pwc-archive/papers-with-abstracts huggingface dataset:
The Function Transformation Omics - Funomics
Authors: Yongshuai Jiang, Jing Xu, Simeng Hu, Di Liu, Linna Zhao, Xu Zhou
Date: 2018-08-17 00:00:00
Tasks: Math

There are no two identical leaves in the world, so how to find effective markers or features to distinguish them is an important issue. Function transformation, such as f(x,y) and f(x,y,z), can transform two, three, or multiple input/observation variables (in biology, it generally refers to the observed/measured value of biomarkers, biological characteristics, or other indicators) into a new output variable (new characteristics or indicators). This provided us a chance to re-cognize objective things or relationships beyond the original measurements. For example, Body Mass Index, which transform weight and high into a new indicator BMI=x/y^2 (where x is weight and y is high), is commonly used in to gauge obesity. Here, we proposed a new system, Funomics (Function Transformation Omics), for understanding the world in a different perspective. Funome can be understood as a set of math functions consist of basic elementary functions (such as power functions and exponential functions) and basic mathematical operations (such as addition, subtraction). By scanning the whole Funome, researchers can identify some special functions (called handsome functions) which can generate the novel important output variable (characteristics or indicators). We also start "the Funome project" to develop novel methods, function library and analysis software for Funome studies. The Funome project will accelerate the discovery of new useful indicators or characteristics, will improve the utilization efficiency of directly measured data, and will enhance our ability to understand the world. The analysis tools and data resources about the Funome project can be found gradually at http://www.funome.com.
ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information
Authors: Abad Zahra Shakeri Hossein, Gervasi Vincenzo, Zowghi Didar, Barker Ken
Date: 2018-07-21 00:00:00

Requirements elicitation requires extensive knowledge and deep understanding
of the problem domain where the final system will be situated. However, in many
software development projects, analysts are required to elicit the requirements
from an unfamiliar domain, which often causes communication barriers between
analysts and stakeholders. In this paper, we propose a requirements ELICitation
Aid tool (ELICA) to help analysts better understand the target application
domain by dynamic extraction and labeling of requirements-relevant knowledge.
To extract the relevant terms, we leverage the flexibility and power of
Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural
language processing tasks. In addition to the information conveyed through
text, ELICA captures and processes non-linguistic information about the
intention of speakers such as their confidence level, analytical tone, and
emotions. The extracted information is made available to the analysts as a set
of labeled snippets with highlighted relevant terms which can also be exported
as an artifact of the Requirements Engineering (RE) process. The application
and usefulness of ELICA are demonstrated through a case study. This study shows
how pre-existing relevant information about the application domain and the
information captured during an elicitation meeting, such as the conversation
and stakeholders' intentions, can be captured and used to support analysts
achieving their tasks.
Using path signatures to predict a diagnosis of Alzheimer's disease
Date: 2018-08-16 00:00:00
Tasks: Hippocampus

The path signature is a means of feature generation that can encode nonlinear
interactions in the data as well as the usual linear features. It can
distinguish the ordering of time-sequenced changes: for example whether or not
the hippocampus shrinks fast, then slowly or the converse. It provides
interpretable features and its output is a fixed length vector irrespective of
the number of input points so it can encode longitudinal data of varying length
and with missing data points. In this paper we demonstrate the path signature
in providing features to distinguish a set of people with Alzheimer's disease
from a matched set of healthy individuals. The data used are volume
measurements of the whole brain, ventricles and hippocampus from the
Alzheimer's Disease Neuroimaging Initiative (ADNI). The path signature method
is shown to be a useful tool for the processing of sequential data which is
becoming increasingly available as monitoring technologies are applied.
A High Order Method for Pricing of Financial Derivatives using Radial Basis Function generated Finite Differences
Date: 2018-08-20 00:00:00

In this paper, we consider the numerical pricing of financial derivatives
using Radial Basis Function generated Finite Differences in space. Such
discretization methods have the advantage of not requiring Cartesian grids.
Instead, the nodes can be placed with higher density in areas where there is a
need for higher accuracy. Still, the discretization matrix is fairly sparse. As
a model problem, we consider the pricing of European options in 2D. Since such
options have a discontinuity in the first derivative of the payoff function
which prohibits high order convergence, we smooth this function using an
established technique for Cartesian grids. Numerical experiments show that we
acquire a fourth order scheme in space, both for the uniform and the nonuniform
node layouts that we use. The high order method with the nonuniform node layout
achieves very high accuracy with relatively few nodes. This renders the
potential for solving pricing problems in higher spatial dimensions since the
computational memory and time demand become much smaller with this method
compared to standard techniques.
Exploring how innovation strategies at time of crisis influence performance: a cluster analysis perspective
Date: 2018-08-17 00:00:00

This paper analyzes the connection between innovation activities of companies
-- implemented before crisis -- and their performance -- measured at time of
crisis. The companies listed in the STAR Market Segment of the Italian Stock
Exchange are analyzed. Innovation is measured through the level of investments
in total tangible and intangible fixed assets in 2006-2007, while performance
is captured through growth -- expressed by variations of sales, total assets
and employees -- profitability -- through ROI or ROS -- and productivity --
through asset turnover or sales per employee in the period 2008-2010. The
variables of interest are analyzed and compared through statistical techniques
and by adopting cluster analysis. In particular, a Voronoi tessellation is also
implemented in a varying centroids framework. In accord with a large part of
the literature, we find that the behavior of the performance of the companies
is not univocal when they innovate.
Recuperation of Regenerative Braking Energy in Electric Rail Transit Systems
Date: 2018-08-17 00:00:00

Electric rail transit systems are large consumers of energy. In trains with
regenerative braking capability, a fraction of the energy used to power a train
is regenerated during braking. This regenerated energy, if not properly
captured, is typically dumped in the form of heat to avoid overvoltage. Finding
a way to recuperate regenerative braking energy can result in economic as well
as technical merits. In this comprehensive paper, the various methods and
technologies that were proposed for regenerative energy recuperation have been
analyzed, investigated and compared. These technologies include: train
timetable optimization, energy storage systems (onboard and wayside), and
reversible substations.
Attainment Ratings for Graph-Query Recommendation
Authors: Cooper Hal, Iyengar Garud, Lin Ching-Yung
Date: 2018-08-17 00:00:00
Tasks: Recommendation Systems

The video game industry is larger than both the film and music industries
combined. Recommender systems for video games have received relatively scant
academic attention, despite the uniqueness of the medium and its data. In this
paper, we introduce a graph-based recommender system that makes use of
interactivity, arguably the most significant feature of video gaming. We show
that the use of implicit data that tracks user-game interactions and levels of
attainment (e.g. Sony Playstation Trophies, Microsoft Xbox Achievements) has
high predictive value when making recommendations. Furthermore, we argue that
the characteristics of the video gaming hobby (low cost, high duration,
socially relevant) make clear the necessity of personalized, individual
recommendations that can incorporate social networking information. We
demonstrate the natural suitability of graph-query based recommendation for
this purpose.
Quantifying the Computational Advantage of Forward Orthogonal Deviations
Date: 2018-08-17 00:00:00

Under suitable conditions, one-step generalized method of moments (GMM) based
on the first-difference (FD) transformation is numerically equal to one-step
GMM based on the forward orthogonal deviations (FOD) transformation. However,
when the number of time periods ($T$) is not small, the FOD transformation
requires less computational work. This paper shows that the computational
complexity of the FD and FOD transformations increases with the number of
individuals ($N$) linearly, but the computational complexity of the FOD
transformation increases with $T$ at the rate $T^{4}$ increases, while the
computational complexity of the FD transformation increases at the rate $T^{6}$
increases. Simulations illustrate that calculations exploiting the FOD
transformation are performed orders of magnitude faster than those using the FD
transformation. The results in the paper indicate that, when one-step GMM based
on the FD and FOD transformations are the same, Monte Carlo experiments can be
conducted much faster if the FOD version of the estimator is used.
$k$th price auctions and Catalan numbers
Date: 2018-08-17 00:00:00

This paper establishes an interesting link between $k$th price auctions and
Catalan numbers by showing that for distributions that have linear density, the
bid function at any symmetric, increasing equilibrium of a $k$th price auction
with $k\geq 3$ can be represented as a finite series of $k-2$ terms whose
$\ell$th term involves the $\ell$th Catalan number. Using an integral
representation of Catalan numbers, together with some classical combinatorial
identities, we derive the closed form of the unique symmetric, increasing
equilibrium of a $k$th price auction for a non-uniform distribution.
Heuristics for publishing dynamic content as structured data with schema.org
Authors: Kärle Elias, Fensel Dieter
Date: 2018-08-17 00:00:00

Publishing fast changing dynamic data as open data on the web in a scalable
manner is not trivial. So far the only approaches describe publishing as much
data as possible, which then leads to problems, like server capacity overload,
network latency or unwanted knowledge disclosure. With this paper we show ways
how to publish dynamic data in a scalable, meaningful manner by applying
context-dependent publication heuristics. The outcome shows that the
application of the right publication heuristics in the right domain can improve
the publication performance significantly. Good knowledge about the domain help
choosing the right publication heuristic and hence lead to very good
publication results.
Non-equilibrium time dynamics of genetic evolution
Date: 2018-08-18 00:00:00

Biological systems are typically highly open, non-equilibrium systems that
are very challenging to understand from a statistical mechanics perspective.
While statistical treatments of evolutionary biological systems have a long and
rich history, examination of the time-dependent non-equilibrium dynamics has
been less studied. In this paper we first derive a generalized master equation
in the genotype space for diploid organisms incorporating the processes of
selection, mutation, recombination, and reproduction. The master equation is
defined in terms of continuous time and can handle an arbitrary number of gene
loci and alleles, and can be defined in terms of an absolute population or
probabilities. We examine and analytically solve several prototypical cases
which illustrate the interplay of the various processes and discuss the
timescales of their evolution. The entropy production during the evolution
towards steady state is calculated and we find that it agrees with predictions
from non-equilibrium statistical mechanics where it is large when the
population distribution evolves towards a more viable genotype. The stability
of the non-equilibrium steady state is confirmed using the Glansdorff-Prigogine
criterion.
Indoor Coverage Enhancement for mmWave Systems with Passive Reflectors: Measurements and Ray Tracing Simulations
Date: 2018-08-19 00:00:00

The future 5G networks are expected to use millimeter wave (mmWave) frequency
bands, mainly due to the availability of large unused spectrum. However, due to
high path loss at mmWave frequencies, coverage of mmWave signals can get
severely reduced, especially for non-line-of-sight (NLOS) scenarios. In this
work, we study the use of passive metallic reflectors of different shapes/sizes
to improve mmWave signal coverage for indoor NLOS scenarios. Software defined
radio based mmWave transceiver platforms operating at 28 GHz are used for
indoor measurements. Subsequently, ray tracing (RT) simulations are carried out
in a similar environment using Remcom Wireless InSite software. The cumulative
distribution functions of the received signal strength for the RT simulations
in the area of interest are observed to be reasonably close with those obtained
from the measurements. Our measurements and RT simulations both show that there
is significant (on the order of 20 dB) power gain obtained with square metallic
reflectors, when compared to no reflector scenario for an indoor corridor. We
also observe that overall mmWave signal coverage can be improved utilizing
reflectors of different shapes and orientations.
The speciation of Australopithecus and Paranthropus was caused by introgression from the Gorilla lineage
Date: 2018-08-21 00:00:00

The discovery of Paranthropus deyiremeda in 3.3-3.5 million year old fossil
sites in Afar (Haile-Selassie, 2015), together with 30% of the gorilla genome
showing lineage sorting between humans and chimpanzees (Scally, 2012), and a
NUMT ("nuclear mitochondrial DNA segment") that is shared by both gorillas,
humans and chimpanzees, and that dates back to 6 million years ago (Popadin,
2017), is conclusive evidence that introgression from the gorilla lineage
caused the speciation of both the Australopithecus lineage and the Paranthropus
lineage, providing a lens into the gorilla-like features within Paranthropus,
as well as traits within Homo that originate from the gorilla branch, such as a
high opposable thumb index (Alm\'ecija, 2015), an adducted great toe (Tocheri,
2011, McHenry, 2006), and large deposits of subcutaneous fat.
Optimal asset allocation for a DC plan with partial information under inflation and mortality risks
Date: 2018-08-22 00:00:00

We study an asset allocation stochastic problem with restriction for a
defined-contribution pension plan during the accumulation phase. We consider a
financial market with stochastic interest rate, composed of a risk-free asset,
a real zero coupon bond price, the inflation-linked bond and the risky asset. A
plan member aims to maximize the expected power utility derived from the
terminal wealth. In order to protect the rights of a member who dies before
retirement, we introduce a clause which allows to withdraw his premiums and the
difference is distributed among the survival members. Besides the mortality
risk, the fund manager takes into account the salary and the inflation risks.
We then obtain closed form solutions for the asset allocation problem using a
sufficient maximum principle approach for the problem with partial information.
Finally, we give a numerical example.
Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations
Authors: Lacic Emanuel, Kowald Dominik, Lex Elisabeth
Date: 2018-08-20 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

In this paper, we present work-in-progress on applying user pre-filtering to
speed up and enhance recommendations based on Collaborative Filtering. We
propose to pre-filter users in order to extract a smaller set of candidate
neighbors, who exhibit a high number of overlapping entities and to compute the
final user similarities based on this set. To realize this, we exploit features
of the high-performance search engine Apache Solr and integrate them into a
scalable recommender system. We have evaluated our approach on a dataset
gathered from Foursquare and our evaluation results suggest that our proposed
user pre-filtering step can help to achieve both a better runtime performance
as well as an increase in overall recommendation accuracy.
Comparative Study on Millimeter Wave Location-Based Beamforming
Date: 2018-08-21 00:00:00
Tasks: Compressive Sensing

This paper presents a comparative study on millimeter wave (mmWave)
location-based analog beamforming (BF) techniques based on channel estimation.
Localization and compressive sensing (CS) effectively reduces mmWave BF
complexity and enhance the performance of mmWave system comparable to the
conventional mmWave analog BF techniques. BF techniques based on channel state
information (CSI) has high complexity in constructing mmWave channel sensing
matrix using CS. Location services based techniques highly reduce this
complexity by defining the area within which the user equipment (UE) mostly
probable to be exist. In this paper, we study the performance of mmWave
location-based BF using various location services. Where, the BF is conducted
using channel estimation based CS to estimate both the angle of departures
(AoDs) and the angle of arrivals (AoAs) of the mmWave channel.
Boundaries of Operation for Refurbished Parallel AC-DC Reconfigurable Links in Distribution Grids
Date: 2018-12-15 00:00:00

Parallel ac-dc reconfigurable link technology can find interesting
applications in medium voltage power distribution. A given system can operate
in different configurations while maintaining equivalent capacity during (n-1)
contingencies. It is proved that within the defined operating boundaries, a
parallel ac-dc configuration has higher efficiency as compared to pure ac or
pure dc power delivery. Using sensitivity analysis, the variations in these
efficiency boundaries with power demand, power factor, grid voltages, link
lengths, conductor areas and converter efficiency is described. It is shown
that parallel ac-dc system can have smaller payback time as compared to a
purely dc power transmission for the same capacity due to lower investment cost
in converter station and superior efficiency. As compared to a purely ac
system, the payback of a refurbished parallel acdc configuration can be less
than 5 years for a 10 km, 10 kV distribution link within the specified
assumptions and operating conditions.
Experimental Results of a 3D Millimeter-Wave Compressive-Reflector-Antenna Imaging System
Date: 2018-07-28 00:00:00
Tasks: 3D Reconstruction, Compressive Sensing, Diversity

This letter presents the first experimental results of our three-dimensional
(3D) millimeter-wave (mm-wave) Compressive-Reflector-Antenna (CRA) imaging
system. In this prototype, the CRA is 3D-printed and coated with a metallic
spray to easily introduce pseudo-random scatterers on the surface of a
traditional reflector antenna (TRA). The CRA performs a pseudo random coding of
the incident wavefront, thus adding spatial diversity in the imaging region and
enabling the effective use of compressive sensing (CS) and imaging techniques.
The CRA is fed with a multiple-input-multiple-output (MIMO) radar, which
consists of four transmitting and four receiving ports. Consequently, the
mechanical scanning parts and phase shifters, which are necessary in
conventional physical or synthetic aperture arrays, are not needed in this
system. Experimental results show the effectiveness of the prototype to perform
a successful 3D reconstruction of a T-shaped metallic target.
Universal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder
Date: 2019-04-30 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), ERP

The process of recording Electroencephalography (EEG) signals is onerous and
requires massive storage to store signals at an applicable frequency rate. In
this work, we propose the EventRelated Potential Encoder Network (ERPENet); a
multi-task autoencoder-based model, that can be applied to any ERP-related
tasks. The strength of ERPENet lies in its capability to handle various kinds
of ERP datasets and its robustness across multiple recording setups, enabling
joint training across datasets. ERPENet incorporates Convolutional Neural
Networks (CNNs) and Long Short-Term Memory (LSTM), in an autoencoder setup,
which tries to simultaneously compress the input EEG signal and extract related
P300 features into a latent vector. Here, we can infer the process for
generating the latent vector as universal joint feature extraction. The network
also includes a classification part for attended and unattended events
classification as an auxiliary task. We experimented on six different P300
datasets. The results show that the latent vector exhibits better compression
capability than the previous state-of-the-art semi-supervised autoencoder
model. For attended and unattended events classification, pre-trained weights
are adopted as initial weights and tested on unseen P300 datasets to evaluate
the adaptability of the model, which shortens the training process as compared
to using random Xavier weight initialization. At the compression rate of 6.84,
the classification accuracy outperforms conventional P300 classification
models: XdawnLDA, DeepConvNet, and EEGNet achieving 79.37% - 88.52%
classification accuracy depending on the dataset.
Paradoxical LTP maintenance with inhibition of protein synthesis and the proteasome suggests a novel protein synthesis requirement for early LTP reversal
Date: 2018-08-20 00:00:00

The transition from early long-term potentiation (E-LTP) to late LTP (L-LTP)
involves protein synthesis and degradation. L-LTP is blocked by inhibiting
either protein synthesis or proteasome-dependent degradation prior to and
during a tetanic stimulus, but paradoxically, L-LTP is not blocked when
synthesis and degradation are inhibited simultaneously, suggesting
counter-acting positive and negative proteins regulate L-LTP. To investigate
this paradox, we modeled LTP at the Schaffer collateral synapse. Nine
differential equations describe the levels of positive and negative regulator
proteins (PP and NP) and transitions among five discrete synaptic states, a
basal state (BAS), three E-LTP states (EP1, EP2, ED), and a L-LTP state (LP). A
stimulus initiates the transition from BAS to EP1 and from EP1 to EP2,
initiates the synthesis of PP and NP, and activates the ubiquitin-proteasome
system (UPS). UPS mediates transitions of EP1 and EP2 to ED and the degradation
of NP. The conversion of E-LTP to L-LTP is mediated by a PP-dependent
transition from ED to LP. NP mediates reversal of EP2 to BAS. This model
simulates empirical observations: 1) normal L-LTP, 2) block by either
proteasome inhibitor or protein synthesis inhibitor alone, and 3) preservation
of L-LTP when both inhibitors are applied together. Elements of this abstract
model can be correlated with specific molecules and processes. Moreover, the
model makes testable predictions, such as a unique synaptic state ED that
precedes the transition to L-LTP, and a time window for the action of the UPS
(during the transitions from EP1 and EP2 to ED). Tests of these predictions
will provide insights into the processes of long-term synaptic plasticity.