pwc-archive/papers-with-abstracts huggingface dataset:
Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition
Date: 2019-01-18 00:00:00
Tasks: Decision Making

We present the epithelial-to-mesenchymal transition (EMT) from two
perspectives: experimental/technological and theoretical. We review the state
of the current understanding of the regulatory networks that underlie EMT in
three physiological contexts: embryonic development, wound healing, and
metastasis. We describe the existing experimental systems and manipulations
used to better understand the molecular participants and factors that influence
EMT and metastasis. We review the mathematical models of the regulatory
networks involved in EMT, with a particular emphasis on the network motifs
(such as coupled feedback loops) that can generate intermediate hybrid states
between the epithelial and mesenchymal states. Ultimately, the understanding
gained about these networks should be translated into methods to control
phenotypic outcomes, especially in the context of cancer therapeutic
strategies. We present emerging theories of how to drive the dynamics of a
network toward a desired dynamical attractor (e.g. an epithelial cell state)
and emerging synthetic biology technologies to monitor and control the state of
cells.
The Potential of Learned Index Structures for Index Compression
Authors: Oosterhuis Harrie, Culpepper J. Shane, de Rijke Maarten
Date: 2019-01-29 00:00:00

Inverted indexes are vital in providing fast key-word-based search. For every
term in the document collection, a list of identifiers of documents in which
the term appears is stored, along with auxiliary information such as term
frequency, and position offsets. While very effective, inverted indexes have
large memory requirements for web-sized collections. Recently, the concept of
learned index structures was introduced, where machine learned models replace
common index structures such as B-tree-indexes, hash-indexes, and
bloom-filters. These learned index structures require less memory, and can be
computationally much faster than their traditional counterparts. In this paper,
we consider whether such models may be applied to conjunctive Boolean querying.
First, we investigate how a learned model can replace document postings of an
inverted index, and then evaluate the compromises such an approach might have.
Second, we evaluate the potential gains that can be achieved in terms of memory
requirements. Our work shows that learned models have great potential in
inverted indexing, and this direction seems to be a promising area for future
research.
Fairness for Multi-Self Agents
Authors: Sophie Bade, Erel Segal-haLevi
Date: 2018-11-16 00:00:00
Tasks: Fairness

We investigate whether fairness is compatible with efficiency in economies with multi-self agents, who may not be able to integrate their multiple objectives into a single complete and transitive ranking. We adapt envy-freeness, egalitarian-equivalence and the fair-share guarantee in two different ways. An allocation is unambiguously-fair if it satisfies the chosen criterion of fairness according to every objective of any agent; it is aggregate-fair if it satisfies the criterion for some aggregation of each agent's objectives. While efficiency is always compatible with the unambiguous fair-share guarantee, it is incompatible with unambiguous envy-freeness in economics with at least three agents. Two agents are enough for efficiency and unambiguous egalitarian-equivalence to clash. Efficiency and the unambiguous fair-share guarantee can be attained together with aggregate envy-freeness, or aggregate egalitarian-equivalence.
Outage Analysis of $2\times2 $ MIMO-MRC in Correlated Rician Fading
Date: 2018-11-16 00:00:00

This paper addresses one of the classical problems in random matrix theory--
finding the distribution of the maximum eigenvalue of the correlated Wishart
unitary ensemble. In particular, we derive a new exact expression for the
cumulative distribution function (c.d.f.) of the maximum eigenvalue of a
$2\times 2$ correlated non-central Wishart matrix with rank-$1$ mean. By using
this new result, we derive an exact analytical expression for the outage
probability of $2\times 2$ multiple-input multiple-output
maximum-ratio-combining (MIMO-MRC) in Rician fading with transmit correlation
and a strong line-of-sight (LoS) component (rank-$1$ channel mean). We also
show that the outage performance is affected by the relative alignment of the
eigen-spaces of the mean and correlation matrices. In general, when the LoS
path aligns with the least eigenvector of the correlation matrix, in the {\it
high} transmit signal-to-noise ratio (SNR) regime, the outage gradually
improves with the increasing correlation. Moreover, we show that as $K$ (Rician
factor) grows large, the outage event can be approximately characterized by the
c.d.f. of a certain Gaussian random variable.
Joint Range and Angle Estimation for FMCW MIMO Radar and Its Application
Date: 2018-11-16 00:00:00
Tasks: Simultaneous Localization and Mapping

Recently, frequency-modulated continuous wave (FMCW) radars with array
antennas are gaining in popularity on a wide variety of commercial
applications. A usual approach of the range and angle estimation of a target
with an array FMCW radar is to form a range-angle matrix with deramped receive
signal, and then apply the two-dimensional fast Fourier transformation (2D-FFT)
on the range-angle matrix. However, such frequency estimation approaches give
bias error because two frequencies on the range-angle matrix are not
independent to each other, unlike the 2D angle estimation with a passive planar
antenna array. We propose a new maximum-likelihood based algorithm for the
joint range and angle estimation of multiple targets with an array FMCW radar,
and show that the proposed algorithm achieves the Cramer-Rao bounds (CRBs) both
for the range and angle estimation. The proposed algorithm is also compared
with other algorithms for a simultaneous localization and mapping (SLAM)
problem.
Ferrimagnetic organelles in multicellular organisms
Date: 2018-11-16 00:00:00

In this paper, it was revealed by means of methods of atomic force microscopy
and magnetic force microscopy that the biogenic magnetic nanoparticles are
localized in the form of chains in the walls of the capillaries of animals and
the walls of the conducting tissue of plants and fungi. The biogenic magnetic
nanoparticles are part of the transport system in multicellular organisms. In
this connection, a new idea of function of biogenic magnetic nanoparticles is
discussed in the paper that the chains of biogenic magnetic nanoparticles
represent a ferrimagnetic organelles of a specific purpose.
Technical Analysis and Discrete False Discovery Rate: Evidence from MSCI Indices
Date: 2019-06-13 00:00:00

We investigate the performance of dynamic portfolios constructed using more
than 21,000 technical trading rules on 12 categorical and country-specific
markets over the 2004-2015 study period, on rolling forward structures of
different lengths. We also introduce a discrete false discovery rate (DFRD+/-)
method for controlling data snooping bias. Compared to the existing methods,
DFRD+/- is adaptive and more powerful, and accommodates for discrete p-values.
The profitability, persistence and robustness of the technical rules are
examined. Technical analysis still has short-term value in advanced, emerging
and frontier markets. Financial stress, the economic environment and market
development seem to affect the performance of trading rules. A cross-validation
exercise highlights the importance of frequent rebalancing and the variability
of profitability in trading with technical analysis.
Evolution and structure of technological systems - An innovation output network
Date: 2018-11-16 00:00:00

This study examines the network of supply and use of significant innovations
across industries in Sweden, 1970-2013. It is found that 30% of innovation
patterns can be predicted by network stimulus from backward and forward
linkages. The network is hierarchical, characterized by hubs that connect
diverse industries in closely knitted communities. To explain the network
structure, a preferential weight assignment process is proposed as an
adaptation of the classical preferential attachment process to weighted
directed networks. The network structure is strongly predicted by this process
where historical technological linkages and proximities matter, while human
capital flows and economic input-output flows have conflicting effects on link
formation. The results are consistent with the idea that innovations emerge in
closely connected communities, but suggest that the transformation of
technological systems are shaped by technological requirements, imbalances and
opportunities that are not straightforwardly related to other proximities.
Building and Evaluation of a Real Room Impulse Response Dataset
Date: 2019-05-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Room Impulse Response (RIR), Speaker Recognition, speech-recognition, Speech Recognition

This paper presents BUT ReverbDB - a dataset of real room impulse responses
(RIR), background noises and re-transmitted speech data. The retransmitted data
includes LibriSpeech test-clean, 2000 HUB5 English evaluation and part of 2010
NIST Speaker Recognition Evaluation datasets. We provide a detailed description
of RIR collection (hardware, software, post-processing) that can serve as a
"cook-book" for similar efforts. We also validate BUT ReverbDB in two sets of
automatic speech recognition (ASR) experiments and draw conclusions for
augmenting ASR training data with real and artificially generated RIRs. We show
that a limited number of real RIRs, carefully selected to match the target
environment, provide results comparable to a large number of artificially
generated RIRs, and that both sets can be combined to achieve the best ASR
results. The dataset is distributed for free under a non-restrictive license
and it currently contains data from 8 rooms, which is growing. The distribution
package also contains a Kaldi-based recipe for augmenting publicly available
AMI close-talk meeting data and test the results on an AMI single distant
microphone set, allowing it to reproduce our experiments.
SoundSignaling: Realtime, Stylistic Modification of a Personal Music Corpus for Information Delivery
Authors: Ananthabhotla Ishwarya, Paradiso Joseph A.
Date: 2018-11-16 00:00:00

Drawing inspiration from the notion of cognitive incongruence associated with
Stroop's famous experiment, from musical principles, and from the observation
that music consumption on an individual basis is becoming increasingly
ubiquitous, we present the SoundSignaling system -- a software platform
designed to make real-time, stylistically relevant modifications to a personal
corpus of music as a means of conveying information or notifications. In this
work, we discuss in detail the system's technical implementation and its
motivation from a musical perspective, and validate these design choices
through a crowd-sourced signal identification experiment consisting of 200
independent tasks performed by 50 online participants. We then qualitatively
discuss the potential implications of such a system from the standpoint of
switch cost, cognitive load, and listening behavior by considering the
anecdotal outcomes of a small-scale, in-the-wild experiment consisting of over
180 hours of usage from 6 participants. Through this work, we suggest a
re-evaluation of the age-old paradigm of binary audio notifications in favor of
a system designed to operate upon the relatively unexplored medium of a user's
musical preferences.
Optogenetic vision restoration with high resolution
Date: 2018-11-16 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The majority of inherited retinal degenerations are due to photoreceptor cell
death. In many cases ganglion cells are spared making it possible to stimulate
them to restore visual function. Several studies (Bi et al., 2006; Lin et al.,
2008; Sengupta et al., 2016; Caporale et al., 2011; Berry et al., 2017) have
shown that it is possible to express an optogenetic protein in ganglion cells
and make them light sensitive. This is a promising strategy to restore vision
since optical targeting may be more precise than electrical stimulation with a
retinal prothesis. However the spatial resolution of
optogenetically-reactivated retinas has not been measured with fine-grained
stimulation patterns. Since the optogenetic protein is also expressed in axons,
it is unclear if these neurons will only be sensitive to the stimulation of a
small region covering their somas and dendrites, or if they will also respond
to any stimulation overlapping with their axon, dramatically impairing spatial
resolution. Here we recorded responses of mouse and macaque retinas to random
checkerboard patterns following an in vivo optogenetic therapy. We show that
optogenetically activated ganglion cells are each sensitive to a small region
of visual space. A simple model based on this small receptive field predicted
accurately their responses to complex stimuli. From this model, we simulated
how the entire population of light sensitive ganglion cells would respond to
letters of different sizes. We then estimated the maximal acuity expected by a
patient, assuming it could make an optimal use of the information delivered by
this reactivated retina. The obtained acuity is above the limit of legal
blindness. This high spatial resolution is a promising result for future
clinical studies.
Ontology based Approach for Precision Agriculture
Authors: Ngo Quoc Hung, Le-Khac Nhien-An, Kechadi Tahar
Date: 2018-11-16 00:00:00

In this paper, we propose a framework of knowledge for an agriculture
ontology which can be used for the purpose of smart agriculture systems. This
ontology not only includes basic concepts in the agricultural domain but also
contains geographical, IoT, business subdomains, and other knowledge extracted
from various datasets. With this ontology, any users can easily understand
agricultural data links between them collected from many different data
resources. In our experiment, we also import country, sub-country and disease
entities into this ontology as basic entities for building agricultural linked
datasets later.
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
Authors: Hang Mengyue, Pytlarz Ian, Neville Jennifer
Date: 2018-09-25 00:00:00

With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).
Multi-scale variability in neuronal competition
Date: 2018-11-16 00:00:00
Tasks: Open-Ended Question Answering

We examine whether a single biophysical cortical circuit model can explain
both spiking and perceptual variability. We consider perceptual rivalry, which
provides a window into intrinsic neural processing since neural activity in
some brain areas is correlated to the alternating perception rather than the
constant ambiguous stimulus. The prevalent theory for spiking variability is a
chaotic attractor called the balanced state; whereas, the source of perceptual
variability is an open question. We present a dynamical model with a chaotic
attractor that explains both spiking and perceptual variability and adheres to
a broad set of strict experimental constraints. The model makes quantitative
predictions for how both spiking and perceptual variability will change as the
stimulus changes.
Multipath-enabled private audio with noise
Date: 2019-03-13 00:00:00

We address the problem of privately communicating audio messages to multiple
listeners in a reverberant room using a set of loudspeakers. We propose two
methods based on emitting noise. In the first method, the loudspeakers emit
noise signals that are appropriately filtered so that after echoing along
multiple paths in the room, they sum up and descramble to yield distinct
meaningful audio messages only at specific focusing spots, while being
incoherent everywhere else. In the second method, adapted from wireless
communications, we project noise signals onto the nullspace of the MIMO channel
matrix between the loudspeakers and listeners. Loudspeakers reproduce a sum of
the projected noise signals and intended messages. Again because of echoes, the
MIMO nullspace changes across different locations in the room. Thus, the
listeners at focusing spots hear intended messages, while the acoustic channel
of an eavesdropper at any other location is jammed. We show, using both
numerical and real experiments, that with a small number of speakers and a few
impulse response measurements, audio messages can indeed be communicated to a
set of listeners while ensuring negligible intelligibility elsewhere.
Higher order tensor decomposition for proportional myoelectric control based on muscle synergies
Authors: Ahmed Ebied, Eli Kinney-Lang, Javier Escudero
Date: 2018-11-17 00:00:00
Tasks: Tensor Decomposition

Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is limited in terms of task-dimensionality. Here, the potential application of higher-order tensor decomposition as a framework for proportional myoelectric control is demonstrated. A novel constrained Tucker decomposition (consTD) technique of synergy extraction is proposed for synergy-based myoelectric control model and compared with state-of-the-art matrix factorisation models. The extracted synergies were used to estimate control signals for the wrist's Degree of Freedom (DoF) through direct projection. The consTD model was able to estimate the control signals for each DoF by utilising all data in one 3rd-order tensor. This is contrast with matrix factorisation models where data are segmented for each DoF and then the synergies often have to be realigned. Moreover, the consTD method offers more information by providing additional shared synergies, unlike matrix factorisation methods. The extracted control signals were fed to a ridge regression to estimate the wrist's kinematics based on real glove data. The Coefficient of Determination (R2) for the reconstructed wrist position showed that the proposed consTD was higher than matrix factorisation methods. In sum, this study provides the first proof of concept for the use of higher-order tensor decomposition in proportional myoelectric control and it highlights the potential of tensors to provide an objective and direct approach to identify synergies.
Dynamics and biological reference points in a stochastic age-structured fish population model with an illustration of the Patagonian toothfish population
Date: 2018-11-17 00:00:00

In this manuscript we investigate the long-term behavior of a single-species
fishery, which is harvested by several fleets. The time evolution of this
population is modeled by a discrete time stochastic age-structured model. We
assume that incertitude only affects the recruitment. First, for the
deterministic version of this model, we characterize the equilibrium yields in
terms of the fishing mortality. Then, for the stochastic version, we introduce
the concepts of maximum expected, log expected and harmonic expected
sustainable yield, and we analyze how the incertitude affects the behavior of
these yields and their stationary distribution. All the numerical simulations
are performed with data obtained from Patagonian Tooth-fish fishery, which is
harvested by four different type of fleets: Chilean Industrial fleet, Chilean
Artisanal fleet, Argentinean longline fleet, and Argentinean Artisanal fleet.
JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-wide Associations on Multiple Diseases
Date: 2018-11-17 00:00:00

Taking advantages of high-throughput genotyping technology of single
nucleotide polymorphism (SNP), large genome-wide association studies (GWASs)
have been considered as the promise to unravel the complex relationships
between genotypes and phenotypes, in particularly common diseases. However,
current multi-locus-based methods are insufficient, in terms of computational
cost and discrimination power, to detect statistically significant interactions
and they are lacking in the ability of finding diverse genetic effects on
multifarious diseases. Especially, multiple statistic tests for high-order
epistasis ($ \geq $ 2 SNPs) will raise huge analytical challenges because the
computational cost increases exponentially as the growth of the cardinality of
SNPs in an epistatic module. In this paper, we develop a simple, fast and
powerful method, named JS-MA, using the Jensen-Shannon divergence and a
high-dimensional $ k $-mean clustering algorithm for mapping the genome-wide
multi-locus epistatic interactions on multiple diseases. Compared with some
state-of-the-art association mapping tools, our method is demonstrated to be
more powerful and efficient from the experimental results on the systematical
simulations. We also applied JS-MA to the GWAS datasets from WTCCC for two
common diseases, i.e. Rheumatoid Arthritis and Type 1 Diabetes. JS-MA not only
confirms some recently reported biologically meaningful associations but also
identifies some novel findings. Therefore, we believe that our method is
suitable and efficient for the full-scale analysis of multi-disease-related
interactions in the large GWASs.
Directional Adaptive MUSIC-like Algorithm under {\alpha}-Stable Distributed Noise
Date: 2018-11-17 00:00:00

An algorithm called MUSIC-like algorithm was originally proposed as an
alternative method to the MUltiple SIgnal Classification (MUSIC) algorithm for
direction-of-arrival (DOA) estimation. Without requiring explicit model order
estimation, it was shown to have robust performance particularly in low
signal-to-noise ratio (SNR) scenarios. In this letter, the working principle of
a relaxation parameter {\beta}, a parameter which was introduced into the
formulation of the MUSIC-like algorithm, is provided based on geometrical
interpretation. To illustrate its robustness, the algorithm will be examined
under symmetric {\alpha}-stable distributed noise environment. An adaptive
framework is then developed and proposed in this letter to further optimize the
algorithm. The proposed adaptive framework is compared with the original
MUSIC-like, MUSIC, FLOM-MUSIC, and SSCM-MUSIC algorithms. A notable improvement
in terms of targets resolvability of the proposed method is observed under
different impulse noise scenarios as well as different SNR levels.