pwc-archive/papers-with-abstracts huggingface dataset:
Hyper-rational choice theory
Date: 2018-02-26 00:00:00

The rational choice theory is based on this idea that people rationally
pursue goals for increasing their personal interests. In most conditions, the
behavior of an actor is not independent of the person and others' behavior.
Here, we present a new concept of rational choice as a hyper-rational choice
which in this concept, the actor thinks about profit or loss of other actors in
addition to his personal profit or loss and then will choose an action which is
desirable to him. We implement the hyper-rational choice to generalize and
expand the game theory. Results of this study will help to model the behavior
of people considering environmental conditions, the kind of behavior
interactive, valuation system of itself and others and system of beliefs and
internal values of societies. Hyper-rationality helps us understand how human
decision makers behave in interactive decisions.
Performance Comparison of 112 Gb/s DMT, Nyquist PAM4 and Partial-Response PAM4 for Future 5G Ethernet-based Fronthaul Architecture
Date: 2018-01-31 00:00:00

For a future 5G Ethernet-based fronthaul architecture, 100G trunk lines of a
transmission distance up to 10 km standard single mode fiber (SSMF) in
combination with cheap grey optics to daisy chain cell site network interfaces
are a promising cost- and power-efficient solution. For such a scenario,
different intensity modulation and direct detect (IMDD) Formats at a data rate
of 112 Gb/s, namely Nyquist four-level pulse amplitude modulation (PAM4),
discrete multi-tone Transmission (DMT) and partial-response (PR) PAM4 are
experimentally investigated, using a low-cost electro-absorption modulated
laser (EML), a 25G driver and current state-of-the-art high Speed 84 GS/s CMOS
digital-to-analog converter (DAC) and analog-to-digital converter (ADC) test
chips. Each modulation Format is optimized independently for the desired
scenario and their digital signal processing (DSP) requirements are
investigated. The performance of Nyquist PAM4 and PR PAM4 depend very much on
the efficiency of pre- and post-equalization. We show the necessity for at
least 11 FFE-taps for pre-emphasis and up to 41 FFE coefficients at the
receiver side. In addition, PR PAM4 requires an MLSE with four states to decode
the signal back to a PAM4 signal. On the contrary, bit- and power-loading (BL,
PL) is crucial for DMT and an FFT length of at least 512 is necessary. With
optimized parameters, all Modulation formats result in a very similar
performances, demonstrating a transmission distance of up to 10 km over SSMF
with bit error rates (BERs) below a FEC threshold of 4.4E-3, allowing error
free transmission.
Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures
Date: 2018-01-31 00:00:00
Tasks: Time Series Analysis

Due to the liberalization of markets, the change in the energy mix and the
surrounding energy laws, electricity research is a dynamically altering field
with steadily changing challenges. One challenge especially for investment
decisions is to provide reliable short to mid-term forecasts despite high
variation in the time series of electricity prices. This paper tackles this
issue in a promising and novel approach. By combining the precision of
econometric autoregressive models in the short-run with the expectations of
market participants reflected in future prices for the short- and mid-run we
show that the forecasting performance can be vastly increased while maintaining
hourly precision. We investigate the day-ahead electricity price of the EPEX
Spot for Germany and Austria and setup a model which incorporates the Phelix
future of the EEX for Germany and Austria. The model can be considered as an
AR24-X model with one distinct model for each hour of the day. We are able to
show that future data contains relevant price information for future time
periods of the day-ahead electricity price. We show that relying only on
deterministic external regressors can provide stability for forecast horizons
of multiple weeks. By implementing a fast and efficient lasso estimation
approach we demonstrate that our model can outperform several other models in
the literature.
ILPS at TREC 2017 Common Core Track
Authors: Van Gysel Christophe, Li Dan, Kanoulas Evangelos
Date: 2018-01-31 00:00:00
Tasks: Bayesian Optimization, Retrieval

The TREC 2017 Common Core Track aimed at gathering a diverse set of
participating runs and building a new test collection using advanced pooling
methods.
  In this paper, we describe the participation of the IlpsUvA team at the TREC
2017 Common Core Track. We submitted runs created using two methods to the
track: (1) BOIR uses Bayesian optimization to automatically optimize retrieval
model hyperparameters. (2) NVSM is a latent vector space model where
representations of documents and query terms are learned from scratch in an
unsupervised manner.
  We find that BOIR is able to optimize hyperparameters as to find a system
that performs competitively amongst track participants. NVSM provides rankings
that are diverse, as it was amongst the top automated unsupervised runs that
provided the most unique relevant documents.
Reaching Optimized Parameter Set, Protein Secondary Structure Prediction Using Neural Network
Date: 2018-01-31 00:00:00
Tasks: Protein Secondary Structure Prediction, Specificity

We propose an optimized parameter set for protein secondary structure
prediction using three layer feed forward back propagation neural network. The
methodology uses four parameters viz. encoding scheme, window size, number of
neurons in the hidden layer and type of learning algorithm. The input layer of
the network consists of neurons changing from 3 to 19, corresponding to
different window sizes. The hidden layer chooses a natural number from 1 to 20
as the number of neurons. The output layer consists of three neurons, each
corresponding to known secondary structural classes viz. alpha helix, beta
strands and coils respectively. It also uses eight different learning
algorithms and nine encoding schemes. Exhaustive experiments were performed
using non-homologues dataset. The experimental results were compared using
performance measures like Q3, sensitivity, specificity, Mathew correlation
coefficient and accuracy. The paper also discusses the process of obtaining a
stabilized cluster of 2530 records from a collection of 11340 records. The
graphs of these stabilized clusters of records with respect to accuracy are
concave, convergence is monotonic increasing and rate of convergence is
uniform. The paper gives BLOSUM62 as the encoding scheme, 19 as the window
size, 19 as the number of neurons in the hidden layer and One- Step Secant as
the learning algorithm with the highest accuracy of 78%. These parameter values
are proposed as the optimized parameter set for the three layer feed forward
back propagation neural network for the protein secondary structure predictionv
Modeling epidemics on d-cliqued graphs
Date: 2018-01-31 00:00:00

Since social interactions have been shown to lead to symmetric clusters, we
propose here that symmetries play a key role in epidemic modeling. Mathematical
models on d-ary tree graphs were recently shown to be particularly effective
for modeling epidemics in simple networks [Seibold & Callender, 2016]. To
account for symmetric relations, we generalize this to a new type of networks
modeled on d-cliqued tree graphs, which are obtained by adding edges to regular
d-trees to form d-cliques. This setting gives a more realistic model for
epidemic outbreaks originating, for example, within a family or classroom and
which could reach a population by transmission via children in schools.
Specifically, we quantify how an infection starting in a clique (e.g. family)
can reach other cliques through the body of the graph (e.g. public places).
Moreover, we propose and study the notion of a safe zone, a subset that has a
negligible probability of infection.
Quantification of systemic risk from overlapping portfolios in the financial system
Date: 2018-01-31 00:00:00

Financial markets are exposed to systemic risk, the risk that a substantial
fraction of the system ceases to function and collapses. Systemic risk can
propagate through different mechanisms and channels of contagion. One important
form of financial contagion arises from indirect interconnections between
financial institutions mediated by financial markets. This indirect
interconnection occurs when financial institutions invest in common assets and
is referred to as overlapping portfolios. In this work we quantify systemic
risk from indirect interconnections between financial institutions. Having
complete information of security holdings of major Mexican financial
intermediaries and the ability to uniquely identify securities in their
portfolios, allows us to represent the Mexican financial system as a bipartite
network of securities and financial institutions. This makes it possible to
quantify systemic risk arising from overlapping portfolios. We show that
focusing only on direct exposures underestimates total systemic risk levels by
up to 50%. By representing the financial system as a multi-layer network of
direct exposures (default contagion) and indirect exposures (overlapping
portfolios) we estimate the mutual influence of different channels of
contagion. The method presented here is the first objective data-driven
quantification of systemic risk on national scales that includes overlapping
portfolios.
Centralized and distributed cognitive task processing in the human connectome
Date: 2018-09-24 00:00:00

A key question in modern neuroscience is how cognitive changes in a human
brain can be quantified and captured by functional connectomes (FC) . A
systematic approach to measure pairwise functional distance at different brain
states is lacking. This would provide a straight-forward way to quantify
differences in cognitive processing across tasks; also, it would help in
relating these differences in task-based FCs to the underlying structural
network. Here we propose a framework, based on the concept of Jensen-Shannon
divergence, to map the task-rest connectivity distance between tasks and
resting-state FC. We show how this information theoretical measure allows for
quantifying connectivity changes in distributed and centralized processing in
functional networks. We study resting-state and seven tasks from the Human
Connectome Project dataset to obtain the most distant links across tasks. We
investigate how these changes are associated to different functional brain
networks, and use the proposed measure to infer changes in the information
processing regimes. Furthermore, we show how the FC distance from resting state
is shaped by structural connectivity, and to what extent this relationship
depends on the task. This framework provides a well grounded mathematical
quantification of connectivity changes associated to cognitive processing in
large-scale brain networks.
Biomedical Signals Reconstruction Under the Compressive Sensing Approach
Date: 2018-01-31 00:00:00
Tasks: Anatomy, Compressive Sensing

The paper analyses the possibility to recover different biomedical signals if
limited number of samples is available. Having in mind that monitoring of
health condition is done by measuring and observing key parameters such as
heart activity through electrocardiogram or anatomy and body processes through
magnetic resonance imaging, it is important to keep the quality of the
reconstructed signal as better as possible. To recover the signal from limited
set of available coefficients, the Compressive Sensing approach and
optimization algorithms are used. The theory is verified by the experimental
results.
Satellite Image Scene Classification via ConvNet with Context Aggregation
Date: 2018-07-12 00:00:00
Tasks: Classification, Scene Classification

Scene classification is a fundamental problem to understand the
high-resolution remote sensing imagery. Recently, convolutional neural network
(ConvNet) has achieved remarkable performance in different tasks, and
significant efforts have been made to develop various representations for
satellite image scene classification. In this paper, we present a novel
representation based on a ConvNet with context aggregation. The proposed
two-pathway ResNet (ResNet-TP) architecture adopts the ResNet as backbone, and
the two pathways allow the network to model both local details and regional
context. The ResNet-TP based representation is generated by global average
pooling on the last convolutional layers from both pathways. Experiments on two
scene classification datasets, UCM Land Use and NWPU-RESISC45, show that the
proposed mechanism achieves promising improvements over state-of-the-art
methods.
Hierarchical evolutive systems, fuzzy categories and the living single cell
Authors: Alejandro M. Mesón, C. Manuel Carlevaro, Fernando Vericat
Date: 2018-01-31 00:00:00
Tasks: Math

In this article, the theory of hierarchical evolutive systems of Ehresmann and Vandremeersch [Bull. Math. Bio. 49, 13-50 (1987)] is improved by considering the categories of the theory as fuzzy sets whose elements are the composite objects formed by the arrows and corresponding vertices of their embedded graphs. This way each category can be represented as a point in the states space [0.1]**N. The introduction of a diffeomorphism, that acts in this context as a functor between categories, allows to define a measure preserving dynamical system. In particular, we apply this formalism to describe a living single cell. We propose for its state at a given time a hirerchical category with three levels (molecular, coarse-grained and cellular levels) related by adequate colimits. Each level involves the main functional and structural modules in which the cell can be partitioned. The time evolution of the cell is drived by a transformation which is a N-dimensional generalization of the Ricker map whose parameters we propose to be determined by requiring that, as hallmark of its behavior, the living cell to evolve at the edge of chaos. From the dynamical point of view this property manifests in the fact that the largest Lyapunov exponent is equal to zero. Since in a rather complete model of the living cell the huge number of involved parameters can make of the calculations a hard task, we also propose a toy model, with fewer parameters to be determined, which emphasizes the cellular fission.
On the interplay between multiscaling and stocks dependence
Date: 2019-03-31 00:00:00
Tasks: Time Series, Time Series Analysis

We find a nonlinear dependence between an indicator of the degree of
multiscaling of log-price time series of a stock and the average correlation of
the stock with respect to the other stocks traded in the same market. This
result is a robust stylized fact holding for different financial markets. We
investigate this result conditional on the stocks' capitalization and on the
kurtosis of stocks' log-returns in order to search for possible confounding
effects. We show that a linear dependence with the logarithm of the
capitalization and the logarithm of kurtosis does not explain the observed
stylized fact, which we interpret as being originated from a deeper
relationship.
Asian Option Pricing with Orthogonal Polynomials
Date: 2018-09-14 00:00:00
Tasks: Numerical Integration

In this paper we derive a series expansion for the price of a continuously
sampled arithmetic Asian option in the Black-Scholes setting. The expansion is
based on polynomials that are orthogonal with respect to the log-normal
distribution. All terms in the series are fully explicit and no numerical
integration nor any special functions are involved. We provide sufficient
conditions to guarantee convergence of the series. The moment indeterminacy of
the log-normal distribution introduces an asymptotic bias in the series,
however we show numerically that the bias can safely be ignored in practice.
Seasonal Stochastic Volatility and the Samuelson Effect in Agricultural Futures Markets
Date: 2018-11-22 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce a multi-factor stochastic volatility model for commodities that
incorporates seasonality and the Samuelson effect. Conditions on the seasonal
term under which the corresponding volatility factor is well-defined are given,
and five different specifications of the seasonality pattern are proposed. We
calculate the joint characteristic function of two futures prices for different
maturities in the risk-neutral measure. The model is then presented under the
physical measure, and its state-space representation is derived, in order to
estimate the parameters with the Kalman filter for time series of corn, cotton,
soybean, sugar and wheat futures from 2007 to 2017. The seasonal model
significantly outperforms the nested non-seasonal model in all five markets,
and we show which seasonality patterns are particularly well-suited in each
case. We also confirm the importance of correctly modelling the Samuelson
effect in order to account for futures with different maturities. Our results
are clearly confirmed in a robustness check carried out with an alternative
dataset of constant maturity futures for the same agricultural markets.
Volatility options in rough volatility models
Authors: Blanka Horvath, Antoine Jacquier, Peter Tankov
Date: 2018-02-05 00:00:00

We discuss the pricing and hedging of volatility options in some rough volatility models. First, we develop efficient Monte Carlo methods and asymptotic approximations for computing option prices and hedge ratios in models where log-volatility follows a Gaussian Volterra process. While providing a good fit for European options, these models are unable to reproduce the VIX option smile observed in the market, and are thus not suitable for VIX products. To accommodate these, we introduce the class of modulated Volterra processes, and show that they successfully capture the VIX smile.
Detection of persistent signals and its relation to coherent feedforward loops
Date: 2018-10-11 00:00:00
Tasks: Relation

Many studies have shown that cells use temporal dynamics of signalling
molecules to encode information. One particular class of temporal dynamics is
persistent and transient signals, i.e. signals of long and short durations
respectively. It has been shown that the coherent type-1 feedforward loop with
an AND logic at the output (or C1-FFL for short) can be used to discriminate a
persistent input signal from a transient one. This has been done by modelling
the C1-FFL, and then use the model to show that persistent and transient input
signals give, respectively, a non-zero and zero output. Instead of assuming the
structure of C1-FFL, this paper shows that it is possible to deduce the C1-FFL
model from the requirement of discriminating a persistent signal. We do this by
first formulating a statistical detection problem of distinguishing persistent
signals from transient ones. The solution of the detection problem is to
compute the log-likelihood ratio of observing a persistent signal to a
transient signal. We show that, if this log-likelihood ratio is positive, which
happens when the signal is likely to be persistent, then it can be
approximately computed by a C1-FFL. Although the capability of C1-FFL to
discriminate persistent signals is known, this paper adds an information
processing interpretation on how a C1-FFL works as a detector of persistent
signals.
An Experimental Investigation of Preference Misrepresentation in the Residency Match
Date: 2018-08-30 00:00:00

The development and deployment of matching procedures that incentivize
truthful preference reporting is considered one of the major successes of
market design research. In this study, we test the degree to which these
procedures succeed in eliminating preference misrepresentation. We administered
an online experiment to 1,714 medical students immediately after their
participation in the medical residency match--a leading field application of
strategy-proof market design. When placed in an analogous, incentivized
matching task, we find that 23% of participants misrepresent their preferences.
We explore the factors that predict preference misrepresentation, including
cognitive ability, strategic positioning, overconfidence, expectations, advice,
and trust. We discuss the implications of this behavior for the design of
allocation mechanisms and the social welfare in markets that use them.
Combinatorial views on persistent characters in phylogenetics
Date: 2020-03-27 00:00:00

The so-called binary perfect phylogeny with persistent characters has
recently been thoroughly studied in computational biology as it is less
restrictive than the well known binary perfect phylogeny. Here, we focus on the
notion of (binary) persistent characters, i.e. characters that can be realized
on a phylogenetic tree by at most one $0 \rightarrow 1$ transition followed by
at most one $1 \rightarrow 0$ transition in the tree, and analyze these
characters under different aspects. First, we illustrate the connection between
persistent characters and Maximum Parsimony, where we characterize persistent
characters in terms of the first phase of the famous Fitch algorithm.
Afterwards we focus on the number of persistent characters for a given
phylogenetic tree. We show that this number solely depends on the balance of
the tree. To be precise, we develop a formula for counting the number of
persistent characters for a given phylogenetic tree based on an index of tree
balance, namely the Sackin index. Lastly, we consider the question of how many
(carefully chosen) binary characters together with their persistence status are
needed to uniquely determine a phylogenetic tree and provide an upper bound for
the number of characters needed.
Plasma Brain Dynamics (PBD): A Mechanism for EEG Waves Under Human Consciousness
Date: 2018-01-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

EEG signals are records of nonlinear solitary waves in human brains. The
waves have several types (e.g., a, b, g, q, d) in response to different levels
of consciousness. They are classified into two groups: Group-1 consists of
complex storm-like waves (a, b, and g); Group-2 is composed of simple
quasilinear waves (q and d). In order to elucidate the mechanism of EEG wave
formation and propagation, this paper extends the Vlasov-Maxwell equations of
Plasma Brain Dynamics (PBD) to a set of two-fluid, self-similar, nonlinear
solitary wave equations. Numerical simulations are performed for different EEG
signals. Main results include: (1) The excitation and propagation of the EEG
wave packets are dependent of electric and magnetic fields, brain aqua-ions,
electron and ion temperatures, masses, and their initial fluid speeds; (2)
Group-1 complex waves contain three ingredients: the high-frequency
ion-acoustic (IA) mode, the intermediate-frequency lower-hybrid (LH) mode, and
the low-frequency ion-cyclotron (IC) mode; (3) Group-2 simple waves fall within
the IA band, featured by one or a combination of the three envelopes:
sinusoidal, sawtooth, and spiky/bipolar. The study proposes an alternative
model to Quantum Brain Dynamics (QBD) by suggesting that the formation and
propagation of the nonlinear solitary EEG waves in the brain have the same
mechanism as that of the waves in space plasmas
Molecular Regulation of Histamine Synthesis
Date: 2018-05-31 00:00:00

Histamine is a critical mediator of IgE/ cell-mediated anaphylaxis, a
neurotransmitter and a regulator of gastric acid secretion. Histamine is a
monoamine synthesized from the amino acid histidine through a reaction
catalyzed by the enzyme histidine decarboxylase (HDC), which removes carboxyl
group from histidine. Despite the importance of histamine, transcriptional
regulation of HDC gene expression in mammals is still poorly understood. In
this Review, we focus on discussing advances in the understanding of molecular
regulation of mammalian histamine synthesis.