pwc-archive/papers-with-abstracts huggingface dataset:
The evolution and structure of biomedical knowledge on cytochrome P450
Authors: David Fajardo-Ortiz, Miguel Lara, Victor M. Castano
Date: 2017-11-27 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Cytochrome P450 are fundamental proteins to the metabolism of drugs and other relevant processes. through a combination of text mining and network analysis of the P450 literature we mapped the emergence and evolution of the biomedical research communities working on this family of proteins. Our results suggest that the historical research communities that worked on P450 emerged and were organized mainly around methodological achievements like the induction of animal liver microsomal P450 by drugs, the use of chemical inhibitors of P450 enzymes in in-vitro metabolism studies and the development of E. coli expression systems. We found clear evidence that P450 research indeed constitutes a material scientific culture, as we discuss in the text.
Valuing Exchange Options Under an Ornstein-Uhlenbeck Covariance Model
Date: 2017-11-27 00:00:00

In this paper we study the pricing of exchange options under a dynamic
described by stochastic correlation with random jumps. In particular, we
consider a Ornstein-Uhlenbeck covariance model with Levy Background Noise
Process driven by Inverse Gaussian subordinators. We use expansion in terms of
Taylor polynomials and cubic splines to approximately compute the price of the
derivative contract. Our findings show that this approach provides an efficient
way to compute the price when compared with a Monte Carlo method while
maintaining an equivalent degree of accuracy with the latter.
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model
Date: 2018-01-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Multilingual models for Automatic Speech Recognition (ASR) are attractive as
they have been shown to benefit from more training data, and better lend
themselves to adaptation to under-resourced languages. However, initialisation
from monolingual context-dependent models leads to an explosion of
context-dependent states. Connectionist Temporal Classification (CTC) is a
potential solution to this as it performs well with monophone labels.
  We investigate multilingual CTC in the context of adaptation and
regularisation techniques that have been shown to be beneficial in more
conventional contexts. The multilingual model is trained to model a universal
International Phonetic Alphabet (IPA)-based phone set using the CTC loss
function. Learning Hidden Unit Contribution (LHUC) is investigated to perform
language adaptive training. In addition, dropout during cross-lingual
adaptation is also studied and tested in order to mitigate the overfitting
problem.
  Experiments show that the performance of the universal phoneme-based CTC
system can be improved by applying LHUC and it is extensible to new phonemes
during cross-lingual adaptation. Updating all the parameters shows consistent
improvement on limited data. Applying dropout during adaptation can further
improve the system and achieve competitive performance with Deep Neural Network
/ Hidden Markov Model (DNN/HMM) systems on limited data.
Constructive Identification of Heterogeneous Elasticities in the Cobb-Douglas Production Function
Date: 2017-11-27 00:00:00

This paper presents the identification of heterogeneous elasticities in the
Cobb-Douglas production function. The identification is constructive with
closed-form formulas for the elasticity with respect to each input for each
firm. We propose that the flexible input cost ratio plays the role of a control
function under "non-collinear heterogeneity" between elasticities with respect
to two flexible inputs. The ex ante flexible input cost share can be used to
identify the elasticities with respect to flexible inputs for each firm. The
elasticities with respect to labor and capital can be subsequently identified
for each firm under the timing assumption admitting the functional
independence.
On the Constructing Bifurcation Diagram of the Quadratic Map With Floating-Point Arithmetic
Date: 2017-11-28 00:00:00

This paper presents an analysis on the effects of floating-point arithmetic
on the constructing bifurcation diagram of the quadratic map. More precisely,
we are interested in showing the dependence of initial conditions to obtain
some specific features of the diagram. With this study, it was possible to
observe that when there is a restriction regarding the initial condition, the
results present aspects with significant differences of the ones found in the
literature regarding the behaviour of the map, consequently there is a
considerable modification in its bifurcation diagram. We show that these
difference are related to floating-point arithmetic
The effects of energy and commodity prices on commodity output in a three-factor, two-good general equilibrium trade model
Date: 2018-11-28 00:00:00
Tasks: Position

We analyze the effects of energy and commodity prices on commodity output
using a three-factor, two-good general equilibrium trade model with three
factors: capital, labor, and imported energy. We derive a sufficient condition
for each sign pattern of each relationship to hold, which no other studies have
derived. We assume factor-intensity ranking is constant and use the EWS
(economy-wide substitution)-ratio vector and the Hadamard product in the
analysis. The results reveal that the position of the EWS-ratio vector
determines the relationships. Specifically, the strengthening (resp. reduction)
of import restrictions can increase (resp. decrease) the commodity output of
exportables, if capital and labor, domestic factors, are economy-wide
complements. This seems paradoxical.
Comment on Suzuki's rebuttal of Batra and Casas
Date: 2017-11-28 00:00:00

Batra and Casas (1976) claimed that 'a strong Rybczynski result' arises in
the three-factor two-good general equilibrium trade model. In subsequent
comments, Suzuki (1983) contended that this could not be the case. Among his
comments, Suzuki found that the set of three equations holds for the
Allen-partial elasticity of substitution under the assumption of perfect
complementarity, and he applied these to his analysis. In the following, I
demonstrate that these are impossible, hence his dissenting proof is not
plausible.
Source Localisation Using Binary Measurements
Date: 2018-09-12 00:00:00

This paper considers the problem of localising a stationary signal source
using a team of mobile agents which only take binary measurements. Background
false detection rates and missed detection probabilities are incorporated into
the framework. A Bayesian estimation algorithm that discretises the search
environment is employed, and analytical convergence and consistency results for
this are derived. Fisher Information is then used as a metric for the design of
optimal agent geometries. Knowledge of the probability of detection as a
function of the source and agent locations is assumed in the analysis, with
special attention given to range-dependent functions. The behaviour of the
algorithm under inexact knowledge of the probability of detection is also
analysed. Finally, simulation results are presented to demonstrate the
effectiveness of the algorithm.
Complex network analysis of brain functional connectivity under a multi-step cognitive task
Date: 2017-11-28 00:00:00
Tasks: Functional Connectivity

Functional brain network has been widely studied to understand the
relationship between brain organization and behavior. In this paper, we aim to
explore the functional connectivity of brain network under a \emph{multi-step}
cognitive task involving with consecutive behaviors, and further understand the
effect of behaviors on the brain organization. The functional brain networks
are constructed base on a high spatial and temporal resolution fMRI dataset and
analyzed via complex network based approach. We find that at voxel level the
functional brain network shows robust small-worldness and scale-free
characteristics, while its assortativity and rich-club organization are
slightly restricted to order of behaviors performed. More interestingly, the
functional connectivity of brain network in activated ROIs strongly correlates
with behaviors and behaves obvious differences restricted to order of behaviors
performed. These empirical results suggest that the brain organization has the
generic properties of small-worldness and scale-free characteristics, and its
diverse function connectivity emerging from activated ROIs is strongly driven
by these behavioral activities via the plasticity of brain.
Screening of Fungi for the Application of Self-Healing Concrete
Authors: Rakenth R. Menon, Jing Luo, Xiaobo Chen, Hui Zhou, Zhiyong Liu, Guangwen Zhou, Ning Zhang, Congrui Jin
Date: 2017-11-23 00:00:00

Concrete is susceptible to cracking owing to drying shrinkage, freeze-thaw cycles, delayed ettringite formation, reinforcement corrosion, creep and fatigue, etc. Since maintenance and inspection of concrete infrastructure require onerous labor and high costs, self-healing of harmful cracks without human interference or intervention could be of great attraction. The goal of this study is to explore a new self-healing approach in which fungi are used as a self-healing agent to promote calcium carbonate precipitation to fill the cracks in concrete structures. Recent research results in the field of geomycology have shown that many species of fungi could play an important role in promoting calcium carbonate mineralization, but their application in self-healing concrete has not been reported. Therefore, a screening of different species of fungi has been conducted in this study. Our results showed that, despite the drastic pH increase owing to the leaching of calcium hydroxide from concrete, Aspergillus nidulans (MAD1445), a pH regulatory mutant, could grow on concrete plates and promote calcium carbonate precipitation.
Inferring users' preferences through leveraging their social relationships
Authors: Deng Xiaofang, Wu Leilei, Ren Xiaolong, Jia Chunxiao, Zhong Yuansheng, Lü Linyuan
Date: 2017-11-28 00:00:00
Tasks: Recommendation Systems

Recommender systems, inferring users' preferences from their historical
activities and personal profiles, have been an enormous success in the last
several years. Most of the existing works are based on the similarities of
users, objects or both that derived from their purchases records in the online
shopping platforms. Such approaches, however, are facing bottlenecks when the
known information is limited. The extreme case is how to recommend products to
new users, namely the so-called cold-start problem. The rise of the online
social networks gives us a chance to break the glass ceiling. Birds of a
feather flock together. Close friends may have similar hidden pattern of
selecting products and the advices from friends are more trustworthy.
  In this paper, we integrate the individual's social relationships into
recommender systems and propose a new method, called Social Mass Diffusion
(SMD), based on a mass diffusion process in the combined network of users'
social network and user-item bipartite network. The results show that the SMD
algorithm can achieve higher recommendation accuracy than the Mass Diffusion
(MD) purely on the bipartite network. Especially, the improvement is striking
for small degree users. Moreover, SMD provides a good solution to the
cold-start problem. The recommendation accuracy for new users significantly
higher than that of the conventional popularity-based algorithm. These results
may shed some light on the new designs of better personalized recommender
systems and information services.
Identification of and correction for publication bias
Date: 2017-11-28 00:00:00

Some empirical results are more likely to be published than others. Such
selective publication leads to biased estimates and distorted inference. This
paper proposes two approaches for identifying the conditional probability of
publication as a function of a study's results, the first based on systematic
replication studies and the second based on meta-studies. For known conditional
publication probabilities, we propose median-unbiased estimators and associated
confidence sets that correct for selective publication. We apply our methods to
recent large-scale replication studies in experimental economics and
psychology, and to meta-studies of the effects of minimum wages and de-worming
programs.
Power Control and Scheduling In Low SNR Region In The Uplink of Two Cell Networks
Date: 2018-01-20 00:00:00
Tasks: Scheduling

In this paper we investigate the sub-channel assignment and power control to
maximize the total sum rate in the uplink of two-cell network. It is assumed
that there are some sub-channels in each cell which should be allocated among
some users. Also, each user is subjected to a power constraint. The underlying
problem is a non-convex mixed integer non-linear optimization problem which
does not have a trivial solution. To solve the problem, having fixed the
consumed power of each user, and assuming low co-channel interference region,
the sub-channel allocation problem is reformulate into a more mathematically
tractable problem which is shown can be tackled through the so-called Hungarian
algorithm. Then, the consumed power of each user is reformulated as a quadratic
fractional problem which can be numerically derived. Numerical results
demonstrate the superiority of the proposed method in low SNR region as
compared to existing works addressed in the literature
Identifying species network features from gene tree quartets under the coalescent model
Date: 2017-11-28 00:00:00

We show that many topological features of level-1 species networks are
identifiable from the distribution of the gene tree quartets under the network
multi-species coalescent model. In particular, every cycle of size at least 4
and every hybrid node in a cycle of size at least 5 is identifiable. This is a
step toward justifying the inference of such networks which was recently
implemented by Sol\'is-Lemus and An\'e. We show additionally how to compute
quartet concordance factors for a network in terms of simpler networks, and
explore some circumstances in which cycles of size 3 and hybrid nodes in
4-cycles can be detected.
Predicting variation of DNA shape preferences in protein-DNA interaction in cancer cells with a new biophysical model
Date: 2017-10-31 00:00:00

DNA shape readout is an important mechanism of target site recognition by
transcription factors, in addition to the sequence readout. Several models of
transcription factor-DNA binding which consider DNA shape have been developed
in recent years. We present a new biophysical model of protein-DNA interaction
by considering the DNA shape features, which is based on a neighbour
dinucleotide dependency model BayesPI2. The parameters of the new model are
restricted to a subspace spanned by the 2-mer DNA shape features, which
allowing a biophysical interpretation of the new parameters as
position-dependent preferences towards certain values of the features. Using
the new model, we explore the variation of DNA shape preferences in several
transcription factors across cancer cell lines and cellular conditions. We find
evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after
treatment with steroids. The new model is useful for elucidating finer details
of transcription factor-DNA interaction. It may be used to improve the
prediction of cancer mutation effects in the future.
An efficient dual sampling algorithm with Hamming distance filtration
Date: 2017-10-31 00:00:00

Recently, a framework considering RNA sequences and their RNA secondary
structures as pairs, led to some information-theoretic perspectives on how the
semantics encoded in RNA sequences can be inferred. In this context, the
pairing arises naturally from the energy model of RNA secondary structures.
Fixing the sequence in the pairing produces the RNA energy landscape, whose
partition function was discovered by McCaskill. Dually, fixing the structure
induces the energy landscape of sequences. The latter has been considered for
designing more efficient inverse folding algorithms. We present here the
Hamming distance filtered, dual partition function, together with a Boltzmann
sampler using novel dynamic programming routines for the loop-based energy
model. The time complexity of the algorithm is $O(h^2n)$, where $h,n$ are
Hamming distance and sequence length, respectively, reducing the time
complexity of samplers, reported in the literature by $O(n^2)$. We then present
two applications, the first being in the context of the evolution of natural
sequence-structure pairs of microRNAs and the second constructing neutral
paths. The former studies the inverse fold rate (IFR) of sequence-structure
pairs, filtered by Hamming distance, observing that such pairs evolve towards
higher levels of robustness, i.e.,~increasing IFR. The latter is an algorithm
that constructs neutral paths: given two sequences in a neutral network, we
employ the sampler in order to construct short paths connecting them,
consisting of sequences all contained in the neutral network.
Using nonlinear stochastic and deterministic (chaotic tools) to test the EMH of two Electricity Markets the case of Italy and Greece
Date: 2017-11-27 00:00:00

Utilization of non-linear tools to characterize the state of development of
the electricity markets in Italy and Greece. This is equivalent to testing the
Efficient Market Hypothesis on these markets. The tools include a variety of
complexity measures like Maximal Lyapunov and Hurst exponents and HHI index for
market concentration and Entropy, a measure of uncertainty and complexity in a
dynamical system, applied on the electricity wholesale marginal prices PUN and
SMP of Italy and Greece.Our aim is to measure the complexity and dimensionality
of the manifold on which the underlying stochastic dynamical system, govenring
the prices, evolve. We also use the conditional volatility of prices, which is
a measure of the market risk, and its connection with stability, and Hurst
exponent to investigate the properties of the fluctuations of the prices which
are the footprints of the idiosyncrracies of each market.
Effect of fixation target on the contrast sensitivity in the foveal and parafoveal area
Date: 2017-11-27 00:00:00
Tasks: Sensitivity

Purpose
  To determine the influence on the contrast sensitivity when the stimulus
contains a fixation target in two retinal locations, foveal and parafoveal.
  Methods
  Four young adults with 0.0 logMar acuity participated in this study. The
stimulus was based on vertical sinusoidal gratings masked by a circular (for
foveal area) or a ring (for parafoveal area). To increase the luminance
resolution of the display a bit-stealing technique was used. Four different
sets of stimuli were generated, two for exploring the foveal sensitivity and
two for the parafoveal area. The difference between the sets designed for the
same area was the presence, or absence, of a fixation target (a white cross) in
the centre of the stimulus. A modified staircase method was implemented.
  Results
  The results show a drop in the contrast sensitivity when the fixation target
was present on the stimulus for frequencies smaller than 4 cycles per degree.
  Conclusions
  The presence of fixation targets diminishes the contrast sensitivity for low
to mid frequencies over different concentric areas of the retina. This could be
due to the fixational eye movements, different patterns of eye movements were
found using an eye tracker. The relationship between the sensitivity in the
foveal area and the parafoveal agrees with those reported by other authors
using different designs confirming that the new stimulus design is suitable to
measure the contrast sensitivity outside the foveal area.
Proactive Caching for Energy-Efficiency in Wireless Networks: A Markov Decision Process Approach
Date: 2017-11-29 00:00:00

Content caching in wireless networks provides a substantial opportunity to
trade off low cost memory storage with energy consumption, yet finding the
optimal causal policy with low computational complexity remains a challenge.
This paper models the Joint Pushing and Caching (JPC) problem as a Markov
Decision Process (MDP) and provides a solution to determine the optimal
randomized policy. A novel approach to decouple the influence from buffer
occupancy and user requests is proposed to turn the high-dimensional
optimization problem into three low-dimensional ones. Furthermore, a
non-iterative algorithm to solve one of the sub-problems is presented,
exploiting a structural property we found as \textit{generalized monotonicity},
and hence significantly reduces the computational complexity. The result
attains close performance in comparison with theoretical bounds from
non-practical policies, while benefiting from higher time efficiency than the
unadapted MDP solution.
$L_2$-Box Optimization for Green Cloud-RAN via Network Adaptation
Date: 2017-11-29 00:00:00

In this paper, we propose a reformulation for the Mixed Integer Programming
(MIP) problem into an exact and continuous model through using the $\ell_2$-box
technique to recast the binary constraints into a box with an $\ell_2$ sphere
constraint. The reformulated problem can be tackled by a dual ascent algorithm
combined with a Majorization-Minimization (MM) method for the subproblems to
solve the network power consumption problem of the Cloud Radio Access Network
(Cloud-RAN), and which leads to solving a sequence of Difference of Convex (DC)
subproblems handled by an inexact MM algorithm. After obtaining the final
solution, we use it as the initial result of the bi-section Group Sparse
Beamforming (GSBF) algorithm to promote the group-sparsity of beamformers,
rather than using the weighted $\ell_1 / \ell_2$-norm. Simulation results
indicate that the new method outperforms the bi-section GSBF algorithm by
achieving smaller network power consumption, especially in sparser cases, i.e.,
Cloud-RANs with a lot of Remote Radio Heads (RRHs) but fewer users.
A new fMRI data analysis method using cross validation: Negative BOLD responses may be the deactivations of interneurons
Date: 2017-12-10 00:00:00

Although functional magnetic resonance imaging (fMRI) is widely used for the
study of brain functions, the blood oxygenation level dependent (BOLD) effect
is incompletely understood. Particularly, negative BOLD responses(NBRs) is
controversial. This paper presents a new fMRI data analysis method, which is
more accurate than the typical conventional method. The authors conducted the
experiments of simple repetition, and analyzed the data by the new method. The
results strongly suggest that the deactivations(NBRs) detected by the new
method are the deactivations of interneurons, because the deactivation ratios
obtained by the new method approximately equals the deactivation ratios of
interneurons obtained by the study of interneurons. The (de)activations
detected by the new method are largely different from those detected by the
conventional method. The new method is more accurate than the conventional
method, and therefore the (de)activations detected by the new method may be
correct and the (de)activations detected by the conventional method may be
incorrect. A large portion of the deactivations of inhibitory interneurons is
also considered to be activations. Therefore, the right-tailed t-test, which is
usually performed in the conventional method, does not detect the whole
activation, because the right-tailed t-test only detects the activations of
excitatory neurons, and neglect the deactivations of inhibitory interneurons. A
lot of fMRI studies so far by the conventional method should be re-examined by
the new method, and many results obtained so far will be modified.