pwc-archive/papers-with-abstracts huggingface dataset:
Identity, Kinship, and the Evolution of Cooperation
Date: 2018-09-18 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Extensive cooperation among biologically unrelated individuals is uniquely
human and much current research attempts to explain this fact. We draw upon
social, cultural, and psychological aspects of human uniqueness to present an
integrated theory of human cooperation that explains aspects of human
cooperation that are problematic for other theories (e.g., defector invasion
avoidance, preferential assortment to exclude free riders, and the second order
free rider problem). We propose that the evolution of human cooperative
behavior required (1) a capacity for self-sustained, self-referential thought
manifested as an integrated worldview, including a sense of identity and point
of view, and (2) the cultural formation of kinship-based social organizational
systems within which social identities can be established and transmitted
through enculturation. Human cooperative behavior arose, we argue, through the
acquisition of a culturally grounded social identity that included the
expectation of cooperation among kin. This identity is linked to basic survival
instincts by emotions that are mentally experienced as culture laden feelings.
As a consequence, individuals are motivated to cooperate with those perceived
culturally as kin, while deviations from expected social behavior are
experienced as threatening to one's social identity, leading to punishment of
those seen as violating cultural expectations regarding socially proper
behavior.
The distortion principle for insurance pricing: properties, identification and robustness
Date: 2018-09-18 00:00:00
Tasks: Sensitivity

Distortion (Denneberg 1990) is a well known premium calculation principle for
insurance contracts. In this paper, we study sensitivity properties of
distortion functionals w.r.t. the assumptions for risk aversion as well as
robustness w.r.t. ambiguity of the loss distribution. Ambiguity is measured by
the Wasserstein distance. We study variances of distances for probability
models and identify some worst case distributions. In addition to the direct
problem we also investigate the inverse problem, that is how to identify the
distortion density on the basis of observations of insurance premia.
Modular decomposition of protein structure using community detection
Date: 2018-09-18 00:00:00
Tasks: Community Detection, domain classification, Protein Design

As the number of solved protein structures increases, the opportunities for
meta-analysis of this dataset increase too. Protein structures are known to be
formed of domains; structural and functional subunits that are often repeated
across sets of proteins. These domains generally form compact, globular
regions, and are therefore often easily identifiable by inspection, yet the
problem of automatically fragmenting the protein into these compact
substructures remains computationally challenging. Existing domain
classification methods focus on finding subregions of protein structure that
are conserved, rather than finding a decomposition which spans the full protein
structure. However, such a decomposition would find ready application in
coarse-graining molecular dynamics, analysing the protein's topology, in de
novo protein design and in fitting electron microscopy maps. Here, we present a
tool for performing this modular decomposition using the Infomap community
detection algorithm. The protein structure is abstracted into a network in
which its amino acids are the nodes, and where the edges are generated using a
simple proximity test. Infomap can then be used to identify highly
intra-connected regions of the protein. We perform this decomposition
systematically across 4000 distinct protein structures, taken from the Protein
Data Bank. The decomposition obtained correlates well with existing PFAM
sequence classifications, but has the advantage of spanning the full protein,
with the potential for novel domains. The coarse-grained network formed by the
communities can also be used as a proxy for protein topology at the
single-chain level; we demonstrate that grouping these proteins by their
coarse-grained network results in a functionally significant classification.
A Consistent Stochastic Model of the Term Structure of Interest Rates for Multiple Tenors
Date: 2018-09-18 00:00:00

Explicitly taking into account the risk incurred when borrowing at a shorter
tenor versus lending at a longer tenor ("roll-over risk"), we construct a
stochastic model framework for the term structure of interest rates in which a
frequency basis (i.e. a spread applied to one leg of a swap to exchange one
floating interest rate for another of a different tenor in the same currency)
arises endogenously. This rollover risk consists of two components, a credit
risk component due to the possibility of being downgraded and thus facing a
higher credit spread when attempting to roll over short-term borrowing, and a
component reflecting the (systemic) possibility of being unable to roll over
short-term borrowing at the reference rate (e.g., LIBOR) due to an absence of
liquidity in the market. The modelling framework is of "reduced form" in the
sense that (similar to the credit risk literature) the source of credit risk is
not modelled (nor is the source of liquidity risk). However, the framework has
more structure than the literature seeking to simply model a different term
structure of interest rates for each tenor frequency, since relationships
between rates for all tenor frequencies are established based on the modelled
roll-over risk. We proceed to consider a specific case within this framework,
where the dynamics of interest rate and roll-over risk are driven by a
multifactor Cox/Ingersoll/Ross-type process, show how such model can be
calibrated to market data, and used for relative pricing of interest rate
derivatives, including bespoke tenor frequencies not liquidly traded in the
market.
The phylogenetic effective sample size and jumps
Date: 2018-09-18 00:00:00

The phylogenetic effective sample size is a parameter that has as its goal
the quantification of the amount of independent signal in a phylogenetically
correlated sample. It was studied for Brownian motion and Ornstein-Uhlenbeck
models of trait evolution. Here, we study this composite parameter when the
trait is allowed to jump at speciation points of the phylogeny. Our numerical
study indicates that there is a non-trivial limit as the effect of jumps grows.
The limit depends on the value of the drift parameter of the Ornstein-Uhlenbeck
process.
Reconfiguration of Brain Network between Resting-state and Oddball Paradigm
Date: 2018-09-18 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

The oddball paradigm is widely applied to the investigation of multiple
cognitive functions. Prior studies have explored the cortical oscillation and
power spectral differing from the resting-state conduction to oddball paradigm,
but whether brain networks existing the significant difference is still
unclear. Our study addressed how the brain reconfigures its architecture from a
resting-state condition (i.e., baseline) to P300 stimulus task in the visual
oddball paradigm. In this study, electroencephalogram (EEG) datasets were
collected from 24 postgraduate students, who were required to only mentally
count the number of target stimulus; afterwards the functional EEG networks
constructed in different frequency bands were compared between baseline and
oddball task conditions to evaluate the reconfiguration of functional network
in the brain. Compared to the baseline, our results showed the significantly (p
< 0.05) enhanced delta/theta EEG connectivity and decreased alpha default mode
network in the progress of brain reconfiguration to the P300 task. Furthermore,
the reconfigured coupling strengths were demonstrated to relate to P300
amplitudes, which were then regarded as input features to train a classifier to
differentiate the high and low P300 amplitudes groups with an accuracy of
77.78%. The findings of our study help us to understand the changes of
functional brain connectivity from resting-state to oddball stimulus task, and
the reconfigured network pattern has the potential for the selection of good
subjects for P300-based brain- computer interface.
Capsule Deep Neural Network for Recognition of Historical Graffiti Handwriting
Authors: Gordienko Nikita, Kochura Yuriy, Taran Vlad, Peng Gang, Gordienko Yuri, Stirenko Sergii
Date: 2018-09-11 00:00:00
Tasks: Data Augmentation

Automatic recognition of the historical letters (XI-XVIII centuries) carved
on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine) was demonstrated
by means of capsule deep learning neural network. It was applied to the image
dataset of the carved Glagolitic and Cyrillic letters (CGCL), which was
assembled and pre-processed recently for recognition and prediction by machine
learning methods
(https://www.kaggle.com/yoctoman/graffiti-st-sophia-cathedral-kyiv). CGCL
dataset contains >4000 images for glyphs of 34 letters which are hardly
recognized by experts even in contrast to notMNIST dataset with the better
images of 10 letters taken from different fonts. Despite the much worse quality
of CGCL dataset and extremely low number of samples (in comparison to notMNIST
dataset) the capsule network model demonstrated much better results than the
previously used convolutional neural network (CNN). The validation accuracy
(and validation loss) was higher (lower) for capsule network model than for CNN
without data augmentation even. The area under curve (AUC) values for receiver
operating characteristic (ROC) were also higher for the capsule network model
than for CNN model: 0.88-0.93 (capsule network) and 0.50 (CNN) without data
augmentation, 0.91-0.95 (capsule network) and 0.51 (CNN) with lossless data
augmentation, and similar results of 0.91-0.93 (capsule network) and 0.9 (CNN)
in the regime of lossless data augmentation only. The confusion matrixes were
much better for capsule network than for CNN model and gave the much lower type
I (false positive) and type II (false negative) values in all three regimes of
data augmentation. These results supports the previous claims that capsule-like
networks allow to reduce error rates not only on MNIST digit dataset, but on
the other notMNIST letter dataset and the more complex CGCL handwriting
graffiti letter dataset also.
EEG-based Subjects Identification based on Biometrics of Imagined Speech using EMD
Authors: Moctezuma Luis Alfredo, Molinas Marta
Date: 2018-09-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

When brain activity is translated into commands for real applications, the
potential for human capacities augmentation is promising. In this paper, EMD is
used to decompose EEG signals during Imagined Speech in order to use it as a
biometric marker for creating a Biometric Recognition System. For each EEG
channel, the most relevant Intrinsic Mode Functions (IMFs) are decided based on
the Minkowski distance, and for each IMF 4 features are computed: Instantaneous
and Teager energy distribution and Higuchi and Petrosian Fractal Dimension. To
test the proposed method, a dataset with 20 subjects who imagined 30
repetitions of 5 words in Spanish, is used. Four classifiers are used for this
task - random forest, SVM, naive Bayes, and k-NN - and their performances are
compared. The accuracy obtained (up to 0.92 using Linear SVM) after 10-folds
cross-validation suggest that the proposed method based on EMD can be valuable
for creating EEG-based biometrics of imagined speech for Subjects
identification.
Surface Wave-Based Underwater Radio Communication
Date: 2018-09-17 00:00:00

An underwater portable radio antenna operating in the 50 MHz band and
efficient for launching surface electromagnetic waves at the seawater/air
interface is presented. The antenna operation is based on the field enhancement
at the antenna tip and on an impedance matching antenna enclosure, which is
filled with de-ionized water. This enclosure allows us to reduce antenna
dimensions and improve the coupling of electromagnetic energy to the
surrounding salt water medium. Since surface wave propagation length far
exceeds the skin depth of conventional radio waves at the same frequency, this
technique is useful for broadband underwater wireless communication over
several meters distances.
Sorting and filtering as effective rational choice procedures
Authors: Paulo Oliva, Philipp Zahn
Date: 2018-09-18 00:00:00
Tasks: Navigate

Many online shops offer functionality that help their customers navigate the available alternatives. For instance, options to filter and to sort goods are wide-spread. In this paper we show that sorting and filtering can be used by rational consumers to find their most preferred choice -- quickly. We characterize the preferences which can be expressed through filtering and sorting and show that these preferences exhibit a simple and intuitive logical structure.
Selling Information
Date: 2018-10-17 00:00:00
Tasks: Decision Making

I consider the monopolistic pricing of informational good. A buyer's
willingness to pay for information is from inferring the unknown payoffs of
actions in decision making. A monopolistic seller and the buyer each observes a
private signal about the payoffs. The seller's signal is binary and she can
commit to sell any statistical experiment of her signal to the buyer. Assuming
that buyer's decision problem involves rich actions, I characterize the profit
maximizing menu. It contains a continuum of experiments, each containing
different amount of information. I also find a complementarity between buyer's
private information and information provision: when buyer's private signal is
more informative, the optimal menu contains more informative experiments.
Visual Speech Language Models
Date: 2018-09-14 00:00:00
Tasks: Language Modeling, Language Modelling, Lipreading

Language models (LM) are very powerful in lipreading systems. Language models
built upon the ground truth utterances of datasets learn grammar and structure
rules of words and sentences (the latter in the case of continuous speech).
However, visual co-articulation effects in visual speech signals damage the
performance of visual speech LM's as visually, people do not utter what the
language model expects. These models are commonplace but while higher-order
N-gram LM's may improve classification rates, the cost of this model is
disproportionate to the common goal of developing more accurate classifiers. So
we compare which unit would best optimize a lipreading (visual speech) LM to
observe their limitations. We compare three units; visemes (visual speech
units) \cite{lan2010improving}, phonemes (audible speech units), and words.
Competing evolutionary paths in growing populations with applications to multidrug resistance
Date: 2019-01-23 00:00:00

Investigating the emergence of a particular cell type is a recurring theme in
models of growing cellular populations. The evolution of resistance to therapy
is a classic example. Common questions are: when does the cell type first
occur, and via which sequence of steps is it most likely to emerge? For growing
populations, these questions can be formulated in a general framework of
branching processes spreading through a graph from a root to a target vertex.
Cells have a particular fitness value on each vertex and can transition along
edges at specific rates. Vertices represents cell states, say \mic{genotypes
}or physical locations, while possible transitions are acquiring a mutation or
cell migration. We focus on the setting where cells at the root vertex have the
highest fitness and transition rates are small. Simple formulas are derived for
the time to reach the target vertex and for the probability that it is reached
along a given path in the graph. We demonstrate our results on \mic{several
scenarios relevant to the emergence of drug resistance}, including: the
orderings of resistance-conferring mutations in bacteria and the impact of
imperfect drug penetration in cancer.
Matching in Dynamic Imbalanced Markets
Date: 2019-06-13 00:00:00

We study dynamic matching in exchange markets with easy- and hard-to-match
agents. A greedy policy, which attempts to match agents upon arrival, ignores
the positive externality that waiting agents generate by facilitating future
matchings. We prove that this trade-off between a ``thicker'' market and faster
matching vanishes in large markets; A greedy policy leads to shorter waiting
times, and more agents matched than any other policy. We empirically confirm
these findings in data from the National Kidney Registry. Greedy matching
achieves as many transplants as commonly-used policies (1.6\% more than
monthly-batching), and shorter patient waiting times.
Error-Control-Coding Assisted Imaging
Date: 2018-09-18 00:00:00

Error-control-coding (ECC) techniques are widely used in modern digital
communication systems to minimize the effect of noisy channels on the quality
of received signals. Motivated by the fact that both communication and imaging
can be considered as an information transfer process, in this paper we
demonstrate that ECC could yield significant improvements in the image quality
by reducing the effect of noise in the physical process of image acquisition.
In the demonstrated approach, the object is encoded by ECC structured light
fields generated by a digital-micromirror-device (DMD) while its image is
obtained by decoding the received signal collected by a bucket-detector (BD).
By applying a Luby Transform (LT) code as an example, our proof-of-concept
experiment validates that the object image can be effectively reconstructed
while errors induced by noisy reception can be significantly reduced. The
demonstrated approach informs a new imaging technique: ECC assisted imaging,
which can be scaled into applications such as remote sensing, spectroscopy and
biomedical imaging.
Aberrated dark-field imaging systems
Date: 2018-10-25 00:00:00

We study generalized dark-field imaging systems. These are a subset of linear
shift-invariant optical imaging systems, that exhibit arbitrary aberrations,
and for which normally-incident plane-wave input yields zero output. We write
down the theory for the forward problem of imaging coherent scalar optical
fields using such arbitrarily-aberrated dark-field systems, and give numerical
examples. The associated images may be viewed as a form of dark-field Gabor
holography, utilizing arbitrary outgoing Green functions as generalized
Huygens-type wavelets, and with the Young-type boundary wave forming the
holographic reference.
Transient crosslinking kinetics optimize gene cluster interactions
Date: 2019-05-21 00:00:00
Tasks: Clustering, Community Detection

Our understanding of how chromosomes structurally organize and dynamically
interact has been revolutionized through the lens of long-chain polymer
physics. Major protein contributors to chromosome structure and dynamics are
condensin and cohesin that stochastically generate loops within and between
chains, and entrap proximal strands of sister chromatids. In this paper, we
explore the ability of transient, protein-mediated, gene-gene crosslinks to
induce clusters of genes, thereby dynamic architecture, within the highly
repeated ribosomal DNA that comprises the nucleolus of budding yeast. We
implement three approaches: live cell microscopy; computational modeling of the
full genome during G1 in budding yeast, exploring four decades of timescales
for transient crosslinks between 5k bp domains in the nucleolus on Chromosome
XII; and, temporal network models with automated community detection algorithms
applied to the full range of 4D modeling datasets. The data analysis tools
detect and track gene clusters, their size, number, persistence time, and their
plasticity. Of biological significance, our analysis reveals an optimal mean
crosslink lifetime that promotes pairwise and cluster gene interactions through
"flexible" clustering. In this state, large gene clusters self-assemble yet
frequently interact, marked by gene exchanges between clusters, which in turn
maximizes global gene interactions in the nucleolus. This regime stands between
two limiting cases each with far less global gene interactions: with shorter
crosslink lifetimes, "rigid" clustering emerges with clusters that interact
infrequently; with longer crosslink lifetimes, there is a dissolution of
clusters. These observations are compared with imaging experiments on a normal
yeast strain and two condensin-modified mutant cell strains, applying the same
image analysis pipeline to the experimental and simulated datasets.
Argumentation Mining: Exploiting Multiple Sources and Background Knowledge
Authors: Lytos Anastasios, Lagkas Thomas, Sarigiannidis Panagiotis, Bontcheva Kalina
Date: 2018-09-18 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

The field of Argumentation Mining has arisen from the need of determining the
underlying causes from an expressed opinion and the urgency to develop the
established fields of Opinion Mining and Sentiment Analysis. The recent
progress in the wider field of Artificial Intelligence in combination with the
available data through Social Web has create great potential for every
sub-field of Natural Language Process including Argumentation Mining.
Analysis of the Risk-Sharing Principal-Agent problem through the Reverse-H{ö}lder inequality
Authors: Jessica Martin, Anthony Réveillac
Date: 2018-09-19 00:00:00

In this paper we provide an alternative framework to tackle the first-best Principal-Agent problem under CARA utilities. This framework leads to both a proof of existence and uniqueness of the solution to the Risk-Sharing problem under very general assumptions on the underlying contract space. Our analysis relies on an optimal decomposition of the expected utility of the Principal in terms of the reservation utility of the Agent and works both in a discrete time and continuous time setting. As a by-product this approach provides a novel way of characterizing the optimal contract in the CARA setting, which is as an alternative to the widely used Lagrangian method, and some analysis of the optimum.
Risk evaluation and behaviour: defining appropriate frames of reference
Date: 2018-09-19 00:00:00

Many biological, psychological and economic experiments have been designed
where an organism or individual must choose between two options that have the
same expected reward but differ in the variance of reward received. In this
way, designed empirical approaches have been developed for evaluating risk
preferences. Here, however, we show that if the experimental subject is
inferring the reward distribution (to optimize some process), they will never
agree in finite time that the expected rewards are equal. In turn, we argue
that this makes discussions of risk preferences, and indeed the motivations of
behaviour, not so simple or straightforward to interpret. We use this
particular experiment to highlight the serious need to consider the frame of
reference of the experimental subject in studies of behaviour.