pwc-archive/papers-with-abstracts huggingface dataset:
Anomaly Classification in Distribution Networks Using a Quotient Gradient System
Date: 2018-05-14 00:00:00
Tasks: Anomaly Classification, Classification

The classification of anomalies or sudden changes in power networks versus
normal abrupt changes or switching actions is essential to take appropriate
maintenance actions that guarantee the quality of power delivery. This issue
has increased in importance and has become more complicated with the
proliferation of volatile resources that introduce variability, uncertainty,
and intermittency in circuit behavior that can be observed as variations in
voltage and current phasors. This makes diagnostics applications more
challenging. This paper proposes using quotient gradient system (QGS) to train
two-stage partially recurrent neural network to improve anomaly classification
rate in power distribution networks using high-fidelity data from micro-phasor
measurement units (PMUs). QGS is a systematic approach to finding solutions of
constraint satisfaction problems. We transform the PMUs data from the power
network into a constraint satisfaction problem and use QGS to train a neural
network by solving the resulting optimization problem. Simulation results show
that the proposed supervised classification method can reliably distinguish
between different anomalies in power distribution networks. Comparison with
other neural network classifiers shows that QGS trained networks provide
significantly better classification. Sensitivity analysis is performed
concerning the number of PMUs, reporting rates, noise level and early versus
late data stream fusion frameworks.
Utilizing Probase in Open Directory Project-based Text Classification
Authors: Jun So-Young, Aliyeva Dinara, Lee Ji-Min, Lee SangKeun
Date: 2018-05-14 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Open Directory Project (ODP) has been successfully utilized in text
classification due to its representation ability of various categories.
However, ODP includes a limited number of entities, which play an important
role in classification tasks. In this paper, we enrich the semantics of ODP
categories with Probase entities. To effectively incorporate Probase entities
in ODP categories, we first represent each ODP category and Probase entity in
terms of concepts. Next, we measure the semantic relevance between an ODP
category and a Probase entity based on the concept vector. Finally, we use
Probase entity to enrich the semantics of the ODP categories. Our experimental
results show that the proposed methodology exhibits a significant improvement
over state-of-the-art techniques in the ODP-based text classification.
Saikosaponins with similar structures but different mechanisms lead to combined hepatotoxicity
Authors: Qianqian Zhang, Wanqiu Huang, Yiqiao Gao, Yingtong Lv, Wei zhang, Zunjian Zhang, Fengguo Xu
Date: 2018-05-14 00:00:00

Radix Bupleuri is a hepatoprotective traditional Chinese medicine (TCM) used for thousands of years in clinical, which was reported to be linked with liver damage. Previous studies have revealed that saikosaponins are the major types of components that contribute to the hepatotoxicity of Radix Bupleuri. However the underlying molecular mechanism is far from being understood. In order to clarify whether these structural analogues exert toxicity effects through the same molecular targets, a systematic comparison study was done in this paper. The effects of SSa, b2, c, and d on isolated rat liver mitochondria and human hepatocyte L02 cells were explored, respectively. The collective results indicated that although saikosaponins share the similar structures but they have quite different mechanisms. SSb2 and SSd showed most serious damage on the function of mitochondria and survival rate of cell, respectively. SSb2 could cause mitochondrial permeability transition pore (mPTP) opening and collapse of mitochondrial membrane potential ({\Delta}{\Psi}m) by impairing the mitochondrial respiratory chain complex III. While SSd destroyed plasma membrane and led to the release of lactate dehydrogenase (LDH) mainly through activating caspase-1. Furthermore, the combine index (CI) demonstrated that the combined hepatotoxicity of SSb2 and SSd could be additive. This finding might yield more in depth understanding of hepatotoxicity of Radix Bupleuri possess many different saikosaponins.
Collaborative Item Embedding Model for Implicit Feedback Data
Authors: Nguyen ThaiBinh, Aihara Kenro, Takasu Atsuhiro
Date: 2018-05-14 00:00:00
Tasks: Collaborative Filtering, model, Recommendation Systems

Collaborative filtering is the most popular approach for recommender systems.
One way to perform collaborative filtering is matrix factorization, which
characterizes user preferences and item attributes using latent vectors. These
latent vectors are good at capturing global features of users and items but are
not strong in capturing local relationships between users or between items. In
this work, we propose a method to extract the relationships between items and
embed them into the latent vectors of the factorization model. This combines
two worlds: matrix factorization for collaborative filtering and item embed-
ding, a similar concept to word embedding in language processing. Our
experiments on three real-world datasets show that our proposed method
outperforms competing methods on top-n recommendation tasks.
Integrating Hypertension Phenotype and Genotype with Hybrid Non-negative Matrix Factorization
Authors: Luo Yuan, Mao Chengsheng, Yang Yiben, Wang Fei, Ahmad Faraz S., Arnett Donna, Irvin Marguerite R., Shah Sanjiv J.
Date: 2018-05-18 00:00:00

Hypertension is a heterogeneous syndrome in need of improved subtyping using
phenotypic and genetic measurements so that patients in different subtypes
share similar pathophysiologic mechanisms and respond more uniformly to
targeted treatments. Existing machine learning approaches often face challenges
in integrating phenotype and genotype information and presenting to clinicians
an interpretable model. We aim to provide informed patient stratification by
introducing Hybrid Non-negative Matrix Factorization (HNMF) on phenotype and
genotype matrices. HNMF simultaneously approximates the phenotypic and genetic
matrices using different appropriate loss functions, and generates patient
subtypes, phenotypic groups and genetic groups. Unlike previous methods, HNMF
approximates phenotypic matrix under Frobenius loss, and genetic matrix under
Kullback-Leibler (KL) loss. We propose an alternating projected gradient method
to solve the approximation problem. Simulation shows HNMF converges fast and
accurately to the true factor matrices. On real-world clinical dataset, we used
the patient factor matrix as features to predict main cardiac mechanistic
outcomes. We compared HNMF with six different models using phenotype or
genotype features alone, with or without NMF, or using joint NMF with only one
type of loss. HNMF significantly outperforms all comparison models. HNMF also
reveals intuitive phenotype-genotype interactions that characterize cardiac
abnormalities.
Individuality and slow dynamics in bacterial growth homeostasis
Date: 2018-05-14 00:00:00

Microbial growth and division are fundamental processes relevant to many
areas of life science. Of particular interest are homeostasis mechanisms, which
buffer growth and division from accumulating fluctuations over multiple cycles.
These mechanisms operate within single cells, possibly extending over several
division cycles. However, all experimental studies to date have relied on
measurements pooled from many distinct cells. Here, we disentangle long-term
measured traces of individual cells from one another, revealing subtle
differences between temporal and pooled statistics. By analyzing correlations
along up to hundreds of generations, we find that the parameter describing
effective cell-size homeostasis strength varies significantly among cells. At
the same time, we find an invariant cell size which acts as an attractor to all
individual traces, albeit with different effective attractive forces. Despite
the common attractor, each cell maintains a distinct average size over its
finite lifetime with suppressed temporal fluctuations around it, and
equilibration to the global average size is surprisingly slow (> 150 cell
cycles). To demonstrate a possible source of variable homeostasis strength, we
construct a mathematical model relying on intracellular interactions, which
integrates measured properties of cell size with those of highly expressed
proteins. Effective homeostasis strength is then influenced by interactions and
by noise levels, and generally varies among cells. A predictable and measurable
consequence of variable homeostasis strength appears as distinct oscillatory
patterns in cell size and protein content over many generations. We discuss the
implications of our results to understanding mechanisms controlling division in
single cells and their characteristic timescales
Self and turbo iterations for MIMO receivers and large-scale systems
Date: 2019-01-10 00:00:00
Tasks: Decoder

We investigate a turbo soft detector based on the expectation propagation
(EP) algorithm for large-scale multiple-input multiple-output (MIMO) systems.
Optimal detection in MIMO systems becomes computationally unfeasible for
high-order modulations and/or large number of antennas. In this situation, the
linear minimum mean square error (LMMSE) exhibits a low-complexity with a good
performance, however far from optimal. To improve the performance, the EP
algorithm can be used. In this paper, we review previous EP-based detectors and
enhance their estimation in terms of complexity and performance. Specifically,
we improve the convergence of the self-iterated EP stage by replacing the
uniform prior by a non-uniform one, which better characterizes the information
returned by the decoder once the turbo procedure starts. We also review the EP
parameters to avoid instabilities when using high-order modulations and to
reduce the computational complexity. Simulation results illustrate the
robustness and enhanced performance of this novel detector in comparison with
previous approaches found in the literature. Results also show that the
proposed detector is robust in the presence of imperfect channel state
information (CSI).
The Finite Sample Performance of Treatment Effects Estimators based on the Lasso
Date: 2018-05-14 00:00:00
Tasks: BIG-bench Machine Learning

This paper contributes to the literature on treatment effects estimation with
machine learning inspired methods by studying the performance of different
estimators based on the Lasso. Building on recent work in the field of
high-dimensional statistics, we use the semiparametric efficient score
estimation structure to compare different estimators. Alternative weighting
schemes are considered and their suitability for the incorporation of machine
learning estimators is assessed using theoretical arguments and various Monte
Carlo experiments. Additionally we propose an own estimator based on doubly
robust Kernel matching that is argued to be more robust to nuisance parameter
misspecification. In the simulation study we verify theory based intuition and
find good finite sample properties of alternative weighting scheme estimators
like the one we propose.
Nonautonomous Dynamics of Acute Cell Injury
Authors: Donald J. DeGracia, Doaa Taha, Fika Tri Anggraini, Zhi-Feng Huang
Date: 2018-05-14 00:00:00

Medical conditions due to acute cell injury, such as stroke and heart attack, are of tremendous impact and have attracted huge amounts of research effort. The biomedical research that seeks cures for these conditions has been dominated by a qualitative, inductive mindset. Although the inductive approach has not been effective in developing medical treatments, it has amassed enough information to allow construction of quantitative, deductive models of acute cell injury. In this work we develop a modeling approach by extending an autonomous nonlinear dynamic theory of acute cell injury that offered new ways to conceptualize cell injury but possessed limitations that decrease its effectiveness. Here we study the global dynamics of the cell injury theory using a nonautonomous formulation. Different from the standard scenario in nonlinear dynamics that is determined by the steady state and fixed points of the model equations, in this nonautonomous model with a trivial fixed point, the system property is dominated by the transient states and the corresponding dynamic processes. The model gives rise to four qualitative types of dynamical patterns that can be mapped to the behavior of cells after clinical acute injuries. The nonautonomous theory predicts the existence of a latent stress response capacity (LSRC) possessed by injured cells. The LSRC provides a theoretical explanation of how therapies, such as hypothermia, can prevent cell death after lethal injuries. The nonautonomous theory of acute cell injury provides an improved quantitative framework for understanding cell death and recovery and lays a foundation for developing effective therapeutics for acute injury.
The strong Fatou property of risk measures
Date: 2018-05-14 00:00:00

In this paper, we explore several Fatou-type properties of risk measures. The
paper continues to reveal that the strong Fatou property, which was introduced
in [17], seems to be most suitable to ensure nice dual representations of risk
measures. Our main result asserts that every quasiconvex law-invariant
functional on a rearrangement invariant space $\mathcal{X}$ with the strong
Fatou property is $\sigma(\mathcal{X},L^\infty)$ lower semicontinuous and that
the converse is true on a wide range of rearrangement invariant spaces. We also
study inf-convolutions of law-invariant or surplus-invariant risk measures that
preserve the (strong) Fatou property.
Neurofeedback: principles, appraisal and outstanding issues
Date: 2018-05-14 00:00:00

Neurofeedback is a form of brain training in which subjects are fed back
information about some measure of their brain activity which they are
instructed to modify in a way thought to be functionally advantageous. Over the
last twenty years, NF has been used to treat various neurological and
psychiatric conditions, and to improve cognitive function in various contexts.
However, despite its growing popularity, each of the main steps in NF comes
with its own set of often covert assumptions. Here we critically examine some
conceptual and methodological issues associated with the way general objectives
and neural targets of NF are defined, and review the neural mechanisms through
which NF may act, and the way its efficacy is gauged. The NF process is
characterised in terms of functional dynamics, and possible ways in which it
may be controlled are discussed. Finally, it is proposed that improving NF will
require better understanding of various fundamental aspects of brain dynamics
and a more precise definition of functional brain activity and brain-behaviour
relationships.
Mathematical model of immune response to hepatitis B
Date: 2018-05-11 00:00:00
Tasks: model

A new detailed mathematical model for dynamics of immune response to
hepatitis B is proposed, which takes into account contributions from innate and
adaptive immune responses, as well as cytokines. Stability analysis of
different steady states is performed to identify parameter regions where the
model exhibits clearance of infection, maintenance of a chronic infection, or
periodic oscillations. Effects of nucleoside analogues and interferon
treatments are analysed, and the critical drug efficiency is determined.
'Bosons' and 'fermions' in social and economic systems
Date: 2018-05-13 00:00:00

We analyze social and economic systems with a hierarchical structure and show
that for such systems, it is possible to construct thermostatistics, based on
the intermediate Gentile statistics. We show that in social and economic
hierarchical systems there are elements that obey the Fermi-Dirac statistics
and can be called fermions, as well as elements that are approximately subject
to Bose-Einstein statistics and can be called bosons. We derive the first and
second laws of thermodynamics for the considered economic system and show that
such concepts as temperature, pressure and financial potential (which is an
analogue of the chemical potential in thermodynamics) that characterize the
state of the economic system as a whole, can be introduced for economic
systems.
Theoretical open-loop model of respiratory mechanics in the extremely preterm infant
Date: 2018-05-14 00:00:00

Non-invasive ventilation is increasingly used for respiratory support in
preterm infants, and is associated with a lower risk of chronic lung disease.
However, this mode is often not successful in the extremely preterm infant in
part due to their markedly increased chest wall compliance that does not
provide enough structure against which the forces of inhalation can generate
sufficient pressure. To address the continued challenge of studying treatments
in this fragile population, we developed a nonlinear lumped-parameter model of
respiratory system mechanics of the extremely preterm infant that incorporates
nonlinear lung and chest wall compliances and lung volume parameters tuned to
this population. In particular we developed a novel empirical representation of
progressive volume loss based on compensatory alveolar pressure increase
resulting from collapsed alveoli. The model demonstrates increased rate of
volume loss related to high chest wall compliance, and simulates laryngeal
braking for elevation of end-expiratory lung volume and constant positive
airway pressure (CPAP). The model predicts that low chest wall compliance
(chest stiffening) in addition to laryngeal braking and CPAP enhance breathing
and delay lung volume loss. These results motivate future data collection
strategies and investigation into treatments for chest wall stiffening.
Reconstruction of the deep history of "Parent-Daughter" relationships among vertebrate paralogs
Date: 2018-05-22 00:00:00

Gene duplication is a major mechanism through which new genetic material is
generated. Although numerous methods have been developed to differentiate the
ortholog and paralogs, very few differentiate the "Parent-Daughter"
relationship among paralogous pairs. As coined by the Mira et al, we refer the
"Parent" copy as the paralogous copy that stays at the original genomic
position of the "original copy" before the duplication event, while the
"Daughter" copy occupies a new genomic locus.
  Here we present a novel method which combines the phylogenetic reconstruction
of duplications at different evolutionary periods and the synteny evidence
collected from the preserved homologous gene orders. We reconstructed for the
first time a deep evolutionary history of "Parent-Daughter" relationships among
genes that were descendants from 2 rounds of whole genome duplications (2R
WGDs) at early vertebrates and were further duplicated in later ceancestors
like early Mammalia and early Primates.
  Our analysis reveals that the "Parent" copy has significantly fewer
accumulated mutations compared with the "Daughter" copy since their divergence
after the duplication event. More strikingly, we found that the "Parent" copy
in a duplication event continues to be the "Parent" of the younger successive
duplication events which lead to "grand-daughters".
Computational mechanisms in genetic regulation by RNA
Authors: J. M. Deutsch
Date: 2018-05-14 00:00:00

The evolution of the genome has led to very sophisticated and complex regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell, different species will promiscuously associate with each other, suggesting collective dynamics similar to artificial neural networks. Here we present a simple mechanism allowing ncRNA to perform computations equivalent to neural network algorithms such as Boltzmann machines and the Hopfield model. The quantities analogous to the neural couplings are the equilibrium constants between different RNA species. The relatively rapid equilibration of RNA binding and unbinding is regulated by a slower process that degrades and creates new RNA. The model requires that the creation rate for each species be an increasing function of the ratio of total to unbound RNA. Similar mechanisms have already been found to exist experimentally for ncRNA regulation. With the overall concentration of RNA regulated, equilibrium constants can be chosen to store many different patterns, or many different input-output relations. The network is also quite insensitive to random mutations in equilibrium constants. Therefore one expects that this kind of mechanism will have a much higher mutation rate than ones typically regarded as being under evolutionary constraint.
Social diversity for reducing the impact of information cascades on social learning
Date: 2018-05-15 00:00:00
Tasks: Diversity

Collective behavior in online social media and networks is known to be
capable of generating non-intuitive dynamics associated with crowd wisdom and
herd behaviour. Even though these topics have been well-studied in social
science, the explosive growth of Internet computing and e-commerce makes urgent
to understand their effects within the digital society. In this work we explore
how the stochasticity introduced by social diversity can help agents involved
in a inference process to improve their collective performance. Our results
show how social diversity can reduce the undesirable effects of information
cascades, in which rational agents choose to ignore personal knowledge in order
to follow a predominant social behaviour. Situations where social diversity is
never desirable are also distinguished, and consequences of these findings for
engineering and social scenarios are discussed.
Aggregating multiple types of complex data in stock market prediction: A model-independent framework
Date: 2018-05-15 00:00:00
Tasks: Prediction, Stock Market Prediction

The increasing richness in volume, and especially types of data in the
financial domain provides unprecedented opportunities to understand the stock
market more comprehensively and makes the price prediction more accurate than
before. However, they also bring challenges to classic statistic approaches
since those models might be constrained to a certain type of data. Aiming at
aggregating differently sourced information and offering type-free capability
to existing models, a framework for predicting stock market of scenarios with
mixed data, including scalar data, compositional data (pie-like) and functional
data (curve-like), is established. The presented framework is
model-independent, as it serves like an interface to multiple types of data and
can be combined with various prediction models. And it is proved to be
effective through numerical simulations. Regarding to price prediction, we
incorporate the trading volume (scalar data), intraday return series
(functional data), and investors' emotions from social media (compositional
data) through the framework to competently forecast whether the market goes up
or down at opening in the next day. The strong explanatory power of the
framework is further demonstrated. Specifically, it is found that the intraday
returns impact the following opening prices differently between bearish market
and bullish market. And it is not at the beginning of the bearish market but
the subsequent period in which the investors' "fear" comes to be indicative.
The framework would help extend existing prediction models easily to scenarios
with multiple types of data and shed light on a more systemic understanding of
the stock market.