pwc-archive/papers-with-abstracts huggingface dataset:
On the American swaption in the linear-rational framework
Date: 2018-02-24 00:00:00

We study American swaptions in the linear-rational (LR) term structure model
introduced in [5]. The American swaption pricing problem boils down to an
optimal stopping problem that is analytically tractable. It reduces to a
free-boundary problem that we tackle by the local time-space calculus of [7].
We characterize the optimal stopping boundary as the unique solution to a
nonlinear integral equation that can be readily solved numerically. We obtain
the arbitrage-free price of the American swaption and the optimal exercise
strategies in terms of swap rates for both fixed-rate payer and receiver swaps.
Finally, we show that Bermudan swaptions can be efficiently priced as well.
An ergodic BSDE approach to forward entropic risk measures: representation and large-maturity behavior
Date: 2017-04-17 00:00:00

Using elements from the theory of ergodic backward stochastic differential
equations (BSDE), we study the behavior of forward entropic risk measures. We
provide their general representation results (via both BSDE and convex duality)
and examine their behavior for risk positions of long maturities. We show that
forward entropic risk measures converge to some constant exponentially fast. We
also compare them with their classical counterparts and derive a parity result.
Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?
Date: 2016-09-14 00:00:00

Recently, Basel Committee for Banking Supervision proposed to replace all
approaches, including Advanced Measurement Approach (AMA), for operational risk
capital with a simple formula referred to as the Standardised Measurement
Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of
SMA such as instability, risk insensitivity, super-additivity and the implicit
relationship between SMA capital model and systemic risk in the banking sector.
We also discuss the issues with closely related operational risk
Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor
to the SMA. In conclusion, we advocate to maintain the AMA internal model
framework and suggest as an alternative a number of standardization
recommendations that could be considered to unify internal modelling of
operational risk. The findings and views presented in this paper have been
discussed with and supported by many OpRisk practitioners and academics in
Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in
London.
Deep Learning for Mortgage Risk
Date: 2018-03-10 00:00:00
Tasks: Deep Learning

We develop a deep learning model of multi-period mortgage risk and use it to
analyze an unprecedented dataset of origination and monthly performance records
for over 120 million mortgages originated across the US between 1995 and 2014.
Our estimators of term structures of conditional probabilities of prepayment,
foreclosure and various states of delinquency incorporate the dynamics of a
large number of loan-specific as well as macroeconomic variables down to the
zip-code level. The estimators uncover the highly nonlinear nature of the
relationship between the variables and borrower behavior, especially
prepayment. They also highlight the effects of local economic conditions on
borrower behavior. State unemployment has the greatest explanatory power among
all variables, offering strong evidence of the tight connection between housing
finance markets and the macroeconomy. The sensitivity of a borrower to changes
in unemployment strongly depends upon current unemployment. It also
significantly varies across the entire borrower population, which highlights
the interaction of unemployment and many other variables. These findings have
important implications for mortgage-backed security investors, rating agencies,
and housing finance policymakers.
Narrow escape to small windows on a small ball modeling the viral entry into the cell nucleus
Date: 2016-07-18 00:00:00

A certain class of viruses replicates inside a cell if they can enter the
nucleus through one of many small target pores, before being permanently
trapped or degraded. We adopt for viral motion a switching stochastic process
model and we estimate here the probability and the conditional mean first
passage time for a viral particle to attain alive the nucleus. The cell nucleus
is covered with thousands of small absorbing nuclear pores and the minimum
distance between them defines the smallest spatial scale that limits the
efficiency of stochastic simulations. Using the Neuman-Green's function method
to solve the steady-state Fokker-Planck equation, we derive asymptotic formula
for the probability and mean arrival time to a small window for various pores'
distributions, that agree with stochastic simulations. These formulas reveal
how key geometrical parameters defines the cytoplasmic stage of viral
infection.
Visual Motion Onset Brain-computer Interface
Date: 2016-09-30 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), ERP

The paper presents a study of two novel visual motion onset stimulus-based
brain-computer interfaces (vmoBCI). Two settings are compared with afferent and
efferent to a computer screen center motion patterns. Online vmoBCI experiments
are conducted in an oddball event-related potential (ERP) paradigm allowing for
"aha-responses" decoding in EEG brainwaves. A subsequent stepwise linear
discriminant analysis classification (swLDA) classification accuracy comparison
is discussed based on two inter-stimulus-interval (ISI) settings of 700 and 150
ms in two online vmoBCI applications with six and eight command settings. A
research hypothesis of classification accuracy non-significant differences with
various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as
well as with various numbers of ERP response averaging scenarios. The efferent
in respect to display center visual motion patterns allowed for a faster
interfacing and thus they are recommended as more suitable for the
no-eye-movements requiring visual BCIs.
Hybrid Recommender System Based on Personal Behavior Mining
Authors: Fang Zhiyuan, Zhang Lingqi, Chen Kun
Date: 2016-07-10 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Sequential Pattern Mining

Recommender systems are mostly well known for their applications in
e-commerce sites and are mostly static models. Classical personalized
recommender algorithm includes item-based collaborative filtering method
applied in Amazon, matrix factorization based collaborative filtering algorithm
from Netflix, etc. In this article, we hope to combine traditional model with
behavior pattern extraction method. We use desensitized mobile transaction
record provided by T-mall, Alibaba to build a hybrid dynamic recommender
system. The sequential pattern mining aims to find frequent sequential pattern
in sequence database and is applied in this hybrid model to predict customers'
payment behavior thus contributing to the accuracy of the model.
Significance of Natural Scene Statistics in Understanding the Anisotropies of Perceptual Filling-in at the Blind Spot
Date: 2017-01-12 00:00:00

Psychophysical experiments reveal our horizontal preference in perceptual
filling-in at the blind spot. On the other hand, vertical preference is
exhibited in the case of tolerance in filling-in. What causes this anisotropy
in our perception? Building upon the general notion, that the functional
properties of the early visual system are shaped by the innate specification as
well as the statistics of the environment, we reasoned that the anisotropy in
filling-in could be understood in terms of anisotropy in orientation
distribution inherent to natural scene statistics. We examined this proposition
by investigating filling-in of bar stimuli on a Hierarchical Predictive Coding
model network. In response to bar stimuli, the model network, trained with
natural images, exhibited anisotropic filling-in performance at the blind spot
similar to reported in psychophysical experiments i.e. horizontal preference in
filling-in and vertical preference in tolerance of filling-in. We suggest that
the over-representation of horizontal contours in the natural scene contribute
to the observed horizontal superiority while the broader distribution of
vertical contours contributes to the observed vertical superiority in
tolerance. These results indicate that natural scene statistics plays a
significant role in determining the filling-in performance at the blind spot
and shaping the associated anisotropies.
Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements
Date: 2016-07-11 00:00:00
Tasks: Bayesian Inference

FRET measurements can provide dynamic spatial information on length scales
smaller than the diffraction limit of light. Several methods exist to measure
FRET between fluorophores, including Fluorescence Lifetime Imaging Microscopy
(FLIM), which relies on the reduction of fluorescence lifetime when a
fluorophore is undergoing FRET. FLIM measurements take the form of histograms
of photon arrival times, containing contributions from a mixed population of
fluorophores both undergoing and not undergoing FRET, with the measured
distribution being a mixture of exponentials of different lifetimes. Here, we
present an analysis method based on Bayesian inference that rigorously takes
into account several experimental complications. We test the precision and
accuracy of our analysis on controlled experimental data and verify that we can
faithfully extract model parameters, both in the low-photon and low-fraction
regimes.
Mass spectrometry analysis using MALDIquant
Date: 2016-07-11 00:00:00

MALDIquant and associated R packages provide a versatile and completely free
open-source platform for analyzing 2D mass spectrometry data as generated for
instance by MALDI and SELDI instruments. We first describe the various methods
and algorithms available in MALDIquant . Subsequently, we illustrate a typical
analysis workflow using MALDIquant by investigating an experimental cancer data
set, starting from raw mass spectrometry measurements and ending at
multivariate classification.
Stock Market Market Crash of 2008: an empirical study of the deviation of share prices from company fundamentals
Date: 2016-07-12 00:00:00
Tasks: regression

The aim of this study is to investigate quantitatively whether share prices
deviated from company fundamentals in the stock market crash of 2008. For this
purpose, we use a large database containing the balance sheets and share prices
of 7,796 worldwide companies for the period 2004 through 2013. We develop a
panel regression model using three financial indicators--dividends per share,
cash flow per share, and book value per share--as explanatory variables for
share price. We then estimate individual company fundamentals for each year by
removing the time fixed effects from the two-way fixed effects model, which we
identified as the best of the panel regression models. One merit of our model
is that we are able to extract unobservable factors of company fundamentals by
using the individual fixed effects.
  Based on these results, we analyze the market anomaly quantitatively using
the divergence rate--the rate of the deviation of share price from a company's
fundamentals. We find that share prices on average were overvalued in the
period from 2005 to 2007, and were undervalued significantly in 2008, when the
global financial crisis occurred. Share prices were equivalent to the
fundamentals on average in the subsequent period. Our empirical results clearly
demonstrate that the worldwide stock market fluctuated excessively in the time
period before and just after the global financial crisis of 2008.
A Cross-Platform Collection of Social Network Profiles
Authors: Veiga Maria Han, Eickhoff Carsten
Date: 2016-07-12 00:00:00
Tasks: User Identification

The proliferation of Internet-enabled devices and services has led to a
shifting balance between digital and analogue aspects of our everyday lives. In
the face of this development there is a growing demand for the study of privacy
hazards, the potential for unique user de-anonymization and information leakage
between the various social media profiles many of us maintain. To enable the
structured study of such adversarial effects, this paper presents a dedicated
dataset of cross-platform social network personas (i.e., the same person has
accounts on multiple platforms). The corpus comprises 850 users who generate
predominantly English content. Each user object contains the online footprint
of the same person in three distinct social networks: Twitter, Instagram and
Foursquare. In total, it encompasses over 2.5M tweets, 340k check-ins and 42k
Instagram posts. We describe the collection methodology, characteristics of the
dataset, and how to obtain it. Finally, we discuss a common use case,
cross-platform user identification.
Implicit Negative Feedback in Clinical Information Retrieval
Authors: Kuhn Lorenz, Eickhoff Carsten
Date: 2016-07-12 00:00:00
Tasks: Information Retrieval, Retrieval

In this paper, we reflect on ways to improve the quality of bio-medical
information retrieval by drawing implicit negative feedback from negated
information in noisy natural language search queries. We begin by studying the
extent to which negations occur in clinical texts and quantify their
detrimental effect on retrieval performance. Subsequently, we present a number
of query reformulation and ranking approaches that remedy these shortcomings by
resolving natural language negations. Our experimental results are based on
data collected in the course of the TREC Clinical Decision Support Track and
show consistent improvements compared to state-of-the-art methods. Using our
novel algorithms, we are able to reduce the negative impact of negations on
early precision by up to 65%.
Dual representations for systemic risk measures
Date: 2019-07-31 00:00:00

The financial crisis showed the importance of measuring, allocating and
regulating systemic risk. Recently, the systemic risk measures that can be
decomposed into an aggregation function and a scalar measure of risk, received
a lot of attention. In this framework, capital allocations are added after
aggregation and can represent bailout costs. More recently, a framework has
been introduced, where institutions are supplied with capital allocations
before aggregation. This yields an interpretation that is particularly useful
for regulatory purposes. In each framework, the set of all feasible capital
allocations leads to a multivariate risk measure. In this paper, we present
dual representations for scalar systemic risk measures as well as for the
corresponding multivariate risk measures concerning capital allocations. Our
results cover both frameworks: aggregating after allocating and allocating
after aggregation. As examples, we consider the aggregation mechanisms of the
Eisenberg-Noe model as well as those of the resource allocation and network
flow models.
Statistical thermodynamic basis in drug-receptor interactions: double annihilation and double decoupling alchemical theories, revisited
Date: 2016-07-13 00:00:00
Tasks: Drug Discovery

Alchemical theory is emerging as a promising tool in the context of molecular
dynamics simulations for drug discovery projects. In this theoretical
contribution, I revisit the statistical mechanics foundation of non covalent
interactions in drug-receptor systems, providing a unifying treatment that
encompasses the most important variants in the alchemical approaches, from the
seminal Double Annihilation Method by Jorgensen and Ravimohan [W.L. Jorgensen
and C. Ravimohan, J. Chem. Phys. 83,3050, 1985], to the Gilson's Double
Decoupling Method [M. K. Gilson and J. A. Given and B. L. Bush and J. A.
McCammon, Biophys. J. 72, 1047 1997] and the Deng and Roux alchemical theory
[Y. Deng and B. Roux, J. Chem. Theory Comput., 2, 1255 2006]. Connections and
differences between the various alchemical approaches are highlighted and
discussed, and finally placed into the broader context of nonequilibrium
thermodynamics.
Effect of memory in non-Markovian Boolean networks
Date: 2016-07-13 00:00:00

One successful model of interacting biological systems is the Boolean
network. The dynamics of a Boolean network, controlled with Boolean functions,
is usually considered to be a Markovian (memory-less) process. However, both
self organizing features of biological phenomena and their intelligent nature
should raise some doubt about ignoring the history of their time evolution.
Here, we extend the Boolean network Markovian approach: we involve the effect
of memory on the dynamics. This can be explored by modifying Boolean functions
into non-Markovian functions, for example, by investigating the usual
non-Markovian threshold function, - one of the most applied Boolean functions.
By applying the non-Markovian threshold function on the dynamical process of a
cell cycle network, we discover a power law memory with a more robust dynamics
than the Markovian dynamics.
A draft genome assembly of southern bluefin tuna Thunnus maccoyii
Date: 2016-07-13 00:00:00

Tuna are large pelagic fish whose populations are close to panmixia. In
addition, they are threatened species, so it is important for the maintenance
and monitoring of genetic diversity that genetic information at a genome level
be obtained. Here we report the draft assembly of the southern bluefin tuna
genome and the collection of genome-wide sequence data for five other tuna
species. We sampled five tuna species of the genus Thunnus, the northern and
southern bluefin, yellowfin, albacore, and bigeye, as well as the skipjack
(Katsuwonis pelamis), a tuna-like species. Genome assembly was facilitated at
k-mer=25 while k-mer=51 generated assembly artefacts. The estimated size of the
southern bluefin tuna genome was 795 Mb. We assembled two southern bluefin tuna
individuals independently using both paired end and mate pair sequence. This
resulted in scaffolds with N50>174,000 bp and maximum scaffold lengths>1.4 Mb.
Our estimate of the size of the assembled genome was the scaffolded sequences
in common to both assemblies, which amounted to 721 Mb of the 795 Mb of the
southern bluefin tuna genome sequence. Using BLAST, there were matches between
13,039 of 14,341 (91%) refseq mRNA of the zebrafish Danio rerio to the tuna
assembly indicating that most of a generic fish transcriptome was covered by
the assembly.
Precision of readout at the hunchback gene: analyzing short transcription time traces in living fly embryos
Date: 2016-11-20 00:00:00

The simultaneous expression of the hunchback gene in the numerous nuclei of
the developing fly embryo gives us a unique opportunity to study how
transcription is regulated in living organisms. A recently developed MS2-MCP
technique for imaging nascent messenger RNA in living Drosophila embryos allows
us to quantify the dynamics of the developmental transcription process. The
initial measurement of the morphogens by the hunchback promoter takes place
during very short cell cycles, not only giving each nucleus little time for a
precise readout, but also resulting in short time traces of transcription.
Additionally, the relationship between the measured signal and the promoter
state depends on the molecular design of the reporting probe. We develop an
analysis approach based on tailor made autocorrelation functions that overcomes
the short trace problems and quantifies the dynamics of transcription
initiation. Based on live imaging data, we identify signatures of bursty
transcription initiation from the hunchback promoter. We show that the
precision of the expression of the hunchback gene to measure its position along
the anterior-posterior axis is low both at the boundary and in the anterior
even at cycle 13, suggesting additional post-transcriptional averaging
mechanisms to provide the precision observed in fixed embryos.
A Principal-Agent Model of Trading Under Market Impact -Crossing networks interacting with dealer markets-
Date: 2016-08-16 00:00:00

We use a principal-agent model to analyze the structure of a book-driven
dealer market when the dealer faces competition from a crossing network or dark
pool. The agents are privately informed about their types (e.g. their
portfolios), which is something that the dealer must take into account when
engaging his counterparties. Instead of trading with the dealer, the agents may
chose to trade in a crossing network. We show that the presence of such a
network results in more types being serviced by the dealer and that, under
certain conditions and due to reduced adverse selection effects, the book's
spread shrinks. We allow for the pricing on the dealer market to determine the
structure of the crossing network and show that the same conditions that lead
to a reduction of the spread imply the existence of an equilibrium
book/crossing network pair.
Functional Cliques in Developmentally Correlated Neural Networks
Date: 2017-03-12 00:00:00

We consider a sparse random network of excitatory leaky integrate-and-fire
neurons with short-term synaptic depression. Furthermore to mimic the dynamics
of a brain circuit in its first stages of development we introduce for each
neuron correlations among in-degree and out-degree as well as among
excitability and the corresponding total degree, We analyze the influence of
single neuron stimulation and deletion on the collective dynamics of the
network. We show the existence of a small group of neurons capable of
controlling and even silencing the bursting activity of the network. These
neurons form a functional clique since only their activation in a precise order
and within specific time windows is capable to ignite population bursts.
On the Ribosomal Density that Maximizes Protein Translation Rate
Date: 2016-07-14 00:00:00
Tasks: Translation

During mRNA translation, several ribosomes attach to the same mRNA molecule
simultaneously translating it into a protein. This pipelining increases the
protein production rate. A natural and important question is what ribosomal
density maximizes the protein production rate. Using mathematical models of
ribosome flow along both a linear and a circular mRNA molecule we prove that
typically the steady-state production rate is maximized when the ribosomal
density is one half of the maximal possible density. We discuss the
implications of our results to endogenous genes under natural cellular
conditions and also to synthetic biology.
Insurance valuation: a computable multi-period cost-of-capital approach
Date: 2016-07-14 00:00:00

We present an approach to market-consistent multi-period valuation of
insurance liability cash flows based on a two-stage valuation procedure. First,
a portfolio of traded financial instrument aimed at replicating the liability
cash flow is fixed. Then the residual cash flow is managed by repeated
one-period replication using only cash funds. The latter part takes capital
requirements and costs into account, as well as limited liability and risk
averseness of capital providers. The cost-of-capital margin is the value of the
residual cash flow. We set up a general framework for the cost-of-capital
margin and relate it to dynamic risk measurement. Moreover, we present explicit
formulas and properties of the cost-of-capital margin under further assumptions
on the model for the liability cash flow and on the conditional risk measures
and utility functions. Finally, we highlight computational aspects of the
cost-of-capital margin, and related quantities, in terms of an example from
life insurance.