pwc-archive/papers-with-abstracts huggingface dataset:
Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification
Authors: Nugroho Suryanto, Prihandoko
Date: 2018-09-20 00:00:00
Tasks: Opinion Mining, POS, POS Tagging

The selection of West Java governor is one event that seizes the attention of
the public is no exception to social media users. Public opinion on a
prospective regional leader can help predict electability and tendency of
voters. Data that can be used by the opinion mining process can be obtained
from Twitter. Because the data is very varied form and very unstructured, it
must be managed and uninformed using data pre-processing techniques into
semi-structured data. This semi-structured information is followed by a
classification stage to categorize the opinion into negative or positive
opinions. The research methodology uses a literature study where the research
will examine previous research on a similar topic. The purpose of this study is
to find the right architecture to develop it into the application of twitter
opinion mining to know public sentiments toward the election of the governor of
west java. The result of this research is that Twitter opinion mining is part
of text mining where opinions in Twitter if they want to be classified, must go
through the preprocessing text stage first. The preprocessing step required
from twitter data is cleansing, case folding, POS Tagging and stemming. The
resulting text mining architecture is an architecture that can be used for text
mining research with different topics.
How production networks amplify economic growth
Authors: James McNerney, Charles Savoie, Francesco Caravelli, Vasco M. Carvalho, J. Doyne Farmer
Date: 2018-10-17 00:00:00

Technological improvement is the most important cause of long-term economic growth. We study the effects of technology improvement in the setting of a production network, in which each producer buys input goods and converts them to other goods, selling the product to households or other producers. We show how this network amplifies the effects of technological improvements as they propagate along chains of production. Longer production chains for an industry bias it towards faster price reduction, and longer production chains for a country bias it towards faster GDP growth. These predictions are in good agreement with data and improve with the passage of time, demonstrating a key influence of production chains in price change and output growth over the long term.
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice
Authors: Pang Kunkun, Dong Mingzhi, Wu Yang, Hospedales Timothy M.
Date: 2018-09-29 00:00:00
Tasks: Active Learning

Active learning aims to reduce annotation cost by predicting which samples
are useful for a human teacher to label. However it has become clear there is
no best active learning algorithm. Inspired by various philosophies about what
constitutes a good criteria, different algorithms perform well on different
datasets. This has motivated research into ensembles of active learners that
learn what constitutes a good criteria in a given scenario, typically via
multi-armed bandit algorithms. Though algorithm ensembles can lead to better
results, they overlook the fact that not only does algorithm efficacy vary
across datasets, but also during a single active learning session. That is, the
best criteria is non-stationary. This breaks existing algorithms' guarantees
and hampers their performance in practice. In this paper, we propose dynamic
ensemble active learning as a more general and promising research direction. We
develop a dynamic ensemble active learner based on a non-stationary multi-armed
bandit with expert advice algorithm. Our dynamic ensemble selects the right
criteria at each step of active learning. It has theoretical guarantees, and
shows encouraging results on $13$ popular datasets.
MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities
Authors: Woszczyk Dominika, Spanakis Gerasimos
Date: 2018-10-13 00:00:00
Tasks: Decision Making, Feature Engineering

Urbanism is no longer planned on paper thanks to powerful models and 3D
simulation platforms. However, current work is not open to the public and lacks
an optimisation agent that could help in decision making. This paper describes
the creation of an open-source simulation based on an existing Dutch
liveability score with a built-in AI module. Features are selected using
feature engineering and Random Forests. Then, a modified scoring function is
built based on the former liveability classes. The score is predicted using
Random Forest for regression and achieved a recall of 0.83 with 10-fold
cross-validation. Afterwards, Exploratory Factor Analysis is applied to select
the actions present in the model. The resulting indicators are divided into 5
groups, and 12 actions are generated. The performance of four optimisation
algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three
established criteria of quality: cardinality, the spread of the solutions,
spacing, and the resulting score and number of turns. Although all four
algorithms show different strengths, eps-MOEA is selected to be the most
suitable for this problem. Ultimately, the simulation incorporates the model
and the selected AI module in a GUI written in the Kivy framework for Python.
Tests performed on users show positive responses and encourage further
initiatives towards joining technology and public applications.
Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
Date: 2019-02-26 00:00:00
Tasks: feature selection, Medical Image Analysis, regression

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and
behavioral treatment interventions have shown promise for young children with
ASD. However, there is limited progress in understanding the effect of each
type of treatment. In this project, we aim to detect structural changes in the
brain after treatment and select structural features associated with treatment
outcomes. The difficulty in building large databases of patients who have
received specific treatments and the high dimensionality of medical image
analysis problems are the challenges in this work. To select predictive
features and build accurate models, we use the sure independence screening
(SIS) method. SIS is a theoretically and empirically validated method for
ultra-high dimensional general linear models, and it achieves both predictive
accuracy and correct feature selection by iterative feature selection. Compared
with step-wise feature selection methods, SIS removes multiple features in each
iteration and is computationally efficient. Compared with other linear models
such as elastic-net regression, support vector regression (SVR) and partial
least squares regression (PSLR), SIS achieves higher accuracy. We validated the
superior performance of SIS in various experiments: First, we extract brain
structural features from FreeSurfer, including cortical thickness, surface
area, mean curvature and cortical volume. Next, we predict different measures
of treatment outcomes based on structural features. We show that SIS achieves
the highest correlation between prediction and measurements in all tasks.
Furthermore, we report regions selected by SIS as biomarkers for ASD.
Modes of rapid polygenic adaptation
Date: 2018-10-18 00:00:00

Many experimental and field studies have shown that adaptation can occur very
rapidly. Two qualitatively different modes of fast adaptation have been
proposed: selective sweeps wherein large shifts in the allele frequencies occur
at a few loci and evolution via small changes in the allele frequencies at many
loci. While the first process has been thoroughly investigated within the
framework of population genetics, the latter is based on quantitative genetics
and is much less understood. Here we summarize results from our recent
theoretical studies of a quantitative genetic model of polygenic adaptation
that makes explicit reference to population genetics to bridge the gap between
the two frameworks. Our key results are that polygenic adaptation may be a
rapid process and can proceed via subtle or dramatic changes in the allele
frequency depending on the sizes of the phenotypic effects relative to a
threshold value. We also discuss how the signals of polygenic selection may be
detected in the genome. While powerful methods are available to identify
signatures of selective sweeps at loci controling quantitative traits, the
development of statistical tests for detecting small shifts of allele
frequencies at quantitative trait loci is still in its infancy.
Interference effects of deleterious and beneficial mutations in large asexual populations
Date: 2018-10-18 00:00:00

Linked beneficial and deleterious mutations are known to decrease the
fixation probability of a favorable mutation in large asexual populations.
While the hindering effect of strongly deleterious mutations on adaptive
evolution has been well studied, how weak deleterious mutations, either in
isolation or with superior beneficial mutations, influence the fixation of a
beneficial mutation has not been fully explored. Here, using a multitype
branching process, we obtain an accurate analytical expression for the fixation
probability when deleterious effects are weak, and exploit this result along
with the clonal interference theory to investigate the joint effect of linked
beneficial and deleterious mutations on the rate of adaptation. We find that
when the mutation rate is increased beyond the beneficial fitness effect, the
fixation probability of the beneficial mutant decreases from Haldane's
classical result towards zero. This has the consequence that above a critical
mutation rate that may depend on the population size, the adaptation rate
decreases exponentially with the mutation rate and is independent of the
population size. In addition, we find that for a range of mutation rates, both
beneficial and deleterious mutations interfere and impede the adaptation
process in large populations. We also study the evolution of mutation rates in
adapting asexual populations, and conclude that linked beneficial mutations
have a stronger influence on mutator fixation than the deleterious mutations.
Power Minimization in Distributed Antenna Systems using Non-Orthogonal Multiple Access and Mutual Successive Interference Cancellation
Date: 2018-10-17 00:00:00

This paper introduces new approaches for combining non-orthogonal multiple
access with distributed antenna systems. The study targets a minimization of
the total transmit power in each cell, under user rate and power multiplexing
constraints. Several new suboptimal power allocation techniques are proposed.
They are shown to yield very close performance to an optimal power allocation
scheme. Also, a new approach based on mutual successive interference
cancellation of paired users is proposed. Different techniques are designed for
the joint allocation of subcarriers, antennas, and power, with a particular
care given to maintain a moderate complexity. The coupling of non-orthogonal
multiple access to distributed antenna systems is shown to greatly outperform
any other combination of orthogonal/non-orthogonal multiple access schemes with
distributed or centralized deployment scenarios.
Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns
Date: 2018-10-18 00:00:00
Tasks: Dictionary Learning, regression, Retrieval

This paper proposes a novel algorithm for image phase retrieval, i.e., for
recovering complex-valued images from the amplitudes of noisy linear
combinations (often the Fourier transform) of the sought complex images. The
algorithm is developed using the alternating projection framework and is aimed
to obtain high performance for heavily noisy (Poissonian or Gaussian)
observations. The estimation of the target images is reformulated as a sparse
regression, often termed sparse coding, in the complex domain. This is
accomplished by learning a complex domain dictionary from the data it
represents via matrix factorization with sparsity constraints on the code
(i.e., the regression coefficients). Our algorithm, termed dictionary learning
phase retrieval (DLPR), jointly learns the referred to dictionary and
reconstructs the unknown target image. The effectiveness of DLPR is illustrated
through experiments conducted on complex images, simulated and real, where it
shows noticeable advantages over the state-of-the-art competitors.
Robust Distributed Cooperative RSS-based Localization for Directed Graphs in Mixed LoS/NLoS Environments
Date: 2018-12-11 00:00:00

The accurate and low-cost localization of sensors using a wireless sensor
network is critically required in a wide range of today's applications. We
propose a novel, robust maximum likelihood-type method for distributed
cooperative received signal strength-based localization in wireless sensor
networks. To cope with mixed LoS/NLoS conditions, we model the measurements
using a two-component Gaussian mixture model. The relevant channel parameters,
including the reference path loss, the path loss exponent and the variance of
the measurement error, for both LoS and NLoS conditions, are assumed to be
unknown deterministic parameters and are adaptively estimated. Unlike existing
algorithms, the proposed method naturally takes into account the (possible)
asymmetry of links between nodes. The proposed approach has a communication
overhead upper-bounded by a quadratic function of the number of nodes and
computational complexity scaling linearly with it. The convergence of the
proposed method is guaranteed for compatible network graphs and compatibility
can be tested a priori by restating the problem as a graph coloring problem.
Simulation results, carried out in comparison to a centralized benchmark
algorithm, demonstrate the good overall performance and high robustness in
mixed LoS/NLoS environments.
A Non-Intrusive Low-Rank Approximation Method for Assessing the Probabilistic Available Transfer Capability
Date: 2018-10-18 00:00:00

In this paper, a mathematical formulation of the probabilistic available
transfer capability (PATC) problem is proposed to incorporate uncertainties
from the large-scale renewable energy generation (e.g., wind farms and solar PV
power plants). Moreover, a novel non-intrusive low-rank approximation (LRA) is
developed to assess PATC, which can accurately and efficiently estimate the
probabilistic characteristics (e.g., mean, variance, probability density
function (PDF)) of the PATC. Numerical studies on the IEEE 24-bus reliability
test system (RTS) and IEEE 118-bus system show that the proposed method can
achieve accurate estimations for the probabilistic characteristics of the PATC
with much less computational effort compared to the Latin hypercube sampling
(LHS)-based Monte Carlo simulations (MCS). The proposed LRA-PATC method offers
an efficient and effective way to determine the available transfer capability
so as to fully utilize the transmission assets while maintaining the security
of the grid.
On Optimal Sensing and Capacity Trade-off in Cognitive Radio Systems with Directional Antennas
Date: 2018-10-18 00:00:00

We consider a cognitive radio system, in which the secondary users (SUs) and
primary users (PUs) coexist. The SUs are equipped with steerable directional
antennas. In our system, the secondary transmitter (SUtx) first senses the
spectrum (with errors) for a duration of {\tau}, and, then transmits data to
the secondary receiver (SUrx) if spectrum is sensed idle. The sensing time as
well as the orientation of SUtx's antenna affect the accuracy of spectrum
sensing and yield a trade-off between spectrum sensing and capacity of the
secondary network. We formulate the ergodic capacity of secondary network which
uses energy detection for spectrum sensing. We obtain optimal SUtx transmit
power, the optimal sensing time {\tau} and the optimal directions of SUtx
transmit antenna and SUrx receive antenna by maximizing the ergodic capacity,
subject to peak transmit power and outage interference probability constraints.
Our simulation results show the effectiveness of these optimizations to
increase the ergodic capacity of the secondary network.
Social learning of prescribing behavior can promote population optimum of antibiotic use
Date: 2018-10-18 00:00:00

The rise and spread of antibiotic resistance causes worsening medical cost
and mortality especially for life-threatening bacteria infections, thereby
posing a major threat to global health. Prescribing behavior of physicians is
one of the important factors impacting the underlying dynamics of resistance
evolution. It remains unclear when individual prescribing decisions can lead to
the overuse of antibiotics on the population level, and whether population
optimum of antibiotic use can be reached through an adaptive social learning
process that governs the evolution of prescribing norm. Here we study a
behavior-disease interaction model, specifically incorporating a feedback loop
between prescription behavior and resistance evolution. We identify the
conditions under which antibiotic resistance can evolve as a result of the
tragedy of the commons in antibiotic overuse. Furthermore, we show that fast
social learning that adjusts prescribing behavior in prompt response to
resistance evolution can steer out cyclic oscillations of antibiotic usage
quickly towards the stable population optimum of prescribing. Our work
demonstrates that provision of prompt feedback to prescribing behavior with the
collective consequences of treatment decisions and costs that are associated
with resistance helps curb the overuse of antibiotics.
Multilinear SVD for Millimeter Wave Channel Parameter Estimation
Date: 2018-10-19 00:00:00
Tasks: parameter estimation

Fifth generation (5G) cellular standards are set to utilize millimeter wave
(mmWave) frequencies, which enable data speeds greater than 10 Gbps and
sub-centimeter localization accuracy. These capabilities rely on accurate
estimates of the channel parameters, which we define as the angle of arrival,
angle of departure, and path distance for each path between the transmitter and
receiver. Estimating the channel parameters in a computationally efficient
manner poses a challenge because it requires estimation of parameters from a
high-dimensional measurement -- particularly for multi-carrier systems since
each subcarrier must be estimated separately. Additionally, channel parameter
estimation must be able to handle hybrid beamforming, which uses a combination
of digital and analog beamforming to reduce the number of required analog to
digital converters. This paper introduces a channel parameter estimation
technique based on the multilinear singular value decomposition (MSVD), a
tensor analog of the singular value decomposition, for massive multiple input
multiple output (MIMO) multi-carrier systems with hybrid beamforming. The MSVD
tensor estimation approach provides a computationally efficient method and is
shown to closely match the Cramer-Rao bound (CRB) of parameter estimates
through simulations. Limitations of channel parameter estimation and
communication waveform effects are also studied.
Optimal hedging under fast-varying stochastic volatility
Authors: Josselin Garnier, Knut Solna
Date: 2018-10-19 00:00:00

In a market with a rough or Markovian mean-reverting stochastic volatility there is no perfect hedge. Here it is shown how various delta-type hedging strategies perform and can be evaluated in such markets in the case of European options. A precise characterization of the hedging cost, the replication cost caused by the volatility fluctuations, is presented in an asymptotic regime of rapid mean reversion for the volatility fluctuations. The optimal dynamic asset based hedging strategy in the considered regime is identified as the so-called `practitioners' delta hedging scheme. It is moreover shown that the performances of the delta-type hedging schemes are essentially independent of the regularity of the volatility paths in the considered regime and that the hedging costs are related to a vega risk martingale whose magnitude is proportional to a new market risk parameter. It is also shown via numerical simulations that the proposed hedging schemes which derive from option price approximations in the regime of rapid mean reversion, are robust: the `practitioners' delta hedging scheme that is identified as being optimal by our asymptotic analysis when the mean reversion time is small seems to be optimal with arbitrary mean reversion times.
Treatment Effect Models with Strategic Interaction in Treatment Decisions
Authors: Tadao Hoshino, Takahide Yanagi
Date: 2018-10-19 00:00:00

This study considers treatment effect models in which others' treatment decisions can affect both one's own treatment and outcome. Focusing on the case of two-player interactions, we formulate treatment decision behavior as a complete information game with multiple equilibria. Using a latent index framework and assuming a stochastic equilibrium selection, we prove that the marginal treatment effect from one's own treatment and that from the partner can be identified separately. Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation. We show that the proposed estimator is uniformly consistent and asymptotically normally distributed. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents' academic performance.
Pico-Newton mechanical forces promote neurite growth
Date: 2018-10-19 00:00:00
Tasks: PICO

Investigations over half a century have indicated that mechanical forces
induce neurite growth - with neurites elongating at a rate of
0.1-0.3{\mu}mh^{-1} per pico-Newton (pN) of applied force - when mechanical
tension exceeds a threshold, with this being identified as 400-1000 pN for
neurites of PC12 cells. Here we demonstrate that there is no threshold for
neurite elongation of PC12 cells in response to applied mechanical forces.
Instead, this proceeds at the same previously identified rate, on the
application of tensions with intensity below 1pN. This supports the idea of
mechanical tension as an endogenous signal used by neurons for promoting
neurite elongation.
QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks
Authors: Ranjbar Vahid, Salehi Mostafa, Jandaghi Pegah, Jalili Mahdi
Date: 2018-10-19 00:00:00
Tasks: Anomaly Detection, Clustering, Tensor Decomposition

Complex networks have now become integral parts of modern information
infrastructures. This paper proposes a user-centric method for detecting
anomalies in heterogeneous information networks, in which nodes and/or edges
might be from different types. In the proposed anomaly detection method, users
interact directly with the system and anomalous entities can be detected
through queries. Our approach is based on tensor decomposition and clustering
methods. We also propose a network generation model to construct synthetic
heterogeneous information network to test the performance of the proposed
method. The proposed anomaly detection method is compared with state-of-the-art
methods in both synthetic and real-world networks. Experimental results show
that the proposed tensor-based method considerably outperforms the existing
anomaly detection methods.