pwc-archive/papers-with-abstracts huggingface dataset:
Proteins analysed as virtual knots
Date: 2016-11-18 00:00:00

Long, flexible physical filaments are naturally tangled and knotted, from
macroscopic string down to long-chain molecules. The existence of knotting in a
filament naturally affects its configuration and properties, and may be very
stable or disappear rapidly under manipulation and interaction. Knotting has
been previously identified in protein backbone chains, for which these
mechanical constraints are of fundamental importance to their molecular
functionality, despite their being open curves in which the knots are not
mathematically well defined; knotting can only be identified by closing the
termini of the chain somehow. We introduce a new method for resolving knotting
in open curves using virtual knots, a wider class of topological objects that
do not require a classical closure and so naturally capture the topological
ambiguity inherent in open curves. We describe the results of analysing
proteins in the Protein Data Bank by this new scheme, recovering and extending
previous knotting results, and identifying topological interest in some new
cases. The statistics of virtual knots in protein chains are compared with
those of open random walks and Hamiltonian subchains on cubic lattices,
identifying a regime of open curves in which the virtual knotting description
is likely to be important.
Interplay between endogenous and exogenous fluctuations in financial markets
Date: 2016-11-19 00:00:00

We address microscopic, agent based, and macroscopic, stochastic, modeling of
the financial markets combining it with the exogenous noise. The interplay
between the endogenous dynamics of agents and the exogenous noise is the
primary mechanism responsible for the observed long-range dependence and
statistical properties of high volatility return intervals. By exogenous noise
we mean information flow or/and order flow fluctuations. Numerical results
based on the proposed model reveal that the exogenous fluctuations have to be
considered as indispensable part of comprehensive modeling of the financial
markets.
3D Reconstruction of Heterogeneous Virus Particles with Statistical Geometric Symmetry
Date: 2016-11-21 00:00:00
Tasks: 3D Reconstruction, Object

In 3-D reconstruction problems, the image data obtained from cryo electron
microscopy is the projection of many heterogeneous instances of the object
under study (e.g., a virus). When the object is heterogeneous but has an
overall symmetry, it is natural to describe the object as stochastic with
symmetrical statistics. This paper presents a maximum likelihood reconstruction
approach which allows each object to lack symmetry while constraining the {\it
statistics} of the ensemble of objects to have symmetry. This algorithm is
demonstrated on bacteriophage HK97 and is contrasted with an existing algorithm
in which each object, while still heterogeneous, has the symmetry.
Reconstruction results show that the proposed algorithm eliminates
long-standing distortions in previous heterogeneity calculations associated
with symmetry axes, and provides estimates that make more biologically sense
than the estimates of existing algorithms.
A Business Zone Recommender System Based on Facebook and Urban Planning Data
Authors: Lin Jovian, Oentaryo Richard J., Lim Ee-Peng, Vu Casey, Vu Adrian, Kwee Agus T., Prasetyo Philips K.
Date: 2016-11-21 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

We present ZoneRec---a zone recommendation system for physical businesses in
an urban city, which uses both public business data from Facebook and urban
planning data. The system consists of machine learning algorithms that take in
a business' metadata and outputs a list of recommended zones to establish the
business in. We evaluate our system using data of food businesses in Singapore
and assess the contribution of different feature groups to the recommendation
quality.
MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
Authors: Cui Qiang, Wu Shu, Liu Qiang, Zhong Wen, Wang Liang
Date: 2018-11-20 00:00:00
Tasks: Collaborative Filtering, Sequential Recommendation

Sequential recommendation is a fundamental task for network applications, and
it usually suffers from the item cold start problem due to the insufficiency of
user feedbacks. There are currently three kinds of popular approaches which are
respectively based on matrix factorization (MF) of collaborative filtering,
Markov chain (MC), and recurrent neural network (RNN). Although widely used,
they have some limitations. MF based methods could not capture dynamic user's
interest. The strong Markov assumption greatly limits the performance of MC
based methods. RNN based methods are still in the early stage of incorporating
additional information. Based on these basic models, many methods with
additional information only validate incorporating one modality in a separate
way. In this work, to make the sequential recommendation and deal with the item
cold start problem, we propose a Multi-View Recurrent Neural Network (MV-RNN})
model. Given the latent feature, MV-RNN can alleviate the item cold start
problem by incorporating visual and textual information. First, At the input of
MV-RNN, three different combinations of multi-view features are studied, like
concatenation, fusion by addition and fusion by reconstructing the original
multi-modal data. MV-RNN applies the recurrent structure to dynamically capture
the user's interest. Second, we design a separate structure and a united
structure on the hidden state of MV-RNN to explore a more effective way to
handle multi-view features. Experiments on two real-world datasets show that
MV-RNN can effectively generate the personalized ranking list, tackle the
missing modalities problem and significantly alleviate the item cold start
problem.
Systemic Risk and Interbank Lending
Date: 2017-03-30 00:00:00

We propose a simple model of the banking system incorporating a game feature
where the evolution of monetary reserve is modeled as a system of coupled
Feller diffusions. The Markov Nash equilibrium generated through minimizing the
linear quadratic cost subject to Cox-Ingersoll-Ross type processes creates
liquidity and deposit rate. The adding liquidity leads to a flocking effect but
the deposit rate diminishes the growth rate of the total monetary reserve
causing a large number of bank defaults. In addition, the corresponding Mean
Field Game and the infinite time horizon stochastic game with the discount
factor are also discussed.
Modeling contact networks of patients and MRSA spread in Swedish hospitals
Date: 2016-11-21 00:00:00

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat
infection that only in the European Union affects about 150,000 patients and
causes extra costs of 380 million Euros annually to the health-care systems.
Increasing efforts have been taken to mitigate the epidemics and to avoid
potential outbreaks in low endemic settings. Understanding the population
dynamics of MRSA through modeling is essential to identify the causal
mechanisms driving the epidemics and to generalize conclusions to different
contexts. We develop an innovative high-resolution spatiotemporal contact
network model of interactions between patients to reproduce the hospital
population in the context of the Stockholm County in Sweden and simulate the
spread of MRSA within this population. Our model captures the spatial and
temporal heterogeneity caused by human behavior and by the dynamics of mobility
within wards and hospitals. We estimate that in this population the epidemic
threshold is at about 0.008. We also identify that these heterogeneous contact
patterns cause the emergence of super-spreader patients and a polynomial growth
of the epidemic curve. We finally study the effect of standard intervention
control strategies and identify that screening is more effective than improved
hygienic in order to cause smaller or null outbreaks.
Differential response of the retinal neural code with respect to the sparseness of natural images
Authors: Cesar Ravello, Maria-Jose Escobar, Adrian Palacios, Laurent Perrinet
Date: 2016-11-21 00:00:00

Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. To investigate the role of this sparseness in the efficiency of the neural code, we designed a new class of random textured stimuli with a controlled sparseness value inspired by measurements of natural images. Then, we tested the impact of this sparseness parameter on the firing pattern observed in a population of retinal ganglion cells recorded ex vivo in the retina of a rodent, the Octodon degus. These recordings showed in particular that the reliability of spike timings varies with respect to the sparseness with globally a similar trend than the distribution of sparseness statistics observed in natural images. These results suggest that the code represented in the spike pattern of ganglion cells may adapt to this aspect of the statistics of natural images.
CMOS based image cytometry for detection of phytoplankton in ballast water
Date: 2016-11-21 00:00:00
Tasks: Benchmarking

We introduce an image cytometer (I-CYT) for the analysis of phytoplankton in
fresh and marine water environments. A linear quantification of cell numbers
was observed covering several orders of magnitude using cultures of Tetraselmis
and Nannochloropsis measured by autofluorescence in a laboratory environment.
We assessed the functionality of the system outside the laboratory by
phytoplankton quantification of samples taken from a marine water environment
(Dutch Wadden Sea, The Netherlands) and a fresh water environment (Lake Ijssel,
The Netherlands). The I-CYT was also employed to study the effects of two
ballast water treatment systems (BWTS), based on chlorine electrolysis and UV
sterilization, with the analysis including the vitality of the phytoplankton.
For comparative study and benchmarking of the I-CYT, a standard flow cytometer
was used. Our results prove a limit of detection (LOD) of 10 cells/ml with an
accuracy between 0.7 and 0.5 log, and a correlation of 88.29% in quantification
and 96.21% in vitality, with respect to the flow cytometry results.
Rising Novelties on Evolving Networks: Recent Behavior Dominant and Non-Dominant Model
Authors: Abbas Khushnood
Date: 2016-11-21 00:00:00
Tasks: Information Retrieval, Retrieval

Novelty attracts attention like popularity. Hence predicting novelty is as
important as popularity. Novelty is the side effect of competition and aging in
evolving systems. Recent behavior or recent link gain in networks plays an
important role in emergence or trend. We exploited this wisdom and came up with
two models considering different scenarios and systems. Where recent behavior
dominates over total behavior (total link gain) in the first one, and recent
behavior is as important as total behavior for future link gain in second one.
It suppose that random walker walks on a network and can jump to any node, the
probablity of jumping or making connection to other node is based on which node
is recently more active or receiving more links. In our assumption random
walker can also jump to node which is already popular but recently not popular.
We are able to predict rising novelties or popular nodes which is generally
suppressed under preferential attachment effect. To show performance of our
model we have conducted experiments on four real data sets namely, MovieLens,
Netflix, Facebook and Arxiv High Energy Physics paper citation. For testing our
model we used four information retrieval indices namely Precision, Novelty,
Area Under Receiving Operating Characteristic(AUC) and Kendal's rank
correlation coefficient. We have used four benchmark models for validating our
proposed models. Although our model doesn't perform better in all the cases
but, it has theoretical significance in working better for recent behavior
dominant systems.
Leaf growth is conformal
Date: 2016-11-21 00:00:00

Growth pattern dynamics lie at the heart of morphogenesis. Here, we
investigate the growth of plant leaves. We compute the conformal transformation
that maps the contour of a leaf at a given stage onto the contour of the same
leaf at a later stage. Based on the mapping we predict the local displacement
field in the leaf blade and find it to agree with the experimentally measured
displacement field to 92%. This approach is applicable to any two-dimensional
system with locally isotropic growth, enabling the deduction of the whole
growth field just from observation of the tissue contour.
Derivation of human chromatic discrimination ability from an information-theoretical notion of distance in color space
Date: 2016-11-22 00:00:00

The accuracy with which humans can detect small chromatic differences varies
throughout color space. For example, we are far more precise when
discriminating two similar orange stimuli than two similar green stimuli. In
order for two colors to be perceived as different, the neurons representing
chromatic information must respond differently, and the difference must be
larger than the trial-to-trial variability of the response to each separate
color. Photoreceptors constitute the first stage in the processing of color
information; many more stages are required before humans can consciously report
whether two stimuli are perceived as chromatically distinguishable or not.
Therefore, although photoreceptor absorption curves are expected to influence
the accuracy of conscious discriminability, there is no reason to believe that
they should suffice to explain it. Here we develop information-theoretical
tools based on the Fisher metric that demonstrate that photoreceptor absorption
properties explain ~87% of the variance of human color discrimination ability,
as tested by previous behavioral experiments. In the context of this theory,
the bottleneck in chromatic information processing is determined by
photoreceptor absorption characteristics. Subsequent encoding stages modify
only marginally the chromatic discriminability at the photoreceptor level.
"Chaos" in energy and commodity markets: a controversial matter
Date: 2017-03-29 00:00:00
Tasks: Time Series, Time Series Analysis

We test whether the futures prices of some commodity and energy markets are
determined by stochastic rules or exhibit nonlinear deterministic endogenous
fluctuations. As for the methodologies, we use the maximal Lyapunov exponents
(MLE) and a determinism test, both based on the reconstruction of the phase
space. In particular, employing a recent methodology, we estimate a coefficient
$\kappa$ that describes the determinism rate of the analyzed time series. We
find that the underlying system for futures prices shows a reliability level
$\kappa$ near to $1$ while the MLE is positive for all commodity futures
series. Thus, the empirical evidence suggests that commodity and energy futures
prices are the measured footprint of a nonlinear deterministic, rather than a
stochastic, system.
A statistical method for analyzing and comparing spatiotemporal cortical activation patterns
Date: 2016-11-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We present a new statistical method to analyze multichannel steady-state
local field potentials (LFP) recorded within different sensory cortices of
different rodent species. Our spatiotemporal multi-dimensional cluster
statistics (MCS) method enables statistical analyzing and comparing clusters of
data points in n-dimensional space. We demonstrate that using this approach
stimulus-specific attractor-like spatiotemporal activity patterns can be
detected and be significantly different from each other during stimulation with
long-lasting stimuli. Our method may be applied to other types of multichannel
neuronal data, like EEG, MEG or spiking responses and used for the development
of new read-out algorithms of brain activity and by that opens new perspectives
for the development of brain-computer interfaces.
Specificity-determining DNA triplet code for positioning of human pre-initiation complex
Date: 2016-11-23 00:00:00
Tasks: Specificity, Triplet

The notion that transcription factors bind DNA only through specific,
consensus binding sites has been recently questioned. In a pioneering study by
Pugh and Venters no specific consensus motif for the positioning of the human
pre-initiation complex (PIC) has been identified. Here, we reveal that
nonconsensus, statistical, DNA triplet code provides specificity for the
positioning of the human PIC. In particular, we reveal a highly non-random,
statistical pattern of repetitive nucleotide triplets that correlates with the
genome-wide binding preferences of PIC measured by Chip-exo. We analyze the
triplet enrichment and depletion near the transcription start site (TSS) and
identify triplets that have the strongest effect on PIC-DNA nonconsensus
binding. Our results constitute a proof-of-concept for a new design principle
for protein-DNA recognition in the human genome, which can lead to a better
mechanistic understanding of transcriptional regulation.
NMRProcFlow: A graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics
Date: 2016-11-23 00:00:00
Tasks: Experimental Design

Concerning NMR-based metabolomics, 1D spectra processing often requires an
expert eye for disentangling the intertwined peaks, and so far the best way is
to proceed interactively with a spectra viewer. NMRProcFlow is a graphical and
interactive 1D NMR (1H \& 13C) spectra processing tool dedicated to metabolic
fingerprinting and targeted metabolomic, covering all spectra processing steps
including baseline correction, chemical shift calibration, alignment. It does
not require programming skills. Biologists and NMR spectroscopists can easily
interact and develop synergies by visualizing the NMR spectra along with their
corresponding experimental-factor levels, thus setting a bridge between
experimental design and subsequent statistical analyses.
Visual motion processing and human tracking behavior
Date: 2016-11-23 00:00:00
Tasks: Visual Tracking

The accurate visual tracking of a moving object is a human fundamental skill
that allows to reduce the relative slip and instability of the object's image
on the retina, thus granting a stable, high-quality vision. In order to
optimize tracking performance across time, a quick estimate of the object's
global motion properties needs to be fed to the oculomotor system and
dynamically updated. Concurrently, performance can be greatly improved in terms
of latency and accuracy by taking into account predictive cues, especially
under variable conditions of visibility and in presence of ambiguous retinal
information. Here, we review several recent studies focusing on the integration
of retinal and extra-retinal information for the control of human smooth
pursuit.By dynamically probing the tracking performance with well established
paradigms in the visual perception and oculomotor literature we provide the
basis to test theoretical hypotheses within the framework of dynamic
probabilistic inference. We will in particular present the applications of
these results in light of state-of-the-art computer vision algorithms.
Explicitly Linking Regional Activation and Function Connectivity: Community Structure of Weighted Networks with Continuous Annotation
Date: 2016-11-23 00:00:00
Tasks: Attribute, Community Detection, Functional Connectivity

A major challenge in neuroimaging is understanding the mapping of
neurophysiological dynamics onto cognitive functions. Traditionally, these maps
have been constructed by examining changes in the activity magnitude of regions
related to task performance. Recently, network neuroscience has produced
methods to map connectivity patterns among many regions to certain cognitive
functions by drawing on tools from network science and graph theory. However,
these two different views are rarely addressed simultaneously, largely because
few tools exist that account for patterns between nodes while simultaneously
considering activation of nodes. We address this gap by solving the problem of
community detection on weighted networks with continuous (non-integer)
annotations by deriving a generative probabilistic model. This model generates
communities whose members connect densely to nodes within their own community,
and whose members share similar annotation values. We demonstrate the utility
of the model in the context of neuroimaging data gathered during a motor
learning paradigm, where edges are task-based functional connectivity and
annotations to each node are beta weights from a general linear model that
encoded a linear decrease in blood-oxygen-level-dependent signal with practice.
Interestingly, we observe that individuals who learn at a faster rate exhibit
the greatest dissimilarity between functional connectivity and activation
magnitudes, suggesting that activation and functional connectivity are distinct
dimensions of neurophysiology that track behavioral change. More generally, the
tool that we develop offers an explicit, mathematically principled link between
functional activation and functional connectivity, and can readily be applied
to a other similar problems in which one set of imaging data offers network
data, and a second offers a regional attribute.
The effects of stochasticity at the single-cell level and cell size control on the population growth
Date: 2017-09-18 00:00:00

Establishing a quantitative connection between the population growth rate and
the generation times of single cells is a prerequisite for understanding
evolutionary dynamics of microbes. However, existing theories fail to account
for the experimentally observed correlations between mother-daughter generation
times that are unavoidable when cell size is controlled for - which is
essentially always the case. Here, we study population-level growth in the
presence of cell size control and corroborate our theory using experimental
measurements of single-cell growth rates. We derive a closed formula for the
population growth rate and demonstrate that it only depends on the single-cell
growth rate variability, not other sources of stochasticity. Our work provides
an evolutionary rationale for the narrow growth rate distributions often
observed in nature: when single-cell growth rates are less variable but have a
fixed mean, the population will exhibit an enhanced population growth rate, as
long as the correlations between the mother and daughter cells' growth rates
are not too strong.