pwc-archive/papers-with-abstracts huggingface dataset:
Continuous Phase Modulation of Phase Coded Transmit Waveforms using Multi-Tone Sinusoidal Frequency Modulation
Authors: David A. Hague
Date: 2022-01-20 00:00:00

Phase Coded (PC) waveforms possess desirable Auto-Correlation Function (ACF) properties for use in radar and sonar systems. However, their spectra possess high spectral leakage due to the abrupt phase transitions between the chips in the waveform. This paper describes a method of Continuous Phase Modulation (CPM) to reduce a PC waveform's spectral leakage using the Multi-Tone Sinusoidal Frequency Modulation (MTSFM) model. The MTSFM-CPM model represents the PC waveform's instantaneous phase as a finite Fourier series. This representation smooths the abrupt phase transitions between chips resulting in a spectrally compact waveform. This smoothing of the PC waveform's instantaneous phase introduces perturbations to the waveform's ACF mainlobe and sidelobe structure. Adjusting the MTSFM-CPM waveform's parameters refines its ACF mainlobe and sidelobe structure while also preserving its compact spectral shape.
Application of Particle Swarm Optimization method to On-going Monitoring for estimating vehicle-bridge interaction system
Authors: Kyosuke Yamamoto, Kakeru Murakami, Ryota Shin, Yukihiko Okada
Date: 2022-01-20 00:00:00

This study proposes a method for estimating the mechanical parameters of vehicles and bridges and the road unevenness, using only vehicle vibration and position data. In the proposed method, vehicle input and bridge vibration are estimated using randomly assumed vehicle and bridge parameters. Then, the road profiles at the front and rear wheels can be determined from the vehicle input and bridge vibration. The difference between the two road profiles is used as the objective function because they are expected to coincide when synchronized. Using the particle swarm optimization (PSO) method, the vehicle and bridge parameters and the road unevenness can be estimated by updating the parameters to minimize the objective function. Numerical experiments also verify the applicability of this method. In the numerical experiments, it is confirmed that the proposed method can estimate the vehicle weight with reasonable accuracy, but the accuracy of other parameters is not sufficient. It is necessary to improve the accuracy of the proposed method in the future.
Fusion Learning for 1-Bit CS-based Superimposed CSI Feedback with Bi-Directional Channel Reciprocity
Authors: Chaojin Qing, Qing Ye, Wenhui Liu, Jiafan Wang
Date: 2022-01-20 00:00:00
Tasks: compressed sensing

Due to the discarding of downlink channel state information (CSI) amplitude and the employing of iteration reconstruction algorithms, 1-bit compressed sensing (CS)-based superimposed CSI feedback is challenged by low recovery accuracy and large processing delay. To overcome these drawbacks, this letter proposes a fusion learning scheme by exploiting the bi-directional channel reciprocity. Specifically, a simplified version of the conventional downlink CSI reconstruction is utilized to extract the initial feature of downlink CSI, and a single hidden layer-based amplitude-learning network (AMPL-NET) is designed to learn the auxiliary feature of the downlink CSI amplitude. Then, based on the extracted and learned amplitude features, a simple but effective amplitude-fusion network (AMPF-NET) is developed to perform the amplitude fusion of downlink CSI and thus improves the reconstruction accuracy for 1-bit CS-based superimposed CSI feedback while reducing the processing delay. Simulation results show the effectiveness of the proposed feedback scheme and the robustness against parameter variations.
Network-ELAA Beamforming and Coverage Analysis for eMBB/URLLC in Spatially Non-Stationary Rician Channels
Authors: Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli, Fan Wang
Date: 2022-01-19 00:00:00

In vehicle-to-infrastructure (V2I) networks, a cluster of multi-antenna access points (APs) can collaboratively conduct transmitter beamforming to provide data services (e.g., eMBB or URLLC). The collaboration between APs effectively forms a networked linear antenna-array with extra-large aperture (i.e., network-ELAA), where the wireless channel exhibits spatial nonstationarity. Major contribution of this work lies in the analysis of beamforming gain and radio coverage for network-ELAA non-stationary Rician channels considering the AP clustering. Assuming that: 1) the total transmit-power is fixed and evenly distributed over APs, 2) the beam is formed only based on the line-of-sight (LoS) path, it is found that the beamforming gain is concave to the cluster size. The optimum size of the AP cluster varies with respect to the user's location, channel uncertainty as well as data services. A user located farther from the ELAA requires a larger cluster size. URLLC is more sensitive to the channel uncertainty when comparing to eMBB, thus requiring a larger cluster size to mitigate the channel fading effect and extend the coverage. Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.
An Effective Spatial Modulation Based Scheme for Indoor VLC Systems
Authors: Shimaa Naser, Lina Bariah, Sami Muhaidat, Mahmoud Al-Qutayri, Paschalis C. Sofotasios
Date: 2022-01-19 00:00:00

We propose an enhanced spatial modulation (SM)-based scheme for indoor visible light communication systems. This scheme enhances the achievable throughput of conventional SM schemes by transmitting higher order complex modulation symbol, which is decomposed into three different parts. These parts carry the amplitude, phase, and quadrant components of the complex symbol, which are then represented by unipolar pulse amplitude modulation (PAM) symbols. Superposition coding is exploited to allocate a fraction of the total power to each part before they are all multiplexed and transmitted simultaneously, exploiting the entire available bandwidth. At the receiver, a two-step decoding process is proposed to decode the active light emitting diode index before the complex symbol is retrieved. It is shown that at higher spectral efficiency values, the proposed modulation scheme outperforms conventional SM schemes with PAM symbols in terms of average symbol error rate (ASER), and hence, enhancing the system throughput. Furthermore, since the performance of the proposed modulation scheme is sensitive to the power allocation factors, we formulated an ASER optimization problem and propose a sub-optimal solution using successive convex programming (SCP). Notably, the proposed algorithm converges after only few iterations, whilst the performance with the optimized power allocation coefficients outperforms both random and fixed power allocation.
A Non-Expert's Introduction to Data Ethics for Mathematicians
Authors: Mason A. Porter
Date: 2022-01-18 00:00:00
Tasks: Articles, Ethics

I give a short introduction to data ethics. I begin with some background information and societal context for data ethics. I then discuss data ethics in mathematical-science education and indicate some available course material. I briefly highlight a few efforts -- at my home institution and elsewhere -- on data ethics, society, and social good. I then discuss open data in research, research replicability and some other ethical issues in research, and the tension between privacy and open data and code, and a few controversial studies and reactions to studies. I then discuss ethical principles, institutional review boards, and a few other considerations in the scientific use of human data. I then briefly survey a variety of research and lay articles that are relevant to data ethics and data privacy. I conclude with a brief summary and some closing remarks. My focal audience is mathematicians, but I hope that this chapter will also be useful to others. I am not an expert about data ethics, and this chapter provides only a starting point on this wide-ranging topic. I encourage you to examine the resources that I discuss and to reflect carefully on data ethics, its role in mathematics education, and the societal implications of data and data analysis. As data and technology continue to evolve, I hope that such careful reflection will continue throughout your life.
The spatial scale dimension of speech processing in the human brain
Authors: Philipp Kellmeyer, Roland Berkemeier, Tonio Ball
Date: 2022-01-19 00:00:00

In the past three decades, neuroimaging has provided important insights into structure-function relationships in the human brain. Recently, however, the methods for analyzing functional magnetic resonance imaging (fMRI) data have come under scrutiny, with studies questioning cross-software comparability, the validity of statistical inference and interpretation, and the influence of the spatial filter size on neuroimaging analyses. As most fMRI studies only use a single filter for analysis, much information on the size and shape of the BOLD signal in Gaussian scale space remains hidden and constrains the interpretation of fMRI studies. To investigate the influence of the spatial observation scale on fMRI analysis, we use a spatial multiscale analysis with a range of Gaussian filters from 1-20 mm (full width at half maximum) to analyze fMRI data from a speech repetition paradigm in 25 healthy subjects. We show that analyzing the fMRI data over a range of Gaussian filter kernels reveals substantial variability in the neuroanatomical localization and the average signal strength and size of suprathreshold clusters depending on the filter size. We also demonstrate how small spatial filters bias the results towards subcortical and cerebellar clusters. Furthermore, we describe substantially different scale-dependent cluster size dynamics between cortical and cerebellar clusters. We discuss how spatial multiscale analysis may substantially improve the interpretation of fMRI data. We propose to further develop a spatial multiscale analysis to fully explore the deep structure of the BOLD signal in Gaussian scale space.
Insights from a computational analysis of the SARS-CoV-2 Omicron variant: Host-pathogen interaction, pathogenicity and possible therapeutics
Authors: Md Sorwer Alam Parvez, Manash Kumar Saha, Md. Ibrahim, Yusha Araf, Md. Taufiqul Islam, Gen Ohtsuki, Mohammad Jakir Hosen
Date: 2022-01-20 00:00:00
Tasks: Management, Molecular Docking

Prominently accountable for the upsurge of COVID-19 cases as the world attempts to recover from the previous two waves, Omicron has further threatened the conventional therapeutic approaches. Omicron is the fifth variant of concern (VOC), which comprises more than 10 mutations in the receptor-binding domain (RBD) of the spike protein. However, the lack of extensive research regarding Omicron has raised the need to establish correlations to understand this variant by structural comparisons. Here, we evaluate, correlate, and compare its genomic sequences through an immunoinformatic approach with wild and mutant RBD forms of the spike protein to understand its epidemiological characteristics and responses towards existing drugs for better patient management. Our computational analyses provided insights into infectious and pathogenic trails of the Omicron variant. In addition, while the analysis represented South Africa's Omicron variant being similar to the highly-infectious B.1.620 variant, mutations within the prominent proteins are hypothesized to alter its pathogenicity. Moreover, docking evaluations revealed significant differences in binding affinity with human receptors, ACE2 and NRP1. Owing to its characteristics of rendering existing treatments ineffective, we evaluated the drug efficacy against their target protein encoded in the Omicron through molecular docking approach. Most of the tested drugs were proven to be effective. Nirmatrelvir (Paxlovid), MPro 13b, and Lopinavir displayed increased effectiveness and efficacy, while Ivermectin showed the best result against Omicron.
On the origins of the Omicron variant of the SARS-CoV-2 virus
Authors: Robert Penner, Minus van Baalen
Date: 2021-12-08 00:00:00
Tasks: Virology

A possible explanation based on first principles for the appearance of the Omicron variant of the SARS-CoV-2 virus is proposed involving coinfection with HIV. The gist is that the resultant HIV-induced immunocompromise allows SARS-CoV-2 greater latitude to explore its own mutational space. This latitude is not withoutr estriction, and a specific biophysical constraint is explored. Specifically, a nearly two- to five-fold discrepancy in backbone hydrogen bonding is observed between sub-molecules in Protein Data Bank files of the spike glycoprotein yielding two conclusions: mutagenic residues in the receptor-binding subunit of the spike much more frequently do not participate in backbone hydrogen bonds; and a technique of viral escape is therefore to remove such bonds within physico-chemical and functional constraints. Earlier work, from which the previous discussion is entirely independent, explains these phenomena from general principles of free energy, namely, the metastability of the glycoprotein. The conclusions therefore likely hold more generally as principles in virology.
Coexistence in spatiotemporally fluctuating environments
Authors: Evan Johnson, Alan Hastings
Date: 2022-01-20 00:00:00

Ecologists have put forward many explanations for coexistence, but these are only partial explanations; nature is complex, so it is reasonable to assume that in any given ecological community, multiple mechanisms of coexistence are operating at the same time. Here, we present a methodology for quantifying the relative importance of different explanations for coexistence, based on an extension of Modern Coexistence Theory. Current versions of Modern Coexistence Theory only allow for the analysis of communities that are affected by spatial or temporal environmental variation, but not both. We show how to analyze communities with spatiotemporal fluctuations, how to parse the importance of spatial variation and temporal variation, and how to measure everything with either mathematical expressions or simulation experiments. Our extension of Modern Coexistence Theory allows empiricists to use realistic models and more data to better infer the mechanisms of coexistence in real communities.
Resolving conceptual issues in Modern Coexistence Theory
Authors: Evan Johnson, Alan Hastings
Date: 2022-01-20 00:00:00
Tasks: Misconceptions

In this paper, we discuss the conceptual underpinnings of Modern Coexistence Theory (MCT), a quantitative framework for understanding ecological coexistence. In order to use MCT to infer how species are coexisting, one must relate a complex model (which simulates coexistence in the real world) to simple models in which previously proposed explanations for coexistence have been codified. This can be accomplished in three steps: 1) relating the construct of coexistence to invasion growth rates, 2) mathematically partitioning the invasion growth rates into coexistence mechanisms (i.e., classes of explanations for coexistence), and 3) relating coexistence mechanisms to simple explanations for coexistence. Previous research has primarily focused on step 2. Here, we discuss the other crucial steps and their implications for inferring the mechanisms of coexistence in real communities. Our discussion of step 3 -- relating coexistence mechanisms to simple explanations for coexistence -- serves a heuristic guide for hypothesizing about the causes of coexistence in new models; but also addresses misconceptions about coexistence mechanisms. For example, the storage effect has little to do with bet-hedging or "storage" via a robust life-history stage; relative nonlinearity is more likely to promote coexistence than originally thought; and fitness-density covariance is an amalgam of a large number of previously proposed explanations for coexistence (e.g., the competition-colonization trade-off, heteromyopia, spatially-varying resource supply ratios). Additionally, we review a number of topics in MCT, including the role of "scaling factors"; whether coexistence mechanisms are approximations; whether the magnitude or sign of invasion growth rates matters more; whether Hutchinson solved the paradox of the plankton; the scale-dependence of coexistence mechanisms; and much more.
A Mathematical Model of Transmission Dynamics of SARS-Cov-2 (Covid-19) with an Underlying Condition of Diabetes
Authors: Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah
Date: 2022-01-12 00:00:00

It is well established that people with diabetes are more likely to have serious complications from COVID-19. Nearly 1 in 5 COVID-19 deaths in the African region are linked to diabetes. World Health Organization (WHO) finds that 18.3% of COVID-19 deaths in Africa are among people with diabetes. In this paper, we have formulated and analysed a mathematical comorbidity model of diabetes - COVID-19 of the deterministic type. The basic properties of the model were explored. The basic reproductive number, equilibrium points and stability of the equilibrium points were examined. Sensitivity analysis of the model was carried on to determine the impact of the model parameters on the basic reproduction number of the model. The model had a unique endemic equilibrium point, which was stable for R_0>1. Time-dependent optimal controls were incorporated into the model with the sole aim of determining the best strategy for curtailing the spread of the disease. COVID-19 cases from March to September 2020 in Ghana were used to validate the model. Results of the numerical simulation suggest a greater number of individuals deceased when the infected individual had an underlying condition of diabetes. More so COVID-19 is endemic in Ghana with the basic reproduction number found to be R_0=1.4722. The numerical simulation of the optimal control model reveals the lockdown control minimized the rate of decay of the susceptible individuals whereas the vaccination led to a number of susceptible individuals becoming immune to COVID-19 infections. In all the two preventive control measures were both effective in curbing the spread of the COVID-19 disease as the number of COVID-19 infections was greatly reduced. We conclude that more attention should be paid to COVID-19 patients with an underlying condition of diabetes as the probability of death in this population was significantly higher.
Effect of Mammalian Tissue Source on the Molecular and Macroscopic Characteristics of UV-Cured Type I Collagen Hydrogel Networks
Authors: Charles Brooker, Giuseppe Tronci
Date: 2022-01-20 00:00:00
Tasks: Vocal Bursts Type Prediction

The tissue source of type I collagen is critical to ensure scalability and regulation-friendly clinical translation of new medical device prototypes. However, the selection of a commercial source of collagen that fulfils both aforementioned requirements and is compliant with new manufacturing routes is challenging. This study investigates the effect that type I collagen extracted from three different mammalian tissues has on the molecular and macroscopic characteristics of a new UV-cured collagen hydrogel. Pepsin-solubilised bovine atelocollagen (BA) and pepsin-solubilised porcine atelocollagen (PA) were selected as commercially available raw materials associated with varying safety risks and compared with in-house acid-extracted type I collagen from rat tails (CRT). All raw materials displayed the typical dichroic and electrophoretic characteristics of type I collagen, while significantly decreased lysine content was measured on samples of PA. Following covalent functionalisation with 4-vinylbenzyl chloride (4VBC), BA and CRT products generated comparable UV-cured hydrogels with significantly increased averaged gel content (G > 97 wt.%), while the porcine variants revealed the highest swelling ratio (SR = 2224 +/- 242 wt.%) and an order of magnitude reduction in compression modulus (Ec = 6 +/- 2 kPa). Collectively, these results support the use of bovine tissues as a chemically viable source of type I collagen for the realisation of UV-cured hydrogels with competitive mechanical properties and covalent network architectures.
HOG + CNN Net: Diagnosing COVID-19 and Pneumonia by Deep Neural Network from Chest X-Ray Images.
Authors: Mohammad Marufur Rahman, Sheikh Nooruddin, K. M. Azharul Hasan, Nahin Kumar Dey
Date: 2021-07-08 00:00:00
Proceeding: journal 2021 7

Coronavirus disease 2019 in short COVID-19 is a contagious disease caused by coronavirus SARS-CoV-2, which has caused
a global pandemic and still infecting millions around the globe. COVID-19 has made an enormous impact on everybody’s
day-to-day life. One of the main strengths of COVID-19 is its extraordinary infectious capability. Early detection systems
can thus play a big role in curbing the exponential growth of COVID-19. Some medical radiography techniques, such as
chest X-rays and chest CT scans, are used for fast and reliable detection of coronavirus-induced pneumonia. In this paper,
we propose a histogram of oriented gradients and deep convolutional network-based model that can fnd out the specifc
abnormality in frontal chest X-ray images and efectively classify the data into COVID-19 positive, pneumonia positive, and
normal classes. The proposed system performed efectively in terms of various performance measures and proved capable
as an efective early detection system.
Sentiment analysis for Urdu online reviews using deep learning models
Authors: qra Safder, Zainab Mahmood, Raheem Sarwar, Saeed-Ul Hassan, Farooq Zaman, Rao Muhammad Adeel Nawab, Faisal Bukhari, Rabeeh Ayaz Abbasi, Salem Alelyani, Naif Radi Aljohani, Raheel Nawaz
Date: 2021-06-28 00:00:00
Proceeding: Expert Systems 2021 6
Tasks: Binary Classification, Classification, Deep Learning, Sentiment Analysis

Most existing studies are focused on popular languages like English, Spanish, Chinese,
Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learning
model for the sentiments expressed in this under-resourced language. We develop
an open-source corpus of 10,008 reviews from 566 online threads on the topics of
sports, food, software, politics, and entertainment. The objectives of this work are bifold (a) the creation of a human-annotated corpus for the research of sentiment analysis in Urdu; and (b) measurement of up-to-date model performance using a corpus.
For their assessment, we performed binary and ternary classification studies utilizing
another model, namely long short-term memory (LSTM), recurrent convolutional neural network (RCNN) Rule-Based, N-gram, support vector machine , convolutional neural network, and LSTM. The RCNN model surpasses standard models with 84.98%
accuracy for binary classification and 68.56% accuracy for ternary classification. To
facilitate other researchers working in the same domain, we have open-sourced the
corpus and code developed for this research
Planar Structure Matching Under Projective Uncertainty for Geolocation
Authors: Ang Li, Vlad I. Morariu, Larry S. Davis
Date: 2014-01-01 00:00:00
Proceeding: ECCV 2014 1
Tasks: Geometric Matching

Image based geolocation aims to answer the question: where
was this ground photograph taken? We present an approach to geolocalating a single image based on matching human delineated line segments
in the ground image to automatically detected line segments in ortho
images. Our approach is based on distance transform matching. By observing that the uncertainty of line segments is non-linearly amplified
by projective transformations, we develop an uncertainty based representation and incorporate it into a geometric matching framework. We
show that our approach is able to rule out a considerable portion of false
candidate regions even in a database composed of geographic areas with
similar visual appearances.