pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning-Based Least Square Forward-Backward Stochastic Differential Equation Solver for High-Dimensional Derivative Pricing
Authors: Jian Liang, Zhe Xu, Peter Li
Date: 2019-07-24 00:00:00
Tasks: regression

We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the efficiency and accuracy of our least square backward deep neural network solver and its capability to provide accurate prices for complex early exercise derivatives such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an efficient means for pricing high-dimensional derivatives with early exercises features.
The method of Eneström and Phragmén for parliamentary elections by means of approval voting
Authors: Rosa Camps, Xavier Mora, Laia Saumell
Date: 2019-07-23 00:00:00

We study a method for proportional representation that was proposed at the turn from the nineteenth to the twentieth century by Gustav Enestr\"om and Edvard Phragm\'en. Like Phragm\'en's better-known iterative minimax method, it is assumed that the voters express themselves by means of approval voting. In contrast to the iterative minimax method, however, here one starts by fixing a quota, i.e. the number of votes that give the right to a seat. As a matter of fact, the method of Enestr\"om and Phragm\'en can be seen as an extension of the method of largest remainders from closed lists to open lists, or also as an adaptation of the single transferable vote to approval rather than preferential voting. The properties of this method are studied and compared with those of other methods of the same kind.
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Splitting
Date: 2019-07-23 00:00:00
Tasks: Quantization

We study the distributed average consensus problem in multi-agent systems
with directed communication links that are subject to quantized information
flow. The goal of distributed average consensus is for the nodes, each
associated with some initial value, to obtain the average (or some value close
to the average) of these initial values. In this paper, we present and analyze
a distributed averaging algorithm which operates exclusively with quantized
values (specifically, the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and rely
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We characterize the
properties of the proposed distributed averaging protocol, illustrate its
operation with an example, and show that its execution, on any timeinvariant
and strongly connected digraph, will allow all agents to reach, in finite time,
a common consensus value that is equal to the quantized average. We conclude
with comparisons against existing quantized average consensus algorithms that
illustrate the performance and potential advantages of the proposed algorithm.
Cell-to-cell variability in organelle abundance reveals mechanisms of organelle biogenesis
Authors: Sandeep Choubey, Dipjyoti Das, Saptarshi Majumdar
Date: 2019-07-24 00:00:00

How cells regulate the number of organelles is a fundamental question in cell biology. While decades of experimental work have uncovered four fundamental processes that regulate organelle biogenesis, namely, de novo synthesis, fission, fusion and decay, a comprehensive understanding of how these processes together control organelle abundance remains elusive. Recent fluorescence microscopy experiments allow for the counting of organelles at the single-cell level. These measurements provide information about the cell-to-cell variability in organelle abundance in addition to the mean level. Motivated by such measurements, we build upon a recent study and analyze a general stochastic model of organelle biogenesis. We compute the exact analytical expressions for the probability distribution of organelle numbers, their mean, and variance across a population of single cells. It is shown that different mechanisms of organelle biogenesis lead to distinct signatures in the distribution of organelle numbers which allows us to discriminate between these various mechanisms. By comparing our theory against published data for peroxisome abundance measurements in yeast, we show that a widely believed model of peroxisome biogenesis that involves de novo synthesis, fission, and decay is inadequate in explaining the data. Also, our theory predicts bimodality in certain limits of the model. Overall, the framework developed here can be harnessed to gain mechanistic insights into the process of organelle biogenesis.
Cell Motion Alignment as Polarity Memory Effect
Authors: Katsuyoshi Matsushita, Kazuya Horibe, Naoya Kamamoto, Koichi Fujimoto
Date: 2019-07-25 00:00:00

The clarification of the motion alignment mechanism in collective cell migration is an important issue commonly in physics and biology. In analogy with the self-propelled disk, the polarity memory effect of eukaryotic cell is a fundamental candidate for this alignment mechanism. In the present paper, we theoretically examine the polarity memory effect for the motion alignment of cells on the basis of the cellular Potts model. We show that the polarity memory effect can align motion of cells. We also find that the polarity memory effect emerges for the persistent length of cell trajectories longer than average cell-cell distance.
Approximate calculation of the binding energy between 17$\beta$-estradiol and human estrogen receptor alpha
Date: 2019-07-25 00:00:00

Estrogen receptors (ERs) are a group of proteins activated by
17$\beta$-estradiol. The endocrine-disrupting chemicals (EDCs) mimic estrogen
action by bind directly to the ligand binding domain of ER. From this
perspective, ER represent a good model for identifying and assessing the health
risk of potential EDCs. This ability is best reflected by the ligand-ER binding
energy. Multilayer fragment molecular orbital (MFMO) calculations were
performed which allowed us to obtain the binding energy using a calculation
scheme that considers the molecular interactions that occur on the following
model systems: the bound and free receptor, 17$\beta$-estradiol and a water
cluster. The bound and free receptor and 17$\beta$-estradiol were surrounded by
a water shell containing the same number of molecules as the water cluster. The
structures required for MFMO calculations were obtained from molecular dynamics
simulations and cluster analysis. Attractive dispersion interactions were
observed between 17$\beta$-estradiol and the binding site hydrophobic residues.
In addition, strong electrostatic interactions were found between
17$\beta$-estradiol and the following charged/polarized residues: Glu 353, His
524 and Arg 394. The FMO2-RHF/STO-3G:MP2/6-31G(d) weighted binding energy was
of -67.2 kcal/mol. We hope that the model developed in this study can be useful
for identifying and assessing the health risk of potential EDCs.
A Tensor-based Approach to Joint Channel Estimation / Data Detection in Flexible Multicarrier MIMO Systems
Date: 2019-07-23 00:00:00

Filter bank-based multicarrier (FBMC) systems have attracted increasing
attention recently in view of their many advantages over the classical cyclic
prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM)
modulation. However, their more advanced structure (resulting in, for example,
self interference) complicates signal processing tasks at the receiver,
including synchronization, channel estimation and equalization. In a
multiple-input multiple-output (MIMO) configuration, the multi-antenna
interference has also to be taken into account. (Semi-) blind receivers, of
increasing interest in (massive) MIMO systems, have been little studied so far
for FBMC and mainly for the single-antenna case only. The design of such
receivers for flexible MIMO FBMC systems, unifying a number of existing FBMC
schemes, is considered in this paper through a tensor-based approach, which is
shown to encompass existing joint channel estimation and data detection
approaches as special cases, adding to their understanding and paving the way
to further developments. Simulation-based results are included, for realistic
transmission models, demonstrating the estimation and detection performance
gains from the adoption of these receivers over their training only-based
counterparts.
Molecular Brightness analysis of GPCR oligomerization in the presence of spatial heterogeneity
Authors: Paolo Annibale, Martin J Lohse
Date: 2019-07-25 00:00:00

Measuring the oligomerization of plasma membrane proteins is rife with biophysical and biomedical implications. This is particularly true for GPCRs, a large family of proteins representing the targets of over one third of all FDA approved medications. Over the last thirty years, fluorescence microscopy has been the leading approach to address this problem. However, in spite of a large number of studies and approaches, for most GPCRs the results have remained highly contentious, possibly due to the large spectrum of specific methods employed.
Multicell MIMO Communications Relying on Intelligent Reflecting Surface
Date: 2020-04-24 00:00:00

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless
communication technique capable of creating a controllable propagation
environment. In this paper, we propose to invoke an IRS at the cell boundary of
multiple cells to assist the downlink transmission to cell-edge users, whilst
mitigating the inter-cell interference, which is a crucial issue in multicell
communication systems. We aim for maximizing the weighted sum rate (WSR) of all
users through jointly optimizing the active precoding matrices at the base
stations (BSs) and the phase shifts at the IRS subject to each BS's power
constraint and unit modulus constraint. Both the BSs and the users are equipped
with multiple antennas, which enhances the spectral efficiency by exploiting
the spatial multiplexing gain. Due to the non-convexity of the problem, we
first reformulate it into an equivalent one, which is solved by using the block
coordinate descent (BCD) algorithm, where the precoding matrices and phase
shifts are alternately optimized. The optimal precoding matrices can be
obtained in closed form, when fixing the phase shifts. A pair of efficient
algorithms are proposed for solving the phase shift optimization problem,
namely the Majorization-Minimization (MM) Algorithm and the Complex Circle
Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least
locally optimal solutions. We also extend the proposed algorithms to the more
general multiple-IRS and network MIMO scenarios. Finally, our simulation
results confirm the advantages of introducing IRSs in enhancing the cell-edge
user performance.
Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning
Date: 2019-12-16 00:00:00
Tasks: BIG-bench Machine Learning

Analysis of microscope images is a tedious work which requires patience and
time, usually done manually by the microscopist after data collection. Here we
introduce an approach of automatic image analysis, which is based on locally
applied Fourier Transform and Machine Learning methods. In this approach, a
whole image is scanned by a local moving window with defined size and the 2D
Fourier Transform is calculated for each window. Then, all the Local Fourier
Transforms are fed into Machine Learning processing. Firstly, a number of
components in the data is estimated from Principal Component Analysis (PCA)
Scree Plot performed on the data. Secondly, the data are decomposed blindly by
Non-Negative Matrix Factorization (NMF) into interpretable spatial maps
(loadings) and corresponding Fourier Transforms (factors). The microscopic
image is analyzed and the features on the image are automatically discovered,
based on the local changes in Fourier Transform. The user selects only a size
and movement of the scanning local window which defines the final analysis
resolution. This automatic approach was successfully applied to analysis of
various microscopic images with and without local periodicity i.e. atomically
resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron
Microscopy (STEM) image of Au nanoisland of fcc and Au hcp phases, Scanning
Tunneling Microscopy (STM) image of Au-induced reconstruction on Ge(001)
surface, Scanning Electron Microscopy (SEM) image of metallic nanoclusters
grown on GaSb surface, and Fluorescence microscopy image of HeLa cell line of
cervical cancer. The proposed approach could be used to automatically analyze
the local structure of microscopic images within a time of about a minute for a
single image on a modern desktop/notebook computer and it is freely available
as a Python analysis notebook and Python program for batch processing.
Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network
Date: 2019-07-25 00:00:00

Thin-plate splines can be used for interpolation of image values, but can
also be used to represent a smooth surface, such as the boundary between two
structures. We present a method for partitioning vertebra segmentation masks
into two substructures, the vertebral body and the posterior elements, using a
convolutional neural network that predicts the boundary between the two
structures. This boundary is modeled as a thin-plate spline surface defined by
a set of control points predicted by the network. The neural network is trained
using the reconstruction error of a convolutional autoencoder to enable the use
of unpaired data.
Optimal make take fees in a multi market maker environment
Authors: Bastien Baldacci, Dylan Possamaï, Mathieu Rosenbaum
Date: 2019-07-25 00:00:00

Following the recent literature on make take fees policies, we consider an exchange wishing to set a suitable contract with several market makers in order to improve trading quality on its platform. To do so, we use a principal-agent approach, where the agents (the market makers) optimise their quotes in a Nash equilibrium fashion, providing best response to the contract proposed by the principal (the exchange). This contract aims at attracting liquidity on the platform. This is because the wealth of the exchange depends on the arrival of market orders, which is driven by the spread of market makers. We compute the optimal contract in quasi explicit form and also derive the optimal spread policies for the market makers. Several new phenomena appears in this multi market maker setting. In particular we show that it is not necessarily optimal to have a large number of market makers in the presence of a contracting scheme.
Resolution of the St. Petersburg paradox using Von Mises axiom of randomness
Date: 2019-06-27 00:00:00
Tasks: Decision Making

In this article we will propose a completely new point of view for solving
one of the most important paradoxes concerning game theory. The solution
develop shifts the focus from the result to the strategy s ability to operate
in a cognitive way by exploiting useful information about the system. In order
to determine from a mathematical point of view if a strategy is cognitive, we
use Von Mises' axiom of randomness. Based on this axiom, the knowledge of
useful information consequently generates results that cannot be reproduced
randomly. Useful information in this case may be seen as a significant datum
for the recipient, for their present or future decision-making process.
Finally, by resolving the paradox from this new point of view, we will
demonstrate that an expected gain that tends toward infinity is not always a
consequence of a cognitive and non-random strategy. Therefore, this result
leads us to define a hierarchy of values in decision-making, where the
cognitive aspect, whose statistical consequence is a divergence from random
behaviour, turns out to be more important than the expected gain.
Special Drawing Rights in a New Decentralized Century
Date: 2019-06-25 00:00:00
Tasks: Philosophy

Unfulfilled expectations from macro-economic initiatives during the Great
Recession and the massive shift into globalization echo today with political
upheaval, anti-establishment propaganda, and looming trade/currency wars that
threaten domestic and international value chains. Once stable entities like the
EU now look fragile and political instability in the US presents unprecedented
challenges to an International Monetary System (IMS) that predominantly relies
on the USD and EUR as reserve currencies. In this environment, it is critical
for an international organization mandated to ensure stability to plan and act
ahead. This paper argues that Decentralized Ledger-based technology (DLT) is
key for the International Monetary Fund (IMF) to mitigate some of those risks,
promote stability and safeguard world prosperity. Over the last two years, DLT
has made headline news globally and created a worldwide excitement not seen
since the internet entered the mainstream. The rapid adoption and open-to-all
philosophy of DLT has already redefined global socioeconomics, promises to
shake up the world of commerce/finance and challenges the workings of central
governments/regulators. This paper examines DLT core premises and proposes a
two-step approach for the IMF to expand Special Drawing Rights (SDR) into that
sphere so as to become the originally envisioned numeraire and reserve currency
for cross-border transactions in this new decentralized century.
On the Statistical Differences between Binary Forecasts and Real World Payoffs
Authors: Nassim Nicholas Taleb
Date: 2019-07-24 00:00:00

What do binary (or probabilistic) forecasting abilities have to do with overall performance? We map the difference between (univariate) binary predictions, bets and "beliefs" (expressed as a specific "event" will happen/will not happen) and real-world continuous payoffs (numerical benefits or harm from an event) and show the effect of their conflation and mischaracterization in the decision-science literature. We also examine the differences under thin and fat tails. The effects are: A- Spuriousness of many psychological results particularly those documenting that humans overestimate tail probabilities and rare events, or that they overreact to fears of market crashes, ecological calamities, etc. Many perceived "biases" are just mischaracterizations by psychologists. There is also a misuse of Hayekian arguments in promoting prediction markets. We quantify such conflations with a metric for "pseudo-overestimation". B- Being a "good forecaster" in binary space doesn't lead to having a good actual performance}, and vice versa, especially under nonlinearities. A binary forecasting record is likely to be a reverse indicator under some classes of distributions. Deeper uncertainty or more complicated and realistic probability distribution worsen the conflation . C- Machine Learning: Some nonlinear payoff functions, while not lending themselves to verbalistic expressions and "forecasts", are well captured by ML or expressed in option contracts. D- Fattailedness: The difference is exacerbated in the power law classes of probability distributions.
Mathematical Modeling of Plasticity and Heterogeneity in EMT
Authors: Shubham Tripathi, Jianhua Xing, Herbert Levine, Mohit Kumar Jolly
Date: 2019-07-25 00:00:00

Epithelial-Mesenchymal Transition (EMT), and the corresponding reverse process, Mesenchymal-Epithelial Transition (MET), are dynamic and reversible cellular programs orchestrated by many changes at biochemical and morphological levels. A recent surge in identifying the molecular mechanisms underlying EMT/MET has led to the development of various mathematical models that have contributed to our improved understanding of dynamics at single-cell and population levels: a) multi-stability (how many phenotypes can cells attain en route EMT/MET?), b) reversibility/irreversibility (what time and/or concentration of an EMT inducer marks the 'tipping point' when cells induced to undergo EMT cannot revert?), c) symmetry in EMT/MET (do cells take the same path while reverting as they took during the induction of EMT?), and d) non-cell autonomous mechanisms (how does a cell undergoing EMT alter the tendency of its neighbors to undergo EMT?). These dynamical traits may facilitate a heterogeneous response within a cell population undergoing EMT/MET. Here, we present a few examples of designing different mathematical models that can contribute to decoding EMT/MET dynamics.
Deep convolutional neural networks for uncertainty propagation in random fields
Date: 2019-07-24 00:00:00
Tasks: Uncertainty Quantification

The development of a reliable and robust surrogate model is often constrained
by the dimensionality of the problem. For a system with high-dimensional
inputs/outputs (I/O), conventional approaches usually use a low-dimensional
manifold to describe the high-dimensional system, where the I/O data is first
reduced to more manageable dimensions and then the condensed representation is
used for surrogate modeling. In this study, we present a new solution scheme
for this type of problems based on a deep learning approach. The proposed
surrogate is based on a particular network architecture, i.e. the convolutional
neural networks. The surrogate architecture is designed in a hierarchical style
containing three different levels of model structures, advancing the efficiency
and effectiveness of the model in the aspect of training and deploying. To
assess the model performance, we carry out uncertainty quantification in a
continuum mechanics benchmark problem. Numerical results suggest the proposed
model is capable of directly inferring a wide variety of I/O mapping
relationships. Uncertainty analysis results obtained via the proposed surrogate
have successfully characterized the statistical properties of the output fields
compared to the Monte Carlo estimates.
Does the short-term boost of renewable energies guarantee their stable long-term growth? Assessment of the dynamics of feed-in tariff policy
Date: 2019-07-11 00:00:00
Tasks: Management

Feed in tariff (FiT) is one of the most efficient ways that many governments
throughout the world use to stimulate investment in renewable energies (REs)
technology. For governments, financial management of the policy is very
challenging as that it needs a considerable amount of budget to support RE
producers during the long remuneration period. In this paper, we illuminate
that the early growth of REs capacity could be a temporary boost and the system
elements would backlash the policy if financial circumstances are not handled
well. To show this, we chose Iran as the case, which is in the infancy period
of FiT implementation. Iran started the implementation of FiT policy in 2015
aiming to achieve 5 GW of renewable capacity until 2021. Analyses show that the
probable financial crisis will not only lead to inefficient REs development
after the target time (2021), but may also cause the existing plants to fail.
Social tolerance for paying REs tax and potential investors trust emanated from
budget related mechanisms are taken into consideration in the system dynamics
model developed in this research to reflect those financial effects, which have
rarely been considered in the previous researches. To prevent the financial
crisis of the FiT funding and to maintain the stable growth in long term, three
policy scenarios are analyzed: continuation of the current program with higher
FiT rates, adjusting the FiT rates based on the budget status, and adjusting
the tax on electricity consumption for the development of REs based on the
budget status. The results demonstrate that adjusting the tax on electricity
consumption for the development of REs based on budget status leads to the best
policy result for a desired installed capacity development without any negative
social effects and financial crises.