pwc-archive/papers-with-abstracts huggingface dataset:
Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations
Authors: Piao Guangyuan, Breslin John G.
Date: 2017-07-28 00:00:00
Tasks: Learning-To-Rank, Recommendation Systems

With the popularity of Linked Open Data (LOD) and the associated rise in
freely accessible knowledge that can be accessed via LOD, exploiting LOD for
recommender systems has been widely studied based on various approaches such as
graph-based or using different machine learning models with LOD-enabled
features. Many of the previous approaches require construction of an additional
graph to run graph-based algorithms or to extract path-based features by
combining user- item interactions (e.g., likes, dislikes) and background
knowledge from LOD. In this paper, we investigate Factorization Machines (FMs)
based on particularly lightweight LOD-enabled features which can be directly
obtained via a public SPARQL Endpoint without any additional effort to
construct a graph. Firstly, we aim to study whether using FM with these
lightweight LOD-enabled features can provide competitive performance compared
to a learning-to-rank approach leveraging LOD as well as other well-established
approaches such as kNN-item and BPRMF. Secondly, we are interested in finding
out to what extent each set of LOD-enabled features contributes to the
recommendation performance. Experimental evaluation on a standard dataset shows
that our proposed approach using FM with lightweight LOD-enabled features
provides the best performance compared to other approaches in terms of five
evaluation metrics. In addition, the study of the recommendation performance
based on different sets of LOD-enabled features indicate that property-object
lists and PageRank scores of items are useful for improving the performance,
and can provide the best performance through using them together for FM. We
observe that subject-property lists of items does not contribute to the
recommendation performance but rather decreases the performance.
Self-organization and fractality created by gluconeogenesis in the metabolic process
Date: 2017-07-14 00:00:00

Within a mathematical model, the process of interaction of the metabolic
processes such as glycolysis and gluconeogenesis is studied. As a result of the
running of two opposite processes in a cell, the conditions for their
interaction and the self-organization in a single dissipative system are
created.
Correlations and Flow of Information between The New York Times and Stock Markets
Date: 2017-07-18 00:00:00
Tasks: Sentiment Analysis, Time Series, Time Series Analysis

We use Random Matrix Theory (RMT) and information theory to analyze the
correlations and flow of information between 64,939 news from The New York
Times and 40 world financial indices during 10 months along the period
2015-2016. The set of news was quantified and transformed into daily polarity
time series using tools from sentiment analysis. Results from RMT shows that a
common factor lead the world indices and news, and even share the same
dynamics. Furthermore, the global correlation structure has found preserved
when adding white noise, which indicate that correlations are not due to sample
size effects. Likewise, we found a lot of information flowing from news to
world indices for specific delay, being of practical interest for trading
purpose. Our results suggest a deep relationship between news and world
indices, and show a situation where news drive world market movements, giving a
new evidence to support behavioral finance as the current economic paradigm.
A guide to performing systematic literature reviews in bioinformatics
Date: 2017-07-18 00:00:00
Tasks: Decision Making, Systematic Literature Review

Bioinformatics research depends on high-quality databases to provide accurate
results. In silico experiments, correctly performed, may prospect novel
discoveries and elucidates pathways for biological experiments through data
analysis in large scale. However, most biological databases have presented
mistakes, such as data incorrectly classified or incomplete information. Also,
sometimes, data mining algorithms cannot treat these errors, leading to serious
problems for the in silico analysis. Manual curation of data extracted from
literature is a possible solution for this problem. Systematic Literature
Review (SLR), or Systematic Review, is a method to identify, evaluate and
summarize the state-of-the-art of a specific theme. Moreover, SLR allows the
collection from databases restrictively, which allows an analysis with lower
bias than traditional reviews. The SRL approaches have been widely used for
decision-making in medical and environmental studies. However, other research
areas, such as bioinformatics, do not have a specific step-by-step to guide
researchers undertaking the procedures of an SLR. In this study, we propose a
guideline, called BiSRL, to perform SLR in bioinformatics. Our procedures cover
the most traditional guides to produce SLRs adapted to bioinformatics. To
evaluate our method, we propose a case study to detect and summarize SLRs
developed for bioinformatics data. We used two databases: PubMed and
ScienceDirect. A total of 207 papers were screened in four steps: title,
abstract, diagonal and full-text reading. Four evaluators performed the SLR
independently to reduce bias risk. A total of 8 papers was included in the SLR
case study. The case study demonstrates how to implement the methods of BiSLR
to procedure SLR for bioinformatics. BiSLR may guide bioinformaticians to
perform systematic reviews reproducible to collect accurate data for higher
quality analysis.
Improving the Economic Complexity Index
Date: 2017-07-24 00:00:00

How much knowledge is there in an economy? In recent years, data on the mix
of products that countries export has been used to construct measures of
economic complexity that estimate the knowledge available in an economy and
predict future economic growth. Here we introduce a new and simpler metric of
economic complexity (ECI+) that measures the total exports of an economy
corrected by how difficult it is to export each product. We use data from 1973
to 2013 to compare the ability of ECI+, the Economic Complexity Index (ECI),
and Fitness complexity, to predict future economic growth using 5, 10, and
20-year panels in a pooled OLS, a random effects model, and a fixed effects
model. We find that ECI+ outperforms ECI and Fitness in its ability to predict
economic growth and in the consistency of its estimators across most
econometric specifications. On average, one standard deviation increase in ECI+
is associated with an increase in annualized growth of about 4% to 5%. We then
combine ECI+ with measures of physical capital, human capital, and
institutions, to find a robust model of economic growth. The ability of ECI+ to
predict growth, and the value of its coefficient, is robust to these controls.
Also, we find that human capital, political stability, and control of
corruption; are positively associated with future economic growth, and that
income is negatively associated with growth, in agreement with the traditional
growth literature. Finally, we use ECI+ to generate economic growth predictions
for the next 20 years and compare these predictions with the ones obtained
using ECI and Fitness. These findings improve the methods available to estimate
the knowledge intensity of economies and predict future economic growth.
Contagious disruptions and complexity traps in economic development
Date: 2017-07-19 00:00:00

Poor economies not only produce less; they typically produce things that
involve fewer inputs and fewer intermediate steps. Yet the supply chains of
poor countries face more frequent disruptions---delivery failures, faulty
parts, delays, power outages, theft, government failures---that systematically
thwart the production process. To understand how these disruptions affect
economic development, we model an evolving input--output network in which
disruptions spread contagiously among optimizing agents. The key finding is
that a poverty trap can emerge: agents adapt to frequent disruptions by
producing simpler, less valuable goods, yet disruptions persist. Growing out of
poverty requires that agents invest in buffers to disruptions. These buffers
rise and then fall as the economy produces more complex goods, a prediction
consistent with global patterns of input inventories. Large jumps in economic
complexity can backfire. This result suggests why "big push" policies can fail,
and it underscores the importance of reliability and of gradual increases in
technological complexity.
Session-aware Information Embedding for E-commerce Product Recommendation
Authors: Wu Chen, Yan Ming, Si Luo
Date: 2017-11-20 00:00:00
Tasks: Product Recommendation, Recommendation Systems, User Identification

Most of the existing recommender systems assume that user's visiting history
can be constantly recorded. However, in recent online services, the user
identification may be usually unknown and only limited online user behaviors
can be used. It is of great importance to model the temporal online user
behaviors and conduct recommendation for the anonymous users. In this paper, we
propose a list-wise deep neural network based architecture to model the limited
user behaviors within each session. To train the model efficiently, we first
design a session embedding method to pre-train a session representation, which
incorporates different kinds of user search behaviors such as clicks and views.
Based on the learnt session representation, we further propose a list-wise
ranking model to generate the recommendation result for each anonymous user
session. We conduct quantitative experiments on a recently published dataset
from an e-commerce company. The evaluation results validate the effectiveness
of the proposed method, which can outperform the state-of-the-art
significantly.
Oscillations and irregular persistent firing patterns in a homogeneous network of excitatory stochastic neurons with gap junctions in the mean-field limit
Date: 2017-07-19 00:00:00

We continue the work of a series of previous studies of a mathematical model
that describes the mean-field limit behavior of a homogeneous network of
excitatory point spiking neurons. Contrary to other models, here noise is
intrinsic to the neurons through a membrane-potential dependent spiking rate
probability, that we assume to be given by a power law. This allows one to
collapse several sources of neural noise into a single function, which aids for
a more treatable mathematical and comptuational analysis. In particular, we
give pseudo-analytical expressions for the invariant distributions of membrane
potentials across the population and study their stability computationally. The
neurons are assumed to be connected both by chemical and possibly electrical
(gap junction) synapses and can also undergo a leakage of ions with the
extracellular medium. The distributions are of compact support whenever leakage
or gap junction rates are non-zero and an infinite discontinuity appears at the
maximum potential in the population. This happens invariably for the leakage
(with no gap junctions) and gap junction (without leakage) cases. However,
these discontinuous distributions might only be stable for linear spiking rate
functions. It was recently shown how the network can present highly synchronous
states of global oscillations in its activity when leakage is not present and
gap junctions are strong enough. Here we confirm how oscillations persist also
when weak leakage is considered. Thus, these dendritic (rather than axonal) gap
junctions could play a role in the high levels of neural synchrony necessary
for the development of neural systems at young ages. The model, thus, presents
a rich phenomenology and capability to reproduce several biological features of
neural networks despite of its mathematical simplicity, hence presenting a
powerful tool for high performance computing.
Recommendation via matrix completion using Kolmogorov complexity
Authors: Ramos Guilherme, Saude Joao, Caleiro Carlos, Kar Soummya
Date: 2017-07-19 00:00:00
Tasks: Collaborative Filtering, Matrix Completion, Recommendation Systems

A usual way to model a recommendation system is as a matrix completion
problem. There are several matrix completion methods, typically using
optimization approaches or collaborative filtering. Most approaches assume that
the matrix is either low rank, or that there are a small number of latent
variables that encode the full problem. Here, we propose a novel matrix
completion algorithm for recommendation systems, without any assumptions on the
rank and that is model free, i.e., the entries are not assumed to be a function
of some latent variables. Instead, we use a technique akin to information
theory. Our method performs hybrid neighborhood-based collaborative filtering
using Kolmogorov complexity. It decouples the matrix completion into a vector
completion problem for each user. The recommendation for one user is thus
independent of the recommendation for other users. This makes the algorithm
scalable because the computations are highly parallelizable. Our results are
competitive with state-of-the-art approaches on both synthetic and real-world
dataset benchmarks.
Microblog Retrieval for Post-Disaster Relief: Applying and Comparing Neural IR Models
Authors: Khosla Prannay, Basu Moumita, Ghosh Kripabandhu, Ghosh Saptarshi
Date: 2017-07-19 00:00:00
Tasks: Retrieval

Microblogging sites like Twitter and Weibo have emerged as important
sourcesof real-time information on ongoing events, including socio-political
events, emergency events, and so on. For instance, during emergency events
(such as earthquakes, floods, terror attacks), microblogging sites are very
useful for gathering situational information in real-time. During such an
event, typically only a small fraction of the microblogs (tweets) posted are
relevant to the information need. Hence, it is necessary to design effective
methodologies for microblog retrieval, so that the relevant tweets can be
automatically extracted from large sets of documents (tweets).
  In this work, we apply and compare various neural network-based IR models for
microblog retrieval for a specific application, as follows. In a disaster
situation, one of the primary and practical challenges in coordinating the
post-disaster relief operations is to know about what resources are needed and
what resources are available in the disaster-affected area. Thus, in this
study, we focus on extracting these two specific types of microblogs or tweets
namely need tweets and avail tweets, which are tweets which define some needs
of the people and the tweets which offer some solutions or aid for the people,
respectively.
American Options with Discontinuous Two-Level Caps
Date: 2017-07-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper examines the valuation of American capped call options with
two-level caps. The structure of the immediate exercise region is significantly
more complex than in the classical case with constant cap. When the cap grows
over time, making extensive use of probabilistic arguments and local time, we
show that the exercise region can be the union of two disconnected set.
Alternatively, it can consist of two sets connected by a line. The problem then
reduces to the characterization of the upper boundary of the first set, which
is shown to satisfy a recursive integral equation. When the cap decreases over
time, the boundary of the exercise region has piecewise constant segments
alternating with non-increasing segments. General representation formulas for
the option price, involving the exercise boundaries and the local time of the
underlying price process, are derived. An efficient algorithm is developed and
numerical results are provided.
Chemical Boltzmann Machines
Date: 2017-07-19 00:00:00

How smart can a micron-sized bag of chemicals be? How can an artificial or
real cell make inferences about its environment? From which kinds of
probability distributions can chemical reaction networks sample? We begin
tackling these questions by showing four ways in which a stochastic chemical
reaction network can implement a Boltzmann machine, a stochastic neural network
model that can generate a wide range of probability distributions and compute
conditional probabilities. The resulting models, and the associated theorems,
provide a road map for constructing chemical reaction networks that exploit
their native stochasticity as a computational resource. Finally, to show the
potential of our models, we simulate a chemical Boltzmann machine to classify
and generate MNIST digits in-silico.
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
Authors: Tay Yi, Phan Minh C., Tuan Luu Anh, Hui Siu Cheung
Date: 2017-07-20 00:00:00
Tasks: Feature Engineering, Learning-To-Rank, Sentence

We describe a new deep learning architecture for learning to rank question
answer pairs. Our approach extends the long short-term memory (LSTM) network
with holographic composition to model the relationship between question and
answer representations. As opposed to the neural tensor layer that has been
adopted recently, the holographic composition provides the benefits of scalable
and rich representational learning approach without incurring huge parameter
costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified
architecture for both deep sentence modeling and semantic matching.
Essentially, our model is trained end-to-end whereby the parameters of the LSTM
are optimized in a way that best explains the correlation between question and
answer representations. In addition, our proposed deep learning architecture
requires no extensive feature engineering. Via extensive experiments, we show
that HD-LSTM outperforms many other neural architectures on two popular
benchmark QA datasets. Empirical studies confirm the effectiveness of
holographic composition over the neural tensor layer.
Fast Reversible Learning based on Neurons functioning as Anisotropic Multiplex Hubs
Date: 2017-07-20 00:00:00

Neural networks are composed of neurons and synapses, which are responsible
for learning in a slow adaptive dynamical process. Here we experimentally show
that neurons act like independent anisotropic multiplex hubs, which relay and
mute incoming signals following their input directions. Theoretically, the
observed information routing enriches the computational capabilities of neurons
by allowing, for instance, equalization among different information routes in
the network, as well as high-frequency transmission of complex time-dependent
signals constructed via several parallel routes. In addition, this kind of hubs
adaptively eliminate very noisy neurons from the dynamics of the network,
preventing masking of information transmission. The timescales for these
features are several seconds at most, as opposed to the imprint of information
by the synaptic plasticity, a process which exceeds minutes. Results open the
horizon to the understanding of fast and adaptive learning realities in higher
cognitive functionalities of the brain.
Toward understanding of the role of reversibility of phenotypic switching in the evolution of resistance to therapy
Date: 2018-03-30 00:00:00

Reversibility of state transitions is intensively studied topic in many
scientific disciplines over many years. In cell biology, it plays an important
role in epigenetic variation of phenotypes, known as phenotypic plasticity.
More interestingly, the cell state reversibility is probably crucial in the
adaptation of population phenotypic heterogeneity to environmental fluctuations
by evolving bet-hedging strategy, which might confer to cancer cells resistance
to therapy. In this article, we propose a formalization of the evolution of
highly reversible states in the environments of periodic variability. Two
interrelated models of heterogeneous cell populations are proposed and their
behavior is studied. The first model captures selection dynamics of the cell
clones for the respective levels of phenotypic reversibility. The second model
focuses on the interplay between reversibility and drug resistance in the
particular case of cancer. Overall, our results show that the threshold
dependencies are emergent features of the investigated model with eventual
therapeutic relevance. Presented examples demonstrate importance of taking into
account cell to cell heterogeneity within a system of clones with different
reversibility quantified by appropriately chosen genetic and epigenetic entropy
measures.
Vitality of Neural Networks under Reoccurring Catastrophic Failures
Date: 2017-07-20 00:00:00

Catastrophic failures are complete and sudden collapses in the activity of
large networks such as economics, electrical power grids and computer networks,
which typically require a manual recovery process. Here we experimentally show
that excitatory neural networks are governed by a non-Poissonian reoccurrence
of catastrophic failures, where their repetition time follows a multimodal
distribution characterized by a few tenths of a second and tens of seconds
timescales. The mechanism underlying the termination and reappearance of
network activity is quantitatively shown here to be associated with nodal
time-dependent features, neuronal plasticity, where hyperactive nodes damage
the response capability of their neighbors. It presents a complementary
mechanism for the emergence of Poissonian catastrophic failures from damage
conductivity. The effect that hyperactive nodes degenerate their neighbors
represents a type of local competition which is a common feature in the
dynamics of real-world complex networks, whereas their spontaneous recoveries
represent a vitality which enhances reliable functionality.
Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity
Date: 2017-07-20 00:00:00

The increasing number of recording electrodes enhances the capability of
capturing the network's cooperative activity, however, using too many monitors
might alter the properties of the measured neural network and induce noise.
Using a technique that merges simultaneous multi-patch-clamp and
multi-electrode array recordings of neural networks in-vitro, we show that the
membrane potential of a single neuron is a reliable and super-sensitive probe
for monitoring such cooperative activities and their detailed rhythms.
Specifically, the membrane potential and the spiking activity of a single
neuron are either highly correlated or highly anti-correlated with the
time-dependent macroscopic activity of the entire network. This surprising
observation also sheds light on the cooperative origin of neuronal burst in
cultured networks. Our findings present an alternative flexible approach to the
technique based on a massive tiling of networks by large-scale arrays of
electrodes to monitor their activity.
Geopolitical Model of Investment Project Implementation
Date: 2017-07-21 00:00:00
Tasks: model

Two geopolitical actors implement a geopolitical project that involves
transportaion and storage of some commodities. They interact with each other
through a transport network. The network consists of several interconnected
vertices. Some of the vetrices are trading hubs, storage spaces, production
hubs and goods buyers. Actors wish to satify the demand of buyers and recieve
the highest possible profit subject to compromise solution principle. A
numerical example is given.
Should Evolution Necessarily be Egolution?
Date: 2017-07-21 00:00:00

In the article I study the evolutionary adaptivity of two simple population
models, based on either altruistic or egoistic law of energy exchange. The
computational experiments show the convincing advantage of the altruists, which
brings us to a small discussion about genetic algorithms and extraterrestrial
life.