pwc-archive/papers-with-abstracts huggingface dataset:
Dionysius: A Framework for Modeling Hierarchical User Interactions in Recommender Systems
Authors: Wang Jian, Kenthapadi Krishnaram, Rangadurai Kaushik, Hardtke David
Date: 2017-06-12 00:00:00
Tasks: Recommendation Systems

We address the following problem: How do we incorporate user item interaction
signals as part of the relevance model in a large-scale personalized
recommendation system such that, (1) the ability to interpret the model and
explain recommendations is retained, and (2) the existing infrastructure
designed for the (user profile) content-based model can be leveraged? We
propose Dionysius, a hierarchical graphical model based framework and system
for incorporating user interactions into recommender systems, with minimal
change to the underlying infrastructure. We learn a hidden fields vector for
each user by considering the hierarchy of interaction signals, and replace the
user profile-based vector with this learned vector, thereby not expanding the
feature space at all. Thus, our framework allows the use of existing
recommendation infrastructure that supports content based features. We
implemented and deployed this system as part of the recommendation platform at
LinkedIn for more than one year. We validated the efficacy of our approach
through extensive offline experiments with different model choices, as well as
online A/B testing experiments. Our deployment of this system as part of the
job recommendation engine resulted in significant improvement in the quality of
retrieved results, thereby generating improved user experience and positive
impact for millions of users.
Stochastic mechanisms forming large clones during colonisation of new areas
Date: 2017-06-13 00:00:00

In species reproducing both sexually and asexually clones are often more
common in recently established populations. Earlier studies have suggested that
this pattern arises from natural selection favouring asexual recruitment in
young populations. Alternatively, as we show here, this pattern may result from
stochastic processes during species-range expansions. We model a dioecious
species expanding into a new area in which all individuals are capable of both
sexual and asexual reproduction, and all individuals have equal survival rates
and dispersal distances. Even under conditions that eventually favour sexual
recruitment, colonisation starts with an asexual wave. Long after colonisation
is completed, a sexual wave erodes clonal dominance. If individuals reproduce
more than one season, and with only local dispersal, a few large clones
typically dominate for thousands of reproductive seasons. Adding occasional
long-distance dispersal, more dominant clones emerge, but they persist for a
shorter period of time. The general mechanism involved is simple: edge effects
at the expansion front favour asexual (uniparental) recruitment where potential
mates are rare. Specifically, our stochastic model makes detailed predictions
different from a selection model, and comparing these with empirical data from
a postglacially established seaweed species (Fucus radicans) shows that in this
case a stochastic mechanism is strongly supported.
RELink: A Research Framework and Test Collection for Entity-Relationship Retrieval
Authors: Saleiro Pedro, Milic-Frayling Natasa, Rodrigues Eduarda Mendes, Soares Carlos
Date: 2017-06-13 00:00:00
Tasks: Natural Language Queries, Retrieval

Improvements of entity-relationship (E-R) search techniques have been
hampered by a lack of test collections, particularly for complex queries
involving multiple entities and relationships. In this paper we describe a
method for generating E-R test queries to support comprehensive E-R search
experiments. Queries and relevance judgments are created from content that
exists in a tabular form where columns represent entity types and the table
structure implies one or more relationships among the entities. Editorial work
involves creating natural language queries based on relationships represented
by the entries in the table. We have publicly released the RELink test
collection comprising 600 queries and relevance judgments obtained from a
sample of Wikipedia List-of-lists-of-lists tables. The latter comprise tuples
of entities that are extracted from columns and labelled by corresponding
entity types and relationships they represent. In order to facilitate research
in complex E-R retrieval, we have created and released as open source the
RELink Framework that includes Apache Lucene indexing and search specifically
tailored to E-R retrieval. RELink includes entity and relationship indexing
based on the ClueWeb-09-B Web collection with FACC1 text span annotations
linked to Wikipedia entities. With ready to use search resources and a
comprehensive test collection, we support community in pursuing E-R research at
scale.
Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models
Date: 2017-06-13 00:00:00
Tasks: Reading Comprehension

With the tremendous increase in the amount of biological literature,
developing automated methods for extracting big data from papers, building
models and explaining big mechanisms becomes a necessity. We describe here our
approach to translating machine reading outputs, obtained by reading bio-
logical signaling literature, to discrete models of cellular networks. We use
out- puts from three different reading engines, and describe our approach to
translating their different features, using examples from reading cancer
literature. We also outline several issues that still arise when assembling
cellular network models from state-of-the-art reading engines. Finally, we
illustrate the details of our approach with a case study in pancreatic cancer.
A Theoretical Solution of the Mind-Body Problem: An Operationalized Proof that no Purely Physical System Can Exhibit all the Properties of Human Consciousness
Date: 2017-10-31 00:00:00
Tasks: All, Logical Reasoning

This article presents an operationalized solution to the mind-body problem
which relies on rigorously defined theoretical reasoning rather than
philosophical argument. We identify a specific operation which is a necessary
property of all healthy human conscious individuals -- specifically the
operation of self-certainty, or the capacity of healthy conscious humans to
"know" with certainty that they are conscious. This operation is shown to be
inconsistent with the properties possible in any meaningful definition of a
physical system. This inconsistency is demonstrated by proving a "no-go"
theorem for any physical system capable of human logical reasoning, if this
reasoning is required to be both sound and consistent. The proof of this
theorem is both general -- it applies to any function whereby evidence affects
the state of some physical system -- and recursive, since any physical process
subserving a function of this type is shown to imply another such function.
Thus for at least one aspect of human consciousness, the mind-body problem is
now conclusively resolved.
Generalized Quasispecies Model on Finite Metric Spaces: Isometry Groups and Spectral Properties of Evolutionary Matrices
Date: 2017-06-13 00:00:00

The quasispecies model introduced by Eigen in 1971 has close connections with
the isometry group of the space of binary sequences relative to the Hamming
distance metric. Generalizing this observation we introduce an abstract
quasispecies model on a finite metric space $X$ together with a group of
isometries $\Gamma$ acting transitively on $X$. We show that if the domain of
the fitness function has a natural decomposition into the union of $t$
$G$-orbits, $G$ being a subgroup of $\Gamma$, then the dominant eigenvalue of
the evolutionary matrix satisfies an algebraic equation of degree at most
$t\cdot {\rm rk}_{\mathbf Z} R$, where $R$ is what we call the orbital ring.
The general theory is illustrated by two examples, in both of which $X$ is
taken to be the metric space of vertices of a regular polytope with the "edge"
metric; namely, the case of a regular $m$-gon and of a hyperoctahedron are
considered.
Hybrid Collaborative Recommendation via Semi-AutoEncoder
Authors: Zhang Shuai, Yao Lina, Xu Xiwei, Wang Sen, Zhu Liming
Date: 2017-08-16 00:00:00
Tasks: Collaborative Filtering

In this paper, we present a novel structure, Semi-AutoEncoder, based on
AutoEncoder. We generalize it into a hybrid collaborative filtering model for
rating prediction as well as personalized top-n recommendations. Experimental
results on two real-world datasets demonstrate its state-of-the-art
performances.
Evaluating Personal Assistants on Mobile devices
Authors: Kiseleva Julia, de Rijke Maarten
Date: 2017-06-14 00:00:00

The iPhone was introduced only a decade ago in 2007 but has fundamentally
changed the way we interact with online information. Mobile devices differ
radically from classic command-based and point-and-click user interfaces, now
allowing for gesture-based interaction using fine-grained touch and swipe
signals. Due to the rapid growth in the use of voice-controlled intelligent
personal assistants on mobile devices, such as Microsoft's Cortana, Google Now,
and Apple's Siri, mobile devices have become personal, allowing us to be online
all the time, and assist us in any task, both in work and in our daily lives,
making context a crucial factor to consider.
  Mobile usage is now exceeding desktop usage, and is still growing at a rapid
rate, yet our main ways of training and evaluating personal assistants are
still based on (and framed in) classical desktop interactions, focusing on
explicit queries, clicks, and dwell time spent. However, modern user
interaction with mobile devices is radically different due to touch screens
with a gesture- and voice-based control and the varying context of use, e.g.,
in a car, by bike, often invalidating the assumptions underlying today's user
satisfaction evaluation.
  There is an urgent need to understand voice- and gesture-based interaction,
taking all interaction signals and context into account in appropriate ways. We
propose a research agenda for developing methods to evaluate and improve
context-aware user satisfaction with mobile interactions using gesture-based
signals at scale.
Realized volatility and parametric estimation of Heston SDEs
Authors: Robert Azencott, Peng Ren, Ilya Timofeyev
Date: 2017-06-14 00:00:00

We present a detailed analysis of \emph{observable} moments based parameter estimators for the Heston SDEs jointly driving the rate of returns $R_t$ and the squared volatilities $V_t$. Since volatilities are not directly observable, our parameter estimators are constructed from empirical moments of realized volatilities $Y_t$, which are of course observable. Realized volatilities are computed over sliding windows of size $\varepsilon$, partitioned into $J(\varepsilon)$ intervals. We establish criteria for the joint selection of $J(\varepsilon)$ and of the sub-sampling frequency of return rates data. We obtain explicit bounds for the $L^q$ speed of convergence of realized volatilities to true volatilities as $\varepsilon \to 0$. In turn, these bounds provide also $L^q$ speeds of convergence of our observable estimators for the parameters of the Heston volatility SDE. Our theoretical analysis is supplemented by extensive numerical simulations of joint Heston SDEs to investigate the actual performances of our moments based parameter estimators. Our results provide practical guidelines for adequately fitting Heston SDEs parameters to observed stock prices series.
Computational models of long term plasticity and memory
Date: 2017-06-15 00:00:00

Memory is often defined as the mental capacity of retaining information about
facts, events, procedures and more generally about any type of previous
experience. Memories are remembered as long as they influence our thoughts,
feelings, and behavior at the present time. Memory is also one of the
fundamental components of learning, our ability to acquire any type of
knowledge or skills. In the brain it is not easy to identify the physical
substrate of memory. Basically, any long-lasting alteration of a biochemical
process can be considered a form of memory, although some of these alterations
last only a few milliseconds, and most of them, if taken individually, cannot
influence our behavior. However, if we want to understand memory, we need to
keep in mind that memory is not a unitary phenomenon, and it certainly involves
several distinct mechanisms that operate at different spatial and temporal
levels. One of the goals of theoretical neuroscience is to try to understand
how these processes are orchestrated to store memories rapidly and preserve
them over a lifetime. Theorists have mostly focused on synaptic plasticity, as
it is one of the most studied memory mechanisms in experimental neuroscience
and it is known to be highly effective in training artificial neural networks
to perform real world tasks. Some of the synaptic plasticity models are purely
phenomenological, some others have been designed to solve computational
problems. In this article I will review some of these models and I will try to
identify computational principles that underlie memory storage and
preservation.
Research Topics Map: rtopmap
Authors: Hossain Md Iqbal, Kobourov Stephen
Date: 2017-06-15 00:00:00

In this paper we describe a system for visualizing and analyzing worldwide
research topics, {\tt rtopmap}. We gather data from google scholar academic
research profiles, putting together a weighted topics graph, consisting of over
35,000 nodes and 646,000 edges. The nodes correspond to self-reported research
topics, and edges correspond to co-occurring topics in google scholar profiles.
The {\tt rtopmap} system supports zooming/panning/searching and other
google-maps-based interactive features. With the help of map overlays, we also
visualize the strengths and weaknesses of different academic institutions in
terms of human resources (e.g., number of researchers in different areas), as
well as scholarly output (e.g., citation counts in different areas). Finally,
we also visualize what parts of the map are associated with different academic
departments, or with specific documents (such as research papers, or calls for
proposals). The system itself is available at
\url{http://rtopmap.arl.arizona.edu/}.
Measuring Personalization of Web Search
Authors: Hannák Anikó, Sapieżyński Piotr, Khaki Arash Molavi, Lazer David, Mislove Alan, Wilson Christo
Date: 2017-06-15 00:00:00
Tasks: Attribute

Web search is an integral part of our daily lives. Recently, there has been a
trend of personalization in Web search, where different users receive different
results for the same search query. The increasing level of personalization is
leading to concerns about Filter Bubble effects, where certain users are simply
unable to access information that the search engines' algorithm decides is
irrelevant. Despite these concerns, there has been little quantification of the
extent of personalization in Web search today, or the user attributes that
cause it.
  In light of this situation, we make three contributions. First, we develop a
methodology for measuring personalization in Web search results. While
conceptually simple, there are numerous details that our methodology must
handle in order to accurately attribute differences in search results to
personalization. Second, we apply our methodology to 200 users on Google Web
Search and 100 users on Bing. We find that, on average, 11.7% of results show
differences due to personalization on Google, while 15.8% of results are
personalized on Bing, but that this varies widely by search query and by result
ranking. Third, we investigate the user features used to personalize on Google
Web Search and Bing. Surprisingly, we only find measurable personalization as a
result of searching with a logged in account and the IP address of the
searching user. Our results are a first step towards understanding the extent
and effects of personalization on Web search engines today.
The thermodynamic efficiency of computations made in cells across the range of life
Date: 2017-06-15 00:00:00
Tasks: Computational Efficiency

Biological organisms must perform computation as they grow, reproduce, and
evolve. Moreover, ever since Landauer's bound was proposed it has been known
that all computation has some thermodynamic cost -- and that the same
computation can be achieved with greater or smaller thermodynamic cost
depending on how it is implemented. Accordingly an important issue concerning
the evolution of life is assessing the thermodynamic efficiency of the
computations performed by organisms. This issue is interesting both from the
perspective of how close life has come to maximally efficient computation
(presumably under the pressure of natural selection), and from the practical
perspective of what efficiencies we might hope that engineered biological
computers might achieve, especially in comparison with current computational
systems. Here we show that the computational efficiency of translation, defined
as free energy expended per amino acid operation, outperforms the best
supercomputers by several orders of magnitude, and is only about an order of
magnitude worse than the Landauer bound. However this efficiency depends
strongly on the size and architecture of the cell in question. In particular,
we show that the {\it useful} efficiency of an amino acid operation, defined as
the bulk energy per amino acid polymerization, decreases for increasing
bacterial size and converges to the polymerization cost of the ribosome. This
cost of the largest bacteria does not change in cells as we progress through
the major evolutionary shifts to both single and multicellular eukaryotes.
However, the rates of total computation per unit mass are nonmonotonic in
bacteria with increasing cell size, and also change across different biological
architectures including the shift from unicellular to multicellular eukaryotes.
Community interactions determine role of species in parasite spread amplification: the ecomultiplex network model
Date: 2017-06-16 00:00:00

Most of zoonoses are multi-host parasites with multiple transmission routes
that are usually investigated separately despite their potential interplay. As
a unifying framework for modelling parasite spread through different paths of
infection, we suggest "ecomultiplex" networks, i.e. multiplex networks
representing interacting animal communities with (i) spatial structure and (ii)
metabolic scaling. We exploit this ecological framework for testing potential
control strategies for $T. cruzii$ spread in two real-world ecosystems. Our
investigation highlights two interesting results. Firstly, the ecomultiplex
topology can be as efficient as more data-demanding epidemiological measures in
identifying which species facilitate parasite spread. Secondly, the interplay
between predator-prey and host-parasite interactions leads to a phenomenon of
parasite amplification in which top predators facilitate $T. cruzii$ spread,
offering theoretical interpretation of previous empirical findings. Our
approach is broadly applicable and could provide novel insights in designing
immunisation strategies for pathogens with multiple transmission routes in
real-world ecosystems.
Two-phase Age-Structured Model of Solitarious and Gregarious Locust Population Dynamics
Date: 2017-06-16 00:00:00

In this paper we study a nonlinear age structured model of locust population
dynamics with variable time of egg incubation that describes the phase
polyphenism and behaviour of desert locust, Schistocerca gregaria. The analysis
of asymptotical stability of trivial and nontrivial equilibriums of autonomous
system allows us to derive the conditions and understand the particularities of
bidirectional phase transitions between solitarious and gregarious. Simulation
of locust population dynamics with different conditions of phase transitions
for autonomous and non-autonomous dynamical systems of our two phase
age-structured competitive model with time delay exhibits the features of
behaviour of documented outbreak dynamics of Schistocerca gregaria.
Twigraph: Discovering and Visualizing Influential Words between Twitter Profiles
Authors: Sundararaman Dhanasekar, Srinivasan Sudharshan
Date: 2017-06-29 00:00:00
Tasks: Articles, Clustering

The social media craze is on an ever increasing spree, and people are
connected with each other like never before, but these vast connections are
visually unexplored. We propose a methodology Twigraph to explore the
connections between persons using their Twitter profiles. First, we propose a
hybrid approach of recommending social media profiles, articles, and
advertisements to a user.The profiles are recommended based on the similarity
score between the user profile, and profile under evaluation. The similarity
between a set of profiles is investigated by finding the top influential words
thus causing a high similarity through an Influence Term Metric for each word.
Then, we group profiles of various domains such as politics, sports, and
entertainment based on the similarity score through a novel clustering
algorithm. The connectivity between profiles is envisaged using word graphs
that help in finding the words that connect a set of profiles and the profiles
that are connected to a word. Finally, we analyze the top influential words
over a set of profiles through clustering by finding the similarity of that
profiles enabling to break down a Twitter profile with a lot of followers to
fine level word connections using word graphs. The proposed method was
implemented on datasets comprising 1.1 M Tweets obtained from Twitter.
Experimental results show that the resultant influential words were highly
representative of the relationship between two profiles or a set of profiles
Synaptic mechanisms of interference in working memory
Date: 2017-07-24 00:00:00

Information from preceding trials of cognitive tasks can bias performance in
the current trial, a phenomenon referred to as interference. Subjects
performing visual working memory tasks exhibit interference in their
trial-to-trial response correlations: the recalled target location in the
current trial is biased in the direction of the target presented on the
previous trial. We present modeling work that (a) develops a probabilistic
inference model of this history-dependent bias, and (b) links our probabilistic
model to computations of a recurrent network wherein short-term facilitation
accounts for the dynamics of the observed bias. Network connectivity is
reshaped dynamically during each trial, providing a mechanism for generating
predictions from prior trial observations. Applying timescale separation
methods, we can obtain a low-dimensional description of the trial-to-trial bias
based on the history of target locations. The model has response statistics
whose mean is centered at the true target location across many trials, typical
of such visual working memory tasks. Furthermore, we demonstrate task protocols
for which the plastic model performs better than a model with static
connectivity: repetitively presented targets are better retained in working
memory than targets drawn from uncorrelated sequences.
Transfer entropy between communities in complex networks
Date: 2017-06-17 00:00:00

With the help of transfer entropy, we analyze information flows between
communities of complex networks. We show that the transfer entropy provides a
coherent description of interactions between communities, including non-linear
interactions. To put some flesh on the bare bones, we analyze transfer
entropies between communities of five largest financial markets, represented as
networks of interacting stocks. Additionally, we discuss information transfer
of rare events, which is analyzed by R\'enyi transfer entropy.