pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the consequences of lack of closure in codon models
Date: 2017-09-15 00:00:00

Models of codon evolution are commonly used to identify positive selection.
Positive selection is typically a heterogeneous process, i.e., it acts on some
branches of the evolutionary tree and not others. Previous work on DNA models
showed that when evolution occurs under a heterogeneous process it is important
to consider the property of model closure, because non-closed models can give
biased estimates of evolutionary processes. The existing codon models that
account for the genetic code are not closed; to establish this it is enough to
show that they are not linear (meaning that the sum of two codon rate matrices
in the model is not a matrix in the model). This raises the concern that a
single codon model fit to a heterogeneous process might mis-estimate both the
effect of selection and branch lengths.
  Codon models are typically constructed by choosing an underlying DNA model
(e.g., HKY) that acts identically and independently at each codon position, and
then applying the genetic code via the parameter $\omega$ to modify the rate of
transitions between codons that code for different amino acids. Here we use
simulation to investigate the accuracy of estimation of both the selection
parameter $\omega$ and branch lengths in cases where the underlying DNA process
is heterogeneous but $\omega$ is constant. We find that both $\omega$ and
branch lengths can be mis-estimated in these scenarios. Errors in $\omega$ were
usually less than 2% but could be as high as 17%. We also assessed if choosing
different underlying DNA models had any affect on accuracy, in particular we
assessed if using closed DNA models gave any advantage. However, a DNA model
being closed does not imply that the codon model constructed from it is closed,
and in general we found that using closed DNA models did not decrease errors in
the estimation of $\omega$.
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
Authors: Chen Laming, Zhang Guoxin, Zhou Hanning
Date: 2018-05-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Diversity

The determinantal point process (DPP) is an elegant probabilistic model of
repulsion with applications in various machine learning tasks including
summarization and search. However, the maximum a posteriori (MAP) inference for
DPP which plays an important role in many applications is NP-hard, and even the
popular greedy algorithm can still be too computationally expensive to be used
in large-scale real-time scenarios. To overcome the computational challenge, in
this paper, we propose a novel algorithm to greatly accelerate the greedy MAP
inference for DPP. In addition, our algorithm also adapts to scenarios where
the repulsion is only required among nearby few items in the result sequence.
We apply the proposed algorithm to generate relevant and diverse
recommendations. Experimental results show that our proposed algorithm is
significantly faster than state-of-the-art competitors, and provides a better
relevance-diversity trade-off on several public datasets, which is also
confirmed in an online A/B test.
Crowdsourcing Paper Screening in Systematic Literature Reviews
Authors: Krivosheev Evgeny, Casati Fabio, Caforio Valentina, Benatallah Boualem
Date: 2017-09-15 00:00:00
Tasks: Systematic Literature Review

Literature reviews allow scientists to stand on the shoulders of giants,
showing promising directions, summarizing progress, and pointing out existing
challenges in research. At the same time conducting a systematic literature
review is a laborious and consequently expensive process. In the last decade,
there have a few studies on crowdsourcing in literature reviews. This paper
explores the feasibility of crowdsourcing for facilitating the literature
review process in terms of results, time and effort, as well as to identify
which crowdsourcing strategies provide the best results based on the budget
available. In particular we focus on the screening phase of the literature
review process and we contribute and assess methods for identifying the size of
tests, labels required per paper, and classification functions as well as
methods to split the crowdsourcing process in phases to improve results.
Finally, we present our findings based on experiments run on Crowdflower.
Clustering of Musical Pieces through Complex Networks: an Assessment over Guitar Solos
Authors: Ferretti Stefano
Date: 2017-09-14 00:00:00
Tasks: Clustering, Music Generation

Musical pieces can be modeled as complex networks. This fosters innovative
ways to categorize music, paving the way towards novel applications in
multimedia domains, such as music didactics, multimedia entertainment and
digital music generation. Clustering these networks through their main metrics
allows grouping similar musical tracks. To show the viability of the approach,
we provide results on a dataset of guitar solos.
Relatedness, Knowledge Diffusion, and the Evolution of Bilateral Trade
Date: 2017-09-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

During the last decades two important contributions have reshaped our
understanding of international trade. First, countries trade more with those
with whom they share history, language, and culture, suggesting that trade is
limited by information frictions. Second, countries are more likely to start
exporting products that are similar to their current exports, suggesting that
knowledge diffusion among related industries is a key constrain shaping the
diversification of exports. But does knowledge about how to export to a
destination also diffuses among related products and geographic neighbors? Do
countries need to learn how to trade each product to each destination? Here, we
use bilateral trade data from 2000 to 2015 to show that countries are more
likely to increase their exports of a product to a destination when: (i) they
export related products to it, (ii) they export the same product to the
neighbor of a destination, (iii) they have neighbors who export the same
product to that destination. Then, we explore the magnitude of these effects
for new, nascent, and experienced exporters, (exporters with and without
comparative advantage in a product) and also for groups of products with
different level of technological sophistication. We find that the effects of
product and geographic relatedness are stronger for new exporters, and also,
that the effect of product relatedness is stronger for more technologically
sophisticated products. These findings support the idea that international
trade is shaped by information frictions that are reduced in the presence of
related products and experienced geographic neighbors.
Semi-Static and Sparse Variance-Optimal Hedging
Date: 2017-09-16 00:00:00
Tasks: Position

We consider hedging of a contingent claim by a 'semi-static' strategy
composed of a dynamic position in one asset and static (buy-and-hold) positions
in other assets. We give general representations of the optimal strategy and
the hedging error under the criterion of variance-optimality and provide
tractable formulas using Fourier-integration in case of the Heston model. We
also consider the problem of optimally selecting a sparse semi-static hedging
strategy, i.e. a strategy which only uses a small subset of available hedging
assets. The developed methods are illustrated in an extended numerical example
where we compute a sparse semi-static hedge for a variance swap using European
options as static hedging assets.
A computational study of the mechanisms of growth-driven folding patterns on shells, with application to the developing brain
Date: 2017-11-23 00:00:00

We consider the mechanisms by which folds, or sulci (troughs) and gyri
(crests), develop in the brain. This feature, common to many gyrencephalic
species including humans, has attracted recent attention from soft matter
physicists. It occurs due to inhomogeneous, and predominantly tangential,
growth of the cortex, which causes circumferential compression, leading to a
bifurcation of the solution path to a folded configuration. The problem can be
framed as one of buckling in the regime of linearized elasticity. However, the
brain is a very soft solid, which is subject to large strains due to
inhomogeneous growth. As a consequence, the morphomechanics of the developing
brain demonstrates an extensive post-bifurcation regime. Nonlinear elasticity
studies of growth-driven brain folding have established the conditions
necessary for the onset of folding, and for its progression to configurations
broadly resembling gyrencephalic brains. The reference, unfolded,
configurations in these treatments have a high degree of symmetry--typically,
ellipsoidal. Depending on the boundary conditions, the folded configurations
have symmetric or anti-symmetric patterns. However, these configurations do not
approximate the actual morphology of, e.g., human brains, which display
unsymmetric folding. More importantly, from a neurodevelopmental standpoint,
many of the unsymmetric sulci and gyri are notably robust in their locations.
Here, we initiate studies on the physical mechanisms and geometry that control
the development of primary sulci and gyri. In this preliminary communication we
carry out computations with idealized geometries, boundary conditions and
parameters, seeking a pattern resembling one of the first folds to form: the
Central Sulcus.
GDP growth rates as confined L\'evy flights
Date: 2017-08-26 00:00:00

A new model that combines economic growth rate fluctuations at the
microscopic and macroscopic level is presented. At the microscopic level, firms
are growing at different rates while also being exposed to idiosyncratic shocks
at the firm and sector level. We describe such fluctuations as independent
L\'evy-stable fluctuations, varying over multiple orders of magnitude. These
fluctuations are aggregated and measured at the macroscopic level in averaged
economic output quantities such as GDP. A fundamental question is thereby to
what extend individual firm size fluctuations can have a noticeable impact on
the overall economy. We argue that this question can be answered by considering
the L\'evy fluctuations as embedded in a steep confining potential well,
ensuring nonlinear mean-reversal behavior, without having to rely on
microscopic details of the system. The steepness of the potential well directly
controls the extend towards which idiosyncratic shocks to firms and sectors are
damped at the level of the economy. Additionally, the theory naturally accounts
for business cycles, represented in terms of a bimodal economic output
distribution, and thus connects two so far unrelated fields in economics. By
analyzing 200 years of US GDP growth rates, we find that the model is in good
agreement with the data.
Closing the loop between neural network simulators and the OpenAI Gym
Date: 2017-09-17 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Since the enormous breakthroughs in machine learning over the last decade,
functional neural network models are of growing interest for many researchers
in the field of computational neuroscience. One major branch of research is
concerned with biologically plausible implementations of reinforcement
learning, with a variety of different models developed over the recent years.
However, most studies in this area are conducted with custom simulation scripts
and manually implemented tasks. This makes it hard for other researchers to
reproduce and build upon previous work and nearly impossible to compare the
performance of different learning architectures. In this work, we present a
novel approach to solve this problem, connecting benchmark tools from the field
of machine learning and state-of-the-art neural network simulators from
computational neuroscience. This toolchain enables researchers in both fields
to make use of well-tested high-performance simulation software supporting
biologically plausible neuron, synapse and network models and allows them to
evaluate and compare their approach on the basis of standardized environments
of varying complexity. We demonstrate the functionality of the toolchain by
implementing a neuronal actor-critic architecture for reinforcement learning in
the NEST simulator and successfully training it on two different environments
from the OpenAI Gym.
Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions
Date: 2017-09-17 00:00:00

Improvements in sequencing technologies and reduced experimental costs have
resulted in a vast number of studies generating high-throughput data. Although
the number of methods to analyze these "omics" data has also increased,
computational complexity and lack of documentation hinder researchers from
analyzing their high-throughput data to its true potential. In this chapter we
detail our data-driven, transkingdom network (TransNet) analysis protocol to
integrate and interrogate multi-omics data. This systems biology approach has
allowed us to successfully identify important causal relationships between
different taxonomic kingdoms (e.g. mammals and microbes) using diverse types of
data.
Anticipating Information Needs Based on Check-in Activity
Authors: Benetka Jan R., Balog Krisztian, Nørvåg Kjetil
Date: 2017-09-18 00:00:00

In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.
A new approach to the modeling of financial volumes
Date: 2017-09-18 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we study the high frequency dynamic of financial volumes of
traded stocks by using a semi-Markov approach. More precisely we assume that
the intraday logarithmic change of volume is described by a weighted-indexed
semi-Markov chain model. Based on this assumptions we show that this model is
able to reproduce several empirical facts about volume evolution like time
series dependence, intra-daily periodicity and volume asymmetry. Results have
been obtained from a real data application to high frequency data from the
Italian stock market from first of January 2007 until end of December 2010.
Quantum origin of life: methodological, epistemological and ontological issues
Date: 2017-09-15 00:00:00

The aim of this essay is to analyze the role of quantum mechanics as an
inherent characteristic of life. During the last ten years the problem of the
origin of life has become an innovative research subject approached by many
authors. The essay is divided in to three parts: the first deals with the
problem of life from a philosophical and biological perspective. The second
presents the conceptual and methodological basis of the essay which is founded
on the Information Theory and the Quantum Theory. This basis is then used, in
the third part, to discuss the different arguments and conjectures of a quantum
origin of life. There are many philosophical views on the problem of life, two
of which are especially important at the moment: reductive physicalism and
biosystemic emergentism. From a scientific perspective, all the theories and
experimental evidences put forward by Biology can be summed up in to two main
research themes: the RNA world and the vesicular theory. The RNA world, from a
physicalist point of view, maintains that replication is the essence of life
while the vesicular theory, founded on biosystemic grounds, believes the
essence of life can be found in cellular metabolism. This essay uses the
Information Theory to discard the idea of a spontaneous emergency of life
through replication. Understanding the nature and basis of quantum mechanics is
fundamental in order to be able to comprehend the advantages of using quantum
computation to be able increase the probabilities of existence of auto
replicative structures. Different arguments are set forth such as the inherence
of quantum mechanics to the origin of life. Finally, in order to try to resolve
the question of auto replication, three scientific propositions are put
forward: Q-life, the quantum combinatory library and the role of algorithms in
the origin of genetic language.
From metagenomic data to personalized computational microbiotas: Predicting dietary supplements for Crohn's disease
Date: 2017-09-18 00:00:00

Crohn's disease (CD) is associated with an ecological imbalance of the
intestinal microbiota, consisting of hundreds of species. The underlying
complexity as well as individual differences between patients contributes to
the difficulty to define a standardized treatment. Computational modeling can
systematically investigate metabolic interactions between gut microbes to
unravel novel mechanistic insights. In this study, we integrated metagenomic
data of CD patients and healthy controls with genome-scale metabolic models
into personalized in silico microbiotas. We predicted short chain fatty acid
(SFCA) levels for patients and controls, which were overall congruent with
experimental findings. As an emergent property, low concentrations of SCFA were
predicted for CD patients and the SCFA signatures were unique to each patient.
Consequently, we suggest personalized dietary treatments that could improve
each patient's SCFA levels. The underlying modeling approach could aid clinical
practice to find novel dietary treatment and guide recovery by rationally
proposing food aliments.
Discrete Dynamic Causal Modeling and Its Relationship with Directed Information
Date: 2017-09-18 00:00:00

This paper explores the discrete Dynamic Causal Modeling (DDCM) and its
relationship with Directed Information (DI). We prove the conditional
equivalence between DDCM and DI in characterizing the causal relationship
between two brain regions. The theoretical results are demonstrated using fMRI
data obtained under both resting state and stimulus based state. Our numerical
analysis is consistent with that reported in previous study.
Optimal Training for Residual Self-Interference for Full Duplex One-way Relays
Date: 2018-07-18 00:00:00

Channel estimation and optimal training sequence design for full-duplex
one-way relays are investigated. We propose a training scheme to estimate the
residual self-interference (RSI) channel and the channels between nodes
simultaneously. A maximum likelihood estimator is implemented with
Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. In the presence of RSI, the
overall source-to-destination channel becomes an inter-symbol-interference
(ISI) channel. With the help of estimates of the RSI channel, the destination
is able to cancel the ISI through equalization. We derive and analyze the
Cramer-Rao bound (CRB) in closed-form by using the asymptotic properties of
Toeplitz matrices. The optimal training sequence is obtained by minimizing the
CRB. Extensions for the fundamental one-way relay model to the
frequency-selective fading channels and the multiple relays case are also
considered. For the former, we propose a training scheme to estimate the
overall channel, and for the latter the CRB and the optimal number of relays
are derived when the distance between the source and the destination is fixed.
Simulations using LTE parameters corroborate our theoretical results.
Cell Sequence and Mitosis Affect Fibroblast Directional Decision-Making during Chemotaxis in Tissue-Mimicking Microfluidic Mazes
Date: 2017-09-18 00:00:00
Tasks: Decision Making

Directed fibroblast migration is central to highly proliferative processes in
regenerative medicine and developmental biology, such as wound healing and
embryogenesis. However, the mechanisms by which single fibroblasts affect each
other's directional decisions, while chemotaxing in microscopic tissue pores,
are not well understood. Therefore, we explored the effects of two types of
relevant social interactions on fibroblast PDGF-BB-induced migration in
microfluidic tissue-mimicking mazes: cell sequence and mitosis. Surprisingly,
it was found that in both cases, the cells display behavior that is
contradictory to the chemoattractant gradient established in the maze. In case
of the sequence, the cells do not like to take the same path through the maze
as their predecessor, when faced with a bifurcation. To the contrary, they tend
to alternate - if a leading cell takes the shorter (steeper gradient) path, the
cell following it chooses the longer (weaker gradient) path, and vice versa.
Additionally, we found that when a mother cell divides, its two daughters go in
opposite directions (even if it means migrating against the chemoattractant
gradient and overcoming on-going cell traffic). Therefore, it is apparent that
fibroblasts modify each other's directional decisions in a manner that is
counter-intuitive to what is expected from classical chemotaxis theory.
Consequently, accounting for these effects could lead to a better understanding
of tissue generation in vivo, and result in more advanced engineered tissue
products in vitro.
Universal L\'evy's stable law of stock market and its characterization
Date: 2018-02-20 00:00:00

Price fluctuations in financial markets can be characterized by L\'evy's
stable distribution, which is supported by the generalized central limit
system. When the stable parameters were estimated from four different stock
markets in long term, they similarly indicated an unique value. On the other
hand, when analyzed in short term, parameters and the stock prices fluctuated
with correlation, which shows that the stock markets are instable.