pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Method for Extrinsic Calibration of Multiple RGB-D Cameras Using Descriptor-Based Patterns
Date: 2018-09-10 00:00:00
Tasks: Robot Navigation, Translation

This letter presents a novel method to estimate the relative poses between
RGB-D cameras with minimal overlapping fields of view in a panoramic RGB-D
camera system. This calibration problem is relevant to applications such as
indoor 3D mapping and robot navigation that can benefit from a 360$^\circ$
field of view using RGB-D cameras. The proposed approach relies on
descriptor-based patterns to provide well-matched 2D keypoints in the case of a
minimal overlapping field of view between cameras. Integrating the matched 2D
keypoints with corresponding depth values, a set of 3D matched keypoints are
constructed to calibrate multiple RGB-D cameras. Experiments validated the
accuracy and efficiency of the proposed calibration approach, both superior to
those of existing methods (800 ms vs. 5 seconds; rotation error of 0.56 degrees
vs. 1.6 degrees; and translation error of 1.80 cm vs. 2.5 cm.
Exploring Combinations of Ontological Features and Keywords for Text Retrieval
Authors: Cao Tru H., Le Khanh C., Ngo Vuong M.
Date: 2018-07-20 00:00:00
Tasks: Information Retrieval, Retrieval, Text Retrieval

Named entities have been considered and combined with keywords to enhance
information retrieval performance. However, there is not yet a formal and
complete model that takes into account entity names, classes, and identifiers
together. Our work explores various adaptations of the traditional Vector Space
Model that combine different ontological features with keywords, and in
different ways. It shows better performance of the proposed models as compared
to the keyword-based Lucene, and their advantages for both text retrieval and
representation of documents and queries.
Combining Named Entities with WordNet and Using Query-Oriented Spreading Activation for Semantic Text Search
Authors: Ngo Vuong M., Cao Tru H., Le Tuan M. V.
Date: 2018-07-20 00:00:00

Purely keyword-based text search is not satisfactory because named entities
and WordNet words are also important elements to define the content of a
document or a query in which they occur. Named entities have ontological
features, namely, their aliases, classes, and identifiers. Words in WordNet
also have ontological features, namely, their synonyms, hypernyms, hyponyms,
and senses. Those features of concepts may be hidden from their textual
appearance. Besides, there are related concepts that do not appear in a query,
but can bring out the meaning of the query if they are added. We propose an
ontology-based generalized Vector Space Model to semantic text search. It
exploits ontological features of named entities and WordNet words, and develops
a query-oriented spreading activation algorithm to expand queries. In addition,
it combines and utilizes advantages of different ontologies for semantic
annotation and searching. Experiments on a benchmark dataset show that, in
terms of the MAP measure, our model is 42.5% better than the purely
keyword-based model, and 32.3% and 15.9% respectively better than the ones
using only WordNet or named entities.
  Keywords: semantic search, spreading activation, ontology, named entity,
WordNet.
PGLasso: Microbial Community Detection through Phylogenetic Graphical Lasso
Date: 2018-07-20 00:00:00
Tasks: Community Detection

Due to the recent advances in high-throughput sequencing technologies, it
becomes possible to directly analyze microbial communities in the human body
and in the environment. Knowledge of how microbes interact with each other and
form functional communities can provide a solid foundation to understand
microbiome related diseases; this can serve as a key step towards precision
medicine. In order to understand how microbes form communities, we propose a
two step approach: First, we infer the microbial co-occurrence network by
integrating a graph inference algorithm with phylogenetic information obtained
directly from metagenomic data. Next, we utilize a network-based community
detection algorithm to cluster microbes into functional groups where microbes
in each group are highly correlated. We also curate a "gold standard" network
based on the microbe-metabolic relationships which are extracted directly from
the metagenomic data. Utilizing community detection on the resulting microbial
metabolic pathway bipartite graph, the community membership for each microbe
can be viewed as the true label when evaluating against other existing methods.
Overall, our proposed framework Phylogenetic Graphical Lasso (PGLasso)
outperforms existing methods with gains larger than 100% in terms of Adjusted
Rand Index (ARI) which is commonly used to quantify the goodness of
clusterings.
A Line in the Sand: Recommendation or Ad-hoc Retrieval?
Authors: Kallumadi Surya, Mitra Bhaskar, Iofciu Tereza
Date: 2018-07-21 00:00:00
Tasks: Collaborative Filtering, Learning-To-Rank, Music Recommendation, Retrieval, Sand

The popular approaches to recommendation and ad-hoc retrieval tasks are
largely distinct in the literature. In this work, we argue that many
recommendation problems can also be cast as ad-hoc retrieval tasks. To
demonstrate this, we build a solution for the RecSys 2018 Spotify challenge by
combining standard ad-hoc retrieval models and using popular retrieval tools
sets. We draw a parallel between the playlist continuation task and the task of
finding good expansion terms for queries in ad-hoc retrieval, and show that
standard pseudo-relevance feedback can be effective as a collaborative
filtering approach. We also use ad-hoc retrieval for content-based
recommendation by treating the input playlist title as a query and associating
all candidate tracks with meta-descriptions extracted from the background data.
The recommendations from these two approaches are further supplemented by a
nearest neighbor search based on track embeddings learned by a popular neural
model. Our final ranked list of recommendations is produced by a learning to
rank model. Our proposed solution using ad-hoc retrieval models achieved a
competitive performance on the music recommendation task at RecSys 2018
challenge---finishing at rank 7 out of 112 participating teams and at rank 5
out of 31 teams for the main and the creative tracks, respectively.
Optimal Dividend of Compound Poisson Process under a Stochastic Interest Rate
Date: 2018-07-21 00:00:00

In this paper we assume the insurance wealth process is driven by the
compound Poisson process. The discounting factor is modelled as a geometric
Brownian motion at first and then as an exponential function of an integrated
Ornstein-Uhlenbeck process. The objective is to maximize the cumulated value of
expected discounted dividends up to the time of ruin. We give an explicit
expression of the value function and the optimal strategy in the case of
interest rate following a geometric Brownian motion. For the case of the
Vasicek model, we explore some properties of the value function. Since we can
not find an explicit expression for the value function in the second case, we
prove that the value function is the viscosity solution of the corresponding
HJB equation.
EMU and ECB Conflicts
Date: 2018-07-21 00:00:00
Tasks: Management

In dynamical framework the conflict between government and the central bank
according to the exchange Rate of payment of fixed rates and fixed rates of
fixed income (EMU) convergence criteria such that the public debt / GDP ratio
The method consists of calculating private public debt management in a public
debt management system purpose there is no mechanism to allow naturally for
this adjustment.
Creativity: Linchpin in the Quest for a Viable Theory of Cultural Evolution
Date: 2019-03-13 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

This paper outlines the implications of neural-level accounts of insight, and
models of the conceptual interactions that underlie creativity, for a theory of
cultural evolution. Since elements of human culture exhibit cumulative,
adaptive, open-ended change, it seems reasonable to view culture as an
evolutionary process, one fueled by creativity. Associative memory models of
creativity and mathematical models of how concepts combine and transform
through interaction with a context, support a view of creativity that is
incompatible with a Darwinian (selectionist) framework for cultural evolution,
but compatible with a non-Darwinian (Self-Other Reorganization) framework. A
theory of cultural evolution in which creativity is centre stage could provide
the kind of integrative framework for the behavioral sciences that Darwin
provided for the life sciences.
Backward SDEs for Control with Partial Information
Date: 2018-07-22 00:00:00

This paper considers a non-Markov control problem arising in a financial
market where asset returns depend on hidden factors. The problem is non-Markov
because nonlinear filtering is required to make inference on these factors, and
hence the associated dynamic program effectively takes the filtering
distribution as one of its state variables. This is of significant difficulty
because the filtering distribution is a stochastic probability measure of
infinite dimension, and therefore the dynamic program has a state that cannot
be differentiated in the traditional sense. This lack of differentiability
means that the problem cannot be solved using a Hamilton-Jacobi-Bellman (HJB)
equation. This paper will show how the problem can be analyzed and solved using
backward stochastic differential equations (BSDEs), with a key tool being the
problem's dual formulation.
Regions of multistationarity in cascades of Goldbeter-Koshland loops
Date: 2018-07-23 00:00:00

We consider cascades of enzymatic Goldbeter-Koshland loops with any number n
of layers, for which there exist two layers involving the same phosphatase.
Even if the number of variables and the number of conservation laws grow
linearly with n, we find explicit regions in reaction rate constant and total
conservation constant space for which the associated mass-action kinetics
dynamical system is multistationary. Our computations are based on the
theoretical results of our companion paper in arXiv:1807.05157, which are
inspired by results in real algebraic geometry by Bihan, Santos and
Spaenlehauer.
An Impossibility Theorem for Wealth in Heterogeneous-agent Models with Limited Heterogeneity
Date: 2019-01-26 00:00:00

It has been conjectured that canonical Bewley--Huggett--Aiyagari
heterogeneous-agent models cannot explain the joint distribution of income and
wealth. The results stated below verify this conjecture and clarify its
implications under very general conditions. We show in particular that if (i)
agents are infinitely-lived, (ii) saving is risk-free, and (iii) agents have
constant discount factors, then the wealth distribution inherits the tail
behavior of income shocks (e.g., light-tailedness or the Pareto exponent). Our
restrictions on utility require only that relative risk aversion is bounded,
and a large variety of income processes are admitted. Our results show
conclusively that it is necessary to go beyond standard models to explain the
empirical fact that wealth is heavier-tailed than income. We demonstrate
through examples that relaxing any of the above three conditions can generate
Pareto tails.
Deciphering gene regulation from gene expression dynamics using deep neural network
Authors: Jingxiang Shen, Mariela D. Petkova, Yuhai Tu, Feng Liu, Chao Tang
Date: 2018-07-23 00:00:00

Complex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation - the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN "black box". Our interpretable DNN approach should have broad applications in genotype-phenotype mapping.
The Semiparametric Cram\'er-Rao Bound for Complex Elliptically Symmetric Distributions
Date: 2019-02-25 00:00:00

This letter aims at extending the Constrained Semiparametric Cramer-Rao Bound
(CSCRB) for the joint estimation of mean vector and scatter matrix of Real
Elliptically Symmetric (RES) distributions to Complex Elliptically Symmetric
(CES) distributions. A closed form expression for the complex CSCRB (CCSCRB) is
derived by exploiting the so-called \textit{Wirtinger} or
$\mathbb{C}\mathbb{R}$-\textit{calculus}. Finally, the CCSCRB for the
estimation of the complex mean vector and scatter matrix of a set of complex
$t$-distributed random vectors is provided as an example of application.
Big data analyses reveal patterns and drivers of the movements of southern elephant seals
Date: 2018-07-24 00:00:00
Tasks: Diversity

The growing number of large databases of animal tracking provides an
opportunity for analyses of movement patterns at the scales of populations and
even species. We used analytical approaches, developed to cope with big data,
that require no a priori assumptions about the behaviour of the target agents,
to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina)
in the Southern Ocean, that was comprised of >500,000 location estimates
collected over more than a decade. Our analyses showed that the displacements
of these seals were described by a truncated power law distribution across
several spatial and temporal scales, with a clear signature of directed
movement. This pattern was evident when analysing the aggregated tracks despite
a wide diversity of individual trajectories. We also identified marine
provinces that described the migratory and foraging habitats of these seals.
Our analysis provides evidence for the presence of intrinsic drivers of
movement, such as memory, that cannot be detected using common models of
movement behaviour. These results highlight the potential for big data
techniques to provide new insights into movement behaviour when applied to
large datasets of animal tracking.
Randomized Greedy Sensor Selection: Leveraging Weak Submodularity
Date: 2018-07-19 00:00:00

We study the problem of estimating a random process from the observations
collected by a network of sensors that operate under resource constraints. When
the dynamics of the process and sensor observations are described by a
state-space model and the resource are unlimited, the conventional Kalman
filter provides the minimum mean-square error (MMSE) estimates. However, at any
given time, restrictions on the available communications bandwidth and
computational capabilities and/or power impose a limitation on the number of
network nodes whose observations can be used to compute the estimates. We
formulate the problem of selecting the most informative subset of the sensors
as a combinatorial problem of maximizing a monotone set function under a
uniform matroid constraint. For the MMSE estimation criterion we show that the
maximum element-wise curvature of the objective function satisfies a certain
upper-bound constraint and is, therefore, weak submodular. We develop an
efficient randomized greedy algorithm for sensor selection and establish
guarantees on the estimator's performance in this setting. Extensive simulation
results demonstrate the efficacy of the randomized greedy algorithm compared to
state-of-the-art greedy and semidefinite programming relaxation methods.
Training Humans and Machines
Authors: Nikolaidis Aki
Date: 2018-06-29 00:00:00
Tasks: BIG-bench Machine Learning

For many years, researchers in psychology, education, statistics, and machine
learning have been developing practical methods to improve learning speed,
retention, and generalizability, and this work has been successful. Many of
these methods are rooted in common underlying principles that seem to drive
learning and overlearning in both humans and machines. I present a review of a
small part of this work to point to potentially novel applications in both
machine and human learning that may be worth exploring.
AI for 5G: Research Directions and Paradigms
Date: 2018-07-23 00:00:00

The 5th wireless communication (5G) techniques not only fulfil the
requirement of $1,000$ times increase of internet traffic in the next decade,
but also offer the underlying technologies to the entire industry and ecology
for internet of everything. Compared to the existing mobile communication
techniques, 5G techniques are more-widely applicable and the corresponding
system design is more complicated. The resurgence of artificial intelligence
(AI) techniques offers as an alternative option, which is possibly superior
over traditional ideas and performance. Typical and potential research
directions to which AI can make promising contributions need to be identified,
evaluated, and investigated. To this end, this overview paper first combs
through several promising research directions of AI for 5G, based on the
understanding of the 5G key techniques. Also, the paper devotes itself in
providing design paradigms including 5G network optimization, optimal resource
allocation, 5G physical layer unified acceleration, end-to-end physical layer
joint optimization, and so on.
Storing and retrieving long-term memories: cooperation and competition in synaptic dynamics
Date: 2018-07-23 00:00:00

We first review traditional approaches to memory storage and formation,
drawing on the literature of quantitative neuroscience as well as statistical
physics. These have generally focused on the fast dynamics of neurons; however,
there is now an increasing emphasis on the slow dynamics of synapses, whose
weight changes are held to be responsible for memory storage. An important
first step in this direction was taken in the context of Fusi's cascade model,
where complex synaptic architectures were invoked, in particular, to store
long-term memories. No explicit synaptic dynamics were, however, invoked in
that work. These were recently incorporated theoretically using the techniques
used in agent-based modelling, and subsequently, models of competing and
cooperating synapses were formulated. It was found that the key to the storage
of long-term memories lay in the competitive dynamics of synapses. In this
review, we focus on models of synaptic competition and cooperation, and look at
the outstanding challenges that remain.
Detecting T-cell receptors involved in immune responses from single repertoire snapshots
Date: 2018-07-23 00:00:00

Hypervariable T-cell receptors (TCR) play a key role in adaptive immunity,
recognising a vast diversity of pathogen-derived antigens. High throughput
sequencing of TCR repertoires (RepSeq) produces huge datasets of T-cell
receptor sequences from blood and tissue samples. However, our ability to
extract clinically relevant information from RepSeq data is limited, mainly
because little is known about TCR-disease associations. Here we present a
statistical approach called ALICE (Antigen-specific Lymphocyte Identification
by Clustering of Expanded sequences) that identifies TCR sequences that are
actively involved in the current immune response from a single RepSeq sample,
and apply it to repertoires of patients with a variety of disorders -
autoimmune disease (ankylosing spondylitis), patients under cancer
immunotherapy, or subject to an acute infection (live yellow fever vaccine).
The method's robustness is demonstrated by the agreement of its predictions
with independent assays, and is supported by its ability to selectively detect
responding TCR in the memory but not in the naive subset. ALICE requires no
longitudinal data collection nor large cohorts, and is thus directly applicable
to most RepSeq datasets. Its results facilitate the identification of TCR
variants associated with a wide variety of diseases and conditions, which can
be used for diagnostics, rational vaccine design and evaluation of the adaptive
immune system state.
Inferring interaction partners from protein sequences using mutual information
Date: 2018-11-13 00:00:00

Functional protein-protein interactions are crucial in most cellular
processes. They enable multi-protein complexes to assemble and to remain
stable, and they allow signal transduction in various pathways. Functional
interactions between proteins result in coevolution between the interacting
partners, and thus in correlations between their sequences. Pairwise
maximum-entropy based models have enabled successful inference of pairs of
amino-acid residues that are in contact in the three-dimensional structure of
multi-protein complexes, starting from the correlations in the sequence data of
known interaction partners. Recently, algorithms inspired by these methods have
been developed to identify which proteins are functional interaction partners
among the paralogous proteins of two families, starting from sequence data
alone. Here, we demonstrate that a slightly higher performance for partner
identification can be reached by an approximate maximization of the mutual
information between the sequence alignments of the two protein families. Our
mutual information-based method also provides signatures of the existence of
interactions between protein families. These results stand in contrast with
structure prediction of proteins and of multi-protein complexes from sequence
data, where pairwise maximum-entropy based global statistical models
substantially improve performance compared to mutual information. Our findings
entail that the statistical dependences allowing interaction partner prediction
from sequence data are not restricted to the residue pairs that are in direct
contact at the interface between the partner proteins.
Robust Retrospective Frequency and Phase Correction for Single-voxel MR Spectroscopy
Date: 2018-09-14 00:00:00

Purpose: subject motion and static field (B$_0$) drift are known to reduce
the quality of single voxel MR spectroscopy data due to incoherent averaging.
Retrospective correction has previously been shown to improve data quality by
adjusting the phase and frequency offset of each average to match a reference
spectrum. In this work, a new method (RATS) is developed to be tolerant to
large frequency shifts (greater than 7Hz) and baseline instability resulting
from inconsistent water suppression. Methods: in contrast to previous
approaches, the variable-projection method and baseline fitting is incorporated
into the correction procedure to improve robustness to fluctuating baseline
signals and optimization instability. RATS is compared to an alternative
method, based on time-domain spectral registration (TDSR), using simulated data
to model frequency, phase and baseline instability. In addition, a J-difference
edited glutathione in-vivo dataset is processed using both approaches and
compared. Results: RATS offers improved accuracy and stability for large
frequency shifts and unstable baselines. Reduced subtraction artifacts are
demonstrated for glutathione edited MRS when using RATS, compared with
uncorrected or TDSR corrected spectra. Conclusion: the RATS algorithm has been
shown to provide accurate retrospective correction of SVS MRS data in the
presence of large frequency shifts and baseline instability. The method is
rapid, generic and therefore readily incorporated into MRS processing pipelines
to improve lineshape, SNR and aid quality assessment.