Date: 2020-08-06 00:00:00
Tasks: Point Cloud Quality Assessment, Quantization

In this paper, we focus on subjective and objective Point Cloud Quality Assessment (PCQA) in an immersive environment and study the effect of geometry and texture attributes in compression distortion. Using a Head-Mounted Display (HMD) with six degrees of freedom, we establish a subjective PCQA database, named SIAT Point Cloud Quality Database (SIAT-PCQD). Our database consists of 340 distorted point clouds compressed by the MPEG point cloud encoder with the combination of 20 sequences and 17 pairs of geometry and texture quantization parameters. The impact of distorted geometry and texture attributes is further discussed in this paper. Then, we propose two projection-based objective quality evaluation methods, i.e., a weighted view projection based model and a patch projection based model. Our subjective database and findings can be used in point cloud processing, transmission, and coding, especially for virtual reality applications. The subjective dataset has been released in the public repository.
Online Estimation of Diameter at Breast Height (DBH) of Forest Trees Using a Handheld LiDAR
Authors: Alexander Proudman, Milad Ramezani, Maurice Fallon
Date: 2021-08-03 00:00:00

While mobile LiDAR sensors are increasingly used to scan in ecology and forestry applications, reconstruction and characterisation are typically carried out offline (to the best of our knowledge). Motivated by this, we present an online LiDAR system which can run on a handheld device to segment and track individual trees and identify them in a fixed coordinate system. Segments relating to each tree are accumulated over time, and tree models are completed as more scans are captured from different perspectives. Using this reconstruction we then fit a cylinder model to each tree trunk by solving a least-squares optimisation over the points to estimate the Diameter at Breast Height (DBH) of the trees. Experimental results demonstrate that our system can estimate DBH to within $\sim$7 cm accuracy for 90% of individual trees in a forest (Wytham Woods, Oxford)
CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation
Authors: Bowen Huang, Xiao Yan, Jinjia Zhou, Yibo Fan
Date: 2021-08-03 00:00:00
Tasks: Compressive Sensing, Motion Estimation, Video Compressive Sensing

Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN. In this paper, a deep neural network with interpretable motion estimation named CSMCNet is proposed. The network is able to realize high-quality reconstruction of video compressive sensing by unfolding the iterative steps of optimization based algorithms. A DNN based, multi-hypothesis motion estimation module is designed to improve the reconstruction quality, and a residual module is employed to further narrow down the gap between re-construction results and original signal in our proposed method. Besides, we propose an interpolation module with corresponding training strategy to realize scalable CS reconstruction, which is capable of using the same model to decode various compression ratios. Experiments show that a PSNR of 29.34dB can be achieved at 2% CS ratio (compressed by 98%), which is superior than other state-of-the-art methods. Moreover, the interpolation module is proved to be effective, with significant cost saving and acceptable performance losses.
Simultaneous reconstruction and displacement estimation for spectral-domain optical coherence elastography
Authors: Jonathan H. Mason, Yvonne Reinwald, Ying Yang, Sarah Waters, Alicia El Haj, Pierre O. Bagnaninchi
Date: 2021-08-02 00:00:00
Tasks: Denoising

Optical coherence elastography allows the characterization of the mechanical properties of tissues, and can be performed through estimating local displacement maps from subsequent acquisitions of a sample under different loads. This displacement estimation is limited by noise in the images, which can be high in dynamic systems due to the inability to perform long exposures or B-scan averaging. In this work, we propose a framework for simultaneously enhancing both the image quality and displacement map for elastography, by motion compensated denoising with the block-matching and 4D filtering (BM4D) method, followed by a re-estimation of displacement. We adopt the interferometric synthetic aperture microscopy (ISAM) method to enhance the lateral resolution away from the focal plane, and use sub-pixel cross correlation block matching for non-uniform deformation estimation. We validate this approach on data from a commercial spectral domain optical coherence tomography system, whereby we observe an enhancement of both image and displacement accuracy of up to 33% over a standard approach.
Model-based iterative reconstruction for spectral-domain optical coherence tomography
Authors: Jonathan H. Mason, Yvonne Reinwald, Ying Yang, Sarah Waters, Alicia El Haj, Pierre O. Bagnaninchi
Date: 2021-08-02 00:00:00

Spectral domain optical coherence tomography (OCT) offers high resolution multidimensional imaging, but generally suffers from defocussing, intensity falloff and shot noise, causing artifacts and image degradation along the imaging depth. In this work, we develop an iterative statistical reconstruction technique, based upon the interferometric synthetic aperture microscopy (ISAM) model with additive noise, to actively compensate for these effects. For the ISAM re-sampling, we use a non uniform FFT with Kaiser-Bessel interpolation, offering efficiency and high accuracy. We then employ an accelerated gradient descent based algorithm, to minimize the negative log-likelihood of the model, and include spatial or wavelet sparsity based penalty functions, to provide appropriate regularization for given image structures. We evaluate our approach with titanium oxide micro-bead and cucumber samples with a commercial spectral domain OCT system, under various subsampling regimes, and demonstrate superior image quality over traditional reconstruction and ISAM methods.
Sexing Caucasian 2D footprints using convolutional neural networks
Authors: Marcin Budka, Matthew R. Bennet, Sally Reynolds, Shelby Barefoot, Sarah Reel, Selina Reidy, Jeremy Walker
Date: 2021-07-23 00:00:00
Tasks: Friction

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N=196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N=2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N=267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint.
Open Set Wireless Standard Classification Using Convolutional Neural Networks
Authors: Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer
Date: 2021-08-03 00:00:00
Tasks: Classification

In congested electromagnetic environments, cognitive radios require knowledge about other emitters in order to optimize their dynamic spectrum access strategy. Deep learning classification algorithms have been used to recognize the wireless signal standards of emitters with high accuracy, but are limited to classifying signal classes that appear in their training set. This diminishes the performance of deep learning classifiers deployed in the field because they cannot accurately identify signals from classes outside of the training set. In this paper, a convolution neural network based open set classifier is proposed with the ability to detect if signals are not from known classes by thresholding the output sigmoid activation. The open set classifier was trained on 4G LTE, 5G NR, IEEE 802.11ax, Bluetooth Low Energy 5.0, and Narrowband Internet-of-Things signals impaired with Rayleigh or Rician fading, AWGN, frequency offsets, and in-phase/quadrature imbalances. Then, the classifier was tested on OFDM, SC-FDMA, SC, AM, and FM signals, which did not appear in the training set classes. The closed set classifier achieves an average accuracy of 94.5% for known signals with SNR's greater than 0 dB, but by design, has a 0% accuracy detecting signals from unknown classes. On the other hand, the open set classifier retains an 86% accuracy for known signal classes, but can detect 95.5% of signals from unknown classes with SNR's greater than 0 dB.
mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter
Authors: Ossi Kaltiokallio, Yu Ge, Jukka Talvitie, Henk Wymeersch, Mikko Valkama
Date: 2021-08-03 00:00:00
Tasks: Position, Simultaneous Localization and Mapping

Future cellular networks that utilize millimeter wave signals provide new opportunities in positioning and situational awareness. Large bandwidths combined with large antenna arrays provide unparalleled delay and angle resolution, allowing high accuracy localization but also building up a map of the environment. Even the most basic filter intended for simultaneous localization and mapping exhibits high computational overhead since the methods rely on sigma point or particle-based approximations. In this paper, a first order Taylor series based Gaussian approximation of the filtering distribution is used and it is demonstrated that the developed extended Kalman probability hypothesis density filter is computationally very efficient. In addition, the results imply that efficiency does not come with the expense of estimation accuracy since the method nearly achieves the position error bound.
Progressive Transmission using Recurrent Neural Networks
Authors: Mohammad Sadegh Safari, Vahid Pourahmadi, Patrick Mitran, Hamid Sheikhzadeh
Date: 2021-08-03 00:00:00

In this paper, we investigate a new machine learning-based transmission strategy called progressive transmission or ProgTr. In ProgTr, there are b variables that should be transmitted using at most T channel uses. The transmitter aims to send the data to the receiver as fast as possible and with as few channel uses as possible (as channel conditions permit) while the receiver refines its estimate after each channel use. We use recurrent neural networks as the building block of both the transmitter and receiver where the SNR is provided as an input that represents the channel conditions. To show how ProgTr works, the proposed scheme was simulated in different scenarios including single/multi-user settings, different channel conditions, and for both discrete and continuous input data. The results show that ProgTr can achieve better performance compared to conventional modulation methods. In addition to performance metrics such as BER, bit-wise mutual information is used to provide some interpretation to how the transmitter and receiver operate in ProgTr.
Rectangular Planar vs Concentric Circular Array for 5G Beamforming
Authors: Md Imrul Hasan, Mohammad Saquib
Date: 2021-08-02 00:00:00

The transmitter signals in the fifth generation (5G) wireless networks suffer from significant path loss due to the use of higher frequencies in Sub-6 GHz and millimeter-wave (mmWave) bands. Therefore, beamforming is an essential and powerful tool in 5G to enhance signal for the desired direction and suppress interferences from other directions. For a given number of antenna elements, the geometry of the array dictates its beam pattern. Most of the works in the existing literature address the use of rectangular planar antenna (RPA) arrays for 5G beamforming. In this work, we demonstrate that a concentric circular antenna (CCA) array is capable of generating a significantly narrower beam than an RPA array while operating with a notably lesser number of antenna elements on a considerably smaller area. This capability of the CCA array can be exploited and utilized in enhancing the performance of a 5G network in both the link and the system levels.