pwc-archive/papers-with-abstracts huggingface dataset:
Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach
Date: 2015-11-21 00:00:00
Tasks: All

Complex phenotypic differences among different acute leukemias cannot be
fully captured by analyzing the expression levels of one single molecule, such
as a miR, at a time, but requires systematic analysis of large sets of miRs.
While a popular approach for analysis of such datasets is principal component
analysis (PCA), this method is not designed to optimally discriminate different
phenotypes. Moreover, PCA and other low-dimensional representation methods
yield linear or non-linear combinations of all measured miRs. Global human miR
expression was measured in AML, B-ALL, and T-ALL cell lines and patient RNA
samples. By systematically applying support vector machines to all measured
miRs taken in dyad and triad groups, we built miR networks using cell line data
and validated our findings with primary patient samples. All the coordinately
transcribed members of the miR-23a cluster (which includes also miR-24 and
miR-27a), known to function as tumor suppressors of acute leukemias, appeared
in the AML, B-ALL and T-ALL centric networks. Subsequent qRT-PCR analysis
showed that the most connected miR in the B-ALL-centric network, miR-708, is
highly and specifically expressed in B-ALLs, suggesting that miR-708 might
serve as a biomarker for B-ALL. This approach is systematic, quantitative,
scalable, and unbiased. Rather than a single signature, our approach yields a
network of signatures reflecting the redundant nature of biological signaling
pathways. The network representation allows for visual analysis of all
signatures by an expert and for future integration of additional information.
Furthermore, each signature involves only small sets of miRs, such as dyads and
triads, which are well suited for in depth validation through laboratory
experiments such as loss- and gain-of-function assays designed to drive changes
in leukemia cell survival, proliferation and differentiation.
Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models
Date: 2016-02-21 00:00:00

In Part II of this paper, we concentrate our analysis on the price dynamical
model with the moving average rules developed in Part I of this paper. By
decomposing the excessive demand function, we reveal that it is the interplay
between trend-following and contrarian actions that generates the price chaos,
and give parameter ranges for the price series to change from divergence to
chaos and to oscillation. We prove that the price dynamical model has an
infinite number of equilibrium points but all these equilibrium points are
unstable. We demonstrate the short-term predictability of the return volatility
and derive the detailed formula of the Lyapunov exponent as function of the
model parameters. We show that although the price is chaotic, the volatility
converges to some constant very quickly at the rate of the Lyapunov exponent.
We extract the formula relating the converged volatility to the model
parameters based on Monte-Carlo simulations. We explore the circumstances under
which the returns show independency and illustrate in details how the
independency index changes with the model parameters. Finally, we plot the
strange attractor and return distribution of the chaotic price model to
illustrate the complex structure and fat-tailed distribution of the returns.
Dynamical Models of Stock Prices Based on Technical Trading Rules Part III: Application to Hong Kong Stocks
Date: 2016-02-21 00:00:00

In Part III of this study, we apply the price dynamical model with big buyers
and big sellers developed in Part I of this paper to the daily closing prices
of the top 20 banking and real estate stocks listed in the Hong Kong Stock
Exchange. The basic idea is to estimate the strength parameters of the big
buyers and the big sellers in the model and make buy/sell decisions based on
these parameter estimates. We propose two trading strategies: (i)
Follow-the-Big-Buyer which buys when big buyer begins to appear and there is no
sign of big sellers, holds the stock as long as the big buyer is still there,
and sells the stock once the big buyer disappears; and (ii) Ride-the-Mood which
buys as soon as the big buyer strength begins to surpass the big seller
strength, and sells the stock once the opposite happens. Based on the testing
over 245 two-year intervals uniformly distributed across the seven years from
03-July-2007 to 02-July-2014 which includes a variety of scenarios, the net
profits would increase 67% or 120% on average if an investor switched from the
benchmark Buy-and-Hold strategy to the Follow-the-Big-Buyer or Ride-the-Mood
strategies during this period, respectively.
Methods of Information Theory and Algorithmic Complexity for Network Biology
Date: 2015-12-11 00:00:00

We survey and introduce concepts and tools located at the intersection of
information theory and network biology. We show that Shannon's information
entropy, compressibility and algorithmic complexity quantify different local
and global aspects of synthetic and biological data. We show examples such as
the emergence of giant components in Erdos-Renyi random graphs, and the
recovery of topological properties from numerical kinetic properties simulating
gene expression data. We provide exact theoretical calculations, numerical
approximations and error estimations of entropy, algorithmic probability and
Kolmogorov complexity for different types of graphs, characterizing their
variant and invariant properties. We introduce formal definitions of complexity
for both labeled and unlabeled graphs and prove that the Kolmogorov complexity
of a labeled graph is a good approximation of its unlabeled Kolmogorov
complexity and thus a robust definition of graph complexity.
On the Measurement of Economic Tail Risk
Date: 2015-08-16 00:00:00

This paper attempts to provide a decision-theoretic foundation for the
measurement of economic tail risk, which is not only closely related to utility
theory but also relevant to statistical model uncertainty. The main result is
that the only risk measures that satisfy a set of economic axioms for the
Choquet expected utility and the statistical property of elicitability (i.e.
there exists an objective function such that minimizing the expected objective
function yields the risk measure) are the mean functional and the median
shortfall, which is the median of tail loss distribution. Elicitability is
important for backtesting. We also extend the result to address model
uncertainty by incorporating multiple scenarios. As an application, we argue
that median shortfall is a better alternative than expected shortfall for
setting capital requirements in Basel Accords.
From the Samuelson Volatility Effect to a Samuelson Correlation Effect: Evidence from Crude Oil Calendar Spread Options
Date: 2015-02-20 00:00:00

We introduce a multi-factor stochastic volatility model based on the
CIR/Heston stochastic volatility process. In order to capture the Samuelson
effect displayed by commodity futures contracts, we add expiry-dependent
exponential damping factors to their volatility coefficients. The pricing of
single underlying European options on futures contracts is straightforward and
can incorporate the volatility smile or skew observed in the market. We
calculate the joint characteristic function of two futures contracts in the
model in analytic form and use the one-dimensional Fourier inversion method of
Caldana and Fusai (JBF 2013) to price calendar spread options. The model leads
to stochastic correlation between the returns of two futures contracts. We
illustrate the distribution of this correlation in an example. We then propose
analytical expressions to obtain the copula and copula density directly from
the joint characteristic function of a pair of futures. These expressions are
convenient to analyze the term-structure of dependence between the two futures
produced by the model. In an empirical application we calibrate the proposed
model to volatility surfaces of vanilla options on WTI. In this application we
provide evidence that the model is able to produce the desired stylized facts
in terms of volatility and dependence. In a separate appendix, we give guidance
for the implementation of the proposed model and the Fourier inversion results
by means of one and two-dimensional FFT methods.
Elimination of systemic risk in financial networks by means of a systemic risk transaction tax
Date: 2016-02-17 00:00:00

Financial markets are exposed to systemic risk (SR), the risk that a major
fraction of the system ceases to function, and collapses. It has recently
become possible to quantify SR in terms of underlying financial networks where
nodes represent financial institutions, and links capture the size and maturity
of assets (loans), liabilities, and other obligations, such as derivatives. We
demonstrate that it is possible to quantify the share of SR that individual
liabilities within a financial network contribute to the overall SR. We use
empirical data of nationwide interbank liabilities to show that the marginal
contribution to overall SR of liabilities for a given size varies by a factor
of a thousand. We propose a tax on individual transactions that is proportional
to their marginal contribution to overall SR. If a transaction does not
increase SR it is tax-free. With an agent-based model (CRISIS macro-financial
model) we demonstrate that the proposed "Systemic Risk Tax" (SRT) leads to a
self-organised restructuring of financial networks that are practically free of
SR. The SRT can be seen as an insurance for the public against costs arising
from cascading failure. ABM predictions are shown to be in remarkable agreement
with the empirical data and can be used to understand the relation of credit
risk and SR.
The evolution of carrying capacity in constrained and expanding tumour cell populations
Date: 2015-08-14 00:00:00
Tasks: Specificity, Variable Selection

Cancer cells are known to modify their micro-environment such that it can
sustain a larger population, or, in ecological terms, they construct a niche
which increases the carrying capacity of the population. It has however been
argued that niche construction, which benefits all cells in the tumour, would
be selected against since cheaters could reap the benefits without paying the
cost. We have investigated the impact of niche specificity on tumour evolution
using an individual based model of breast tumour growth, in which the carrying
capacity of each cell consists of two components: an intrinsic,
subclone-specific part and a contribution from all neighbouring cells. Analysis
of the model shows that the ability of a mutant to invade a resident population
depends strongly on the specificity. When specificity is low selection is
mostly on growth rate, while high specificity shifts selection towards
increased carrying capacity. Further, we show that the long-term evolution of
the system can be predicted using adaptive dynamics. By comparing the results
from a spatially structured vs.\ well-mixed population we show that spatial
structure restores selection for carrying capacity even at zero specificity,
which a poses solution to the niche construction dilemma. Lastly, we show that
an expanding population exhibits spatially variable selection pressure, where
cells at the leading edge exhibit higher growth rate and lower carrying
capacity than those at the centre of the tumour.
A comparison of Euclidean metrics and their application in statistical inferences in the spike train space
Date: 2015-06-10 00:00:00

Statistical analysis and inferences on spike trains are one of the central
topics in neural coding. It is of great interest to understand the underlying
distribution and geometric structure of given spike train data. However, a
fundamental obstacle is that the space of all spike trains is not an Euclidean
space, and non-Euclidean metrics have been commonly used in the literature to
characterize the variability and pattern in neural observations. Over the past
few years, two Euclidean-like metrics were independently developed to measure
distance in the spike train space. An important benefit of these metrics is
that the spike train space will be suitable for embedding in Euclidean spaces
due to their Euclidean properties. In this paper, we systematically compare
these two metrics on theory, properties, and applications. Because of its
Euclidean properties, one of these metrics has been further used in defining
summary statistics (i.e. mean and variance) and conducting statistical
inferences in the spike train space. Here we provide equivalent definitions
using the other metric and show that consistent statistical inferences can be
conducted. We then apply both inference frameworks in a neural coding problem
for a recording in geniculate ganglion stimulated by different tastes. It is
found that both frameworks achieve desirable results and provide useful new
tools in statistical inferences in neural spike train space.
A unifying framework for quantifying the nature of animal interactions
Date: 2016-03-18 00:00:00

Collective phenomena, whereby agent-agent interactions determine spatial
patterns, are ubiquitous in the animal kingdom. On the other hand, movement and
space use are also greatly influenced by the interactions between animals and
their environment. Despite both types of interaction fundamentally influencing
animal behaviour, there has hitherto been no unifying framework for the models
proposed in both areas. Here, we construct a general method for inferring
population-level spatial patterns from underlying individual movement and
interaction processes, a key ingredient in building a statistical mechanics for
ecological systems. We show that resource selection functions, as well as
several examples of collective motion models, arise as special cases of our
framework, thus bringing together resource selection analysis and collective
animal behaviour into a single theory. In particular, we focus on combining the
various mechanistic models of territorial interactions in the literature with
step selection functions, by incorporate interactions into the step selection
framework and demonstrating how to derive territorial patterns from the
resulting models. We demonstrate the efficacy of our model by application to a
population of insectivore birds in the Amazon rainforest.
TrendLearner: Early Prediction of Popularity Trends of User Generated Content
Authors: Figueiredo Flavio, Almeida Jussara M., Gonçalves Marcos André, Benevenuto Fabrício
Date: 2016-02-14 00:00:00
Tasks: Prediction

We here focus on the problem of predicting the popularity trend of user
generated content (UGC) as early as possible. Taking YouTube videos as case
study, we propose a novel two-step learning approach that: (1) extracts
popularity trends from previously uploaded objects, and (2) predicts trends for
new content. Unlike previous work, our solution explicitly addresses the
inherent tradeoff between prediction accuracy and remaining interest in the
content after prediction, solving it on a per-object basis. Our experimental
results show great improvements of our solution over alternatives, and its
applicability to improve the accuracy of state-of-the-art popularity prediction
methods.
Model-independent Superhedging under Portfolio Constraints
Date: 2015-06-12 00:00:00
Tasks: model

In a discrete-time market, we study model-independent superhedging, while the
semi-static superhedging portfolio consists of {\it three} parts: static
positions in liquidly traded vanilla calls, static positions in other tradable,
yet possibly less liquid, exotic options, and a dynamic trading strategy in
risky assets under certain constraints. By considering the limit order book of
each tradable exotic option and employing the Monge-Kantorovich theory of
optimal transport, we establish a general superhedging duality, which admits a
natural connection to convex risk measures. With the aid of this duality, we
derive a model-independent version of the fundamental theorem of asset pricing.
The notion "finite optimal arbitrage profit", weaker than no-arbitrage, is also
introduced. It is worth noting that our method covers a large class of Delta
constraints as well as Gamma constraint.
The geometry of relative arbitrage
Date: 2015-07-28 00:00:00
Tasks: Diversity

Consider an equity market with $n$ stocks. The vector of proportions of the
total market capitalizations that belong to each stock is called the market
weight. The market weight defines the market portfolio which is a buy-and-hold
portfolio representing the performance of the entire stock market. Consider a
function that assigns a portfolio vector to each possible value of the market
weight, and we perform self-financing trading using this portfolio function. We
study the problem of characterizing functions such that the resulting portfolio
will outperform the market portfolio in the long run under the conditions of
diversity and sufficient volatility. No other assumption on the future behavior
of stock prices is made. We prove that the only solutions are functionally
generated portfolios in the sense of Fernholz. A second characterization is
given as the optimal maps of a remarkable optimal transport problem. Both
characterizations follow from a novel property of portfolios called
multiplicative cyclical monotonicity.
Accelerating Implicit Finite Difference Schemes Using a Hardware Optimized Tridiagonal Solver for FPGAs
Date: 2015-10-14 00:00:00

We present a design and implementation of the Thomas algorithm optimized for
hardware acceleration on an FPGA, the Thomas Core. The hardware-based algorithm
combined with the custom data flow and low level parallelism available in an
FPGA reduces the overall complexity from 8N down to 5N serial arithmetic
operations, and almost halves the overall latency by parallelizing the two
costly divisions. Combining this with a data streaming interface, we reduce
memory overheads to 2 N-length vectors per N-tridiagonal system to be solved.
The Thomas Core allows for multiple independent tridiagonal systems to be
continuously solved in parallel, providing an efficient and scalable
accelerator for many numerical computations. Finally we present applications
for derivatives pricing problems using implicit finite difference schemes on an
FPGA accelerated system and we investigate the use and limitations of
fixed-point arithmetic in our algorithm.
Evolution-Development Congruence in Pattern Formation Dynamics: Bifurcations in Gene Expressions and Regulation of Networks Structures
Date: 2015-03-31 00:00:00

Search for possible relationships between phylogeny and ontogeny is one of
the most important issues in the field of evolutionary developmental biology.
By representing developmental dynamics of spatially located cells with gene
expression dynamics with cell-to-cell interaction under external morphogen
gradient, evolved are gene regulation networks under mutation and selection
with the fitness to approach a prescribed spatial pattern of expressed genes.
For most of thousands of numerical evolution experiments, evolution of pattern
over generations and development of pattern by an evolved network exhibit
remarkable congruence. Here, both the pattern dynamics consist of several
epochs to form successive stripe formations between quasi-stationary regimes.
In evolution, the regimes are generations needed to hit relevant mutations,
while in development, they are due to the emergence of slowly varying
expression that controls the pattern change. Successive pattern changes are
thus generated, which are regulated by successive combinations of feedback or
feedforward regulations under the upstream feedforward network that reads the
morphogen gradient. By using a pattern generated by the upstream feedforward
network as a boundary condition, downstream networks form later stripe
patterns. These epochal changes in development and evolution are represented as
same bifurcations in dynamical-systems theory, and this agreement of
bifurcations lead to the evolution-development congruences. Violation of the
evolution-development congruence, observed exceptionally, is shown to be
originated in alteration of the boundary due to mutation at the upstream
feedforward network. Our results provide a new look on developmental stages,
punctuated equilibrium, developmental bottlenecks, and evolutionary acquisition
of novelty in morphogenesis.
Trading with Small Price Impact
Date: 2015-03-30 00:00:00

An investor trades a safe and several risky assets with linear price impact
to maximize expected utility from terminal wealth. In the limit for small
impact costs, we explicitly determine the optimal policy and welfare, in a
general Markovian setting allowing for stochastic market, cost, and preference
parameters. These results shed light on the general structure of the problem at
hand, and also unveil close connections to optimal execution problems and to
other market frictions such as proportional and fixed transaction costs.
Systemic Risk and Default Clustering for Large Financial Systems
Date: 2015-02-18 00:00:00
Tasks: Clustering

As it is known in the finance risk and macroeconomics literature,
risk-sharing in large portfolios may increase the probability of creation of
default clusters and of systemic risk. We review recent developments on
mathematical and computational tools for the quantification of such phenomena.
Limiting analysis such as law of large numbers and central limit theorems allow
to approximate the distribution in large systems and study quantities such as
the loss distribution in large portfolios. Large deviations analysis allow us
to study the tail of the loss distribution and to identify pathways to default
clustering. Sensitivity analysis allows to understand the most likely ways in
which different effects, such as contagion and systematic risks, combine to
lead to large default rates. Such results could give useful insights into how
to optimally safeguard against such events.
Estimating the Energetic State of Malignant Cells from RNA Transcription and Protein Interaction Network Data
Date: 2015-05-31 00:00:00

Gene expression data, or transcription data, are surrogates for actual
protein concentrations in the cells. In addition protein-protein interactions
are static diagrams of all the protein-protein interactions in the cell. These
interactions may consist of covalent bonding or maybe just secondary bonding
such as hydrogen bonding. Given these two surrogate data types we show a
technique to compute the Gibbs free energy of a cell. We apply this to yeast
cell cycle and to cancer.
Clinical Facts Along With a Feedback Control Perspective Suggest That Increased Response Time Might be the Cause of Parkinsonian Rest Tremor
Date: 2017-07-26 00:00:00

Parkinson's disease (PD) is a neurodegenerative disorder characterized by
increased response times leading to a variety of biomechanical symptoms such as
tremors, stooping and gait instability. Although the deterioration in
biomechanical control can intuitively be related to sluggish response times,
how the delay leads to such biomechanical symptoms as tremor is not yet
understood. Only recently has it been explained from the perspective of
feedback control theory that delay beyond a threshold can be the cause of
Parkinsonian tremor [1]. This paper correlates several observations from this
perspective to clinical facts and reinforces them with simple numerical and
experimental examples. This work provides a framework towards developing a
deeper conceptual understanding of the mechanism behind PD symptoms.
Furthermore, it lays a foundation for developing tools for diagnosis and
progress tracking of the disease by identifying some key trends.