pwc-archive/papers-with-abstracts huggingface dataset:
Investigating the Application of Common-Sense Knowledge-Base for Identifying Term Obfuscation in Adversarial Communication
Authors: Agarwal Swati, Sureka Ashish
Date: 2017-01-18 00:00:00
Tasks: Common Sense Reasoning, Sentence

Word obfuscation or substitution means replacing one word with another word
in a sentence to conceal the textual content or communication. Word obfuscation
is used in adversarial communication by terrorist or criminals for conveying
their messages without getting red-flagged by security and intelligence
agencies intercepting or scanning messages (such as emails and telephone
conversations). ConceptNet is a freely available semantic network represented
as a directed graph consisting of nodes as concepts and edges as assertions of
common sense about these concepts. We present a solution approach exploiting
vast amount of semantic knowledge in ConceptNet for addressing the technically
challenging problem of word substitution in adversarial communication. We frame
the given problem as a textual reasoning and context inference task and utilize
ConceptNet's natural-language-processing tool-kit for determining word
substitution. We use ConceptNet to compute the conceptual similarity between
any two given terms and define a Mean Average Conceptual Similarity (MACS)
metric to identify out-of-context terms. The test-bed to evaluate our proposed
approach consists of Enron email dataset (having over 600000 emails generated
by 158 employees of Enron Corporation) and Brown corpus (totaling about a
million words drawn from a wide variety of sources). We implement word
substitution techniques used by previous researches to generate a test dataset.
We conduct a series of experiments consisting of word substitution methods used
in the past to evaluate our approach. Experimental results reveal that the
proposed approach is effective.
Structural Effects and Competition Mechanisms Targeting the Interactions between p53 and Mdm2 for Cancer Therapy
Date: 2017-01-18 00:00:00

About half of human cancers show normal TP53 gene and aberrant overexpression
of Mdm2 and/or MdmX. This fact promotes a promising cancer therapeutic strategy
which targeting the interactions between p53 and Mdm2/MdmX. For developing the
inhibitors to disrupt the p53-Mdm2/MdmX interactions, we systematically
investigate structural and interaction characteristics of p53 and inhibitors
with Mdm2 and MdmX from atomistic level by exploiting stochastic molecular
dynamics simulations. We find that some specific $\alpha$ helices in Mdm2 and
MdmX structure play key role in their bindings with inhibitors and the hydrogen
bond formed by residue Trp23 of p53 with its counterpart in Mdm2/MdmX
determines dynamical competition processes of the disruption of Mdm2-p53
interaction and replacement of p53 from Mdm2-p53 complex {\it in vivo}. We hope
that the results reported in this paper provide basic information for designing
functional inhibitors and realizing cancer gene therapy.
Mean-Reverting Portfolio Design with Budget Constraint
Date: 2017-01-18 00:00:00

This paper considers the mean-reverting portfolio design problem arising from
statistical arbitrage in the financial markets. We first propose a general
problem formulation aimed at finding a portfolio of underlying component assets
by optimizing a mean-reversion criterion characterizing the mean-reversion
strength, taking into consideration the variance of the portfolio and an
investment budget constraint. Then several specific problems are considered
based on the general formulation, and efficient algorithms are proposed.
Numerical results on both synthetic and market data show that our proposed
mean-reverting portfolio design methods can generate consistent profits and
outperform the traditional design methods and the benchmark methods in the
literature.
Individual versus collective cognition in social insects
Date: 2017-01-17 00:00:00

The concerted responses of eusocial insects to environmental stimuli are
often referred to as collective cognition on the level of the colony.To achieve
collective cognitiona group can draw on two different sources: individual
cognitionand the connectivity between individuals.Computation in
neural-networks, for example,is attributedmore tosophisticated communication
schemes than to the complexity of individual neurons. The case of social
insects, however, can be expected to differ. This is since individual insects
are cognitively capable units that are often able to process information that
is directly relevant at the level of the colony.Furthermore, involved
communication patterns seem difficult to implement in a group of insects since
these lack clear network structure.This review discusses links between the
cognition of an individual insect and that of the colony. We provide examples
for collective cognition whose sources span the full spectrum between
amplification of individual insect cognition and emergent group-level
processes.
A geometrical imaging of the real gap between economies of China and the United States
Date: 2019-04-24 00:00:00
Tasks: Geometrical View

GDP of China is about 11 trillion dollars and GDP of the United States is
about 18 trillion dollars. Suppose that we know for the coming years, economy
of the US will experience a real growth rate equal to \%3 and economy of China
will experience a real growth as of \%6. Now, the question is how long does it
take for economy of China to catch the economy of the United States. The early
impression is that the desired time is the answer of the equation
$11\times1.06^X=18\times1.03^X$. The correct answer however is quite different.
GDP is not a simple number and the gap between two countries can not be
addressed simply through their sizes. It is rather a geometrical object.
Countries pass different paths in the space of production. The gaps between GDP
of different countries depend on the path that each country passes through and
local metric. To address distance between economies of China and of the US we
need to know their utility preferences and the path that China passes to reach
the US size. The true gap then can be found if we calculate local metric along
this path. It resembles impressions about measurements in the General Theory of
Relativity. Path dependency of aggregate indexes is widely discussed in the
Index Number Theory. Our aim is to stick to the geometrical view presented in
the General Relativity to provide a visual understanding of the matter. We show
that different elements in the general relativity have their own counterparts
in economics. We claim that national agencies who provide aggregate data
resemble falling observers into a curved space time. It is while the World Bank
or international organizations are outside observers. The vision provided here,
leaves readers with a clear conclusion. If China keeps its growth rate, then
the economy of China should catch the economy of the United States sooner than
what we expect.
Integrated information and dimensionality in continuous attractor dynamics
Date: 2017-01-20 00:00:00
Tasks: Time Series Analysis

There has been increasing interest in the integrated information theory (IIT)
ofconsciousness, which hypothesizes that consciousness is integrated
information withinneuronal dynamics. However, the current formulation of IIT
poses both practical andtheoretical problems when we aim to empirically test
the theory by computingintegrated information from neuronal signals. For
example, measuring integratedinformation requires observing all the elements in
the considered system at the sametime, but this is practically rather
difficult. In addition, the interpretation of the spatialpartition needed to
compute integrated information becomes vague in continuous time-series
variables due to a general property of nonlinear dynamical systems known
as"embedding." Here, we propose that some aspects of such problems are resolved
byconsidering the topological dimensionality of shared attractor dynamics as an
indicatorof integrated information in continuous attractor dynamics. In this
formulation, theeffects of unobserved nodes on the attractor dynamics can be
reconstructed using atechnique called delay embedding, which allows us to
identify the dimensionality of anembedded attractor from partial observations.
We propose that the topologicaldimensionality represents a critical property of
integrated information, as it is invariantto general coordinate
transformations. We illustrate this new framework with simpleexamples and
discuss how it fits together with recent findings based on neuralrecordings
from awake and anesthetized animals. This topological approach extendsthe
existing notions of IIT to continuous dynamical systems and offers a
much-neededframework for testing the theory with experimental data by
substantially relaxing theconditions required for evaluating integrated
information in real neural systems.
Dynamic Prize Linked Savings: Maximizing Savings and Managing Risk
Date: 2017-01-18 00:00:00

Prize linked savings accounts provide a return in the form of randomly chosen
accounts receiving large cash prizes, in lieu of a guaranteed and uniform
interest rate. This model became legal for American national banks upon
bipartisan passage of the American Savings Promotion Act in December 2014, and
many states have deregulated this option for state chartered banks and credit
unions in recent years. Prize linked savings programs have unique appeal and
proven societal benefits, but the product is still not available to the vast
majority of Americans. There is demonstrated interest in these products, but
the supply side may be the bottleneck, because the prevailing consensus is that
prize linked savings primarily appeal to low income consumers. This paper
examines a less common, dynamic prize, model of prize linked savings and shows
why it might result in a larger average account size. The paper proposes three
methods of managing risk under this model, and tests two of them using a Monte
Carlo simulation. We conclude that both tested methods are effective at
mitigating the most severe risks.
Intermittent Preventive Treatment (IPT): Its role in averting disease-induced mortalities in children and in promoting the spread of antimalarial drug resistance
Date: 2017-01-18 00:00:00

We develop a variable population age-structured ODE model to investigate the
role of Intermittent Preventive Treatment (IPT) in averting malaria-induced
mortalities in children, as well as its related cost in promoting the spread of
anti-malarial drug resistance. IPT, a malaria control strategy in which a full
curative dose of an antimalarial medication is administered to vulnerable
asymptomatic individuals at specified intervals, has been shown to have a
positive impact on reducing malaria transmission and deaths in children and
pregnant women. However, it can also promote drug resistance spread. Our
mathematical model is used to explore IPT effects on drug resistance in
holoendemic malaria regions while quantifying the benefits in deaths averted.
Our model includes both drug-sensitive and drug-resistant strains of the
parasite as well as interactions between human hosts and mosquitoes. The basic
reproduction numbers for both strains as well as the invasion reproduction
numbers are derived and used to examine the role of IPT on drug resistance.
Numerical simulations show the individual and combined effects of IPT and
treatment of symptomatic infections on the prevalence levels of both parasite
strains and on the number of lives saved. The results suggest that while IPT
can indeed save lives, particularly in the high transmission region, certain
combinations of drugs used for IPT and drugs used to treat symptomatic
infection may result in more deaths when resistant parasite strains are
circulating. Moreover, the half-lives of the treatment and IPT drugs used play
an important role in the extent to which IPT may influence the rate of spread
of the resistant strain. A sensitivity analysis indicates the model outcomes
are most sensitive to the reduction factor of transmission for the resistant
strain, rate of immunity loss, and the clearance rate of sensitive infections.
Climate change alters diffusion of forest pest: A model study
Date: 2017-01-19 00:00:00

Population dynamics with spatial information is applied to understand the
spread of pests. We introduce a model describing how pests spread in discrete
space. The number of pest descendants at each site is controlled by local
information such as temperature, precipitation, and the density of pine trees.
Our simulation leads to a pest spreading pattern comparable to the real data
for pine needle gall midge in the past. We also simulate the model in two
different climate conditions based on two different representative
concentration pathways scenarios for the future. We observe that after an
initial stage of a slow spread of pests, a sudden change in the spreading speed
occurs, which is soon followed by a large-scale outbreak. We found that a
future climate change causes the outbreak point to occur earlier and that the
detailed spatio-temporal pattern of the spread depends on the source position
from which the initial pest infection starts.
A mechanistic model quantifies artemisinin-induced parasite growth retardation in blood-stage Plasmodium falciparum infection
Date: 2017-01-19 00:00:00

Falciparum malaria is a major parasitic disease causing widespread morbidity
and mortality globally. Artemisinin derivatives---the most effective and
widely-used antimalarials that have helped reduce the burden of malaria by 60%
in some areas over the past decade---have recently been found to induce growth
retardation of blood-stage Plasmodium falciparum when applied at clinically
relevant concentrations. To date, no model has been designed to quantify the
growth retardation effect and to predict the influence of this property on in
vivo parasite killing. Here we introduce a mechanistic model of parasite growth
from the ring to trophozoite stage of the parasite's life cycle, and by
modelling the level of staining with an RNA-binding dye, we demonstrate that
the model is able to reproduce fluorescence distribution data from in vitro
experiments using the laboratory 3D7 strain. We quantify the dependence of
growth retardation on drug concentration and demonstrate the model's utility as
a platform to propose experimentally-testable mechanisms of growth retardation.
Furthermore we illustrate that a drug-induced delay in growth may significantly
influence in vivo parasite dynamics, demonstrating the importance of
considering growth retardation in the design of optimal artemisinin-based
dosing regimens.
IRE1 alpha may be causing abnormal loss of p53 at post transcriptional level in chronic myeloid leukemia
Date: 2017-01-19 00:00:00
Tasks: Prognosis

Current treatment strategy for chronic myeloid leukemia (CML) mainly includes
inhibition of tyrosine kinase activity, which has dramatically improved the
prognosis of the disease but without cure. In addition some patients may become
drug resistant. Thus there is still the need for other therapies to avoid
resistance and if possible to cure the disease. Loss of p53 is known to play an
important role in the disease progression of CML and causes drug resistance.
Here I propose that in CML, inositol requiring enzyme 1 alpha (IRE1 alpha) may
cause abnormal degradation of p53 mRNA resulting in inhibition of apoptosis in
leukemic clonal cells, which has not been elucidated before. Hence, I propose
that inhibition of endoribonuclease activity of IRE1 alpha with small molecule
inhibitors may provide a novel strategy to enhance p53 function in CML leukemic
clones to overcome the limitations of current treatment regimens.
Translating ceRNA susceptibilities into correlation functions
Date: 2017-01-19 00:00:00

Competition to bind microRNAs induces an effective positive crosstalk between
their targets, therefore known as `competing endogenous RNAs' or ceRNAs. While
such an effect is known to play a significant role in specific conditions,
estimating its strength from data and, experimentally, in physiological
conditions appears to be far from simple. Here we show that the susceptibility
of ceRNAs to different types of perturbations affecting their competitors (and
hence their tendency to crosstalk) can be encoded in quantities as intuitive
and as simple to measure as correlation functions. We confirm this scenario by
extensive numerical simulations and validate it by re-analyzing PTEN's
crosstalk pattern from TCGA breast cancer dataset. These results clarify the
links between different quantities used to estimate the intensity of ceRNA
crosstalk and provide new keys to analyze transcriptional datasets and
effectively probe ceRNA networks in silico.
The competitive nature of STAT complex formation drives phenotype switching of T cells
Date: 2017-01-19 00:00:00

Signal transducers and activators of transcription (STATs) are key molecular
determinants of T cell fate and effector function. A number of inflammatory
diseases are characterized by an altered balance of T cell phenotypes and
cytokine secretion. STATs, therefore, represent viable therapeutic targets in
numerous pathologies. However, the underlying mechanisms of how the same STAT
proteins regulate both the development of different T cell phenotypes and their
plasticity during changes in extracellular conditions remain unclear. In this
study, we investigated the STAT mediated regulation of T cell phenotype
formation and plasticity using mathematical modeling and experimental data for
intracellular STAT signaling proteins. The close fit of our model predictions
to the experimental data for IFN-{\gamma} to IL-10 switching allows us to
propose a potential mechanism for T cell switching that regulates human Th1/Tr1
responses. According to this mechanism, T cell phenotype switching is due to
the relative redistribution of STAT dimer complexes caused by the extracellular
cytokine-dependent STAT competition effects. The proposed model is applicable
to a number of STAT signaling circuits.
Convolutional Neural Networks for Automated Annotation of Cellular Cryo-Electron Tomograms
Date: 2017-06-11 00:00:00
Tasks: Cryogenic Electron Tomography

Cellular Electron Cryotomography (CryoET) offers the ability to look inside
cells and observe macromolecules frozen in action. A primary challenge for this
technique is identifying and extracting the molecular components within the
crowded cellular environment. We introduce a method using neural networks to
dramatically reduce the time and human effort required for subcellular
annotation and feature extraction. Subsequent subtomogram classification and
averaging yields in-situ structures of molecular components of interest.
The Parallel Distributed Image Search Engine (ParaDISE)
Authors: Markonis Dimitrios, Schaer Roger, de Herrera Alba García Seco, Müller Henning
Date: 2017-01-19 00:00:00
Tasks: Image Retrieval, Medical Image Retrieval, Retrieval

Image retrieval is a complex task that differs according to the context and
the user requirements in any specific field, for example in a medical
environment. Search by text is often not possible or optimal and retrieval by
the visual content does not always succeed in modelling high-level concepts
that a user is looking for. Modern image retrieval techniques consist of
multiple steps and aim to retrieve information from large--scale datasets and
not only based on global image appearance but local features and if possible in
a connection between visual features and text or semantics. This paper presents
the Parallel Distributed Image Search Engine (ParaDISE), an image retrieval
system that combines visual search with text--based retrieval and that is
available as open source and free of charge. The main design concepts of
ParaDISE are flexibility, expandability, scalability and interoperability.
These concepts constitute the system, able to be used both in real-world
applications and as an image retrieval research platform. Apart from the
architecture and the implementation of the system, two use cases are described,
an application of ParaDISE in retrieval of images from the medical literature
and a visual feature evaluation for medical image retrieval. Future steps
include the creation of an open source community that will contribute and
expand this platform based on the existing parts.
Increased Efficiency in the Second-Hand Tire Trade Provides Opportunity for Dengue Control
Date: 2017-01-19 00:00:00

Dengue fever is increasing in geographical range, spread by invasion of its
vector mosquitoes. The trade in second-hand tires has been implicated as a
factor in this process as they act as mobile reservoirs of mosquito eggs and
larvae. Regional transportation of tires can create linkages between rural
areas with dengue to disease-free urban areas, potentially giving rise to
outbreaks even in areas with strong local control measures. In this work we
sought to model the dynamics of mosquito transportation via the tire trade, in
particular to predict its role in causing unexpected dengue outbreaks through
vertical transmission of the virus across generations of mosquitoes. We also
aimed to identify strategies for regulating the trade in second-hand tires,
improving disease control. We created a mathematical model which captures the
dynamics of dengue between rural and urban areas, taking into account the
movement, storage time of tires, and mosquito diapause. We simulate a series of
scenarios. First a mosquito population is introduced to a dengue-free area via
movement of tires, either as single or multiple events, increasing the
likelihood of a dengue outbreak. An endemic state can be induced regardless of
whether urban conditions for an outbreak are met, and an existing endemic state
can be enhanced by vector input. Finally we assess the potential for regulation
of tire processing as a means of reducing the transmission of dengue fever
using a specific case study from Puerto Rico. Our work demonstrates the
importance of the second-hand tire trade in modulating the spread of dengue
fever across regions, in particular its role in introducing dengue to
disease-free areas. We propose that regulation of tire storage and movement can
play a crucial role in containing outbreaks and dengue spread.
Topological transformations in proteins: effects of heating and proximity of an interface
Date: 2017-01-19 00:00:00

Using a structure-based coarse-grained model of proteins, we study the
mechanism of unfolding of knotted proteins through heating. We find that the
dominant mechanisms of unfolding depend on the temperature applied and are
generally distinct from those identified for folding at its optimal
temperature. In particular, for shallowly knotted proteins, folding usually
involves formation of two loops whereas unfolding through high-temperature
heating is dominated by untying of single loops. Untying the knots is found to
generally precede unfolding unless the protein is deeply knotted and the
heating temperature exceeds a threshold value. We then use a phenomenological
model of the air-water interface to show that such an interface can untie
shallow knots, but it can also make knots in proteins that are natively
unknotted.
Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution
Date: 2017-01-19 00:00:00
Tasks: Model Selection

We re-examined data from the classic Luria-Delbruck fluctuation experiment,
which is often credited with establishing a Darwinian basis for evolution. We
argue that, for the Lamarckian model of evolution to be ruled out by the
experiment, the experiment must favor pure Darwinian evolution over both the
Lamarckian model and a model that allows both Darwinian and Lamarckian
mechanisms. Analysis of the combined model was not performed in the original
1943 paper. The Luria-Delbruck paper also did not consider the possibility of
neither model fitting the experiment. Using Bayesian model selection, we find
that the Luria-Delbruck experiment, indeed, favors the Darwinian evolution over
purely Lamarckian. However, our analysis does not rule out the combined model,
and hence cannot rule out Lamarckian contributions to the evolutionary
dynamics.