pwc-archive/papers-with-abstracts huggingface dataset:
Fallopian tube anatomy predicts pregnancy and pregnancy outcomes after tubal reversal surgery
Authors: Rafael S. de Souza, Gary S. Berger
Date: 2019-04-21 00:00:00
Tasks: Additive models, Anatomy

We conducted this study to determine whether fallopian tube anatomy can predict the likelihood of pregnancy and pregnancy outcomes after tubal sterilization reversal. We built a flexible, non-parametric, multivariate model via generalized additive models to assess the effects of the following tubal parameters observed during tubal reparative surgery: tubal lengths; differences in tubal segment location, and diameters at the anastomosis sites; and, fibrosis of the tubal muscularis. In this study population, age and tubal length - in that order - were the primary factors predicting the likelihood of pregnancy. For pregnancy outcomes, tubal length was the most influential predictor of birth and ectopic pregnancy, while age was the primary predictor of miscarriage. Segment location and diameters contributed slightly to the odds of miscarriage and ectopic pregnancy. Tubal muscularis fibrosis had a little apparent effect. This study is the first to show that a statistical learning predictive model based on fallopian tube anatomy can predict pregnancy and pregnancy outcome probabilities after tubal reversal surgery.
Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits
Date: 2019-04-21 00:00:00
Tasks: All

Winner-Take-All (WTA) refers to the neural operation that selects a
(typically small) group of neurons from a large neuron pool. It is conjectured
to underlie many of the brain's fundamental computational abilities. However,
not much is known about the robustness of a spike-based WTA network to the
inherent randomness of the input spike trains. In this work, we consider a
spike-based $k$--WTA model wherein $n$ randomly generated input spike trains
compete with each other based on their underlying statistics, and $k$ winners
are supposed to be selected. We slot the time evenly with each time slot of
length $1\, ms$, and model the $n$ input spike trains as $n$ independent
Bernoulli processes. The Bernoulli process is a good approximation of the
popular Poisson process but is more biologically relevant as it takes the
refractory periods into account. Due to the randomness in the input spike
trains, no circuits can guarantee to successfully select the correct winners in
finite time. We focus on analytically characterizing the minimal amount of time
needed so that a target minimax decision accuracy (success probability) can be
reached.
  We first derive an information-theoretic lower bound on the decision time. We
show that to have a (minimax) decision error $\le \delta$ (where $\delta \in
(0,1)$), the computation time of any WTA circuit is at least \[ ((1-\delta)
\log(k(n -k)+1) -1)T_{\mathcal{R}}, \] where $T_{\mathcal{R}}$ is a difficulty
parameter of a WTA task that is independent of $\delta$, $n$, and $k$. We then
design a simple WTA circuit whose decision time is \[ O(
\log\frac{1}{\delta}+\log k(n-k))T_{\mathcal{R}}). \] It turns out that for any
fixed $\delta \in (0,1)$, this decision time is order-optimal in terms of its
scaling in $n$, $k$, and $T_{\mathcal{R}}$.
Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models
Date: 2019-04-23 00:00:00

Sensory neurons often have variable responses to repeated presentations of
the same stimulus, which can significantly degrade the stimulus information
contained in those responses. This information can in principle be preserved if
variability is shared across many neurons, but depends on the structure of the
shared variability and its relationship to sensory encoding at the population
level. The structure of this shared variability in neural activity can be
characterized by latent variable models, although they have thus far typically
been used under restrictive mathematical assumptions. Here we introduce two
nonlinear latent variable models for analyzing large-scale neural recordings.
We first present a general nonlinear latent variable model that is agnostic to
the stimulus tuning properties of the individual neurons, and is hence well
suited for exploring neural populations whose tuning properties are not well
characterized. This motivates a second class of model, the Generalized Affine
Model, which simultaneously determines each neuron's stimulus selectivity and a
set of latent variables that modulate these stimulus-driven responses both
additively and multiplicatively. While these approaches can detect very general
nonlinear relationships in shared neural variability, we find that neural
activity recorded in anesthetized primary visual cortex (V1) is best described
by a single additive and single multiplicative latent variable, i.e. an `affine
model'. In contrast, application of the same models to recordings in awake
macaque prefrontal cortex discover more general nonlinearities to compactly
describe the population response variability. These results thus demonstrate
how nonlinear latent variable models can be used to describe population
response variability, and suggest that a range of methods is necessary to study
different brain regions under different experimental conditions.
ECG Reconstruction via PPG: A Pilot Study
Date: 2019-04-23 00:00:00

In this paper, the relation between electrocardiogram (ECG) and
photoplethysmogram (PPG) signals is studied, and the waveform of ECG is
inferred via the PPG signals. In order to address this inverse problem, a
transform is proposed to map the discrete cosine transform (DCT) coefficients
of each PPG cycle to those of the corresponding ECG cycle. The resulting DCT
coefficients of the ECG cycle are inversely transformed to obtain the
reconstructed ECG waveform. The proposed method is evaluated on a benchmark
dataset of subjects with a variety of combinations of age and weight.
Experimental results show that the proposed method can achieve a high accuracy
at 0.98 in averaged correlation.
Information Based Centralization of Locomotion in Animals and Robots
Date: 2019-04-17 00:00:00

Movement in biology is often achieved with distributed control of coupled
subcomponents, e.g. muscles and limbs. Coupling could range from weak and
local, i.e. decentralized, to strong and global, i.e. centralized. We developed
a model-free measure of centralization that compares information shared between
control signals and both global and local states. A second measure, called
co-information, quantifies the net redundant information the control signal
shares with both states. We first validate our measures through simulations of
coupled oscillators and show that it successfully reconstructs the shift from
low to high coupling strengths. We then measure centralization in freely
running cockroaches. Surprisingly, extensor muscle activity in the middle leg
is more informative of movements of all legs combined than the movements of
that particular leg. Cockroach centralization successfully recapitulates a
specific model of a strongly coupled oscillator network previously used to
model cockroach leg kinematics. When segregated by stride frequency, slower
cockroach strides exhibit more shared information per stride between control
and output states than faster strides, indicative of an information bandwidth
limitation. However, centralization remains consistent between the two groups.
We then used a robotic model to show that centralization can be affected by
mechanical coupling independent of neural coupling. The mechanically coupled
bounding gait is decentralized and becomes more decentralized as mechanical
coupling decreases while internal parameters of control remain constant. The
results of these systems span a design space of centralization and
co-information that can be used to test biological hypotheses and advise the
design of robotic control.
Putting Mind Back into Nature: A Tribute to Henry P. Stapp
Date: 2019-04-16 00:00:00

Henry Stapp has for 60 years been a leader, perhaps the leader, in exploring
the role of mind (psyche, consciousness, experience) in the ontology of quantum
mechanics. Henry's contention is that the very structure of quantum mechanics
implies a central and irreducible role for mind: an experiential aspect of
nature distinct from that of the physical matter and energy described by the
dynamical equations of physics. The task then becomes generating interest
across disciplines in the exploration of this thesis, and in particular to seek
connections with neuroscience and with empirical psychology.
Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge
Date: 2019-04-23 00:00:00
Tasks: Image Registration

In brain tumor surgery, the quality and safety of the procedure can be
impacted by intra-operative tissue deformation, called brain shift. Brain shift
can move the surgical targets and other vital structures such as blood vessels,
thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a
convenient and cost-effective imaging tool to track brain shift and tumor
resection. Accurate image registration techniques that update pre-surgical MRI
based on iUS are crucial but challenging. The MICCAI Challenge 2018 for
Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018)
provided a public platform to benchmark MRI-iUS registration algorithms on
newly released clinical datasets. In this work, we present the data, setup,
evaluation, and results of CuRIOUS 2018, which received 6 fully automated
algorithms from leading academic and industrial research groups. All algorithms
were first trained with the public RESECT database, and then ranked based on
test dataset of 10 additional cases with identical data curation and annotation
protocols as the RESECT database. The article compares the results of all
participating teams and discusses the insights gained from the challenge, as
well as future work.
Backbone chemical shift assignments of human 14-3-3$\sigma$
Date: 2019-04-24 00:00:00

14-3-3 proteins are a group of seven dimeric adapter proteins that exert
their biological function by interacting with hundreds of phosphorylated
proteins, thus influencing their sub-cellular localization, activity or
stability in the cell. Due to this remarkable interaction network, 14-3-3
proteins have been associated with several pathologies and the protein-protein
interactions established with a number of partners are now considered promising
drug targets. The activity of 14-3-3 proteins is often isoform specific and to
our knowledge only one out of seven isoforms, 14-3-3$\zeta$, has been assigned.
Despite the availability of the crystal structures of all seven isoforms of
14-3-3, the additional NMR assignments of 14-3-3 proteins are important for
both biological mechanism studies and chemical biology approaches. Herein, we
present a robust backbone assignment of 14-3-3$\sigma$, which will allow
advances in the discovery of potential therapeutic compounds. This assignment
is now being applied to the discovery of both inhibitors and stabilizers of
14-3-3 protein-protein interactions.
Aliased seabed detection in fisheries acoustic data
Date: 2019-04-24 00:00:00
Tasks: Survey

Aliased seabed echoes, also known as "false bottoms" or "shadow bottoms", are
a form of echogram corruption caused by seabed reverberation from preceding
pings coinciding with echoes from the current ping. These aliases are usually
either avoided by adjusting the survey parameters, or identified and removed by
hand - a subjective and laborious process. This paper describes a simple
algorithm that uses volume backscatter and split-beam angle to detect and
remove aliased seabed using single frequency, split-beam echo sounder data
without the need for bathymetry.
Cognitive Radar Using Reinforcement Learning in Automotive Applications
Authors: PengFei Liu, Yimin Liu, Tianyao Huang, Yuxiang Lu, Xiqin Wang
Date: 2019-04-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The concept of cognitive radar (CR) enables radar systems to achieve intelligent adaption to a changeable environment with feedback facility from receiver to transmitter. However, the implementation of CR in a fast-changing environment usually requires a well-known environmental model. In our work, we stress the learning ability of CR in an unknown environment using a combination of CR and reinforcement learning (RL), called RL-CR. Less or no model of the environment is required. We also apply the general RL-CR to a specific problem of automotive radar spectrum allocation to mitigate mutual interference. Using RL-CR, each vehicle can autonomously choose a frequency subband according to its own observation of the environment. Since radar's single observation is quite limited compared to the overall information of the environment, a long short-term memory (LSTM) network is utilized so that radar can decide the next transmitted subband by aggregating its observations over time. Compared with centralized spectrum allocation approaches, our approach has the advantage of reducing communication between vehicles and the control center. It also outperforms some other distributive frequency subband selecting policies in reducing interference under certain circumstances.
Observing Actions in Bayesian Games
Date: 2019-04-24 00:00:00

We study Bayesian coordination games where agents receive noisy private
information over the game's payoff structure, and over each others' actions. If
private information over actions is precise, we find that agents can coordinate
on multiple equilibria. If private information over actions is of low quality,
equilibrium uniqueness obtains like in a standard global games setting. The
current model, with its flexible information structure, can thus be used to
study phenomena such as bank-runs, currency crises, recessions, riots, and
revolutions, where agents rely on information over each others' actions.
The Analogue Computer as a Voltage-Controlled Synthesiser
Date: 2019-04-25 00:00:00

This paper re-appraises the role of analogue computers within electronic and
computer music and provides some pointers to future areas of research. It
begins by introducing the idea of analogue computing and placing in the context
of sound and music applications. This is followed by a brief examination of the
classic constituents of an analogue computer, contrasting these with the
typical modular voltage-controlled synthesiser. Two examples are presented,
leading to a discussion on some parallels between these two technologies. This
is followed by an examination of the current state-of-the-art in analogue
computation and its prospects for applications in computer and electronic
music.
Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation
Date: 2019-06-28 00:00:00

Index Modulations, in the form of Spatial Modulation or Polarized Modulation,
are gaining traction for both satellite and terrestrial next generation
communication systems. Adaptive Index Modulation based links are needed to
fully exploit the transmission capacity of time-variant channels. The
adaptation of code and/or modulation requires a real-time evaluation of the
channel achievable rates. Some existing results in the literature present a
computational complexity which scales quadratically with the number of transmit
antennas and the constellation order. Moreover, the accuracy of these
approximations is low and it can lead to wrong Modulation and Coding Scheme
selection. In this work we apply a Multilayer Feedforward Neural Network to
compute the achievable rate of a generic Index Modulation link. The case of two
antennas/polarizations is analyzed throughly showing the neural network not
only a one-hundred fold decrement of the Mean Square Error in the estimation of
the capacity compared with existing analytical approximations, but it also
reduces fifty times the computational complexity. Moreover, the extension to an
arbitrary number of antennas is explained and supported with simulations. More
generally, neural networks can be considered as promising candidates for the
practical estimation of complex metrics in communication related settings.
Seeding hESCs to achieve optimal colony clonality
Authors: Laura E Wadkin, Sirio Orozco-Fuentes, Irina Neganova, Sanja Bojic, Alex Laude, Majlina Lako, Nicholas G Parker, Anvar Shukurov
Date: 2019-04-16 00:00:00

Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have promising clinical applications which often rely on clonally-homogeneous cell populations. To achieve this, cross-contamination and merger of colonies should be avoided. This motivates us to experimentally study and quantitatively model the growth of hESC colonies. The colony population is unexpectedly found to be multi-modal. We associate these sub-populations with different numbers of founding cells, and predict their occurrence by considering the role of cell-cell interactions and cell behaviour on randomly seeded cells. We develop a multi-population stochastic exponential model for the colony population which captures our experimental observations, and apply this to calculate the timescales for colony merges and over which colony size no longer predicts the number of founding cells. These results can be used to achieve the best outcome for homogeneous colony growth from different cell seeding densities.
Pre-distortion and Pre-equalization for Non-Linearities and Low-Pass Effect Mitigation in OFDM-VLC Systems
Date: 2019-04-24 00:00:00

The orthogonal frequency division multiplexing (OFDM) transmission has shown
promise in applications of visible light communication (VLC). However, the
variation of the nonlinearity of the optical power emitted by the high power
light emitting diode (HPLED) as a function of current and temperature implies
in drastic OFDM-VLC performance degradation. The first part of this work,
experimentally confirms and models this degradation due to temperature in a
high power white HPLED. The higher attenuation at high frequencies, which is
inherent to the HPLED and which is accentuated by the effect of the intrinsic
capacitance of the photodiode, is another factor of degradation due to the
reduction of the signal-to-noise ratio (SNR) at the receiver for such
frequencies. For the mitigation of these effects, we propose a pre-distortion
and digital pre-equalization scheme using a luminous feedback signal in the
transmitter module. The system is modeled so that the operating points are
mathematically deduced and evaluated by simulations and by an experimental
setup. By allowing the linearization of the transmitted light signal and the
maintenance of an average SNR in all OFDM subcarriers, the performance
improvement is confirmed in comparison with other schemes, such as with
non-predistortion, pre-distortion with fixed parameters, and simple
post-equalization.
Outage Analysis of Cooperative NOMA in Millimeter Wave Vehicular Network at Intersections
Date: 2019-11-26 00:00:00

In this paper, we study the impact and the improvement of using cooperative
non-orthogonal multiple access scheme (NOMA) on a millimeter wave (mmWave)
vehicular network at intersection roads. The intersections consists of two
perpendicular roads. The transmission occurs between a source, and two
destinations nodes with a help of a relay. We assume that the interference
comes from as set of vehicles that are distributed as a one dimensional
homogeneous Poisson point process (PPP). We derive closed form outage
probability expressions for cooperative NOMA, and compare them with cooperative
orthogonal multiple access (OMA). We show that, NOMA offers a significant
improvement, especially for high data rates. However, there a condition imposed
to the data rate, otherwise, the performance of NOMA will decreases
dramatically. We show that as the nodes approach the intersection, the outage
probability increases. Counter-intuitively, We show that, the non line of sigh
(NLOS) scenario has a better performance than the line of sigh (LOS) scenario.
The analysis is conducted using tools from stochastic geometry and is verified
with Monte Carlo simulations.
Normal Approximation in Large Network Models
Authors: Michael P. Leung, Hyungsik Roger Moon
Date: 2019-04-24 00:00:00

We prove a central limit theorem for network formation models with strategic interactions and homophilous agents. Since data often consists of observations on a single large network, we consider an asymptotic framework in which the network size diverges. We argue that a modification of ``stabilization'' conditions from the literature on geometric graphs provides a useful high-level formulation of weak dependence, which we utilize to establish an abstract central limit theorem. In the context of strategic network formation, we derive primitive conditions for stabilization using results in branching process theory. We outline a methodology for deriving primitive conditions that can be applied more broadly to other large network models with strategic interactions. Finally, we discuss practical inference procedures justified by our results.
Robust Sequential Steady-State Analysis of Cascading Outages
Date: 2019-11-22 00:00:00

Simulating potential cascading failures can be useful for avoiding or
mitigating such events. Currently, existing steady-state analysis tools are
ill-suited for simulating cascading outages as they do not model frequency
dependencies, they require good initial conditions to converge, and they are
unable to distinguish between a collapsed grid state from a hard-to-solve test
case. In this paper, we extend a circuit-theoretic approach for simulating the
steady-state of a power grid to incorporate frequency deviations and implicit
models for underfrequency and undervoltage load shedding. Using these models,
we introduce a framework capable of robustly solving cascading outages of
large-scale systems that can also locate infeasible regions. We demonstrate the
efficacy of our approach by simulating entire cascading outages on more than
8000 nodes sample testcase.
Latent Class Model with Application to Speaker Diarization
Date: 2019-04-25 00:00:00
Tasks: model, speaker-diarization, Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker
diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in
that it uses soft information and avoids premature hard decisions in its
iterations. In contrast to the VB method, which is based on a generative model,
LCM provides a framework allowing both generative and discriminative models.
The discriminative property is realized through the use of i-vector (Ivec),
probabilistic linear discriminative analysis (PLDA), and a support vector
machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid are introduced. In addition, three further improvements are
applied to enhance its performance. 1) Adding neighbor windows to extract more
speaker information for each short segment. 2) Using a hidden Markov model to
avoid frequent speaker change points. 3) Using an agglomerative hierarchical
cluster to do initialization and present hard and soft priors, in order to
overcome the problem of initial sensitivity. Experiments on the National
Institute of Standards and Technology Rich Transcription 2009 speaker
diarization database, under the condition of a single distant microphone, show
that the diarization error rate (DER) of the proposed methods has substantial
relative improvements compared with mainstream systems. Compared to the VB
method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments
on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial
conditions also show that the proposed LCM-Ivec-Hybrid system has the best
overall performance.