pwc-archive/papers-with-abstracts huggingface dataset:
Phase-of-firing code
Date: 2015-04-15 00:00:00

Definition. The phase-of-firing code is a neural coding scheme whereby
neurons encode information using the time at which they fire spikes within a
cycle of the ongoing oscillatory pattern of network activity. This coding
scheme may allow neurons to use their temporal pattern of spikes to encode
information that is not encoded in their firing rate.
First passage times in integrate-and-fire neurons with stochastic thresholds
Date: 2015-04-16 00:00:00

We consider a leaky integrate-and-fire neuron with deterministic subthreshold
dynamics and a firing threshold that evolves as an Ornstein-Uhlenbeck process.
The formulation of this minimal model is motivated by the experimentally
observed widespread variation of neural firing thresholds. We show numerically
that the mean first passage time can depend non-monotonically on the noise
amplitude. For sufficiently large values of the correlation time of the
stochastic threshold the mean first passage time is maximal for non-vanishing
noise. We provide an explanation for this effect by analytically transforming
the original model into a first passage time problem for Brownian motion. This
transformation also allows for a perturbative calculation of the first passage
time histograms. In turn this provides quantitative insights into the
mechanisms that lead to the non-monotonic behaviour of the mean first passage
time. The perturbation expansion is in excellent agreement with direct
numerical simulations. The approach developed here can be applied to any
deterministic subthreshold dynamics and any Gauss-Markov processes for the
firing threshold. This opens up the possibility to incorporate biophysically
detailed components into the subthreshold dynamics, rendering our approach a
powerful framework that sits between traditional integrate-and-fire models and
complex mechanistic descriptions of neural dynamics.
The Equilibrium Statistical Model of Economic Systems using Concepts and Theorems of Statistical Physics
Date: 2015-04-16 00:00:00

Economic systems are similar with physic systems for their large number of
individuals and the exist of equilibrium. In this paper, we present a model
applying the equilibrium statistical model in economic systems. Consistent with
statistical physics, we define a series of concepts, such as economic
temperature, economic pressure, economic potential, wealth and population.
Moreover, we suggest that these parameters show pretty close relationship with
the concepts in economy. This paper presents related concepts in the
equilibrium economic model and constructs significant theorems and corollaries,
which is derived from the priori possibility postulate, getting theorems
including the equilibrium theorem between open systems, the distribution
theorem of wealth and population along with related corollaries. More
importantly, we demonstrate a method constructing optimal density of states,
optimizing a macroscopic parameter depending on need to get the distribution of
density of states utilizing the variation method, which is significant for the
development of a society. In addition, we calculate a simple economic system as
an example, indicating that the system occupied mostly by the middle class
could develop stably and soundly, explaining the reason for resulting
distributions of macroscopic parameters.
Contextualization of topics - browsing through terms, authors, journals and cluster allocations
Authors: Koopman Rob, Wang Shenghui, Scharnhorst Andrea
Date: 2015-04-16 00:00:00
Tasks: Astronomy, Clustering, Information Retrieval, Retrieval

This paper builds on an innovative Information Retrieval tool, Ariadne. The
tool has been developed as an interactive network visualization and browsing
tool for large-scale bibliographic databases. It basically allows to gain
insights into a topic by contextualizing a search query (Koopman et al., 2015).
In this paper, we apply the Ariadne tool to a far smaller dataset of 111,616
documents in astronomy and astrophysics. Labeled as the Berlin dataset, this
data have been used by several research teams to apply and later compare
different clustering algorithms. The quest for this team effort is how to
delineate topics. This paper contributes to this challenge in two different
ways. First, we produce one of the different cluster solution and second, we
use Ariadne (the method behind it, and the interface - called LittleAriadne) to
display cluster solutions of the different group members. By providing a tool
that allows the visual inspection of the similarity of article clusters
produced by different algorithms, we present a complementary approach to other
possible means of comparison. More particular, we discuss how we can - with
LittleAriadne - browse through the network of topical terms, authors, journals
and cluster solutions in the Berlin dataset and compare cluster solutions as
well as see their context.
Profitability of simple technical trading rules of Chinese stock exchange indexes
Date: 2015-04-15 00:00:00

Although technical trading rules have been widely used by practitioners in
financial markets, their profitability still remains controversial. We here
investigate the profitability of moving average (MA) and trading range break
(TRB) rules by using the Shanghai Stock Exchange Composite Index (SHCI) from
May 21, 1992 through December 31, 2013 and Shenzhen Stock Exchange Composite
Index (SZCI) from April 3, 1991 through December 31, 2013. The $t$-test is
adopted to check whether the mean returns which are conditioned on the trading
signals are significantly different from unconditioned returns and whether the
mean returns conditioned on the buy signals are significantly different from
the mean returns conditioned on the sell signals. We find that TRB rules
outperform MA rules and short-term variable moving average (VMA) rules
outperform long-term VMA rules. By applying White's Reality Check test and
accounting for the data snooping effects, we find that the best trading rule
outperforms the buy-and-hold strategy when transaction costs are not taken into
consideration. Once transaction costs are included, trading profits will be
eliminated completely. Our analysis suggests that simple trading rules like MA
and TRB cannot beat the standard buy-and-hold strategy for the Chinese stock
exchange indexes.
Estimating the Algorithmic Complexity of Stock Markets
Date: 2015-04-16 00:00:00
Tasks: Time Series, Time Series Analysis

Randomness and regularities in Finance are usually treated in probabilistic
terms. In this paper, we develop a completely different approach in using a
non-probabilistic framework based on the algorithmic information theory
initially developed by Kolmogorov (1965). We present some elements of this
theory and show why it is particularly relevant to Finance, and potentially to
other sub-fields of Economics as well. We develop a generic method to estimate
the Kolmogorov complexity of numeric series. This approach is based on an
iterative "regularity erasing procedure" implemented to use lossless
compression algorithms on financial data. Examples are provided with both
simulated and real-world financial time series. The contributions of this
article are twofold. The first one is methodological : we show that some
structural regularities, invisible with classical statistical tests, can be
detected by this algorithmic method. The second one consists in illustrations
on the daily Dow-Jones Index suggesting that beyond several well-known
regularities, hidden structure may in this index remain to be identified.
The Long Memory of Order Flow in the Foreign Exchange Spot Market
Date: 2015-10-22 00:00:00

We study the long memory of order flow for each of three liquid currency
pairs on a large electronic trading platform in the foreign exchange (FX) spot
market. Due to the extremely high levels of market activity on the platform,
and in contrast to existing empirical studies of other markets, our data
enables us to perform statistically stable estimation without needing to
aggregate data from different trading days. We find strong evidence of long
memory, with a Hurst exponent of approximately 0.7, for each of the three
currency pairs and on each trading day in our sample. We repeat our
calculations using data that spans different trading days, and we find no
significant differences in our results. We test and reject the hypothesis that
the apparent long memory of order flow is an artifact caused by structural
breaks, in favour of the alternative hypothesis of true long memory. We
therefore conclude that the long memory of order flow in the FX spot market is
a robust empirical property that persists across daily boundaries.
Mathematical modeling of physical capital using the spatial Solow model
Date: 2015-04-16 00:00:00

This research deals with the mathematical modeling of the physical capital
diffusion through the borders of the countries. The physical capital is
considered an important variable for the economic growth of a country. Here we
use an extension of the economic Solow model to describe how the smuggling
affects the economic growth of the countries. In this study we rely on a
production function that is non-concave instead of the classical Cobb-Douglas
production function. In order to model the physical capital diffusion through
the borders of the country, we developed a model based on a parabolic partial
differential equation that describes the dynamics of physical capital and
boundary conditions of Neumann type. Smuggling is present in many borders
between countries and may include fuel, machinery and food. This smuggling
through the borders is a problematic issue for the country's economies. The
smuggling problem usually is related mainly to a non-official exchange rate
that is different than the official rate or subsides. Numerical simulations are
obtained using an explicit finite difference scheme that shows how the physical
capital diffusion through the border of the countries. The study of physical
capital is a paramount issue for the economic growth of many countries for the
next years. The results show that the dynamics of the physical capital when
boundary conditions of Neumann type are different than zero differ from the
classical economic behavior observed in the classical spatial Solow model
without physical capital flux through the borders of countries. Finally, it can
be concluded that avoiding the smuggling through the frontiers is an important
factor that affects the economic growth of the countries.
Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data
Date: 2015-11-18 00:00:00

To characterize natural selection, various analytical methods for detecting
candidate genomic regions have been developed. We propose to perform
genome-wide scans of natural selection using principal component analysis. We
show that the common Fst index of genetic differentiation between populations
can be viewed as a proportion of variance explained by the principal
components. Considering the correlations between genetic variants and each
principal component provides a conceptual framework to detect genetic variants
involved in local adaptation without any prior definition of populations. To
validate the PCA-based approach, we consider the 1000 Genomes data (phase 1)
after removal of recently admixed individuals resulting in 850 individuals
coming from Africa, Asia, and Europe. The number of genetic variants is of the
order of 36 millions obtained with a low-coverage sequencing depth (3X). The
correlations between genetic variation and each principal component provide
well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and
also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and non-coding
RNAs. In addition to identifying genes involved in biological adaptation, we
identify two biological pathways involved in polygenic adaptation that are
related to the innate immune system (beta defensins) and to lipid metabolism
(fatty acid omega oxidation). An additional analysis of European data shows
that a genome scan based on PCA retrieves classical examples of local
adaptation even when there are no well-defined populations. PCA-based
statistics, implemented in the PCAdapt R package and the PCAdapt open-source
software, retrieve well-known signals of human adaptation, which is encouraging
for future whole-genome sequencing project, especially when defining
populations is difficult.
A Picture Tells a Thousand Words -- About You! User Interest Profiling from User Generated Visual Content
Authors: You Quanzeng, Bhatia Sumit, Luo Jiebo
Date: 2015-04-17 00:00:00
Tasks: Recommendation Systems

Inference of online social network users' attributes and interests has been
an active research topic. Accurate identification of users' attributes and
interests is crucial for improving the performance of personalization and
recommender systems. Most of the existing works have focused on textual content
generated by the users and have successfully used it for predicting users'
interests and other identifying attributes. However, little attention has been
paid to user generated visual content (images) that is becoming increasingly
popular and pervasive in recent times. We posit that images posted by users on
online social networks are a reflection of topics they are interested in and
propose an approach to infer user attributes from images posted by them. We
analyze the content of individual images and then aggregate the image-level
knowledge to infer user-level interest distribution. We employ image-level
similarity to propagate the label information between images, as well as
utilize the image category information derived from the user created
organization structure to further propagate the category-level knowledge for
all images. A real life social network dataset created from Pinterest is used
for evaluation and the experimental results demonstrate the effectiveness of
our proposed approach.
Dirac Processes and Default Risk
Date: 2015-04-17 00:00:00

We introduce Dirac processes, using Dirac delta functions, for
short-rate-type pricing of financial derivatives. Dirac processes add spikes to
the existing building blocks of diffusions and jumps. Dirac processes are
Generalized Processes, which have not been used directly before because the
dollar value of non-Real numbers is meaningless. However, short-rate pricing is
based on integrals so Dirac processes are natural. This integration directly
implies that jumps are redundant whilst Dirac processes expand expressivity of
short-rate approaches. Practically, we demonstrate that Dirac processes enable
high implied volatility for CDS swaptions that has been otherwise problematic
in hazard rate setups.
A Posteriori Error Estimator for a Front-Fixing Finite Difference Scheme for American Options
Date: 2015-04-17 00:00:00
Tasks: Position

For the numerical solution of the American option valuation problem, we
provide a script written in MATLAB implementing an explicit finite difference
scheme. Our main contribute is the definition of a posteriori error estimator
for the American options pricing which is based on Richardson's extrapolation
theory. This error estimator allows us to find a suitable grid where the
computed solution, both the option price field variable and the free boundary
position, verify a prefixed error tolerance.
Structural Learning of Diverse Ranking
Authors: Zhu Yadong, Lan Yanyan, Guo Jiafeng, Cheng Xueqi
Date: 2015-04-20 00:00:00
Tasks: Diversity, Task 2

Relevance and diversity are both crucial criteria for an effective search
system. In this paper, we propose a unified learning framework for
simultaneously optimizing both relevance and diversity. Specifically, the
problem is formalized as a structural learning framework optimizing
Diversity-Correlated Evaluation Measures (DCEM), such as ERR-IA, a-NDCG and
NRBP. Within this framework, the discriminant function is defined to be a
bi-criteria objective maximizing the sum of the relevance scores and
dissimilarities (or diversity) among the documents. Relevance and diversity
features are utilized to define the relevance scores and dissimilarities,
respectively. Compared with traditional methods, the advantages of our approach
lie in that: (1) Directly optimizing DCEM as the loss function is more
fundamental for the task; (2) Our framework does not rely on explicit diversity
information such as subtopics, thus is more adaptive to real application; (3)
The representation of diversity as the feature-based scoring function is more
flexible to incorporate rich diversity-based features into the learning
framework. Extensive experiments on the public TREC datasets show that our
approach significantly outperforms state-of-the-art diversification approaches,
which validate the above advantages.
Optimal Multiple Trading Times Under the Exponential OU Model with Transaction Costs
Date: 2015-04-18 00:00:00

This paper studies the timing of trades under mean-reverting price dynamics
subject to fixed transaction costs. We solve an optimal double stopping problem
to determine the optimal times to enter and subsequently exit the market, when
prices are driven by an exponential Ornstein-Uhlenbeck process. In addition, we
analyze a related optimal switching problem that involves an infinite sequence
of trades, and identify the conditions under which the double stopping and
switching problems admit the same optimal entry and/or exit timing strategies.
Among our results, we find that the investor generally enters when the price is
low, but may find it optimal to wait if the current price is sufficiently close
to zero. In other words, the continuation (waiting) region for entry is
disconnected. Numerical results are provided to illustrate the dependence of
timing strategies on model parameters and transaction costs.
Towards Detecting Rumours in Social Media
Authors: Zubiaga Arkaitz, Liakata Maria, Procter Rob, Bontcheva Kalina, Tolmie Peter
Date: 2015-04-18 00:00:00
Tasks: Rumour Detection

The spread of false rumours during emergencies can jeopardise the well-being
of citizens as they are monitoring the stream of news from social media to stay
abreast of the latest updates. In this paper, we describe the methodology we
have developed within the PHEME project for the collection and sampling of
conversational threads, as well as the tool we have developed to facilitate the
annotation of these threads so as to identify rumourous ones. We describe the
annotation task conducted on threads collected during the 2014 Ferguson unrest
and we present and analyse our findings. Our results show that we can collect
effectively social media rumours and identify multiple rumours associated with
a range of stories that would have been hard to identify by relying on existing
techniques that need manual input of rumour-specific keywords.
LookAhead: Augmenting Crowdsourced Website Reputation Systems With Predictive Modeling
Authors: Bhattacharya Sourav, Huhta Otto, Asokan N.
Date: 2015-04-18 00:00:00
Tasks: General Classification

Unsafe websites consist of malicious as well as inappropriate sites, such as
those hosting questionable or offensive content. Website reputation systems are
intended to help ordinary users steer away from these unsafe sites. However,
the process of assigning safety ratings for websites typically involves humans.
Consequently it is time consuming, costly and not scalable. This has resulted
in two major problems: (i) a significant proportion of the web space remains
unrated and (ii) there is an unacceptable time lag before new websites are
rated.
  In this paper, we show that by leveraging structural and content-based
properties of websites, it is possible to reliably and efficiently predict
their safety ratings, thereby mitigating both problems. We demonstrate the
effectiveness of our approach using four datasets of up to 90,000 websites. We
use ratings from Web of Trust (WOT), a popular crowdsourced web reputation
system, as ground truth. We propose a novel ensemble classification technique
that makes opportunistic use of available structural and content properties of
webpages to predict their eventual ratings in two dimensions used by WOT:
trustworthiness and child safety. Ours is the first classification system to
predict such subjective ratings and the same approach works equally well in
identifying malicious websites. Across all datasets, our classification
performs well with average F$_1$-score in the 74--90\% range.
Composite Correlation Quantization for Efficient Multimodal Retrieval
Authors: Long Mingsheng, Cao Yue, Wang Jianmin, Yu Philip S.
Date: 2016-05-22 00:00:00
Tasks: Cross-Modal Retrieval, Quantization, Retrieval

Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval.
Forecasting the term structure of crude oil futures prices with neural networks
Date: 2015-04-19 00:00:00
Tasks: regression

The paper contributes to the rare literature modeling term structure of crude
oil markets. We explain term structure of crude oil prices using dynamic
Nelson-Siegel model, and propose to forecast them with the generalized
regression framework based on neural networks. The newly proposed framework is
empirically tested on 24 years of crude oil futures prices covering several
important recessions and crisis periods. We find 1-month, 3-month, 6-month and
12-month-ahead forecasts obtained from focused time-delay neural network to be
significantly more accurate than forecasts from other benchmark models. The
proposed forecasting strategy produces the lowest errors across all times to
maturity.
Triander: A new program for visual analysis of nucleotide sequences
Date: 2015-04-21 00:00:00
Tasks: Position

The Triander program is an interactive software package for nucleotide
sequence visualization. The program was developed using the freeware Pascal RAD
IDE Lazarus, and its source code and binaries compiled for Windows are freely
accessible at http://www.icbge.org.ua/eng/Triander . Triander can produce four
types of plots. It is possible to build three DNA walks independently for each
nucleotide position in triplets. The use of nucleotide vectors with unequal
modulus leads to a significant reduction in the visual information lost in DNA
walks. The program can be used in the investigation of the fine structure of
sequences, to find standard patterns in them and to locate nontrivial regions
for further detailed analysis.