pwc-archive/papers-with-abstracts huggingface dataset:
Effects of pitch and timing expectancy on musical emotion
Date: 2017-08-11 00:00:00
Tasks: Time Series, Time Series Analysis

Pitch and timing information work hand in hand to create a coherent piece of
music; but what happens when this information goes against the norm?
Relationships between musical expectancy and emotional responses were
investigated in a study conducted with 40 participants: 20 musicians and 20
non-musicians. Participants took part in one of two behavioural paradigms
measuring continuous expectancy or emotional responses (arousal and valence)
while listening to folk melodies that exhibited either high or low pitch
predictability and high or low onset predictability. The causal influence of
pitch predictability was investigated in an additional condition where pitch
was artificially manipulated and a comparison conducted between original and
manipulated forms; the dynamic correlative influence of pitch and timing
information and its perception on emotional change during listening was
evaluated using cross-sectional time series analysis. The results indicate that
pitch and onset predictability are consistent predictors of perceived
expectancy and emotional response, with onset carrying more weight than pitch.
In addition, musicians and non-musicians do not differ in their responses,
possibly due to shared cultural background and knowledge. The results
demonstrate in a controlled lab-based setting a precise, quantitative
relationship between the predictability of musical structure, expectation and
emotional response.
Spatial heterogeneity analyses identify limitations of epidemic alert systems: Monitoring influenza-like illness in France
Date: 2018-03-29 00:00:00
Tasks: Epidemiology, Time Series, Time Series Analysis

Surveillance data serving for epidemic alert systems are typically fully
aggregated in space. However, epidemics may be spatially heterogeneous,
undergoing distinct dynamics in distinct regions of the surveillance area. We
unveil this in retrospective analyses by classifying incidence time series. We
use Pearson correlation to quantify the similarity between local time series
and then classify them using modularity maximization. The surveillance area is
thus divided into regions with different incidence patterns. We analyzed 31
years of data on influenza-like-illness from the French system Sentinelles and
found spatial heterogeneity in 19/31 influenza seasons. However, distinct
epidemic regions could be identified only 4-5 weeks after the nationwide alert.
The impact of spatial heterogeneity on influenza epidemiology was complex.
First, when the nationwide alert was triggered, 32-41% of the administrative
regions were experiencing an epidemic, while the others were not. Second, the
nationwide alert was timely for the whole surveillance area, but, subsequently,
regions experienced distinct epidemic dynamics. Third, the epidemic dynamics
were homogeneous in space. Spatial heterogeneity analyses can provide the
timing of the epidemic peak and finish, in various regions, to tailor disease
monitoring and control.
Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation
Authors: Xu Zhenghua, Chen Cheng, Lukasiewicz Thomas, Miao Yishu
Date: 2017-08-12 00:00:00
Tasks: TAG

Matrix factorization has now become a dominant solution for personalized
recommendation on the Social Web. To alleviate the cold start problem, previous
approaches have incorporated various additional sources of information into
traditional matrix factorization models. These upgraded models, however,
achieve only "marginal" enhancements on the performance of personalized
recommendation. Therefore, inspired by the recent development of deep-semantic
modeling, we propose a hybrid deep-semantic matrix factorization (HDMF) model
to further improve the performance of tag-aware personalized recommendation by
integrating the techniques of deep-semantic modeling, hybrid learning, and
matrix factorization. Experimental results show that HDMF significantly
outperforms the state-of-the-art baselines in tag-aware personalized
recommendation, in terms of all evaluation metrics, e.g., its mean reciprocal
rank (resp., mean average precision) is 1.52 (resp., 1.66) times as high as
that of the best baseline.
Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method
Date: 2017-08-14 00:00:00
Tasks: Feature Engineering

Non protein coding regions of the human genome contain many complex patterns
which regulate the cellular activity. Studying the human genome is limited by
the lack of understanding of its features and their complex interactions.
However, recent advances in AI research have enabled automatically learning
representations of high dimensional complex data without feature engineering,
using deep neural networks. Therefore, in this paper, we demonstrate that a
convolutional neural network can learn a representation of DNA sequence without
specifying any motifs or patterns, such that it becomes capable of predicting
whether a DNA sequence is natural or artificial. The trained model could
distinguish scrambled vs real DNA sequences for scrambling lengths of 2 bp, 10
bp, 50 bp and even 100 bp, with a significantly higher accuracy than linear
SVMs. With this study, we have discovered that regions of non protein coding
DNA might have meaningful interactions at even longer than 100 bp distances
even though they do not code proteins.
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
Date: 2019-05-30 00:00:00

In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area.
Valuation of a Bermudan DB underpin hybrid pension benefit
Date: 2017-08-14 00:00:00

In this paper we consider three types of embedded options in pension benefit
design.
  The first is the Florida second election (FSE) option, offered to public
employees in the state of Florida in 2002. Employees were given the option to
convert from a defined contribution (DC) plan to a defined benefit (DB) plan at
a time of their choosing. The cost of the switch was assessed in terms of the
ABO (Accrued Benefit Obligation), which is the expected present value of the
accrued DB pension at the time of the switch. If the ABO was greater than the
DC account, the employee was required to fund the difference.
  The second is the DB Underpin option, also known as a floor offset, under
which the employee participates in a DC plan, but with a guaranteed minimum
benefit based on a traditional DB formula.
  The third option can be considered a variation on each of the first two. We
remove the requirement from the FSE option for employees to fund any shortfall
at the switching date. The resulting option is very similar to the DB underpin,
but with the possibility of early exercise. Since we assume that exercise is
only permitted at discrete, annual intervals, this option is a Bermudan
variation on the DB Underpin.
  We adopt an arbitrage-free pricing methodology to value the option. We
analyse and value the optimal switching strategy for the employee by
constructing an exercise frontier, and illustrate numerically the difference
between the FSE, DB Underpin and Bermudan DB Underpin options.
Improved Answer Selection with Pre-Trained Word Embeddings
Authors: Chakravarti Rishav, Navratil Jiri, Santos Cicero Nogueira dos
Date: 2017-08-14 00:00:00
Tasks: Answer Selection, Information Retrieval, Learning-To-Rank, Retrieval, Word Embeddings

This paper evaluates existing and newly proposed answer selection methods
based on pre-trained word embeddings. Word embeddings are highly effective in
various natural language processing tasks and their integration into
traditional information retrieval (IR) systems allows for the capture of
semantic relatedness between questions and answers. Empirical results on three
publicly available data sets show significant gains over traditional term
frequency based approaches in both supervised and unsupervised settings. We
show that combining these word embedding features with traditional
learning-to-rank techniques can achieve similar performance to state-of-the-art
neural networks trained for the answer selection task.
Optimal placement of a small order in a diffusive limit order book
Date: 2017-08-14 00:00:00

We study the optimal placement problem of a stock trader who wishes to clear
his/her inventory by a predetermined time horizon t, by using a limit order or
a market order. For a diffusive market, we characterize the optimal limit order
placement policy and analyze its behavior under different market conditions. In
particular, we show that, in the presence of a negative drift, there exists a
critical time t0>0 such that, for any time horizon t>t0, there exists an
optimal placement, which, contrary to earlier work, is different from one that
is placed "infinitesimally" close to the best ask, such as the best bid and
second best bid. We also propose a simple method to approximate the critical
time t0 and the optimal order placement.
Optimum thresholding using mean and conditional mean square error
Date: 2017-08-14 00:00:00

We consider a univariate semimartingale model for (the logarithm of) an asset
price, containing jumps having possibly infinite activity (IA). The
nonparametric threshold estimator of the integrated variance IV proposed in
Mancini 2009 is constructed using observations on a discrete time grid, and
precisely it sums up the squared increments of the process when they are below
a threshold, a deterministic function of the observation step and possibly of
the coefficients of X. All the threshold functions satisfying given conditions
allow asymptotically consistent estimates of IV, however the finite sample
properties of the truncated realized variation can depend on the specific
choice of the threshold. We aim here at optimally selecting the threshold by
minimizing either the estimation mean square error (MSE) or the conditional
mean square error (cMSE). The last criterion allows to reach a threshold which
is optimal not in mean but for the specific volatility (and jumps paths) at
hand. A parsimonious characterization of the optimum is established, which
turns out to be asymptotically proportional to the L\'evy's modulus of
continuity of the underlying Brownian motion. Moreover, minimizing the cMSE
enables us to propose a novel implementation scheme for approximating the
optimal threshold. Monte Carlo simulations illustrate the superior performance
of the proposed method.
Towards Learning Reward Functions from User Interactions
Authors: Li Ziming, Kiseleva Julia, de Rijke Maarten, Grotov Artem
Date: 2017-08-15 00:00:00
Tasks: Recommendation Systems

In the physical world, people have dynamic preferences, e.g., the same
situation can lead to satisfaction for some humans and to frustration for
others. Personalization is called for. The same observation holds for online
behavior with interactive systems. It is natural to represent the behavior of
users who are engaging with interactive systems such as a search engine or a
recommender system, as a sequence of actions where each next action depends on
the current situation and the user reward of taking a particular action. By and
large, current online evaluation metrics for interactive systems such as search
engines or recommender systems, are static and do not reflect differences in
user behavior. They rarely capture or model the reward experienced by a user
while interacting with an interactive system. We argue that knowing a user's
reward function is essential for an interactive system as both for learning and
evaluation. We propose to learn users' reward functions directly from observed
interaction traces. In particular, we present how users' reward functions can
be uncovered directly using inverse reinforcement learning techniques. We also
show how to incorporate user features into the learning process. Our main
contribution is a novel and dynamic approach to restore a user's reward
function. We present an analytic approach to this problem and complement it
with initial experiments using the interaction logs of a cultural heritage
institution that demonstrate the feasibility of the approach by uncovering
different reward functions for different user groups.
Balanced Information Storage and Transfer in Modular Spiking Neural Networks
Date: 2017-08-15 00:00:00
Tasks: Relation

While information processing in complex systems can be described in abstract,
general terms, there are cases in which the relation between these computations
and the physical substrate of the underlying system is itself of interest.
Prominently, the brain is one such case. With the aim of relating information
and dynamics in biological neural systems, we study a model network of spiking
neurons with different coupling configurations, and explore the relation
between its informational, dynamical, and topological properties. We find that
information transfer and storage peak at two separate points for different
values of the coupling parameter, and are balanced at an intermediate point. In
this configuration, avalanches in the network follow a long-tailed, power
law-like distribution. Furthermore, the avalanche statistics at this point
reproduce empirical findings in the biological brain.
Ensemble Methods for Personalized E-Commerce Search Challenge at CIKM Cup 2016
Authors: Wu Chen, Yan Ming, Si Luo
Date: 2017-08-15 00:00:00

Personalized search has been a hot research topic for many years and has been
widely used in e-commerce. This paper describes our solution to tackle the
challenge of personalized e-commerce search at CIKM Cup 2016. The goal of this
competition is to predict search relevance and re-rank the result items in SERP
according to the personalized search, browsing and purchasing preferences.
Based on a detailed analysis of the provided data, we extract three different
types of features, i.e., statistic features, query-item features and session
features. Different models are used on these features, including logistic
regression, gradient boosted decision trees, rank svm and a novel deep match
model. With the blending of multiple models, a stacking ensemble model is built
to integrate the output of individual models and produce a more accurate
prediction result. Based on these efforts, our solution won the champion of the
competition on all the evaluation metrics.
SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation
Authors: Cai Chenwei, He Ruining, McAuley Julian
Date: 2017-06-16 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Dealing with sparse, long-tailed datasets, and cold-start problems is always
a challenge for recommender systems. These issues can partly be dealt with by
making predictions not in isolation, but by leveraging information from related
events; such information could include signals from social relationships or
from the sequence of recent activities. Both types of additional information
can be used to improve the performance of state-of-the-art matrix
factorization-based techniques. In this paper, we propose new methods to
combine both social and sequential information simultaneously, in order to
further improve recommendation performance. We show these techniques to be
particularly effective when dealing with sparsity and cold-start issues in
several large, real-world datasets.
Bayesian Non-Exhaustive Classification for Active Online Name Disambiguation
Authors: Zhang Baichuan, Dundar Murat, Hasan Mohammad Al
Date: 2017-08-12 00:00:00
Tasks: Classification, General Classification

The name disambiguation task partitions a collection of records pertaining to
a given name, such that there is a one-to-one correspondence between the
partitions and a group of people, all sharing that given name. Most existing
solutions for this task are proposed for static data. However, more realistic
scenarios stipulate emergence of records in a streaming fashion where records
may belong to known as well as unknown persons all sharing the same name. This
requires a flexible name disambiguation algorithm that can not only classify
records of known persons represented in the train- ing data by their existing
records but can also identify records of new ambiguous persons with no existing
records included in the initial training dataset. Toward achieving this
objective, in this paper we propose a Bayesian non-exhaustive classification
frame- work for solving online name disambiguation. In particular, we present a
Dirichlet Process Gaussian Mixture Model (DPGMM) as a core engine for online
name disambiguation task. Meanwhile, two online inference algorithms, namely
one-pass Gibbs sampler and Sequential Importance Sampling with Resampling (also
known as particle filtering), are proposed to simultaneously perform online
classification and new class discovery. As a case study we consider
bibliographic data in a temporal stream format and disambiguate authors by
partitioning their papers into homogeneous groups.Our experimental results
demonstrate that the proposed method is significantly better than existing
methods for performing online name disambiguation task. We also propose an
interactive version of our online name disambiguation method designed to
leverage user feedback to improve prediction accuracy.
Some stylized facts of the Bitcoin market
Date: 2017-08-12 00:00:00
Tasks: Time Series, Time Series Analysis

In recent years a new type of tradable assets appeared, generically known as
cryptocurrencies. Among them, the most widespread is Bitcoin. Given its
novelty, this paper investigates some statistical properties of the Bitcoin
market. This study compares Bitcoin and standard currencies dynamics and
focuses on the analysis of returns at different time scales. We test the
presence of long memory in return time series from 2011 to 2017, using
transaction data from one Bitcoin platform. We compute the Hurst exponent by
means of the Detrended Fluctuation Analysis method, using a sliding window in
order to measure long range dependence. We detect that Hurst exponents changes
significantly during the first years of existence of Bitcoin, tending to
stabilize in recent times. Additionally, multiscale analysis shows a similar
behavior of the Hurst exponent, implying a self-similar process.
Structured illumination microscopy for dual-modality 3D sub-diffraction resolution fluorescence and refractive-index reconstruction
Date: 2017-08-15 00:00:00
Tasks: Super-Resolution

Though structured illumination (SI) microscopy is a popular imaging technique
conventionally associated with fluorescent super-resolution, recent works have
suggested its applicability towards sub-diffraction coherent imaging with
quantitative endogenous biological contrast. Here, we demonstrate that SI can
efficiently integrate the principles of fluorescent super-resolution and
coherent synthetic aperture to achieve 3D dual-modality sub-diffraction
resolution, fluorescence and refractive-index (RI) visualizations of biological
samples. Such a demonstration can enable future biological studies to
synergistically explore molecular and biophysical/biochemical mechanisms at
sub-diffraction resolutions. We experimentally demonstrate this framework by
introducing a SI microscope capable of 3D sub-diffraction fluorescence and RI
imaging, and verify its biological visualization capabilities by experimentally
reconstructing 3D RI/fluorescence visualizations of fluorescent calibration
microspheres as well as alveolar basal epithelial adenocarcinoma (A549) and
human colorectal adenocarcinmoa (HT-29) cells, fluorescently stained for
F-actin.
Low-Dimensional Manifold of Actin Polymerization Dynamics
Date: 2017-08-15 00:00:00

Actin filaments are critical components of the eukaryotic cytoskeleton,
playing important roles in a number of cellular functions, such as cell
migration, organelle transport, and mechanosensation. They are helical polymers
with a well-defined polarity, composed of globular monomers that bind
nucleotides in one of three hydrolysis states (ATP, ADP-Pi, or ADP). Mean-field
models of the dynamics of actin polymerization have succeeded in, among other
things, determining the nucleotide profile of an average filament and resolving
the mechanisms of accessory proteins, however these models require numerical
solution of a high-dimensional system of nonlinear ODE's. By truncating a set
of recursion equations, the Brooks-Carlsson model reduces dimensionality to 11,
but it remains nonlinear and does not admit an analytical solution, hence,
significantly hindering understanding of its resulting dynamics. In this work,
by taking advantage of the fast timescales of the hydrolysis states of the
filament tips, we propose two model reduction schemes that achieve low
dimensionality and linearity. We provide an exact solution of the resulting
linear equations and use it to shed light on the dynamical behaviors of the
full BC model, highlighting the relative ordering of the timescales of various
collective processes, and explaining some unusual dependence of the
steady-state behavior on initial conditions.
Generalizations of Szpilrajn's Theorem in economic and game theories
Date: 2017-08-15 00:00:00
Tasks: LEMMA, Vocal Bursts Type Prediction

Szpilrajn's Lemma entails that each partial order extends to a linear order.
Dushnik and Miller use Szpilrajn's Lemma to show that each partial order has a
relizer. Since then, many authors utilize Szpilrajn's Theorem and the
Well-ordering principle to prove more general existence type theorems on
extending binary relations. Nevertheless, we are often interested not only in
the existence of extensions of a binary relation $R$ satisfying certain axioms
of orderability, but in something more: (A) The conditions of the sets of
alternatives and the properties which $R$ satisfies to be inherited when one
passes to any member of a subfamily of the family of extensions of $R$ and: (B)
The size of a family of ordering extensions of $R$, whose intersection is $R$,
to be the smallest one. The key to addressing these kinds of problems is the
szpilrajn inherited method. In this paper, we define the notion of
$\Lambda(m)$-consistency, where $m$ can reach the first infinite ordinal
$\omega$, and we give two general inherited type theorems on extending binary
relations, a Szpilrajn type and a Dushnik-Miller type theorem, which generalize
all the well known existence and inherited type extension theorems in the
literature. \keywords{Consistent binary relations, Extension theorems,
Intersection of binary relations.