pwc-archive/papers-with-abstracts huggingface dataset:
Enumeration of ancestral configurations for matching gene trees and species trees
Date: 2016-10-24 00:00:00

Given a gene tree and a species tree, ancestral configurations represent the
combinatorially distinct sets of gene lineages that can reach a given node of
the species tree. They have been introduced as a data structure for use in the
recursive computation of the conditional probability under the multispecies
coalescent model of a gene tree topology given a species tree, the cost of this
computation being affected by the number of ancestral configurations of the
gene tree in the species tree. For matching gene trees and species trees, we
obtain enumerative results on ancestral configurations. We study ancestral
configurations in balanced and unbalanced families of trees determined by a
given seed tree, showing that for seed trees with more than one taxon, the
number of ancestral configurations increases for both families exponentially in
the number of taxa $n$. For fixed $n$, the maximal number of ancestral
configurations tabulated at the species tree root node and the largest number
of labeled histories possible for a labeled topology occur for trees with
precisely the same unlabeled shape. For ancestral configurations at the root,
the maximum increases with $k_0^n$, where $k_0 \approx 1.5028$ is a quadratic
recurrence constant. Under a uniform distribution over the set of labeled trees
of given size, the mean number of root ancestral configurations grows with
$\sqrt{3/2}(4/3)^n$ and the variance with approximately $1.4048(1.8215)^n$. The
results provide a contribution to the combinatorial study of gene trees and
species trees.
Exact probabilities for the indeterminacy of complex networks as perceived through press perturbations
Date: 2016-10-25 00:00:00

We consider the goal of predicting how complex networks respond to chronic
(press) perturbations when characterizations of their network topology and
interaction strengths are associated with uncertainty. Our primary result is
the derivation of exact formulas for the expected number and probability of
qualitatively incorrect predictions about a system's responses under
uncertainties drawn form arbitrary distributions of error. These formulas
obviate the current use of simulations, algorithms, and qualitative modeling
techniques. Additional indices provide new tools for identifying which links in
a network are most qualitatively and quantitatively sensitive to error, and for
determining the volume of errors within which predictions will remain
qualitatively determinate (i.e. sign insensitive). Together with recent
advances in the empirical characterization of uncertainty in ecological
networks, these tools bridge a way towards probabilistic predictions of network
dynamics.
Bi-phase age-related brain gray matter magnetic resonance T1rho relaxation time change
Date: 2016-10-25 00:00:00
Tasks: Hippocampus

Objectives: To investigate normative value and age-related change of brain
magnetic resonance T1rho relaxation at 1.5 T. Methods: 20 males (age:
40.7+/-15.5 years, range: 22-68 years) and 22 females (age: 38.5 +/-14.8 years,
range: 21-62 years), were scanned at 1.5 Tesla using 3D fluid suppressed turbo
spin echo sequence. Regions-of-interests (ROIs) were obtained by atlas-based
tissue segmentation and T1rho was calculated by fitting the mean value to
monoexponential model. Correlation between T1rho relaxation of brain gray
matter regions and age was investigated. Results: A regional difference among
individual gray matter areas was noted; with hippocampus (98.37+/-5.37 msec)
and amygdala (94.95+/-4.34 msec) have the highest measurement, while pallidum
(83.81+/-5.49) and putamen (83.93+4.76) the lowest measurement. T1rho values
decreased slowly (mean slope: -0.256) and significantly (p<0.05) with age in
gray matter for subjects younger than 40 years old, while for subjects older
than 40 years old there was no significant correlation between T1rho relaxation
and age. Conclusion: T1rho relaxation demonstrates a bi-phase change with age
in adults of 22-68 years.
A recommender system for efficient discovery of new anomalies in large-scale access logs
Authors: Jiang Heju, Algatt Scott, Ahammad Parvez
Date: 2016-10-25 00:00:00
Tasks: Management, Recommendation Systems

We present a novel, non-standard recommender system for large-scale security
policy management(SPM). Our system Helios discovers and recommends unknown and
unseen anomalies in large-scale access logs with minimal supervision and no
starting information on users and items. Typical recommender systems assume
availability of user- and item-related information, but such information is not
usually available in access logs. To resolve this problem, we first use
discrete categorical labels to construct categorical combinations from access
logs in a bootstrapping manner. Then, we utilize rank statistics of entity rank
and order categorical combinations for recommendation. From a double-sided cold
start, with minimal supervision, Helios learns to recommend most salient
anomalies at large-scale, and provides visualizations to security experts to
explain rationale behind the recommendations. Our experiments show Helios to be
suitable for large-scale applications: from cold starts, in less than 60
minutes, Helios can analyze roughly 4.6 billion records in logs of 400GB with
about 300 million potential categorical combinations, then generate ranked
categorical combinations as recommended discoveries. We also show that, even
with limited computing resources, Helios accelerates unknown and unseen anomaly
discovery process for SPM by 1 to 3 orders of magnitude, depending on use
cases. In addition, Helios' design is flexible with metrics and measurement
fields used for discoveries and recommendations. Overall, our system leads to
more efficient and customizable SPM processes with faster discoveries of unseen
and unknown anomalies.
Optimal Risk-Averse Timing of an Asset Sale: Trending vs Mean-Reverting Price Dynamics
Date: 2016-10-26 00:00:00

This paper studies the optimal risk-averse timing to sell a risky asset. The
investor's risk preference is described by the exponential, power, or log
utility. Two stochastic models are considered for the asset price -- the
geometric Brownian motion and exponential Ornstein-Uhlenbeck models -- to
account for, respectively, the trending and mean-reverting price dynamics. In
all cases, we derive the optimal thresholds and certainty equivalents to sell
the asset, and compare them across models and utilities, with emphasis on their
dependence on asset price, risk aversion, and quantity. We find that the timing
option may render the investor's value function and certainty equivalent
non-concave in price. Numerical results are provided to illustrate the
investor's strategies and the premium associated with optimally timing to sell.
Online Bayesian phylogenetic inference: theoretical foundations via Sequential Monte Carlo
Date: 2016-10-26 00:00:00

Phylogenetics, the inference of evolutionary trees from molecular sequence
data such as DNA, is an enterprise that yields valuable evolutionary
understanding of many biological systems. Bayesian phylogenetic algorithms,
which approximate a posterior distribution on trees, have become a popular if
computationally expensive means of doing phylogenetics. Modern data collection
technologies are quickly adding new sequences to already substantial databases.
With all current techniques for Bayesian phylogenetics, computation must start
anew each time a sequence becomes available, making it costly to maintain an
up-to-date estimate of a phylogenetic posterior. These considerations highlight
the need for an \emph{online} Bayesian phylogenetic method which can update an
existing posterior with new sequences.
  Here we provide theoretical results on the consistency and stability of
methods for online Bayesian phylogenetic inference based on Sequential Monte
Carlo (SMC) and Markov chain Monte Carlo (MCMC). We first show a consistency
result, demonstrating that the method samples from the correct distribution in
the limit of a large number of particles. Next we derive the first reported set
of bounds on how phylogenetic likelihood surfaces change when new sequences are
added. These bounds enable us to characterize the theoretical performance of
sampling algorithms by bounding the effective sample size (ESS) with a given
number of particles from below. We show that the ESS is guaranteed to grow
linearly as the number of particles in an SMC sampler grows. Surprisingly, this
result holds even though the dimensions of the phylogenetic model grow with
each new added sequence.
Phenotypic heterogeneity in modeling cancer evolution
Date: 2016-10-26 00:00:00

The unwelcome evolution of malignancy during cancer progression emerges
through a selection process in a complex heterogeneous population structure. In
the present work, we investigate evolutionary dynamics in a phenotypically
heterogeneous population of stem cells (SCs) and their associated progenitors.
The fate of a malignant mutation is determined not only by overall stem cell
and differentiated cell growth rates but also differentiation and
dedifferentiation rates. We investigate the effect of such a complex population
structure on the evolution of malignant mutations. We derive exact analytic
results for the fixation probability of a mutant arising in each of the
subpopulations. The analytic results are in almost perfect agreement with the
numerical simulations. Moreover, a condition for evolutionary advantage of a
mutant cell versus the wild type population is given in the present study. We
also show that microenvironment-induced plasticity in invading mutants leads to
more aggressive mutants with higher fixation probability. Our model predicts
that decreasing polarity between stem and differentiated cells turnover would
raise the survivability of non-plastic mutants; while it would suppress the
development of malignancy for plastic mutants. We discuss our model in the
context of colorectal/intestinal cancer (at the epithelium). This novel
mathematical framework can be applied more generally to a variety of problems
concerning selection in heterogeneous populations, in other contexts such as
population genetics, and ecology.
A clustering tool for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Models
Date: 2016-10-26 00:00:00
Tasks: Clustering

We propose a new procedure for clustering nucleotide sequences based on the
"Laplacian Eigenmaps" and Gaussian Mixture modelling. This proposal is then
applied to a set of 100 DNA sequences from the mitochondrially encoded NADH
dehydrogenase 3 (ND3) gene of a collection of Platyhelminthes and Nematoda
species. The resulting clusters are then shown to be consistent with the gene
phylogenetic tree computed using a maximum likelihood approach. This comparison
shows in particular that the clustering produced by the methodology combining
Laplacian Eigenmaps with Gaussian Mixture models is coherent with the phylogeny
as well as with the NCBI taxonomy. We also developed a Python package for this
procedure which is available online.
Short term prediction of extreme returns based on the recurrence interval analysis
Date: 2016-10-26 00:00:00
Tasks: Management

Being able to predict the occurrence of extreme returns is important in
financial risk management. Using the distribution of recurrence intervals---the
waiting time between consecutive extremes---we show that these extreme returns
are predictable on the short term. Examining a range of different types of
returns and thresholds we find that recurrence intervals follow a
$q$-exponential distribution, which we then use to theoretically derive the
hazard probability $W(\Delta t |t)$. Maximizing the usefulness of extreme
forecasts to define an optimized hazard threshold, we indicates a financial
extreme occurring within the next day when the hazard probability is greater
than the optimized threshold. Both in-sample tests and out-of-sample
predictions indicate that these forecasts are more accurate than a benchmark
that ignores the predictive signals. This recurrence interval finding deepens
our understanding of reoccurring extreme returns and can be applied to forecast
extremes in risk management.
Inferring individual attributes from search engine queries and auxiliary information
Authors: Soldaini Luca, Yom-Tov Elad
Date: 2016-10-26 00:00:00

Internet data has surfaced as a primary source for investigation of different
aspects of human behavior. A crucial step in such studies is finding a suitable
cohort (i.e., a set of users) that shares a common trait of interest to
researchers. However, direct identification of users sharing this trait is
often impossible, as the data available to researchers is usually anonymized to
preserve user privacy. To facilitate research on specific topics of interest,
especially in medicine, we introduce an algorithm for identifying a trait of
interest in anonymous users. We illustrate how a small set of labeled examples,
together with statistical information about the entire population, can be
aggregated to obtain labels on unseen examples. We validate our approach using
labeled data from the political domain.
  We provide two applications of the proposed algorithm to the medical domain.
In the first, we demonstrate how to identify users whose search patterns
indicate they might be suffering from certain types of cancer. In the second,
we detail an algorithm to predict the distribution of diseases given their
incidence in a subset of the population at study, making it possible to predict
disease spread from partial epidemiological data.
Science In the Cloud (SIC): A use case in MRI Connectomics
Date: 2017-02-14 00:00:00
Tasks: Cloud Computing

Modern technologies are enabling scientists to collect extraordinary amounts
of complex and sophisticated data across a huge range of scales like never
before. With this onslaught of data, we can allow the focal point to shift
towards answering the question of how we can analyze and understand the massive
amounts of data in front of us. Unfortunately, lack of standardized sharing
mechanisms and practices often make reproducing or extending scientific results
very difficult. With the creation of data organization structures and tools
which drastically improve code portability, we now have the opportunity to
design such a framework for communicating extensible scientific discoveries.
Our proposed solution leverages these existing technologies and standards, and
provides an accessible and extensible model for reproducible research, called
"science in the cloud" (sic). Exploiting scientific containers, cloud computing
and cloud data services, we show the capability to launch a computer in the
cloud and run a web service which enables intimate interaction with the tools
and data presented. We hope this model will inspire the community to produce
reproducible and, importantly, extensible results which will enable us to
collectively accelerate the rate at which scientific breakthroughs are
discovered, replicated, and extended.
Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: novel insights from a dataset with a high genomic coverage
Date: 2016-10-26 00:00:00

Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil
bacterium able to grow on a diversity of carbon sources like sugars and organic
acids. It is a biotechnological relevant organism because of its highly
efficient ability to biosynthesize amino acids, such as L-glutamic acid and
L-lysine. Here, we reconstructed the most complete C. glutamicum regulatory
network to date and comprehensively analyzed its global organizational
properties, systems-level features and functional architecture. Our analyses
show the tremendous power of Abasy Atlas to study the functional organization
of regulatory networks. We created two models of the C. glutamicum regulatory
network: all-evidences (containing both weak and strong supported interactions,
genomic coverage = 73%) and strongly-supported (only accounting for strongly
supported evidences, genomic coverage = 71%). Using state-of-the-art
methodologies, we prove that power-law behaviors truly govern the connectivity
and clustering coefficient distributions. We found a non-previously reported
circuit motif that we named complex feed-forward motif. We highlighted the
importance of feedback loops for the functional architecture, beyond whether
they are statistically over-represented or not in the network. We show that the
previously reported top-down approach is inadequate to infer the hierarchy
governing a regulatory network because feedback bridges different hierarchical
layers, and the top-down approach disregards the presence of intermodular genes
shaping the integration layer. Our findings all together further support a
diamond-shaped, three-layered hierarchy exhibiting some feedback between
processing and coordination layers, which is shaped by four classes of
systems-level elements: global regulators, locally autonomous modules, basal
machinery and intermodular genes.
Portfolio Benchmarking under Drawdown Constraint and Stochastic Sharpe Ratio
Date: 2016-10-26 00:00:00
Tasks: Benchmarking

We consider an investor who seeks to maximize her expected utility derived
from her terminal wealth relative to the maximum performance achieved over a
fixed time horizon, and under a portfolio drawdown constraint, in a market with
local stochastic volatility (LSV). In the absence of closed-form formulas for
the value function and optimal portfolio strategy, we obtain approximations for
these quantities through the use of a coefficient expansion technique and
nonlinear transformations. We utilize regularity properties of the risk
tolerance function to numerically compute the estimates for our approximations.
In order to achieve similar value functions, we illustrate that, compared to a
constant volatility model, the investor must deploy a quite different portfolio
strategy which depends on the current level of volatility in the stochastic
volatility model.
Quantifying word salad: The structural randomness of verbal reports predicts negative symptoms and Schizophrenia diagnosis 6 months later
Date: 2016-10-30 00:00:00
Tasks: Prognosis

Background: The precise quantification of negative symptoms is necessary to
improve differential diagnosis and prognosis prediction in Schizophrenia. In
chronic psychotic patients, the representation of verbal reports as word graphs
provides automated sorting of schizophrenia, bipolar disorder and control
groups based on the degree of speech connectedness. Here we aim to use machine
learning to verify whether speech connectedness during first clinical contact
can predict negative symptoms and Schizophrenia diagnosis six months later.
Methods: PANSS scores and memory reports were collected from 21 patients
undergoing first clinical contact for recent-onset psychosis and followed for 6
months to establish DSM-IV diagnosis, and 21 healthy controls. Each report was
represented as a graph in which words corresponded to nodes, and node temporal
succession corresponded to edges. Three connectedness attributes were extracted
from each graph, z-scores to random graph distributions were measured,
correlated with the PANSS negative subscale, combined into a single
Fragmentation Index, and used for predictions. Findings: Random-like speech was
prevalent among Schizophrenia patients (64% x 5% in Control group, p=0.0002).
Connectedness explained 92% of the PANSS negative subscale variance (p=0.0001).
The Fragmentation Index classified low versus high scores of PANSS negative
subscale with 93% accuracy (AUC=1), predicted Schizophrenia diagnosis with 89%
accuracy (AUC=0.89), and was validated in an independent cohort of chronic
psychotic patients. Interpretation: The structural randomness of speech graph
connectedness is increased in Schizophrenia. It provides a quantitative
measurement of word salad as a Fragmentation Index that tightly correlates with
negative symptoms and predicts Schizophrenia diagnosis during first clinical
contact of recent-onset psychosis.
Random matrix analysis for gene interaction networks in cancer cells
Date: 2018-07-26 00:00:00

Investigations of topological uniqueness of gene interaction networks in
cancer cells are essential for understanding this disease. Based on the random
matrix theory, we study the distribution of the nearest neighbor level spacings
$P(s)$ of interaction matrices for gene networks in human cancer cells. The
interaction matrices are computed using the Cancer Network Galaxy (TCNG)
database, which is a repository of gene interactions inferred by a Bayesian
network model. 256 NCBI GEO entries regarding gene expressions in human cancer
cells have been selected for the Bayesian network calculations in TCNG. We
observe the Wigner distribution of $P(s)$ when the gene networks are dense
networks that have more than $\sim 38,000$ edges. In the opposite case, when
the networks have smaller numbers of edges, the distribution $P(s)$ becomes the
Poisson distribution. We investigate relevance of $P(s)$ both to the size of
the networks and to edge frequencies that manifest reliance of the inferred
gene interactions.
Utility Maximization and Indifference Value under Risk and Information Constraints for a Market with a Change Point
Date: 2016-10-27 00:00:00

In this article we consider an optimization problem of expected utility
maximization of continuous-time trading in a financial market. This trading is
constrained by a benchmark for a utility-based shortfall risk measure. The
market consists of one asset whose price process is modeled by a Geometric
Brownian motion where the market parameters change at a random time. The
information flow is modeled by initially and progressively enlarged filtrations
which represent the knowledge about the price process, the Brownian motion and
the random time. We solve the maximization problem and give the optimal
terminal wealth depending on these different filtrations for general utility
functions by using martingale representation results for the corresponding
filtration. Moreover, for a special utility function and risk measure we
calculate the utility indifference value which measures the gain of further
information for the investor.
$\kappa$-generalized models of income and wealth distributions: A survey
Date: 2016-10-27 00:00:00
Tasks: Survey

The paper provides a survey of results related to the "$\kappa$-generalized
distribution", a statistical model for the size distribution of income and
wealth. Topics include, among others, discussion of basic analytical
properties, interrelations with other statistical distributions as well as
aspects that are of special interest in the income distribution field, such as
the Gini index and the Lorenz curve. An extension of the basic model that is
most able to accommodate the special features of wealth data is also reviewed.
The survey of empirical applications given in this paper shows the
$\kappa$-generalized models of income and wealth to be in excellent agreement
with the observed data in many cases.
Extinction threshold of a population in spatial and stochastic model
Date: 2016-10-26 00:00:00

In this study, spatial stochastic and logistic model (SSLM) describing
dynamics of a population of a certain species was analysed. The behaviour of
the extinction threshold as a function of model parameters was studied. More
specifically, we studied how the critical values for the model parameters that
separate the cases of extinction and persistence depend on the spatial scales
of the competition and dispersal kernels. We compared the simulations and
analytical results to examine if and how the mathematical approximations break
down at the vicinity of the extinction threshold, and found a functional form
of the naive approximation for which higher-order term of analytical
approximation converges.