pwc-archive/papers-with-abstracts huggingface dataset:
Chemical Heredity as Group Selection at the Molecular Level
Date: 2018-02-22 00:00:00

Many examples of cooperation exist in biology. In chemical systems however,
which can sometimes be quite complex, we do not appear to observe intricate
cooperative interactions. A key question for the origin of life, is then how
can molecular cooperation first arise in an abiotic system prior to the
emergence of biological replication. We postulate that selection at the
molecular level is a driving force behind the complexification of chemical
systems, particularly during the origins of life. In the theory of multilevel
selection the two selective forces are: within-group and between-group, where
the former tends to favor "selfish" replication of individuals and the latter
favor cooperation between individuals enhancing the replication of the group as
a whole. These forces can be quantified using the Price equation, which is a
standard tool used in evolutionary biology to quantify evolutionary change. Our
central claim is that replication and heredity in chemical systems are subject
to selection, and quantifiable using the multilevel Price equation. We
demonstrate this using the Graded Autocatalysis Replication Domain computer
model, describing simple protocell composed out of molecules and its
replication, which respectively analogue to the group and the individuals. In
contrast to previous treatments of this model, we treat the lipid molecules
themselves as replicating individuals and the protocells they form as groups of
individuals. Our goal is to demonstrate how evolutionary biology tools and
concepts can be applied in chemistry and we suggest that molecular cooperation
may arise as a result of group selection. Further, the biological relation of
parent-progeny is proposed to be analogue to the reactant-product relation in
chemistry, thus allowing for tools from evolutionary biology to be applied to
chemistry and would deepen the connection between chemistry and biology.
MEC-assisted End-to-End Latency Evaluations for C-V2X Communications
Date: 2018-02-22 00:00:00
Tasks: Edge-computing

The efficient design of fifth generation (5G) mobile networks is driven by
the need to support the dynamic proliferation of several vertical market
segments. Considering the automotive sector, different Cellular
Vehicle-to-Everything (C-V2X) use cases have been identified by the industrial
and research world, referring to infotainment, automated driving and road
safety. A common characteristic of these use cases is the need to exploit
collective awareness of the road environment towards satisfying performance
requirements. One of these requirements is the End-to-End (E2E) latency when,
for instance, Vulnerable Road Users (VRUs) inform vehicles about their status
(e.g., location) and activity, assisted by the cellular network. In this paper,
focusing on a freeway-based VRU scenario, we argue that, in contrast to
conventional, remote cloud-based cellular architecture, the deployment of
Multi-access Edge Computing (MEC) infrastructure can substantially prune the
E2E communication latency. Our argument is supported by an extensive
simulation-based performance comparison between the conventional and the
MEC-assisted network architecture.
On the Effects of Resistive and Reactive Loads on Signal Amplification
Date: 2018-02-12 00:00:00

The effects of reactive loads into amplification is studied. A simplified
common emitter circuit configuration was adopted and respective
time-independent and time-dependent voltage and current equations were
obtained. As phasor analysis cannot be used because of the non-linearity, the
voltage at the capacitor was represented in terms of the respective integral,
implying a numerical approach. The effect of purely resistive loads was
investigated first, and it was shown that the fanned structure of the
transistor isolines can severely distort the amplification, especially for
$V_a$ small and $s$ large. The total harmonic distortion was found not to
depend on $V_a$, being determined by $s$ and the load resistance $R$. An
expression was obtained for the current gain in terms of the base current and
it was shown that it decreases in an almost perfectly linearly fashion with
$I_B$. Remarkably, no gain variation, and hence perfectly linear amplification,
is obtained when $R=0$, provided maximum power dissipation limits are not
exceeded. Capacitive loads imply the detachment of the circuit trajectory from
a straight line to an "ellipsoidal"-like loop. This implies a gain asymmetry
along upper or lower arcs of this loop. By using the time-dependent circuit
equations, it was possible to show numerically and by an analytical
approximation that, at least for the adopted circuit and parameter values, the
asymmetry induced by capacitive loads is not substantial. However, capacitive
loads will imply lag between the output voltage and current and, hence,
low-pass filtering. It was shown that smaller $V_a$ and larger $s$ can
substantially reduce the phase lag, but at the cost of severe distortion.
Analysis of Fourier ptychographic microscopy with half of the captured images
Date: 2018-02-20 00:00:00

Fourier ptychography microscopy (FPM) is a new computational imaging
technique that can provide gigapixel images with both high resolution and a
wide field of view (FOV). However, time consuming of the data-acquisition
process is a critical issue. In this paper, we make an analysis on the FPM
imaging system with half number of the captured images. Based on the image
analysis of the conventional FPM system, we then compare the reconstructed
images with different number of captured data. Simulation and experiment
results show that the reconstructed image with half number captured data do not
show obvious resolution degradation compared to that with all the captured
data, except a contrast reduction. In particular in the case when the object is
close to phase-only/amplitude only, the quality of the reconstructed image with
half of the captured data is nearly as good as the one reconstructed with full
data.
SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis
Date: 2018-02-22 00:00:00

Bakground: With the proliferation of available microarray and high throughput
sequencing experiments in the public domain, the use of meta-analysis methods
increases. In these experiments, where the sample size is often limited,
meta-analysis offers the possibility to considerably enhance the statistical
power and give more accurate results. For those purposes, it combines either
effect sizes or results of single studies in a appropriate manner. R packages
metaMA and metaRNASeq perform meta-analysis on microarray and NGS data,
respectively. They are not interchangeable as they rely on statistical modeling
specific to each technology.
  Results: SMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates
metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to
carry out meta-analysis of gene expression data, while taking care of their
specificities. We have developed this tool suite to analyse microarray data
from Gene Expression Omnibus (GEO) database or custom data from affymetrix
microarrays. These data are then combined to carry out meta-analysis using
metaMA package. SMAGEXP also offers to combine raw read counts from Next
Generation Sequencing (NGS) experiments using DESeq2 and metaRNASeq package. In
both cases, key values, independent from the technology type, are reported to
judge the quality of the meta-analysis. These tools are available on the Galaxy
main tool shed. Source code, help and installation instructions are available
on github.
  Conclusion: The use of Galaxy offers an easy-to-use gene expression
meta-analysis tool suite based on the metaMA and metaRNASeq packages.
Implementing a Concept Network Model
Date: 2019-03-20 00:00:00
Tasks: Diversity, model

The same concept can mean different things or be instantiated in different
forms depending on context, suggesting a degree of flexibility within the
conceptual system. We propose that a compositional network model can be used to
capture and predict this flexibility. We modeled individual concepts (e.g.,
BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g.,
YELLOW, SWEET) were represented as nodes and their associations as edges. In
this framework, networks capture the within-concept statistics that reflect how
properties correlate with each other across instances of a concept. We ran a
classification analysis using graph eigendecomposition to validate these
models, and find that these models can successfully discriminate between object
concepts. We then computed formal measures from these concept networks and
explored their relationship to conceptual structure. We find that diversity
coefficients and core-periphery structure can be interpreted as network-based
measures of conceptual flexibility and stability, respectively. These results
support the feasibility of a concept network framework and highlight its
ability to formally capture important characteristics of the conceptual system.
Application of Multivariate Data Analysis to machine power measurements as a means of tool life Predictive Maintenance for reducing product waste
Date: 2018-02-26 00:00:00
Tasks: Decision Making, Scheduling

Modern manufacturing industries are increasingly looking to predictive
analytics to gain decision making information from process data. This is driven
by high levels of competition and a need to reduce operating costs. The
presented work takes data in the form of a power measurement recorded during a
medical device manufacturing process and uses multivariate data analysis (MVDA)
to extract information leading to the proposal of a predictive maintenance
scheduling algorithm. The proposed MVDA model was able to predict with 100 %
accuracy the condition of a grinding tool.
Time Consistent Stopping For The Mean-Standard Deviation Problem --- The Discrete Time Case
Date: 2019-04-19 00:00:00

Inspired by Strotz's consistent planning strategy, we formulate the infinite
horizon mean-variance stopping problem as a subgame perfect Nash equilibrium in
order to determine time consistent strategies with no regret. Equilibria among
stopping times or randomized stopping times may not exist. This motivates us to
consider the notion of liquidation strategies, which lets the stopping right to
be divisible. We then argue that the mean-standard deviation variant of this
problem makes more sense for this type of strategies in terms of time
consistency. It turns out that an equilibrium liquidation strategy always
exists. We then analyze whether optimal equilibrium liquidation strategies
exist and whether they are unique and observe that neither may hold.
Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
Authors: Liu Feng, Tang Ruiming, Li Xutao, Ye Yunming, Guo Huifeng, He Xiuqiang
Date: 2018-02-23 00:00:00
Tasks: Decision Making, Information Retrieval, Retrieval, Sequential Decision Making

Recently, some studies have utilized the Markov Decision Process for
diversifying (MDP-DIV) the search results in information retrieval. Though
promising performances can be delivered, MDP-DIV suffers from a very slow
convergence, which hinders its usability in real applications. In this paper,
we aim to promote the performance of MDP-DIV by speeding up the convergence
rate without much accuracy sacrifice. The slow convergence is incurred by two
main reasons: the large action space and data scarcity. On the one hand, the
sequential decision making at each position needs to evaluate the
query-document relevance for all the candidate set, which results in a huge
searching space for MDP; on the other hand, due to the data scarcity, the agent
has to proceed more "trial and error" interactions with the environment. To
tackle this problem, we propose MDP-DIV-kNN and MDP-DIV-NTN methods. The
MDP-DIV-kNN method adopts a $k$ nearest neighbor strategy, i.e., discarding the
$k$ nearest neighbors of the recently-selected action (document), to reduce the
diversification searching space. The MDP-DIV-NTN employs a pre-trained
diversification neural tensor network (NTN-DIV) as the evaluation model, and
combines the results with MDP to produce the final ranking solution. The
experiment results demonstrate that the two proposed methods indeed accelerate
the convergence rate of the MDP-DIV, which is 3x faster, while the accuracies
produced barely degrade, or even are better.
Market Impact: A Systematic Study of Limit Orders
Authors: Emilio Said, Ahmed Bel Hadj Ayed, Alexandre Husson, Frédéric Abergel
Date: 2018-02-23 00:00:00

This paper is devoted to the important yet little explored subject of the market impact of limit orders. Our analysis is based on a proprietary database of metaorders - large orders that are split into smaller pieces before being sent to the market. We first address the case of aggressive limit orders and then, that of passive limit orders. In both cases, we provide empirical evidence of a power law behaviour for the temporary market impact. The relaxation of the price following the end of the metaorder is also studied, and the long-term impact is shown to stabilize at a level of approximately two-thirds of the maximum impact. Finally, a fair pricing condition during the life cycle of the metaorders is empirically validated.
Total Array Gains of Millimeter-Wave Mobile Phone Antennas Under Practical Conditions
Date: 2018-02-23 00:00:00

This paper studies a gain of an antenna array embedded on a mobile device
operating at a millimeter-wave radio frequency. Assuming that mobile phones at
millimeter-wave range operate with a single baseband unit and analog
beamforming like phased arrays, we define a total array gain denoting a path
gain of the phased antenna array in excess to the omni-directional path gain.
The total array gain circumvents the ambiguity of conventional array gain which
cannot be uniquely defined as there are multiple choices of a reference
single-element antenna in an array. Two types of 8-element patch antenna arrays
implemented on a mobile phone chassis, i.e., uniform linear array (ULA) and
distributed array (DA) both operating at 60 GHz, are studied. The gain
evaluated in a small-cell scenario in an airport shows that DA achieves higher
median and outage gain by up to 8 and 6 dB than ULA when different orientations
of the mobile phone are considered along with body torso and finger shadowing.
There are always postures of the mobile phone where ULA cannot see the
line-of-sight due to directionality of the patch antenna and of body and finger
shadowing, leading to outage gain of -15 dB in the worst case. The DA has much
smaller variation of the gain across different orientations of the phone, even
when the human torso shadowing and user's finger effects are considered.
An investigation of the classifiers to detect android malicious apps
Authors: Sharma Ashu, Sahay Sanjay K.
Date: 2018-02-23 00:00:00
Tasks: Malware Detection

Android devices are growing exponentially and are connected through the
internet accessing billion of online websites. The popularity of these devices
encourages malware developer to penetrate the market with malicious apps to
annoy and disrupt the victim. Although, for the detection of malicious apps
different approaches are discussed. However, proposed approaches are not
suffice to detect the advanced malware to limit/prevent the damages. In this,
very few approaches are based on opcode occurrence to classify the malicious
apps. Therefore, this paper investigates the five classifiers using opcodes
occurrence as the prominent features for the detection of malicious apps. For
the analysis, we use WEKA tool and found that FT detection accuracy (79.27%) is
best among the investigated classifiers. However, true positives rate i.e.
malware detection rate is highest (99.91%) by RF and fluctuate least with the
different number of prominent features compared to other studied classifiers.
The analysis shows that overall accuracy is majorly affected by the false
positives of the classifier.
PV-Powered CoMP-Based Green Cellular Networks with a Standby Grid Supply
Date: 2018-02-23 00:00:00
Tasks: energy management, Management

This paper proposes a novel framework for PV-powered cellular networks with a
standby grid supply and an essential energy management technique for achieving
envisaged green networks. The proposal considers an emerging cellular network
architecture employing two types of coordinated multipoint (CoMP) transmission
techniques for serving the subscribers. Under the proposed framework, each base
station (BS) is powered by an individual PV solar energy module having an
independent storage device. BSs are also connected to the conventional grid
supply for meeting additional energy demand. We also propose a dynamic inter-BS
solar energy sharing policy through a transmission line for further greening
the proposed network by minimizing the consumption from the grid supply. An
extensive simulation-based study in the downlink of a Long-Term Evolution (LTE)
cellular system is carried out for evaluating the energy efficiency performance
of the proposed framework. System performance is also investigated for
identifying the impact of various system parameters including storage factor,
storage capacity, solar generation capacity, transmission line loss, and
different CoMP techniques.
Kinematic Flexibility Analysis: Hydrogen Bonding Patterns Impart a Spatial Hierarchy of Protein Motion
Date: 2018-02-23 00:00:00

Elastic network models (ENM) and constraint-based, topological rigidity
analysis are two distinct, coarse-grained approaches to study conformational
flexibility of macromolecules. In the two decades since their introduction,
both have contributed significantly to insights into protein molecular
mechanisms and function. However, despite a shared purpose of these approaches,
the topological nature of rigidity analysis, and thereby the absence of motion
modes, has impeded a direct comparison. Here, we present an alternative,
kinematic approach to rigidity analysis, which circumvents these drawbacks. We
introduce a novel protein hydrogen bond network spectral decomposition, which
provides an orthonormal basis for collective motions modulated by non-covalent
interactions, analogous to the eigenspectrum of normal modes, and decomposes
proteins into rigid clusters identical to those from topological rigidity. Our
kinematic flexibility analysis bridges topological rigidity theory and ENM, and
enables a detailed analysis of motion modes obtained from both approaches. Our
analysis reveals that collectivity of protein motions, reported by the Shannon
entropy, is significantly lower for rigidity theory versus normal mode
approaches. Strikingly, kinematic flexibility analysis suggests that the
hydrogen bonding network encodes a protein-fold specific, spatial hierarchy of
motions, which goes nearly undetected in ENM. This hierarchy reveals distinct
motion regimes that rationalize protein stiffness changes observed from
experiment and molecular dynamics simulations. A formal expression for changes
in free energy derived from the spectral decomposition indicates that motions
across nearly 40% of modes obey enthalpy-entropy compensation. Taken together,
our analysis suggests that hydrogen bond networks have evolved to modulate
protein structure and dynamics.
Biophysical inference of epistasis and the effects of mutations on protein stability and function
Date: 2018-03-30 00:00:00

Understanding the relationship between protein sequence, function, and
stability is a fundamental problem in biology. While high-throughput methods
have produced large numbers of sequence-function pairs, functional assays do
not distinguish whether mutations directly affect function or are destabilizing
the protein. Here, we introduce a statistical method to infer the underlying
biophysics from a high-throughput binding assay by combining information from
many mutated variants. We fit a thermodynamic model describing the bound,
unbound, and unfolded states to high quality data of protein G domain B1
binding to IgG-Fc. We infer an energy landscape with distinct folding and
binding energies for each substitution providing a detailed view of how
mutations affect binding and stability across the protein. We accurately infer
folding energy of each variant in physical units, validated by independent
data, whereas previous high-throughput methods could only measure indirect
changes in stability. While we assume an additive sequence-energy relationship,
the binding fraction is epistatic due its non-linear relation to energy.
Despite having no epistasis in energy, our model explains much of the observed
epistasis in binding fraction, with the remaining epistasis identifying
conformationally dynamic regions.
Data-driven brain network models predict individual variability in behavior
Date: 2018-02-23 00:00:00

The relationship between brain structure and function has been probed using a
variety of approaches, but how the underlying structural connectivity of the
human brain drives behavior is far from understood. To investigate the effect
of anatomical brain organization on human task performance, we use a
data-driven computational modeling approach and explore the functional effects
of naturally occurring structural differences in brain networks. We construct
personalized brain network models by combining anatomical connectivity
estimated from diffusion spectrum imaging of individual subjects with a
nonlinear model of brain dynamics. By performing computational experiments in
which we measure the excitability of the global brain network and spread of
synchronization following a targeted computational stimulation, we quantify how
individual variation in the underlying connectivity impacts both local and
global brain dynamics. We further relate the computational results to
individual variability in the subjects' performance of three language-demanding
tasks both before and after transcranial magnetic stimulation to the
left-inferior frontal gyrus. Our results show that task performance correlates
with either local or global measures of functional activity, depending on the
complexity of the task. By emphasizing differences in the underlying structural
connectivity, our model serves as a powerful tool to predict individual
differences in task performances, to dissociate the effect of targeted
stimulation in tasks that differ in cognitive complexity, and to pave the way
for the development of personalized therapeutics.
Multispectral Focal Stack Acquisition Using A Chromatic Aberration Enlarged Camera
Date: 2018-02-24 00:00:00

Capturing more information, e.g. geometry and material, using optical cameras
can greatly help the perception and understanding of complex scenes. This paper
proposes a novel method to capture the spectral and light field information
simultaneously. By using a delicately designed chromatic aberration enlarged
camera, the spectral-varying slices at different depths of the scene can be
easily captured. Afterwards, the multispectral focal stack, which is composed
of a stack of multispectral slice images focusing on different depths, can be
recovered from the spectral-varying slices by using a Local Linear
Transformation (LLT) based algorithm. The experiments verify the effectiveness
of the proposed method.