pwc-archive/papers-with-abstracts huggingface dataset:
Eco-evolutionary dynamics and collective dispersal: implications for salmon metapopulation robustness
Date: 2017-09-21 00:00:00

The spatial dispersal of individuals is known to play an important role in
the dynamics of populations, and is central to metapopulation theory. At the
same time, local adaptation to environmental conditions creates a geographic
mosaic of evolutionary forces, where the combined drivers of selection and gene
flow interact. Although the dispersal of individuals from donor to recipient
populations provides connections within the metapopulation, promoting
demographic and evolutionary rescue, it may also introduce maladapted
individuals into habitats host to different environmental conditions,
potentially lowering the fitness of the recipient population. Here we explore a
model of the eco-evolutionary dynamics between two populations connected by
dispersal, where the productivity of each is defined by a trait complex that is
subject to local selection. Although general in nature, our model is inspired
by salmon metapopulations, where dispersal between populations is defined in
terms of the straying rate, which has been shown to be density-dependent. The
results of our model reveal that increased straying between evolving
populations leads to alternative stable states, which has large and nonlinear
effects on two measures of metapopulation robustness: the portfolio effect and
the time to recovery following an induced disturbance. We show that
intermediate levels of straying result in large gains in robustness, and that
increased habitat heterogeneity promotes robustness when straying rates are
low, and erodes robustness when straying rates are high. Finally, we show that
density-dependent straying promotes robustness, particularly when the aggregate
biomass is low and straying is correspondingly high, which has important
ramifications for the conservation of salmon metapopulations facing both
natural and anthropogenic disturbances.
Bolt Detection Signal Analysis Method Based on ICEEMD
Date: 2018-02-24 00:00:00

The construction quality of the bolt is directly related to the safety of the
project, and as such, it must be tested. In this paper, the improved complete
ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt
detection signal analysis. The ICEEMD is used in order to decompose the anchor
detection signal according to the approximate entropy of each intrinsic mode
function (IMF). The noise of the IMFs is eliminated by the wavelet soft
threshold de-noising technique. Based on the approximate entropy, and the
wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is
proposed. From the analysis of the vibration analog signal, as well as the bolt
detection signal, the result shows that the ICEEMD-De method is capable of
correctly separating the different IMFs under noisy conditions, and also that
the IMF can effectively identify the reflection signal of the end of the bolt.
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint
Date: 2018-04-25 00:00:00
Tasks: Graph Generation, Time Series, Time Series Analysis

This paper presents a simple method for a posteriori (historical)
multi-variate multi-stage optimal trading under transaction costs and a
diversification constraint. Starting from a given amount of money in some
currency, we analyze the stage-wise optimal allocation over a time horizon with
potential investments in multiple currencies and various assets. Three variants
are discussed, including unconstrained trading frequency, a fixed number of
total admissable trades, and the waiting of a specific time-period after every
executed trade until the next trade. The developed methods are based on
efficient graph generation and consequent graph search, and are evaluated
quantitatively on real-world data. The fundamental motivation of this work is
preparatory labeling of financial time-series data for supervised machine
learning.
Mining User Queries with Information Extraction Methods and Linked Data
Authors: Chardonnens Anne, Rizza Ettore, Coeckelbergs Mathias, van Hooland Seth
Date: 2017-09-22 00:00:00

Purpose: Advanced usage of Web Analytics tools allows to capture the content
of user queries. Despite their relevant nature, the manual analysis of large
volumes of user queries is problematic. This paper demonstrates the potential
of using information extraction techniques and Linked Data to gather a better
understanding of the nature of user queries in an automated manner.
  Design/methodology/approach: The paper presents a large-scale case-study
conducted at the Royal Library of Belgium consisting of a data set of 83 854
queries resulting from 29 812 visits over a 12 month period of the historical
newspapers platform BelgicaPress. By making use of information extraction
methods, knowledge bases and various authority files, this paper presents the
possibilities and limits to identify what percentage of end users are looking
for person and place names.
  Findings: Based on a quantitative assessment, our method can successfully
identify the majority of person and place names from user queries. Due to the
specific character of user queries and the nature of the knowledge bases used,
a limited amount of queries remained too ambiguous to be treated in an
automated manner.
  Originality/value: This paper demonstrates in an empirical manner both the
possibilities and limits of gaining more insights from user queries extracted
from a Web Analytics tool and analysed with the help of information extraction
tools and knowledge bases. Methods and tools used are generalisable and can be
reused by other collection holders.
Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures
Authors: Luo Shuangqi, Wu Hongmin, Lin Hongbin, Duan Shuangda, Guan Yisheng, Rojas Juan
Date: 2018-06-20 00:00:00
Tasks: Event Detection

Event detection is a critical feature in data-driven systems as it assists
with the identification of nominal and anomalous behavior. Event detection is
increasingly relevant in robotics as robots operate with greater autonomy in
increasingly unstructured environments. In this work, we present an accurate,
robust, fast, and versatile measure for skill and anomaly identification. A
theoretical proof establishes the link between the derivative of the
log-likelihood of the HMM filtered belief state and the latest emission
probabilities. The key insight is the inverse relationship in which gradient
analysis is used for skill and anomaly identification. Our measure showed
better performance across all metrics than related state-of-the art works. The
result is broadly applicable to domains that use HMMs for event detection.
A novel recommendation system to match college events and groups to students
Authors: Qazanfari Kazem, Youssef Abdou, Keane Kai, Nelson Joseph
Date: 2017-09-26 00:00:00
Tasks: Recommendation Systems

With the recent increase in data online, discovering meaningful opportunities
can be time-consuming and complicated for many individuals. To overcome this
data overload challenge, we present a novel text-content-based recommender
system as a valuable tool to predict user interests. To that end, we develop a
specific procedure to create user models and item feature-vectors, where items
are described in free text. The user model is generated by soliciting from a
user a few keywords and expanding those keywords into a list of weighted
near-synonyms. The item feature-vectors are generated from the textual
descriptions of the items, using modified tf-idf values of the users' keywords
and their near-synonyms. Once the users are modeled and the items are
abstracted into feature vectors, the system returns the maximum-similarity
items as recommendations to that user. Our experimental evaluation shows that
our method of creating the user models and item feature-vectors resulted in
higher precision and accuracy in comparison to well-known
feature-vector-generating methods like Glove and Word2Vec. It also shows that
stemming and the use of a modified version of tf-idf increase the accuracy and
precision by 2% and 3%, respectively, compared to non-stemming and the standard
tf-idf definition. Moreover, the evaluation results show that updating the user
model from usage histories improves the precision and accuracy of the system.
This recommender system has been developed as part of the Agnes application,
which runs on iOS and Android platforms and is accessible through the Agnes
website.
Compressed Air Energy Storage-Part I: An Accurate Bi-linear Cavern Model
Authors: Junpeng Zhan, Osama Aslam Ansari, C. Y. Chung
Date: 2017-09-24 00:00:00

Compressed air energy storage (CAES) is suitable for large-scale energy storage and can help to increase the penetration of wind power in power systems. A CAES plant consists of compressors, expanders, caverns, and a motor/generator set. Currently used cavern models for CAES are either accurate but highly non-linear or linear but inaccurate. Highly non-linear cavern models cannot be directly utilized in power system optimization problems. In this regard, an accurate bi-linear cavern model for CAES is proposed in this first paper of a two-part series. The charging and discharging processes in a cavern are divided into several virtual states and then the first law of thermodynamics and ideal gas law are used to derive a cavern model, i.e., model for the variation of temperature and pressure in these processes. Thereafter, the heat transfer between the air in the cavern and the cavern wall is considered and integrated into the cavern model. By subsequently eliminating several negligible terms, the cavern model reduces to a bi-linear (linear) model for CAES with multiple (single) time steps. The accuracy of the proposed cavern model is verified via comparison with an accurate non-linear model.
Compressed Air Energy Storage-Part II: Application to Power System Unit Commitment
Authors: Junpeng Zhan, Yunfeng Wen, Osama Aslam Ansari, C. Y. Chung
Date: 2017-09-24 00:00:00

Unit commitment (UC) is one of the most important power system operation problems. To integrate higher penetration of wind power into power systems, more compressed air energy storage (CAES) plants are being built. Existing cavern models for the CAES used in power system optimization problems are not accurate, which may lead to infeasible solutions, e.g., the air pressure in the cavern is outside its operating range. In this regard, an accurate CAES model is proposed for the UC problem based on the accurate bi-linear cavern model proposed in the first paper of this two-part series. The minimum switch time between the charging and discharging processes of CAES is considered. The whole model, i.e., the UC model with an accurate CAES model, is a large-scale mixed integer bi-linear programming problem. To reduce the complexity of the whole model, three strategies are proposed to reduce the number of bi-linear terms without sacrificing accuracy. McCormick relaxation and piecewise linearization are then used to linearize the whole model. To decrease the solution time, a method to obtain an initial solution of the linearized model is proposed. A modified RTS-79 system is used to verify the effectiveness of the whole model and the solution methodology.
A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering
Authors: Rani Monika, Muyeba Maybin K., Vyas O. P.
Date: 2017-10-29 00:00:00
Tasks: Clustering, Information Retrieval, Question Answering, Retrieval

One of the challenges in information retrieval is providing accurate answers
to a user's question often expressed as uncertainty words. Most answers are
based on a Syntactic approach rather than a Semantic analysis of the query. In
this paper, our objective is to present a hybrid approach for a Semantic
question answering retrieval system using Ontology Similarity and Fuzzy logic.
We use a Fuzzy Co-clustering algorithm to retrieve the collection of documents
based on Ontology Similarity. The Fuzzy Scale uses Fuzzy type-1 for documents
and Fuzzy type-2 for words to prioritize answers. The objective of this work is
to provide retrieval system with more accurate answers than non-fuzzy Semantic
Ontology approach.
A default system with overspilling contagion
Authors: Delia Coculescu, Gabriele Visentin
Date: 2017-09-26 00:00:00

In classical contagion models, default systems are Markovian conditionally on the observation of their stochastic environment, with interacting intensities. This necessitates that the environment evolves autonomously and is not influenced by the history of the default events. We extend the classical literature and allow a default system to have a contagious impact on its environment. In our framework, contagion can either be contained within the default system (i.e., direct contagion from a counterparty to another) or spill from the default system over its environment (indirect contagion). This type of model is of interest whenever one wants to capture within a model possible impacts of the defaults of a class of debtors on the more global economy and vice versa.
Scaling Author Name Disambiguation with CNF Blocking
Authors: Kim Kunho, Sefid Athar, Giles C. Lee
Date: 2017-09-27 00:00:00
Tasks: Blocking, Clustering

An author name disambiguation (AND) algorithm identifies a unique author
entity record from all similar or same publication records in scholarly or
similar databases. Typically, a clustering method is used that requires
calculation of similarities between each possible record pair. However, the
total number of pairs grows quadratically with the size of the author database
making such clustering difficult for millions of records. One remedy for this
is a blocking function that reduces the number of pairwise similarity
calculations. Here, we introduce a new way of learning blocking schemes by
using a conjunctive normal form (CNF) in contrast to the disjunctive normal
form (DNF). We demonstrate on PubMed author records that CNF blocking reduces
more pairs while preserving high pairs completeness compared to the previous
methods that use a DNF with the computation time significantly reduced. Thus,
these concepts in scholarly data can be better represented with CNFs. Moreover,
we also show how to ensure that the method produces disjoint blocks so that the
rest of the AND algorithm can be easily paralleled. Our CNF blocking tested on
the entire PubMed database of 80 million author mentions efficiently removes
82.17% of all author record pairs in 10 minutes.
Towards a Semantic Search Engine for Scientific Articles
Authors: Latard Bastien, Weber Jonathan, Forestier Germain, Hassenforder Michel
Date: 2017-09-28 00:00:00
Tasks: Articles

Because of the data deluge in scientific publication, finding relevant
information is getting harder and harder for researchers and readers. Building
an enhanced scientific search engine by taking semantic relations into account
poses a great challenge. As a starting point, semantic relations between
keywords from scientific articles could be extracted in order to classify
articles. This might help later in the process of browsing and searching for
content in a meaningful scientific way. Indeed, by connecting keywords, the
context of the article can be extracted. This paper aims to provide ideas to
build such a smart search engine and describes the initial contributions
towards achieving such an ambitious goal.
Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report
Authors: Vagliano Iacopo, Monti Diego, Scherp Ansgar, Morisio Maurizio
Date: 2017-10-28 00:00:00
Tasks: Recommendation Systems

Nowadays, most recommender systems exploit user-provided ratings to infer
their preferences. However, the growing popularity of social and e-commerce
websites has encouraged users to also share comments and opinions through
textual reviews. In this paper, we introduce a new recommendation approach
which exploits the semantic annotation of user reviews to extract useful and
non-trivial information about the items to recommend. It also relies on the
knowledge freely available in the Web of Data, notably in DBpedia and Wikidata,
to discover other resources connected with the annotated entities. We evaluated
our approach in three domains, using both DBpedia and Wikidata. The results
showed that our solution provides a better ranking than another recommendation
method based on the Web of Data, while it improves in novelty with respect to
traditional techniques based on ratings. Additionally, our method achieved a
better performance with Wikidata than DBpedia.
Measuring the Eccentricity of Items
Authors: Park Chanyoung, Kim Songkuk
Date: 2017-09-28 00:00:00

The long-tail phenomenon tells us that there are many items in the tail.
However, not all tail items are the same. Each item acquires different kinds of
users. Some items are loved by the general public, while some items are
consumed by eccentric fans. In this paper, we propose a novel metric, item
eccentricity, to incorporate this difference between consumers of the items.
Eccentric items are defined as items that are consumed by eccentric users. We
used this metric to analyze two real-world datasets of music and movies and
observed the characteristics of items in terms of eccentricity. The results
showed that our defined eccentricity of an item does not change much over time,
and classified eccentric and noneccentric items present significantly distinct
characteristics. The proposed metric effectively separates the eccentric and
noneccentric items mixed in the tail, which could not be done with the previous
measures, which only consider the popularity of items.
Optimal Reserve Price for Online Ads Trading Based on Inventory Identification
Authors: Xie Zhihui, Lee Kuang-Chih, Wang Liang
Date: 2017-08-23 00:00:00

The online ads trading platform plays a crucial role in connecting publishers
and advertisers and generates tremendous value in facilitating the convenience
of our lives. It has been evolving into a more and more complicated structure.
In this paper, we consider the problem of maximizing the revenue for the seller
side via utilizing proper reserve price for the auctions in a dynamical way.
  Predicting the optimal reserve price for each auction in the repeated auction
marketplaces is a non-trivial problem. However, we were able to come up with an
efficient method of improving the seller revenue by mainly focusing on
adjusting the reserve price for those high-value inventories. Previously, no
dedicated work has been performed from this perspective. Inspired by Paul and
Michael, our model first identifies the value of the inventory by predicting
the top bid price bucket using a cascade of classifiers. The cascade is
essential in significantly reducing the false positive rate of a single
classifier. Based on the output of the first step, we build another cluster of
classifiers to predict the price separations between the top two bids. We
showed that although the high-value auctions are only a small portion of all
the traffic, successfully identifying them and setting correct reserve price
would result in a significant revenue lift. Moreover, our optimization is
compatible with all other reserve price models in the system and does not
impact their performance. In other words, when combined with other models, the
enhancement on exchange revenue will be aggregated. Simulations on randomly
sampled Yahoo ads exchange (YAXR) data showed stable and expected lift after
applying our model.
Towards Understanding the Evolution of Vocabulary Terms in Knowledge Graphs
Authors: Abdel-Qader Mohammad, Scherp Ansgar
Date: 2017-09-30 00:00:00
Tasks: Knowledge Graphs

Vocabularies are used for modeling data in Knowledge Graphs (KG) like the
Linked Open Data Cloud and Wikidata. During their lifetime, the vocabularies of
the KGs are subject to changes. New terms are coined, while existing terms are
modified or declared as deprecated. We first quantify the amount and frequency
of changes in vocabularies. Subsequently, we investigate to which extend and
when the changes are adopted in the evolution of the KGs. We conduct our
experiments on three large-scale KGs for which time-stamped snapshots are
available, namely the Billion Triples Challenge datasets, Dynamic Linked Data
Observatory dataset, and Wikidata. Our results show that the change frequency
of terms is rather low, but can have high impact when adopted on a large amount
of distributed graph data on the web. Furthermore, not all coined terms are
used and most of the deprecated terms are still used by data publishers. There
are variations in the adoption time of terms coming from different vocabularies
ranging from very fast (few days) to very slow (few years). Surprisingly, there
are also adoptions we could observe even before the vocabulary changes are
published. Understanding this adoption is important, since otherwise it may
lead to wrong assumptions about the modeling status of data published on the
web and may result in difficulties when querying the data from distributed
sources.
Quality factor of a transmission line coupled coplanar waveguide resonator
Date: 2018-05-23 00:00:00

We investigate analytically the coupling of a coplanar waveguide resonator to
a coplanar waveguide feedline. Using a conformal mapping technique we obtain an
expression for the characteristic mode impedances and coupling coefficients of
an asymmetric multi-conductor transmission line. Leading order terms for the
external quality factor and frequency shift are calculated. The obtained
analytical results are relevant for designing circuit-QED quantum systems and
frequency division multiplexing of superconducting bolometers, detectors and
similar microwave-range multi-pixel devices.
Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries
Authors: Oh Sejoon, Park Namyong, Sael Lee, Kang U
Date: 2018-02-25 00:00:00
Tasks: Missing Values

Given sparse multi-dimensional data (e.g., (user, movie, time; rating) for
movie recommendations), how can we discover latent concepts/relations and
predict missing values? Tucker factorization has been widely used to solve such
problems with multi-dimensional data, which are modeled as tensors. However,
most Tucker factorization algorithms regard and estimate missing entries as
zeros, which triggers a highly inaccurate decomposition. Moreover, few methods
focusing on an accuracy exhibit limited scalability since they require huge
memory and heavy computational costs while updating factor matrices. In this
paper, we propose P-Tucker, a scalable Tucker factorization method for sparse
tensors. P-Tucker performs alternating least squares with a row-wise update
rule in a fully parallel way, which significantly reduces memory requirements
for updating factor matrices. Furthermore, we offer two variants of P-Tucker: a
caching algorithm P-Tucker-Cache and an approximation algorithm
P-Tucker-Approx, both of which accelerate the update process. Experimental
results show that P-Tucker exhibits 1.7-14.1x speed-up and 1.4-4.8x less error
compared to the state-of-the-art. In addition, P-Tucker scales near linearly
with the number of observable entries in a tensor and number of threads. Thanks
to P-Tucker, we successfully discover hidden concepts and relations in a
large-scale real-world tensor, while existing methods cannot reveal latent
features due to their limited scalability or low accuracy.