pwc-archive/papers-with-abstracts huggingface dataset:
An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures
Authors: Tu Zhucheng, Crane Matt, Sequiera Royal, Zhang Junchen, Lin Jimmy
Date: 2017-07-26 00:00:00
Tasks: Reranking

We explore different approaches to integrating a simple convolutional neural
network (CNN) with the Lucene search engine in a multi-stage ranking
architecture. Our models are trained using the PyTorch deep learning toolkit,
which is implemented in C/C++ with a Python frontend. One obvious integration
strategy is to expose the neural network directly as a service. For this, we
use Apache Thrift, a software framework for building scalable cross-language
services. In exploring alternative architectures, we observe that once trained,
the feedforward evaluation of neural networks is quite straightforward.
Therefore, we can extract the parameters of a trained CNN from PyTorch and
import the model into Java, taking advantage of the Java Deeplearning4J library
for feedforward evaluation. This has the advantage that the entire end-to-end
system can be implemented in Java. As a third approach, we can extract the
neural network from PyTorch and "compile" it into a C++ program that exposes a
Thrift service. We evaluate these alternatives in terms of performance (latency
and throughput) as well as ease of integration. Experiments show that
feedforward evaluation of the convolutional neural network is significantly
slower in Java, while the performance of the compiled C++ network does not
consistently beat the PyTorch implementation.
Effects of population- and seed bank noise on neutral evolution and efficacy of natural selection
Date: 2017-12-11 00:00:00
Tasks: Dimensionality Reduction

Population genetics models typically consider a fixed population size and a
unique selection coefficient. However, population dynamics inherently generate
noise in numbers of individuals and selection acts on various components of the
individuals' fitness. In plant species with seed banks, the size of both the
above- and below-ground compartments present noise depending on seed production
and the state of the seed bank. We investigate if this noise has consequences
on 1)~the rate of genetic drift, and 2)~the efficacy of selection. We consider
four variants of two-allele Moran-type models defined by combinations of
presence and absence of noise in above-ground and seed bank compartments. Time
scale analysis and dimension reduction methods allow us to reduce the
corresponding Fokker-Planck equation to a one-dimensional diffusion
approximation of a Moran model. We first show that if the above-ground noise
classically affects the rate of genetic drift, below-ground noise reduces the
diversity storage effect of the seed bank. Second, we consider that selection
can act on four different components of the plant fitness: plant or seed death
rate, seed production or seed germination. Our striking result is that the
efficacy of selection for seed death rate or germination rate is reduced by
seed bank noise, whereas selection occurring on plant death rate or seed
production is not affected. We derive the expected site-frequency spectrum
reflecting this heterogeneity in selection efficacy between genes underpinning
different plant fitness components. Our results highlight the importance to
consider the effect of ecological noise to predict the impact of seed banks on
neutral and selective evolution.
Discrete Latent Factor Model for Cross-Modal Hashing
Authors: Jiang Qing-Yuan, Li Wu-Jun
Date: 2017-07-26 00:00:00
Tasks: model, Retrieval

Due to its storage and retrieval efficiency, cross-modal hashing~(CMH) has
been widely used for cross-modal similarity search in multimedia applications.
According to the training strategy, existing CMH methods can be mainly divided
into two categories: relaxation-based continuous methods and discrete methods.
In general, the training of relaxation-based continuous methods is faster than
discrete methods, but the accuracy of relaxation-based continuous methods is
not satisfactory. On the contrary, the accuracy of discrete methods is
typically better than relaxation-based continuous methods, but the training of
discrete methods is time-consuming. In this paper, we propose a novel CMH
method, called discrete latent factor model based cross-modal hashing~(DLFH),
for cross modal similarity search. DLFH is a discrete method which can directly
learn the binary hash codes for CMH. At the same time, the training of DLFH is
efficient. Experiments on real datasets show that DLFH can achieve
significantly better accuracy than existing methods, and the training time of
DLFH is comparable to that of relaxation-based continuous methods which are
much faster than existing discrete methods.
Statistics of shared components in complex component systems
Date: 2018-04-23 00:00:00

Many complex systems are modular. Such systems can be represented as
"component systems", i.e., sets of elementary components, such as LEGO bricks
in LEGO sets. The bricks found in a LEGO set reflect a target architecture,
which can be built following a set-specific list of instructions. In other
component systems, instead, the underlying functional design and constraints
are not obvious a priori, and their detection is often a challenge of both
scientific and practical importance, requiring a clear understanding of
component statistics. Importantly, some quantitative invariants appear to be
common to many component systems, most notably a common broad distribution of
component abundances, which often resembles the well-known Zipf's law. Such
"laws" affect in a general and non-trivial way the component statistics,
potentially hindering the identification of system-specific functional
constraints or generative processes. Here, we specifically focus on the
statistics of shared components, i.e., the distribution of the number of
components shared by different system-realizations, such as the common bricks
found in different LEGO sets. To account for the effects of component
heterogeneity, we consider a simple null model, which builds
system-realizations by random draws from a universe of possible components.
Under general assumptions on abundance heterogeneity, we provide analytical
estimates of component occurrence, which quantify exhaustively the statistics
of shared components. Surprisingly, this simple null model can positively
explain important features of empirical component-occurrence distributions
obtained from data on bacterial genomes, LEGO sets, and book chapters. Specific
architectural features and functional constraints can be detected from
occurrence patterns as deviations from these null predictions, as we show for
the illustrative case of the "core" genome in bacteria.
Supermetric Search
Authors: Connor Richard, Vadicamo Lucia, Cardillo Franco Alberto, Rabitti Fausto
Date: 2017-10-22 00:00:00

Metric search is concerned with the efficient evaluation of queries in metric
spaces. In general,a large space of objects is arranged in such a way that,
when a further object is presented as a query, those objects most similar to
the query can be efficiently found. Most mechanisms rely upon the triangle
inequality property of the metric governing the space. The triangle inequality
property is equivalent to a finite embedding property, which states that any
three points of the space can be isometrically embedded in two-dimensional
Euclidean space. In this paper, we examine a class of semimetric space which is
finitely four-embeddable in three-dimensional Euclidean space. In mathematics
this property has been extensively studied and is generally known as the
four-point property. All spaces with the four-point property are metric spaces,
but they also have some stronger geometric guarantees. We coin the term
supermetric space as, in terms of metric search, they are significantly more
tractable. Supermetric spaces include all those governed by Euclidean, Cosine,
Jensen-Shannon and Triangular distances, and are thus commonly used within many
domains. In previous work we have given a generic mathematical basis for the
supermetric property and shown how it can improve indexing performance for a
given exact search structure. Here we present a full investigation into its use
within a variety of different hyperplane partition indexing structures, and go
on to show some more of its flexibility by examining a search structure whose
partition and exclusion conditions are tailored, at each node, to suit the
individual reference points and data set present there. Among the results
given, we show a new best performance for exact search using a well-known
benchmark.
High-Dimensional Simplexes for Supermetric Search
Authors: Connor Richard, Vadicamo Lucia, Rabitti Fausto
Date: 2017-07-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In 1953, Blumenthal showed that every semi-metric space that is isometrically
embeddable in a Hilbert space has the n-point property; we have previously
called such spaces supermetric spaces. Although this is a strictly stronger
property than triangle inequality, it is nonetheless closely related and many
useful metric spaces possess it. These include Euclidean, Cosine and
Jensen-Shannon spaces of any dimension. A simple corollary of the n-point
property is that, for any (n+1) objects sampled from the space, there exists an
n-dimensional simplex in Euclidean space whose edge lengths correspond to the
distances among the objects. We show how the construction of such simplexes in
higher dimensions can be used to give arbitrarily tight lower and upper bounds
on distances within the original space. This allows the construction of an
n-dimensional Euclidean space, from which lower and upper bounds of the
original space can be calculated, and which is itself an indexable space with
the n-point property. For similarity search, the engineering tradeoffs are
good: we show significant reductions in data size and metric cost with little
loss of accuracy, leading to a significant overall improvement in search
performance.
Mutation Clusters from Cancer Exome
Date: 2017-07-26 00:00:00
Tasks: Clustering

We apply our statistically deterministic machine learning/clustering
algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to
10,656 published exome samples for 32 cancer types. A majority of cancer types
exhibit mutation clustering structure. Our results are in-sample stable. They
are also out-of-sample stable when applied to 1,389 published genome samples
across 14 cancer types. In contrast, we find in- and out-of-sample
instabilities in cancer signatures extracted from exome samples via nonnegative
matrix factorization (NMF), a computationally costly and non-deterministic
method. Extracting stable mutation structures from exome data could have
important implications for speed and cost, which are critical for early-stage
cancer diagnostics such as novel blood-test methods currently in development.
NMR microscopy for in vivo metabolomics, digitally twinned by computational systems biology, needs a sensitivity boost
Date: 2017-07-27 00:00:00
Tasks: Sensitivity

The metabolism of an organism is regulated at the cellular level, yet is
strongly influenced by its environment. The precise metabolomic study of living
organisms is currently hampered by measurement sensitivity: most metabolomic
measurement techniques involve some compromise, in that averaging is performed
over a volume significantly larger than a single cell, or require invasion of
the organism, or arrest the state of the organism. NMR is an inherently
non-invasive chemometric and imaging method, and hence in principle suitable
for metabolomic measurements. The digital twin of metabolomics is computational
systems biology, so that NMR microscopy is potentially a viable approach with
which to join the theoretical and experimental exploration of the metabolomic
and behavioural response of organisms. This prospect paper considers the
challenge of performing in vivo NMR-based metabolomics on the small organism C.
elegans, points the way towards possible solutions created using MEMS
techniques, and highlights currently insurmountable challenges.
Topological Data Analysis of Clostridioides difficile Infection and Fecal Microbiota Transplantation
Date: 2017-07-31 00:00:00
Tasks: Descriptive, Topological Data Analysis

Computational topologists recently developed a method, called persistent
homology to analyze data presented in terms of similarity or dissimilarity.
Indeed, persistent homology studies the evolution of topological features in
terms of a single index, and is able to capture higher order features beyond
the usual clustering techniques. There are three descriptive statistics of
persistent homology, namely barcode, persistence diagram and more recently,
persistence landscape. Persistence landscape is useful for statistical
inference as it belongs to a space of $p-$integrable functions, a separable
Banach space. We apply tools in both computational topology and statistics to
DNA sequences taken from Clostridioides difficile infected patients treated
with an experimental fecal microbiota transplantation. Our statistical and
topological data analysis are able to detect interesting patterns among
patients and donors. It also provides visualization of DNA sequences in the
form of clusters and loops.
Multi-Stakeholder Recommendation: Applications and Challenges
Authors: Zheng Yong
Date: 2017-07-27 00:00:00
Tasks: Decision Making, Recommendation Systems

Recommender systems have been successfully applied to assist decision making
by producing a list of item recommendations tailored to user preferences.
Traditional recommender systems only focus on optimizing the utility of the end
users who are the receiver of the recommendations. By contrast,
multi-stakeholder recommendation attempts to generate recommendations that
satisfy the needs of both the end users and other parties or stakeholders. This
paper provides an overview and discussion about the multi-stakeholder
recommendations from the perspective of practical applications, available data
sets, corresponding research challenges and potential solutions.
Transition to Chaos in the Kinetic Model of Cellulose Hydrolysis Under Enzyme Biosynthesis Control
Date: 2017-07-27 00:00:00

In the paper the kinetic model of the biochemical process of cellulose
hydrolysis with cell application is presented. The model includes enzyme
biosynthesis control and is open conditions it represents the dynamical system
in the preturbulent regime. The limit cycle and its five consequence
bifurcations of the doubling-period type are found. Also the limit regime of
the system - the strange attractor - is presented.
Protein Lipograms
Date: 2017-07-25 00:00:00

Linguistic analysis of protein sequences is an underexploited technique.
Here, we capitalize on the concept of the lipogram to characterize sequences at
the proteome levels. A lipogram is a literary composition which omits one or
more letters. A protein lipogram likewise omits one or more types of amino
acid. In this article, we establish a usable terminology for the decomposition
of a sequence collection in terms of the lipogram. Next, we characterize
Uniref50 using a lipogram decomposition. At the global level, protein lipograms
exhibit power-law properties. A clear correlation with metabolic cost is seen.
Finally, we use the lipogram construction to differentiate proteomes between
the four branches of the tree-of-life: archaea, bacteria, eukaryotes and
viruses. We conclude from this pilot study that the lipogram demonstrates
considerable potential as an additional tool for sequence analysis and proteome
classification.
On Biased Correlation Estimation
Date: 2017-07-24 00:00:00
Tasks: Asset Management, Management

In general, underestimation of risk is something which should be avoided as
far as possible. Especially in financial asset management, equity risk is
typically characterized by the measure of portfolio variance, or indirectly by
quantities which are derived from it. Since there is a linear dependency of the
variance and the empirical correlation between asset classes, one is compelled
to control or to avoid the possibility of underestimating correlation
coefficients. In the present approach, we formalize common practice and
classify these approaches by computing their probability of underestimation. In
addition, we introduce a new estimator which is characterized by having the
advantage of a constant and controllable probability of underestimation. We
prove that the new estimator is statistically consistent.
The minimal model of light harvesting complex with dipole-quadrupole interaction derived from ab initio calculation
Date: 2017-07-27 00:00:00

A minimal, one-parameter model of the excitonic Hamiltonian for the light
harvesting complex of purple bacteria (LH2) based on ab initio calculation of
the excitonic energies is proposed. The set of input parameters contains
positions of atoms in 27 bacteriochlorophyll pigments only. The excitation
energies, transition dipole and quadrupole moments of the bacteriochlorophyll
units were calculated by advanced multiconfigurational multireference method.
In the work we investigate influence of the dipole-quadrupole interaction on
the particularities of the absorption spectra and circular dichroism and on the
wave-functions localization. We demonstrate that although the spectra changes
are small the dipole-quadrupole interaction term leads, however, to almost
complete separation of the system onto two subsystems: the small B800 ring of
pigments and the large B850 ring. The latter observation is seemed essential
for formulation of energy transport models in light harvesting complexes.
Early Fusion Strategy for Entity-Relationship Retrieval
Authors: Saleiro Pedro, Milic-Frayling Natasa, Rodrigues Eduarda Mendes, Soares Carlos
Date: 2017-10-19 00:00:00
Tasks: Articles, Entity Linking, Retrieval

We address the task of entity-relationship (E-R) retrieval, i.e, given a
query characterizing types of two or more entities and relationships between
them, retrieve the relevant tuples of related entities. Answering E-R queries
requires gathering and joining evidence from multiple unstructured documents.
In this work, we consider entity and relationships of any type, i.e,
characterized by context terms instead of pre-defined types or relationships.
We propose a novel IR-centric approach for E-R retrieval, that builds on the
basic early fusion design pattern for object retrieval, to provide extensible
entity-relationship representations, suitable for complex, multi-relationships
queries. We performed experiments with Wikipedia articles as entity
representations combined with relationships extracted from ClueWeb-09-B with
FACC1 entity linking. We obtained promising results using 3 different query
collections comprising 469 E-R queries.
A hydrodynamic model for cooperating solidary countries
Date: 2017-07-28 00:00:00
Tasks: model

The goal of international trade theories is to explain the exchange of goods
and services between different countries, aiming to benefit from it. Albeit the
idea is very simple and known since ancient history, smart policy and business
strategies need to be implemented by each subject, resulting in a complex as
well as not obvious interplay. In order to understand such a complexity,
different theories have been developed since the sixteenth century and today
new ideas still continue to enter the game. Among them, the so called classical
theories are country-based and range from Absolute and Comparative Advantage
theories by A. Smith and D. Ricardo to Factor Proportions theory by E.
Heckscher and B. Ohlin. In this work we build a simple hydrodynamic model, able
to reproduce the main conclusions of Comparative Advantage theory in its
simplest setup, i.e. a two-country world with country A and country B
exchanging two goods within a genuine exchange-based economy and a trade flow
ruled only by market forces. The model is further generalized by introducing
money in order to discuss its role in shaping trade patterns. Advantages and
drawbacks of the model are also discussed together with perspectives for its
improvement.
Extracting Event-Centric Document Collections from Large-Scale Web Archives
Authors: Gossen Gerhard, Demidova Elena, Risse Thomas
Date: 2017-07-28 00:00:00
Tasks: Retrieval

Web archives are typically very broad in scope and extremely large in scale.
This makes data analysis appear daunting, especially for non-computer
scientists. These collections constitute an increasingly important source for
researchers in the social sciences, the historical sciences and journalists
interested in studying past events. However, there are currently no access
methods that help users to efficiently access information, in particular about
specific events, beyond the retrieval of individual disconnected documents.
Therefore we propose a novel method to extract event-centric document
collections from large scale Web archives. This method relies on a specialized
focused extraction algorithm. Our experiments on the German Web archive
(covering a time period of 19 years) demonstrate that our method enables the
extraction of event-centric collections for different event types.
Patterns of Multistakeholder Recommendation
Authors: Burke Robin, Abdollahpouri Himan
Date: 2017-07-28 00:00:00
Tasks: Recommendation Systems

Recommender systems are personalized information systems. However, in many
settings, the end-user of the recommendations is not the only party whose needs
must be represented in recommendation generation. Incorporating this insight
gives rise to the notion of multistakeholder recommendation, in which the
interests of multiple parties are represented in recommendation algorithms and
evaluation. In this paper, we identify patterns of stakeholder utility that
characterize different multistakeholder recommendation applications, and
provide a taxonomy of the different possible systems, only some of which have
currently been implemented.
Robustly simulating biochemical reaction kinetics using multi-level Monte Carlo approaches
Date: 2018-11-26 00:00:00

In this work, we consider the problem of estimating summary statistics to
characterise biochemical reaction networks of interest. Such networks are often
described using the framework of the Chemical Master Equation (CME). For
physically-realistic models, the CME is widely considered to be analytically
intractable. A variety of Monte Carlo algorithms have therefore been developed
to explore the dynamics of such networks empirically. Amongst them is the
multi-level method, which uses estimates from multiple ensembles of sample
paths of different accuracies to estimate a summary statistic of interest. {In
this work, we develop the multi-level method in two directions: (1) to increase
the robustness, reliability and performance of the multi-level method, we
implement an improved variance reduction method for generating the sample paths
of each ensemble; and (2) to improve computational performance, we demonstrate
the successful use of a different mechanism for choosing which ensembles should
be included in the multi-level algorithm.
A network model for clonal differentiation and immune memory
Date: 2017-07-11 00:00:00

A model of bit-strings, that uses the technique of multi-spin coding, was
previously used to study the time evolution of B-cell clone repertoire, in a
paper by Lagreca, Almeida and Santos. In this work we extend that simplified
model to include independently the role of the populations of antibodies, in
the control of the immune response, producing mechanisms of differentiation and
regulation in a more complete way. Although the antibodies have the same
molecular shape of the B-cells receptors (BCR), they should present a different
time evolution and thus should be treated separately. We have also studied a
possible model for the network immune memory, suggesting a random memory
regeneration, which is self-perpetuating.