pwc-archive/papers-with-abstracts huggingface dataset:
Learning Mid-Level Auditory Codes from Natural Sound Statistics
Date: 2017-10-15 00:00:00

Interaction with the world requires an organism to transform sensory signals
into representations in which behaviorally meaningful properties of the
environment are made explicit. These representations are derived through
cascades of neuronal processing stages in which neurons at each stage recode
the output of preceding stages. Explanations of sensory coding may thus involve
understanding how low-level patterns are combined into more complex structures.
Although models exist in the visual domain to explain how mid-level features
such as junctions and curves might be derived from oriented filters in early
visual cortex, little is known about analogous grouping principles for
mid-level auditory representations. We propose a hierarchical generative model
of natural sounds that learns combinations of spectrotemporal features from
natural stimulus statistics. In the first layer the model forms a sparse
convolutional code of spectrograms using a dictionary of learned
spectrotemporal kernels. To generalize from specific kernel activation
patterns, the second layer encodes patterns of time-varying magnitude of
multiple first layer coefficients. Because second-layer features are sensitive
to combinations of spectrotemporal features, the representation they support
encodes more complex acoustic patterns than the first layer. When trained on
corpora of speech and environmental sounds, some second-layer units learned to
group spectrotemporal features that occur together in natural sounds. Others
instantiate opponency between dissimilar sets of spectrotemporal features. Such
groupings might be instantiated by neurons in the auditory cortex, providing a
hypothesis for mid-level neuronal computation.
Day of the Week Effect in biotechnology stocks: An Application of the GARCH processes
Date: 2017-01-25 00:00:00

This study examines the presence of the day-of-the-week effect on daily
returns of biotechnology stocks over a 16-year period from January 2002 to
December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI),
we find that the stock returns were the lowest on Mondays, and compared to the
Mondays the stock returns were significantly higher on Wednesdays, Thursdays,
and Fridays. Moreover, the results from using the asymmetric GARCH processes
reveal that momentum and small-firm effect were positively associated with the
market risk-adjusted returns of the biotechnology stocks during this period.
The findings of our study suggest that active portfolio managers need to
consider the day of the week, momentum, and small-firm effect when making
trading decisions for biotechnology stocks.
Premium valuation for a multiple state model containing manifold premium-paid states
Date: 2017-01-25 00:00:00

The aim of this contribution is to derive a general matrix formula for the
net period premium paid in more than one state. For this purpose we propose to
combine actuarial technics with the graph optimization methodology. The
obtained result is useful for example to more advanced models of dread disease
insurances allowing period premiums paid by both healthy and ill person (e.g.
not terminally yet). As an application, we provide analysis of dread disease
insurances against the risk of lung cancer based on the actual data for the
Lower Silesian Voivodship in Poland.
Context-Aware Prediction of Pathogenicity of Missense Mutations Involved in Human Disease
Date: 2017-01-25 00:00:00

Amino-acid substitutions are implicated in a wide range of human diseases,
many of which are lethal. Distinguishing such mutations from polymorphisms
without significant effect on human health is a necessary step in understanding
the etiology of such diseases. Computational methods can be used to select
interesting mutations within a larger set, to corroborate experimental findings
and to elucidate the cause of the deleterious effect. In this work, we show
that taking into account the sequence context in which the mutation appears
allows to improve the predictive and explanatory power of such methods. We
present an unsupervised approach based on the direct-coupling analysis of
homologous proteins. We show its capability to quantify mutations where methods
without context dependence fail. We highlight cases where the context
dependence is interpretable as functional or structural constraints and show
that our simple and unsupervised method has an accuracy similar to
state-of-the-art methods, including supervised ones.
Research and Teaching Efficiencies of Turkish Universities with Heterogeneity Considerations: Application of Multi-Activity DEA and DEA by Sequential Exclusion of Alternatives Methods
Date: 2016-11-12 00:00:00

The research and teaching efficiencies of 45 Turkish state universities are
evaluated by using Multi-Activity Data Envelopment Analysis (MA-DEA) model
developed by Beasley (1995). Universities are multi-purpose institutions,
therefore they face multiple production functions simultaneously associated
with research and teaching activities. MA-DEA allows assigning priorities and
allocating shared resources to these activities.
Heterogeneity of the educational system: an introduction to the problem
Date: 2016-11-12 00:00:00

We analyze a heterogeneity of the educational system on the basis of one
parameter: input grades of university students. We propose a mathematical model
based on the construction of universities interval order. We use the Hamming
distance to evaluate the heterogeneity of the educational system, and the
Unified State Examination (USE) scores of Russian students to illustrate the
application of the model. We show that institutions taking weak students turn
the whole system of universities into a poorly structured nonhomogeneous
system. In contrast, after deleting the weakest part, the remaining set of
universities becomes a well-structured system
Supply based on demand dynamical model
Date: 2017-01-23 00:00:00
Tasks: model

We propose and analyze numerically a simple dynamical model that describes
the firm behaviors under uncertainty of demand forecast. Iterating this simple
model and varying some parameters values we observe a wide variety of market
dynamics such as equilibria, periodic and chaotic behaviors. Interestingly the
model is also able to reproduce market collapses.
Fractional Calculus and the ESR test
Date: 2016-12-29 00:00:00

We consider a partial differential equation associated with a mathematical
model describing the concentration of nutrients in blood which interferes
directly on the erythrocyte sedimentation rate in the case of an average fluid
velocity equal to zero. Introducing the fractional derivative in the Caputo
sense, we propose a time-fractional mathematical model which contains, as a
particular case, the model proposed by Sharma et al. Our main purpose is to
obtain an analytic solution of this time-fractional partial differential
equation in terms of the Mittag-Leffler function and Wright function.
Brain State Flexibility Accompanies Motor-Skill Acquisition
Date: 2017-01-26 00:00:00

Learning requires the traversal of inherently distinct cognitive states to
produce behavioral adaptation. Yet, tools to explicitly measure these states
with non-invasive imaging -- and to assess their dynamics during learning --
remain limited. Here, we describe an approach based on a novel application of
graph theory in which points in time are represented by network nodes, and
similarities in brain states between two different time points are represented
as network edges. We use a graph-based clustering technique to identify
clusters of time points representing canonical brain states, and to assess the
manner in which the brain moves from one state to another as learning
progresses. We observe the presence of two primary states characterized by
either high activation in sensorimotor cortex or high activation in a
frontal-subcortical system. Flexible switching among these primary states and
other less common states becomes more frequent as learning progresses, and is
inversely correlated with individual differences in learning rate. These
results are consistent with the notion that the development of automaticity is
associated with a greater freedom to use cognitive resources for other
processes. Taken together, our work offers new insights into the constrained,
low dimensional nature of brain dynamics characteristic of early learning,
which give way to less constrained, high-dimensional dynamics in later
learning.
Contact Mechanics of a Small Icosahedral Virus
Date: 2017-01-26 00:00:00
Tasks: Contact mechanics

Virus binding to a surface results at least locally, at the contact area, in
stress and potential structural perturbation of the virus cage. Here we address
the question of the role of substrate-induced deformation in the overall virus
mechanical response to the adsorption event. This question may be especially
important for the broad category of viruses that have their shells stabilized
by weak, non-covalent interactions. We utilize atomic force microscopy to
measure the height change distributions of the brome mosaic virus upon
adsorption from liquid on atomically flat substrates and present a continuum
model which captures well the behavior. Height data fitting according the model
provides, without recourse to indentation, estimates of virus elastic
properties and of the interfacial energy.
A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control
Date: 2017-01-26 00:00:00
Tasks: counterfactual

How does our motor system solve the problem of anticipatory control in spite
of a wide spectrum of response dynamics from different musculo-skeletal
systems, transport delays as well as response latencies throughout the central
nervous system? To a great extent, our highly-skilled motor responses are a
result of a reactive feedback system, originating in the brain-stem and spinal
cord, combined with a feed-forward anticipatory system, that is adaptively
fine-tuned by sensory experience and originates in the cerebellum. Based on
that interaction we design the counterfactual predictive control (CFPC)
architecture, an anticipatory adaptive motor control scheme in which a
feed-forward module, based on the cerebellum, steers an error feedback
controller with counterfactual error signals. Those are signals that trigger
reactions as actual errors would, but that do not code for any current or
forthcoming errors. In order to determine the optimal learning strategy, we
derive a novel learning rule for the feed-forward module that involves an
eligibility trace and operates at the synaptic level. In particular, our
eligibility trace provides a mechanism beyond co-incidence detection in that it
convolves a history of prior synaptic inputs with error signals. In the context
of cerebellar physiology, this solution implies that Purkinje cell synapses
should generate eligibility traces using a forward model of the system being
controlled. From an engineering perspective, CFPC provides a general-purpose
anticipatory control architecture equipped with a learning rule that exploits
the full dynamics of the closed-loop system.
Multi-locus data distinguishes between population growth and multiple merger coalescents
Date: 2018-04-19 00:00:00
Tasks: Experimental Design, Model Selection, parameter estimation

We introduce a low dimensional function of the site frequency spectrum that
is tailor-made for distinguishing coalescent models with multiple mergers from
Kingman coalescent models with population growth, and use this function to
construct a hypothesis test between these model classes. The null and
alternative sampling distributions of the statistic are intractable, but its
low dimensionality renders them amenable to Monte Carlo estimation. We
construct kernel density estimates of the sampling distributions based on
simulated data, and show that the resulting hypothesis test dramatically
improves on the statistical power of a current state-of-the-art method. A key
reason for this improvement is the use of multi-locus data, in particular
averaging observed site frequency spectra across unlinked loci to reduce
sampling variance. We also demonstrate the robustness of our method to nuisance
and tuning parameters. Finally we show that the same kernel density estimates
can be used to conduct parameter estimation, and argue that our method is
readily generalisable for applications in model selection, parameter inference
and experimental design.
Private Information Retrieval from MDS Coded Data with Colluding Servers: Settling a Conjecture by Freij-Hollanti et al.
Authors: Sun Hua, Jafar Syed A.
Date: 2017-01-30 00:00:00
Tasks: Information Retrieval, Retrieval

A $(K, N, T, K_c)$ instance of the MDS-TPIR problem is comprised of $K$
messages and $N$ distributed servers. Each message is separately encoded
through a $(K_c, N)$ MDS storage code. A user wishes to retrieve one message,
as efficiently as possible, while revealing no information about the desired
message index to any colluding set of up to $T$ servers. The fundamental limit
on the efficiency of retrieval, i.e., the capacity of MDS-TPIR is known only at
the extremes where either $T$ or $K_c$ belongs to $\{1,N\}$. The focus of this
work is a recent conjecture by Freij-Hollanti, Gnilke, Hollanti and Karpuk
which offers a general capacity expression for MDS-TPIR. We prove that the
conjecture is false by presenting as a counterexample a PIR scheme for the
setting $(K, N, T, K_c) = (2,4,2,2)$, which achieves the rate $3/5$, exceeding
the conjectured capacity, $4/7$. Insights from the counterexample lead us to
capacity characterizations for various instances of MDS-TPIR including all
cases with $(K, N, T, K_c) = (2,N,T,N-1)$, where $N$ and $T$ can be arbitrary.
Intelligent Topic Selection for Low-Cost Information Retrieval Evaluation: A New Perspective on Deep vs. Shallow Judging
Authors: Kutlu Mucahid, Elsayed Tamer, Lease Matthew
Date: 2017-09-19 00:00:00
Tasks: Information Retrieval, Retrieval

While test collections provide the cornerstone for Cranfield-based evaluation
of information retrieval (IR) systems, it has become practically infeasible to
rely on traditional pooling techniques to construct test collections at the
scale of today's massive document collections. In this paper, we propose a new
intelligent topic selection method which reduces the number of search topics
needed for reliable IR evaluation. To rigorously assess our method, we
integrate previously disparate lines of research on intelligent topic selection
and deep vs. shallow judging. While prior work on intelligent topic selection
has never been evaluated against shallow judging baselines, prior work on deep
vs. shallow judging has largely argued for shallowed judging, but assuming
random topic selection. We argue that for evaluating any topic selection
method, ultimately one must ask whether it is actually useful to select topics,
or should one simply perform shallow judging over many topics? In seeking a
rigorous answer to this over-arching question, we conduct a comprehensive
investigation over a set of relevant factors never previously studied together
1) topic selection method 2) the effect of topic familiarity on human judging
speed and 3) how different topic generation processes impact (i) budget
utilization and (ii) the resultant quality of judgments. Experiments on NIST
TREC Robust 2003 and Robust 2004 test collections show that not only can we
reliably evaluate IR systems with fewer topics, but also that 1) when topics
are intelligently selected, deep judging is often more cost-effective than
shallow judging in evaluation reliability and 2) topic familiarity and topic
generation costs greatly impact the evaluation cost vs. reliability trade-off.
Our findings challenge conventional wisdom in showing that deep judging is
often preferable to shallow judging when topics are selected intelligently.
Diversity and coevolutionary dynamics in high-dimensional phenotype spaces
Date: 2017-02-10 00:00:00
Tasks: Diversity, Vocal Bursts Intensity Prediction

We study macroevolutionary dynamics by extending microevolutionary
competition models to long time scales. It has been shown that for a general
class of competition models, gradual evolutionary change in continuous
phenotypes (evolutionary dynamics) can be non-stationary and even chaotic when
the dimension of the phenotype space in which the evolutionary dynamics unfold
is high. It has also been shown that evolutionary diversification can occur
along non-equilibrium trajectories in phenotype space. We combine these lines
of thinking by studying long-term coevolutionary dynamics of emerging lineages
in multi-dimensional phenotype spaces. We use a statistical approach to
investigate the evolutionary dynamics of many different systems. We find: 1)
for a given dimension of phenotype space, the coevolutionary dynamics tends to
be fast and non-stationary for an intermediate number of coexisting lineages,
but tends to stabilize as the evolving communities reach a saturation level of
diversity; and 2) the amount of diversity at the saturation level increases
rapidly (exponentially) with the dimension of phenotype space. These results
have implications for theoretical perspectives on major macroevolutionary
patterns such as adaptive radiation, long-term temporal patterns of phenotypic
changes, and the evolution of diversity.
Inferring clonal composition from multiple tumor biopsies
Date: 2019-11-22 00:00:00

Explicit accounting for copy number alterations can dramatically improve
mutation frequency estimates, leading to more accurate phylogeny
reconstructions and subclone characterizations.
KMC 3: counting and manipulating k-mer statistics
Date: 2017-01-27 00:00:00

Summary: Counting all k-mers in a given dataset is a standard procedure in
many bioinformatics applications. We introduce KMC3, a significant improvement
of the former KMC2 algorithm together with KMC tools for manipulating k-mer
databases. Usefulness of the tools is shown on a few real problems.
Availability: Program is freely available at
http://sun.aei.polsl.pl/REFRESH/kmc. Contact: sebastian.deorowicz@polsl.pl
Cell-to-cell variability and robustness in S-phase duration from genome replication kinetics
Date: 2017-05-25 00:00:00

Genome replication, a key process for a cell, relies on stochastic initiation
by replication origins, causing a variability of replication timing from cell
to cell. While stochastic models of eukaryotic replication are widely
available, the link between the key parameters and overall replication timing
has not been addressed systematically.We use a combined analytical and
computational approach to calculate how positions and strength of many origins
lead to a given cell-to-cell variability of total duration of the replication
of a large region, a chromosome or the entire genome.Specifically, the total
replication timing can be framed as an extreme-value problem, since it is due
to the last region that replicates in each cell. Our calculations identify two
regimes based on the spread between characteristic completion times of all
inter-origin regions of a genome. For widely different completion times, timing
is set by the single specific region that is typically the last to replicate in
all cells. Conversely, when the completion time of all regions are
comparable,an extreme-value estimate shows that the cell-to-cell variability of
genome replication timing has universal properties. Comparison with available
data shows that the replication program of three yeast species falls in this
extreme-value regime.
A stage-structured Bayesian hierarchical model for salmon lice populations at individual salmon farms - Estimated from multiple farm data sets
Date: 2017-01-27 00:00:00

Salmon farming has become a prosperous international industry over the last
decades. Along with growth in the production farmed salmon, however, an
increasing threat by pathogens has emerged. Of special concern is the
propagation and spread of the salmon louse, Lepeophtheirus salmonis. In order
to gain insight into this parasites population dynamics in large scale salmon
farming system, we present a fully mechanistic stage-structured population
model for the salmon louse, also allowing for complexities involved in the
hierarchical structure of full scale salmon farming. The model estimates
parameters controlling a wide range of processes, including temperature
dependent demographic rates, fish size and abundance effects on louse
transmission rates, effects sizes of various salmon louse control measures, and
distance based between farm transmission rates. Model parameters were estimated
from data including 32 salmon farms, except the last production months for five
farms which were used to evaluate model predictions. We used a Bayesian
estimation approach, combining the prior distributions and the data likelihood
into a joint posterior distribution for all model parameters. The model
generated expected values that fitted the observed infection levels of the
chalimus, adult female and other mobile stages of salmon lice, reasonably well.
Predictions for the time periods not used for fitting the model were also
consistent with the observational data. We argue that the present model for the
population dynamics of the salmon louse in aquaculture farm systems may
contribute to resolve the complexity of processes that drive that drive this
host-parasite relationship, and hence may improve strategies to control the
parasite in this production system.
Who With Whom And How?: Extracting Large Social Networks Using Search Engines
Authors: Siersdorfer Stefan, Kemkes Philipp, Ackermann Hanno, Zerr Sergej
Date: 2017-01-28 00:00:00

Social network analysis is leveraged in a variety of applications such as
identifying influential entities, detecting communities with special interests,
and determining the flow of information and innovations. However, existing
approaches for extracting social networks from unstructured Web content do not
scale well and are only feasible for small graphs. In this paper, we introduce
novel methodologies for query-based search engine mining, enabling efficient
extraction of social networks from large amounts of Web data. To this end, we
use patterns in phrase queries for retrieving entity connections, and employ a
bootstrapping approach for iteratively expanding the pattern set. Our
experimental evaluation in different domains demonstrates that our algorithms
provide high quality results and allow for scalable and efficient construction
of social graphs.
Arbitrage-Free Pricing Of Derivatives In Nonlinear Market Models
Date: 2018-04-10 00:00:00

The objective of this paper is to provide a comprehensive study no-arbitrage
pricing of financial derivatives in the presence of funding costs, the
counterparty credit risk and market frictions affecting the trading mechanism,
such as collateralization and capital requirements. To achieve our goals, we
extend in several respects the nonlinear pricing approach developed in El
Karoui and Quenez (1997) and El Karoui et al. (1997), which was subsequently
continued in Bielecki and Rutkowski (2015).