pwc-archive/papers-with-abstracts huggingface dataset:
The role of evolutionary selection in the dynamics of protein structure evolution
Date: 2016-10-25 00:00:00

Emergence of new protein structures has proved difficult to trace in nature
and engineer in the laboratory. However, one aspect of structure evolution has
proved immensely helpful for determining the three-dimensional structure of
proteins from their sequences: in the vast majority of cases, proteins that
share more than 30% sequence identity have similar structures. Below this mark
is the "twilight zone" where proteins may have identical or very different
structures. These observations form the foundational intuition behind structure
homology modeling. Despite their importance, however, they have never received
a comprehensive biophysical justification. Here we show that the onset of the
twilight zone is more gradual for proteins with low contact density, a proxy
for low thermodynamic stability, than proteins with high contact density. Then
we present an analytical model that treats divergent fold evolution as an
activated process, in analogy to chemical kinetics, where sequence evolution
must overcome thermodynamically unstable evolutionary intermediates to discover
new folds. This model explains the existence of a twilight zone and explains
why its onset is more abrupt for some classes of proteins than for others. We
test the assumptions of the model and characterize the dynamics of fold
evolution using evolutionary simulations of model proteins and cell
populations. Overall these results show how fundamental biophysical constraints
directed evolutionary dynamics leading to the Universe of modern protein
structures and sequences.
GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation
Authors: Zhao Shenglin, Zhao Tong, King Irwin, Lyu Michael R.
Date: 2016-06-19 00:00:00
Tasks: Recommendation Systems

Point-of-interest (POI) recommendation is an important application in
location-based social networks (LBSNs), which learns the user preference and
mobility pattern from check-in sequences to recommend POIs. However, previous
POI recommendation systems model check-in sequences based on either tensor
factorization or Markov chain model, which cannot capture contextual check-in
information in sequences. The contextual check-in information implies the
complementary functions among POIs that compose an individual's daily check-in
sequence. In this paper, we exploit the embedding learning technique to capture
the contextual check-in information and further propose the
\textit{{\textbf{SE}}}quential \textit{{\textbf{E}}}mbedding
\textit{{\textbf{R}}}ank (\textit{SEER}) model for POI recommendation. In
particular, the \textit{SEER} model learns user preferences via a pairwise
ranking model under the sequential constraint modeled by the POI embedding
learning method. Furthermore, we incorporate two important factors, i.e.,
temporal influence and geographical influence, into the \textit{SEER} model to
enhance the POI recommendation system. Due to the temporal variance of
sequences on different days, we propose a temporal POI embedding model and
incorporate the temporal POI representations into a temporal preference ranking
model to establish the \textit{T}emporal \textit{SEER} (\textit{T-SEER}) model.
In addition, We incorporate the geographical influence into the \textit{T-SEER}
model and develop the \textit{\textbf{Geo-Temporal}} \textit{{\textbf{SEER}}}
(\textit{GT-SEER}) model.
A new decomposition of portfolio return
Date: 2016-06-19 00:00:00

For a functionally generated portfolio, there is a natural decomposition of
the relative log-return into the log-change in the generating function and a
drift process. In this note, this decomposition is extended to arbitrary stock
portfolios by an application of Fisk-Stratonovich integration. With the
extended methodology, the generating function is represented by a structural
process, and the drift process is subsumed into a trading process that measures
the profit and loss to the portfolio from trading.
Coexistence of competing metabolic pathways in well-mixed populations
Date: 2016-06-19 00:00:00

Understanding why strains with different metabolic pathways that compete for
a single limiting resource coexist is a challenging issue within a theoretical
perspective. Previous investigations rely on mechanisms such as group or
spatial structuring to achieve a stable coexistence between competing metabolic
strategies. Nevertheless, coexistence has been experimentally reported even in
situations where it cannot be attributed to spatial effects [Heredity {\bf
100}, 471 (2008)]. According to that study a toxin expelled by one of the
strains can be responsible for the stable maintenance of the two strain types.
We propose a resource-based model in which an efficient strain with a slow
metabolic rate competes with a second strain type which presents a fast but
inefficient metabolism. Moreover, the model assumes that the inefficient strain
produces a toxin as a byproduct. This toxin affects the growth rate of both
strains with different strength. Through an extensive exploration of the
parameter space we determine the situations at which the coexistence of the two
strains is possible. Interestingly, we observe that the resource influx rate
plays a key role in the maintenance of the two strain types. In a scenario of
resource scarcity the inefficient is favored, though as the resource influx
rate is augmented the coexistence becomes possible and its domain is enlarged.
M3A: Model, MetaModel, and Anomaly Detection in Web Searches
Authors: Juan Da-Cheng, Shah Neil, Tang Mingyu, Qian Zhiliang, Marculescu Diana, Faloutsos Christos
Date: 2016-06-20 00:00:00
Tasks: Anomaly Detection

'Alice' is submitting one web search per five minutes, for three hours in a
row - is it normal? How to detect abnormal search behaviors, among Alice and
other users? Is there any distinct pattern in Alice's (or other users') search
behavior? We studied what is probably the largest, publicly available, query
log that contains more than 30 million queries from 0.6 million users. In this
paper, we present a novel, user-and group-level framework, M3A: Model,
MetaModel and Anomaly detection. For each user, we discover and explain a
surprising, bi-modal pattern of the inter-arrival time (IAT) of landed queries
(queries with user click-through). Specifically, the model Camel-Log is
proposed to describe such an IAT distribution; we then notice the correlations
among its parameters at the group level. Thus, we further propose the metamodel
Meta-Click, to capture and explain the two-dimensional, heavy-tail distribution
of the parameters. Combining Camel-Log and Meta-Click, the proposed M3A has the
following strong points: (1) the accurate modeling of marginal IAT
distribution, (2) quantitative interpretations, and (3) anomaly detection.
Using String Invariants for Prediction Searching for Optimal Parameters
Date: 2016-06-20 00:00:00
Tasks: Prediction

We have developed a novel prediction method based on string invariants. The
method does not require learning but a small set of parameters must be set to
achieve optimal performance. We have implemented an evolutionary algorithm for
the parametric optimization. We have tested the performance of the method on
artificial and real world data and compared the performance to statistical
methods and to a number of artificial intelligence methods. We have used data
and the results of a prediction competition as a benchmark. The results show
that the method performs well in single step prediction but the methods
performance for multiple step prediction needs to be improved. The method works
well for a wide range of parameters.
How Relevant is the Long Tail? A Relevance Assessment Study on Million Short
Authors: Schaer Philipp, Mayr Philipp, Sünkler Sebastian, Lewandowski Dirk
Date: 2016-06-20 00:00:00

Users of web search engines are known to mostly focus on the top ranked
results of the search engine result page. While many studies support this well
known information seeking pattern only few studies concentrate on the question
what users are missing by neglecting lower ranked results. To learn more about
the relevance distributions in the so-called long tail we conducted a relevance
assessment study with the Million Short long-tail web search engine. While we
see a clear difference in the content between the head and the tail of the
search engine result list we see no statistical significant differences in the
binary relevance judgments and weak significant differences when using graded
relevance. The tail contains different but still valuable results. We argue
that the long tail can be a rich source for the diversification of web search
engine result lists but it needs more evaluation to clearly describe the
differences.
A conditional likelihood is required to estimate the selection coefficient in ancient DNA
Date: 2016-07-11 00:00:00
Tasks: Time Series, Time Series Analysis

Time-series of allele frequencies are a useful and unique set of data to
determine the strength of natural selection on the background of genetic drift.
Technically, the selection coefficient is estimated by means of a likelihood
function built under the hypothesis that the available trajectory spans a
sufficiently large portion of the fitness landscape. Especially for ancient
DNA, however, often only one single such trajectories is available and the
coverage of the fitness landscape is very limited. In fact, one single
trajectory is more representative of a process conditioned both in the initial
and in the final condition than of a process free to visit the available
fitness landscape. Based on two models of population genetics, here we show how
to build a likelihood function for the selection coefficient that takes the
statistical peculiarity of single trajectories into account. We show that this
conditional likelihood delivers a precise estimate of the selection coefficient
also when allele frequencies are close to fixation whereas the unconditioned
likelihood fails. Finally, we discuss the fact that the traditional,
unconditioned likelihood always delivers an answer, which is often
unfalsifiable and appears reasonable also when it is not correct.
Deviations from universality in the fluctuation behavior of a heterogeneous complex system reveal intrinsic properties of components: The case of the international currency market
Date: 2018-06-21 00:00:00
Tasks: Diversity

Identifying behavior that is relatively invariant under different conditions
is a challenging task in far-from-equilibrium complex systems. As an example of
how the existence of a semi-invariant signature can be masked by the
heterogeneity in the properties of the components comprising such systems, we
consider the exchange rate dynamics in the international currency market. We
show that the exponents characterizing the heavy tails of fluctuation
distributions for different currencies systematically diverge from a putative
universal form associated with the median value (~2) of the exponents. We
relate the degree of deviation of a particular currency from such an "inverse
square law" to fundamental macroscopic properties of the corresponding economy,
viz., measures of per capita production output and diversity of export
products. We also show that in contrast to uncorrelated random walks exhibited
by the exchange rate dynamics for currencies belonging to developed economies,
those of the less developed economies show characteristics of sub-diffusive
processes which we relate to the anti-correlated nature of the corresponding
fluctuations. Approaches similar to that presented here may help in identifying
invariant features obscured by the heterogeneous nature of components in other
complex systems.
Modeling the distribution of distance data in Euclidean space
Date: 2016-06-20 00:00:00

Phylogenetic inference-the derivation of a hypothesis for the common
evolutionary history of a group of species- is an active area of research at
the intersection of biology, computer science, mathematics, and statistics. One
assumes the data contains a phylogenetic signal that will be recovered with
varying accuracy due to the quality of the method used, and the quality of the
data.
  The input for distance-based inference methods is an element of a Euclidean
space with coordinates indexed by the pairs of organisms. For several
algorithms there exists a subdivision of this space into polyhedral cones such
that inputs in the same cone return the same tree topology. The geometry of
these cones has been used to analyze the inference algorithms. In this chapter,
we model how input data points drawn from DNA sequences are distributed
throughout Euclidean space in relation to the space of tree metrics, which in
turn can also be described as a collection of polyhedral cones.
Quantifying Neural Efficiency and Capacity: A Differential Equation Interpretation of Polynomial Contrasts
Date: 2016-06-20 00:00:00

Task based neuroimaging tools for the study of cognitive neuroscience provide
insight into understanding how the brain responds to increasing cognitive
demand. Theoretical models of neural-cognitive relationships have been
developed based on observations of linear and non-linear increases in brain
activity. Neural efficiency and capacity are two parameters of current
theoretical models. These two theoretical parameters describe the rate of
increase of brain activity and the upper limits of the increases, respectively.
The current work demonstrates that a quadratic model of increasing brain
activity in response to the n-back task is a solution to a differential
equation model. This reinterpretation of a standard approach to analyzing a
common cognitive task provides a wealth of new insight. The results include
brain wide measures of neural efficiency and capacity. The quantification of
neural-cognitive relationships provides evidence to support current cognitive
neuroscience theories. In addition, the methods provide a framework for
understanding the neural mechanisms of working memory. This allows estimation
of the effects of experimental manipulations within a conceptual research
framework. The proposed methods were applied to twenty-one healthy young adults
while engaging in four levels of the n-back task. All methods are easily
applicable using standard current software packages for neuroimaging.
Predicting Novel Tick Vectors of Zoonotic Disease
Date: 2016-06-20 00:00:00
Tasks: regression

With the resurgence of tick-borne diseases such as Lyme disease and the
emergence of new pathogens such as Powassan virus, understanding what
distinguishes vector from non-vector species, and predicting undiscovered tick
vectors is an important step towards mitigating human disease risk. We apply
generalized boosted regression to interrogate over 90 features for over 240
species of Ixodes ticks. Our model predicted vector status with ~97% accuracy
and implicated 14 tick species whose intrinsic trait profiles confer high
probabilities (~80%) that they are capable of transmitting infections from
animal hosts to humans. Distinguishing characteristics of zoonotic tick vectors
include several anatomical structures that facilitate efficient host seeking
and blood-feeding from a wide variety of host species. Boosted regression
analysis produced both actionable predictions to guide ongoing surveillance as
well as testable hypotheses about the biological underpinnings of vectorial
capacity across tick species.
Excess of genomic defects in a woolly mammoth on Wrangel island
Date: 2017-01-19 00:00:00

Woolly mammoths (Mammuthus primigenius) populated Siberia, Beringia, and
North America during the Pleistocene and early Holocene. Recent breakthroughs
in ancient DNA sequencing have allowed for complete genome sequencing for two
specimens of woolly mammoths (Palkopoulou et al. 2015). One mammoth specimen is
from a mainland population ~45,000 years ago when mammoths were plentiful. The
second, a 4300 yr old specimen, is derived from an isolated population on
Wrangel island where mammoths subsisted with small effective population size
more than 43-fold lower than previous populations. These extreme differences in
effective population size offer a rare opportunity to test nearly neutral
models of genome architecture evolution within a single species. Using these
previously published mammoth sequences, we identify deletions, retrogenes, and
non-functionalizing point mutations. In the Wrangel island mammoth, we identify
a greater number of deletions, a larger proportion of deletions affecting gene
sequences, a greater number of candidate retrogenes, and an increased number of
premature stop codons. This accumulation of detrimental mutations is consistent
with genomic meltdown in response to low effective population sizes in the
dwindling mammoth population on Wrangel island. In addition, we observe high
rates of loss of olfactory receptors and urinary proteins, either because these
loci are non-essential or because they were favored by divergent selective
pressures in island environments. Finally, at the locus of FOXQ1 we observe two
independent loss-of-function mutations, which would confer a satin coat
phenotype in this island woolly mammoth.
Multi-Period Portfolio Optimization: Translation of Autocorrelation Risk to Excess Variance
Date: 2016-09-19 00:00:00
Tasks: Portfolio Optimization, Translation

Growth-optimal portfolios are guaranteed to accumulate higher wealth than any
other investment strategy in the long run. However, they tend to be risky in
the short term. For serially uncorrelated markets, similar portfolios with more
robust guarantees have been recently proposed. This paper extends these robust
portfolios by accommodating non-zero autocorrelations that may reflect
investors' beliefs about market movements. Moreover, we prove that the risk
incurred by such autocorrelations can be absorbed by modifying the covariance
matrix of asset returns.
Competitive binding of transcription factors drives Mendelian dominance in regulatory genetic pathways
Date: 2016-08-27 00:00:00

We report a new mechanism for allelic dominance in regulatory genetic
interactions that we call binding dominance. We investigated a biophysical
model of gene regulation, where the fractional occupancy of a transcription
factor (TF) on the cis-regulated promoter site it binds to is determined by
binding energy (-{\Delta}G) and TF dosage. Transcription and gene expression
proceed when the TF is bound to the promoter. In diploids, individuals may be
heterozygous at the cis-site, at the TF's coding region, or at the TF's own
promoter, which determines allele-specific dosage. We find that when the TF's
coding region is heterozygous, TF alleles compete for occupancy at the cis
sites and the tighter-binding TF is dominant in proportion to the difference in
binding strength. When the TF's own promoter is heterozygous, the TF produced
at the higher dosage is also dominant. Cis-site heterozygotes have additive
expression and therefore codominant phenotypes. Binding dominance propagates to
affect the expression of downstream loci and it is sensitive in both magnitude
and direction to genetic background, but its detectability often attenuates.
While binding dominance is inevitable at the molecular level, it is difficult
to detect in the phenotype under some biophysical conditions, more so when TF
dosage is high and allele-specific binding affinities are similar. A body of
empirical research on the biophysics of TF binding demonstrates the
plausibility of this mechanism of dominance, but studies of gene expression
under competitive binding in heterozygotes in a diversity of genetic
backgrounds are needed.
A mathematical model of demand-supply dynamics with collectability and saturation factors
Date: 2016-06-21 00:00:00

We introduce a mathematical model on the dynamics of demand and supply
incorporating collectability and saturation factors. Our analysis shows that
when the fluctuation of the determinants of demand and supply is strong enough,
there is chaos in the demand-supply dynamics. Our numerical simulation shows
that such a chaos is not an attractor (i.e. dynamics is not approaching the
chaos), instead a periodic attractor (of period 3 under the Poincar\'e period
map) exists near the chaos, and co-exists with another periodic attractor (of
period 1 under the Poincar\'e period map) near the market equilibrium. Outside
the basins of attraction of the two periodic attractors, the dynamics
approaches infinity indicating market irrational exuberance or flash crash. The
period 3 attractor represents the product's market cycle of growth and
recession, while period 1 attractor near the market equilibrium represents the
regular fluctuation of the product's market. Thus our model captures more
market phenomena besides Marshall's market equilibrium. When the fluctuation of
the determinants of demand and supply is strong enough, a three leaf danger
zone exists where the basins of attraction of all attractors intertwine and
fractal basin boundaries are formed. Small perturbations in the danger zone can
lead to very different attractors. That is, small perturbations in the danger
zone can cause the market to experience oscillation near market equilibrium,
large growth and recession cycle, and irrational exuberance or flash crash.
Learning Optimal Card Ranking from Query Reformulation
Authors: Hong Liangjie, Shi Yue, Rajan Suju
Date: 2016-06-22 00:00:00
Tasks: Learning-To-Rank

Mobile search has recently been shown to be the major contributor to the
growing search market. The key difference between mobile search and desktop
search is that information presentation is limited to the screen space of the
mobile device. Thus, major search engines have adopted a new type of search
result presentation, known as \textit{information cards}, in which each card
presents summarized results from one domain/vertical, for a given query, to
augment the standard blue-links search results. While it has been widely
acknowledged that information cards are particularly suited to mobile user
experience, it is also challenging to optimize such result sets. Typically,
user engagement metrics like query reformulation are based on whole ranked list
of cards for each query and most traditional learning to rank algorithms
require per-item relevance labels. In this paper, we investigate the
possibility of interpreting query reformulation into effective relevance labels
for query-card pairs. We inherit the concept of conventional learning-to-rank,
and propose pointwise, pairwise and listwise interpretations for query
reformulation. In addition, we propose a learning-to-label strategy that learns
the contribution of each card, with respect to a query, where such
contributions can be used as labels for training card ranking models. We
utilize a state-of-the-art ranking model and demonstrate the effectiveness of
proposed mechanisms on a large-scale mobile data from a major search engine,
showing that models trained from labels derived from user engagement can
significantly outperform ones trained from human judgment labels.
Brexit or Bremain ? Evidence from bubble analysis
Date: 2016-06-22 00:00:00

We applied the Johansen-Ledoit-Sornette (JLS) model to detect possible
bubbles and crashes related to the Brexit/Bremain referendum scheduled for 23rd
June 2016. Our implementation includes an enhanced model calibration using
Genetic Algorithms. We selected a few historical financial series sensitive to
the Brexit/Bremain scenario, representative of multiple asset classes. We found
that equity and currency asset classes show no bubble signals, while rates,
credit and real estate show super-exponential behaviour and instabilities
typical of bubble regime. Our study suggests that, under the JLS model, equity
and currency markets do not expect crashes or sharp rises following the
referendum results. Instead, rates and credit markets consider the referendum a
risky event, expecting either a Bremain scenario or a Brexit scenario
edulcorated by central banks intervention. In the case of real estate, a crash
is expected, but its relationship with the referendum results is unclear.