pwc-archive/papers-with-abstracts huggingface dataset:
Shift-enabled graphs: Graphs where shift-invariant filters are representable as polynomials of shift operations
Date: 2018-06-10 00:00:00

In digital signal processing, shift-invariant filters can be represented as a
polynomial expansion of a shift operation,that is, the Z-transform
representation. When extended to graph signal processing (GSP), this would mean
that a shift-invariant graph filter can be represented as a polynomial of the
adjacency (shift) matrix of the graph. However, the characteristic and minimum
polynomials of the adjacency matrix must be identical for the property to hold.
While it has been suggested that this condition might be ignored as it is
always possible to find a polynomial transform to represent the original
adjacency matrix by another adjacency matrix that satisfies the condition, this
letter shows that a filter that is shift invariant in terms of the original
graph may not be shift invariant anymore under the modified graph and vice
versa. We introduce the notion of "shift-enabled graph" for graphs that satisfy
the aforementioned condition, and present a concrete example of a graph that is
not "shift-enabled" and a shift-invariant filter that is not a polynomial of
the shift operation matrix. The result provides a deeper understanding of
shift-invariant filters when applied in GSP and shows that further
investigation of shift-enabled graphs is needed to make it applicable to
practical scenarios.
Feasibility Study of OFDM-MFSK Modulation Scheme for Smart Metering Technology
Date: 2017-10-28 00:00:00

The Orthogonal Frequency Division Multiplexing based M-ary Frequency Shift
Keying (OFDM-MFSK) is a noncoherent modulation scheme which merges MFSK with
the OFDM waveform. It is designed to improve the receiver sensitivity in the
hard environments where channel estimation is very difficult to perform. In
this paper, the OFDM-MFSK is suggested for the smart metering technology and
its performance is measured and compared with the ordinary OFDM-BPSK. Our
results show that, depending on the MFSK size value (M), the Packet Error Rate
(PER) has dramatically improved for OFDM-MFSK. Additionally, the adaptive
OFDM-MFSK, which selects the best M value that gives the minimum PER and higher
throughput for each Smart Meter (SM), has better coverage than OFDM-BPSK.
Although its throughput and capacity are lower than OFDMBPSK, the connected SMs
per sector are higher. Based on the smart metering technology requirements
which imply the need for high coverage and low amount of data exchanged between
the network and the SMs, The OFDM-MFSK can be efficiently used in this
technology.
High-Resolution, Three-Dimensional Reconstruction of the Outflow Tract Demonstrates Segmental Differences in Cleared Eyes
Date: 2017-10-28 00:00:00

Purpose: The rate of conventional aqueous humor outflow is the highest
nasally. We hypothesized that this is reflected in regionally different outflow
structures and analyzed the entire limbus by high-resolution, full-thickness
ribbon-scanning confocal microscopy (RSCM). Methods: We perfused pig eyes by
anterior chamber cannulation with eight lectin-fluorophore conjugates, followed
by optical clearance with benzyl alcohol benzyl benzoate (BABB). RSCM and an
advanced analysis software (Imaris) were used to reconstruct a
three-dimensional, whole-specimen rendering of the perilimbal outflow
structures. We performed morphometric analyses of the outflow tract from the
level of the trabecular meshwork (TM) to the scleral vascular plexus (SVP).
Results: Except for pigmented structures, BABB cleared the entire eye.
Rhodamine-conjugated Glycine max agglutinin (soybean, SBA) labeled the outflow
tract evenly and retained fluorescence for months. RSCM produced terabyte-sized
files allowing for in silico dissection of outflow tract vessel at a high
resolution and in 3D. Networks of interconnected lumina were traced from the TM
to downstream drainage structures. The collector channel (CC) volumes were ten
times smaller than the receiving SVP vessels, the largest of which were in the
inferior limbus. Proximal CC diameters were up to four times the size of distal
diameters and more elliptical at their proximal ends. The largest CCs were
found in the superonasal and inferonasal quadrants where the highest outflow
occurs. Conclusions: RSCM of cleared eyes enabled high-resolution, volumetric
analysis of the outflow tract. The proximal structures had greater diameters
nasally while the SVP was larger in the inferior limbus.
Research on ruin probability of risk model based on AR(1) series
Date: 2017-10-29 00:00:00

In this text, we establish the risk model based on AR(1) series and propose
the basic model which has a dependent structure under intensity of claim
number. Considering some properties of the risk model, we take advantage of
newton iteration method to figure out the adjustment coefficient and estimate
the exponential upper bound of ruin probability. This is significant to refine
the research of ruin theory. As a result, our theory will help develop
insurance industry stably.
Probabilistic Distribution Power Flow Based on Finite Smoothing of Data Samples Considering Plug-in Hybrid Electric Vehicles
Date: 2017-10-30 00:00:00

The ever increasing penetration of plug-in hybrid electric vehicles in
distribution systems has triggered the need for a more accurate and at the same
time fast solution to probabilistic distribution power flow problem. In this
paper a novel algorithm is introduced based on finite sample points to
determine probabilistic density function of probabilistic distribution power
flow results. A modified probabilistic charging behavior of plug-in hybrid
electric vehicles at charging stations and their overlap with residential peak
load is evaluated in probabilistic distribution power flow problem. The
proposed algorithm is faster than Monte Carlo Simulation and at the same time
keeps adequate accuracy. It is applied to solve probabilistic distribution
power flow for two dimensionally different test systems and is compared with
recent probabilistic solutions. Simulation results show the accuracy and
efficiency of the proposed algorithm to calculate probability density function
of uncertain outputs.
Drift and behavior of E. coli cells
Date: 2017-10-30 00:00:00

Chemotaxis of the bacterium Escherichia coli is well understood in shallow
chemical gradients, but its swimming behavior remains difficult to interpret in
steep gradients. By focusing on single-cell trajectories from simulations, we
investigated the dependence of the chemotactic drift velocity on attractant
concentration in an exponential gradient. While maxima of the average drift
velocity can be interpreted within analytical linear-response theory of
chemotaxis in shallow gradients, limits in drift due to steep gradients and
finite number of receptor-methylation sites for adaptation go beyond
perturbation theory. For instance, we found a surprising pinning of the cells
to the concentration in the gradient at which cells run out of methylation
sites. To validate the positions of maximal drift, we recorded single-cell
trajectories in carefully designed chemical gradients using microfluidics.
Content-based Representations of audio using Siamese neural networks
Authors: Manocha Pranay, Badlani Rohan, Kumar Anurag, Shah Ankit, Elizalde Benjamin, Raj Bhiksha
Date: 2018-02-15 00:00:00
Tasks: Retrieval

In this paper, we focus on the problem of content-based retrieval for audio,
which aims to retrieve all semantically similar audio recordings for a given
audio clip query. This problem is similar to the problem of query by example of
audio, which aims to retrieve media samples from a database, which are similar
to the user-provided example. We propose a novel approach which encodes the
audio into a vector representation using Siamese Neural Networks. The goal is
to obtain an encoding similar for files belonging to the same audio class, thus
allowing retrieval of semantically similar audio. Using simple similarity
measures such as those based on simple euclidean distance and cosine similarity
we show that these representations can be very effectively used for retrieving
recordings similar in audio content.
Initial cell density encodes proliferative potential in cancer cell populations
Authors: Chiara Enrico Bena, Marco Del Giudice, Alice Grob, Thomas Gueudré, Mattia Miotto, Dimitra Gialama, Matteo Osella, Emilia Turco, Francesca Ceroni, Andrea De Martino, Carla Bosia
Date: 2017-10-30 00:00:00

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells.
Bibliometric-Enhanced Information Retrieval: 5th International BIR Workshop
Authors: Mayr Philipp, Frommholz Ingo, Cabanac Guillaume
Date: 2017-10-30 00:00:00
Tasks: Information Retrieval, Retrieval

Bibliometric-enhanced Information Retrieval (BIR) workshops serve as the
annual gathering of IR researchers who address various information-related
tasks on scientific corpora and bibliometrics. The workshop features original
approaches to search, browse, and discover value-added knowledge from
scientific documents and related information networks (e.g., terms, authors,
institutions, references). We welcome contributions elaborating on dedicated IR
systems, as well as studies revealing original characteristics on how
scientific knowledge is created, communicated, and used. In this paper we
introduce the BIR workshop series and discuss some selected papers presented at
previous BIR workshops.
Mathematical Model and Analysis of Transmission Dynamics of Hepatitis B Virus
Authors: Blessing O. Emerenini, Simeon C. Inyama
Date: 2017-10-31 00:00:00

Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus (HBV). In this paper, the transmission dynamics of hepatitis B is formulated with a mathematical model with considerations of different classes of individuals, namely immunized, susceptible, latent,infected and recovered class. The role of vaccination of new born babies against hepatitis B and the treatment of both latently and actively infected individuals in controlling the spread are factored into the model. The model in this study is based on the standard SEIR model. The disease-free equilibrium state of the model was established and its stability analyzed using the Routh-Hurwitz theorem. The result of the analysis of the stability of the disease-free equilibrium state shows that hepatitis B can totally be eradicated if effort is made to ensure that the sum of the rate of recovery of the latent class, the rate at which latently infected individuals become actively infected and the rate of natural death must have a lower bound.
Related Fact Checks: a tool for combating fake news
Authors: Guha Sreya
Date: 2017-10-31 00:00:00
Tasks: Misinformation

The emergence of "Fake News" and misinformation via online news and social
media has spurred an interest in computational tools to combat this phenomenon.
In this paper we present a new "Related Fact Checks" service, which can help a
reader critically evaluate an article and make a judgment on its veracity by
bringing up fact checks that are relevant to the article. We describe the core
technical problems that need to be solved in building a "Related Fact Checks"
service, and present results from an evaluation of an implementation.
Identifying the Effects of a Program Offer with an Application to Head Start
Authors: Vishal Kamat
Date: 2017-11-06 00:00:00

I propose a treatment selection model that introduces unobserved heterogeneity in both choice sets and preferences to evaluate the average effects of a program offer. I show how to exploit the model structure to define parameters capturing these effects and then computationally characterize their identified sets under instrumental variable variation in choice sets. I illustrate these tools by analyzing the effects of providing an offer to the Head Start preschool program using data from the Head Start Impact Study. I find that such a policy affects a large number of children who take up the offer, and that they subsequently have positive effects on test scores. These effects arise from children who do not have any preschool as an outside option. A cost-benefit analysis reveals that the earning benefits associated with the test score gains can be large and outweigh the net costs associated with offer take up.
The perverse incentive for insurance instruments that are derivatives: solving the jackpot problem with a clawback lien for default insurance notes
Authors: Brian P. Hanley
Date: 2017-10-18 00:00:00

When an insurance note is also a derivative a serious problem arises because a derivative must be fulfilled immediately. This feature of derivatives prevents claims processing procedures that screen out ineligible claims. This, in turn, creates a perverse incentive for insured holders of notes to commit acts that result in payment. This problem first surfaced with CDS contracts, which are part of a class of loan insurance I term a default insurance note. Without an address to this problem, within the average range of returns for a large venture capital portfolio, a venture-bank makes less money the better their investments do, in a continuous function. The highest rate of return is a total loss, 64% more than a top portfolio. Here, a strategy for removing this perverse incentive is defined, consisting of a clawback lien that returns part of the payment value as a lien on the firm that is the beneficiary of the insurance. This is presented as the final major component for implementing a default insurance note system so that venture-banking can operate to maximum benefit. Removing the perverse incentive also minimizes disincentive for underwriters to deny DIN coverage to new venture capital firms, or to those firms that have historical earnings which are below average.
Food Recommender Systems: Important Contributions, Challenges and Future Research Directions
Authors: Trattner Christoph, Elsweiler David
Date: 2017-11-10 00:00:00
Tasks: Food recommendation, Recommendation Systems

The recommendation of food items is important for many reasons. Attaining
cooking inspiration via digital sources is becoming evermore popular; as are
systems, which recommend other types of food, such as meals in restaurants or
products in supermarkets. Researchers have been studying these kinds of systems
for many years, suggesting not only that can they be a means to help people
find food they might want to eat, but also help them nourish themselves more
healthily. This paper provides a summary of the state-of-the-art of so-called
food recommender systems, highlighting both seminal and most recent approaches
to the problem, as well as important specializations, such as food
recommendation systems for groups of users or systems which promote healthy
eating. We moreover discuss the diverse challenges involved in designing recsys
for food, summarise the lessons learned from past research and outline what we
believe to be important future directions and open questions for the field. In
providing these contributions we hope to provide a useful resource for
researchers and practitioners alike.
Defining Tourism Domains for Semantic Annotation of Web Content
Authors: Panasiuk Oleksandra, Kärle Elias, Simsek Umutcan, Fensel Dieter
Date: 2018-02-16 00:00:00

Schema.org is an initiative by Bing, Google, Yahoo! and Yandex that publishes
a vocabulary for creating structured data markup on web pages. The use of
schema.org is necessary to increase the visibility of a website, making the
content understandable to different automated agents (e.g. search engines,
chatbots or personal assistant systems). The domain specifications are the
subsets of types from the schema.org vocabulary, each associated with a set of
properties. The challenge is to choose the right classes and properties for an
annotation in a given domain. In this paper we address the problem of finding a
subset of types and properties for complete and correct annotation of different
tourism domains. The approach provides a collection of domain specifications
that were built based on domain analysis and vocabulary selection.
A distributed system for SearchOnMath based on the Microsoft BizSpark program
Authors: Oliveira Ricardo M., Gonzaga Flavio B., Barbosa Valmir C., Xexéo Geraldo B.
Date: 2017-11-11 00:00:00
Tasks: Information Retrieval, Retrieval

Mathematical information retrieval is a relatively new area, so the first
search tools capable of retrieving mathematical formulas began to appear only a
few years ago. The proposals made public so far mostly implement searches on
internal university databases, small sets of scientific papers, or Wikipedia in
English. As such, only modest computing power is required. In this context,
SearchOnMath has emerged as a pioneering tool in that it indexes several
different databases and is compatible with several mathematical representation
languages. Given the significantly greater number of formulas it handles, a
distributed system becomes necessary to support it. The present study is based
on the Microsoft BizSpark program and has aimed, for 38 different
distributed-system scenarios, to pinpoint the one affording the best response
times when searching the SearchOnMath databases for a collection of 120
formulas.
Orbital-angular-momentum mode-group multiplexed transmission over a graded-index ring-core fiber based on receive diversity and maximal ratio combining
Date: 2017-11-10 00:00:00
Tasks: Diversity

An orbital-angular-momentum (OAM) mode-group multiplexing (MGM) scheme based
on a graded-index ring-core fiber (GIRCF) is proposed, in which a single-input
two-output (or receive diversity) architecture is designed for each MG channel
and simple digital signal processing (DSP) is utilized to adaptively resist the
mode partition noise resulting from random intra-group mode crosstalk. There is
no need of complex multiple-input multiple-output (MIMO) equalization in this
scheme. Furthermore, the signal-to-noise ratio (SNR) of the received signals
can be improved if a simple maximal ratio combining (MRC) technique is employed
on the receiver side to efficiently take advantage of the diversity gain of
receiver. Intensity-modulated direct-detection (IM-DD) systems transmitting
three OAM mode groups with total 100-Gb/s discrete multi-tone (DMT) signals
over a 1-km GIRCF and two OAM mode groups with total 40-Gb/s DMT signals over
an 18-km GIRCF are experimentally demonstrated, respectively, to confirm the
feasibility of our proposed OAM-MGM scheme.
Evaluating deep variational autoencoders trained on pan-cancer gene expression
Date: 2017-11-13 00:00:00
Tasks: Denoising, Dimensionality Reduction

Cancer is a heterogeneous disease with diverse molecular etiologies and
outcomes. The Cancer Genome Atlas (TCGA) has released a large compendium of
over 10,000 tumors with RNA-seq gene expression measurements. Gene expression
captures the diverse molecular profiles of tumors and can be interrogated to
reveal differential pathway activations. Deep unsupervised models, including
Variational Autoencoders (VAE) can be used to reveal these underlying patterns.
We compare a one-hidden layer VAE to two alternative VAE architectures with
increased depth. We determine the additional capacity marginally improves
performance. We train and compare the three VAE architectures to other
dimensionality reduction techniques including principal components analysis
(PCA), independent components analysis (ICA), non-negative matrix factorization
(NMF), and analysis of gene expression by denoising autoencoders (ADAGE). We
compare performance in a supervised learning task predicting gene inactivation
pan-cancer and in a latent space analysis of high grade serous ovarian cancer
(HGSC) subtypes. We do not observe substantial differences across algorithms in
the classification task. VAE latent spaces offer biological insights into HGSC
subtype biology.
Analysis of non-processive molecular motor transport using renewal reward theory
Authors: Christopher E. Miles, Sean D. Lawley, James P. Keener
Date: 2017-11-13 00:00:00
Tasks: Clustering, Position

We propose and analyze a mathematical model of cargo transport by non-processive molecular motors. In our model, the motors change states by random discrete events (corresponding to stepping and binding/unbinding), while the cargo position follows a stochastic differential equation (SDE) that depends on the discrete states of the motors. The resulting system for the cargo position is consequently an SDE that randomly switches according to a Markov jump process governing motor dynamics. To study this system we (1) cast the cargo position in a renewal theory framework and generalize the renewal reward theorem and (2) decompose the continuous and discrete sources of stochasticity and exploit a resulting pair of disparate timescales. With these mathematical tools, we obtain explicit formulas for experimentally measurable quantities, such as cargo velocity and run length. Analyzing these formulas then yields some predictions regarding so-called non-processive clustering, the phenomenon that a single motor cannot transport cargo, but that two or more motors can. We find that having motor stepping, binding, and unbinding rates depend on the number of bound motors, due to geometric effects, is necessary and sufficient to explain recent experimental data on non-processive motors.
Tree-Based Unrooted Nonbinary Phylogenetic Networks
Date: 2017-11-20 00:00:00

Phylogenetic networks are a generalisation of phylogenetic trees that allow
for more complex evolutionary histories that include hybridisation-like
processes. It is of considerable interest whether a network can be considered
`tree-like' or not, which lead to the introduction of \textit{tree-based}
networks in the rooted, binary context. Tree-based networks are those networks
which can be constructed by adding additional edges into a given phylogenetic
tree, called the \textit{base tree}. Previous extensions have considered
extending to the binary, unrooted case and the nonbinary, rooted case. We
extend tree-based networks to the context of unrooted, nonbinary networks in
three ways, depending on the types of additional edges that are permitted. A
phylogenetic network in which every embedded tree is a base tree is termed a
\textit{fully tree-based} network. We also extend this concept to unrooted,
nonbinary phylogenetic networks and classify the resulting networks. We also
derive some results on the colourability of tree-based networks, which can be
useful to determine whether a network is tree-based.