pwc-archive/papers-with-abstracts huggingface dataset:
Data-driven modeling for different stages of pandemic response
Date: 2020-09-21 00:00:00
Tasks: Decision Making

Some of the key questions of interest during the COVID-19 pandemic (and all
outbreaks) include: where did the disease start, how is it spreading, who is at
risk, and how to control the spread. There are a large number of complex
factors driving the spread of pandemics, and, as a result, multiple modeling
techniques play an increasingly important role in shaping public policy and
decision making. As different countries and regions go through phases of the
pandemic, the questions and data availability also changes. Especially of
interest is aligning model development and data collection to support response
efforts at each stage of the pandemic. The COVID-19 pandemic has been
unprecedented in terms of real-time collection and dissemination of a number of
diverse datasets, ranging from disease outcomes, to mobility, behaviors, and
socio-economic factors. The data sets have been critical from the perspective
of disease modeling and analytics to support policymakers in real-time. In this
overview article, we survey the data landscape around COVID-19, with a focus on
how such datasets have aided modeling and response through different stages so
far in the pandemic. We also discuss some of the current challenges and the
needs that will arise as we plan our way out of the pandemic.
Complexity in economic and social systems: cryptocurrency market at around COVID-19
Authors: Stanisław Drożdż, Jarosław Kwapień, Paweł Oświęcimka, Tomasz Stanisz, Marcin Wątorek
Date: 2020-09-21 00:00:00

Social systems are characterized by an enormous network of connections and factors that can influence the structure and dynamics of these systems. All financial markets, including the cryptocurrency market, belong to the economical sphere of human activity that seems to be the most interrelated and complex. The cryptocurrency market complexity can be studied from different perspectives. First, the dynamics of the cryptocurrency exchange rates to other cryptocurrencies and fiat currencies can be studied and quantified by means of multifractal formalism. Second, coupling and decoupling of the cryptocurrencies and the conventional assets can be investigated with the advanced cross-correlation analyses based on fractal analysis. Third, an internal structure of the cryptocurrency market can also be a subject of analysis that exploits, for example, a network representation of the market. We approach this subject from all three perspectives based on data recorded between January 2019 and June 2020. This period includes the Covid-19 pandemic and we pay particular attention to this event and investigate how strong its impact on the structure and dynamics of the market was. Besides, the studied data covers a few other significant events like double bull and bear phases in 2019. We show that, throughout the considered interval, the exchange rate returns were multifractal with intermittent signatures of bifractality that can be associated with the most volatile periods of the market dynamics like a bull market onset in April 2019 and the Covid-19 outburst in March 2020. The topology of a minimal spanning tree representation of the market also used to alter during these events from a distributed type without any dominant node to a highly centralized type with a dominating hub of USDT. However, the MST topology during the pandemic differs in some details from other volatile periods.
A flux-limited model for glioma patterning with hypoxia-induced angiogenesis
Authors: Pawan Kumar, Christina Surulescu
Date: 2020-09-21 00:00:00

We propose a model for glioma patterns in a microlocal tumor environment under the influence of acidity, angiogenesis, and tissue anisotropy. The bottom-up model deduction eventually leads to a system of reaction-diffusion-taxis equations for glioma and endothelial cell population densities, of which the former infers flux limitation both in the self-diffusion and taxis terms. The model extends a recently introduced [34] description of glioma pseudopalisade formation, with the aim of studying the effect of hypoxia-induced tumor vascularization on the establishment and maintenance of these histological patterns which are typical for high grade brain cancer. Numerical simulations of the population level dynamics are performed to investigate several model scenarios containing this and further effects.
Sensitivity of BPA SAR Image Formation to Initial Position, Velocity, and Attitude Navigation Errors
Authors: Colton Lindstrom, Randall Christensen, Jacob Gunther
Date: 2020-09-21 00:00:00
Tasks: Position, Sensitivity

The Back-Projection Algorithm (BPA) is a time domain matched filtering technique to form synthetic aperture radar (SAR) images. To produce high quality BPA images, precise navigation data for the radar platform must be known. Any error in position, velocity, or attitude results in improperly formed images corrupted by shifting, blurring, and distortion. This paper develops analytical expressions that characterize the relationship between navigation errors and image formation errors. These analytical expressions are verified via simulated image formation and real data image formation.
Improving Maximal Safe Brain Tumor Resection with Photoacoustic Remote Sensing Microscopy
Authors: Benjamin R. Ecclestone, Kevan Bell, Saad Abbasi, Deepak Dinakaran, Frank K. H. van Landeghem, John R. Mackey, Paul Fieguth, Parsin Haji Reza
Date: 2020-09-21 00:00:00
Tasks: Diagnostic

Malignant brain tumors are among the deadliest neoplasms with the lowest survival rates of any cancer type. In considering surgical tumor resection, suboptimal extent of resection is linked to poor clinical outcomes and lower overall survival rates. Currently available tools for intraoperative histopathological assessment require an average of 20 minutes processing and are of limited diagnostic quality for guiding surgeries. Consequently, there is an unaddressed need for a rapid imaging technique to guide maximal resection of brain tumors. Working towards this goal, presented here is an all optical non-contact label-free reflection mode photoacoustic remote sensing (PARS) microscope. By using a tunable excitation laser, PARS takes advantage of the endogenous optical absorption peaks of DNA and cytoplasm to achieve virtual contrast analogous to standard hematoxylin and eosin (H and E) staining. In conjunction, a fast 266 nm excitation is used to generate large grossing scans and rapidly assess small fields in real-time with hematoxylin-like contrast. Images obtained using this technique show comparable quality and contrast to the current standard for histopathological assessment of brain tissues. Using the proposed method, rapid, high-throughput, histological-like imaging was achieved in unstained brain tissues, indicating PARS utility for intraoperative guidance to improve extent of surgical resection.
Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays
Authors: Tamas G. Molnar, Andrew W. Singletary, Gabor Orosz, Aaron D. Ames
Date: 2020-09-22 00:00:00

We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions (such as quarantining or social distancing) as control input. We consider a generalized compartmental model that represents the form of the most popular epidemiological models and we design safety-critical controllers that formally guarantee safe evolution with respect to keeping certain populations of interest under prescribed safe limits. Furthermore, we discuss how measurement delays originated from incubation period and testing delays affect safety and how delays can be compensated via predictor feedback. We demonstrate our results by synthesizing active intervention policies that bound the number of infections, hospitalizations and deaths for epidemiological models capturing the spread of COVID-19 in the USA.
Performance Prediction for Coherent Noise Radars Using the Correlation Coefficient
Authors: David Luong, Bhashyam Balaji, Sreeraman Rajan
Date: 2020-09-22 00:00:00

Noise radars can be understood in terms of a correlation coefficient which characterizes their detection performance. Although most results in the literature are stated in terms of the signal-to-noise ratio (SNR), we show that it is possible to carry out performance prediction in terms of the correlation coefficient. To this end, we derive the range dependence of the correlation coefficient. We then combine our result with a previously-derived expression for the receiver operating characteristic (ROC) curve of a coherent noise radar, showing that we can obtain ROC curves for varying ranges. A comparison with corresponding results for a conventional radar employing coherent integration shows that our results are sensible. The aim of our work is to show that the correlation coefficient is a viable adjunct to SNR in understanding radar performance.
Temperature dependence of mycosubtilin homologue production in Bacillus subtilis ATCC6633
Authors: Patrick Fickers, Valérie Leclére, Jean-Sebastien Guez, Max Béchet, Françoise Coucheney, Bernard Joris, Philippe Jacques
Date: 2020-09-22 00:00:00

Bacillus subtilis ATCC6633 produces mycosubtilin, a non-ribosomally synthesized lipopeptide of the iturin family which presents antagonistic activities toward various phytopathogens. Different homologues with fatty acid moiety varying from C 15 to C 17 are usually co-produced, with their biological activities increasing with the number of carbons in the fatty acid chain. In the present report, we highlight that growth temperature modulates both the extent of mycosubtilin production and the relative abundance of the different homologues. A 30-fold increase in mycosubtilin production was observed when the temperature was decreased from 37 C to 25 C for both strain ATCC6633 and its derivative BBG100, a constitutive mycosubtilin overproducer. However, no significant difference in either the expression of the mycosubtilin synthetase encoding genes or in the intracellular synthetase concentration could be found, suggesting that the observed phenotype originated from a higher mycosubtilin synthetase turnover at lower temperature. We also point out that lower growth temperature leads to an increased proportion of odd-numbered fatty acid homologues as a consequence of de novo synthesis of C 17 anteiso fatty acid following cell adaptation to low temperatures.
Mycosubtilin Overproduction by Bacillus subtilis BBG100 Enhances the Organism's Antagonistic and Biocontrol Activities
Authors: Valérie Leclére, Max Béchet, Akram Adam, Jean-Sebastien Guez, Bernard Wathelet, Marc Ongena, Philippe Thonart, Frédérique Gancel, Marléne Chollet-Imbert, Philippe Jacques
Date: 2020-09-22 00:00:00
Tasks: Vocal Bursts Type Prediction

A Bacillus subtilis derivative was obtained from strain ATCC 6633 by replacement of the native promoter of the mycosubtilin operon by a constitutive promoter originating from the replication gene repU of the Staphylococcus aureus plasmid pUB110. The recombinant strain, designated BBG100, produced up to 15-fold more mycosubtilin than the wild type produced. The overproducing phenotype was related to enhancement of the antagonistic activities against several yeasts and pathogenic fungi. Hemolytic activities were also clearly increased in the modified strain. Mass spectrometry analyses of enriched mycosubtilin extracts showed similar patterns of lipopeptides for BBG100 and the wild type. Interestingly, these analyses also revealed a new form of mycosubtilin which was more easily detected in the BBG100 sample. When tested for its biocontrol potential, wild-type strain ATCC 6633 was almost ineffective for reducing a Pythium infection of tomato seedlings. However, treatment of seeds with the BBG100 overproducing strain resulted in a marked increase in the germination rate of seeds. This protective effect afforded by mycosubtilin overproduction was also visualized by the significantly greater fresh weight of emerging seedlings treated with BBG100 compared to controls or seedlings inoculated with the wild-type strain.
Distillation of News Flow into Analysis of Stock Reactions
Authors: Junni L. Zhang, Wolfgang Karl Härdle, Cathy Y. Chen, Elisabeth Bommes
Date: 2020-09-22 00:00:00
Tasks: Articles

The gargantuan plethora of opinions, facts and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical technology the informative elements of this prodigious and indeed colossal data source. Using mixed text sources from professional platforms, blog fora and stock message boards we distill via different lexica sentiment variables. These are employed for an analysis of stock reactions: volatility, volume and returns. An increased sentiment, especially for those with negative prospection, will influence volatility as well as volume. This influence is contingent on the lexical projection and different across Global Industry Classification Standard (GICS) sectors. Based on review articles on 100 S&P 500 constituents for the period of October 20, 2009, to October 13, 2014, we project into BL, MPQA, LM lexica and use the distilled sentiment variables to forecast individual stock indicators in a panel context. Exploiting different lexical projections to test different stock reaction indicators we aim at answering the following research questions: (i) Are the lexica consistent in their analytic ability? (ii) To which degree is there an asymmetric response given the sentiment scales (positive v.s. negative)? (iii) Are the news of high attention firms diffusing faster and result in more timely and efficient stock reaction? (iv) Is there a sector-specific reaction from the distilled sentiment measures? We find there is significant incremental information in the distilled news flow and the sentiment effect is characterized as an asymmetric, attention-specific and sector-specific response of stock reactions.
In-phase and Quadrature Chirp Spread Spectrum for IoT Communications
Authors: Ivo Bizon Franco de Almeida, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis
Date: 2020-09-22 00:00:00

This paper describes a coherent chirp spread spectrum (CSS) technique based on the Long-Range (LoRa) physical layer (PHY) framework. LoRa PHY employs CSS on top of a variant of frequency shift keying (FSK), and non-coherent detection is employed at the receiver for obtaining the transmitted data symbols. In this paper, we propose a scheme that encodes information bits on both in-phase and quadrature components of the chirp signal, and rather employs a coherent detector at the receiver. Hence, channel equalization is required for compensating the channel induced phase rotation on the transmit signal. Moreover, a simple channel estimation technique exploits the LoRa reference sequences used for synchronization to obtain the complex channel coefficient used in the equalizer. Performance evaluation using numerical simulation shows that the proposed scheme achieves approximately 1 dB gain in terms of energy efficiency, and it doubles the spectral efficiency when compared to the conventional LoRa PHY scheme. This is due to the fact that the coherent receiver is able to exploit the orthogonality between in-phase and quadrature components of the transmit signal.
Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm
Date: 2020-09-25 00:00:00

Existing generator parameterization methods, typically developed for large
turbine generator units, are difficult to apply to small kW-level diesel
generators in microgrid applications. This paper presents a model
parameterization method that estimates a complete set of kW-level diesel
generator parameters simultaneously using only load-step-change tests with
limited measurement points. This method provides a more cost-efficient and
robust approach to achieve high-fidelity modeling of diesel generators for
microgrid dynamic simulation. A two-stage hybrid box-constrained
Levenberg-Marquardt (H-BCLM) algorithm is developed to search the optimal
parameter set given the parameter bounds. A heuristic algorithm, namely
Generalized Opposition-based Learning Genetic Algorithm (GOL-GA), is applied to
identify proper initial estimates at the first stage, followed by a modified
Levenberg-Marquardt algorithm designed to fine tune the solution based on the
first-stage result. The proposed method is validated against dynamic simulation
of a diesel generator model and field measurements from a 16kW diesel generator
unit.
A word recurrence based algorithm to extract genomic dictionaries
Authors: Vincenzo Bonnici, Giuditta Franco, Vincenzo Manca
Date: 2020-09-22 00:00:00

Genomes may be analyzed from an information viewpoint as very long strings, containing functional elements of variable length, which have been assembled by evolution. In this work an innovative information theory based algorithm is proposed, to extract significant (relatively small) dictionaries of genomic words. Namely, conceptual analyses are here combined with empirical studies, to open up a methodology for the extraction of variable length dictionaries from genomic sequences, based on the information content of some factors. Its application to human chromosomes highlights an original inter-chromosomal similarity in terms of factor distributions.
The Two Growth Rates of the Economy
Date: 2020-09-22 00:00:00
Tasks: Vocal Bursts Valence Prediction

Economic growth is measured as the rate of relative change in gross domestic
product (GDP) per capita. Yet, when incomes follow random multiplicative
growth, the ensemble-average (GDP per capita) growth rate is higher than the
time-average growth rate achieved by each individual in the long run. This
mathematical fact is the starting point of ergodicity economics. Using the
atypically high ensemble-average growth rate as the principal growth measure
creates an incomplete picture. Policymaking would be better informed by
reporting both ensemble-average and time-average growth rates. We analyse
rigorously these growth rates and describe their evolution in the United States
and France over the last fifty years. The difference between the two growth
rates gives rise to a natural measure of income inequality, equal to the mean
logarithmic deviation. Despite being estimated as the average of individual
income growth rates, the time-average growth rate is independent of income
mobility.
An unnoticed side effect of electric vehicles
Date: 2020-09-22 00:00:00

We illustrate that the electrification of our transport system might impose
unnecessary extra congestion and delay for daily commuting passengers. By
modelling travel behaviors of these passengers, it is found that more of them
tend to depart at a narrower peak-hour time window. The occurrence of this
shift is mainly caused by (1) the energy consumption of electric vehicles (EVs)
is much lower than that of traditional vehicles and (2) the energy consumption
of EVs is less sensitive to congestion than that of traditional vehicles. We
further examine the role of congestion toll in minimizing the extra congestion
and delay.
Solutions for 80 km DWDM Systems
Authors: Annika Dochhan, Helmut Griesser, Michael Eiselt, Jörg-Peter Elbers
Date: 2020-09-22 00:00:00

We review currently discussed solutions for 80 km DWDM transmission targeting inter data-center connections at 100G and 400G line rates. PDM-64QAM, PAM4 and DMT are investigated, while the focus lies on directly detected solutions.
Practical Solutions for 400 Gbit/s Data Center Transmission
Authors: Annika Dochhan, Nicklas Eiselt, Jinlong Wei, Helmut Griesser, Michael Eiselt, Juan José Vegas Olmos, Idelfonso Tafur Monroy, Jörg-Peter Elbers
Date: 2020-09-22 00:00:00

We review three solutions for low-cost data center interconnects with a target reach of up to 80 km. Directly detected DMT, PAM-4 and multi-band CAP are promising modulation schemes, enabling 400 Gbit/s by combining eight channels of 56 Gbit/s.
Optimizing Discrete Multi-tone Transmission for 400G Data Center Interconnects
Authors: Annika Dochhan, Helmut Griesser, Nicklas Eiselt, Michael Eiselt, Jörg-Peter Elbers
Date: 2020-09-22 00:00:00

Discrete multi-tone transmission (DMT) is a promising candidate for future 400G data center interconnects. Eight channels, each carrying 56 Gb/s of data can be combined in a 50-GHz channel grid to form a 400 Gb/s superchannel. For a fully loaded 96 channel DWDM system this leads to a total capacity of 4.8 Tb/s. To meet the requirements of the targeted application in terms of cost efficiency and low power consumption, it is important to keep the complexity for the digital signal processing as low as possible. For DMT, the complexity is mainly determined by the length of the fast Fourier transformation (FFT). Since data center interconnects only have to bridge a distance of typically 80 km, we here investigate among other parameters the influence of the FFT length among other parameters on the achievable performance for 56 Gb/s DMT only for this distance. Transmission is performed in C-band to enable DWDM and no optical dispersion compensation is applied. We consider double sideband (DSB) as well as vestigial sideband (VSB) DMT. It can be seen that an FFT length of 128 is sufficient to reach the required performance in terms of bit error ratio, however, a higher length can significantly improve the performance.
Spinopelvic Anatomic Parameters Prediction Model of NSLBP based on data mining
Authors: Hua Cheng
Date: 2020-09-14 00:00:00
Tasks: regression

Objective: The purpose of this study is to perform analysis through the low back pain open data set to predict the incidence of non-specific chronic low back pain (NSLBP) to obtain a more accurate and convenient sagittal spinopelvic parameter model. Methods: The logistic regression analysis and multilayer perceptron(MLP) algorithm is used to construct a NSLBP prediction model based on the parameters of the spinopelvic parameters from open data source. Results: Degree of spondylolisthesis(DS), Pelvic radius (PR), Sacral slope (SS), Pelvic tilt (PT) are four predictors screened out by regression analysis that have significant predictive power for the risk of NSLBP. The overall accuracy of the equation prediction model is 85.8%.The MLP network algorithm determines that DS is the most powerful predictor of NSLBP through more precise modeling. The model has good predictive ability of 95.2% of accuracy. Conclusions: MLP models play a more accurate role in the construction of predictive models. Computer science is playing a greater role in helping precision medicine clinical research.
The fitness landscapes of translation
Authors: Mario Josupeit, Joachim Krug
Date: 2020-09-22 00:00:00
Tasks: Translation

Motivated by recent experiments on an antibiotic resistance gene, we investigate genetic interactions between synonymous mutations in the framework of exclusion models of translation. We show that the range of possible interactions is markedly different depending on whether translation efficiency is assumed to be proportional to ribosome current or ribosome speed. In the first case every mutational effect has a definite sign that is independent of genetic background, whereas in the second case the effect-sign can vary depending on the presence of other mutations. The latter result is demonstrated using configurations of multiple translational bottlenecks induced by slow codons.