pwc-archive/papers-with-abstracts huggingface dataset:
Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization
Date: 2017-07-25 00:00:00
Tasks: Dictionary Learning, Dimensionality Reduction, EEG, Electroencephalogram (EEG)

Two techniques are proposed to alleviate the computational burden of MUltiple
SIgnal Classification (MUSIC) algorithm applied to Electroencephalogram (EEG)
source localization. A significant reduction was achieved by parsing the cortex
surface into smaller regions and nominating only a few regions for the
exhaustive search inherent in the MUSIC algorithm. The nomination procedure
involves a dictionary learning phase in which each region is assigned an atom
matrix. Moreover, a dimensionality reduction step provided by excluding some of
the electrodes is designed such that the Cramer-Rao bound of localization is
maintained. It is shown by simulation that computational complexity of the
MUSIC-based localization can be reduced by up to $80\%$.
Structural entanglements in protein complexes
Date: 2017-07-12 00:00:00

We consider multi-chain protein native structures and propose a criterion
that determines whether two chains in the system are entangled or not. The
criterion is based on the behavior observed by pulling at both temini of each
chain simultaneously in the two chains. We have identified about 900 entangled
systems in the Protein Data Bank and provided a more detailed analysis for
several of them. We argue that entanglement enhances the thermodynamic
stability of the system but it may have other functions: burying the
hydrophobic residues at the interface, and increasing the DNA or RNA binding
area. We also study the folding and stretching properties of the knotted
dimeric proteins MJ0366, YibK and bacteriophytochrome. These proteins have been
studied theoretically in their monomeric versions so far. The dimers are seen
to separate on stretching through the tensile mechanism and the characteristic
unraveling force depends on the pulling direction.
OMICtools: a community-driven search engine for biological data analysis
Authors: Perrin Helene, Denorme Marion, Grosjean Julien, community OMICtools, Dynomant Emeric, Henry Vincent J., Pichon Fabien, Darmoni Stefan, Desfeux Arnaud, Gonzalez Bruno J.
Date: 2017-07-12 00:00:00

With high-throughput biotechnologies generating unprecedented quantities of
data, researchers are faced with the challenge of locating and comparing an
exponentially growing number of programs and websites dedicated to
computational biology, in order to maximize the potential of their data.
OMICtools is designed to meet this need with its open-access search engine
offering an easy means of locating the right tools corresponding to each
researcher and their specific biological data analyses. The OMICtools website
(https://OMICtools.com) centralizes more than 18,500 software tools and
databases, manually classified, by a team of biocurators including many
scientific experts. Key information, a direct link, and access to discussions
and evaluations by the biomedical community are provided for each tool. Anyone
can join the OMICtools community and create a profile page, to share their
expertise and comment on tools. In addition, developers can directly upload
their code versions which are registered for identification and citation in
scientific publications, improving research traceability. The OMICtools
community links thousands of life scientists and developers worldwide, who use
this bioinformatics platform to accelerate their projects and biological data
analyses.
Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution
Date: 2017-07-11 00:00:00
Tasks: Time Series, Time Series Analysis

The realized GARCH framework is extended to incorporate the two-sided Weibull
distribution, for the purpose of volatility and tail risk forecasting in a
financial time series. Further, the realized range, as a competitor for
realized variance or daily returns, is employed in the realized GARCH
framework. Further, sub-sampling and scaling methods are applied to both the
realized range and realized variance, to help deal with inherent
micro-structure noise and inefficiency. An adaptive Bayesian Markov Chain Monte
Carlo method is developed and employed for estimation and forecasting, whose
properties are assessed and compared with maximum likelihood, via a simulation
study. Compared to a range of well-known parametric GARCH, GARCH with two-sided
Weibull distribution and realized GARCH models, tail risk forecasting results
across 7 market index return series and 2 individual assets clearly favor the
realized GARCH models incorporating two-sided Weibull distribution, especially
models employing the sub-sampled realized variance and sub-sampled realized
range, over a six year period that includes the global financial crisis.
Collective excitability in a mesoscopic neuronal model of epileptic activity
Date: 2017-07-12 00:00:00

The brain can be understood as a collection of interacting neuronal
oscillators, but the extent to which its sustained activity is due to coupling
among brain areas is still unclear. Here we study the joint dynamics of two
cortical columns described by Jansen-Rit neural mass models, and show that
coupling between the columns gives rise to stochastic initiations of sustained
collective activity, which can be interpreted as epileptic events. For large
enough coupling strengths, termination of these events results mainly from the
emergence of synchronization between the columns, and thus is controlled by
coupling instead of noise. Stochastic triggering and noise-independent
durations are characteristic of excitable dynamics, and thus we interpret our
results in terms of collective excitability.
Discordance between genomic divergence and phenotypic variation in a rapidly evolving avian genus (Motacilla)
Date: 2017-07-12 00:00:00

Generally, genotypes and phenotypes are expected to be spatially congruent,
however, in widespread species complexes with few barriers to dispersal,
multiple contact zones, and limited reproductive isolation, discordance between
phenotypes and phylogeographic groups is more probable. Wagtails (Aves:
Motacilla) are a genus of birds with striking plumage pattern variation across
Eurasia. Up to 13 subspecies are recognized within a single species, yet
previous studies using mitochondrial DNA have supported phylogeographic groups
that are inconsistent with subspecies plumage characteristics. In this study,
we investigate the link between phenotypes and genotype by comparing
populations thought to be at different stages along the speciation continuum.
We take a phylogeographic approach by estimating population structure, testing
for isolation by distance, conducting demographic modeling, and estimating the
first time-calibrated species tree for the genus. Our study provides strong
evidence for species-level patterns of differentiation in wagtails, however
population-level differentiation is less pronounced. We find evidence that
three of four widespread Eurasian species exhibit an east-west divide that
contradicts both subspecies taxonomy and phenotypic variation. Both the
geographic location of this divide and time estimates from demographic models
are overlapping in two sympatric species, indicating that coincident
Pleistocene events shaped their histories.
Environmental engineering is an emergent feature of diverse ecosystems and drives community structure
Date: 2017-07-13 00:00:00

A central question in ecology is to understand the ecological processes that
shape community structure. Niche-based theories have emphasized the important
role played by competition for maintaining species diversity. Many of these
insights have been derived using MacArthur's consumer resource model (MCRM) or
its generalizations. Most theoretical work on the MCRM has focused on small
ecosystems with a few species and resources. However theoretical insights
derived from small ecosystems many not scale up large ecosystems with many
resources and species because large systems with many interacting components
often display new emergent behaviors that cannot be understood or deduced from
analyzing smaller systems. To address this shortcoming, we develop a
sophisticated statistical physics inspired cavity method to analyze MCRM when
both the number of species and the number of resources is large. We find that
in this limit, species generically and consistently perturb their environments
and significantly modify available ecological niches. We show how our cavity
approach naturally generalizes niche theory to large ecosystems by accounting
for the effect of this emergent environmental engineering on species invasion
and ecological stability. Our work suggests that environmental engineering is a
generic feature of large, natural ecosystems and must be taken into account
when analyzing and interpreting community structure. It also highlights the
important role that statistical-physics inspired approaches can play in
furthering our understanding of ecology.
How do fishery policies affect Hawaii's longline fishing industry? Calibrating a positive mathematical programming model
Date: 2017-07-13 00:00:00
Tasks: Management

We present a vessel and target-specific positive mathematical programming
model (PMP) for Hawaii's longline fishing fleet. Although common in
agricultural economics, PMP modeling is rarely attempted in fisheries. To
demonstrate the flexibility of the PMP framework, we separate tuna and
swordfish production technologies into three policy relevant fishing targets.
We find the model most accurately predicts vessel-specific annual bigeye catch
in the WCPO, with an accuracy of 12% to 35%, and a correlation between 0.30 and
0.53. To demonstrate the model's usefulness to policy makers, we simulate the
economic impact to individual vessels from increasing and decreasing the bigeye
catch limit in the WCPO by 10%. Our results suggest that such policy changes
will have moderate impacts on most vessels, but large impacts on a few
generating a fat tailed distribution. These results offer insights into the
range of winners and losers resulting from changes in fishery policies, and
therefore, which policies are more likely to gain widespread industry support.
As a tool for fishery management, the calibrated PMP model offers a flexible
and easy-to-use framework, capable of capturing the heterogeneous response of
fishing vessels to evaluate policy changes.
A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality
Date: 2017-07-13 00:00:00
Tasks: Retrieval

No generic function for the minicolumn, i.e., one that would apply equally
well to all cortical areas and species, has yet been proposed. I propose that
the minicolumn does have a generic functionality, which only becomes clear when
seen in the context of the function of the higher-level, subsuming unit, the
macrocolumn. I propose that: a) a macrocolumn's function is to store sparse
distributed representations of its inputs and to be a recognizer of those
inputs; and b) the generic function of the minicolumn is to enforce
macrocolumnar code sparseness. The minicolumn, defined here as a physically
localized pool of ~20 L2/3 pyramidals, does this by acting as a winner-take-all
(WTA) competitive module, implying that macrocolumnar codes consist of ~70
active L2/3 cells, assuming ~70 minicolumns per macrocolumn. I describe an
algorithm for activating these codes during both learning and retrievals, which
causes more similar inputs to map to more highly intersecting codes, a property
which yields ultra-fast (immediate, first-shot) storage and retrieval. The
algorithm achieves this by adding an amount of randomness (noise) into the code
selection process, which is inversely proportional to an input's familiarity. I
propose a possible mapping of the algorithm onto cortical circuitry, and adduce
evidence for a neuromodulatory implementation of this familiarity-contingent
noise mechanism. The model is distinguished from other recent columnar cortical
circuit models in proposing a generic minicolumnar function in which a group of
cells within the minicolumn, the L2/3 pyramidals, compete (WTA) to be part of
the sparse distributed macrocolumnar code.
Asymptotics for Greeks under the constant elasticity of variance model
Date: 2017-07-14 00:00:00

This paper is concerned with the asymptotics for Greeks of European-style
options and the risk-neutral density function calculated under the constant
elasticity of variance model. Formulae obtained help financial engineers to
construct a perfect hedge with known behaviour and to price any options on
financial assets.
Modeling Hormesis Using a Non-Monotonic Copula Method
Date: 2017-07-13 00:00:00

This paper presents a probabilistic method for capturing non-monotonic
behavior under the biphasic dose-response regime observed in many biological
systems experiencing different types of stress. The proposed method is based on
the rolling-pin method introduced earlier to estimate highly nonlinear and
non-monotonic joint probability distributions from continuous domain data. We
show that the proposed method outperforms the conventional parametric methods
in terms of the error (namely RMSE) and it needs fewer parameters to be
estimated a priori, while offering high flexibility. The application and
performance of the proposed method are shown through an example.
One-shot learning and behavioral eligibility traces in sequential decision making
Date: 2019-11-12 00:00:00
Tasks: Decision Making, Learning Theory, One-Shot Learning, Pupil Dilation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

In many daily tasks we make multiple decisions before reaching a goal. In
order to learn such sequences of decisions, a mechanism to link earlier actions
to later reward is necessary. Reinforcement learning theory suggests two
classes of algorithms solving this credit assignment problem: In classic
temporal-difference learning, earlier actions receive reward information only
after multiple repetitions of the task, whereas models with eligibility traces
reinforce entire sequences of actions from a single experience (one-shot). Here
we asked whether humans use eligibility traces. We developed a novel paradigm
to directly observe which actions and states along a multi-step sequence are
reinforced after a single reward. By focusing our analysis on those states for
which RL with and without eligibility trace make qualitatively distinct
predictions, we find direct behavioral (choice probability) and physiological
(pupil dilation) signatures of reinforcement learning with eligibility trace
across multiple sensory modalities.
Clonal Dominance and Transplantation Dynamics in Hematopoietic Stem Cell Compartments
Date: 2017-10-11 00:00:00

Hematopoietic stem cells in mammals are known to reside mostly in the bone
marrow, but also transitively passage in small numbers in the blood.
Experimental findings have suggested that they exist in a dynamic equilibrium,
continuously migrating between these two compartments. Here we construct an
individual-based mathematical model of this process, which is parametrised
using existing empirical findings from mice. This approach allows us to
quantify the amount of migration between the bone marrow niches and the
peripheral blood. We use this model to investigate clonal hematopoiesis, which
is a significant risk factor for hematologic cancers. We also analyse the
engraftment of donor stem cells into non-conditioned and conditioned hosts,
quantifying the impact of different treatment scenarios. The simplicity of the
model permits a thorough mathematical analysis, providing deeper insights into
the dynamics of both the model and of the real-world system. We predict the
time taken for mutant clones to expand within a host, as well as chimerism
levels that can be expected following transplantation therapy, and the
probability that a preconditioned host is reconstituted by donor cells.
Modeling Harmony with Skip-Grams
Authors: Sears David R. W., Arzt Andreas, Frostel Harald, Sonnleitner Reinhard, Widmer Gerhard
Date: 2017-07-18 00:00:00

String-based (or viewpoint) models of tonal harmony often struggle with data
sparsity in pattern discovery and prediction tasks, particularly when modeling
composite events like triads and seventh chords, since the number of distinct
n-note combinations in polyphonic textures is potentially enormous. To address
this problem, this study examines the efficacy of skip-grams in music research,
an alternative viewpoint method developed in corpus linguistics and natural
language processing that includes sub-sequences of n events (or n-grams) in a
frequency distribution if their constituent members occur within a certain
number of skips.
  Using a corpus consisting of four datasets of Western classical music in
symbolic form, we found that including skip-grams reduces data sparsity in
n-gram distributions by (1) minimizing the proportion of n-grams with
negligible counts, and (2) increasing the coverage of contiguous n-grams in a
test corpus. What is more, skip-grams significantly outperformed contiguous
n-grams in discovering conventional closing progressions (called cadences).
Reduced-form framework under model uncertainty
Date: 2018-03-19 00:00:00
Tasks: Form, model

In this paper we introduce a sublinear conditional expectation with respect
to a family of possibly nondominated probability measures on a progressively
enlarged filtration. In this way, we extend the classic reduced-form setting
for credit and insurance markets to the case under model uncertainty, when we
consider a family of priors possibly mutually singular to each other.
Furthermore, we study the superhedging approach in continuous time for payment
streams under model uncertainty, and establish several equivalent versions of
dynamic robust superhedging duality. These results close the gap between robust
framework for financial market, which is recently studied in an intensive way,
and the one for credit and insurance markets, which is limited in the present
literature only to some very specific cases.
Coexistence of critical sensitivity and subcritical specificity can yield optimal population coding
Date: 2017-07-14 00:00:00
Tasks: Diversity, Sensitivity, Specificity

The vicinity of phase transitions selectively amplifies weak stimuli,
yielding optimal sensitivity to distinguish external input. Along with this
enhanced sensitivity, enhanced levels of fluctuations at criticality reduce the
specificity of the response. Given that the specificity of the response is
largely compromised when the sensitivity is maximal, the overall benefit of
criticality for signal processing remains questionable. Here it is shown that
this impasse can be solved by heterogeneous systems incorporating functional
diversity, in which critical and subcritical components coexist. The subnetwork
of critical elements has optimal sensitivity, and the subnetwork of subcritical
elements has enhanced specificity. Combining segregated features extracted from
the different subgroups, the resulting collective response can maximise the
tradeoff between sensitivity and specificity measured by the
dynamic-range-to-noise-ratio. Although numerous benefits can be observed when
the entire system is critical, our results highlight that optimal performance
is obtained when only a small subset of the system is at criticality.
Discovery of new drug therapeutic indications from gene mutation information for hepatocellular carcinoma
Date: 2017-07-14 00:00:00

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy
and is a leading cause of cancer-related death worldwide. However, cure is not
possible with currently used therapies, and there is not so much approved
targeted therapy for HCC despite numerous attempts and clinical trials. So, it
is essential to identify additional therapeutic strategies to block the growth
of HCC tumors. As a cancer disease, it is associated with aberrant genomic and
transcriptional landscapes. We sought to use a systematic drug repositioning
bioinformatics approach to identify novel candidate drugs to treat HCC, which
considers not only aberrant genomic information, but also the changes of
transcriptional landscapes. First, we screen the collection of HCC feature
genes that frequently mutated in most samples of HCC based on human mutation
data. Then, the gene expression data of HCC in TCGA are combined to classify
the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is
calculated based on the kolmogorov-smirnov statistical method. Using this
strategy, we identified five drugs that associated with HCC, including three
drugs that could treat HCC and two drugs that might have side-effect on HCC. In
addition, we also make Connectivity Map (CMap) profiles similarity analysis and
KEGG enrichment analysis on drug targets. All these findings suggest that our
approach is effective for accurate discovering novel therapeutic options for
HCC and easily to be extended to other tumors.
Large Deviation Theory for Parameter Estimation in Simple Neuron Models
Date: 2017-07-14 00:00:00
Tasks: parameter estimation

To investigate the complex dynamics of a biological neuron that is subject to
small random perturbations we can use stochastic neuron models. While many
techniques have already been developed to study properties of such models,
especially the analysis of the (expected) first-passage time or (E)FPT remains
difficult. In this thesis I apply the large deviation theory (LDT), which is
already well-established in physics and finance, to the problem of determining
the EFPT of the mean-reverting Ornstein-Uhlenbeck (OU) process. The OU process
instantiates the Stochastic Leaky Integrate and Fire model and thus serves as
an example of a biologically inspired mathematical neuron model. I derive
several classical results using much simpler mathematics than the original
publications from neuroscience and I provide a few conceivable interpretations
and perspectives on these derivations. Using these results I explore some
possible applications for parameter estimation and I provide an additional
mathematical justification for using a Poisson process as a small-noise
approximation of the full model. Finally I perform several simulations to
verify these results and to reveal systematic biases of this estimator.
Early MFCC And HPCP Fusion for Robust Cover Song Identification
Authors: Tralie Christopher J.
Date: 2017-07-15 00:00:00
Tasks: Cover song identification

While most schemes for automatic cover song identification have focused on
note-based features such as HPCP and chord profiles, a few recent papers
surprisingly showed that local self-similarities of MFCC-based features also
have classification power for this task. Since MFCC and HPCP capture
complementary information, we design an unsupervised algorithm that combines
normalized, beat-synchronous blocks of these features using cross-similarity
fusion before attempting to locally align a pair of songs. As an added bonus,
our scheme naturally incorporates structural information in each song to fill
in alignment gaps where both feature sets fail. We show a striking jump in
performance over MFCC and HPCP alone, achieving a state of the art mean
reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new
medium-sized hand designed benchmark dataset called "Covers 1000," which
consists of 395 cliques of cover songs for a total of 1000 songs, and we show
that our algorithm achieves an MRR of 0.9 on this dataset for the first
correctly identified song in a clique. We provide the precomputed HPCP and MFCC
features, as well as beat intervals, for all songs in the Covers 1000 dataset
for use in further research.