pwc-archive/papers-with-abstracts huggingface dataset:
Impaired neurovascular coupling in the APPxPS1 mouse model of Alzheimerâs disease
Date: 2019-05-10 00:00:00

The tight coupling between neuronal activity and the local increase of blood
flow termed neurovascular coupling is essential for normal brain function. This
mechanism of regulation is compromised in Alzheimer's Disease (AD). In order to
determine whether a purely vascular dysfunction or a neuronal alteration of
blood vessels diameter control could be responsible for the impaired
neurovascular coupling observed in AD, blood vessels reactivity in response to
different pharmacological stimulations was examined in double transgenic
APPxPS1 mice model of AD. Blood vessels movements were monitored using infrared
videomicroscopy ex vivo, in cortical slices of 8 month-old APPxPS1 and wild
type (WT) mice. We quantified vasomotor responses induced either by direct
blood vessel stimulation with a thromboxane A 2 analogue, the U46619
(9,11-dideoxy-11a,9a-epoxymethanoprostaglandin F2) or via the stimulation of
interneurons with the nicotinic acetylcholine receptor (nAChRs) agonist DMPP
(1,1-Dimethyl-4-phenylpiperazinium iodide). Using both types of stimulation, no
significant differences were detected for the amplitude of blood vessel
diameter changes between the transgenic APPxPS1 mice model of AD and WT mice,
although the kinetics of recovery were slower in APPxPS1 mice. We find that
activation of neocortical interneurons with DMPP induced both vasodilation via
Nitric Oxide (NO) release and constriction via Neuropeptide Y (NPY) release.
However, we observed a smaller proportion of reactive blood vessels following a
neuronal activation in transgenic mice compared with WT mice. Altogether, these
results suggest that in this mouse model of AD, deficiency in the cortical
neurovascular coupling essentially results from a neuronal rather than a
vascular dysfunction.
Demand and Welfare Analysis in Discrete Choice Models with Social Interactions
Authors: Debopam Bhattacharya, Pascaline Dupas, Shin Kanaya
Date: 2019-05-10 00:00:00
Tasks: counterfactual, Discrete Choice Models, Econometrics

Many real-life settings of consumer-choice involve social interactions, causing targeted policies to have spillover-effects. This paper develops novel empirical tools for analyzing demand and welfare-effects of policy-interventions in binary choice settings with social interactions. Examples include subsidies for health-product adoption and vouchers for attending a high-achieving school. We establish the connection between econometrics of large games and Brock-Durlauf-type interaction models, under both I.I.D. and spatially correlated unobservables. We develop new convergence results for associated beliefs and estimates of preference-parameters under increasing-domain spatial asymptotics. Next, we show that even with fully parametric specifications and unique equilibrium, choice data, that are sufficient for counterfactual demand-prediction under interactions, are insufficient for welfare-calculations. This is because distinct underlying mechanisms producing the same interaction coefficient can imply different welfare-effects and deadweight-loss from a policy-intervention. Standard index-restrictions imply distribution-free bounds on welfare. We illustrate our results using experimental data on mosquito-net adoption in rural Kenya.
Intelligent User Association for Symbiotic Radio Networks using Deep Reinforcement Learning
Date: 2019-05-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this paper, we are interested in symbiotic radio networks, in which an
Internet-of-Things (IoT) network parasitizes in a primary network to achieve
spectrum-, energy-, and infrastructure-efficient communications. Specifically,
the BS serves multiple cellular users using time division multiple access
(TDMA) and each IoT device is associated with one cellular user for information
transmission. We focus on the user association problem, whose objective is to
link each IoT device to an appropriate cellular user by maximizing the sum rate
of all IoT devices. However, the difficulty in obtaining the full real-time
channel information makes it difficult to design an optimal policy for this
problem. To overcome this issue, we propose two deep reinforcement learning
(DRL) algorithms, both use the historical information to infer the current
information in order to make appropriate decisions. One algorithm, centralized
DRL, makes decisions for all IoT devices at one time with global information.
The other algorithm, distributed DRL, makes a decision only for one IoT device
at one time using local information. Finally, simulation results show that the
two DRL algorithms achieve comparable performance as the optimal user
association policy which requires perfect real-time information, and the
distributed DRL algorithm has the advantage of scalability.
Effect of curvature and normal forces on motor regulation of cilia
Authors: Pablo Sartori
Date: 2019-05-10 00:00:00

Cilia are ubiquitous organelles involves in eukaryotic motility. They are long, slender, and motile protrusions from the cell body. They undergo active regular oscillatory beating patterns that can propel cells, such as the algae Chlamydomonas, through fluids. When many cilia beat in synchrony they can also propel fluid along the surfaces of cells, as is the case of nodal cilia. The main structural elements inside the cilium are microtubules. There are also molecular motors of the dynein family that actively power the motion of the cilium. These motors transform chemical energy in the form of ATP into mechanical forces that produce sliding displacement between the microtubules. This sliding is converted to bending by constraints at the base and/or along the length of the cilium. Forces and displacements within the cilium can regulate dyneins and provide a feedback mechanism: the dyneins generate forces, deforming the cilium; the deformations, in turn, regulate the dyneins. This feedback is believed to be the origin of the coordination of dyneins in space and time which underlies the regularity of the beat pattern. Goals and approach. While the mechanism by which dyneins bend the cilium is understood, the feedback mechanism is much less clear. The two key questions are: which forces and displacements are the most relevant in regulating the beat? and how exactly does this regulation occur? In this thesis we develop a framework to describe the spatio-temporal patterns of a cilium with different mechanisms of motor regulation. Characterizing and comparing the predicted shapes and beat patterns of these different mechanisms to those observed in experiments provides us with further understanding on how dyneins are regulated. This comparison is done both, with a linear model that can be analytically solved, as with a non-linear model that we solve numerically.
Optical synthetic sampling imaging: concept and an example of microscopy
Date: 2019-05-13 00:00:00

Digital two-dimensional (2D) spatial sampling devices (such as charge-coupled
device) have been widely used in various imaging systems, especially in
computational imaging systems. However, the undersampling of digital sampling
devices is a problem that limits the resolution of the acquired images. In this
study, we present a synthetic sampling imaging (SSI) concept to solve the
undersampling problem. It combines the structured illumination system and
conventional 2D image detection system to simultaneously sample the specimen
from the illumination and the detection sides. Then, we synthesize the
illumination sampling rate and the detection sampling rate to reconstruct a
high sampling rate image. The concept of the proposed SSI is demonstrated by an
example of microscopy. Experimental results confirm that the proposed method
can double the sampling resolution of the microscope. The synthetic sampling
scheme, where the sampling task is shared by the illumination and detection
sides, provides insight for resolving the undersampling problem of the digital
imaging system.
Exploration of Gibbs-Laguerre tessellations for three-dimensional stochastic modeling
Date: 2019-11-21 00:00:00

Random tessellations are well suited for probabilistic modeling of
three-dimensional (3D) grain microstructures of polycrystalline materials. The
present paper is focused on so-called Gibbs-Laguerre tessellations, in which
the generators of the Laguerre tessellation form a Gibbs point process. The
goal is to construct an energy function of the Gibbs point process such that
the resulting tessellation matches some desired geometrical properties. Since
the model is analytically intractable, our main tool of analysis is stochastic
simulation based on Markov chain Monte Carlo. Such simulations enable us to
investigate the properties of the models, and, in the next step, to apply the
knowledge gained to the statistical reconstruction of the 3D microstructure of
an aluminum alloy extracted from 3D tomographic image data.
Interference Mitigation and Resource Allocation in Underlay Cognitive Radio Networks
Date: 2019-05-11 00:00:00

Due to ever increasing usage of wireless devices and data hungry
applications, it has become necessary to improve the spectral efficiency of
existing wireless networks. One way of improving spectral efficiency is to
share the spectrum amongst different coexisting networks and serve multiple
devices simultaneously. Spectrum sharing mechanisms for coexistence of a
licensed network, such as LTE, with an unlicensed network, such as Wi-Fi, are
being considered in the recent literature and standardizations. In order to
enable the coexistence between licensed and unlicensed users, it is necessary
to include interference mitigation techniques to protect the licensed primary
users (PUs) from harmful interference. Typical interference mitigation
mechanisms are based on spectrum sensing and cognitive radio (CR), wherein
unlicensed secondary users (SUs) observe the spectrum and utilize it when
licensed PUs are inactive. Thus, the SUs utilize empty time-slots in the shared
spectrum to avoid the interference. The spectral efficiency can be further
improved if the SUs are allowed to transmit concurrently with PUs by exploiting
the spatial dimension provided by multiple antenna techniques. The underlay CR
paradigm allows such coexistence where SUs transmit its signal in the same
time-slots as PUs by exploiting the spatial and frequency resources in the
network. In order to exploit the spatial dimension, SUs can utilize the
location coordinates of PUs to steer its signal away from PUs to mitigate the
interference. The SU transmitter can also employ multiple antenna techniques to
serve a large number of devices. Further, the SUs can utilize frequency bands
occupied by PUs by dynamically selecting the frequency band that provides the
highest rate. In this work, we develop techniques for PU location estimation,
spatial resource allocation and frequency band selection for SUs in underlay CR
networks.
Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques
Date: 2019-05-11 00:00:00
Tasks: Dimensionality Reduction, EEG, Electroencephalogram (EEG)

We propose a novel unsupervised approach for sleep dynamics exploration and
automatic annotation by combining modern harmonic analysis tools. Specifically,
we apply diffusion-based algorithms, diffusion map (DM) and alternating
diffusion (AD) algorithms, to reconstruct the intrinsic geometry of sleep
dynamics by reorganizing the spectral information of an electroencephalogram
(EEG) extracted from a nonlinear-type time frequency analysis tool, the
synchrosqueezing transform (SST). The visualization is achieved by the
nonlinear dimension reduction properties of DM and AD. Moreover, the
reconstructed nonlinear geometric structure of the sleep dynamics allows us to
achieve the automatic annotation purpose. The hidden Markov model is trained to
predict the sleep stage. The prediction performance is validated on a publicly
available benchmark database, Physionet Sleep-EDF [extended] SC* and ST*, with
the leave-one-subject-out cross validation. The overall accuracy and macro F1
achieve 82:57% and 76% in Sleep-EDF SC* and 77.01% and 71:53% in Sleep-EDF ST*,
which is compatible with the state-of-the-art results by supervised
learning-based algorithms. The results suggest the potential of the proposed
algorithm for clinical applications.
Deep Layered LMS Predictor
Date: 2019-05-11 00:00:00

In this study, we present a new approach to design a Least Mean Squares (LMS)
predictor. This approach exploits the concept of deep neural networks and their
supremacy in terms of performance and accuracy. The new LMS predictor is
implemented as a deep neural network using multiple non linear LMS filters. The
network consists of multiple layers with nonlinear activation functions, where
each neuron in the hidden layers corresponds to a certain FIR filter output
which goes through nonlinearity. The output of the last layer is the
prediction. We hypothesize that this approach will outperform the traditional
adaptive filters.
Mobile XR over 5G: A way forward with mmWaves and Edge
Date: 2019-05-11 00:00:00

This e-letter summarizes our most recent work and contributed approaches to
increase the capacity, cut down on the latency and provide higher reliability
in several extended reality (XR) scenarios. To that end, several technologies
from emerging 5G communications systems are weaved together towards enabling a
fully immersive XR experience.
A New Valuation Measure for the Stock Market
Authors: Andrey Sarantsev
Date: 2019-05-11 00:00:00
Tasks: Time Series Analysis

We propose a new valuation measure for the American stock market. We split total returns into three components: earnings growth, dividend yield, and valuation change. The first two components are fundamental, the third is speculative. We treat earnings growth as exogenous. Combining the other two components gives us a new valuation measure, which fits autoregression of order 1 with Gaussian innovations, centered at 4.6%. Therefore, long-term total returns equals long-term earnings growth plus 4.6%. We confirm the classic 4% withdrawal rule. A retiree should invest in stocks and withdraw 4% of initial wealth after adjusting for inflation.
Using statistical techniques and replication samples for imputation of metabolite missing values
Date: 2019-05-12 00:00:00
Tasks: Imputation, Missing Values

Background: Data preparation, such as missing values imputation and
transformation, is the first step in any data analysis and requires crucial
attention. Particularly, analysis of metabolites demands more preparation since
those small compounds have recently been measurable in large scales with mass
spectrometry techniques. We introduce novel statistical techniques for
metabolite missing values imputation by utilizing replication samples. Results:
To understand the nature of the missing values using replication samples, we
obtained the empirical distribution of missing values and observed that the
rate of missing values is approximately distributed as uniform across the
metabolite range. Therefore, the missing values cannot be imputed with the
lowest values. Using the identified distribution, we illustrated a simulation
study to find an optimal imputation approach for metabolites. Conclusions: We
demonstrated that the missing values in metabolomic data sets might not be
necessarily low value. After identification of the nature of missing values, we
validated K nearest neighborhood as an optimal approach for imputation.
Improving Opus Low Bit Rate Quality with Neural Speech Synthesis
Date: 2020-08-10 00:00:00
Tasks: Decoder, Speech Synthesis

The voice mode of the Opus audio coder can compress wideband speech at bit
rates ranging from 6 kb/s to 40 kb/s. However, Opus is at its core a waveform
matching coder, and as the rate drops below 10 kb/s, quality degrades quickly.
As the rate reduces even further, parametric coders tend to perform better than
waveform coders. In this paper we propose a backward-compatible way of
improving low bit rate Opus quality by re-synthesizing speech from the decoded
parameters. We compare two different neural generative models, WaveNet and
LPCNet. WaveNet is a powerful, high-complexity, and high-latency architecture
that is not feasible for a practical system, yet provides a best known
achievable quality with generative models. LPCNet is a low-complexity,
low-latency RNN-based generative model, and practically implementable on mobile
phones. We apply these systems with parameters from Opus coded at 6 kb/s as
conditioning features for the generative models. A listening test shows that
for the same 6 kb/s Opus bit stream, synthesized speech using LPCNet clearly
outperforms the output of the standard Opus decoder. This opens up ways to
improve the decoding quality of existing speech and audio waveform coders
without breaking compatibility.
A Variational Approach to Sparse Model Error Estimation in Cardiac Electrophysiological Imaging
Date: 2019-05-13 00:00:00

Noninvasive reconstruction of cardiac electrical activity from surface
electrocardiograms (ECG) involves solving an ill-posed inverse problem. Cardiac
electrophysiological (EP) models have been used as important a priori knowledge
to constrain this inverse problem. However, the reconstruction suffer from
inaccuracy and uncertainty of the prior model itself which could be mitigated
by estimating a priori model error. Unfortunately, due to the need to handle an
additional large number of unknowns in a problem that already suffers from
ill-posedness, model error estimation remains an unresolved challenge. In this
paper, we address this issue by modeling and estimating the a priori model
error in a low dimensional space using a novel sparse prior based on the
variational approximation of L0 norm. This prior is used in a posterior
regularized Bayesian formulation to quantify the error in a priori EP model
during the reconstruction of transmural action potential from ECG data. Through
synthetic and real-data experiments, we demonstrate the ability of the
presented method to timely capture a priori model error and thus to improve
reconstruction accuracy compared to approaches without model error correction.
Optimal multi-asset trading with linear costs: a mean-field approach
Authors: Matt Emschwiller, Benjamin Petit, Jean-Philippe Bouchaud
Date: 2019-05-13 00:00:00
Tasks: Position

Optimal multi-asset trading with Markovian predictors is well understood in the case of quadratic transaction costs, but remains intractable when these costs are $L_1$. We present a mean-field approach that reduces the multi-asset problem to a single-asset problem, with an effective predictor that includes a risk averse component. We obtain a simple approximate solution in the case of Ornstein-Uhlenbeck predictors and maximum position constraints. The optimal strategy is of the "bang-bang" type similar to that obtained in [de Lataillade et al., 2012]. When the risk aversion parameter is small, we find that the trading threshold is an affine function of the instantaneous global position, with a slope coefficient that we compute exactly. We relate the risk aversion parameter to the desired target risk and provide numerical simulations that support our analytical results.
Low Noise Non-Linear Equalization Using Neural Networks and Belief Propagation
Date: 2019-05-14 00:00:00

Nonlinearities can be introduced into communication systems by the physical
components such as the power amplifier, or during signal propagation through a
nonlinear channel. These nonlinearities can be compensated by a nonlinear
equalizer at the receiver side. The nonlinear equalizer also operates on the
additive noise, which can lead to noise enhancement. In this work we evaluate
this trade-off between distortion reduction and noise-enhancement via nonlinear
equalization techniques. We first, evaluate the trade-off between nonlinearity
compensation and noise enhancement for the Volterra equalizer, and propose a
method to determine the training SNR that optimizes this performance trade-off.
We then propose a new approach for nonlinear equalization that alternates
between neural networks (NNs) for nonlinearity compensation, and belief
propagation (BP) for noise removal. This new approach achieves a 0.6 dB gain
compared to the Volterra equalizer with the optimal training SNR, and a 1.7 dB
gain compared to a system with no nonlinearity compensation.
Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation
Date: 2019-05-13 00:00:00

To enable realistic studies of massive multiple-input multiple-output
systems, the COST 2100 channel model is extended based on measurements. First,
the concept of a base station-side visibility region (BS-VR) is proposed to
model the appearance and disappearance of clusters when using a
physically-large array. We find that BS-VR lifetimes are exponentially
distributed, and that the number of BS-VRs is Poisson distributed with
intensity proportional to the sum of the array length and the mean lifetime.
Simulations suggest that under certain conditions longer lifetimes can help
decorrelating closely-located users. Second, the concept of a multipath
component visibility region (MPC-VR) is proposed to model birth-death processes
of individual MPCs at the mobile station side. We find that both MPC lifetimes
and MPC-VR radii are lognormally distributed. Simulations suggest that unless
MPC-VRs are applied the channel condition number is overestimated. Key
statistical properties of the proposed extensions, e.g., autocorrelation
functions, maximum likelihood estimators, and Cramer-Rao bounds, are derived
and analyzed.
Phase-coherent lightwave communications with frequency combs
Date: 2019-05-13 00:00:00

Fiber-optical networks are a crucial telecommunication infrastructure in
society. Wavelength division multiplexing allows for transmitting parallel data
streams over the fiber bandwidth, and coherent detection enables the use of
sophisticated modulation formats and electronic compensation of signal
impairments. In the future, optical frequency combs may replace multiple lasers
used for the different wavelength channels. We demonstrate two novel signal
processing schemes that take advantage of the broadband phase coherence of
optical frequency combs. This approach allows for a more efficient estimation
and compensation of optical phase noise in coherent communication systems,
which can significantly simplify the signal processing or increase the
transmission performance. With further advances in space division multiplexing
and chip-scale frequency comb sources, these findings pave the way for compact
energy-efficient optical transceivers.