pwc-archive/papers-with-abstracts huggingface dataset:
Universality of macroscopic neuronal dynamics in Caenorhabditis elegans
Date: 2017-11-22 00:00:00
Tasks: Dimensionality Reduction

Recordings of whole brain activity with single neuron resolution are now
feasible in simple organisms. Yet, it is still challenging to appropriately
simplify such complex, noisy, and multivariate data in order to reveal general
principles of nervous system function. Here, we develop a method that allows us
to extract global brain dynamics from pan-neuronal imaging. Success of this
method is rooted in a surprising mathematical connection between dimensionality
reduction and a general class of thermodynamic systems. Application of this
theoretical framework to the nervous system of C. elegans reveals the manifold
that sculpts global brain dynamics. This manifold allows us to predict switches
between worm behaviors across individuals, implying that macroscopic dynamics
embodied by the manifold are universal. In contrast, activation of individual
neurons differs consistently between worms. These findings suggest that brains
of genetically identical individuals express distinct microscopic neuronal
configurations which nonetheless yield equivalent macroscopic dynamics.
Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems
Date: 2017-11-23 00:00:00
Tasks: Decoder, Scheduling

The conventional grant-based network relies on the handshaking between base
station and active users to achieve dynamic multi-user scheduling, which may
cost large signaling overheads as well as system latency. To address those
problems, the grant-free receiver design is considered in this paper based on
sparse code multiple access (SCMA), one of the promising air interface
technologies for 5G wireless networks. With the presence of unknown multipath
fading, the proposed receiver blindly performs joint channel estimation and
data decoding without knowing the user activity in the network. Based on the
framework of belief propagation (BP), we formulate a messagepassing receiver
for uplink SCMA that performs joint estimation iteratively. However, the direct
application of BP for the multivariable detection problem is complex. Motivated
by the idea of approximate inference, we use expectation propagation to project
the intractable distributions into Gaussian families such that a linear
complexity decoder is obtained.Simulation results show that the proposed
receiver can detect active users in the network with a high accuracy and can
achieve an improved bit-error-rate performance compared with existing methods.
Singing voice correction using canonical time warping
Date: 2017-11-23 00:00:00

Expressive singing voice correction is an appealing but challenging problem.
A robust time-warping algorithm which synchronizes two singing recordings can
provide a promising solution. We thereby propose to address the problem by
canonical time warping (CTW) which aligns amateur singing recordings to
professional ones. A new pitch contour is generated given the alignment
information, and a pitch-corrected singing is synthesized back through the
vocoder. The objective evaluation shows that CTW is robust against
pitch-shifting and time-stretching effects, and the subjective test
demonstrates that CTW prevails the other methods including DTW and the
commercial auto-tuning software. Finally, we demonstrate the applicability of
the proposed method in a practical, real-world scenario.
Wiki-MetaSemantik: A Wikipedia-derived Query Expansion Approach based on Network Properties
Authors: Puspitaningrum D., Yulianti G., Prasetya I. S. W. B.
Date: 2017-11-23 00:00:00

This paper discusses the use of Wikipedia for building semantic ontologies to
do Query Expansion (QE) in order to improve the search results of search
engines. In this technique, selecting related Wikipedia concepts becomes
important. We propose the use of network properties (degree, closeness, and
pageRank) to build an ontology graph of user query concepts which is derived
directly from Wikipedia structures. The resulting expansion system is called
Wiki-MetaSemantik. We tested this system against other online thesauruses and
ontology based QE in both individual and meta-search engines setups. Despite
that our system has to build a Wikipedia ontology graph in order to do its
work, the technique turns out to work very fast (1:281) compared to another
ontology QE baseline (Wikipedia Persian ontology QE). It has thus the potential
to be utilized online. Furthermore, it shows significant improvement in
accuracy. Wiki-MetaSemantik also shows better performance in a meta-search
engine (MSE) set up rather than in an individual search engine set up.
Paper evolution graph: Multi-view structural retrieval for academic literature
Authors: Liao Danping, Qian Yuntao
Date: 2017-11-24 00:00:00
Tasks: Articles, Retrieval

Academic literature retrieval is concerned with the selection of papers that
are most likely to match a user's information needs. Most of the retrieval
systems are limited to list-output models, in which the retrieval results are
isolated from each other. In this work, we aim to uncover the relationships of
the retrieval results and propose a method for building structural retrieval
results for academic literatures, which we call a paper evolution graph (PEG).
A PEG describes the evolution of the diverse aspects of input queries through
several evolution chains of papers. By utilizing the author, citation and
content information, PEGs can uncover the various underlying relationships
among the papers and present the evolution of articles from multiple
viewpoints. Our system supports three types of input queries: keyword,
single-paper and two-paper queries. The construction of a PEG mainly consists
of three steps. First, the papers are soft-clustered into communities via
metagraph factorization during which the topic distribution of each paper is
obtained. Second, topically cohesive evolution chains are extracted from the
communities that are relevant to the query. Each chain focuses on one aspect of
the query. Finally, the extracted chains are combined to generate a PEG, which
fully covers all the topics of the query. The experimental results on a
real-world dataset demonstrate that the proposed method is able to construct
meaningful PEGs.
Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval
Authors: Yu Yi, Tang Suhua, Raposo Francisco, Chen Lei
Date: 2017-11-29 00:00:00
Tasks: Retrieval

Little research focuses on cross-modal correlation learning where temporal
structures of different data modalities such as audio and lyrics are taken into
account. Stemming from the characteristic of temporal structures of music in
nature, we are motivated to learn the deep sequential correlation between audio
and lyrics. In this work, we propose a deep cross-modal correlation learning
architecture involving two-branch deep neural networks for audio modality and
text modality (lyrics). Different modality data are converted to the same
canonical space where inter modal canonical correlation analysis is utilized as
an objective function to calculate the similarity of temporal structures. This
is the first study on understanding the correlation between language and music
audio through deep architectures for learning the paired temporal correlation
of audio and lyrics. Pre-trained Doc2vec model followed by fully-connected
layers (fully-connected deep neural network) is used to represent lyrics. Two
significant contributions are made in the audio branch, as follows: i)
pre-trained CNN followed by fully-connected layers is investigated for
representing music audio. ii) We further suggest an end-to-end architecture
that simultaneously trains convolutional layers and fully-connected layers to
better learn temporal structures of music audio. Particularly, our end-to-end
deep architecture contains two properties: simultaneously implementing feature
learning and cross-modal correlation learning, and learning joint
representation by considering temporal structures. Experimental results, using
audio to retrieve lyrics or using lyrics to retrieve audio, verify the
effectiveness of the proposed deep correlation learning architectures in
cross-modal music retrieval.
Neural Ranking Models with Multiple Document Fields
Authors: Zamani Hamed, Mitra Bhaskar, Song Xia, Craswell Nick, Tiwary Saurabh
Date: 2017-11-25 00:00:00
Tasks: Learning-To-Rank, Retrieval

Deep neural networks have recently shown promise in the ad-hoc retrieval
task. However, such models have often been based on one field of the document,
for example considering document title only or document body only. Since in
practice documents typically have multiple fields, and given that non-neural
ranking models such as BM25F have been developed to take advantage of document
structure, this paper investigates how neural models can deal with multiple
document fields. We introduce a model that can consume short text fields such
as document title and long text fields such as document body. It can also
handle multi-instance fields with variable number of instances, for example
where each document has zero or more instances of incoming anchor text. Since
fields vary in coverage and quality, we introduce a masking method to handle
missing field instances, as well as a field-level dropout method to avoid
relying too much on any one field. As in the studies of non-neural field
weighting, we find it is better for the ranker to score the whole document
jointly, rather than generate a per-field score and aggregate. We find that
different document fields may match different aspects of the query and
therefore benefit from comparing with separate representations of the query
text. The combination of techniques introduced here leads to a neural ranker
that can take advantage of full document structure, including multiple instance
and missing instance data, of variable length. The techniques significantly
enhance the performance of the ranker, and also outperform a learning to rank
baseline with hand-crafted features.
Option Pricing with Orthogonal Polynomial Expansions
Authors: Damien Ackerer, Damir Filipovic
Date: 2017-11-25 00:00:00

We derive analytic series representations for European option prices in polynomial stochastic volatility models. This includes the Jacobi, Heston, Stein-Stein, and Hull-White models, for which we provide numerical case studies. We find that our polynomial option price series expansion performs as efficiently and accurately as the Fourier transform based method in the nested affine cases. We also derive and numerically validate series representations for option Greeks. We depict an extension of our approach to exotic options whose payoffs depend on a finite number of prices.
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
Authors: Oosterhuis Harrie, de Rijke Maarten
Date: 2017-11-26 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval

In Online Learning to Rank (OLTR) the aim is to find an optimal ranking model
by interacting with users. When learning from user behavior, systems must
interact with users while simultaneously learning from those interactions.
Unlike other Learning to Rank (LTR) settings, existing research in this field
has been limited to linear models. This is due to the speed-quality tradeoff
that arises when selecting models: complex models are more expressive and can
find the best rankings but need more user interactions to do so, a requirement
that risks frustrating users during training. Conversely, simpler models can be
optimized on fewer interactions and thus provide a better user experience, but
they will converge towards suboptimal rankings. This tradeoff creates a
deadlock, since novel models will not be able to improve either the user
experience or the final convergence point, without sacrificing the other. Our
contribution is twofold. First, we introduce a fast OLTR model called Sim-MGD
that addresses the speed aspect of the speed-quality tradeoff. Sim-MGD ranks
documents based on similarities with reference documents. It converges rapidly
and, hence, gives a better user experience but it does not converge towards the
optimal rankings. Second, we contribute Cascading Multileave Gradient Descent
(C-MGD) for OLTR that directly addresses the speed-quality tradeoff by using a
cascade that enables combinations of the best of two worlds: fast learning and
high quality final convergence. C-MGD can provide the better user experience of
Sim-MGD while maintaining the same convergence as the state-of-the-art MGD
model. This opens the door for future work to design new models for OLTR
without having to deal with the speed-quality tradeoff.
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
Authors: Oosterhuis Harrie, de Rijke Maarten
Date: 2017-11-26 00:00:00

Multileaved comparison methods generalize interleaved comparison methods to
provide a scalable approach for comparing ranking systems based on regular user
interactions. Such methods enable the increasingly rapid research and
development of search engines. However, existing multileaved comparison methods
that provide reliable outcomes do so by degrading the user experience during
evaluation. Conversely, current multileaved comparison methods that maintain
the user experience cannot guarantee correctness. Our contribution is two-fold.
First, we propose a theoretical framework for systematically comparing
multileaved comparison methods using the notions of considerateness, which
concerns maintaining the user experience, and fidelity, which concerns reliable
correct outcomes. Second, we introduce a novel multileaved comparison method,
Pairwise Preference Multileaving (PPM), that performs comparisons based on
document-pair preferences, and prove that it is considerate and has fidelity.
We show empirically that, compared to previous multileaved comparison methods,
PPM is more sensitive to user preferences and scalable with the number of
rankers being compared.
Analysis of Dual-Hop AF Relay Systems in Mixed RF and FSO Links
Date: 2017-11-27 00:00:00

We analysis the performances for the dual-hop fixed gain amplify-and-forward
(AF) relay systems operating over mixed radio-frequency (RF) and free-space
optical (FSO) links. The RF link is subject to Rician fading and the FSO link
experiences Gamma-Gamma turbulence fading. We derive the closed-form
expressions for the outage probability and the average symbol error rate in
terms of the Meijer's G function.All analytical results are corroborated by
simulation results and the effects of fading parameters on the system
performances are also studied.
Stress, Depression and Neuroplasticity
Date: 2017-11-27 00:00:00

Modifications of signaling pathways and synapses owing to changing behaviors,
environments, numerous neural modulation as well as brain-tissue injuries is
defined as neuroplasticity in developmental neurology. The central purpose of
the review is to gain a better understanding of the relation between stress,
depression and neuroplasticity and explore potential therapeutic interventions
for enhancing neural resilience. We have also reviewed the role of different
factors like age, stress and sex on inducing neuroplasticity within various
brain regions.
Why People Search for Images using Web Search Engines
Authors: Xie Xiaohui, Liu Yiqun, de Rijke Maarten, He Jiyin, Zhang Min, Ma Shaoping
Date: 2017-11-27 00:00:00
Tasks: Image Retrieval

What are the intents or goals behind human interactions with image search
engines? Knowing why people search for images is of major concern to Web image
search engines because user satisfaction may vary as intent varies. Previous
analyses of image search behavior have mostly been query-based, focusing on
what images people search for, rather than intent-based, that is, why people
search for images. To date, there is no thorough investigation of how different
image search intents affect users' search behavior.
  In this paper, we address the following questions: (1)Why do people search
for images in text-based Web image search systems? (2)How does image search
behavior change with user intent? (3)Can we predict user intent effectively
from interactions during the early stages of a search session? To this end, we
conduct both a lab-based user study and a commercial search log analysis.
  We show that user intents in image search can be grouped into three classes:
Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals
different user behavior patterns under these three intents, such as first click
time, query reformulation, dwell time and mouse movement on the result page.
Based on user interaction features during the early stages of an image search
session, that is, before mouse scroll, we develop an intent classifier that is
able to achieve promising results for classifying intents into our three intent
classes. Given that all features can be obtained online and unobtrusively, the
predicted intents can provide guidance for choosing ranking methods immediately
after scrolling.
Minor climatic fluctuations lead to species extinction in a conceptual ecosystem model
Date: 2017-11-27 00:00:00
Tasks: Diversity

The extinction of species is a core process that affects the diversity of
life on Earth. One way of investigating the causes and consequences of
extinctions is to build conceptual ecological models, and to use the dynamical
outcomes of such models to provide quantitative formalization of changes to
Earth's biosphere. In this paper we propose and study a conceptual resource
model that describes a simple and easily understandable mechanism for resource
competition, generalizes the well-known Huisman and Weissing model, and takes
into account species self-regulation, extinctions, and time dependence of
resources. We use analytical investigations and numerical simulations to study
the dynamics of our model under chaotic and periodic climate oscillations, and
show that the stochastic dynamics of our model exhibit strong dependence on
initial parameters. We also demonstrate that extinctions in our model are
inevitable if an ecosystem has the maximal possible biodiversity and uses the
maximal amount of resources. Our conceptual modeling provides theoretical
support for suggestions that non-linear processes were important during major
extinction events in Earth history.
Linking connectivity, dynamics and computations in low-rank recurrent neural networks
Date: 2018-08-28 00:00:00

Large scale neural recordings have established that the transformation of
sensory stimuli into motor outputs relies on low-dimensional dynamics at the
population level, while individual neurons exhibit complex selectivity.
Understanding how low-dimensional computations on mixed, distributed
representations emerge from the structure of the recurrent connectivity and
inputs to cortical networks is a major challenge. Here, we study a class of
recurrent network models in which the connectivity is a sum of a random part
and a minimal, low-dimensional structure. We show that, in such networks, the
dynamics are low dimensional and can be directly inferred from connectivity
using a geometrical approach. We exploit this understanding to determine
minimal connectivity required to implement specific computations, and find that
the dynamical range and computational capacity quickly increase with the
dimensionality of the connectivity structure. This framework produces testable
experimental predictions for the relationship between connectivity,
low-dimensional dynamics and computational features of recorded neurons.
Classifier Selection with Permutation Tests
Authors: Arias Marta, Arratia Argimiro, Duarte-Lopez Ariel
Date: 2017-11-27 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

This work presents a content-based recommender system for machine learning
classifier algorithms. Given a new data set, a recommendation of what
classifier is likely to perform best is made based on classifier performance
over similar known data sets. This similarity is measured according to a data
set characterization that includes several state-of-the-art metrics taking into
account physical structure, statis- tics, and information theory. A novelty
with respect to prior work is the use of a robust approach based on permutation
tests to directly assess whether a given learning algorithm is able to exploit
the attributes in a data set to predict class labels, and compare it to the
more commonly used F-score metric for evalu- ating classifier performance. To
evaluate our approach, we have conducted an extensive experimentation including
8 of the main machine learning classification methods with varying
configurations and 65 bi- nary data sets, leading to over 2331 experiments. Our
results show that using the information from the permutation test clearly
improves the quality of the recommendations.
Better results with homogeneous biological macromolecules
Date: 2017-11-27 00:00:00

Pure and homogeneous biological macromolecules (i.e. proteins, nucleic acids,
protein-protein or protein-nucleic acid complexes, and functional assemblies
such as ribosomes and viruses) are the key for consistent and reliable
biochemical and biophysical measurements, as well as for reproducible
crystallizations, best crystal diffraction properties, and exploitable electron
microscopy images. Highlights: Pure and homogeneous macromolecules are the key
for the best experimental results; They warrant the consistency and the
reliability of biochemical and biophysical data; They give more reproducible
crystallography and electron microscopy results as well.
Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance
Date: 2017-11-27 00:00:00

In cognitive network neuroscience, the connectivity and community structure
of the brain network is related to cognition. Much of this research has focused
on two measures of connectivity - modularity and flexibility - which frequently
have been examined in isolation. By using resting state fMRI data from 52 young
adults, we investigate the relationship between modularity, flexibility and
performance on cognitive tasks. We show that flexibility and modularity are
highly negatively correlated. However, we also demonstrate that flexibility and
modularity make unique contributions to explain task performance, with
modularity predicting performance for simple tasks and flexibility predicting
performance on complex tasks that require cognitive control and executive
functioning. The theory and results presented here allow for stronger links
between measures of brain network connectivity and cognitive processes.
Pricing Derivatives under Multiple Stochastic Factors by Localized Radial Basis Function Methods
Date: 2018-08-17 00:00:00
Tasks: Unity

We propose two localized Radial Basis Function (RBF) methods, the Radial
Basis Function Partition of Unity method (RBF-PUM) and the Radial Basis
Function generated Finite Differences method (RBF-FD), for solving financial
derivative pricing problems arising from market models with multiple stochastic
factors. We demonstrate the useful features of the proposed methods, such as
high accuracy, sparsity of the differentiation matrices, mesh-free nature and
multi-dimensional extendability, and show how to apply these methods for
solving time-dependent higher-dimensional PDEs in finance. We test these
methods on several problems that incorporate stochastic asset, volatility, and
interest rate dynamics by conducting numerical experiments. The results
illustrate the capability of both methods to solve the problems to a sufficient
accuracy within reasonable time. Both methods exhibit similar orders of
convergence, which can be further improved by a more elaborate choice of the
method parameters. Finally, we discuss the parallelization potentials of the
proposed methods and report the speedup on the example of RBF-FD.