pwc-archive/papers-with-abstracts huggingface dataset:
How to build a cross-impact model from first principles: Theoretical requirements and empirical results
Authors: Mehdi Tomas, Iacopo Mastromatteo, Michael Benzaquen
Date: 2020-04-03 00:00:00

Trading a financial instrument pushes its price and those of other assets, a phenomenon known as cross-impact. To be of use, cross-impact models must fit data and be well-behaved so they can be applied in applications such as optimal trading. To address these issues, we introduce a set of desirable properties which constrain cross-impact models. We classify cross-impact models according to which properties they satisfy and stress them on three different asset classes to evaluate goodness-of-fit. We find that two models are robust across markets, but only one satisfies all desirable properties and is appropriate for applications.
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression
Date: 2020-06-15 00:00:00
Tasks: Gaussian Processes, regression

The deployment of autonomous systems that operate in unstructured
environments necessitates algorithms to verify their safety. This can be
challenging due to, e.g., black-box components in the control software, or
undermodelled dynamics that prevent model-based verification. We present a
novel verification framework for an unknown dynamical system from a given set
of noisy observations of the dynamics. Using Gaussian processes trained on this
data set, the framework abstracts the system as an uncertain Markov process
with discrete states defined over the safe set. The transition bounds of the
abstraction are derived from the probabilistic error bounds between the
regression and underlying system. An existing approach for verifying safety
properties over uncertain Markov processes then generates safety guarantees. We
demonstrate the versatility of the framework on several examples, including
switched and nonlinear systems.
Inside the Mind of a Stock Market Crash
Authors: Stefano Giglio, Matteo Maggiori, Johannes Stroebel, Stephen Utkus
Date: 2020-04-04 00:00:00

We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20\%, and (iii) on April 16-17, after the market had rallied 25\% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-year) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations, and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash.
COVID-19: Development of a Robust Mathematical Model and Simulation Package with Consideration for Ageing Population and Time Delay for Control Action and Resusceptibility
Date: 2020-05-27 00:00:00

The current global health emergency triggered by the pandemic COVID-19 is one
of the greatest challenges mankind face in this generation. Computational
simulations have played an important role to predict the development of the
current pandemic. Such simulations enable early indications on the future
projections of the pandemic and is useful to estimate the efficiency of control
action in the battle against the SARS-CoV-2 virus. The SEIR model is a
well-known method used in computational simulations of infectious viral
diseases and it has been widely used to model other epidemics such as Ebola,
SARS, MERS, and influenza A. This paper presents a modified SEIRS model with
additional exit conditions in the form of death rates and resusceptibility,
where we can tune the exit conditions in the model to extend prediction on the
current projections of the pandemic into three possible outcomes; death,
recovery, and recovery with a possibility of resusceptibility. The model also
considers specific information such as ageing factor of the population, time
delay on the development of the pandemic due to control action measures, as
well as resusceptibility with temporal immune response. Owing to huge
variations in clinical symptoms exhibited by COVID-19, the proposed model aims
to reflect better on the current scenario and case data reported, such that the
spread of the disease and the efficiency of the control action taken can be
better understood. The model is verified using two case studies for
verification and prediction studies, based on the real-world data in South
Korea and Northern Ireland, respectively.
Tau affects P53 function and cell fate during the DNA damage response
Authors: Martina Sola, Claudia Magrin, Giona Pedrioli, Sandra Pinton, Agnese Salvade, Stephanie Papin, Paolo Paganetti
Date: 2020-04-05 00:00:00

Cells are constantly exposed to DNA damaging insults. To protect the organism, cells developed a complex molecular response coordinated by P53, the master regulator of DNA repair, cell division and cell fate. DNA damage accumulation and abnormal cell fate decision may represent a pathomechanism shared by aging-associated disorders such as cancer and neurodegeneration. Here, we examined this hypothesis in the context of tauopathies, a neurodegenerative disorder group characterized by Tau protein deposition. For this, the response to an acute DNA damage was studied in neuroblastoma cells with depleted Tau, as a model of loss-of-function. Under these conditions, altered P53 stability and activity result in reduced cell death and increased cell senescence. This newly discovered function of Tau involves abnormal modification of P53 and its E3 ubiquitin ligase MDM2. Considering the medical need with vast social implications caused by neurodegeneration and cancer, our study may reform our approach to disease-modifying therapies.
Joint Modelling and Calibration of SPX and VIX by Optimal Transport
Authors: Ivan Guo, Gregoire Loeper, Jan Obloj, Shiyi Wang
Date: 2020-04-05 00:00:00

This paper addresses the joint calibration problem of SPX options and VIX options or futures. We show that the problem can be formulated as a semimartingale optimal transport problem under a finite number of discrete constraints, in the spirit of [arXiv:1906.06478]. We introduce a PDE formulation along with its dual counterpart. The solution, a calibrated diffusion process, can be represented via the solutions of Hamilton-Jacobi-Bellman equations arising from the dual formulation. The method is tested on both simulated data and market data. Numerical examples show that the model can be accurately calibrated to SPX options, VIX options and VIX futures simultaneously.
Course of the first month of the COVID 19 outbreak in the New York State counties
Date: 2020-04-10 00:00:00

We illustrate and study the evolution of reported infections over the month
from March 1st to April 1st in the New York State as a whole, as well as in
each individual county. We search for exponential trends, and try to understand
whether there is any correlation of the timing and dynamics of these trends
with state mandated measures on social distancing and testing. We conclude that
the reports on April 1st may be dramatically under-representing the actual
number of state-wide infections, and we propose reassessment of the data over
the coming weeks, to monitor for effects of the PAUSE directive, and for the
increasing number of casualties as a validating measure.
Signal processing on simplicial complexes
Authors: Feng Ji, Giacomo Kahn, Wee Peng Tay
Date: 2020-04-06 00:00:00

Theoretical development and applications of graph signal processing (GSP) have attracted much attention. In classical GSP, the underlying structures are restricted in terms of dimensionality. A graph is a combinatorial object that models binary relations, and it does not directly model complex n-ary relations. One possible high dimensional generalization of graphs are simplicial complexes. They are a step between the constrained case of graphs and the general case of hypergraphs. In this paper, we develop a signal processing framework on simplicial complexes, such that we recover the traditional GSP theory when restricted to signals on graphs. It is worth mentioning that the framework works much more generally, though the focus of the paper is on simplicial complexes. We demonstrate how to perform signal processing with the framework using numerical examples.
Gradient-based Feature Extraction From Raw Bayer Pattern Images
Authors: Wei Zhou, Ling Zhang, Shengyu Gao, Xin Lou
Date: 2020-04-06 00:00:00
Tasks: Demosaicking, Pedestrian Detection

In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed. It is shown both theoretically and experimentally that the Bayer pattern images are applicable to the central difference gradient-based feature extraction algorithms without performance degradation, or even with superior performance in some cases. The color difference constancy assumption, which is widely used in various demosaicing algorithms, is applied in the proposed Bayer pattern image-based gradient extraction pipeline. Experimental results show that the gradients extracted from Bayer pattern images are robust enough to be used in histogram of oriented gradients (HOG)-based pedestrian detection algorithms and shift-invariant feature transform (SIFT)-based matching algorithms.
Reaction Cycles of Halogen Species in the Immune Defense: Implications for Human Health and Diseases and the Pathology and Treatment of COVID-19
Authors: Qing-Bin Lu
Date: 2020-04-06 00:00:00

There is no vaccine or specific antiviral treatment for COVID-19. One current focus is drug repurposing research, but those drugs have limited therapeutic efficacies and known adverse effects. The pathology of COVID-19 is essentially unknown. It is therefore challenging to discover a successful treatment to be approved for clinical use. This paper addresses several key biological processes of reactive oxygen, halogen and nitrogen species (ROS, RHS and RNS) that play crucial physiological roles in organisms from plants to humans. These include why superoxide dismutases, the enzymes to catalyze the formation of H2O2, are required for protecting ROS-induced injury in cell metabolism, why the amount of ROS/RNS produced by ionizing radiation at clinically relevant doses is ~1000 fold lower than the endogenous ROS/RNS level routinely produced in the cell and why a low level of endogenous RHS plays a crucial role in phagocytosis for immune defense. Herein we propose a plausible amplification mechanism in immune defense: ozone-depleting-like halogen cyclic reactions enhancing RHS effects are responsible for all the mentioned physiological functions, which are activated by H2O2 and deactivated by NO signaling molecule. Our results show that the reaction cycles can be repeated thousands of times and amplify the RHS pathogen-killing (defense) effects by 100,000 fold in phagocytosis, resembling the cyclic ozone-depleting reactions in the stratosphere. It is unraveled that H2O2 is a required protective signaling molecule (angel) in the defense system for human health and its dysfunction can cause many diseases or conditions such as autoimmune disorders, aging and cancer. We also identify a class of potent drugs for effective treatment of invading pathogens such as HIV and SARS-CoV-2 (COVID-19), cancer and other diseases, and provide a molecular mechanism of action of the drugs or candidates.
Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems
Date: 2020-08-11 00:00:00
Tasks: Distributed Optimization

We propose a novel algorithm for solving convex, constrained and distributed
optimization problems defined on multi-agent-networks, where each agent has
exclusive access to a part of the global objective function. The agents are
able to exchange information over a directed, weighted communication graph,
which can be represented as a column-stochastic matrix. The algorithm combines
an adjusted push-sum consensus protocol for information diffusion and a
gradient descent-ascent on the local cost functions, providing convergence to
the optimum of their sum. We provide results on a reformulation of the push-sum
into single matrix-updates and prove convergence of the proposed algorithm to
an optimal solution, given standard assumptions in distributed optimization.
The algorithm is applied to a distributed economic dispatch problem, in which
the constraints can be expressed in local and global subsets.
A Comprehensive Study of Reconfigurable Intelligent Surfaces in Generalized Fading
Date: 2020-04-10 00:00:00

Leveraging on the reconfigurable intelligent surface (RIS) paradigm for
enabling the next Internet of Things (IoT) and 6G era, this paper develops a
comprehensive theoretical framework characterizing the performance of
RIS-assisted communications in a plethora of propagation environments. We
derive unified mathematical models for the outage probability and ergodic
capacity of single and multiple-element RIS over Fox's H fading channel, which
includes as special cases nearly all linear and non linear multi-path and
shadowing fading models adopted in the open literature. For gleaning further
insights, we capitalize on the algebraic asymptotic expansions of the
H-transform to further analyze the outage probability and capacity at high
signal-to-noise ratio (SNR) in a unified fashion. Asymptotic analysis shows two
scaling rates of the outage probability at large average SNR. Moreover, by
harnessing its tractability, the developed statistical machinery is employed to
characterize the performance of multiple randomly distributed RIS-assisted
communications over Fox's H fading channels. We show that there is a great
potential to improve the outage performance and thereby the capacity when fewer
RISs are deployed each with more reflecting elements.
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments
Authors: Kyle Colangelo, Ying-Ying Lee
Date: 2020-04-06 00:00:00
Tasks: BIG-bench Machine Learning

We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or high-dimensional nuisance functions. Our double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial effects are asymptotically normal with non-parametric convergence rates. The first-step estimators for the nuisance conditional expectation function and the conditional density can be nonparametric or ML methods. Utilizing a kernel-based doubly robust moment function and cross-fitting, we give high-level conditions under which the nuisance function estimators do not affect the first-order large sample distribution of the DML estimators. We provide sufficient low-level conditions for kernel, series, and deep neural networks. We justify the use of kernel to localize the continuous treatment at a given value by the Gateaux derivative. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation
Adaptive Fuzzy Control for Fractional-Order Interconnected Systems with Unknown Control Directions
Date: 2020-04-12 00:00:00

This paper concentrates on the study of the decentralized fuzzy control
method for a class of fractional-order interconnected systems with unknown
control directions. To overcome the difficulties caused by the multiple unknown
control directions in fractional-order systems, a novel fractional-order
Nussbaum function technique is proposed. This technique is much more general
than those of existing works since it not only handles single/multiple unknown
control directions but is also suitable for fractional/integer-order
single/interconnected systems. Based on this technique, a new decentralized
adaptive control method is proposed for fractional-order interconnected
systems. Smooth functions are introduced to compensate for unknown interactions
among subsystems adaptively. Furthermore, fuzzy logic systems are utilized to
approximate unknown nonlinearities. It is proven that the designed controller
can guarantee the boundedness of all signals in interconnected systems and the
convergence of tracking errors. Two examples are given to show the validity of
the proposed method.
Optimized Energy and Information Relaying in Self-Sustainable IRS-Empowered WPCN
Authors: Bin Lyu, Parisa Ramezani, Dinh Thai Hoang, Shimin Gong, Zhen Yang, Abbas Jamalipour
Date: 2020-04-07 00:00:00

This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS.
Distributed Coordination of Deferrable Loads: A Real-time Market with Self-fulfilling Forecasts
Date: 2020-06-15 00:00:00
Tasks: Privacy Preserving, Scheduling

Increased uptake of variable renewable generation and further electrification
of energy demand necessitate efficient coordination of flexible demand
resources to make most efficient use of power system assets. Flexible
electrical loads are typically small, numerous, heterogeneous and owned by
self-interested agents. Considering the multi-temporal nature of flexibility
and the uncertainty involved, scheduling them is a complex task. This paper
proposes a forecast-mediated real-time market-based control approach (F-MBC)
for cost minimizing coordination of uninterruptible time-shiftable (i.e.
deferrable) loads. F-MBC is scalable, privacy preserving, and usable by device
agents with small computational power. Moreover, F-MBC is proven to overcome
the challenge of mutually conflicting decisions from equivalent devices.
Simulations in a simplified but challenging case study show that F-MBC produces
near-optimal behaviour over multiple time-steps.
Effect of protein structure on evolution of cotranslational folding
Authors: Victor Zhao, William M. Jacobs, Eugene I. Shakhnovich
Date: 2020-04-07 00:00:00
Tasks: Protein Folding, Translation

Cotranslational folding depends on the folding speed and stability of the nascent protein. It remains difficult, however, to predict which proteins cotranslationally fold. Here, we simulate evolution of model proteins to investigate how native structure influences evolution of cotranslational folding. We developed a model that connects protein folding during and after translation to cellular fitness. Model proteins evolved improved folding speed and stability, with proteins adopting one of two strategies for folding quickly. Low contact order proteins evolve to fold cotranslationally. Such proteins adopt native conformations early on during the translation process, with each subsequently translated residue establishing additional native contacts. On the other hand, high contact order proteins tend not to be stable in their native conformations until the full chain is nearly extruded. We also simulated evolution of slowly translating codons, finding that slower translation speeds at certain positions enhances cotranslational folding. Finally, we investigated real protein structures using a previously published dataset that identified evolutionarily conserved rare codons in E. coli genes and associated such codons with cotranslational folding intermediates. We found that protein substructures preceding conserved rare codons tend to have lower contact orders, in line with our finding that lower contact order proteins are more likely to fold cotranslationally. Our work shows how evolutionary selection pressure can cause proteins with local contact topologies to evolve cotranslational folding.
A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and Visual Features
Date: 2020-10-11 00:00:00

SARS-CoV-2 has characteristics of wide contagion and quick propagation
velocity. To analyse the visual information of it, we build a SARS-CoV-2
Microscopic Image Dataset (SC2-MID) with 48 electron microscopic images and
also prepare their ground truth images. Furthermore, we extract multiple
classical features and novel deep learning features to describe the visual
information of SARS-CoV-2. Finally, it is proved that the visual features of
the SARS-CoV-2 images which are observed under the electron microscopic can be
extracted and analysed.
Epidemics with containment measures
Date: 2020-10-28 00:00:00

We propose a tractable epidemic model that includes containment measures. In
the absence of containment measures, the epidemics spread exponentially fast
whenever the infectivity rate is positive, $\lambda>0$. The containment
measures are modeled by considering a time-dependent modulation of the bare
infectivity $\lambda$ leading to effective infectivity that decays in time for
each infected individual, mimicking for instance the combined effect of the
asymptomatic onset of the disease, testing policies and quarantine. We consider
a wide range of temporal kernels for effective infectivity and we investigate
the effect of the considered containment measures. We find that not all kernels
are able to push the epidemic dynamics below the epidemic threshold, with some
containment measures only able to reduce the rate of the exponential growth of
newly infected individuals. We also propose a pandemic model caused by a
growing number of separated foci.