pwc-archive/papers-with-abstracts huggingface dataset:
Hodge Decomposition of the wall shear stress vector fields characterizing biological flows
Date: 2018-11-21 00:00:00

A discrete boundary-sensitive Hodge decomposition is proposed as a central
tool for the analysis of wall shear stress (WSS) vector fields in aortic blood
flows. The method is based on novel results for the smooth and discrete
Hodge-Morrey-Friedrichs decomposition on manifolds with boundary and subdivides
the WSS vector field into five components: gradient (curl-free), co-gradient
(divergence-free), and three harmonic fields induced from the boundary, which
are called the center, Neumann and Dirichlet fields. First, an analysis of WSS
in several simulated simplified phantom geometries (duct and idealized aorta)
was performed in order to understand the impact of the five components. It was
shown that the decomposition is able to distinguish harmonic blood flow arising
from the inlet from harmonic circulations induced by the interior topology of
the geometry. Finally, a comparative analysis of 11 patients with coarctation
of the aorta (CoA) before and after treatment as well as 10 controls patient
was done.
  The study shows a significant difference between the CoA patients and the
healthy controls before and after the treatment. This means a global difference
between aortic shapes of diseased and healthy subjects, thus leading to a new
type of WSS-based analysis and classification of pathological and physiological
blood flow.
Design an Advance computer-aided tool for Image Authentication and Classification
Date: 2018-06-16 00:00:00
Tasks: Image Compression, Image Forgery Detection

Over the years, advancements in the fields of digital image processing and
artificial intelligence have been applied in solving many real-life problems.
This could be seen in facial image recognition for security systems, identity
registrations. Hence a bottleneck of identity registration is image processing.
These are carried out in form of image preprocessing, image region extraction
by cropping, feature extraction using Principal Component Analysis (PCA) and
image compression using Discrete Cosine Transform (DCT). Other processing
includes filtering and histogram equalization using contrast stretching is
performed while enhancing the image as part of the analytical tool. Hence, this
research work presents a universal integration image forgery detection analysis
tool with image facial recognition using Back Propagation Neural Network (BPNN)
processor. The proposed designed tool is a multi-function smart tool with the
novel architecture of programmable error goal and light intensity. Furthermore,
its advance dual database increases the efficiency of a high-performance
application. With the fact that, the facial image recognition will always, give
a matching output or closest possible output image for every input image
irrespective of the authenticity, the universal smart GUI tool is proposed and
designed to perform image forgery detection with the high accuracy of 2% error
rate. Meanwhile, a novel structure that provides efficient automatic image
forgery detection for all input test images for the BPNN recognition is
presented. Hence, an input image will be authenticated before being fed into
the recognition tool.
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
Authors: Du Changde, Du Changying, Wang Hao, Li Jinpeng, Zheng Wei-Long, Lu Bao-Liang, He Huiguang
Date: 2018-07-27 00:00:00
Tasks: Emotion Recognition, Imputation

There are threefold challenges in emotion recognition. First, it is difficult
to recognize human's emotional states only considering a single modality.
Second, it is expensive to manually annotate the emotional data. Third,
emotional data often suffers from missing modalities due to unforeseeable
sensor malfunction or configuration issues. In this paper, we address all these
problems under a novel multi-view deep generative framework. Specifically, we
propose to model the statistical relationships of multi-modality emotional data
using multiple modality-specific generative networks with a shared latent
space. By imposing a Gaussian mixture assumption on the posterior approximation
of the shared latent variables, our framework can learn the joint deep
representation from multiple modalities and evaluate the importance of each
modality simultaneously. To solve the labeled-data-scarcity problem, we extend
our multi-view model to semi-supervised learning scenario by casting the
semi-supervised classification problem as a specialized missing data imputation
task. To address the missing-modality problem, we further extend our
semi-supervised multi-view model to deal with incomplete data, where a missing
view is treated as a latent variable and integrated out during inference. This
way, the proposed overall framework can utilize all available (both labeled and
unlabeled, as well as both complete and incomplete) data to improve its
generalization ability. The experiments conducted on two real multi-modal
emotion datasets demonstrated the superiority of our framework.
Fisher information matrix for single molecules with stochastic trajectories
Date: 2020-02-26 00:00:00

Tracking of objects in cellular environments has become a vital tool in
molecular cell biology. A particularly important example is single molecule
tracking which enables the study of the motion of a molecule in cellular
environments and provides quantitative information on the behavior of
individual molecules in cellular environments, which were not available before
through bulk studies. Here, we consider a dynamical system where the motion of
an object is modeled by stochastic differential equations (SDEs), and
measurements are the detected photons emitted by the moving fluorescently
labeled object, which occur at discrete time points, corresponding to the
arrival times of a Poisson process, in contrast to uniform time points which
have been commonly used in similar dynamical systems. The measurements are
distributed according to optical diffraction theory, and therefore, they would
be modeled by different distributions, e.g., a Born and Wolf profile for an
out-of-focus molecule. For some special circumstances, Gaussian image models
have been proposed. In this paper, we introduce a stochastic framework in which
we calculate the maximum likelihood estimates of the biophysical parameters of
the molecular interactions, e.g., diffusion and drift coefficients. More
importantly, we develop a general framework to calculate the Cram\'er-Rao lower
bound (CRLB), given by the inverse of the Fisher information matrix, for the
estimation of unknown parameters and use it as a benchmark in the evaluation of
the standard deviation of the estimates. There exists no established method,
even for Gaussian measurements, to systematically calculate the CRLB for the
general motion model that we consider in this paper. We apply the developed
methodology to simulated data of a molecule with linear trajectories and show
that the standard deviation of the estimates matches well with the square root
of the CRLB.
Optimal Subpattern Assignment Metric for Multiple Tracks (OSPAMT Metric)
Date: 2019-04-16 00:00:00

In this paper, we propose a new metric which measures the distance between
two finite sets of tracks (a track is a path of either a real or estimated
target). This metric is based on the same principle as the Optimal Subpattern
Assignment (OSPA) metric devised by Schuhmacher et al. Importantly however, the
new metric measures the distance between two finite sets of tracks whereas the
OSPA metric measures the distance between two finite sets of target states. By
also considering the properties of false tracks, missed tracks and many tracks
assigned to one track situations caused by missed detections and false alarms,
the minimization of all distances between tracks across two finite sets of
tracks employed by the new OSPAMT metric enables performance evaluation of
multi-target tracking (MTT) algorithms in a more comprehensive and accurate
manner than existing metrics such as the OSPA metric and the enhanced OSPAT
metric introduced by Ristic et al which measures the distance between two
finite sets of labeled target states.
On the behavior of random RNA secondary structures near the glass transition
Date: 2019-01-15 00:00:00

RNA forms elaborate secondary structures through intramolecular base pairing.
These structures perform critical biological functions within each cell. Due to
the availability of a polynomial algorithm to calculate the partition function
over these structures, they are also a suitable system for the statistical
physics of disordered systems. In this model, below the denaturation
temperature, random RNA secondary structures exist in one of two phases: a
strongly disordered, low-temperature glass phase, and a weakly disordered,
high-temperature molten phase. The probability of two bases to pair decays with
their distance with an exponent 3/2 in the molten phase, and about 4/3 in the
glass phase. Inspired by previous results from a renormalized field theory of
the glass transition separating the two phases, we numerically study this
transition. We introduce distinct order parameters for each phase, that both
vanish at the critical point. We finally explore the driving mechanism behind
this transition.
Objective and Subjective Solomonoff Probabilities in Quantum Mechanics
Authors: Randall Allan F.
Date: 2018-07-30 00:00:00

Algorithmic probability has shown some promise in dealing with the
probability problem in the Everett interpretation, since it provides an
objective, single-case probability measure. Many find the Everettian cosmology
to be overly extravagant, however, and algorithmic probability has also
provided improved models of subjective probability and Bayesian reasoning. I
attempt here to generalize algorithmic Everettianism to more Bayesian and
subjectivist interpretations. I present a general framework for applying
generative probability, of which algorithmic probability can be considered a
special case. I apply this framework to two commonly vexing thought experiments
that have immediate application to quantum probability: the Sleeping Beauty and
Replicator experiments.
Pricing Financial Derivatives using Radial Basis Function generated Finite Differences with Polyharmonic Splines on Smoothly Varying Node Layouts
Date: 2018-08-17 00:00:00

In this paper, we study the benefits of using polyharmonic splines and node
layouts with smoothly varying density for developing robust and efficient
radial basis function generated finite difference (RBF-FD) methods for pricing
of financial derivatives. We present a significantly improved RBF-FD scheme and
successfully apply it to two types of multidimensional partial differential
equations in finance: a two-asset European call basket option under the
Black--Scholes--Merton model, and a European call option under the Heston
model. We also show that the performance of the improved method is equally high
when it comes to pricing American options. By studying convergence,
computational performance, and conditioning of the discrete systems, we show
the superiority of the introduced approaches over previously used versions of
the RBF-FD method in financial applications.
Generating VaR scenarios with product beta distributions
Date: 2019-01-18 00:00:00

We propose a Monte Carlo simulation method to generate stress tests by VaR
scenarios under Solvency II for dependent risks on the basis of observed data.
This is of particular interest for the construction of Internal Models and
requirements on evaluation processes formulated in the Commission Delegated
Regulation. The approach is based on former work on partition-ofunity copulas,
however with a direct scenario estimation of the joint density by product beta
distributions after a suitable transformation of the original data.
Advanced Frequency Identification Power Metering System for Energy Usage
Date: 2018-06-10 00:00:00
Tasks: Management

Energy meter measures the amount of power consumed by electrical loads in
residential, industrial and commercial applications. In this project, the focus
goes to the implementation of a smart power measurement system to allocate
identification for individuals and determine the clients energy usage. The
incorporation of two PIC 16F877A microcontrollers and radio-frequency
identification (RFID) reader in this research work make the system operation
smooth and reliable. This paper presents the development of an intelligent
prepaid power metering system enabling power utilities to collect electricity
bills from consumers prior to the usage of power. Homeowners are able to
monitor reliable power consumption data for efficient power management. To
conclude, a graphical user interface (GUI) has been designed to be applied for
data transmission between the personal computer and RFID a smart card which
allows the credit to be transferred to the smart card.
Spectral Efficiency Analysis of the Decoupled Access for Downlink and Uplink in Two Tier Network
Date: 2019-03-14 00:00:00

This paper analyzes the efficacy of decoupled wireless access in a two-tier
heterogeneous network. The decoupled wireless access and its performance
benefits have been studied in different scenarios recently. In this paper, an
in-depth analysis on its efficacy from spectral efficiency perspective is
provided. To achieve this task, (i) new closed form expressions for probability
of association of user equipment with different tiers employing different
frequency bands (i.e., microwave and millimeter wave) with different pathloss
exponents are derived using univariate Fox's H-functions; (ii) Distributions of
the distance to the serving base stations are also derived; (iii) Exact
expressions of spectral efficiency for different association cases are further
obtained using bivariate Fox's H-functions. Furthermore, rigorous simulation
results are provided which validate the aforementioned analytical results. In
addition to that, a detailed discussion on the decoupling gain of decoupled
wireless access and its efficacy is also provided. Lastly, despite the
improvement provided by the decoupled wireless access, which is evident from
the results presented in this paper, few questions are raised on its pragmatic
value.
Existence of Equilibrium Prices: A Pedagogical Proof
Date: 2018-09-23 00:00:00

Under the same assumptions made by Mas-Colell et al. (1995), I develop a
short, simple, and complete proof of existence of equilibrium prices based on
excess demand functions. The result is obtained by applying the Brouwer fixed
point theorem to a trimmed simplex which does not contain prices equal to zero.
The mathematical techniques are based on some results obtained in Neuefeind
(1980) and Geanakoplos (2003).
Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention
Authors: Ahmed Mahtab, Islam Jumayel, Samee Muhammad Rifayat, Mercer Robert E.
Date: 2018-07-27 00:00:00

Identifying interactions between proteins is important to understand
underlying biological processes. Extracting a protein-protein interaction (PPI)
from the raw text is often very difficult. Previous supervised learning methods
have used handcrafted features on human-annotated data sets. In this paper, we
propose a novel tree recurrent neural network with structured attention
architecture for doing PPI. Our architecture achieves state of the art results
(precision, recall, and F1-score) on the AIMed and BioInfer benchmark data
sets. Moreover, our models achieve a significant improvement over previous best
models without any explicit feature extraction. Our experimental results show
that traditional recurrent networks have inferior performance compared to tree
recurrent networks for the supervised PPI problem.
Ethnographie de la structuration d'un corpus collectif de messages de soutien social en ligne
Authors: Ninova Goritsa  Tech-CICO, Atifi Hassan  Tech-CICO
Date: 2018-07-27 00:00:00

In this paper, we propose a study of progressive development of the structure
of corpus collected starting from the discussion forums. These are the first
results from an ethnographic description of constitution and analysis practices
of a corpus located within an interdisciplinary project. At first, we describe
the personal digital documents folder of a researcher, relative to his corpus
analysis of interactions of social support online. Next, we examine the
constitution of a shared corpus, result of the pooling of individual corpora,
small size, produced by researchers participating in the project, each
individual corpus reflecting his producer's perspectives of research.
Trend without hiccups: a Kalman filter approach
Date: 2018-07-13 00:00:00

Have you ever felt miserable because of a sudden whipsaw in the price that
triggered an unfortunate trade? In an attempt to remove this noise, technical
analysts have used various types of moving averages (simple, exponential,
adaptive one or using Nyquist criterion). These tools may have performed
decently but we show in this paper that this can be improved dramatically
thanks to the optimal filtering theory of Kalman filters (KF). We explain the
basic concepts of KF and its optimum criterion. We provide a pseudo code for
this new technical indicator that demystifies its complexity. We show that this
new smoothing device can be used to better forecast price moves as lag is
reduced. We provide 4 Kalman filter models and their performance on the SP500
mini-future contract. Results are quite illustrative of the efficiency of KF
models with better net performance achieved by the KF model combining smoothing
and extremum position.
Continuous Authentication of Smartphones Based on Application Usage
Authors: Mahbub Upal, Komulainen Jukka, Ferreira Denzil, Chellappa Rama
Date: 2018-07-18 00:00:00

An empirical investigation of active/continuous authentication for
smartphones is presented in this paper by exploiting users' unique application
usage data, i.e., distinct patterns of use, modeled by a Markovian process.
Variations of Hidden Markov Models (HMMs) are evaluated for continuous user
verification, and challenges due to the sparsity of session-wise data, an
explosion of states, and handling unforeseen events in the test data are
tackled. Unlike traditional approaches, the proposed formulation does not
depend on the top N-apps, rather uses the complete app-usage information to
achieve low latency. Through experimentation, empirical assessment of the
impact of unforeseen events, i.e., unknown applications and unforeseen
observations, on user verification is done via a modified edit-distance
algorithm for simple sequence matching. It is found that for enhanced
verification performance, unforeseen events should be incorporated in the
models by adopting smoothing techniques with HMMs. For validation, extensive
experiments on two distinct datasets are performed. The marginal smoothing
technique is the most effective for user verification in terms of equal error
rate (EER) and with a sampling rate of 1/30s^{-1} and 30 minutes of historical
data, and the method is capable of detecting an intrusion within ~2.5 minutes
of application use.
Computational Complexity of Observing Evolution in Artificial-Life Forms
Authors: Misra Janardan
Date: 2018-06-24 00:00:00
Tasks: Artificial Life

Observations are an essential component of the simulation based studies on
artificial-evolutionary systems (AES) by which entities are identified and
their behavior is observed to uncover higher-level "emergent" phenomena.
Because of the heterogeneity of AES models and implicit nature of observations,
precise characterization of the observation process, independent of the
underlying micro-level reaction semantics of the model, is a difficult problem.
Building upon the multiset based algebraic framework to characterize
state-space trajectory of AES model simulations, we estimate bounds on
computational resource requirements of the process of automatically discovering
life-like evolutionary behavior in AES models during simulations. For
illustration, we consider the case of Langton's Cellular Automata model and
characterize the worst case computational complexity bounds for identifying
entity and population level reproduction.
DCA for genome-wide epistasis analysis: the statistical genetics perspective
Date: 2018-08-10 00:00:00

Direct Coupling Analysis (DCA) is a now widely used method to leverage
statistical information from many similar biological systems to draw meaningful
conclusions on each system separately. DCA has been applied with great success
to sequences of homologous proteins, and also more recently to whole-genome
population-wide sequencing data. We here argue that the use of DCA on the
genome scale is contingent on fundamental issues of population genetics. DCA
can be expected to yield meaningful results when a population is in the
Quasi-Linkage Equilibrium (QLE) phase studied by Kimura and others, but not,
for instance, in a phase of Clonal Competition. We discuss how the exponential
(Potts model) distributions emerge in QLE, and compare couplings to
correlations obtained in a study of about 3,000 genomes of the human pathogen
Streptococcus pneumoniae.
Cross-location wind speed forecasting for wind energy applications using machine learning based models
Date: 2018-08-10 00:00:00
Tasks: BIG-bench Machine Learning

The widespread utilisation of grid-integrated wind electricity necessitates
accurate and reliable wind speed forecasting to ensure stable grid and quality
power. Machine learning algorithm based wind speed forecasting models are
getting increased attention in the literature owing to its superior ability to
learn by effectively capturing the changing patterns from the data. Most of the
reported wind forecasting models built on machine learning algorithms are
location specific and tested against data adjacent to the training data. In
this work, we develop the machine learning based wind speed forecasting models
and analyse their performance when applied to data from different cross-
locations up to a year ahead. Two distinct machine learning models based on
Support Vector Machine (SVM) and Random Forest (RF) algorithms have been
developed and tested separately for a relatively large geographical area. The
results of analysis of 1-hour forecasts obtained at various cross-locations and
points of time up to a year ahead show 80% of predictions within a Root Mean
Square Error (RMSE) of 1.5 m/s, 95% within 2.5 m/s and 98% within an RMSEof 3.5
m/s. The 75% of 2-hour predictions are within RMSE of 1.5 m/s, 16-hour
predictions within RMSE of 2.5 m / s and 48-hour predictions within RMSE of 3.5
m/s. When applied to thesame location of training data, the models generate
reliable forecasts for periods up to 22 hours, with the added advantage that
the models perform consistently throughout the year ahead horizon, independent
of the lead time from the training data. The output of the analysis is highly
promising to the wind energy industry in wind forecasting for locations where
historical wind speed data are not available for model building and training.
Concave Shape of the Yield Curve and No Arbitrage
Date: 2018-08-10 00:00:00

In fixed income sector, the yield curve is probably the most observed
indicator by the market for trading and fifinancing purposes. A yield curve
plots interest rates across different contract maturities from short end to as
long as 30 years. For each currency, the corresponding curve shows the relation
between the level of the interest rates (or cost of borrowing) and the time to
maturity. For example, the U.S. dollar interest rates paid on U.S. Treasury
securities for various maturities are plotted as the US treasury curve. For the
same currency, if the swap market is used, we could also plot the swap rates
across the tenors which would be called the swap curve.Even the yield curve can
be at, upward or downward (inverted), however, yield curve is generally
concave. There is a lack of explanation of the concavity of the yield curve
shape from economics theory. We offer in this article an explanation of the
concavity shape of the yield curve from trading perspectives.