pwc-archive/papers-with-abstracts huggingface dataset:
Data-driven network alignment
Authors: Shawn Gu, Tijana Milenkovic
Date: 2019-02-08 00:00:00

Biological network alignment (NA) aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for transfer of functional knowledge between the aligned nodes. However, current NA methods do not end up aligning functionally related nodes. A likely reason is that they assume it is topologically similar nodes that are functionally related. However, we show that this assumption does not hold well. So, a paradigm shift is needed with how the NA problem is approached. We redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity, like traditional NA methods do. TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns. We find that TARA is able to make accurate predictions. TARA then takes each pair of nodes that are predicted as related to be part of an alignment. Like traditional NA methods, TARA uses this alignment for the across-species transfer of functional knowledge. Clearly, TARA as currently implemented uses topological but not protein sequence information for this task. We find that TARA outperforms existing state-of-the-art NA methods that also use topological information, WAVE and SANA, and even outperforms or complements a state-of-the-art NA method that uses both topological and sequence information, PrimAlign. Hence, adding sequence information to TARA, which is our future work, is likely to further improve its performance.
Use Symmetry to Elucidate the Roles of Global Shape and Local Interactions in Protein Dynamics and Cooperativity
Date: 2019-02-10 00:00:00

Shape had been intuitively recognized to play a dominant role in determining
the global motion patterns of bio-molecular assemblies. However, it is not
clear exactly how shape determines the motion patterns. What about the local
interactions that hold a structure together to a certain shape? The
contributions of global shape and local interactions usually mix together and
are difficult to tease part. In this work, we use symmetry to elucidate the
distinct roles of global shape and local interactions in protein dynamics.
Symmetric complexes provide an ideal platform for this task since in them the
effects of local interactions and global shape are separable, allowing their
distinct roles to be identified. Our key findings based on symmetric assemblies
are: (i) the motion patterns of each subunit are determined primarily by
intra-subunit interactions (IRSi), and secondarily by inter-subunit
interactions (IESi); (ii) the motion patterns of the whole assembly are fully
dictated by the global symmetry/shape and have nothing to do with local iESi or
IRSi. This is followed by a discussion on how the findings may be generalized
to complexes in any shape, with or without symmetry.
Helping Blind People in Their Meeting Locations to Find Each Other Using RFID Technology
Date: 2019-02-10 00:00:00
Tasks: Position, TAG

This paper presents a new specific system based on RFID technology to help
blind people find the other party in their meeting place. This system, uses a
device called Smart Director or SD, equipped with Active Tag and RFID Reader.
The blind person, the visitor, adjusts the SD with the identification number of
the other party who has shared it previously for example on a telephone call,
and takes the device to the meeting place. When the blind person arrives at the
location, the reader of the device receives signals from the active tag of the
other party's SD. It then identifies his/her position based on the intensity
and direction of the received signals and tell it to the blind person. In this
way, blind people can simply identify the position of the other party in
crowded places and find each other.
Physics and Derivatives: Effective-Potential Path-Integral Approximations of Arrow-Debreu Densities
Date: 2019-02-10 00:00:00
Tasks: Computational Efficiency

We show how effective-potential path-integrals methods, stemming on a simple
and nice idea originally due to Feynman and successfully employed in Physics
for a variety of quantum thermodynamics applications, can be used to develop an
accurate and easy-to-compute semi-analytical approximation of transition
probabilities and Arrow-Debreu densities for arbitrary diffusions. We
illustrate the accuracy of the method by presenting results for the
Black-Karasinski and the GARCH linear models, for which the proposed
approximation provides remarkably accurate results, even in regimes of high
volatility, and for multi-year time horizons. The accuracy and the
computational efficiency of the proposed approximation makes it a viable
alternative to fully numerical schemes for a variety of derivatives pricing
applications.
Towards the Internet of Underground Things: A Systematic Survey
Date: 2019-08-26 00:00:00
Tasks: Sand, Survey

This paper provides recent advances in the area of Internet of Underground
Things (IoUT) with emphasis on enabling communication technologies, networking
issues, and localization techniques. IoUT is enabled by underground things
(sensors), communication technology, and networking protocols. This new
paradigm of IoUT facilitates the integration of sensing and communication in
the underground environment for various industries such as oil and gas,
agriculture, seismic mapping, and border monitoring. These applications require
to gather relevant information from the deployed underground things. However,
the harsh underground propagation environment including sand, rock, and
watersheds do not allow the use of single communication technology for
information transfer between the surface and the underground things. Therefore,
various wireless and wired communication technologies are used for underground
communication. The wireless technologies are based on acoustic waves,
electromagnetic waves, magnetic induction and visible light communication while
the wired technologies use coaxial cable and optical fibers. In this paper,
state-of-art communication technologies are surveyed, and the respective
networking and localization techniques for IoUT are presented. Moreover, the
advances and applications of IoUT are also reported. Also, new research
challenges for the design and implementation of IoUT are identified.
Mixed RF/FSO Relaying Systems with Hardware Impairments
Date: 2019-01-16 00:00:00

In this work, we provide a detailed analysis of a dual-hop fixed gain (FG)
amplify-and-forward relaying system, consisting of a hybrid radio frequency
(RF) and free-space optical (FSO) channels. We introduce an impairment model
which is the soft envelope limiter (SEL). Additionally, we propose the partial
relay selection (PRS) protocol with outdated channel state information (CSI)
based on the knowledge of the RF channels in order to select one relay for the
communication. Moreover, the RF channels of the first hop experience Rayleigh
fading while we propose a unified fading model for the FSO channels, called the
unified Gamma Gamma (GG), taking into account the atmospheric turbulence, the
path loss and the misalignment between the transmitter and the receiver
aperture also called the pointing error. Novel closed-forms of the outage
probability (OP), the bit error probability (BEP) and the average ergodic
capacity (EC) are derived in terms of Meijer-G and Fox-H functions.
Capitalizing on these metrics, we also derive the asymptotical high
signal-to-noise ratio (SNR) in order to get engineering insights into the
impacts of the hardware impairments and the system parameters as well. Finally,
using Monte Carlo simulations, we validate numerically the derived mathematical
formulations.
A complete data processing workflow for CryoET and subtomogram averaging
Date: 2019-02-11 00:00:00
Tasks: Cryogenic Electron Tomography

Electron cryotomography (CryoET) is currently the only method capable of
visualizing cells in 3D at nanometer resolutions. While modern instruments
produce massive amounts of tomography data containing extremely rich structural
information, the data processing is very labor intensive and results are often
limited by the skills of the personnel rather than the data. We present an
integrated workflow that covers the entire tomography data processing pipeline,
from automated tilt series alignment to subnanometer resolution subtomogram
averaging. This workflow greatly reduces human effort and increases throughput,
and is capable of determining protein structures at state-of-the-art
resolutions for both purified macromolecules and cells.
Unified Bayesian Conditional Autoregressive Risk Measures using the Skew Exponential Power Distribution
Date: 2019-09-30 00:00:00

Conditional Autoregressive Value-at-Risk and Conditional Autoregressive
Expectile have become two popular approaches for direct measurement of market
risk. Since their introduction several improvements both in the Bayesian and in
the classical framework have been proposed to better account for asymmetry and
local non-linearity. Here we propose a unified Bayesian Conditional
Autoregressive Risk Measures approach by using the Skew Exponential Power
distribution. Further, we extend the proposed models using a semiparametric
P-spline approximation answering for a flexible way to consider the presence of
non-linearity. To make the statistical inference we adapt the MCMC algorithm
proposed in Bernardi et al. (2018) to our case. The effectiveness of the whole
approach is demonstrated using real data on daily return of five stock market
indices.
Adaptive Scan for Atomic Force Microscopy Based on Online Optimisation: Theory and Experiment
Date: 2019-02-12 00:00:00

A major challenge in Atomic Force Microscopy (AFM) is to reduce the scan
duration while retaining the image quality. Conventionally, the scan rate is
restricted to a sufficiently small value in order to ensure a desirable image
quality as well as a safe tip-sample contact force. This usually results in a
conservative scan rate for samples that have a large variation in aspect ratio
and/or for scan patterns that have a varying linear velocity. In this paper, an
adaptive scan scheme is proposed to alleviate this problem. A scan line-based
performance metric balancing both imaging speed and accuracy is proposed, and
the scan rate is adapted such that the metric is optimised online in the
presence of aspect ratio and/or linear velocity variations. The online
optimisation is achieved using an extremum-seeking (ES) approach, and a
semi-global practical asymptotic stability (SGPAS) result is shown for the
overall system. Finally, the proposed scheme is demonstrated via both
simulation and experiment.
Location-Based Optimum Relay Selection in Random Spatial Networks
Date: 2019-02-12 00:00:00

This paper investigates the location-based relay selection problem, where the
source node chooses its relay from a set of spatially deployed
decode-and-forward relays. The advantages of location-based relay selection are
the elimination of excessive relay switching rate and the feedback reduction
avoiding the requirement of having full channel state information at the source
node. For a homogeneous Poisson point process of candidate relays, we first
derive the distribution for the distance of the relay (relative to the source
and destination nodes) selected by the optimum location-based relay selection
policy. This result is independent of the functional form of the path-loss
function as long as it is a non-increasing function of the transmitter-receiver
separation. By utilizing the derived optimum relay distance distribution, we
then obtain analytical expressions for the average rate and outage probability
by considering the power-law decaying path-loss function for the no-fading and
Rayleigh fading communication scenarios. It is observed that the optimum relay
selection policy outperforms the other common selection strategies notably,
including the ones choosing the relay closest to the source, the relay closest
to the destination and the relay closest to the mid-point between source and
destination.
Active Sampling for Approximately Bandlimited Graph Signals
Date: 2019-02-16 00:00:00

This paper investigates the active sampling for estimation of approximately
bandlimited graph signals. With the assistance of a graph filter, an
approximately bandlimited graph signal can be formulated by a Gaussian random
field over the graph. In contrast to offline sampling set design methods which
usually rely on accurate prior knowledge about the model, unknown parameters in
signal and noise distribution are allowed in the proposed active sampling
algorithm. The active sampling process is divided into two alternating stages:
unknown parameters are first estimated by Expectation Maximization (EM), with
which the next node to sample is selected based on historical observations
according to predictive uncertainty. Validated by simulations compared with
related approaches, the proposed algorithm can reduce the sample size to reach
a certain estimation accuracy.
A Joint Solution for Scheduling and Precoding in Multiuser MISO Downlink Channels
Date: 2019-02-13 00:00:00
Tasks: Management, Scheduling

The long-term average performance of the MISO downlink channel, with a large
number of users compared to transmit antennas of the BS, depends on the
interference management which necessitates the joint design problem of
scheduling and precoding. Unlike the previous works which do not offer a truly
joint design, this paper focuses on formulating a problem amenable for the
joint update of scheduling and precoding. Novel optimization formulations are
investigated to reveal the hidden difference of convex/ concave structure for
three classical criteria (weighted sum rate, max-min SINR, and power
minimization) and associated constraints are considered. Thereafter, we propose
a convex-concave procedure framework based iterative algorithm where scheduling
and precoding variables are updated jointly in each iteration. Finally, we show
the superiority in performance of joint solution over the state-of-the-art
designs through Monte-Carlo simulations.
Building arbitrage-free implied volatility: Sinkhorn's algorithm and variants
Authors: Hadrien De March, Pierre Henry-Labordere
Date: 2019-02-12 00:00:00

We consider the classical problem of building an arbitrage-free implied volatility surface from bid-ask quotes. We design a fast numerical procedure, for which we prove the convergence, based on the Sinkhorn algorithm that has been recently used to solve efficiently (martingale) optimal transport problems.
Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30
Authors: Brian F. Tivnan, David Rushing Dewhurst, Colin M. Van Oort, John H. Ring IV, Tyler J. Gray, Brendan F. Tivnan, Matthew T. K. Koehler, Matthew T. McMahon, David Slater, Jason Veneman, Christopher M. Danforth
Date: 2019-02-13 00:00:00

Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity marketplace are relatively common and persist for longer than what physical constraints may suggest. Information feeds reported different prices for the same equity more than 120 million times, with almost 64 million dislocation segments featuring meaningfully longer duration and higher magnitude. During this period, roughly 22% of all trades occurred while the SIP and aggregated direct feeds were dislocated. The current market configuration resulted in a realized opportunity cost totaling over $160 million when compared with a single feed, single exchange alternative---a conservative estimate that does not take into account intra-day offsetting events.
Indirect Microwave Holography with Resolution Enhancement in Metallic Imaging
Date: 2019-11-21 00:00:00
Tasks: Translation

The development of compact indirect microwave holographic set-up by the
implementation of low cost, specifically designed directive antennae as
transmitter and receiver is proposed. Microwave holograms are recorded by 2D
scanning over a plane using motorized translation stage. The recorded
interference pattern i.e. holograms are then processed numerically to
reconstruct the amplitude and phase information employing the angular spectrum
diffraction method. The quality of the reconstructed amplitude image is further
enhanced through the deep neural network, in order to combat with the low
resolution of reconstructed images. The qualitative experimental results
exploit the possibility of developing the miniaturized, and low cost indirect
microwave holographic system for near field applications.
SHEAR-net: An End-to-End Deep Learning Approach for Single Push Ultrasound Shear Wave Elasticity Imaging
Date: 2019-02-13 00:00:00
Tasks: Lesion Segmentation, Multi-Task Learning, SSIM

Ultrasound Shear Wave Elastography (USWE) with conventional B-mode imaging
demonstrates better performance in lesion segmentation and classification
problems. In this article, we propose SHEAR-net, an end-to-end deep neural
network, to reconstruct USWE images from tracked tissue displacement data at
different time instants induced by a single acoustic radiation force (ARF) with
100% or 50% of the energy in conventional use. The SHEAR-net consists of a
localizer called the S-net to first localize the lesion location and then uses
recurrent layers to extract temporal correlations from wave patterns using
different time frames, and finally, with an estimator, it reconstructs the
shear modulus image from the concatenated outputs of S-net and recurrent
layers. The network is trained with 800 simulation and a limited number of CIRS
tissue mimicking phantom data and is optimized using a multi-task learning loss
function where the tasks are: inclusion localization and modulus estimation.
The efficacy of the proposed SHEAR-net is extensively evaluated both
qualitatively and quantitatively on 125 test set of motion data obtained from
simulation and CIRS phantoms. We show that the proposed approach consistently
outperforms the current state-of-the-art method and achieves overall 4-5 dB
improvement in PSNR and SNR. In addition, an average gain of 0.15 in DSC and
SSIM values indicate that the SHEAR-net has a better inclusion coverage area
and structural similarity of the two approaches. The proposed real-time deep
learning based technique can accurately estimate shear modulus for a minimum
tissue displacement of 0.5$\mu$m and image multiple inclusions with a single
push ARF.
Ca2+ release via IP3 receptors shapes the cytosolic Ca2+ transient for hypertrophic signalling in ventricular cardiomyocytes
Authors: Hilary Hunt, Agne Tilunaite, Greg Bass, Christian Soeller, H. Llewelyn Roderick, Vijay Rajagopal, Edmund J. Crampin
Date: 2019-02-13 00:00:00

Calcium (Ca2+) plays a central role in mediating both contractile function and hypertrophic signalling in ventricular cardiomyocytes. L-type Ca2+ channels trigger release of Ca2+ from ryanodine receptors (RyRs) for cellular contraction, while signalling downstream of Gq coupled receptors stimulates Ca2+ release via inositol 1,4,5-trisphosphate receptors (IP3Rs), engaging hypertrophic signalling pathways. Modulation of the amplitude, duration, and duty cycle of the cytosolic Ca2+ contraction signal, and spatial localisation, have all been proposed to encode this hypertrophic signal. Given current knowledge of IP3Rs, we develop a model describing the effect of functional interaction (cross-talk) between RyR and IP3R channels on the Ca2+ transient, and examine the sensitivity of the Ca2+ transient shape to properties of IP3R activation. A key result of our study is that IP3R activation increases Ca2+ transient duration for a broad range of IP3R properties, but the effect of IP3R activation on Ca2+ transient amplitude is dependent on IP3 concentration. Furthermore we demonstrate that IP3-mediated Ca2+ release in the cytosol increases the duty cycle of the Ca2+ transient, the fraction of the cycle for which [Ca2+] is elevated, across a broad range of parameter values and IP3 concentrations. When coupled to a model of downstream transcription factor (NFAT) activation, we demonstrate that there is a high correspondence between the Ca transient duty cycle and the proportion of activated NFAT in the nucleus. These findings suggest increased cytosolic Ca2+ duty cycle as a plausible mechanism for IP3-dependent hypertrophic signalling via Ca2+-sensitive transcription factors such as NFAT in ventricular cardiomyocytes.
Improvements of computational ghost imaging by using Special-Hadamard patterns
Date: 2019-02-12 00:00:00
Tasks: Object

We introduced a new kind of patterns named Special-Hadamard patterns, which
could be used as structured illuminations of computational ghost imaging.
Special-Hadamard patterns can get a better image quality than Hadamard patterns
in a noisy environment. We can completely reconstruct the original object in a
noiseless environment by using Special-Hadamard patterns, and the size of
object also can be adjusted arbitrarily, these advantages cannot be achieved by
other common patterns. We also performed simulations to compare the results of
Special Hadamard patterns with the results of Hadamard patterns. We found
Special Hadamard patterns can greatly improve the image quality of
computational ghost imaging.
Ordered Hexagonal Patterns via Notch-Delta Signaling
Authors: Eial Teomy, David A. Kessler, Herbert Levine
Date: 2019-02-13 00:00:00

Many developmental processes in biology utilize Notch-Delta signaling to construct an ordered pattern of cellular differentiation. This signaling modality is based on nearest-neighbor contact, as opposed to the more familiar mechanism driven by the release of diffusible ligands. Here, exploiting this "juxtacrine" property, we present an exact treatment of the pattern formation problem via a system of nine coupled ordinary differential equations. The possible patterns that are realized for realistic parameters can be analyzed by considering a co-dimension 2 pitchfork bifurcation of this system. This analysis explains the observed prevalence of hexagonal patterns with high Delta at their center, as opposed to those with central high Notch levels. We show that outside this range of parameters, in particular for low cis-coupling, a novel kind of pattern is produced, where high Delta cells have high Notch as well. It also suggests that the biological system is only weakly first order, so that an additional mechanism is required to generate the observed defect-free patterns. We construct a simple strategy for producing such defect-free patterns.
Knowledge-aided Two-dimensional Autofocus for Spotlight SAR Filtered Backprojection Imagery
Date: 2019-02-13 00:00:00
Tasks: Motion Compensation, parameter estimation, Vocal Bursts Valence Prediction

Filtered backprojection (FBP) algorithm is a popular choice for complicated
trajectory SAR image formation processing due to its inherent nonlinear motion
compensation capability. However, how to efficiently autofocus the defocused
FBP imagery when the motion measurement is not accurate enough is still a
challenging problem. In this paper, a new interpretation of the FBP derivation
is presented from the Fourier transform point of view. Based on this new
viewpoint, the property of the residual 2-D phase error in FBP imagery is
analyzed in detail. Then, by incorporating the derived a priori knowledge on
the 2-D phase error, an accurate and efficient 2-D autofocus approach is
proposed. The new approach performs the parameter estimation in a
dimension-reduced parameter subspace by exploiting the a priori analytical
structure of the 2-D phase error, therefore possesses much higher accuracy and
efficiency than conventional blind methods. Finally, experimental results
clearly demonstrate the effectiveness and robustness of the proposed method.