pwc-archive/papers-with-abstracts huggingface dataset:
A logical representation of Arabic questions toward automatic passage extraction from the Web
Authors: Bellot Patrice  LSIS, DIMAG, Bakari Wided, Neji Mahmoud
Date: 2018-05-23 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval

With the expanding growth of Arabic electronic data on the web, extracting
information, which is actually one of the major challenges of the
question-answering, is essentially used for building corpus of documents. In
fact, building a corpus is a research topic that is currently referred to among
some other major themes of conferences, in Natural Language Processing (NLP),
such as, Information Retrieval (IR), Question-Answering (QA), Automatic Summary
(AS), etc. Generally, a question-answering system provides various passages to
answer the user questions. To make these passages truly informative, this
system needs access to an underlying knowledge base; this requires the
construction of a corpus. The aim of our research is to build an Arabic
question-answering system. In addition, analyzing the question must be the
first step. Next, it is essential to retrieve a passage from the web that can
serve as an appropriate answer. In this paper, we propose a method to analysis
the question and retrieve the passage answer in the Arabic language. For the
question analysis, five factual question types are processed. Additionally, our
purpose is to experiment with the generation of a logic representation from the
declarative form of each question. Several studies, deal with the logic
approaches in question-answering, are discussed in other languages than the
Arabic language. This representation is very promising because it helps us
later in the selection of a justifiable answer. The accuracy of questions that
are correctly analyzed and translated into the logic form achieved 64%. And
then, the results of passages of texts that are automatically generated
achieved an 87% score for accuracy and a 98% score for c@1.
Comments on "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform"
Date: 2018-05-23 00:00:00

The recently introduced coder based on region-adaptive hierarchical transform
(RAHT) for the compression of point clouds attributes, was shown to have a
performance competitive with the state-of-the-art, while being much less
complex. In the paper "Compression of 3D Point Clouds Using a Region-Adaptive
Hierarchical Transform", top performance was achieved using arithmetic coding
(AC), while adaptive run-length Golomb-Rice (RLGR) coding was presented as a
lower-performance lower-complexity alternative. However, we have found that by
reordering the RAHT coefficients we can largely increase the runs of zeros and
significantly increase the performance of the RLGR-based RAHT coder. As a
result, the new coder, using ordered coefficients, was shown to outperform all
other coders, including AC-based RAHT, at an even lower computational cost. We
present new results and plots that should enhance those in the work of Queiroz
and Chou to include the new results for RLGR-RAHT. We risk to say, based on the
results herein, that RLGR-RAHT with sorted coefficients is the new
state-of-the-art in point cloud compression.
A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing
Date: 2018-05-22 00:00:00
Tasks: Speaker Verification

The second Automatic Speaker Verification Spoofing and Countermeasures
challenge (ASVspoof 2017) focused on "replay attack" detection. The best
deep-learning systems to compete in ASVspoof 2017 used Convolutional Neural
Networks (CNNs) as a feature extractor. In this paper, we study their
performance in an end-to-end setting. We find that these architectures show
poor generalization in the evaluation dataset, but find a compact architecture
that shows good generalization on the development data. We demonstrate that for
this dataset it is not easy to obtain a similar level of generalization on both
the development and evaluation data. This leads to a variety of open questions
about what the differences are in the data; why these are more evident in an
end-to-end setting; and how these issues can be overcome by increasing the
training data.
Why the brain knows more than we do: non-conscious representations and their role in the construction of conscious experience
Date: 2018-05-23 00:00:00

Scientific studies have shown that non-conscious stimuli and representations
influence information processing during conscious experience. In the light of
such evidence, questions about potential functional links between non-conscious
brain representations and conscious experience arise. This article discusses a
neural coding model capable of explaining how statistical learning mechanisms
in dedicated resonant circuits could generate specific temporal activity traces
of non-conscious representations in the brain. How reentrant signaling,
top-down matching, and statistical coincidence of such activity traces may lead
to the progressive consolidation of temporal patterns that constitute the
neural signatures of conscious experience in networks extending across large
distances beyond functionally specialized brain regions is then explained.
Identification in Nonparametric Models for Dynamic Treatment Effects
Date: 2019-01-14 00:00:00

This paper develops a nonparametric model that represents how sequences of
outcomes and treatment choices influence one another in a dynamic manner. In
this setting, we are interested in identifying the average outcome for
individuals in each period, had a particular treatment sequence been assigned.
The identification of this quantity allows us to identify the average treatment
effects (ATE's) and the ATE's on transitions, as well as the optimal treatment
regimes, namely, the regimes that maximize the (weighted) sum of the average
potential outcomes, possibly less the cost of the treatments. The main
contribution of this paper is to relax the sequential randomization assumption
widely used in the biostatistics literature by introducing a flexible
choice-theoretic framework for a sequence of endogenous treatments. We show
that the parameters of interest are identified under each period's two-way
exclusion restriction, i.e., with instruments excluded from the
outcome-determining process and other exogenous variables excluded from the
treatment-selection process. We also consider partial identification in the
case where the latter variables are not available. Lastly, we extend our
results to a setting where treatments do not appear in every period.
General multilevel Monte Carlo methods for pricing discretely monitored Asian options
Date: 2018-09-28 00:00:00

We describe general multilevel Monte Carlo methods that estimate the price of
an Asian option monitored at $m$ fixed dates. Our approach yields unbiased
estimators with standard deviation $O(\epsilon)$ in $O(m + (1/\epsilon)^{2})$
expected time for a variety of processes including the Black-Scholes model,
Merton's jump-diffusion model, the Square-Root diffusion model, Kou's double
exponential jump-diffusion model, the variance gamma and NIG exponential Levy
processes and, via the Milstein scheme, processes driven by scalar stochastic
differential equations. Using the Euler scheme, our approach estimates the
Asian option price with root mean square error $O(\epsilon)$ in
$O(m+(\ln(\epsilon)/\epsilon)^{2})$ expected time for processes driven by
multidimensional stochastic differential equations. Numerical experiments
confirm that our approach outperforms the conventional Monte Carlo method by a
factor of order $m$.
A network biology-based approach to evaluating the effect of environmental contaminants on human interactome and diseases
Date: 2018-05-24 00:00:00

Environmental contaminant exposure can pose significant risks to human
health. Therefore, evaluating the impact of this exposure is of great
importance; however, it is often difficult because both the molecular mechanism
of disease and the mode of action of the contaminants are complex. We used
network biology techniques to quantitatively assess the impact of environmental
contaminants on the human interactome and diseases with a particular focus on
seven major contaminant categories: persistent organic pollutants (POPs),
dioxins, polycyclic aromatic hydrocarbons (PAHs), pesticides,
perfluorochemicals (PFCs), metals, and pharmaceutical and personal care
products (PPCPs). We integrated publicly available data on toxicogenomics, the
diseasome, protein-protein interactions (PPIs), and gene essentiality and found
that a few contaminants were targeted to many genes, and a few genes were
targeted by many contaminants. The contaminant targets were hub proteins in the
human PPI network, whereas the target proteins in most categories did not
contain abundant essential proteins. Generally, contaminant targets and
disease-associated proteins were closely associated with the PPI network, and
the closeness of the associations depended on the disease type and chemical
category. Network biology techniques were used to identify environmental
contaminants with broad effects on the human interactome and
contaminant-sensitive biomarkers. Moreover, this method enabled us to quantify
the relationship between environmental contaminants and human diseases, which
was supported by epidemiological and experimental evidence. These methods and
findings have facilitated the elucidation of the complex relationship between
environmental exposure and adverse health outcomes.
A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership
Authors: Vall Andreu, Dorfer Matthias, Schedl Markus, Widmer Gerhard
Date: 2018-05-24 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Automated music playlist continuation is a common task of music recommender
systems, that generally consists in providing a fitting extension to a given
playlist. Collaborative filtering models, that extract abstract patterns from
curated music playlists, tend to provide better playlist continuations than
content-based approaches. However, pure collaborative filtering models have at
least one of the following limitations: (1) they can only extend playlists
profiled at training time; (2) they misrepresent songs that occur in very few
playlists. We introduce a novel hybrid playlist continuation model based on
what we name "playlist-song membership", that is, whether a given playlist and
a given song fit together. The proposed model regards any playlist-song pair
exclusively in terms of feature vectors. In light of this information, and
after having been trained on a collection of labeled playlist-song pairs, the
proposed model decides whether a playlist-song pair fits together or not.
Experimental results on two datasets of curated music playlists show that the
proposed playlist continuation model compares to a state-of-the-art
collaborative filtering model in the ideal situation of extending playlists
profiled at training time and where songs occurred frequently in training
playlists. In contrast to the collaborative filtering model, and as a result of
its general understanding of the playlist-song pairs in terms of feature
vectors, the proposed model is additionally able to (1) extend non-profiled
playlists and (2) recommend songs that occurred seldom or never in
training~playlists.
Affinity Network Fusion and Semi-supervised Learning for Cancer Patient Clustering
Date: 2018-05-22 00:00:00
Tasks: Clustering, Few-Shot Learning

Defining subtypes of complex diseases such as cancer and stratifying patient
groups with the same disease but different subtypes for targeted treatments is
important for personalized and precision medicine. Approaches that incorporate
multi-omic data are more advantageous to those using only one data type for
patient clustering and disease subtype discovery. However, it is challenging to
integrate multi-omic data as they are heterogeneous and noisy. In this paper,
we present Affinity Network Fusion (ANF) to integrate multi-omic data for
patient clustering. ANF first constructs patient affinity networks for each
omic data type, and then calculates a fused network for spectral clustering. We
applied ANF to a processed harmonized cancer dataset downloaded from GDC data
portal consisting of 2193 patients, and generated promising results on
clustering patients into correct disease types. Moreover, we developed a
semi-supervised model combining ANF and neural network for few-shot learning.
In several cases, the model can achieve greater than 90% acccuracy on test set
with training less than 1% of the data. This demonstrates the power of ANF in
learning a good representation of patients, and shows the great potential of
semi-supervised learning in cancer patient clustering.
Forecasting the sustainable status of the labor market in agriculture
Date: 2018-05-23 00:00:00

In this article, a game-theoretic model is constructed that is related to the
problem of optimal assignments. Examples are considered. A compromise point is
found, the Nash equilibriums and the decision of the Nash arbitration scheme
are constructed.
A self-organized criticality participative pricing mechanism for selling zero-marginal cost products
Authors: Daniel Fraiman
Date: 2018-05-24 00:00:00

In today's economy, selling a new zero-marginal cost product is a real challenge, as it is difficult to determine a product's "correct" sales price based on its profit and dissemination. As an example, think of the price of a new app or video game. New sales mechanisms for selling this type of product need to be designed, in particular ones that consider consumer preferences and reality. Current auction mechanisms establish a time deadline for the auction to take place. This deadline is set to increase the number of bidders and thus the final offering price. Consumers want to obtain the product as quickly as possible from the moment they become interested in it, and this time does not always coincide with the seller's deadline. Naturally, consumers also want to pay a price they consider "fair". Here we introduce an auction model where buyers continuously place bids and the challenge is to decide quickly whether or not to accept them. The model does not include a deadline for placing bids, and exhibits self-organized criticality; it presents a critical price from which a bid is accepted with probability one, and avalanches of sales above this value are observed. This model is of particular interest for startup companies interested in profit as well as making the product known on the market.
Learning with precise spike times: A new decoding algorithm for liquid state machines
Date: 2019-07-13 00:00:00
Tasks: Binary Classification

There is extensive evidence that biological neural networks encode
information in the precise timing of the spikes generated and transmitted by
neurons, which offers several advantages over rate-based codes. Here we adopt a
vector space formulation of spike train sequences and introduce a new liquid
state machine (LSM) network architecture and a new forward orthogonal
regression algorithm to learn an input-output signal mapping or to decode the
brain activity. The proposed algorithm uses precise spike timing to select the
presynaptic neurons relevant to each learning task. We show that using precise
spike timing to train the LSM and selecting the readout neurons leads to a
significant increase in performance on binary classification tasks as well as
in decoding neural activity from multielectrode array recordings, compared with
what is achieved using the standard architecture and training method.
Tie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems
Date: 2018-05-24 00:00:00
Tasks: Clustering, Distributed Optimization, Graph Clustering

The convergence performance of distributed optimization algorithms is of
significant importance to solve optimal power flow (OPF) in a distributed
fashion. In this paper, we aim to provide some insights on how to partition a
power system to achieve a high convergence rate of distributed algorithms for
the solution of an OPF problem. We analyzed several features of the power
network to find a set of suitable partitions with the aim of convergence
performance improvement. We model the grid as a graph and decompose it based on
the edge betweenness graph clustering. This technique provides several
partitions. To find an effective partitioning, we merge the partitions obtained
by clustering technique and analyze them based on characteristics of tie-lines
connecting neighboring partitions. The main goal is to find the best set of
partitions with respect to the convergence speed. We deploy analytical target
cascading (ATC) method to distributedly solve optimization subproblems. We test
the proposed algorithm on the IEEE 118-bus system. The results show that the
algorithm converges faster with a proper partitioning, whereas improper
partitioning leads to a large number of iterations
X-ray tomography of extended objects: a comparison of data acquisition approaches
Date: 2018-07-12 00:00:00

The penetration power of x-rays allows one to image large objects. For
example, centimeter-sized specimens can be imaged with micron-level resolution
using synchrotron sources. In this case, however, the limited beam diameter and
detector size preclude the acquisition of the full sample in a single take,
necessitating strategies for combining data from multiple regions. Object
stitching involves the combination of local tomography data from overlapping
regions, while projection stitching involves the collection of projections at
multiple offset positions from the rotation axis followed by data merging and
reconstruction. We compare these two approaches in terms of radiation dose
applied to the specimen, and reconstructed image quality. Object stitching
involves an easier data alignment problem, and immediate viewing of subregions
before the entire dataset has been acquired. Projection stitching is more
dose-efficient, and avoids certain artifacts of local tomography; however, it
also involves a more difficult data assembly and alignment procedure, in that
it is more sensitive to accumulative registration error.
Ridge Regression Estimated Linear Probability Model Predictions of O-glycosylation in Proteins with Structural and Sequence Data
Date: 2019-02-16 00:00:00

The likelihood of O-GlcNAc glycosylation in human proteins is predicted using
the ridge regression estimated linear probability model (LPM). To achieve this,
sequences from three similar post-translational modifications (PTMs) of
proteins occurring at, or very near, the S or T site are analyzed:
N-glycosylation, O-mucin type (O-GalNAc) glycosylation, and phosphorylation.
Results found include: 1) The consensus composite sequon for O-glycosylation
does NOT have W on either side of the glycosylation site. 2) The same holds for
the consensus sequon for phosphorylation. 3) For LPM estimation, N-glycosylated
sequences are found to be good approximations to non-O-glycosylatable
sequences. 4) The selective positioning of an amino acid along the sequence,
differentiates the PTMs of proteins. 5) Some N-glycosylated sequences are also
phosphorylated at the S or T site. 6) ASA values for N-glycosylated sequences
are stochastically larger than those for O-GlcNAc glycosylated sequences. 7)
Structural attributes (beta turn II, II', helix, beta bridges, beta hairpin,
and the phi angle) are significant LPM predictors of O-GlcNAc glycosylation.
The LPM with sequence and structural data as explanatory variables yields a
Kolmogorov-Smirnov (KS) statistic value of 99%. 8) With only sequence data, the
KS statistic erodes to 80%, underscoring the germaneness of structural
information, which is sparse on O-glycosylated sequences. With 50% as the
cutoff probability for predicting O-GlcNAc glycosylation, this LPM mispredicts
21% of out-of-sample O-GlcNAc glycosylated sequences as not being glycosylated.
The 95% confidence interval around this mispredictions rate is 16% to 26%
An experimental comparison of label selection methods for hierarchical document clusters
Authors: Moura Maria Fernanda, Santos Fabiano Fernandes dos, Rezende Solange Oliveira
Date: 2018-05-24 00:00:00
Tasks: Clustering, Information Retrieval, Retrieval

The focus of this paper is on the evaluation of sixteen labeling methods for
hierarchical document clusters over five datasets. All of the methods are
independent from clustering algorithms, applied subsequently to the dendrogram
construction and based on probabilistic dependence relations among labels and
clusters. To reach a fair comparison as well as a standard benchmark, we
rewrote and presented the labeling methods in a similar notation. The
experimental results were analyzed through a proposed evaluation methodology
based on: (i) data standardization before applying the cluster labeling methods
and over the labeling results; (ii) a particular information retrieval process,
using the obtained labels and their hierarchical relations to construct the
search queries; (iii) evaluation of the retrieval process through precision,
recall and F measure; (iv) variance analysis of the retrieval results to better
understanding the differences among the labeling methods; and, (v) the
emulation of a human judgment through the analysis of a topic observed
coherence measure - normalized Pointwise Mutual Information (PMI). Applying the
methodology, we are able to highlight the advantages of certain methods: to
capture specific information; for a better document hierarchy comprehension at
different levels of granularity; and, to capture the most coherent labels
through the label selections. Finally, the experimental results demonstrated
that the label selection methods which hardly consider hierarchical relations
had the best results.
An Efficient Nonlinear Beamformer Based on P^{th} Root of Detected Signals for Linear-Array Photoacoustic Tomography: Application to Sentinel Lymph Node Imaging
Date: 2018-05-24 00:00:00

In linear-array transducer based photoacoustic (PA) imaging, B-scan PA images
are formed using the raw channel PA signals. Delay-and-Sum (DAS) is the most
prevalent algorithm due to its simple implementation, but it leads to low
quality images. Delay-Multiply-and-Sum (DMAS) provides a higher image quality
in comparison with DAS while it imposes a computational burden of O(M^2). In
this work, we introduce a nonlinear (NL) beamformer for linear-array PA
imaging, which uses the p^{th} root of the detected signals and imposes the
complexity of DAS (O(M)). The proposed algorithm is evaluated numerically and
experimentally (wire-target and in vivo sentinel lymph node (SLN) imaging), and
the effects of the parameter p are investigated. The results show that the NL
algorithm, using a root of p (NL_p), leads to lower sidelobes and higher
signal-to-noise ratio (SNR) compared to DAS and DMAS, for (p > 2). The
sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are
about -31 dB, -52 dB, -52 dB, -67 dB, -88 dB and -109 dB, for DAS, DMAS, NL_2,
NL_3, NL_4 and NL_5, respectively, indicating the superiority of the NL_p
algorithm. In addition, the best value of p for SLN imaging is reported to be
12.
Wireless Transmission of Big Data: Data-oriented Performance Limits and Their Applications
Date: 2019-04-16 00:00:00

The growing popularity of big data and Internet of Things (IoT) applications
bring new challenges to the wireless communication community. Wireless
transmission systems should more efficiently support the large amount of data
traffics from diverse types of information sources. In this article, we
introduce a novel data-oriented approach for the design and optimization of
wireless transmission strategies. Specifically, we define new performance
metrics for individual data transmission session and apply them to compare two
popular channel-adaptive transmission strategies. We develop several
interesting and somewhat counterintuitive observations on these transmission
strategies, which would not be possible with conventional approach. We also
present several interesting future research directions that are worth pursuing
with the data-oriented approach.