pwc-archive/papers-with-abstracts huggingface dataset:
Perfect spike detection via time reversal
Date: 2017-06-18 00:00:00

Spiking neuronal networks are usually simulated with three main simulation
schemes: the classical time-driven and event-driven schemes, and the more
recent hybrid scheme. All three schemes evolve the state of a neuron through a
series of checkpoints: equally spaced in the first scheme and determined
neuron-wise by spike events in the latter two. The time-driven and the hybrid
scheme determine whether the membrane potential of a neuron crosses a threshold
at the end of of the time interval between consecutive checkpoints. Threshold
crossing can, however, occur within the interval even if this test is negative.
Spikes can therefore be missed. The present work derives, implements, and
benchmarks a method for perfect retrospective spike detection. This method can
be applied to neuron models with affine or linear subthreshold dynamics. The
idea behind the method is to propagate the threshold with a time-inverted
dynamics, testing whether the threshold crosses the neuron state to be evolved,
rather than vice versa. Algebraically this translates into a set of
inequalities necessary and sufficient for threshold crossing. This test is
slower than the imperfect one, but faster than an alternative perfect tests
based on bisection or root-finding methods. Comparison confirms earlier results
that the imperfect test rarely misses spikes (less than a fraction $1/10^8$ of
missed spikes) in biologically relevant settings. This study offers an
alternative geometric point of view on neuronal dynamics.
Modeling credit default swap premiums with stochastic recovery rate
Date: 2017-06-18 00:00:00

There are many studies on development of models for analyzing some
derivatives such as credit default swaps .
Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli
Date: 2017-06-19 00:00:00

We investigate the sparse functional identification of complex cells and the
decoding of visual stimuli encoded by an ensemble of complex cells. The
reconstruction algorithm of both temporal and spatio-temporal stimuli is
formulated as a rank minimization problem that significantly reduces the number
of sampling measurements (spikes) required for decoding. We also establish the
duality between sparse decoding and functional identification, and provide
algorithms for identification of low-rank dendritic stimulus processors. The
duality enables us to efficiently evaluate our functional identification
algorithms by reconstructing novel stimuli in the input space. Finally, we
demonstrate that our identification algorithms substantially outperform the
generalized quadratic model, the non-linear input model and the widely used
spike-triggered covariance algorithm.
Distributed synaptic weights in a LIF neural network and learning rules
Date: 2017-06-19 00:00:00

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large
number of neurons, used to describe neural networks. We consider inhomogeneous
networks structured by a connec-tivity parameter (strengths of the synaptic
weights) with the effect of processing the input current with different
intensities. We first study the properties of the network activity depending on
the distribution of synaptic weights and in particular its discrimination
capacity. Then, we consider simple learning rules and determine the synaptic
weight distribution it generates. We outline the role of noise as a selection
principle and the capacity to memorized a learned signal.
General Equilibrium Under Convex Portfolio Constraints and Heterogeneous Risk Preferences
Date: 2018-06-18 00:00:00

This paper characterizes the equilibrium in a continuous time financial
market populated by heterogeneous agents who differ in their rate of relative
risk aversion and face convex portfolio constraints. The model is studied in an
application to margin constraints and found to match real world observations
about financial variables and leverage cycles. It is shown how margin
constraints increase the market price of risk and decrease the interest rate by
forcing more risk averse agents to hold more risky assets, producing a higher
equity risk premium. In addition, heterogeneity and margin constraints are
shown to produce both pro- and counter-cyclical leverage cycles. Beyond two
types, it is shown how constraints can cascade and how leverage can exhibit
highly non-linear dynamics. Finally, empirical results are given, documenting a
novel stylized fact which is predicted by the model, namely that the leverage
cycle is both pro- and counter-cyclical.
Speed and biases of Fourier-based pricing choices: A numerical analysis
Date: 2018-05-11 00:00:00
Tasks: CPU

We compare the CPU effort and pricing biases of seven Fourier-based
implementations. Our analyses show that truncation and discretization errors
significantly increase as we move away from the Black-Scholes-Merton framework.
We rank the speed and accuracy of the competing choices, showing which methods
require smaller truncation ranges and which are the most efficient in terms of
sampling densities. While all implementations converge well in the Bates
jump-diffusion model, Attari's formula is the only Fourier-based method that
does not blow up for any Variance Gamma parameter values. In terms of speed,
the use of strike vector computations significantly improves the computational
burden, rendering both fast Fourier transforms (FFT) and plain
delta-probability decompositions inefficient. We conclude that the multi-strike
version of the COS method is notably faster than any other implementation,
whereas the strike-optimized Carr Madan's formula is simultaneously faster and
more accurate than the FFT, thus questioning its use.
Leveraging web resources for keyword assignment to short text documents
Authors: Singhal Ayush, Kasturi Ravindra, Sharma Ankit, Srivastava Jaideep
Date: 2017-06-19 00:00:00
Tasks: Keyword Extraction, Management, Retrieval

Assigning relevant keywords to documents is very important for efficient
retrieval, clustering and management of the documents. Especially with the web
corpus deluged with digital documents, automation of this task is of prime
importance. Keyword assignment is a broad topic of research which refers to
tagging of document with keywords, key-phrases or topics. For text documents,
the keyword assignment techniques have been developed under two sub-topics:
automatic keyword extraction (AKE) and automatic key-phrase abstraction.
However, the approaches developed in the literature for full text documents
cannot be used to assign keywords to low text content documents like twitter
feeds, news clips, product reviews or even short scholarly text. In this work,
we point out several practical challenges encountered in tagging such low text
content documents. As a solution to these challenges, we show that the proposed
approaches which leverage knowledge from several open source web resources
enhance the quality of the tags (keywords) assigned to the low text content
documents. The performance of the proposed approach is tested on real world
corpus consisting of scholarly documents with text content ranging from only
the text in the title of the document (5-10 words) to the summary text/abstract
(100- 150 words). We find that the proposed approach not just improves the
accuracy of keyword assignment but offer a computationally efficient solution
which can be used in real world applications.
Complete Semantics to empower Touristic Service Providers
Authors: Akbar Zaenal, Kärle Elias, Panasiuk Oleksandra, Şimşek Umutcan, Toma Ioan, Fensel Dieter
Date: 2017-09-15 00:00:00

The tourism industry has a significant impact on the world's economy,
contributes 10.2% of the world's gross domestic product in 2016. It becomes a
very competitive industry, where having a strong online presence is an
essential aspect for business success. To achieve this goal, the proper usage
of latest Web technologies, particularly schema.org annotations is crucial. In
this paper, we present our effort to improve the online visibility of touristic
service providers in the region of Tyrol, Austria, by creating and deploying a
substantial amount of semantic annotations according to schema.org, a widely
used vocabulary for structured data on the Web. We started our work from
Tourismusverband (TVB) Mayrhofen-Hippach and all touristic service providers in
the Mayrhofen-Hippach region and applied the same approach to other TVBs and
regions, as well as other use cases. The rationale for doing this is
straightforward. Having schema.org annotations enables search engines to
understand the content better, and provide better results for end users, as
well as enables various intelligent applications to utilize them. As a direct
consequence, the region of Tyrol and its touristic service increase their
online visibility and decrease the dependency on intermediaries, i.e. Online
Travel Agency (OTA).
On the accuracy of ancestral sequence reconstruction for ultrametric trees with parsimony
Date: 2017-06-19 00:00:00

We examine a mathematical question concerning the reconstruction accuracy of
the Fitch algorithm for reconstructing the ancestral sequence of the most
recent common ancestor given a phylogenetic tree and sequence data for all taxa
under consideration. In particular, for the symmetric 4-state substitution
model which is also known as Jukes-Cantor model, we answer affirmatively a
conjecture of Li, Steel and Zhang which states that for any ultrametric
phylogenetic tree and a symmetric model, the Fitch parsimony method using all
terminal taxa is more accurate, or at least as accurate, for ancestral state
reconstruction than using any particular terminal taxon or any particular pair
of taxa. This conjecture had so far only been answered for two-state data by
Fischer and Thatte. Here, we focus on answering the biologically more relevant
case with four states, which corresponds to ancestral sequence reconstruction
from DNA or RNA data.
Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression
Date: 2017-06-19 00:00:00

Electrocorticography (ECoG) provides direct measurements of synchronized
postsynaptic potentials at the exposed cortical surface. Patterns of signal
covariance across ECoG sensors have been associated with diverse cognitive
functions and remain a critical marker of seizure onset, progression, and
termination. Yet, a systems level understanding of these patterns (or networks)
has remained elusive, in part due to variable electrode placement and sparse
cortical coverage. Here, we address these challenges by constructing
inter-regional ECoG networks from multi-subject recordings, demonstrate
similarities between these networks and those constructed from
blood-oxygen-level-dependent signal in functional magnetic resonance imaging,
and predict network topology from anatomical connectivity, interregional
distance, and correlated gene expression patterns. Our models accurately
predict out-of-sample ECoG networks and perform well even when fit to data from
individual subjects, suggesting shared organizing principles across persons. In
addition, we identify a set of genes whose brain-wide co-expression is highly
correlated with ECoG network organization. Using gene ontology analysis, we
show that these same genes are enriched for membrane and ion channel
maintenance and function, suggesting a molecular underpinning of ECoG
connectivity. Our findings provide fundamental understanding of the factors
that influence interregional ECoG networks, and open the possibility for
predictive modeling of surgical outcomes in disease.
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings
Date: 2017-06-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis, Uncertainty Quantification

We deal with estimation of multiple dipoles from combined MEG and EEG
time--series. We use a sequential Monte Carlo algorithm to characterize the
posterior distribution of the number of dipoles and their locations. By
considering three test cases, we show that using the combined data the method
can localize sources that are not easily (or not at all) visible with either of
the two individual data alone. In addition, the posterior distribution from
combined data exhibits a lower variance, i.e. lower uncertainty, than the
posterior from single device.
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Authors: Wang Hao, Fu Yanmei, Wang Qinyong, Yin Hongzhi, Du Changying, Xiong Hui
Date: 2017-06-20 00:00:00
Tasks: Decision Making, Recommendation Systems

Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.
An Explicit Default Contagion Model and Its Application to Credit Derivatives Pricing
Date: 2018-08-30 00:00:00

We propose a novel credit default model that takes into account the impact of
macroeconomic information and contagion effect on the defaults of obligors. We
use a set-valued Markov chain to model the default process, which is the set of
all defaulted obligors in the group. We obtain analytic characterizations for
the default process, and use them to derive pricing formulas in explicit forms
for synthetic collateralized debt obligations (CDOs). Furthermore, we use
market data to calibrate the model and conduct numerical studies on the tranche
spreads of CDOs. We find evidence to support that systematic default risk
coupled with default contagion could have the leading component of the total
default risk.
Deep Learning in (and of) Agent-Based Models: A Prospectus
Date: 2017-06-20 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

A very timely issue for economic agent-based models (ABMs) is their empirical
estimation. This paper describes a line of research that could resolve the
issue by using machine learning techniques, using multi-layer artificial neural
networks (ANNs), or so called Deep Nets. The seminal contribution by Hinton et
al. (2006) introduced a fast and efficient training algorithm called Deep
Learning, and there have been major breakthroughs in machine learning ever
since. Economics has not yet benefited from these developments, and therefore
we believe that now is the right time to apply Deep Learning and multi-layered
neural networks to agent-based models in economics.
Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach
Authors: Liu Qiang, Yu Feng, Wu Shu, Wang Liang
Date: 2017-06-20 00:00:00
Tasks: Misinformation

With the rapid growth of social media, massive misinformation is also
spreading widely on social media, such as microblog, and bring negative effects
to human life. Nowadays, automatic misinformation identification has drawn
attention from academic and industrial communities. For an event on social
media usually consists of multiple microblogs, current methods are mainly based
on global statistical features. However, information on social media is full of
noisy and outliers, which should be alleviated. Moreover, most of microblogs
about an event have little contribution to the identification of
misinformation, where useful information can be easily overwhelmed by useless
information. Thus, it is important to mine significant microblogs for a
reliable misinformation identification method. In this paper, we propose an
Attention-based approach for Identification of Misinformation (AIM). Based on
the attention mechanism, AIM can select microblogs with largest attention
values for misinformation identification. The attention mechanism in AIM
contains two parts: content attention and dynamic attention. Content attention
is calculated based textual features of each microblog. Dynamic attention is
related to the time interval between the posting time of a microblog and the
beginning of the event. To evaluate AIM, we conduct a series of experiments on
the Weibo dataset and the Twitter dataset, and the experimental results show
that the proposed AIM model outperforms the state-of-the-art methods.
Complex Correlation Approach for High Frequency Financial Data
Date: 2017-11-22 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We propose a novel approach that allows to calculate Hilbert transform based
complex correlation for unevenly spaced data. This method is especially
suitable for high frequency trading data, which are of a particular interest in
finance. Its most important feature is the ability to take into account
lead-lag relations on different scales, without knowing them in advance. We
also present results obtained with this approach while working on Tokyo Stock
Exchange intraday quotations. We show that individual sectors and subsectors
tend to form important market components which may follow each other with small
but significant delays. These components may be recognized by analysing
eigenvectors of complex correlation matrix for Nikkei 225 stocks.
Interestingly, sectorial components are also found in eigenvectors
corresponding to the bulk eigenvalues, traditionally treated as noise.
Investigating Exploratory Search Activities based on the Stratagem Level in Digital Libraries
Authors: Carevic Zeljko, Lusky Maria, van Hoek Wilko, Mayr Philipp
Date: 2017-06-20 00:00:00

In this paper we present the results of a user study on exploratory search
activities in a social science digital library. We conducted a user study with
32 participants with a social sciences background -- 16 postdoctoral
researchers and 16 students -- who were asked to solve a task on searching
related work to a given topic. The exploratory search task was performed in a
10-minutes time slot. The use of certain search activities is measured and
compared to gaze data recorded with an eye tracking device. We use a novel tree
graph representation to visualise the users' search patterns and introduce a
way to combine multiple search session trees. The tree graph representation is
capable to create one single tree for multiple users and to identify common
search patterns. In addition, the information behaviour of students and
postdoctoral researchers is being compared. The results show that search
activities on the stratagem level are frequently utilised by both user groups.
The most heavily used search activities were keyword search, followed by
browsing through references and citations, and author searching. The eye
tracking results showed an intense examination of documents metadata,
especially on the level of citations and references. When comparing the group
of students and postdoctoral researchers we found significant differences
regarding gaze data on the area of the journal name of the seed document. In
general, we found a tendency of the postdoctoral researchers to examine the
metadata records more intensively with regards to dwell time and the number of
fixations.
Towards a Recommender System for Undergraduate Research
Authors: del-Rio Felipe, Parra Denis, Kuzmicic Jovan, Svec Erick
Date: 2017-06-20 00:00:00
Tasks: Recommendation Systems

Several studies indicate that attracting students to research careers
requires to engage them from early undergraduate years. Following this
paradigm, our Engineering School has developed an undergraduate research
program that allows students to enroll in research in exchange for course
credits. Moreover, we developed a web portal to inform students about the
program and the opportunities, but participation remains lower than expected.
In order to promote student engagement, we attempt to build a personalized
recommender system of research opportunities to undergraduates. With this goal
in mind we investigate two tasks. First, one that identifies students that are
more willing to participate on this kind of program. A second task is
generating a personalized list of recommendations of research opportunities for
each student. To evaluate our approach, we perform a simulated prediction
experiment with data from our School, which has more than 4,000 active
undergraduate students nowadays. Our results indicate that there is a big
potential to create a personalized recommender system for this purpose. Our
results can be used as a baseline for colleges seeking strategies to encourage
research activities within undergraduate students.
Singular Fourier-Pad\'e Series Expansion of European Option Prices
Date: 2017-11-13 00:00:00

We apply a new numerical method, the singular Fourier-Pad\'e (SFP) method
invented by Driscoll and Fornberg (2001, 2011), to price European-type options
in L\'evy and affine processes. The motivation behind this application is to
reduce the inefficiency of current Fourier techniques when they are used to
approximate piecewise continuous (non-smooth) probability density functions.
When techniques such as fast Fourier transforms and Fourier series are applied
to price and hedge options with non-smooth probability density functions, they
cause the Gibbs phenomenon, accordingly, the techniques converge slowly for
density functions with jumps in value or derivatives. This seriously adversely
affects the efficiency and accuracy of these techniques. In this paper, we
derive pricing formulae and their option Greeks using the SFP method to resolve
the Gibbs phenomenon and restore the global spectral convergence rate.
Moreover, we show that our method requires a small number of terms to yield
fast error convergence, and it is able to accurately price any European-type
option deep in/out of the money and with very long/short maturities.
Furthermore, we conduct an error-bound analysis of the SFP method in option
pricing. This new method performs favourably in numerical experiments compared
with existing techniques.