pwc-archive/papers-with-abstracts huggingface dataset:
Regulation of Pom cluster dynamics in Myxococcus xanthus
Date: 2018-01-18 00:00:00
Tasks: Friction

Precise positioning of the cell division site is essential for the correct
segregation of the genetic material into the two daughter cells. In the
bacterium Myxococcus xanthus, the proteins PomX and PomY form a cluster on the
chromosome that performs a biased random walk to midcell and positively
regulates cell division there. PomZ, an ATPase, is necessary for tethering of
the cluster to the nucleoid and regulates its movement towards midcell. It has
remained unclear how the cluster dynamics change when the biochemical
parameters, such as the attachment rates of PomZ to the nucleoid and the
cluster, the ATP hydrolysis rate of PomZ or the mobility of PomZ dimers
interacting with the nucleoid and cluster, are varied. To answer these
questions, we investigate a one-dimensional model that includes the nucleoid,
the Pom cluster and the PomZ protein. We find that a mechanism based on the
diffusive PomZ fluxes on the nucleoid into the cluster can explain the latter's
midnucleoid localization for a broad parameter range. Furthermore, there is an
ATP hydrolysis rate that minimizes the time the cluster needs to reach
midnucleoid. If the dynamics of PomZ dimers on the nucleoid is slow relative to
the cluster's velocity, we observe oscillatory cluster movements around
midnucleoid. To understand midnucleoid localization, we developed a
semi-analytical approach that dissects the net movement of the cluster into its
components: the difference in PomZ fluxes into the cluster from either side,
the force exerted by a single PomZ dimer on the cluster and the effective
friction coefficient of the cluster. Importantly, we predict that the Pom
cluster oscillates around midnucleoid if the diffusivity of PomZ on the
nucleoid is reduced. A similar approach to that applied here may also prove
useful for cargo localization in ParABS systems.
A First Option Calibration of the GARCH Diffusion Model by a PDE Method
Date: 2018-01-18 00:00:00
Tasks: Time Series, Time Series Analysis

Time-series calibrations often suggest that the GARCH diffusion model could
also be a suitable candidate for option (risk-neutral) calibration. But unlike
the popular Heston model, it lacks a fast, semi-analytic solution for the
pricing of vanilla options, perhaps the main reason why it is not used in this
way. In this paper we show how an efficient finite difference-based PDE solver
can effectively replace analytical solutions, enabling accurate option
calibrations in less than a minute. The proposed pricing engine is shown to be
robust under a wide range of model parameters and combines smoothly with
black-box optimizers. We use this approach to produce a first PDE calibration
of the GARCH diffusion model to SPX options and present some benchmark results
for future reference.
Computational model of avian nervous system nuclei governing learned song
Date: 2018-01-18 00:00:00
Tasks: Functional Connectivity

The means by which neuronal activity yields robust behavior is a ubiquitous
question in neuroscience. In the songbird, the timing of a highly stereotyped
song motif is attributed to the cortical nucleus HVC, and to feedback to HVC
from downstream nuclei in the song motor pathway. Control of the acoustic
structure appears to be shared by various structures, whose functional
connectivity is largely unknown. Currently there exists no model for functional
synaptic architecture that links HVC to song output in a manner consistent with
experiments. Here we build on a previous model of HVC in which a distinct
functional architecture may act as a pattern generator to drive downstream
regions. Using a specific functional connectivity of the song motor pathway, we
show how this HVC mechanism can generate simple representations of the driving
forces for song. The model reproduces observed correlations between neuronal
and respiratory activity and acoustic features of song. It makes testable
predictions regarding the electrophysiology of distinct populations in the
robust nucleus of the arcopallium (RA), the connectivity within HVC and RA and
between them, and the activity patterns of vocal-respiratory neurons in the
brainstem.
Plagiarism: Taxonomy, Tools and Detection Techniques
Authors: Chowdhury Hussain A, Bhattacharyya Dhruba K
Date: 2018-01-19 00:00:00

To detect plagiarism of any form, it is essential to have broad knowledge of
its possible forms and classes, and existence of various tools and systems for
its detection. Based on impact or severity of damages, plagiarism may occur in
an article or in any production in a number of ways. This survey presents a
taxonomy of various plagiarism forms and include discussion on each of these
forms. Over the years, a good number tools and techniques have been introduced
to detect plagiarism. This paper highlights few promising methods for
plagiarism detection based on machine learning techniques. We analyse the pros
and cons of these methods and finally we highlight a list of issues and
research challenges related to this evolving research problem.
Disentangling population strategies of two cladocerans adapted to different ultraviolet regimes
Date: 2018-01-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

Zooplankton have evolved several mechanisms to deal with environmental
threats, such as ultraviolet radiation (UVR), and in order to identify
strategies inherent to organisms exposed to different UVR environments, we here
examine life-history traits of two lineages of Daphnia pulex. The lineages
differed in the UVR dose they had received at their place of origin from
extremely high UVR stress at high-altitude Bolivian lakes to low UVR stress
near the sea level in temperate Sweden. Nine life-history variables of each
lineage were analyzed in laboratory experiments in the presence and the absence
of sub-lethal doses of UVR (UV-A band), and we identified trade-offs among
variables through structural equation modeling (SEM). The UVR treatment was
detrimental to almost all life-history variables of both lineages; however, the
Daphnia historically exposed to higher doses of UVR (HighUV) showed a higher
overall fecundity than those historically exposed to lower doses of UVR
(LowUV). The total offspring and ephippia production, as well as the number of
clutches and number of offspring at first reproduction, was directly affected
by UVR in both lineages. Main differences between lineages involved indirect
effects that affected offspring production as the age at first reproduction. We
here show that organisms within the same species have developed different
strategies as responses to UVR, although no increased physiological tolerance
or plasticity was shown by the HighUV lineage. In addition to known tolerance
strategies to UVR, including avoidance, prevention, or repairing of damages, we
here propose a population strategy that includes early reproduction and high
fertility, which we show compensated for the fitness loss imposed by UVR
stress.
Predicting crypto-currencies using sparse non-Gaussian state space models
Date: 2018-02-13 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis

In this paper we forecast daily returns of crypto-currencies using a wide
variety of different econometric models. To capture salient features commonly
observed in financial time series like rapid changes in the conditional
variance, non-normality of the measurement errors and sharply increasing
trends, we develop a time-varying parameter VAR with t-distributed measurement
errors and stochastic volatility. To control for overparameterization, we rely
on the Bayesian literature on shrinkage priors that enables us to shrink
coefficients associated with irrelevant predictors and/or perform model
specification in a flexible manner. Using around one year of daily data we
perform a real-time forecasting exercise and investigate whether any of the
proposed models is able to outperform the naive random walk benchmark. To
assess the economic relevance of the forecasting gains produced by the proposed
models we moreover run a simple trading exercise.
Affine forward variance models
Date: 2018-10-30 00:00:00

We introduce the class of affine forward variance (AFV) models of which both
the conventional Heston model and the rough Heston model are special cases. We
show that AFV models can be characterized by the affine form of their cumulant
generating function, which can be obtained as solution of a convolution Riccati
equation. We further introduce the class of affine forward order flow intensity
(AFI) models, which are structurally similar to AFV models, but driven by jump
processes, and which include Hawkes-type models. We show that the cumulant
generating function of an AFI model satisfies a generalized convolution Riccati
equation and that a high-frequency limit of AFI models converges in
distribution to the AFV model.
Medical Photoacoustic Beamforming Using Minimum Variance-Based Delay Multiply and Sum
Date: 2018-01-18 00:00:00

Delay-and-Sum (DAS) beamformer is the most common beamforming algorithm in
Photoacoustic imaging (PAI) due to its simple implementation and real time
imaging. However, it provides poor resolution and high levels of sidelobe. A
new algorithm named Delay-Multiply-and-Sum (DMAS) was introduced. Using DMAS
leads to lower levels of sidelobe compared to DAS, but resolution is not
satisfying yet. In this paper, a novel beamformer is introduced based on the
combination of Minimum Variance (MV) adaptive beamforming and DMAS, so-called
Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS
equation leads to some terms which contain a DAS equation. It is proposed to
use MV adaptive beamformer instead of existing DAS inside the DMAS algebra
expansion. MVB-DMAS is evaluated numerically compared to DAS, DMAS and MV and
Signal-to-noise ratio (SNR) metric is presented. It is shown that MVB-DMAS
leads to higher image quality and SNR for about 13 dB, 3 dB and 2 dB in
comparison with DAS, DMAS and MV, respectively.
Ergodic robust maximization of asymptotic growth
Date: 2018-01-19 00:00:00

We consider the problem of robustly maximizing the growth rate of investor
wealth in the presence of model uncertainty. Possible models are all those
under which the assets' region $E$ and instantaneous covariation $c$ are known,
and where additionally the assets are stable in that their occupancy time
measures converge to a law with density $p$. This latter assumption is
motivated by the observed stability of ranked relative market capitalizations
for equity markets. We seek to identify the robust optimal growth rate, as well
as a trading strategy which achieves this rate in all models. Under minimal
assumptions upon $(E,c,p)$, we identify the robust growth rate with the
Donsker-Varadhan rate function from occupancy time Large Deviations theory. We
also prove existence of, and explicitly identify, the optimal trading strategy.
We then apply our results in the case of drift uncertainty for ranked relative
market capitalizations. Assuming regularity under symmetrization for the
covariance and limiting density of the ranked capitalizations, we explicitly
identify the robust optimal trading strategy in this setting.
Region of Interest (ROI) Coding for Aerial Surveillance Video using AVC & HEVC
Date: 2018-01-19 00:00:00
Tasks: Motion Compensation

Aerial surveillance from Unmanned Aerial Vehicles (UAVs), i.e. with moving
cameras, is of growing interest for police as well as disaster area monitoring.
For more detailed ground images the camera resolutions are steadily increasing.
Simultaneously the amount of video data to transmit is increasing
significantly, too. To reduce the amount of data, Region of Interest (ROI)
coding systems were introduced which mainly encode some regions in higher
quality at the cost of the remaining image regions. We employ an existing ROI
coding system relying on global motion compensation to retain full image
resolution over the entire image. Different ROI detectors are used to
automatically classify a video image on board of the UAV in ROI and non-ROI. We
propose to replace the modified Advanced Video Coding (AVC) video encoder by a
modified High Efficiency Video Coding (HEVC) encoder. Without any change of the
detection system itself, but by replacing the video coding back-end we are able
to improve the coding efficiency by 32% on average although regular HEVC
provides coding gains of 12-30% only for the same test sequences and similar
PSNR compared to regular AVC coding. Since the employed ROI coding mainly
relies on intra mode coding of new emerging image areas, gains of HEVC-ROI
coding over AVC-ROI coding compared to regular coding of the entire frames
including predictive modes (inter) depend on sequence characteristics. We
present a detailed analysis of bit distribution within the frames to explain
the gains. In total we can provide coding data rates of 0.7-1.0 Mbit/s for full
HDTV video sequences at 30 fps at reasonable quality of more than 37 dB.
DSP-Enhanced OTDR for Detection and Estimation of Events in PONs
Date: 2018-01-17 00:00:00

To plan a rapid response and minimize operational costs, passive optical
network operators require to automatically detect and identify faults that may
occur in the optical distribution network. In this work, we present
DSP-Enhanced OTDR, a novel methodology for remote fault analysis based on
conventional optical time-domain reflectometry complemented with reference
traces. Together with the mathematical formalism, we derive the detection tests
that result to be uniformly most powerful, which are optimal according to the
Neyman-Pearson criterion. To identify the type of fault and fully characterize
it, the detection stage is followed by the estimation of its characteristic
parameters, such as return loss and insertion loss. We experimentally
demonstrate that this approach allows to detect faults inside the event dead
zone, which overcomes the shortage of conventional event-marking algorithms.
The Bibliotelemetry of Information and Environment: an Evaluation of IoT-Powered Recommender Systems
Authors: Hahn Jim
Date: 2018-03-29 00:00:00
Tasks: Recommendation Systems

Internet of Things (IoT) infrastructure within the physical library
environment is the basis for an integrative, hybrid approach to digital
resource recommenders. The IoT infrastructure provides mobile, dynamic
wayfinding support for items in the collection, which includes features for
location-based recommendations. A modular evaluation and analysis herein
clarified the nature of users' requests for recommendations based on their
location and describes subject areas of the library for which users request
recommendations. The modular mobile design allowed for deep exploration of
users' bibliographic identifiers throughout the global module system, serving
to provide context to the browsing data that are the focus of this study.
Bibliotelemetry is introduced as an evaluation method for IoT middleware within
library collections.
USDA Forecasts: A meta-analysis study
Date: 2018-01-19 00:00:00

The primary goal of this study is doing a meta-analysis research on two
groups of published studies. First, the ones that focus on the evaluation of
the United States Department of Agriculture (USDA) forecasts and second, the
ones that evaluate the market reactions to the USDA forecasts. We investigate
four questions. 1) How the studies evaluate the accuracy of the USDA forecasts?
2) How they evaluate the market reactions to the USDA forecasts? 3) Is there
any heterogeneity in the results of the mentioned studies? 4) Is there any
publication bias? About the first question, while some researchers argue that
the forecasts are unbiased, most of them maintain that they are biased,
inefficient, not optimal, or not rational. About the second question, while a
few studies claim that the forecasts are not newsworthy, most of them maintain
that they are newsworthy, provide useful information, and cause market
reactions. About the third and the fourth questions, based on our findings,
there are some clues that the results of the studies are heterogeneous, but we
didn't find enough evidences of publication bias.
Modelo de maturidade em gerenciamento de riscos em projetos (Project Risk Management Model Maturity)
Date: 2018-01-19 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Management

The globalization feeded by the technology explosion that begans in the end
of the last century, started the world to change faster every day. The only
today's certain is the tomorrow's uncertain. Risk is defined as uncertain where
one or many causes composed of ocurrence probality can generate an impact or
consequence (threat if negative and oportunity if positive, to a determinated
goal). The Risk Management is composed of culture, procedure and process of an
organization or individual care of uncertain, aiming to minimize threats e
maximizing the oportunities, to reach a desired goal. The "Risk maturity model
in projects" proposed on this document, wants to measure the organizations
capacity and skills to manage the riks involved in projects when adopting a
generic risk management methodology.
G protein-coupled receptor-mediated calcium signaling in astrocytes
Date: 2018-01-20 00:00:00

Astrocytes express a large variety of G~protein-coupled receptors (GPCRs)
which mediate the transduction of extracellular signals into intracellular
calcium responses. This transduction is provided by a complex network of
biochemical reactions which mobilizes a wealth of possible calcium-mobilizing
second messenger molecules. Inositol 1,4,5-trisphosphate is probably the best
known of these molecules whose enzymes for its production and degradation are
nonetheless calcium-dependent. We present a biophysical modeling approach based
on the assumption of Michaelis-Menten enzyme kinetics, to effectively describe
GPCR-mediated astrocytic calcium signals. Our model is then used to study
different mechanisms at play in stimulus encoding by shape and frequency of
calcium oscillations in astrocytes.
Capital Structure in U.S., a Quantile Regression Approach with Macroeconomic Impacts
Date: 2018-01-20 00:00:00
Tasks: quantile regression, regression

The major perspective of this paper is to provide more evidence into the
empirical determinants of capital structure adjustment in different
macroeconomics states by focusing and discussing the relative importance of
firm-specific and macroeconomic characteristics from an alternative scope in
U.S. This study extends the empirical research on the topic of capital
structure by focusing on a quantile regression method to investigate the
behavior of firm-specific characteristics and macroeconomic variables across
all quantiles of distribution of leverage (total debt, long-terms debt and
short-terms debt). Thus, based on a partial adjustment model, we find that
long-term and short-term debt ratios varying regarding their partial adjustment
speeds; the short-term debt raises up while the long-term debt ratio slows down
for same periods.
Ontology-based Adaptive e-Textbook Platform for Student and Machine Co-Learning
Authors: Tay Noel Nuo Wi, Yang Sheng-Chi, Lee Chang-Shing, Kubota Naoyuki
Date: 2018-01-20 00:00:00
Tasks: Information Retrieval, Retrieval

The use of electronic textbooks (e-book) has been heavily studied over the
years due to their flexibility, accessibility, interactivity and extensibility.
Yet current shortcomings of e-book, which is often just a digitized version of
the original book, does not encourage adoption. Consequently, this leads to a
rethinking of e-book that should incorporate current technologies to augment
its capabilities, where inclusion of information search and organization tools
have shown to be favorable. This paper is on a preliminary work to add
intelligence into such tools in terms of information retrieval. Construction of
knowledge graph for e-book material with little overhead is first introduced.
Information retrieval through typed similarity query is then performed via
random walk. Case study demonstrate the applicability of the e-book platform,
with promising application and advancement in the area of electronic textbooks.
Translating the Architectural Complexity of the Colon or Polyp into a Sinusoid Wave for Classification via the Fast Fourier Transform
Date: 2018-01-21 00:00:00

There is no method to quantify the spatial complexity within colon polyps.
This paper describes a spatial transformation that translates the tissue
architecture within a polyp, or a normal colon lining, into a complex sinusoid
wave composed of discrete points. This sinusoid wave can then undergo the Fast
Fourier Transform to obtain a spectrum of frequencies that represents the
sinusoid wave. This spectrum can then serve as a signature of the spatial
complexity [an index] within the polyp. By overlaying vertical lines that
radiate from the bottom middle [like a fold-out fan] of an image of a polyp
stained by hematoxylin and eosin, the image is segmented into sectors. Each
vertical line also forms an angle with the horizontal axis of the image,
ranging from 0 degrees to 180 degrees rising counter clockwise. Each vertical
line will intersect with various features of the polyp [border of lumens,
border of epithelial lining]. Each of these intersections is a point that can
be characterized by its distance from the origin [this distance is also a
magnitude of that point]. Thus, each intersection between radial line and polyp
feature can be mapped by polar coordinates [radius length, angle measure]. By
summing the distance of all points along the same radial line, each radial line
that divides the image becomes one value. Plotting these values [y variable]
against the angle of each radial line from the horizontal axis [x variable]
results in a sinusoid wave consisting of discrete points. This method is
referred to as the Linearized Compressed Polar Coordinates [LCPC] Transform.
The LCPC transform, in conjunction with the Fast Fourier Transform, can reduce
the complexity of visually hidden histological grades in colon polyps into
categories of similar wave frequencies [each histological grade has a signature
consisting of a handful of frequencies].
Learning to Speed Up Query Planning in Graph Databases
Authors: Namaki Mohammad Hossain, Chowdhury F A Rezaur Rahman, Islam Md Rakibul, Doppa Janardhan Rao, Wu Yinghui
Date: 2018-01-21 00:00:00
Tasks: Computational Efficiency, Knowledge Graphs

Querying graph structured data is a fundamental operation that enables
important applications including knowledge graph search, social network
analysis, and cyber-network security. However, the growing size of real-world
data graphs poses severe challenges for graph databases to meet the
response-time requirements of the applications. Planning the computational
steps of query processing - Query Planning - is central to address these
challenges. In this paper, we study the problem of learning to speedup query
planning in graph databases towards the goal of improving the
computational-efficiency of query processing via training queries.We present a
Learning to Plan (L2P) framework that is applicable to a large class of query
reasoners that follow the Threshold Algorithm (TA) approach. First, we define a
generic search space over candidate query plans, and identify target search
trajectories (query plans) corresponding to the training queries by performing
an expensive search. Subsequently, we learn greedy search control knowledge to
imitate the search behavior of the target query plans. We provide a concrete
instantiation of our L2P framework for STAR, a state-of-the-art graph query
reasoner. Our experiments on benchmark knowledge graphs including DBpedia,
YAGO, and Freebase show that using the query plans generated by the learned
search control knowledge, we can significantly improve the speed of STAR with
negligible loss in accuracy.