pwc-archive/papers-with-abstracts huggingface dataset:
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning
Authors: Wang Menghan, Zheng Xiaolin, Zhang Kun
Date: 2018-04-28 00:00:00
Tasks: Clustering, Multi-Task Learning

This paper considers recommendation algorithm ensembles in a user-sensitive
manner. Recently researchers have proposed various effective recommendation
algorithms, which utilized different aspects of the data and different
techniques. However, the "user skewed prediction" problem may exist for almost
all recommendation algorithms -- algorithms with best average predictive
accuracy may cover up that the algorithms may perform poorly for some part of
users, which will lead to biased services in real scenarios. In this paper, we
propose a user-sensitive ensemble method named "UREC" to address this issue. We
first cluster users based on the recommendation predictions, then we use
multi-task learning to learn the user-sensitive ensemble function for the
users. In addition, to alleviate the negative effects of new user problem to
clustering users, we propose an approximate approach based on a spectral
relaxation. Experiments on real-world datasets demonstrate the superiority of
our methods.
Ladder Networks for Emotion Recognition: Using Unsupervised Auxiliary Tasks to Improve Predictions of Emotional Attributes
Date: 2018-04-28 00:00:00
Tasks: Attribute, Decoder, Denoising, Emotion Recognition

Recognizing emotions using few attribute dimensions such as arousal, valence
and dominance provides the flexibility to effectively represent complex range
of emotional behaviors. Conventional methods to learn these emotional
descriptors primarily focus on separate models to recognize each of these
attributes. Recent work has shown that learning these attributes together
regularizes the models, leading to better feature representations. This study
explores new forms of regularization by adding unsupervised auxiliary tasks to
reconstruct hidden layer representations. This auxiliary task requires the
denoising of hidden representations at every layer of an auto-encoder. The
framework relies on ladder networks that utilize skip connections between
encoder and decoder layers to learn powerful representations of emotional
dimensions. The results show that ladder networks improve the performance of
the system compared to baselines that individually learn each attribute, and
conventional denoising autoencoders. Furthermore, the unsupervised auxiliary
tasks have promising potential to be used in a semi-supervised setting, where
few labeled sentences are available.
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation
Date: 2018-04-28 00:00:00
Tasks: Denoising, Stereo Matching

Acquired 3D point cloud data, whether from active sensors directly or from
stereo-matching algorithms indirectly, typically contain non-negligible noise.
To address the point cloud denoising problem, we propose a fast graph-based
local algorithm. Specifically, given a k-nearest-neighbor graph of the 3D
points, we first approximate it with a bipartite graph(independent sets of red
and blue nodes) using a KL divergence criterion. For each partite of nodes (say
red), we first define surface normal of each red node using 3D coordinates of
neighboring blue nodes, so that red node normals n can be written as a linear
function of red node coordinates p. We then formulate a convex optimization
problem, with a quadratic fidelity term ||p-q||_2^2 given noisy observed red
coordinates q and a graph total variation (GTV) regularization term for surface
normals of neighboring red nodes. We minimize the resulting l2-l1-norm using
alternating direction method of multipliers (ADMM) and proximal gradient
descent. The two partites of nodes are alternately optimized until convergence.
Experimental results show that compared to state-of-the-art schemes with
similar complexity, our proposed algorithm achieves the best overall denoising
performance objectively and subjectively.
Application of Probabilistic Graphical Models in Forecasting Crude Oil Price
Date: 2018-04-29 00:00:00

The dissertation investigates the application of Probabilistic Graphical
Models (PGMs) in forecasting the price of Crude Oil.
  This research is important because crude oil plays a very pivotal role in the
global economy hence is a very critical macroeconomic indicator of the
industrial growth. Given the vast amount of macroeconomic factors affecting the
price of crude oil such as supply of oil from OPEC countries, demand of oil
from OECD countries, geopolitical and geoeconomic changes among many other
variables - probabilistic graphical models (PGMs) allow us to understand by
learning the graphical structure. This dissertation proposes condensing data
numerous Crude Oil factors into a graphical model in the attempt of creating a
accurate forecast of the price of crude oil.
  The research project experiments with using different libraries in Python in
order to construct models of the crude oil market. The experiments in this
thesis investigate three main challenges commonly presented while trading oil
in the financial markets. The first challenge it investigates is the process of
learning the structure of the oil markets; thus allowing crude oil traders to
understand the different physical market factors and macroeconomic indicators
affecting crude oil markets and how they are \textit{causally} related. The
second challenge it solves is the exploration and exploitation of the available
data and the learnt structure in predicting the behaviour of the oil markets.
The third challenge it investigates is how to validate the performance and
reliability of the constructed model in order for it to be deployed in the
financial markets.
  A design and implementation of a probabilistic framework for forecasting the
price of crude oil is also presented as part of the research.
Interpreting Quantile Independence
Date: 2018-04-29 00:00:00

How should one assess the credibility of assumptions weaker than statistical
independence, like quantile independence? In the context of identifying causal
effects of a treatment variable, we argue that such deviations should be chosen
based on the form of selection on unobservables they allow. For quantile
independence, we characterize this form of treatment selection. Specifically,
we show that quantile independence is equivalent to a constraint on the average
value of either a latent propensity score (for a binary treatment) or the cdf
of treatment given the unobservables (for a continuous treatment). In both
cases, this average value constraint requires a kind of non-monotonic treatment
selection. Using these results, we show that several common treatment selection
models are incompatible with quantile independence. We introduce a class of
assumptions which weakens quantile independence by removing the average value
constraint, and therefore allows for monotonic treatment selection. In a
potential outcomes model with a binary treatment, we derive identified sets for
the ATT and QTT under both classes of assumptions. In a numerical example we
show that the average value constraint inherent in quantile independence has
substantial identifying power. Our results suggest that researchers should
carefully consider the credibility of this non-monotonicity property when using
quantile independence to weaken full independence.
The Bayesian optimist's guide to adaptive immune receptor repertoire analysis
Date: 2018-04-29 00:00:00

Probabilistic modeling is fundamental to the statistical analysis of complex
data. In addition to forming a coherent description of the data-generating
process, probabilistic models enable parameter inference about given data sets.
This procedure is well-developed in the Bayesian perspective, in which one
infers probability distributions describing to what extent various possible
parameters agree with the data. In this paper we motivate and review
probabilistic modeling for adaptive immune receptor repertoire data then
describe progress and prospects for future work, from germline haplotyping to
adaptive immune system deployment across tissues. The relevant quantities in
immune sequence analysis include not only continuous parameters such as gene
use frequency, but also discrete objects such as B cell clusters and lineages.
Throughout this review, we unravel the many opportunities for probabilistic
modeling in adaptive immune receptor analysis, including settings for which the
Bayesian approach holds substantial promise (especially if one is optimistic
about new computational methods). From our perspective the greatest prospects
for progress in probabilistic modeling for repertoires concern ancestral
sequence estimation for B cell receptor lineages, including uncertainty from
germline genotype, rearrangement, and lineage development.
Block Sparse Multi-lead ECG Compression Exploiting between-lead Collaboration
Date: 2018-07-17 00:00:00

Multilead ECG compression (MlEC) has attracted tremendous attention in
long-term monitoring of the patients heart behavior. This paper proposes a
method denoted by block sparse MlEC (BlS MlEC) in order to exploit between-lead
correlations to compress the signals in a more efficient way. This is due to
the fact that multi-lead ECG signals are multiple observations of the same
source (heart) from different locations. Consequently, they have high
correlation in terms of the support set of their sparse models which leads them
to share dominant common structure. In order to obtain the block sparse model,
the collaborative version of lasso estimator is applied. In addition, we have
shown that raised cosine kernel has advantages over conventional Gaussian and
wavelet (Daubechies family) due to its specific properties. It is demonstrated
that using raised cosine kernel in constructing the sparsifying basis matrix
gives a sparser model which results in higher compression ratio and lower
reconstruction error. The simulation results show the average improvement of
37%, 88% and 90-97% for BlS M-lEC compared to the non-collaborative case with
raised cosine kernel, Gaussian kernel and collaborative case with Daubechies
wavelet kernels, respectively, in terms of reconstruction error while the
compression ratio is considered fixed.
Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy
Date: 2018-04-30 00:00:00
Tasks: parameter estimation

A large number of recent genome-wide association studies (GWASs) for complex
phenotypes confirm the early conjecture for polygenicity, suggesting the
presence of large number of variants with only tiny or moderate effects.
However, due to the limited sample size of a single GWAS, many associated
genetic variants are too weak to achieve the genome-wide significance. These
undiscovered variants further limit the prediction capability of GWAS.
Restricted access to the individual-level data and the increasing availability
of the published GWAS results motivate the development of methods integrating
both the individual-level and summary-level data. How to build the connection
between the individual-level and summary-level data determines the efficiency
of using the existing abundant summary-level resources with limited
individual-level data, and this issue inspires more efforts in the existing
area.
  In this study, we propose a novel statistical approach, LEP, which provides a
novel way of modeling the connection between the individual-level data and
summary-level data. LEP integrates both types of data by \underline{LE}veraing
\underline{P}leiotropy to increase the statistical power of risk variants
identification and the accuracy of risk prediction. The algorithm for parameter
estimation is developed to handle genome-wide-scale data. Through comprehensive
simulation studies, we demonstrated the advantages of LEP over the existing
methods. We further applied LEP to perform integrative analysis of Crohn's
disease from WTCCC and summary statistics from GWAS of some other diseases,
such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP
was able to significantly increase the statistical power of identifying risk
variants and improve the risk prediction accuracy from 63.39\% ($\pm$ 0.58\%)
to 68.33\% ($\pm$ 0.32\%) using about 195,000 variants.
Acute lymphoblastic leukemia may develop as a result of rapid transformation of a lymphoblast triggered by repeated bone-remodeling during bone-growth
Date: 2018-04-30 00:00:00
Tasks: All

Acute lymphoblastic leukemia (ALL) and chronic lymphocytic leukemia (CLL) are
two major forms of leukemia that arise from lymphoid cells (LCs). ALL occurs
mostly in children and CLL occurs mainly in old people. However, the
Philadelphia-chromosome-positive ALL (Ph+-ALL) and the Ph-like ALL occur in
both children and adults. To understand childhood leukemia/lymphoma, we have
recently proposed two hypotheses on the causes and the mechanism of cell
transformation of a LC. Hypothesis A is: repeated bone-remodeling during
bone-growth and bone-repair may be a source of cell injuries of marrow cells
including hematopoietic stem cells (HSCs), myeloid cells, and LCs. Hypothesis B
is: a LC may have three pathways on transformation: a slow, a rapid, and an
accelerated. We discuss in the present paper the developing mechanisms of ALL
and CLL by these hypotheses. Having a peak incidence in young children, ALL may
develop mainly as a result of rapid cell transformation of a lymphoblast (or
pro-lymphocyte). Differently, Ph+-ALL and Ph-like ALL may develop as results of
transformation of a lymphoblast via accelerated pathway. Occurring mainly in
adults, CLL may be a result of transformation of a memory B-cell via slow
pathway. By causing cell injuries of HSCs and LCs, repeated bone-remodeling
during bone-growth and bone-repair may be related to the cell transformation of
a LC. In conclusion, ALL may develop as a result of cell transformation of a
lymphoblast via rapid or accelerated pathway; and repeated bone-remodeling
during bone-growth may be a trigger for the cell transformation of a
lymphoblast in a child.
Pediatric lymphoma may develop by "one-step" cell transformation of a lymphoid cell
Date: 2018-04-30 00:00:00

Lymphomas are a large group of neoplasms developed from lymphoid cells (LCs)
in lymph nodes (LNs) or lymphoid tissues (LTs). Some forms of lymphomas,
including Burkitt lymphoma (BL), ALK+ anaplastic large cell lymphoma
(ALK+-ALCL), and T-cell lymphoblastic lymphoma/leukemia (T-LBL), occur mainly
in children and teenagers. Hodgkin's lymphoma (HL) has a peak incidence at age
20s. To understand pediatric lymphoma, we have recently proposed two hypotheses
on the causes and the mechanism of cell transformation of a LC. Hypothesis A
is: repeated bone-remodeling during bone-growth and bone-repair may be a source
of cell injuries of marrow cells including hematopoietic stem cells (HSCs),
myeloid cells, and LCs, and thymic involution may be a source of damage to the
developing T-cells in thymus. Hypothesis B is: a LC may have three pathways on
transformation: a slow, a rapid, and an accelerated. In this paper, we discuss
pediatric lymphomas by this hypothesis. Having a peak incidence at young age,
BL, T-LBL, ALK+-ALCL, and HL develop more likely as a result of rapid
transformation of a LC. In BL, ALK+-ALCL, and HL, the cell transformations may
be triggered by severe viral infections. In T-LBL, the cell transformation may
be related to thymic involution. Occurring in both adults and children, diffuse
large B-cell lymphoma (DLBCL) may develop via slow or accelerated pathway. In
conclusion, pediatric lymphoma may develop as a result of "one-step" cell
transformation of a LC, and severe viral infections may be the main trigger for
the rapid transformation of a LC in a LN/LT.
Visualizing mitochondrial FoF1-ATP synthase as the target of the immunomodulatory drug Bz-423
Date: 2018-04-30 00:00:00

Targeting the mitochondrial enzyme FoF1-ATP synthase and modulating its
catalytic activities with small molecules is a promising new approach for
treatment of autoimmune diseases. The immuno-modulatory compound Bz-423 is such
a drug that binds to subunit OSCP of the mitochondrial FoF1-ATP synthase and
induces apoptosis via increased reactive oxygen production in coupled, actively
respiring mitochondria. Here we review the experimental progress to reveal the
binding of Bz-423 to the mitochondrial target and discuss how subunit rotation
of FoF1-ATP synthase is affected by Bz-423. Briefly, we report how F\"orster
resonance energy transfer (FRET) can be employed to colocalize the enzyme and
the fluorescently tagged Bz-423 within the mitochondria of living cells with
nanometer resolution.
Development of pediatric myeloid leukemia may be related to the repeatedbone-remodeling during bone-growth
Date: 2018-04-30 00:00:00

Acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) are two major
formsof leukemia developed from myeloid cells (MCs). To understand why AML and
CML occurin children, we analyzed the causes and the mechanism of cell
transformation of a MC. I. Forthe MCs in marrow cavity, repeated
bone-remodeling during bone-growth may be a source ofcell injuries. II. As a
type of blood cell, a MC may have higher survivability from DNAchanges and
require obtaining fewer cancerous properties for cell transformation than a
tissuecell. III. Point DNA mutations (PDMs) and chromosome changes (CCs) are
the two majortypes of DNA changes. CCs have three subtypes by effects on a
cell: great effect CCs(GECCs), mild-effect CCs (MECCs), and intermediate-effect
CCs (IECCs). A GECC affectsone or more genes and can alone trigger cell
transformation. PDMs/MECCs are mostly mildand can accumulate in cells. Some of
the PDMs/MECCs contribute to cell transformation. AnIECC affects one or more
genes and participates in cell transformation. IV. Based on II andIII, we
hypothesize that a MC may have two pathways on transformation: a slow and
anaccelerated. Slow pathway is driven by accumulation of PDMs/MECCs.
Accelerated pathwayis driven by accumulation of PDMs/MECCs/IECC(s). A
transformation via slow pathwayoccurs at old age; whereas that via accelerated
pathway occurs at any age. Thus, CML andpediatric AML may develop via
accelerated pathway, and adult AML may develop via bothpathways. In conclusion,
pediatric AML and CML may develop as a result of transformationof a MC via
accelerated pathway; and repeated bone-remodeling for bone-growth may be
atrigger for the transformation of a MC in a child.
Author-topic profiles for academic search
Authors: Verberne Suzan, de Vries Arjen P., Kraaij Wessel
Date: 2018-04-30 00:00:00
Tasks: Re-Ranking, Retrieval

We implemented and evaluated a two-stage retrieval method for personalized
academic search in which the initial search results are re-ranked using an
author-topic profile. In academic search tasks, the user's own data can help
optimizing the ranking of search results to match the searcher's specific
individual needs. The author-topic profile consists of topic-specific terms,
stored in a graph. We re-rank the top-1000 retrieved documents using ten
features that represent the similarity between the document and the
author-topic graph. We found that the re-ranking gives a small but significant
improvement over the reproduced best method from the literature. Storing the
profile as a graph has a number of advantages: it is flexible with respect to
node and relation types; it is a visualization of knowledge that is
interpretable by the user, and it offers the possibility to view relational
characteristics of individual nodes.
A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs, A Genetic-Algorithm Approach
Date: 2018-04-26 00:00:00
Tasks: feature selection

High false alarm rate in intensive care units (ICUs) has been identified as
one of the most critical medical challenges in recent years. This often results
in overwhelming the clinical staff by numerous false or unurgent alarms and
decreasing the quality of care through enhancing the probability of missing
true alarms as well as causing delirium, stress, sleep deprivation and
depressed immune systems for patients. One major cause of false alarms in
clinical practice is that the collected signals from different devices are
processed individually to trigger an alarm, while there exists a considerable
chance that the signal collected from one device is corrupted by noise or
motion artifacts. In this paper, we propose a low-computational complexity yet
accurate game-theoretic feature selection method which is based on a genetic
algorithm that identifies the most informative biomarkers across the signals
collected from various monitoring devices and can considerably reduce the rate
of false alarms.
Sample Size for Concurrent Species Detection in a Species-Rich Assemblage
Date: 2018-05-11 00:00:00

Monitoring the distribution of microfossils in stratigraphic successions is
an essential tool for biostratigraphic, evolutionary and
paleoecologic/paleoceanographic studies. To estimate the relative abundance (%)
of a given species, it is necessary to estimate in advance the minimum number
of specimens to be used in the count (n). This requires an a priori assumption
about a specified level of confidence, and about the species population
proportion (p). It is common use to apply the binomial distribution to
determine n to detect the presence of more than one species in the same sample,
although the multinomial distribution should necessarily be used instead.
  The mathematical theory of sample size computation using the multinomial
distribution is adapted to the computation of n for any number of species to be
detected together (K) at any level of confidence. Easy-to-use extensive tables
show n, for a combination of K and p. These tables indicate a large difference
for n between that indicated by the binomial and those by the multinomial
distribution when many species are to be detected simultaneously. Counting only
300 specimens (with 95 % confidence level) or 500 (99 %) is not enough to
detect more than one taxon.
  The reconstructed history of the micro-biosphere may therefore, in many
instances, need to be largely revised. This revision should affect our
understanding of the ecological and evolutionary relationships between the past
changes in the biosphere and the other major reservoirs (hydrosphere, geosphere
and atmosphere). In biostratigraphy and biochronology, using a much larger
sample size, when more than one marker species is to be detected in the
neighborhood of the same biozone boundary, may help clarifying the nature of
the apparent inconsistencies given by the observed reversals in the ordinal
(rank) biostratigraphic data shown as intersections of the correlation lines
Multi-Class Management with Sub-Class Service for Autonomous Electric Mobility On-Demand Systems
Date: 2018-04-30 00:00:00
Tasks: Management

Despite the significant advances in vehicle automation and electrification,
the next-decade aspirations for massive deployments of autonomous electric
mobility on demand (AEMoD) services are still threatened by two major
bottlenecks, namely the computational and charging delays. This paper proposes
a solution for these two challenges by suggesting the use of fog computing for
AEMoD systems, and developing an optimized charging scheme for its vehicles
with and multi-class dispatching scheme for the customers. A queuing model
representing the proposed multi-class management scheme with sub-class service
is first introduced. The stability conditions of the system in a given city
zone are then derived. Decisions on the proportions of each class vehicles to
partially/fully charge, or directly serve customers of possible sub-classes are
then optimized in order to minimize the maximum response time of the system.
Results show the merits of our optimized model compared to a previously
proposed scheme and other non-optimized policies.