pwc-archive/papers-with-abstracts huggingface dataset:
Defining Fundamental Frequency for Almost Harmonic Signals
Authors: Filip Elvander, Andreas Jakobsson
Date: 2020-03-24 00:00:00

In this work, we consider the modeling of signals that are almost, but not quite, harmonic, i.e., composed of sinusoids whose frequencies are close to being integer multiples of a common frequency. Typically, in applications, such signals are treated as perfectly harmonic, allowing for the estimation of their fundamental frequency, despite the signals not actually being periodic. Herein, we provide three different definitions of a concept of fundamental frequency for such inharmonic signals and study the implications of the different choices for modeling and estimation. We show that one of the definitions corresponds to a misspecified modeling scenario, and provides a theoretical benchmark for analyzing the behavior of estimators derived under a perfectly harmonic assumption. The second definition stems from optimal mass transport theory and yields a robust and easily interpretable concept of fundamental frequency based on the signals' spectral properties. The third definition interprets the inharmonic signal as an observation of a randomly perturbed harmonic signal. This allows for computing a hybrid information theoretical bound on estimation performance, as well as for finding an estimator attaining the bound. The theoretical findings are illustrated using numerical examples.
Self-Organized Networks, Darwinian Evolution of Self-Organized Networks, Darwinian Evolution of Dynein Rings, Stalks and Stalk Heads
Authors: J. C. Phillips
Date: 2020-03-21 00:00:00

Cytoskeletons are self organized networks based on polymerized proteins, actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality self organized criticality. Dynein has three distinct titled subunits, but how these units connect to function as a molecular motor is mysterious. Dynein binds to tubulin through two coiled coil stalks and a stalk head. The energy used to alter the head binding and propel cargo along tubulin is supplied by ATP at a ring 1500 amino acids away.
Genotyping coronavirus SARS-CoV-2: methods and implications
Authors: Changchuan Yin
Date: 2020-03-24 00:00:00

The emerging global infectious COVID-19 coronavirus disease by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) presents critical threats to global public health and the economy since it was identified in late December 2019 in China. The virus has gone through various pathways of evolution. For understanding the evolution and transmission of SARS-CoV-2, genotyping of virus isolates is of great importance. We present an accurate method for effectively genotyping SARS-CoV-2 viruses using complete genomes. The method employs the multiple sequence alignments of the genome isolates with the SARS-CoV-2 reference genome. The SNP genotypes are then measured by Jaccard distances to track the relationship of virus isolates. The genotyping analysis of SARS-CoV-2 isolates from the globe reveals that specific multiple mutations are the predominated mutation type during the current epidemic. Our method serves a promising tool for monitoring and tracking the epidemic of pathogenic viruses in their gradual and local genetic variations. The genotyping analysis shows that the genes encoding the S proteins and RNA polymerase, RNA primase, and nucleoprotein, undergo frequent mutations. These mutations are critical for vaccine development in disease control.
Bemerkungen zum paarweisen Vergleich
Date: 2020-03-24 00:00:00

The simple pairwise comparison is a method to provide different criteria with
weights. We show that the values of those weights (in particular the maximum)
depend just on the number of criteria. Additionally, it is shown that the
distance between the weights is always the same.
  -----
  Der einfache paarweise Vergleich ist ein Verfahren verschiedene Kriterien mit
einer Gewichtung zu versehen. Wir zeigen, dass die Werte dieser Gewichte
(insbesondere auch der maximale Wert) ausschlie{\ss}lich von der Anzahl der
Kriterien abh\"angt. Dar\"uber hinaus wird gezeigt, dass der Abstand der
Gewichtungen stets gleich ist.
Experimental Investigation of Frequency Domain Channel Extrapolation in Massive MIMO Systems for Zero-Feedback FDD
Authors: Thomas Choi, François Rottenberg, Jorge Gomez-Ponce, Akshay Ramesh, Peng Luo, Jianzhong Zhang, Andreas F. Molisch
Date: 2020-03-24 00:00:00
Tasks: parameter estimation

Estimating downlink (DL) channel state information (CSI) in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems generally requires downlink pilots and feedback overheads. Accordingly, this paper investigates the feasibility of zero-feedback FDD massive MIMO systems based on channel extrapolation. We use the high-resolution parameter estimation (HRPE), specifically the space-alternating generalized expectation-maximization (SAGE) algorithm, to extrapolate the DL CSI based on the extracted parameters of multipath components in the uplink channel. We apply the HRPE to two different channel models: the vector spatial signature (VSS) model and the direction of arrival (DOA) model. We verify these methods through real-world channel data acquired from channel measurement campaigns with two different types of channel sounders: a) a switched array-based, real-time, time-domain, outdoors setup at 3.5 GHz, and b) a virtual array-based, high-accuracy, frequency-domain, indoors setup at 2.4 and 5-7 GHz. The performance metrics of the extrapolated channels that we evaluate include the mean squared error, beamforming efficiency, and spectral efficiency in multiuser MIMO scenarios. The results show that the HRPE-based channel extrapolation performs best under the simple VSS model, which does not require array calibration, and if the BS is in an open outdoor environment having line-of-sight (LOS) paths to well-separated users.
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy
Authors: Gabriele Amorosino, Denis Peruzzo, Pietro Astolfi, Daniela Redaelli, Paolo Avesani, Filippo Arrigoni, Emanuele Olivetti
Date: 2020-03-24 00:00:00
Tasks: Anatomy, Segmentation

Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of congenital or acquired brain distortion. We compare traditional pipelines and a deep learning model, i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional pipelines completely fail to segment the tissues with strong anatomical distortion. Surprisingly, the 3D U-Net provides useful segmentations that can be a valuable starting point for manual refinement by experts/neuroradiologists.
Phenomenological analysis of the 2020 COVID-19 outbreak dynamics
Date: 2020-03-24 00:00:00
Tasks: Descriptive

In the wake of the COVID-19 virus outbreak, a brief phenomenological
(descriptive, comparative) analysis of the dynamics of the disease spread among
different countries is presented. Results indicate that the infection spread
dynamics is currently the most pronounced in the USA (confirmed cases are
currently doubling every 2.16 days, with a decreasing doubling time tendency),
while other countries with the most confirmed cases show different values, and
tendencies. The reported number of deaths is currently doubled every 2.28 days
in Germany, 2.56 days in France, 2.57 days in Switzerland, 2.59 days in France,
and 2.62 days in USA, while only France and USA are currently exhibiting
further acceleration (diminishing doubling time).
Logistic approximations used to describe new outbreaks in the 2020 COVID-19 pandemic
Date: 2020-03-24 00:00:00

In this investigation I used the Logistic Model to fit the COVID-19 pandemic
data for some countries. The data modeled is the death numbers per day in
China, Iran, Italy, South Korea, Spain and United States. Considering the
current growth rate of the pandemic, it was possible to show that the death
toll should be between 3,277-3,327 deaths in China, 2,035-2,107 in Iran,
120-134 in South Korea, 11,227-12,793 in Italy and 6,217-7,405 in Spain. Also,
with this toy model it was possible to show a clear emergence of a new outbreak
within the same country (Iran, China and the United States). The growth rate of
deaths found for South Korea was the lowest among the countries studied
($0.14701\pm0.00923$) and for China ($0.16667\pm0.00284$). Italy
($0.22594\pm0.00599$) and Spain ($0.31213\pm0.02337$) had the highest rates and
in the second wave in Iran ($0.37893\pm0.02712$).
Event-Triggered Consensus of Homogeneous and Heterogeneous Multi-Agent Systems with Jointly Connected Switching Topologies
Date: 2020-09-22 00:00:00

This paper investigates the distributed event-based consensus problem of
switching networks satisfying the jointly connected condition. Both the state
consensus of homogeneous linear networks and output consensus of heterogeneous
networks are studied. Two kinds of event-based protocols based on local sampled
information are designed, without the need to solve any matrix equation or
inequality. Theoretical analysis indicates that the proposed event-based
protocols guarantee the achievement of consensus and the exclusion of Zeno
behaviors for jointly connected undirected switching graphs. These protocols,
relying on no global knowledge of the network topology and independent of
switching rules, can be devised and utilized in a completely distributed
manner. They are able to avoid continuous information exchanges for either
controllers' updating or triggering functions' monitoring, which ensures the
feasibility of the presented protocols.
SIR Model for COVID-19 calibrated with existing data and projected for Colombia
Date: 2020-09-24 00:00:00

In this paper we develop a SIR epidemiological model with parameters
calculated according to existing data at the time of writing (24/03/2020); the
data is from Italy, South Korea and Colombia, the model is then used to project
the evolution of the COVID-19 epidemic in Colombia for different scenarios
using the data of population for the country and known initial conditions at
the start of the simulation.
Measures and LMIs for Lateral F-16 MRAC Validation
Date: 2020-03-25 00:00:00

Occupation measures and linear matrix inequality (LMI) relax-ations (called
the moment sums of squares or Lasserre hierarchy) are state-of-the-art methods
for verification and validation (VV) in aerospace. In this document, we extend
these results to a full F-16 closed-loop nonlinear dutch roll polynomial model
complete with model reference adaptive control (MRAC). This is done through a
new technique of approximating the reference trajectory by exploiting sparse
ordinary differential equations (ODEs) with parsimony. The VV problem is then
solved directly using moment LMI relaxations and off-the-shelf-software. The
main results are then compared to their numerical counterparts obtained using
traditional Monte-Carlo simulations.
On wind farm wake mixing strategies using dynamic individual pitch control
Date: 2020-03-25 00:00:00
Tasks: Pitch control

Dynamic wind farm control is a new strategy that aims to apply time-varying,
often periodic, control signals on upstream wind turbines to increase the wake
mixing behind the turbine. As a result, wake recovery is accelerated, leading
to a higher power production of downstream turbines. As the amount of interest
in dynamic control strategies for wind turbines in a wind farm is increasing,
different approaches are being proposed. One such novel approach is called
Dynamic Individual Pitch Control (DIPC). In DIPC, each blade pitch angle of a
turbine is controlled independently to dynamically manipulate the direction of
the thrust force vector exerted on the wind. Hence, the direction of the wake
is varied, inducing wake mixing without significant thrust force magnitude
variations on the rotor. In this paper, the effectiveness of different
variations in the thrust direction are evaluated and compared using Large Eddy
Simulation (LES) experiments.
Predictive coding underlies adaptation in the subcortical sensory pathway
Date: 2020-03-25 00:00:00

The subcortical sensory pathways are the fundamental channels for mapping the
outside world to our minds. Sensory pathways efficiently transmit information
by adapting neural responses to the local statistics of the sensory input. The
longstanding mechanistic explanation for this adaptive behaviour is that
neuronal habituation scales activity to the local statistics of the stimuli. An
alternative account is that neural coding is directly driven by expectations of
the sensory input. Here we used abstract rules to manipulate expectations
independently of local stimulus statistics. The ultra-high-field functional-MRI
data show that expectations, and not habituation, are the main driver of the
response amplitude to tones in the human auditory pathway. These results
provide first unambiguous evidence of predictive coding and abstract processing
in a subcortical sensory pathway, indicating that the brain only holds
subjective representations of the outside world even at initial points of the
processing hierarchy.
What Matters to Individual Investors: Price Setting in Online Auctions of P2P Consumer Loans
Authors: Andreas Dietrich, Reto Rey
Date: 2020-03-25 00:00:00

We analyze how retail investors price the credit risk of online P"P consumer loans in a reverse auction framework where personal interaction is absent. The explained interest rate variance is considerably larger than in comparable studies using bank loan data. This is unexpected, given the difference in experience and the limited set of information provided to the investor. Factors representing economic status significantly influence lender evaluations of the borrower's credit risk. Previous studies on gender discrimination found mixed results. We show that this is likely due to simplified specifications and find surprising results: male borrowers are charged a higher interest rates, conditional on being married and having children. Overall, our results indicate that retail investors exhibit a strong degree of predictability in this weakly regulated market for online P2P consumer loans.
Cryptocurrency Trading: A Comprehensive Survey
Authors: Fan Fang, Carmine Ventre, Michail Basios, Leslie Kanthan, Lingbo Li, David Martinez-Regoband, Fan Wu
Date: 2020-03-25 00:00:00
Tasks: Management, Survey

In recent years, the tendency of the number of financial institutions including cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects(contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.
A hybrid P3HT-Graphene interface for efficient photostimulation of neurons
Authors: Mattia L. DiFrancesco, Elisabetta Colombo, Ermanno D. Papaleo, José Fernando Maya-Vetencourt, Giovanni Manfredi, Guglielmo Lanzani, Fabio Benfenati
Date: 2020-03-25 00:00:00

Graphene conductive properties have been long exploited in the field of organic photovoltaics and optoelectronics by the scientific community worldwide. We engineered and characterized a hybrid biointerface in which graphene is coupled with photosensitive polymers, and tested its ability to elicit lighttriggered neural activity modulation in primary neurons and blind retina explants. We designed such a graphene-based device by modifying a photoactive P3HT-based retinal interface, previously reported to rescue light sensitivity in blind rodents, with a CVD graphene layer replacing the conductive PEDOT:PSS layer to enhance charge separation. The new graphene-based device was characterized for its electrochemical features and for the ability to photostimulate primary neurons and blind retina explants, while preserving biocompatibility. Light-triggered responses, recorded by patch-clamp in vitro or MEA ex vivo, show a stronger light-transduction efficiency when the neurons are interfaced with the graphene-based device with respect to the PEDOT:PSS-based one. The possibility to ameliorate flexible photo-stimulating devices via the insertion of graphene, paves the way for potential biomedical applications of graphenebased neuronal interfaces in the context of retinal implants.
A Novel Wide-Area Control Strategy for Damping of Critical Frequency Oscillations via Modulation of Active Power Injections
Date: 2020-03-25 00:00:00

This paper proposes a novel wide-area control strategy for modulating the
active power injections to damp the critical frequency oscillations in power
systems, this includes the inter-area oscillations and the transient frequency
swing. The proposed method pursues an efficient utilization of the limited
power reserve of existing distributed energy resources (DERs) to mitigate these
oscillations. This is accomplished by decoupling the damping control actions at
different sites using the oscillation signals of the concerned mode as the
power commands. A theoretical basis for this decoupled modulating control is
provided. Technically, the desired sole modal oscillation signals are filtered
out by linearly combining the system-wide frequencies, which is determined by
the linear quadratic regulator based sparsity-promoting (LQRSP) technique. With
the proposed strategy, the modulation of each active power injection can be
effectively engineered considering the response limit and steady-state output
capability of the supporting device. The method is validated based on a
two-area test system and is further demonstrated based on the New England
39-bus test system.
Physics and Derivatives -- Interview Questions and Answers
Authors: Alexander Lipton
Date: 2020-03-15 00:00:00

Answers to interview questions sent to a selected group of former physicists working in finance. The interview will be published as part of a Special Issue on Physics and Derivatives by The Journal of Derivatives in the second half of 2020.
EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm
Authors: Yang Chen, Emerson Li
Date: 2020-03-13 00:00:00
Tasks: Decision Making, Generative Adversarial Network, Time Series, Time Series Analysis

Stock prices are influenced over time by underlying macroeconomic factors. Jumping out of the box of conventional assumptions about the unpredictability of the market noise, we modeled the changes of stock prices over time through the Markov Decision Process, a discrete stochastic control process that aids decision making in a situation that is partly random. We then did a "Region of Interest" (RoI) Pooling of the stock time-series graphs in order to predict future prices with existing ones. Generative Adversarial Network (GAN) is then used based on a competing pair of supervised learning algorithms, to regenerate future stock price projections on a real-time basis. The supervised learning algorithm used in this research, moreover, is original to this study and will have wider uses. With the ensemble of these algorithms, we are able to identify, to what extent, each specific macroeconomic factor influences the change of the Brownian/random market movement. In addition, our model will have a wider influence on the predictions of other Brownian movements.
Gender Differences in Wage Expectations
Date: 2020-03-25 00:00:00

Using a survey on wage expectations among students at two Swiss institutions
of higher education, we examine the wage expectations of our respondents along
two main lines. First, we investigate the rationality of wage expectations by
comparing average expected wages from our sample with those of similar
graduates; we further examine how our respondents revise their expectations
when provided information about actual wages. Second, using causal mediation
analysis, we test whether the consideration of a rich set of personal and
professional controls, namely concerning family formation and children in
addition to professional preferences, accounts for the difference in wage
expectations across genders. We find that males and females overestimate their
wages compared to actual ones, and that males respond in an overconfident
manner to information about outside wages. Despite the attenuation of the
gender difference in wage expectations brought about by the comprehensive set
of controls, gender generally retains a significant direct, unexplained effect
on wage expectations.
The Millennial Boom, the Baby Bust, and the Housing Market
Authors: Marijn A. Bolhuis, Judd N. L. Cramer
Date: 2020-03-25 00:00:00

As baby boomers have begun to downsize and retire, their preferences now overlap with millennials' predilection for urban amenities and smaller living spaces. This confluence in tastes between the two largest age segments of the U.S. population has meaningfully changed the evolution of home prices in the United States. Utilizing a Bartik shift-share instrument for demography-driven demand shocks, we show that from 2000 to 2018 (i) the price growth of four- and five-bedroom houses has lagged the prices of one- and two-bedroom homes, (ii) within local labor markets, the relative home prices in baby boomer-rich zip codes have declined compared with millennial-rich neighborhoods, and (iii) the zip codes with the largest relative share of smaller homes have grown fastest. These patterns have become more pronounced during the latest economic cycle. We show that the effects are concentrated in areas where housing supply is most inelastic. If this pattern in the housing market persists or expands, the approximately 16.5 trillion in real estate wealth held by households headed by those aged 55 or older will be significantly affected. We find little evidence that these upcoming changes have been incorporated into current prices.