pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Virtualization Framework for Cellular Networks with Downlink Rate Coverage Probability Constraints
Date: 2018-04-26 00:00:00
Tasks: Stochastic Optimization

Wireless network virtualization is emerging as an important technology for
next-generation (5G) wireless networks. A key advantage of introducing
virtualization in cellular networks is that service providers can robustly
share virtualized network resources (e.g., infrastructure and spectrum) to
extend coverage, increase capacity, and reduce costs. {However, the inherent
features of wireless networks, i.e., the uncertainty in user equipment (UE)
locations and channel conditions impose significant challenges on
virtualization and sharing of the network resources.} In this context, we
propose a stochastic optimization-based virtualization framework that enables
robust sharing of network resources. Our proposed scheme aims at
probabilistically guaranteeing UEs' Quality of Service (QoS) demand
satisfaction, while minimizing the cost for service providers, with reasonable
computational complexity and affordable network overhead.
Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains
Date: 2018-04-25 00:00:00
Tasks: Point Processes

The number of neurons that can be simultaneously recorded doubles every seven
years. This ever increasing number of recorded neurons opens up the possibility
to address new questions and extract higher dimensional stimuli from the
recordings. Modeling neural spike trains as point processes, this task of
extracting dynamical signals from spike trains is commonly set in the context
of nonlinear filtering theory. Particle filter methods relying on importance
weights are generic algorithms that solve the filtering task numerically, but
exhibit a serious drawback when the problem dimensionality is high: they are
known to suffer from the 'curse of dimensionality' (COD), i.e. the number of
particles required for a certain performance scales exponentially with the
observable dimensions. Here, we first briefly review the theory on filtering
with point process observations in continuous time. Based on this theory, we
investigate both analytically and numerically the reason for the COD of
weighted particle filtering approaches: Similarly to particle filtering with
continuous-time observations, the COD with point-process observations is due to
the decay of effective number of particles, an effect that is stronger when the
number of observable dimensions increases. Given the success of unweighted
particle filtering approaches in overcoming the COD for continuous- time
observations, we introduce an unweighted particle filter for point-process
observations, the spike-based Neural Particle Filter (sNPF), and show that it
exhibits a similar favorable scaling as the number of dimensions grows.
Further, we derive rules for the parameters of the sNPF from a maximum
likelihood approach learning. We finally employ a simple decoding task to
illustrate the capabilities of the sNPF and to highlight one possible future
application of our inference and learning algorithm.
On the complexity of solving a decision problem with flow-depending costs: the case of the IJsselmeer dikes
Date: 2018-04-25 00:00:00

We consider a fundamental integer programming (IP) model for cost-benefit
analysis flood protection through dike building in the Netherlands, due to
Verweij and Zwaneveld.
  Experimental analysis with data for the Ijsselmeer lead to integral optimal
solution of the linear programming relaxation of the IP model.
  This naturally led to the question of integrality of the polytope associated
with the IP model.
  In this paper we first give a negative answer to this question by
establishing non-integrality of the polytope.
  Second, we establish natural conditions that guarantee the linear programming
relaxation of the IP model to be integral.
  We then test the most recent data on flood probabilities, damage and
investment costs of the IJsselmeer for these conditions.
  Third, we show that the IP model can be solved in polynomial time when the
number of dike segments, or the number of feasible barrier heights, are
constant.
Lane formation and critical coarsening in a model of bacterial competition
Date: 2019-04-15 00:00:00

We study competition of two non-motile bacterial strains in a
three-dimensional channel numerically, and analyze how their configuration
evolves in space and time. We construct a lattice model that takes into account
self-replication, mutation, and killing of bacteria. When mutation is not
significant, the two strains segregate and form stripe patterns along the
channel. The formed lanes are gradually rearranged, with increasing length
scales in the two-dimensional cross-sectional plane. We characterize it in
terms of coarsening and phase ordering in statistical physics. In particular,
for the simple model without mutation and killing, we find logarithmically slow
coarsening, which is characteristic of the two-dimensional voter model. With
mutation and killing, we find a phase transition from a monopolistic phase, in
which lanes are formed and coarsened until the system is eventually dominated
by one of the two strains, to an equally mixed and disordered phase without
lane structure. Critical behavior at the transition point is also studied and
compared with the generalized voter class and the Ising class. These results
are accounted for by continuum equations, obtained by applying a mean field
approximation along the channel axis. Our findings indicate relevance of
critical coarsening of two-dimensional systems in the problem of bacterial
competition within anisotropic three-dimensional geometry.
System Description of CITlab's Recognition & Retrieval Engine for ICDAR2017 Competition on Information Extraction in Historical Handwritten Records
Authors: Strauß Tobias, Weidemann Max, Michael Johannes, Leifert Gundram, Grüning Tobias, Labahn Roger
Date: 2018-04-26 00:00:00
Tasks: Retrieval

We present a recognition and retrieval system for the ICDAR2017 Competition
on Information Extraction in Historical Handwritten Records which successfully
infers person names and other data from marriage records. The system extracts
information from the line images with a high accuracy and outperforms the
baseline. The optical model is based on Neural Networks. To infer the desired
information, regular expressions are used to describe the set of feasible words
sequences.
Dynamic Signal Measurements Based on Quantized Data
Date: 2018-04-26 00:00:00
Tasks: Quantization

The estimation of the parameters of a dynamic signal, such as a sine wave,
based on quantized data is customarily performed using the least-square
estimator (LSE), such as the sine fit. However, the characteristics of the
experiments and the measurement setup hardly satisfy the requirements ensuring
the LSE to be optimal in the minimum mean-square-error sense. This occurs if
the input signal is characterized by a large signal-to-noise ratio resulting in
the deterministic component of the quantization error dominating the random
error component and when the ADC transition levels are not uniformly
distributed over the quantizer input range. In this paper, it is first shown
that the LSE applied to quantized data does not perform as expected when the
quantizer is not uniform. Then, an estimator is introduced that overcomes these
limitations. It uses the values of the transition levels so that a prior
quantizer calibration phase is necessary. The estimator properties are analyzed
and both numerical and experimental results are described to illustrate its
performance. It is shown that the described estimator outperforms the LSE and
it also provides an estimate of the probability distribution function of the
noise before quantization.
TOA Positioning for a TDMA Localization System
Date: 2018-04-26 00:00:00
Tasks: Position

Positioning with one single communication between base stations and user
devices can effectively save air time and thus expand the user volume to
infinite. However, this usually demands accurate synchronization between base
stations. Wireless synchronization between base stations can simplify the
deployment of the positioning system but requires accurate clock offset
estimation between base stations. A time division multiple access (TDMA)
localization system in which user devices only receive signals from base
stations to generate time of arrival (TOA) measurements to position themselves
and no cables are needed to interconnect base stations for clock
synchronization is proposed, implemented and tested. In this system, the user
devices can easily join in or exit without influence to other users and the
update rate of each user can be easily adjusted independently according to its
specific requirement.
A GPP algorithm for hippocampal interneuron characterization
Date: 2018-04-26 00:00:00

Correctly identifying neuronal subsets is critical to multiple downstream
methods in several areas of neuroscience research. The hippocampal interneuron
characterization technology has achieved rapid development in recent years.
However, capturing true neuronal features for accurate interneuron
characterization and segmentation has remained elusive. In the current study, a
novel global preserving estimate algorithm is used to capture the non-linearity
in the features of hippocampal interneurons after factor Algorithm. Our results
provide evidence for the effective integration of the original linear and
nonlinear neuronal features and achieves better characterization performance on
multiple hippocampal interneuron databases through array matching.
Kinetics of Protein-DNA Interactions: First-Passage Analysis
Date: 2018-04-26 00:00:00

All living systems can function only far away from equilibrium, and for this
reason chemical kinetic methods are critically important for uncovering the
mechanisms of biological processes. Here we present a new theoretical method of
investigating dynamics of protein-DNA interactions, which govern all major
biological processes. It is based on a first-passage analysis of biochemical
and biophysical transitions, and it provides a fully analytic description of
the processes. Our approach is explained for the case of a single protein
searching for a specific binding site on DNA. In addition, the application of
the method to investigations of the effect of DNA sequence heterogeneity, and
the role multiple targets and traps in the protein search dynamics are
discussed.
Nonlinearity in stock networks
Date: 2018-06-26 00:00:00
Tasks: Time Series, Time Series Analysis

Stock networks, constructed from stock price time series, are a
well-established tool for the characterization of complex behavior in stock
markets. Following Mantegna's seminal paper, the linear Pearson's correlation
coefficient between pairs of stocks has been the usual way to determine network
edges. Recently, possible effects of nonlinearity on the graph-theoretical
properties of such networks have been demonstrated when using nonlinear
measures such as mutual information instead of linear correlation. In this
paper, we quantitatively characterize the nonlinearity in stock time series and
the effect it has on stock network properties. This is achieved by a systematic
multi-step approach that allows us to quantify the nonlinearity of coupling;
correct its effects wherever it is caused by simple univariate non-Gaussianity;
potentially localize in space and time any remaining strong sources of this
nonlinearity; and, finally, study the effect nonlinearity has on global network
properties. By applying this multi-step approach to stocks included in three
prominent indices (NYSE100, FTSE100 and SP500), we establish that the apparent
nonlinearity that has been observed is largely due to univariate
non-Gaussianity. Furthermore, strong nonstationarity in a few specific stocks
may play a role. In particular, the sharp decrease in some stocks during the
global financial crisis of 2008 gives rise to apparent nonlinear dependencies
among stocks.
From product recommendation to cyber-attack prediction: Generating attack graphs and predicting future attacks
Authors: Polatidis Nikolaos, Pimenidis Elias, Pavlidis Michalis, Papastergiou Spyridon, Mouratidis Haralambos
Date: 2018-04-26 00:00:00
Tasks: Management, Product Recommendation, Recommendation Systems

Modern information society depends on reliable functionality of information
systems infrastructure, while at the same time the number of cyber-attacks has
been increasing over the years and damages have been caused. Furthermore,
graphs can be used to show paths than can be exploited by attackers to intrude
into systems and gain unauthorized access through vulnerability exploitation.
This paper presents a method that builds attack graphs using data supplied from
the maritime supply chain infrastructure. The method delivers all possible
paths that can be exploited to gain access. Then, a recommendation system is
utilized to make predictions about future attack steps within the network. We
show that recommender systems can be used in cyber defense by predicting
attacks. The goal of this paper is to identify attack paths and show how a
recommendation method can be used to classify future cyber-attacks in terms of
risk management. The proposed method has been experimentally evaluated and
validated, with the results showing that it is both practical and effective.
In-field Remote Fingerprint Authentication using Human Body Communication and On-Hub Analytics
Date: 2018-04-26 00:00:00

In this emerging data-driven world, secure and ubiquitous authentication
mechanisms are necessary prior to any confidential information delivery.
Biometric authentication has been widely adopted as it provides a unique and
non-transferable solution for user authentication. In this article, the authors
envision the need for an in-field, remote and on-demand authentication system
for a highly mobile and tactical environment, such as critical information
delivery to soldiers in a battlefield. Fingerprint-based in-field biometric
authentication combined with the conventional password-based techniques would
ensure strong security of critical information delivery. The proposed in-field
fingerprint authentication system involves: (i) wearable fingerprint sensor,
(ii) template extraction (TE) algorithm, (iii) data encryption, (iv) on-body
and long-range communications, all of which are subject to energy constraints
due to the requirement of small form-factor wearable devices. This paper
explores the design space and provides an optimized solution for resource
allocation to enable energy-efficient in-field fingerprint-based
authentication. Using Human Body Communication (HBC) for the on-body data
transfer along with the analytics (TE algorithm) on the hub allows for the
maximum lifetime of the energy-sparse sensor. A custom-built hardware prototype
using COTS components demonstrates the feasibility of the in-field fingerprint
authentication framework.
Simulating dysarthric speech for training data augmentation in clinical speech applications
Date: 2018-04-27 00:00:00
Tasks: Data Augmentation

Training machine learning algorithms for speech applications requires large,
labeled training data sets. This is problematic for clinical applications where
obtaining such data is prohibitively expensive because of privacy concerns or
lack of access. As a result, clinical speech applications are typically
developed using small data sets with only tens of speakers. In this paper, we
propose a method for simulating training data for clinical applications by
transforming healthy speech to dysarthric speech using adversarial training. We
evaluate the efficacy of our approach using both objective and subjective
criteria. We present the transformed samples to five experienced
speech-language pathologists (SLPs) and ask them to identify the samples as
healthy or dysarthric. The results reveal that the SLPs identify the
transformed speech as dysarthric 65% of the time. In a pilot classification
experiment, we show that by using the simulated speech samples to balance an
existing dataset, the classification accuracy improves by about 10% after data
augmentation.
Information and Statistical Efficiency When Quantizing Noisy DC Values
Date: 2018-04-27 00:00:00
Tasks: Quantization

This paper considers estimation of a quantized constant in noise when using
uniform and nonuniform quantizers. Estimators based on simple arithmetic
averages, on sample statistical moments and on the maximum-likelihood procedure
are considered. It provides expressions for the statistical efficiency of the
arithmetic mean by comparing its variance to the proper Cram\'er-Rao lower
bound. It is conjectured that the arithmetic mean is optimal among all
estimators with an exactly known bias. Conditions under which its statistical
performance are improved by the other estimation procedures when the exact bias
is not known are found and analyzed. Using simulations and analysis of
experimental data, it is shown that both moment-based and
maximum-likelihood-based estimators provide better results, when the noise
standard deviation is comparable with the quantization step and the noise model
of quantization can not be applied.
The mre11 A470 alleles influence the hereditability and the segregation of telosomes in Saccharomyces cerevisiae (original with corrected raw data)
Authors: In-Joon Baek, Daniel S Moss, Arthur J Lustig
Date: 2018-04-26 00:00:00
Tasks: Vocal Bursts Type Prediction

Telomeres, the nucleoprotein complexes at the termini of linear chromosomes, are essential for the processes of end replication, end protection, and chromatin segregation. The Mre11 complex is involved in multiple cellular roles in DNA repair and structure in the regulation and function of telomere size homeostasis. In this study, we characterize yeast telomere chromatin structure, phenotypic heritability, and chromatin segregation in both wild-type [MRE11] and A470 motif alleles. MRE11 strains confer a telomere size of 300 base pairs of G+T irregular simple sequence repeats. This DNA and a portion of subtelomeric DNA is embedded in a telosome: a MNase-resistant non-nucleosomal particle. Chromatin immunoprecipitation shows a three to four-fold lower occupancy of Mre11A470T proteins than wild-type proteins in telosomes. Telosomes containing the Mre11A470T protein confer a greater resistance to MNase digestion than wild-type telosomes. The integration of a wild-type MRE11 allele into an ectopic locus in the genome of an mre11A470T mutant and the introduction of an mre11A470T allele at an ectopic site in a wild-type strain lead to unexpectedly differing results. In each case, the replicated sister chromatids inherit telosomes containing only the protein encoded by the genomic mre11 locus, even in the presence of protein encoded by the opposing ectopic allele. We hypothesize that the telosome segregates by a conservative mechanism. These data support a mechanism for the linkage between sister chromatid replication and maintenance of either identical mutant or identical wild-type telosomes after replication of sister chromatids. These data suggest the presence of an active mechanism for chromatin segregation in yeast.
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges
Date: 2018-04-27 00:00:00
Tasks: Drug Design, Pose Prediction, Prediction

Advanced mathematics, such as multiscale weighted colored graph and element
specific persistent homology, and machine learning including deep neural
networks were integrated to construct mathematical deep learning models for
pose and binding affinity prediction and ranking in the last two D3R grand
challenges in computer-aided drug design and discovery. D3R Grand Challenge 2
(GC2) focused on the pose prediction and binding affinity ranking and free
energy prediction for Farnesoid X receptor ligands. Our models obtained the top
place in absolute free energy prediction for free energy Set 1 in Stage 2. The
latest competition, D3R Grand Challenge 3 (GC3), is considered as the most
difficult challenge so far. It has 5 subchallenges involving Cathepsin S and
five other kinase targets, namely VEGFR2, JAK2, p38-$\alpha$, TIE2, and ABL1.
There is a total of 26 official competitive tasks for GC3. Our predictions were
ranked 1st in 10 out of 26 official competitive tasks.
Compressive Sensing Imaging of 3-D Object by a Holographic Algorithm
Date: 2018-04-27 00:00:00
Tasks: Compressive Sensing

Existing three-dimensional (3-D) compressive sensing-based millimeter-wave
(MMW) imaging methods require a large-scale storage of the sensing matrix and
immense computations owing to the high dimension matrix-vector model employed
in the optimization. To overcome this shortcoming, we propose an efficient
compressive sensing (CS) method based on a holographic algorithm for near-field
3-D MMW imaging. An interpolation-free holographic imaging algorithm is
developed and used as a sensing operator, in lieu of the nominal sensing matrix
typically used in the CS iterative optimization procedure. In so doing, the
problem induced by the large-scale sensing matrix is avoided. With no
interpolations required, both the computational speed and the image quality can
be improved. Simulation and experimental results are provided to demonstrate
the performance of the proposed method in comparison with those of the Omega-K
based CS and the traditional Fourier-based imaging techniques.
Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data
Date: 2018-04-28 00:00:00
Tasks: Quantization

The estimation of the amplitude of a sine wave from the sequence of its
quantized samples is a typical problem in instrumentation and measurement. A
standard approach for its solution makes use of a least squares estimator (LSE)
that, however, does not perform optimally in the presence of quantization
errors. In fact, if the quantization error cannot be modeled as an additive
noise source, as it often happens in practice, the LSE returns biased
estimates. In this paper, we consider the estimation of the amplitude of a
noisy sine wave after quantization. The proposed technique is based on a
uniform distribution of signal phases and it does not require that the
quantizer has equally spaced transition levels. The experimental results show
that this technique removes the estimation bias associated with the usage of
the LSE and that it is sufficiently robust with respect to small uncertainties
in the known values of transition levels.
Arbitrage-free pricing of American options in nonlinear markets
Date: 2018-07-14 00:00:00

We re-examine and extend the findings from the recent paper by Dumitrescu,
Quenez and Sulem (2018) who studied American and game options in a particular
market model using the nonlinear arbitrage-free pricing approach developed in
El Karoui and Quenez (1997). In the first part, we provide a detailed study of
unilateral valuation problems for the two counterparties in an American-style
contract within the framework of a general nonlinear market. We extend results
from Bielecki and Rutkowski (2015) and Bielecki, Cialenco and Rutkowski (2018)
who examined the case of a European-style contract. In the second part, we
present a BSDE approach, which is used to establish more explicit pricing,
hedging and exercising results when solutions to reflected BSDEs have
additional desirable properties.